Abstract: A system and a method for parameter optimization with adaptive search space and a user interface using the same are provided. The system includes a data acquisition unit, an adaptive adjustment unit and an optimization search unit. The data acquisition unit obtains a set of executed values of several operating parameters and a target parameter. The adaptive adjustment unit includes a parameter space transformer and a search range definer. The parameter space transformer performs a space transformation on a parameter space of the operating parameters according to the executed values. The search range definer defines a parameter search range in a transformed parameter space based on the sets of the executed values. The optimization search unit takes the parameter search range as a limiting condition and takes optimizing the target parameter as a target to search for a set of recommended values of the operating parameters.

Abstract: A computer system includes a device having instructions to implement a training application programming interface that is configured to receive a first set of data that includes training observable events and a model builder engine that is configured to provide estimated outcomes of said training observable events using a kernel function, wherein said kernel function is configured by a plurality of parameters including a match reference vector and a mismatch measurement vector. The model builder engine is also configured to calculate values of said mismatch measurement vector to provide a calculated mismatch measurement vector calculate a distance between the value of said match reference vector and the value of said calculated mismatch measurement vector corresponding to each training observable event. The model builder engine is also configured to determine an accuracy of said kernel function in providing estimated outcomes of said training observable events.

Abstract: Some embodiments include a method of training an electronic transaction event classifier model to rank electronic transaction events. The method can include: receiving a training set of observable events corresponding to a set of quantifiable feature vectors, a set of known outcome labels, and a set of known cost values; setting initial parameters of a kernel function of a support vector machine; and iterating through potential weight vectors of the kernel function until the kernel function, as configured by a working weight vector of an iteration, is capable of estimating at least polarities of the known outcome labels within a threshold tolerance of accuracy based on the quantifiable feature vectors, wherein said iterating includes computing the working weight vector in a feature space of the quantifiable features in each iteration by solving variable constraints.

Abstract: A magnetic clip is provided. The magnetic clip includes a first plate and a second plate. The first plate includes a first surface and a second surface. The second plate includes a first surface and a second surface. The first plate and the second plate are oppositely arranged, such that the first surface of the first plate and the first surface of the second plate are oppositely positioned. At least one magnet is provided at the first surface of the first plate. At least one metal ball is provided at the second plate correspondingly to the magnet. When the magnet approaches to the metal ball, the metal ball is magnetized, and becomes another ball-shaped magnet. Therefore, small metal objects such as a key ring, paper clips, or drawing pins can be directly put on the metal ball without opening the first plate 1 and the second plate.

Abstract: Techniques are described for determining and using information regarding expected road traffic flow conditions information for vehicles traveling on roads. The expected road traffic flow conditions for a particular portion of a road may be generated by combining historical representative information about road traffic flow conditions for that road portion with current information about actual traffic flow on or near that road portion. The combination may, for example, provide benefits for estimating expected traffic flow conditions information for roads with structural flow obstructions that cause reduced traffic flow at certain road locations and times—for example, the expected traffic flow conditions information may be based at least in part on fitting or otherwise adapting partial actual traffic flow information about a vehicle's actual travel path to a historical travel profile for a road that includes representative traffic flow information for various combinations of road locations and time periods.