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Introduction
Published in Limiao Deng, Cognitive and Neural Modelling for Visual Information Representation and Memorization, 2022
Although CLS provides a neural computing framework that combines episodic and semantic memory, there are still some drawbacks. Bogacz and Brown71 pointed out that the neocortex's ability to discriminate familiarity was much lower than that of human recognition of memory. Much of the problem was due to the fact that Hebbian's learning rules did not yet have a good enough sense of how to adjust synaptic weights. Moreover, Hebbian learning has a large capacity limit and therefore has less computing power than other error-based learning mechanisms72. At the same time, the CLS model is very complex, and the extension of this model makes the model more complex, and it makes little sense to explain some simple phenomena with an overly complex model73.
Organisation and use of the stack
Published in Stuart Anderson, Microprocessor Technology, 2012
The label ‘CLS:’ is equated with 01B9H which is a memory address in the MARC micro operating system. When the processor meets the instruction ‘CALL CLS’ it pushes the program counter (PC) contents onto the stack. Then it rushes off to 01B9H which contains a clear screen routine, kindly inserted there by the manufacturers for our great convenience. This ‘user routine’ as it's called, has a RET statement at the end of it, which has the effect of popping the address stored on the stack and putting it back into the PC. Program execution then continues, in this case with the instruction ‘LD HL,VIDEO’.
A review and comparison of control charts for ordinal samples
Published in Journal of Quality Technology, 2023
Sebastian Ottenstreuer, Christian H. Weiß, Murat Caner Testik
We declare the monitored process to be in control as long as it follows the specified in-control distribution, given by and respectively. If the sample at time is the first one with distribution being different to then τ is said to be a change point, and the process is called out of control for In SPC, a control chart is applied to the samples by sequentially computing a certain statistic for each sampling time and these statistics are used to decide about the state of the process: if a statistic exceeds the given control limits (CLs), an alarm is triggered and entails an inspection of the process for a possible out-of-control situation. Obviously, we do not wish to intervene in the process if this is in control (false alarm, i.e., if ), while a true change () should be detected as soon as possible. A common way of evaluating a control chart’s performance with respect to these two opposing poles is to compute its ARL for a given scenario, i.e., the mean time until the first alarm is triggered. The ARL should be large if the process is in control, whereas it should be small if the process is out of control. For these and further basics on SPC and control charts, see the textbook by Montgomery (2009).