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Context Effects in Lexical Ambiguity Processing in Chinese: A Meta-Analysis
Jia Guo,Hua Shu,Ping Li 서울대학교 인지과학연구소 2007 Journal of Cognitive Science Vol.8 No.1
Context effects in lexical ambiguity processing have been extensively examined in various languages including Chinese. A meta-analysis was performed on seven studies conducted in Chinese in order to determine how the Chinese data as a whole agree or disagree with previous findings in other languages. All seven studies reviewed in our analysis used the priming technique to determine the degree of activation of alternative meanings of an ambiguous word in sentence context. The analysis reveals a small but consistent effect of context on lexical access: the contextually appropriate interpretation of a word consistently shows greater priming than the inappropriate interpretation. We further show that sentence contexts interact closely with the meaning frequency of an ambiguous word. We also identify variables in these studies such as length of context and timing of presentation that could influence the strength of the context effect.
Characterization of Three A-type Cyclin Genes in Tomato (Solanum Lycopersicum) Treated with Auxins
( Jia Guo ),( Hye Kyoung Kwon ),( Myeong Hyeon Wang ) 한국응용생명화학회 2010 Applied Biological Chemistry (Appl Biol Chem) Vol.53 No.3
We characterized three A-type cyclins from tomato: SlCycA1 (AJ243451), SlCycA2 (AJ243452), and SlCycA3 (AJ243453). All of them had the typical cyclin box and LVEVxEEY motif, and were homologous to other A-type cyclins. SlCycA1 and SlCycA2 were significantly expressed in meristematic tissues, but the transcript of SlCycA3 maintains relatively low levels in the various tissues we tested. To investigate the effect of various hormones on the expression level of A-type cyclins, two-week-old tomato seedlings were treated with 10 μM indole-3-acetic acid (IAA), 3-Indolebutyric acid (IBA), or α-naphthalene acetic acid (NAA) for 0, 0.5, 1, 2, 5, or 10 d. Treatment with IAA or IBA significantly increased the cells in the G2 phase after treatment for 10 days, whereas the proportion of cells in the S phase was strongly increased by NAA treatment. RT-PCR analysis showed that the SlCycA1 and SlCycA2 transcripts accumulated at 5 days and 2 days after IAA treatment, respectively. Also, the SlCycA3 transcripts were strongly induced after 10 days of IBA or NAA treatment. These data provide new insights into the cell division accompanying hormone treatment in tomato.
Jia Guo,Hye Kyoung Kwon,Myeong Hyeon Wang 한국응용생명화학회 2010 Journal of Applied Biological Chemistry (J. Appl. Vol.53 No.3
We characterized three A-type cyclins from tomato: SlCycA1 (AJ243451), SlCycA2 (AJ243452), and SlCycA3 (AJ243453). All of them had the typical cyclin box and LVEVxEEY motif, and were homologous to other A-type cyclins. SlCycA1 and SlCycA2 were significantl
Jia Guo,왕명현 한국원예학회 2011 Horticulture, Environment, and Biotechnology Vol.52 No.1
A dehydration responsive element binding (DREB) gene, designated LeDREB2, was isolated from tomato. It was classified as an A-2 group member of the DREB family. LeDREB2 is a single copy gene in the tomato genome, and it was strongly expressed in young leaves and roots but weakly expressed in mature leaves and shoots. Transcripts of the LeDREB2 genes were induced by various environmental stresses such as drought and cold. Expression analysis demonstrated that various oxidative stresses (e.g., salt (NaCl), abscisic acid (ABA), hydrogen peroxide (H2O2), methyl viologen (MV)) could induce the transcripts of LeDREB2 gene after application of time courses treatments except ABA. These results indicate that the LeDREB2 gene is a DREB transcription factor, which may play a role in both abiotic and oxidative stress responses.
Modified GMDH networks for oilfield production prediction
Jia Guo,Wei Huang,Qiong Mao,Xudong Wang,Xinying Wang,Tao Song 한국자원공학회 2018 Geosystem engineering Vol.21 No.4
The self-organizing Group Method of Data Handling (GMDH) functional network is effective in predicting oilfield production. During operation the division of data sample depending on artificial classification cannot lead to global optimum in great probability and the variables are probably eliminated early in the iterative process in traditional GMDH algorithm. Recent years, GMDH model has been improved through many artificial intelligent models, but few people take the optimization of the model structure into account. In this paper, different training and testing set grouping and the effects of variables transmission were studied. The modified GMDH algorithm was optimized using the original variables preservation method and the random sample method, which was applied to the oilfield production forecasting simulation. The results of the modified GMDH algorithm, the traditional GMDH algorithm, ANNs and the empirical equations for predicting annual oil production were compared. The simulative results indicated that the modified GMDH model was the best tool for data-fitting with lowest error (RMSE = 13.9440, MAPE = 0.1121 and SI = 0.0378) and highest accuracy (R = 0.9984).
Efficient Greedy Algorithms for Influence Maximization in Social Networks
Lv, Jiaguo,Guo, Jingfeng,Ren, Huixiao Korea Information Processing Society 2014 Journal of information processing systems Vol.10 No.3
Influence maximization is an important problem of finding a small subset of nodes in a social network, such that by targeting this set, one will maximize the expected spread of influence in the network. To improve the efficiency of algorithm KK_Greedy proposed by Kempe et al., we propose two improved algorithms, Lv_NewGreedy and Lv_CELF. By combining all of advantages of these two algorithms, we propose a mixed algorithm Lv_MixedGreedy. We conducted experiments on two synthetically datasets and show that our improved algorithms have a matching influence with their benchmark algorithms, while being faster than them.