The effects of concordance-based electronic glosses on L2 vocabulary learning

The present study investigates the effects of two different vocabulary learning conditions in digital reading environments equipped with electronic textual glossing. The first condition presents the concordance lines of a target lexical item, thereby making learners infer its meaning by reading the referenced sentences. The second condition additionally offers the definition of a target lexical item after learners consult the concordance lines, thus enabling learners to confirm their meaning inference. A total of 138 English as a Foreign Language students completed a meaning-recall vocabulary pre-test, and three different reading tasks, which were followed by meaning-recall vocabulary post-tests in a repeated measures design with a control condition. Overall, the findings showed that the second condition resulted in higher vocabulary gains than both the first condition andthe control condition. Yet, a closer look at the interactions of (a) the participants’ clicking behaviors, (b) the difficulty of selected concordance lines, (c) the surrounding contexts around target lexical items, and (d) the participants’ prior knowledge of the target lexical items showed that each target lexical item may require different treatments for it to be recalled most efficiently and effectively. Through this investigation, the present study suggests that glossary information, such as concordance lines, may involve more complex and unexpected learner interactions

Effects of EMI-CLIL on secondary-level students’ English learning: A multilevel meta-analysis

This meta-analysis synthesized the effects of the English medium instruction and content and language integrated learning (EMI-CLIL) approach on secondary-level students’ English learning. The dataset included 44 samples (N = 7,434) from 38 primary studies. The results revealed EMI-CLIL’s overall effectiveness for the development of English competence compared to the mainstream condition in the short term (d = 0.73, SE = 0.06, 95% CI [0.61, 0.86]) and longer term (d = 1.01, SE = 0.06, 95% CI [0.88, 1.15]). Additionally, we found that EMI-CLIL’s overall effectiveness was influenced by several moderator variables. Its effectiveness was significantly: (1) higher for learners whose first language (L1) was linguistically related to English; (2) lower for primary studies which confirmed the homogeneity of the EMI-CLIL and comparison groups; (3) lower when studies targeted the productive (rather than receptive or overall) dimension of English learning; and (4) higher when outcome measures focused on vocabulary. Implications for pedagogy and future research are discussed

The role of motivation and vocabulary learning strategies in L2 vocabulary knowledge: A structural equation modeling analysis

This study explores the complex relationships between language learning motivation, vocabulary learning strategies, and two components of second language vocabulary knowledge (i.e., vocabulary size and depth), within the framework of self-regulated learning. Responses to questionnaires were gathered from 185 secondary-level Korean adolescent learners of English as a foreign language, regarding their motivation and vocabulary learning strategy use; additionally, the results of their vocabulary size and depth tests were collected. We adopted structural equation modeling for analysis, with vocabulary learning strategies consisting of memory, cognitive, and metacognitive categories, and vocabulary knowledge consisting of vocabulary size and depth. The results showed that motivation directly predicted vocabulary learning strategies and vocabulary knowledge, and indirectly predicted vocabulary knowledge via vocabulary learning strategies. When further classified, intrinsic motivation was found to have a stronger influence on the use of vocabulary learning strategies and vocabulary knowledge than extrinsic motivation. We discuss the implications of increasing learners’ motivation and repertoire of strategies for improving vocabulary size and depth.This study explores the complex relationships between language learning motivation, vocabulary learning strategies, and two components of second language vocabulary knowledge (i.e., vocabulary size and depth), within the framework of self-regulated learning. Responses to questionnaires were gathered from 185 secondary-level Korean adolescent learners of English as a foreign language, regarding their motivation and vocabulary learning strategy use; additionally, the results of their vocabulary size and depth tests were collected. We adopted structural equation modeling for analysis, with vocabulary learning strategies consisting of memory, cognitive, and metacognitive categories, and vocabulary knowledge consisting of vocabulary size and depth. The results showed that motivation directly predicted vocabulary learning strategies and vocabulary knowledge, and indirectly predicted vocabulary knowledge via vocabulary learning strategies. When further classified, intrinsic motivation was found to have a stronger influence on the use of vocabulary learning strategies and vocabulary knowledge than extrinsic motivation. We discuss the implications of increasing learners’ motivation and repertoire of strategies for improving vocabulary size and depth

A protein domain interaction interface database: InterPare.

BACKGROUND: Most proteins function by interacting with other molecules. Their interaction interfaces are highly conserved throughout evolution to avoid undesirable interactions that lead to fatal disorders in cells. Rational drug discovery includes computational methods to identify the interaction sites of lead compounds to the target molecules. Identifying and classifying protein interaction interfaces on a large scale can help researchers discover drug targets more efficiently. DESCRIPTION: We introduce a large-scale protein domain interaction interface database called InterPare http://interpare.net. It contains both inter-chain (between chains) interfaces and intra-chain (within chain) interfaces. InterPare uses three methods to detect interfaces: 1) the geometric distance method for checking the distance between atoms that belong to different domains, 2) Accessible Surface Area (ASA), a method for detecting the buried region of a protein that is detached from a solvent when forming multimers or complexes, and 3) the Voronoi diagram, a computational geometry method that uses a mathematical definition of interface regions. InterPare includes visualization tools to display protein interior, surface, and interaction interfaces. It also provides statistics such as the amino acid propensities of queried protein according to its interior, surface, and interface region. The atom coordinates that belong to interface, surface, and interior regions can be downloaded from the website. CONCLUSION: InterPare is an open and public database server for protein interaction interface information. It contains the large-scale interface data for proteins whose 3D-structures are known. As of November 2004, there were 10,583 (Geometric distance), 10,431 (ASA), and 11,010 (Voronoi diagram) entries in the Protein Data Bank (PDB) containing interfaces, according to the above three methods. In the case of the geometric distance method, there are 31,620 inter-chain domain-domain interaction interfaces and 12,758 intra-chain domain-domain interfaces

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Neural phoneme models with Feature embedding

학위논문 (석사) -- 서울대학교 대학원 : 인문대학 언어학과, 2020. 8. 신효필.본 연구는 다양한 자연어처리 과제의 해결을 위해 단어 내 정보에 대한 효과적인 신경망 음소 모델을 찾고자 하였다. 신경망 음소 모델을 개선하기 위해 자질(feature) 정보를 자질 임베딩(feature embedding)을 이용하여 반영하였다. 신경망을 이용하여 음소배열제약을 학습한 기존 연구에서는 자질(feature) 정보를 1, -1, 0으로 매핑하여 음소 임베딩을 초기화했다. 이렇게 자질을 반영한 모델은 자질이 반영되지 않은 모델과 비교하여 더 높은 성능을 보여주지 못했다. 이런 자질 정보의 활용을 소극적이라고 보고, 본 연구에서는 자질 임베딩(feature embedding)을 도입하여 각 자질마다의 임베딩을 구축하도록 모델을 설계하였다. 자질 정보를 포함하지 않는 음소 임베딩(phone embedding)을 이용하는 방식, 자질 임베딩만으로 음소 임베딩(phone embedding)을 만드는 음소-자질 임베딩(phone by feature embedding)을 사용하는 방식과 음소-자질 임베딩(phone by feature embedding)에 음소 임베딩(phone embedding)을 결합하는 방식을 실험함으로써 자질 정보의 포함이 모델의 성능을 높일 수 있는지를 알아보고, 자질 정보를 포함하는 최적의 방식을 확인하고자 한다. 실험은 한국어와 영어를 대상으로 진행하였으며, 모델은 트랜스포머 인코더(Transformer Encoder)를 사용하였다. 실험의 결과로 음소-자질 임베딩(phone by feature embedding)만을 활용한 모델이 음소 임베딩(phone embedding)을 사용한 모델보다 평균적으로 4~5%의 성능향상을 보였으며 이 방식이 음소 모델 구축에 효과적임을 확인할 수 있었다. 훈련한 임베딩을 군집화 한 결과로 음소-자질 임베딩(phone by feature embedding)만을 활용하는 방식이 자질 정보를 가장 뚜렷하게 구분하였다. 이는 실험에서 가장 높은 성능을 보인 음소-자질 임베딩(phone by feature embedding)만을 활용하는 방식이 설계의 의도대로 자질(feature) 정보를 반영하였음을 보였다.The purpose of this thesis is to reveal the usefulnes of feature information in neural network phoneme model. In previous research, for the aim of comparing feature-aware condition to feature-naive condition in neural network phoneme model, phone embeddings are usualy initialized by mapping feature information to 1, -1, and 0. The model using this initialization did not show higher performance compared to those without feature information. To make more use of feature information, this study aims to design neural phoneme model by introducing feature embedding. To ases the obligatorines of phonological distinctive features for phonotactic learning, experiments have done with thre diferent embedding methods: phone embedding, phone by feature embedding, and phone & feature embedding. It was conducted with English data and Korean data, and base structure of our model is Transformer Encoder. As the results, the model using phone by feature embedding outperforms the others about 4~5%, sugesting that this knowledge of distinctive features would be regarded as a help rather than a slight hindrance. As a result of clustering with learned embedding, the model of phone by feature embedding clearly distinguishes the most feature information, which supports the use of feature information is efective in phonotactic learning through neural network.1. 서론 1 2. 선행 연구 5 2.1. 음소 임베딩 6 2.2. LSTM 기반의 음소 모델 10 2.3. RNN 기반의 음소 모델 12 3. Transformer Encoder 18 4. 모델 25 4.1. 모델의 구조 27 4.2. Input representation 28 4.2.1. Phone embedding 29 4.2.2. Phone by Feature embedding 31 4.2.3. Phone & Feature embedding 34 5. 실험 36 5.1. 데이터 36 5.1.1. 사전학습 37 5.1.2. 사후학습 37 5.2. 학습 환경 43 5.2.1. 사전학습 44 5.2.2. 사후학습 46 5.3. 평가 47 5.4. 결과 48 5.4.1. 영어 음소 모델 50 5.4.2. 한국어 음소 모델 54 6. 분석 57 7. 결론 66 참고문헌 71 부록 78 Abstract 85Maste

Solar energy-based devices for the production of fresh water

We have developed a new solar energy-based device that produces fresh water and electricity simultaneously. Cellulose nanofibers were obrained by Tempo-mediated oxidation of bleached pulp and used to prepare a porous CNF substrate. The top surface of the CNF substrate was coated with thin layers of polypyrrole and ion exchange polymer to absorb sun light and to maintain a salt concentration gradient, respectively. The substrate was exposed to sunlight after being placed in sea water. The sea water absorbed through the hydrophilic substrate evaporated from the surface due to the solar heat, resulting in a salt concentration gradient between the upper and lower surfaces. More electricity was generated as more concentration gradient was created.2