Fashion MNIST¶
This guide is a copy of Tensorflow’s tutorial Basic classification: Classify images of clothing. It does NOT use a complex database. It just serves to test the correct work of the CVNN layers and compare it to a known working example.
It trains a neural network model to classify images of clothing, like sneakers and shirts. It’s okay if you don’t understand all the details; this is a fast-paced overview of a complete TensorFlow program with the details explained as you go.
This guide uses tf.keras
, a high-level API to build and train models in TensorFlow.
[1]:
# TensorFlow and tf.keras
import tensorflow as tf
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
# My library!
from cvnn import layers
print(tf.__version__)
2.1.0
Import the Fashion MNIST dataset¶
This guide uses the Fashion MNIST dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:
Fashion MNIST is intended as a drop-in replacement for the classic MNIST dataset—often used as the “Hello, World” of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc.) in a format identical to that of the articles of clothing you’ll use here.
This guide uses Fashion MNIST for variety, and because it’s a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They’re good starting points to test and debug code.
Here, 60,000 images are used to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow. Import and load the Fashion MNIST data directly from TensorFlow:
[2]:
fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
Loading the dataset returns four NumPy arrays:
The
train_images
andtrain_labels
arrays are the training set—the data the model uses to learn.The model is tested against the test set, the
test_images
, andtest_labels
arrays.
The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255. The labels are an array of integers, ranging from 0 to 9. These correspond to the class of clothing the image represents:
Label |
Class |
|---|---|
0 |
T-shirt/top |
1 |
Trouser |
2 |
Pullover |
3 |
Dress |
4 |
Coat |
5 |
Sandal |
6 |
Shirt |
7 |
Sneaker |
8 |
Bag |
9 |
Ankle boot |
Each image is mapped to a single label. Since the class names are not included with the dataset, store them here to use later when plotting the images:
[3]:
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
Explore the data¶
Let’s explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels:
[4]:
train_images.shape
[4]:
(60000, 28, 28)
Likewise, there are 60,000 labels in the training set:
[5]:
len(train_labels)
[5]:
60000
Each label is an integer between 0 and 9:
[6]:
train_labels
[6]:
array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)
There are 10,000 images in the test set. Again, each image is represented as 28 x 28 pixels:
[7]:
test_images.shape
[7]:
(10000, 28, 28)
And the test set contains 10,000 images labels:
[8]:
len(test_labels)
[8]:
10000
Preprocess the data¶
The data must be preprocessed before training the network. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:
[9]:
plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
Scale these values to a range of 0 to 1 before feeding them to the neural network model. To do so, divide the values by 255. It’s important that the training set and the testing set be preprocessed in the same way:
[10]:
train_images = train_images / 255.0
test_images = test_images / 255.0
To verify that the data is in the correct format and that you’re ready to build and train the network, let’s display the first 25 images from the training set and display the class name below each image.
[11]:
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i], cmap=plt.cm.binary)
plt.xlabel(class_names[train_labels[i]])
plt.show()
Build the model¶
Building the neural network requires configuring the layers of the model, then compiling the model.
Set up the layers¶
The basic building block of a neural network is the layer. Layers extract representations from the data fed into them. Hopefully, these representations are meaningful for the problem at hand.
Most of deep learning consists of chaining together simple layers. Most layers, such as tf.keras.layers.Dense, have parameters that are learned during training.
[12]:
model = tf.keras.Sequential([
layers.ComplexFlatten(input_shape=(28, 28)),
layers.ComplexDense(128, activation='cart_relu', dtype=np.float32),
layers.ComplexDense(10, dtype=np.float32)
])
As this CVNN
library goal is to work with complex-valued datasets, ComplexLayer’s have np.complex64 dtype
by default. Therefore, for all layers we should add the parameter dtype=np.float32
(our database is real)
The first layer in this network, layers.ComplexFlatten
, transforms the format of the images from a two-dimensional array (of 28 by 28 pixels) to a one-dimensional array (of 28 * 28 = 784 pixels). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.
After the pixels are flattened, the network consists of a sequence of two layers.ComplexDense
layers. These are densely connected, or fully connected, neural layers. The first Dense layer has 128 nodes (or neurons). The second (and last) layer returns a logits array with length of 10. Each node contains a score that indicates the current image belongs to one of the 10 classes.
Compile the model¶
Before the model is ready for training, it needs a few more settings. These are added during the model’s compile step:
Loss function —This measures how accurate the model is during training. You want to minimize this function to “steer” the model in the right direction.
Optimizer —This is how the model is updated based on the data it sees and its loss function.
Metrics —Used to monitor the training and testing steps. The following example uses accuracy, the fraction of the images that are correctly classified.
[13]:
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
Train the model¶
Training the neural network model requires the following steps:
Feed the training data to the model. In this example, the training data is in the train_images and train_labels arrays.
The model learns to associate images and labels.
You ask the model to make predictions about a test set—in this example, the test_images array.
Verify that the predictions match the labels from the test_labels array.
Feed the model¶
To start training, call the model.fit method—so called because it “fits” the model to the training data:
[14]:
model.fit(train_images, train_labels, epochs=10)
Train on 60000 samples
Epoch 1/10
32/60000 [..............................] - ETA: 28:33
---------------------------------------------------------------------------
InternalError Traceback (most recent call last)
<ipython-input-14-93ea666c821d> in <module>
----> 1 model.fit(train_images, train_labels, epochs=10)
~\.conda\envs\tf-2-gpu\lib\site-packages\tensorflow_core\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
817 max_queue_size=max_queue_size,
818 workers=workers,
--> 819 use_multiprocessing=use_multiprocessing)
820
821 def evaluate(self,
~\.conda\envs\tf-2-gpu\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
340 mode=ModeKeys.TRAIN,
341 training_context=training_context,
--> 342 total_epochs=epochs)
343 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
344
~\.conda\envs\tf-2-gpu\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
126 step=step, mode=mode, size=current_batch_size) as batch_logs:
127 try:
--> 128 batch_outs = execution_function(iterator)
129 except (StopIteration, errors.OutOfRangeError):
130 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~\.conda\envs\tf-2-gpu\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
96 # `numpy` translates Tensors to values in Eager mode.
97 return nest.map_structure(_non_none_constant_value,
---> 98 distributed_function(input_fn))
99
100 return execution_function
~\.conda\envs\tf-2-gpu\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
566 xla_context.Exit()
567 else:
--> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
~\.conda\envs\tf-2-gpu\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
630 # Lifting succeeded, so variables are initialized and we can run the
631 # stateless function.
--> 632 return self._stateless_fn(*args, **kwds)
633 else:
634 canon_args, canon_kwds = \
~\.conda\envs\tf-2-gpu\lib\site-packages\tensorflow_core\python\eager\function.py in __call__(self, *args, **kwargs)
2361 with self._lock:
2362 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2363 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2364
2365 @property
~\.conda\envs\tf-2-gpu\lib\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs)
1609 if isinstance(t, (ops.Tensor,
1610 resource_variable_ops.BaseResourceVariable))),
-> 1611 self.captured_inputs)
1612
1613 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~\.conda\envs\tf-2-gpu\lib\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1690 # No tape is watching; skip to running the function.
1691 return self._build_call_outputs(self._inference_function.call(
-> 1692 ctx, args, cancellation_manager=cancellation_manager))
1693 forward_backward = self._select_forward_and_backward_functions(
1694 args,
~\.conda\envs\tf-2-gpu\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
543 inputs=args,
544 attrs=("executor_type", executor_type, "config_proto", config),
--> 545 ctx=ctx)
546 else:
547 outputs = execute.execute_with_cancellation(
~\.conda\envs\tf-2-gpu\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [
~\.conda\envs\tf-2-gpu\lib\site-packages\six.py in raise_from(value, from_value)
InternalError: Blas GEMM launch failed : a.shape=(32, 784), b.shape=(784, 128), m=32, n=128, k=784
[[node sequential/complex_dense/MatMul (defined at W:\HardDiskDrive\Documentos\GitHub\cvnn\cvnn\layers.py:127) ]] [Op:__inference_distributed_function_668]
Errors may have originated from an input operation.
Input Source operations connected to node sequential/complex_dense/MatMul:
sequential/complex_flatten/Cast (defined at W:\HardDiskDrive\Documentos\GitHub\cvnn\cvnn\layers.py:64)
Function call stack:
distributed_function
As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.91 (or 91%) on the training data.
Evaluate accuracy¶
Next, compare how the model performs on the test dataset:
[ ]:
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)
It turns out that the accuracy on the test dataset is a little less than the accuracy on the training dataset. This gap between training accuracy and test accuracy represents overfitting. Overfitting happens when a machine learning model performs worse on new, previously unseen inputs than it does on the training data. An overfitted model “memorizes” the noise and details in the training dataset to a point where it negatively impacts the performance of the model on the new data. For more information, see the following:
Make predictions¶
With the model trained, you can use it to make predictions about some images. The model’s linear outputs, logits. Attach a softmax layer to convert the logits to probabilities, which are easier to interpret.
[ ]:
probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
predictions = probability_model.predict(test_images)
Here, the model has predicted the label for each image in the testing set. Let’s take a look at the first prediction:
[ ]:
predictions[0]
A prediction is an array of 10 numbers. They represent the model’s “confidence” that the image corresponds to each of the 10 different articles of clothing. You can see which label has the highest confidence value:
[ ]:
np.argmax(predictions[0])
So, the model is most confident that this image is an ankle boot, or class_names[9]
. Examining the test label shows that this classification is correct:
[ ]:
test_labels[0]
Graph this to look at the full set of 10 class predictions.
[1]:
def plot_image(i, predictions_array, true_label, img):
true_label, img = true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = 'blue'
else:
color = 'red'
plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100*np.max(predictions_array),
class_names[true_label]),
color=color)
def plot_value_array(i, predictions_array, true_label):
true_label = true_label[i]
plt.grid(False)
plt.xticks(range(10))
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)
thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('blue')
Verify predictions¶
With the model trained, you can use it to make predictions about some images.
Let’s look at the 0th image, predictions, and prediction array. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percentage (out of 100) for the predicted label.
[ ]:
i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i], test_labels)
plt.show()
[ ]:
i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i], test_labels)
plt.show()
Let’s plot several images with their predictions. Note that the model can be wrong even when very confident.
[ ]:
# Plot the first X test images, their predicted labels, and the true labels.
# Color correct predictions in blue and incorrect predictions in red.
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2*num_cols, 2*i+1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(num_rows, 2*num_cols, 2*i+2)
plot_value_array(i, predictions[i], test_labels)
plt.tight_layout()
plt.show()
Use the trained model¶
Finally, use the trained model to make a prediction about a single image.
[ ]:
# Grab an image from the test dataset.
img = test_images[1]
print(img.shape)
tf.keras
models are optimized to make predictions on a batch, or collection, of examples at once. Accordingly, even though you’re using a single image, you need to add it to a list:
[ ]:
# Add the image to a batch where it's the only member.
img = (np.expand_dims(img,0))
print(img.shape)
Now predict the correct label for this image:
[ ]:
predictions_single = probability_model.predict(img)
print(predictions_single)
[ ]:
plot_value_array(1, predictions_single[0], test_labels)
_ = plt.xticks(range(10), class_names, rotation=45)
tf.keras.Model.predict
returns a list of lists—one list for each image in the batch of data. Grab the predictions for our (only) image in the batch:
[ ]:
np.argmax(predictions_single[0])
[ ]:


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9/2/2019, 3:14:20 AM
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public class Lexer
extends java.lang.Object
| Modifier and Type | Field | Description |
|---|---|---|
private int |
curr |
|
private boolean |
isEnd |
|
private int |
line |
|
private static java.util.Map<java.lang.String,TokenType> |
resKeywords |
|
private java.lang.String |
sourceCode |
Attributes of the lexer
|
private int |
startOfToken |
|
private java.util.List<Token> |
tokenList |
| Constructor | Description |
|---|---|
Lexer(java.lang.String sourceCode) |
This constructor is fed sourcecode from the EZlang client for lexical analysis.
|
| Modifier and Type | Method | Description |
|---|---|---|
private boolean |
checkEnd() |
method that checks if all chars have been iterated through i.e.
|
private boolean |
checkLetter(char c) |
|
private boolean |
checkNumber(char c) |
|
private char |
getCurrChar() |
method that returns current char
|
private char |
getNextChar() |
Returns next char or terminates string if end of file has been reached.
|
java.util.List<Token> |
getTokenList() |
|
void |
LexicalAnalysis() |
This method performs lexical analysis on the source code.
|
private boolean |
match(char input) |
method that checks for second char in 2 character tokens.
|
private char |
next() |
method that iterates to the next char in the source code and returns the
previous char
|
private void |
tokenize(TokenType tokenType) |
Method that makes tokens for tokens with no values
|
private void |
tokenize(TokenType tokenType,
java.lang.Object literal) |
Method that makes tokens for tokens with values
|
private void |
tokenizeChar() |
Helper method for turning chars into char tokens
|
private void |
tokenizeID() |
Helper method for turning identifiers into ID tokens
|
private void |
tokenizeNumber() |
Helper method for tokenizing numbers
|
private void |
tokenizeString() |
Helper method for turning strings into string tokens
|
private final java.lang.String sourceCode
private final java.util.List<Token> tokenList
private static final java.util.Map<java.lang.String,TokenType> resKeywords
private int startOfToken
private int curr
private int line
private boolean isEnd
public Lexer(java.lang.String sourceCode)
sourceCode
- public java.util.List<Token> getTokenList()
public void LexicalAnalysis()
private void tokenizeChar()
private void tokenizeString()
private void tokenizeID()
private char next()
private boolean match(char input)
input
- checks if current char matches input charprivate char getCurrChar()
private char getNextChar()
private boolean checkLetter(char c)
input
- char that is checked if it is a letter or _private boolean checkNumber(char c)
input
- char that is checked if it is a number or notprivate void tokenizeNumber()
private void tokenize(TokenType tokenType)
The
- token type of a corresponding tokenprivate void tokenize(TokenType tokenType, java.lang.Object literal)
tokenType
- The type of the tokenliteral
- The value of the tokenprivate boolean checkEnd()


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Virtualized Browser
Technologies used: Django, Javascript, MariaDB, Python, SQL, Xojo.
The Objective
Develop and maintain a browser solution that protects you and your network from threats on the internet.
The Process
This was a project I worked on at a previous company. I can get into a bit more detail in person.
The Result
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General
What is Go kit?
 It was originally conceived as a toolkit to help larger (so-called modern enterprise) organizations adopt Go as an implementation language. But it very quickly “grew downward”, and now serves smaller startups and organizations just as well. For more about the origins of Go kit, see Go kit: Go in the modern enterprise.
Why should I use Go kit?
 Go kit will help you structure and build out your services, avoid common pitfalls, and write code that grows with grace.
Go kit can also help to justify Go as an implementation language to stakeholders like engineering managers or tech leads.
Who is behind Go kit?
Go kit was originally conceived by Peter Bourgon, but is now built and maintained by a large group of contributors from a diverse set of backgrounds and organizations. Go kit is presently an all-volunteer effort, and has no commercial backing.
Is Go kit production-ready?
Yes. Go kit is being used in production in several organizations, large and small.
Which organizations are using Go kit?
Watch this space :)
How does Go kit compare to Micro?
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Architecture and design
What is the design of a Go kit microservice?
Go kit models a microservice like an onion, with many layers. The layers can be divided into three domains.
The innermost service domain is where everything is based on your specific service definition, and where all of the business logic is implemented. The middle endpoint domain is where each method of your service is abstracted to the generic endpoint.Endpoint, and where safety and antifragile logic is implemented. Finally, the outermost transport domain is where endpoints are bound to concrete transports like HTTP or gRPC.
You implement the core business logic by defining an interface for your service and providing a concrete implementation. Then, you write service middlewares to provide additional functionality, like logging, analytics, instrumentation — anything that needs knowledge of your business domain.
Go kit provides endpoint and transport domain middlewares, for functionality like rate limiting, circuit breaking, load balancing, and distributed tracing — all of which are generally agnostic to your business domain.
In short, Go kit tries to enforce strict separation of concerns through studious use of the middleware (or decorator) pattern.
Why is func main always so big?
 Experience has taught me that the most comprehensible, maintainable, and expressive method of defining and wiring up the component graph in a microservice is through an explicit and declarative composition in a large func main.
This style reinforces two important virtues. By strictly keeping component lifecycles in main, you avoid leaning on global state as a shortcut, which is critical for testability. And if components are scoped to main, the only way to provide them as dependencies to other components is to pass them explicitly as parameters to constructors. This keeps dependencies explicit, which stops a lot of technical debt before it starts.
For more general Go design tips, see Go best practices, six years in.
How should I deploy Go kit services?
It’s totally up to you. You can build a static binary, scp it to your server, and use a supervisor like runit. Or you can use a tool like Packer to create an AMI, and deploy it into an EC2 autoscaling group. Or you can package your service up into a container, ship it to a registry, and deploy it onto a cloud-native platform like Kubernetes.
Go kit is mostly concerned with good software engineering within your service; it tries to integrate well with any kind of platform or infrastructure.
How should I encode errors?
Your service methods will probably return errors. You have two options for encoding them in your endpoints. You can include an error field in your response struct, and return your business domain errors there. Or, you can choose to return your business domain errors in the endpoint error return value.
Both methods can be made to work. But errors returned directly by endpoints are recognized by middlewares that check for failures, like circuit breakers. It’s unlikely that a business-domain error from your service should cause a circuit breaker to trip in a client. So, it’s likely that you want to encode errors in your response struct.
addsvc contains examples of both methods.
Specific components
Which transports are supported?
Go kit ships with support for HTTP, gRPC, Thrift, and net/rpc. It’s straightforward to add support for new transports; just file an issue if you need something that isn’t already offered.
Which service discovery systems are supported?
Go kit ships with support for Consul, etcd, ZooKeeper, and DNS SRV records.
Do I even need to use package sd?
It depends on your infrastructure.
Some platforms, like Kubernetes, take care of registering services instances and making them discoverable automatically, via platform-specific concepts. So, if you run on Kubernetes, you probably don’t need to use package sd.
But if you’re putting together your own infrastructure or platform with open-source components, then your services will likely need to register themselves with the service registry. Or if you have reached a scale where internal load balancers become a bottleneck, you may need to have your services subscribe to the system of record directly, and maintain their own connection pools. (This is the client-side discovery pattern.) In these situations, package sd will be useful.
Which monitoring systems are supported?
Go kit ships with support for modern monitoring systems like Prometheus and InfluxDB, as well as more traditional systems like statsd, Graphite, and expvar, and hosted systems like Datadog via DogStatsD and Circonus.
Which monitoring system should I use?
Why is package log so different?
Experience has taught us that a good logging package should be based on a minimal interface and should enforce so-called structured logging. Based on these invariants, Go kit’s package log has evolved through many design iterations, extensive benchmarking, and plenty of real-world use to arrive at its current state.
With a well-defined core contract, ancillary concerns like levels, colorized output, and synchronization can be easily bolted on using the familiar decorator pattern. It may feel a little unfamiliar at first, but we believe package log strikes the ideal balance between usability, maintainability, and performance.
For more details on the evolution of package log, see issues and PRs 63, 76, 131, 157, and 252. For more on logging philosophy, see The Hunt for a Logger Interface, Let’s talk about logging, and Logging v. instrumentation.
How should I aggregate my logs?
Collecting, shipping, and aggregating logs is the responsibility of the platform, not individual services. So, just make sure you’re writing logs to stdout/stderr, and let another component handle the rest.


Govt. Model Boys Higher Secondary School Haripad
Higher Secondary Allotment Crowd Management System
Name Of Student
Address
Contact Number
Name Of Parent
Allotment Number
Enter a secret token id . You should remember this id as a token of your identity
Select Department
Bio science Malayalam
commerce
humanities
Computer Science
Bio Science Hindi
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Permenent
Temporory
Upload Photo of your Application


The Cold Standby feature allows one or more clients to connect to a primary instance and ensure automatic on-the-fly synchronization of the repository state from the master to the client(s). The sync process is one-way only. Data stored on the master is never changed. The only purpose of this client installation(s) is to guarantee an (almost live) data copy and enable a quick switch from the master to a client installation without data loss.
The Cold Standby feature does not guarantee file, filesystem or even repository integrity! If the content of a tar file is corrupted, a file is missing or anything similar happens to the locally stored files the installation will break because these situation or not checked, detected or treated!
On the master a TCP port is opened and listening to incoming messages. Currently there a two messages implemented:
The clients periodically request the segment id of the current head of the master. If the segment is locally unknown it will be retrieved. If it’s already present the segments are compared and referenced segments (if necessary) will be requested, too.
You can add the additional configuration option secure=true if you like a SSL secured connection between the client and the master. It must be garantueed that all clients and the master either use secure or standard connections! A mixed configuration will definitely fail.
The clients specify the master host using the host (default is localhost) and port (default is 8023) configuration options. For monitoring reasons (see below) the client(s) must be distinctable. Therefore a generic UUID is automatically created for each running client and this UUID is used to identify the client on the master. If you want to specify the name of the client you can set a system property standbyID.
The master can define the TCP port the feature is listening (default is 8023) using the port configuration option. If you want to restrict the communication you can specify a list of allowed IPs or IP ranges….
The data flow is designed to detect and handle connection and network related problems automatically. All packets are bundled with checksums and as soon as problems with the connection or damaged packets occur retry mechanisms are triggered.
The Cold Standby feature exposes informations using JMX/MBeans. Doing so you can inspect the current state of the client(s) and the master using standard tools like jconsole or jmc (if running JDK 1.7 or higher). The information can be found if you look for a org.apache.jackrabbit.oak:type="Standby" MBean named Status.
Observing a client you will notice exactly one node (the id is either a generic UUID or the name specified by the standbyID system property). This node has five readonly attributes:
There are also two invokable methods:
Observing the master exposes some general (non client-specific) informations via a MBean whose id value is the port number the Cold Standby service is using (usually 8023). There are almost the same attributes and methods as described above but the values differ:
Furthermore informations for each (up to 10) clients can be retrieved. The MBean id is the name of the client (see above). There are no invokable methods for these MBeans but some very useful readonly attributes:
A typical state might look like this:
Running on the master enabling the Cold Standby feature has almost no measurable impact on the performance. The additional CPU consumption is very low and the extra harddisk and network IO shouldn’t have any drawbacks.
Things look differently on the client! During a sync process you can expect at least one CPU core running close to 100% for all the time. Due to the fact that the procedure is not multithreaded you can’t speed up the process by using multiple cores. If no data is changed/transferred there will be no measurable activity. The expected throughput is about 700 KB / sec. Obviously this number will vary depending on the hardware and network environment but it does not depend on the size of the repository or whether you use SSL encryption or not. You should keep this in mind when estimating the time needed for an initial sync or when much data was changed in the meantime on the master node.
Assuming that the client(s) and the master run in the same intranet security zone there should be no security issue enabling the Cold Standby feature. Nevertheless you can add extra security by enabling SSL connections between the client(s) and the master (see above). Doing so reduces the possibility that the data is compromised by a man-in-the-middle. Furthermore you can specify the allowed client(s) by restricting the IP-address of incoming requests. This should help to garantuee that no one in the intranet can copy the repository (by accident).


Module to perform matrix multiplication on two mini-batch inputs, producing a mini-batch.
NB: This implementation is almost the same as "com.intel.analytics.bigdl.nn.Timedistributed" except for some bug fixing.
NB: This implementation is almost the same as "com.intel.analytics.bigdl.nn.Timedistributed" except for some bug fixing. We need to merge this back to BigDL in the next release.
This layer is intended to apply contained layer to each temporal time slice of input tensor.
For instance, The TimeDistributed Layer can feed each time slice of input tensor to the Linear layer.
The input data format is [Batch, Time, Other dims]. For the contained layer, it must not change the Other dims length.
data type, which can be Double or Float
Module to perform matrix multiplication on two mini-batch inputs, producing a mini-batch.


property FocalPoint: TFloatPoint; //read and write
The focal point defines the center of the gradient.
[See also]:


This document is to introduce how to upgrade the Zabbix from 1.8.3 to 1.8.12 to suit OpenSuse 11.4.
Note: As this is not a full new configuration, so this document is only for Zabbix version update from 1.8.3 to 1.8.12, cannot be treated as a full installation guide.
As this is a little update during 1.8.x, so no need to touch the DB. Steps attached here, please follow the steps strictly, all the command are provided here.
Reference:
You may find more information from below links:
Install zabbix from Source: https://www.zabbix.com/documentation/1.8/manual/installation/installation_from_source
Upgrade proceduler: (section 3.6) https://www.zabbix.com/documentation/1.8/manual/about/installation_and_upgrade
Dependency packages: (Chinese) http://os.51cto.com/art/201104/252989.htm
Download:
You may get all the required packages from here: Download
Steps:
-
Remove the old version Zabbix 1.8.3.
You may run:
rpm -qa | grep zabbix
to check all the installed zabbix packages. Please confirm all the installed packages have been removed before perform the new installation.Before remove:
After:
-
Install the dependency packages:
gcc make mysql-devel curl-devel net-snmp-devel
(Above packages could be find from the OpenSuse 11.4 OS iso file, and most of the Linux distrubution should have includes these tools already.)
iksemel-1.4.tar.gz
install this package with below steps:
-
Upload file iksemel-1.4.tar.gz to /tmp, then
cd /tmp
-
tar –zxf iksemel-1.4.tar.gz
-
cd iksemel-1.4
-
./configure
-
make
-
make install
-
-
Upload the file zabbix-1.8.12.tar.gz to /tmp, then
cd /tmp
. -
tar –zxf zabbix-1.8.12.tar.gz
-
cd zabbix-1.8.12
-
./configure --enable-server --enable-proxy --enable-agent --with-mysql --with-net-snmp --with-jabber --with-libcurl
-
make install
-
ln -sf /usr/local/lib/libiksemel.so.3 /lib/
Basically, till now, the new version Zabbix have been installed, please run
zabbix_server –V
to verify the version of zabbix. -
Copy the config file to folder: /etc/zabbix:
cd /tmp/zabbix-1.8.12 cp misc/conf/zabbix_server.conf /etc/zabbix/ cp misc/conf/zabbix_proxy.conf /etc/zabbix/ cp misc/conf/zabbix_agent.conf /etc/zabbix/ cp misc/conf/zabbix_agentd.conf /etc/zabbix/
-
vi /etc/zabbix/zabbix_server.conf
Find out below 2 lines, then modify the DB username and password:
(find function in VI: type
/ DBUser
in command mode)DBUser=zabbix DBPassword=zabbix
-
Upload the files: zabbix_server and zabbix_agentd to /etc/init.d/, then add execute permission:</div>
chmod +x /etc/init.d/zabbix_server chmod +x /etc/init.d/zabbix_agentd
-
Start and stop the zabbix_server and zabbix_agentd to verify the installation with below commands:
/etc/init.d/zabbix_server start /etc/init.d/zabbix_server stop ps –ef | grep zabbix /etc/init.d/zabbix_agentd start /etc/init.d/zabbix_agentd stop ps –ef | grep zabbix
After confirmed there’s no issue, start the zabbix_server and zabbix_agentd:
/etc/init.d/zabbix_server start /etc/init.d/zabbix_agentd start
Steer into the web interface configuration:
-
cd /tmp/zabbix-1.8.12/frontends/php/
-
mkdir /srv/www/htdocs/zabbix
-
cp -a . /srv/www/htdocs/zabbix
-
Now we don’t need to work with the command line any more, instead, we need to open a Web browser to access the zabbix web interface to configure our web view.
Open a browser, input the address:
http://<IP address here>/zabbix/queue.php
- If you are working locally on the Linux server, you may open the embeded Firefox (I guess all the popular Linux distribution have Firefox installed by default).
If you are working with Linux server remotely, you will have some choices:
- Find out any PC which could access the remote server via port 80, then use PC browser to access the web page.
- Configure remote X session from local PC, you may use Putty+xming (free), or xterm (not free), and then access the Firefox on the server from local PC directly.
-
Click
Next>>
Button. Then read and accept GPL v2 license. -
Make sure that all software pre-requisites are met.
-
Configure database settings. Zabbix database must already be created.
Please input “zabbix” in “Name”, “User” and “Password” fields..Then click
Test Connection
button, “Ok” means connection is fine. -
Click Next. See summary of settings. Click next.
-
Download configuration file and place it under conf/.
-
Click the “Save configuration file” button.
-
After saved, upload the file zabbix.conf.php to /srv/www/htdocs/zabbix/conf/.
-
-
Click
Retry
button, there should be “Ok” appears. Then finishing installation.
Till now, all the configuration is done, input the monitoring address on browser http://IP Address/zabbix/
to work with the new environment.


POST COITUM, ANIMAL TRISTE By Harvey Karten, Ph.D. Ognon Pictures Director: Brigitte Rouan Writer: Brigitte Rouan, Santiago Amigorena, Jean-Louis Richard, Guy Zilberstein, Philippe Le Guay Cast: Brigitte Rouan, Patrick Chesnais, Boris Terral, Nils Tavernier, Jean-Louis Richard, Francoise Arnoul A teen-aged boy tells this story, which is at the heart of the movie...A mouse wanders around the railroad tracks when a train comes and cuts off his tail. The mouse does not notice this until he arrives home. The next day he runs back to the tracks to search for what's missing when a train comes and cuts off his head.  "Post Coitum, Animal Triste" is the story of a woman who chases tail, so to speak, who loses her head, but who gets an opportunity to set it right back on again. It's a love story--and the French know about such things--and it goes against the stereotype that French romances are talk, talk, talk and little action. There is some interesting talk, all right, but the passions are so strong that the visceral reactions of the principal character Diane Clovier (Brigitte Rouan) will make you squirm. This girl-gets-boy, girl-loses-boy story engages us because of the way director Brigitte Rouan cleverly weaves a subplot into her principal focus, but even more because of Ms. Rouan's remarkable acting. She has the uncanny ability to change from a writhing, despair-ridden, suicidal victim of lost love, as we see her in the opening scene, to a woman involved in her career, then on to a peak of sexual frenzy and ecstasy and back again, playing all parts beautifully. Diane proves that women can have mid-life crises that easily rival those of the male gender. She has hit the big 4-0, has a job of some power at a small publishing company, enjoys the love of her husband and two teen kids. What's wrong with this picture? Nothing, that is, provided that no outside force intervene. But when the 20-year-old friend of a writer she is encouraging, Emilio (Boris Terral), literally sweeps her off her feet, her love-at-first-sight becomes an addiction for both. They meet, sometimes openly in a park, in one instance brazenly in a public ladies' room, where they consummate their affection with utter freedom. All the while Diane must try to get one of her authors, Francois (Nils Tavernier) to overcome writers' block--he claims he cannot understand a woman's desires. Conveniently enough, Diane has a new experience to help him over the hurdles. In a fascinating subplot involving the gruesome and sudden murder of a cheating man by his wife of 43 years, Madame Lepluche (Francoise Arnoul), Rouann, successfully adapting the screenplay she wrote with four others, shows us yet another way a mouse can lose his head by chasing tail. Helpfully, Mme. Lepluche is defended in court by Diane's loving husband Philippe (Patrick Chesnais), and will be able to use his own cuckoldry to try to move the jury. "Post Coitum Animal Triste," which bears the French title "Un Certain Regard," closed the 50th Cannes International Film Festival 1997 before being screened at New York's own annual festival. It includes one visual which is particularly humorous of Diane literally floating on a cloud while in the throes of her new passion and a convincing chemistry between Diane and the lover who is twenty years her junior. The enormous trouble which marital betrayal brings hits home in this movie through both a swift but graphic murder scene involving a woman who stabs her cheating husband in the neck with a carving fork--and the break-up of Diane's own family through her own mad fling. "Post Coitum" is wonderful at bringing out an adage repeated by Diane: "When you're twenty and love goes wrong, you cry because you think you'll never love anyone again. After 40, you cry because it's far more likely that you never will again." Not Rated. Running Time: 95 minutes. (C) 1997 Harvey Karten
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Related links: index of all rec.arts.movies.reviews reviews


Luga Validator was designed with extensibility in mind. There will always be the need for custom validation. Luga Validator stores all rules inside tmt.validator.rules, a JavaScript object, acting as a container for both built-in and custom validation rules.
In order to invoke the custom validation rule, you need to add a custom attribute to the field that exactly matches the name of the custom validation. Then the validator will call our custom method passing the field's DOM node as an argument and expecting a boolean as return value:
luga.validator.rules.lowercase = function(fieldNode){
const lowerStr = fieldNode.value.toLowerCase();
return (lowerStr === fieldNode.value);
};
The HTML will look like:
<input type="text" name="mylowercase"
data-lugavalidator-required="true"
data-lugavalidator-lowercase="true"
data-lugavalidator-message="Only lowercase!"
value="MixedCase"/>


As you can see, this is a very dynamic pallete... not
I prefer the cold, I hate the heat
It is my company's chosen training provider for my software development apprenticeship
I didn't really have any as I have covered an extensive amount of HTML/JS/CSS in the past


The remarkable life of the undaunted science voyager.
Four achievements and events from Carl Sagan's chronicle that are sources of inspiration in my daily life:
Best known for his TV series Cosmos: A Personal Voyage, he was, above all, an indefatigable science communicator. His alluring style helped many viewers discover the veiled truths of nature.
Quoting William Poundstone(author of "Carl Sagan: A Life in the Cosmos): "He worked hard for his students so that they got a good job, he cared about their education so that they had a promising future".
He tried to convey a new dimension to the human life on Earth by superbly explaining the insignificance of our evil and ephemeral deeds on a scale of the universe.
During the peak of the Cold War, Sagan focused on promoting nuclear desarmament ideas by exposing the catastrophic effects a nuclear war would unlesh.
His famous quotes evince his approach of critical thinking and skepticism in science as well as his humble human nature. Here are some of my favourites:
-For tiny creatures like us the immensity is only bearable through love.
-If you want an apple pie from scrath, first invent the universe.
 When it is used we feel good. Understanding is joy.
Fun facts:
Did you know that the film 'Contact' is based on the homonymous novel written by Carl Sagan in 1985?
What is the connection between Stanley Kubrick and Carl Sagan? Surprising question to say the least but I'glad you ask.The answer is that Carl Sagan suggested to insinuate the existence of extraterrestial life instead of explicitly showing it in the film '2001: A Space Odyssey'.
For more info about this man's life go visit his Wikipedia entry.


For general inquires, email us at<EMAIL_ADDRESS>For press inquires, contact us at<EMAIL_ADDRESS>For more information about sponsorships, visit our sponsors page to learn more
Hyperloop UC
523 Rhodes Hall
University of Cincinnati
Cincinnati, OH 45220


Now
<% include partials/current %>
Next trips
<% futureEvents.forEach(function (event) { %> <% include partials/event-item %> <% }); %>
<% include partials/form %>


--- layout: product.hbs title: "Cree LED Light Bar, LED Offroad Light, LED Search Light, LED Work Light, LED Automobile Lamp, Vehicle Replace Bulb, China LED Light Bar Suppliers Manufacturers, Cree LED Light Bar Wholesalers" description: "MICZE has a strong technical force of production Cree LED Light Bar Offroad, LED Truck Light, LED Work Light, and we are China LED Light Bar Suppliers Manufacturers, LED Light Bar Wholesalers" keywords: - Cree LED Light Bar Offroad - LED Light Bar USA/Dubai/Canada/Australia - LED Light Bar for trucks - "LLB-1-120W" - "LLB-3A-120W" - LED Offroad Light - LED Truck Light - Agriculture Light - China LED Light Bar Suppliers Manufacturers - Cree LED Light Bar Wholesalers - LED Work Light - LED Search Light ---
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MODEL NO:
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Feature: • Operation Voltage: 10-30VDC • Waterproof rate: IP67 • Color: Black • Color Temperature: 6000K • Material: Extrusion die aluminum housing • Lens material: PC • Mounting Bracket: Alu firm bracket • Beam: Flood beam pattern (20degreen) 10 degree Spot beam optional /Combo(20+10degree) • 30000 hours above life time |
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Reported BUG
assignment
IssueId
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Title
Assign to
Status
More info..
{{value}}
priority_high
More Info
close
close bug
check
Solved
warning
reassigned
warning
Solved


Thank you ' . $name .'
for registering to attend
Sony's Press Conference
at CES 2014
You have schedule a booth tour appointment on
Wednesday, Jan 9 at 11:30 a.m.
Where
Las Vegas Convention Center
Sony Booth 14200
Central Hall, LVCC
When
Monday January 6, 2014
5:00 PM
For more information please contact:


[console_scripts] easy_install = setuptools.command.easy_install:main easy_install-3.8 = setuptools.command.easy_install:main [distutils.commands] alias = setuptools.command.alias:alias bdist_egg = setuptools.command.bdist_egg:bdist_egg bdist_rpm = setuptools.command.bdist_rpm:bdist_rpm bdist_wininst = setuptools.command.bdist_wininst:bdist_wininst build_clib = setuptools.command.build_clib:build_clib build_ext = setuptools.command.build_ext:build_ext build_py = setuptools.command.build_py:build_py develop = setuptools.command.develop:develop dist_info = setuptools.command.dist_info:dist_info easy_install = setuptools.command.easy_install:easy_install egg_info = setuptools.command.egg_info:egg_info install = setuptools.command.install:install install_egg_info = setuptools.command.install_egg_info:install_egg_info install_lib = setuptools.command.install_lib:install_lib install_scripts = setuptools.command.install_scripts:install_scripts rotate = setuptools.command.rotate:rotate saveopts = setuptools.command.saveopts:saveopts sdist = setuptools.command.sdist:sdist setopt = setuptools.command.setopt:setopt test = setuptools.command.test:test upload_docs = setuptools.command.upload_docs:upload_docs [distutils.setup_keywords] convert_2to3_doctests = setuptools.dist:assert_string_list dependency_links = setuptools.dist:assert_string_list eager_resources = setuptools.dist:assert_string_list entry_points = setuptools.dist:check_entry_points exclude_package_data = setuptools.dist:check_package_data extras_require = setuptools.dist:check_extras include_package_data = setuptools.dist:assert_bool install_requires = setuptools.dist:check_requirements namespace_packages = setuptools.dist:check_nsp package_data = setuptools.dist:check_package_data packages = setuptools.dist:check_packages python_requires = setuptools.dist:check_specifier setup_requires = setuptools.dist:check_requirements test_loader = setuptools.dist:check_importable test_runner = setuptools.dist:check_importable test_suite = setuptools.dist:check_test_suite tests_require = setuptools.dist:check_requirements use_2to3 = setuptools.dist:assert_bool use_2to3_exclude_fixers = setuptools.dist:assert_string_list use_2to3_fixers = setuptools.dist:assert_string_list zip_safe = setuptools.dist:assert_bool [egg_info.writers] PKG-INFO = setuptools.command.egg_info:write_pkg_info dependency_links.txt = setuptools.command.egg_info:overwrite_arg depends.txt = setuptools.command.egg_info:warn_depends_obsolete eager_resources.txt = setuptools.command.egg_info:overwrite_arg entry_points.txt = setuptools.command.egg_info:write_entries namespace_packages.txt = setuptools.command.egg_info:overwrite_arg requires.txt = setuptools.command.egg_info:write_requirements top_level.txt = setuptools.command.egg_info:write_toplevel_names [setuptools.finalize_distribution_options] 2to3_doctests = setuptools.dist:Distribution._finalize_2to3_doctests keywords = setuptools.dist:Distribution._finalize_setup_keywords parent_finalize = setuptools.dist:_Distribution.finalize_options [setuptools.installation] eggsecutable = setuptools.command.easy_install:bootstrap


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Multi-Post Stories
A daily selection of privately personalized reads; no accounts or sign-ups required. has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a standard dummy text.
I can help you build a product, feature or website Look trough some of my work and experience! If you like what you see and have a project you need coded, don't heastiate contact me.
A daily selection of privately personalized reads; no accounts or sign-ups required. has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a standard dummy text.
Hello I’m a software developer! I can help you build a product , feature or website Look through some of my work and experience! If you like what you see and have a project you need coded, don’t hestiate to contact me.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essent


Company profile - Canadian Company Capabilities
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Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
ITS Electronics Inc.
Mailing Address:
B-3280 Langstaff RdCONCORD, Ontario
L4K 5B6
Location Address:
B-3280 Langstaff RdCONCORD, Ontario
L4K 5B6
Tel.:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Company Profile
ITS Electronics is a leading global supplier of advanced microwave and millimetre wave solid state power amplifiers (SSPA), frequency converters, transceivers, radios and intelligent wireless subsystems for commercial, government and defence customers. The company is housed in a modern 36,000 Sq foot Canadian facility that has state of the art design, development and manufacturing capabilities including clean room automated processes. Canadian owned and founded in 1987, ITS Electronics
Inc. supplies the following products and services:
. Standard components from a diversified product line
. Custom design and manufacturing of microwave components and
subsystems
. Build-to-spec and build-to-print of RF and microwave
components and subsystems
. Design and consulting services on a system and subsystem
level in a variety of fields concerning applications of RF,
microwave and millimetre wave based systems.
RF and Microwave Technologies are utilized in a growing number of
fields and applications such as:
. Terrestrial and Satellite communications
. Radar
. Broadcasting and Cable TV
. Navigation
. Electronic Warfare
. Law Enforcement and Intrusion Detection
. Electrooptics and Fiber Optic Information Transmission
. Environment Monitoring
. Air and Ground Traffic Control
. Remote Sensing
. Medical Science
OUR PRODUCTS:
Among our products you will find:
Components
Oscillators
. Dielectric Resonator Oscillators
. Voltage Controlled Oscillators
. Phase-Locked Loop Oscillators
Amplifiers
. Low Noise Amplifiers
. Linear Power Amplifiers
. Pulsed Power Amplifiers
Signal Processing
. Precision Special Ratio Low Loss Power Dividers
. Power Limiters
Subsystems
. Upconverters
. Downconverters
. Integrated Subassemblies
. Interference Canceling Systems
. Waveguide Subsystems
. Switchable Oscillator Banks
. High Stability Oscillator/Multiplier Subsystems
. High Power Frequency Sources
. Altimeter RF Subsystems
. Communication Products
OUR CUSTOMERS:
RF and Microwave based systems are used by both Commercial and
Defense Establishments and pose a broad range of trade-off
constraints such as size, weight, cost, power efficiency, severe
environments and high reliability.
From its first day of operation, I.T.S. Electronics Inc. has
worked in close relationship with Canadian, American, European,
and Asian Customers supplying products and design services to:
. SPAR Aerospace
. MEL DSL Ltd.
. Ontario Hydro
. Quebec Hydro
. Telesat Canada
. Canadian Astronautics Ltd.
. Northern Telecom
. Com Dev Ltd.
. British Aerospace
. ESA (European Space Agency)
. Valmet Ltd.
. Maclean Hunter Cable
. Rogers Cable
. Canadian Broadcasting Corporation
. Bell Canada
. Amtech Technology Corporation
. Canadian Space Agency
. Sciex Inc.
. California Microwave
. Williams Communications Services Ltd.
. Department of National Defence
. Government of Japan
. General Electric-Plessey Telecom (GPT)
. Wavemat Inc.
. Anorad Corporation
. D.O.D.
. Canadian Centre for Remote Sensing (CCRS)
. Alcatel
. Marconi
. Siemens
Our list of Customers is continuously growing!
ITS Electronics designs and manufactures advanced microwave solid
state components and sub-systems for telecommunications
satellite, radar and wireless multipoint microwave distribution
systems (MMDS).
This company manufactures LNA/Diplexer/PA sub systems for
Inmarsat applications and also designs and manufactures microwave
passive components and sub-systems.
Our products are designed and manufactured to exacting standards
and to well established military and commercial quality control
standards.
Over 80% of this companies products are exported.
RESPONSE, INNOVATION, CRAFTSMANSHIP
At ITS Electronics Inc you will find an organization that is in
tune with your needs. Standard and custom components and
subsystems and high reliability manufacturing, subsystem design
and integration, rapid prototyping, system analysis and
engineering, are the things that we do best.
We thrive on challenge and are here to solve your most difficult
problems.
ITS Electronics Inc. offers the ultimate choice in support and
reliability
Call us today: find out how we can add to your future success.
Tel:<PHONE_NUMBER> Fax:<PHONE_NUMBER>.
Full profile
Full profile
Contact information
Company description
Product / Service / Licensing
Also for defence applications.
telecommunications satellite and wireless systems.
MMDS and LMCS applications also for defense applications.
including filters, switches, couplers and combiners.
These class A GaAs FET amplifiers set a new price/performance
standard for wide band power and reliability. They offer very
high 3rd order intercept point, best harmonic performance in the
industry (-25 dBc in-band and -40 dBc outside the band @ P1 dB
GCP) and high efficiency and dynamic range.
Applications include EMC, EW, ECM, communication, instrumentation
and intermodulation and device testing. Fully protected outputs
enable safe operation into any load mismatch including open and
short circuits. Thermal shutdown feature, continuously monitors
the base-plate temperature and automatically shuts the amplifier
off and provides visual front panel alarm if the base-plate
temperature exceeds factory preset limit.
3 300LFM quiet fans provide sufficient cooling to ensure low
junction temperature to guarantee high MTBF.
The unit comes with 24 months warranty.
test source!
This new wideband synthesized test translator, P/N:
UC-140/1485-003, is part of the ITS growing family of high
performance products. It is used as a transmission path
simulator for DBS systems and provides a convenient way to
accurately characterize these systems. Fully digital compatible,
it includes a variable power noise source, adjustable power
output from -10 dBm down to -70 dBm and computer controlled
frequency selection via RS-232.
The input frequency range, variable in 10 MHz steps, is 920 to
2060 MHz.
The spurious signals level is lower than -40 dBc and image
rejection is better than -50 dBc.
The unit is self-contained in a type 2U (3.5" high) housing
suitable for 19" rack mount.
Oscillators
. Dielectric Resonator Oscillators
. Voltage Controlled Oscillators
. Phase-Locked Loop Oscillators
Amplifiers
. Low Noise Amplifiers
. Linear Power Amplifiers
. Pulsed Power Amplifiers
Signal Processing
. Precision Special Ratio Low Loss Power Dividers
. Power Limiters
Subsystems
. Upconverters
. Downconverters
. Integrated Subassemblies
. Interference Canceling Systems
. Waveguide Subsystems
. Switchable Oscillator Banks
. High Stability Oscillator/Multiplier Subsystems
. High Power Frequency Sources
. Altimeter RF Subsystems
. Communication Products
5865-Electronic Countermeasures, Counter- Countermeasures and Quick Reaction Capability Equipment
5895-Miscellaneous Communication Equipment
5915-Filters and Networks
5955-Oscillators and Piezoelectric Crystals
5962-Microcircuits, Electronic
5963-Electronic Modules
5998-Electrical and Electronic Assemblies; Boards, Cards, and Associated Hardware
5999-Miscellaneous Electrical and Electronic Components
6005-Couplers, Splitters, and Mixers
6007-Filters
6625-Electrical and Electronic Properties Measuring and Testing Instruments
AC00-Defense Systems
AD00-Defense - Other
AR00-Space
B207-Technology Studies
Technology profile
ITS designs and builds High Power Solid State Transmitters and Transceivers for SOTMTM, SOTPTM , Fly-Away Airborne and Marine platforms in L, X , Ku, and K/Ka bands. ITS delivered the 125W Ka-Band SSPA.
Market profile
Alliances:
- Sales/Marketing
- Technology
Industry sector market interests:
- Information Technology and Telecommunications
- Aerospace
- Defence
Geographic markets:
Export experience:
- Australia
- Germany
- New Zealand
- United Kingdom
- United States
- California
- Florida
- Georgia
- Maryland
- Massachusetts
- New Hampshire
- New York
- Washington
Actively pursuing:
- Alabama
- Alaska
- Arizona
- Arkansas
- Colorado
- Connecticut
- Delaware
- District of Columbia
- Hawaii
- Idaho
- Illinois
- Indiana
- Iowa
- Kansas
- Kentucky
- Louisiana
- Maine
- Michigan
- Minnesota
- Mississippi
- Missouri
- Montana
- Nebraska
- Nevada
- New Jersey
- New Mexico
- North Carolina
- North Dakota
- Ohio
- Oklahoma
- Oregon
- Pennsylvania
- Rhode Island
- South Carolina
- South Dakota
- Tennessee
- Texas
- Utah
- Vermont
- Virginia
- West Virginia
- Wisconsin
- Wyoming
Sector information
Classification codes
Total plant size:
includes Cleanroom 1,800 sq. feet
Distribution of employees:
Total = 65Production = 29
Engineering, R&D = 20
Admin = 16
Key / Major clients:
Boeing CompanyRockwell- Collins
EMS Technologies
BAE Systems
Harris
US DOD
Canada DND
Lockheed Martin
Northrop Grumman
Hughes Network Systems
General Dynamics
Rantec Microwave
Raytheon Canada
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
Products, services and licensing
Product / Service / Licensing
Also for defence applications.
telecommunications satellite and wireless systems.
MMDS and LMCS applications also for defense applications.
including filters, switches, couplers and combiners.
These class A GaAs FET amplifiers set a new price/performance
standard for wide band power and reliability. They offer very
high 3rd order intercept point, best harmonic performance in the
industry (-25 dBc in-band and -40 dBc outside the band @ P1 dB
GCP) and high efficiency and dynamic range.
Applications include EMC, EW, ECM, communication, instrumentation
and intermodulation and device testing. Fully protected outputs
enable safe operation into any load mismatch including open and
short circuits. Thermal shutdown feature, continuously monitors
the base-plate temperature and automatically shuts the amplifier
off and provides visual front panel alarm if the base-plate
temperature exceeds factory preset limit.
3 300LFM quiet fans provide sufficient cooling to ensure low
junction temperature to guarantee high MTBF.
The unit comes with 24 months warranty.
test source!
This new wideband synthesized test translator, P/N:
UC-140/1485-003, is part of the ITS growing family of high
performance products. It is used as a transmission path
simulator for DBS systems and provides a convenient way to
accurately characterize these systems. Fully digital compatible,
it includes a variable power noise source, adjustable power
output from -10 dBm down to -70 dBm and computer controlled
frequency selection via RS-232.
The input frequency range, variable in 10 MHz steps, is 920 to
2060 MHz.
The spurious signals level is lower than -40 dBc and image
rejection is better than -50 dBc.
The unit is self-contained in a type 2U (3.5" high) housing
suitable for 19" rack mount.
Oscillators
. Dielectric Resonator Oscillators
. Voltage Controlled Oscillators
. Phase-Locked Loop Oscillators
Amplifiers
. Low Noise Amplifiers
. Linear Power Amplifiers
. Pulsed Power Amplifiers
Signal Processing
. Precision Special Ratio Low Loss Power Dividers
. Power Limiters
Subsystems
. Upconverters
. Downconverters
. Integrated Subassemblies
. Interference Canceling Systems
. Waveguide Subsystems
. Switchable Oscillator Banks
. High Stability Oscillator/Multiplier Subsystems
. High Power Frequency Sources
. Altimeter RF Subsystems
. Communication Products
5865-Electronic Countermeasures, Counter- Countermeasures and Quick Reaction Capability Equipment
5895-Miscellaneous Communication Equipment
5915-Filters and Networks
5955-Oscillators and Piezoelectric Crystals
5962-Microcircuits, Electronic
5963-Electronic Modules
5998-Electrical and Electronic Assemblies; Boards, Cards, and Associated Hardware
5999-Miscellaneous Electrical and Electronic Components
6005-Couplers, Splitters, and Mixers
6007-Filters
6625-Electrical and Electronic Properties Measuring and Testing Instruments
AC00-Defense Systems
AD00-Defense - Other
AR00-Space
B207-Technology Studies
Technology
Technology profile
ITS designs and builds High Power Solid State Transmitters and Transceivers for SOTMTM, SOTPTM , Fly-Away Airborne and Marine platforms in L, X , Ku, and K/Ka bands. ITS delivered the 125W Ka-Band SSPA.
Market
Market profile
Alliances:
- Sales/Marketing
- Technology
Industry sector market interests:
- Information Technology and Telecommunications
- Aerospace
- Defence
Geographic markets:
Export experience:
- Australia
- Germany
- New Zealand
- United Kingdom
- United States
- California
- Florida
- Georgia
- Maryland
- Massachusetts
- New Hampshire
- New York
- Washington
Actively pursuing:
- Alabama
- Alaska
- Arizona
- Arkansas
- Colorado
- Connecticut
- Delaware
- District of Columbia
- Hawaii
- Idaho
- Illinois
- Indiana
- Iowa
- Kansas
- Kentucky
- Louisiana
- Maine
- Michigan
- Minnesota
- Mississippi
- Missouri
- Montana
- Nebraska
- Nevada
- New Jersey
- New Mexico
- North Carolina
- North Dakota
- Ohio
- Oklahoma
- Oregon
- Pennsylvania
- Rhode Island
- South Carolina
- South Dakota
- Tennessee
- Texas
- Utah
- Vermont
- Virginia
- West Virginia
- Wisconsin
- Wyoming
Sector
Sector information
Classification codes
Total plant size:
includes Cleanroom 1,800 sq. feet
Distribution of employees:
Total = 65Production = 29
Engineering, R&D = 20
Admin = 16
Key / Major clients:
Boeing CompanyRockwell- Collins
EMS Technologies
BAE Systems
Harris
US DOD
Canada DND
Lockheed Martin
Northrop Grumman
Hughes Network Systems
General Dynamics
Rantec Microwave
Raytheon Canada


|
Bay Bridge |
The Neighborhood |
|
Coit Tower |
420 on Hippie Hill |
|
Embarcadero |
Alamo Square 2 |
|
Aerial Backyard |
PanHandle |
|
Alamo Square |
The City |
|
Aerial of Coit Tower |
Cupids Span |
|
Ferry Building & Coit Tower |
Painted Ladies |


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public class SparkSchemaUtil
extends java.lang.Object
| Modifier and Type | Method and Description |
|---|---|
static Type |
convert(org.apache.spark.sql.types.DataType sparkType)
Convert a Spark
struct to a Type with new field ids. |
static org.apache.spark.sql.types.StructType |
convert(Schema schema)
Convert a
Schema to a Spark type . |
static Schema |
convert(Schema baseSchema,
org.apache.spark.sql.types.StructType sparkType)
Convert a Spark
struct to a Schema based on the given schema. |
static Schema |
convert(org.apache.spark.sql.types.StructType sparkType)
Convert a Spark
struct to a Schema with new field ids. |
static org.apache.spark.sql.types.DataType |
convert(Type type)
Convert a
Type to a Spark type . |
static Schema |
prune(Schema schema,
org.apache.spark.sql.types.StructType requestedType)
Prune columns from a
Schema using a Spark type projection. |
static Schema |
prune(Schema schema,
org.apache.spark.sql.types.StructType requestedType,
Expression filter,
boolean caseSensitive)
Prune columns from a
Schema using a Spark type projection. |
static Schema |
prune(Schema schema,
org.apache.spark.sql.types.StructType requestedType,
java.util.List<Expression> filters)
Prune columns from a
Schema using a Spark type projection. |
static Schema |
schemaForTable(org.apache.spark.sql.SparkSession spark,
java.lang.String name)
Returns a
Schema for the given table with fresh field ids. |
static PartitionSpec |
specForTable(org.apache.spark.sql.SparkSession spark,
java.lang.String name)
Returns a
PartitionSpec for the given table. |
public static Schema schemaForTable(org.apache.spark.sql.SparkSession spark, java.lang.String name)
Schema
for the given table with fresh field ids.
This creates a Schema for an existing table by looking up the table's schema with Spark and converting that schema. Spark/Hive partition columns are included in the schema.
spark
- a Spark sessionname
- a table name and (optional) databasepublic static PartitionSpec specForTable(org.apache.spark.sql.SparkSession spark, java.lang.String name) throws org.apache.spark.sql.AnalysisException
PartitionSpec
for the given table.
This creates a partition spec for an existing table by looking up the table's schema and creating a spec with identity partitions for each partition column.
spark
- a Spark sessionname
- a table name and (optional) databaseorg.apache.spark.sql.AnalysisException
- if thrown by the Spark catalogpublic static org.apache.spark.sql.types.StructType convert(Schema schema)
Schema
to a Spark type
.schema
- a Schemajava.lang.IllegalArgumentException
- if the type cannot be converted to Sparkpublic static org.apache.spark.sql.types.DataType convert(Type type)
Type
to a Spark type
.type
- a Typejava.lang.IllegalArgumentException
- if the type cannot be converted to Sparkpublic static Schema convert(org.apache.spark.sql.types.StructType sparkType)
struct
to a Schema
with new field ids.
This conversion assigns fresh ids.
Some data types are represented as the same Spark type. These are converted to a default type.
To convert using a reference schema for field ids and ambiguous types, use
convert(Schema, StructType)
.
sparkType
- a Spark StructTypejava.lang.IllegalArgumentException
- if the type cannot be convertedpublic static Type convert(org.apache.spark.sql.types.DataType sparkType)
struct
to a Type
with new field ids.
This conversion assigns fresh ids.
Some data types are represented as the same Spark type. These are converted to a default type.
To convert using a reference schema for field ids and ambiguous types, use
convert(Schema, StructType)
.
sparkType
- a Spark DataTypejava.lang.IllegalArgumentException
- if the type cannot be convertedpublic static Schema convert(Schema baseSchema, org.apache.spark.sql.types.StructType sparkType)
struct
to a Schema
based on the given schema.
This conversion does not assign new ids; it uses ids from the base schema.
Data types, field order, and nullability will match the spark type. This conversion may return a schema that is not compatible with base schema.
baseSchema
- a Schema on which conversion is basedsparkType
- a Spark StructTypejava.lang.IllegalArgumentException
- if the type cannot be converted or there are missing idspublic static Schema prune(Schema schema, org.apache.spark.sql.types.StructType requestedType)
Schema
using a Spark type
projection.
This requires that the Spark type is a projection of the Schema. Nullability and types must match.
schema
- a SchemarequestedType
- a projection of the Spark representation of the Schemajava.lang.IllegalArgumentException
- if the Spark type does not match the Schemapublic static Schema prune(Schema schema, org.apache.spark.sql.types.StructType requestedType, java.util.List<Expression> filters)
Schema
using a Spark type
projection.
This requires that the Spark type is a projection of the Schema. Nullability and types must match.
The filters list of Expression
is used to ensure that columns referenced by filters
are projected.
schema
- a SchemarequestedType
- a projection of the Spark representation of the Schemafilters
- a list of filtersjava.lang.IllegalArgumentException
- if the Spark type does not match the Schemapublic static Schema prune(Schema schema, org.apache.spark.sql.types.StructType requestedType, Expression filter, boolean caseSensitive)
Schema
using a Spark type
projection.
This requires that the Spark type is a projection of the Schema. Nullability and types must match.
The filters list of Expression
is used to ensure that columns referenced by filters
are projected.
schema
- a SchemarequestedType
- a projection of the Spark representation of the Schemafilter
- a filtersjava.lang.IllegalArgumentException
- if the Spark type does not match the Schema


<< back
7 August 2020
Who Failed
Challenge Source: Defcon 28 - Red Team Village CTFChallenge Category: logs
Logs - Who Failed
The challenge seems easy enough:
How many different IP addresses were banned?
However, we don’t want to have to go through and count these manually. Looking at the file, we can see how a ban is formatted:
So we can combine grep and sort as such:
This gives us unique instances of any strings that look like “Ban X.X.X.X” (11 characters) through “Ban XXX.XXX.XXX.XXX” (19 characters), where X is a number between 0 and 9 or a period.
With a bigger list we would also probably want to leverage the wc command to count the number of lines. So a complete, single line solution for this challenge could be:
Tags: Defcon28-RTVCTF


Hello, My name is Simon Nicolas and I am the general manager of IFSD Inc. international company ( http://ifsd-finance.net ) and I would like to make you a favorable offer. Now there is some information about our company. Our activity lies in planning for private persons and the companies how they receive their current payments. We help the ones who want to get the remittances from their foreign clients without any problem. Our company is engaged in a wide international activity; we work with the organizations and private persons from the various countries worldwide. We keep broadening our activity, we offer new kinds of services, we expand the staff we open our representative offices all over the world. As our company is expanding its international activity we are in need of new employees in various countries. Now we are looking for new people to take the vacant places of regional financial managers. A regional financial manager deals with bank transfers and checks. As our clients are private persons and companies from different countries, we hire people to be our region financial managers to make working with different types of remittances easier. The duties of a Regional Financial Manager: - to transfer bank payments on a bank account - to transfer money resources to our representatives in other countries The scheme of work is the next one: a Regional Financial Manager receives money resources and informs us of it. Then we immediately put the needed sum of money on our client�s account. It saves a lot of time - now the client does not need to wait till the money from abroad will be sent to him. This scheme saves your money. Your interest is 5 % of the sum of each remittance. The basic requirements: - to be 21-60 years old - to have access to the Internet, to have e-mail - to be able to work at least 3 hours every day For you this is a really good chance to make good money! Start working with us right now! To get this job the only thing you need is sign the labour contract. --- Best regards, Simon Nicolas. http://ifsd-finance.net


R1(config)#router rip
R1(config-router)#network <IP_ADDRESS>
R1(config-router)#network <IP_ADDRESS>
Above is a basic RIP configuration on R1. The network command in RIP does 2 things.
Only two steps are needed to configure RIP.
router rip
RIP exchanges UDP encapsulated packets over UDP port 520. RIP has two message types:
When a RIP router process starts, it broadcasts a Request message out each RIP-enabled interface. The begins the state machine with neighbors receiving a RIP request and responding with a RIP Response.
When the requesting router receives the Response message, it processes the enclosed information and responds in one of 3 ways, depending on the information in the RIP Response.
RIP does not store information received in a Topology table, nor do routers have a picture of the entire topology. They receive routes in Response messages, process the information in those messages, and then move on. The RIP Response messages contain that router’s full routing table, with the exception of routes that are stressed by the split-horizon rule.
Another configuration issue is that RIPv1 does not support discontiguous networks. Discontiguous networks arise when a major network is separated by a major network. The problem arises because RIP summarizes at the major network boundary. This summarization is done at the classful boundary. If there is one major network between the two discontiguous networks, the RIP routers will receive a summarized route they already have in their routing table, so they will silently drop it. The result is that only half the of the discontiguous network is reachable. If there are two major networks between, then each major network believes it has a route to the summarized network, so they will install two equal-cost routes to the discontiguous network. This again would result in only half the communication being received by the destination network.
The workarounds for discontiguous networks with RIP would be to install static routes for the individual networks. The other solution is to apply secondary IPs to the interfaces of the in-between major network. These IPs would be from the summarized discontiguous network in question. The RIP routing process sees secondary addresses as separate data links.
RIPv2 is defined in RFC 1723. It is not a new protocol, but rather just RIPv1 with some extensions bringing it more up to date with modern routing environments. The improvements include:
Operation of RIPv2 is similar to RIPv1, and all the timer mechanisms remain the same. One difference is that updates are sent to the multicast address <IP_ADDRESS> instead of the broadcast address <IP_ADDRESS> that RIPv1 used.
The RIPv2 message format is similar to RIPv1.
Command is set to 1 for a request message or 2 for a response message.
Version will be 2 for RIPv2. Note that RIPv2 will process valid RIPv1 messages.
Address Family Identifier is set to 2 for IPv4. The only exception is a request for a router’s full routing table, in which case it will be 0.
Route tag provides a field for tagging external routes or routes that have been distributed into the RIPv2 process. One possible use is to carry the AS number of of routes that have been imported into RIP. This might be needed in case the information is carried across the RIP domain and then shared back with the external routing protocol in another location.
IP Address is the IPv4 address of the destination of the route. It may be a major network address, a subnet, or a host.
Subnet Mask is a 32 bit mask that identifies the host and network portions of the IPv4 address.
Next Hop identifies a better next-hop address, if one exists, than the address of the advertising router. It will be populated if there is another router on the same subnet that is closer to the destination than the advertising router. If the field is set to <IP_ADDRESS>, then the advertising router is the best next-hop address.
Metric is the hop count, between 1 and 16.
As RIPv2 is a classless routing protocol, when it goes to look up a destination in the routing table, it does not pay attention to the class of the destination address. Instead, it performs a bit-by-bit best match between the destination address and all of its known routes.
One interesting note in regards to all classless routing protocols - Cisco IOS rejects an attempt to configure an all-zeroes subnet. To override this enter the global configuration command ip subnet-zero
We saw with RIPv1 that discontiguous subnets could be a problem. Classless routing protocols do not have the same issue, as each route update contains a mask, so subnets of one major network can be advertised into another major network. But the default behavior of RIPv2 is to summarize at network boundaries just like RIPv1. To turn off this summarization and allow subnets to be advertised across network boundaries, use the command no auto-summary in the RIP router configuration mode.


--- title: "Find Your Autumn Sanctuary at Red Butte Garden" date: "2017-10-05 00:00:01 -06:00" categories: - garden-journal layout: "post" blog-banner: "garden-journal-fall.jpg" post-date: "Oct 5, 2017" post-time: "03:00 PM" blog-image: "2017-10-05-wh.jpg" published: true ---
By: Kate Randall, Red Butte Garden Marketing Communications Specialist
In the fall, many people drive their cars through the canyons and foothills to view autumn-colored leaves through car windows.  Smell the flowers, touch the leaves, take photographs, feel the breeze, walk the paths, listen to the birds along Red Butte Creek, and enjoy crisp mountain air?
As the State Arboretum of Utah, Red Butte Garden is a showpiece in the fall. There are almost five miles of natural area hiking trails, and the formal gardens provide a variety of botanical displays year-round where you can enjoy the seasons with all your senses. Take a stroll down the “Floral Walk” and find yourself in a fiery tunnel of orange, yellow, and red Serviceberry, and Pear trees. Look for the yellow and orange tones of Smokebushes in the Rose Garden. Go off the beaten path into the “Natural Area” and walk underneath colorful Bigtooth Maples near Red Butte Creek. Hike up the foothills and enjoy the view of Salt Lake City.
Trees are not alone in the spotlight this season, and leaves are not the only players. The stems of Red and Yellow Twig Dogwood become very colorful. The exfoliating (peeling) bark of Paperbark Maple and our native River Birch are cinnamon in color. Many perennials are also great fall bloomers such as: Black-Eyed Susan, Purple Coneflower, Aster, Autumn Crocus, Anemone, Hibiscus, Toadlily, and you can still find late-blooming roses.
By fall, the inflorescence (or seed heads) of many native and ornamental grasses have matured and seem to glow when caught by the sunlight and are delightful to watch dancing in the breeze. Some autumn favorites include: Switch Grass ‘Heavy Metal’ and Northern Sea Oats in the Four Seasons Garden. Autumn Moor Grass, Little Bluestem 'The Blues,' and Feather Reed Grass along the Floral Walk, and you’ll find Eulalia 'Purpurascens' and Plume Grass near the Water Pavilion.
There are plenty of gardening opportunities in the fall. At home, it’s a good time to plant cold-season crops like lettuce, spinach, radish, garlic, and onion. Fall is also a great time to plant spring-time blooming bulbs, trees, shrubs, perennials, and cool-season annuals. Harvest seeds from your favorite annuals to plant in the spring.
Keep deadheading your flowers now to enjoy Indian-summer color, or let bird-friendly varieties go to seed. Let roses form their decorative rosehips. Fall is also the best time to treat lawns for weed control and deeply water trees before the ground freezes, especially conifers and shallow-rooted trees.


Delhi , officially the National Capital Territory of Delhi (NCT), is a city and a union
territory of India containing New Delhi, the capital of India.It is bordered by the state of Haryana on
three sides and by Uttar Pradesh to the east. The NCT covers an area of 1,484 square kilometres (573 sq
mi). According to the 2011 census, Delhi's city proper population was over 11 million, the
second-highest in India after Mumbai, while the whole NCT's population was about 16.8 million. Delhi's
urban area is now considered to extend beyond the NCT boundaries, and include the neighbouring satellite
cities of Ghaziabad, Faridabad, Gurgaon and Noida in an area now called National Capital Region (NCR)
and had an estimated 2016 population of over 26 million people, making it the world's second-largest
urban area according to the United Nations.As of 2016, recent estimates of the metro economy of its
urban area have ranked Delhi either the most or second-most productive metro area of India. Delhi is the
second-wealthiest city in India after Mumbai and is home to 18 billionaires and 23,000 millionaires.
Delhi ranks fifth among the Indian states and union territories in human development index.[14] Delhi
has the second-highest GDP per capita in India. It is one of the world's most polluted cities by
particulate matter concentration.


package extraction
Ordering
- Alphabetic
Visibility
- Public
- All
Type Members
-
class
OWLHorstSchemaExtractor extends SchemaExtractor
An extractor of the schema for OWL Horst.
An extractor of the schema for OWL Horst.
Currently, it's supports the extraction of triples
(s,p,o)
withp
being- rdfs:subClassOf
- rdfs:subPropertyOf
- rdfs:domain
- rdfs:range
- owl:equivalentProperty
- owl:equivalentClass
- owl:inverseOf
- owl:someValuesFrom
- owl:allValuesFrom
- owl:hasValue
- owl:onProperty
or
o
being- owl:TransitiveProperty
- owl:FunctionalProperty
- owl:InverseFunctionalProperty
- owl:SymmetricProperty
-
class
RDFSSchemaExtractor extends SchemaExtractor
An extractor of the schema for RDFS.
An extractor of the schema for RDFS.
Currently, it's supports the extraction of triples
(s,p,o)
withp
being- rdfs:subClassOf
- rdfs:subPropertyOf
- rdfs:domain
- rdfs:range
- abstract class SchemaExtractor extends Logging with Serializable
Value Members
- object RDFSSchemaExtractor extends Serializable


insightfulUBC
Courses
Rooms
Scheduler
Rooms Explorer
Building Name
Room Number
Room Size
>
Room Type
OR
Furniture Type
Within
meters of
Result


{% if request.args["sort_by"] == "priority" %} sort by time {% else %} sort by transaction {% endif %}


{% extends "global/Page.html" %} {% load staticfiles otree %} {% block title %} Results {% endblock %} {% block content %}
{% if player.role == 'dictator' %}
You will be paid for your decision about {{ player.treatment }}.
You decided to keep {{ paying_round_kept }} for yourself.
{% else %}
Participant 1 decided to keep {{ person_kept }}, so
you got {{ offer }}.
{% endif %}
{% next_button %}


[Gecode] Bug tracking?
Christian Schulte
schulte at imit.kth.se
Wed May 19 19:30:57 CEST 2004
Bugzilla is fine with me.
For obvious reasons, www.ps.uni-sb.de/gecode is completely out of question!
Well, at least for anything to the public. If it goes there, then only with
password. I think it is so obvious, that I don't have to explain, do I?
Christian
--
Christian Schulte, http://www.imit.kth.se/~schulte/
-----Original Message-----
From: gecode-bounces at ps.uni-sb.de [mailto:gecode-bounces at ps.uni-sb.de] On
Behalf Of Guido Tack
Sent: Wednesday, May 19, 2004 6:47 PM
To: Technical discussions about Gecode
Subject: Re: [Gecode] Bug tracking?
On Wed, 19 May 2004 duchier at ps.uni-sb.de wrote:
> My recommendation is to go with a _standard_ and _modern_ tool. Forget
> jitterbug it doesn't satisfy either criterion. While bugzilla is
> rather complex, it has become so ubiquitous that people have become
> familiar with it anyway. I like the fact that bugzilla supports
> attachments: the lack of them is a big drawback in jitterbug. I also
> like the "activity log".
Ok, cool. If you all agree, I could set up a gecode bugzilla on Friday or
Monday (depending on whether I need root privileges, as Thorsten is on
vacation). BTW, we will need some kind of gecode homepage anyway if we
want to release sometime ;-)
I could of course offer http://www.ps.uni-sb.de/gecode, and that's where
I'd put up the bug tracking system.
Guido
--
Guido Tack
mailto:tack at ps.uni-sb.de
http://www.ps.uni-sb.de/~tack/
_______________________________________________
Gecode mailing list
Gecode at ps.uni-sb.de http://www.ps.uni-sb.de/mailman/listinfo/gecode
More information about the gecode-users
mailing list


Ecosystem
ES6 Introduction - Sets And Maps
Sets and Maps didn't exist in JavaScript before ES6,
which was a pain each time you need this kind of functionality.
Happily, they are now available!
You can use any object for keys, but equality of keys is checked through the
'===' operator, which can be error prone :
['hello'] === ['hello'] //false
//Map
class Person{
constructor(id, firstName, lastName, age){
//...
this.id= id;
this.friends= new Map();
}
addFriend(friend){
this.friends.set(friend.id, friend);
}
getFriendById(id){
this.friends.get(id);
}
}
//Set
let friend= new Person('a', 'b');
let friends= new Set();
friends.add(friend);
friends.has(friend); // true


The displacements in a general datatype are relative to some initial buffer address. Absolute addresses can be substituted for these displacements: we treat them as displacements relative to ``address zero,'' the start of the address space. This initial address zero is indicated by the constant MPI_BOTTOM. Thus, a datatype can specify the absolute address of the entries in the communication buffer, in which case the buf argument is passed the value MPI_BOTTOM.
The address of a location in memory can be found by invoking the
function
MPI_ADDRESS.
MPI_ADDRESS(location, address)
[ IN location] location in caller memory (choice)
[ OUT address] address of location (integer)
int MPI_Address(void* location, MPI_Aint *address)
MPI_ADDRESS(LOCATION, ADDRESS, IERROR)
<type> LOCATION(*)
INTEGER ADDRESS, IERROR
Returns the (byte) address of location.
Example
Using MPI_ADDRESS for an array.
REAL A(100,100) INTEGER I1, I2, DIFF CALL MPI_ADDRESS(A(1,1), I1, IERROR) CALL MPI_ADDRESS(A(10,10), I2, IERROR) DIFF = I2 - I1 ! The value of DIFF is 909*sizeofreal; the values of I1 and I2 are ! implementation dependent.
[] Advice to users.
C users may be tempted to avoid the usage of MPI_ADDRESS and rely on
the availability of the address operator &. Note, however, that
& cast-expression is a pointer, not an
address. ANSI C does not require that the value of a pointer
(or the pointer cast to int) be
the absolute address of the object pointed at --- although this is
commonly the case.
Furthermore, referencing may not have a unique
definition on machines with a segmented address space.
The use of MPI_ADDRESS to ``reference'' C
variables guarantees portability to such machines as well.
( End of advice to users.)
The following auxiliary functions provide useful information on
derived datatypes.
MPI_TYPE_EXTENT(datatype, extent)
[ IN datatype] datatype (handle)
[ OUT extent] datatype extent (integer)
int MPI_Type_extent(MPI_Datatype datatype, MPI_Aint *extent)
MPI_TYPE_EXTENT(DATATYPE, EXTENT, IERROR)
INTEGER DATATYPE, EXTENT, IERROR
Returns the extent of a datatype, where extent is as defined.
MPI_TYPE_SIZE(datatype, size)
[ IN datatype] datatype (handle)
[ OUT size] datatype size (integer)
int MPI_Type_size(MPI_Datatype datatype, int *size)
MPI_TYPE_SIZE(DATATYPE, SIZE, IERROR)
INTEGER DATATYPE, SIZE, IERROR
MPI_TYPE_SIZE returns the total size, in bytes, of the entries in the type signature associated with datatype; i.e., the total size of the data in a message that would be created with this datatype. Entries that occur multiple times in the datatype are counted with their multiplicity.


User stories:
- I can get the image URLs, alt text and page urls for a set of images relating to a given search string.
- I can paginate through the responses by adding a ?offset=2 parameter to the URL.
- I can get a list of the most recently submitted search strings.
Entering:
https://image-search-microservice-pa.glitch.me/api/imagesearch/gorilla
will give something like:
[
{
"species": "Gorilla gorilla",
"status": "critically endangered",
"thumbnail": "https://upload.wikimedia.org/wikipedia/commons/thumb/5/50/Male_gorilla_in_SF_zoo.jpg/220px-Male_gorilla_in_SF_zoo.jpg",
"context": "https://en.wikipedia.org/wiki/Gorilla",
"alt-text": "Western gorillas are ground dwelling herbivorous apes from central Sub-Saharan Africa"
},
{
"species": "Gorilla beringei",
"status": "critically endangered",
"thumbnail": "https://upload.wikimedia.org/wikipedia/commons/thumb/9/98/Mountain_gorillas_%288209001529%29.jpg/220px-Mountain_gorillas_%288209001529%29.jpg",
"context": "https://en.wikipedia.org/wiki/Eastern_gorilla",
"alt-text": "Eastern Gorillas are the largest living primate"
}
]
The meta-data of at most 10 images are listed: use ?offset= to list yet more meta-data.
Entering:
https://image-search-microservice-pa.glitch.me/api/latest/imagesearch/
will give something like:
{
"find": "gorilla",
"found": 2,
"stamp": "Sun, 18 Mar 2018 17:31:23 GMT"
},
Note the queries are presented newest first, oldest last. Only the last 10 are listed: use ?offset= to see older queries.
The following keywords should retrieve meta-data for an image or two:
The following keywords may retrieve enough meta-data to make the `?offset=` worth using.