Is there a difference between the CPU of the server and the CPU of the computer? Whether the computer’s CPU can use the server’s CPU

# Tag: difference

## Difference between impersonation and masquerading attacks

According to a book that I am reading, an impersonation attack is a more active attack than masquerading but I thought they are the same thing. How is this so.

## post processing – Difference between DxO’s Nik Collection 3 and DxO PhotoLab 3?

Can someone tell me the difference between DxO’s Nik Collection 3 and DxO PhotoLab 3? They seem so similar but I can’t find any comparison to help me decide.

(If this is somehow the wrong sort of question to post here, please accept my apologies and help me find the appropriate forum. Thanks)

## Problem with summation by method of difference

**Question:** What would be the result of: $$sum_{k=1}^{n}frac{1}{n(n+2)}$$

**My Approach:**

Let $T_n$ denote the $n^{th}$ term of the given series. Then we have

$$T_1=frac12 left(frac11-frac13right)$$

$$T_2=frac12 left(frac12-frac14right)$$

$$T_3=frac12 left(frac13-frac15right)$$

And so on up till

$$T_n=frac12 left(frac1n-frac1{n+2}right)$$

I can see that the series telescopes and the terms start to cut each other after an interval of one. My only problem is, *how do I find the terms that remain in the end?*

## sharepoint online – Calculated Column – Days Difference – Syntax Error

I have a SharePoint List and I’d like to have a column to calculate the days it take to solve a ticket (Solution date – Submission Date).

I tried to create a calculated column, by using a formula `= DATEDIF((Submission Date),(Solution Date),"D")`

, and return data with “Number”. It didn’t allow me to create this as it says Syntax error.

I researched and tried to replaced “,” with “;” `= DATEDIF((Submission Date);(Solution Date);"D")`

, still didn’t work.

Both Solution date & Submission Date columns are formatted as “Date and Time” Type.

## What is the main difference between Bootstrap and Microsoft Fluid?

I can’t figure out if these are competing products or they complement one another. Thanks

## optimization – Clustering sets by set difference

Suppose you have $n$ nonequal sets $S_1, ldots, S_n$ and some constant $0 le k < n$. The goal of *set clustering* is to find a partition of the set $mathbf{S} = {S_1, ldots, S_n}$ such that the sum of the *total distance* for each subset of $mathbf{S}$ is minimized and such that $mathrm{cardinality}(mathbf{S}) = k$ (in reality, this latter constraint is not quite so tight, but the size of $mathbf{S}$ must be less than $k$, and hopefully near it). The *total distance* $d$ of a set $X in mathbf{S}$ is $d(X) = sum_{A, B in X} mathrm{cardinality}(A ominus B)$ where $ominus$ is symmetric difference. Assume for the purposes of the problem that the sets consume $O(1)$ space (so symmetric difference can be computed in constant time).

Is there a good greedy linear(ithmic) heuristic for this problem? Is there any literature on this problem, or similar ones?

All I’ve come up with so far is an $O(n^2 log n)$ heuristic that looks like:

- Set $I = {1, ldots, n}$
- Choose some $i in I$
- Emit a cluster $C subset I$ containing all $m in I$ such that $mathrm{cardinality}(S_i ominus S_m) le d_mathrm{max}$.
- Set $I = I – C$
- Go to step 2.

where this process occurs in each step of a binary search that finds the best value of $d_mathrm{max}$ for a given $k$.

I was thinking that if there were some way to sort the list of sets such that nearby sets in the list have small symmetric difference, then a linearithmic solution might be easy to write.

## usa – What exactly was/is the difference between “Disney World” and “Disneyland”?

The big difference is the scale.

Disneyland (in California) was the first theme park Walt Disney built. Originally consisting of one theme park and one hotel, though after Walt’s death it was expanded into a resort with a second theme park, more hotels and some other attractions.

Disney world (in Florida) was a much bigger project, this time Walt Disney wanted to control not just the immediate theme park but the whole area surrounding it. While sadly Walt died before the park was actually built the result was nevertheless a Disney-controlled resort with four theme parks, two water parks and loads of hotels and other attractions.

There are also a number of foreign (outside the US) Disneylands. As far as I can tell these are much closer in scale to the original Disneyland than they are to Disney World.

## Filling stations and petrol stations in Nigeria, is there any difference?

No, this is the same thing.

Apparently, according to Wikipedia, the most common name in the world is a *filling station*:

A filling stationthat sells only electric energy is also known as a charging station, while a typical filling station can also be known as a fueling or gas station (United States and Canada), gasbar (Canada), gasoline stand or SS(Note 1) (Japan), petrol pump or petrol bunk (India, Pakistan and Bangladesh), garage,petrol station(Australia, Hong Kong, New Zealand, Singapore, South Africa, United Kingdom and Ireland), service station (Australia, France, Italy, Japan, New Zealand and United Kingdom and Ireland), servo (Australia), or fuel station (Northern Europe and Israel).

This is probably a naming preference/incosistency on the map.

## What is the exact difference between Google Analytics Sessions by Type and Top Channels, why am I getting different results on my reports?

In Google Analytics -> Acquisition -> Overview you can get a month to month comparison of Top Channels which shows incoming traffic for direct, organic, etc. In my Customization -> Dashboards I have a custom report that has a widget, it is defined as sessions grouped by traffic type. This should show the same data, right? If so, why am I getting different numbers, and if not, what is the difference between the two? The image below is Sessions by Type, the second image is Top Channels. I have searched for an answer but all I get is instruction on how to set up reports for them, not what the distinction between the two might be.