Variation of Rumen Bacterial Diversity in Steers after the Beginning of Grazing

Holstein heifers before or after puberty often are herded on public pastures in Japan. The herbage intake and rumen fermentation of grazing heifers that are not adapted for fresh herbage decreases due to a change of feed from stall-fed dried forage to fresh herbage. This limits their performance during the first several weeks on pasture. Thus, the feeding program such as supplementation before and after the beginning of grazing is important. An increase in ammonia concentration and a decline in fibre degradation in the rumen of a heifer (both of which occur simultaneously with low herbage intake and rumen fermentation) would be caused by the reduced capacity of various bacteria to produce peptides and degrade fibre (Oshio and Tahata 1981). This suggests that variation in rumen bacterial diversity plays an important role in herbage intake and rumen fermentation. However, less information is available on how bacterial diversity in heifers varies during the first few weeks of grazing. This information will provide the basis for designing nutritional management programs for heifers before and after the beginning of grazing. The objective of this study was to determine how herbage intake, digestibility, and rumen bacterial diversity vary in steers that have started grazing without adaptation for fresh herbage during the first 4 weeks after the beginning of grazing

Efficiency of the Symmetry Bias in Grammar Aquisition

It is well known that the symmetry bias greatly accelerates vocabulary learning. In particular, the bias helps infants to connect objects with their names easily. However, grammar learning is another important aspect of language acquisition. In this study, we propose that the symmetry bias also helps to acquire grammar rules faster. We employ the Iterated Learning Model, and revise it to include the symmetry bias. The result of the simulations shows that infants could abduce the meanings from unrecognized utterances using the symmetry bias, and acquire compositional grammar from a reduced amount of learning data.Special Issue: 3rd International Conference on Language and Automata Theory and Applications (LATA 2009

Simulation of the Emergence of Language Groups Using the Iterated Learning Model on Social Networks

In evolutionary linguistics, the Iterated Learning Model (ILM) is often used for simulating the first language acquisition. Our purpose in this paper is to develop an agent-based model for language contact based on ILM. We put a learning agent on each node in the social network. Our experimental result showed that the language exposure rather deteriorates the emergence of local common languages, and grammars become non-compositional, which is different from our expectation. However, we have shown that an excessive string-clipping as well as a language exposure may constrain the appearance of local language community, independent of the shape of networks

Utility for Communicability by Profit and Cost of Agreement

Abstract. The inflection of words based on agreement, such as number, gender and case, is considered to contribute to clarify the dependency between words in a sentence. Our purpose in this study is to investigate the efficiency of word inflections with HPSG (Head–driven Phrase Structure Grammar), which is able to deal with these features directly. Using a notion of utility, we measure the efficiency of a grammar in terms of the balance between the number of semantic structures of a sentence, and the cost of agreement according to the number of unification processes. In our experiments, we showed how these were balanced in two different corpora. One, WSJ (Wall Street Journal), includes long and complicated sentences, while the other corpus, ATIS (Air Travel Information System) does shorter colloquial sentences. In the both corpora, agreement is surely important to reduce ambiguity. However, the importance of agreement in the ATIS corpus became salient as personal pronouns were so often employed in it, compared with the WSJ corpus.

Application of Loose Symmetry Bias to Multiple Meaning Environment

It is well known that the cognitive biases much accelerate the vocabulary learning. In addition, other works suggest that cognitive biases help to acquire grammar rules faster. The efficacy of the cognitive biases enables infants to connect an utterance to its meaning; even a single uttered situation contains many possible meanings. In this study, we focus on the symmetry bias which is one of the cognitive biases. The aim of this study is to evaluate the efficacy of the symmetry bias in the multiple meaning environment. In the experiments, two symmetry bias patters are utilized to evaluate the developed Meaning Selection Iterated Learning Model. The patterns are strict/loose symmetry bias with distance in languages and expressivity

Process Acceleration in the Iterated Learning Model with String Clipping

In evolutionary linguistics, the Iterated Learning Model (ILM) is often used for simulating a first language acquisition. Whereas an infant agent acquires a grammar through communication with his/her parent in ILM, the length of syntax rules tends to increase rapidly over generations due to the addition of symbols of meaningless terminal symbols. In the case learning agents potentially have more than one teacher agent, this problem causes an unnatural learning, which results in a combinatorial explosion. In this paper, we propose a learning method in ILM to solve the problem by string clipping. Our experimental result showed that the length of utterances decreases without potential influence in intergenerational language propagation