Bernard of Morlaix was a Cluniac monk who flourished around 1140. What little is known about him, including his visit to Rome, is examined in relation to the affairs of the Cluniac family in his day. A new conjecture is advanced that he was prior of Saint-Denis de Nogent-le-Rotrou. His poems are discussed as examples of the genre of complaint literature. His treatment of the end of the world, and of death, judgement, heaven and hell, is discussed in relation to twelfth-century monasticism. His castigation of the sins of his time includes some of the earliest estates satire. His anticlericalism and his misogyny are compared with those of his contemporaries, and discussed in the context of twelfth-century monastic culture. Bernard’s classical learning is analysed and compared with that of his contemporaries, especially John of Salisbury and Saint Bernard of Clairvaux. His use of metre and rhyme is examined in the context of the development of metre based on stress rather than quantity and of systematic and sustained rhyme in the Latin verse of the twelfth century. Bernard’s use of interpretive and compositional allegory is explored. Bernard is seen as a man of his time, exemplifying a number of twelfth-century characteristics, religious, educational and cultural. Special attention is paid to the Latin literary tradition, and it is suggested that the culture of the twelfth-century was in many respects a culmination rather than a renaissance.

Reinforcement learning has proven itself very useful in certain areas, such as games. However, the approach has been seen as quite limited. Reinforcement-based learning has for instance not been commonly used for classification tasks as it is receiving feedback on how well it did for an action performed on a specific input. This slows the performance convergence rate as compared to other classification approaches which has the input and the corresponding output to train on. Nevertheless, this thesis aims to investigate whether reinforcement-based learning could successfully be employed on a classification task. Moreover, as sensor fusion is an expanding field which can for instance assist autonomous vehicles in understanding its surroundings, it is also interesting to see how sensor fusion, i.e., fusion between lidar and RGB images, could increase the performance in a classification task. In this thesis, a reinforcement-based learning approach is compared to a semi-classical approach. As an example of a reinforcement learning model, a deep Q-learning network was chosen, and a support vector machine classifier built on top of a deep neural network, was chosen as an example of a semi-classical model. In this work, these frameworks are compared with and without sensor fusion to see whether fusion improves their performance. Experiments show that the evaluated reinforcement-based learning approach underperforms in terms of metrics but mainly due to its slow learning process, in comparison to the semi-classical approach. However, on the other hand using reinforcement-based learning to carry out a classification task could still in some cases be advantageous, as it still performs fairly well in terms of the metrics presented in this work, e.g. F1-score, or for instance imbalanced datasets. As for the impact of sensor fusion, a notable improvement can be seen, e.g. when training the deep Q-learning model for 50 episodes, the F1-score increased with 0.1329; especially, when taking into account that the most of the lidar data used in the fusion is lost since this work projects the 3D lidar data onto the same 2D plane as the RGB images.

由於顏元著「四書正誤」一書是針對朱熹「四書集註」的不滿而提出反對的觀點，換言之，其「正誤」意即正「四書集註」之誤。本文的緒論為研究動機、研究目的、研究程序及研究範圍，亦對前人研究的文獻大致作檢討。第二章是探討顏元四書學的背景，從橫向如清初對「四書」的研究情形及當時的學風作敘述；縱向從顏元本身的學習歷程、思想的轉變等亦在此章分述。第三章和第四章分別是從顏元「一破一立」的主張去呈顯其四書學。第三章是探討顏元反對「四書集註」之觀點。在形式上，顏元反對朱熹以傳注訓詁的方式去註解古籍；在內容上，他反對以心性義理的角度去詮釋「四書」。此外，他也對「四書集註」有妄改經義或經文處提出反對的意見。第四章是顏元四書學之要旨，從中剖析顏元所認為的訓解「四書」之方式為何。本章是從其主張以實際的事物去訓解古籍；主張透過行為去實踐經書中的道理；主張回復傳統儒學，從「四書」經典本身求義理以及其對「四書」的文句疏通方面去作剖析。第五章是對顏元的四書學作評述，主要是從其價值貢獻及缺失兩方面來談。再者，顏元四書學的定位及影響也一併在此章作探討。第六章是結論以總結全文。