dougallj

# 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

AndrasKovacs

{-# 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

MattPD

Program Analysis Resources

(draft; work in progress)

See also:

• Compilers
  • correctness
• Program analysis:
• Dynamic analysis - instrumentation, translation, sanitizers

joom

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

eborden

{-# LANGUAGE BangPatterns #-}
{-# LANGUAGE GeneralizedNewtypeDeriving #-}
module LossyHistogram
  ( Histogram
  , histResolution
  , histMagnitude
  , histBuckets
  , mkHistogram
  , fromList
  , keys

eddies

Standalone Spark 2.0.0 with s3

###Tested with:

• Spark 2.0.0 pre-built for Hadoop 2.7
• Mac OS X 10.11
• Python 3.5.2

Goal

Use s3 within pyspark with minimal hassle.

david-christiansen

#!/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

andrejbauer

Are all functions continuous?

Mathematical background

Brouwer's statement "all functions are continuous" can be formulated without reference to topology as follows.

Definition: A functional f : (N → N) → N is continuous at a : N → N when there exists m : N such that, for all b : N → N, if ∀ k < m, a k = b k then f a = f b.

baraldilorenzo

##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

matthieubulte

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