A small tool to view real-world ActivityPub objects as JSON! Enter a URL
or username from Mastodon or a similar service below, and we'll send a
request with
the right
Accept
header
to the server to view the underlying object.
{
"@context": [
"https://www.w3.org/ns/activitystreams",
{
"ostatus": "http://ostatus.org#",
"atomUri": "ostatus:atomUri",
"inReplyToAtomUri": "ostatus:inReplyToAtomUri",
"conversation": "ostatus:conversation",
"sensitive": "as:sensitive",
"toot": "http://joinmastodon.org/ns#",
"votersCount": "toot:votersCount",
"litepub": "http://litepub.social/ns#",
"directMessage": "litepub:directMessage",
"Hashtag": "as:Hashtag"
}
],
"id": "https://neuromatch.social/users/fabrice13/statuses/111266377079312737",
"type": "Note",
"summary": null,
"inReplyTo": null,
"published": "2023-10-20T08:22:57Z",
"url": "https://neuromatch.social/@fabrice13/111266377079312737",
"attributedTo": "https://neuromatch.social/users/fabrice13",
"to": [
"https://www.w3.org/ns/activitystreams#Public"
],
"cc": [
"https://neuromatch.social/users/fabrice13/followers"
],
"sensitive": false,
"atomUri": "https://neuromatch.social/users/fabrice13/statuses/111266377079312737",
"inReplyToAtomUri": null,
"conversation": "tag:neuromatch.social,2023-10-20:objectId=6830812:objectType=Conversation",
"content": "<p>Yesterday I attended a very interesting seminar with professors Alessandra Bertoldo, Alessandro Chiusi and Marco Zorzi from <a href=\"https://neuromatch.social/tags/unipd\" class=\"mention hashtag\" rel=\"tag\">#<span>unipd</span></a> , from departments of information engineering, psychology, and neuroscience.<br />It was mostly about popularizing <a href=\"https://neuromatch.social/tags/fMRI\" class=\"mention hashtag\" rel=\"tag\">#<span>fMRI</span></a> and the clinical potential of such studies.<br />My mind was captured by two things, <a href=\"https://neuromatch.social/tags/effectiveconnectivity\" class=\"mention hashtag\" rel=\"tag\">#<span>effectiveconnectivity</span></a> and the use of neural networks for <a href=\"https://neuromatch.social/tags/FunctionalConnectivity\" class=\"mention hashtag\" rel=\"tag\">#<span>FunctionalConnectivity</span></a> to symptoms mapping.<br />The core for me was: they are not talking about using deep learning, or the most apt deep learning architecture for the problems.<br />For EffectiveC., they were speaking about dynamical systems modeling (which is great!); for functional connectivity they cited convolutional autoencoders on the image or matrix of functional connectivity, which I really don't like unless number of channels and more importantly kernel dimension are discussed.<br />Overall, we are dealing with directed and undirected weighted graphs respectively, and we have architectures for those</p>",
"contentMap": {
"it": "<p>Yesterday I attended a very interesting seminar with professors Alessandra Bertoldo, Alessandro Chiusi and Marco Zorzi from <a href=\"https://neuromatch.social/tags/unipd\" class=\"mention hashtag\" rel=\"tag\">#<span>unipd</span></a> , from departments of information engineering, psychology, and neuroscience.<br />It was mostly about popularizing <a href=\"https://neuromatch.social/tags/fMRI\" class=\"mention hashtag\" rel=\"tag\">#<span>fMRI</span></a> and the clinical potential of such studies.<br />My mind was captured by two things, <a href=\"https://neuromatch.social/tags/effectiveconnectivity\" class=\"mention hashtag\" rel=\"tag\">#<span>effectiveconnectivity</span></a> and the use of neural networks for <a href=\"https://neuromatch.social/tags/FunctionalConnectivity\" class=\"mention hashtag\" rel=\"tag\">#<span>FunctionalConnectivity</span></a> to symptoms mapping.<br />The core for me was: they are not talking about using deep learning, or the most apt deep learning architecture for the problems.<br />For EffectiveC., they were speaking about dynamical systems modeling (which is great!); for functional connectivity they cited convolutional autoencoders on the image or matrix of functional connectivity, which I really don't like unless number of channels and more importantly kernel dimension are discussed.<br />Overall, we are dealing with directed and undirected weighted graphs respectively, and we have architectures for those</p>"
},
"attachment": [],
"tag": [
{
"type": "Hashtag",
"href": "https://neuromatch.social/tags/unipd",
"name": "#unipd"
},
{
"type": "Hashtag",
"href": "https://neuromatch.social/tags/fmri",
"name": "#fmri"
},
{
"type": "Hashtag",
"href": "https://neuromatch.social/tags/effectiveconnectivity",
"name": "#effectiveconnectivity"
},
{
"type": "Hashtag",
"href": "https://neuromatch.social/tags/functionalconnectivity",
"name": "#functionalconnectivity"
}
],
"replies": {
"id": "https://neuromatch.social/users/fabrice13/statuses/111266377079312737/replies",
"type": "Collection",
"first": {
"type": "CollectionPage",
"next": "https://neuromatch.social/users/fabrice13/statuses/111266377079312737/replies?only_other_accounts=true&page=true",
"partOf": "https://neuromatch.social/users/fabrice13/statuses/111266377079312737/replies",
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}
},
"likes": {
"id": "https://neuromatch.social/users/fabrice13/statuses/111266377079312737/likes",
"type": "Collection",
"totalItems": 1
},
"shares": {
"id": "https://neuromatch.social/users/fabrice13/statuses/111266377079312737/shares",
"type": "Collection",
"totalItems": 0
}
}