recolic

android qq聊天记录导出大致流程

tested on android 6 tencent qq

1. 设法将/data/data/com.tencent.*/databases目录拷贝出来，我假设你了解如何做到这一点。

2. 运行以下命令。我假设你了解如何安装/使用sqlite，我假设你了解linux基本知识。

$ sqlite3 872222222-IndexQQMsg.db

myungsub

Papers from Super SloMo references

• Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation [Paper]
  • Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz
  • CVPR 2018 (splotlight)
• Video frame synthesis using deep voxel flow [Paper] [Code]
  • Z. Liu, R. Yeh, X. Tang, Y. Liu, and A. Agarwala.
  • ICCV 2017
• Video frame interpolation via adaptive separable convolution. [Paper] [Code]

normalize_image.ipynb

kdplus

require 'torch'
require 'xlua'
require 'image'
require 'cunn'
require 'cudnn'
require 'nn'
require 'torch'
require 'optim'
require 'paths'

ryerh

注意：本文内容适用于 Tmux 2.3 及以上的版本，但是绝大部分的特性低版本也都适用，鼠标支持、VI 模式、插件管理在低版本可能会与本文不兼容。

Tmux 快捷键 & 速查表 & 简明教程

启动新会话：

tmux [new -s 会话名 -n 窗口名]

恢复会话：

toshi-k

# = = = = = include = = = = = #

library(MASS)

# = = = = = function = = = = = #

Obj_func <- function(Y,B,A,Lambda){
  MAT <- t(Y) %*% Y - Y %*% t(A) %*% solve(A%*%t(A)+Lambda) %*% t(Y%*%t(A)) - Lambda
  return <- sum(diag(MAT))

szagoruyko

require 'nn'

local vgg = nn.Sequential()
-- building block
local function ConvBNReLU(nInputPlane, nOutputPlane)
  vgg:add(nn.SpatialConvolution(nInputPlane, nOutputPlane, 3,3, 1,1, 1,1))
  vgg:add(nn.SpatialBatchNormalization(nOutputPlane,1e-3))
  vgg:add(nn.ReLU(true))
  return vgg
end

paidi

m = nn.ParallelTable()
layer = nn.SparseLinear(inputSize,outputSize)
m:add(nn.Sequential():add(layer):add(nn.Reshape(1,outputSize)))
for i=2,batchSize do
  local repLayer = layer:clone('weight', 'bias', 'gradWeight', 'gradBias')
  m:add(nn.Sequential():add(repLayer):add(nn.Reshape(1,outputSize)))
end
batchLayer = nn.Sequential():add(m):add(nn.JoinTable(1))

gcr

------- AlexNet: Using my own weight initialization
model = nn.Sequential()
model:add(cudnn.SpatialConvolution(3,96,11,11,4,4,2,2))
    model.modules[#model.modules].weight:normal(0, 0.01)
    model.modules[#model.modules].bias:fill(0)
model:add(cudnn.ReLU())
model:add(inn.SpatialCrossResponseNormalization(5, 0.0001, 0.75, 1))
model:add(nn.SpatialMaxPooling(3,3,2,2))
model:add(cudnn.SpatialConvolution(96,256,5,5,1,1,2,2))
    model.modules[#model.modules].weight:normal(0, 0.01)

dwf

"""
L1-penalized minimization using the feature sign search algorithm.
"""

import logging
import numpy as np
log = logging.getLogger("feature_sign")
log.setLevel(logging.INFO)