Extensive Resection of Invasive Recurrent Left Parotid Gland Carcinoma ex Pleomorphic Adenoma

A 50-year-old man experienced tumor recurrence with invasion into the skull base. Preoperative neuroimaging showed partial destruction of the body of the mandible and the anterior and middle cranial bases. Further, there was tumor involvement of the left internal carotid artery. Preoperative embolization of the left internal carotid artery and an extensive resection of the tumor were successfully performed. Even though salivary gland tumors are rarely treated by neurosurgeons, a multidisciplinary approach allowed effective treatment of this tumor. The optimal therapeutic strategy for this rare type of intracranial invasion is described

Self-Indexed Grammar-Based Compression by Edit Sensitive Parsing

A space-economical self-index on the grammar-based compression is proposed. The algorithm by (Sakamoto et al. 2009) approximates the optimum compression for a given string within O(log*u log u) ratio for the string length u. Adopting this algorithm with a new succinct data structure for full binary tree, a grammar-based compression represented by a CFG in Chomsky normal form is transformed to a compressed index. The index size is 2nlog n+hlog n+nlog h+4n+o(nlog n) bits, where n is the number of different variables in the grammar-based compression G and h is the height of G bounded by O(log u). The time to count occurrences for a pattern P of length m is estimated to O(mlog^2 n + occ_c(log nlog m+h)), where occ_c is the occurrence number of core of P, which is derived from alphabet reduction in (Cormode and Muthukrishnan 2007). The parameter occ_c is almost equal to the occurrence of P for sufficiently long P. Experiments for large text data also show the time/space efficiency and the high performance for long pattern search compared with other compact index structures

Self-Indexed Grammar-Based Compression by Edit Sensitive Parsing

A space-economical self-index on the grammar-based compression is proposed. The algorithm by (Sakamoto et al. 2009) approximates the optimum compression for a given string within O(log*u log u) ratio for the string length u. Adopting this algorithm with a new succinct data structure for full binary tree, a grammar-based compression represented by a CFG in Chomsky normal form is transformed to a compressed index. The index size is 2nlog n+hlog n+nlog h+4n+o(nlog n) bits, where n is the number of different variables in the grammar-based compression G and h is the height of G bounded by O(log u). The time to count occurrences for a pattern P of length m is estimated to O(mlog^2 n + occ_c(log nlog m+h)), where occ_c is the occurrence number of core of P, which is derived from alphabet reduction in (Cormode and Muthukrishnan 2007). The parameter occ_c is almost equal to the occurrence of P for sufficiently long P. Experiments for large text data also show the time/space efficiency and the high performance for long pattern search compared with other compact index structures