Emergence of highly-designable protein-backbone conformations in an off-lattice model

Despite the variety of protein sizes, shapes, and backbone configurations found in nature, the design of novel protein folds remains an open problem. Within simple lattice models it has been shown that all structures are not equally suitable for design. Rather, certain structures are distinguished by unusually high designability: the number of amino-acid sequences for which they represent the unique ground state; sequences associated with such structures possess both robustness to mutation and thermodynamic stability. Here we report that highly designable backbone conformations also emerge in a realistic off-lattice model. The highly designable conformations of a chain of 23 amino acids are identified, and found to be remarkably insensitive to model parameters. While some of these conformations correspond closely to known natural protein folds, such as the zinc finger and the helix-turn-helix motifs, others do not resemble known folds and may be candidates for novel fold design.Comment: 7 figure

Extracting generic text information from images

University of Technology, Sydney. Faculty of Engineering and Information Technology.As a vast amount of text appears everywhere, including natural scene, web pages and videos, text becomes very important information for different applications. Extracting text information from images and video frames is the first step of applying them to a specific application and this task is completed by a text information extraction (TIE) system. TIE consists of text detection, text binarisation and text recognition. For different applications or projects, one or more of these three TIE components may be embedded. Although many efforts have been made to extract text from images and videos, this problem is far from being solved due to the difficulties existing in different scenarios. This thesis focuses on the research of text detection and text binarisation. For the work on text detection in born-digital images, a new scheme for coarse text detection and a texture-based feature for fine text detection are proposed. In the coarse detection step, a novel scheme based on Maximum Gradient Difference (MGD) response of text lines is proposed. MGD values are classified into multiple clusters by a clustering algorithm to create multiple layer images. Then, the text line candidates are detected in different layer images. An SVM classifier trained by a novel texture-based feature is utilized to filter out the non-text regions. The superiority of the proposed feature is demonstrated by comparing with other features for text/non-text classification capability. Another algorithm is designed for detecting texts from natural scene images. Maximally Stable Extremal Regions (MSERs) as character candidates are classified into character MSERs and non-character MSERs based on geometry-based, stroke-based, HOG-based and colour-based features. Two types of misclassified character MSERs are retrieved by two different schemes respectively. A false alarm elimination step is performed for increasing the text detection precision and the bootstrap strategy is used to enhance the power of suppressing false positives. Both promising recall rate and precision rate are achieved. In the aspect of text binarisation research, the combination of the selected colour channel image and graph-based technique are explored firstly. The colour channel image with the histogram having the biggest distance, estimated by mean-shift procedure, between the two main peaks is selected before the graph model is constructed. Then, Normalised cut is employed on the graph to get the binarisation result. For circumventing the drawbacks of the grayscale-based method, a colour-based text binarisation method is proposed. A modified Connected Component (CC)-based validation measurement and a new objective segmentation evaluation criterion are applied as sequential processing. The experimental results show the effectiveness of our text binarisation algorithms

A hidden constant in the anomalous Hall effect of a high-purity magnet MnSi

Measurements of the Hall conductivity in MnSi can provide incisive tests of theories of the anomalous Hall (AH) effect, because both the mean-free-path and magnetoresistance (MR) are unusually large for a ferromagnet. The large MR provides an accurate way to separate the AH conductivity $\sigma_{xy}^A$ from the ordinary Hall conductivity $\sigma_{xy}^N$. Below the Curie temperature $T_C$, $\sigma_{xy}^A$ is linearly proportional to $M$ (magnetization) with a proportionality constant $S_H$ that is independent of both $T$ and $H$. In particular, $S_H$ remains a constant while $\sigma_{xy}^N$ changes by a factor of 100 between 5 K and $T_C$. We discuss implications of the hidden constancy in $S_H$.Comment: 5 pages, 4 figures. Minor change

Deriving N-soliton solutions via constrained flows

The soliton equations can be factorized by two commuting x- and t-constrained flows. We propose a method to derive N-soliton solutions of soliton equations directly from the x- and t-constrained flows.Comment: 8 pages, AmsTex, no figures, to be published in Journal of Physics

Deterministic Quantum Key Distribution Using Gaussian-Modulated Squeezed States

A continuous variable ping-pong scheme, which is utilized to generate deterministically private key, is proposed. The proposed scheme is implemented physically by using Gaussian-modulated squeezed states. The deterministic way, i.e., no basis reconciliation between two parties, leads a two-times efficiency comparing to the standard quantum key distribution schemes. Especially, the separate control mode does not need in the proposed scheme so that it is simpler and more available than previous ping-pong schemes. The attacker may be detected easily through the fidelity of the transmitted signal, and may not be successful in the beam splitter attack strategy.Comment: 7 pages, 4figure