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Least Frequently Used (LFU) is a type of cache algorithm used to manage memory within a computer. The standard characteristics of this method involve the system keeping track of the number of times a block is referenced in memory. When the cache is full and requires more room the system will purge the item with the lowest reference frequency.
When a program/algorithm computes the answer to a problem, it uses a set of data (input and intermediate data) to complete the work. For any given instance of the problem, the program has one such data set, which is called the working set. The Working Set Size (WSS) is the size of this data set.
Adaptive Replacement Cache (ARC) is a page replacement algorithm with better performance [1] than LRU (least recently used). This is accomplished by keeping track of both frequently used and recently used pages plus a recent eviction history for both. The algorithm was developed [2] at the IBM Almaden Research Center.
Bélády's algorithm is the optimal cache replacement policy, but it requires knowledge of the future to evict lines that will be reused farthest in the future. A number of replacement policies have been proposed which attempt to predict future reuse distances from past access patterns, [23] allowing them to approximate the optimal replacement ...
Iterative adaptive filtering algorithms use Kalman filter to estimate transformation from low-resolution frame to high-resolution one. [10] To improve the final result these methods consider temporal correlation among low-resolution sequences.
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YouTube's content recommendation algorithm is designed to keep the user engaged as long as possible, which Roose calls the "rabbit hole effect". [5] The podcast features interviews with a variety of people involved with YouTube and the "rabbit hole effect". [6] For instance, in episode four Roose interviews Susan Wojcicki—the CEO of YouTube. [2]