[pt] Essa tese é composta por dois capítulos. O primeiro deles refere-se ao problema de aprendizado sequencial, útil em diversos campos de pesquisa e aplicações práticas. Exemplos incluem problemas de apreçamento dinâmico, desenhos de leilões e de incentivos, além de programas e tratamentos sequenciais. Neste capítulo, propomos a extensão de uma das mais populares regras de aprendizado, epsilon-greedy, para contextos de alta-dimensão, levando em consideração uma diretriz conservadora. Em particular, nossa proposta consiste em alocar parte do tempo que a regra original utiliza na adoção de ações completamente novas em uma busca focada em um conjunto restrito de ações promissoras. A regra resultante pode ser útil para aplicações práticas nas quais existem restrições suaves à adoção de ações não-usuais, mas que eventualmente, valorize surpresas positivas, ainda que a uma taxa decrescente. Como parte dos resultados, encontramos limites plausíveis, com alta probabilidade, para o remorso cumulativo para a regra epsilon-greedy conservadora em alta-dimensão. Também, mostramos a existência de um limite inferior para a cardinalidade do conjunto de ações viáveis que implica em um limite superior menor para o remorso da regra conservadora, comparativamente a sua versão não-conservadora. Adicionalmente, usuários finais possuem suficiente flexibilidade em estabelecer o nível de segurança que desejam, uma vez que tal nível não impacta as propriedades teóricas da regra de aprendizado proposta. Ilustramos nossa proposta tanto por meio de simulação, quanto por meio de um exercício utilizando base de dados de um problema real de sistemas de classificação. Por sua vez, no segundo capítulo, investigamos efeitos de tratamento determinísticos quando a regra de aloção é complexa e desconhecida, talvez por razões éticas, ou para evitar manipulação ou competição desnecessária. Mais especificamente, com foco na metodologia de regressão discontínua sharp, superamos a falta de conhecimento de pontos de corte na alocação de unidades, pela implementação de uma floresta de árvores de classificação, que também utiliza aprendizado sequencial na sua construção, para garantir que, assintoticamente, as regras de alocação desconhecidas sejam identificadas corretamente. A estrutura de árvore também é útil nos casos em que a regra de alocação desconhecida é mais complexa que as tradicionais univariadas. Motivado por exemplos da vida prática, nós mostramos nesse capítulo que, com alta probabilidade e baseado em premissas razoáveis, é possível estimar consistentemente os efeitos de tratamento sob esse cenário. Propomos ainda um algoritmo útil para usuários finais que se mostrou robusto para diferentes especificações e que revela com relativa confiança a regra de alocação anteriormente desconhecida. Ainda, exemplificamos os benefícios da metodologia proposta pela sua aplicação em parte do P900, um programa governamental Chileno de suporte para escolas, que se mostrou adequado ao cenário aqui estudado. / [en] Sequential learning problems are common in several fields of research and practical applications. Examples include dynamic pricing and assortment, design of auctions and incentives and permeate a large number of sequential treatment experiments. In this essay, we extend one of the most popular learning solutions, the epsilon-greedy heuristics, to high-dimensional contexts considering a conservative directive. We do this by allocating part of the time the original rule uses to adopt completely new actions to a more focused search in a restrictive set of promising actions. The resulting rule might be useful for practical applications that still values surprises, although at a decreasing rate, while also has restrictions on the adoption of unusual actions. With high probability, we find reasonable bounds for the cumulative regret of a conservative high-dimensional decaying epsilon-greedy rule. Also, we provide a lower bound for the cardinality of the set of viable actions that implies in an improved regret bound for the conservative version when compared to its non-conservative counterpart. Additionally, we show that end-users have sufficient flexibility when establishing how much safety they want, since it can be tuned without impacting theoretical properties. We illustrate our proposal both in a simulation exercise and using a real dataset. The second essay studies deterministic treatment effects when the assignment rule is both more complex than traditional ones and unknown to the public perhaps, among many possible causes, due to ethical reasons, to avoid data manipulation or unnecessary competition. More specifically, sticking to the well-known sharp RDD methodology, we circumvent the lack of knowledge of true cutoffs by employing a forest of classification trees which also uses sequential learning, as in the last essay, to guarantee that, asymptotically, the true unknown assignment rule is correctly identified. The tree structure also turns out to be suitable if the program s rule is more sophisticated than traditional univariate ones. Motivated by real world examples, we show in this essay that, with high probability and based on reasonable assumptions, it is possible to consistently estimate treatment effects under this setup. For practical implementation we propose an algorithm that not only sheds light on the previously unknown assignment rule but also is capable to robustly estimate treatment effects regarding different specifications imputed by end-users. Moreover, we exemplify the benefits of our methodology by employing it on part of the Chilean P900 school assistance program, which proves to be suitable for our framework.

The discovery of the Dead Sea Scrolls has provided an opportunity to reexamine the formation of the Gospel of John. This study will utilize Dead Sea finds coupled with other Second Temple literature to examine how the Gospel of John portrays Jesus as being a king. The approach of this study to use a narrative approach that builds on the Gospel of John as a finished text. The contribution of a source critical approach is not disparaged but the narrative approach will allow the Johannine community to be seen in the context of the immediate post-Second Temple era. The limited literacy of the probable first audience of this text suggests that a narrative approach will best be able to understand the background to the formation of the Gospel of John. A central contention of this study is that the Gospel of John was composed after the Jewish Revolt and after the Synoptics. Thus it deserves the appellation of the Fourth Gospel and is called such in this study. The Fourth Gospel was composed at a time when Roman interest in anything connected to Judaism was sure to attract special interest. Thus the portrayal of Jesus as the Davidic Messiah needed to be handled carefully. The imagery of the new David found in 4Q504 compared with the imagery of Jesus being the Good Shepherd becomes an important part of the argument of this study on whether this Gospel portrays Jesus as being the Davidic Messiah. Jesus as the Good Shepherd showed Jews that Jesus is the Davidic Messiah without overtly offending Roman sensibilities. Furthermore evidence from Christian and Jewish sources indicates that an interest in a Third Temple was still stirring between the Jewish and Bar-Kochba Revolts. The Fourth Gospel shows Jesus as the Davidic Messiah who replaces the Temple because the Good Shepherd was the perfect sacrifice. / New Testament / D. Th. (New Testament)

承襲清末地方軍事主義與西方條約特權體系脈絡，加以民初軍閥亂政影響，民國時期的中國社會逐漸出現兩種類型的特權人物：一個是手握槍桿子的「丘八爺」，另一個則是同時操持著條約與砲艦的「洋大人」。 「丘八爺」是軍閥割據與頻繁內戰的產物。數以百萬計的「丘八爺」平素身著戎裝，打著軍人名義，動輒打打殺殺，作戰失敗或軍隊欠餉時，即譁變作亂，化為草莽土匪，到處打家劫舍。但遇軍閥招安，則又由匪轉兵，形成兵、匪間的惡性循環。雖然嚴格說來兵、匪之間不無差異，但是民國以來，尤其北京政府時期，「丘八爺」往往以兵、匪不分的兵匪面目，烙印在一般人的印象中。 另一方面，歐美國家自清末挾帶條約體系與船堅砲利來到中國之後，經由一連串戰爭，透過砲艦外交模式教導中國人：條約必須遵守、外人生命財產安全必須獲得保障。歐美國家常設性駐華外交、領事機構，則是扮演關鍵性的角色，手持上帝之鞭，多年來宣傳、馴化中國政府與中國人。如此，中國逐漸從抗拒、反彈，到接受西方權威，承認外國人在華的地位不容輕蔑與挑戰。「洋大人」的權威形象，由此在中國樹立起來。 理論上，「丘八爺」為禍雖大，百姓亦深以為苦，但畢竟是中國內政問題，與外交事務無涉。可是，清末以來，隨著大批洋人進入中國內陸通商、傳教與居住、外國資金流入中國市場、外國軍艦巡弋重要內河、外國領事機構與軍隊駐紮各通商要衢，條約特權體制進一步內化為中國權勢結構的一部份。當數量龐大、散佈全國的「丘八爺」，與深入中國內陸的「洋大人」頻繁接觸，「丘八爺」將魔爪伸向「洋大人」之後，也就變成嚴重的外交問題。 歐戰後的東亞秩序，歷經巴黎和會、華盛頓會議的凝聚共識，歐美列強（包括日本）逐漸形成一個觀念：將美國對華政策擴大為各國對華政策，也就是放棄原先帝國主義競爭、權力均勢的模式，改以「門戶開放政策」作為處理中國事務的中心指標。與此同時，經過馬列主義、無產階級革命洗禮的蘇聯，也重新回到中國：一方面利用放棄在華特權博取中國同情，另一方面以打倒歐美帝國主義為口號，大肆宣傳民族主義式理念。列強任何強硬的舉動，常被布爾什維克宣傳家渲染為帝國主義侵華鐵證，激發更強大的反帝浪潮。在這樣的國際場景之下，當「丘八爺」遇上了「洋大人」，會發生什麼問題？當「丘八爺」以打、殺、搶、綁等各種暴力形式，一再挑釁「洋大人」威嚴，甚至任意污衊指為「洋鬼子」時，「洋大人」該如何應付？蘇聯宣傳家環伺在側、伺機扯後腿的情況下，「洋大人」是否還能重施故技，以帝國主義的老路子—砲艦外交來施以懲罰？另謀他法？抑或默認中國現狀的發展？ 此外，1920年代前後，中國軍閥割據、南北對立逐漸發展到高峰，北京政府對外雖有中央之名、對內則無約束地方之實力。面對如此威信不足、欲振乏力的北京政府，以及地方分裂割據的現狀，「洋大人」顯然無法經由「代理人機制」，透過北京政府有效地制約地方問題。 因此，1920年代上半期，當意氣風發、趾高氣昂的「丘八爺」，遇上了受到外在與內部多重制約、看似有些跛腳的「洋大人」，勢必會產生許多值得深入討論、分析的有趣問題。 其次，當我們回顧以往被定位為帝國主義侵華—國賊賣國史的北洋外交史，面對民族主義史觀與俯拾皆是的情緒性指責字眼，是否應該嘗試以新的理解取代原先的敵意，重新建構不同視野的北洋外交史？處於中國軍閥主義高漲的1920年代上半期，體現中國內政不安因子、大肆肆虐中國社會各個角落的「丘八爺」現象，與「洋大人」之間的互動，及其衍生出的種種外交交涉，乃是當時最常見的外交與內政問題。但是囿於傳統民族主義史觀，外交史家往往只著重關係國家民族大義的重大外交事件，以及國際會議層次，屬於政府高層官方外交(high diplomacy)往來交涉模式。對於發生於社會下層，一些名不見經傳的「丘八爺」與「洋大人」衝突問題，外交史家若非視而不見，即是匆匆帶過，而忽略華洋衝突背後所隱藏的重要文化意涵。究其實際，當西方列強以帝國主義強權之姿在中國樹立起條約特權體制，「洋大人」頻頻以傳教士、商人角色深入中國內陸之際，中外之間國家主權界線因此變動不居，產生許多不明確的灰色地帶。而發生在中國內部，由「丘八爺」與「洋大人」構織出的華洋衝突事件，就是徘徊在邊境與邊界之間的重要問題。一方面華洋衝突涉及到兩國之間條約權利與義務的界定，屬於官方外交層次、明確的國家主權邊界設定。另一方面，華洋衝突在本質上，同時也涉及到不同文化邂逅下的族群衝突問題，以及彼此認知、設想的民族偏見問題，如中國人心中的「洋大人形象」，與洋人心中的中國的「兵匪問題」均屬於這個層次。而在文化邊際效應的作用下，華洋勢力之間的滲透、爭執與磨合，構成中外往來的真實文化底蘊，正是當時日常生活的寫實反映。透過發生在邊境地帶的華洋衝突研究，與中外官方外交進行對話、討論，或許可以思索出與傳統相當不一樣的中外關係史。 因此，本文試圖從複雜的華洋糾葛角度，以「丘八爺」與「洋大人」的互動切入點，經由複線式歷史論述與中外不同觀點的探討，由下往上地剖析外交問題，探究當時中外重疊地帶上的北洋外交史。

L’allocation des ressources spectrales à des services de communications sans fil, sans cesse plus nombreux et plus gourmands, a récemment mené la communauté radio à vouloir remettre en question la stratégie de répartition des bandes de fréquences imposée depuis plus d’un siècle. En effet une étude rendue publique en 2002 par la commission fédérale des communications aux Etats-Unis (Federal Communications Commission - FCC) mit en évidence une pénurie des ressources spectrales dans une large bande de fréquences comprise entre quelques mégahertz à plusieurs gigahertz. Cependant, cette même étude expliqua cette pénurie par une allocation statique des ressources aux différents services demandeurs plutôt que par une saturation des bandes de fréquences. Cette explication fut par la suite corroborée par de nombreuses mesures d’occupation spectrale, réalisées dans plusieurs pays, qui montrèrent une forte sous-utilisation des bandes de fréquences en fonction du temps et de l’espace, représentant par conséquent autant d’opportunité spectrale inexploitée. Ces constations donnèrent naissance à un domaine en plein effervescence connu sous le nom d’Accès Opportuniste au Spectre (Opportunistic Spectrum Access). Nos travaux suggèrent l’étude de mécanismes d’apprentissage pour la radio intelligente (Cognitive Radio) dans le cadre de l’Accès Opportuniste au Spectre (AOS) afin de permettre à des équipements radio d’exploiter ces opportunités de manière autonome. Pour cela, nous montrons que les problématiques d’AOS peuvent être fidèlement représentées par des modèles d’apprentissage par renforcement. Ainsi, l’équipement radio est modélisé par un agent intelligent capable d’interagir avec son environnement afin d’en collecter des informations. Ces dernières servent à reconnaître, au fur et à mesure des expériences, les meilleurs choix (bandes de fréquences, configurations, etc.) qui s’offrent au système de communication. Nous nous intéressons au modèle particulier des bandits manchots (Multi-Armed Bandit appliqué à l’AOS). Nous discutons, lors d’une phase préliminaire, différentes solutions empruntées au domaine de l’apprentissage machine (Machine Learning). Ensuite, nous élargissons ces résultats à des cadres adaptés à la radio intelligente. Notamment, nous évaluons les performances de ces algorithmes dans le cas de réseaux d’équipements qui collaborent en prenant en compte, dans le modèle suggéré, les erreurs d’observations. On montre de plus que ces algorithmes n’ont pas besoin de connaître la fréquence des erreurs d’observation afin de converger. La vitesse de convergence dépend néanmoins de ces fréquences. Dans un second temps nous concevons un nouvel algorithme d’apprentissage destiné à répondre à des problèmes d’exploitation des ressources spectrales dans des conditions dites de fading. Tous ces travaux présupposent néanmoins la capacité de l’équipement intelligent à détecter efficacement l’activité d’autres utilisateurs sur la bande (utilisateurs prioritaires dits utilisateurs primaires). La principale difficulté réside dans le fait que l’équipement intelligent ne suppose aucune connaissance a priori sur son environnement (niveau du bruit notamment) ou sur les utilisateurs primaires. Afin de lever le doute sur l’efficacité de l’approche suggérée, nous analysons l’impact de ces incertitudes sur le détecteur d’énergie. Ce dernier prend donc le rôle d’observateur et envoie ses observations aux algorithmes d’apprentissage. Nous montrons ainsi qu’il est possible de quantifier les performances de ce détecteur dans des conditions d’incertitude sur le niveau du bruit ce qui le rend utilisable dans le contexte de la radio intelligente. Par conséquent, les algorithmes d’apprentissage utilisés pourront exploiter les résultats du détecteur malgré l’incertitude inhérente liée à l’environnement considéré et aux hypothèses (sévères) d’incertitude liées au problème analysé. / During the last century, most of the meaningful frequency bands were licensed to emerging wireless applications. Because of the static model of frequency allocation, the growing number of spectrum demanding services led to a spectrum scarcity. However, recently, series of measurements on the spectrum utilization showed that the different frequency bands were underutilized (sometimes even unoccupied) and thus that the scarcity of the spectrum resource is virtual and only due to the static allocation of the different bands to specific wireless services. Moreover, the underutilization of the spectrum resource varies on different scales in time and space offering many opportunities to an unlicensed user or network to access the spectrum. Cognitive Radio (CR) and Opportunistic Spectrum Access (OSA) were introduced as possible solutions to alleviate the spectrum scarcity issue.In this dissertation, we aim at enabling CR equipments to exploit autonomously communication opportunities found in their vicinity. For that purpose, we suggest decision making mechanisms designed and/or adapted to answer CR related problems in general, and more specifically, OSA related scenarios. Thus, we argue that OSA scenarios can be modeled as Multi-Armed Bandit (MAB) problems. As a matter of fact, within OSA contexts, CR equipments are assumed to have no prior knowledge on their environment. Acquiring the necessary information relies on a sequential interaction between the CR equipment and its environment. Finally, the CR equipment is modeled as a cognitive agent whose purpose is to learn while providing an improving service to its user. Thus, firstly we analyze the performance of UCB1 algorithm when dealing with OSA problems with imperfect sensing. More specifically, we show that UCB1 can efficiently cope with sensing errors. We prove its convergence to the optimal channel and quantify its loss of performance compared to the case with perfect sensing. Secondly, we combine UCB1 algorithm with collaborative and coordination mechanism to model a secondary network (i.e. several SUs). We show that within this complex scenario, a coordinated learning mechanism can lead to efficient secondary networks. These scenarios assume that a SU can efficiently detect incumbent users’ activity while having no prior knowledge on their characteristics. Usually, energy detection is suggested as a possible approach to handle such task. Unfortunately, energy detection in known to perform poorly when dealing with uncertainty. Consequently, we ventured in this Ph.D. to revisit the problem of energy detection limits under uncertainty. We present new results on its performances as well as its limits when the noise level is uncertain and the uncertainty is modeled by a log-normal distribution (as suggested by Alexander Sonnenschein and Philip M. Fishman in 1992). Within OSA contexts, we address a final problem where a sensor aims at quantifying the quality of a channel in fading environments. In such contexts, UCB1 algorithms seem to fail. Consequently, we designed a new algorithm called Multiplicative UCB (UCB) and prove its convergence. Moreover, we prove that MUCB algorithms are order optimal (i.e., the order of their learning rate is optimal). This last work provides a contribution that goes beyond CR and OSA. As a matter of fact, MUCB algorithms are introduced and solved within a general MAB framework.

Multi-armed bandit (MAB) algorithms could be used to select a subset of the k most informative summary statistics, from a pool of m possible summary statistics, by reformulating the subset selection problem as a MAB problem. This is suggested by experiments that tested five MAB algorithms (Direct, Halving, SAR, OCBA-m, and Racing) on the reformulated problem and comparing the results to two established subset selection algorithms (Minimizing Entropy and Approximate Sufficiency). The MAB algorithms yielded errors at par with the established methods, but in only a fraction of the time. Establishing MAB algorithms as a new standard for summary statistics subset selection could therefore save numerous scientists substantial amounts of time when selecting summary statistics for approximate bayesian computation.

Cette thèse porte sur l'étude des Systèmes de Recommandation dans un cadre séquentiel, où les retours des utilisateurs sur des articles arrivent dans le système l'un après l'autre. Après chaque retour utilisateur, le système doit le prendre en compte afin d'améliorer les recommandations futures. De nombreuses techniques de recommandation ou méthodologies d'évaluation ont été proposées par le passé pour les problèmes de recommandation. Malgré cela, l'évaluation séquentielle, qui est pourtant plus réaliste et se rapproche davantage du cadre d'évaluation d'un vrai système de recommandation, a été laissée de côté. Le contexte séquentiel nécessite de prendre en considération différents aspects non visibles dans un contexte fixe. Le premier de ces aspects est le dilemme dit d'exploration vs. exploitation: le modèle effectuant les recommandations doit trouver le bon compromis entre recueillir de l'information sur les goûts des utilisateurs à travers des étapes d'exploration, et exploiter la connaissance qu'il a à l'heure actuelle pour maximiser le feedback reçu. L'importance de ce premier point est mise en avant à travers une première évaluation, et nous proposons une approche à la fois simple et efficace, basée sur la Factorisation de Matrice et un algorithme de Bandit Manchot, pour produire des recommandations appropriées. Le second aspect pouvant apparaître dans le cadre séquentiel surgit dans le cas où une liste ordonnée d'articles est recommandée au lieu d'un seul article. Dans cette situation, le feedback donné par l'utilisateur est multiple: la partie explicite concerne la note donnée par l'utilisateur concernant l'article choisi, tandis que la partie implicite concerne les articles cliqués (ou non cliqués) parmi les articles de la liste. En intégrant les deux parties du feedback dans un modèle d'apprentissage, nous proposons une approche basée sur la Factorisation de Matrice, qui peut recommander de meilleures listes ordonnées d'articles, et nous évaluons cette approche dans un contexte séquentiel particulier pour montrer son efficacité. / This thesis is dedicated to the study of Recommendation Systems under a sequential setting, where the feedback given by users on items arrive one after another in the system. After each feedback, the system has to integrate it and try to improve future recommendations. Many techniques or evaluation methods have already been proposed to study the recommendation problem. Despite that, such sequential setting, which is more realistic and represent a closer framework to a real Recommendation System evaluation, has surprisingly been left aside. Under a sequential context, recommendation techniques need to take into consideration several aspects which are not visible for a fixed setting. The first one is the exploration-exploitation dilemma: the model making recommendations needs to find a good balance between gathering information about users' tastes or items through exploratory recommendation steps, and exploiting its current knowledge of the users and items to try to maximize the feedback received. We highlight the importance of this point through the first evaluation study and propose a simple yet efficient approach to make effective recommendation, based on Matrix Factorization and Multi-Armed Bandit algorithms. The second aspect emphasized by the sequential context appears when a list of items is recommended to the user instead of a single item. In such a case, the feedback given by the user includes two parts: the explicit feedback as the rating, but also the implicit feedback given by clicking (or not clicking) on other items of the list. By integrating both feedback into a Matrix Factorization model, we propose an approach which can suggest better ranked list of items, and we evaluate it in a particular setting.

近幾年由於「山寨手機」的興起，吸引了大家對於山寨產品的注意力，改變了一般民眾對山寨產品的想法，然而，山寨手機的出現，已經不單是一個社會的獨特現象，它更影響了手機產業供應鏈的生態。因此本研究透過個案研究方法，以B公司做為研究對象，從收集的初級及次級資料中，利用SWOT分析與五力分析作為分析方法，試圖回答下列兩個問題：（1）瞭解「山寨產業」對於PCB產業有何衝擊？（2）面對「山寨產業」的衰退，台灣PCB廠商應如何因應？ 本研究發現，由於B公司初期的企業能力能夠滿足山寨手機的需求，因此能夠在山寨手機興起時從中獲利，然而，隨著山寨手機產業的衰退，本研究認為B公司須採用下列策略因應：（1）淡出山寨手機供應鏈，不再陷入削價競爭的窘境，將目標客戶轉為「白牌手機」廠商，（2）善用大陸市場與低價勞力，由於大陸市場已經從「世界工廠」轉變為「世界市場」，為了服務廣大的消費者，國際大廠紛紛在大陸設立據點，因此B公司應繼續增加昆山廠的生產線，同時，（3）要提高手機用PCB的研發費用，由於傳統山寨手機產業已經衰減，新的需求轉向高階智慧型手機，因此B公司必須手機產品主力從低階四層板，提升為高階的HDI板，除了可以回應智慧型手機的需要外，也慢慢將目標客戶轉移至品牌手機廠商。 本研究主要貢獻是提供企業深入了解山寨手機對手機產業供應鏈之影響，雖然仿冒抄襲是一個新興產業成長的必經之路，然而大陸的山寨手機產業所產生的影響不容小覷，因此本研究以一個曾經受惠於在山寨手機的PCB廠作為研究對象，試圖提出面對後山寨時代的來臨，企業應該用如何回應。 / The rise of bandit mobile phone industry has attracted people’s eye and change people’s mind on bandit products. “Bandit” is not just a unique social phenomenon, but an impact on the outside environment of mobile phone supply chain. This research used case study to investigate the B company, a Taiwanese PCB company as our research object. We utilized SWOT analysis and Porter’s five force analysis as tools with our primary and secondary data to clarify the following two questions: (1) what the influence did bandit industry arise on the PCB industry, and (2) what is the PCB company’s strategy when facing downturn of the bandit mobile phone industry. Our study has found that the B company’s capability matched the need of bandit mobile phone industry at the early stage. However, with the industry declined, we suggest the B company should adapt the following strategies in order to maintain its profit: (1) fade out from bandit mobile phone industry, and turn to white-box mobile phone industry; (2) take advantage of cheap labor cost in China and extend existing production lines; (3) enhance the R&D investment on mobile phone PCB. The major contribution of this research is to help firms understand the influence of bandit mobile phones on mobile phone’s supply chain. Although copy is the necessary evil in the developing industry, however, the impact from it still can’t be ignored. We choose a Taiwanese PCB company, which has been benefited from bandit mobile phones, as our case to identify what’s next for the PCB companies in post bandit mobile phone age.

The discovery of the Dead Sea Scrolls has provided an opportunity to reexamine the formation of the Gospel of John. This study will utilize Dead Sea finds coupled with other Second Temple literature to examine how the Gospel of John portrays Jesus as being a king. The approach of this study to use a narrative approach that builds on the Gospel of John as a finished text. The contribution of a source critical approach is not disparaged but the narrative approach will allow the Johannine community to be seen in the context of the immediate post-Second Temple era. The limited literacy of the probable first audience of this text suggests that a narrative approach will best be able to understand the background to the formation of the Gospel of John. A central contention of this study is that the Gospel of John was composed after the Jewish Revolt and after the Synoptics. Thus it deserves the appellation of the Fourth Gospel and is called such in this study. The Fourth Gospel was composed at a time when Roman interest in anything connected to Judaism was sure to attract special interest. Thus the portrayal of Jesus as the Davidic Messiah needed to be handled carefully. The imagery of the new David found in 4Q504 compared with the imagery of Jesus being the Good Shepherd becomes an important part of the argument of this study on whether this Gospel portrays Jesus as being the Davidic Messiah. Jesus as the Good Shepherd showed Jews that Jesus is the Davidic Messiah without overtly offending Roman sensibilities. Furthermore evidence from Christian and Jewish sources indicates that an interest in a Third Temple was still stirring between the Jewish and Bar-Kochba Revolts. The Fourth Gospel shows Jesus as the Davidic Messiah who replaces the Temple because the Good Shepherd was the perfect sacrifice. / New Testament / D. Th. (New Testament)

Allocation of objects or resources to competing agents is a ubiquitous problem in the real world. For example, a federal government may wish to allocate different types of spectrum licenses to telecom service providers; a search engine has to assign different sponsored slots to the ads of advertisers; etc. The agents involved in such situations have private preferences over the allocations. The agents, being strategic, may manipulate the allocation procedure to get a favourable allocation. If the objects to be allocated are heterogeneous (rather than homogeneous), the problem becomes quite complex. The allocation problem becomes even more formidable in the presence of a dynamic supply and/or demand. This doctoral work is motivated by such problems involving strategic agents, heterogeneous objects, and dynamic supply and/or demand. In this thesis, we model such problems in a standard game theoretic setting and use mechanism design to propose novel solutions to the problems. We extend the current state-of-the-art in a non-trivial way by solving the following problems: Optimal combinatorial auctions with single minded bidders, generalizing the existing methods to take into account multiple units of heterogeneous objects Multi-armed bandit mechanisms for sponsored search auctions with multiple slots, generalizing the current methods that only consider a single slot. Strategyproof redistribution mechanisms for heterogeneous objects, expanding the scope of the current state of practice beyond homogeneous objects Online allocation mechanisms without money for one-sided and two-sided matching markets, extending the existing methods for static settings.

<p>Online decision making with switching costs has received considerable attention in many practical problems that face uncertainty in the inputs and key problem parameters. Because of the switching costs that penalize the change of decisions, making good online decisions under such uncertainty is known to be extremely challenging. This thesis aims at providing new online algorithms with strong performance guarantees to address this challenge.</p> <p><br></p> <p>In part 1 and part 2 of this thesis, motivated by Network Functions Virtualization and smart grid, we study competitive online convex optimization with switching costs. Specifically, in part 1, we focus on the setting with an uncertainty set (one type of prediction) and hard infeasibility constraints. We develop new online algorithms that can attain optimized competitive ratios, while ensuring feasibility at all times. Moreover, we design a robustification procedure that helps these algorithms obtain good average-case performance simultaneously. In part 2, we focus on the setting with look-ahead (another type of prediction). We provide the first algorithm that attains a competitive ratio that not only decreases to 1 as the look-ahead window size increases, but also remains upper-bounded for any ratio between the switching-cost coefficient and service-cost coefficient.</p> <p><br></p> <p>In part 3 of this thesis, motivated by edge computing with artificial intelligence, we study bandit learning with switching costs where, in addition to bandit feedback, full feedback can be requested at a cost. We show that, when only 1 arm can be chosen at a time, adding costly full-feedback is not helpful in fundamentally reducing the Θ(<em>T</em>2/3) regret over a time-horizon <em>T</em>. In contrast, when 2 (or more) arms can be chosen at a time, we provide a new online learning algorithm that achieves a significantly smaller regret equal to <em>O</em>(√<em>T</em>), without even using full feedback. To the best of our knowledge, this type of sharp transition from choosing 1 arm to choosing 2 (or more) arms has never been reported in the literature.</p>