(draft; work in progress)
See also:
- Compilers
- Program analysis:
- Dynamic analysis - instrumentation, translation, sanitizers
| # IDA (disassembler) and Hex-Rays (decompiler) plugin for Apple AMX | |
| # | |
| # WIP research. (This was edited to add more info after someone posted it to | |
| # Hacker News. Click "Revisions" to see full changes.) | |
| # | |
| # Copyright (c) 2020 dougallj | |
| # Based on Python port of VMX intrinsics plugin: | |
| # Copyright (c) 2019 w4kfu - Synacktiv |
| {-# language Strict, BangPatterns #-} | |
| data Tm = Var Int | App Tm Tm | Lam Tm | Fix Tm deriving (Show, Eq) | |
| data Val = VVar Int | VApp Val Val | VLam [Val] Tm | VFix [Val] Tm | |
| isCanonical :: Val -> Bool | |
| isCanonical VLam{} = True | |
| isCanonical _ = False | |
| eval :: [Val] -> Tm -> Val |
(draft; work in progress)
See also:
| let val plus = \(n:nat) \(m:nat) rec m { | |
| z => n | |
| | s(x) with y => s(y) | |
| } | |
| in let val pred = \(n : nat) rec n { | |
| z => z | |
| | s(x) with y => x | |
| } | |
| in let val minus = \(n:nat) \(m:nat) rec m { | |
| z => n |
| {-# LANGUAGE BangPatterns #-} | |
| {-# LANGUAGE GeneralizedNewtypeDeriving #-} | |
| module LossyHistogram | |
| ( Histogram | |
| , histResolution | |
| , histMagnitude | |
| , histBuckets | |
| , mkHistogram | |
| , fromList | |
| , keys |
| #!/bin/bash | |
| for i in *.mp4; do | |
| if [ ! -a "${i%.mp4}-fixed.mp4" ]; then | |
| ffmpeg -i "$i" -vcodec copy -ac 1 "${i%.mp4}-fixed.mp4" | |
| fi | |
| done |
Brouwer's statement "all functions are continuous" can be formulated without reference to topology as follows.
Definition: A functional
f : (N → N) → Nis continuous ata : N → Nwhen there existsm : Nsuch that, for allb : N → N, if∀ k < m, a k = b kthenf a = f b.
##VGG19 model for Keras
This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| let slice = Array.prototype.slice; | |
| let str = s => slice.call(s) | |
| let unstr = cs => cs.reduce((c, cs) => c + cs, "") | |
| let concat = xss => xss.reduce((xs, ys) => xs.concat(ys), []) | |
| let id = x => x | |
| let negate = x => -x | |
| let add = (x,y) => x + y | |
| let sub = (x,y) => x - y | |
| // type Parser a = () -> [Char] -> a |