Baryon-Pion Couplings from Large-N QCD

We derive a set of consistency conditions for the pion-baryon coupling constants in the large-N limit of QCD. The consistency conditions have a unique solution which are precisely the values for the pion-baryon coupling constants in the Skyrme model. We also prove that non-relativistic $SU(2N_f)$ spin-flavor symmetry (where $N_f$ is the number of light flavors) is a symmetry of the baryon-pion couplings in the large-N limit of QCD. The symmetry breaking corrections to the pion-baryon couplings vanish to first order in $1/N$. Consistency conditions for other couplings, such as the magnetic moments are also derived.Comment: (12 pages, 2 figs, uses harvmac and uufiles), UCSD/PTH 93-1

Improving the performance of cascade correlation neural networks on multimodal functions

Intrinsic qualities of the cascade correlation algorithm make it a popular choice for many researchers wishing to utilize neural networks. Problems arise when the outputs required are highly multimodal over the input domain. The mean squared error of the approximation increases significantly as the number of modes increases. By applying ensembling and early stopping, we show that this error can be reduced by a factor of three. We also present a new technique based on subdivision that we call patchworking. When used in combination with early stopping and ensembling the mean improvement in error is over 10 in some cases

Terrorist Attacks On Public Bus Transportation: A Preliminary Empirical Analysis, MTI Report WP 09-01

This report provides data on terrorist attacks against public bus transportation targets and serious crimes committed against such targets throughout the world. The data are drawn from the MTI database of attacks on public surface transportation, which is expanded and updated as information becomes available. This analysis is based on the database as of December 17, 2009. Data include the frequency and lethality with which buses, bus stations, and bus stops are attacked; the relationship between fatalities and attacks against bus targets and the relationship between injuries and attacks against those targets; how often, relative to other surface transportation targets, buses are attacked, first with all weapons and then with only explosive and incendiary devices; the relative lethality of attacks; and the distribution of attacks. It then presents some preliminary observations drawn from those data that can help stakeholders governments, transit managers, and employees to focus on the ways the most frequent and/or most lethal attacks are carried out as they consider measures to prevent or mitigate attacks that may be considered likely to happen in the United States

An optimal factor analysis approach to improve the wavelet-based image resolution enhancement techniques

The existing wavelet-based image resolution enhancement techniques have many assumptions, such as limitation of the way to generate low-resolution images and the selection of wavelet functions, which limits their applications in different fields. This paper initially identifies the factors that effectively affect the performance of these techniques and quantitatively evaluates the impact of the existing assumptions. An approach called Optimal Factor Analysis employing the genetic algorithm is then introduced to increase the applicability and fidelity of the existing methods. Moreover, a new Figure of Merit is proposed to assist the selection of parameters and better measure the overall performance. The experimental results show that the proposed approach improves the performance of the selected image resolution enhancement methods and has potential to be extended to other methods

A study of early stopping, ensembling, and patchworking for cascade correlation neural networks

The constructive topology of the cascade correlation algorithm makes it a popular choice for many researchers wishing to utilize neural networks. However, for multimodal problems, the mean squared error of the approximation increases significantly as the number of modes increases. The components of this error will comprise both bias and variance and we provide formulae for estimating these values from mean squared errors alone. We achieve a near threefold reduction in the overall error by using early stopping and ensembling. Also described is a new subdivision technique that we call patchworking. Patchworking, when used in combination with early stopping and ensembling, can achieve an order of magnitude improvement in the error. Also presented is an approach for validating the quality of a neural network’s training, without the explicit use of a testing dataset

Spin-Flavor Structure of Large N Baryons

The spin-flavor structure of large N baryons is described in the 1/N expansion of QCD using quark operators. The complete set of quark operator identities is obtained, and used to derive an operator reduction rule which simplifies the 1/N expansion. The operator reduction rule is applied to the axial currents, masses, magnetic moments and hyperon non-leptonic decay amplitudes in the $SU(3)$ limit, to first order in $SU(3)$ breaking, and without assuming $SU(3)$ symmetry. The connection between the Skyrme and quark representations is discussed. An explicit formula is given for the quark model operators in terms of the Skyrme model operators to all orders in $1/\N$ for the two flavor case.Comment: 36 pages, 2 eps figures, uses revte

Bosonic Operator Methods for the Quark Model

Quark model matrix elements can be computed using bosonic operators and the holomorphic representation for the harmonic oscillator. The technique is illustrated for normal and exotic baryons for an arbitrary number of colors. The computations are much simpler than those using conventional quark model wavefunctions

Prediction of earnings per share for industry

Prediction of Earnings Per Share (EPS) is the fundamental problem in finance industry. Various Data Mining technologies have been widely used in computational finance. This research work aims to predict the future EPS with previous values through the use of data mining technologies, thus to provide decision makers a reference or evidence for their economic strategies and business activity. We created three models LR, RBF and MLP for the regression problem. Our experiments with these models were carried out on the real datasets provided by a software company. The performance assessment was based on Correlation Coefficient and Root Mean Squared Error. These algorithms were validated with the data of six different companies. Some differences between the models have been observed. In most cases, Linear Regression and Multilayer Perceptron are effectively capable of predicting the future EPS. But for the high nonlinear data, MLP gives better performance

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Optimisation of the surfboard fin shape using computational fluid dynamics and genetic algorithms

During the sport of wave surfing, the fins on a surfboard play a major role in the overall performance of the surfer. This article presents the optimisation of a surfboard fin shape, using coupled genetic algorithms with the FLUENT® solver, aiming at the maximisation of the lift per drag ratio. The design-variable vector includes six components namely the chord length, the depth and the sweep angle of the fin as well as the maximum camber, the maximum camber position and the thickness of the hydrofoil (the four-digit NACA parametrization). The Latin hypercube sampling technique is utilised to explore the design space, resulting in 42 different fin designs. Fin and control volume models are created (using CATIA® V5) and meshed (unstructured using ANSYS® Workbench). Steady-state computations were performed using the FLUENT SST k−ω (shear stress transport k−ω) turbulence model at the velocity of 10 m/s and 10° angle of attack. Using the obtained lift and drag values, a response surface based model was constructed with the aim to maximise the lift-to-drag ratio. The optimisation problem was solved using the genetic algorithm provided by the MATLAB® optimisation toolbox and the response surface based model was iteratively improved. The resultant optimal fin design is compared with the experimental data for the fin demonstrating an increase in lift-to-drag ratio by approximately 62% for the given angle of attack of 10°