Degrading -Webkit-Text-Security

Java performance gradually degrades

After much fruitless mucking about with jvisualvm's profiler, I resorted to adding a ton of logging. This gave me a clue that the problem was in a Hibernate operation that was performed to populate the BigObject. I was able to fix the performance issue by evicting each object that I retrieved as soon as I retrieved it.

I'm not much of a Hibernate expert, but I think that what was happening is that even though the objects were going out of scope in my code (and thus getting garbage collected), Hibernate was keeping a copy of each object in its cache. Then when it performed the next retrieval, it compared the retrieved object to all the cached ones. As the cache population rose, this operation would take longer and longer.

Degrading performance when increasing number of cores

I added this as a comment, but I'm going to throw it in there as answer too. Because your test is doing file I/O, you have probably hit a point with that 6th thread where you are now doing too much I/O and thus slowing everything down. If you really want to see the benefit of the 16 cores you have, you should re-write your file reading thread to use non-blocking I/O.

Plotting a degrading consine function in MatPlotLib

Here is a sample solution to your problem using NumPy's vectorized approach without using any for loops. I choose some sample input data to produce the result. I used np.cos and np.exp to make vectorized operations as math.exp and math.cos doesn't allow them.

SAMPLE_TIME = 100
SAMPLE_RATE = 0.2
x = np.arange(0, SAMPLE_TIME, SAMPLE_RATE)
deflection = 20
damping_coefficent = 0.1
w = 2*np.pi

el = deflection * np.exp(-x*damping_coefficent) * np.cos(w * x)
plt.plot(x, el)
plt.xlabel('x')
plt.ylabel('$f(x)$')

Degrading the services automatically by autoscaling in azure services - vCPU

There is a chance of auto scaling for the normal services in azure cloud services, that means for stipulated time you can increase or decrease as mentioned in the link.

When it comes for vCPU which is cannot be performed automatically. vCPU can be scaled up based on the request criteria and in the same manner we need to request the support team to scale those down to the normal.

There is no specific procedure to make the auto scaling for vCPU operations. We can increase the capacity of core, but to reduce to the normal, we need to approach the support system for manual changing. You can change it from 10 cores to next level 16 cores, but cannot be performed automatic scaling down from 16 cores to 10 cores.

Degrading Data Randomly with Pre-Existing Missingness

How about this?

degradefunction <- function(x, del.amount){
  # 1) indicate which cells are NA (works with matrix or df)
  preNAs     <- is.na(x)
  # 2) how many cells are eligible to be degraded?
  OpenSpots  <- prod(dim(x)) - sum(preNAs)
  # 3) of these, select del.amount for replacement with NA
  newNas     <- sample(1:OpenSpots, size = del.amount, replace = FALSE)
  # 4) impute these NAs, ignoring the original NAs
  x[!preNAs][newNas] <- NA
  x
}
degradefunction(mypractice,16)

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