aka what i did to get from nothing to done.
note: these are designed to be primarily a re-install guide for myself (writing things down helps me memorize the knowledge), as such don't take any of this on blind faith - some areas are well tested and the docs are very robust, some items, less so). YMMV
CustomJS is used to load two JavaScript classes: create.js and dated.js
daily.md, weekly.md, monthly.md, and yearly.md are Templater templates which use instances of the previous classes to parse dates and generate headers and links.
Bonus is I was able to create a small test project to verify the output of dated.js, as it has no other dependencies aside from moment.js. The test project contains:
package.json sets up a simple node project (such that npm install works)main.js runs the test: node main.js
List of desktop Nvidia GPUS ordered by CUDA core count
Compute Capability from (https://developer.nvidia.com/cuda-gpus) Check the card / architecture / gencode info: (https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/)
| GPU | CUDA cores | Memory | Processor frequency | Compute Capability | CUDA Support |
|---|---|---|---|---|---|
| GeForce GTX TITAN Z | 5760 | 12 GB | 705 / 876 | 3.5 | until CUDA 11 |
| NVIDIA TITAN Xp | 3840 | 12 GB | 1582 | 6.1 | CUDA 8+ |
| [opcache] | |
| ; Determines if Zend OPCache is enabled | |
| opcache.enable=1 | |
| ; Determines if Zend OPCache is enabled for the CLI version of PHP | |
| ;opcache.enable_cli=1 | |
| ; The OPcache shared memory storage size. | |
| opcache.memory_consumption=512 |
| #!/bin/bash | |
| # $1 = # of seconds | |
| # $@ = What to print after "Waiting n seconds" | |
| countdown() { | |
| secs=$1 | |
| shift | |
| msg=$@ | |
| while [ $secs -gt 0 ] | |
| do | |
| printf "\r\033[KWaiting %.d seconds $msg" $((secs--)) |
Ubuntu, Fedora, openSUSE, CentOS, SUSE Linux Enterprise, Debian,... users can finally use Autodesk Fusion 360 in the Linux Browser now.
On Chromium 55.0.2843.0 I get NET::ERR_CERTIFICATE_TRANSPARENCY_REQUIRED.
| translate([0,0,0]) sector(30, 20, 10, 90); | |
| translate([22,0,0]) sector(30, 20, 300, 30); | |
| translate([0,22,0]) sector(30, 20, 30, 300); | |
| translate([22,22,0]) sector(30, 20, 10, 190); | |
| module sector(h, d, a1, a2) { | |
| if (a2 - a1 > 180) { | |
| difference() { | |
| cylinder(h=h, d=d); | |
| translate([0,0,-0.5]) sector(h+1, d+1, a2-360, a1); |
Convolutional neural networks for emotion classification from facial images as described in the following work:
Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015
Project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/
If you find our models useful, please add suitable reference to our paper in your work.
