Manipulations:
- Scaling
- Cropping and
- Rotation
- Angle you take the photo and the
- Ambient light
- Colours that are shown on a pc monitor or the printed photo.
https://poly.google.com/view/9-b6-yqrwEe
reasons why we need view source
declarativeness:
how do i reproduce this? i can download the model, but how do i get that lighting? that sky? how do i even learn these words? how do i position the camera? how would i even learn about the existance of a camera?
holographic projections. how do i learn about that?
The routing problem
in content-centric networks is making (efficiently and resiliently) a connection between content names
and hosts.
The overall idea of the user-oriented name system
is to address a subset of the problem where the content is addressed by author
first and filename
second (but in a manner that still de-couples content from hosts).
UNS, much like DNS, works as a hierarchical resolution algorithm. It talks to root-level ANS servers to find trackers given an author name
.
Trackers, much like bittorrent, enable hosts to register their ability/desire to serve content for a specific content name, which is then later used to answer user queries of where to find content.
UNS servers exchange data between themselves with a gossip protocol, propagating the routing tables (mapping usernames to trackers) to each other, eventually converging.
Discovery
<link rel="alternate" href="/api" type="application/vnd.microforms">
Self-described payloads
{
JSON-LD encapsulation of forms:
{
@context: "https://example.com",
@type: MyType,
action: {
@context: "https://w3c.org/2018/forms",
@type: Form,
...
CoreML/ONNX/TF JSON-LD based for serving.
{
@context: "https://w3c.org/2018/deeplearning",
@type: Model,
network: {
@type: NeuralNetwork,
...
}
ATOM feeds in JSON-LD.
{
"@context": "http://www.w3.org/2005/Atom",
"@type": "Feed",
"title": "Example Feed",
"subtitle": "A subtitle.",
"links": [{
Alternatives considered
TODO(goto): go over this.
ARML is an XML-based data format to describe an interact with AR scenes.
ARML
10-15% cost of interpreation seems like a non-starter to me when they are trying to squeeze in every level of performance. I'm seeing some convergence in the industry regarding "inference models" file formats / representations
I would challenge that assertion. Your going to get a larger drop on an android phone when thermal throttling kicks in.