Abstract: Cerebrovascular treatment at an intracranial location beyond the petrous segment of the carotid artery can be challenging due to blood vessel size and tortuosity. First pass cerebrovascular thrombectomy success rate under only fluoroscopic guidance can be low (e.g., 25.1%) but an angioscope can help improve efficacy. A sheath catheter can be advanced toward the cerebrovascular pathology. Its distal balloon can be inflated. An angioscope can be inserted via its working lumen for viewing. The sheath catheter can have a stepped-down lateral profile and can extend the working channel a distance beyond the balloon. A dual concentric lumen structure can include an inner body and an outer body, defining an inflation lumen therebetween, with one or more portions of one or more layers stretched or cut or both, such as to provide bending flexibility. Reflow techniques can be used to help bond layers together.

Abstract: An approach for two stage log normalization is provided. The approach retrieves a message format and a plurality of parameters from one or more log files. The approach determines a classification for one or more first sequence files, wherein the one or more first sequence files includes the message format from the one or more log files. The approach determines a classification of error for the one or more first sequence files. The approach determines whether there is a high confidence in the classification of error for the one or more first sequence files. The approach determines whether there is an improvement in confidence in the classification of error from one or more second sequence files, wherein the one or more second sequence files includes the message format and the plurality of parameters from the one or more log files.

Abstract: An approach for two stage log normalization is provided. The approach retrieves a message format and a plurality of parameters from one or more log files. The approach determines a classification for one or more first sequence files, wherein the one or more first sequence files includes the message format from the one or more log files. The approach determines a classification of error for the one or more first sequence files. The approach determines whether there is a high confidence in the classification of error for the one or more first sequence files. The approach determines whether there is an improvement in confidence in the classification of error from one or more second sequence files, wherein the one or more second sequence files includes the message format and the plurality of parameters from the one or more log files.

Abstract: An approach for two stage log normalization is provided. The approach retrieves a message format and a plurality of parameters from one or more log files. The approach determines a classification for one or more first sequence files, wherein the one or more first sequence files includes the message format from the one or more log files. The approach determines a classification of error for the one or more first sequence files. The approach determines whether there is a high confidence in the classification of error for the one or more first sequence files. The approach determines whether there is an improvement in confidence in the classification of error from one or more second sequence files, wherein the one or more second sequence files includes the message format and the plurality of parameters from the one or more log files.

Abstract: An approach for two stage log normalization is provided. The approach retrieves a message format and a plurality of parameters from one or more log files. The approach determines a classification for one or more first sequence files, wherein the one or more first sequence files includes the message format from the one or more log files. The approach determines a classification of error for the one or more first sequence files. The approach determines whether there is a high confidence in the classification of error for the one or more first sequence files. The approach determines whether there is an improvement in confidence in the classification of error from one or more second sequence files, wherein the one or more second sequence files includes the message format and the plurality of parameters from the one or more log files.

Abstract: An approach for two stage log normalization is provided. The approach retrieves a message format and a plurality of parameters from one or more log files. The approach determines a classification for one or more first sequence files, wherein the one or more first sequence files includes the message format from the one or more log files. The approach determines a classification of error for the one or more first sequence files. The approach determines whether there is a high confidence in the classification of error for the one or more first sequence files. The approach determines whether there is an improvement in confidence in the classification of error from one or more second sequence files, wherein the one or more second sequence files includes the message format and the plurality of parameters from the one or more log files.

Abstract: An approach for two stage log normalization is provided. The approach retrieves a message format and a plurality of parameters from one or more log files. The approach determines a classification for one or more first sequence files, wherein the one or more first sequence files includes the message format from the one or more log files. The approach determines a classification of error for the one or more first sequence files. The approach determines whether there is a high confidence in the classification of error for the one or more first sequence files. The approach determines whether there is an improvement in confidence in the classification of error from one or more second sequence files, wherein the one or more second sequence files includes the message format and the plurality of parameters from the one or more log files.