No description available.

by Zhang Xiao-dong. / Thesis (M.Ph.)--Chineses University of Hong Kong, 1987. / Bibliography: leaves 88-92.

Yeung, Hau Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 59-63). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.7 / Chapter 2 --- Work Flows and Kernel Methods --- p.9 / Chapter 2.1 --- Work Flows --- p.9 / Chapter 2.2 --- Frequency Vector --- p.11 / Chapter 2.3 --- Motivation for Kernel Based Distance --- p.12 / Chapter 2.3.1 --- Similarity between sequences --- p.13 / Chapter 2.3.2 --- Distance between sequences --- p.14 / Chapter 2.4 --- Kernels for DNA Sequence --- p.15 / Chapter 2.4.1 --- Kernels based on evolution model --- p.15 / Chapter 2.4.2 --- Kernels based on empirical data --- p.17 / Chapter 2.5 --- Kernels for Peptide Sequence --- p.18 / Chapter 3 --- Dataset for DNA Sequence and Results --- p.25 / Chapter 3.1 --- Dataset and Goal --- p.25 / Chapter 3.1.1 --- Mitochondrial DNA dataset --- p.26 / Chapter 3.1.2 --- 18S ribosomal RNA --- p.28 / Chapter 3.2 --- Results --- p.28 / Chapter 4 --- Dataset for Peptide Sequence and Results --- p.35 / Chapter 4.1 --- Dataset and Goal --- p.36 / Chapter 4.2 --- Classification and Evaluation Methods --- p.39 / Chapter 4.2.1 --- Partition of training and testing datasets --- p.39 / Chapter 4.2.2 --- Classification methods --- p.40 / Chapter 4.3 --- Results --- p.45 / Chapter 4.3.1 --- KNN performs better than the FDSM --- p.45 / Chapter 4.3.2 --- BLOSUM62 performs best and window length not important --- p.46 / Chapter 4.3.3 --- Distance formula (2.4) performs better --- p.49 / Chapter 5 --- Discussion --- p.51 / Chapter 5.1 --- Sequence Length and Window Length --- p.51 / Chapter 5.2 --- Possible Kernels --- p.52 / Chapter 5.3 --- Distance Formulae --- p.53 / Chapter 5.4 --- Protein Structural Problem --- p.54 / Chapter 6 --- Appendix --- p.55 / Chapter 6.1 --- Kernel for Peptide Sequences --- p.55 / Bibliography --- p.59

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 83-90). Also available in electronic version.

In many instances of virtual machine deployments today, virtual machine instances are created to support a single application. Traditional operating systems provide an extensive framework for protecting one process from another. In such deployments, this protection layer becomes an additional source of overhead as isolation between services is provided at an operating system level and each instance of an operating system supports only one service. This makes the operating system the equivalent of a process from the traditional operating system perspective. Isolation between these operating systems and indirectly the services they support, is ensured by the virtual machine monitor in these deployments. In these scenarios the process protection provided by the operating system becomes redundant and a source of additional overhead. We propose a new model for these scenarios with operating systems that bypass this redundant protection offered by the traditional operating systems. We prototyped such an operating system by executing parts of the operating system in the same protection ring as user applications. This gives processes more power and access to kernel memory bypassing the need to copy data from user to kernel and vice versa as is required when the traditional ring protection layer is enforced. This allows us to save the system call trap overhead and allows application program mers to directly call kernel functions exposing the rich kernel library. This does not compromise security on the other virtual machines running on the same physical machine, as they are protected by the VMM. We illustrate the design and implementation of such a system with the Xen hypervisor and the XenoLinux kernel.

Abstract In the age of deep submicron VLSI, we can design various system applications in a single chip. In such system-on-a-chip designs, hardware and software function are integrated and managed by application-specific operating system functions. It motivates us to study design structures of current OS kernels. In this research, we applied an executable specification method for the software design of a UNIX kernel. We studied first the overall software structure of UNIX kernels. Then, we analyzed the detailed designs of process management and memory management. We applied object-oriented analysis and design techniques as well as a hierarchical state machine control design method. We will then map this design onto an executable specification framework to produce system prototype designs for collecting early experimental results and tuning application-specific kernel functionalities.

Thesis (Ph. D.)--Carnegie Mellon University, 2003. / "September 2003." Includes bibliographical references.

Machine learning techniques are now essential for a diverse set of applications in computer vision, natural language processing, software analysis, and many other domains. As more applications emerge and the amount of data continues to grow, there is a need for increasingly powerful and scalable techniques. Kernel methods, which generalize linear learning methods to non-linear ones, have become a cornerstone for much of the recent work in machine learning and have been used successfully for many core machine learning tasks such as clustering, classification, and regression. Despite the recent popularity in kernel methods, a number of issues must be tackled in order for them to succeed on large-scale data. First, kernel methods typically require memory that grows quadratically in the number of data objects, making it difficult to scale to large data sets. Second, kernel methods depend on an appropriate kernel function--an implicit mapping to a high-dimensional space--which is not clear how to choose as it is dependent on the data. Third, in the context of data clustering, kernel methods have not been demonstrated to be practical for real-world clustering problems. This thesis explores these questions, offers some novel solutions to them, and applies the results to a number of challenging applications in computer vision and other domains. We explore two broad fundamental problems in kernel methods. First, we introduce a scalable framework for learning kernel functions based on incorporating prior knowledge from the data. This frame-work scales to very large data sets of millions of objects, can be used for a variety of complex data, and outperforms several existing techniques. In the transductive setting, the method can be used to learn low-rank kernels, whose memory requirements are linear in the number of data points. We also explore extensions of this framework and applications to image search problems, such as object recognition, human body pose estimation, and 3-d reconstructions. As a second problem, we explore the use of kernel methods for clustering. We show a mathematical equivalence between several graph cut objective functions and the weighted kernel k-means objective. This equivalence leads to the first eigenvector-free algorithm for weighted graph cuts, which is thousands of times faster than existing state-of-the-art techniques while using significantly less memory. We benchmark this algorithm against existing methods, apply it to image segmentation, and explore extensions to semi-supervised clustering. / text

In many instances of virtual machine deployments today, virtual machine instances are created to support a single application. Traditional operating systems provide an extensive framework for protecting one process from another. In such deployments, this protection layer becomes an additional source of overhead as isolation between services is provided at an operating system level and each instance of an operating system supports only one service. This makes the operating system the equivalent of a process from the traditional operating system perspective. Isolation between these operating systems and indirectly the services they support, is ensured by the virtual machine monitor in these deployments. In these scenarios the process protection provided by the operating system becomes redundant and a source of additional overhead. We propose a new model for these scenarios with operating systems that bypass this redundant protection offered by the traditional operating systems. We prototyped such an operating system by executing parts of the operating system in the same protection ring as user applications. This gives processes more power and access to kernel memory bypassing the need to copy data from user to kernel and vice versa as is required when the traditional ring protection layer is enforced. This allows us to save the system call trap overhead and allows application program mers to directly call kernel functions exposing the rich kernel library. This does not compromise security on the other virtual machines running on the same physical machine, as they are protected by the VMM. We illustrate the design and implementation of such a system with the Xen hypervisor and the XenoLinux kernel.

No description available.