Kiuas
Finnish heavy metal band from Espoo, Finland
Before the formation of Kiuas, three musicians (Markku Näreneva, Teemu
Tuominen and Ilja Jalkanen) played in a band known as Iconofear, which
plays a genre of music known as dark heavy metal. Iconofear had formed
after the break-up of a blackened death metal band known as Agonia.
They released a single EP before those three musicians left the band
to concentrate on Kiuas. Kiuas was formed in 2000 when the four
original members (Mikko Salovaara, Markku Näreneva, Teemu Tuominen and
Atte Tanskanen) found a lead singer, Ilja Jalkanen.
In 2002, funded only by themselves, they released their first demo
known as The Discipline of Steel, that featured four songs, and was
rooted deep in the mysticism and importance of steel, and its effect
on Finnish culture.
In 2003, they successfully released Born
Under the Northern Lights, and soon had many offers for record deals.
With fans on their side, and a deal from Rage of Achilles, they signed
the contract.
In 2004, Kiuas's first label-aided release was
completed. Winter in June (MCD EP) was released in Finland and was
well received in the metal and hard rock community. Their ability to
combine extreme subgenres of metal with hard rock and classical guitar
have led them to become a recognizable force.
Later that year, Rage of Achilles reported that they were going out of
business due to the fact they could not support themselves due to
belonging to so many record deals in such short amount of time. For
this reason, Kiuas, and their co-workers, and associated acts; Amoral,
Elenium, Omnium Gatherum, Manitou were cast out without a label, and
some without any official release. A few months later, an offer came
from the major-label Spinefarm Records.
In 2005, they completed and released their first album, The Spirit of
Ukko, which was based on the Pagan gods, primarily Ukko, hence the
name. In May, 2006 their second album Reformation was released. It is
generally more structured and technical, while keeping the signature
sound of Kiuas.
Their third studio album, The New Dark Age, was released on March 12,
2008 in Finland with other dates to be announced. This album was a
slight departure from their previous, as it adopted a much heavier,
more serious sound. Their new single, "Of Sacrifice, Loss and Reward",
was released on February 20, 2008.
Kiuas also supported Children of Bodom in their show at London's
Astoria alongside another Finnish folk metal act, Moonsorrow. Kiuas
also performed with Serenity Dies and Nothnegal on 5 September 2008 at
Alimas Carnival, Malé, Maldives. The show was called "Revolution
begins". Vocalist Ilja Jalkanen stated in an interview that guitarist
and primary songwriter Mikko Salovaara wrote material for a follow up
to 2008's The New Dark Age in Summer 2009. The album was released
March 31, 2010 and is titled "Lustdriven."
On October 27, Kiuas announced the departure of singer Ilja Jalkanen.
In Ilja’s own words: “My heart is not in heavy metal anymore, and a
great band like Kiuas deserves a singer completely dedicated to the
music.”
On January 16, 2011, at Nosturi, Helsinki, stand-in
vocalist Asim Searah was officially inaugurated as the new frontman of
Kiuas.
On September 11, 2012, Kiuas announced that they had fired their new
vocalist, Asim Searah.
On August 29, 2013, Kiuas officially
announced they were disbanding after a final show in Helsinki on
October 18 of the same year.
On February 24, 2021, Kiuas
announced that it has been reformed and are planning on releasing new
music and playing gigs during 2021. The reformed lineup consists of
the original members of the band: Tuominen, Näreneva, Salovaara,
Tanskanen and Jalkanen


The Slick Akka Http is a very simple json rest api showing one way of using akka http with slick 3 library for database access.
It supports the following features:
Utils: Typesafe config for property management and Typesafe Scala Logging (LazyLogging)
The project was thought to be used as a seed for creating akka-http projects with slick, so the implementation is minimal. The model is very simple, is a supplier with a name and description.
The rest api has a get and a post (json), a get for get all the suppliers, and a post to add suppliers. You should view this info in swagger with more detail.
For running the project go to section run, and run it.
For running the project in sbt:
$ sbt
> run
Models as case classes using slick-repo to have generic repos
This seed uses cake pattern to add the possibility to use alternative implementations of some modules, for instance, in tests, the seed uses an alternative implementation for persistence.
The seed are organized in modules: ActorModule, ConfigurationModule and PersistenceModule. When we are implementing a class/actor that uses some of this dependencies, we just mix them with the modules, or put a parameter that receives the injected modules, for instance:
class RoutesActor(modules: Configuration with PersistenceModule)


- Lazy Image Loading. The images will not load when the page is opened but only when you navigate to the slide. Also, you can choose to preload the nearby images.
- Multiple Navigation Modes. You can navigate throught the slides using the slider's controls but also using the keyboard (left and right arrow buttons). The thumbnail scroller also supports mouse scroll and mouse wheel navigation.


Classifying MNIST digits using Logistic Regression¶
Note
This sections assumes familiarity with the following Theano concepts: shared variables , basic arithmetic ops , T.grad , floatX. If you intend to run the code on GPU also read GPU.
Note
The code for this section is available for download here.
In this section, we show how Theano can be used to implement the most basic classifier: the logistic regression. We start off with a quick primer of the model, which serves both as a refresher but also to anchor the notation and show how mathematical expressions are mapped onto Theano graphs.
In the deepest of machine learning traditions, this tutorial will tackle the exciting problem of MNIST digit classification.
The Model¶
Logistic regression is a probabilistic, linear classifier. It is parametrized by a weight matrix W and a bias vector b. Classification is done by projecting an input vector onto a set of hyperplanes, each of which corresponds to a class. The distance from the input to a hyperplane reflects the probability that the input is a member of the corresponding class.
Mathematically, the probability that an input vector x is a member of a class i, a value of a stochastic variable Y, can be written as:
P(Y=i|x, W,b) &= softmax_i(W x + b) \\ &= \frac {e^{W_i x + b_i}} {\sum_j e^{W_j x + b_j}}
The model’s prediction y_{pred} is the class whose probability is maximal, specifically:
y_{pred} = {\rm argmax}_i P(Y=i|x,W,b)
The code to do this in Theano is the following:
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
self.W = theano.shared(
value=numpy.zeros(
(n_in, n_out),
dtype=theano.config.floatX
),
name='W',
borrow=True
)
# initialize the biases b as a vector of n_out 0s
self.b = theano.shared(
value=numpy.zeros(
(n_out,),
dtype=theano.config.floatX
),
name='b',
borrow=True
)
# symbolic expression for computing the matrix of class-membership
# probabilities
# Where:
# W is a matrix where column-k represent the separation hyperplane for
# class-k
# x is a matrix where row-j represents input training sample-j
# b is a vector where element-k represent the free parameter of
# hyperplane-k
self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)
# symbolic description of how to compute prediction as class whose
# probability is maximal
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
Since the parameters of the model must maintain a persistent state throughout
training, we allocate shared variables for W,b. This declares them both
as being symbolic Theano variables, but also initializes their contents. The
dot and softmax operators are then used to compute the vector P(Y|x,
W,b). The result p_y_given_x
is a symbolic variable of vector-type.
To get the actual model prediction, we can use the T.argmax
operator, which
will return the index at which p_y_given_x
is maximal (i.e. the class with
maximum probability).
Now of course, the model we have defined so far does not do anything useful yet, since its parameters are still in their initial state. The following section will thus cover how to learn the optimal parameters.
Note
For a complete list of Theano ops, see: list of ops
Defining a Loss Function¶
Learning optimal model parameters involves minimizing a loss function. In the case of multi-class logistic regression, it is very common to use the negative log-likelihood as the loss. This is equivalent to maximizing the likelihood of the data set \cal{D} under the model parameterized by \theta. Let us first start by defining the likelihood \cal{L} and loss \ell:
\mathcal{L} (\theta=\{W,b\}, \mathcal{D}) = \sum_{i=0}^{|\mathcal{D}|} \log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\ \ell (\theta=\{W,b\}, \mathcal{D}) = - \mathcal{L} (\theta=\{W,b\}, \mathcal{D})
While entire books are dedicated to the topic of minimization, gradient descent is by far the simplest method for minimizing arbitrary non-linear functions. This tutorial will use the method of stochastic gradient method with mini-batches (MSGD). See Stochastic Gradient Descent for more details.
The following Theano code defines the (symbolic) loss for a given minibatch:
# y.shape[0] is (symbolically) the number of rows in y, i.e.,
# number of examples (call it n) in the minibatch
# T.arange(y.shape[0]) is a symbolic vector which will contain
# [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of
# Log-Probabilities (call it LP) with one row per example and
# one column per class LP[T.arange(y.shape[0]),y] is a vector
# v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,
# LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is
# the mean (across minibatch examples) of the elements in v,
# i.e., the mean log-likelihood across the minibatch.
return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
Note
Even though the loss is formally defined as the sum, over the data set,
of individual error terms, in practice, we use the mean (T.mean
)
in the code. This allows for the learning rate choice to be less dependent
of the minibatch size.
Creating a LogisticRegression class¶
We now have all the tools we need to define a LogisticRegression
class, which
encapsulates the basic behaviour of logistic regression. The code is very
similar to what we have covered so far, and should be self explanatory.
class LogisticRegression(object):
"""Multi-class Logistic Regression Class
The logistic regression is fully described by a weight matrix :math:`W`
and bias vector :math:`b`. Classification is done by projecting data
points onto a set of hyperplanes, the distance to which is used to
determine a class membership probability.
"""
def __init__(self, input, n_in, n_out):
""" Initialize the parameters of the logistic regression
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
# start-snippet-1
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
self.W = theano.shared(
value=numpy.zeros(
(n_in, n_out),
dtype=theano.config.floatX
),
name='W',
borrow=True
)
# initialize the biases b as a vector of n_out 0s
self.b = theano.shared(
value=numpy.zeros(
(n_out,),
dtype=theano.config.floatX
),
name='b',
borrow=True
)
# symbolic expression for computing the matrix of class-membership
# probabilities
# Where:
# W is a matrix where column-k represent the separation hyperplane for
# class-k
# x is a matrix where row-j represents input training sample-j
# b is a vector where element-k represent the free parameter of
# hyperplane-k
self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)
# symbolic description of how to compute prediction as class whose
# probability is maximal
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
# end-snippet-1
# parameters of the model
self.params = [self.W, self.b]
# keep track of model input
self.input = input
def negative_log_likelihood(self, y):
"""Return the mean of the negative log-likelihood of the prediction
of this model under a given target distribution.
.. math::
\frac{1}{|\mathcal{D}|} \mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
\frac{1}{|\mathcal{D}|} \sum_{i=0}^{|\mathcal{D}|}
\log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
\ell (\theta=\{W,b\}, \mathcal{D})
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
Note: we use the mean instead of the sum so that
the learning rate is less dependent on the batch size
"""
# start-snippet-2
# y.shape[0] is (symbolically) the number of rows in y, i.e.,
# number of examples (call it n) in the minibatch
# T.arange(y.shape[0]) is a symbolic vector which will contain
# [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of
# Log-Probabilities (call it LP) with one row per example and
# one column per class LP[T.arange(y.shape[0]),y] is a vector
# v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,
# LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is
# the mean (across minibatch examples) of the elements in v,
# i.e., the mean log-likelihood across the minibatch.
return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
# end-snippet-2
def errors(self, y):
"""Return a float representing the number of errors in the minibatch
over the total number of examples of the minibatch ; zero one
loss over the size of the minibatch
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
"""
# check if y has same dimension of y_pred
if y.ndim != self.y_pred.ndim:
raise TypeError(
'y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', self.y_pred.type)
)
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
return T.mean(T.neq(self.y_pred, y))
else:
raise NotImplementedError()
We instantiate this class as follows:
# generate symbolic variables for input (x and y represent a
# minibatch)
x = T.matrix('x') # data, presented as rasterized images
y = T.ivector('y') # labels, presented as 1D vector of [int] labels
# construct the logistic regression class
# Each MNIST image has size 28*28
classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10)
We start by allocating symbolic variables for the training inputs x and
their corresponding classes y. Note that x
and y
are defined
outside the scope of the LogisticRegression
object. Since the class
requires the input to build its graph, it is passed as a parameter of the
__init__
function. This is useful in case you want to connect instances of
such classes to form a deep network. The output of one layer can be passed as
the input of the layer above. (This tutorial does not build a multi-layer
network, but this code will be reused in future tutorials that do.)
Finally, we define a (symbolic) cost
variable to minimize, using the instance
method classifier.negative_log_likelihood
.
# the cost we minimize during training is the negative log likelihood of
# the model in symbolic format
cost = classifier.negative_log_likelihood(y)
Note that x
is an implicit symbolic input to the definition of cost
,
because the symbolic variables of classifier
were defined in terms of x
at initialization.
Learning the Model¶
To implement MSGD in most programming languages (C/C++, Matlab, Python), one would start by manually deriving the expressions for the gradient of the loss with respect to the parameters: in this case \partial{\ell}/\partial{W}, and \partial{\ell}/\partial{b}, This can get pretty tricky for complex models, as expressions for \partial{\ell}/\partial{\theta} can get fairly complex, especially when taking into account problems of numerical stability.
With Theano, this work is greatly simplified. It performs automatic differentiation and applies certain math transforms to improve numerical stability.
To get the gradients \partial{\ell}/\partial{W} and \partial{\ell}/\partial{b} in Theano, simply do the following:
g_W = T.grad(cost=cost, wrt=classifier.W)
g_b = T.grad(cost=cost, wrt=classifier.b)
g_W
and g_b
are symbolic variables, which can be used as part
of a computation graph. The function train_model
, which performs one step
of gradient descent, can then be defined as follows:
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs.
updates = [(classifier.W, classifier.W - learning_rate * g_W),
(classifier.b, classifier.b - learning_rate * g_b)]
# compiling a Theano function `train_model` that returns the cost, but in
# the same time updates the parameter of the model based on the rules
# defined in `updates`
train_model = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
updates
is a list of pairs. In each pair, the first element is the symbolic
variable to be updated in the step, and the second element is the symbolic
function for calculating its new value. Similarly, givens
is a dictionary
whose keys are symbolic variables and whose values specify
their replacements during the step. The function train_model
is then defined such
that:
- the input is the mini-batch index
index
that, together with the batch size (which is not an input since it is fixed) defines x with corresponding labels y - the return value is the cost/loss associated with the x, y defined by
the
index
- on every function call, it will first replace
x
andy
with the slices from the training set specified byindex
. Then, it will evaluate the cost associated with that minibatch and apply the operations defined by theupdates
list.
Each time train_model(index)
is called, it will thus compute and return the
cost of a minibatch, while also performing a step of MSGD. The entire learning
algorithm thus consists in looping over all examples in the dataset, considering
all the examples in one minibatch at a time,
and repeatedly calling the train_model
function.
Testing the model¶
As explained in Learning a Classifier, when testing the model we are
interested in the number of misclassified examples (and not only in the likelihood).
The LogisticRegression
class therefore has an extra instance method, which
builds the symbolic graph for retrieving the number of misclassified examples in
each minibatch.
The code is as follows:
def errors(self, y):
"""Return a float representing the number of errors in the minibatch
over the total number of examples of the minibatch ; zero one
loss over the size of the minibatch
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
"""
# check if y has same dimension of y_pred
if y.ndim != self.y_pred.ndim:
raise TypeError(
'y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', self.y_pred.type)
)
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
return T.mean(T.neq(self.y_pred, y))
else:
raise NotImplementedError()
We then create a function test_model
and a function validate_model
,
which we can call to retrieve this value. As you will see shortly,
validate_model
is key to our early-stopping implementation (see
Early-Stopping). These functions take a minibatch index and compute,
for the examples in that minibatch, the number that were misclassified by the
model. The only difference between them is that test_model
draws its
minibatches from the testing set, while validate_model
draws its from the
validation set.
# compiling a Theano function that computes the mistakes that are made by
# the model on a minibatch
test_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]
}
)
validate_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]
}
)
Putting it All Together¶
The finished product is as follows.
"""
This tutorial introduces logistic regression using Theano and stochastic
gradient descent.
Logistic regression is a probabilistic, linear classifier. It is parametrized
by a weight matrix :math:`W` and a bias vector :math:`b`. Classification is
done by projecting data points onto a set of hyperplanes, the distance to
which is used to determine a class membership probability.
Mathematically, this can be written as:
.. math::
P(Y=i|x, W,b) &= softmax_i(W x + b) \\
&= \frac {e^{W_i x + b_i}} {\sum_j e^{W_j x + b_j}}
The output of the model or prediction is then done by taking the argmax of
the vector whose i'th element is P(Y=i|x).
.. math::
y_{pred} = argmax_i P(Y=i|x,W,b)
This tutorial presents a stochastic gradient descent optimization method
suitable for large datasets.
References:
- textbooks: "Pattern Recognition and Machine Learning" -
Christopher M. Bishop, section 4.3.2
"""
from __future__ import print_function
__docformat__ = 'restructedtext en'
import six.moves.cPickle as pickle
import gzip
import os
import sys
import timeit
import numpy
import theano
import theano.tensor as T
class LogisticRegression(object):
"""Multi-class Logistic Regression Class
The logistic regression is fully described by a weight matrix :math:`W`
and bias vector :math:`b`. Classification is done by projecting data
points onto a set of hyperplanes, the distance to which is used to
determine a class membership probability.
"""
def __init__(self, input, n_in, n_out):
""" Initialize the parameters of the logistic regression
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
# start-snippet-1
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
self.W = theano.shared(
value=numpy.zeros(
(n_in, n_out),
dtype=theano.config.floatX
),
name='W',
borrow=True
)
# initialize the biases b as a vector of n_out 0s
self.b = theano.shared(
value=numpy.zeros(
(n_out,),
dtype=theano.config.floatX
),
name='b',
borrow=True
)
# symbolic expression for computing the matrix of class-membership
# probabilities
# Where:
# W is a matrix where column-k represent the separation hyperplane for
# class-k
# x is a matrix where row-j represents input training sample-j
# b is a vector where element-k represent the free parameter of
# hyperplane-k
self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)
# symbolic description of how to compute prediction as class whose
# probability is maximal
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
# end-snippet-1
# parameters of the model
self.params = [self.W, self.b]
# keep track of model input
self.input = input
def negative_log_likelihood(self, y):
"""Return the mean of the negative log-likelihood of the prediction
of this model under a given target distribution.
.. math::
\frac{1}{|\mathcal{D}|} \mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
\frac{1}{|\mathcal{D}|} \sum_{i=0}^{|\mathcal{D}|}
\log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
\ell (\theta=\{W,b\}, \mathcal{D})
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
Note: we use the mean instead of the sum so that
the learning rate is less dependent on the batch size
"""
# start-snippet-2
# y.shape[0] is (symbolically) the number of rows in y, i.e.,
# number of examples (call it n) in the minibatch
# T.arange(y.shape[0]) is a symbolic vector which will contain
# [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of
# Log-Probabilities (call it LP) with one row per example and
# one column per class LP[T.arange(y.shape[0]),y] is a vector
# v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,
# LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is
# the mean (across minibatch examples) of the elements in v,
# i.e., the mean log-likelihood across the minibatch.
return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
# end-snippet-2
def errors(self, y):
"""Return a float representing the number of errors in the minibatch
over the total number of examples of the minibatch ; zero one
loss over the size of the minibatch
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
"""
# check if y has same dimension of y_pred
if y.ndim != self.y_pred.ndim:
raise TypeError(
'y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', self.y_pred.type)
)
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
return T.mean(T.neq(self.y_pred, y))
else:
raise NotImplementedError()
def load_data(dataset):
''' Loads the dataset
:type dataset: string
:param dataset: the path to the dataset (here MNIST)
'''
#############
# LOAD DATA #
#############
# Download the MNIST dataset if it is not present
data_dir, data_file = os.path.split(dataset)
if data_dir == "" and not os.path.isfile(dataset):
# Check if dataset is in the data directory.
new_path = os.path.join(
os.path.split(__file__)[0],
"..",
"data",
dataset
)
if os.path.isfile(new_path) or data_file == 'mnist.pkl.gz':
dataset = new_path
if (not os.path.isfile(dataset)) and data_file == 'mnist.pkl.gz':
from six.moves import urllib
origin = (
'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz'
)
print('Downloading data from %s' % origin)
urllib.request.urlretrieve(origin, dataset)
print('... loading data')
# Load the dataset
with gzip.open(dataset, 'rb') as f:
try:
train_set, valid_set, test_set = pickle.load(f, encoding='latin1')
except:
train_set, valid_set, test_set = pickle.load(f)
# train_set, valid_set, test_set format: tuple(input, target)
# input is a numpy.ndarray of 2 dimensions (a matrix)
# where each row corresponds to an example. target is a
# numpy.ndarray of 1 dimension (vector) that has the same length as
# the number of rows in the input. It should give the target
# to the example with the same index in the input.
def shared_dataset(data_xy, borrow=True):
""" Function that loads the dataset into shared variables
The reason we store our dataset in shared variables is to allow
Theano to copy it into the GPU memory (when code is run on GPU).
Since copying data into the GPU is slow, copying a minibatch everytime
is needed (the default behaviour if the data is not in a shared
variable) would lead to a large decrease in performance.
"""
data_x, data_y = data_xy
shared_x = theano.shared(numpy.asarray(data_x,
dtype=theano.config.floatX),
borrow=borrow)
shared_y = theano.shared(numpy.asarray(data_y,
dtype=theano.config.floatX),
borrow=borrow)
# When storing data on the GPU it has to be stored as floats
# therefore we will store the labels as ``floatX`` as well
# (``shared_y`` does exactly that). But during our computations
# we need them as ints (we use labels as index, and if they are
# floats it doesn't make sense) therefore instead of returning
# ``shared_y`` we will have to cast it to int. This little hack
# lets ous get around this issue
return shared_x, T.cast(shared_y, 'int32')
test_set_x, test_set_y = shared_dataset(test_set)
valid_set_x, valid_set_y = shared_dataset(valid_set)
train_set_x, train_set_y = shared_dataset(train_set)
rval = [(train_set_x, train_set_y), (valid_set_x, valid_set_y),
(test_set_x, test_set_y)]
return rval
def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000,
dataset='mnist.pkl.gz',
batch_size=600):
"""
Demonstrate stochastic gradient descent optimization of a log-linear
model
This is demonstrated on MNIST.
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: the path of the MNIST dataset file from
http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz
"""
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] // batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size
######################
# BUILD ACTUAL MODEL #
######################
print('... building the model')
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
# generate symbolic variables for input (x and y represent a
# minibatch)
x = T.matrix('x') # data, presented as rasterized images
y = T.ivector('y') # labels, presented as 1D vector of [int] labels
# construct the logistic regression class
# Each MNIST image has size 28*28
classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10)
# the cost we minimize during training is the negative log likelihood of
# the model in symbolic format
cost = classifier.negative_log_likelihood(y)
# compiling a Theano function that computes the mistakes that are made by
# the model on a minibatch
test_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]
}
)
validate_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]
}
)
# compute the gradient of cost with respect to theta = (W,b)
g_W = T.grad(cost=cost, wrt=classifier.W)
g_b = T.grad(cost=cost, wrt=classifier.b)
# start-snippet-3
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs.
updates = [(classifier.W, classifier.W - learning_rate * g_W),
(classifier.b, classifier.b - learning_rate * g_b)]
# compiling a Theano function `train_model` that returns the cost, but in
# the same time updates the parameter of the model based on the rules
# defined in `updates`
train_model = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
# end-snippet-3
###############
# TRAIN MODEL #
###############
print('... training the model')
# early-stopping parameters
patience = 5000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience // 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = numpy.inf
test_score = 0.
start_time = timeit.default_timer()
done_looping = False
epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
minibatch_avg_cost = train_model(minibatch_index)
# iteration number
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [validate_model(i)
for i in range(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print(
'epoch %i, minibatch %i/%i, validation error %f %%' %
(
epoch,
minibatch_index + 1,
n_train_batches,
this_validation_loss * 100.
)
)
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
# test it on the test set
test_losses = [test_model(i)
for i in range(n_test_batches)]
test_score = numpy.mean(test_losses)
print(
(
' epoch %i, minibatch %i/%i, test error of'
' best model %f %%'
) %
(
epoch,
minibatch_index + 1,
n_train_batches,
test_score * 100.
)
)
# save the best model
with open('best_model.pkl', 'wb') as f:
pickle.dump(classifier, f)
if patience <= iter:
done_looping = True
break
end_time = timeit.default_timer()
print(
(
'Optimization complete with best validation score of %f %%,'
'with test performance %f %%'
)
% (best_validation_loss * 100., test_score * 100.)
)
print('The code run for %d epochs, with %f epochs/sec' % (
epoch, 1. * epoch / (end_time - start_time)))
print(('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.1fs' % ((end_time - start_time))), file=sys.stderr)
def predict():
"""
An example of how to load a trained model and use it
to predict labels.
"""
# load the saved model
classifier = pickle.load(open('best_model.pkl'))
# compile a predictor function
predict_model = theano.function(
inputs=[classifier.input],
outputs=classifier.y_pred)
# We can test it on some examples from test test
dataset='mnist.pkl.gz'
datasets = load_data(dataset)
test_set_x, test_set_y = datasets[2]
test_set_x = test_set_x.get_value()
predicted_values = predict_model(test_set_x[:10])
print("Predicted values for the first 10 examples in test set:")
print(predicted_values)
if __name__ == '__main__':
sgd_optimization_mnist()
The user can learn to classify MNIST digits with SGD logistic regression, by typing, from within the DeepLearningTutorials folder:
python code/logistic_sgd.py
The output one should expect is of the form:
...
epoch 72, minibatch 83/83, validation error 7.510417 %
epoch 72, minibatch 83/83, test error of best model 7.510417 %
epoch 73, minibatch 83/83, validation error 7.500000 %
epoch 73, minibatch 83/83, test error of best model 7.489583 %
Optimization complete with best validation score of 7.500000 %,with test performance 7.489583 %
The code run for 74 epochs, with 1.936983 epochs/sec
On an Intel(R) Core(TM)2 Duo CPU E8400 @ 3.00 Ghz the code runs with approximately 1.936 epochs/sec and it took 75 epochs to reach a test error of 7.489%. On the GPU the code does almost 10.0 epochs/sec. For this instance we used a batch size of 600.
Prediction Using a Trained Model¶
sgd_optimization_mnist
serialize and pickle the model each time new
lowest validation error is reached. We can reload this model and predict
labels of new data. predict
function shows an example of how
this could be done.
def predict():
"""
An example of how to load a trained model and use it
to predict labels.
"""
# load the saved model
classifier = pickle.load(open('best_model.pkl'))
# compile a predictor function
predict_model = theano.function(
inputs=[classifier.input],
outputs=classifier.y_pred)
# We can test it on some examples from test test
dataset='mnist.pkl.gz'
datasets = load_data(dataset)
test_set_x, test_set_y = datasets[2]
test_set_x = test_set_x.get_value()
predicted_values = predict_model(test_set_x[:10])
print("Predicted values for the first 10 examples in test set:")
print(predicted_values)
Footnotes
| [1] | For smaller datasets and simpler models, more sophisticated descent algorithms can be more effective. The sample code logistic_cg.py demonstrates how to use SciPy’s conjugate gradient solver with Theano on the logistic regression task. |


{% extends 'base.html' %} {% block title %} Analytics | Bear Blog {% endblock %} {% block nav %} {% include '../snippets/dashboard_nav.html' %} {% endblock %} {% block content %} Analytics (7 days) Unique reads: {{ unique_reads.hit_count__sum }} Unique visitors: {{ unique_visitors }} {{ chart|safe }} Need more in-depth analytics? For more in-depth analytics sign up for Fathom Analytics who are comitted to simple, fast and privacy-focused analytics. Check out the example analytics and use this link to get $10 off {% csrf_token %} {{ form.as_p }} Save {% endblock %}
For more in-depth analytics sign up for Fathom Analytics who are comitted to simple, fast and privacy-focused analytics.
Check out the example analytics and use this link to get $10 off


Protects the user from exposure to moving parts during the lifting action of the jack. Flap is lifted by the draw as it rises to the surface
The bracket is mounted on a rod which is connected to a frame that is capable of moving laterally. This allows for it to slide horizontally into a complementary slot in the drawer. The design of the bracket was optimised to ensure an even distribution of the load of the drawer
The lifting bracket is immediately mounted onto this rod. This rod which extends vertically runs pallalel and is connected to a motorised lead screw to enable vertical motion of the bracket
User selects their desired compartment to be lifted on the interface


Human Computer Interaction (HCI)
Management Information Systems (MIS)
Knowledge Management (KM) / Information Management (IM)
All courses are offered in-person and online
Preprint PDF Project Source Document
PDF Cite Project Project Source Document DOI
PDF Cite Project Slides Source Document DOI
Preprint Project DOI
More media work


What is it?expect-lite is a mature, quick and easy command line automation tool.Written in expect, it is designed to directly map an interactive terminal session into an automation script. expect-lite scripts use special character(s) at the beginning of each line to indicate the action. Basic expect-lite scripts can be created by simply cutting and pasting text from a terminal window into a script, and adding '>' '<' characters. No knowledge of expect is required! Includes a Powerful Debugger with breakpoints, step/skip, copy/paste expect-lite lines right into a running script. Structured programming via Code Blocks, enables easy to create while loops, foreach loops and conditionals (if statements). Full IPv6 support, using telnet or ssh over IPv6, and IPv6 addresses in the script. See Linux Symposium
presentations for a quick overview. Expect-lite is targeted at the verification testing environment, and will produce a Pass/Fail result at the end of the script. However, its use is not limited to this environment and can be used anywhere a text-based interface needs a bit of automation. What is it for?A simple to use automation tool to do:
What New?Latest version 4.9.0 improvements include:
The main purpose is keep it simple. However, the advanced features allow pretty creative scripting. The central ideas behind the design are:
ExamplesCheck the IP address of the localhost >ifconfig ssh to a remote host >ssh root@host-021While loops using code blocks $i=1Conditional using code blocks ? $IP == 2001:db8::f00d ? [ expect-lite includes over 25 example scripts covering topics such as: if statements, looping, multiple sessions, random number generation, and more. Where to get it?The most recent version is always available on SourceForge:
|


Google Earth Enterprise Documentation Home | Release notes
Release notes: GEE 5.1.1
GEE 5.1.1 is an incremental release of GEE 5.1. It contains several bug fixes in Fusion and GEE Server, and updates to third-party libraries.
Supported Platforms
The Google Earth Enterprise 5.1.1 release is supported on 64-bit versions of the following operating systems:
- Red Hat Enterprise Linux versions 6.0 to 6.5, including the most recent security patches
- CentOS 6.5
- Ubuntu 10.04 and 12.04 LTS
Google Earth Enterprise 5.1.1 is compatible with Google Earth Enterprise Client (EC) version 7.1.4 and Google Earth Plug-in versions 7.0.1 - <IP_ADDRESS>8 for Windows, Mac, and Linux.
Library updates
| Library | Updated version |
|---|---|
| GDAL | 1.11.1 (with libcurl support) |
| libjpeg | v8d1 |
| OpenSSL | 0.9.8ze |
| libcurl | 7.39.0 |
| MesaLib | 7.11 |
| NumPy | 1.9.1 |
| OpenLDAP | 2.4.40 |
| mod_wsgi | 3.5 |
| libgeotiff | 1.4.0 |
| PostgreSQL | 8.4.22 |
| PostGIS | 1.5.8 |
| GEOS | 3.4.2 |
| Libxml2 | 2.9.1 |
| Google Maps JS API V3 | 3.19 |
Resolved Issues
| Number | Description | Resolution |
|---|---|---|
| 12239387, 18719211 | Under certain conditions, historical imagery is not displayed correctly. | Fixed. To display historical imagery correctly, take the following steps:
|
| 15212134 | Cuts of 2D maps fail if no polygon is specified. | Fixed in GEE Server. See Create portable globes and maps. |
| 15290004 | When attempting to cancel a cut using the Cutter tool, the Cancel button does not properly kill any running task on GEE Server. | Fixed. The Cutter cancel process now kills any running task on GEE Server. |
| 16981234 | Portable Server fails to run on RHEL 6.4, returning Error Loading python lib libpython2.7.so.1.0 . |
Fixed. See Portable release notes. |
| 18476901 | When attempting to publish databases using disconnected publishing, gedisconnectedsend reports incorrect suffix for bytes. |
Fixed. See Publishing databases using disconnected publishing. |
| 18506290 | Serving 2D Database with Mercator on the Fly imagery fails when imagery has no worldwide coverage. | Fixed. See Add flat imagery to Mercator map databases. |
| 18509863 | HTTPS protocol not respected in URL-based search tab. | Fixed. See Create search tabs. |
| 18572866 | GEE 4.x virtual servers (default_ge , default_map ) are not deleted when upgrading to GEE 5.1.x. |
Fixed. |
| 18668563 | During the installation process, the installer returns java.lang.NullPointerException error. |
Fixed. |
| 18791567 | Building a 2D database without an imagery project crashes gesystemmanager . |
Fixed. See Creating a map database. |
| 19003972 | Improve Mercator on the Fly reprojection performance. | Fixed. See Add flat imagery to Mercator map databases. |
| 19020117 | WMS GetCapabilities returns inaccurate bounding box information; transposed coordinates on GetMap 1.3.0 requests. | Fixed. |
| 18980809 | When publishing, the Delete button may not be available for a search service that has been added to a database. | Fixed. |
| 18724718 | Additional support in the UI for new configuration options for search service deployment:
|
Fixed. |
| 18935285 | When new resources have been added, Fusion GUI produces unnecessary gepackgen tasks during project builds. This occurs when: a new resource is added using the Fusion GUI, which has previously been created with console tool; the new resource is created without specifying an acquisition date and also has the same base resolution as other resources in the existing raster project. |
Fixed. |
| 19286893 | Uncaught TypeError: Cannot read property value of null when publishing 2D databases. |
Fixed. |
| 19276438 | During push, Fusion GUI processes at 100%. | Fixed. |
| 18724718 | Default search tab cannot be hidden, and custom ones are not displayed by default. | Fixed. Modified management of search services deployment for 3D Databases: you can configure a search service to deploy to an EC search tab (services with one search field) or to a Supplemental Search. |
| 11333524 | When pushing a database: improve provision of database push status. | Fixed.
|
| 19412572 | Publishing an updated map at the same target point causes gehttpd to crash when serving Databases that use Mercator on the Fly, caused by a request to a non-existent version of imagery layer. Returns a 404 error code. |
Fixed. |
| 19280022 | When using URL-based search tabs, erroneous search requests may occur. | Fixed. |
| 19338090 | Support added for Google Geocoder responses for 2D searches. | Fixed. |
| 5447870 | Segmentation fault when using the geraster2kml tool. |
Fixed. |
| 19709212 | Icon file cannot be read. | Fixed. |
| 20185775 | Missing bundle.hdr file in published database, resulting in blurry imagery in some areas. | Fixed. |


Follows API
Read and create follow relationships between sites.
import {follows} from 'dat://unwalled.garden/index.js'
await follows.list({
query: {authors, subjects, visibility},
sortBy,
offset,
limit,
reverse
})
await follows.get(author, subject)
await follows.add(subject, {visibility})
await follows.edit(subject, {visibility})
await follows.remove(subject)
Follow
The values returned by follow functions will fit the following object shape:
| Attribute |
Type |
Usage |
| author |
Object |
The site doing the following |
| url |
string |
|
| title |
string |
|
| description |
string |
|
| type |
string |
|
| subject |
Object |
The site being followed |
| url |
string |
|
| title |
string |
|
| description |
string |
|
| type |
string |
|
| visibility |
string |
The visibility of the “follow” record |
list(opts)
List follows by authors and/or subjects.
| Param |
Type |
Default |
Usage |
| opts |
Object |
|
|
| query |
Object |
|
|
| authors |
string|string[] |
|
Site URLs |
| subjects |
string|string[] |
|
Site URLs |
| visibility |
string |
'all' |
See visibility |
| sortBy |
string |
'subject' |
One of: 'subject' |
| offset |
number |
0 |
|
| limit |
number |
|
|
| reverse |
boolean |
false |
|
| Returns |
Promise<Follow[]> |
get(author, subject)
Get a follow by author and subject.
| Param |
Type |
Default |
Usage |
| author |
string |
|
Site URL (required) |
| subject |
string |
|
Site URL (required) |
add(subject, opts)
Add a follow to the current user’s site.
| Param |
Type |
Default |
Usage |
| subject |
string |
|
Site URL (required) |
| opts |
Object |
|
|
| visibility |
string |
'public' |
One of: 'public' , 'private' |
edit(subject, opts)
Edit a follow on the current user’s site.
| Param |
Type |
Default |
Usage |
| subject |
string |
|
Site URL (required) |
| opts |
Object |
|
|
| visibility |
string |
'public' |
One of: 'public' , 'private' |
remove(subject)
Remove a follow from the current user’s site.
| Param |
Type |
Default |
Usage |
| subject |
string |
|
Site URL (required) |


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Weebly makes it surprisingly easy to create a high-quality website, blog or online store. Over 25 million people use Weebly to bring their unique ideas to life. You've visited this page 4 times.  What you'll be making. Before we ask you to write a bunch of code, we think it's helpful for you to see exactly what you'll be making in ...
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domestic | structure | introduction | 2010
Introduction
1.0
1.0.1 Background
The structure of a
building
is fundamental to ensuring the safety of people in
or around new and existing
buildings
and can be affected by a number of
factors inside and outside the
building
including environmental factors. These
factors should be considered to prevent the collapse, excessive deformation
or the disproportionate collapse of
buildings
.
Climatic conditions
The climatic conditions in Scotland including temperature, snow, wind, driving
rain and flooding and the impact of climate change should be carefully
considered in the assessment of loading (actions) and in the structural design
of
buildings
.
The Standing Committee on Structural Safety (SCOSS) is an independent
body supported by the Institution of Civil Engineers, the Institution of
Structural Engineers and the Health and Safety Executive to maintain a
continuing review of
building
and civil engineering matters affecting the safety
of structures. The prime function of SCOSS is to identify in advance those
trends and developments that might contribute to an increasing risk to
structural safety. The collation and dissemination of information relating to
matters of structural concern is a vital element of achieving safe structures.
SCOSS has established a UK wide confidential reporting system (CROSS) to
gather data on structural problems.
Appraisal of existing
Conversions
to existing
buildings
present particular problems and an
appraisal of the existing structure for its new occupation or use should be
undertaken to ensure that appropriate measures are taken to meet the
requirements of regulation 12. Guidance on how a structural appraisal may
be undertaken is given in ’Appraisal of Existing Structures’, 1996 published
by the Institution of Structural Engineers.
Procurement
The contractual arrangements used by clients to procure a
building
can have
important consequences for the reliability of the design and the adequacy of
the
construction
. Frequently,
building
design procurement will involve the
appointment of a number of designers who may be employed by more than
one organisation. Detailed design of individual structural details and
components can be passed to specialist contractors. In these circumstances
the client should appoint a lead designer or other appropriately experienced
and qualified person to oversee the design process.
1.0.2 Aims
The intention of this section is to ensure that the structure of a
building
shall
be designed and executed in such a way that, during its intended life, it will
not pose a threat to the safety of people in and around the
building
with an
appropriate degree of reliability. To achieve a structure with adequate
structural resistance, serviceability and durability the following should be
taken into account:
a. the loadings (actions) on the
building;
b. nature of the ground;
c. collapse or deformations;
e. climatic conditions;
f. materials;
g. structural analysis; and
h. details of
construction
.
1.0.1
—
1.0.2
domestic | structure | introduction | 2010
The actions on the
building
will comprise a set of loads applied directly to the
structure that will include dynamic, concentrated and peak load effects,
together with a set of imposed deformations caused for example by
temperature changes or uneven settlement.
1.0.3 Latest changes
The following is a summary of the changes made to this section since 1 May
2009.
1.0.1
reference to SCOSS updated;
1.0.1
guidance for clients on procurement added;
1.0.2
aims re-written to reflect Structural Eurocodes;
1.0.4
guidance on relevant legislation added for CDM regulations;
1.0.5
guidance on alternative approaches added including the use
of withdrawn British Standards;
clause on certification introduced;
1.0.7
1.1.1
reference to guidance on fixings and stone masonry added;
1.1.2
reference to Structural Eurocodes added for loadings;
1.1.3
reference to Structural Eurocodes added for design and
1.1.4
reference to BS EN 1997-2:2007 added for geotechnical
investigation of the
site
;
1.1.5
reference to BS EN 1997-1:2004 added for design of
foundations
adjacent to existing
buildings;
and
1.2.1
guidance in relation to disproportionate collapse updated to
align with Structural Eurocodes.
Annex A - list of structural Eurocodes and corresponding
British Standards to be withdrawn added.
1.A.
The previous annexes forming the Small Buildings Structural Guidance
(SBSG) have been removed from the Technical Handbook and are now
referenced in clause 1.0.5.
Minor alterations and corrections have also been made. A full list of changes
to this edition of the Technical Handbooks is available on the Building
Standards website.
The Construction
(Design and
Management)
Regulations 2007
The Construction (Design and Management) Regulations 2007 are intended
to protect people working in
construction
and others who may be affected by
their activities. The regulations require the systematic management of
projects from concept to completion and throughout the life cycle of the
structure, including eventual demolition. Clients have a duty to ensure that
competent people are employed to do the
work
, that sufficient time is
allocated for the
work
to be undertaken and that the various members of the
design and
construction
teams co-operate and exchange information.
1.0.2
—
1.0.4
domestic | structure | introduction | 2010
1.0.5 Alternative approaches
Where alternative approaches to the structural design are proposed other
than using the guidance contained in this section, the structural design
should take account of all of the factors identified in clause 1.0.2 above. For
example, care should be taken where alternative numerical values are placed
on factors of safety as this may have a detrimental effect on the overall
stability of the structure.
The British Standards Institution (BSI) agreement with the European
Committee for Standardisation (CEN) obliges it to withdraw UK national
standards after a harmonised European Standard with the same scope and
field of application has been produced. Withdrawal of a standard implies that
while documents will still be available there will be no support or five-year
review by a BSI committee to consider the currency of the standard and to
decide whether it should be confirmed, revised or withdrawn. BSI, in line with
this commitment, will by 31 March 2010 replace the British Standards relating
to loading and structural design with the European Standards and associated
National Annexes listed in sections 1.1.2 and 1.1.3 of this guidance.
Whilst other guidance documents or international standards, including
withdrawn national standards might be used in alternative approaches to
satisfy building regulations, designers, verifiers, or in the case of certified
projects, the approved certifiers of design (
building
structures) will need to
satisfy themselves that the use of such guidance is appropriate for a specific
project. Care should be exercised, particularly with withdrawn standards, in
relation to wind and snow loadings (actions) where the effects of climate
change may render these unsafe.
Where alternative approaches use design methods or codes other than those
listed in this guidance then these must be used within the context of the
assumptions set out in section 1.1.3. Designs must be checked in order to
deliver similar levels of design reliability.
Small
Buildings
Structural Guidance
The Small Buildings Structural Guidance (SBSG) provides structural
guidance to designers of small
domestic buildings
on how to comply with
standard 1.1. The guidance was published in section 1 (annexes 1.A to 1.F)
of the Technical Handbooks in May 2005 (updated in 2007) and based on
British Standards which have now been withdrawn. The SBSG, together with
research on the impact of the Structural Eurocodes on masonry
construction
,
is available on the Building Standards Division website
The SBSG has been written for those with expertise in
building
design and
construction
but not necessarily in structural engineering design. Where the
conditions or parameters fall outside the scope of the guidance, then
specialist advice should be sought from approved certifiers of design,
chartered engineers or other appropriately qualified persons.
1.0.5
—
1.0.5
domestic | structure | introduction | 2010
1.0.6 Annex
A list of structural Eurocodes and corresponding British Standards to be
withdrawn is contained in Annex 1A.
1.0.7 Certification
Scottish Ministers can, under Section 7 of the Building (Scotland) Act 2003,
approve schemes for the certification of design or
construction
for compliance
with the mandatory functional standards. Such schemes are approved on the
basis that the procedures adopted by the scheme will take account of the
need to co-ordinate the
work
of various designers and specialist contractors.
Individuals approved to provide certification services under the scheme are
assessed to ensure that they have the qualifications, skills and experience
required to certify compliance for the
work
covered by the scope of the
scheme. Checking procedures adopted by Approved Certifiers will deliver
design or installation reliability in accordance with legislation.
The Certification of Design (Building Structures) scheme has been approved
by Scottish Ministers to confirm compliance with standards 1.1 and
1.2. Details are available on the Building Standards Division website
1.0.6
—
1.0.7


|
Garage Home General Settings SIRI Setup Instructions Steve Does Stuff! YouTube GitHub |
How to Setup Siri to Tell the Status of the Garage Door(s) How to Setup Siri to Open or Close Garage Door(s) Every Door Needs 2 Shortcuts, 1 for Opening the Door, 1 for Closing You can duplicate 1 Shortcut & edit the copy. Change "dowhat" field from Open to Close |


CloudASR API Monitor
Running workers:
[[container.model]]
[[container.address]]
[[container.status]]


(#) Valid Network Security Config File
!!! ERROR: Valid Network Security Config File
This is an error, and is also enforced at build time when
supported by the build system. For Android this means it will
run during release builds.
Id
: `NetworkSecurityConfig`
Summary
: Valid Network Security Config File
Severity
: Fatal
Category
: Correctness
Platform
: Android
Vendor
: Android Open Source Project
Feedback
: https://issuetracker.google.com/issues/new?component=192708
Affects
: Resource files
Editing
: This check runs on the fly in the IDE editor
See
: https://developer.android.com/preview/features/security-config.html
Implementation
: [Source Code](https://cs.android.com/android-studio/platform/tools/base/+/mirror-goog-studio-main:lint/libs/lint-checks/src/main/java/com/android/tools/lint/checks/NetworkSecurityConfigDetector.java)
Tests
: [Source Code](https://cs.android.com/android-studio/platform/tools/base/+/mirror-goog-studio-main:lint/libs/lint-tests/src/test/java/com/android/tools/lint/checks/NetworkSecurityConfigDetectorTest.java)
Copyright Year
: 2016
Ensures that a `` file, which is pointed to by
an `android:networkSecurityConfig` attribute in the manifest file, is
valid.
!!! Tip
This lint check has an associated quickfix available in the IDE.
(##) Example
Here is an example of lint warnings produced by this check:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~text
res/xml/network_config.xml:4:Error: Unexpected element
[NetworkSecurityConfig]
<include domain="file"/>
-------
res/xml/network_config.xml:7:Error: Nested elements are
not allowed in base-config [NetworkSecurityConfig]
<domain-config>
-------------
res/xml/network_config.xml:12:Error: No elements in
[NetworkSecurityConfig]
<domain-config>
-------------
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Here is the source file referenced above:
`res/xml/network_config.xml`:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~xml linenumbers
<?xml version="1.0" encoding="utf-8"?>
<network-security-config>
<!-- Invalid element as child. -->
<include domain="file"/>
<!-- Invalid base-config with nested domain-config element -->
<base-config>
<domain-config>
<domain>android.com</domain>
</domain-config>
</base-config>
<!-- Invalid domain-config without domain child element -->
<domain-config>
</domain-config>
</network-security-config>
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You can also visit the
[source code](https://cs.android.com/android-studio/platform/tools/base/+/mirror-goog-studio-main:lint/libs/lint-tests/src/test/java/com/android/tools/lint/checks/NetworkSecurityConfigDetectorTest.java)
for the unit tests for this check to see additional scenarios.
The above example was automatically extracted from the first unit test
found for this lint check, `NetworkSecurityConfigDetector.testInvalidElementAndMissingDomain`.
To report a problem with this extracted sample, visit
https://issuetracker.google.com/issues/new?component=192708.
(##) Suppressing
You can suppress false positives using one of the following mechanisms:
* Adding the suppression attribute
`tools:ignore="NetworkSecurityConfig"` on the problematic XML
element (or one of its enclosing elements). You may also need to add
the following namespace declaration on the root element in the XML
file if it's not already there:
`xmlns:tools="http://schemas.android.com/tools"`.
* Using a special `lint.xml` file in the source tree which turns off
the check in that folder and any sub folder. A simple file might look
like this:
```xml
<?xml version="1.0" encoding="UTF-8"?>
<lint>
<issue id="NetworkSecurityConfig" severity="ignore" />
</lint>
```
Instead of `ignore` you can also change the severity here, for
example from `error` to `warning`. You can find additional
documentation on how to filter issues by path, regular expression and
so on
[here](https://googlesamples.github.io/android-custom-lint-rules/usage/lintxml.md.html).
* In Gradle projects, using the DSL syntax to configure lint. For
example, you can use something like
```gradle
lintOptions {
disable 'NetworkSecurityConfig'
}
```
In Android projects this should be nested inside an `android { }`
block.
* For manual invocations of `lint`, using the `--ignore` flag:
```
$ lint --ignore NetworkSecurityConfig ...`
```
* Last, but not least, using baselines, as discussed
[here](https://googlesamples.github.io/android-custom-lint-rules/usage/baselines.md.html).


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Follow these steps to remove a library from a project, open a terminal in your organization workspace that stores the project and then run lean library remove "<projectName>" "Library/<libraryName>"
.
$ lean library remove "My Project" "Library/MyLibrary" Removing D:\qc\workspaces\Quant Organization\Library\MyLibrary from 'My Project\My Project.csproj' Restoring packages in 'My Project' Determining projects to restore... Restored D:\qc\workspaces\Quant Organization\My Project\My Project.csproj (in 399 ms).
$ lean library remove "My Project" "Library/MyLibrary"


Compatibility patch and weapon/armor/alchemy/projectile stat overhaul creator for T3nd0's Skyrim Redone
Use the settings below to toggle the patching of different content. Remember to re-run the patcher whenever you change your load order!


IStudentInfo
Student info like name and a pair of year id and register id for every available school year.
Fields
name: string - full name
classId: string - class id
years: IStudentInfo.IYear[]


Jose Mourinho says he is humble and will not repeat mistakes
The Portuguese boss spoke to reporters for the first time at Spurs
by Sport.esFormer Chelsea, Man United and Real Madrid boss Jose Mourinho admits he made mistakes but claims he will not repeat them as he spoke for the first time after taking over at Tottenham.
"I realised I made mistakes," the Portuguese gaffer told reporters at a news conference.
"I'm not going to make the same mistakes. I'll make new mistakes, but not the same."
The boastful Portuguese boss also claimed he was humble.
"I am humble, humble enough to try and analyse my career - not just the last year, but the whole thing, the evolution, the problems and the solutions. Not to blame anyone else," added Jose.
"It was a great thing. I went really deep with that analysis. A break was very positive for me. It was the first summer I did not work and I felt a little bit at a loss during that pre-season.
"I was always humble, in my way. The problem is you didn't understand that."


Buttermilk Pancakes
15.99$
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on it tumblr kickstarter thundercats migas everyday carry squid palo santo leggings. Food truck truffaut
skateboard fam synth authentic semiotics. Live-edge lyft af, edison bulb yuccie crucifix microdosing.
skateboard fam synth authentic semiotics. Live-edge lyft af, edison bulb yuccie crucifix microdosing.


Pizzeria Avalon philosophy is to create a special moment while you're eating your pizza. OUr pizzas are famous for particular tastes, colours and flavours. In our pizzas you can find even our personal stories and our passion. We use only high quality products, and we always choose Slow Food Presidia. Our pizza rises some hours and we work it following PIzza New School rules: we bake only soft and crunchy pizzas. Our pizza menu changes according to seasonality. Lorenzo, our pizza chef, dresses pizza when it’s raw in order to intensify all flavours and ingredients. Lorenzo loves his pizza and Paolo will take care of you during your stay with us.
Here are some of our pizzas that have won your heart.
If you love real pizza you will love Avalon’s pizzas! Everything was very well cooke; this is the real pizza napotetana. Also fried are crunchy. Good craft beers and good desserts.
Here you can find the real pizza. Theis pizzas really deserve the trip to come here. Choose your pizza and say … wow!
Congrats for pizzas and desserts! I really enjoyed the atmosphere and the cheerfulness here. See you soon.
We take reservations only through our booking system AVALON BOOKINGS
We are open thursday-tuesday:
19.30-22.00
Via Sacco e Vanzetti, 13, 60131 Ancona
Pizzeria Avalon is Cashback partner, click here to find out more:
Cashback
AVALON di Stecconi Lorenzo & C. S.A.S
P.Iva<PHONE_NUMBER>3


IN THE HIGH COURT OF JHARKHAND, RANCHI L.P.A No.188 of 2013 ---
M/s Balaji Agrotech Industries ...... ....... .... Appellant
--Versus--
Jharkhand State Pollution Control Board & Anr. ..Respondents
---
CORAM :- HON'BLE THE CHIEF JUSTICE HON'BLE MR. JUSTICE P. P. BHATT
---
For the Appellant :- Mr. Ashok Kumar Yadav, Advocate For the J.S.P.C. Board : Mr. Prabhash Kumar, Advocate Mr. A.K. Pandey, Advocate.
---
Order No.5 Dated 3rd July, 2014
1. The appellant is represented by learned counsel Mr. Ashok Kumar Yadav and Jharkhand State Pollution Control Board is represented by Mr. Prabhash Kumar and Mr. A.K. Pandey, Advocates.
2. The present appeal is filed setting aside the order and judgment dated 22.4.2013 passed in W.P.C No. 5703 of 2012, whereby the writ petition was disposed of by giving direction to the appellant to install the Effluent Treatment Plant within twelve weeks from the date of order with further direction that if the installation of the Effluent Treatment Plant and any other compliance of Air Act or Water Act are not found to have been complied by the appellant, it will be open for the respondent Board to proceed in accordance with law.
3. This Letters Patent Appeal is listed for hearing, but after some arguments, learned counsel appearing for the appellant upon instructions received from his client seeks permission to withdraw this appeal.
4. Learned counsel for the Pollution Control Board has submitted that dispite the specific direction given by the Court for installation of Effluent Treatment Plant within 12 weeks from the date of the order, the said plant has not been made functional. As against that the learned counsel for the appellant has submitted that due to technical reasons, the said plant could not be successfully installed.
5. Accordingly, this Letters Patent Appeal is dismissed as withdrawn.
(R. BANUMATHI, C.J.) (P. P. BHATT, J.) SD/SI


What is Emotional Intelligence?
Emotional Intelligence is being able to understand your emotions and also being able to manage them.
Having Emotional Intelligence also usually means that someone can not only understand their own
emotions but also understand the emotions of others and also influence the emotions of others which can
be a very powerful tool to have.
Five Elements of Emotional Intelligence are:
- Self Awareness
- Self Regulation
- Motivation
- Empathy
- Social Skills
How is it different to IQ?
Emotional Intelligence is different to IQ as it more based on humans thoughts and feelings and how you interact with other humans. Where as IQ is more based on facts and academics, understanding the world as opposed to people. One of the most simple comparisons of IQ vs EQ would be Book Smart vs Street Smart.
Why is it important?
Emotional Intelligence is extremely important because as humans we are naturally social creatures and human interaction is a very important aspect of that.


Settings
General settings such as, site title, site description, address and so on.
Application settings, language, time zones and other environments.
Email SMTP settings, notifications and others related to email.
Settings about appearance such as themes, icon and color scheme.
Legal Notice
Copyright (c) 2019 Brian Luna. All rights reserved.
Bootstrap
The MIT License (MIT)
Copyright 2011-2018 Twitter, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
jQuery JavaScript Library
The MIT License (MIT)
Copyright jQuery Foundation and other contributors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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DataTables
The MIT License (MIT)
Copyright 2008-2018 SpryMedia Ltd
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
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all copies or substantial portions of the Software.
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Chart.js
The MIT License (MIT)
Copyright 2018 Chart.js Contributors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Air Datepicker
The MIT License (MIT)
Copyright (c) 2016 Timofey Marochkin
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
MDTimePicker
The MIT License (MIT)
Copyright (c) 2017 Dionlee Uy
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Fontawesome
The MIT License (MIT)
Font Awesome Free License
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Flag Icon CSS
The MIT License (MIT)
Copyright (c) 2013 Panayiotis Lipiridis
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Full Calendar
The MIT License (MIT)
Copyright (c) 2019 Adam Shaw
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Summernote
The MIT License (MIT)
Copyright (c) 2015- Summernote Team
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.


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| fmt_time {gt} | R Documentation |
Format input time values that are character-based and expressed according to
the ISO 8601 time format (HH:MM:SS
). Once the appropriate data cells are
targeted with columns
(and, optionally, rows
), we can simply apply a
preset time style to format the times. The following time styles are
available for simpler formatting of ISO times (all using the input time of
14:35:00
in the example output times):
hms: 14:35:00
hm: 14:35
hms_p: 2:35:00 PM
hm_p: 2:35 PM
h_p: 2 PM
We can use the info_time_style()
function for a useful reference on all of
the possible inputs to time_style
.
fmt_time(data, columns, rows = everything(), time_style = 2)
data |
A table object that is created using the |
columns |
The columns to format. Can either be a series of column names
provided in |
rows |
Optional rows to format. Providing either |
time_style |
The time style to use. Supply a number (from |
Targeting of values is done through columns
and additionally by rows
(if
nothing is provided for rows
then entire columns are selected). A number of
helper functions exist to make targeting more effective. Conditional
formatting is possible by providing a conditional expression to the rows
argument. See the Arguments section for more information on this.
An object of class gt_tbl
.
3-6
Other Format Data:
data_color()
,
fmt_currency()
,
fmt_datetime()
,
fmt_date()
,
fmt_markdown()
,
fmt_missing()
,
fmt_number()
,
fmt_passthrough()
,
fmt_percent()
,
fmt_scientific()
,
fmt()
,
text_transform()
# Use `exibble` to create a gt table; # keep only the `date` and `time` columns; # format the `time` column to have # times formatted as `hms_p` # (time style `3`) tab_1 <- exibble %>% dplyr::select(date, time) %>% gt() %>% fmt_time( columns = time, time_style = 3 ) # Use `exibble` to create a gt table; # keep only the `date` and `time` columns; # format the `time` column to have mixed # time formats (times after 16:00 will # be different than the others) tab_2 <- exibble %>% dplyr::select(date, time) %>% gt() %>% fmt_time( columns = time, rows = time > "16:00", time_style = 3 ) %>% fmt_time( columns = time, rows = time <= "16:00", time_style = 4 )


Welcome to the second page
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size
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black
36
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three
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Mr Lewis Musasike Development Bank of Southern Africa. 1258 Lever Road Headway Hill, MIDRAND Republic Of South Africa<EMAIL_ADDRESS>Hello, I am Mr Lewis Musasike, General Manager(Treasury) of Development Bank of Southern Africa.This is an urgent and very confidential business proposition. On May 27, 2001,a Foreign Oil consultant/contractor with the South African Institute of Mining and Metallurgy, Mr.Kelvin Smith made a numbered time(Fixed) Deposit for twelve calendar months, valued at US$10,500,000.00, (Ten Million, five hundred thousand Dollars). Upon maturity,I sent a routine notification to his forwarding address but got no reply. After a month,we sent a reminder and finally we discovered from his contract employers, the National Petroleum Corporation that Mr.Kelvin Smith died from an automobile accident.On further investigation,I found out that he died without making a WILL,and all attempts to trace his next of kin was fruitless. I therefore made further investigation and discovered that Mr.Kelvin Smith did not declare any kin or relations in all his official documents,including his Bank Deposit paperwork in my Bank. This sum of US$10,500,000.00 has carefully been fixed in my bank for safekeeping. No one will ever come forward to claim it.According to South African Law,at the expiration of 5 (five) years, the money will revert to the ownership of the Government if nobody applies to claim the fund. Consequently, my proposal is that I will like you as a Foreigner to stand in as the owner of the money which was fixed deposited in my bank. I am writing you because I as a public servant,i cannot operate a foreign account. I want to present you as the owner of the funds so you can be able to claim them with the help of my attorney. This is simple.I will like you to provide immediately your full names and address so that the Attorney will prepare the necessary documents which will put you in place as the beneficiary of the funds. The money will be moved out for us to share in the ratio of 60% for me and 40% for you. The paperwork for this transaction will be done by the Attorney.If you are interested, please reply immediately via my email address and Upon your response,I shall then provide you with more details and relevant documents that will help you understand the transaction.Please observe utmost confidentiality, and be rest assured that this transaction would be most profitable for both of us because I shall require your assistance to invest my share in real estate within your country. Awaiting your urgent reply via my email Above. Thanks and my regards. Lewis Musasike. ------------------------------------------------------------------------------ deArriba me paga por navegar. Vos tambien podes ganar $$$ navegando. Entra a www.dearriba.com.


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if ($_GET['button']) {
echo htmlspecialchars($_GET['form']);
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On 30 March 2020, 6 new articles were published in 6 journals.
Equal Under the Law: Officers' Perceptions of Equitable Treatment and Justice in Policing
Cascades: War, Crime, Peace, and Crime Prevention: Reaction Essay
Operational Stress Levels and Adjustment-Neuroticism Among Police Personnel in Goa
Discourses of Good Motherhood and the Policing of Young Parenthood


Filters
Filters are a mechanism in Coach that allows doing pre-processing and post-processing of the internal agent information. There are two filter categories -
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Input filters - these are filters that process the information passed into the agent from the environment. This information includes the observation and the reward. Input filters therefore allow rescaling observations, normalizing rewards, stack observations, etc.
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Output filters - these are filters that process the information going out of the agent into the environment. This information includes the action the agent chooses to take. Output filters therefore allow conversion of actions from one space into another. For example, the agent can take discrete actions, that will be mapped by the output filter onto continuous actions.
Filters can be stacked on top of each other in order to build complex processing flows of the inputs or outputs.
Input Filters
The input filters are separated into two categories - observation filters and reward filters.
Observation Filters
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ObservationClippingFilter - Clips the observation values to a given range of values. For example, if the observation consists of measurements in an arbitrary range, and we want to control the minimum and maximum values of these observations, we can define a range and clip the values of the measurements.
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ObservationCropFilter - Crops the size of the observation to a given crop window. For example, in Atari, the observations are images with a shape of 210x160. Usually, we will want to crop the size of the observation to a square of 160x160 before rescaling them.
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ObservationMoveAxisFilter - Reorders the axes of the observation. This can be useful when the observation is an image, and we want to move the channel axis to be the last axis instead of the first axis.
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ObservationNormalizationFilter - Normalizes the observation values with a running mean and standard deviation of all the observations seen so far. The normalization is performed element-wise. Additionally, when working with multiple workers, the statistics used for the normalization operation are accumulated over all the workers.
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ObservationReductionBySubPartsNameFilter - Allows keeping only parts of the observation, by specifying their name. For example, the CARLA environment extracts multiple measurements that can be used by the agent, such as speed and location. If we want to only use the speed, it can be done using this filter.
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ObservationRescaleSizeByFactorFilter - Rescales an image observation by some factor. For example, the image size can be reduced by a factor of 2.
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ObservationRescaleToSizeFilter - Rescales an image observation to a given size. The target size does not necessarily keep the aspect ratio of the original observation.
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ObservationRGBToYFilter - Converts a color image observation specified using the RGB encoding into a grayscale image observation, by keeping only the luminance (Y) channel of the YUV encoding. This can be useful if the colors in the original image are not relevant for solving the task at hand.
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ObservationSqueezeFilter - Removes redundant axes from the observation, which are axes with a dimension of 1.
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ObservationStackingFilter - Stacks several observations on top of each other. For image observation this will create a 3D blob. The stacking is done in a lazy manner in order to reduce memory consumption. To achieve this, a LazyStack object is used in order to wrap the observations in the stack. For this reason, the ObservationStackingFilter must be the last filter in the inputs filters stack.
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ObservationUint8Filter - Converts a floating point observation into an unsigned int 8 bit observation. This is mostly useful for reducing memory consumption and is usually used for image observations. The filter will first spread the observation values over the range 0-255 and then discretize them into integer values.
Reward Filters
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RewardClippingFilter - Clips the reward values into a given range. For example, in DQN, the Atari rewards are clipped into the range -1 and 1 in order to control the scale of the returns.
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RewardNormalizationFilter - Normalizes the reward values with a running mean and standard deviation of all the rewards seen so far. When working with multiple workers, the statistics used for the normalization operation are accumulated over all the workers.
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RewardRescaleFilter - Rescales the reward by a given factor. Rescaling the rewards of the environment has been observed to have a large effect (negative or positive) on the behavior of the learning process.
Output Filters
The output filters only process the actions.
Action Filters
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AttentionDiscretization - Discretizes an AttentionActionSpace. The attention action space defines the actions as choosing sub-boxes in a given box. For example, consider an image of size 100x100, where the action is choosing a crop window of size 20x20 to attend to in the image. AttentionDiscretization allows discretizing the possible crop windows to choose into a finite number of options, and map a discrete action space into those crop windows.
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BoxDiscretization - Discretizes a continuous action space into a discrete action space, allowing the usage of agents such as DQN for continuous environments such as MuJoCo. Given the number of bins to discretize into, the original continuous action space is uniformly separated into the given number of bins, each mapped to a discrete action index. For example, if the original actions space is between -1 and 1 and 5 bins were selected, the new action space will consist of 5 actions mapped to -1, -0.5, 0, 0.5 and 1.
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BoxMasking - Masks part of the action space to enforce the agent to work in a defined space. For example, if the original action space is between -1 and 1, then this filter can be used in order to constrain the agent actions to the range 0 and 1 instead. This essentially masks the range -1 and 0 from the agent.
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PartialDiscreteActionSpaceMap - Partial map of two countable action spaces. For example, consider an environment with a MultiSelect action space (select multiple actions at the same time, such as jump and go right), with 8 actual MultiSelect actions. If we want the agent to be able to select only 5 of those actions by their index (0-4), we can map a discrete action space with 5 actions into the 5 selected MultiSelect actions. This will both allow the agent to use regular discrete actions, and mask 3 of the actions from the agent.
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FullDiscreteActionSpaceMap - Full map of two countable action spaces. This works in a similar way to the PartialDiscreteActionSpaceMap, but maps the entire source action space into the entire target action space, without masking any actions.
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LinearBoxToBoxMap - A linear mapping of two box action spaces. For example, if the action space of the environment consists of continuous actions between 0 and 1, and we want the agent to choose actions between -1 and 1, the LinearBoxToBoxMap can be used to map the range -1 and 1 to the range 0 and 1 in a linear way. This means that the action -1 will be mapped to 0, the action 1 will be mapped to 1, and the rest of the actions will be linearly mapped between those values.


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Billionaire and former New York City Mayor Michael Bloomberg launches Democratic presidential bid
NEW YORK - NEW YORK (AP) - Billionaire and former New York City Mayor Michael Bloomberg launches Democratic presidential bid.
Copyright 2019 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.


Chinese authorities plan to ditch all foreign-made tech by 2023
See how you don't get to like them Apples
by Chris MerrimanCHINA HAS DEMANDED that all foreign computer equipment and software be stripped out from government and public offices by the end of 2022.
The move, reported by the Financial Times, adds a new layer of permafrost to the ongoing spat between the US and China. It comes after the White House added several Chinese companies to its "Entity List" of firms considered to be a national security risk and therefore excluded from trading with American companies, without a specific licence.
The FT estimates that 20 to 30 million pieces of equipment would need replacing, with the bulk of the work happening in 2021.
This is far from the first time that China has threatened to boycott Western tech. Around the time of Windows 8, Chinese TV News carried a prime time report claiming that the operating system was "spyware".
Around the same time, the country announced a process of "Dewindowsifying" (its words), with plans to move to a homegrown Linux variant. That plan was abandoned after Microsoft agreed to produce a special edition of Windows for the country that complied with local laws - and you can draw your own conclusions as to what that means.
It's not clear how strict the Chinese authorities plan to be. For example, Lenovo is a Chinese company, but the chips come from Intel, which is American.
One option could be to create special edition machines using Huawei's Kirin chips instead of Intel or AMD, but that's just our little theory.
Although this is the first tangible evidence of China sanctioning Western technology since the Entity List shenanigans began, it doesn't seem to be aimed at the US specifically, but rather is part of ongoing self-sufficiency drives in the country, and in fact, was going to happen at some point anyway.
That being the case, if the idea wasn't to annoy Baby Trump, the timing of this move couldn't be much worse. But that's a big 'if'. μ
Further reading
If you're buying a phone in China, you'll need to get your face scanned
- 02 Dec 2019
China to introduce an online gaming curfew for under 18s
- 08 Nov 2019
China is rolling out one of the world's biggest 5G networks
- 01 Nov 2019
China's APT10 hacking group linked to attacks on Airbus suppliers
- 27 Sep 2019


Seth Meyers Has To ‘Think Fast!’ In Interview With Jimmy Fallon
by Corey AtadIt was a late-night crossover event for the ages.
On Monday night, “Late Night” host Seth Meyers appeared as a guest on “The Tonight Show”, where he and Jimmy Fallon played a game of “Think Fast!”.
Each TV host had to ask the other a series of rapid-fire questions, with the other person responding with the first thing to pop in their head.
Things got very silly, very fast, of course.
Later during their interview, Meyers talked about his once-in-a-lifetime experience of going day drinking with Rihanna.
“Is there one thing that she doesn’t do well?” Fallon asked him.
There was one thing, Meyers said, revealing, “She, at one point, took my phone and took a bunch of pictures while we were day drinking. And then, when I went through them and looked at them, I’m like, ‘Rihanna’s bad at taking pictures.'”
Fallon then showed off one of those pictures, a very blurry one of Meyers looking at something and smiling.