We propose a method named Super Characters for sentiment classification. This method converts the sentiment classification problem into image classification problem by projecting texts into images and then applying CNN models for classification. Text features are extracted automatically from the generated Super Characters images, hence there is no need of any explicit step of embedding the words or characters into numerical vector representations. Experimental results on large social media corpus show that the Super Characters method consistently outperforms other methods for sentiment classification and topic classification tasks on ten large social media datasets of millions of contents in four different languages, including Chinese, Japanese, Korean and English. …
Factor-Augmented Markov Switching (FAMS)
This paper investigates the role of high-dimensional information sets in the context of Markov switching models with time varying transition probabilities. Markov switching models are commonly employed in empirical macroeconomic research and policy work. However, the information used to model the switching process is usually limited drastically to ensure stability of the model. Increasing the number of included variables to enlarge the information set might even result in decreasing precision of the model. Moreover, it is often not clear a priori which variables are actually relevant when it comes to informing the switching behavior. Building strongly on recent contributions in the field of dynamic factor analysis, we introduce a general type of Markov switching autoregressive models for non-linear time series analysis. Large numbers of time series are allowed to inform the switching process through a factor structure. This factor-augmented Markov switching (FAMS) model overcomes estimation issues that are likely to arise in previous assessments of the modeling framework. More accurate estimates of the switching behavior as well as improved model fit result. The performance of the FAMS model is illustrated in a simulated data example as well as in an US business cycle application. …
Greedy Randomized Adaptive Search Procedures (GRASP)
The greedy randomized adaptive search procedure (also known as GRASP) is a metaheuristic algorithm commonly applied to combinatorial optimization problems. GRASP typically consists of iterations made up from successive constructions of a greedy randomized solution and subsequent iterative improvements of it through a local search. The greedy randomized solutions are generated by adding elements to the problem’s solution set from a list of elements ranked by a greedy function according to the quality of the solution they will achieve. To obtain variability in the candidate set of greedy solutions, well-ranked candidate elements are often placed in a restricted candidate list (also known as RCL), and chosen at random when building up the solution. This kind of greedy randomized construction method is also known as a semi-greedy heuristic, first described in Hart and Shogan (1987). GRASP was first introduced in Feo and Resende (1989). Survey papers on GRASP include Feo and Resende (1995), Pitsoulis and Resende (2002), and Resende and Ribeiro (2003). An annotated bibliography of GRASP can be found in Festa, G. C Resende (2002). …
The application Bio7 is an integrated development environment for ecological modelling and contains powerful tools for model creation, scientific image analysis and statistical analysis. The application itself is based on an RCP-Eclipse-Environment (Rich-Client-Platform) which offers a huge flexibility in configuration and extensibility because of its plug-in structure and the possibility of customization. Features:
· Creation and analysis of simulation models.
· Statistical analysis.
· Advanced R Graphical User Interface with editor, spreadsheet, ImageJ plot device and debugging interface.
· Spatial statistics (possibility to send values from a specialized panel to R).
· Image Analysis (embedded ImageJ).
· Fast transfer of image data from ImageJ to R and vice versa.
· Fast communication between R and Java (with RServe) and the possibilty to use R methods inside Java.
· Interpretation of Java and script creation (BeanShell, Groovy, Jython).
· Dynamic compilation of Java.
· Creation of methods for Java, BeanShell, Groovy, Jython and R (integrated editors for Java, R, BeanShell, Groovy, Jython).
· Sensitivity analysis with an embedded flowchart editor in which scripts, macros and compiled code can be dragged and executed.
· Creation of 3d OpenGL (Jogl) models.
· Visualizations and simulations on an embedded 3d globe (World Wind Java SDK).
· Creation of Graphical User Interfaces with the embedded JavaFX SceneBuilder. …