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Awake FILE is a secure Open Source framework that allows to program very easily file uploads/downloads and RPC through http. File transfers include powerful features like file chunking and automatic recovery mechanism. Security has been taken into account from the design: server side allows to specify strong security rules in order to protect the files and to secure the RPC calls.

COBYLA2 is an implementation of Powell's nonlinear derivative free constrained optimization that uses a linear approximation approach. The algorithm is a sequential trust region algorithm that employs linear approximations to the objective and constraint functions, where the approximations are formed by linear interpolation at n + 1 points in the space of the variables and tries to maintain a regular shaped simplex over iterations. It solves nonsmooth NLP with a moderate number of variables (about 100). Inequality constraints only. The initial point X is taken as one vertex of the initial simplex with zero being another, so, X should not be entered as the zero vector.

A Java component which displays bytes in Hex- and ASCII-format. The component is designed as a standalone lib. A complete interface which provides the function of an HexEditor based on this lib exisits under the Fork of this project. \r\n This library is simply taken directly from sourceforge at https://sourceforge.net/projects/hexlib/ and made it available in maven central repository. No other changes have been made (including keeping the same license).

Samply.Common.MDRFaces is a Java Server Faces library which eases the user interface design of web applications where the respective data model relies on well defined data elements. Especially in case of systems for electronic data capturing, where the necessary data model is not known beforehand and can even vary over time, the user interface has to be easily adjustable. This often means the user instead of the developer designs the various forms for data entry and therefore an easy to use mechanism has to be provided. By using Samply.MDRFaces the developer can focus on the implementation of that mechanism, e.g. some editor component, but does not have to cope with the visualization of every single data element as for that is taken care of automatically.

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid. In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>'). GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well. The best filter/classifier setup can be accessed after the buildClassifier call via the getBestFilter/getBestClassifier methods. Note on the implementation: after the data has been passed through the filter, a default NumericCleaner filter is applied to the data in order to avoid numbers that are getting too small and might produce NaNs in other schemes.