Abstract: A magnetic resonance (MR) imaging acceleration method is provided. The method includes applying, by an MR system, a pulse sequence having a k-space trajectory of a plurality of blades being rotated in k-space, each blade including a plurality of views, wherein the k-space trajectory has an undersampling pattern in the k-space. The method also includes receiving k-space data of a subject acquired by the pulse sequence, reconstructing MR images of the subject based on the k-space data using compressed sensing, and outputting the reconstructed images.

Abstract: A differential compression method and computer program product combines hash value techniques and suffix array techniques. The invention finds the best matches for every offset of the version file, with respect to a certain granularity and above a certain length threshold. The invention has two variations depending on block size choice. If the block size is kept fixed, the compression performance of the invention is similar to that of the greedy algorithm, without the expensive space and time requirements. If the block size is varied linearly with the reference file size, the invention can run in linear-time and constant-space. It has been shown empirically that the invention performs better than certain known differential compression algorithms in terms of compression and speed.

Abstract: A method for learning signatures of a target class using a sequential covering phased rule-induction. The method balances recall and precision for the target class. A first phase aims for high recall by inducing rules with high support and a reasonable level of accuracy. A second phase improves the precision by learning rules to remove false positives in the collection of the records covered by the first phase rules, while keeping the overall recall at a desirable level. The method constructs a mechanism to assign prediction probability scores to each classification decision. The model includes a set of positive rules that predict presence of the target class, a set of negative rules that predict absence of the target class, and a set of prediction score values corresponding to each pair-wise combination of positive and negative rules. The two-phase method is extensible to a multiphase approach.

Abstract: A method for learning signatures of a target class using a sequential covering phased rule-induction. The method balances recall and precision for the target class. A first phase aims for high recall by inducing rules with high support and a reasonable level of accuracy. A second phase improves the precision by learning rules to remove false positives in the collection of the records covered by the first phase rules, while keeping the overall recall at a desirable level. The method constructs a mechanism to assign prediction probability scores to each classification decision. The model includes a set of positive rules that predict presence of the target class, a set of negative rules that predict absence of the target class, and a set of prediction score values corresponding to each pair-wise combination of positive and negative rules. The two-phase method is extensible to a multiphase approach.