Abstract: Disclosed herein are ophthalmic cleaning systems and methods for their use, which comprise an ophthalmic solution and lens case, wherein the solution includes dual disinfectants and the lens case includes silver. When the lens case is combined with the solution according to the system of the present invention, it surprisingly exhibits synergistic activity which results in a faster antimicrobial activity.

Abstract: Disclosed herein are ophthalmic cleaning systems and methods for their use, which comprise an ophthalmic solution and lens case, wherein the solution includes dual disinfectants and the lens case includes silver. When the lens case is combined with the solution according to the system of the present invention, it surprisingly exhibits synergistic activity which results in a faster antimicrobial activity.

Abstract: Disclosed herein are ophthalmic cleaning systems and methods for their use, which comprise an ophthalmic solution and lens case, wherein the solution includes dual disinfectants and the lens case includes silver. When the lens case is combined with the solution according to the system of the present invention, it surprisingly exhibits synergistic activity which results in a faster antimicrobial activity.

Abstract: A method of anomaly detection applicable to telecommunications or retail fraud or software vulnerabilities uses inductive logic programming to develop anomaly characterization rules from relevant background knowledge and a training data set, which includes positive anomaly samples of data covered by rules. Data samples include 1 or 0 indicating association or otherwise with anomalies. An anomaly is detected by a rule having condition set which the anomaly fu,lfils. Rules are developed by addition of conditions and unification of variables, and are filtered to remove duplicates, equivalents, symmetric rules and unnecessary conditions. Overfitting of noisy data is avoided by an encoding cost criterion. Termination of rule construction involves criteria of rule length, absence of negative examples, rule significance and accuracy, and absence of recent refinement.

Abstract: A method of anomaly detection applicable to telecommunications or retail fraud or software vulnerabilities uses inductive logic programming to develop anomaly characterisation rules from relevant background knowledge and a training data set, which includes positive anomaly samples of data covered by rules. Data samples include 1 or 0 indicating association or otherwise with anomalies. An anomaly is detected by a rule having condition set which the anomaly fu,lfils. Rules are developed by addition of conditions and unification of variables, and are filtered to remove duplicates, equivalents, symmetric rules and unnecessary conditions. Overfitting of noisy data is avoided by an encoding cost criterion. Termination of rule construction involves criteria of rule length, absence of negative examples, rule significance and accuracy, and absence of recent refinement.