京都大学 / 新制・課程博士 / 博士(薬科学) / 甲第23839号 / 薬科博第154号 / 新制||薬科||17(附属図書館) / 京都大学大学院薬学研究科薬科学専攻 / (主査)教授 金子 周司, 教授 土居 雅夫, 教授 竹島 浩 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(薬学) / 甲第23847号 / 薬博第854号 / 新制||薬||242(附属図書館) / 京都大学大学院薬学研究科薬学専攻 / (主査)教授 金子 周司, 教授 土居 雅夫, 教授 竹島 浩 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

本研究之目的：一為探究年輕思覺失調症患者，於荒野臨界中如何形塑其此際體態；二為探討患者主要照顧者面對精神分裂體驗，從何接應患者，如何賦予其心理照顧意義。而後，以對元結構方式，探討患者及其照顧者雙方的心理經驗。研究者將患者「生命經驗」及其主要照顧者「照顧經驗」並置於現象場脈絡中，透過深度訪談，進行現象還原，以接近患者的內在經驗，反思照顧者照顧行動意義。方法上，本研究採取立意抽樣，以年輕思覺失調症患者與其主要照顧者作為研究對象，以配對方式進行資料蒐集，共三對受訪者（6位）接受訪談，患者部分為三位男性，而雙親照顧者部分為一位男性、兩位女性。資料蒐集後，以Henri Ellenberger之現象學分析方法（林耀盛，2002）分析。研究結果發現，年輕思覺失調症患者與其雙親主要照顧者共構之置身結構經驗的定向軌跡為：（1）超常隱匿於日常偽裝之下；（2）超常現身，即特異性經驗被看見，包含病者與雙親照顧者；（3）芻思心緒，反覆思索當前之事件；（4）經驗賦義，理解所發生的事，並給出位置，將其涵容於自我之中；（5）存有的流變：希望與憂懼心思。研究貢獻為在臨床心理實務上可能的幫助：可協助狀態逐漸穩定之個案進行經驗之整理，使其看見自己的狀態，而非僅僅使其擁有病識感、願意配合醫療處遇。而對雙親做為主要照顧者，可協助其釐清對於疾病狀態的未知，及自我照顧技藝的發展。礙於現實收案狀況，難以擴大取樣具異質性的思覺失調症患者，資料豐富度較為不足，為未來研究可改善之處。 / Purpose: This study is exploring (1) how the young schizophrenic patient perceived who he/she is in “the wilderness”; (2) how their caregivers faced with the schizophrenic experiences and constructed the meaning of caring; and (3) disclosing the patients and their caregiver’s mutual situated experiences by the meta-dyadic structure. Methods: Taking a qualitative approach grounded in phenomenological psychology, this study conducted in-depth interviews by which researchers strived to observe, analyze and understand, from their first-person perspective, how they live under such situation. The participants were recruited through purposive sampling. Three patient-caregiver dyads (three male schizophrenic patients with one male and two female caregivers) were interviewed. The analysis step is adopted by Henri Ellenberger’s existential-phenomenological method (Yaw-Sheng Lin, 2002). Results: Five over-arching themes emerged in the analysis on the transformation of experiences of suffering and caring. (1) Camouflaging the “extra-normal” in daily life. (2) “Extra-normal” is understood through the singularity in the daily life, including the patient and caregiver can “encounter” the unique experience. (3) Ruminative reflection about what happening? (4) Make sense of schizophrenic experiences through blurry the boundary between the normal and abnormal to integrate self into life orientation. (5) Existential experience as becoming process through the state of mind of “hope” and “Angst”. Conclusions: The outcome is implicated in clinical practice: (1) Return to young schizophrenic patients’ lifeworld beyond psychopathology comprehension to articulate the intersubjective understanding relationships. (2) Help caregiver to understand their uncanny situation and cultivate the caring ethics for self and the other. Limitation, future research agenda and suggestion are discussed and proposed.

abstract: 最近几十年对高管人员个人特征的研究成为组织战略决策领域的重要议题，但其中对 于高管人员的心理感受却几乎从未触及。心理学研究表明，心理感受会很大程度上影响人 的行为。荣耀感，作为最主要的一种与自我意识相关的心理感受，对于工作行为和结果的 影响都非常大。因而，研究企业高管的荣耀感，对理解和预测他们的行为，进而对预测其 个人意志扮演重要角色的公司战略决策有显著意义。 本论文对企业高管的荣耀感进行了系统研究。通过两个研究，中国企业高管的问卷 调查数据，回答了两个问题：1. 企业高管工作荣耀感的内涵是什么？2. 企业高管荣耀感 的高低如何影响其在公司中的战略决策。具体而言，通过 6 人深度访谈和 50 人的开放式 问题问卷的定性研究提炼了企业高管荣耀感的双维度模型，即世俗荣耀感和神圣荣耀感， 202 名企业高管样本的问卷开发了信度和效度达标的两种荣耀感的测量量表（研究一）； 运用 159 名企业高管的问卷数据（研究二）检验了有关两种荣耀感影响公司战略变革意愿 和行为，以及社会创新的不同作用及其边界条件的理论模型和假设。 本研究的主要结论是： 1. 企业高管的世俗荣耀感和神圣荣耀感均正向影响企业战略主动性、战略变革，以 及社会创新战略，其中神圣荣耀感相比世俗荣耀感，对于公司社会创新战略的影响效应更 大；2.企业高管神圣的荣耀感，相比世俗荣耀刚对公司战略主动性的积极作用，受到其感 知到的基于政策环境变化的心理契约违背的影响更大，而世俗的荣耀感，相比神圣荣耀感 对公司社会创新的正向作用，受到其感知到的基于政策环境变化的心理契约违背的影响更 大。 本论文对战略领导力的研究做出了重要贡献。战略管理研究越来越多地关注人的因 素，尤其是企业高管对企业决策、行为以及绩效的影响，但以往对高阶的研究都集中在对 高管人员认知、背景及经验的探讨，本论文首次聚焦于高管人员的情感体验，通过强调高 管人员的荣耀感对组织战略决策的重要性，本研究大大地扩展了战略领导的研究范畴。 论文从心理学的视角出发，首次从内容和来源视角区分荣耀感的两种类型，对原先 从表现形式（momentary experiences and chronic dispositional tendencies）对荣耀感的 分类是个补充，对心理学领域有关荣耀感的研究做出了贡献。本文创造性地将荣耀感的研 究扩展到了企业管理中的高级管理人员，显著地提高了有关个体荣耀感受的理论与管理学 领域理论和实践运用的相关性。 / Dissertation/Thesis / Doctoral Dissertation Business Administration 2018

目前國內針對精神障礙者就業相關文獻，主要著重於就業影響因素。因此，本研究以復元統合模式為架構，探究精神障礙者在職業抉擇歷程裡有哪些影響因子以及其與復元之關係。本研究為理解精神障礙者如何解讀自己曾經歷的職業抉擇歷程，採半結構訪談之方式訪談七位具有多年就業經驗的精神障礙者，研究結果如下： 1.精神障礙者職業抉擇影響因素包含： (1)個人因素：清楚身體現況、過去就業經驗、職業倦怠。 (2)家庭因素：家人促進職涯發展情況與家人阻礙職涯發展情況。 (3)非正式支持因素：朋友與宗教組織提供就業資訊與建議。 (4)工作環境因素：交通、工作穩定性、職場友善度、工作機會。 (5)正式支持因素：就業服務單位協助轉介、就業服務員之協助。 (6)社會大眾汙名化：雇主對於精神障礙者的不友善、民眾對於精神障礙者的偏見與歧視、疾病自我汙名化。 2.職業抉擇歷程與復元：從受訪者的生命故事裡得知精神障礙者的求職之路蜿蜒迂迴，呼應復元是一個來來回回的過程。而本研究發現雖然工作動機並不一定影響精神障礙者的職業選擇，但在復元過程裡是重要激發強健心理能量的來源。本研究亦看到生涯成熟度對於精神障礙者職業選擇的影響。 3.精神障礙者就業與復元：精神障礙者在參與工作的過程裡，會發展個人策略及尋求相關資源協助，從而增強工作效能。而穩定就業讓精神障礙者得以提高生涯自主性、開始關注生活品質，及具有回饋社會的能力，這都是一種復元的展現。 最後，研究者結合上述研究結果與復元統合模式，嘗試繪製出精神障礙者職業抉擇與復元統合模式之關係，並提出本研究限制與建議，提供精神障礙者、精神障礙者之家屬、服務提供者和政策制定者。 / This research has based on the unity model of recovery to explore the factors affecting career choice of people with mental disorder, as well as to illustrate the relations between these factors and one’s recovery. Departing from domestic correlational literature that mostly underlines the factors impacting employment of people with mental illness, this study aims to shed new light on this research area by employing the framework above. Thereupon, semi-structured interviews are conducted with 7 interlocutors of multi-year working experiences to establish elaborated understandings of their self-narratives on the career choice process. The findings are: 1.Factors influencing the career choice of people with mental illness are as follows: (1)Personal factor: including clear understanding of personal health condition, past employment experiences, and occupational burnout. (2)Family factor: varied conditions where family members may be encouraging or discouraging in one’s career development. (3)Informal supporting factor: such as employment information and advices from friends or religious organizations. (4)Work environment factor: including transportation convenience, job stability, workplace friendliness, and job opportunities. (5)Formal supporting factor: referral from employment service providers, assistance from employment service officers. (6)Public stigma: such as antagonistic attitudes received from employers, prejudice and discrimination from the public, and even more, self-stigma one experiencing. 2.Career choice process and recovery: From the life stories disclosed by the interlocutors, their twist-and-turn paths toward employment could clearly be seen, echoing that of the recovery “is not a perfectly linear process”. Although work motivation might not be the determining cause of their career choice, it is crucial for inciting mental strength during recovery process. Furthermore, this research has also demonstrated the influence of career maturity towards career choice of people with mental disorder. 3.Employment and recovery: During their participation in employment, people with mental illness would develop personal strategies and seek for assistance from related resources in order to thrive at work environment. Moreover, stable employment aids one’s recovery in many ways, such as promoting one’s career autonomy, instilling life qualities, and also strengthening one’s potential to give back to the society. In accordance with the findings mentioned above, this study has made an attempt to depict the relations between career choices of people with mental illness and the unity model of recovery. Lastly, research limitations along with suggestions for further research are subsequently raised, in the hope of serving as a reference for them with mental illness, their families, mental health services providers, and policy makers.

本篇文章使用了增強學習與捲積深度學習結合的DQCN模型製作交易系統，希望藉由此交易系統能自行判斷是否買賣ETF，由於ETF屬於穩定性高且手續費高的衍生性金融商品，所以該系統不即時性的做買賣，採用每二十個開盤日進行一次買賣，並由這20個開盤日進行買賣的預測，希望該系統能最大化我們未來的報酬。 DQN是一種增強學習的模型，並在其中使用深度學習進行動作價值的預測，利用增強學習的自我更新動作價值的機制，再用深度學習強大的學習能力成就了人工智慧，並在其取得良好的成效。 / In this paper, we used DCQN model, which is combined with reinforcement learning and CNN to train a trading system and hope the trading system could judge whether buy or sell ETFs. Since ETFs is a derivative financial good with high stability and related fee, the system does not perform real-time trading and it performs every 20 trading day. The system predicts value of action based on data in the last 20 opening days to maximize our future rewards. DQN is a reinforcement learning model, using deep learning to predict value of actions in model. Combined with the RL's mechanism, which updates value of actions, and deep learning, which has a strong ability of learning, to finish an artificial intelligence. We got a perfect effect.

互聯網的發展與近年來數據平台受到公私部門重視，資訊的取得與流通變得便捷，中國房地產文化目前有別於台灣，尚無實價登錄機制且地域面積廣大，傳統估價模型可能無法直接應用，面對房地產背後眾多的影響因素，本研究將預測建模目標放在泡沫化尚不嚴重且較具有潛力的中國新一線城市杭州市，自新浪二手房網爬取杭州市房地產數據，並自國家統計局取得各地區行政支出數據，作為實證分析資料。結合自動程序爬蟲抓取數據、統計分析與機器學習方法，期望對中國房地產建立一混合非監督式與監督式學習之模型。 在分群結果之後建構模型採用之技術為C5.0、三層CHAID、五層CHAID與Neural Network，挑選出最適合的模型為使用混合模型後的C5.0決策樹方法，達到降低變數維度亦提升或達到相當的預測準確率的雙贏目標，模型中行政地區、面積、總樓層為最頻出現的重要變數。 另外透過集群分析於行政支出的應用，發現2016年度杭州市投入的行政支出集中於余杭區、蕭山區、濱江區，成為賣屋及購屋者的第二項決策標準。 / In recent years, with the growth of the Internet and the importance of data platform on public sector and private sector. Getting and sharing information are made easily. The culture of real estate in China is different from Taiwan. For instance, there is no actual house price registration system. Furthermore, traditional estimate model may not be directly applicable to China which has the vast geographical area of the mainland. There are many factors to influence house price model. This study focus on Hangzhou city. Because the burst of real estate bubbles is not serious as first-tier cities and it is one of new first-tier cities in China. The research data were crawler from Sina second-hand housing website and National Bureau of Statistics. By using auto web crawler skill, statistical analysis, and machine learning method to build a real estate model in China, which was combining unsupervised learning method with supervised learning method. After clustering Hangzhou second-hand housing data, this study used C5.0, three layers Chi-Square Automatic Interaction Detector(CHAID), five layers CHAID, and Neural Network(NN). The study goal are both reducing dimension and getting better forecast accuracy. Choosing clustering- C5.0 model as appropriate house price model to achieve win-win situation after comparing final result. Administrative region, area, and total floor are the top three high frequency influential factors. Applying Clustering Analysis to administrative expenses data in Hangzhou, the study found that the government resource focus on Yuhang, Xiaoshan, and Binjiang. It can be the second decision-making criterion for house sellers and house buyers.

近年來，房地產之投資及買賣廣為盛行，而房地產依舊為人們投資的方向之一。屬於人工智慧範疇之類神經網路，其具有學習能力，可以進一步的歸納推演所要預估的結果，也適合應用於非線性的問題中，但以往類神經網路的機器學習模型，皆採用中央處理器(CPU)進行運算，在計算量龐大時往往耗費大量時間於訓練上。而圖形處理器(GPU)之崛起，將增進機器學習的速率。 本研究利用穩健學習程序搭配信封模組的概念，建立一類神經網路系統，利用GPU設備及機器學習工具–Tensorflow實作，針對民國一零四年之台北市不動產交易之住宅資料，並使用1276筆資料，隨機選取60%資料作為訓練範例並分別進行以假設有5%為可能離群值及沒有之情況做學習，並選取影響房地產價格之11個變數做為輸入變數，對網路進行訓練，實證結果發現類神經網路的速度有顯著的提升；且在假定有5%離群值之狀況下學習有較好的預測水準；另外在對資料依價格進行分組後，顯示此網路在對中價位以上的資料有較好的預測能力。就實務應用方面，藉由類神經網路適合應用於非線性問題的特性，對未來房地產之估價系統輔助做為參考。 / Real estate investment and transcation prevails in recent year. And it is still one of the choices for people to invest. The Neural Network which belongs to the category of Arificial Intelligence has the ability to learn and it can deduce to reach the goal. In addi-tion, it is also suitable for the application of non-linear problems. However, the machine learning model of the Neural Network use CPU to operate before and it will always spend a lot of time on training when the calculation is large.However, the rise of GPU speeds up the machine learing system. This study will implement resistant learning procedure with the concept of Enve-lope Bulk focus to built a Neural Network system. Using TensorFlow and graphics pro-cessing unit (GPU) to speed up the original Arificial Intelligence system. According to the real estate transaction data of Taipei City in 2015, 1276 data will be used. We will pick 60% of the data in a random way as training data of our two experiment , one of it will assume that there are 5% of outlier and another won’t. Then select 11 variables which may impact the value of real estate as input. As the experiment result, it makes the operation more efficient and faster , training of the Neural Network really speed up a lot. The experiment which has assume that there are 5% of outlier shows the better effect of predicting than the another. And we got a better prediction on the part of the higher price after we divided the data into six groups by their price.In the other hand, Neural Network is good at solving the problem of non-linear. It can be a reference of the sup-port system of real estate appraisal in the future.

因應近年來數位典藏的趨勢日漸發展，越來越多珍貴中文歷史文本 選擇進行數保存，而保存的同時會面對文本的作者遺失或從缺，進而 影響文本的完整性，而本論文提出了一個適用於中文史料文本作者分 析的方法，主要是透過語言模型的建構，為每一位潛在的作者訓練出 一個專屬的語言模型，而搭配不同的平滑方法能避免掉某一受測文本 單詞出現的機率為零的機率進而造成計算上的錯誤，而本論文主要採 用改良式 Kneser–Ney 平滑方法，該平滑方法因其會同時考慮到 N 詞彙 語言模型的高低頻詞的影響，而使其成為建構語言模型普遍選擇的平 滑方式。 若僅將每一位潛在作者的所有文章進行合併訓練成單一的語言模型 會忽略掉許多特性，所以本篇論文在取得附有價值的歷史文本之外， 又加入後設資料 (Metadata) 進行綜合分析，包括人工標記的主題分類 的統計資訊，使建構出來的語言模型更適配受測文本，增加預測結果 的準確性。和加入額外的自定義的字詞以符合文本專有名詞的用詞習 慣，還會在一般建構語言模型的基礎上，加入長字詞的權重，以確定 字詞長度對預測準確度的關係。最後還會採用遞歸神經網路 (Recursive neural networks) 結合語言模型進行作者預測，與傳統的語言模型分析 作進一步的比較。 / In recent years, the trend of digital collections has been developing day by day, and more and more precious Chinese historical corpora have been selected for preservation. The preservation of the corpora at the same time will face the loss or lack of the authors, thus affecting the integrity of the corpora. A method for analyzing the author of the Chinese historical text is mainly through the construction of the language model, for each potential author to train a specific language model, and with a different smoothing method can be avoided zero probability of words and the error is caused by the calculation. This paper mainly adopts the Interpolated Modified Kneser-Ney smoothing method, which will take into account the influence of higher order and lower order n-grams string frequency. So, Interpolated Modified Kneser-Ney smoothing is become a very popular way to construct a general choice of language models. The combination of all the articles of each potential author into a single language model will ignore many of the features, so this paper in addition to the value of the historical corpora, but also to add the metadata to integrate analysis, including the statistical information of the subject matter classification of the artificial mark, so that the constructed language model is more suitable for the measured text, increase the accuracy of the forecast results, add additional custom words to match the language of the proper nouns, in addition. But also on the basis of the general construction language model, the weight of the long word to join, to determine the length of the word on the relationship between the accuracy of prediction. Finally, recursive neural networks language models are also used to predict the authors and to make further comparisons with the traditional language model analysis.

本文分析近年盛行的電子遊戲實況活動。從分心、不專注於電玩的現象開始，本文以「如何玩」這個提問來引導論證「嬉玩」與「遊戲」這組概念在分析上的效力，藉此，本文論證了電玩實況活動中的心智狀態與活動是游移在電玩活動與嬉鬧互動，在嚴肅遊戲與嬉戲玩耍之間游移，這樣的分神狀態之中也產生了有樂趣、具有實效的遊戲性。 從技術架構的觀點來看，以即時串流的技術為基礎，電玩實況活動結合了即時傳輸與即時互動。使用者所獲致的遊戲聆賞經驗以及互動是即時且與他人同步，便也構成了網路媒介之下的共時經驗，實況中以電玩活動為對象的起鬨也便具共在的實感。本文從聊天室起鬨、技術戲耍挪用等現象中論證了基於遊戲性旨趣的共謀嬉玩。嘲諷、干擾的對抗性是為了製造分神的條件，從中產生張力並獲致遊戲性，而包括實況主在內的所有參與者，皆是在這樣的分神狀態中嬉玩，維繫歡騰的共感情緒。 電子遊戲具有將遊戲性限定在其中的封閉性質，也因此對於電玩的遊戲經驗多有私密、個人的想像，即時串流的電玩實況活動便是打開了這樣的封閉性質。透過網路媒介將人匯聚在遊戲活動周圍，不僅共享了電玩活動本身的樂趣，也在即時串流之下共時的互動跟聆賞經驗中以遊戲為對象群聚嬉戲。這樣的網路活動之所以盛行，顯示了人對遊戲性的需求不僅是來自遊戲中，也來自與他人玩在一起的連結共感，電玩實況便是實現了這樣的需求。 / Taking the prevailing "video game live streaming" activities as research object, this thesis takes "play" and "game" as a set of core concepts, and begins the exploration into their conceptual effect. Setting out the arguments with "How to Play" question, this thesis concludes that the mentality and activities in video game live streaming are in an "in-between" condition. Between video game activity and playful disturbance, seriousness and playfulness, there are gameplay with actual effect. Taking the technical conditions into consideration, video game live streaming consists of live media transportation and live interaction. Users' experiences synchronize with each other, thus contribute to the "togetherness" mediated through the Internet, and also the carnivalistic interactions and disturbance in the live channel. From the fuss and disturbance in the chatroom to the appropriation of streaming "plugins", this thesis argues that these actions conducted by streamer and users contribute to the "distractive" scenario, and the conducts are all based on "the interest of play". Different from serious and immersive mentality which is deemed as the fundamental of playing and gaming, the gameplay in live stream is based on distraction, which is the core condition that brings tension and thus playfulness. All the users in video game live streaming, the streamer included, are connected and forged into a synchronized togetherness by this kind of distractive, carnivalistic playfulness. Digital video games have tributes of enclosure, which require concentration, seriousness and absorption. Video game live streaming opens up this kind of enclosure, and brings people in and around the video game activities. In the live channel, users experience the joy and fun of the game itself together in a synchronized way, and also act on the interest of play, creating distraction and disturbance that bring to playful experiences, and that explains why this kind of Internet activity prevails. This thesis shows that the need of gameplay comes not only from game activities themselves, but also from the desire to "play together". In a digital age, video game live streaming is just the fulfillment of this kind of desire.