京都大学 / 新制・課程博士 / 博士(薬学) / 甲第25235号 / 薬博第870号 / 京都大学大学院薬学研究科薬学専攻 / (主査)教授 山下 富義, 准教授 白川 久志, 教授 寺田 智祐 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

迴歸分析與類神經網路此兩種方法皆是預測領域上的主要工具。本論文嘗試在線性迴歸模式及非線性迴歸模式的條件下，隨機產生不同特性的資料以完整探討資料特性對迴歸分析與類神經網路之預測效果的影響。這些特性包括常態分配、偏態分配、不等變異、Michaelis-Menten關係模式及指數迴歸模式。 再者，我們使用區域搜尋法(local search methods)中的演化策略法(evolution strategies，ES)作為類神經網路的學習(learning)方法以提高其預測功能。我們稱這種類型的類神經網路為ESNN。 模擬結果顯示，ESNN確實可以取代常用來與迴歸分析做比較的倒傳遞類神經網路(back-propagation neural network，BPNN)，成為類神經網路的新選擇。針對不同特性的資料，我們建議：如果原始的資料適合以常態線性迴歸模式配適，則使用者可考慮使用迴歸方法做預測。如果原始的資料經由圖形分析或由檢定方法得知違反誤差項為均等變異之假設時，若能找到合適的權數，可使用加權最小平方法，但若權數難以決定時，則使用ESNN做預測。如果資料呈現韋伯偏態分佈時，可考慮使用ESNN或韋伯迴歸方法。資料適合以非線性迴歸模式做配適時，則選擇以ESNN做預測。 關鍵詞：迴歸分析，類神經網路，區域搜尋法，演化策略法類神經網路，倒傳遞類神經網路 / Both regression analysis and artificial neural networks are the main techniques for prediction. In this research, we tried to randomly generate different types of data, so as to completely explore the effect of data characteristics on the predictive performance of regression analysis and artificial neural networks. The data characteristics include normal distribution, skew distribution, unequal variances, Michaelis-Menten relationship model and exponential regression model. In addition, we used the evolution strategies, which is one of the local search methods for training artificial neural networks, to further improve its predictive performance. We name this type of artificial neural networks ESNN. Simulation studies indicate that ESNN could indeed replace BPNN to be the new choice of artificial neural networks. For different types of data, we commend that users can use regression analysis for their prediction if the original data is fit for linear regression model. When the residuals of the data are unequal variances, users can use weighted least squares if the optimal weights could be found. Otherwise, users can use ESNN. If the data is fit for weibull distribution, users can use ESNN or weibull regression. If the data is fit for nonlinear regression model, users can choose ESNN for the prediction. Keywords: Regression Analysis, Artificial Neural Networks, Local Search Methods, Evolution Strategies Neural Network (ESNN), Back-propagation Neural Network (BPNN)

本研究嘗試檢視原發性失眠 (Primary insomnia) 患者在接受完認知行為治療後，其生理激發系統與恆定系統相關生理指標的改變，並探討其主、客觀睡眠改善與生理指標變化之間的關聯，藉以瞭解不同的生理系統在影響原發性失眠患者其主、客觀睡眠改善程度上可能的重要性。本研究透過醫師轉介，共18名原發性失眠患者 (男5人，女13人，平均年齡37.4歲) 接受7週6次的失眠認知行為治療，在接受治療的前後，分別進行一個晚上的多頻道睡眠記錄檢查與主、客觀睡眠評估。再進一步分析高頻率腦波Beta波 (14~35Hz) ，用來反應其中樞神經系統的激發，低頻率腦波Delta波 (0.5~2.5Hz) 則用來反應個案的恆定系統；另外，透過心跳變異率分析得出的參數，包括低頻率 (Low frequency，簡稱LF) 功率與高頻率 (High frequency，簡稱HF) 功率，以LF/HF的比率值測量其交感神經系統的活動，而HF/ (LF+HF) 則是測量副交感神經系統的活動。研究結果顯示個案的失眠問題在主觀睡眠評估指標上有顯著改善，而客觀睡眠評估指標與睡眠結構於入睡時間與入睡後清醒時間有顯著降低，其餘則無顯著改善。而各項生理系統指標，僅後半夜階段二的腦波的Delta波有顯著上升，其餘均無顯著地改善，而LF/HF的下降與失眠嚴重度的下降有顯著地關聯。因此，本研究顯示原發性失眠患者接受認知行為治療前後，其交感神經系統的下降與失眠嚴重度之改善有明顯關聯，推論失眠認知行為治療對於原發性失眠患者的交感神經活動的改善，可能是使其失眠嚴重度改善的關鍵。 / The present study evaluated the changes in beta and delta ranges of electroencephalogram (EEG) power and heart rate variability (HRV) after cognitive behavioural therapy for insomnia (CBT-I) to understand the effect of CBT-I on arousal system and homeostatic system. The study also examined the correlations between change of sleep measurement and the physiological index to clarify underlying mechanisms of sleep improved by CBT-I. Eighteen primary insomnia patients (5 males, 13 female, mean age = 37.4) participated in this study. The participants were scheduled to come to the sleep laboratory for polysomnographic (PSG) recording twice, one prior to CBT-I and one following CBT-I. A course of 6-session CBT-I was conducted during a period of seven weeks. Subjects’ changes in subjective ratings of sleep quality and quantity and sleep parameters in PSG were calculated. Spectrum analyses were conducted for their EEG and electrocardiogram (EKG). Beta EEG activity (14~35 Hz) was used to indicate the central nervos system (CNS) arousal level and Delta EEG activity (0.5~2.5 Hz) for the intensity of homeostatic system. Low frequency power (LF) and high frequency power (HF) of the R-R interval were calculated for heart rate variability (HRV). LF/HF ratio was used as a index of sympathetic nervous system activity and the HF/ (LF+HF) ratio as a index of parasympathetic nervous system activity. The results show subjective sleep quality of subjects were significantly improved after CBT-I. PSG shows shortened sleep onset latency and decreased wake time after sleep onset, but not in the other measures. For EEG spectrum and HRV parameters, only Delta EEG activity in stage2 of the second half of the night was significantly improved. In addition, the decrease of LF/HF significantly correlated with the improvement of the insomnia severity index. Thus, the results suggests that sleep improvements by CBT-I may be associated with the reduction of sympathetic arousal.

為了瞭解影響演化性神經網路（ENT）預測表現的四項重要的機制：輸入資料性質、訓練樣本大小、網路搜尋密度以及控制模型複雜度，進而找出能使ENT充分發揮效果的組合。在本論文中首先設計ENT在模擬資料上的實驗，探討上述四項機制個別對預測表現的影響，再依照實驗結果的建議，設計能讓ENT發揮功效的組合，並以實際金融高頻率資料：香港恆生指數在一九九八年十二月報酬率為標的，探討模擬資料的結果在實際金融資料需要調整的部份。實驗結果顯示，當輸入資料經過線性過濾後，搭配大樣本訓練、高搜尋強度與適當地模型複雜度控制，會是能讓神經網路提高預測能力的組合。在實際金融資料的實驗當中同時發現，資料中偶而出現特別高或特別低的變化，會對ENT的預測表現有相當程度的影響。 / In this thesis, Evolutionary Neural Trees (ENTs) are applied to forecast the artificial data generated by financial and chaos models — iid random, linear process (Auto Regressive-Moving Average；ARMA), nonlinear processes (AutoRegressive Conditional Heteroskedasticity；ARCH, General AutoRegressive Conditional Heteroskedasticity；GARCH, Bilinear), mixed linear and nonlinear process (AR and GARCH). Experiments of the artificial data were conducted to understand the characteristics of ENTs mechanism. – data pre-processing procedures, search intensity, sample size and complexity regularization. From the experiment results of artificial data, the combination of pure linear or nonlinear time series, large sample size, intensive search and simple neural trees are suggested for the parameters setting of ENTs. And for the sake of computational burden, we have a trade-off between search intensity and sample size. Ten experiments are designed for ENTs modeling on the high-frequency stock returns of Heng Sheng stock index on December, 1998, in order to have an efficient combination of the factors of ENTs. The results show that ENTs would perform more efficiently if data are pre-processed by a linear filter, for ENTs will concentrate on searching in the space of nonlinear signals. Also, as is well demonstrated in this study, the infrequent bursts (outliers) appearing in the data set can be very disturbing for the ENTs modeling.

博士(神学) / Doctor of Theology / 同志社大学 / Doshisha University

本研究考慮重要且對台股大盤指數走勢有連動影響的因素，主要納入對台股有領頭作用的美國三大股市，那斯達克(NASDAQ)指數、道瓊工業(Dow Jones)指數、標準普爾500(S&P500)指數;其他對台股緊密連動效果的國際股票市場，香港恆生指數、上海證券綜合指數、深圳證券綜合指數、日經225指數;以及納入左右國際經濟表現的國際原油價格走勢，美國西德州原油、中東杜拜原油和歐洲北海布蘭特原油;在宏觀經濟因素方面則考量失業率、消費者物價指數、匯率、無風險利率、美國製造業重要指標的存貨/銷貨比率、影響貨幣數量甚鉅的M1B;在技術分析方面則納入多種重要的指標，心理線 (PSY) 指標、相對強弱(RSI) 指標、威廉(WMS%R) 指標、未成熟隨機(RSV) 指標、K-D隨機指標、移動平均線(MA)、乖離率(BIAS)、包寧傑%b和包寧傑帶狀寬度(BandWidth%);所有考量因素共計35項，因為納入重要因子比較多，所以完備性較高。 本研究先採用的贏者全拿(Winner-Take-All) 競爭學習策略的自組織映射網路(Self-Organizing Feature Maps, SOM)，藉由將相似資料歸屬到已身的神經元萃取出關聯分類且以計算距離來衡量神經元的離散特徵，對於探索大量且高維度的非線性複雜特徵俱有優良的因素相依性投射效果，將有利於提高預測模式精準度。在線性擬合部分則結合倒傳遞(Back-Propagation, BP)、Elman反饋式和徑向基底函數類網路(Radial-Basis-Function Network, RBF)模式為指數預測輸出，並對台股加權指數隔日收盤指數進行預測和評量。而在傳統的Elman反饋式網路只在隱藏層存在反饋機制，本研究則在輸入層和隱藏層皆建立反饋機制，將儲存在輸入層和隱藏層的過去時間資訊回饋給網路未來參考。在徑向基底函數網路方面，一般選取中心聚類點採用隨機選取方式，若能有效降低中心點個數，可降低網路複雜度，本研究導入垂直最小平方法以求取誤差最小的方式強化非監督式學習選取中心點的能力，以達到網路快速收斂，提昇網路學習品質。 研究資料為台股指數交易收盤價，日期自2001/1/2，至2011/10/31共2676筆資料。訓練資料自2001/1/2至2009/12/31，共2223筆;實證測試資料自2010/1/4至2011/10/31，計453個日數。主要評估指標採用平均相對誤差(AMRE)和平均絕對誤差 (AAE)。在考慮因子較多的狀況下，實證結果顯示，在先透過SOM進行因子聚類分析之後，預測因子被分成四個組別，分別再透過BP、Elman recurrent和RBF方法進行線性擬合，平均表現方面，以RBF模式下的四個群組因子表現最佳，其中RBF模式之下的群組4，其AMRE可達到0.63%，最差的AMRE則是群組1，約為1.05%;而Elman recurrent模式下的四組群組因子之ARME則介於1.01%和1.47%之間;其中預測效果表現最差則是BP模式的預測結果。顯示RBF具有絕佳的股價預測能力。最後，在未來研究建議可以運用本文獻所探討之其他數種類神經網路模式進行股價預測。 / In this study, we considering the impact factors for TWSE index tendency, mainly aimed at the three major American stock markets, NASDAQ index, Dow Jones index, S&P 500, which leading the Taiwan stock market trend; the other international stock markets, such as the Hong Kong Hang-Seng Index, Shanghai Stock Exchange Composite Index, Shenzhen Stock Exchange Composite Index, NIKKEI 225 index, which have close relationship with Taiwan stock market; we also adopt the international oil price trend, such as the West Texas Intermediate Crude Oil in American, the Dubai crude oil in Middle Eastern, North Sea Brent crude oil in European, which affects international economic performance widely; On the side of macroeconomic factors, we considering the Unemployed rate, Consumer Price Index, exchange rate, riskless rate, the Inventory to Sales ratio which it is important index of American manufacturing industry, and the M1b factor which did greatly affect to currency amounts; In the part of Technical Analysis index, we adopt several important indices, such as the Psychology Line Index (PSY), Relative Strength Index (RSI), the Wechsler Memory Scale—Revised Index (WMS%R), Row Stochastic Value Index (RSV), K-D Stochastics Index, Moving Average Line (MA), BIAS, Bollinger %b (%b), Bollinger Band Width (Band Width%);All factors total of 35 which we have considered the important factor is numerous, so the integrity is high. In this study, at first we adopt the Self-Organizing Feature Maps Network which based on the Winner-Take-All competition learning strategy, Similar information by the attribution to the body of the neuron has been extracted related categories and to calculate the distance to measure the discrete characteristics of neurons, it has excellent projection effect by exploring large and complex high-dimensional non-linear characteristics for all the dependency factors , would help to improve the accuracy of prediction models, would be able to help to improve the accuracy of prediction models. The part of the curve fitting combine with the back-propagation (Back-Propagation, BP), Elman recurrent model and radial basis function network (Radial-Basis-Function Network, RBF) model for the index prediction outputs, forecast and assessment the next close price of Taiwan stocks weighted index. In the traditional Elman recurrent network exists only one feedback mechanism in the hidden layer, in this study in the input and hidden layer feedback mechanisms are established, the previous information will be stored in the input and hidden layer and will be back to the network for future reference. In the radial basis function network, the general method is to selecting cluster center points by random selection, if we have the effectively way to reduce the number of the center points, which can reduces network complexity, in this study introduce the Orthogonal Least Squares method in order to obtain the smallest way to strengthen unsupervised learning center points selecting ability, in order to achieve convergence of the network fast, and improve network learning quality. Research data for the Trading close price of Taiwan Stock Index, the date since January 2, 2001 until September 30, 2011, total data number of 2656. since January 2, 2001 to December 31, 2009 a total number of 2223 trading close price as training data; empirical testing data, from January 4, 2010 to September 30, 2011, a total number of 433. The primary evaluation criteria adopt the Average Mean Relative Error (AMRE) and the Average Absolute Error (AAE). In the condition for consider more factors, the empirical results show that, by first through SOM for factor clustering analysis, the prediction factors were divided into four categories and then through BP, Elman recurrent and RBF methods for curve fitting, at the average performance , the four group factors of the RBF models get the best performance, the group 4 of the RBF model, the AMRE can reach 0.63%, the worst AMRE is group 1, about 1.05%; and the four groups of Elman recurrent model of ARME is between 1.01% and 1.47%; the worst prediction model is BP method. RBF has shown excellent predictive ability for stocks index. Finally, the proposal can be used in future studies of the literatures that we have explore several other methods of neural network model for stock trend forecasting.

序論では研究史を踏まえ、マイモニデスの時代の政治・社会的背景、彼の生涯と著作を概観し、中世ユダヤ思想における彼の思想の位置づけと影響関係を考察した上で『迷える者の手引き』読解の方法論を提示した。本論では「神の本質と属性」、「戒律と人間の自由意志」、「マイモニデスにおける神への道程」の三つを中心的テーマに据え、それらを副題の「神を知解することと人間の生き方との相関性」に結び付けて考察した。 / In the Introduction, based on the History of Maimonidean Study, I surveyed the position of Maimonides' thought and the influential relations among the Medieval Jewish Thought, and then, presented the methodology to read "the Guide of the Perplexed." In the main part, I set the following three subjects, i.e., 'the Essence of God and His Attributes,' 'God's Commandments and Man's Freedom of the Will,' and 'the Transitional Path to God in Maimonides,' to the central theme, and examined these three subjects by connecting with the subtitle, 'the Correlativity between Man's Comprehension of God and His Way of Life.' / 博士(一神教研究) / Doctor of Philosophy in Monotheistic Studies / 同志社大学 / Doshisha University

This paper investigates French entrepreneurs’ motivations to start a business in Taiwan as well as what they find most satisfying and challenging in the process. The data used for the research was collected from interviews of 11 French entrepreneurs and 4 foreign entrepreneurs. The results show that there are two groups of French entrepreneurs: those who came to Taiwan with the intention of starting a business and those who were already in Taiwan before starting a business. The key findings are the following: even if the two groups have different personal motivations to first come to Taiwan, on average, French start a business in Taiwan because of the business opportunities of the country. Indeed, most of them started a business in a niche market. The analysis shows that French and foreigners have the same motivations to start a business in Taiwan. French entrepreneurs are satisfied about the rapidity of business creation and the help received from many people. They also acknowledge the growing startup community but highlight that there are still a lot to be done in order to improve the foreign startup ecosystem.

19世紀英格蘭社會，對於運動史研究來說是很重要的時期。本研究將從19世紀的英格蘭談起，藉由公學校在運動發展中的影響力，來討論與理解跑步運動在19世紀公學校內的發展脈絡以及現代化的過程為何？19世紀在有能力就讀公學校，還有劍橋、牛津大學等中上階級們的影響之下，促使了「現代運動」的形成。同時，這些在運動發展上握有權力的中上階級們，在運動發展的過程中，塑造出了「業餘精神」，藉此來強調運動的高尚和階級的優越性。本文將從19世紀初期討論到西元1908年倫敦奧運。從公學校早期具有娛樂和暴力性質的跑步運動談起，在19世紀中歷經校長改革和學生們投入後，跑步運動中娛樂與暴力性質越來越少，加入更多工業革命發展下所帶來的現代元素。19世紀後半葉，公學校內跑步運動發展逐漸成熟，討論規則制定的情況漸少，描述比賽舉辦情況漸多。在公學校外，許多田徑俱樂部和越野賽跑俱樂部在公學校或是大學校友們的支持下成立。1880年業餘田徑協會成立，統一各地俱樂部和規則紛亂的局面，負責掌管英格蘭地區有關田徑運動的一切事宜，這也是公學校內討論跑步運動規則情況減少的原因。1908年倫敦在畢業於公學校校友的牽線下舉辦奧運，並與美國在跑步賽場上因為「業餘精神」和規則發生了些紛爭。至此，我們可以看出19世紀英格蘭公學校跑步運動的發展狀況，以及了解公學校跑步運動發展與現代運動出現間，部份的互動關係和影響。 / The nineteenth cenry is an essential age for Sport history, especially in England. This research attempts to comprehend the revolutionary transformation of British sporting culture by studying running. During the Victorian era, running, as a sport category, includes hare and hounds, cross-country running, handicap, steeplechase and track events which is a little bit different from the modern Athletics. The research focuses on the public schools’ students, their relationship with athleticism, running sport and the society in the nineteenth-century England. As its main concern is the role of the public schools played in the the transformation of British sporting culture, the dissertation begins with the education system of the public schools and the running in the old times . Then, it turns to the transformation of athleticism and the renovation of running in the public schools by discussing what, why and how it happened? The third chapter moves to the modern running games held in the public schools, and the interaction between the public schools and the amateur clubs. The Amateur Athletic Association, established in 1880, played an important role in the history of running sports and had significant effects on Amateurism. Finally, the dissertation concludes with the 1908 London Summer Olympic to see the spread of public schools’ amateurism.

研究目的：本研究釐清不同成人依戀風格在羅夏克墨漬測驗上的表現，並檢證Fonagy的精神分析理論，進而反思臨床心理學上的應用。 研究程序：研究期間，共招募到302位大學部學生填寫《親密關係經驗量表—關係結構版》。而後，根據量表信、效度分析後，篩選出高焦慮依戀分數、高逃避依戀分數及兩分數均低之控制組各25人，邀請其參與羅夏克墨漬測驗。最終收取焦慮依戀組12人、逃避依戀組12人、安全依戀控制組15人進行資料分析。 研究結果：認知及投入向度方面，焦慮依戀者與逃避依戀者在F%顯著高於安全依戀者；同時，焦慮依戀者在Complexity、Sy、Blend、M及MC 表現亦差於安全依戀者，答題使用範圍則顯示Dd%較高、W%較低。情緒指標方面，焦慮依戀者在V變數上顯著高於其他兩組。人際互動方面，焦慮依戀者的SumH及H顯著低於安全依戀者。 研究貢獻：本研究初步支持Fonagy精神分析論於不安全依戀者的認知與心智化、情緒控制與人際表徵分化等證據。此一結果亦可增加臨床實務中對不安全依戀者的衡鑑遞增效度，並探究其背後的心理病理機制。 / Objective: When attachment comes to assessment, many clinicians would utilize self-report inventories to gather information. However, different attachment styles, in fact, represent diverse behavior patterns; thus, using instruments adopting multi-rating techniques such as the Rorschach would be ideal. Based on Fonagy’s psychodynamic theory and past Rorschach results, we predict participants with different attachment styles will have different pattern in Rorschach task. Methods: We conducted a double-blind research design and recruited 302 volunteers. All participants were required to fill out the Experiences in Close Relationship - Relationship Structure inventory, a measurement used to classify the adult attachment styles. The top 7% of highest scoring participants on the Anxiety Dimension were assigned to the Anxiety Group(ANG), and the same strategy was applied to those in the Avoidance Group(AVG). The Control Group(CG) consisted of 7% of the randomly-selected individuals who scored below the means of both Anxiety and Avoidance Dimensions. Participants in these three groups were invited and encouraged to take the Rorschach test according to the R-PAS. Results: Current research collected 39 participants Rorschach protocols (n = 12 for both the ANG and ACG; n = 15 for the CG). Results shows that both ANG and AVG have higher F% then CG, and ANG have lower Complexity, Sy, Blend, M, W%, MC, SumH and H then CG. ANG have higher V then AVG and CG. Conclusion: Current results partially supported Fonagy’s psychodynamic theory. Results also support using multi-rating tasks to increase assessment validity when evaluating different attachment styles in clinical settings.