Translation invariant topological superconductors on lattice

In this paper we introduce four Z_2 topological indices zeta_k=0,1 at k=(0,0), (0,pi), (pi, 0), (pi, pi) characterizing 16 universal classes of 2D superconducting states that have translation symmetry but may break any other symmetries. The 16 classes of superconducting states are distinguished by their even/odd numbers of fermions on even-by-even, even-by-odd, odd-by-even, and odd-by-odd lattices. As a result, the 16 classes topological superconducting states exist even for interacting systems. For non-interacting systems, we find that zeta_k is the number of electrons on k=(0,0), (0,pi), (pi, 0), or (pi,pi) orbitals (mod 2) in the ground state. For 3D superconducting states with only translation symmetry, there are 256 different types of topological superconductors.Comment: 4 pages, RevTeX

Predicting Bankruptcy After The Sarbanes-Oxley Act Using The Most Current Data Mining Approaches

Our study proposes several current data mining methods to predict bankruptcy after the Sarbanes-Oxley Act (2002) using 2007-2008 U.S. data.  The Sarbanes-Oxley Act (SOX) of 2002 was introduced to improve the quality of financial reporting and minimize corporate fraud in the U.S.  Because of this SOX implementation, a company’s financial statements are assumed to provide higher quality financial information for investors and other stakeholders. The results of our data mining approaches in our bankruptcy prediction study show that Bayesian Net method performs the best (85% overall prediction rate with 94% in AUC), followed by J48 (85% with 82% AUC), Decision Table (83.52%), and Decision Tree (82%) methods using financial and other data from the 10-K report and Compustat.  These results are better than previous bankruptcy prediction studies before the SOX implementation using most current data mining approaches

Predicting Auditor Changes Using Financial Distress Variables And The Multiple Criteria Linear Programming (MCLP) And Other Data Mining Approaches

Our study evaluates a multiple criteria linear programming (MCLP) and other data mining approaches to predict auditor changes using a portfolio of financial statement measures to capture financial distress. The results of the MCLP approach and the other data mining approaches show that these methods perform reasonably well to predict auditor changes using financial distress variables. Overall accuracy rates are more than 60 percent, and true positive rates exceed 80 percent. Our study is designed to establish a starting point for auditor-change prediction using financial distress variables. Further research should incorporate additional explanatory variables and a longer study period to improve prediction rates

Multi-Level Opinion Dynamics under Bounded Confidence

Opinion dynamics focuses on the opinion evolution in a social community. Recently, some models of continuous opinion dynamics under bounded confidence were proposed by Deffuant and Krause, et al. In the literature, agents were generally assumed to have a homogeneous confidence level. This paper proposes an extended model for a group of agents with heterogeneous confidence levels. First, a social differentiation theory is introduced and a social group is divided into opinion subgroups with distinct confidence levels. Second, a multi-level heterogeneous opinion formation model is formulated under the framework of bounded confidence. Finally, computer simulations are conducted to study the collective opinion evolution, focusing on three key factors: the fractions of heterogeneous agents, the initial opinions, and the group size. The simulation results demonstrate that the number of final opinions depends on the fraction of closeminded agents when the group size and the initial opinions are fixed; the final opinions converge more easily when the initial opinions are closer; and the number of final opinions can be approximately modeled by a linear increasing function of the group size and the increasing rate is the fraction of close-minded agents

Using Optimization-Based Classification Method for Massive Datasets

Optimization-based algorithms, such as Multi-Criteria Linear programming (MCLP), have shown their effectiveness in classification. Nevertheless, due to the limitation of computation power and memory, it is difficult to apply MCLP, or similar optimization methods, to huge datasets. As the size of today’s databases is continuously increasing, it is highly important that data mining algorithms are able to perform their functions regardless of dataset sizes. The objectives of this paper are: (1) to propose a new stratified random sampling and majority-vote ensemble approach, and (2) to compare this approach with the plain MCLP approach (which uses only part of the training set), and See5 (which is a decision-tree-based classification tool designed to analyze substantial datasets), on KDD99 and KDD2004 datasets. The results indicate that this new approach not only has the potential to handle arbitrary-size of datasets, but also outperforms the plain MCLP approach and achieves comparable classification accuracy to See5

Overall Performance Evaluation of Tubular Scraper Conveyors Using a TOPSIS-Based Multiattribute Decision-Making Method

Properly evaluating the overall performance of tubular scraper conveyors (TSCs) can increase their overall efficiency and reduce economic investments, but such methods have rarely been studied. This study evaluated the overall performance of TSCs based on the technique for order of preference by similarity to ideal solution (TOPSIS). Three conveyors of the same type produced in the same factory were investigated. Their scraper space, material filling coefficient, and vibration coefficient of the traction components were evaluated. A mathematical model of the multiattribute decision matrix was constructed; a weighted judgment matrix was obtained using the DELPHI method. The linguistic positive-ideal solution (LPIS), the linguistic negative-ideal solution (LNIS), and the distance from each solution to the LPIS and the LNIS, that is, the approximation degrees, were calculated. The optimal solution was determined by ordering the approximation degrees for each solution. The TOPSIS-based results were compared with the measurement results provided by the manufacturer. The ordering result based on the three evaluated parameters was highly consistent with the result provided by the manufacturer. The TOPSIS-based method serves as a suitable evaluation tool for the overall performance of TSCs. It facilitates the optimal deployment of TSCs for industrial purposes

Predicting Material Weaknesses In Internal Control Systems After The Sarbanes-Oxley Act Using Multiple Criteria Linear Programming And Other Data Mining Approaches

Our study proposes a multiple criteria linear programming (MCLP) and other data mining methods to predict material weaknesses in a firm’s internal control system after the Sarbanes-Oxley Act (SOX) using 2003-2004 U.S. data.  The results of the MCLP and other data mining approaches in our prediction study show that the MCLP method performs better overall than the other data mining approaches using financial and other data from the Form 10-K report.  Consistent with prior research, firms that disclosed material weaknesses in their SOX Section 302 disclosures were more complex (based on the existence of foreign currency translations), more often used Big 4 auditors, and had lower operating cash flows-to-total assets ratios than the non-material weakness control firms.  Because of mixed results on several profitability measures and marginal predictive ability for the MCLP and other methods used, more research is needed to identify firm characteristics that help investors, auditors, and others predict material weaknesses