@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.ApplicationConfig @this( govuk_wrapper: views.html.govuk_wrapper, govukPanel: GovukPanel ) @(name: String, dateOfBirth: String, nino: String, currentYearResidencyStatus: String, nextYearResidencyStatus: Option[String], currentTaxYear: Int, nextTaxYear: Int)(implicit request: Request[_], messages: Messages, appConfig: ApplicationConfig) @panelContent = { @if(nextYearResidencyStatus.nonEmpty) {
@messages("tax.year.period", currentTaxYear.toString, (currentTaxYear + 1).toString)
@messages(currentYearResidencyStatus)
@messages("tax.year.period", currentTaxYear.toString, (currentTaxYear + 1).toString)
@messages(nextYearResidencyStatus.get)
@messages("tax.year.period", currentTaxYear.toString, (currentTaxYear + 1).toString)
@messages(currentYearResidencyStatus)
@messages("match.found.top")
@if(nextYearResidencyStatus.isEmpty) {@messages("match.found.bottom.current-year.bottom", (currentTaxYear + 1).toString, name, (currentTaxYear + 1).toString, (currentTaxYear + 2).toString)
}@Html(messages("choose.something.else"))
@messages("feedback.link") @messages("feedback.text")
}


Before reporting a bug
- Search issue tracker for similar issues.
- Try the latest dev branch version of three.js.
- Refer to the Migration Guide when upgrading to the dev version.
How to report a bug
- Specify the revision number of the three.js library where the bug occurred.
- Specify your browser version, operating system, and graphics card. (for example, Chrome 23.0.1271.95, Windows 7, Nvidia Quadro 2000M)
- Describe the problem in detail. Explain what happened, and what you expected would happen.
- Provide a small test-case (http://jsfiddle.net). If a test-case is not possible, provide a link to a live version of your application.
- If helpful, include a screenshot. Annotate the screenshot for clarity.
How to contribute to three.js
- Make sure you have a GitHub account.
- Fork the repository on GitHub.
- Check the Contribution Guidelines.
- Make changes to your clone of the repository.
- Submit a pull request.


Jamie is a psychology PhD student based at the University of Sussex.
The categorize html plugin shows an HTML object on the screen. The subject responds by pressing a key. Feedback indicating the correctness of the response is given.
Parameters with a default value of undefined must be specified. Other parameters can be left unspecified if the default value is acceptable.
| Parameter | Type | Default Value | Description |
|---|---|---|---|
| stimulus | html string | undefined | The HTML stimulus to display. |
| choices | array of keycodes | jsPsych.ALL_KEYS |
This array contains the keys that the subject is allowed to press in order to respond to the stimulus. Keys can be specified as their numeric key code or as characters (e.g., 'a' , 'q' ). The default value of jsPsych.ALL_KEYS means that all keys will be accepted as valid responses. Specifying jsPsych.NO_KEYS will mean that no responses are allowed. |
| key_answer | numeric | undefined | The numeric key code indicating the correct response. |
| text_answer | string | ”” | A label that is associated with the correct answer. Used in conjunction with the correct_text and incorrect_text parameters. |
| correct_text | string | “Correct.” | String to show when the correct answer is given. Can contain HTML formatting. The special string %ANS% can be used within the string. If present, the plugin will put the text_answer for the trial in place of the %ANS% string (see example below). |
| incorrect_text | string | “Wrong.” | String to show when the wrong answer is given. Can contain HTML formatting. The special string %ANS% can be used within the string. If present, the plugin will put the text_answer for the trial in place of the %ANS% string (see example below). |
| prompt | string | null | This string can contain HTML markup. Any content here will be displayed below the stimulus. The intention is that it can be used to provide a reminder about the action the subject is supposed to take (e.g., which key to press). |
| force_correct_button_press | boolean | false | If set to true, then the subject must press the correct response key after feedback is given in order to advance to the next trial. |
| show_stim_with_feedback | boolean | true | If set to true, then the stimulus will be shown during feedback. If false, then only the text feedback will display during feedback. |
| show_feedback_on_timeout | boolean | false | If true, then category feedback will be displayed for an incorrect response after a timeout (timing_response is exceeded). If false, then a timeout message will be shown. |
| timeout_message | string | “Please respond faster.” | The message to show on a timeout non-response. |
| stimulus_duration | numeric | null | How long to show the stimulus for (milliseconds). If null, then the stimulus is shown until a response is given. |
| feedback_duration | numeric | 2000 | How long to show the feedback for (milliseconds). |
| trial_duration | numeric | null | The maximum time allowed for a response. If null, then the experiment will wait indefinitely for a response. |
In addition to the default data collected by all plugins, this plugin collects the following data for each trial.
| Name | Type | Value |
|---|---|---|
| stimulus | string | Either the path to the image file or the string containing the HTML formatted content that the subject saw on this trial. |
| key_press | numeric | Indicates which key the subject pressed. The value is the numeric key code corresponding to the subject’s response. |
| rt | numeric | The response time in milliseconds for the subject to make a response. The time is measured from when the stimulus first appears on the screen until the subject’s response. |
| correct | boolean | true if the subject got the correct answer, false otherwise. |
var categorization_trial = {
type: 'categorize',
stimulus: '
B
',
key_answer: 80,
text_answer: 'letter',
choices: [80, 81],
correct_text: "
Correct, this is a %ANS%.
",
incorrect_text: "
Incorrect, this is a %ANS%.
",
prompt: "
Press P for letter. Press Q for number.
"
};


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Prerequisites
- Python Programming
- Basics of Arrays
- Basics of Machine Learning
TensforFlow APIs
- The lowest level TensorFlow API, TensorFlow Core, provides the complete programming control, recommended for machine learning researchers who require fine levels of control over their model.
- The higher level TensorFlow APIs are built on top of TensorFlow Core. These APIs are easier to learn and use. Higher level APIs also provide convinient wrappers for repetitive tasks, e.g. tf.estimator helps to manage datasets, estimators, training, inference etc.
Terminologies
- Computational Graph defines the series of TensorFlow operations arranged in the form of graph nodes.
- Dataset: Similar to placeholders, but Dataset represents a potentially large set of elements that can be accessed using iterators. These are the preferred method of streaming data into a model.
- Node in a TensorFlow graph takes zero or more tensors as inputs and produces a tensor as an output, e.g. Constant is a type of node in TensorFlow that takes no inputs and outputs the value that it stores internally (as defined in the defination of Tensorflow nodes earlier).
- Operations are another kind of node in TensorFlow used to build the computational graphs, that consume and produce tensors.
- Placeholder is a promise to provide value later. These serve the purpose or parameters or arguments to a graph which represents a dynamic function based on inputs.
- Rank: The number of dimensions of a tensor (similar to rank of a matrix).
- Session, used for evaluation of TensorFlow graphs, encapsulates the control and state of the TensorFlow runtime.
- Shape: Often confused with rank, shape refers to the tuple of integers specifying the length of tensor along each dimension.
- Tensor: A set of primitive types shaped into an array of any number of dimensions. It can be considered a higher-dimensional vector. Tensorflow used numpy arrays to represent tensor values.
- TensorBoard is a TensorFlow utility used to visualize the computational graphs.
TensorFlow Imports
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
-
absolute_import: The distinction between absolute and relative imports can be considered to be analogous to the concept of absolute or relative file paths or even URLs, i.e. absolute imports specify the exact path of the imports while the relative imports work w.r.t. the working directory. Therefore for the code that is to be shared among peers, it is recommended to use absolute imports.
-
division: The import belongs to era when the debate on true division vs floor division was on in the python community i.e. for python 2.*. The import in python 3.* is not required as the regular division operator itself is the true division while the floor division is denoted by \\.
-
print_function: This import is again not necessary in python 3.*. It is used to invalidate the print as a statement in python 2.*. Post call to this function, print only has valid represetnation as a function, which has some apparent advantages over the print as a statement, e.g. print function can be used inside lambda function or list and dictionary comprehensions.
Generally all the __future__ imports are recommended to be kept at the top of the file because it changes the way the compiler behaves and the set of rules it follows.
Initialization
Basically, a TensorFlow Core program can be split into two sections:
- Building the computational graph
- Running the computational graph
Tensor initialization is done as follows:
import tensorflow as tf
a = tf.constant(3.0,
dtype=tf.float32,
name='a')
b = tf.constant(4.0,
name='b')
print(a)
print(b)
The output shows that the default type of a tensor is float32.
Tensor("a:0", shape=(), dtype=float32)
Tensor("b:0", shape=(), dtype=float32)
Also, the print statement does not print value assigned to the nodes. The actual values will be displayed only on evaluation of the nodes. In TensorFlow the evaluation of a node can only be done within a session as shown below.
Session
sess = tf.Session()
sess.run([a, b])
More complex TensorFlow graphs are built using operation nodes. For example,
total = tf.add(a, b)
print(total)
sess.run(total)
Mutliple tensors can be passed to a tf.Session.run
, i.e., the run
method handles any combination of tuples dictionaries,
sess.run({'ab': (a, b), 'total': total})
Some tensorflow functions return
tf.Operations
instead oftf.Tensors
. Also, the result of calling run on an Operation isNone
because Operations are run to cause a side-effect and not to retrieve a value.
During a call to tf.Session.run
, and tf.Tensor
holds a single value throughout that run. This is consistent with the notion that state of graph is saved in a session making sure once initialized a tensor will not have updated values unless operated upon.
TensorBoard
In order to visualize the TensorFlow graph, following command can be followed:
tf.summary.FileWriter('/path/to/save/', sess.graph)
or
writer = tf.summary.FileWriter('/path/to/save')
writer.add_graph(tf.get_default_graph())
Now run the following command on the terminal, ensure TensorBoard is installed.
tensorboard --logdir=/path/to/save
It leads to the above graph displayed, which is an apt representation of the minimal graph that has been built so far. But it is a constant graph as the input nodes are constants. In order to build a parameterized graph, placeholders are used as shown below.
Feeding Data using Placeholders
a = tf.placeholder(tf.float32, name='a')
b = tf.placeholder(tf.float32, name='b')
z = a+b
sess = tf.Session()
print(sess.run(z, {a: 3, b: 4}))
print(sess.run(z, feed_dict={a: [1, 2, 3], b: [4, 5, 6]}))
tf.summary.FileWriter('/path/to/save', sess.graph)
The
feed_dict
argument can be used to overwrite any tensor in the graph.
The only difference between placeholders and other tf.Tensors
is that placeholders throw and error if no value is fed to them.
run(
fetches,
feed_dict=None,
options=None,
run_metadata=None
)
Runs operations and evaluates tensors listed in fetches argument. The method will run one step of TensorFlow computation, by running necessary graph consisting of tensors and operations to evaluate the dependencies of Tensors listed in fetches, substituting the values listed in the feed_dict for the corresponding values.
The fetches argument can be a single graph element, nested list, tuple, namedtuple, dict, or OrderedDict that consists of graph elements as its leaves.
The graph element may belong to one of the following classes:
- tf.Operation: fetched value is None.
- tf.Tensor: fetched value is a numpy ndarray containing the value of the tensor.
- tf.SparseTensor: fetched value is a tf.SparseTensorValue.
- get_tensor_handle op: fetched value is a numpy ndarray containing the handle of the tensor.
- string: name of a tensor or operation in the graph.
The value returned by
run()
has the same shape as the fetches argument, where leaves are replaced by corresponding values returned by TensorFlow.
Example code:
import collections
a = tf.constant([10, 20])
b = tf.constant([1.0, 2.0])
data = collections.namedtuple('data', ['a', 'b'])
v = sess.run({'k1': data(a, b), 'k2': [b, a]})
print(v)
It can be observed on executing the code that the output of run saved has the same structure as the input to the fetches argument, i.e. a dictionary of namedtuple of lists and list of lists.
The keys in feed_dict can belong to one of the following:
- If key is
tf.Tensor
: value maybe a scalar, string, list, or numpy array. - If key is
tf.Placeholder
: shape of the value will be checked with shape of the placeholder for compatibility. - If key is
tf.SparseTensor
: value should betf.SparseTensorValue
. - If key is nested tuple of Tensors or SparseTensors, then the value should be follow the same nested structure that maps to corresponding values in the key’s structure.
Each value in the feed_dict must be convertible to a numpy array of the dtype of corresponding key.
The following errors are raised by run function:
RuntimeError
: If the Session is in invalid state or closed.TypeError
: If fetches or feed_dict keys are of inappropriate types.ValueError
: If fetches or feed_dict keys are invalid or refer to a tensor that does not exist.
Importing Data
tf.data
api helps to build complex input pipelines. It basically helps deal with large amounts of data, maybe belonging to different formats and apply complicated transformations to the data such as image augmentation or handling text sequences of different lengths for preprocessing and batch processing use cases. The pipelines help abstract processes and also modularize the code for easier debugging and managing of the code.
Some of the abstractions dataset introduces in TensorFlow are summarized below:
tf.data.Dataset
is used to represent a sequence of elements where each element contains one or more Tensor objects.
Creating a source (Dataset.from_tensor_slices()
) contructs a dataset from one or more tf.Tensor
objects, while applying a transformation (Dataset.batch()
) contructs a dataset from one or more tf.data.Dataset
objects.
tf.data.Iterator
is used to extract elements from a dataset, and acts as an interface between the input pipeline and the models.
The get_next
method of iterator is called for streaming the data.
Simplest iterator is made using make_one_shot_iterator
method as shown below. It is associated with a particular dataset and iterates through it once.
data = [
[0, 1,],
[2, 3,],
[4, 5,],
[6, 7,],
]
slices = tf.data.Dataset.from_tensor_slices(data)
next_item = slices.make_one_shot_iterator().get_next()
while True:
try:
print(sess.run(next_item))
except tf.errors.OutOfRangeError:
break
Reaching the end of dataset, if get_next
is called, OutOfRangeError
is thrown by the Dataset.
For more sophisticated uses, Iterator.initializer
is used that helps reinitialize and parameterize an iterator with different datasets, including running over a single or a set of datasets multiple number of times in the same program.
Placeholders work for simple experiments, but Datasets are the preferred method of streaming data into a model.
A dataset consists of elements that each have exactly the same structure, i.e. an element contains one or more tf.Tensor
objects, called components. A component has tf.DType
representing the type of elements in the tensor, and a tf.TensorShape
(maybe partially specified, for example batch size might be missing but dimension of elements in batch may be present) representing the static shape of each element.
Similarly the Dataset.output_types
and Dataset.output_shapes
inspect the inferred types and shapes of each component of the dataset element.
It is optional but often helps to name the components in a dataset element. To summarize, one can use tuples, collections.namedtuples
or a dictionary mapping strings to tensors to represent a single element in a Dataset
.
Sample code to see the above properties:
def print_dtype(input_data, print_string=""):
if type(input_data) == tuple:
print_string += "("
for _ in input_data:
print_string = print_tuple(_, print_string)
print_string += ")"
else:
print_string += str(input_data.dtype) + ", "
return print_string
def print_dimension(input_data, print_string=""):
if type(input_data) == tuple:
print_string += "("
for _ in input_data:
print_string = print_dimension(_, print_string)
print_string += ")"
else:
print_string += str(input_data.get_shape()) + ", "
return print_string
dataset1 = tf.data.Dataset.from_tensor_slices(tf.random_uniform([4, 10]))
element1 = dataset1.make_initializable_iterator().get_next()
print("dataset1:")
print("\t", print_dtype(element1))
print("\t", print_dimension(element1))
print("\t", dataset1.output_types)
print("\t", dataset1.output_shapes)
dataset2 = tf.data.Dataset.from_tensor_slices(
(tf.random_uniform([4]),
tf.random_uniform([4, 100], maxval=100, dtype=tf.int32)))
element2 = dataset2.make_initializable_iterator().get_next()
print("dataset2:")
print("\t", print_dtype(element2))
print("\t", print_dimension(element2))
print("\t", dataset2.output_types)
print("\t", dataset2.output_shapes)
dataset3 = tf.data.Dataset.zip((dataset1, dataset2))
element3 = dataset3.make_initializable_iterator().get_next()
print("dataset3:")
print("\t", print_dtype(element3))
print("\t", print_dimension(element3))
print("\t", dataset3.output_types)
print("\t", dataset3.output_shapes)
Outputs:
dataset1:
<dtype: 'float32'>,
(10,),
<dtype: 'float32'>
(10,)
dataset2:
(second:<dtype: 'int32'>, first:<dtype: 'float32'>, )
(second:(100,), first:(), )
{'second': tf.int32, 'first': tf.float32}
{'second': TensorShape([Dimension(100)]), 'first': TensorShape([])}
dataset3:
(d1:<dtype: 'float32'>, (second:<dtype: 'int32'>, first:<dtype: 'float32'>, ))
(d1:(10,), (second:(100,), first:(), ))
{'d1': tf.float32, 'd2': {'second': tf.int32, 'first': tf.float32}}
{'d1': TensorShape([Dimension(10)]), 'd2': {'second': TensorShape([Dimension(100)]), 'first': TensorShape([])}}
So, the basic mechanics of import data can be listed as follows:
-
Define a source is the first step in defining an input pipeline. For example, data can be imported from in-memory tensors using
tf.data.Dataset.from_tensors()
ortf.data.Dataset.from_tensor_slices()
. It can also be imported from disk if the data is in recommended TFRecord format usingtf.data.TFRecordDataset
-
Transformations can be applied on any dataset to obtain subsequent dataset objects. This can be achieved by chaining preprocessing or transformation operations. For example, per-element transformations can be applied using
Dataset.map()
or multi-element transformations can be applied usingDataset.batch()
.
Dataset transformations support datasets of any structure. The element structure determines the argument of a function. For example,
dataset1 = dataset1.map(lambda x: ...)
dataset2 = dataset2.flat_map(lambda x, y: ...)
dataset3 = dataset3.filter(lambda x, (y, z): ...)
From the example above it can be seen that the lambda function can take any structure as the input based on the structure of an element in the dataset, however complex it is.
-
Define an iterator to stream data into the model. The iterator can be one-shot iterator as in the example above or one of the types listed below.
-
One-shot iterator is the simplest iterator that supports iterating only once through the dataset and does not require an explicit initialization. Hence, as a by-product, it does not allow parameterization.
-
Initializable iterator requires an explicit
iterator.initializer
operation before using it. At the cost of this inconvinience, it gives the flexibility to parameterize the defination of dataset using placeholders.
-
max_value = tf.placeholder(tf.int64, shape=[])
dataset = tf.data.Dataset.range(max_value)
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
# parameter passing for the placeholder
sess.run(iterator.initializer, feed_dict={max_value: 10})
for i in range(10):
value = sess.run(next_element)
assert i == value
- Reinitializable iterator can be initialized from multiple different dataset objects. Basically, while the datasets may change they have the same structure attributed to each element of the dataset. A reinitializable iterator is defined by its structure and any dataset complying to that structure can used to initialize the iterator.
# two different datasets with same structure
training_dataset = tf.data.Dataset.range(100).map(
lambda x: x + tf.random_uniform([], -10, 10, tf.int64))
validation_dataset = tf.data.Dataset.range(50)
# define the iterator using the structure property
iterator = tf.data.Iterator.from_structure(training_dataset.output_types,
training_dataset.output_shapes)
next_element = iterator.get_next()
training_init = iterator.make_initializer(training_dataset)
validation_init = iterator.make_initializer(validation_dataset)
# initialize for training set
sess.run(training_init)
sess.run(next_element)
# reinitialize for validation set
sess.run(validation_init)
sess.run(next_element)
- Feedable iterator is used along with
tf.placeholder
to selectIterator
to use in each call oftf.Session.run
, via thefeed_dict
mechanism. It does not require one to initialize the iterator from the start of a dataset when switching between the iterators. Thetf.data.Iterator.from_string_handle
can be used to define a feedable iterator.
string_handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(
string_handle,
training_dataset.output_types,
training_dataset.output_shapes
)
next_element = iterator.get_next()
# get string handles of each iterator
training_handle = sess.run(training_iterator.string_handle())
validation_handle = sess.run(validation_iterator.string_handle())
# using training iterator
sess.run(next_element, feed_dict={string_handle: training_handle})
# using validation iterator
sess.run(next_element, feed_dict={string_handle: validation_handle})
Layers
A trainable model is implemented in TensorFlow by means of using Layers which adds trainable parameters to a graph. A layer packages the variables and the operations that act on them. For example, densely-connected layer performs weighted sum across all inputs from each output and applies an optional activation function. The connection weights and biases are managed by the layer object.
In order to apply layer to an input, the layer is called as a function with input as an argument.
After calling the layer as a function, based on the inputs to the layer it sets up shape of weight matrices compatible with the input. Now, the layers contain variables that must be initialized before they can be used.
import tensorflow as tf
x = tf.placeholder(tf.float32, shape=[None, 3], name='x')
linear_model = tf.layers.Dense(units=1, name='dense_layer')
y = linear_model(x)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
sess.run(y, {x: [[1, 2, 3],[4, 5, 6]]})
Calling
tf.global_variables_initializer
only creates and returns a handle to tensorflow operation which will initialize all global variables on call oftf.Session.run
.
For each layer class (like tf.layers.Dense
) there exists a shortcut function in TensorFlow (like tf.layers.dense
) that creates and runs the layer in a single call. But this approach allows no access for the tf.layers.Layer
object which might cause difficulties in debugging and introspection or layer reuse possibilities.
The graph of a linear model looks as shown below.
where the linear_model block internally has the structure as shown in Fig. 4 which abides by \eqref{1}.
where W is the weights being fed by the kernel, x is the input vector being fed by the input placeholder and b is the bias.
Feature columns can be experimented with using
tf.feature_column.input_layer
function for dense columns as input andtf.feature_column.indicator_column
for categorical indicators as input.
features = {
'sales' : [[5], [10], [8], [9]],
'department': ['sports', 'sports', 'gardening', 'gardening']}
department_column = tf.feature_column.categorical_column_with_vocabulary_list(
'department', ['sports', 'gardening'])
department_column = tf.feature_column.indicator_column(department_column)
columns = [
tf.feature_column.numeric_column('sales'),
department_column
]
inputs = tf.feature_column.input_layer(features, columns)
var_init = tf.global_variables_initializer()
table_init = tf.tables_initializer()
sess = tf.Session()
sess.run((var_init, table_init))
print(sess.run(inputs))
Feature columns can have internal state, like layers and so need to be initialized. Similarly, categorical columns use lookup tables internally and hence require tf.table_initializer
additionally.
Categorical input fed using indicator vectors are one-hot encoded.
Training
- Define the data and labels.
x = tf.constant([[1], [2], [3], [4]], dtype=tf.float32)
y_true = tf.constant([[2], [4], [6], [8]], dtype=tf.float32)
- Define the model
y_pred = tf.layers.dense(x, units=1)
At this point, model output can be evaluated
# initialize session and variable
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
# extract model output
sess.run(y_pred)
The output would not be correct as the model is not trained to optimize model parameters for accuracy.
- To optimize the model, a loss has to be defined.
Using mean squared error,
loss = tf.losses.mean_squared_error(
labels=y_true,
predictions=y_pred)
sess.run(loss)
- After defining a loss, one of the optimizers provided by TensorFlow out of box can be used as optimization algorithm.
The optimizers are defined in tf.train.Optimizer
. Using the simplest gradient descent implemented in tf.train.GradientDescentOptimizer
,
Gradient Descent modifies each variable according to the magnitude of the derivative of loss with respect to that variable.
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
- Training the model
At this stage the model graph is built and the only task pending is to call the evaluate train iteratively to minimize loss by optimizing model trainable parameters by updating the corresponding variables.
for i in range(100):
_, loss_value = sess.run((train, loss))
- Evaluate model predictions
predictions = session.run(y_pred)
- Model Graph
The model graph generated from the above training example can be seen below.
Since the model inputs are constants, it can be seen that the dense layer in the graph has no input being fed from outside, rather is a part of the dense block.


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Calumn Dalton, Hyde Benefit Service Centre
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Note
{{ data['handover-note-reallocate'] }}
{% include "includes/v2/date.html" %} at 10:05
Calumn Dalton, Hyde Benefit Service Centre
{% endif %} {% if data['scenario'] === 'agentEditEscalate' %}
Allocated to
{% include "includes/v2/date.html" %} at 11:15
Birkenhead Service Centre
Sent to
Birkenhead Benefit Centre
{% include "includes/v2/date.html" %} at 9:33
Bobby Brown, Hyde Benefit Service Centre
Handover created
JSA / maintenance / Change of address
{% include "includes/v2/date.html" %} at 9:33
Bobby Brown, Hyde Benefit Service Centre
{% elif data['scenario'] === 'customerUpdate' %}
Note
Called customer to inform them they need to provide there last two payslips, but customer didn't answer.
{% include "includes/v2/date.html" %} at 11:15
Callum Dalton, Birkenhead Service Centre
Allocated to
{% include "includes/v2/date.html" %} at 11:15
Birkenhead Service Centre
Sent to
Birkenhead Benefit Centre
{% include "includes/v2/date.html" %} at 9:33
Bobby Brown, Hyde Benefit Service Centre
Handover created
JSA / maintenance / Change of address
{% include "includes/v2/date.html" %} at 9:33
Bobby Brown, Hyde Benefit Service Centre
{% elif data['scenario'] === 'agentClear' %}
Handover created
JSA / maintenance / Change of address
{% include "includes/v2/date.html" %} at 9:33
Bobby Brown, Hyde Benefit Service Centre
{% elif data['scenario'] === 'allocatorEdit' %}
Note
Escalated due to customer contact and not being called back within the 3 hours as stated
{% include "includes/v2/date.html" %} at 12:40pm
Julie Smith, Hyde Benefit Service Centre
Escalated
{% include "includes/v2/date.html" %} at 12:40pm
Julie Smith, Hyde Benefit Service Centre
Allocated to
{% include "includes/v2/date.html" %} at 11:15
Birkenhead Service Centre
Sent to
Birkenhead Benefit Centre
{% include "includes/v2/date.html" %} at 9:33
Bobby Brown, Hyde Benefit Service Centre
Handover created
JSA / New Claim / Payment query
{% include "includes/v2/date.html" %} at 9:33am
Bobby Brown, Hyde Benefit Service Centre
{% else %}
Handover created
JSA / New claim / Payment query
{% include "includes/v2/date.html" %} at 9:33
Bobby Brown, Hyde Benefit Service Centre
{% endif %}


Tony Simon |
AcademicsMasters in Human Resource from St Joseph’s College of Business Administration, Bangalore |
Experience2 years of HR experience as a part of Flipkart talent acquisition team. |
Please join us in welcoming Tony Simon to Riflexions family.
Tony joins us in the capacity of Talent Transformation lead at Riflexions.
In simple terms he will be your Go To person for all HR related queries.
We wish Tony a sucessful career with Riflexions and we look forward to building our strength and dream for a people oriented organisation. Tony will be reaching out to you folks in the coming days and weeks individually.


{% extends 'business/base.html' %} {% load bootstrap3 %} {% block page %} Orders Page Business Statistics Log out


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(Appendix 7)
A message can be left by a user or within a forwarding connection. The recording mechanism is always the same. The recording command is always like : Sx desti @ bbs < exped $ ident + filename Only recipient field is mandatory, all other fields are optional. Appending a filename is a possibility reserved to the sysop. The name must be complete, including logic unit and complete path (C:\FBB\SYSTEM\TEST.TXT). When receiving the command line, a first test checks if a route exists when a route has been specified, or if the message must be automatically routed when no route was specified. The title of the message is then asked to the user. If the title is a missing, the message is canceled and the user returns to the main menu. The text of the message is then asked to the user. The software checks possible preamble lines. These lines give information on the previous BBS having routed this message. They all begin by R: on first column. The BBS callsign is given behind the @ character within the preamble line. All adjacent BBS mentioned in this preamble will be included in the "already forwarded" list, and will not be concerned by this message. This list specific to each message can be displayed with the $ or FN command followed by the message number. When receiving a /EX in first column, or a Ctrl Z, a message number is then assigned, The BID (or MID if private) and the list of adjacent BBS concerned by this message are created. All these information are sent to the user when acknowledging the message. In case of disconnection before the /EX or Ctrl Z, the whole message will be lost, and the texts already stored are deleted. All information about the message (sender, recipient, route, MID, title, etc...) are stored in the DIRMES.SYS file. The text of the message is stored in a sub-directory of the MAIL directory. The sub-directory is MAILn where n is the last digit of the message number. The name of the file corresponds to the message number 123 is M_000123.MES, the number is 6 digits wide, in this case it is in the sub-directory MAIL3. The message number uses a long integer (32 bits), the number boundary is very far (more than 4 billions !). This page was last updated


public interface ZookeeperConfigOptions
Zookeeper options are passed via system properties prior to the ZK Methods/classes being invoked. This implies that:
| Modifier and Type | Field and Description |
|---|---|
static String |
DEFAULT_ZK_ENABLE_SASL_CLIENT
Default flag for the ZK client: "true".
|
static String |
PROP_ZK_ALLOW_FAILED_SASL_CLIENTS
should the ZK Server Allow failed SASL clients: "zookeeper.allowSaslFailedClients".
|
static String |
PROP_ZK_ENABLE_SASL_CLIENT
Enable SASL secure clients: "zookeeper.sasl.client".
|
static String |
PROP_ZK_KINIT_PATH
Path to a kinit binary: "zookeeper.kinit".
|
static String |
PROP_ZK_SASL_CLIENT_CONTEXT
System property for the JAAS client context : "zookeeper.sasl.clientconfig".
|
static String |
PROP_ZK_SASL_CLIENT_USERNAME
The SASL client username: "zookeeper.sasl.client.username".
|
static String |
PROP_ZK_SERVER_MAINTAIN_CONNECTION_DESPITE_SASL_FAILURE
Should ZK Server allow failed SASL clients to downgrade to classic
authentication on a SASL auth failure: "zookeeper.maintain_connection_despite_sasl_failure".
|
static String |
PROP_ZK_SERVER_REALM
Kerberos realm of the server: "zookeeper.server.realm".
|
static String |
PROP_ZK_SERVER_SASL_CONTEXT
The SASL Server context, referring to a context in the JVM's
JAAS context file: "zookeeper.sasl.serverconfig"
|
static String |
SCHEME_DIGEST
ID scheme for digest auth: "digest".
|
static String |
SCHEME_SASL
ID scheme for SASL: "sasl".
|
static final String PROP_ZK_ENABLE_SASL_CLIENT
PROP_ZK_SERVER_MAINTAIN_CONNECTION_DESPITE_SASL_FAILURE
.
As a result, clients will default to attempting SASL-authentication, but revert to classic authentication/anonymous access on failure.
static final String DEFAULT_ZK_ENABLE_SASL_CLIENT
static final String PROP_ZK_SASL_CLIENT_CONTEXT
Default value is derived from
ZooKeeperSaslClient.LOGIN_CONTEXT_NAME_KEY
static final String PROP_ZK_SASL_CLIENT_USERNAME
Set this to the short name of the client, e.g, "user",
not user/host
, or user/host@REALM
static final String PROP_ZK_SERVER_SASL_CONTEXT
static final String PROP_ZK_SERVER_MAINTAIN_CONNECTION_DESPITE_SASL_FAILURE
static final String PROP_ZK_ALLOW_FAILED_SASL_CLIENTS
static final String PROP_ZK_SERVER_REALM
static final String PROP_ZK_KINIT_PATH
"/usr/bin/kinit"
static final String SCHEME_SASL
static final String SCHEME_DIGEST
Copyright © 2020 Apache Software Foundation. All rights reserved.


{block name="style"}{/block}
{block name="header"} {include file="common/header" /} {/block} {block name="page"}
{block name="content"}{/block} {block name="aside"} {include file="common/aside" /} {/block}
{/block} {block name="footer"} {include file="common/footer" /} {/block}
{block name="script"}{/block}


Member Discipline Committee, Vice chairman Grievance Redressal Committee
Dean Student Welfare
Nodal Officer Academics TEQIP-III, OIC Purchase(College), OIC Classroom, OIC Civil and Mechanical Maintenance, OIC Water Supply, Fire, Land Related Matters
Nodal Officer Procurement TEQIP,OIC Electrical Maintenance,Nodal Officer PMKVY
Coordinator TEQIP-III, Nodal Officer,AISHE
Public Information Officer
OIC Exam
Nodal Officer AICTE (approval), Nodal Officer Innovation and Startup Cell & Tinkering Lab, Nodal Officer NPTEL, Nodal Officer IPR, OIC Library
Deputy TEQIP Coordinator, OIC Internship, Nodal Officer Twinning Activities
OIC College Website
OIC College Internet
NBA Coordinator 1(Civil Engineering), OIC T&P
NO,Finance College
Dy. Training and Placement Officer, Dy. OIC Exam
Nodal Officer Equity
OIC Alumni, OIC Personality Development
Coordinator Environment Management Framework
NBA Coordinator 3(Mechanical Engineering)
OIC Cultural Activities, Deputy OIC Personality Development
Dy.OIC T&P, Dy OIC Library
Proctor, OIC Sports
Gate Coordinator
Dy. Proctor
Nodal Officer UBA
Dean Administration and Academic
OIC College Time Table, Nodal Officer-Finance(TEQIP)
Dy. OIC Purchase(College),Dy. OIC Classroom, Dy.OIC Civil and Mechanical Maintenance, Dy.OIC Water Supply, Fire, Land related matters
Nodal Officer Community Services
Dy. Dean Academic ,NBA Coordinator2(CSE)


Holds Number Of Client Operations
counter :Missing
Own currency: Name Error
Destination currency: Name Error
New Rate: Value Error


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Jenny
Lee
Stereotype Threat
072715
What is a stereotype threat?
Stereotype threat is when negative stereotypes surround a specific social group, which in turn can negatively impact one's performance due to that stereotype.
In other words, if you belong to a group of people who are stereotyped in a negative way ( and are aware that you belong to this group ),this might have some negative impact on your performance because of that stereotype.
For example, a girl playing a video game may not play as well as she could have if she is aware of the fact that there is a stereotype that "girls are bad at video games".
The example above is only a small consequence of negative stereotyping. In the bigger picture of things, negative stereotyping can cause all kinds of consequences.
Decreased performance could cause a chain of reactions, one being decreased self-esteem, which could in turn lead to decreased motivation to try new things, which can then lead to something else.
If the girl in the video game example cannot play to her fullest potential because of her knowledge of the idea "girls are bad at video games", she may come to the conclusion that maybe she really is bad, which can eventually discourage her from even trying to play the game altogether.
It is not so hard to see that negative stereotyping could discourage individuals from entering into specific fields in school and work. Such "filtering" of select groups of people can turn away potentially amazing people from achieving what they can, and consequentially prevent the creation of something truly great.


{% load i18n%}
{% blocktrans with code=code expiration_time=expiration_time %}Hello,
You have requested email verification. Your verification code is: {{code}}
Please note, that the code is only valid before: {{ expiration_time }}{% endblocktrans %}


---
subject: This is a request to DELETE a HOLIDAY record
---
HOLIDAY Request This is a request to DELETE a HOLIDAY record. Employee (1018) {\{firstname\}} {\{lastname\}} Start {\{DATE\}} End {\{DATE+2\}} Dates Requested 13 1.00 d 14 1.00 d Total Duration: 2.00 day(s) Requested {\{Date\}} Additonal User Comments Request from {\{Date\}} to {\{Date+2\}}
Please login to the HR Database to authorise or reject this request. You may wish to check the planner within the system to check all of your team members holidays and absences. To do this click on the planner link from the Quick Links menu.


Internship Experience
From the months of January 2015 to the end of June 2015 I worked on the Software Quality Assurance Team for Broadcom Limited.
I mainly worked in developing python scripts to automate manual testing on SoCs, which are used in ethernet modems. I also had to perform manual testing for cases which could not be automated. This gave me a lot of experience dealing with an embedded linux environment.
This was a challenging position because I had to develop different test cases for each feature based off of feedback from customers and our software dev team. These features could be very simple, i.e. USB, PCIe, Ethernet, etc. Nevertheless, they could become more complex when developing test scenarios for booting different types of root filesystems (i.e. NAND, NOR, Initramfs).
My team was responsible for automating these test cases in the most efficient way. Therefore, while I was at Broadcom I worked on a major automation framework change. This was a team effort to develop a framework that could allow for the development of cleaner and more scalable code. This was accomplished by utlizing python's object oriented features to add many abstraction levels to our code, accompanied by a unique configuration file that the QA engineer could edit as she/he tests different products.
Things Learned:
- Embedded Linux OS exposure
- Building images using the open-embedded Yocto Project
- Flashing images
- Understanding of bash scripting
- Python Development
- Better understanding of OO principles
- Creating wrappers for existing libraries to perform speciliazied tasks (i.e communication from local server to SoC to tell the embedded linux OS what commands to perform and understand output)
- Utilizing PEP 8 style guide accompanied with docstring for better documentation of code
- Version Control Tools
- Used Git Lab UI when developing python scripts
- Exposure to using version control user interface as well as understanding the git command line tools
- Was responsible for creating unique branches for each feature, testing the code and providing the results, merging this code back into the master branch to be used by the Software QA team as a whole
- I love software development and being apart of team!


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Code based on twitteroauth by @abraham


|
We extend our deepest condolences and sympathies to the family of one of our late members.
VOLUNTEER OPPORTUNITY: REMINDERS: |
First meeting of 2019! |
Some Bajan trivia - 'How well do you remember'
By: Marjorie McCauley, a CFL Bajan
Can you remember these aspects of old Barbados?
The former name for a district hospital?
Answer: almshouse
What we used to call a toilet?
Answer: chamber
What was the previous name of the Sir Grantley Adams International Airport?
Answer: Seawell
The name for a straw hat worn by school children years ago.
Answer: Panama hat
Today we say chickens, in the olden days they said ____.
Answer: yard fowl
A stiff undergarment worn by women in the olden days.
Answer: a can-can
A moveable cupboard used for storing food.
Answer: a larder
The hat once worn by the plantation owners.
Answer: cork hat
'Yuh know, Patsy did always a fair weather friend'. What does that mean?
Answer: She's unreliable person
'Yuh tell me, why would anybody let a 14-year old back back a car?'. What is the 14-year old doing to the car?
Answer: reversing
Bajan trivia by Marjorie McCauley is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Because when Bajans hangout, they say the darnest things!
Summaries of past meetings
Need to catch up on what happened at a past meeting? Or you wish to re-live the moments? Check out the summaries below.


Chapter 2 Approach
We use cognitive modeling and neuroimaging to understand how people learn and make decisions. We are interested how these processes do, or do not, change as people get older. The ultimate goal of this research is to promote health and wellbeing across adulthood.
Recognize that this work is inherently interdisciplinary, meaning we will use tools and knowledge from traditional fields like psychology, neuroscience, and economics. Because it requires competency in so many different domains, the learning curve can be steep and can feel overwhelming - but this is also what makes it interesting and rewarding work. Hang in there. It will get easier.
2.1 Mentorship
“When you get to the top of the mountain / Pull the next one up.” - Marc Kelly Smith
This quote, from a poem by Marc Kelly Smith, illustrates my mentorship philosophy. As a mentor, I am here to help you grow as a scientist. Depending on where you are in your career, this can mean different things. For instance,
For undergraduate students/post-bacc RAs: This often means helping you decide if science in general, or the cognitive neuroscience of aging and decision making, is the right fit for you. It also means developing skills, like writing, data analysis, and coding, that can be used in many domains. It also means helping you develop foundational knowledge in our research area and whenever possible, helping to connect you to others with shared interests.
For graduate students: This can mean helping you navigate graduate school, develop a deeper understanding of our research area, grow your advanced research and project management skills, and improve your writing and oral commiunication. It also means helping you develop a professional network, and create long-term career plan.
For postdoc trainees: It can mean helping you navigate grantwriting landscape and the academic job search process. It also means helping you broaden your professional network and refine your long-term career plans.
You will be assigned a mentor within the lab. If you are a lab manager, graduate student, or postdoc, the Lab Director will likely be your mentor. If you are an undergraduate, you will likely be reporting to the lab manager, a graduate student, or a postdoc. If you are not sure who your mentor is, ask! It is likely to the person leading the project you are working on.
Postdocs/PhD Students/Lab Managers and others leading a project (for instance, a undergraduate working on an honor’s thesis) can expect to meet with me regularly (i.e. weekly). The purpose of these meetings is to give you a chance to directly ask me for assistance on the project (or projects) you are working on. You should come prepared to each meeting with an agenda - you are expected to lead the meeting. Topics for meetings can include progress reports, questions, new ideas, etc.
Others in the lab will have regular meetings with their assigned mentors.
2.2 Independent Development Plans (IDPs)
To facilitate your grown as a scientist, we will use individual development plans (IDPs). The structure of IDPs will vary depending on your role in the lab, but generally they will set and track short-term and long-term goals. These will be created when you join the lab, shared with your mentor, and be revisited at the beginning of each semester (i.e. Fall, Spring, and Summer).
Postdoc/Graduate Students/Post-bacc RAs
Undergraduate Students
You will complete the Expectations document when you first join the lab and a copy will be saved by your mentor in the AWL Box folder.
You will complete the Letter of Rec document at the beginning of each subsequent semester you work in the laboratory. Copies of these documents will be saved by your mentor in the AWL Box folder.
2.3 Feedback
You should expect to regularly receive feedback from your mentor and your peers. Feedback, especially negative feedback, can be discouraging and overwhelming. Please recognize that the purpose of feedback is to improve your work and help you meet your goals. Also know that giving critical and constructive feedback is an time-consuming effort and try to accept feedback in the spirit in which it is offered. As a group, we will discuss how to give and receive feedback.


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A milestone for Office Delve | Office Blogs
blogs.office.com
Today’s post was written by Julia White, general manager of Office 365 Technical Product Management.
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Minecraft to Join Microsoft
news.xbox.com
Today is an incredibly exciting day for our team as Mojang and the Minecraft franchise join Microsoft. Minecraft is one of the most popular video ...
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Cut off from the outside world by a rare disorder, Windows 8 empowers ...
blogs.microsoft.com
By Paul Nyhan and Aimee Riordan Alejandra Tristan was an active kid in the usual ways: running, jumping, tumbling in gymnastics, dancing ballet, ...
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Digital Business Technologies Dominate Gartner 2014 Emerging Technologies ...
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By Hung LeHong and Jackie Fenn Gartner, Inc. Imagine this: As you leave for work in the morning, your house automatically turns down the heat and ...
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Ex-Raven donates kidney to bro, ex-Steeler
espn.go.com
Ma'ake and Chris Kemoeatu share a lifelong love of football, an alma mater and now, a pair of kidneys.
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7 times to turn down a media interview
www.prdaily.com
You shouldn't take every interview that comes your way. Tell the reporter
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Apple Finally Killed the iPod Classic. RIP iPod Classic.
gizmodo.com
Today is a sad day. Today is also a happy day for several exciting reasons, but today is a sad day for one very specific reason. Today is the day the ...
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Lasker Winner Calls for More Genetic Testing for Cancer
www.nytimes.com
A geneticist wins a prestigious Albert and Mary Lasker Foundation award and uses the spotlight to call for much wider genetic screening for breast ...
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With a Bitcoin ETF, Risk Isn't Virtual
online.wsj.com
Two firms are racing to bring a publicly traded bitcoin fund to market. Potential investors should consider the extraordinary risks of investing in ...
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Stop trying to sell me wrist-worn smartphones
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Smartwatches have a big problem. I don’t blame Google or Android Wear. Hell, I don’t even blame LG, Motorola, Asus, Sony, or Samsung. These guys are ...
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White House Names Google's Megan Smith Chief Technology Officer
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Google and the White House just sidled a step closer together: Google executive Megan Smith will be the United States' next chief technology officer, ...
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Digital Marketing Interview Series with Marketing Communications Manager ...
www.smartt.com
Jennifer Henshaw is a Marketing Communications Manager at Microsoft. Here are her thoughts on digital marketing.
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New stadium deal for Raiders give s team land, pays off debt
www.sfgate.com
[...] Zach Wasserman, an attorney representing backers of a hoped-for sports, housing and retail complex called Coliseum City, says the "basic terms" ...
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Cowboys schedule news conference to announce Sam signing
profootballtalk.nbcsports.com
Michael Sam will be introduced as a Cowboy this morning. The Cowboys have scheduled a news conference to make it official that Sam, the NFL’s first ...
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Partner Profile: JumpStart moves games to Microsoft Azure to improve reach ...
blogs.msdn.com
Steve “Guggs” Guggenheimer is the Corporate Vice President of Developer Platform & Evangelism and Chief Evangelist for Microsoft. Learn more about ...
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OneDrive gets smarter on your phone: Access your work documents, new photo ...
blog.onedrive.com
OneDrive is the one place for your files that you can access from virtually anywhere. Read our blog to hear the latest news and updates!
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Royal Caribbean Announces 40K Windows Tablets
blogs.windows.com
We are thrilled that today, Royal Caribbean International announced that every shipboard employee in the Royal Caribbean International fleet will ...
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This Android Shield Could Encrypt Apps So Invisibly You Forget It's There ...
www.wired.com
In the post-Snowden era, everyone wants to make encryption easier. Now, one group of researchers has created a tool intended to make it invisible. A ...
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MIT Invents A Shapeshifting Display You Can Reach Through And Touch
www.fastcodesign.com
The Tangible Media Group at MIT's Media Lab has unveiled a futuristic display made of atoms, not pixels.
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Workers of the world, log in
www.economist.com
A LOT of room in an office is a sign either of a blossoming company or a shrivelling one. Happily for Frank Han, the empty space at Kenandy, a ...
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How to make Facebook work better for you: Quit the 'Like'
venturebeat.com
"Once I removed the Like function from my own behavior, I almost started to like using Facebook," writer Elan Morgan said.
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No, Celebrity Deaths Do Not Come in Threes
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Robin Williams and Lauren Bacall died this week, but despite the all-too-human desire to find patterns in life, the odds of another celebrity death ...
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Guy Makes Calendar for Losing Fantasy
bleacherreport.com
Most fantasy leagues have an award for the winner, but some have a punishment for the loser, and this one is pretty hilarious. One man was forced to ...
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10 Things You Didn't Know You Could Do With Your iPhone
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VIDEO: We reveal how you can do useful tricks and features hidden in your iPhone.
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This Piece Of Paper Folds Itself Into A Robot | TechCrunch
techcrunch.com
Imagine a ream of flat sheets sent into space, folding themselves to form a satellite, or a flat robot sent into a collapsed building to assemble ...
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The Secret Bataclysm: White Nose Syndrome and Extinction | WIRED
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In just 8 years, bats have gone from the most common mammal in the US to endangered species candidates.
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For all kinds of searches, Bing lets you ask a question the way you would ask a friend. For example, “ who is the President of the United States ” or ...
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9 Financial To-Dos You Can Tackle In Under 10 Minutes
www.forbes.com
Who says important to-dos need to take up a lot of time? You can tackle these financial priorities in under 10 minutes. Really!


7
1 After the wall had been finished and we had put the gates in their places, we assigned to the temple guards and to the members of the sacred choir and the other descendants of Levi the work that they were to do.
2 I appointed my brother Hanani as governor of Jerusalem. He was a faithful man who respected God and honored him, more than many others. In addition, Hananiah was appointed commander of the fortress there in Jerusalem.
3 I said to them, "Do not open the gates of Jerusalem until the sun is hot. And close the gates and put the bars across the doors only when gatekeepers are guarding the gates." I also told them to appoint some people who live in Jerusalem to be guards, and to assign some of them to guard stations around the city, and some would guard near their own houses."
4 The city of Jerusalem covered a large area, but at that time not many people lived in the city, and they had not rebuilt any of the houses.
5 God gave me the idea to summon the leaders and officials and other people, and to enroll them by their families in the books of the records of the families. I also found the records of the people who had been the first ones to return to Jerusalem. This is what I found written in those records.
6 "This is a list of the people who returned to Jerusalem and to other places in Judea. They had been living in Babylonia. Nebuchadnezzar had taken them there. They returned to Jerusalem and to Judah. Each one who returned went back to his own city where his ancestors had lived before the exile.
7 They came back with Zerubbabel, Joshua, Nehemiah, Azariah, Raamiah, Nahamani, Mordecai, Bilshan, Mispereth, Bigvai, Nehum, and Baanah.
This is a list of the number of men from the people who returned were as follows:
8 From the descendants of Parosh, 2,172,
9 from the descendants of Shephatiah, 372,
10 from the descendants of Arah, 652.
11 From the descendants of Pahathmoab, the descendants of Jeshua and Joab, 2,818,
12 from the descendants of Elam, 1,254,
13 from the descendants of Zattu, 845,
14 from the descendants of Zaccai, 760.
15 From the descendants of Bani, 648,
16 from the descendants of Bebai, 628,
17 from the descendants of Azgad, 2,322,
18 from the descendants of Adonikam, 667.
19 From the descendants of Bigvai, 2,067,
20 from the descendants of Adin, 655,
21 from the descendants of Ater, whose other name is Hezekiah, 98,
22 from the descendants of Hashum, 328.
23 From the descendants of Bezai, 324,
24 from the descendants of Hariph, whose other name is Jorah, 112,
25 from the descendants of Gibeon, whose other name is Gibbar, 95.
26 Men whose ancestors had lived in these towns also returned:
Men from Bethlehem and Netophah, 188.
27 There were also men from Anathoth, 128,
28 men from Beth Azmaveth, 42,
29 men from Kiriath Jearim, Kephirah and Beeroth, 743,
30 men from Ramah and Geba, 621.
31 There were men from Micmash, 122,
32 men from Bethel and Ai, 123,
33 men from Nebo, 52,
34 men from Elam, 1,254.
35 There were men from Harim, 320,
36 men from Jericho, 345,
37 men from Lod, Hadid, and Ono, 721,
38 men from Senaah, 3,930.
These priests also returned:
39 The descendants of Jedaiah, who are the family of Jeshua, 973,
40 the descendants of Immer, 1,052,
41 the descendants of Pashhur, 1,247,
42 the descendants of Harim, 1,017.
43 Descendants of Levi who returned were:
the descendants of Jeshua and Kadmiel, who are some of the descendants of Hodeva, 74,
44 singers who are descendants of Asaph,148.
45 Also 138 temple gatekeepers from the descendants of Shallum, Ater, Talmon, Akkub, Hatita, and Shobai, returned.
46 Temple workers who returned were descendants of these men:
Ziha, Hasupha, Tabbaoth,
47 Keros, Sia, Padon,
48 Lebanah, Hagabah, Shalmai,
49 Hanan, Giddel, Gahar,
50 Reaiah, Rezin, Nekoda,
51 Gazzam, Uzza, Paseah,
52 Besai, Meunim, Ephusesim who is also called Nephusim,
53 Bakbuk, Hakupha, Harhur,
54 Bazlith who is also called Bazluth, Mehida, Harsha,
55 Barkos, Sisera, Temah,
56 Neziah, and Hatipha.
57 Descendants of the servants of King Solomon who returned were:
Sotai, Sophereth, Perida,
58 Jaalah, Darkon, Giddel,
59 Shephatiah, Hattil, Pokereth Hazzebaim, and Amon.
60 Altogether, there were 392 temple workers and descendants of Solomon's servants who returned.
61-62 Another group of 642 people from the clans of Delaiah, Tobiah, and Nekoda returned from the towns of Telmelah, Telharsha, Kerub, Addan, also known as Addon, and Immer in Babylonia. But they could not prove that they were descendants of Israel.
63 Priests from the descendants of Hobaiah, Hakkoz, and Barzillai also returned. Barzillai had married a woman who is a descendant of a man named Barzillai from the region of Gilead, and he had taken his wife's family name.
64 They searched in the records that contained the names of people's ancestors, but they could not find the names of those families, so they were not allowed to have the rights and duties that priests had. They did not qualify to be priests because they could not trace their family history.
65 The governor told them they should not be allowed to eat the priests' share of the food, taken from the sacrifices, and they should come who could use the marked stones to find what God said about their being priests once more.
66 Altogether, there were 42,360 people who returned to Judea.
67 There were also 7,337 of their servants, and 245 singers, counting both men and women.
68 The Israelites also brought back from Babylonia 736 horses, 245 mules,
69 435 camels, and 6,720 donkeys.
70 Some of the leaders of the clans gave gifts for the work of rebuilding the temple. The governor gave 8.5 kilograms of gold, fifty bowls to be used in the temple, and 530 robes for the priests.
71 The other leaders gave to the treasury 170 kilograms of gold, and the leaders of the clans gave a total of 1.2 metric tons of silver.
72 The rest of the people gave 170 kilograms of gold, and 1.1 metric tons of silver, and sixty-seven robes for the priests.
73 So the priests, the Levites who helped the priests, the temple guards, the musicians, the temple workers, and many ordinary people, who were all Israelites, started to live in the towns and cities of Judea where their ancestors had lived.


An add-on package to the 'leaflet' package, which provides bindings for 'ESRI' services. This package allows a user to add 'ESRI' provided services such as 'MapService', 'ImageMapService', 'TiledMapService' etc. to a 'leaflet' map.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.1.0), leaflet (≥ 2.0.0), leaflet.extras (≥ 1.0.0) |
| Imports: | htmltools |
| Suggests: | htmlwidgets |
| Published: | 2018-04-23 |
| Author: | Bhaskar Karambelkar [aut, cre], Barret Schloerke [aut], Patrick Arlt [ctb, cph] (esri-leaflet, esri-leaflet-cluster, esri-leaflet-geocoder, and esri-leaflet-heatmap libraries), Rachel Nehmer [ctb, cph] (esri-leaflet-renderers library), Vladimir Agafonkin [ctb, cph] (leaflet.heat and simpleheat libraries), Leaflet [ctb, cph] (leaflet.markercluster library) |
| Maintainer: | Bhaskar Karambelkar <bhaskarvk at gmail.com> |
| BugReports: | https://github.com/bhaskarvk/leaflet.esri/issues |
| License: | GPL-3 | file LICENSE |
| URL: | https://github.com/bhaskarvk/leaflet.esri,https://bhaskarvk.github.io/leaflet.esri/ |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | leaflet.esri results |
| Reference manual: | leaflet.esri.pdf |
| Package source: | leaflet.esri_1.0.0.tar.gz |
| Windows binaries: | r-devel: leaflet.esri_1.0.0.zip, r-release: leaflet.esri_1.0.0.zip, r-oldrel: leaflet.esri_1.0.0.zip |
| OS X binaries: | r-release: leaflet.esri_1.0.0.tgz, r-oldrel: leaflet.esri_1.0.0.tgz |
| Old sources: | leaflet.esri archive |
Please use the canonical form https://CRAN.R-project.org/package=leaflet.esri to link to this page.


A request to add an email address to your Matrix account has been received. If this was you, please click the link below to confirm adding this email:
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DISSERTATION
AN ANALYTICAL TREATMENT OF TWO-PHASE FLOW DURING INFILTRATION
Submitted by Richard Lee Brustkern
In partial fulfillment of the requirements for the Degree of Doctor of Philosophy Colorado State University
Fort Collins, Colorado
March 1970
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


Psalm 23:4. Beloved in Christ, It is a priviledge to hear from God and it gives me joy to relate my testimony to you haven recieved instruction from God through divine revelation.I was a Moslem and a retired top officer in Estrada Government in Philippines that served under past Mr Estrada regime. I was the financial secretary to the Estrad Ruling Council in Philippines (ERC).With my position as a financial secretary, I was able to divert up to $15,000,000.00( Fifteen million U.S Dollars, into my personal Bank Account, hoping to invest the money when I am retired.Immediately I got retired, I was converted from Moslem to Christian when I was preached the words of God through my cousin Pastor HANRY FRED. I then gave my life to Christ and became a born again Christain. Since I gave my life to Christ, I had no rest of mind.Some times I think of my dubious ways I diverted my countrys money into my Personal Bank Account. I then decided to seek the face of God for forgiveness and after fasting and prayer through Divine Revelation, the Almighty God revealed to me that the only way I could have rest of mind is when I used all my money to do the work of God. I immediately disclosed my revelation to my Pastor and he was happy with me.I have been thinking of the particular thing to do for my God before this present civilian president of my country set up a panel ( Panel) to probe the Bank Account of the past Civilan officers like me. Instead of loosing this money to my Government,I quickly withdrew my money out of my Account and packed it in a Trunk before i deposited it with the finance and security company as an valuable gift for safe keeping.My Pastor has adviced me to sow this money into a ministries work abroad. I have decided to sow this money into a ministry. I am making arrangement to come over to your country but you need to receive this money as a consignment from the finance and security company in a trunk box before I come over. declared to the security company containing Valuable Gift items in a consignments trunk box and it's ready for shipment but the security company requires the address of the receiver. Please send to me your names, address and your telephone and fax number where I can fax the Airway Bill to you to I will be coming over to your Country with my family for thanks givingas soon as I sent out this money to you. Please act fast because I dont want the panel to know that intention to leave now that the probe is going on. I look forward to your response God bless you. YOURS FAITHFULLY HUGO ERIC.


The Conference will be hosted at Tramshed Cardiff, in the center of the City of Cardiff, less than 10-minutes walking from Cardiff Central.
The venue is easily accessible using public transportation. If you're coming by train, get off at Cardiff Central, then head left across the River Taff.


Ploror688.github.io
Do My Homework For Me We Can Do Your Assignment Edmodo is an easy way to get your students connected so they can safely collaborate, get and stay organized, and access assignments, grades, and school messages. Good writing skills are essential for effective communication. Learning to write well takes time and practice. Be sure to leave yourself enough time for all of these. Everything a teacher needs. Weekly no prep books from edHelper combine worksheets, reading comprehension, printables, and puzzles that allow kids to pick the pages
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Helping Your Child with Organization and Study Skills. JUNIOR HIGH - Homework Help For Grades 7-8. Infoplease Homework Central MATH. Math.com ENGLISH. BJ Pinchbeck's Homework Helper: English SOCIAL STUDIES. Does Homework Improve Learning? - Alfie. The Importance of Homework and Studying. Homework Helper, for Free Online Homework.
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Does your child have trouble remembering instructions or spelling words? Here are eight working memory boosters you can use to help improve his memory. This is the new website for Woodlands Junior homework help. Hundreds of pages of easy to read information and facts on many homework topics including. The Learning Habit: A Groundbreaking Approach to Homework and Parenting that Helps Our Children Succeed in School and Life Stephanie Donaldson-Pressman, Rebecca.
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Homework is wrecking our kids: The research is clear Online homework and grading tools for instructors and students that reinforce student learning through practice and instant feedback. We can do your homework for you. Any class: Math, Biology, Physics, Programming and Chemistry. ツ Assignments made easy with our expert writing help.⓵ Whenever. The End of Homework: How Homework Disrupts Families, Overburdens Children, and Limits Learning Etta Kralovec, John Buell on Amazon.com. FREE shipping. The Learning Habit: A Groundbreaking Approach to Homework.
Common Sense Media editors help you choose Homework Help Websites. Online resources for tough assignments. The nagging, the battles, the lost papers—do you dread school work as much as the kids do? Here's how to help them hit the books and develop good study habits.
ProQuest powers research in academic, corporate, government, public and school libraries around the world with unique content, including rich academic databases Homework, or a homework assignment, is a set of tasks assigned to students by their teachers to be completed outside the class. Common homework assignments. This is a comprehensive listing of online resources offering homework help to students of all ages. Vetted by our experts for safety and quality, and totally.
At CMA, we conduct abacus and mental arithmetic classes for all ages. Students will acquire useful mental math strategies by the end of our sessions. With increased technology in education, coupled with student interest and study buddies, there is a way to learn how to motivate students to enjoy doing their homework. Edmodo - Connect With Students and Parents


 It’s not a pretty image and certainly not one that most folks want to be associated with. No one wants to think the profits of their efforts feed some singluarly greedy entity, personal or corporate. However, the reality of business is that it REQUIRES profits to maintain it’s ability to run and provide the opportunity for folks to work and provide for themselves and their families. So how do we break out of that mold? Can business be both good and profitable?
Conscious Capitalism
I’ve recently become aware of Conscious Capitalism. It was started by John Mackey, CEO of Whole Foods, and Kip Tindell, CEO of Container Store, as a movement towards a more holistic view of corporate responsibility to a larger audience and a deeper purpose. Basically, profits are not evil but should be focused on the environment that produced them as a way to grow the business. To illustrate this, we have the Stakeholder Model - whereas typical business focuses on the shareholder and customers as the main stakeholders in a business' success, we believe in the larger perspective of stakeholders. The employees who provide value to our customers, industry that we are working in, the suppliers who provide us with the goods we need to produce value, as well as the community we live in. Without any one of these critical stakeholders, the company would be decreased and less effective. These are the relationships that we should focus on and be investing in. By focusing on this larger perspective, we are able to feed back significantly to the ecosystem that we live in.
For me this was a “duh” moment. Of course there’s a broader perspective (there usually is if you are paying attention) and it was great to have a framework and vocabulary to match the things I have believed and been living for a while now. Let me explain…
Improving
One of the founding ideas of Improving was that we would be THE place that people want to work. A place that you can find fullfillment and success in what you do. A place that focused on people and relationships first and foremost. This was a VERY different philosophy than what I had come thru previously in my career. At Improving, we cared about our customers and their success as most businesses do but there was more. Our suppliers would come and hang out with us (one in particular spends mornings at our office to have coffee and chat about family and life). The vast majority of the user groups in the area used our facilities and were given pizza and drinks for free. We spoke at a significant number of conferences and attended them in packs. We fundraised for the local community and found ways to contribute time and money to the types of charities I’ve come to believe make a difference. Most of all, people cared about each other. They truly and honestly were involved in each other’s lives both inside and outside of work. I don’t want to paint things too perfect, they’re not. We have our share of challenges and are always looking at ways to be better but we have a perspective for what could be and now we have a model for speaking to that. It’s been cool to see how the things laid out by Conscious Capitalism are what have been built into Improving from the start. And to see that something that sounds idealistic can be a reality and, even better, a success.


| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | package org.apache.commons.imaging.formats.jpeg.xmp; | |
| 18 | ||
| 19 | import java.io.ByteArrayOutputStream; | |
| 20 | import java.io.File; | |
| 21 | import java.io.IOException; | |
| 22 | import java.io.InputStream; | |
| 23 | import java.io.OutputStream; | |
| 24 | import java.util.ArrayList; | |
| 25 | import java.util.List; | |
| 26 | ||
| 27 | import org.apache.commons.imaging.ImageReadException; | |
| 28 | import org.apache.commons.imaging.ImageWriteException; | |
| 29 | import org.apache.commons.imaging.common.bytesource.ByteSource; | |
| 30 | import org.apache.commons.imaging.common.bytesource.ByteSourceArray; | |
| 31 | import org.apache.commons.imaging.common.bytesource.ByteSourceFile; | |
| 32 | import org.apache.commons.imaging.common.bytesource.ByteSourceInputStream; | |
| 33 | import org.apache.commons.imaging.formats.jpeg.JpegConstants; | |
| 34 | ||
| 35 | /** | |
| 36 | * Interface for Exif write/update/remove functionality for Jpeg/JFIF images. | |
| 37 | */ | |
| 38 | public class JpegXmpRewriter extends JpegRewriter { | |
| 39 | ||
| 40 | /** | |
| 41 | * Reads a Jpeg image, removes all XMP XML (by removing the APP1 segment), | |
| 42 | * and writes the result to a stream. | |
| 43 | * <p> | |
| 44 | * | |
| 45 | * @param src | |
| 46 | * Image file. | |
| 47 | * @param os | |
| 48 | * OutputStream to write the image to. | |
| 49 | * | |
| 50 | * @see java.io.File | |
| 51 | * @see java.io.OutputStream | |
| 52 | */ | |
| 53 | public void removeXmpXml(final File src, final OutputStream os) | |
| 54 | throws ImageReadException, IOException { | |
| 55 | final ByteSource byteSource = new ByteSourceFile(src); | |
| 56 |
1
1. removeXmpXml : removed call to org/apache/commons/imaging/formats/jpeg/xmp/JpegXmpRewriter::removeXmpXml → NO_COVERAGE |
removeXmpXml(byteSource, os); |
| 57 | } | |
| 58 | ||
| 59 | /** | |
| 60 | * Reads a Jpeg image, removes all XMP XML (by removing the APP1 segment), | |
| 61 | * and writes the result to a stream. | |
| 62 | * <p> | |
| 63 | * | |
| 64 | * @param src | |
| 65 | * Byte array containing Jpeg image data. | |
| 66 | * @param os | |
| 67 | * OutputStream to write the image to. | |
| 68 | */ | |
| 69 | public void removeXmpXml(final byte src[], final OutputStream os) | |
| 70 | throws ImageReadException, IOException { | |
| 71 | final ByteSource byteSource = new ByteSourceArray(src); | |
| 72 |
1
1. removeXmpXml : removed call to org/apache/commons/imaging/formats/jpeg/xmp/JpegXmpRewriter::removeXmpXml → NO_COVERAGE |
removeXmpXml(byteSource, os); |
| 73 | } | |
| 74 | ||
| 75 | /** | |
| 76 | * Reads a Jpeg image, removes all XMP XML (by removing the APP1 segment), | |
| 77 | * and writes the result to a stream. | |
| 78 | * <p> | |
| 79 | * | |
| 80 | * @param src | |
| 81 | * InputStream containing Jpeg image data. | |
| 82 | * @param os | |
| 83 | * OutputStream to write the image to. | |
| 84 | */ | |
| 85 | public void removeXmpXml(final InputStream src, final OutputStream os) | |
| 86 | throws ImageReadException, IOException { | |
| 87 | final ByteSource byteSource = new ByteSourceInputStream(src, null); | |
| 88 |
1
1. removeXmpXml : removed call to org/apache/commons/imaging/formats/jpeg/xmp/JpegXmpRewriter::removeXmpXml → NO_COVERAGE |
removeXmpXml(byteSource, os); |
| 89 | } | |
| 90 | ||
| 91 | /** | |
| 92 | * Reads a Jpeg image, removes all XMP XML (by removing the APP1 segment), | |
| 93 | * and writes the result to a stream. | |
| 94 | * <p> | |
| 95 | * | |
| 96 | * @param byteSource | |
| 97 | * ByteSource containing Jpeg image data. | |
| 98 | * @param os | |
| 99 | * OutputStream to write the image to. | |
| 100 | */ | |
| 101 | public void removeXmpXml(final ByteSource byteSource, final OutputStream os) | |
| 102 | throws ImageReadException, IOException { | |
| 103 | final JFIFPieces jfifPieces = analyzeJFIF(byteSource); | |
| 104 | List<JFIFPiece> pieces = jfifPieces.pieces; | |
| 105 | pieces = removeXmpSegments(pieces); | |
| 106 |
1
1. removeXmpXml : removed call to org/apache/commons/imaging/formats/jpeg/xmp/JpegXmpRewriter::writeSegments → KILLED |
writeSegments(os, pieces); |
| 107 | } | |
| 108 | ||
| 109 | /** | |
| 110 | * Reads a Jpeg image, replaces the XMP XML and writes the result to a | |
| 111 | * stream. | |
| 112 | * | |
| 113 | * @param src | |
| 114 | * Byte array containing Jpeg image data. | |
| 115 | * @param os | |
| 116 | * OutputStream to write the image to. | |
| 117 | * @param xmpXml | |
| 118 | * String containing XMP XML. | |
| 119 | */ | |
| 120 | public void updateXmpXml(final byte src[], final OutputStream os, final String xmpXml) | |
| 121 | throws ImageReadException, IOException, ImageWriteException { | |
| 122 | final ByteSource byteSource = new ByteSourceArray(src); | |
| 123 |
1
1. updateXmpXml : removed call to org/apache/commons/imaging/formats/jpeg/xmp/JpegXmpRewriter::updateXmpXml → NO_COVERAGE |
updateXmpXml(byteSource, os, xmpXml); |
| 124 | } | |
| 125 | ||
| 126 | /** | |
| 127 | * Reads a Jpeg image, replaces the XMP XML and writes the result to a | |
| 128 | * stream. | |
| 129 | * | |
| 130 | * @param src | |
| 131 | * InputStream containing Jpeg image data. | |
| 132 | * @param os | |
| 133 | * OutputStream to write the image to. | |
| 134 | * @param xmpXml | |
| 135 | * String containing XMP XML. | |
| 136 | */ | |
| 137 | public void updateXmpXml(final InputStream src, final OutputStream os, final String xmpXml) | |
| 138 | throws ImageReadException, IOException, ImageWriteException { | |
| 139 | final ByteSource byteSource = new ByteSourceInputStream(src, null); | |
| 140 |
1
1. updateXmpXml : removed call to org/apache/commons/imaging/formats/jpeg/xmp/JpegXmpRewriter::updateXmpXml → NO_COVERAGE |
updateXmpXml(byteSource, os, xmpXml); |
| 141 | } | |
| 142 | ||
| 143 | /** | |
| 144 | * Reads a Jpeg image, replaces the XMP XML and writes the result to a | |
| 145 | * stream. | |
| 146 | * | |
| 147 | * @param src | |
| 148 | * Image file. | |
| 149 | * @param os | |
| 150 | * OutputStream to write the image to. | |
| 151 | * @param xmpXml | |
| 152 | * String containing XMP XML. | |
| 153 | */ | |
| 154 | public void updateXmpXml(final File src, final OutputStream os, final String xmpXml) | |
| 155 | throws ImageReadException, IOException, ImageWriteException { | |
| 156 | final ByteSource byteSource = new ByteSourceFile(src); | |
| 157 |
1
1. updateXmpXml : removed call to org/apache/commons/imaging/formats/jpeg/xmp/JpegXmpRewriter::updateXmpXml → NO_COVERAGE |
updateXmpXml(byteSource, os, xmpXml); |
| 158 | } | |
| 159 | ||
| 160 | /** | |
| 161 | * Reads a Jpeg image, replaces the XMP XML and writes the result to a | |
| 162 | * stream. | |
| 163 | * | |
| 164 | * @param byteSource | |
| 165 | * ByteSource containing Jpeg image data. | |
| 166 | * @param os | |
| 167 | * OutputStream to write the image to. | |
| 168 | * @param xmpXml | |
| 169 | * String containing XMP XML. | |
| 170 | */ | |
| 171 | public void updateXmpXml(final ByteSource byteSource, final OutputStream os, | |
| 172 | final String xmpXml) throws ImageReadException, IOException, | |
| 173 | ImageWriteException { | |
| 174 | final JFIFPieces jfifPieces = analyzeJFIF(byteSource); | |
| 175 | List<JFIFPiece> pieces = jfifPieces.pieces; | |
| 176 | pieces = removeXmpSegments(pieces); | |
| 177 | ||
| 178 | final List<JFIFPieceSegment> newPieces = new ArrayList<JFIFPieceSegment>(); | |
| 179 | final byte xmpXmlBytes[] = xmpXml.getBytes("utf-8"); | |
| 180 | int index = 0; | |
| 181 |
2
1. updateXmpXml : changed conditional boundary → KILLED 2. updateXmpXml : negated conditional → KILLED |
while (index < xmpXmlBytes.length) { |
| 182 | final int segmentSize = Math.min(xmpXmlBytes.length, JpegConstants.MAX_SEGMENT_SIZE); | |
| 183 | final byte segmentData[] = writeXmpSegment(xmpXmlBytes, index, | |
| 184 | segmentSize); | |
| 185 | newPieces.add(new JFIFPieceSegment(JpegConstants.JPEG_APP1_Marker, segmentData)); | |
| 186 |
1
1. updateXmpXml : Replaced integer addition with subtraction → KILLED |
index += segmentSize; |
| 187 | } | |
| 188 | ||
| 189 | pieces = insertAfterLastAppSegments(pieces, newPieces); | |
| 190 | ||
| 191 |
1
1. updateXmpXml : removed call to org/apache/commons/imaging/formats/jpeg/xmp/JpegXmpRewriter::writeSegments → KILLED |
writeSegments(os, pieces); |
| 192 | } | |
| 193 | ||
| 194 | private byte[] writeXmpSegment(final byte xmpXmlData[], final int start, final int length) | |
| 195 | throws IOException { | |
| 196 | final ByteArrayOutputStream os = new ByteArrayOutputStream(); | |
| 197 | ||
| 198 |
1
1. writeXmpSegment : removed call to org/apache/commons/imaging/common/BinaryConstant::writeTo → KILLED |
JpegConstants.XMP_IDENTIFIER.writeTo(os); |
| 199 |
1
1. writeXmpSegment : removed call to java/io/ByteArrayOutputStream::write → KILLED |
os.write(xmpXmlData, start, length); |
| 200 | ||
| 201 |
1
1. writeXmpSegment : mutated return of Object value for org/apache/commons/imaging/formats/jpeg/xmp/JpegXmpRewriter::writeXmpSegment to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return os.toByteArray(); |
| 202 | } | |
| 203 | ||
| 204 | } | |
Mutations | ||
| 56 |
1.1 |
|
| 72 |
1.1 |
|
| 88 |
1.1 |
|
| 106 |
1.1 |
|
| 123 |
1.1 |
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| 140 |
1.1 |
|
| 157 |
1.1 |
|
| 181 |
1.1 2.2 |
|
| 186 |
1.1 |
|
| 191 |
1.1 |
|
| 198 |
1.1 |
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| 199 |
1.1 |
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| 201 |
1.1 |


$45 for 30 minutes sessions! About 25-30 photos, 1 location, 1 outfit
$80, 1 hour, 1 location, 1-2 outfits
$75, 1 hour shoot. 1 location, 1-2 outfits
$100, 1 hour.1 location, 1 outfit
$75, 1 hour shoot. 1 location, 1-2 outfits
Sidney has been my photographer on many occasions and each session has been great! She’s not only fun to work with but the pictures turn out amazing every time. I don’t perceive myself as a very photogenic person but her photos always make me beautiful. She has creative ideas and is wonderful through the whole process!
— Angela Scolari


Steven Paul Jobs was an American business magnate, industrial designer, entrepreneur and investor. He was the chairman, chief executive officer (CEO), and co-founder of Apple Inc., the chairman and majority shareholder of Pixar, a member of The Walt Disney Company's board of directors following its acquisition of Pixar, and the founder, chairman, and CEO of NeXT. Jobs is widely recognized as a pioneer of the microcomputer revolution of the 1970s and 1980s, along with Apple co-founder Steve Wozniak. Jobs was born in San Francisco, California and put up for adoption. He was raised in the San Francisco Bay Area. He attended Reed College in 1972 before dropping out that same year, and traveled through India in 1974 seeking enlightenment and studying Zen Buddhism. His declassified FBI report states that he used marijuana and LSD while he was in college, and once told a reporter that taking LSD was "one of the two or three most important things" he had done in his


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--- layout: post title: DDD Group in Cairo Announced date: 2010-02-23 19:39:13.000000000 -07:00 type: post published: true status: publish categories: - News tags: [] meta: _gcid: '176' _gcuser: Vladimir Gitlevich content_type: story Summary: '2' _bitly_trim: http://dddcommunity.org/?p=87 _edit_last: '1' _dependencies: s:6:"a:0:{}"; _custom_background: a:5:{s:3:"url";s:0:"";s:16:"background-color";s:0:"";s:17:"background-repeat";s:6:"repeat";s:21:"background-attachment";s:6:"scroll";s:19:"background-position";s:8:"left top";} _intro_text: default _intro_custom_banner: a:1:{s:3:"url";s:0:"";} author: login: Vladimir Gitlevich email<EMAIL_ADDRESS>display_name: Vladimir Gitlevich first_name: Vladimir Gitlevich last_name: '' ---
Domain Driven Design group in Cairo (DDD Cairo) is intended to be a base of professional Egyptian community for software designers, architects, and professionals interested in software design, specifically DDD.
Join this LinkedIn-based group.


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Fun with images
- Add an img tag with a src attribute.
- Add a width and height attribute (numbers).
- Bonus (not graded): What happens with only a width and a height attribute.
- Bonus (not graded): Save an image into the directory and use it as the src. Can you add it to another directory?


THE
WETZEL FAMILY
The following story is reproduced from a
book titled, the "Combined History and Atlas of Monroe County, Ohio."
The material for this book was taken from two nineteenth century books: (1)
'History of Monroe County Ohio,' a product of the H.H. Hardesty & Co.,
publishers, Chicago and Toledo, 1882 and (2) 'Caldwell's Atlas of Monroe
County, Ohio,' a product of Atlas Publishing Company, Mount Vernon, Ohio, 1898.
The "Combined History and Atlas of Monroe County, Ohio" was reprinted
and is available from the Monroe County Historical Society.
Probably the most noted family, as Indian
fighters, about Wheeling and in eastern Ohio, was the Wetzels. John Wetzel came
out with the Zane in 1770, and settled on Wheeling creek. He had five sons and
two daughters. The sons were Martin, Lewis, Jacob, George and John. The whole
family became celebrated hunters and Indian fighters, but the most daring and
successful was Lewis.
It is said that Martin Wetzel, when a
prisoner among the Indians, was brought about twenty miles up Sunfish Creek.
This would be near where Woodsfield now stands. It is a tradition that Wetzel
said the Indians stopped under a ledge of rock, and then left him under guard
and went off. After being gone about an hour they returned with a large
quantity of lead, which they molded into bullets. They fused the metal in a
large wooden ladle with live coals. After Wetzel had escaped from the Indians he
returned to search for the lead, but was unable to find it.
Click here for the Short-Cut Table of Contents


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github


Model data and motion data Contains.
Materials can we use the Albedo and Metallic and
Normalmap in PBR
Since it is only model data and motion data, please make necessary
script and Animator Controller by yourself when moving in game
(Please create by Animator Controller as you wish.Since the character movement
script is not included, please describe it by yourself and produce it by
yourself)
Model
Character\4Hero\Models
Chara_4Hero.fbx
Material
Character\4Hero\Models\Materials
Mat_Chara_4Hero.mat
Texture
Character\4Hero\Models\Textures
Texture_Chara_4Hero_Albedo.tga
Texture_Chara_4Hero_MetallicSmooth.tga
Texture_Chara_4Hero_Normal.tga
Motion
Character \Hero\Animations
Chara_4Hero_Idle
Chara_4Hero_Run
Chara_4Hero_Jump
Chara_4Hero_Attack01
Chara_4Hero_Attack02
Chara_4Hero_Damage
Chara_4Hero_Dead
Prefab
Character\4Hero\Prefabs
Chara_4Hero.prefab
SampleScene.unity...It becomes a sample where Prefab characters are arranged
-Idle ...Chara_4Hero_Idle
-Run ...Chara_4Hero_Run
-Jump ...Chara_4Hero_Jump
-Punch ...Chara_4Hero_Attack01
-Kick ...Chara_4Hero_Attack02
-Damage ...Chara_4Hero_Damage
-Dead ...Chara_4Hero_Dead
Model data that has been exported in the 2018 format of FBX has been recorded in a folder called Models.
Texture Targa format has been recorded in a folder called
Textures.
Orijinal Texture size is 2048pix.
Texture_Chara_4Hero_MetallicSmooth.tga Description
R Channel is Metallic
G Channel is Occlusion
A Channnel is Smoothness
Sample Animation Check
SampleScene.unity
Since Prefab has SampleAnimation.cs, you can check it
Open SampleScene.unity
When you play Unity and press the following key
Key"Arrow" Run
Key"a" Punch
Key"s" Kick
Key"d" Damage
Key"f" Dead
Key"Space" Jump
If you have any questions, you can contact me at
<EMAIL_ADDRESS>My WEB SITE
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