A Flow Trace Generator using Graph-based Traffic Classification Techniques

We propose a novel methodology to generate realistic network flow traces to enable systematic evaluation of network monitoring systems in various traffic conditions. Our technique uses a graph-based approach to model the communication structure observed in real-world traces and to extract traffic templates. By combining extracted and user-defined traffic templates, realistic network flow traces that comprise normal traffic and customized conditions are generated in a scalable manner. A proof-of-concept implementation demonstrates the utility and simplicity of our method to produce a variety of evaluation scenarios. We show that the extraction of templates from real-world traffic leads to a manageable number of templates that still enable accurate re-creation of the original communication properties on the network flow level.

Parameters of Reform and Unification in Modern Japanese Buddhist Thought: Murakami Senshō and Critical Buddhism

Reform is a word that, one might easily say, characterizes more than any other the history and development of Buddhism. Yet, it must also be said that reform movements in East Asian Buddhism have often taken on another goal—harmony or unification; that is, a desire not only to reconstruct a more worthy form of Buddhism, but to simultaneously bring together all existing forms under a single banner, in theory if not in practice. This paper explores some of the tensions between the desire for reform and the quest for harmony in modern Japanese Buddhism thought, by comparing two developments: the late 19th century movement towards ‘New Buddhism’ (shin Bukkyō) as exemplified by Murakami Senshō 村上専精 (1851–1929), and the late 20th century movement known as ‘Critical Buddhism’ (hihan Bukkyō), as found in the works of Matsumoto Shirō 松本史朗 and Hakamaya Noriaki 袴谷憲昭. In all that has been written about Critical Buddhism, in both Japanese and English, very little attention has been paid to the place of the movement within the larger traditions of Japanese Buddhist reform. Here I reconsider Critical Buddhism in relation to the concerns of the previous, much larger trends towards Buddhist reform that emerged almost exactly 100 years previous—the so-called shin Bukkyō or New Buddhism of the late-Meiji era. Shin Bukkyō is a catch-all term that includes the various writings and activities of Inoue Enryō, Shaku Sōen, and Kiyozawa Manshi, as well as the so-called Daijō-hibussetsuron, a broad term used (often critically) to describe Buddhist writers who suggested that Mahāyāna Buddhism is not, in fact, the Buddhism taught by the ‘historical’ Buddha Śākyamuni. Of these, I will make a few general remarks about Daijō-hibusseturon, before turning attention more specifically to the work of Murakami Senshō, in order to flesh out some of the similarities and differences between his attempt to construct a ‘unified Buddhism’ and the work of his late-20th century avatars, the Critical Buddhists. Though a number of their aims and ideas overlap, I argue that there remain fundamental differences with respect to the ultimate purposes of Buddhist reform. This issue hinges on the implications of key terms such as ‘unity’ and ‘harmony’ as well as the way doctrinal history is categorized and understood, but it also relates to issues of ideology and the use and abuse of Buddhist doctrines in 20th-century politics.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.