學術出版;;Publications

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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Developing team performance: the double-edged nature of justice
(1970-01-01)
Chen, Jui-Yu
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Lin, Chieh-Peng
;
經營管理研究所
;
Institute of Business and Management
This study bridges social cognitive theory and Turner's theory of social interaction to develop a model that explains how work teams in technology industry can be motivated to enhance team performance. The prevalence of the use of work teams in technology industry environments has enhanced the importance of studying the processes involved in their performance development. For that reason, this study uses data from team workers in high-tech firms in Taiwan, and shows that collective efficacy positively relates to team performance via the mediation of social interaction. Furthermore, justice positively moderates the relationship between collective humility and social interaction but negatively moderates the relationship between collective efficacy and social interaction. Lastly, managerial implications and research limitations based on the empirical results are provided.
Six-way decomposition of causal effects: Unifying mediation and mechanistic interaction
(1970-01-01)
Huang, Yen-Tsung
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Tai, An-Shun
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Chou, Meng-Ying
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Lin, Geng-Xian
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Lin, Sheng-Hsuan
The sufficient component cause (SCC) model and counterfactual model are two common methods for causal inference, each with their own advantages: the SCC model allows the mechanistic interaction to be detailed, whereas the counterfactual model features a systemic framework for quantifying causal effects. Hence, integrating the SCC and counterfactual models may facilitate the conceptualization of causation. Based on the marginal SCC (mSCC) model, we propose a novel counterfactual mSCC framework that includes the steps of definition, identification, and estimation. We further propose a six-way effect decomposition for assessing mediation and the mechanistic interaction. The results demonstrate that when all variables are binary, the six-way decomposition is an extension of four-way decomposition and that without agonism, the six-way decomposition is reduced to four-way decomposition. To illustrate the utility of the proposed decomposition, we apply it to a Taiwanese cohort to examine the mechanism of hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC) with liver inflammation measured by alanine aminotransferase (ALT) as a mediator. Among the HCV-induced HCC cases, 62.27% are not explained by either mediation or interaction in relation to ALT; 9.32% are purely mediated by ALT; 16.53% are caused by the synergistic effect of HCV and ALT; and 9.31% are due to the mediated synergistic effect of HCV and ALT. In summary, we introduce an SCC model framework based on counterfactual theory and detail the required identification assumptions and estimation procedures; we also propose a six-way effect decomposition to unify mediation and mechanistic interaction analyses.
A two-step deep learning approach to data classification and modeling and a demonstration on subject type relationship analysis in the Web of Science
(1970-01-01)
Phoa, Frederick Kin Hing
;
Lai, Hsin-Yi
;
Chang, Livia Lin-Hsuan
;
Honda, Keisuke
;
統計學研究所
It is common sense that some subjects have strong relationships while others are perhaps almost mutually independent, but a quantitative and systematic approach to describe such sense is a deficiency. A technique called pointwise mutual information (PMI) from information science helps to fulfill the request, but the calculation through a large-scale database is computationally infeasible if one requires an instantaneous value. This work provides a two-step remedy via deep learning for estimating and predicting relationships among two subject types that are found in the large-scale citation database called the Web of Science. The resulting model successfully replicates existing PMI values among subject types, and it can be used for predicting PMI values of two subject types if one or both subject types does not exist in the database.
Post-Pictet-Spengler Cyclization (PPSC): A Strategy to Synthesize Polycyclic beta-Carboline-Derived Natural Products and Biologically Active N-Heterocycles
(1970-01-01)
Singh, Rahul
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Kumar, Sunil
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Patil, Madhuri T.
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Sun, Chung-Ming
;
Salunke, Deepak B.
Pictet-Spengler reaction is a typical condensation reaction of beta-arylethylamine or Tryptamine with an aldehyde or ketone followed by ring closure to generate tetrahydroisoquinoline (THIQ) and Tetrahydro-beta-carboline (TH beta C), respectively. Both these key skeletons represent huge range of structurally diverse naturally occurring isoquinoline and indole alkaloids. Nature has synthesized structurally complex Reserpine, Ajmalicine as well as other related alkaloids using various biosynthetic pathwaysviaPictet-Spengler reaction followed by post-Pictet-Spengler cyclization (PPSC) stratergy. This biosynthetic process is the short and reliable method to access diverse range of polycyclic heterocycles. Over the period of last sixty years, several researchers have synthesized complex alkaloids without highlighting the synthetic utility of the post-Pictet-Spengler cyclization strategy. We summarized the synthesis of numerous natural products and related natural product inspired heterocyclic scaffolds which follow the PPSC strategy to demonstrate its usefulness for the construction of diverse compound collection. 1. Introduction 2. beta-Carboline Fused Heterocycles Synthesized via post-Pictet-Spengler C1-N2 Cyclization 3. beta-Carboline Fused Heterocycles Synthesized via post Pictet-Spengler C1-N9 Cyclization 4. beta-Carboline Fused Heterocycles Synthesized via post-Pictet-Spengler C3-N2 Cyclization 5. beta-Carboline Fused Heterocycles Synthesized via post-Pictet-Spengler C1-C3 Cyclization 6. Conclusion
Remote sensing image colorization using symmetrical multi-scale DCGAN in YUV color space
(1970-01-01)
Wu, Min
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Jin, Xin
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Jiang, Qian
;
Lee, Shin-jye
;
Liang, Wentao
Image colorization technique is used to colorize the gray-level image or single-channel image, which is a very significant and challenging task in image processing, especially the colorization of remote sensing images. This paper proposes a new method for coloring remote sensing images based on deep convolution generation adversarial network. The adopted generator model is a symmetrical structure using the principle of auto-encoder, and a multi-scale convolutional module is specially designed to introduce into the generator model. Thus, the proposed generator can enable the whole model to retain more image features in the process of up-sampling and down-sampling. Meanwhile, the discriminator uses residual neural network 18 that can compete with the generator, so that the generator and discriminator can effectively optimize each other. In the proposed method, the color space transformation technique is first utilized to convert remote sensing images from RGB to YUV. Then, the Y channel (a gray-level image) is used as the input of the neural network model to predict UV channels. Finally, the predicted UV channels are concatenated with the original Y channel as a whole YUV that is then transformed into RGB space to get the final color image. Experiments are conducted to test the performance of different image colorization methods, and the results show that the proposed method has good performance in both visual quality and objective indexes on the colorization of remote sensing image.
Fixed-dose combination of amlodipine and atorvastatin improves clinical outcomes in patients with concomitant hypertension and dyslipidemia
(1970-01-01)
Lin, Chia-Pin
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Tung, Ying-Chang
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Hsiao, Fu-Chih
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Yang, Chia-Hung
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Kao, Yi-Wei
Hypertension and dyslipidemia are important risk factors for cardiovascular disease. However, the clinical outcomes of fixed-dose combination (FDC) versus free-equivalent combination (FEC) of amlodipine and atorvastatin in the treatment of concurrent hypertension and dyslipidemia remain unknown. In this study, we included patients with newly diagnosed hypertension and dyslipidemia, without previously established cardiovascular disease, and treated with either FDC or FEC of amlodipine and atorvastatin were identified from the National Health Insurance Research Database of Taiwan and follow-up for 5 years. By using 1:1 propensity score matching, a total of 1756 patients were enrolled in this study. The composite of major adverse cardiovascular events, including all-cause mortality, myocardial infarction (MI), stroke, and coronary revascularization, occurred more frequently in the FEC group than in the FDC group (hazard ratio, 1.88; 95% confidence interval [CI], 1.42 to 2.5). Although the all-cause mortality did not differ (hazard ratio, 0.46; 95% CI, 0.36 to 1.59), the FEC group developed increased MI, stroke, and coronary revascularization (hazard ratio, 2.87; 95% CI, 1.07 to 7.68; hazard ratio, 1.97; 95% CI, 1.41 to 2.74; and hazard ratio, 2.44; 95% CI, 1.26 to 4.69, respectively). Furthermore, as an unexpected result, a higher risk to develop new-onset diabetes mellitus was observed with FEC regimens (hazard ratio, 2.19; 95% CI, 1.6 to 3.0). In conclusion, although the all-cause mortality did not differ between the two groups, the FDC regimen of amlodipine and atorvastatin improved clinical outcomes when compared to FEC in patients with newly diagnosed hypertension and dyslipidemia.
Reliability evaluation in terms of flow data mining for multistate networks
(1970-01-01)
Lin, Yi-Kuei
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Chen, Shin-Guang
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工業工程與管理學系
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Department of Industrial Engineering and Management
Network reliability is famous for its problem solving ability in several real-life applications. However, due to its NP-hard nature (Ball in IEEE Trans Reliab 35(3):230-238, 1986), researchers are devoted to the improvement of computational efficiency in various approaches. Although flow in networks depicts its combination properties, only few of them are useful in the calculation of network reliability. In some point of views, we call it mining in flow data. This paper presents techniques of how to efficiently do the flow data mining tasks. A skill based on backtrack and maximal flow is illustrated with examples and benchmarks. The results show that the proposed approach is valuable in the calculation of network reliability.
A Direct Mapping Approach to Understand Carrier Relaxation Dynamics in Varied Regions of a Polycrystalline Perovskite Film
(1970-01-01)
Fathi, Amir
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Jokar, Efat
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Lee, Yuan-Pern
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Diau, Eric Wei-Guang
;
交大名義發表
We developed a direct mapping approach to overlay the image of a polycrystalline perovskite film obtained from the transient absorption microscope (TAM) with that from the scanning electron microscope (SEM). By mapping these imaging data pixel by pixel, we are able to observe the relaxation dynamics of the photo-generated charge carriers on varied regions of the film. The carrier relaxation dynamics contain a dominated single-exponential decay component owing to the recombination of charge carriers. The lifetime distribution of charge recombination shows a bimodal feature, for which the rapid and slow distributions are assigned as free and trapped carriers, respectively. The charge recombination was slower in the grain boundary (GB) region than in the grain interior (GI) region. The small grains have longer lifetimes than the large grains for the crystal size smaller than 500 nm. Therefore, GB with retarded charge recombination might play a positive role in a perovskite solar cell.
Generation of small groups with rich behaviors from natural language interface
(1970-01-01)
Chen, Chien-Yuan
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Wong, Sai-Keung
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Liu, Wen-Yun
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資訊工程學系
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Department of Computer Science
In this article, we develop a system with a natural language interface to generate animation of small groups in environments with ambient crowds. Our system takes simple sentences of English as input and these sentences are parsed to obtain information about character attributes, behaviors, and locations for constructing situation nodes. These situation nodes form an animation graph for producing an animation of small groups. The interface assists in interactively managing and editing the animation graph. We demonstrate the effectiveness of the system in some examples. The user study results indicate that the proposed system is user-friendly and flexible in producing animation of small groups in various scenes.
A Survey on Deep Learning-Based Vehicular Communication Applications
(1970-01-01)
Lin, Chia-Hung
;
Lin, Yu-Chien
;
Wu, Yen-Jung
;
Chung, Wei-Ho
;
Lee, Ta-Sung
Besides the use of information transmission, vehicular communications also perform an essential role in intelligent transportation systems (ITS) for exchanging critical driving information among end users, vehicles, and infrastructures. Moreover, to enhance the understanding of the local environment, increasingly more data are collected by sensors, inducing an extensive use of deep learning (DL)-based algorithms in ITS. To further promote the development of DL-based algorithms in ITS, in this paper, we present a concise introduction of DL technologies. Then, we conduct an in-depth investigation on two popular DL-based applications used in ITS, traffic flow forecasting and trajectory prediction, focusing onwhenandhowthe authors employ different DL models and training schemes in these tasks. Finally, we raise two existing problems while employing DL-based algorithms in practical ITS and further discuss certain recent advances in DL-based research to tackle these challenges. To encourage more researchers to focus on the development of DL-based algorithms in ITS for a better world, we hope this paper can be treated as an informational material for prospective researchers, which contains the essential background knowledge of DL-based ITS applications; we also hope this paper will encourage experienced researchers to counter the open challenges and achieve a technical breakthrough to ITS.

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