Abstract: Systems and techniques train a first large language model (LLM) using domain data to output a second LLM, the second LLM having a first memory size. The systems and techniques generate predictions by both the first LLM and the second LLM using a same input data for generating the predictions. The systems and techniques compute a threshold by comparing the predictions by the first LLM and the second LLM. The systems and techniques iteratively adjusting a set of attention heads of the second LLM by comparing new predictions from the second LLM to the threshold using the same input data. The systems and techniques generate a third LLM having a different set of attention heads than the set of attention heads of the second LLM, the third LLM having a second memory size that is smaller than the first memory size of the second LLM.

Abstract: A plurality of textual log records characterizing operations occurring within a technology landscape may be received and converted into numerical log record vectors. For a current log record vector and a preceding set of log record vectors of the numerical log record vectors, a similarity series may be computed that includes a similarity measure for each of a set of log record vector pairs, with each log record vector pair including the current log record vector and one of the preceding set of log record vectors. A similarity distribution of the similarity series may be generated, and an anomaly in the operations occurring within the technology landscape may be detected, based on the similarity distribution.

Abstract: A plurality of log records characterizing operations occurring within a technology landscape may be received. The plurality of log records may be clustered into at least a first cluster of log records and a second cluster of log records, using at least one similarity algorithm. A first dissimilar subset of log records within the first cluster of log records, and a second dissimilar subset of log records within the second cluster of log record may be identified, using the at least one similarity algorithm. At least one machine learning model may be trained to process new log records characterizing the operations occurring within the technology landscape, using the first dissimilar subset and the second dissimilar subset.

Abstract: A method of managing a processing system that has at least one processor, uses the steps of: measuring MCPS (million cycles per second) utilization in said at least one processor; estimating a cycle count requirement for an algorithm on least one processor based on measured MCPS utilization; and, estimating an ability to run multiple applications on the at least one processor by assessing MCPS requirements and estimated cycle count requirement. Measurement of the MCPS utilization is preferably done by using the steps of: choosing a critical path in the processor, e.g., by taking hard real time requirements into consideration; measuring time taken for processing along said critical path; and, calculating MCPS requirements along said critical path using the measured time taken and a current processor clock speed. The inventive method has application in 802.11 MAC. Also described is an article having a storage medium to execute the described method.