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The Amazon CloudFront endpoint following is required for Storage Gateway to get the list of available AWS Regions.

A Storage Gateway VM is configured to use the following NTP servers.

The following table provides a list of region strings for the available AWS Regions.

Depending on your gateway's AWS Region, replace region in the endpoint with the corresponding region string. For example, if you create a gateway in the US West (Oregon) region, the endpoint looks like this: storagegateway.us-west-2.amazonaws.com:443 .

A security group controls traffic to your Amazon EC2 gateway instance. When you create an instance from the Amazon Machine Image (AMI) for AWS Storage Gateway from AWS Marketplace, you have two choices for launching the instance. To launch the instance by using the 1-Click Launch feature of AWS Marketplace, follow the steps in Deploying a Volume or Tape Gateway on an Amazon EC2 Host . We recommend that you use this 1-Click Launch feature.

You can also launch an instance by using the Manual Launch feature in AWS Marketplace. In this case, an autogenerated security group that is named AWS Storage Gateway-1-0-AutogenByAWSMP- is created. This security group has the correct rule for port 80 to activate your gateway. For more information about security groups, see Security Group Concepts in the Amazon EC2 User Guide for Linux Instances .

Regardless of the security group that you use, we recommend the following:

The security group should not allow incoming connections from the outside internet. It should allow only instances within the gateway security group to communicate with the gateway. If you need to allow instances to connect to the gateway from outside its security group, we recommend that you allow connections only on ports 3260 (for iSCSI connections) and 80 (for activation).

If you want to activate your gateway from an EC2 host outside the gateway security group, allow incoming connections on port 80 from the IP address of that host. If you cannot determine the activating host's IP address, you can open port 80, activate your gateway, and then close access on port 80 after completing activation.

Allow port 22 access only if you are using AWS Support for troubleshooting purposes. For more information, see .

In some cases, you might use an Amazon EC2 instance as an initiator (that is, to connect to iSCSI targets on a gateway that you deployed on Amazon EC2). In such a case, we recommend a two-step approach:

You should launch the initiator instance in the same security group as your gateway.

You should configure access so the initiator can communicate with your gateway.

For information about the ports to open for your gateway, see Port Requirements .

Intuitively, it feels a bit like the two languages have a similar ‘shape’ and that by forcing them to line up at different points, they overlap and other points get pulled into the right positions.

t-SNE visualization of the bilingual word embedding. Green is Chinese, Yellow is English. ( Socher (2013a) )

In bilingual word embeddings, we learn a shared representation for two very similar kinds of data. But we can also learn to embed very different kinds of data in the same space.

Recently, deep learning has begun exploring models that embed images and words in a single representation. 5

The basic idea is that one classifies images by outputting a vector in a word embedding. Images of dogs are mapped near the “dog” word vector. Images of horses are mapped near the “horse” vector. Images of automobiles near the “automobile” vector. And so on.

The interesting part is what happens when you test the model on new classes of images. For example, if the model wasn’t trained to classify cats – that is, to map them near the “cat” vector – what happens when we try to classify images of cats?

( Socher (2013b) )

It turns out that the network is able to handle these new classes of images quite reasonably. Images of cats aren’t mapped to random points in the word embedding space. Instead, they tend to be mapped to the general vicinity of the “dog” vector, and, in fact, close to the “cat” vector. Similarly, the truck images end up relatively close to the “truck” vector, which is near the related “automobile” vector.

( Socher (2013b) )

This was done by members of the Stanford group with only 8 known classes (and 2 unknown classes). The results are already quite impressive. But with so few known classes, there are very few points to interpolate the relationship between images and semantic space off of.

The Google group did a much larger version – instead of 8 categories, they used 1,000 – around the same time ( Frome et al. (2013) ) and has followed up with a new variation ( Norouzi et al. (2014) ). Both are based on a very powerful image classification model (from Krizehvsky et al. (2012) ), but embed images into the word embedding space in different ways.

The results are impressive. While they may not get images of unknown classes to the precise vector representing that class, they are able to get to the right neighborhood. So, if you ask it to classify images of unknown classes and the classes are fairly different, it can distinguish between the different classes.

Even though I’ve never seen a Aesculapian snake or an Armadillo before, if you show me a picture of one and a picture of the other, I can tell you which is which because I have a general idea of what sort of animal is associated with each word. These networks can accomplish the same thing.

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