Aspect-Oriented Programming (AOP)
In computing, aspect-oriented programming (AOP) is a programming paradigm that aims to increase modularity by allowing the separation of cross-cutting concerns. It does so by adding additional behavior to existing code (an advice) without modifying the code itself, instead separately specifying which code is modified via a ‘pointcut’ specification, such as ‘log all function calls when the function’s name begins with ‘set”. This allows behaviors that are not central to the business logic (such as logging) to be added to a program without cluttering the code, core to the functionality. AOP forms a basis for aspect-oriented software development. AOP includes programming methods and tools that support the modularization of concerns at the level of the source code, while ‘aspect-oriented software development’ refers to a whole engineering discipline. …

Three-Way Decisions-Based Conflict Analysis Model (TWDCAM)
Three-way decision theory, which trisects the universe with less risks or costs, is considered as a powerful mathematical tool for handling uncertainty in incomplete and imprecise information tables, and provides an effective tool for conflict analysis decision making in real-time situations. In this paper, we propose the concepts of the agreement, disagreement and neutral subsets of a strategy with two evaluation functions, which establish the three-way decisions-based conflict analysis models(TWDCAMs) for trisecting the universe of agents, and employ a pair of two-way decisions models to interpret the mechanism of the three-way decision rules for an agent. Subsequently, we develop the concepts of the agreement, disagreement and neutral strategies of an agent group with two evaluation functions, which build the TWDCAMs for trisecting the universe of issues, and take a couple of two-way decisions models to explain the mechanism of the three-way decision rules for an issue. Finally, we reconstruct Fan, Qi and Wei’s conflict analysis models(FQWCAMs) and Sun, Ma and Zhao’s conflict analysis models(SMZCAMs) with two evaluation functions, and interpret FQWCAMs and SMZCAMs with a pair of two-day decisions models, which illustrates that FQWCAMs and SMZCAMs are special cases of TWDCAMs. …

Multi Screen Penalty (MSP)
We propose a multi-step method, called Multi Screen Penalty (MSP), to estimate high-dimensional sparse linear models. MSP uses a series of small and adaptive penalty to iteratively estimate the regression coefficients. This structure is shown to greatly improve the model selection and estimation accuracy, i.e., it precisely selects the true model when the irrepresentable condition fails; under mild regularity conditions, MSP estimator can achieve the rate $\sqrt{q \log n /n}$ for the upper bound of l_2-norm error. At each step, we restrict the selection of MSP only on the reduced parameter space obtained from the last step; this makes its computational complexity is roughly comparable to Lasso. This algorithm is found to be stable and reaches to high accuracy over a range of small tuning parameters, hence deletes the cross-validation segment. Numerical comparisons show that the method works effectively both in model selection and estimation and nearly uniformly outperform others. We apply MSP and other methods to financial data. MSP is successful in assets selection and produces more stable and lower rates of fitted/predicted errors. …

Sliding Window Discrete Fourier Transform (SWDFT)
This paper introduces a new tool for time-series analysis: the Sliding Window Discrete Fourier Transform (SWDFT). The SWDFT is especially useful for time-series with local- in-time periodic components. We define a 5-parameter model for noiseless local periodic signals, then study the SWDFT of this model. Our study illustrates several key concepts crucial to analyzing time-series with the SWDFT, in particular Aliasing, Leakage, and Ringing. We also show how these ideas extend to R > 1 local periodic components, using the linearity property of the Fourier transform. Next, we propose a simple procedure for estimating the 5 parameters of our local periodic signal model using the SWDFT. Our estimation procedure speeds up computation by using a trigonometric identity that linearizes estimation of 2 of the 5 parameters. We conclude with a very small Monte Carlo simulation study of our estimation procedure under different levels of noise. …