Technology Council of Central Pennsylvania
As the only trade association seated in the Commonwealth's Capital representing the technology and innovation sector, TCCP enhances Pennsylvaniaís economic development and prosperity by fostering the growth of a strong technology industry.Established in 1990, the TCCP platform has assisted technology companies and individuals to grow and succeed in Pennsylvania. TCCP serves our members in business, government, and academia by:
- Building partnerships and offering business development opportunities
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ss
Utility to investigate sockets.
More information: home page
- Show all TCP/UDP/RAW/UNIX sockets:
ss -a -t|-u|-w|-x
- Filter TCP sockets by states, only/exclude:
ss state/exclude bucket/big/connected/synchronized/...
- Show all TCP sockets connected to the local HTTPS port (443):
ss -t src :443
- Show all TCP sockets listening on the local 8080 port:
ss -lt src :8080
- Show all TCP sockets along with processes connected to a remote ssh port:
ss -pt dst :ssh
- Show all UDP sockets connected on specific source and destination ports:
ss -u 'sport == :source_port and dport == :destination_port'
- Show all TCP IPv4 sockets locally connected on the subnet <IP_ADDRESS>/16:
ss -4t src 192.168/16
- Kill IPv4 or IPv6 Socket Connection with destination IP <IP_ADDRESS> and destination port 8080:
ss --kill dst <IP_ADDRESS> dport = 8080


--- title: Title keywords: fastai sidebar: home_sidebar nb_path: "notebooks/keras_for_engineers.ipynb" ---
Everything you need to know to use Keras & TF 2.0 to build real-world machine learning solutions.
import numpy as np
import tensorflow as tf
from tensorflow import keras
Are you a machine learning looking to use Keras to ship deep-learning powered features in real products? This guide will serve as your first introduction to core Keras API concepts.
In this guide, you will learn about:
tf.data.Dataset
objects).fit()
method, while being.
mindful of checkpointing, metrics monitoring, and fault tolerance.fit()
does, for instance to build a GAN.At the end of this guide, you will get pointers to end-to-end examples to solidify these concepts:
Neural networks don't process raw data, like text files, encoded JPEG image files, or CSV files. They process vectorized & standardized representations.
Let's start with data loading.
Keras models accept three types of inputs:
Dataset
objects. This is a high-performance option that is more suitable for datasets that do not fit in memory and that are streamed from disk or from a distributed filesystem.keras.utils.Sequence
class).Before you start training a model, you will need to make your data available as one of these formats. If you have a large dataset and you are training on GPU(s), consider using Dataset
objects, since they will take care of performance-critical details, such as:
Keras features a range of utilities to help you turn raw data on disk into a Dataset
:
tf.keras.preprocessing.image_dataset_from_directory
turns image files sorted into class-specific folders into a labeled dataset of image tensors.tf.keras.preprocessing.text_dataset_from_directory
does the same for text files.In addition, the TensorFlow tf.data
includes other similar utilities, such as
tf.data.experimental.make_csv_dataset
to load structured data from CSV files.
Example: obtaining a labeled dataset from image files on disk
Supposed you have image files sorted by class in different folders, like this:
main_directory/
...class_a/
......a_image_1.jpg
......a_image_2.jpg
...class_b/
......b_image_1.jpg
......b_image_2.jpg
Then you can do:
# Create a dataset.
dataset = keras.preprocessing.image_dataset_from_directory(
'path/to/main_directory', batch_size=64, image_size=(200, 200))
# For demonstration, iterate over the batches yielded by the dataset.
for data, labels in dataset:
print(data.shape) # (64, 200, 200, 3)
print(data.dtype) # float32
print(labels.shape) # (64,)
print(labels.dtype) # int32
The label of a sample is the rank of its folder in alphanumeric order. Naturally, this can also be configured explicitly by passing, e.g.
class_names=['class_a', 'class_b']
, in which cases label 0
will be class_a
and 1
will be class_b
.
Example: obtaining a labeled dataset from text files on disk
Likewise for text: if you have .txt
documents sorted by class in different folders, you can do:
dataset = keras.preprocessing.text_dataset_from_directory(
'path/to/main_directory', batch_size=64)
# For demonstration, iterate over the batches yielded by the dataset.
for data, labels in dataset:
print(data.shape) # (64,)
print(data.dtype) # string
print(labels.shape) # (64,)
print(labels.dtype) # int32
Once your data is in the form of string/int/float NumpPy arrays, or a Dataset
object (or Python generator) that yields batches of string/int/float tensors,
it is time to preprocess the data. This can mean:
[0, 1]
range.In general, you should seek to do data preprocessing as part of your model as much as possible, not via an external data preprocessing pipeline. That's because external data preprocessing makes your models less portable when it's time to use them in production. Consider a model that processes text: it uses a specific tokenization algorithm and a specific vocabulary index. When you want to ship your model to a mobile app or a JavaScript app, you will need to recreate the exact same preprocessing setup in the target language. This can get very tricky: any small discrepancy between the original pipeline and the one you recreate has the potential to completely invalidate your model, or at least severely degrade its performance.
It would be much easier to be able to simply export an end-to-end model that already includes preprocessing. The ideal model should expect as input something as close as possible to raw data: an image model should expect RGB pixel values in the [0, 255]
range, and a text model should accept strings of utf-8
characters. That way, the consumer of the exported model doesn't have
to know about the preprocessing pipeline.
In Keras, you do in-model data preprocessing via preprocessing layers. This includes:
TextVectorization
layerNormalization
layerThe key advatange of using Keras preprocessing layers is that they can be included directly into your model, either during training or after training, which makes your models portable.
Some preprocessing layers have a state:
TextVectorization
holds an index mapping words or tokens to integer indicesNormalization
holds the mean and variance of your featuresThe state of a preprocessing layers is obtained by calling layer.adapt(data)
on a sample of the training data (or all of it).
Example: turning strings into sequences of integer word indices
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
# Example training data, of dtype `string`.
training_data = np.array([["This is the 1st sample."],
["And here's the 2nd sample."]])
# Create a TextVectorization layer instance. It can be configured to either
# return integer token indices, or a dense token representation (e.g. multi-hot
# or TF-IDF). The text standardization and text splitting algorithms are fully
# configurable.
vectorizer = TextVectorization(output_mode='int')
# Calling `adapt` on an array or dataset makes the layer generate a vocabulary
# index for the data, which can then be reused when seeing new data.
vectorizer.adapt(training_data)
# After calling adapt, the layer is able to encode any n-gram it has seen before
# in the `adapt()` data. Unknown n-grams are encoded via an "out-of-vocabulary"
# token.
integer_data = vectorizer(training_data)
print(integer_data)
Example: turning strings into sequences of one-hot encoded bigrams
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
# Example training data, of dtype `string`.
training_data = np.array([["This is the 1st sample."],
["And here's the 2nd sample."]])
# Create a TextVectorization layer instance. It can be configured to either
# return integer token indices, or a dense token representation (e.g. multi-hot
# or TF-IDF). The text standardization and text splitting algorithms are fully
# configurable.
vectorizer = TextVectorization(output_mode='binary', ngrams=2)
# Calling `adapt` on an array or dataset makes the layer generate a vocabulary
# index for the data, which can then be reused when seeing new data.
vectorizer.adapt(training_data)
# After calling adapt, the layer is able to encode any n-gram it has seen before
# in the `adapt()` data. Unknown n-grams are encoded via an "out-of-vocabulary"
# token.
integer_data = vectorizer(training_data)
print(integer_data)
Example: normalizing features
from tensorflow.keras.layers.experimental.preprocessing import Normalization
# Example image data, with values in the [0, 255] range
training_data = np.random.randint(0, 256, size=(64, 200, 200, 3)).astype('float32')
normalizer = Normalization(axis=-1)
normalizer.adapt(training_data)
normalized_data = normalizer(training_data)
print('var: %.4f' % np.var(normalized_data))
print('mean: %.4f' % np.mean(normalized_data))
Example: rescaling & center-cropping images
Both the Rescaling
layer and the CenterCrop
layer are stateless, so it isn't necessary to call adapt()
in this case.
from tensorflow.keras.layers.experimental.preprocessing import CenterCrop
from tensorflow.keras.layers.experimental.preprocessing import Rescaling
# Example image data, with values in the [0, 255] range
training_data = np.random.randint(0, 256, size=(64, 200, 200, 3)).astype('float32')
cropper = CenterCrop(height=150, width=150)
scaler = Rescaling(scale=1./255)
output_data = scaler(cropper(training_data))
print('shape:', output_data.shape)
print('min:', np.min(output_data))
print('max:', np.max(output_data))
A "layer" is a simple input-output transformation (such as the scaling & center-cropping transformations above). For instance, here's a linear projection layer that maps its inputs to a 16-dimensional feature space:
dense = keras.layers.Dense(units=16)
A "model" is a directed acyclic graph of layers. You can think of a model as a "bigger layer" that encompasses multiple sublayers and that can be trained via exposure to data.
The most common and most powerful way to build Keras models is the Functional API. To build models with the Functional API, you start by specifying the shape (and optionally the dtype) of your inputs. If any dimension of your input can vary, you can specify it as None
. For instance, an input for 200x200 RBG image would have shape (200, 200, 3)
, but an input for RGB images of any size would have shape (None, None, 3)
.
inputs = keras.Input(shape=(None, None, 3))
After defining your input(s), you chain layer transformations on top of your inputs, until your final output:
from tensorflow.keras import layers
# Center-crop images to 150x150
x = CenterCrop(height=150, width=150)(inputs)
# Rescale images to [0, 1]
x = Rescaling(scale=1./255)(x)
# Apply some convolution and pooling layers
x = layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu')(x)
x = layers.MaxPooling2D(pool_size=(3, 3))(x)
x = layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu')(x)
x = layers.MaxPooling2D(pool_size=(3, 3))(x)
x = layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu')(x)
# Apply global average pooling to get flat feature vectors
x = layers.GlobalAveragePooling2D()(x)
# Add a dense classifier on top
num_classes = 10
outputs = layers.Dense(num_classes, activation='softmax')(x)
Once you have defined the directed acylic graph of layers that turns your input(s) into your outputs, instantiate a Model
object:
model = keras.Model(inputs=inputs, outputs=outputs)
This model behaves basically like a bigger layer. You call it on batches of data, like this:
data = np.random.randint(0, 256, size=(64, 200, 200, 3)).astype('float32')
processed_data = model(data)
print(processed_data.shape)
You can print a summary of how your data gets transformed at each stage of the model. This is useful for debugging.
Note that the output shape displayed for each layers includes the batch size. Here the batch size is None, which indicates our model can process batchs of any size.
model.summary()
The Functional API also makes it easy to build models that have multiple inputs (for instance, an image and its metadata) or multiple outputs (for instance, predicting the class of the image and the likelihood that a user will click on it). For a deeper dive into what you can do, see our guide to the Functional API.
fit()
At this point, you know:
tf.data.Dataset
object)The next step is to train your model on your data. The Model
class features a built-in training loop, the fit()
method. It accepts Dataset
objects, Python generators that yield batches of data, or NumPy arrays.
Before you can call fit()
, you need to specify an optimizer and a loss function (we asssume you are already familiar with these concepts). This is the compile()
step:
model.compile(optimizer=keras.optimizers.RMSprop(learning_rate=1e-3),
loss=keras.losses.CategoricalCrossentropy())
Loss and optimizer can be specified via their string identifiers (in this case their default constructor argument values are used):
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
Once your model is compiled, you can start "fitting" the model to the data. Here's what fitting a model looks like with NumPy data:
model.fit(numpy_array_of_samples, numpy_array_of_labels,
batch_size=32, epochs=10)
Besides the data, you have to specify two key parameters: the batch_size
and
the number of epochs (iterations on the data). Here our data will get sliced on batches of 32 samples, and the model will iterate 10 times over the data during training.
Here's what fitting a model looks like with a dataset:
model.fit(dataset_of_samples_and_labels, epochs=10)
Since the data yielded by a dataset is expect to be already batched, you don't need to specify the batch size here.
Let's look at it in practice with a toy example model that learns to classify MNIST digits:
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# Build a simple model
inputs = keras.Input(shape=(28, 28))
x = layers.experimental.preprocessing.Rescaling(1./255)(inputs)
x = layers.Flatten()(x)
x = layers.Dense(128, activation='relu')(x)
x = layers.Dense(128, activation='relu')(x)
outputs = layers.Dense(10, activation='softmax')(x)
model = keras.Model(inputs, outputs)
model.summary()
# Compile the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
# Train the model for 1 epoch from Numpy data
batch_size = 64
print('Fit on NumPy data')
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=1)
# Train the model for 1 epoch using a dataset
dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size)
print('Fit on Dataset')
history = model.fit(dataset, epochs=1)
The fit()
call returns a "history" object which records what happened over the course of training. The history.history
dict contains per-epoch timeseries of metrics values (here we have only one metric, the loss, and one epoch, so we only get a single scalar):
print(history.history)
For a detailed overview of how to use fit()
, see the
guide to training & evaluation with the built-in Keras methods.
As you're training a model, you want to keep of track of metrics such as classification accuracy, precision, recall, AUC, etc. Besides, you want to monitor these metrics not only on the training data, but also on a validation set.
Monitoring metrics
You can pass a list of metric objects to compile()
, like this:
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
metrics=[keras.metrics.SparseCategoricalAccuracy(name='acc')])
history = model.fit(dataset, epochs=1)
Passing validation data to fit()
You can pass validation data to fit()
to monitor your validation loss & validation metrics. Validation metrics get reported at the end of each epoch.
val_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size)
history = model.fit(dataset, epochs=1, validation_data=val_dataset)
If training goes on for more than a few minutes, it's important to save your model at regular intervals during training. You can then use your saved models to restart training in case your training process crashes (this is important for multi-worker distributed training, since with many workers at least one of them is bound to fail at some point).
An important feature of Keras is callbacks, configured in fit()
. Callbacks are objects that get called by the model at different point during training, in particular:
Callbacks are a way to make model trainable entirely scriptable.
You can use callbacks to periodically save your model. Here's a simple example: a ModelCheckpoint
callback
configured to save the model at the end of every epoch. The filename will include the current epoch.
callbacks = [
keras.callbacks.ModelCheckpoint(
filepath='path/to/my/model_{epoch}',
save_freq='epoch')
]
model.fit(dataset, epochs=2, callbacks=callbacks)
You can also use callbacks to do things like periodically changing the learning of your optimizer, streaming metrics to a Slack bot, sending yourself an email notification when training is complete, etc.
For detailed overview of what callbacks are available and how to write your own, see the callbacks API documentation and the guide to writing custom callbacks.
Staring at the Keras progress bar isn't the most ergonomic way to monitor how your loss and metrics are evolving over time. There's a better solution: TensorBoard, a web application that can display real-time graphs of your metrics (and more).
To use TensorBoard with fit()
, simply pass a keras.callbacks.TensorBoard
callback specifying the directory where to store TensorBoard logs:
callbacks = [
keras.callbacks.TensorBoard(log_dir='./logs')
]
model.fit(dataset, epochs=2, callbacks=callbacks)
You can then launch a TensorBoard instance that you can open in your browser to monitor the logs getting written to this location:
tensorboard --logdir=./logs
What's more, you can launch an in-line TensorBoard tab when training models in Jupyter / Colab notebooks:
%load_ext tensorboard
# Create some TensorBoard logs
callbacks = [
keras.callbacks.TensorBoard(log_dir='./logs')
]
model.fit(dataset, epochs=5, callbacks=callbacks)
# Launch in-line TensorBoard
%tensorboard --logdir logs
loss, acc = model.evaluate(val_dataset) # returns loss and metrics
print('loss: %.2f' % loss)
print('acc: %.2f' % acc)
You can also generate NumPy arrays of predictions (the activations of the output layer(s) in the model) via predict()
:
predictions = model.predict(val_dataset)
print(predictions.shape)
fit()
with a custom training step By default, fit()
is configured for supervised learning. If you need a different kind of training loop (for instance, a GAN training loop), you
can provide your own implementation of the Model.train_step()
method. This is the method that is repeatedly called during fit()
.
Metrics, callbacks, etc. will work as usual.
Here's a simple example that reimplements what fit()
normally does:
class CustomModel(keras.Model):
def train_step(self, data):
# Unpack the data. Its structure depends on your model and
# on what you pass to `fit()`.
x, y = data
with tf.GradientTape() as tape:
y_pred = self(x, training=True) # Forward pass
# Compute the loss value
# (the loss function is configured in `compile()`)
loss = self.compiled_loss(y, y_pred,
regularization_losses=self.losses)
# Compute gradients
trainable_vars = self.trainable_variables
gradients = tape.gradient(loss, trainable_vars)
# Update weights
self.optimizer.apply_gradients(zip(gradients, trainable_vars))
# Update metrics (includes the metric that tracks the loss)
self.compiled_metrics.update_state(y, y_pred)
# Return a dict mapping metric names to current value
return {m.name: m.result() for m in self.metrics}
# Construct and compile an instance of CustomModel
inputs = keras.Input(shape=(32,))
outputs = keras.layers.Dense(1)(inputs)
model = CustomModel(inputs, outputs)
model.compile(optimizer='adam', loss='mse', metrics=[...])
# Just use `fit` as usual
model.fit(dataset, epochs=3, callbacks=...)
For a detailed overview of how you customize the built-in training & evaluation loops, see the guide:
"Customizing what happens in fit()
".
If you write custom training steps or custom layers, you will need to debug them. The begugging experience is an integral part of a framework: with Keras, the debugging workflow is designed with the user in mind.
By default, your Keras models are compiled to highly-optimized computation graphs that deliver fast execution times. That means that the Python code you write (e.g. in a custom train_step
) is not the code you are actually excecuting. This introduces a layer of indirection that can make debugging hard.
Debugging is best done step by step. You want to be able to sprinkle your code with print()
statement to see what your data looks like after every operation, you want to be able to use pdb
. You can achieve this by running your model eagerly. With eager execution, the Python code you write is the code that gets executed.
Simply pass run_eagerly=True
to compile()
:
model.compile(optimizer='adam', loss='mse', run_eagerly=True)
Of course, the downside is that it makes your model significantly slower. Make sure to switch it back off to get the benefits of compiled computation graphs once you are done debugging!
In general, you will use run_eagerly=True
every time you need to debug what's happening inside your fit()
call.
Keras has built-in industry-strength support for multi-GPU training and distributed multi-worker training, via the tf.distribute
API.
If you have multiple GPUs on your machine, you can train your model on all of them by:
tf.distribute.MirroredStrategy
objectfit()
and evaluate()
on a dataset as usual# Create a MirroredStrategy.
strategy = tf.distribute.MirroredStrategy()
# Open a strategy scope.
with strategy.scope():
# Everything that creates variables should be under the strategy scope.
# In general this is only model construction & `compile()`.
model = Model(...)
model.compile(...)
# Train the model on all available devices.
train_dataset, val_dataset, test_dataset = get_dataset()
model.fit(train_dataset, epochs=2, validation_data=val_dataset)
# Test the model on all available devices.
model.evaluate(test_dataset)
For a detailed introduction to multi-GPU & distributed training, see this guide.
You've learned about preprocessing, and you've seen example where we put image preprocessing layers (CenterCrop
and Rescaling
) directly inside our model.
Having preprocessing happen as part of the model during training
is great if you want to do on-device preprocessing, for instance, GPU-accelerated feature normalization or image augmentation. But there are kinds of preprocessing that are not suited to this setup: in particular, text preprocessing with the TextVectorization
layer. Due to its sequential nature and due to the fact that it can only run on CPU, it's often a good idea to do asynchronous preprocessing.
With asynchronous preprocessing, your preprocessing operations will run on CPU, and the preprocessed samples will be buffered into a queue while your GPU is busy with previous batch of data. The next batch of preprocessed samples will then be fetched from the queue to the GPU memory right before the GPU becomes available again (prefetching). This ensures that preprocessing will not be blocking and that your GPU can run at full utilization.
To do asynchronous preprocessing, simply use dataset.map
to inject a preprocessing operation into your data pipeline:
samples = np.array([["This is the 1st sample."],
["And here's the 2nd sample."]])
labels = [[0], [1]]
# Prepare a TextVectorization layer.
vectorizer = TextVectorization(output_mode='int')
vectorizer.adapt(samples)
# Asynchronous preprocessing: the text vectorization is part of the tf.data pipeline.
# First, create a dataset
dataset = tf.data.Dataset.from_tensor_slices((samples, labels)).batch(2)
# Apply text vectorization to the samples
dataset = dataset.map(lambda x, y: (vectorizer(x), y))
# Prefetch with a buffer size of 2 batches
dataset = dataset.prefetch(2)
# Our model should expect sequences of integers as inputs
inputs = keras.Input(shape=(None,), dtype='int64')
x = layers.Embedding(input_dim=10, output_dim=32)(inputs)
outputs = layers.Dense(1)(x)
model = keras.Model(inputs, outputs)
model.compile(optimizer='adam', loss='mse', run_eagerly=True)
model.fit(dataset)
Compare this to doing text vectorization as part of the model:
dataset = tf.data.Dataset.from_tensor_slices((samples, labels)).batch(2)
# Our model should expect strings as inputs
inputs = keras.Input(shape=(1,), dtype='string')
x = vectorizer(inputs)
x = layers.Embedding(input_dim=10, output_dim=32)(x)
outputs = layers.Dense(1)(x)
model = keras.Model(inputs, outputs)
model.compile(optimizer='adam', loss='mse', run_eagerly=True)
model.fit(dataset)
When training text models on CPU, you will generally not see any performance difference between the two setups. When training on GPU, however, doing asynchronous buffered preprocessing on the host CPU while the GPU is running the model itself can result in a significant speedup.
After training, if you to export an end-to-end model that includes the preprocessing layer(s), this is easy to do, since TextVectorization
is a layer:
inputs = keras.Input(shape=(1,), dtype='string')
x = vectorizer(inputs)
outputs = trained_model(x)
end_to_end_model = keras.Model(inputs, outputs)
Once you have a working model, you're going to want to optimize its configuration -- architecture choices, layer sizes, etc. Human intuition can only go so far, so you'll want to leverage a systematic approach: hyperparameter search.
You can use
Keras Tuner to find the best hyperparameter for your Keras models. It's as easy as calling fit()
.
Here how it works.
First, place your model definition in a function, that takes a single hp
argument. Inside this function, replace any value you want to tune with a call to hyperparameter sampling methods, e.g. hp.Int()
or hp.Choice()
:
def build_model(hp):
inputs = keras.Input(shape=(784,))
x = layers.Dense(
units=hp.Int('units', min_value=32, max_value=512, step=32),
activation='relu'))(inputs)
outputs = layers.Dense(10, activation='softmax')(x)
model = keras.Model(inputs, outputs)
model.compile(
optimizer=keras.optimizers.Adam(
hp.Choice('learning_rate',
values=[1e-2, 1e-3, 1e-4])),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
The function should return a compiled model.
Next, instantiate a tuner object specifying your optimiation objective and other search parameters:
import kerastuner
tuner = kerastuner.tuners.Hyperband(
build_model,
objective='val_loss',
max_epochs=100,
max_trials=200,
executions_per_trial=2,
directory='my_dir')
Finally, start the search with the search()
method, which takes the same arguments as Model.fit()
:
tuner.search(dataset, validation_data=val_dataset)
When search is over, you can retrieve the best model(s):
models = tuner.get_best_models(num_models=2)
Or print a summary of the results:
tuner.results_summary()
fit()
and evaluate()
.


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:


Greetings From Group Finance Director H.B.O.S Plc Edinburgh EH3 9PE Tel: +44<PHONE_NUMBER> Fax:+44<PHONE_NUMBER><EMAIL_ADDRESS>websit:www. hbosplc.com Greetings, I am Dr. Phil Hodkinson, Group Finance Director (H.B.O.S) I understand that through Internet is not the best way to link up with you because of the confidentiality which the transaction demands. However, I have already sent you this same letter by post one month ago, but I am not sure if it did get to you since I have not heard from you, hence my resending it again. I discovered a dormant account in my office, as Group finance director with Halifax Bank of Scotland. It will be in my interest to transfer this fund worth 15,000,000 million pounds in an account offshore. If you can be a collaborator to this please indicate interest immediately for us to proceed. Your contact phone numbers will be necessary for this effect. Regards and respect, Dr. Phil Hodkinson Group Finance Director Halifax Bank of Scotland


{% include 'includes/head.html' %}
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|---|
| {{person.Name}} |


Sofia – the social Robot?
Sophia is Hanson Robotics’ latest and most advanced robot to date and a cultural icon. She has
become a media darling, appearing on major media outlets around the world, igniting the interest
of people regardless of age, gender, and culture, even gracing the cover of one of the top
fashion magazines. Her press coverage has a potential reach of over ten billion readers in
2017.
Sophia is a highly sought-after speaker in business and showed her prowess and great
potential across many industries. She has met face-to-face with key decision makers in banking,
insurance, auto manufacturing, property development, media, and entertainment. In addition, she
has appeared onstage as a panel member and presenter in high-level conferences, covering how
robotics and artificial intelligence will become a prevalent part of people’s lives. Her
reputation extends beyond business into the global social arena. She was named the world’s first
United Nation Innovation Champion by United Nations Development Program (UNDP) and will have an
official role in working with UNDP to promote sustainable development and safeguard human rights
and equality.
Sophia is an evolving genius machine. Her incredible human likeness, expressiveness, and
remarkable story as an awakening robot over time makes her a fascinating front-page technology
story.
Sophia’s creator, Dr. David Hanson, is the founder of Hanson Robotics and a modern-day
renaissance man who has built a worldwide reputation for creating robots that look and act
amazingly human. After working at Disney as an “Imagineer,” Dr. Hanson aspired to create genius
machines that will surpass human intelligence. Dr. Hanson believes that three distinctively
human traits must be integrated into the artificial intelligence of these genius machines:
Creativity, empathy, and compassion. As an extension of human intelligence, Hanson Robotics’
genius machines can evolve to solve world problems too complex for humans to solve themselves.
Sophia personifies this bold and responsible goal.
Please follow Sophia’s journey to observe and engage with her as she develops into an exciting
platform for artificial general intelligence (AGI) and service robotics applications in
business, medical/healthcare, and education.
Facebook: facebook.com/realsophiarobot
Twitter: twitter.com/realsophiarobot
Instagram: www.instagram.com/realsophiarobot


Windows 10 x64 (18362.418)
_MMSUPPORT_FULL
+0x000 Instance :
_MMSUPPORT_INSTANCE
+0x0c0 Shared :
_MMSUPPORT_SHARED


{% extends "mails/messages/activity_base.html" %} {% load i18n %} {% block message %}
{% blocktrans context 'email' %}Tomorrow is the big day on which your activity "{{ title }}" starts!{% endblocktrans %}
{% blocktrans context 'email' %}This is a good time to send your participants a motivational message to make your activity a success. Send a message to all your participants via the update wall on your activity page.{% endblocktrans %}
{% endblock %}


%! from time import strftime as time %> This page was generated at ${'%H:%M:%S %z %d/%m/%Y' | time}. Please don't edit this page, your changes will be lost in next page publishing.
This page was generated at ${'%H:%M:%S %z %d/%m/%Y' | time}. Please don't edit this page, your changes will be lost in next page publishing.
${diagram['notes']}


{% extends "layout.html" %}
{% block body %}
Performance Status/Scale Reference
Palliative Perfomance Score (PPS)1
| PPS % |
Ambulation |
Activity and Evidence of Disease |
Self-Care |
Intake |
Conscious Level |
| 100 |
Full |
Normal Activity, No Evidence of Disease |
Full |
Normal |
Full |
| 90 |
Full |
Normal Activity, Some Evidence of Disease |
Full |
Normal |
Full |
| 80 |
Full |
Normal Activity with Effort, Some Evidence of Disease |
Full |
Normal or Reduced |
Full |
| 70 |
Reduced |
Unable Normal Job/Work, Some Evidence of Disease |
Full | Normal or Reduced |
Full |
| 60 |
Reduced |
Unable Hobby/House Work, Significant Disease |
Occasional Assistance Necessary |
Normal or Reduced |
Full or Confusion |
| 50 |
Mainly Sit/Lie |
Unable to Do Any Work, Extensive Disease |
Considerable Assistance Necessary |
Normal or Reduced |
Full or Confusion |
| 40 |
Mainly in Bed |
As Above |
Mainly Assistance |
Normal or Reduced |
Full, Drowsy or Confusion |
| 30 |
Totally Bed Bound |
As Above |
Total Care |
Reduced |
Full, Drowsy or Confusion |
| 20 |
As Above |
As Above |
As Above |
Minimal Sips |
Full, Drowsy or Confusion |
| 10 |
As Above |
As Above |
As Above |
Mouth Care Only |
Drowsy or Coma |
| 0 |
Death | - | - | - | - |
PPS Instructions: Starting at the 'Ambulation' column, find the decile that best fits the patient. Stronger performance factors are located on the left and if the patient doesn't fit all in one decile give preference to the ones that are leftmost.
Karnofsky Performance Status2
| KPS % |
Description |
| 100 |
Normal, no complaints. No evidence of disease. |
| 90 |
Able to carry on normal activity, minor signs or symptoms of disease. |
| 80 |
Normal activity with effort, some signs or symptoms of disease. |
| 70 |
Cares for self, unable to carry on normal activity or to do active work. |
| 60 |
Requires occasional assistance, but is able to care for most of his/her personal needs. |
| 50 |
Requires occasional assitance, but is able to care for most of his/her personal needs. |
| 40 |
Disabled. Requires special care and assistance. |
| 30 |
Severely disabled, hospital admission is indicated although death is not imminent. |
| 20 |
Very sick, hospital admission necessary. Active support treatment necessary. |
| 10 |
Moribund, fatal processes progressing rapidly |
| 0 |
Death |
ECOG3
| Score |
Description |
| 0 |
Asymptomatic. Fully active, able to carry on all pre-disease activities without restriction. |
| 1 |
Restricted in physically strenuous activity but ambulatory and able to carry out work of a light or sedentary nature (e.g. light house work, office work). |
| 2 |
Ambulatory and capable of all selfcare but unable to carry out any work activities. Up and about more than 50% of waking hours. |
| 3 |
Capable of only limited selfcare, confined to bed or chair more than 50% of waking hours. |
| 4 |
Completely disabled. Cannot carry on any selfcare. Totally confined to bed or chair |
| 5 |
Death |
Dealing with Differences Between the ECOG, KPS and PPS.
In this tool, we check for agreement among the given performance status variables in two ways. First, we check that the PPS and KPS differ by no more than 20%. Secondly, to compare ECOG with PPS and KPS, we make sure that the estimates are no more than one 'level' apart based on the following paper.4
| ECOG |
KPS/PPS |
| 0 |
100 |
| 1 |
80-90 |
| 2 |
60-70 |
| 3 |
40-50 |
| 4 |
10-30 |
For example, an ECOG of 0 and a PPS/KPS of 80% are reasonable (one level apart), however a ECOG of 4 and a PPS/KPS 60% should raise questions about the accuracy of the scoring and will raise a flag.
Sources:
- Anderson F, Downing GM, Hill J, Casorso L, Lerch N. Palliative Performance Scale (PPS): A New Tool. J Palliative Care. 1996;12(1):5-11.
- Karnofsky DA, Abelmann WH, Craver LF, Burchenal JH. The Use of the Nitrogen Mustards in the Palliative Treatment of Carcinoma, With Particular Reference to Bronchogenic Carcinoma. Cancer. 1948;1(4):634-656.
- Oken MM, Creech RH, Tormey DC, et al. Toxicity And Response Criteria Of The Eastern Cooperative Oncology Group. Am J Clin Oncol. 1982;5:649-655.
- Ma C, Bandukwala S, Burman D, Bryson J, Seccareccia D, et al. Interconversion of Three Measures of Performance Status: An Empirical Analysis. Eur J Cancer. 2010;46(18):3175-83.
{% endblock %}


EUR/USD Technical Analysis: Rising odds for a retest of 1.10 and below
by Pablo Piovano- EUR/USD remains under pressure and eyes a probable move to 1.10.
- Last week’s tops near 1.1100 continues to cap the upside.
EUR/USD has faded the move to daily highs beyond 1.1030 and is now seems to be re-shifting the focus to the 1.1000 neighbourhood.
Initial resistance is seen at the 55-day SMA in the 1.1040 region. Above this level, the downside pressure should mitigate somewhat and allow for a potential test of last week’s highs in levels just shy of the 1.1100 mark.
On the opposite side, if sellers regain the upper hand, another visit to the psychological support at 1.1000 should come to the fore. A breakdown of this important area of contention, should reassert the downside bias.
EUR/USD
| Overview | |
|---|---|
| Today last price | 1.1012 |
| Today Daily Change | 22 |
| Today Daily Change % | -0.10 |
| Today daily open | 1.1023 |
| Trends | |
|---|---|
| Daily SMA20 | 1.1073 |
| Daily SMA50 | 1.1043 |
| Daily SMA100 | 1.1087 |
| Daily SMA200 | 1.1174 |
| Levels | |
|---|---|
| Previous Daily High | 1.1088 |
| Previous Daily Low | 1.1014 |
| Previous Weekly High | 1.1098 |
| Previous Weekly Low | 1.1014 |
| Previous Monthly High | 1.118 |
| Previous Monthly Low | 1.0879 |
| Daily Fibonacci 38.2% | 1.1043 |
| Daily Fibonacci 61.8% | 1.106 |
| Daily Pivot Point S1 | 1.0995 |
| Daily Pivot Point S2 | 1.0968 |
| Daily Pivot Point S3 | 1.0921 |
| Daily Pivot Point R1 | 1.1069 |
| Daily Pivot Point R2 | 1.1116 |
| Daily Pivot Point R3 | 1.1143 |


lola is made for linux users who want to download software fast and easy!
lolacli
lola is made for linux users who want to download software fast and easy!
Now many will ask, Why use lola when we have those software managers?
Well, lola is a Command Line Interface and is used inside the terminal. And as we know, terminal downloads are way more faster than the software managers. While many softwares can be downloaded with a single sudo apt install, most common ones need some more commands.
Lola doesn't support all distributions till now
lola currently uses the apt software manager ONLY.
Debian is an operating system and a distribution of Free Software. It is maintained and updated through the work of many users who volunteer their time and effort.
Ubuntu is the modern, open source operating system on Linux for the enterprise server, desktop, cloud, and IoT.
Linux Mint is a free Linux-based operating system designed for use on desktop computers.
Pop!_OS is an operating system for STEM and creative professionals who use their computer as a tool to discover and create. Unleash your potential on secure, reliable open source software. Based on your exceptional curiosity, we sense you have a lot of it.
elementary OS is designed to be easy to understand and pick up as a new user. But that doesn't mean it's dumbed down; powerful, customizable keyboard shortcuts ensure you'll stay productive whether it's your first day or your thousandth.
Zorin OS is designed to be easy, so you won't need to learn anything to get started. The Zorin Appearance app lets you change the desktop to resemble the environment you're familiar with, whether it's Windows, macOS, or Linux. Rock solid & reliable.
lolacli is built in python, and the python packages used in development are listed below
Click is a Python package for creating beautiful command line interfaces in a composable way with as little code as necessary. It’s the “Command Line Interface Creation Kit”. It’s highly configurable but comes with sensible defaults out of the box.
Learn MoreThe subprocess module allows you to spawn new processes, connect to
their input/output/error pipes, and obtain their return codes. This module intends to replace several older
modules and functions like os.spawn
and os.system
Requests is an elegant and simple HTTP library for Python, built for human beings. Requests allows you to send HTTP/1.1 requests extremely easily. There’s no need to manually add query strings to your URLs, or to form-encode your POST data. Keep-alive and HTTP connection pooling are 100% automatic, thanks to urllib3.
Learn MoreThis module provides a portable way of using operating system dependent functionality. If you just want to read or write a file see open(), if you want to manipulate paths, see the os.path module, and if you want to read all the lines in all the files on the command line see the fileinput module. For creating temporary files and directories see the tempfile module, and for high-level file and directory handling see the shutil module.
Learn More


PoseNet
Gesture Recognition - Camera feed
Pose Estimation - Camera feed demo
Pose Estimation - Image demo


Report and document damage in your area, visually.
{{currentReport.event}}
{{currentReport.type}}
{{currentReport.datetime | date:'MMM d, y h:mm a'}}
{{currentReport.details}}
Images...


We are part of the Machine Perception
organization at Google which tackles the hard
problems of understanding images, sounds, music and video.
If you have questions about the dataset, or would like to be notified of updates, please
subscribe to
Google Group: cartoonset-users.


Hello, my name is Ilya and I'm Python/Django full-stack web developer.
I build and support web appplications based on Django/DRF and AngularJS/Backbone.
I believe that communication and following the specs are keys to successful collaboration.
Feel free to contact. Have a nice day!
Web applications development based on Python/Django and JavaScript frameworks.
VoIP client-side infrastructure development.
 Also, I like active rest like footall, volleyball, skiing and skating.
I enjoy music, especially rock music, but actually, I'm meloman. Beign a child I was studing in music school and played a bass in folk band. Recently, I bought Fender Telecaster and now I'm doing my first steps in playing a rock :)


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DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds Published in Transactions on Graphics (SIGGRAPH 2022).
CNNs on Surfaces using Rotation-Equivariant Features Published in Transactions on Graphics (SIGGRAPH 2020).


While everyone’s eye is on Ethereum and Bitcoin, celebrating their breakthrough through $2000 and $150, one crypto which no one paid attention, bytecoin, has surged almost ten times in May alone, and its market cap now rank 6-8 while its market cap is beyond 20 in the beginning of the month.
Bytecoin daily trading volume increased from $340,000 to a staggering $48 million, and its market cap goes beyond Dash(once 4th) and Monero.
Prior to the existence of Monero, Bytecoin was heavily criticized due to its premine scheme and for that reason, the Monero development team refused to collaborate with the Bytecoin development team. In 2015, Monero lead developer Riccardo Spagni, better known as FluffyPony, stated:
“The reality is that 82% of the coins were already mined before its ’public’ release. Even if the premined coins weren’t done so maliciously, it still means 82% of the coins in the hands of persons unknown and invisible. It basically centralises a decentralised currency.”
So, is it a real uptrend or just scam and a classic pump and dump?
This page is synchronized from the post: ‘Bytecoin, the original anonymous currency returns, market cap now over Monero and Dash’


GR32_Dsgn_Misc.pas contains parts of the design time code used in Graphics32. The contents are not documented as this is mainly used internally. See source code for details.


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World.
How
Are
You?
Content goes here.
Content goes here.
Content goes here.


Hello, my name is Blane. I am a Front End Developer living in Anderson, SC.
I’m a freelance developer who is passionate about communicating ideas and creating a strong online presence through user friendly, mobile-first websites.
Outside of work, I have beautiful a wife and a houseful of pets. Some say I am quiet, but I am a dreamer at heart who is always cooking up new ideas, which is why making other people's vision a reality is so exciting to me.
This site randomly selects user input as well as random "explicit" words to create your future!
Experience the game here.
Interested in doing a project together?


- 5 min read
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--- layout: default page-title: page-description: canonical: https://ketch.partners/articles/ robots: index,follow rating: og-title: og-type: article og-url: og-image: og-description: og-sitename: Ketch Partners fb-id: twitter-summary: twitter-handle: ketchpartners twitter-title: twitter-description: twitter-author: twitter-image: datePublished: dateModified: datePublishedDisplay: author-name: ---
- 5 min read


Athletics
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--- layout: post title: Belated Birthday date: 2013-09-19 00:47:41.000000000 -04:00 categories: [] tags: [] status: draft type: post published: false meta: aktt_notify_twitter: 'no' _edit_last: '1' author: login: cint email<EMAIL_ADDRESS>display_name: Jacinta first_name: Jacinta last_name: '' ---
I have had my heart set on a chair I spied in Ikea earlier in the year, with all the house going-ons, I had to wait a few months for my birthday present to come to fruition. Jairah and I set off to Ikea to purchase my chair. This was all very exciting until I realised I had to get the huge and awkward shaped box onto the trolly, thankfully a lovely Ikea lady came to my rescue. It was actually easier to get in the car although a lovely gentleman came running to help me, bless his kind heart. I couldn't get it home quick enough and try it out.  Of course a new cushion was in order, so I whipped on up using Sweetwaters "" fabric. It has the perfect colour combination to match the lovely grey fabric.


class: center, left # Brains4Publication Brainhack project update **Our goal**: *Make neuroimaging publications more beautiful while promoting best practices for reproducible research.* We wanted to wrap plotting functions to make them into useful command line tools that anyone who is conducting neuroimaging research will be able to use.
We were very successful. We had some really incredible help from the [nilearn]() and [nipy]() teams. ***Thank you!***  Check out ***[mri_glass_brain.py](https://github.com/KirstieJane/BrainsForPublication/blob/master/docs/mni_glass_brain.md)***
--- # Locate clusters Want to see where the clusters in your 3D statistical maps are? Check out ***[show_cluster_in_volume.py](https://github.com/KirstieJane/BrainsForPublication/blob/master/docs/mni_glass_brain.md)***
You get one of these pictures for each of the cluster peaks, and a text file with the coordinates and descriptive statistics. --- # Freesurfer results Want to make beautiful images of your freesurfer results? Check out ***[pysurfer_combined.py](https://github.com/KirstieJane/BrainsForPublication/blob/master/docs/mni_glass_brain.md)***
--- # High res gif Want to make a snazzy gif of your own brain? Check out ***[subj_anat_gif.py](https://github.com/KirstieJane/BrainsForPublication/blob/master/docs/subj_anat_gif.md)***
--- # No installation required!
--- # Run all the code online
--- # Lets all build a hat rack!
--- # Thank you!


What is tapering? Impact on the stock market.
The market is keen on when Powell’s quantitative easing will end.
The beginning of quantitative easing will be tapering.
Tapering means that it is gradually decreasing.
It is the beginning of an exit strategy away from quantitative easing.
There are many ways to reduce the currency on the market.
Interest rate hikes, bond issuance, etc.
At the moment, we will not be able to drastically reduce the market currency sharply.
It is said that the economy is recovering, but the economy due to Corona is still unknown.
The long-term quantitative easing policy of the U.S. also puts pressure on currencies.
Tapering is about reducing the amount of money that increases as calls increase.
How?
In the meantime, large amounts of bond purchases are gradually reduced.
In other words, tapering is about reducing bond purchases.
I have experienced tapering in the past.
There was Chairman Ben Bernanke, nicknamed “Helicopter Ben.”
He argued that if the economy was in trouble, we should boost the economy even by spraying money with helicopters.
When the global financial market panicked in 2008 due to the Lehman Brothers crisis, the company pulled out quantitative easing cards.
His popularity was very high.
Ritually, he was appointed by the Republican government and served a second term in the Democratic government.
His successor, Powell, must have borne the burden of quantitative easing, and has so far adhered to the policy of quantitative easing.
By the way, inflation is a problem.
If there are more currencies on the market, the value of the currency falls and prices rise.
Rising prices reduce corporate investment and reduce employment.
So, ever since quantitative easing, there are always opinions here and there that are wary of inflation.
“At a time when the economy is recovering after the pandemic,” Federal Reserve Bank President Rafael Bostick told Bloomberg TV.
“I’m leaving every possibility open.
Was there no tapering in the past?
On May 23, 2013,
Chairman Ben Bernanke first mentioned tapering.
In June, a detailed roadmap was announced.
At that time, the S&P 500 also experienced a decline of 5 percent compared to its high point.
From now on, it’s my idea.
Currently, the increase rate of raw material prices such as iron ore is too high.
Key figures in charge of the national economy must be afraid of high inflation.
If the current rate of increase in raw material prices continues, tapering will take place.
It will have a bad effect on the stock market.
However, the current economy is a new business.
There are various themes such as electric vehicles, metabuses, blockchain, and space industry.
These themes will give investors hope.
Always listen to market conditions and study.


North Korea conducts new test of 'super-large' rocket launcher: KCNA
SEOUL: North Korea has carried out a "successful" new test of its "super-large multiple rocket launcher" system, state media said on Friday (Nov 1) - the latest in a series of provocations by Pyongyang.
South Korea's military said on Thursday that the North had launched two short-range projectiles from South Pyongan province. They each flew approximately 370 kilometres.
It was the latest in a series of launches by the North but the first since Oct 2, when it fired a sea-launched missile.
If confirmed, a submarine-based missile capability would change the military balance on the Korean peninsula.
Thursday's test was conducted to "verify the security of launchers' continuous fire system," the state Korean Central News Agency reported from Pyongyang.
North Korean leader Kim Jong Un, who had personally supervised previous tests of the "super-large" system, "expressed satisfaction ... and sent congratulations" to the team involved, KCNA said.
Nuclear talks between Pyongyang and Washington are currently at a standstill.
Pyongyang is under multiple sets of international sanctions over its nuclear weapon and ballistic missile programmes, which it says it needs to defend against a possible US invasion.
It is demanding the easing of the measures and has repeatedly urged Washington to come forward with a new offer by the end of this year.
Kim and US President Donald Trump adopted a vaguely-worded statement on the "complete denuclearisation of the Korean peninsula" at their first summit in Singapore in June last year, but little progress has since been made.
The pair met in Hanoi in February, but no deal was reached.
Trump and Kim then agreed to restart working-level talks during a brief meeting at the Demilitarised Zone dividing the peninsula in June.
The two sides met in Sweden earlier this month but Pyongyang walked away, saying it had "no desire" to negotiate unless the United States moves to end its "hostile policies."


5 Spreadsheets
Introduction
Some people consider using spreadsheets the table stakes for getting into data journalism. It’s relatively easy to see what you’re doing and you can easily share your work with your colleagues. In fact, pieces of the Pulitzer-Prize winning COVID-19 coverage from The New York Times was compiled using an elaborate and highly tuned set of Google spreadsheets with dozens of contributors.
This guide uses Google Sheets, which allows students to do the exercises regardless of their computer operating system, Mac, Windows or Linux. The exercises can be easily adapted to Microsoft Excel, which can handle larger datasets and has more options for pivot tables and other more advanced functions. However, Google Sheets have their own advanced functions for scraping websites or importing non-tabular file formats like JSON.
Most of the screen shots and instructions are created with a MacOS Monterey. Some come from earlier Mac versions, but are largely the same now. Windows users should replace any instructions for using the CMD- key with the CTL- key. There is a table that compares keystrokes for Apple desktops, laptops and Windows machines for Excel at the bottom of Spreadsheet Refresher
Tutorials
Spreadsheets are used in almost every workplace in America. This section covers most of what you need in the newsroom, which is a different set of skills than in other businesses.
Spreadsheet Refresher : Start over with good habits
Sorting and filtering to find stories : The first step of interviewing data
Grouping with pivot tables: Aggregating, and the super power of spreadsheets
Spreadsheet Formulas: Percents, sums, and other basic computations used in newsrooms.


I go to Willow Canyon High School, and take Honors Classes. I take IB Coursework, and currently have a 4.875
GPA.
In the West-MEC Coding Program, I have a 4.0 GPA.
Relevant Coursework: Cambridge Spanish 1-2,Cambridge Spanish 3-4(And 5-6), IB Spanish 1-2 HL, AP US Government,
IB Math Studies HL, IB English HL, IB Biology SL, IB Chemistry HL, Physical Education and Health, Cambridge
Biology, Cambridge Chemistry, Cambridge Math, Cambridge World History and US History, and Cambridge English.


Atticus said to Jem one day,
I'd rather you shot at tin cans in the backyard, but I know you'll go
after birds. Shoot all the blue jays you want, if you can hit 'em, but
remember it's a sin to kill a mockingbird.
That was the only time I ever heard Atticus say it was a sin to do
something, and I asked Miss Maudie about it.
Your father's right,
she said.
Mockingbirds don't do one thing except make music for us to enjoy.
They don't eat up people's gardens, don't nest in corn cribs, they
don't do one thing but sing their hearts out for us. That's why it's a
sin to kill a mockingbird.
We believe that we can change the things around us in accordance with
our desires-we believe it because otherwise we can see no favourable
outcome. We do not think of the outcome which generally comes to pass
and is also favourable: we do not succeed in changing things in
accordance with our desires, but gradually our desires change. The
situation that we hoped to change because it was intolerable becomes
unimportant to us. We have failed to surmount the obstacle, as we were
absolutely determined to do, but life has taken us round it, led us
beyond it, and then if we turn round to gaze into the distance of the
past, we can barely see it, so imperceptible has it become.
Below is a table of books that I love to read and their authors:
| Novel | Firstname | Lastname |
|---|---|---|
| To Kill a Mockingbird | Harper | Lee |
| Pride and Prejudice | Jane | Austen |
| David Copperfield | Charles | Dickens |
| Moby-Dick | Herman | Melville |


Python Global Interpreter Lock
Python uses GIL (Global interpreter lock). GIL ensures that python process can only process 1 instruction at a time, regardless the number of cores that CPU is using. This has been the behavior of the default implementation of python, cPython.
However, this could be overcome by using alternative implementations such as cython and by using mutliprocessing module (instead of threading).
To understand the impact of GIL, I ran this program on a multi-core CPU laptop. This is a classic CPU bound problem, generate the nth prime. Think of CPU bound problem as ones that has heavy looping, floating point operations and no IO (databases, disk operations, network, etc).
The full source code is available at this link
@timefn:singleThreadtest took 4.8399810791 seconds.
@timefn:multiThreadtest took 5.75388002396 seconds.
Although, generally speaking, it is a bit suprising to hear that mutli-threaded version performs slower than the single-threaded version, however in python GIL world, it should not be much of a surprise. Because, only single core of CPU is used and only 1 instruction is executed at a given time. So, implementing threading in this case creates overhead of managing threads and introduces waits for shared resources.
Conclusion
- If your program is a CPU bound, think twice about implementing threading. At the least, experiment using cython or multi-processing modules.
- If your program is IO-bound, then multi-threading will generate real speeds. You can go thru my post title “Oracle AWR Using Python”. I’ve tested multi-threading there and performance benefits are exponential.


| Character | Mean |
| . | Matches any single character. |
| ^ | Strings that beginning with some simbol. |
| $ | Strings that end with some simbol. |
| {n} | Repeat a character n times. |
| * | Matches zero or more instances |
| Examples |
| ^A | All sequences that start with A. |
| GC$ | All sequences that end with GC. |
| TTT | All sequences that contain TTT. |
| ^T{3}..ACG$ | All sequences that start with TTT contain two wildcards and end with ACG. |


See red text on failure, green on success.
Insert Test
Attribute Change Test
Ancestor Attribute Change Test
Light DOM


PushUpExerciseName
public struct PushUpExerciseName : ExerciseName
extension PushUpExerciseName: Hashable
Push Up Exercise Name
-
Exercise Name Type
Declaration
Swift
public typealias ExerciseNameType = PushUpExerciseName
-
Exercise Catagory Type
Declaration
Swift
public var catagory: ExerciseCategory { get }
-
Exercise Name
Declaration
Swift
private(set) public var name: String { get }
-
Exercise Name Number
Declaration
Swift
private(set) public var number: UInt16 { get }
-
List of Supported ExerciseNames
Declaration
Swift
static var supportedExerciseNames: [PushUpExerciseName] { get }
-
Creates a ExerciseName Object
Declaration
Swift
static func create(rawValue: UInt16) -> PushUpExerciseName?


@()(implicit ctx: Context) @site.message("You played all the openings!", back = false) { Sorry, we don't have any more openings for you. You played quite a lot of them, didn't you :) }


{% extends "_base.html" %} {% block title %}Metabolism Manager{% endblock %} {% block content %} {% include "metabolism_manager/admin/_entity.menu.html" %}
| Date | Material | Type | Quantity |
|---|---|---|---|
| February 2020 | Wooden crates | Surplus | 49 t |
| March 2020 | Wooden creates | Surplus | 33 t |
| March 2020 | Energy (hot water) | Surplus | 114 ML |
| March 2020 | Cast iron beams | Demand | 22 t |
| April 2020 | Cast iron beams | Demand | 14 t |


Demo (subdirectory at /demos/ ) |
Single Page (title in _index.md front matter) |
Deck (title in deck.md front matter) |
|---|---|---|
| {{ index ( split .Dir "/" ) 1 }} | {{ .Title }} | {{ $deckB := (printf "/%sdeck.md" .Dir) }} {{ with .GetPage $deckB }}{{ .Title }} | {{ end }}
In order for this demo to show up on the Marketing site's "Featured Demos" section, add a category to a{{ end }}categories
key in the demo's_index.md
front matter.
Use one of the following categories:
categories:
{{ range $cats }} - {{ . }}
{{ end }}
Public demos:{{ len ($cats3.Get "workflow")}}
Draft demos:
Total demos: {{ len .Pages }}
{{ else }}Tags:
Please set the{{ end }}video
key in the_index.md
front matter to the YouTube ID where the recorded demo exists.
Example:
video: N1XvFfCAXRk
Please include slide notes in the{{ end }}aside
sections for each slide in yourdeck.md
file.


Mr Key's puny pea-sized brain is empty today, so while we wait for it to be refilled with coruscating insights, might I suggest that you go and read Jimmy Goddard, Tony Wedd, and the Star Fellowship? This is fascinating stuff, as those of who you recall Cosmic Friends will know only too well.


--- layout: post title: Zedboard - Setting up a ARM development environment in Linux date: 2018-02-26 11:23:00.000000000 +02:00 type: post parent_id: '0' published: true password: '' status: publish tags: - FPGAs - Linux/Ubuntu - Xilinx - Zedboard meta: _wpas_skip_16076853: '1' timeline_notification: '1519636983' _rest_api_published: '1' _rest_api_client_id: "-1" _publicize_job_id: '15150543259' _publicize_done_external: a:1:{s:7:"twitter";a:1:{i:16076855;s:56:"https://twitter.com/MphoMphego/status/968053794243338240";}} _publicize_done_16257272: '1' _wpas_done_16076855: '1' publicize_twitter_user: MphoMphego publicize_google_plus_url: https://plus.google.com/+MphoMphego/posts/SKcn8qUkex5 _publicize_done_16966120: '1' _wpas_done_16750486: '1' author: login: mpho112 email<EMAIL_ADDRESS>display_name: Mpho first_name: '' last_name: '' ---
Well now that you have your Zedboard up and running with Petalinux installed, and your network is configured to talk to the outside world, let's get some code running on this thing!
First some environmental comments. I am a big fan of Xubuntu. I like it's feel and it's ease of use when it comes to installing packages using the apt-get command. I will be writing just about all of these blog posts using Xubuntu as my host OS.
I have installed on a development box Xubuntu 16.04 LTS. The only thing I selected during the install was to install OpenSSH so I could actually interface to the thing. We are going to install just a single additional package to allow us to compile ARM code, and it's going to be really really simple to do!
Open up a command terminal on the local machine
In the event that you do not have SSH installed and would like it installed then here is the command:
# sudo apt-get install update
# sudo apt-get install openssh-server openssh-client
Now that you are at the prompt either on your local machine or via SSH, we can install the arm gcc tools:
$ sudo apt-get install gcc-arm-linux-gnueabi
Once the package is installed, you now have the ability to compile applications for your Zedboard!
That simple!!!
I am going to go to my home
$ cd
$ mkdir arm-devel
$ cd arm-devel/
$ ~/arm-devel$ touch helloworld.c
$ ~/arm-devel$ cat > helloworld.c
#include
#include
int main(){
printf("Hello World!\n");
return 0;
}
So we have our source file, now we need to compile it. The application we will use is arm-linux-gnueabi-gcc.
$ arm-linux-gnueabi-gcc -o helloworld helloworld.c
Ok, so now we have our application written and compiled. But now what? We can't run it on our development box because it isn't ARM based. So we need to get it over to our Zedboard. The Zedboard conveniently ships with a SCP running on it! So all we need to do is connect to our Zedboard and drop our file there.
$ scp helloworld root@<IP_ADDRESS>:~/
On Zedboard:
Cool. So now our our Zedboard in the ~/home
directory we have a file called helloworld. We need to change it's properties so our shell knows it can be executed. To do this we will use chmod:
$ ssh<EMAIL_ADDRESS>$ root@avnet-digilent-zedboard-2017_2:~# chmod a+x helloworld
You will notice that the helloworld app now has the ability to be executed. Well, what are you waiting for!? let's do it!
root@avnet-digilent-zedboard-2017_2:~# ./helloworld
Hello World!
Woohoo! How cool is that!?. So now, you are well on your way to application development for the ARM cores running within the Zynq-7000 on you Zedboard.


To ensure fast response to fire emergencies, SPSA partnered with SANED have initiated “AMAN” project. AMAN, aims to protect lives and properties by establishing a safe and reliable connection between all firefighting systems in all properties in Sharjah, with the central monitoring station that is continuously monitored to identify and respond to emergencies instantly ensuring the safety of people and assets.
Enhance the quality of life in the Emirate of Sharjah by applying the highest internationally recognized standards to ensure the protection and safety of individuals and properties. The mission is a Zero Harm Environment.
The 24/7 communication system is:
The user friendly SPSA Mobile App and AMAN online registration portal make it easy to register for your service and will walk you through all the requirements.
Our 24x7 call centre is happy to answer any questions: 600 515152
Website and Mobile App coming soon.Included in the fixed fee for Building Owners are the following:


Click to refresh table
Click to fetch selected rows
Click to refresh table
Click to fetch selected rows
Click to refresh table
Click to fetch selected rows


Introduction
Structure Flex Grid is a flex-box based grid system for quickly creating the page structure for your html pages. Structure follows BEM naming conventions to clearly separate modifiers from elements. You can read more about BEM at getbem.com.
It packs some very useful modifiers for rows and columns to achieve the layout of the page you are looking for. For example my favorite "--shrink" modifier for columns which I use heavily in my designs. OR "--align--justify" modifier for rows which is very useful in making toolbars or rows which should align items to left and right edges.
Scroll down to read more about all the modifiers.


Preparing for Midterm
- Complete all homework (Worksheets 1-5).
- Study regularly and practice deliberately. Make connections to the Learning Outcomes.
- Make notes as you study and review your notes regularly.
- Join a Study Group, study together and test each other.
- Most questions are based on the material you are tasked to complete.
SYST10049 Web Development
@2021-2023 Sheridan College
Midterm
- Written online, starting at the beginning of class in Week 7.
- Length max. 90 minutes, SLATE Quiz with Respondus lockdown.
- You must physically attend the class and present your Student ID card.
- Covers concepts and skills related to the learning objectives (next slide)
- Be prepared to demonstrate your mastery
- of all material from Weeks 1 through 6 (Worksheets 1-5)
- by answering questions in form of multiple-choice (MC) , true-false (F/T), multiple-select (MS), fill-in-blanks (FIB), matching (MAT), ordering (ORD) and coding/written answers (WR).
- approx. 15 MC two points each , approx. 20 F/T one point each, 50 total,
10-12 MS, FIB, MAT, ORD, WR questions, 3-6 points each, 50 total.
SYST10049 Web Development
@2021-2023 Sheridan College
Learning Outcomes
- Create a properly structured web page (HTML5)
- Represent the Document Object Model (DOM) in a hierarchical diagram.
- Recognize the importance of browser compatibility with standards.
- Style a web page (CSS3)
- Create page layouts using media queries.
- Use internet protocols to publish a web site
- Explain how web servers display web pages.
- Describe how to secure a web site.
- Use design techniques to layout a user-friendly web site.
- Design forms that use major controls and form handling logic.
Create Use simple client-side scripts with JavaScript.
SYST10049 Web Development
@2021-2023 Sheridan College
What notes can I use during exam?
 There is no set limit.
- If you are making your notes electronically, print them out and organize in a binder.
- If you are making handwritten notes, bring your notebook or binder.
- Printouts of other sites and publications are not allowed.
- Make sure to organize your notes so that you do not distract your neighbors!

SYST10049 Web Development
@2021-2023 Sheridan College
Preparing for Midterm
More details next week
- Written during regularly scheduled class in Week 7
- Complete all homework (worksheets 1 to 5).
- Study and practice regularly.
- Make notes as you study; review your notes regularly.
- Most questions are based on the material you are asked to study.
- Study together and test each other.
SYST10049 Web Development
2021-2023 © Sheridan College
HTML5 Elements
Review and explore the elements deeper.
Hover over the grid to highlight groupings.
Click on individual cells to go to MDN's reference pages.
Worksheets 1, 2, 3, 4, and 5
Complete and review each worksheet. Discuss on class discussion board.
Worksheet 5 review
- What is an HTML form? form elements? form submission methods? difference between GET and POST?
- What input types are available? labeling controls? explicity vs implicit?
- What does the pattern attribute do? what are regular expressions?
- What other attributes are there? when would you use them?
- What is DOM? DOM node? visual representation of DOM? DOM processing? CSSOM?
- Describe elements:
FORM TEXTAREA BUTTON INPUT OUTPUT FIELDSET LEGEND OPTION OPTGROUP SELECT DATALIST
SYST10049 Web Development
@2021-2022 Sheridan College
«previous — next»
SYST10949 Web Development
@2021-2022 Sheridan College
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SYST10949 Web Development
@2021-2022 Sheridan College


The volatility model is a property of a security. Exactly how volatility is calculated varies a lot between strategies so we've provided an override point here. Volatility models get updated with data each time step and are expected to be updated immediately. This is primarily require for options backtesting.
Volatility models implement the $[IVolatilityModel] interface. We default to the NullVolatilityModel which returns 0 volatility at all times. As a helper we also provide the $[RelativeStandardDeviationVolatilityModel] which calculates the volatility based on standard deviation.


Defines | |
| #define | EDMA3_LLD_DRV_VERSION_ID (0x020B0002) |
| This is the EDMA3 LLD Version. Versions numbers are encoded in the following format: 0xAABBCCDD -> Arch (AA); API Changes (BB); Major (CC); Minor (DD). | |
| #define | EDMA3_LLD_DRV_VERSION_STR "EDMA3 LLD Revision: <IP_ADDRESS>" |
| This is the version string which describes the EDMA3 LLD along with the date and build information. | |
| #define | EDMA3_DRV_E_BASE (-128) |
| EDMA3 Driver Error Codes Base define. | |
| #define | EDMA3_DRV_E_OBJ_NOT_DELETED (EDMA3_DRV_E_BASE) |
| #define | EDMA3_DRV_E_OBJ_NOT_CLOSED (EDMA3_DRV_E_BASE-1) |
| #define | EDMA3_DRV_E_OBJ_NOT_OPENED (EDMA3_DRV_E_BASE-2) |
| #define | EDMA3_DRV_E_RM_CLOSE_FAIL (EDMA3_DRV_E_BASE-3) |
| #define | EDMA3_DRV_E_DMA_CHANNEL_UNAVAIL (EDMA3_DRV_E_BASE-4) |
| #define | EDMA3_DRV_E_QDMA_CHANNEL_UNAVAIL (EDMA3_DRV_E_BASE-5) |
| #define | EDMA3_DRV_E_PARAM_SET_UNAVAIL (EDMA3_DRV_E_BASE-6) |
| #define | EDMA3_DRV_E_TCC_UNAVAIL (EDMA3_DRV_E_BASE-7) |
| #define | EDMA3_DRV_E_TCC_REGISTER_FAIL (EDMA3_DRV_E_BASE-8) |
| #define | EDMA3_DRV_E_CH_PARAM_BIND_FAIL (EDMA3_DRV_E_BASE-9) |
| #define | EDMA3_DRV_E_ADDRESS_NOT_ALIGNED (EDMA3_DRV_E_BASE-10) |
| #define | EDMA3_DRV_E_INVALID_PARAM (EDMA3_DRV_E_BASE-11) |
| #define | EDMA3_DRV_E_INVALID_STATE (EDMA3_DRV_E_BASE-12) |
| #define | EDMA3_DRV_E_INST_ALREADY_EXISTS (EDMA3_DRV_E_BASE-13) |
| #define | EDMA3_DRV_E_FIFO_WIDTH_NOT_SUPPORTED (EDMA3_DRV_E_BASE-14) |
| #define | EDMA3_DRV_E_SEMAPHORE (EDMA3_DRV_E_BASE-15) |
| #define | EDMA3_DRV_E_INST_NOT_OPENED (EDMA3_DRV_E_BASE-16) |
| #define | EDMA3_DRV_CH_NO_PARAM_MAP EDMA3_RM_CH_NO_PARAM_MAP |
| #define | EDMA3_DRV_CH_NO_TCC_MAP EDMA3_RM_CH_NO_TCC_MAP |
| #define | EDMA3_DRV_DMA_CHANNEL_ANY 1002u |
| #define | EDMA3_DRV_QDMA_CHANNEL_ANY 1003u |
| #define | EDMA3_DRV_TCC_ANY 1004u |
| #define | EDMA3_DRV_LINK_CHANNEL 1005u |
| #define | EDMA3_DRV_LINK_CHANNEL_WITH_TCC 1006u |
| #define | EDMA3_DRV_QDMA_CHANNEL_0 (EDMA3_MAX_DMA_CH + EDMA3_MAX_PARAM_SETS) |
| QDMA Channel defines. | |
| #define | EDMA3_DRV_QDMA_CHANNEL_1 (EDMA3_DRV_QDMA_CHANNEL_0+1u) |
| #define | EDMA3_DRV_QDMA_CHANNEL_2 (EDMA3_DRV_QDMA_CHANNEL_0+2u) |
| #define | EDMA3_DRV_QDMA_CHANNEL_3 (EDMA3_DRV_QDMA_CHANNEL_0+3u) |
| #define | EDMA3_DRV_QDMA_CHANNEL_4 (EDMA3_DRV_QDMA_CHANNEL_0+4u) |
| #define | EDMA3_DRV_QDMA_CHANNEL_5 (EDMA3_DRV_QDMA_CHANNEL_0+5u) |
| #define | EDMA3_DRV_QDMA_CHANNEL_6 (EDMA3_DRV_QDMA_CHANNEL_0+6u) |
| #define | EDMA3_DRV_QDMA_CHANNEL_7 (EDMA3_DRV_QDMA_CHANNEL_0+7u) |
| #define | EDMA3_DRV_CHANNEL_CLEAN 0x0000u |
| Channel status defines These defines suggest the current state of the DMA / QDMA channel. They are used while returning the channel status from EDMA3_DRV_getChannelStatus(). | |
| #define | EDMA3_DRV_CHANNEL_EVENT_PENDING 0x0001u |
| #define | EDMA3_DRV_CHANNEL_XFER_COMPLETE 0x0002u |
| #define | EDMA3_DRV_CHANNEL_ERR 0x0004u |
| #define EDMA3_DRV_E_OBJ_NOT_DELETED (EDMA3_DRV_E_BASE) |
EDMA3 Driver Object Not Deleted yet. So it cannot be created.
| #define EDMA3_DRV_E_OBJ_NOT_CLOSED (EDMA3_DRV_E_BASE-1) |
EDMA3 Driver Object Not Closed yet. So it cannot be deleted.
| #define EDMA3_DRV_E_OBJ_NOT_OPENED (EDMA3_DRV_E_BASE-2) |
EDMA3 Driver Object Not Opened yet So it cannot be closed.
| #define EDMA3_DRV_E_RM_CLOSE_FAIL (EDMA3_DRV_E_BASE-3) |
While closing EDMA3 Driver, Resource Manager Close Failed.
| #define EDMA3_DRV_E_DMA_CHANNEL_UNAVAIL (EDMA3_DRV_E_BASE-4) |
The requested DMA Channel not available
| #define EDMA3_DRV_E_QDMA_CHANNEL_UNAVAIL (EDMA3_DRV_E_BASE-5) |
The requested QDMA Channel not available
| #define EDMA3_DRV_E_PARAM_SET_UNAVAIL (EDMA3_DRV_E_BASE-6) |
The requested PaRAM Set not available
| #define EDMA3_DRV_E_TCC_UNAVAIL (EDMA3_DRV_E_BASE-7) |
The requested TCC not available
| #define EDMA3_DRV_E_TCC_REGISTER_FAIL (EDMA3_DRV_E_BASE-8) |
The registration of TCC failed
| #define EDMA3_DRV_E_CH_PARAM_BIND_FAIL (EDMA3_DRV_E_BASE-9) |
The binding of Channel and PaRAM Set failed
| #define EDMA3_DRV_E_ADDRESS_NOT_ALIGNED (EDMA3_DRV_E_BASE-10) |
The address of the memory location passed as argument is not properly aligned. It should be 32 bytes aligned.
| #define EDMA3_DRV_E_INVALID_PARAM (EDMA3_DRV_E_BASE-11) |
Invalid Parameter passed to API
| #define EDMA3_DRV_E_INVALID_STATE (EDMA3_DRV_E_BASE-12) |
Invalid State of EDMA3 HW Obj
| #define EDMA3_DRV_E_INST_ALREADY_EXISTS (EDMA3_DRV_E_BASE-13) |
EDMA3 Driver instance already exists for the specified region
| #define EDMA3_DRV_E_FIFO_WIDTH_NOT_SUPPORTED (EDMA3_DRV_E_BASE-14) |
FIFO width not supported by the requested TC
| #define EDMA3_DRV_E_SEMAPHORE (EDMA3_DRV_E_BASE-15) |
Semaphore related error
| #define EDMA3_DRV_E_INST_NOT_OPENED (EDMA3_DRV_E_BASE-16) |
EDMA3 Driver Instance does not exist, it is not opened yet
| #define EDMA3_DRV_CH_NO_PARAM_MAP EDMA3_RM_CH_NO_PARAM_MAP |
This define is used to specify that a DMA channel is NOT tied to any PaRAM Set and hence any available PaRAM Set could be used for that DMA channel. It could be used in dmaChannelPaRAMMap [EDMA3_MAX_DMA_CH], in global configuration structure EDMA3_RM_GblConfigParams.
This value should mandatorily be used to mark DMA channels with no initial mapping to specific PaRAM Sets.
| #define EDMA3_DRV_CH_NO_TCC_MAP EDMA3_RM_CH_NO_TCC_MAP |
This define is used to specify that the DMA/QDMA channel is not tied to any TCC and hence any available TCC could be used for that DMA/QDMA channel. It could be used in dmaChannelTccMap [EDMA3_MAX_DMA_CH], in global configuration structure EDMA3_RM_GblConfigParams.
This value should mandatorily be used to mark DMA channels with no initial mapping to specific TCCs.
| #define EDMA3_DRV_DMA_CHANNEL_ANY 1002u |
Used to specify any available DMA Channel while requesting one. Used in the API EDMA3_DRV_requestChannel(). DMA channel from the pool of (owned && non_reserved && available_right_now) DMA channels will be chosen and returned.
| #define EDMA3_DRV_QDMA_CHANNEL_ANY 1003u |
Used to specify any available QDMA Channel while requesting one. Used in the API EDMA3_DRV_requestChannel(). QDMA channel from the pool of (owned && non_reserved && available_right_now) QDMA channels will be chosen and returned.
| #define EDMA3_DRV_TCC_ANY 1004u |
Used to specify any available TCC while requesting one. Used in the API EDMA3_DRV_requestChannel(), for both DMA and QDMA channels. TCC from the pool of (owned && non_reserved && available_right_now) TCCs will be chosen and returned.
| #define EDMA3_DRV_LINK_CHANNEL 1005u |
Used to specify any available PaRAM Set while requesting one. Used in the API EDMA3_DRV_requestChannel(), for Link channels. PaRAM Set from the pool of (owned && non_reserved && available_right_now) PaRAM Sets will be chosen and returned.
| #define EDMA3_DRV_LINK_CHANNEL_WITH_TCC 1006u |
Used to specify any available PaRAM Set while requesting one. Used in the API EDMA3_DRV_requestChannel(), for Link channels. TCC code should also be specified and it will be used to populate the LINK field of the PaRAM Set. Without TCC code, the call will fail. PaRAM Set from the pool of (owned && non_reserved && available_right_now) PaRAM Sets will be chosen and returned.
| #define EDMA3_DRV_QDMA_CHANNEL_0 (EDMA3_MAX_DMA_CH + EDMA3_MAX_PARAM_SETS) |
QDMA Channel defines.
They should be used while requesting a specific QDMA channel in API EDMA3_DRV_requestChannel() as the argument (*pLch). Please note that these defines should ONLY be used in the API EDMA3_DRV_requestChannel() and not in any other API to perform further operations. They are only provided to allow user allocate specific QDMA channels. QDMA Channel 0
| #define EDMA3_DRV_QDMA_CHANNEL_1 (EDMA3_DRV_QDMA_CHANNEL_0+1u) |
QDMA Channel 1
| #define EDMA3_DRV_QDMA_CHANNEL_2 (EDMA3_DRV_QDMA_CHANNEL_0+2u) |
QDMA Channel 2
| #define EDMA3_DRV_QDMA_CHANNEL_3 (EDMA3_DRV_QDMA_CHANNEL_0+3u) |
QDMA Channel 3
| #define EDMA3_DRV_QDMA_CHANNEL_4 (EDMA3_DRV_QDMA_CHANNEL_0+4u) |
QDMA Channel 4
| #define EDMA3_DRV_QDMA_CHANNEL_5 (EDMA3_DRV_QDMA_CHANNEL_0+5u) |
QDMA Channel 5
| #define EDMA3_DRV_QDMA_CHANNEL_6 (EDMA3_DRV_QDMA_CHANNEL_0+6u) |
QDMA Channel 6
| #define EDMA3_DRV_QDMA_CHANNEL_7 (EDMA3_DRV_QDMA_CHANNEL_0+7u) |
QDMA Channel 7
| #define EDMA3_DRV_CHANNEL_CLEAN 0x0000u |
Channel status defines These defines suggest the current state of the DMA / QDMA channel. They are used while returning the channel status from EDMA3_DRV_getChannelStatus().
Channel is clean; no pending event, completion interrupt and event miss interrupt
| #define EDMA3_DRV_CHANNEL_EVENT_PENDING 0x0001u |
Pending event is detected on the DMA channel
| #define EDMA3_DRV_CHANNEL_XFER_COMPLETE 0x0002u |
Transfer completion interrupt is detected on the DMA/QDMA channel
| #define EDMA3_DRV_CHANNEL_ERR 0x0004u |
Event miss error interrupt is detected on the DMA/QDMA channel


ActionComponents
04.05.00
Customizable UI and time-saving tools for Android development with Xamarin and Visual Studio.
Main Page
Packages
Classes
Class List
Class Index
Class Hierarchy
Class Members
Class Hierarchy
Go to the textual class hierarchy
Generated on Tue Mar 13 2018 13:15:35 for ActionComponents by
1.8.11


Available resources
Tool for create pdf files by xml notation
Apache FOP
Specifications for pdf files
PDF 1.6 Reference
PDF 1.7 Specification Part I


About Savannah
Savannah is a domestic cat hybrid, cross bred between a african serval and a domestic cat. These cats are idetified as a new registered breed in 2001 by the International Cat Association and are recognized as a champion breed in 2012 by the International Cat Association. Depending on the generation of cross from their wild ancestory; cats are characterized by f1 filial for first generation, f2 for second generation and f3 for third generation with any farther distant generation classified as SBG for Stud Book Tradition. Male hybrids are generally sterile in until the f5 generation, while females f1 females are fertile.
- F1 (~53% Serval)
- F2 (~29% Serval)
- F3 (~16% Serval) and so forth.


class: center, middle # ETLocal ## Sprint meeting --- # Agenda 1. Overview 2. What did we do the previous sprint? 3. What didn't we do? 4. What are we going to do? --- # Overview:
--- # What did we do the previous sprint?
---
---
---
--- # Schematic households analysis
--- # Schematic households analysis (inputs)
--- # Schematic households analysis (inputs / graph)
--- # Schematic households analysis (outputs)
--- # To summarize - Made it possible to run the analyses from the front-end - Showing the results in a separate window - Restructured the interface - Created schematic of the households analysis --- # What didn't we do? **Dependency to analyses** - Make each calculation apparent - Giving an explanation to each initializer input
**Lack of time:** - Try-out the dataset in a separate (ETM) environment - Commit and use as stable dataset in ETM --- # What are we going to do? **Etlocal:** - Focus on the analyses: households (2 days max), buildings (2 days), industry (rest): - Creating a schematic: - Re-evaluate the current ETLocal analysis - Translate the Excel analysis into ETLocal analysis - Agree upon schematic JB, CK, BS - Change the ETLocal analysis - Explain the calculation in the front-end - Adding download/export dataset button (internal use)(1 day) **non-ETlocal:** - CSV dump of a scenario (1 day) - I have 1 day off --- class: center, middle # End


Hot or Not?
Current temperature:
Current usage:
Power Saving is on?
true
+
-
RESET
location:
temperature:
If you look out the window, you'll see:


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Burning CD's of photos to take to in-laws. Found the missing pan--it was in the utility room with the hot peppers.
11/24/2008, 10:05:30 PM
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Burning CD's of photos to take to in-laws. Found the missing pan--it was in the utility room with the hot peppers.
11/24/2008, 10:05:30 PM
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A.i.d.a. substitute all the secretaries in the office environment.
A.i.d.a. could reply to emails, schedule activities, organize folders
and last but not least find files in a smarter way based on the personal users' experience.
A.i.d.a. substitute all the secretaries in the office environment.
A.i.d.a. could reply to emails, schedule activities, organize folders
and last but not least find files in a smarter way based on the personal users' experience.
per info:


Start a room
Create it!
You
Drag your mouse anywhere over your partner to "touch" them with a warm glow.


Test and document all changes, including updates, made to software code and applications.
Testing helps ensure that patches do not introduce new vulnerabilities or break functionality. Changes should be documented to help find the cause of a vulnerability if one is introduced by the changes.
All changes to software code, including patches, should be tested and documented before being deployed.
Perform the following actions to implement change control procedures in compliance with PCI DSS:
Define change-control procedures. PCI DSS requires following change control procedures for all changes to system components. These procedures may be stated explicitly in a separate document or included in the information security policy. The procedures are the ones described in this article and include documentation of impact, management sign-off, functionality testing and back-out procedures.
Designate a testing environment. Design a simulated test environment that uses hardware and software similar to that used in the production environment. If possible, set up the test environment like a scaled-down version of the production environment. Identify the components of the production environment that will be affected by the change being tested. Install and configure hardware and software similar to the affected components in the test environment. Install and review changes in the test environment before deploying them in the production environment. Staff working in the production environment should not be working in the test environment
Include customer impact considerations. While testing changes, consider and document the possible customer impact of each change. Things to consider are the effect the change would have on the user experience and the functionality that may be added or removed. Attach the customer impact considerations to the change-control documentation.
Require a management sign-off. The management should sign off on each change before it is deployed. The signed documents should be included in the change-control documentation.
Document the test results. The objective of testing is to make sure that the change to the code produces only the desirable behavior. The results of how well the code changes have achieved these objectives should be noted. These results should be included in the change-control documentation.
Define back-out procedures. Design and document back-out procedures for each change. Include the descriptions of the back-out procedures in the change-control documentation. Typically, the back-out procedures should describe how to uninstall the patch or how to reinstall the earlier, stable version of the software.


Compass is built to allow extensions to be built off of it. Similar to Drupal modules, each of these provides some extra functionality.
Variables, nesting, inheritance
$green: #66B360;
ul.list {
margin-top: 1em;
li {
list-style-type: none;
color: $green;
&:nth-child(even) {
color: darken($green, 20%);
}
}
}
ul.list {
margin-top: 1em;
}
ul.list li {
list-style-type: none;
color: #66B360;
}
ul.list li:nth-child(even) {
color: #3C7538;
}
Commenting
/**
* Standard CSS commenting.
* This will display
*/
// Sass inline commenting.
// This won't display.
.redness {
color: $red; // We make this red so it is red.
}
/**
* Standard CSS commenting, will display
*/
.redness {
color: #FF0000;
}
Partials
Compass will not compile any .scss file with an underscore before it, so you can have as many lower "partials" that each have a small piece of the styling you want.
By having an "@import 'file_name'; " in your Sass file "file_name.scss" (or "_file_name.scss") into your stylesheet, and include it in a single file.
The Corona distribution of Aurora contains the partial structure we will be using going forward
Compiling Sass to CSS
To run once:
compass compile
To have compass monitor changes in your partials
compass watch
Aurora is a powerful base theme that will provide you with HTML5 templates, cleans core css, provides helpful settings, helps with JavaScript organization... and nothing more.
It is built to be a great starting point to create a custom theme, and not a huge frameworks you have to work against.
github.com/ChinggizKhan/Corona
Corona is just a sub-theme distribution that will start your theme building with an awesome Sass partial structure.
The bulk of every Aurora sub-theme (from the compass extension) is the same. The only difference is the partial structure.
compass create THEME_NAME -r aurora --using aurora/corona
OR to make the little kittens happy:
compass create THEME_NAME -r aurora --using aurora/corona --sass-dir "sass" --css-dir "css" --javascripts-dir "js" --images-dir "img"
Base and Global already have within it a variables and files to start you off, but nothing too complex. Design is completely empty and just waiting for you to start designing.


Prosecutors: Teen killed girl, 14, thinking she was pregnant
by EndPlayCANTON, Ohio (AP) - Authorities say a teen fatally shot a 14-year-old girl because he believed a false rumor the girl was pregnant.
The Repository in Canton reports the 14-year-old boy pleaded guilty to murder Wednesday in Stark County Juvenile Court. His trial on the juvenile equivalent of aggravated murder was scheduled to begin Thursday.
Assistant Stark County Prosecutor Michelle Cordova said the boy told police he believed Sylvia McGee, who wasn't his girlfriend, was pregnant. Cordova says an autopsy conducted after the slaying March 30 showed she was not.
Cordova says there's no evidence other than the pregnancy rumor to indicate why Sylvia was killed.
The boy couldn't be tried in adult court because he was just 13 at the time of the slaying.
Sentencing is scheduled for Jan. 7.
___
Information from: The Repository, http://www.cantonrep.com


{% extends "base.html" %} {% block content %}
A prototype of REST API application via Python and Flask.
Implemented CRUD functionality for REST API.
Supports GET, POST, PUT, DELETE.
The current version of the API lives at https://api-flask-baur.herokuapp.com/api/v1/quotes
| Version | Data | Changes |
|---|---|---|
| version 1 | 08/24/2019 | Initial deployment |
| Endpoint | What it does |
|---|---|
| /quotes | Returns random quote |
| /quotes/{id} | Returns a quote by id, if there is no quote with specific id returns warning message |


Luke Skywalker's peaceful and solitary existence gets upended when he meets Rey, a young woman who shows strong signs of the Force. Stay tuned to this page for more updates regarding movie release dates, plot rumors as well merchandise!
| Opening Hours | |
|---|---|
| Day | Hours |
| Monday to Thursday | 9am - 5pm |
| Friday and Saturday | Closed |


THE TERMINATOR A film review by Raghava S. Setlur Copyright 1996 Raghava S. Setlur
(1984) 3.9 / 4.0
I never liked Arnold S.  His movies were, how shall I put it, mentally negligible. When this movie came out back in 1988 in my hometown of Bangalore, India, I politely declined the invitation from some of my friends. But as weeks went by, one day when there was nothing else to do, and all the other movies I wanted to see were seen, I grudgingly went to see this flick. Let me tell you somethin'. I loved it man, I loved it.
Arnie was perfect in this movie. All that bad press about being as stoical as a robot really pays off here. The story, visuals, action, colors and the effects blend together successfullly, capped by some eerie music.
Anyway the premise of this movie is this: In the future, there will be a nuclear war against, get this, computers and humans. Most of the humans die but a few enterprising souls survive the holocaust. I never did get how they did. Probably it is a crime to watch movies on basic cable. You don't know what they leave out. How did the computers survive. There might be more than a few technical glitches in terms of the effects of nuclear explosions on power supply could be intricate. This and some other questions do come up, inevitably in any science fiction or time travel movie. Another one would be that the man from the future explains that only living things could get through the time machine, so he could not bring his custom made ray gun or his best suit, but came in his birthday suit instead. Pray tell me how hair and nail which are dead tissue survived the arduous journey. The terminator itself, or a cyborg has a metallic skeleton. Would it survive, I wonder? Maybe, just maybe, there is some simple explanation to all this.
The Hollywood way of portraying easy sex. Linda Hamilton eagerly goes to bed with the man from the future immediately after he says that she was the only woman he wanted. Very accomodating. That was not the typical blind date. There are many instances of such inexplicable actions in Hollywood movies that tends to carry the notion of the loose American women to all parts of the world. My idea of American women was formed from the American movies.
These are some nitpickings in an otherwise perfect movie. It has good story to tell and paces it well, making it very entertaining and edgy along the way. Critics who have a bias against action/ sf genre (Gene Siskel comes readily to mind) and keep complaining that every picture is a genre picture should see this one. It is much, much better than those weary drama movies that they keep harping about. The concept of existence and different realities is similar to back to the future movies, but is stronger. The mind boggles at the complexities of all this.
 They rig up a time travel device back to the present. And send a death dealing machine with human features and outer tissues. Fortunately for us, a human soldier manages to follow.
 Here name is Connor, Sarah Connor. The city: L.A. You will have no team and no, yes I mean no, point man. You will also have no clothes. But that is a minor inconvenience.
One of the things I liked about the killing and the mayhem is that the terminator cannot be argued or cajoled with. It does not sit around making small talk and giving away secrets (Bond movies) but just says " I'll be back." Which it does. Then promptly goes about doing its business of killing. If you wanna shoot, shoot, don't talk. The lack of dialogue and emotion actually works in Arnold's favor since that is how a terminator is supposed to act. And he is the bad guy. Nowadays he plays the good guy which is less interesting. One of the mistakes the sequel made. This is vastly superior to T2, which had the mental institution thing that left an unpleasant taste in the mouth. The only thing that T2 did better was its special effects. But that is another review.
The way everything fits into place is pleasant to see. Of course there are the usual shootings and car chases but it seems more relevant than most other pictures as the terminator tries to kill every Sarah Connor in the phone book. The screenplay writers are to be congratulated. The production values are good and the direction very sure footed and stylized. There are some neat tricks and ideas which makes it a pleasure to watch this movie. The terminator can learn the accent and inotation of every person it hears. With a computer screen that pops up occasionally to process data for it. Dogs can detect terminators and start barking at them. Why? No idea.
When you have this wacky idea that somebody in a large American city is picking names from the phone book and shooting them, it is surprising that no one has used motif in real life. We have letter bombers and Zodiac killers, so somebody might still use it.
The rest of the movie deals with the Terminator trying to kill the Connor woman and the man from the future trying to save her. The special effects are ground breaking and the story is inventive till the end.  This is great special effects with the skeleton like robot running around. You still get the feeling it is Arnold S. even though you don't see him on the screen. Mission: Impossible. Even for the terminator. I enthusiastically recommend this movie to anyone who has not seen it. A classic. A near perfect score of: 3.9 / 4.0
--XXX-- -- As always, expecting a reply,
Bye for now, Raghava S. Setlur. SUNY at Stony Brook<EMAIL_ADDRESS>June 21, 1996
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Sondland’s explosive testimony
Sir, – US ambassador to the EU Gordon Sondland’s evidence to Congress on Wednesday was explosive (World News, irishtimes.com, November 20th).
When President Donald Trump was first asked about Mr Sondland’s plan to appear before the impeachment hearing he distanced himself from the Ambassador by asserting that he hardly knew the man.
Well the ambassador certainly put some distance between the two of them today. As I watched the coverage I was reminded of Con Houlihan’s adage, “It’s not a good idea to insult the alligator’s mother before you cross the river”. – Yours, etc,
PADDY GOGARTY,
Portmarnock,
Co Dublin.


TUG
Look, i made a random lil' website that you have found, very good job!
Here is a nice picture of Jarrod for you to enjoy!


Run the SQL in the Ad Hoc command window to the right.
It creates a table with regular LOB column that you will use as a baseline to demonstrate the results of inline LOB.
The LOBDATA table has two LOB columns. The VIEW_TEXT column is in Character Large Object (CLOB) data type. Maximum length of each CLOB value is 2 megabytes. The COLUMN_TEXT column is also in CLOB data type whose value can be up to 20 megabytes long. Note that to create CLOB strings greater than 1 gigabyte, you must specify the NOT LOGGED option.
The NOT LOGGED INITIALLY clause is used here to reduce logging activities performed on the LOBDATA table. Logging is not engaged for any operations or changes made on the table in the same unit of work that NOT LOGGED INITIALLY is used.
NOT LOGGED INITIALLY is very useful when you are not concerned about the recoverability for work done on the table. This reduces logging and improves performance.
It inserts 10,000 rows of data into the LOBDATA table.


RequireJS Sample Code
Download source code from github
Closure
모듈 정의하기
Example using jQuery from a CDN
Simple one page app
Multipage app
Multipage app with shim config