Bott periodicity for the topological classification of gapped states of matter with reflection symmetry

Using a dimensional reduction scheme based on scattering theory, we show that the classification tables for topological insulators and superconductors with reflection symmetry can be organized in two period-two and four period-eight cycles, similar to the Bott periodicity found for topological insulators and superconductors without spatial symmetries. With the help of the dimensional reduction scheme the classification in arbitrary dimensions $d \ge 1$ can be obtained from the classification in one dimension, for which we present a derivation based on relative homotopy groups and exact sequences to classify one-dimensional insulators and superconductors with reflection symmetry. The resulting classification is fully consistent with a comprehensive classification obtained recently by Shiozaki and Sato [Phys.\ Rev.\ B {\bf 90}, 165114 (2014)]. The use of a scattering-matrix inspired method allows us to address the second descendant \bZ_2 phase, for which the topological nontrivial phase was previously reported to be vulnerable to perturbations that break translation symmetry.Comment: 18 pages, 7 figure

Classification of logical vulnerability based on group attacking method

New advancement in the field of e-commerce software technology has also brought many benefits, at the same time developing process always face different sort of problems from design phase to implement phase. Software faults and defects increases the issues of reliability and security, that’s reason why a solution of this problem is required to fortify these issues. The paper addresses the problem associated with lack of clear component-based web application related classification of logical vulnerabilities through identifying Attack Group Method by categorizing two different types of vulnerabilities in component-based web applications. A new classification scheme of logical group attack method is proposed and developed by using a Posteriori Empirically methodology

Pure phase-encoded MRI and classification of solids

Here, the authors combine a pure phase-encoded magnetic resonance imaging (MRI) method with a new tissue-classification technique to make geometric models of a human tooth. They demonstrate the feasibility of three-dimensional imaging of solids using a conventional 11.7-T NMR spectrometer. In solid-state imaging, confounding line-broadening effects are typically eliminated using coherent averaging methods. Instead, the authors circumvent them by detecting the proton signal at a fixed phase-encode time following the radio-frequency excitation. By a judicious choice of the phase-encode time in the MRI protocol, the authors differentiate enamel and dentine sufficiently to successfully apply a new classification algorithm. This tissue-classification algorithm identifies the distribution of different material types, such as enamel and dentine, in volumetric data. In this algorithm, the authors treat a voxel as a volume, not as a single point, and assume that each voxel may contain more than one material. They use the distribution of MR image intensities within each voxel-sized volume to estimate the relative proportion of each material using a probabilistic approach. This combined approach, involving MRI and data classification, is directly applicable to bone imaging and hard-tissue contrast-based modeling of biological solids

2D shape classification and retrieval

We present a novel correspondence-based technique for efficient shape classification and retrieval. Shape boundaries are described by a set of (ad hoc) equally spaced points – avoiding the need to extract “landmark points”. By formulating the correspondence problem in terms of a simple generative model, we are able to efficiently compute matches that incorporate scale, translation, rotation and reflection invariance. A hierarchical scheme with likelihood cut-off provides additional speed-up. In contrast to many shape descriptors, the concept of a mean (prototype) shape follows naturally in this setting. This enables model based classification, greatly reducing the cost of the testing phase. Equal spacing of points can be defined in terms of either perimeter distance or radial angle. It is shown that combining the two leads to improved classification/retrieval performance

Unsupervised two-class and multi-class support vector machines for abnormal traffic characterization

Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this paper we propose a measurement-based classification framework that exploits unsupervised learning to accurately categorise network anomalies to specific classes. We introduce the combinatorial use of two-class and multi-class unsupervised Support Vector Machines (SVM)s to first distinguish normal from anomalous traffic and to further classify the latter category to individual groups depending on the nature of the anomaly

Theory of ferromagnetic unconventional superconductors with spin-triplet electron pairing

A general phenomenological theory is presented for the phase behavior of ferromagnetic superconductors with spin-triplet electron Cooper pairing. The theory describes in details the temperature-pressure phase diagrams of real inter-metallic compounds exhibiting the remarkable phenomenon of coexistence of spontaneous magnetic moment of the itinerant electrons and spin-triplet superconductivity. The quantum phase transitions which may occur in these systems are also described. The theory allows for a classification of these itinerant ferromagnetic superconductors in two types: type I and type II. The classification is based on quantitative criteria.The comparison of theory and experiment is performed and outstanding problems are discussed.Comment: 25 pages, 7 figures; CP-ISSP-BAS preprint; a preliminary version of a review paper; to be submitted for a publicatio