2008年初，正值美國籠罩次級房貸風暴影響、全球經濟景氣趨緩、產油國地緣政治因素造成能源價格創新高…等險峻經濟狀況之際，投資銀行設計一包含 :搭配出局條款之CMS Spread雪球型利率結構商品及結合附加WTI上限、USD/JPY匯率上下限之異型選擇權的混合結構性商品提案。本文依據標的資產屬性，參考相關文獻及近期在頂級期刊發表之利率資產評價模型研究中，選用Extended BGM模型(Ting-Pin Wu, and Son-Nan Chen(2007))、遠期曲線模型及匯率評價模型為個案之基礎評價模型；以無套利觀念依取得之市場各資產相關公開報價資料估算各模型所需之參數；由於屬於雪球利率結構型商品及路徑相關特性，在目前相關文獻無封閉解的條件下，使用蒙地卡羅模擬獲得未來各資產之現金流折現值，進而計算預期理論價值。依據上述方法論評價所獲得之預期理論價格顯示，個案並非具公平價值之交易，依此結論強烈建議客戶不應該承做本交易。 個案相當於投資銀行以買入一個5年期附帶出局條件，隱含看空經濟景氣循環之CMS Spread選擇權及買入一個1年期看空WTI價格選擇權建構此混合結構性商品。為強化客戶承做意願，設立一似乎觸及機會很大，但從交易後至今從未觸及的出局條件，又透過每日數位選擇權計息方式將WTI波動度資產化，提供大於10%之相對LIBOR rate 很高，但實際是被低估之半年收息固定費率。由於雪球型利率結構型商品特性，收益不僅取決於是否達成交易付款條件，更重要因素是達成時間點之速度。 在蒙地卡羅模擬資產價格路徑中，觸及頭一次CMS Spread付款條件天數之眾數區間為125至135，貼近實際136天。從評價結果，交易之付款條件內已隱含透過兩個不同標的資產選擇權之高預期獲利相互達到避險、套利及強化收益等效益;投資銀行可以不用額外對受眾多複雜不確定因數影響之WTI價格採取避險策略，而將所有避險成本轉嫁於選擇權賣方的客戶。在資本計提規範下及確保未來預期收益之考量下，投資銀行唯一要做是以低成本尋求中介銀行進行背對背交易以強化因市場風險所衍生之信用風險。 從研究過程，不禁讚嘆個案是投資銀行設計建構在財務工程科學上的卓越藝術及策略，從它一旦出現世界上之瞬間，個人預估其價值將達34,211,458.09美元! / Early 2008 was a steep economic era when U.S. was enveloped by subprimemortgage crisis, world's economy was slowing down, and energy prices were pushed to a historical record high by oil geopolitical factors. Under this situation, an investmentbank designed a hybrid structure product, which includes a CMS Spread Snowball interest rate structured product with USD/JPY FX rate Knock out condition, a WTIoption of an additional upper limit, a USD/JPY exchange rate combined exotic option of upper and lower limits. After considering assets attributes and reviewing the relevant literature and recent research published in top journals related to the interest rate assetpricing model, Extended BGM model (Ting-Pin Wu, and Son-Nan Chen (2007)), forward curve model, and FX Rate model are selected as the basic pricing models. Tocalculate the expected theoretical value of this structured product, the unavailable model parameters of assets are estimated through the public market data based on thearbitrage-free concept, and the discounted values of the assets future cash flows are obtained by Monte Carlo simulation because of snowball interest rate structured product and path dependency characteristic and no close form solution in current relevant literature. The results of the pricing models shows that the net present value(NPV) received by customers is lower than that received by the investment bank, theconclusion is : Strongly recommend customers should not to do this trade ! In this case, the investment bank used a long position of one 5-year period CMSSpread Option with knock out condition, which implies Bearish on the economic cycle, and a short position of a 1-year period WTI option with up and low limits condition to construct this hybrid structure product. To draw customers’ attention to this proposal, the investment bank designed a knock out condition that seemed to be met very easily,but the price never touched by the article finished date. Additionally, a daily accrued digital option is used to transfer WTI volatility to a semi-annual fixed yield over 10% that, compared to LIBOR Rate, is very high but actually is underestimated. For theSnowball structure product, the total profit depends on not only when but also, more importantly, how soon to meet the payment condition. According to the asset pricing path generated by Monte Carlo simulation, the mode range which CMS Spread payment condition first met is 125 to 135 days after the contract’s value date, very close to the actually history data of 136 days. From pricing results, terms of contract implied that two different options combined to hedge risk and gain profit from each other. Hence, the investment bank does not need to make extrahedge strategy to WTI price which is impacted by more complicated risk factors.However, customer must spend hedge cost because of taking much risk as a sell option role. Under the Capital Charge regulation, to lock up the expected profit, what the investment bank needs to do is only to pay a very low cost fee, which like insurancepremium, to look for an intermediary bank to offer a back to back trade to manage thecredit risk caused by market risk! During the research of this paper, I am amazed what an excellent art and strategy that designed by the investment bank based on financial engineering science! As this structure product appeared in this world, I estimated that it would worth 34,211,458.09 USD.

近年來剖面資料的監控在統計製程控制中有很大範圍的應用。在這篇論文裡，我們針對監控無母數剖面資料提出一個實務上的操作方法。這個操作方法有下列這些重要的特色：(1)使用一個靈活且有計算效率的無母數模型B-spline來描述反應變數與解釋變數的關係；(2)一般迴歸模型中之殘差結構假設是不需要的；(3)允許剖面資料內之觀測值間具有相關性之結構。最後，我們利用一個無線偵測器的實際資料來評估所提出方法的效率。 / Profile monitoring has received increasingly attention in a wide range of applications in statistical process control (SPC). In this work, we propose a practical proposed guide which has the following important features: (i) a flexible and computationally efficient smoothing technique, called the B-spline, is employed to describe the relationship between the response variable and the explanatory variable(s); (ii) the usual structural assumptions on the residuals are not require; and (iii) the dependence structure for the within-profile observations is appropriately accommodated. Finally, a real data set from a wireless sensor is used to evaluate the efficiency of our proposed method.

共著者あり。共著者名: 亘理文夫，今井徹，山方秀一，小林雅博. 日本歯科理工学会, 豊泉裕，亘理文夫，今井徹，山方秀一，小林雅博 = TOYOIZUMI, Hiroshi ; WATARI, Fumio ; IMAI, Tohru ; YAMAGATA, Shuichi ; KOBAYASHI, Masahiro, 光重合法による曲げおよびねじり剛性を有する審美性矯正ワイヤーの試作 = Fabrication of Aesthetic Wires with Flexural and Torsional Stiffness by Photo Curing Method, 歯科材料・器械 = The Journal of the Japanese Society for Dental Materials and Devices, 18(6), 1999 NOV, pp.429-440 / Hokkaido University (北海道大学) / 博士 / 歯学

目前大部分的聽眾主要是透過歌詞與樂曲的搭配來了解音樂所要表達的內容，因此歌詞創作在目前的音樂工業是很重要的一環。一般流行音樂創作是由作曲人與作詞人共同完成，然而有另一種方式是將既有的詩詞做為歌詞，接著重新譜曲的方式產生新的流行音樂。這種創作方式是讓舊有的詞或曲注入新的生命力，得以流傳到現在。因此本研究希望可以為一首旋律推薦適合配唱的歌詞，以對數位音樂達到舊曲新詞的加值應用。本論文包括兩個部分，分別為：(1)自動分析歌詞的詞式，找出每個段落的位置與其段落的標籤；(2)詞曲結構搭配，找出相符合結構的詞與曲，並且同步每個漢字與音符。 本論文的第一部分為詞式分析，首先將歌詞擷取四個面向的特徵值，分別為(1)句字數結構；(2)拼音結構；(3)詞性；(4)聲調音高。第二步驟，利用這四種特徵值分別建立詞行的自相似度矩陣(Self Similarity Matrix)，並且利用這四個特徵的自相似度矩陣產生一個線性組合自相似度矩陣。第三步驟，建立在自相似度矩陣上我們做段落分群以及家族(Family)組合找出最佳的分段方式，最後將找出的分段方式利用我們整理出來的規則讓電腦自動標記段落標籤。第二部分為詞曲結構搭配，首先我們將主旋律的樂句以及歌詞的詞句做第一層粗略的對應，第二步驟，將對應好的樂句與詞句做第二層漢字與音符細部的對應，最後整合兩層對應的成本當做詞曲搭配的分數。 我們以KKBOX音樂網站當做歌詞來源，並且請專家標記華語流行歌詞資料庫的詞式。實驗顯示詞式分析的Pairwise f-score準確率達到0.83，標籤回復準確率達到0.78。詞曲結構搭配中，查詢的歌曲其原本搭配的歌詞，推薦排名皆為第一名。 / Nowadays, lots of pop music audiences understand the content of music via lyrics and melody collocation. In general, a Chinese pop music is produced by composer and lyricist cooperatively. However, another producing manner is composing new melody with ancient poetry. Therefore, we want to recommend present lyrics for a melody and then achieving value-added application for digital music. This thesis includes two subjects. The first subject is lyrics form analysis. This subject is finding the block of verse, chorus, etc., in lyrics. The second subject is structure alignment between lyrics and melody. We utilize the result of lyrics form analysis and then employ a 2-tier alignment to recommend present lyrics which is suitable for singing. In lyrics form analysis, the first step, we investigate four types of feature from lyrics: (1) Word Count Structure; (2) Pinyin Structure; (3) Part of Speech Structure; (4) Word Tone Pitch. For the second step, we utilize these four types of feature to construct a SSM(Self Similarity Matrix), and blend these four types of SSM to produce a linear combination SSM. The third step is clustering blocks and finding the best Family combination based on SSM. Finally, a rule-based technique is employed to label blocks of lyrics. For the second subject, the first step is aligning music phrases and lyrics sentences roughly. The second step is aligning a word and a note for corresponding phrase and sentence. Finally, we integrated the cost of two-level alignment regarded as the lyrics and melody collocation score. We collect lyrics from KKBOX, a music web site, and invite experts label ground truth of lyrics form. The experimental result of lyrics form analysis shows that the proposed method achieves the Pairwise f-score of 0.83, and the Label Recovering Ratio of 0.78. The experiment of structure alignment between lyrics and melody shows that the original lyrics of query melodies are ranked number one.

近幾年來，華語流行音樂的發展越來越多元，而大眾所接收到的資訊是流行音樂當中的組成元素”曲與詞”，兩者分別具有賦予人類感知的功能，使人能夠深刻體會音樂作品當中所表答的內容與意境。然而，作曲與作詞都是屬於專業的創作藝術，作詞者通常在填詞時，會先對樂曲當中的結構進行粗略的分析，找出整首曲子的曲式，而針對可以填詞的部份，再進行更細部的分析將詞填入最適當的位置。流行音樂當中，曲與詞存在著密不可分的關係，瞭解歌曲結構不僅能降低填詞的門檻，亦能夠明白曲子的骨架與脈絡;在音樂教育與音樂檢索方面亦有幫助。 本研究的目標為，使用者輸入流行音樂歌曲，系統會自動分析出曲子的『曲式結構』。方法主要分成三個部分，分別為主旋律擷取、歌句分段與音樂曲式結構擷取。首先，我們利用Support Vector Machine以學習之方式建立模型後，擷取出符號音樂中之主旋律。第二步驟我們以”歌句”為單位，對主旋律進行分段，對於分段之結果建構出Self-Similarity Matrix矩陣。最後再利用Non-Negative Matrix Factorization針對不同特徵值矩陣進行分解並建立第二層之Self-Similarity Matrix矩陣，以歧異度之方式找出曲式邊界。 我們針對分段方式對歌曲結構之影響進行分析與觀察。實驗數據顯示，事先將歌曲以歌句單位分段之效果較未分段佳，而歌句分段之評測結果F-Score為0.82;將音樂中以不同特徵值建構之自相似度矩進行Non-Negative Matrix Factorization後，另一空間中之基底特徵更能有效地分辨出不同的歌曲結構，其F-Score為0.71。 / Music structure analysis is helpful for music information retrieval, music education and alignment between lyrics and music. This thesis investigates the techniques of music structure analysis for Chinese popular music. Our work is to analyze music form automatically by three steps, main melody finding, sentence discovery, and music form discovery. First, we extract main melody based on learning from user-labeled sample using support vector machine. Then, the boundary of music sentence is detected by two-way classification using support vector machine. To discover the music form, the sentence-based Self-Similarity Matrix is constructed for each music feature. Non-negative Matrix Factorization is employed to extract the new features and to construct the second level Self-Similarity Matrix. The checkerboard kernel correlation is utilized to find music form boundaries on the second level Self-Similarity Matrix. Experiments on eighty Chinese popular music are performed for performance evaluation of the proposed approaches. For the main melody finding, our proposed learning-based approach is better than existing methods. The proposed approaches achieve 82% F-score for sentence discovery while 71% F-score for music form discovery.

醫療技術的進步以及生活品質的提升，預計人類平均壽命將持續延長，以臺灣為例，男、女性平均壽命將從2011年的75.98歲、82.65歲，增加到2060年的82.0歲、88.0歲（資料來源：行政院經濟建設委員會2012年推估）。壽命延長意謂更長的退休生活，世界各國在21世紀均面對需求日殷的老年生活照顧，包括退休金制度以及老人醫療等，這些社會福利及保險勢必增加國家財務負擔，因此壽命是否繼續延長或存有極限成為大家關心的議題。近年來，不少研究透過死亡壓縮(Mortality Compression)連結壽命議題，亦即探討死亡年齡是否將集中至更窄的範圍，但因為資料及研究方法的限制，死亡壓縮是否成立仍無定論。 本研究以統計方法、分配假設、資料品質，三個面向來探討死亡壓縮與延壽之間的關係。本研究提出三種數值優化方法：加權最小平方法(Weighted Least Squares；WLS)、非線性極值法(Nonlinear-Maximization；NM)及最大概似估計法(Maximal Likelihood Estimation；MLE)，透過電腦模擬衡量方法優劣，與過去常見的方法比較（Kannisto的SD(M+)），探討何者具有較小的均方誤差(Mean Squared Error；MSE)。其次若死亡年齡之真實死亡分配為t分配時，探討以常態假設代入計算所產生的偏誤；最後則是套入各國實際死亡資料，使用上述較佳的估計方法，檢視死亡壓縮是否存在。 研究結果顯示，NM具有不偏性質且具有較小的均方誤差，過去研究常用的SD(M+)反而有明顯偏誤，且隨著觀察值越多變異數反而增加。而若真實死亡分配若為t分配時，以原先利用常態假設所計算的年金險保費皆有低估的情形，分配的重要性可見一斑，進而探討在實務上常態分配之假設，發現與仍與實際情形有明顯之差異，不論是NM及SD(M+)在死亡壓縮的探討下，皆受到資料的限制而有待商榷。 / Due to the advance in medical technology and the change of life style, the human life expectancy has been increasing since the end of the Second World War II and it is expected to continue the pace of increment. Longer life expectancy also means a longer life after retirement. People living in the 21st century are faced with growing demand for the retirement life, such as the pension funds and medical needs to the individuals, as well as the social welfare and insurance for the elderly to the government. Thus, the issue whether the lifespan has a limit receives a lot of attention. In particular, many studies focus on the topic of mortality compression, which means that the expectancy of lifespan has a limit and variance of lifespan converge. However, due to the availability of elderly data, there is still no consensus if the mortality compression is true. In this study, we propose estimation methods to estimate modal age and variance of the age-at-death. Three types of methods are involved: weighted least squares (WLS) method, nonlinear maximization (NM) method, and maximum likelihood estimation (MLE) method, and they are compared to the method proposed by Kannisto, namely SD(M+), in 2000. We found that the NM method has a smaller MSE, and we cannot decide the mortality compression is true based on the data from Human Mortality Database. We also applied the normality and t distribution assumption to the age-at-death and compute the pure premiums for annuity products. We found that normality distribution would produce larger premiums than using the empirical mortality rates. Similarity, the bankruptcy probability would be higher if the t distribution is used.

關聯結構(Copula)這個字最早由Sklar(1959)以法文所提出，在邊際分配未知的假設下，透過關聯結構的特性，可以容易的建立聯合機率分配，所以關聯結構的觀念廣泛應用在財務領域中。對於資料在配適關聯結構的同時，要如何知道哪種關聯結構函數是最符合資料型態的分配呢?為解決這個問題，本文中參考Dobric and Schmid (2005)所提出的方法--卡方適合度檢定，來看資料配適關聯結構函數是否配適的恰當。所以本文的研究重點就是在利用卡方適合度檢定來探討各類股間日報酬率資料配適關聯結構的情形。在5種不同關聯結構(Normal關聯結構、t關聯結構、Clayton 關聯結構、Frank關聯結構、Gumbel關聯結構)，利用蒙地卡羅模擬方法，來做關聯結構在卡方適合度檢定之模擬，以及檢定力曲線。在檢定統計量、參數估計、顯著水準的估計都還不錯，只有當切割數越大時參數估計會和設定值差異較大。從檢定力曲線可看出這些檢定的檢定力都很好，代表有足夠能力能去辨別出分配的差異性。實證的部份，從台灣上市公司選取4個內需概念股報酬率的日內資料。結果可看出在Normal、Clayton、Frank、Gumbel這4個關聯結構，是不適合用來描述實際報酬的日資料。而當t關聯結構自由度較小時來描述資料型態是表現的不錯。

表面增強雷射脫附遊離/飛行時間質譜(SELDI-TOF-MS)是種屬於高維度的蛋白質質譜儀資料，主要是用來偵測蛋白質分子的表現。由於SELDI技術的限制，導致掃描出來的質譜儀資料往往存在誤差與雜訊，因此在分析前通常會先針對原始資料進行低階的事前處理，步驟包括去除基線、正規化、峰偵測(peak detection)與峰調準(peak alignment)。本文中所探討前列腺癌資料，可分成正常、良性腫瘤、癌症初期與癌症末期四種類別。我們分析及比較兩筆事前處理的蛋白質質譜資料，包括我們自行處理的以及Adam等人所處理的資料。為了解決SELDI在偵測分子質量時常出現的位移誤差以及同位素的問題，我們提出以”質荷比段落”當作新的特徵變數的想法來進行分析。本文利用「ROC曲線下面積」(AUC)當作選取的準則來挑選出重要的質荷比段落，而分類方法則採用支援向量機(SVM)。在四分類的分類結果中，我們自行處理的事前處理資可以得到訓練資料89%及測試資料63 %的正確率。而Adam等人所處理的事前處理資料，則得到訓練資料94%及測試資料86 %的正確率。本研究結果指出不同事前處理的方法對分類結果確實有影響，同時也驗證了利用”特徵變數段落”的方法來進行分析的可行性。 / The surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a technique for presenting the expression of molecular masses. It is obvious that every spectrum has a huge dimension of features. In order to analyze these types of spectra samples, preprocessing steps are necessary. The steps of preprocessing include baseline subtraction, normalization, peak detection, and alignment. In our study, we use a prostate cancer data for demonstration. This prostate cancer data can be classified into four categories, namely, healthy men, benign prostate hyperplasia, early stage prostate cancer, and late stage prostate cancer. We analyzed both the preprocessed data processed by ourselves and the preprocessed data done by Adam et al.. In this thesis, we use segmentations of features as “new features” in attempt to solve problems due to location shifts and isotopes. The selection of important segmentations was based on the values of AUC and the SVM was applied for classification. For four-class classification, 94 % and 86 % of accuracy were obtained for training samples and validation samples, respectively, by using Dr. Adam et al.’s preprocessed data, and 89% for training samples, and 63% for validation samples by using our preprocessed data. This study suggested that the preprocessed method does have effect on classification result and a reasonable classification result can be obtained by using segmentations of features.

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鋼琴，樂器之王；李斯特，鋼琴之王。他以目眩神迷的鋼琴技巧、誇張的表現力、優美的歌唱旋律征服所有人。詩人杜甫曾說過：「讀書破萬卷，下筆如有神。」。筆者認為李斯特則是「彈琴破萬次，音樂如有神。」。在鋼琴之王李斯特七十五年的生涯中，所創作的音樂作品包羅萬象。這些形形色色的鋼琴作品中，大致上可分為原創性作品及改編作品，本篇論文探討的作品屬於原創性作品。李斯特一生中創作兩首敘事曲，第一號敘事曲創作於1848年，完成於1849年； b小調第二號敘事曲，創作於1853年也完成於同一年。鋼琴家阿勞（Claudio Arrau, 1903-1991）曾經在霍洛維茲（Vladimir Horowitz, 1903-1989）所著的《與鋼琴家阿勞的對話》（Conversations with Arrau）中表示李斯特《第二號敘事曲》是遵照「席洛與黎安德」（Hero and Leander）的愛情故事來描寫的。李斯特在《第二號敘事曲》中賦予這個故事新的生命，運用主題變形的手法描繪黎安德以及席落在故事裡的性格以及種種感受。由於筆者受到此神話故事的感動，因此想要以音樂的角度來體會其故事中角色的變化與情境。 / Piano is the king of musical instruments; Liszt, the king of the piano. His brilliant techniques on piano, exaggerated expressions in music and beautiful melody have conquered all the people. A great Chinese poet Du Fu once said, “Read wild, and you will wisely write.” The author sincerely believes that Liszt’s works have the same vein“Practice wild and you will wisely play.” In his 75 years’ career, he has composed all sorts of piano works. In these works, they in general can be classified into two types: originals and transcriptions. The paper investigated the original one. Liszt composed two ballades during his life: Ballade No.1 was composed in 1848 and completed in 1849 while Ballade No.2 in b minor was composed and accomplished in 1853. Pianist Claude Arrau, in the book “Conversation with Arrau” written by Valdimir Horowitz（1903-1989）, once claimed “Liszt’s Ballade No.2 was based on a Greek mythology also known as a love story– Hero and Leander. Touched by the mythology, the author would like to experience the scenarios of those characters in the mythology in the context of music. / 中文摘要…………………………………………………………………………………………………………I Abstract……………………………………………………………………………………………………II 目錄………………………………………………………………………………………………………………III 表目錄……………………………………………………………………………………………………………VI 圖目錄………………………………………………………………………………………………………………Ⅴ 譜例目錄…………………………………………………………………………………………………………Ⅵ 第一章緒論………………………………………………………………………………………………………1 第一節研究動機與目的…………………………………………………………………………………1 第二節研究範圍與方法…………………………………………………………………………………3 第二章李斯特的時代………………………………………………………………………………………4 第一節浪漫主義興起與時代背景…………………………………………………………………4 第二節李斯特生平與鋼琴作品分期………………………………………………………………7 第三節李斯特的炫技與對鋼琴技巧貢獻……………………………………………………20 第三章李斯特《b小調第二號敘事曲》創作背景探討……………………………35 第一節敘事曲歷史發展簡述…………………………………………………………………………35 第二節創作背景及神話故事…………………………………………………………………………39 第四章李斯特《b小調第二號敘事曲》樂曲分析與演奏詮釋………………43 第一節呈示部……………………………………………………………………………………………………46 第二節發展部……………………………………………………………………………………………………52 第三節再現部……………………………………………………………………………………………………69 第五章結論…………………………………………………………………………………………………………80 參考書目……………………………………………………………………………………………………………89