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Neural Network (Deep Learning)
Mini-project 1
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A base class for Neural Network Algorithms. More...
#include "NeuralNetwork.h"
Public Member Functions | |
| NeuralNetwork () | |
| double | getLSError (double target, double predicted, int distFromCenter) |
| int | makeWeightCpy () |
| A function called when local minimum in the validation error is achieved, to store the combination of weights. More... | |
| double | deltaCompute (int mb) |
| double | deltaComputeNodeParallel (int mb) |
| int | feedforward (int mb) |
| int | feedforwardNodeParallel (int mb) |
| int | feedforwardNodeParallelCompSparse (int mb) |
| double | backPropagateU (int mb, int la, int ii) |
| void | initialise (ParamsInit parameters) |
| void | initialiseDropOut () |
| A function to initialise the vectors holding positions of the nodes to be set to 0. More... | |
| void | initialiseStatsHiddenUnits () |
| Allocate memory for the array holding the statistics about hidden units. More... | |
| void | initialiseWeights (ParamsInit parameters) |
| void | initialiseDeltaBias (ParamsInit parameters) |
| void | initWeightsFromFile (std::string path) |
| double | getGrad (int kk, int jj, int ii) |
| double | dotProduct (int mb, int kk, int jj) |
| void | dotProductProp (int mb, int kk, int jj) |
| void | setLayerUOutput (int mb, int la, int u, double val) |
| int | setDropOut () |
| A function to set the values of the drop out (shuffle the vectors) More... | |
| void | setLayerUDelta (int mb, int la, int u, double val) |
| void | swapWeights () |
| A function to swap the weights (current with the save with which the local minimum in the error is obtained) More... | |
| void | setTimeStep (int step) |
| The function to set timestep. More... | |
| virtual int | updateWeights (int ii, int jj, int kk, double val, double penalty)=0 |
| A virtual function implemented by CAE and AE to w = w-lr*grad with momentum and adaGrad. More... | |
| virtual int | updateWeightsFast (int ii, int jj, int kk, double val, double penalty)=0 |
| A virtual function implemented by CAE and AE to w = w-lr*grad without momentum and adaGrad. More... | |
| virtual int | backpropagate (double learningRate, double lambda)=0 |
| A virtual function implemented by CAE and AE to compute the backpropagation. More... | |
| virtual int | backpropagateNodeParallel (double learningRate, double lambda, int flagRelease)=0 |
| A virtual function implemented by CAE and AE to compute the backpropagation in parallel. More... | |
| virtual | ~NeuralNetwork () |
| A virtual destructor. More... | |
| virtual std::string | type () const =0 |
| A virtual function giving the type of the encder (string) More... | |
| Layer * | getLayer (int mb, int id) const |
| int | getNumbLayers () const |
| int | getMiniBatch () const |
| double | getLayerUDelta (int mb, int la, int u) const |
| int | getLayerNumbU (int la) const |
| double | getLayerUOutput (int mb, int la, int u) const |
| float | getStats (int ii, int jj) const |
| int | getLayerAct (int mb, int la) const |
| double | getWeightO (int ii, int jj, int kk) const |
| double | getMomentum () const |
| double | getDropOut () const |
| double | getAdaGrad () const |
| int | getStatsFlag () const |
| bool | getSwapped () const |
Protected Types | |
| enum | Flag { disabled, enabled } |
| Flag to hold activated and deactivated values. More... | |
Protected Attributes | |
| Flag | m_flagFast |
| Flag used to mark whether fast versions of the functions can be used. More... | |
| Flag | m_statsFlag |
| Flag to mark whether the user wants to gather statistics. More... | |
| bool | m_flagSwapped |
| Flag to mark whether the weights have been swapped (i.e. local minimum reached) More... | |
| int | m_numberLayers |
| Number of layers in the neural network. More... | |
| int | m_miniBatch |
| Mini-batch size. More... | |
| int | m_itTest |
| Number of test iterations. More... | |
| int | m_itTrain |
| Number of train iterations. More... | |
| int | m_timeStep |
| Time step. More... | |
| float | m_adaGrad |
| Magnitude of the adaptive gradient [0;1) More... | |
| float | m_momentum |
| Magnitude of the momentum [0;1) More... | |
| float | m_dropOut |
| Fraction of the units set to 0. More... | |
| double | m_sparse |
| Magnitude of the penalty. More... | |
| double | m_sparsityParameter |
| The maximum value of the output neurons expected. More... | |
| Objective | m_objective |
| Variable to hold objective function. More... | |
| vector< vector< Layer * > > | m_layers |
| Matrix of layers (1D - minibatch, 2D - layers) More... | |
| vector< int > | m_layersSizeVec |
| Vector to hold the sizes of layers. More... | |
| vector< vector< float > > | m_statsHiddenUnits |
| Matrix to hold the stats about output of the hidden units. More... | |
| vector< double > | m_avUnits |
| Vector to hold regularization for the sparse autoencoder. More... | |
| vector< double > | m_sumDelta |
| Vector used for OpenMP to store calculations produced by different threads. More... | |
| vector< vector< double > > | m_dropOutVec |
| Vector with elements 1 and 0 marking the neurons set to 0. More... | |
| vector< vector< vector< double > > > | m_weights |
| Cube of weights. More... | |
| vector< vector< vector< double > > > | m_weightsMinCp |
| Cube of weights with which local minimum is achieved. More... | |
| vector< vector< vector< double > > > | m_momentumArr |
| Holding momentum for each weight. More... | |
| vector< vector< vector< double > > > | m_sumDeltaArr |
| Holding deltas for the adaGrad. More... | |
A base class for Neural Network Algorithms.
A base class for contractive autoencoder and simple autoencoder.
Definition at line 27 of file NeuralNetwork.h.
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protected |
Flag to hold activated and deactivated values.
| Enumerator | |
|---|---|
| disabled | |
| enabled |
Definition at line 32 of file NeuralNetwork.h.
| NeuralNetwork::NeuralNetwork | ( | ) |
Definition at line 3 of file neuralnetwork.cpp.
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virtual |
A virtual destructor.
Definition at line 476 of file neuralnetwork.cpp.
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pure virtual |
A virtual function implemented by CAE and AE to compute the backpropagation.
Implemented in Autoencoder, and ContractiveAutoencoder.
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pure virtual |
A virtual function implemented by CAE and AE to compute the backpropagation in parallel.
Implemented in Autoencoder, and ContractiveAutoencoder.
| double NeuralNetwork::backPropagateU | ( | int | mb, |
| int | la, | ||
| int | ii | ||
| ) |
A function to apply the derivative of the non-linearity and get the propagated value
| mb | mini-batch |
| la | layer |
| ii | unit in the layer |
Definition at line 251 of file neuralnetwork.cpp.
| double NeuralNetwork::deltaCompute | ( | int | mb | ) |
A function to compute deltas for a given mini-batch
| mb | mini-batch |
Definition at line 212 of file neuralnetwork.cpp.
| double NeuralNetwork::deltaComputeNodeParallel | ( | int | mb | ) |
A parallel function to compute deltas for a given mini-batch
| mb | mini-batch |
m_avUnits[jj]=0.0; YOU CANNOT 0, beacuse the value is used in the next iteration
Definition at line 133 of file neuralnetwork.cpp.
| double NeuralNetwork::dotProduct | ( | int | mb, |
| int | kk, | ||
| int | jj | ||
| ) |
| void NeuralNetwork::dotProductProp | ( | int | mb, |
| int | kk, | ||
| int | jj | ||
| ) |
A function called from the feedforward to compute the dot product of the neuron outputs with associated weights and pass it through the non-linearity
| mb | |
| kk | |
| jj |
Definition at line 124 of file neuralnetwork.cpp.
| int NeuralNetwork::feedforward | ( | int | mb | ) |
| int NeuralNetwork::feedforwardNodeParallel | ( | int | mb | ) |
Node parallel feedforward
| mb | - mini-batch |
Definition at line 93 of file neuralnetwork.cpp.
| int NeuralNetwork::feedforwardNodeParallelCompSparse | ( | int | mb | ) |
A feed forward function used to compute the penalty for the sparse autoencoder
| mb | mini-batch |
Definition at line 72 of file neuralnetwork.cpp.
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inline |
Definition at line 252 of file NeuralNetwork.h.
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inline |
Definition at line 250 of file NeuralNetwork.h.
| double NeuralNetwork::getGrad | ( | int | kk, |
| int | jj, | ||
| int | ii | ||
| ) |
A function used to get the gradient in the backporapagation stage
| kk | - layer |
| jj | - unit in the kk layer |
| ii | - unit in the (kk-1) layer |
Definition at line 256 of file neuralnetwork.cpp.
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inline |
Definition at line 230 of file NeuralNetwork.h.
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inline |
Definition at line 244 of file NeuralNetwork.h.
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inline |
Definition at line 238 of file NeuralNetwork.h.
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inline |
Definition at line 236 of file NeuralNetwork.h.
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inline |
Definition at line 240 of file NeuralNetwork.h.
| double NeuralNetwork::getLSError | ( | double | target, |
| double | predicted, | ||
| int | distFromCenter | ||
| ) |
The function to get the Squared error between the target and predicted values used in to weight error on the edges of the patches
| target | - target value |
| predicted | - predicted value |
| distFromCenter | - distance of the point from the center of the patch |
Definition at line 8 of file neuralnetwork.cpp.
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inline |
Definition at line 234 of file NeuralNetwork.h.
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inline |
Definition at line 248 of file NeuralNetwork.h.
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inline |
Definition at line 232 of file NeuralNetwork.h.
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inline |
Definition at line 242 of file NeuralNetwork.h.
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inline |
Definition at line 254 of file NeuralNetwork.h.
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inline |
Definition at line 256 of file NeuralNetwork.h.
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inline |
Definition at line 246 of file NeuralNetwork.h.
| void NeuralNetwork::initialise | ( | ParamsInit | parameters | ) |
A function to initialise the Neural netowrk
| parameters | used to stor contents of the config file |
Drop out initialise
Stats hidden units initialise
Bias set
Function to Initialise Weights
Definition at line 276 of file neuralnetwork.cpp.
| void NeuralNetwork::initialiseDeltaBias | ( | ParamsInit | parameters | ) |
A function to set bias and delta values
| parameters | - used to stor the results from the config file |
Definition at line 327 of file neuralnetwork.cpp.
| void NeuralNetwork::initialiseDropOut | ( | ) |
A function to initialise the vectors holding positions of the nodes to be set to 0.
Definition at line 344 of file neuralnetwork.cpp.
| void NeuralNetwork::initialiseStatsHiddenUnits | ( | ) |
Allocate memory for the array holding the statistics about hidden units.
Definition at line 366 of file neuralnetwork.cpp.
| void NeuralNetwork::initialiseWeights | ( | ParamsInit | parameters | ) |
A function to initialise weights
| parameters | - used to stor the results from the config file |
Definition at line 382 of file neuralnetwork.cpp.
| void NeuralNetwork::initWeightsFromFile | ( | std::string | path | ) |
A function used to initialise weights from the file
| path | - a path to the weights file |
Definition at line 431 of file neuralnetwork.cpp.
| int NeuralNetwork::makeWeightCpy | ( | ) |
A function called when local minimum in the validation error is achieved, to store the combination of weights.
Definition at line 21 of file neuralnetwork.cpp.
| int NeuralNetwork::setDropOut | ( | ) |
A function to set the values of the drop out (shuffle the vectors)
Definition at line 40 of file neuralnetwork.cpp.
| void NeuralNetwork::setLayerUDelta | ( | int | mb, |
| int | la, | ||
| int | u, | ||
| double | val | ||
| ) |
A function to set the delta of the unit
| mb | - mini-batch |
| la | - layer |
| u | - unit |
| val | - value |
Definition at line 271 of file neuralnetwork.cpp.
| void NeuralNetwork::setLayerUOutput | ( | int | mb, |
| int | la, | ||
| int | u, | ||
| double | val | ||
| ) |
A function to set the output of the unit
| mb | - mini-batch |
| la | - layer |
| u | - unit |
| val | - value |
Definition at line 267 of file neuralnetwork.cpp.
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inline |
The function to set timestep.
Definition at line 209 of file NeuralNetwork.h.
| void NeuralNetwork::swapWeights | ( | ) |
A function to swap the weights (current with the save with which the local minimum in the error is obtained)
Definition at line 15 of file neuralnetwork.cpp.
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pure virtual |
A virtual function giving the type of the encder (string)
Implemented in Autoencoder, and ContractiveAutoencoder.
|
inlinepure virtual |
A virtual function implemented by CAE and AE to w = w-lr*grad with momentum and adaGrad.
Implemented in Autoencoder, and ContractiveAutoencoder.
|
inlinepure virtual |
A virtual function implemented by CAE and AE to w = w-lr*grad without momentum and adaGrad.
Implemented in Autoencoder, and ContractiveAutoencoder.
|
protected |
Magnitude of the adaptive gradient [0;1)
Definition at line 59 of file NeuralNetwork.h.
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protected |
Vector to hold regularization for the sparse autoencoder.
Definition at line 86 of file NeuralNetwork.h.
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protected |
Fraction of the units set to 0.
Definition at line 65 of file NeuralNetwork.h.
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protected |
Vector with elements 1 and 0 marking the neurons set to 0.
Definition at line 92 of file NeuralNetwork.h.
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protected |
Flag used to mark whether fast versions of the functions can be used.
Definition at line 35 of file NeuralNetwork.h.
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protected |
Flag to mark whether the weights have been swapped (i.e. local minimum reached)
Definition at line 41 of file NeuralNetwork.h.
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protected |
Number of test iterations.
Definition at line 50 of file NeuralNetwork.h.
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Number of train iterations.
Definition at line 53 of file NeuralNetwork.h.
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protected |
Matrix of layers (1D - minibatch, 2D - layers)
Definition at line 77 of file NeuralNetwork.h.
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protected |
Vector to hold the sizes of layers.
Definition at line 80 of file NeuralNetwork.h.
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protected |
Mini-batch size.
Definition at line 47 of file NeuralNetwork.h.
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protected |
Magnitude of the momentum [0;1)
Definition at line 62 of file NeuralNetwork.h.
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Holding momentum for each weight.
Definition at line 101 of file NeuralNetwork.h.
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protected |
Number of layers in the neural network.
Definition at line 44 of file NeuralNetwork.h.
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Variable to hold objective function.
Definition at line 74 of file NeuralNetwork.h.
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protected |
Magnitude of the penalty.
Definition at line 68 of file NeuralNetwork.h.
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The maximum value of the output neurons expected.
Definition at line 71 of file NeuralNetwork.h.
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protected |
Flag to mark whether the user wants to gather statistics.
Definition at line 38 of file NeuralNetwork.h.
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Matrix to hold the stats about output of the hidden units.
Definition at line 83 of file NeuralNetwork.h.
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protected |
Vector used for OpenMP to store calculations produced by different threads.
Definition at line 89 of file NeuralNetwork.h.
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protected |
Holding deltas for the adaGrad.
Definition at line 104 of file NeuralNetwork.h.
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Time step.
Definition at line 56 of file NeuralNetwork.h.
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Cube of weights.
Definition at line 95 of file NeuralNetwork.h.
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Cube of weights with which local minimum is achieved.
Definition at line 98 of file NeuralNetwork.h.


This post explains how to build a choropleth map with R. It shows how to load geospatial data, bind it to a numeric value and draw the map.
Two inputs are needed to build a choropleth map:
Note: this step is described in detail here. Read it if you are not familiar with geospatial data management in R.
The region boundaries required to make maps are usually stored in geospatial objects. Those objects can come from shapefiles, geojson files or provided in a R package. See the map section for possibilities.
Let’s get a geospatial object from a shape file available here.
# Download the shapefile. (note that I store it in a folder called DATA. You have to change that if needed.)
download.file("http://thematicmapping.org/downloads/TM_WORLD_BORDERS_SIMPL-0.3.zip" , destfile="DATA/world_shape_file.zip")
# You now have it in your current working directory, have a look!
# Unzip this file. You can do it with R (as below), or clicking on the object you downloaded.
system("unzip DATA/world_shape_file.zip")
# -- > You now have 4 files. One of these files is a .shp file! (TM_WORLD_BORDERS_SIMPL-0.3.shp)
And let’s load it in R
# Read this shape file with the rgdal library.
library(rgdal)
my_spdf <- readOGR(
dsn= paste0(getwd(),"/DATA/world_shape_file/") ,
layer="TM_WORLD_BORDERS_SIMPL-0.3",
verbose=FALSE
)
# Select Africa only
africa <- my_spdf[my_spdf@data$REGION==2 , ]
# -- > Now you have a Spdf object (spatial polygon data frame). You can start doing maps!
The hardest part is to build the color palette that will be used on
the map. The RColorBrewer
package makes it a breeze to
get a nice set of colors thanks to its
brewer.pal()
function.
Once the palette is created, POP2005
is cut in several
bins using the cut()
function, and the approriate color
is attributed to each bin.
Finally, build the map with plot()
and give the color
vector to the col
argument to get the choropleth.
In a choropleth map, each region has a color that represents the value of a numeric variable.
In this example, this variable is already available in the
data
slot of the geospatial object. You can have a look
to it typing africa@data
in the terminal.
This object is a data frame. It is thus possible to visualize the
distribution of POP2005
with a classic histogram made
in ggplot2
# library
library(dplyr)
library(ggplot2)
# Make sure the variable you are studying is numeric
africa@data$POP2005 <- as.numeric( africa@data$POP2005 )
# Distribution of the population per country?
africa@data %>%
ggplot( aes(x=as.numeric(POP2005))) +
geom_histogram(bins=20, fill='#69b3a2', color='white')
The hardest part is to build the color palette that will be used on
the map. The RColorBrewer
package makes it a breeze to
get a nice set of colors thanks to its
brewer.pal()
function.
Once the palette is created, POP2005
is cut in several
bins using the cut()
function, and the approriate color
is attributed to each bin.
Finally, build the map with plot()
and give the color
vector to the col
argument to get the choropleth.
# Palette of 30 colors
library(RColorBrewer)
my_colors <- brewer.pal(9, "Reds")
my_colors <- colorRampPalette(my_colors)(30)
# Attribute the appropriate color to each country
class_of_country <- cut(africa@data$POP2005, 30)
my_colors <- my_colors[as.numeric(class_of_country)]
# Make the plot
plot(africa , xlim=c(-20,60) , ylim=c(-40,40), col=my_colors , bg = "#A6CAE0")


The UI package contains interfaces for various types of displays. The most common are GUI (Graphic User Interface) and CLI (Command Line Interface. Classes here are unlikely, as not many widgets can be used for mulitple games.


If the cursor is outside of a statement (for example, on a blank line or within a comment), perform one of the following actions:


Version 0.2.1 (2014-02-25)
- Fix edge case when (accidentally) supplying only one argument, and that
argument happens to be a falsy value such as
undefined
or null
.
Version 0.2.0 (2014-02-24)
- Disallow passing 0 arguments. It’s weird and inconsistent between browsers.
(Backwards incompatible change.)
Version 0.1.0 (2014-02-23)


15 things to do in every city
Here is a list things I want to do in every city I have lived:
- see sunrise
- see sunset
- go grocery shopping and cook one dinner
- go to a movie
- go to a concert
- go to a museum or a gallery
- go to a live show (stand-up comedy, opera, etc)
- stroll around a public park
- take a walking tour in downtown
- attend a carvinal, a festival, or a free performance
- new year’s countdown
- visit an iconic building (libraries, churches, cathedrals, etc)
- see one street performance
- help one person in need
- have a conversation with one local


A Website For Gamers
NOOB FROM INDIA
gmail.com
Use 8 or more characters with a mix of letters, numbers & symbols
Sign in instead
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Baby found dead in Texas Applebee's trashcan was stillborn: police
by Brie StimsonA baby that was found lifeless in the restroom trashcan of a Texas Applebee’s last July was stillborn, police confirmed Thursday, according to a report.
The baby was discovered by an employee who went into the restroom about 30 minutes after the baby’s mother gave birth at the Irving restaurant, police said, according to The Dallas Morning News.
NEWBORN FOUND DEAD INSIDE APPLEBEE'S RESTROOM IN TEXAS
The 29-year-old mother, who police did not identify, may be charged with abuse of a corpse.
CLICK HERE TO GET THE FOX NEWS APP
The case has been referred to the Dallas County District Attorney’s Office, which will decide if she should be charged, The Morning News reported.


This is a control test file to verify that a JavaScript appended link to a slow-responding stylesheet will block render. If this text is not visible for 5 seconds or so, assumptions about CSS blocking render are valid in your browser.


ZIKV Aedes virus Zika albopictus human Ae. mosquito vector CHIKV African Dis Africa Gabon Gabonese epidemic infections outbreaks species aegypti transmission Central Trop mosquitoes previously strain (.) Med dengue new pools Chikungunya Soc chikungunya outbreak urban Figure Gabon, Hyg Libreville Phylogenetic analysis cycle obtained performed positive sequences spread viral viruses Africa, Asian DENV Infect JP, Vector evolutionary geographic patients phylogenetic resulting similar study suggesting AJ, Alenkiri Asia, Borne Bull Grard Paupy PhyML Virol Virus Yap Zoonotic albopictus, arboviral arboviruses cases collected emergence evidence infection involving isolated maximum occurrence outbreaks. potential sera species, suggested sylvan tree AM, Asia Biol Charrel Culex Exot Leroy Mansonia Nkoghe PCR Pathol Table Trans Uganda Weaver albopictus. associated blood capital detected detection emerging envelope fever global implemented invasive lineage method model molecular range relationships required review role sampling screening serological suburbs virus.


Selena Tutorial¶
SELENA provides a simple Python API which allows easy description, deployment automation and execution of experiments. The API builds on top of the XAPI service and provides enhances configuration primitives to describe network topologies, resource policies and execute commands on VMs.
In order to describe a SELENA experiment, the experimenter must extend the Scenario class and implement custom methods for two functions: The init_scenario function, describing the topology of the experiment, and the run_scenario function, describing the sequence of commands of the experiment on the guest VM. Extensive details on the Selena API can be found here .
In order to walk the user through the required steps, we use a simple a simple topology with two hosts interconnected through a switch, running a netperf session, depicted in the following figure.
SELENA scenario¶
In order to run an experiment, a user must implement a Python class which inherits the Scenario class. The custom scenario class must follow a specific naming pattern. Specifically, the filename must be the same as the name of the Scenario class. In this tutorial, we create a Tutorial class which must be saved in a Tutorial.py file in order to allow execution with the SELENA execution tools.
A python script that can replicate the previous topology and experimental scenario is the following::
from Scenario import Scenario, NodeTypes
import time
class Tutorial(Scenario):
def __init__(self):
super(Tutorial, self).__init__()
def initScenario(self):
if not super(Tutorial, self).initScenario():
return False
self.name = "Tutorial"
# Add the server
self.addNode(self.newNode(
0, # Unique id for the Node
'server', # Name of the node
NodeTypes.LINUX_HOST, # The node is a Linux host
# definition of the list of interfaces and respective
# network configuration primitives
[(1000, "RANDOM", "<IP_ADDRESS>", "<IP_ADDRESS>", "<IP_ADDRESS>")],
# Nunber of vCPUs used by the node
2,
# Identification of the core on which the node is Pinned
'4',
# Available RAM for the node
536870912))
# Add the clients
self.addNode(self.newNode(1, 'client', NodeTypes.LINUX_HOST,
[(1000, "RANDOM", "<IP_ADDRESS>", "<IP_ADDRESS>", "<IP_ADDRESS>")],
1, '6,7', 536870912))
# Switch definition. The switch has a different node type and
# has two interfaces, one for each host, which don't have any
# IP configuration.
self.addNode(self.newNode(2, 'switch', NodeTypes.LINUX_OVS,
[(1000, "RANDOM", "", "", ""), (1000, "RANDOM", "", "", "")],
1, '5', 536870912))
# link between client and switch
self.addLink(self.newLink((1, 0), (2, 1), 1000, 0.1))
# link between server and switch
self.addLink(self.newLink((0, 0), (2, 0), 1000, 0.1))
return True
def runScenario(self):
try:
# A method to configure each device queue sizes and
# disable network optimizations and enable the openvswitch
# instance.
self.setupNetworking()
# A method to configure link delays.
self.setupNetworkEmulation()
# Once everything is setted up, we run a command to run the
# netperf session and output statistics in a file.
self.pushCommand(1, "netperf -H <IP_ADDRESS> -C -c -t TCP_STREAM -l 60 -D 1 > /tmp/throughput.log")
# wait for the emulation to complete and return to terminate
# the experiment.
time.sleep(60)
except KeyboardInterrupt:
return
def getLinuxInterfaceCmd(self, ix, addr, mask, gw, queue):
# interface 0 is the management. subsequent interfaces follow a
# naming similar to what is used in the link definitions.
intf = 'eth'+str(ix+1)
cmdLine = "ifconfig %s up" % (intf)
cmdLine += "; ifconfig %s txqueuelen %s" % (intf, str(queue))
cmdLine += "; ethtool -K %s tso off tx off rx off" % (intf)
if addr:
cmdLine = "ifconfig %s %s netmask %s" % (intf, addr, mask)
if gw:
cmdLine += "; ip route del 0/0"
cmdLine += "; route add default gw %s %s" % (gw, intf)
return (intf, cmdLine)
def setupNetworking(self):
# Setup device interface and port grouping in the switch.
for nId, node in self.EMU_NODES.items():
for ix in range(0, len(node['NETIFS'])):
dev = node['NETIFS'][ix]
(_, cmd) = self.getLinuxInterfaceCmd(ix, dev[2], dev[3], dev[4], dev[0])
self.pushCommand(nId, cmd)
if node['TYPE'] == NodeTypes.LINUX_OVS:
bridge = 'br0'
self.pushCommand(nId, 'ovs-vsctl del-br %s; ovs-vsctl add-br %s' % (bridge, bridge))
for ix in range(0, len(node['NETIFS'])):
dev = node['NETIFS'][ix]
(intf, _) = self.getLinuxInterfaceCmd(ix, dev[2], dev[3], dev[4], dev[0])
self.pushCommand(nId, 'ovs-vsctl add-port %s %s' % (bridge, intf))
time.sleep(5)
def getLinkEmulationCmd(self, ix, rate, delay=None):
# configure an emulation of the link delay.
intf = 'eth'+str(ix+1)
cmdLine = "tc qdisc del root dev %s ; " % (intf)
if delay:
cmdLine += "tc qdisc add dev %s root handle 1: netem delay %sms ; " % (intf, str(delay))
return cmdLine
def setupNetworkEmulation(self):
# Set all the network interfaces
for link in self.LINKS:
delay = link['LATENCY']
if delay:
nodeA = self.EMU_NODES[link['NODE_A']]
ifA = link['IFACE_A']
nodeB = self.EMU_NODES[link['NODE_B']]
ifB = link['IFACE_B']
rate = link['RATE']
# we only configure link latencies on the edge nodes
# of the host.
if (nodeA['TYPE'] != NodeTypes.LINUX_OVS):
# Set the first interface of the link
cmd = self.getLinkEmulationCmd(ifA, rate, delay)
self.pushCommand(nodeA['ID'], cmd)
if nodeB['TYPE'] != NodeTypes.LINUX_OVS:
# Set the second interface of the link
cmd = self.getLinkEmulationCmd(ifB, rate, delay)
self.pushCommand(nodeB['ID'], cmd)
time.sleep(5)
As mentioned earlier a SELENA experimental definition must extend the Scenario class and implement two main functions: init_scenario and run_scenario. The init_scenario method must initialize the topology of the network. When the function is invoked the SELENA instance has only connected to the XEN server and awaits the topology in order to map experimental nodes in guest VMs. The Scenario class provides four methods to specify the topology of the experiment of the network. The newNode and addNode methods allow the experimenter to define the hosts of the topology along with the available interfaces and address configurations. Each node has a unique id and string name, defined by the experimenter, a node type, summarised in the table below, an array with network interfaces and CPU number and affinity and memory limitations for each guest. Furthermore, in order to define the topology links the SELENA API provides the commands addLink and newLink. Each link in the SELENA abstraction consists of node and interface index pairs and the API allows the user to define the link bandwidth and latency.
In the case of this tutorial, our sample code configure two nodes with 1 interface, which represent the client and the server of our example, and 1 node with 2 interfaces, which will represent the switch of the topology. Furthermore, the method defines two links, connecting the first interface of the client and server with an interface of the switch.
| NodeTypes | Configuration | Purpose |
|---|---|---|
| LINUX_HOST | Linux 3.10.11, Debian Wheezy | Simple end-host to run traffic generators and applications |
| LINUX_OVS | Linux 3.10.11, Debian Wheezy, Open vSwitch 1.4.2 | A switch abstraction running an instance of open vSwitch |
| MIRAGE_SWITCH | Mirage 1.0, ocaml-openflow | A Mirage based switch emulator |
| OF_CONTROLLER | Linux 3.10.11, Debian Wheezy, Java 1.6, Nox, Pox, Floodlight, FlowVisor | A node configure to run all popular OpenFlow controllers |
| LINUX_WEBSERVER | Linux 3.10.11, Debian Wheezy, Apache 2.0, Nginx, PHP, MySQL, memcached, redis | An experimental containing all required software to run web applications |
The run_scenario method is executed once the topology is up and configured and the nodes have all connected to the signalling service in Dom0. The SELENA abstraction allows a Scenario class to manipulate the guest and execute commands within them. In order to achieve this, the scenario object has the pushCmd method which runs a command in the guest with the respective node index. In the case of the tutorial, the run_scenario executes in the client node a netperf command towards the controller.
Scenario execution¶
In order to execute a Selena experiment, the selena-control python package provides a simple tool. By default, the execution script is install in /usr/bin/ by the install process of the setup script. The tool by default provides the following options:
Usage: selena [options] [experimentDescription]
This selena utility creates a Selena emulation management instance which can
create / configure / execute / remove a virtual-network experiment specified
by the user
Options:
-h, --help show this help message and exit
-v, --version Print the version and exit.
-i, --install Attempt to create the VMs for the specified experiment
description
-u, --uninstall Attempt to shutdown all VMs of the specified
experiment and destroy them
-k, --kill Shutdown all running VMs of the specified experiment
-s, --start Start and configure all VMs of the specified
experiment
-r, --restart Restart and re-configure all VMs of the specified
experiment (halt all, reset net, start all, configure)
-t, --test Run a quick diagnostic test
-l LOGLEVEL, --logging=LOGLEVEL
Specifies the logging level:DEBUG | INFO | WARNING |
CRITICAL | ERROR
-c JSONCONFIG, --config=JSONCONFIG
Specifies a custom JSON configuration file for Selena
The tool provides three basic operations: install (-i), which creates the relevant VMs and network configurations, start (-s), which runs the execution scenario, kill (-k), which stops all VMs and uninstall (-u), which destroys all create VMs and networks.
Along with the scenario source code, the tool requires a JSON file to describe the selena templates. The selena configuration file is defined through the -c option and an example of a selena configuration file is the following:
{
"XenServerConfig": {
"XCP_url": "http://localhost",
"XCP_uname": "root",
"XCP_passwd": "selenasrg"
},
"SelenaMetaInfo": {
"SELENA_VM_KEYWORD": "SELENA_VM",
"SELENA_NET_KEYWORD": "SELENA_NET",
"SELENA_VDI_KEYWORD": "SELENA_VDI",
"SELENA_VBD_KEYWORD": "SELENA_VBD"
},
"SelenaManagementNet": {
"SELENA_MGMT_NET_NAME": "SELENA_MANAGEMENT_NET",
"SELENA_MGMT_NET_IP": "<IP_ADDRESS>",
"SELENA_MGMT_NETADDR": "<IP_ADDRESS>",
"SELENA_MGMT_NET_MASK": "<IP_ADDRESS>"
},
"SelenaTemplates": [
{
"Name": "LINUX_HOST",
"PV_kernel": "/boot/guest/vmlinuz-3.10.11-1tdf-amd64",
"PV_ramdisk": "/boot/guest/initrd.img-3.10.11-1tdf-amd64",
"PV_args": "root=UUID=45dd90ef-23c2-479b-86a5-78fa864dd249 ro console=hvc0 quiet",
"PV_bootloader": "",
"PV_bootloader_args": "",
"VDI_UUID": "41c7c041-e056-47dc-9520-d144a61b5ba3"
},
{
"Name": "LINUX_OVS",
"PV_kernel": "/boot/guest/vmlinuz-3.10.11-1tdf-amd64",
"PV_ramdisk": "/boot/guest/initrd.img-3.10.11-1tdf-amd64",
"PV_args": "root=UUID=45dd90ef-23c2-479b-86a5-78fa864dd249 ro console=hvc0 quiet",
"PV_bootloader": "",
"PV_bootloader_args": "",
"VDI_UUID": "55eda677-c2cc-4d88-b23e-4505df5e0d26"
},
{
"Name": "MIRAGE_SWITCH",
"PV_kernel": "/boot/guest/ofswitch.xen",
"PV_ramdisk": "",
"PV_args": "",
"PV_bootloader": "",
"PV_bootloader_args": "",
"VDI_UUID": ""
},
{
"Name": "OF_CONTROLLER",
"PV_kernel": "/boot/guest/vmlinuz-3.10.11-1tdf-amd64",
"PV_ramdisk": "/boot/guest/initrd.img-3.10.11-1tdf-amd64",
"PV_args": "root=UUID=45dd90ef-23c2-479b-86a5-78fa864dd249 ro console=hvc0 quiet",
"PV_bootloader": "",
"PV_bootloader_args": "",
"VDI_UUID": "5c0331e9-64c8-451a-a132-7df78dcc6eb9"
}
]
}
In the previous setup the user is critical to update the username and password fields, using the username and password of a system user with privileges to create new VM, as well as the vdi uuid of the Selena VM templates.


Snapshot Test
Snapshot Mode:
Browser
R
ender View
Whole
D
esktop
Snapshot Rectangle:
W
hole Rectangle
S
cript:
Run Snapshot
B
ackground Color for Snapshot
Browser Render View
:
T
ransparent
Close


snapchat
Businessmodel of Snapchat
Customer Segments
The Snapchat app is aimed at the general smart phone user base. It has more than 100 million daily active users. The app’s automatic delete feature makes it popular among users that value privacy or wish to save memory. The platform is particularly popular among millennials.
According to Snapchat’s own statistics, users over the age of 34 account for only 14% of the apps monthly users, with 18 to 24 year olds representing the largest segment, accounting for 37% of the app’s monthly users. The US is Snapchat’s largest market, where more than 60% of smart phone users aged 13 to 34 use the Snapchat app.
Snapchat also serves a number of advertisers and brand marketers, to which it provides a range of advertising solutions.
Value Propositions
Snapchat’s initial value was based on its privacy capabilities, with the app gaining popularity because of its self-delete feature, which ensured that pictures were only accessible for a user-defined timeframe. Users also find value in the app as it is free to use and provides a broad range of personalisation and editing options, such as its collection of overlays and filters.
For advertisers, the apps’ greatest is its user base – 100 million daily active users reportedly accounting for 10 billion daily views – through which brands are able to extend their reach among their target markets.
Channels
The Snapchat platform is available as a mobile app for iOS and Android market. The Company also operates a website, accessible through mobile and desktop browsers, at www.snapchat.com which hosts further information regarding the platform and the Company.
Customer Relationships
The Snapchat app is free to download from the Apple App Store and the Google Play marketplace. Once downloaded, users are able to register for an account directly via the app, following which they are given access to Snapchat’s full functionality. The process requires no interaction with Snapchat representatives.
Snapchat is primarily community-driven, with users creating and sharing ‘stories’ and ‘snaps’. The Snapchat website also hosts a community section where users are provided with guidelines, safety tips and technical support guides. The Company keeps its customers updated with news and events via snaps sent directly to user inboxes, as well as through its blog and Twitter account.
Key Activities
Snapchat develops, operates and maintains a mobile application for messaging and photo-sharing. The app allows users to create and share multimedia messages, known as ‘snaps’. These can be photos or short videos, and can be edited to include filters and effects, text captions, and drawings. Snaps can be published as ‘stories’, a feed of chained content automatically shared with the user’s contact list of family and friends, or can be sent as private messages.
‘Snaps’ can be viewed for a user-specified length of time from one to ten seconds before they are permanently deleted. Snapchat also manages a number of partnerships with media houses and broadcast networks, which share content and user-created coverage of events.
Key Partners
Snapchat partners with a range of companies, primarily comprising media houses, broadcast networks and tech companies. As part of the application’s Discover feature, Snapchat has partnered with a number of news outlets and publishing houses to provide users with daily news updates. These partners include Daily Mail, Vice, CNN, ESPN, Cosmopolitan and Yahoo News.
The Company also collaborates with broadcast networks and events. It has a content production partnership with World Wrestling Entertainment, recently agreed a partnership with NBC and BuzzFeed to bring coverage of the upcoming 2016 Olympic Games to Snapchat, and works with Live Nation to bring coverage of live music events to Snapchat users. Snapchat has also partnered with the Tribeca Film Festival in the form of the Tribeca Snapchat Shorts program, which invites users to submit 200-second long Snapchat stories for a chance to win prizes.
Additionally, in 2014 the Company launched a partnership with payment solutions company Square to enable users to make payments via the Snapchat app.
Key Resources
Snapchat’s key resources are its software and technology, its IT infrastructure, its personnel and its user base. The Company has 17 patent applications filed with the US Patent and Trademark Office, including patents titled ‘Storage and processing of ephemeral messages’, ‘Message queues for rapid re-hosting of client devices’, ‘Device and method for photo and video capture’ and ‘Geo-location based event gallery’. Snapchat’s user base and production partners are also key as they are responsible for generating the platform’s content.
Cost Structure
Snapchat incurs costs in relation to the research and development of its app, its maintenance of IT infrastructure, and the retention of its personnel. The Company employs more than 330 workers who all represent costs in the form of salaries and benefits, and operates an office in Venice, California and an engineering centre in Seattle, which accrue fixed costs relating to rental fees and utilities.
Revenue Streams
In 2015 Snapchat recorded an annual revenue of $50 million. The Company is reportedly projecting a revenue of between $300 million and $350 million for 2016. Its principal revenue stream is advertising, in the form of native advertising solutions and media branded posts. Snapchat’s Discover media hub allows media publishers – including National Geographic, Comedy Central and CNN – to have daily content featured on the app. These customers are reportedly charged on a cost per thousand views (CPM) basis estimated at $100 CPM. If the publisher decides to use its space to publish its own advertisements, the publisher splits its profits with Snapchat.
Companies can also pay to have their brand featured in Snapchat’s Stories feed where public snaps from friends and international events are typically found. According to some reports, these branded posts can cost as much as $750,000 per day. Additionally, brands can sponsor their own location-specific geofilters and animated overlays. Snapchat’s digital advertising revenue is expected to grow following the release of the Company’s advertising API that is set to be released in the near future.


NOTE: camera and screen can be casted over HTTPS only in Chrome. For the screen the --enable-usermedia-screen-capturing flag must be set. window or application casting is only supported in Firefox 44 on.
NOTE: except for camera, to enable screen, window or application casting open about:config URL
and set media.getusermedia.screensharing.enabled to true
and permanently add the current domain to the list in media.getusermedia.screensharing.allowed_domains.
NOTE: if you want to cast music, for better audio quality disable echo-cancellation.
NOTE: if you want to cast music, for better audio quality disable echo-cancellation,
and aec, noise-suppression, agc in the browser configuration (about:config).


Restoring seeks to bring the vitality of Jesus into the neighborhoods, marriages, and families of Detroit.
About Restoring
Joshua and Yvette Rock have been living in Detroit for the past 16 years, building friendships and raising their family, hoping to witness and encourage God’s restoration among the people and in the neighborhoods of Detroit. Restoring has developed out of this desire, as a way to support and encourage friends in the process of believing, following, submitting, and abiding in Jesus. As Joshua and Yvette share life with their friends in the city, they work to strengthen marriages and families, meet with men and women, and serve their neighbors in practical ways.
Joshua and Yvette’s call and service involves making and supporting disciples of Jesus through prayer and the continuing command to love God and love one another. Joshua and Yvette do this work while reflecting their passion for teaching, art, and community.
Donate to Restoring
In order to sustain their work of bringing vitality to neighborhoods, marriages, and families, Restoring receives donations as part of Abide Ministries, a 501(c)(3) organization located in Rochester, MI that provides financial and organizational oversight. All donations made to Restoring are tax-deductible through Abide Ministries. Abide partners with Donorbox to enable donors to make one time and recurring donations, while allowing them to manage their own giving in a safe, secure, and reliable environment.
ONLINE GIFTS
Online gifts can be made using the form on this page, or at https://donorbox.org/restoring. You can make one-time or recurring gifts using credit cards, debit cards, or bank transfers. All transactions are processed through Donorbox (a Stripe partner) for the highest level of security and convenience. Once you make a gift, you can manage your gift at www.donorbox.org.
CHECK GIFTS
Check gifts can be made out to:
Abide Ministries
323 Linwood Dr
Rochester, MI 48307


The Sorcerer
Spell book
This menu is not accessible in
dungeons
This tutorial is also in the game
Navigating the Spell book
There are 10 levels of Magick, each page of the book displays 2 levels of
spells. You can use the arrow keys to move the pointer. To turn the page of the
book move the cursor over to one of the arrows on the right and left side of
the screen then press enter. The left arrow goes back a page and the right
arrow goes forward. To get a description of a spell move the pointer over it
and press enter. To exit the spell book move the cursor over the word
"Done"
Acquiring/Scribing Spells
Samurai and Sorcerers gain spells by increasing their level. When you level up,
if you are granted a new spell there will be a box that says new spell.
Pressing enter over the box will take you to this screen where you must a
select one spell that you will keep permanently. Pressing escape over a spell
icon will give you a description of what it does, be careful in choosing the
spell you want because you cannot go back and change it. Once you have made
your choice you can use this spell anytime, it does not need to be memorized.
Back


Money play. How should Red play 52?
pips Red=82 White=139
(CorrectAnswer= 0/0, ErrorScore= 0)
This one generated an argument at the Battersea chouette but it shouldn’t really have done so.
This is a classic ‘pay me now, pay me later problem’ with everything in favour of paying now. If Red doesn't play 13/6, White’s board will improve while Red’s weakens.
Not paying now is worse than a double blunder.
For the record, Red played 13/6 and White rolled 61!
This is a classic ‘pay me now, pay me later problem’ with everything in favour of paying now. If Red doesn't play 13/6, White’s board will improve while Red’s weakens.
Not paying now is worse than a double blunder.
For the record, Red played 13/6 and White rolled 61!
1. Cubeful 0-ply 13/6 Eq.: +0.413 0.665 0.226 0.008 - 0.335 0.037 0.000 2. Cubeful 0-ply 6/1 4/2 Eq.: +0.155 (-0.258) 0.579 0.206 0.003 - 0.421 0.068 0.000 3. Cubeful 0-ply 6/4 6/1 Eq.: -0.032 (-0.445) 0.523 0.192 0.004 - 0.477 0.111 0.001 4. Cubeful 0-ply 13/11 13/8 Eq.: -0.083 (-0.495) 0.506 0.196 0.006 - 0.494 0.124 0.003 5. Cubeful 0-ply 13/11 6/1 Eq.: -0.169 (-0.582) 0.481 0.201 0.006 - 0.519 0.157 0.005


| Pauses the playing of a sound channel. |
| channel_handle = variable assigned to the channel when played |
Command Description:
| When you are playing a sound channel, there may come a time you wish to pause the sound for whatever reason (like to play another sound effect). This command does this - and the channel can be resumed with the ResumeChannel command. You can use StopChannel to actually halt the sound. This works with any channel playback (WAV, MP3, MIDI, etc.). |
Example:
| ; Channel examples Print "Loading sound ..." ; Load the sample - you'll need to point this to a sound on your computer ; For best results, make it about 5-10 seconds... sndWave=LoadSound("level1.wav") ; Prepare the sound for looping LoopSound sndWave chnWave=PlaySound(sndWave) Print "Playing sound for 2 seconds ..." Delay 2000 Print "Pausing sound for 2 seconds ..." PauseChannel chnWave Delay 2000 Print "Restarting sound ..." ResumeChannel chnWave Delay 2000 Print "Changing Pitch of sound ..." ;StopChannel chnWave ChannelPitch chnWave, 22000 Delay 2000 Print "Playing new pitched sound ..." Delay 2000 Print "Left speaker only" ChannelPan chnWave,-1 Delay 2000 Print "Right speaker only" ChannelPan chnWave,1 Delay 2000 Print "All done!" StopChannel chnWave |


Account Settings |
|---|
| Username |
|
|
Password |
|
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Email Address |
|
|
Recovery Email: |
|
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Secret Question |
|
Answer:
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Account Status: |
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What is Elathon?
This site, created by Ennio Gualandi, is a site created to facilitate the various daily activities that can also be presented at school. Currently, there are three different sections that perform completely different functions. The first section is that of Random Numbers/Random Choice, the second is the Crypted Notepad while the last currently available is the notes of the various subjects. You can get more information about them by scrolling the page.
Random Generators
Elathon's Random Generator has 2 different generation types. The first type of generation is that of random numbers while the second is the random choice. You can find out more about them by going to the Random Generators section.
Crypted Notepad
Crypted Notepad is like a simple note, you will simply have more privacy options. As soon as the notepad is created, you can choose the date on which other people will be able to view the content or provide a key to access the notepad. If a user enters the link to the notepad page without having the access key or before the release date has arrived, they will only see random letters.
Subjects Notes
Subjects Notes is the section of Elathon where you can find notes on various subjects. The notes were written by students of a scientific high school. If you are behind with school this is the section for you to use wherever and whenever you want.


Hopefully you clicked on the "School Info" section of the image map in order to arrive at this page. Is that correct? If so, press the "BACK" button in the browser bar and try the next portion of the image map!


'Apocalyptic disaster': Photos from Venice, submerged by historic tide
13 Nov 2019, 21:26 by Reuters
Venice was hit by the second highest tide recorded in the lagoon city on Tuesday, which flooded its historic basilica and left many of its squares and alleyways deep underwater.
Mayor Luigi Brugnaro has declared a state of emergency and warned of severe damage.
City officials said the tide peaked at 187 cm at 10.50 p.m. (2150 GMT), just short of the record 194 cm set in 1966.
“The situation is dramatic,” Brugnaro said on Twitter. “We ask the government to help us. The cost will be high. This is the result of climate change.”
Saint Mark’s Square was submerged by more than one meter of water, while the adjacent Saint Mark’s Basilica was flooded for the sixth time in 1,200 years.
The floods, made worse by driving rains and strong winds, ravaged areas beyond the city itself.
“Venice has been tortured, but there are also other parts of the Veneto region besides Venice. It is an apocalyptic disaster,” regional governor Luca Zaia told reporters.
Here are photos of the aftermath:
Artwork by street artist Banksy portrays a migrant child wearing a lifejacket and holding a neon pink flare in Venice after the second-highest tide ever recorded swept through the city on Nov. 13, 2019.MARCO BERTORELLO / AFP via Getty Images
People wade through a flooded St. Mark’s Square after the second-highest tide ever recorded in Venice swept through the city on Nov. 13, 2019.Luca Bruno / The Associated Press
A man cleans in a church after the second-highest tide ever recorded in Venice swept through the city on Nov. 13, 2019.FLAVIO LO SCALZO / REUTERS
A man cleans in a shop in St. Mark’s Square in Venice after the highest tide in more than 50 years on Nov. 12, 2019.FLAVIO LO SCALZO / REUTERS
A firefighter in scuba diver gear wades through floodwaters in Venice after the highest tide in more than 50 years on Nov. 12, 2019.Vigili del Fuoco / via REUTERS
A man walks through Venice after the highest tide in more than 50 years on Nov. 12, 2019.MANUEL SILVESTRI / REUTERS
A man with his dog walks past an art gallery in Venice after the highest tide in more than 50 years on Nov. 12, 2019.MANUEL SILVESTRI / REUTERS
A woman walks in a flooded art gallery in Venice after the highest tide in more than 50 years on Nov. 12, 2019.MANUEL SILVESTRI / REUTERS
Tourists snap photos in a flooded arcade street in Venice after the highest tide in more than 50 years on Nov. 12, 2019.MARCO BERTORELLO / AFP via Getty Images
Pedestrians walk on a footbridge across the flooded Riva degli Schiavoni embankment in Venice after the highest tide in more than 50 years on Nov. 12, 2019.MARCO BERTORELLO / AFP via Getty Images
Pedestrians walk across the flooded St. Mark’s Square in Venice after the highest tide in more than 50 years on Nov. 12, 2019.MARCO BERTORELLO / AFP via Getty Images
People walk on a footbridge across a flooded street in Venice after the highest tide in more than 50 years on Nov. 12, 2019.MARCO BERTORELLO / AFP via Getty Images
A woman crosses the flooded St. Mark’s Square in Venice after the highest tide in more than 50 years on Nov. 12, 2019.MARCO BERTORELLO / AFP via Getty Images
A general view of the flooded embankment by the Hotel Rialto on the Grand Canal channel in Venice after the highest tide in more than 50 years on Nov. 12, 2019.MARCO BERTORELLO / AFP via Getty Images
A man pushes a wheelbarrow on a flooded street in Venice, Italy, on Nov. 12, 2019, in this picture obtained from social media.INSTAGRAM/@AQUAAPARTMENTS/via REUTERS
The flooded crypt of St Mark’s Basilica is pictured during an exceptionally high tide in Venice, Italy, on Nov. 13, 2019.MANUEL SILVESTRI / REUTERS
The Mayor of Venice Luigi Brugnaro walks on flooded St Mark’s Square in Venice on Nov. 13, 2019.MANUEL SILVESTRI / REUTERS
People take photos at the flooded St. Mark’s Square after an exceptional overnight high tide on Nov. 13, 2019 in Venice.MARCO BERTORELLO / AFP via Getty Images
A woman walks her dog through a flooded street after an exceptional overnight high tide on Nov. 13, 2019 in Venice.MARCO BERTORELLO / AFP via Getty Images
A room in the flooded Gritti Palace is pictured during an exceptional “Alta Acqua” high tide water level on Nov. 12, 2019, in Venice, Italy.MARCO BERTORELLO / AFP via Getty Images
A room in the flooded Gritti Palace is pictured during an exceptional “Alta Acqua” high tide water level on Nov. 12, 2019, in Venice, Italy.MARCO BERTORELLO / AFP via Getty Images


INEC polling officer reports bribes to supervisor — Official
by Halimah YahayaAn official of the Independent National Electoral Commission(INEC) has returned N50,000 in alleged bribes for him to compromise results in Igalamela/Odolu Local Government Area, Kogi State.
Evans Azubuike, an associate professor and returning officer for the local government, disclosed the development while announcing results from the LGA on Sunday afternoon.
Mr Azubuike said a supervising presiding officer (SPO) told him that a presiding officer (PO) handed over the money to him.
READ ALSO: KogiDecides: Sporadic shootings as thugs invade Dekina
“The SPO reported to me that the PO reported to him that he was given money to influence the result,” Mr Azubuike said in the broadcast announcement. “He collected the money and the money is N50,000.”
“Then he handed over the money to the SPO, then to the PO and finally to me. I have the exhibit here,” he said before displaying the envelope that allegedly contained the money the audience.
When asked of the money source, Mr Azubuike said the PO has a report and it is indicated that the money was from an unknown person.
Mr Azubuike said there were cases of disruption in five polling units of the local government.


Recap • February 26, 2014
Brian Granger (Cal Poly SLO)
“Compose and share reproducible stories that involve code and data”
Olivier Grisel (Parietal, INRIA)
Machine learning in Python
from INRIA
Hadley Wickham (RStudio)
More awesome R packages from Hadley.
† cassandra-like key/value store, fronted by an API over caches, queues, storage
Increment a counter
Stats.incr(“some_important_counter”)
Store the value of a variable at a particular time
Stats.addGauge("current_temperature") {
myThermometer.temperature
}
Record running time
Stats.time("translation") {
document.translate("de", "en")
}
(service,source(host,process),metric)
Chris Ré (Stanford)
Examples of trained systems:
Avery Ching (Facebook)
“iterative graph processing system”
MLBase is a machine learning libraries that run over Spark (like Mahout/Hadoop)
Monica Rogati (Jawbone)


The Growth of Big Data - Big Data is one of the key forces driving the growth and popularity of NoSQL for business. The almost limitless array of data collection technologies ranging from simple online actions to point of sale systems to GPS tools to smartphones and tablets to sophisticated sensors – and many more – act as force multipliers for data growth.
In fact, one of the first reasons to use NoSQL is because we have a Big Data project to tackle. A Big Data project is normally typified by:
- High data velocity – lots of data coming in very quickly, possibly from different locations.
- Data variety – storage of data that is structured, semi-structured and unstructured.
- Data volume – data that involves many terabytes or petabytes in size.
- Data complexity – data that is stored and managed in different locations or data centers.
| Datamodel | Performance | Scalability | Flexibility | Complexity | Functionality |
|---|---|---|---|---|---|
| Key-value store | High | High | High | None | Variable (None) |
| Column Store | High | High | Loq | Moderate | Low Minimal |
| Document Store | High | Variable | High | Low | Variable |
| Graph Database | Variable | Variable | High | High | Graph Theory |
Cassandra is perfect for managing large amounts of structured, semi-structured, and unstructured data across multiple data centers and the cloud. Cassandra delivers continuous availability, linear scalability, and operational simplicity across many commodity servers with no single point of failure, along with a powerful dynamic data model designed for maximum flexibility and fast response times. Built-for-scale architecture means that it is capable of handling petabytes of information and thousands of concurrent users/operations per second.
An apache Software Foundation project, Cassandra is column oriented database and is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra does not support joins or subqueries. Rather, Cassandra emphasizes denormalization through features like collections.
Each node in a cluster can accept read and write requests, regardless of where the data is actually located in the cluster.
When a node goes down, read/write requests can be served from other nodes in the network.
The key components of Cassandra are as follows:
Node
− It is the place where data is stored.Data center
− It is a collection of related nodes.Cluster
− A cluster is a component that contains one or more data centers.Commit log
− The commit log is a crash-recovery mechanism in Cassandra. Every write operation is written to the commit log.Mem-table
− A mem-table is a memory-resident data structure. After commit log, the data will be written to the mem-table. Sometimes, for a single-column family, there will be multiple mem-tables.SSTable
− It is a disk file to which the data is flushed from the mem-table when its contents reach a threshold value.Bloom filter
− These are nothing but quick, nondeterministic, algorithms for testing whether an element is a member of a set. It is a special kind of cache. Bloom filters are accessed after every query.
Commands:
nodetool cfstats :
displays statistics for each table and keyspace.
nodetool cfhistograms :
provides statistics about a table, including read/write latency, row size, column count, and number of SSTables.
nodetool netstats :
provides statistics about network operations and connections.
nodetool tpstats :
provides statistics about the number of active, pending, and completed tasks for each stage of Cassandra operations by thread pool.
nodetool status :
cqlsh machine_ip
- connects to the machine cqlsh
cqlsh command list:
HELP
- Displays help topics for all cqlsh commands.
CAPTURE
- Captures the output of a command and adds it to a file.
CONSISTENCY
- Shows the current consistency level, or sets a new consistency level.
COPY
- Copies data to and from Cassandra.
DESCRIBE
- Describes the current cluster of Cassandra and its objects.
EXPAND
- Expands the output of a query vertically.
EXIT
- Using this command, you can terminate cqlsh.
PAGING
- Enables or disables query paging.
SHOW
- Displays the details of current cqlsh session such as Cassandra version, host, or data type assumptions.
SOURCE -
Executes a file that contains CQL statements.
TRACING -
Enables or disables request tracing.
Upgrading:
To upgrade an existing cassandra installation, you can follow the below instructions:
mkdir ~/cassandra_backup
sudo cp -r /etc/cassandra/* ~/cassandra_backup
sudo vi /etc/cassandra/cassandra.yaml
and edit num_tokens to 1 and uncomment the initial_token and set it to 1
nodetool upgradesstables
nodetool drain
sudo service cassandra stop
sudo cp -r /etc/cassandra/* ~/cassandra_backup_new
sudo apt-get install cassandra=2.1.12
Configuration file '/etc/cassandra/cassandra.yaml' ==> Modified (by you or by a script) since installation. ==> Package distributor has shipped an updated version. What would you like to do about it ? Your options are: Y or I : install the package maintainer's version N or O : keep your currently-installed version D : show the differences between the versions Z : start a shell to examine the situation The default action is to keep your current version. *** cassandra.yaml (Y/I/N/O/D/Z) [default=N] ?
Inserting values into tables
CREATE KEYSPACE key_space WITH replication = {
'class': 'NetworkTopologyStrategy',
'cdr_record': '2'
};
INSERT INTO key_space.emp (emp_id,emp_city,emp_name,emp_phone,emp_sal) VALUES(3,'Kolkata','Stag1',4412,60);
UPDATE TABLE emp(
emp_id int PRIMARY KEY,
emp_name text,
emp_city text,
emp_sal varint,
emp_phone varint);
INSERT INTO TABLE emp(emp_id int,emp_name,emp_city,emp_sal,emp_phone) VALUES(1,'foo','Bangalore',24,1234567);
Nodetool Command Set:
1.nodetool status
2.
nodetool info
3.
nodetool -host <IP_ADDRESS> ring


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Competition for LibreOffice?
OnlyOffice, the Open Source Office Suite is Now Available on Flathub
by By Joey SneddonBig fan of productivity software? If so, you may be interested to know that the OnlyOffice Desktop Editors are now available on Flathub.
Yes, Flathub, aka the de facto app store for Flatpak, the cross-distro containerised app distribution method.
OnlyOffice Desktop Editors — note: the suite is technically styled “ONLYOFFICE Desktop Editors” but I refuse to shout 😆 — is a free, open source set of office apps inspired (partly) by the look, feel and feature set of Microsoft Office.
The suite “…combines text, spreadsheet and presentation editors allowing to create, view and edit documents […] without an Internet connection. It is fully compatible with Office Open XML formats: .docx, .xlsx, .pptx,” according to the Flathub app store listing page.
It’s not a Flathub exclusive though; users can still download OnlyOffice Linux builds from OnlyOffice website in a variety of formats, and the suite is available as on the Snapcraft.io Snap store too.
There are also official builds for macOS and Windows platforms, plus an ‘enterprise’ option and support for integrating with cloud storage platforms like NextCloud.
But in adding itself to the shelves of the Flathub store, OnlyOffice can now reach more users, particularly on Linux distributions that support Flathub out of the box, like Linux Mint 19 does.
If you’re keen to try it out for yourself you can learn more on the Flathub store:


Proof of Concept
Progressive Web App
By Hui Yee Tan
Progressive Web App (PWA)
- Use modern web capabilities to deliver fast, engaging and reliable mobile web experience
- Neutral to most operating system but dependent to the browser used as it is website based
- Only runs over HTTPS or localhost to prevent man-in-the-middle attack
Features
- Work Offline
- Response relatively faster by serving pages from cache storage
- Use device features such as Microphone, Camera, Location and Background Sync
- Send notifications to subscribers (not supported by iOS)
Web App Manifest
A simple JSON file that tells the browser about your web application and how it should behave when installed
Introducing Service Worker
Service Worker
- Service Worker manages how network requests from and to your application are handled
- Runs separately from main browser thread in the background, and independent to your web application
- Enhance user experience
Lifecycle of the service worker
Example of Serving strategies
- Cache, falling back to network
- Network, falling back to cache
- Cache & network race
- Cache then network
- Generic fallback
Business Benefits of PWA
- Enhance user experience
- Reduce network traffic from and to the server
- App can be installed by user, and hence increases user interactions
Limitations
- You hardly knows if the installation of serviceworker fails unless you go to application tab in dev tools
- PWA cannot access many features on devices
- Cannot rely on the app's global state persisting between events
- IndexedDB and localStorage has size limits
Storage Limits
Challenges


--- layout: page title: About header-img: "img/about-bg.png" ---
I will be making posts regarding my progression of programming knowledge


Welcome to
GrapesJS Code Editor
This is a demo content from index.html. For the development, you shouldn't edit this file, instead you can
copy and rename it to _index.html, on next server start the new file will be served, and it will be ignored by
git.


I have been working on a from scratch (minus a math libaray) neural network framework written in C# named Neural.NET. My goal has been to not only use this as a way to better understand the inner workins of neural networks and machine learning in general, but to make it more accessible to developers. I helped my brother with a Python neural network for one of his classes and have been interested ever since.
I have experience with TensorFlow and used their library when they first open sources it back in November of 2015. While it is extremely powerful and fairly easy to use, it definitely requires a decent understanding of how neural networks work. I will go over my implementation in later posts, as that should be enough backstore for now.
While working through version 2, where I will add convolutional network support, I came across a method I haven't seen in most expainations of convolutional neural networks that allows you to do all the convolutions for a given layer, regardless of how many input dimenstions or kernels you have in a layer, in a single matrix matrix multiplication. While this may require a larger chunk of memory at a given time, computationally I feel this is a major improvement. Especially when I start using my network with CUDA processing, most APIs require the object to be copied to the GPU, then the computation can be done, then it needs to be copied back. If you condense all of the operations to one large operation, that should greatly reduce the amount of time CUDA operations copy and instead focus on one large operation to speed up a network.
Most people imagine a convolutional layer as having many kernels that slide across an image. The few bits of pseudo code that is shown usually involves actually figuring out what the kernel needs to be looking at, and moving it by the stride side, and repeating. They then say this is repeated for the number of kernels you have in that layer. When talking through this with my brother, he asked if there was any way to do this in bulk.
It was then that it dawned on me that a kernel convolution is idential to a vector vector multiplication. If you then can somehow calculate what pixels the kernel will convolve over, you can line those up as columns and you can do a vector matrix multiplication of a filter over an entire image. Each column of the matrix is the patch of pixels the kernel is colvolving over. You can flatten a kernel (say 5x5) into a vector (in this case length 25) by wrapping each row onto the row above it. At the end of this vector vector multiplication you have a row of pixels that is a 1D representation of the image with the filter passed over each patch.
We can still maintain spacial data, since you can construct the transformed image (mask as I refer to it later) that the filter multiplies with so the first row get processed first, then the second, and so on. You can easily put the image back into a 2D form by taking the square root (assuming square images) of the length of the resulting row and 'chopping' the image at each interval of that number. You will then have a square image that is identical to what you would have gotten by sliding the kernel over the image as most illustrations show.
In addition to this, you might easily be able to tell how you can add in all of the other filters into this operation. Instead of a vector matrix multiplication where the vector is the filter and the matrix is the 'pre convolution mask', you can stack more filters with the original filter and now you end up with a matrix matrix multiplication. Each row in the first matrix is a flattened kernel, and each column in the second matrix is the data patch that will be convolved. You will then end up with a matrix as output, where each row is a dimension of the image you can pass to another layer (be it ReLU, pooling, another convolution, or a fully connected).
In part 2 I will go over more of my code and the actual implementation details.


Thank you for taking your time to help me to improve the platform
Would you recommend the platform to a friend?
What would you like to see improved? (Check all that apply)
Any comments or suggestions?


Programs, regardless of how complex, need an instance of the manager class. It’s the starting point for adding communication channels, tapping into the logging sub-system, etc. The manager class is given a number when it is constructed. This parameter tells the stack how many threads to allocate to its thread pool. Even though the underlying stack is completely asynchronous and uses all non-blocking calls, the thread pool allows the stack to scale up and stay responsive. Even when a callback to users code is slow, the other connections keep on doing work. Here are the simplest possible programs that instantiate a manager instance. The rest of the tutorials will assume you have this basic infrastructure.
// C++11
#include <opendnp3/DNP3Manager.h>
#include <thread>
using namespace opendnp3;
int main(int argc, char* argv[])
{
// hardware_concurrency() gives hint of number of hardware thread contexts
DNP3Manager manager(std::thread::hardware_concurrency());
}
// Java
import com.automatak.dnp3.DNP3Manager;
import com.automatak.dnp3.impl.DNP3ManagerFactory;
public class Main
{
public static void main(String[] args)
{
DNP3Manager manager = DNP3ManagerFactory.createManager(
Runtime.getRuntime().availableProcessors()
);
manager.shutdown();
}
}
// C#
using DNP3.Adapter;
using DNP3.Interface;
class Main
{
static void Main(string[] args)
{
var manager = DNP3ManagerFactory.CreateManager(
Environment.ProcessorCount
);
}
}


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Let us admit it people…We all love Chapati madondo! In fact, I always feel some sense of betrayal when I have chapati with some other kind of sauce, say Beef stew for example rather than with madondo. Chapati and madondo (beans) belong together,as they somehow just complement each other taste wise. Chapati is flat bread made from all purpose flour, while madondo is minimally stewed beans. This dish here i’d say is the heart of Kenya, and still remains to be the most popular meal.
On the recipe today, I tried to make it as basic as possible exactly the way it was inherently prepared back home. You can however adjust it to your preference and still enjoy your chapati madondo.


Will the following statement work?
DataOutputStream dataOut = new DataOutputStream( "myFile.dat" );
No. You need a FileOutputStream
to connect to
a disk file.
A DataOutputStream
is used to write bytes representing primitive data types.
It writes to another output stream
(such as a BufferedOutputStream
or FileOutputStream
).
A DataOutputStream
ensures portability
because it uses the same data format on all computer platforms.
For example writeInt()
writes the four bytes of an int
in the same order (high byte to low byte) on all computers.
Constructor
public DataOutputStream(OutputStream out) Construct a data output stream.
Methods
public void flush() throws IOException Flushes the stream. public final int size() Return the number of bytes written so far. public void write(int b) throws IOException Writes the low eight bits of the argument. public final void writeBoolean(boolean b) throws IOException Writes a boolean as a 1-byte value. True is written as 00000001, false is written as 00000000. public final void writeByte(int b) throws IOException public void writeBytes(String s) throws IOException Writes the low eight bits of each character in the string. public final void writeChar(int c) throws IOException public void writeDouble(double v) throws IOException public void writeFloat(float f) throws IOException public void writeInt(int i) throws IOException public void writeLong(long l) throws IOException public final void writeShort(int s) throws IOException
Bug Alert: The argument to writeByte()
is an int
.
The low eight bits of the argument are written to the stream.
The same is true for writeChar
.


We are going to be in the hotel, night and day. That is why, we have chosen the AlpHoliday Dolomiti Wellness & Fun Hotel in Dimaro (Trento) as our location for the unconference.
Since one of the important aspects of SoCraTes is being close to the “action”, everything will happen within
the location: lodging, eating and mingling. As a result, we can only accept as many participants as the
number of available beds.
The unconference’s beverages will be covered by the sponsors.


| fix.family.link {mgcv} | R Documentation |
Generalized Additive Model fitting by ‘outer’ iteration, requires extra derivatives of the variance and link functions to be added to family objects. The first 3 functions add what is needed. Model checking can be aided by adding quantile and random deviate generating functions to the family. The final two functions do this.
fix.family.link(fam) fix.family.var(fam) fix.family.ls(fam) fix.family.qf(fam) fix.family.rd(fam)
fam |
A |
Consider the first 3 function first.
Outer iteration GAM estimation requires derivatives of the GCV, UBRE/gAIC, GACV, REML or ML score, which are obtained by finding the derivatives of the model coefficients w.r.t. the log smoothing parameters, using the implicit function theorem. The expressions for the derivatives require the second and third derivatives of the link w.r.t. the mean (and the 4th derivatives if Fisher scoring is not used). Also required are the first and second derivatives of the variance function w.r.t. the mean (plus the third derivative if Fisher scoring is not used). Finally REML or ML estimation of smoothing parameters requires the log saturated likelihood and its first two derivatives w.r.t. the scale parameter. These functions add functions evaluating these quantities to a family.
If the family already has functions dvar
, d2var
, d3var
, d2link
,
d3link
, d4link
and for RE/ML ls
, then these functions simply
return the family unmodified: this allows non-standard links
to be used with gam
when using outer iteration (performance
iteration operates with unmodified families). Note that if you only need Fisher scoring then
d4link
and d3var
can be dummy, as they are ignored. Similalry ls
is only needed for
RE/ML.
The dvar
function is a function of a mean vector, mu
, and returns
a vector of corresponding first derivatives of the family variance
function. The d2link
function is also a function of a vector of mean
values, mu
: it returns a vector of second derivatives of the link,
evaluated at mu
. Higher derivatives are defined similarly.
If modifying your own family, note that you can often get away with supplying
only a dvar
and d2var
, function if your family only requires links that occur in
one of the standard families.
The second two functions are useful for investigating the distribution of residuals and are used by
qq.gam
. If possible the functions add quantile (qf
) or random deviate (rd
) generating functions
to the family. If a family already has qf
or rd
functions then it is left unmodified. qf
functions are only available for some families, and for quasi families
neither type of function is available.
A family object with extra component functions dvar
,
d2var
, d2link
, d3link
, d4link
, ls
, and possibly qf
and rd
,
depending on which functions are called. fix.family.var
also adds a variable scale
set to
negative to indicate that family has a free scale parameter.
Simon N. Wood simon.wood@r-project.org


Create & change seasons according to your needs. Only the active season can be seen and edited by members. Only desactivated seasons can be permanently deleted!
| Name | First pickup | Last pickup | Number of pickups | Pickup options | Options |
|---|---|---|---|---|---|
| Current {{season.name}} | {{$ctrl.getDateForInterval(season, 0) | date:'yyyy.MM.dd'}} | {{$ctrl.getDateForInterval(season, season.numberOfEvents-1) | date:'yyyy.MM.dd'}} | {{season.numberOfEvents}} | {{season.activePickupOptions.length}} | |


Watch the trailer for terrifying remake of ‘The Invisible Man’
The horror hits cinemas next year
The trailer for the 2020 remake of The Invisible Man has been shared.
The adaptation of the 1897 HG Wells novel, which was given its first iteration on the big screen in 1933, will star Oliver Jackson-Cohen (the Invisible Man) and Elisabeth Moss.
The dark and dangerous new trailer comes with a tagline: “What you can’t see can hurt you” – see it below.
The Invisible Man revolves around Cecilia Kass (played by Moss) who escapes an abusive relationship and disappears into the dead of night before her ex comes back to haunt her.
The synopsis for the film reads: “Trapped in a violent, controlling relationship with a wealthy and brilliant scientist, Cecilia Kass (Moss) escapes in the dead of night and disappears into hiding, aided by her sister (Harriet Dyer), their childhood friend (Aldis Hodge, Hidden Figures) and his teenage daughter (Storm Reid).
“But when Cecilia’s abusive ex (Oliver Jackson-Cohen) commits suicide and leaves her a generous portion of his vast fortune, Cecilia suspects his death was a hoax.
“As a series of eerie coincidences turns lethal, threatening the lives of those she loves, Cecilia’s sanity begins to unravel as she desperately tries to prove that she is being hunted by someone nobody can see.”
The lead role in the new film was initially set to be given to Johnny Depp, who was being lined up for the role back in 2016.
Director Leigh Whennell (Saw) told EW of his ideas for the film, saying: “I wanted to kind of get away from [the original version] and make something that was really modern, really grounded, or as grounded as you can be when you’re dealing with a film called The Invisible Man.”
The Invisible Man comes to cinemas on February 28, 2020.


USL could bring soccer club to Buffalo, NY
BUFFALO, N.Y. (AP) - The United Soccer League has reached an agreement that could bring a Division Two soccer franchise to Buffalo, New York in 2023.
The league says the move is dependent on the construction of a soccer-specific stadium and completion of economic feasibility studies.
The co-owner of soccer club FC Buffalo, Nick Mendola, says the potential in a city that “loves soccer” is “through the roof.”
Connecticut real estate developer John McClutchy, who’s working with a local developer, said they have four sites in mind for the 10,000-seat stadium. All are in central Buffalo or close to downtown.
Mayor Byron Brown said the city his working with developers to help find a suitable location.
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Html can be inserted with UI.Html(ressourceId).
UI.Html(ressourceId)
Of course the html build action must be EmbeddedRessource and have a unique ressource id.


Traditional Offerings
Reg D
Federal law only allows Accredited Investors to legally invest in startups using Regulation D 506. Accredited Investors have no limits.
Accredited investors:
- earn more than $200,000 per year, or a joint income of $300,000, in the last two years and expect the same this year.
- or, have a net worth exceeding $1 million not including their home
Reg A+
Non-accredited investors may legally invest no more than 10% of their income or net worth - whichever is greater. Accredited investors (those earning $200k+ or with net worth over $1m) have no investing limit.
Crowdfunded Offerings
Regulation Crowdfunding became legal on May 2016. The SEC really made the investment limits super complicated.
- Everyone can invest at least $2,200
- If either your net worth or income are below $107k, you may legally invest a maximum of 5% of the lesser number.
- If both your net worth or income are above $107k, you may legally invest a maximum of 10% of the lesser number.
- No one may invest more than $107,000. Further, accredited investors are subject to the same investment limitations as everyone else, no matter how silly that is.
- Investment limits are for every 12 month period. Every other investment in a Regulation Crowdfunding offering counts towards the annual limit.


jmlr jmlr2013 jmlr2013-7 jmlr2013-7-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Paramveer S. Dhillon, Dean P. Foster, Sham M. Kakade, Lyle H. Ungar
Abstract: We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a ﬁnite dimensional subspace (as speciﬁed by a principal component analysis) and then performs an ordinary (un-regularized) least squares regression in this subspace. This note shows that the risk of this ordinary least squares method (PCA-OLS) is within a constant factor (namely 4) of the risk of ridge regression (RR). Keywords: risk inﬂation, ridge regression, pca
D. P. Foster and E. I. George. The risk inﬂation criterion for multiple regression. The Annals of Statistics, pages 1947–1975, 1994. A. N. Tikhonov. Solution of incorrectly formulated problems and the regularization method. Soviet Math Dokl 4, pages 501–504, 1963. 1511


Tanker fire, Singapore Strait UPDATE
by NIKOLAY TORKINFire erupted on board of product tanker HOYU on Nov 7 near Horsburgh Lighthouse, eastern Singapore Strait. The ship woth cargo of oil products was en route from Singapore to Philippines. Fire according to MPA Singapore statement, was extinguished, crew evacuated. Last AIS received at around 0530 UTC Nov 7, when tanker was under way full speed near lighthouse.
Nov 8 Update: judging from photo published by channelnewsasia.com, fire broke out in superstructure, engulfing the bridge.
Product tanker HOYU, IMO 9511105, dwt 8925, built 2010, flag Panama, manager SAMPO UNYU CO LTD, Japan.


Frequency (MHz)
dBm / Hz
dBm
nV / rtHz
0
1
DDS Frequency (MHz)
| OUT 0 | ||
| OUT 1 |
File
Laser
| Power (µW) |


6 Boks in Barbarians starting XV to face Fiji
Cape Town - There are six Springboks in the Barbarians starting XV for Saturday's match against Fiji at Twickenham.
World Cup winner Makazole Mapimpi features at left wing, while three Boks who were not part of the World Cup party - Curwin Bosch (flyhalf) Andre Esterhuizen (centre) and Dillyn Leyds (wing) - also feature in the backline.
Up front, World Cup winner Tendai 'Beast' Mtawarira starts at loosehead prop, while Marco van Staden is another Springbok starting at flank.
Coach Eddie Jones has picked Irish veteran Rory Best as captain in what will be the hooker's last international.
There are three more Springboks on the bench - scrumhalf Jano Vermaak, centre Lukhanyo Am and flyhalf Morne Steyn.
South African-born Josh Strauss, who plays for Scotland, also starts at No 8, while Hencus van Wyk, a former Bulls prop currently playing in Japan, is on the bench.
Kick-off for Saturday's clash is scheduled for 16:30 (SA time).
The BaaBaas will also face Brazil in Sao Paolo (November 20) and Wales in Cardiff (November 30).
Teams:
Barbarians
15 David Havili, 14 Dillyn Leyds, 13 Mathieu Bastareaud, 12 Andre Esterhuizen, 11 Makazole Mapimpi, 10 Curwin Bosch, 9 Joe Powell, 8 Josh Strauss, 7 Marco van Staden, 6 Pete Samu, 5 Tyler Ardron, 4 Luke Jones, 3 Enrique Pieretto, 2 Rory Best (captain), 1 Tendai Mtawarira
Substitutes: 16 Andrew Makalio, 17 Campese Maafu, 18 Hencus van Wyk, 19 Angus Cotrell, 20 Matt Philip, 21 Jano Vermaak, 22 Lukhanyo Am, 23 Morne Steyn
Fiji
TBA
- Compiled by Herman Mostert


| Completion Design Parameters |
| Chemical |
Max Design |
Notes |
| Slurry Rate (bbl/min) |
38.00 |
|
| Prop Conc (lb/gal) |
4.00 |
7 ramps |
| FR Conc (gal/1000) |
0.40 |
|
| Biocide Conc (gal/1000) |
0.20 |
|
| Surfac. Conc (gal/1000) |
2.00 |
|
| Clay St. Conc (gal/1000) |
1.00 |
|
| Max Pressure (psi) |
10,000 |
80% safety factor |
| Threshold- Pressure (%) |
10% |
|
| Threshold- Chems (%) |
20% |
|
|
| Casing |
Top Depth |
Bot. Depth |
OD |
ID |
Wt |
Grade |
| Surface |
23.5 |
230.3 |
13 3/8 |
12.615 |
54.5 |
J-55 |
| Intermed |
23.5 |
3461.0 |
9 5/8 |
8.835 |
36 |
J-55 |
| Prod. |
23.5 |
18759.9.0 |
5 1/2 |
4892 |
17 |
P110RY |
| Geological Feature |
Depth |
Notes |
| Fault 1 |
18,400.00 |
Fracturing expected |
| Fault 2 |
15,354.00 |
|
| Out of Zone |
15,300.00 |
Ductile shale, possible scrnout |
| |
|
|
|


Michael Hammond's Website
CIS Security Controls - Guidelines
OS Hardening Benchmarks
Security Vulnerability Database
Web Vectors by Vecteezy


RT @nathancarnes: If anyone at #ROA4 is interested in homebrewing and wants a nerdier book, may I recommend Designing Great Beers?
cc @jas…
3/6/2014, 3:43:26 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
RT @nathancarnes: If anyone at #ROA4 is interested in homebrewing and wants a nerdier book, may I recommend Designing Great Beers?
cc @jas…
3/6/2014, 3:43:26 PM
Favs: 0
Retweets: 0
link


Me and my friends created a volleyball team. A fun and healthy sport.
I published two app (SamRobot and GX Password).
Participated of Hacktoberfest 2016.


This is a limited-reward-limited-risk strategy. The payoff is
$$ \begin{array}{rcll} P^{OTM}_T & = & (K^{OTM} - S_T)^{+}\\ P^{ITM}_T & = & (K^{ITM} - S_T)^{+}\\ P_T & = & (P^{OTM}_T - P^{ITM}_T + P^{ITM}_0 - P^{OTM}_0)\times m - fee\\ \end{array} $$ $$ \begin{array}{rcll} \textrm{where} & P^{OTM}_T & = & \textrm{OTM put value at time T}\\ & P^{ITM}_T & = & \textrm{ITM put value at time T}\\ & S_T & = & \textrm{Underlying asset price at time T}\\ & K^{OTM} & = & \textrm{OTM put strike price}\\ & K^{ITM} & = & \textrm{ITM put strike price}\\ & P_T & = & \textrm{Payout total at time T}\\ & P^{ITM}_0 & = & \textrm{ITM put value at position opening (credit received)}\\ & P^{OTM}_0 & = & \textrm{OTM put value at position opening (debit paid)}\\ & m & = & \textrm{Contract multiplier}\\ & T & = & \textrm{Time of expiration} \end{array} $$The following chart shows the payoff at expiration:
The maximum profit is the net credit you received when opening the position, $P^{ITM}_0 - P^{OTM}_0$. If the underlying price is higher than the strike prices of both put contracts at expiration, both puts expire worthless.
The maximum loss is $K^{ITM} - K^{OTM} + P^{ITM}_0 - P^{OTM}_0$.
If the Option is American Option, there is a risk of early assignment on the sold contract.