layout: post title: ASUS DiGiMatrix created: 1065535860 categories:

• Home Server
• Multimedia PC

[image:590,left,5,5]

I got wind of this barebones/media center box from Asus through The Register, but a quick Google search come up with a much better story by X-Bit Labs.

Some of the best features: 5-in-1 card reader, built in 802.11b wireless access point, dual Ethernet (10/100 and Gigabit), listen to audio (incl. radio, CD, and MP3-CD) without turning the computer on. Read more for labelled pics of the front and back plus some musings.

It’s amazing how many features are packed into this. I think that manufacturers are reallizing that small operators simply don’t have the resources to re-purpose a more standard barebones box into one customized for multimedia. Hence the decision to drop a bunch of PCI or AGP expansion slots and build it in from the start.

[image:592,middle,5,5]

Software is the second issue. From the X-bit Labs article:

Maybe ASUS will provide a special version of Windows XP for this kind of personal computers, but since Windows XP costs an extra $75 ~ $100[US], ASUS may supply only its own software with the DiGiMatrix.

XP Media Center is a) unlikely to be certified for use by ASUS (they’re too friendly with DIY’ers) and b) I imagine the licensing cost is even higher than for “regular” XP Home. If ASUS is working on its own software, that would be amazing. Basically, your choices are to run Windows XP plus a bunch of third-party software (software isn’t designed to work together, full-blown GUI system when all you need is a bunch of on-screen menus, potential driver isssues, expensive) or struggle to get drivers working for a multimedia PC Linux distribution (difficult, if not impossible, especially for such integrated/customized hardware).

Hmmm…makes me think of some sort of QT- or wxWindows-based GUI front-end that implements all the standard multimedia PC functions: TV, photo albums, MP3s, etc. etc. With the cross platform frameworks I mentioned, it has the potential to work on multiple hardware/OS platforms.

[image:591,middle,5,5]

The only down sides that I could see pointed out were part of the strength here, too — lack of expandability. But, this is not a gaming machine — it’s a multimedia PC/server, and it seems to have everything required. The one bone-headed move: placing the S/PDIF output on the front panel. This means you need to have the front panel permanently flipped down, with a cable coming out the front and (presumably) going into the back of your receiver.

• a [[project]].
  • [[agora bot]] for [[matrix]]
  • [[git]] https://github.com/flancian/agora-bot-matrix
  • #go [[git]]

• [[2022-05-08 22:33:09]] [[@flancian:matrix.org]] (link):
  • #psa [[agora bot matrix]] now supports hashtags, starts threads again instead of sending unthreaded responses which is noisier

• [[2022-02-27 19:32:04]]
  • [[@flancian:matrix.org]] https://develop.element.io/#/room/!fHgbulHAlFZEKtEnIM:matrix.org/$0hQZxMq3DDxGPOsQ8PPdJoE6yaREQswng6Jg71d3NZM

• [[2022-03-12 13:02:18]] [[@flancian:matrix.org]] (link):
  • [[agora matrix]] mautrix.errors.request.MUnknown: Cannot start threads from an event with a relation
• [[2022-03-12 13:08:27]] [[@flancian:matrix.org]] (link):
  • working on [[agora matrix]] after having lunch :)
• [[2022-03-12 17:06:35]] [[@flancian:matrix.org]] (link):
  • [[agora matrix]] interop is back up — with some better logging / code structure :)

confusion matrix§

Go back to the [[AI Glossary]]

An NxN table that summarizes how successful a classification model’s predictions were; that is, the correlation between the label and the model’s classification. One axis of a confusion matrix is the label that the model predicted, and the other axis is the actual label. N represents the number of classes. In a binary classification problem, N=2. For example, here is a sample confusion matrix for a binary classification problem:

The preceding confusion matrix shows that of the 19 samples that actually had tumors, the model correctly classified 18 as having tumors (18 true positives), and incorrectly classified 1 as not having a tumor (1 false negative). Similarly, of 458 samples that actually did not have tumors, 452 were correctly classified (452 true negatives) and 6 were incorrectly classified (6 false positives).

The confusion matrix for a multi-class classification problem can help you determine mistake patterns. For example, a confusion matrix could reveal that a model trained to recognize handwritten digits tends to mistakenly predict 9 instead of 4, or 1 instead of 7.

Confusion matrices contain sufficient information to calculate a variety of performance metrics, including precision and recall.

• [[flancian]] https://social.coop/@flancian/109002487229737983 pull

• [[flancian]] https://twitter.com/flancian/status/1570394480146284547 pull

• [[flancian@social.coop]] https://social.coop/@flancian/110508136520564029 pull

• [[2023-06-08 12:18:06]] [[@flancian:matrix.org]] (link):
  • it would be nice to find a [[fediverse matrix]] summarizing what each activitypub server will do with each of these objects — which ones it will produce, which ones it will render fully, which ones it will render in degraded mode, and which ones it will ignore altogether.

• [[2022-03-20 17:36:04]] [[@flancian:matrix.org]] (link):
  • [[hypha matrix]] https://matrix.to/#/#hyphacoop-open:tomesh.net

item matrix§

Go back to the [[AI Glossary]]

#recsystems

In recommendation systems, a matrix of embeddings generated by matrix factorization that holds latent signals about each item. Each row of the item matrix holds the value of a single latent feature for all items. For example, consider a movie recommendation system. Each column in the item matrix represents a single movie. The latent signals might represent genres, or might be harder-to-interpret signals that involve complex interactions among genre, stars, movie age, or other factors.

The item matrix has the same number of columns as the target matrix that is being factorized. For example, given a movie recommendation system that evaluates 10,000 movie titles, the item matrix will have 10,000 columns.

• a [[matrix channel]].
  • [[moa]]
  • #go https://matrix.to/#/#moaparty:matrix.org

• [[2022-05-11 22:02:27]] [[@flancian:matrix.org]] (link):
  • btw there’s a [[moa matrix]] channel if anybody’s interested, where there’s discussion of how to add git support to moa that is relevant also to the agora

• #go https://matrix.to/#/#social.coop:matrix.org

Seemingly a case of [[Mistaken Identity]] where the anagora [[Matrix Bridge]] thought that a [[hashtag]] used to indentify a matrix room was just a normal hashtag

[[Agora Bug]]

Seemingly a case of [[Mistaken Identity]] where the anagora [[Matrix Bridge]] thought that a [[hashtag]] used to indentify a matrix room was just a normal hashtag

[[Agora Bug]]

Seemingly a case of [[Mistaken Identity]] where the anagora [[Matrix Bridge]] thought that a [[hashtag]] used to indentify a matrix room was just a normal hashtag

[[Agora Bug]]

Seemingly a case of [[Mistaken Identity]] where the anagora [[Matrix Bridge]] thought that a [[hashtag]] used to indentify a matrix room was just a normal hashtag

[[Agora Bug]]

• [[film]]
  • [[two pills]] [[third pill]]
  • [[nebuchadnezzar]]
  • [[zion]]

The Matrix§

• [[flancian@social.coop]] https://social.coop/@flancian/110158548615164638 pull

• [[flancian]] https://twitter.com/flancian/status/1467526126302023687 pull
• [[flancian]] https://twitter.com/flancian/status/1548317778368614405 pull

user matrix§

Go back to the [[AI Glossary]]

#recsystems

In recommendation systems, an embedding generated by matrix factorization that holds latent signals about user preferences. Each row of the user matrix holds information about the relative strength of various latent signals for a single user. For example, consider a movie recommendation system. In this system, the latent signals in the user matrix might represent each user’s interest in particular genres, or might be harder-to-interpret signals that involve complex interactions across multiple factors.

The user matrix has a column for each latent feature and a row for each user. That is, the user matrix has the same number of rows as the target matrix that is being factorized. For example, given a movie recommendation system for 1,000,000 users, the user matrix will have 1,000,000 rows.

V§

• [[2022-02-27 22:18:25]] [[@flancian:matrix.org]] (link):
  • PSA: I’ve now enabled the agora bot feature to [[write to the agora from matrix]]. if you include a wikilink in any message in a room with the [[agora bot]], the bot will not only link to your message but include the message text (attributed to your username). like this :)

• [[2022-02-27 22:13:21]] [[@flancian:matrix.org]] 🔗
  • this is an example of [[writing to the agora from matrix]]