Ecological limits of high-density milkfish farming

In the Philippines at present, milkfish farming in ponds includes a wide range of intensities, systems and practices. To make aquaculture possible, ecosystems are used as sources of energy and resources and as sinks for wastes. The growth of aquaculture is limited by the life-support functions of the ecosystem, and sustainability depends on matching the farming techniques with the processes and functions of the ecosystems, for example, by recycling some degraded resources. The fish farm has many interactions with the external environment. Serious environmental problems may be avoided if high-intensity farms are properly planned in the first place, at the farm level and at the level of the coastal zone where it can be integrated with other uses by other sectors. It is believed that the key to immediate success in the mass production of milkfish for local consumption and for export of value-added forms may be in semi-intensive farming at target yields of 3 tons per ha per year, double the current national average. Intensive milkfish farming will be limited by environmental, resource and market constraints. Integrated intensive farming systems are the appropriate long-term response to the triple needs of the next century: more food, more income, and more jobs for more people, all from less land, less resources, and less non-renewable energy.

Unsupervised intra-lingual and cross-lingual speaker adaptation for HMM-based speech synthesis using two-pass decision tree construction

Hidden Markov model (HMM)-based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to estimate the transcription of the adaptation data. This paper first presents an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for such supplementary acoustic models. This is achieved by defining a mapping between HMM-based synthesis models and ASR-style models, via a two-pass decision tree construction process. Second, it is shown that this mapping also enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data. Third, this paper demonstrates how this technique lends itself to the task of unsupervised cross-lingual adaptation of HMM-based speech synthesis models, and explains the advantages of such an approach. Finally, listener evaluations reveal that the proposed unsupervised adaptation methods deliver performance approaching that of supervised adaptation.

NATURAL FOODS AND THEIR ECOLOGICAL IMPLICATIONS FOR MACACA-THIBETANA AT MOUNT EMEI, CHINA

Natural food plants of partly provisioned groups of Macaca thibetana included about 196 species belonging to 135 genera and 72 families. The macaques consumed mainly bamboo shoots and fruits for about 2 months in autumn, whereas they relied on active or passive provisions from visitors, a variety of structural parts of plants and a small amount of invertebrates in late spring and summer and ate mainly mature leaves and bark for the rest of the year. About half of the species eaten came from the dense herb and shrub layers. This forest-dwelling species shows a distinctive feeding and foraging pattern in comparison with other macaques, explaining why M. thibetana has the largest body weight of all macaques.

Capture fisheries in Uganda: why Bujagali hydro-power project and ecological monitoring?

Human use of water resow-ces in Uganda has grown and intensified along with population growth and increasing demand to meet the diverse human needs. In the case of Uganda's rivers, the main uses include fisheries, hydropower generation, abstraction for potable water supply, discharge of sewage and navigation. All these uses can disrupt the integrity of the aquatic ecosystem and may affect the survival of the diversity of organisms. In consideration of the need to increase electricity to meet demand, the Bujagali Hydro-power Project (BHPP) and the National Environment Management Authority (NEMA) recognised the importance of safeguards to mitigate impacts of the project. The National Fisheries Resources Research Institute (NaFIRRI) was assigned the role of providing baseline information on the aquatic ecosystem of the Upper Victoria Nile and to follow up the findings with a monitoring framework during construction and post-commissioning phases.

The role of ecological studies in the rational management of fish stocks

Studies on the ecology of freshwaters are basic to the rational development and management of their fisheries. and the relationships between different abiotic and biotic components of aquatic ecosystems are discussed in this paper. Examples are given of the ways in which such studies have been used to establish factors that may limit or increase yields from various Zambian waters.

High Altitude Adaptation and Phylogenetic Analysis of Tibetan Horse Based on the Mitochondrial Genome

研究测定了西藏那曲(4,500 m)、云南中甸(3,300 m)、云南德钦(3,300 m)地区3匹藏马线粒体全基因组序列.3个地区的藏马线粒体基因组全长以及结构均与韩国济州岛的马类似,但比瑞典马线粒体基因组短.藏马基因组在DNA序列上的两两相似性达99.3%.通过对线粒体蛋白编码区的分析发现,NADH6基因的蛋白序列在三匹藏马中均表现快速进化的现象.这表明NADH6基因在藏马高原适应进化过程中扮演着重要角色.此外,利用7匹藏马的D-loop区域序列以及与其亲缘关系较近的马的序列首次构建的藏马的系统发育树显示,那曲藏马与中甸、德钦藏马属于不同的分支,且存在较大的遗传多样性,表明藏马可能为多地区起源.

Active dendrites: Adaptation to spike-based communication

Computational analyses of dendritic computations often assume stationary inputs to neurons, ignoring the pulsatile nature of spike-based communication between neurons and the moment-to-moment fluctuations caused by such spiking inputs. Conversely, circuit computations with spiking neurons are usually formalized without regard to the rich nonlinear nature of dendritic processing. Here we address the computational challenge faced by neurons that compute and represent analogue quantities but communicate with digital spikes, and show that reliable computation of even purely linear functions of inputs can require the interplay of strongly nonlinear subunits within the postsynaptic dendritic tree.Our theory predicts a matching of dendritic nonlinearities and synaptic weight distributions to the joint statistics of presynaptic inputs. This approach suggests normative roles for some puzzling forms of nonlinear dendritic dynamics and plasticity.

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.

Integrated online speaker clustering and adaptation

For many applications, it is necessary to produce speech transcriptions in a causal fashion. To produce high quality transcripts, speaker adaptation is often used. This requires online speaker clustering and incremental adaptation techniques to be developed. This paper presents an integrated approach to online speaker clustering and adaptation which allows efficient clustering of speakers using the same accumulated statistics that are normally used for adaptation. Using a consistent criterion for both clustering and adaptation should yield gains for both stages. The proposed approach is evaluated on a meetings transcription task using audio from multiple distant microphones. Consistent gains over standard clustering and adaptation were obtained. 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.

Generalization in Adaptation to Stable and Unstable Dynamics

Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization. © 2012 Kadiallah et al.

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.