I am not sure but if I remember correctly the NFL teams play 15-16 games during regular season and a couple more in the play-offs if they can make it the whole way. Why such a short season, in comparison to other leagues such as NBA where teams play 82 games in regular season and playoffs are usually a few games each round (best of 5-7-7-7)?
Why i should be choosing deployer extension over tridion events? i have to perform some operations on publishing. i heard there are performance benefits of deployer extension but want to understand how it improves performance over tridion events.
Thanks for replies. On publishing, i just need to get list of all static pages published so i can use that list in my .net custom utility i'm making for cachging those pages in advance before user hits them. So i just need to confirm whether i should be using deployer extension or tridion events but firstly need to understand benefits of deployer extension over tridion events.
We have a 2 year old son. He is our first kid. Recently he started throwing things at others kids which hurts them. I have done the following things to stop that behavior but they didn’t work:
I don’t know how to make him stop throwing things. It really hurts other kids and sometimes he throws things at us, which hurts us too.
How can we stop him throwing things?
I have a site that has many articles on a specific dog breed. I want to help Google and other search engines to understand this article is specifcally on this breed or a topic realted to this breed. I thought about using productontology.org to define an additional type.
For example I have the following Schema.org markup:
<article itemscope itemtype="http://schema.org/Article"> <link itemprop="additionalType" href="http://www.productontology.org/id/dog_breed" /> <!-- Additional Code + Schema.org markup --> </article>
Is this the correct way to indicate with Schema.org markup that this article is on (or related to) this specific breed? If not, what is the proper way using Schema.org?
Note: I understand fully that the best way is with great content that uses keywords. However, I am looking to know how to do this from a schema.org perspective.
The house I am in has a fireplace that was originally wood burning, then turned into a gas fire place. What would be required to change it back?
I am in the market for a new tennis racquet and have been out of the loop for a while. When I bought my last racquet the oversized heads were all the rage but now they seem to have returned back to a more standard size.
What are the key differences to my game with different racquet sizes?
I have two SATA drives, but their names
/dev/sdb are chosen randomly by Linux.
How can I correctly set up LILO? How can I put a stable reference to the hard drives in
ADDED. Can I use udev's names (like /dev/disk/by-id/*) in lilo.conf? LILO doesn't read lilo.conf at each boot, but only once on setup MBR when I run 'lilo' without arguments. At this stage udev names are available. But how do LILO store information about disks to MBR and boot-files?
I use dual-boot with Windows, but NTFS disks have no UUID. I want to use udev's id.
I have a data classification problem and I'm wondering what is the best machine learning approach to use for the particular constraints of my problem.
My constraints are as follows: - the data points are not linearly separable (in the original space) - I can generate as many training samples of either positive or negative labels - I would like to minimize the number of false positives (i.e. negative samples being wrongly classified as positives) - the classification speed needs to be very fast
I am currently using an SVM but it's not giving me result, particularly on speed. I posted a related question about my problem here: SVM model selection for datasets with sharp corners I'm still waiting for an answer on that, but I started questioning whether I'm even taking the right approach, which is why I've posted this as a separate question.
In case it's relevant, my problem is to use a classifier (or other ML method) as a fast approximate collision detection method (i.e. faster than doing exact collision detection)
Drugs that activate the serotonergic 5-HT2A receptor (e.g. LSD, psilocybin, mescaline) are notorious for causing psychedelic experiences and I would like to know the neurochemical mechanism behind this.
I would like to test detection of a stealth keylogger for Windows ( preferably one that works well on Windows 10 ). Where can I find either a closed or open source keylogger that does not appear in the Task Manager?