class: center, middle ### W4995 Applied Machine Learning # More Neural Networks 04/18/18 Andreas C. Müller ??? --- class:center,middle #Getting Flexible and Scaling Up ??? So today we’ll talk about using neural nets beyond scikit-learn. There are two main motivations for this: scaling to larger datasets and larger networks using GPUs, and having more flexible ways to create the neural network architectures. If you want to get serious about neural nets, you are likely to include some modifications to the standard network. So how could you go about doing that? --- class:spacious # Write your own neural networks .smaller[ ```python class NeuralNetwork(object): def __init__(self): # initialize coefficients and biases pass def forward(self, x): activation = x for coef, bias in zip(self.coef_, self.bias_): activation = self.nonlinearity(np.dot(activation, coef) + bias) return activation def backward(self, x): # compute gradient of stuff in forward pass pass ```] ??? It’s actually pretty straight-forward to write the prediction functions, or forward pass of a neural network. There is matrix multiplications and nonlinearities. It quickly becomes more complicated when you use some of the tricks we’ll discuss today, but even for this writing down the gradient can be a bit tricky and something people used to get wrong all the time. So they came up with a trick that avoided having to write down the gradients yourself, also known as the backward pass. That trick is autodiff. --- #Autodiff .smaller[ ```python # http://mxnet.io/architecture/program_model.html class array(object) : """Simple Array object that support autodiff.""" def __init__(self, value, name=None): self.value = value if name: self.grad = lambda g : {name : g} def __add__(self, other): assert isinstance(other, int) ret = array(self.value + other) ret.grad = lambda g : self.grad(g) return ret def __mul__(self, other): assert isinstance(other, array) ret = array(self.value * other.value) def grad(g): x = self.grad(g * other.value) x.update(other.grad(g * self.value)) return x ret.grad = grad return ret ```] ??? Here is a toy implementation of the idea behind autodiff. It’s a class called array with some operations such as addition of integer and multiplication with other arrays. It also has a method that returns the gradient called grad. The gradient of the array is just the identity function. The trick is what happens with the addition and multiplication. If you add something to an array or you multiply two arrays, the result again is an array, and has again a gradient. The product actually has two gradients, one for each array involved. The magic here is that while we are doing some computation, we are keep track of that computation and building up a graph of how to compute the gradient of it. --- .smaller[ ```python a = array(np.array([1, 2]), 'a') b = array(np.array([3, 4]), 'b') c = b * a d = c + 1 print(d.value) print(d.grad(1)) ```] ``` [4 9] {'b': array([1, 2]), 'a': array([3, 4])} ``` ??? On the right you can see the result of adding two arrays and then computing the gradient at a particular location. FIXME the gradient of multiplication? Any computation in the neural network is a simple operation like a matrix multiplication, addition or nonlinearity. If we write down the derivative of each of these, we can keep track of our computation and automatically get the derivative. It’s really easy to implement but really helpful. Keep in mind that we actually hard-code the derivative for each operation, there is no symbolic differentiation involved. --- # GPU Support .center[ ![:scale 45%](images/gpu_support.png) ] - From http://www.nvidia.com/object/gpu-accelerated-applic ations-tensorflow-benchmarks.html - Take with a grain of salt. ??? An important limitation of GPUs is that they usually have much less memory than the RAM, and memory copies between RAM and GPU are somewhat expensive. --- #Computation Graph .center[ ![:scale 100%](images/computation_graph.png) ] ??? Autodiff doesn’t solve all of the problems, though. Depending on which derivations you need to compute, you need to store different intermediate results (the net activation in backprob for example). Given the limited amount of memory available, it’s important to know what to store and what can be discarded. That requires an end-to-end view of the computation, which can be summarized as a graph. Then you can use knowledge about the computation graph to decide what results to cache, what results to throw away and when to communicate between CPU and GPU. Having a representation of the graph also helps with visual debugging and understanding your network structure. The computation graph is more complex than the network graph because it includes intermediate results and gradient computations. --- # All I want from a deep learning framework - Autodiff - GPU support - Optimization and inspection of computation graph - on-the-fly generation of the graph? - distribution over cluster? - Choices: – TensorFlow – Theano – Torch (lua) ??? So create deep learning model efficiently, I need support for auto diff, computation on a GPGPU, and optimization and inspection of the computation graph. There is some more advanced features that can come in handy for research, like on-the-fly generation of computation graphs, and distributing the network over multiple GPUs or over a cluster. At least the first three are all provided by the TensorFlow, Theano and Torch frameworks, which are the most established deep learning frameworks. These don’t provide deep learning models, they are infrastructure, more like numpy on steroids than sklearn. Theano was an early deep learning library for python, but I think it has become less relevant since tensorflow got released. --- #Deep Learning Libraries - tf.learn (Tensorflow) - Keras (Tensorflow, Theano) - Lasagna (Theano) - Torch.nn / PyTorch (torch) - Chainer (chainer) - MXNet (MXNet) - Also see: http://mxnet.io/architecture/program_model.html ??? Then there are actual deep learning libraries that provide higher level interfaces built on top of this infrastructure. There are a lot of these right now given the deep learning hype, I want to point out a couple. There is tf.learn, a high-level interface to tensorflow, including a scikit-learn compatible API. There’s keras, which supports both Tensorflow and theano, there’s lasagna, which I think has seen less activity recently, and torch.nn (lua) and pytorch building on top of the torch framework. Then there are two projects that are deep learning libraries but that also come with their own framework underneath, chainer and mxnet. I think right now Keras with tensorflow is the most commonly used one, and I want to go into those two a bit more. But these all have their pros and cons. --- #Quick look at TensorFlow - “down to the metal” - don’t use for everyday tasks - Three steps for learning: – Build the computation graph (using array operations and functions etc) – Create an Optimizer (gradient descent, adam, …) attached to the graph. – Run the actual computation. ??? --- .center[ ![:scale 75%](images/tensor_flow_basics.png) ] ??? --- #A little more tf - Everything passed to the graph is a “tensor” - Either “variable”, “constant”, “placeholder” - Learned parameters: variables - Data: placeholders (data is assigned using “feed”) ??? --- # Great Resources! - https://www.tensorflow.org/versions/r0.10/tutorials/ - http://playground.tensorflow.org - https://www.tensorflow.org/versions/r0.10/get_star ted/basic_usage - https://www.tensorflow.org/versions/r0.10/tutorials /mnist/beginners/ - Note: tf.learn is now officially tf.contrib.learn - Tensorboard web interface ??? --- class:center,middle #Introduction to Keras ??? --- #Keras Sequential .smaller[ ```python from keras.models import Sequential from keras.layers import Dense, Activation model = Sequential([ Dense(32, input_shape=(784,)), Activation('relu'), Dense(10), Activation('softmax'), ]) ``` ``` Using TensorFlow backend. ``` ] .smaller[ ```python model = Sequential() model.add(Dense(32, input_dim=784)) model.add(Activation('relu')) ``` ] .smaller[ ```python model = Sequential([ Dense(32, input_shape=(784,), activation='relu'), Dense(10, activation='softmax'), ]) ``` ] ??? There are two interfaces to keras, sequential and the functional, but we’ll only discuss sequential. Sequential is for feed-forward neural networks where one layer follows the other. You specify the layers as a list, similar to a sklearn pipeline. Dense layers are just matrix multiplications. Here we have a neural net with 32 hidden units for the mnist dataset with 10 outputs. The hidden layer nonlinearity is relu, the output if softmax for multi-class classification. You can also instantiate an empty sequential model and then add steps to it. For the first layer we need to specify the input shape so the model knows the sizes of all the matrices. The following layers can infer the sizes from the previous layers. --- .smaller[ ```python model.summary() ``` ] .center[ ![:scale 70%](images/model_summary.png) ] ??? --- # Setting Optimizer .center[ ![:scale 80%](images/optimizer.png) ] .smaller[ ```python model.compile("adam", "categorical_crossentropy", metrics=['accuracy']) ``` ] ??? Compile method picks optimization procedure and loss --- # Training the model .center[ ![:scale 80%](images/training_model.png) ] ??? --- #Preparing MNIST data .smaller[ ```python from keras.datasets import mnist import keras (X_train, y_train), (X_test, y_test) = mnist.load_data() X_train = X_train.reshape(60000, 784) X_test = X_test.reshape(10000, 784) X_train = X_train.astype('float32') X_test = X_test.astype('float32') X_train /= 255 X_test /= 255 print(X_train.shape[0], 'train samples') print(X_test.shape[0], 'test samples') num_classes = 10 # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) ``` ``` 60000 train samples 10000 test samples ``` ] ??? --- # Fit Model .smaller[ ```python model.fit(X_train, y_train, batch_size=128, epochs=10, verbose=1) ``` ] .center[ ![:scale 80%](images/model_fit.png) ] ??? --- #Fit with Validation .smaller[ ```python model.fit(X_train, y_train, batch_size=128, epochs=10, verbose=1, validation_split=.1) ``` ] .center[ ![:scale 90%](images/validation_fit.png) ] ??? --- #Evaluating on Test Set .smaller[ ```python score = model.evaluate(X_test, y_test, verbose=0) print("Test loss: {:.3f}".format(score[0])) print("Test Accuracy: {:.3f}".format(score[1])) ``` ``` Test loss: 0.120 Test Accuracy: 0.966 ``` ] ??? --- # Loggers and Callbacks .smaller[ ```python model = Sequential([ Dense(32, input_shape=(784,), activation='relu'), Dense(10, activation='softmax'), ]) model.compile("adam", "categorical_crossentropy", metrics=['accuracy']) history_callback = model.fit(X_train, y_train, batch_size=128, epochs=100, verbose=1, validation_split=.1) ``` ] .smaller[ ```python pd.DataFrame(history_callback.history).plot() ``` ] .center[ ![:scale 40%](images/logger_callback_plot.png) ] ??? --- #Wrappers for sklearn See https://keras.io/scikit-learn-api/ .smaller[ ```python from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor from sklearn.model_selection import GridSearchCV def make_model(optimizer="adam", hidden_size=32): model = Sequential([ Dense(hidden_size, input_shape=(784,)), Activation('relu'), Dense(10), Activation('softmax'), ]) model.compile(optimizer=optimizer,loss="categorical_crossentropy", metrics=['accuracy']) return model clf = KerasClassifier(make_model) param_grid = {'epochs': [1, 5, 10], # epochs is fit parameter, not in make_model! 'hidden_size': [32, 64, 256]} grid = GridSearchCV(clf, param_grid=param_grid, cv=5) grid.fit(X_train, y_train) ``` ] ??? Useful for grid-search. You need to define a callable that returns a compiled model. You can search parameters that in Keras would be passed to “fit” like the number of epochs. --- .smaller[ ```python res = pd.DataFrame(grid.cv_results_) res.pivot_table(index=["param_epochs", "param_hidden_size"], values=['mean_train_score', "mean_test_score"]) ``` ] .center[ ![:scale 70%](images/keras_api_results.png) ] ??? Training longer overfits more and more units overfit more, but both also lead to better results. We should probably train much longer actually. Setting the number of epochs via cross-validation is a bit silly since it means starting from scratch again each time. Using early stopping would be better. --- # Drop-out Regularization - Randomly set activations to zero. .center[ ![:scale 55%](images/dropout_reg.png) ] - https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf - Rate often as high as .5, i.e. 50% of units set to zero! - Predictions: use all weights, down-weight by rate ??? Drop out is a very successful regularization technique developed in 2014. It is an extreme case of adding noise to the input, a previously established method to avoid overfitting. Instead of adding noise, we actually set given inputs to 0. And not only on the input layer, also the intermediate layer. For each sample, and each iteration we pick different nodes. Randomization avoids overfitting to particular examples. --- class:spacious #Ensemble Interpretation - Every possible configuration represents different network. - With p=.5 we jointly learn `$\binom{n}{n/2}$` networks - Networks share weights - For last layer dropout: prediction is approximate geometric mean of predictions of sub-networks. ??? --- #Implementing Drop-Out .smaller[ ```python from keras.layers import Dropout model_dropout = Sequential([ Dense(1024, input_shape=(784,), activation='relu'), Dropout(.5), Dense(1024, activation='relu'), Dropout(.5), Dense(10, activation='softmax'), ]) model_dropout.compile("adam", "categorical_crossentropy", metrics=['accuracy']) history_dropout = model_dropout.fit(X_train, y_train, batch_size=128, epochs=20, verbose=1, validation_split=.1) ``` ] ??? --- class:spacious # When to use drop-out - Avoids overfitting - Allows using much deeper and larger models - Slows down training somewhat - Wasn’t able to produce better results on MNIST (I don’t have a GPU) but should be possible ??? --- class:center,middle #Convolutional neural networks ??? --- class:spacious # Idea - Translation invariance - Weight sharing ??? --- #Definition of Convolution `$$ (f*g)[n] = \sum\limits_{m=-\infty}^\infty f[m]g[n-m] $$` `$$ = \sum\limits_{m=-\infty}^\infty f[n-m]g[m] $$` .center[ ![:scale 80%](images/convolution.png) ] ??? The definition is symmetric in f, but usually one is the input signal, say f, and g is a fixed “filter” that is applied to it. You can imagine the convolution as g sliding over f. If the support of g is smaller than the support of f (it’s a shorter non-zero sequence) then you can think of it as each entry in f * g depending on all entries of g multiplied with a local window in f. Not that the output is shorter than the input by half the size of g. this is called a valid convolution. We could also extend f with zeros, and get a result that is larger than f by half the size of g, that’s called a full convolution. We can also just pad a little bit and get something that is of the same size as f. Also not that the filter g is flipped as it’s indexed with -m --- #1d example: Gaussian smoothing .center[ ![:scale 80%](images/Gaussian_Smoothing.png) ] ??? --- #2d Smoothing .center[ ![:scale 80%](images/2dsmoothing.png) ] ??? --- #2d Gradients .center[ ![:scale 80%](images/2dgradient.png) ] ??? --- #Convolution Neural Networks .center[ ![:scale 100%](images/CNET1.png) ] - Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. - Gradient-based learning applied to document recognition ??? Here is the architecture of an early convolutional net form 1998. The basic architecture in current networks is still the same. You can multiple layers of convolutions and resampling operations. You start convolving the image, which extracts local features. Each convolutions creates new “feature maps” that serve as input to later convolutions. To allow more global operations, after the convolutions the image resolution is changed. Back then it was subsampling, today it is max-pooling. So you end up with more and more feature maps with lower and lower resolution. At the end, you have some fully connected layers to do the classificattion. --- #Max Pooling .center[ ![:scale 80%](images/maxpool.png) ] - Need to remember position of maximum for back-propagation. - Again not differentiable subgradient → descent ??? --- .center[ ![:scale 80%](images/other_architectures.png) ] ??? Here are two more recent architectures, AlexNet from 2012 and VGG net from 2015. These nets are typically very deep, but often have very small convolutions. In VGG there are 3x3 convolutions and even 1x1 convolutions which serve to summarize multiple feature maps into one. There is often multiple convolutions without pooling in between but pooling is definitely essential. --- class:center,middle #Conv-nets with keras ??? --- #Preparing Data .smaller[ ```python batch_size = 128 num_classes = 10 epochs = 12 # input image dimensions img_rows, img_cols = 28, 28 # the data, shuffled and split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() X_train_images = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) X_test_images = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) ``` ] ??? For convolutional nets the data is n_samples, width, height, channels. MNIST has one channel because it’s grayscale. Often you have RGB channels or possibly Lab. The position of the channels is configurable, using the “channels_first” and “channels_last” options – but you shouldn’t have to worry about that. --- .smaller[ ```python from keras.layers import Conv2D, MaxPooling2D, Flatten num_classes = 10 cnn = Sequential() cnn.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape)) cnn.add(MaxPooling2D(pool_size=(2, 2))) cnn.add(Conv2D(32, (3, 3), activation='relu')) cnn.add(MaxPooling2D(pool_size=(2, 2))) cnn.add(Flatten()) cnn.add(Dense(64, activation='relu')) cnn.add(Dense(num_classes, activation='softmax')) ``` ] ??? For convolutional nets we need 3 new layer types: Conv2d for 2d convolutions, MaxPooling2d for max pooling and Flatten go reshape the input for a dense layer. There are many other options but these are the most commonly used ones. --- #Number of Parameters .left-column[ Convolutional Network for MNIST ![:scale 100%](images/cnn_params_mnist.png) ] .right-column[ Dense Network for MNIST ![:scale 100%](images/dense_params_mnist.png) ] ??? --- #Train and Evaluate .smaller[ ```python cnn.compile("adam", "categorical_crossentropy", metrics=['accuracy']) history_cnn = cnn.fit(X_train_images, y_train, batch_size=128, epochs=20, verbose=1, validation_split=.1) cnn.evaluate(X_test_images, y_test) ``` ``` 9952/10000 [============================>.] - ETA: 0s [0.089020583277629253, 0.98429999999999995] ``` ] .center[ ![:scale 50%](images/train_evaluate.png) ] ??? --- #Batch Normalization .center[ ![:scale 70%](images/batch_norm.png) ] - https://arxiv.org/abs/1502.03167 ??? Another relatively recent advance in neural networks is batch normalization. The idea is that neural networks learn best when the input is zero mean and unit variance. We can scale the data to get that. But each layer inside a neural network is itself a neural network with inputs given by the previous layer. And that output might have much larger or smaller scale (depending on the activation function). Batch normalization re-normalizes the activations for a layer for each batch during training (as the distribution over activation changes). The avoids saturation when using saturating functions. To keep the expressive power of the model, additional scale and shift parameters are learned that are applied after the per-batch normalization. --- #Inspecting Conv-Nets ??? --- #Tensorboard? ??? --- #Deconvolution? ??? --- #Adverserial Samples .center[ ![:scale 70%](images/adverserial.png) ] - Intriguing properties of neural networks https://arxiv.org/abs/1312.6199 ??? Since convolutional neural nets are so good at image recognition, some people think they are pretty infallible. But they are not. There is this interesting paper about intriguing properties of neural networks, that introduces adverserial samples. Adverserial samples are samples that were created by an adversary or attacker to fool your model. Here, they changed images to be classified as Ostrich by AlexNet trained on imagenet. The picture on the left is change just slightly, and went from correctly classified to classified as Ostrich. This technique uses gradient descent on the input and requires access to all the weights in the network to create the samples. Given how high-dimensional the input space is, this is not very surprising from a mathematical perspective, but it might be somewhat unexpected. --- #Using pre trained networks - Train on “large enough” data. - Apply to new “small” dataset. - Take activations of last or second to last fully connected layer. .center[ ![:scale 70%](images/pretrained_network.png) ] ??? Often we have a small but specific image dataset for a particular application. Training a neural net is not feasible unless we have tens of thousands or hundreds of thousands of images. However, if we have a convolutional neural net that was already trained on a large dataset that is similar enough, we can hope that the features it learned are also helpful for our task. The easiest way to adapt a trained network to a new task is to just apply it to our dataset and take the activations of the second to last or last layer. If the original task was rich enough – say 10000 different classes as in imagenet – these layers contain a lot of information about the image. We can then use these activations as features for another classifier like a linear model or smaller dense neural network. --- #Using pre trained networks - Train on “large enough” data. - Apply to new “small” dataset. - Take activations of last or second to last fully connected layer. .center[ ![:scale 70%](images/pretrained_network.png) ] ??? The main point is that we don’t need to retrain all the weights in the network. You can think of it as retraining only the last layer – the classification layer – of the network, while holding all the convolutional filters fixed. If they learned generic patterns like edges and patterns, these will still be useful for your task. You can download pre-trained neural networks for many architectures online. Using a pre-trained network is sometimes also known as transfer learning. This potentially doesn’t work with images from a very different domain, like medical images. --- #Finetuning - Start with pre-trained net - Back-propagate error through all layers - “tune” filters to new data. .center[ ![:scale 70%](images/finetuning.png) ] ??? A more complicated variant of this is to load a network trained on some other dataset, and replace the last layer with your classification task. Instead of training only the last layer, we can also keep training all the previous layers, backpropagating the gradient through the network and adjusting the previously learned filters for our task. You can think of this as warm-starting a neural network from one that was trained on another dataset. If you do that, we often want to train the last layer a little bit before we backpropagate through the network. Otherwise the random initialization of the last layer might destroy the filters that we used for initialization. --- class: middle # Questions ?


| Configuration | 6 Motors (Hexacopter) |
| Maximum take off weight | 5 Kilograms |
| Payload capacity | 2 Kilograms |
| Airframe material | Carbon fiber-Kevlar coating |
| Propulsion system | High grade BLDC motors with ABEC 7 Standard for longevity |
| Telemetry range | 2 kilometers |
| Flight Endurance | 40 minutes (varies on payloads) |
| Wind resistance | Upto 30 kmph |
| Area Coverage in single flight | 100 acres |
| Operator Skills | Automatic Take-off and landing with dyanamic flight planning, automatic route selection using Map. |
| Ground station software | |
| Survey Grid Feature | Available |
| Waypoint setting available | Yes |
| Failsafe Features | Low battery and radio link lost, return to home |
| Intuitive and easy to use | Yes, Graphical User interface |


Skills
- Software Development: Full stack developer, familiar with agile methods and TDD. Able to quickly learn new languages and methodologies. Intuitively able to understand abstractions.
- Data analysis and diagnostics: Skilled at quickly grasping the meaning behind large data sets, understanding them using models, and translating them to relational databases.
- System administration: 12 years experience with Linux. Detailed understanding of networking, security, and operations.
- Teamwork: Experienced at working with diverse teams to build software and get things done. 5+ years experience with Git.
Languages
Proficient in Python, PHP, Ruby, Javascript, Bash, SQL, HTML and CSS. Working knowledge of C, Java, Perl. Some experience with Assembly and Haskell.
Experience
UBC Quantum Devices Group
2012–2014
Research Assistant
- Experimentally characterized strongly interacting 2D electrons in semiconductor devices with applications in quantum computing.
- Managed all aspects of research, including experiment planning, feasibility testing, device design and fabri cation, experimental setup construction, measurement and data collection, data analysis, and presentation.
- Promoted efficiency in the lab by designing and writing software to monitor and automate critical lab systems (including the helium recovery and liquefaction system) running 24/7.
Fusionbox, Inc.
2010–2011
Developer
- Built dynamic web applications that scale well and perform exactly to client specs—from start to finish.
- Responsible for all development phases of projects, including developing specifications, database design, application design, implementation, QA, deployment, and maintenance.
- Successfully managed and improved several legacy applications.
Volunteering
UBC Undergraduate Physics Society
2011–2014
President, various executive roles
- Elected president for 2013–2014 academic year.
- As president, oversaw operations of a 200+ member society with a $14,000 yearly operating budget.
- Guided the executive team to organize events and promote academic growth for undergraduate students.


This page lets you test using the hellosign-embedded
library in an app context.
This is useful for testing development with HelloSign Embedded as well as an example of how the library can be integrated with one of our SDKs (Node.js in this case). Simply enter the API Key for your HelloSign account and your API app's Client ID below, then hit "Open" to generate a signature request and view it
If you haven't already, you will need to create a new API app in order to obtain a unique API Key and Client ID from HelloSign. Don't share these with anyone!


internet!
Internet was the thought of J.C.R. Licklider. He proposed an idea in 1960, where all of the computers in the world are connected to each other. So, the government thinking of this idea created a program called the ARPANET. ARPANET stands for, Advanced Research Projects Agency Network. ARPANET had two computer connected to each other, one at UCLA and one at Stanford. In 1969, the first message was sent “LOGIN”, but only “LO” returned. There were still problems communicating because if one computer failed the entire network failed. So to solve these problems the Internet Protocol was created. Internet Protocol was created by Vint Cerf and Bob Kahn, also known as the Father of Internet. They broke the message into different packets and sent it across the network, so if one computer breaks down other still communicate.
Internet works by breaking up the data into multiple packets and sent it across the network and displaying it on to the computer. The networks consist of powerful computers which can send data across the entire globe, they are the Servers. The server is at everyone's house the router that you have is the server for your house. When you want to search something on Google, your router will ask the google server to access the data that it contains in its data storage. However, to find a single server amongst a million servers is a problematic job for a router. So internet use address called the Internet Protocol or IP. although the Google can show you the result under a second, the most amazing thing about this process is that the router has to ask a lot of servers to get to Google's server itself.
Although the internet is amazing, it is still only 1s and 0s. A human cannot read this type of language, so we created browsers like Chrome, Safari, or Firefox. However, to make a website we needed to create our own language in which we can read it and put data on the internet. So, we created the three main programing language to help us put data on the internet and the web. In 1954, FORTRAN was created by John Backus to program the internet, it the father to the most important internet programing language, HTML. Then came, CSS and then JavaScript. These languages changed the world.
HTML stands for HyperText Markup Language. Created by Tim Berners-Lee in 1990, the same person who invent web or WWW. HTML was created at the last expected place in the CERN lab, it is a lab for particle physics. It was created to collaborate the documents of other scientists around the world through internet. HTML became really popular because it could allow other documents to connect in one, like a Hyperlink, Hyperlink soon came to be known as Hypertext. This was the reason why HTML became popular because it allowed the user to go different pages from one. Otherwise, the user would have to search everything that they wanted to see, BORING!
HTML is the MOST IMPORTANT programing language in the web development world. Since is a markup language, it holds every single element together. Without HTML you cannot put anything on the web, because it is the first thing the web read and then it knows where to look for a particular thing. As we know that www was created by Tim Berners-Lee, so he made it in such a way that the web can only read HTML first and then any other files connected to the website. HTML’s only job is to hold everything together and also keep the content in it. You cannot put content in any other file then HTML. So basically, HTML is a house in the people live in and it held them together.
Although, HTML is very important to in the process of making the website. It has one downside, it cannot make elements presentable. To solve this problem, in 1994 Håkon Wium Lie, Bert Bos and the WWW Consortium, developed CSS. CSS stands for Cascading Styling Sheet, it is a style sheet file. There were other styling sheets before CSS, but they only worked for a specific browser. Tim Berners-Lee didn’t want that and so him and his colleague Håkon Wium Lie, created CSS. It was a major hit, because worked on any browser and required fewer codes to decorate something.
CSS is important because it allows your website looks good, instead of a crappy one. CSS can allow you to make a lot of cool effect like 3d effect, animations, etc. Because of these effects, CSS became popular amongst developer. As you see the effect on the left.
.block1 { height: 210px; width: 210px; background-color: #00C3FF; position: absolute; transform: rotateX(55deg) rotateY(0deg) rotateZ(40deg); box-shadow: 13px 12px 15px -5px rgba(0,0,0,.3); margin-left: 900px; margin-top: -200px; } .block1 img { height: 210px; width: 210px; transition: all 1s; } .block1:hover img { transform: translate(-3%,-3%); }
CSS helped create cool effects and at some point animations too. However, these effects came with a cost that developer had to pay. It consumed a lot of time and effort, and also there really really long codes, and it only helps create few effects not all. So, to make more effects possible, Brendan Eich created JavaScript in 1995. It only took him 10 days to create an entire programing language, Cool!
As people started to understand JavaScript, they started to invent new effects that were never seen before on the web. However, they had to write a lot of code, but they also wanted to share these effects with the developer. For this reason, JavaScript experts created files which were called libraries. This libraries contents all of them .JS file needed to achieve that effect. After releasing the libraries the users just of connect there.JS file into the “head” section of their HTML file.
// JavaScript Document $(document).ready(function() { "use strict"; $('#fullpage').fullpage({ anchors:['firstPage', 'secondPage', 'thirdPage'], navigation: true, autoScrolling: false, controlArrows: false, slidesNavigation: true, onSlideLeave: function(index, nextIndex, direction) { $('.hi').removeClass('animated fadeInLeft'); }, afterSlideLoad: function(anchorLink, index) { $('.hi').addClass('animated fadeInLeft'); }, onLeave: function(index, nextIndex, direction) { $('.hello').removeClass('animated fadeInUp'); }, afterLoad: function(anchorLink, index) { $('.hello').addClass('animated fadeInUp'); }, }); });


I am a student in the 4th year of the computer engineering cycle. I am looking for an internship of 6 months in early february 2021, or a work-study program in early September 2021
Always looking to learn and enhance my knowledge in the IT field.
Please find a part of my personnal project in the section 'MyWork'
I have worked on dozens of projects so I have picked only the latest for you.
This project is a personal project. The goal of this project was to make a Web application called ' Chat App ' . We can chat with others using this application, but for the moment it works only if we use one computer and open several times the application in the browser
The project was developed with the library ReactJs
This project is a personal project. The goal of this project was to make a Web application called ' todolist ' . A todo list, or task list, is a simple and efficient process for managing the tasks of a project. These tasks can be independent or must, on the contrary, be accomplished in a certain order.
The project was developed with the library ReactJs
This project is a personal project. The goal of this project was to make a Mobile application called ' todolist ' . A todo list, or task list, is a simple and efficient process for managing the tasks of a project. These tasks can be independent or must, on the contrary, be accomplished in a certain order.
The project was developed with the library React Native


|
|||||||
|
1. Introduction
2. Installing Karrigell 3. The Web server 4. Configuration options 5. Working with Apache, LightTPD or Xitami 6. Programming 7. Debugging 8. Python scripts 9. CGI scripts 10. Karrigell Services 11. Python Inside HTML 12. HTML Inside Python 13. HTMLTags - generate HTML in Python 14. Including documents 15. Sessions 16. Authentication 17. Translation and Unicode 17.1 Translation 17.2 Unicode support 18. Summary of built-in names |
17. Internationalization and UnicodeAs you'll have guessed by reading this documentation, I'm not from an English-speaking country (I'm French, and more precisely Breton - the name Karrigell is a Breton word). So I've included a program to facilitate internationalization of scripts 17.1 TranslationIn a script, every time you want a message translated into a given language, instead of writing it as a normal string with quotes, it's written using a function called _, this way :
In Python Inside HTML (PIH) you can use the shortcut <%_ > :
Karrigell provides a simple web interface to create and modify translations of
strings. For security reasons, the script that manages translation is reserved to
the administrator. An authentication script is run, relying on md5 digests stored
in a file called With your browser, call the script Fill in the fields and validate the form ; this creates or updates the translations
You can check the effect by calling the script you've just modified and changing the language order in the preferences Translation is held in a file which is common to all the files in the same directory. You can also edit the whole dictionary clicking on the first item in the script list
17.2 Unicode supportNew in version 2.2.2mostly written by Radovan GarabikUnicode is a normalized standard used to represent all the writing styles in the world. For each sign (a letter in any alphabet, an ideogram in an Asiatic language) Unicode defines a unique number, called a "code point". Since computers and networks can only manage bytes, a mapping between "code points" and one or several bytes must be defined ; these mappings are called "encodings" Because there are many different encodings, when a program has to print a sign (a greek letter, a math symbol, a Chinese sign) it must receive two pieces of information : the string representing the sign (a sequence of bytes) and the encoding used. If it receives only a string, the program can try to guess an encoding (this is what a web browser usually does) but with no guarantee of success Karrigell defines two parameters in the configuration file to handle Unicode :
(1) On Microsoft Internet Explorer the language preference is set by Tools/Internet Options/General/Languages ; accepted languages are chosen from a list and ordered by preference. On Firefox use Edition/Preferences/Languages |


|
Biomedical Image Analysis Library
The Biomedical Image Analysis Library is a poweful tool for developers, physicians, researchers, engineers, and so on.
|
Functions | |
| template<class D > | |
| Image< D > | AdaptiveAnisotropicDiffusion (Image< D > img, const DiffusionFunction *diff_func, float init_kappa, float radius=1.0) |
| Computes and returns a filtered image by anisotropic diffusion. More... | |
| template<class D > | |
| Image< D > | AnisotropicDiffusion (Image< D > img, const DiffusionFunction *diff_func, float kappa, size_t iterations, float radius=1.0) |
| Computes and returns a filtered image by anisotropic diffusion. More... | |
| template<class D > | |
| void | AnisotropicDiffusionThread (Image< D > &img, Image< D > &res, double integration_constant, const DiffusionFunction *diff_func, float kappa, Adjacency &adj, size_t thread, size_t total_threads) |
| Threads implementation for anisotropic diffusion filter. More... | |
| template<class D > | |
| double | Flow (const Image< D > &img, const DiffusionFunction *diff_func, float kappa, const size_t pxl, const Adjacency &adj) |
| The flow is computed based the diffusion function. More... | |
| template<class D > | |
| Image< D > | Gaussian (const Image< D > &img, float radius=2.0, float std_dev=2.0) |
| Returns the median filtered image with the given radius. More... | |
| template<class D > | |
| Image< D > | Mean (const Image< D > &img, float radius) |
| Returns the mean filtered image with the given radius. More... | |
| template<class D > | |
| Image< D > | Mean (const Image< D > &img, const Image< D > &msk, float radius) |
| Returns the mean filtered image with the given radius restricted to non-zero mask pixels. More... | |
| template<class D > | |
| Image< D > | Median (const Image< D > &img, float radius) |
| Returns the median filtered image with the given radius. More... | |
| template<class D > | |
| void | MedianThreads (const Image< D > &img, const Adjacency &adj, Image< D > &res, size_t thread, size_t total_threads) |
| Multi-thread implementation of the median filtered image with the given radius. More... | |
| template<class D > | |
| float | EdgeRegionKappa (const Image< D > &source, const Image< D > &mask, const DiffusionFunction *diff_func, float radius) |
| Finds the best edge kappa to filter source image. More... | |
| template<class D > | |
| float | FlatRegionKappa (const Image< D > &source, const Image< D > &mask, const DiffusionFunction *diff_func, float radius, float kappa) |
| Finds the best flat kappa to filter source image. More... | |
| template<class D > | |
| Image< D > | OptimalAnisotropicDiffusion (Image< D > img, const DiffusionFunction *diff_func, float radius, float conservativeness) |
| Threads implementation for anisotropic diffusion filter. More... | |
| template<class D > | |
| Image< D > | OptimalAnisotropicDiffusion (Image< D > img, const DiffusionFunction *diff_func, float radius, float conservativeness, const Image< D > &canny, const Image< D > &backg) |
| Threads implementation for anisotropic diffusion filter. More... | |
| Image< D > Bial::Filtering::AdaptiveAnisotropicDiffusion | ( | Image< D > | img, |
| const DiffusionFunction * | diff_func, | ||
| float | init_kappa, | ||
| float | radius = 1.0 |
||
| ) |
Computes and returns a filtered image by anisotropic diffusion.
| img | Input image. |
| diff_function | diffusion function. |
| init_kappa | initial kappa constant to control the gradient range to be filtered. |
| radius | radius of the adjacency relation. |
| Image< D > Bial::Filtering::AnisotropicDiffusion | ( | Image< D > | img, |
| const DiffusionFunction * | diff_func, | ||
| float | kappa, | ||
| size_t | iterations, | ||
| float | radius = 1.0 |
||
| ) |
Computes and returns a filtered image by anisotropic diffusion.
| img | Input image. |
| diff_function | diffusion function. |
| kappa | constant to control the gradient range to be filtered. |
| iterations | the number of iterations. |
| radius | radius of the adjacency relation. |
| void Bial::Filtering::AnisotropicDiffusionThread | ( | Image< D > & | img, |
| Image< D > & | res, | ||
| double | integration_constant, | ||
| const DiffusionFunction * | diff_func, | ||
| float | kappa, | ||
| Adjacency & | adj, | ||
| size_t | thread, | ||
| size_t | total_threads | ||
| ) |
Threads implementation for anisotropic diffusion filter.
| img | Input image. |
| res | Output image. |
| integration_constant | Constant of integration based on adjacency size. |
| diff_function | diffusion function. |
| kappa | constant to control the gradient range to be filtered. |
| adj | An adjacency relation. |
| thread | Thread number. |
| total_threads | Number of threads. |
| float Bial::Filtering::EdgeRegionKappa | ( | const Image< D > & | source, |
| const Image< D > & | mask, | ||
| const DiffusionFunction * | diff_func, | ||
| float | radius | ||
| ) |
Finds the best edge kappa to filter source image.
| source | Input image. |
| mask | Input mask with edge region. |
| diff_function | diffusion function. |
| radius | Adjacency radius for diff_function. |
| float Bial::Filtering::FlatRegionKappa | ( | const Image< D > & | source, |
| const Image< D > & | mask, | ||
| const DiffusionFunction * | diff_func, | ||
| float | radius, | ||
| float | kappa | ||
| ) |
Finds the best flat kappa to filter source image.
| source | Input image. |
| mask | Input mask with edge region. |
| diff_function | diffusion function. |
| radius | Adjacency radius for diff_function. |
| kappa | Best edge kappa used for initialization. |
| double Bial::Filtering::Flow | ( | const Image< D > & | img, |
| const DiffusionFunction * | diff_func, | ||
| float | kappa, | ||
| const size_t | pxl, | ||
| const Adjacency & | adj | ||
| ) |
The flow is computed based the diffusion function.
| img | Input image. |
| diff_function | diffusion function. |
| kappa | constant to control the gradient range to be filtered. |
| pxl | reference pixel. |
| adj | adjacency relation. |
| Image< D > Bial::Filtering::Gaussian | ( | const Image< D > & | img, |
| float | radius = 2.0 , |
||
| float | std_dev = 2.0 |
||
| ) |
Returns the median filtered image with the given radius.
| img | Input image. |
| neighborhood_radius | radius of the neighborhood. |
Returns the mean filtered image with the given radius.
| img | Input image. |
| neighborhood_radius | radius of the neighborhood. |
| Image< D > Bial::Filtering::Mean | ( | const Image< D > & | img, |
| const Image< D > & | msk, | ||
| float | radius | ||
| ) |
Returns the mean filtered image with the given radius restricted to non-zero mask pixels.
| img | Input image. |
| msk | Restrictive mask. |
| neighborhood_radius | radius of the neighborhood. |
Returns the median filtered image with the given radius.
| img | Input image. |
| neighborhood_radius | radius of the neighborhood. |
| void Bial::Filtering::MedianThreads | ( | const Image< D > & | img, |
| const Adjacency & | adj, | ||
| Image< D > & | res, | ||
| size_t | thread, | ||
| size_t | total_threads | ||
| ) |
Multi-thread implementation of the median filtered image with the given radius.
| img | Input image. |
| adj | Adjacency relation. |
| res | Resulting image. |
| thread | number of the thread. |
| total_threads | total number of threads. |
| Image< D > Bial::Filtering::OptimalAnisotropicDiffusion | ( | Image< D > | img, |
| const DiffusionFunction * | diff_func, | ||
| float | radius, | ||
| float | conservativeness | ||
| ) |
Threads implementation for anisotropic diffusion filter.
| img | Input image. |
| diff_function | diffusion function. |
| radius | radius of the adjacency relation. |
| conservativeness | Conservativeness function. Expected values: 0.0 to 1.0. Lower values keep weak edges and higher values remove stronger noise incidence. |
| Image< D > Bial::Filtering::OptimalAnisotropicDiffusion | ( | Image< D > | img, |
| const DiffusionFunction * | diff_func, | ||
| float | radius, | ||
| float | conservativeness, | ||
| const Image< D > & | canny, | ||
| const Image< D > & | backg | ||
| ) |
Threads implementation for anisotropic diffusion filter.
| img | Input image. |
| diff_function | diffusion function. |
| radius | radius of the adjacency relation. |
| conservativeness | Conservativeness function. Expected values: 0.0 to 1.0. Lower values keep weak edges and higher values remove stronger noise incidence. |
| canny | Canny edges segmentation. |
| backg | Image background segmentation. |


{% extends "layouts/base-error.html" %} {% block title %}Verify-Email{% endblock title %} {% block content %}
You have successfully created your account,Please confirm your email address to complete the registration


You can manage all posts, such as editing, deleting and more.
|
|
Title | Category | Author | Created At | Status |
|---|---|---|---|---|---|
|
|
Laravel 5 Tutorial: Introduction | Web Developer, Tutorial |
Rizal Fakhri
|
2018-01-20 | Published |
|
|
Laravel 5 Tutorial: Installing | Web Developer, Tutorial |
Rizal Fakhri
|
2018-01-20 | Published |
|
|
Laravel 5 Tutorial: MVC | Web Developer, Tutorial |
Rizal Fakhri
|
2018-01-20 | Published |
|
|
Laravel 5 Tutorial: CRUD | Web Developer, Tutorial |
Rizal Fakhri
|
2018-01-20 | Draft |
|
|
Laravel 5 Tutorial: Deployment | Web Developer, Tutorial |
Rizal Fakhri
|
2018-01-20 | Pending |


Dominios disponibles
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| givingspace.com | com | Internet | $1499 | Purchase |
| lifestick.com | com | Internet | $1499 | Purchase |
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RedBook
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(current)
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Redirecting to the correct page. If it doesn't load in a few seconds, go to [https://notes.benjaminreinhardt.com/parpa-2-pager.pdf](https://notes.benjaminreinhardt.com/parpa-2-pager.pdf).


Web Developer, Problem Solver, Energy Drink Consumer.
The Technology
This site was done in AngularJS for the front end using Font-Awesome, Animate.css and of course
Bootstrap. For the server-side code I used ASP.NET MVC 5, Entity Framework 6, WebAPI 2
and Microsoft SQL Server 2017. I host it myself using Windows Server 2012 R2 and IIS.


Example-1 Applying flexbox to the container
In the first example, we can observe what happens to child elements (item 1, item 2 and item 3) when their
parent element (the box surrounding the three elements) has the following applied to it: display :
flex
Note: The parent element has its width set to 90% and the child elements (the three boxes) have their width set at 20%.
Item1
Item2
Item3


Food Factory
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 Initializes, notably, self.xPosInPixels and self.yPosInPixels, that are attributes that
correspond to the last known center of the puck
Args:
i_lowerColor: HSV values of the lower threshold used to identify the puck
i_upperColor: HSV values of the Upper threshold used to identify the puck
i_radius: Radius of the puck in pixels
i_camera: pointer to a concrete implementation of the abstract base class Camera
i_broadcaster : pointer to a concrete implementation of the abstract base class Broadcaster
i_dimensionsConverter : pointer to a DimensionsConverter object
i_displayOutput: Boolean that indicates if the output video feed should be displayed by this script or not
◆ displayFeed()
| def src.VisionPuckDetector.PuckDetector.PuckDetector.displayFeed |
( |
|
self, |
|
|
|
i_frame |
|
) |
| |
Displays one altered frame (calls displayCirclesOnFrame() and displayCirclesPositionOnFrame() on i_frame in order
to draw a circle around the puck on the frame and to write its coordinates
Args:
i_frame: The orginal frame
◆ findPuck()
| def src.VisionPuckDetector.PuckDetector.PuckDetector.findPuck |
( |
|
self | ) |
|
This is the main method of the PuckDetector class. This method iterates indefinitely (or
until the camera fails) and finds the puck in every new frame
◆ updatePosition()
| def src.VisionPuckDetector.PuckDetector.PuckDetector.updatePosition |
( |
|
self, |
|
|
|
i_puck |
|
) |
| |
Updates the position of the puck
Args:
i_puck: (x,y) coordinates of the puck in pixels
The documentation for this class was generated from the following file:
- D:/école/Université/UDES/Session 4/PROJET/HockusPockus/Vision/src/VisionPuckDetector/PuckDetector.py


?6
7
8
9
10
The
New
York
copyright
reserved
botanical
Garden
uew
YORK
BOTANICAL
SARDÈ1
01442717
FLORA
of
THE
ISLAND
OF
DOMINICA
British
West
Indies
No.fr?
-du/'.
Walter
H.
Hodge
Botanic
Gardens,
Roseau
July
16,
1938
01442717
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


PXBlueIcons
air_filter
apple
asparagus
barley
barley_alt
barrel
baseball
basketball_hoop
battery
bearing
boxing_glove
breaker
broccoli
building
bypass_battery
camera
capacitor_large
car
carrot
chili_pepper
combine_harvester
computer
contactor
corn
current
current_circled
current_circled_outline
cybersecurity
cybersecurity_two_tone
department
departments
device
environment
ephesus_fixture
ephesus_gateway
factory
fan
flask
flow
flower
flower_tulip
football_helmet
forklift
fuel
garlic
gas_cylinder
generator
generator_off
glasses
grade_a
grade_b
grade_c
grade_d
grade_f
grass
health
hockey_puck
hockey_sticks
hops
incident
incident_active
incident_resolved
leaf
maintenance
map_marker_multiple
map_marker_three
moisture
network_switch
outline_offline_bolt
overlap
pdu
pepper
pickup_truck
piggy_bank
pipe
pipe_disconnected
pipe_leak
potato
power_circled
power_circled_outline
priority
pump
push_notification
push_notification_solid
pxblue
pxblue_small
pxblue_small_alt
pxgreen
pxred
pxwhite
qrcode
radio_handheld
road
road_variant
running_high
running_low
satellite_uplink
scan_qrcode
server
servers
sign_caution
silo
soybean
sports_car
ssol
stopped
stopping
studioblue
studioblue_outline
sunflower
support
switch
temp
tennis
toggle_off
toggle_off_outlined
toggle_on
toggle_on_outlined
tomato
tractor
traffic_light
tree
trending_down
trending_flat
trending_up
tripped
utility
utility_van
vfd
vmms
voltage
voltage_circled
voltage_circled_outline
water
water_outline
waveform
wheat
xstorage
xstorage_alt


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qwertyuikjhgfdd
dddd
WEEEEEEEEEEEEEEE


Worklists
Filters applied: Showing 2 of 35
| 0003 0866 0007 | New | Mr Howard Jones | Routine | Cardiology/heart failure | Noor Altaf | Rejected | Outpatient |
| 0003 0866 0007 | New | Mr Howard Jones | Routine | Cardiology/heart failure | Noor Altaf | Rejected | Outpatient |
| 0003 9866 9876 | Mr Howard Jones | Routine | Cardiology/heart failure | Noor Altaf | Rejected | Outpatient | |
| 0003 0844 0123 | Mr Howard Jones | Routine | Cardiology/heart failure | Noor Altaf | Rejected | Outpatient | |
| 0003 0866 0023 | Mr Howard Jones | Routine | Cardiology/heart failure | Noor Altaf | Rejected | Outpatient | |
| 0003 0866 0004 | Mr Howard Jones | Routine | Cardiology/heart failure | Noor Altaf | Rejected | Outpatient |


Go To CarnivalClaw.com To Download The App On iTunes & GooglePlay
Then Aim Your Device's Camera At The Orientation Image Above
Tyler Mayberry - Arts Studies Senior
Carnival Claw is an augmented reality video game where users are meant to tap corresponding buttons
to grab items from the top or bottom rows with the claw. Grab all the Rupees and Bags while avoiding the Bombs for your best score! I made all art with
Adobe Illustrator and Blender, I coded and created the app with Unity, and I wrote the website for the game.
Carnival Claw is my submission for the 2017 Code + Art Visualization Contest.
Tutorial
Tips
| Username | Score |
|---|---|
| Wolfpacker_001 | 320 |
| Wolfpacker_002 | 350 |
| Wolfpacker_003 | 2 |
| Wolfpacker_2004 | 12032 |
| Username | Score |
|---|---|
| Wolfpacker_001 | 320 |
| Wolfpacker_002 | 350 |
| Wolfpacker_003 | 2 |
| Wolfpacker_2004 | 12032 |
| Username | Score |
|---|---|
| Wolfpacker_001 | 320 |
| Wolfpacker_002 | 350 |
| Wolfpacker_003 | 2 |
| Wolfpacker_2004 | 12032 |


{% extends "layout.html" %} {% block page_title %} What did you do before using this service? {% endblock %} {% block content %} {% include "includes/phase_banner_alpha.html" %} Back Give feedback All questions in the survey are optional. Were you able to do what you needed to do today? Yes No Why did you come to this service? Select all that apply. View who is on your claim View your next payments Change personal details Change jobs Change earnings Change children Change childcare Other Tell us what else you came to the service to do {% endblock %}
All questions in the survey are optional.
Select all that apply.


def stagepayload(pkgroot)
begin
puts "Creating an applications dir in pkgroot: #{pkgroot + APPS}"
Dir.mkdir("#{pkgroot + APPS}")
puts "Copying payload to pkgroot: #{@source} --> #{pkgroot + APPS}"
cmd = ['/bin/cp',
' -R ',
"\"#{@source}\"",
' ' + "\"#{pkgroot + APPS}\""
]
unless system(cmd.join(" "))
raise QuickPkgErr.new("There was a problem copying the source tree...")
end
puts "Setting file and folder ownership to root:admin..."
cmd = ['/usr/sbin/chown ',
'-R ',
'root:admin ',
"\"#{pkgroot + APPS}\"",
' >&/dev/null'
]
unless system(cmd.join(" "))
raise QuickPkgErr.new("There was a problem setting ownership on the source tree...")
end
puts "Correcting permissions on pkgroot..."
cmd = ['/bin/chmod ',
'-R ',
'o-w ',
"\"#{pkgroot + APPS}\"",
' >&/dev/null'
]
unless system(cmd.join(" "))
raise QuickPkgErr.new("There was a problem setting permissions on the source tree...")
end
rescue
puts "ERROR"
$stderr.puts "#{self.class}: #{$!}"
exit $?
end
end


Coop's tribute to Randotti Skulls, from the golden age of Haunted Mansion merchandise
by Cory DoctorowFrom the 1950s until the 1980s, Randy and Dotti Smith supplied a line of fantastic cast sculptures sold in Disney theme-park gift shops, especially a line of skulls sold in shops associated with the Haunted Mansion and Pirates of the Caribbean rides; these Randotti skulls haven't been sold in decades, you can still find used ones (at high prices) online, as Boing Boing pal and fabulous illustrator Coop discovered when he sourced an impressive collection of Randotti sculpts.
Coop used the skulls as the basis for a set of gorgeous illustrations that he's selling as tees and hoodies through Threadless.
These are some of my favorite designs of all time, and some of my favorite Coop illos: he really captured the spirit of these pieces.
SKULLS [Coop/Threadless]
Original Art [The Art of Coop]


EXPERIENCE
|
| Zaniac
|
New York, NY | 2016-Present |
| Instructor
|
- Instruct K-5 students math or Scratch.
- Assign classwork and grade exams at class.
- Email students' parents about students' progress
|
| Rapid Diagnostic Inc.
|
Freeport, NY | 2015-Present |
| Technician Assistant |
- Serve as an interpreter/translator for ultrasound technicians and patients.
- Work at many different clinics in both Manhattan and Brooklyn.
- Report the number of patients to the company after work.
|
| Chinese Promise Baptist Church
|
Brooklyn, NY | 2015 |
| Teacher Assistant |
- Tutor 5th-grade students in mathematics.
- Make sure students are disciplined and finish their homework on time.
- Bring students to eat lunch.
|


Map data:
Show all links:
Territory:
temp
Unit:
temp
Territory 2:
temp
Unit 2:
temp
Name:
Discription:
Img:
Img Size X:
Y:
Max Distance
Name:
Is supply point:
Supply Marker X:
Y:
Can convoy:
Convoy point X:
Y:
Territory: temp
Army:
Fleet:
Marker X:
Y:
Coast ID:
None
0
1
2
3
Players:


by Joćo Pedro Neto and Derek Hohls
Derek Hohls and me started a small project to create a standard format to translate game rules into XML markups. The goal is to accommodate the games presented at the World of Abstract Games (WAG) website into a data format that can be processed (in the future) by a database management system.The main element is the tag <game> which includes every information concerning a specific game (and its variants).
<game>...</game>
Inside the <game> tag, there are some mandatory tags, namely:
<title>The game title</title>
<meta> [Information about the game] </meta>The <meta> tag includes information not related to the game rules, like the game author and year, the publisher and the webpage writer. An example:
<meta>
<year>1989</year>
<author>
<name>Author's Name</name>
<mail>email@internet</mail>
<url>www.url.com</url>
</author>
<publisher>
<name>Ab Games, Lda.</name>
<url>www.url.com</url>
</publisher>
<writer>
<name>Writer's name</name>
<mail>email@internet</mail>
<url>www.url.com</url>
<year>2003</year>
</writer>
</meta>
The <components> tag includes information about the game features except the rules. An example:
<components>
<board top="5" middle="7" bottom="5">hex</board>
<pieces number="14" color="black" id="soldiers" />
<pieces number="14" color="white" id="soldiers" />

soldier

<features grid="hex"
cancapture="true"
canmove="true"
mustcapture="true"/>
<zillions>http://www.zog.com</zillions>
<diagram type="hex" middle="7">
"5,7,5,wwwww/wwwwww/..www../..xxx../xxxxxx/xxxxx"
</diagram>
<image width="80" height="45"
src="icons/image.jpg"
text="a comment"/>
</components>The <board> gives the typical size of the game board. The <pieces> describe the minimal physical requirements of game pieces in order to play the game.
 provides information to what section the game belongs (soldier, king, stack). <features> denote some rule features (like pieces move, pieces capture, what type of grid, ...). <zillions> points to an optional link with ZoG information. The <diagram> draws a Javascript diagram of a certain board type (the type must be "sq", "hex" or "sqstack"). <diagram> may be used within other tags. The <image> inserts an image, it may be also use on other contexts.
Next, comes the <description> tag, which includes the game rules and samples. The rules and divided in three parts: (i) the game setup, (ii) the rules where it's possible to create rule sections and rules subsections, and (iii) the game goal. An example:
<description>
<setup>Setup description</setup>
<rules>
<rulesection>
<rule><rtitle>MOVING</rtitle>rule 1</rule>
<rule>rule 2</rule>
<rule>rule 3</rule>
<rulesection>
<rule>rule 3.1</rule>
<rule>rule 3.2</rule>
</rulesection>
</rulesection>
</rules>
<goal>Goal definition</goal>
... plus zero or more examples
</description>The format of an example is like the following two:
<example>
<diagram type="hex" middle="7">
"5,7,5,.wwww/wwwwww/w.www../.axxx../.x.xxx/xx.xx"
</diagram>
<title>An example</title>
<note>it's possible to make bolds, newlines

italics, underlines, urls <url>www...</url>
and images
<image width="80" height="45" src="a.jpg"
text="a comment"/>
</note>
</example>
<example>
<diagram type="sqstack" col="5">
"4,5,..[..xWwx]../...../...../....."
</diagram>
<title>Another example</title>
<note>bla blabla blabla blabla bla</note>
</example>
There's also some optional tags for additional information.
<subtitle>The game subtitle</subtitle> game subtitle
<notes>
<note>General notes</note>
<history>Some historical notes.</history>
<rulenote>Rule notes</rulenote>
<authornote>Author's notes</authornote>
</notes>
<related> a related game
<name>Blokus</name>
<url>Blokus.htm</url>
</related>
<variant> a variant
<title>...</title>
<subtitle>...</subtitle>
<meta>...</meta>
<components>...</components>
<notes>...</notes>
<description>...</description>
</variant>
Each <game> element is processed by the following game.xsl file using a transformation tool like Saxon. The format stuff is included at file wagstyle.css.
E.g., given a XML file abalone.xml, if we execute
java -classpath saxon.jar com.icl.saxon.StyleSheet
abalone.xml game.xsl > abalone.html
we get abalone.html.
Some browsers, like IE6, can do this transformation automatically, so a link to the plain abalone.xml is enough.


Black Baby Onesie Short Sleeve with Pink Logo (New born)
BABYONBU000000E63E74NBXX
Variant selection (radio buttons)
BABYONBU000000E63E74NBXX
Available in - hours
The selected SKU is not available.
Your shopping bag contains 0 items () toggle
| SKU | Reference | Unit price | Q.ty | Total | ||
|---|---|---|---|---|---|---|
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| Total |


roles
Written on October 5, 2016
This was made using d3 & jekll & iframes (link to concept chart )
Throughout my bootcamp experience at Telegraph Academy...
I kept wondering... what do software engineers do all day?
I spent the first few weeks of my job figuring it out.
To begin, we must understand how applications are structured.
"A great application has many different front ends accessing a single database."
"A great application has many different front ends accessing a single database (via API)."
This by no means means there are only one version of each site.
And of course in your development flow there will e plenty of branches.
But understanding now the different locations will help us think better about jobs.
(Not shown: Dev Ops, Security Engineers, Fraud)
Full Stack (Data Visualization) & Data Science
Front End & Applications
Back end & API Engineering
All at once again...


Id
Name
Description
Sport
Phone
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{{ league.name }}
{{ league.description }}
{{ league.sport }}
{{ league.phone }}
No leagues found


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have both numbers and letters
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Hello, I’m a undergraduate student at University of South Florida, currently pursing a Bachelor's Degree in Computer Science. I have intermediate experience in web site design & building and customization, also I really passionate about AI as well as Machine Learning. I'm good at
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Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.
Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.


In this assignment, I created a single printable model by merging two meshes together and started working on my lamp design for next week's assignment. You can get my .3dm
files here.
My initial plan for this assignment is to create a model that visualizes the technologies a smartphone has replaced of the past decade. You can imagine this being an iPhone at the front of a model and right behind it is an explosion of different devices like books, old phones, clocks, cameras, television, etc. morphed into a single gigantic structure.
I started tackling this idea by finding all the available 3D models that I wanted. I jumped into Rhino to work on the most important model — the iPhone
To my dismay, the iPhone, like many of the other 3D models, was composed of numerous meshes and many of them did not line up with one another, making it a huge pain to work with.
After a full day of working on this, I decided to scrap the idea in the interest of time, and try something much simpler instead.
The other idea that I came up with was to create a cute little figure of a cat-dog. I was able to find the low-poly aesthetic models that I was looking for and imported them into Rhino.
Upon scaling and rotating them into the right position, I created a plane for each model and used MeshBooleanSplit
to split the models.
The dog shape did not split very well so I had to clean up some vector points and use FillMeshHole
to cover any open ends. I then put them adjacent to each other and attempted to match the two closed meshes together.
This took a little while because it was very hard to select the right points on the meshes and move them without accidentally opening the mesh.
After matching the two shapes together, I then joined them together and exported it as a single .stl
file with no errors/warnings.
To ensure that the model did not have any outstanding errors for printing, I imported the model into Cura as an inspection and the software was able to slice the model with no miscalculations.
The resulting print, as you can see, was also quite successful.
Upon a close inspection though, you can see distortions between where the cat and dog is merged.
For this part of the assignment, I really wanted to create a lamp that is based on something mathematical. So I focused my time this week on researching different theorems and how others have attempted to visualize them.
After looking at a lot of math tutorials and illustrations (while trying to rejog my scrappy Math-mory), I came up with several ideas for my lamp shade.
I stumbled upon a video that visualized prime numbers using a polar plot and the result was absolutely beautiful. There was also an interesting trend where the plot will turn from a spiral pattern to a ray pattern as the prime numbers get larger (as shown in the images below). I think it would be amazing to see if I can turn the "random" nature of this prime polar plot into a lamp shade.
Riemann's Zeta Function can create beautiful plots when you input complex numbers. Here's an introductory video about it if you're interested.
It would be very cool if I am able to extend the Zeta function into some form 3D object, like this post and this parametric plot I found online.
I much more simplistic idea I had was simply creating shapes based on the equation in Fermat's Last Theorem. By increasing the powers in even numbers I can create a lamp made of layers that morph from a circular cone to a pyramid. A simulation of the equations is shown below:


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Find The Top 10 Friendly
Places In Jordan
About Our Website
Our website aims to help the tourists to plan their trip to Jordan based on previous reviews from other tourists, showing the highest recommended places in Jordan, saving them money and time, and providing the best tourism and cultural experience for them.


I use a combination of genetic-engineering, sequencing, and statistical inference to study microbial evolutionary dynamics and quantitative genetics. I am currently a PhD student in Michael Desai's lab at Harvard. I am a proud MEME alumnus.
rego-costa@g.harvard.edu


In Compitetive, Skill Group is used to find matches appropriate for player's skill.
260 WinsIn Wingman, Skill Group is used to find matches appropriate for player's skill.
46 WinsWhen you reach Global General (Rank 40), you’ll have the option to reset your Profile Rank and earn (or upgrade) a Service Medal. After January 1, 2021 you’ll be able to receive the new 2021 Service Medal, an inspectable display item that will be visible wherever your avatar is shown.
New challenges await with the all-new Retakes and the exclusive Broken Fang Premier. Play new maps across a variety of game modes and see how you measure up in the Operation Stats page.
Coupons
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Dragon Lore
$10.0Half-Life: Alyx
$20.0Halo Capsule
$30.0Howling Dawn
$40.0wait time 3:32
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wait time 3:32
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Loyality Badge
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Water Element
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Key
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Chroma Case
Venom Case
GG
Graffitti
Sunday, 03:11
Loss
Monday, 02:14
Victory
Wednesday, 09:25
Victory
| нєłłsиαкє | 21 | 2 | 17 | 56 | ||
| ez | 17 | 0 | 21 | 35 | ||
| hK- | 13 | 1 | 19 | 27 | ||
| Hurricane | 9 | 4 | 20 | 22 | ||
| Majestic | 4 | 4 | 17 | 16 |
| нєłłsиαкє | 21 | 2 | 17 | 56 | ||
| ez | 17 | 0 | 21 | 35 | ||
| hK- | 13 | 1 | 19 | 27 | ||
| Hurricane | 9 | 4 | 20 | 22 | ||
| Majestic | 4 | 4 | 17 | 16 |
| Main Menu Background Scenery | Nuke |
| Color Mode | Computer Monitor |
| Aspect Ratio | WIDESCREEN 16:9 |
| Resolution | 1920x1080 |
| Display Mode | |
| Laptop Power Saving | DISABLED |
| Global Shadow Quaity | VERY LOW |
| Model / Texture Detail | LOW |
| Texture Streaming | DISABLED |


What’s new in iOS 13.3 developer beta 1?
by Chance MillerApple today released the first developer beta of iOS 13.3, as well as iPadOS 13.3, tvOS 13.3, and watchOS 6.1.1. Read on as we roundup all of the changes in these latest updates.
One of the biggest issues in iOS 13.2 is RAM management problem that is causing applications to be killed when running in the background. Early tests indicate that iOS 13.3 does solve this problem, at least to a certain degree. We’ll have to dive deeper to learn more, and it’s likely that further improvements will come with subsequent iOS 13.3 betas.
Spot any major changes in the first developer beta of iOS 13.3? Let us know in the comments or on Twitter, @9to5Mac.
What’s new in iOS 13.3 developer beta 1?
- Apparent fix for iOS 13.2 RAM management problems, though there are mixed reports
- A new option in Settings to disable the Memoji keyboard from always appearing:
- Communication Limits now available through Screen Time, allowing you to set limits based on your contacts.
- Updated Apple Watch icon is back:
- Beats Solo Pro users beware:
FTC: We use income earning auto affiliate links. More.


{{extend 'layout.html'}}
The job will be processed in the background. You should periodically check long jobs to find when they are finished. Results for completed proteins can be viewed before the job is done.
The usual method is to select one or more predictors and appropriate alleles which are
then run at once for all the proteins in the chosen genome. For an entire proteome
using multiple methods, it could take hours to process. Tepitope is fastest for MHC-II so
if you want a quick result just pick this method.
Selection of alleles should always be tailored to your needs or the results
will not be meaningful. There is a limit of 50 alleles to prevent overloading the server.
Some alleles are not available for all methods and will be ignored for those methods.
DP and DQ alleles are netMHCIIpan only.
{{=A('The Allele Frequency Net Database', _href='http://www.allelefrequencies.net/')}}
may be useful for human allele selections.
For convenience you can {{=A('upload a preset allele list', _href=URL(r=request,f='presets'))}}
and then select it in the form to avoid having to re-select the alleles each time.
If the genome is not in the drop down list you should go to the
{{=A('add genome', _href=URL(r=request,f='genomes'))}}
page and add it to the database so that the application is aware of it's availability.


Automaton Generations Exercise (v1.2)
Try out the process of generating successive cell states based on the rule shown.
The auto button will automatically fill in the active generation with the correct
answer. The check button will verify that all the cell values are correct and
will highlight any errors in red. Check will also advance to the next generation.
Clicking on a cell (in the active generation) will cause that cell to change
state. White cells are OFF. Black cells are ON. Cyan cells are in an unspecified
state.
Try auto-generating a generation of cell values then intentionally change a few
cell values by clicking on them. Run check and you should get some error
highlights.
Generation Rule
Generation Space


--- layout: post title: 'Daily Poem 2: Quenya Haiku' date: 2014-10-06 08:00:56.000000000 -05:00 categories: - Poetry tags: - elvish - haiku - poem - poetry - quenya - tolkein status: publish type: post published: true author: image: /assets/images/tengwar-daily-poem-51-e1413039090445.png ---
Lutullonyëi
penda celumë, cenda
lairëli méla
Regular readers knew it was inevitable: A Haiku in Quenya. As always with my Quenya compositions, I will point out that that I am still very new to Quenya and am very uncertain about my grammar.
In English (literal translation with no attempt to respect the meter):
We are floating
downstream, reading
love poetry.


java.lang.Object MailClient
public class MailClient
A class to model a simple email client. The client is run by a particular user, and sends and retrieves mail via a particular server.
| Constructor Summary | |
|---|---|
MailClient(MailServer server,
java.lang.String user)
Create a mail client run by user and attached to the given server. |
| Method Summary | |
|---|---|
MailItem |
getNextMailItem()
Return the next mail item (if any) for this user. |
void |
printNextMailItem()
Print the next mail item (if any) for this user to the text terminal. |
void |
sendMessage(java.lang.String to,
java.lang.String message)
Send the given message to the given recipient via the attached mail server. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public MailClient(MailServer server, java.lang.String user)
| Method Detail |
|---|
public MailItem getNextMailItem()
public void printNextMailItem()
public void sendMessage(java.lang.String to, java.lang.String message)
to
- The intended recipient.mess
- A fully prepared message to be sent.


Name
Appetizers Vegetable Samosas Rs.100
Dosas Rs.100
Onion Pakoras Rs.100
Channa Chat Rs.100
Vegetable Dishes Kadhai Paneer Rs.200
Paalak Paneer Rs.200
Chole Bhature Rs.200
Dum Aaloo Rs.200
Deserts Kheer Rs.70
Kulfi Rs.70
Gulab Jamun Rs.70
Drinks Cold Drinks Rs.50
Cold Coffee Rs.50
Masala Tea Rs.50
Oreo Milkshake Rs.50


Talking C#/CLR with Microsoft
Here. An interview of Peter Hallam, the dev. lead for the C# compiler. Most of the interview covers stuff you're likely to already know, e.g. the design goals for C# and the CLR, etc, but Mr. Hallam does spend some time talking about generics and his opinion on mono (he likes it).


“Bring Up the Bodies”
Like Ben earlier this week, I’m coming back to some of the questions we raised when The Appendix launched. His were on the web and cabinets of curiosity and mine were on the narrative choices that historians make.
I’m inspired to return to the subject by the wonderful address that William Cronon, the president of the American Historical Association, gave two weeks ago at the organization’s annual meeting in New Orleans. It’s been posted on Youtube and I think it’s interesting and urgent enough to include in this post:
In case you don’t have the time to watch, what it says is this: our society’s ability to concentrate on longform reading is waning, and public support and funding for history and other forms of humanistic are in decline. Faced with those challenges, historians have a duty, in Cronon’s words, to “defend the public to the public and in public [defend] the continuing importance of history both in the United States and the wider world.” The best way to do so is by not being boring “alien professionals” but by remembering that the roots of the historical discipline lie in one key word: ‘storytelling.’
“Our core business is resurrection,” Cronon intones, at my favorite moment. “to make the past—the dead past—live again. We forget this most basic task at our peril, for there is no deeper betrayal of the historical imagination than to leave the past inertly, boringly forgettable.”
Again, the whole speech is worth watching. It’s inspiring, sensitive, and moving—especially when Cronon, an environmental historian who got fairly famous a few years ago while standing up for academics’ rights and freedoms, talks about the English professor who inspired him to go on to academia when he was in college.
But what really got me excited was this: in the course of praising the narrative work of journalists like Michael Pollan and makers of historical fiction like the filmmaker John Sayles, Cronon laments that historians’ narrative choices remained constrained “in part because of our discipline’s long-standing and—I might say—too long unquestioned commitment to the omniscient third person narrator,” the narrative voice that was adopted when historians began their professionalized journey in the late nineteenth century.
Cronon defends historians’ loyalty to their epistemological foundation—documents— but he notes that the rhetorical moves historians make to defend that evidence often sap their work of “narrative momentum.” It also builds a “very high wall between us and the inner emotional lives of the human beings about whom we write.”
By contrast, those inner emotional lives are precisely what practitioners of historical fiction can access, following the narrative streams of consciousness initially mapped by the great twentieth century novelists: Virginia Woolf, James Joyce, etc. To that end, Cronon suggests that historians view the work of historical fiction-makers with more sympathy and respect.
But then he says something I’m not so sure about. Before making his excellent call to arms—that historians nonetheless have access to a “literally infinite” number of narratives that can energize our audiences—he states, fairly definitively, that historians cannot emulate the examples of Joyce, Woolf, and the historical fiction-makers who seek to explore their subjects’ inner lives. That’s the other, speculative, novelistic side of the fence, which historians should love, but not encroach upon, lest they lose touch with what makes them historians (fidelity to the limits of the documentary record).
I found it a particularly interesting given that a good friend, a fellow historian, wrote to me the other day to say just how excited she was that I’m writing a serialized novel. It surprised me, given that I didn’t know that I was writing a novel.
I had to stop for a moment and think. And then I realized that she was referring to Death of a Sailor, the first chapter of which ran on The Appendix last week. It’s about one sailor who kills another over a woman in New York City in 1835, and the hidden narrative of slavery, love, and the early nineteenth century that the murder unspools. Structurally inspired in part by Dickens, it’s certainly an experiment in serialized storytelling, but—and this is what tripped me up by friend’s email—I thought had managed to walk the line and write it as a history.
I understood the confusion, though. I even enjoyed it. I had invited being misread by making a very specific narrative choice. If you haven’t read the piece yet, what it does is this: it chops historians’ traditional third person omniscient narrator in two.
This is how it begins:
The pistol fired. A woman screamed. The end of his story began.
Was attention paid? By whom? Begin again.
It was September 4, 1835, just before midnight on a Friday in New York, and a night watchman named Richard Hockman was patrolling the seam between two of Manhattan’s rougher neighborhoods.
Who was Richard Hockman? Why was he a night watchman?
And so on, a series of questions and answers. Now, all of the details—the pistol firing, the woman screaming, the night watchman named Hockman patrolling, the hot summer, the stench—come from historical documents and secondary sources, and I back them up in our version of footnotes, ‘supernotes.’
The voice, however, is something else. Who says our narrators need to be omniscient if they are nonetheless delivering the facts? For Death of a Sailor’s first chapter, I divided the narrator in two, a questioner, and a respondent, and as the piece progresses they begin to poke at each other’s authority. This is the conversation that goes on every historian or writers’ head as they begin to explore the limits of what they can write, but it gets mostly subsumed when we begin to write as third person narrators. But if we recognize that as a narrative convention—we essentially become a character named ‘historical narrator’—why not become two historical narrators? And why not—in the interest of moving the narrative along—put them in conflict?
Were times bad that September?
It depends on who you were.
At first, Death of a Sailor’s respondent is more confident, more romantic, but as the italicized questioner begins to take liberties with his questions, trying to plumb the emotional lives of the historical figures, the respondent sticks more to the facts—an action that I hope begins to feel more inadequate, and makes the reader begin to feel the tension between questions that we ask as people—what did that feel like?—and the questions we must ask as historians: how do we know that?
So what was Jackson to Shoults? Why did Jackson kill Roberts?
“Roberts had taken away his comfort,” explained another newspaper, “and he had now taken him out of the way.”
An answer which explains nothing. Or perhaps everything. Doubt is hopefully conveyed without sacrificing the reader’s trust or interest. The facts are there, but between unanswerable questions and historians’ questioning answers emerges an uncertainty that, I hope, gets at what is often off-limits to academic historians: the lost emotions and inner consciousness of the dead.
What comfort had Roberts taken? What comfort had Jackson lost?
And—confession time—in doing so I was inspired by one of Cronon’s historical unfollowables, James Joyce’s Ulysses. The novel is justly famous for its dissertation-ready, annotation-necessary modernist experimentation with form, culminating in the final chapter, in which Joyce portrays Molly Bloom’s stream of consciousness. But—and I’m not alone in thinking so—Ulysses rises above its word games in the second to last chapter, when Joyce drops the literary pretense to unite his two most important characters, Stephen Dedalus and Leopold Bloom. In the form of a catechism, Joyce finally gives straight answers. Sort of.
Of what did the duumvirate deliberate during their itinerary?
Music, literature, Ireland, Dublin, Paris, friendship, woman, prostitution, diet, the influence of gaslight or the light of arc and glowlamps on the growth of adjoining paraheliotropic trees, exposed corporation emergency dustbuckets, the Roman catholic church, ecclesiastical celibacy, the Irish nation, jesuit education, careers, the study of medicine, the past day, the maleficent influence of the presabbath, Stephen’s collapse.
In its alternating romance and formality; in the way its questions reach for more powerful possibilities than what the text, the real, the documents can ever answer; in the way those answers get more spiritual, and hint at the emotion behind the facts (“Stephen’s collapse”)—the chapter elevates the entire book. It moves past the novelist’s prior cant and self-referentiality to do something more direct, in effect liberating the book from the literature that came before.
In this way, Ulysses is a lot more like academic history in than we’d like to admit. Like Ulysses, academic history has gotten progressively denser, more aware of itself, more beautifully complicated—but less legible beyond the historian’s boundaries. Joyce was aware of that within Ulysses, I think, and added this almost-end, where he seemed to leave behind what had come before, in literature, and in his own novel, to clear the decks for his perhaps overrated but certainly beautiful and momentous final chapter, the inner stream of consciousness of Molly Bloom that made the book famous, and banned.
Can academic historians achieve something similar? I know that ‘he who invokes Ulysses in relation to his own writing’ is already on thin ice.  Perhaps I became a writer of historical fiction, without intending to.
But I don’t think I did, and I think that we can do better. We can play with our forms, without sacrificing our rigor and respect for the sources. We must. It’s a funny thing being in the resurrection business, but Cronon is right. With the right stories, and voices, we can bring back the dead.


Page Structure Code Example
Three examples
- Simple example (Space Teddy Inc)
- Three column example (Stock Market Up)
- ARIA example of level 7 heading (Apples)
Simple example (Space Teddy Inc)
The following HTML code yields a heading structure that corresponds to this list:
- Space Teddy Inc.
- Navigation Menu
- An inside look at the new Space Teddy 6
- Table of Contents
- Cotton Fur
- Related Articles
- Latest Articles

Space Teddy Inc.

Navigation Menu
 …

An inside look at the new Space Teddy 6

Table of Contents
 …
…

Cotton Fur

…

Related Articles

…

Latest Articles

…

Three column example (Stock Market Up
In this example, the main content of the page is in the middle column of a 3-column page. The title of the main content matches the title of the page, and is marked as h1, even though it is not the first thing on the page. The content in the first and third columns is less important, and marked with h2.
Note: It is important to note that the example code below is not intended to prescribe what level of heading should be used for a particular section of the web page. In the example, the layout could be presented with the first heading in each column at the same logical level (such as an h1), or as found in the example, where the logical level reflects its importance in relation to the main content.
 <title>Stock Market Up Today </title>

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Stock Market up today

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ARIA example of level 7 heading
[Copied from ARIA12] This example demonstrates how to implement a level 7 heading using role=heading
and the aria-level attribute. Since HTML only supports headings through level 6, there is no native element to provide these semantics.
...
Fruit Trees
 ...
Apples

Apples grow on trees in areas known as orchards...
 ...
Jonagold

Jonagold is a cross between the Golden Delicious and Jonathan varieties...


Package org.drip.measure.statistics
Class MultivariateMoments
java.lang.Object
org.drip.measure.statistics.MultivariateMoments
public class MultivariateMoments
extends java.lang.Object
MultivariateMoments generates and holds the Specified Multivariate Series Mean, Co-variance, and
other selected Moments.
- Module = Computational Core Module
- Library = Numerical Analysis Library
- Project = Rd Continuous/Discrete Probability Measures
- Package = R1 Rd Thin Thick Moments
- Author:
- Lakshmi Krishnamurthy
-
Method Summary
Modifier and Type Method Description boolean
addCovariance(java.lang.String strVariate1Name, java.lang.String strVariate2Name, double dblCovariance)
Add the Co-variance for the Named Variate Pairboolean
addMean(java.lang.String strVariateName, double dblMean)
Add the Mean for the Named Variatedouble
correlation(java.lang.String strVariate1Name, java.lang.String strVariate2Name)
Retrieve the Correlation between the Named Variate Pairdouble
covariance(java.lang.String strVariate1Name, java.lang.String strVariate2Name)
Retrieve the Co-variance of the Named Variate Pairdouble
mean(java.lang.String strVariateName)
Retrieve the Mean of the Named Variateint
numVariate()
Retrieve the Number of Variates in the Distributionstatic MultivariateMoments
Standard(java.lang.String[] astrVariateName, double[][] aadblVariate)
Generate the MultivariateMetrics Instance from the Series Realizations providedstatic MultivariateMoments
Standard(java.lang.String[] astrVariateName, double[] adblMean, double[][] aadblCovariance)
Generate the MultivariateMetrics Instance from the Specified Mean and Co-variance Inputsdouble
variance(java.lang.String strVariateName)
Retrieve the Variance of the Named Variatejava.util.Set<java.lang.String>
variateList()
Retrieve the Variates for which the Metrics are availableMethods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
-
Method Details
-
Standard
public static final MultivariateMoments Standard(java.lang.String[] astrVariateName, double[][] aadblVariate)Generate the MultivariateMetrics Instance from the Series Realizations provided- Parameters:
astrVariateName
- Array of Variate Name HeadersaadblVariate
- Array of Variate Realization Arrays- Returns:
- The MultivariateMetrics Instance
-
Standard
public static final MultivariateMoments Standard(java.lang.String[] astrVariateName, double[] adblMean, double[][] aadblCovariance)Generate the MultivariateMetrics Instance from the Specified Mean and Co-variance Inputs- Parameters:
astrVariateName
- Array of Variate Name HeadersadblMean
- Array of Variate MeansaadblCovariance
- Double Array of the Variate Co-variance- Returns:
- The MultivariateMetrics Instance
-
numVariate
public int numVariate()Retrieve the Number of Variates in the Distribution- Returns:
- The Number of Variates in the Distribution
-
variateList
public java.util.Set<java.lang.String> variateList()Retrieve the Variates for which the Metrics are available- Returns:
- The Set of Variates
-
addMean
public boolean addMean(java.lang.String strVariateName, double dblMean)Add the Mean for the Named Variate- Parameters:
strVariateName
- The Named VariatedblMean
- The Variate Mean- Returns:
- TRUE - The Variate Mean successfully added
-
mean
public double mean(java.lang.String strVariateName) throws java.lang.ExceptionRetrieve the Mean of the Named Variate- Parameters:
strVariateName
- The Named Variate- Returns:
- Mean of the Named Variate
- Throws:
java.lang.Exception
- Thrown if the Named Variate Mean cannot be retrieved
-
addCovariance
public boolean addCovariance(java.lang.String strVariate1Name, java.lang.String strVariate2Name, double dblCovariance)Add the Co-variance for the Named Variate Pair- Parameters:
strVariate1Name
- The Named Variate #1strVariate2Name
- The Named Variate #2dblCovariance
- The Variate Mean- Returns:
- TRUE - The Variate Pair Co-variance successfully added
-
variance
public double variance(java.lang.String strVariateName) throws java.lang.ExceptionRetrieve the Variance of the Named Variate- Parameters:
strVariateName
- The Named Variate- Returns:
- Variance of the Named Variate
- Throws:
java.lang.Exception
- Thrown if the Named Variate Variance cannot be retrieved
-
covariance
public double covariance(java.lang.String strVariate1Name, java.lang.String strVariate2Name) throws java.lang.ExceptionRetrieve the Co-variance of the Named Variate Pair- Parameters:
strVariate1Name
- The Named Variate #1strVariate2Name
- The Named Variate #2- Returns:
- Co-variance of the Named Variate Pair
- Throws:
java.lang.Exception
- Thrown if the Named Variate Co-variance cannot be retrieved
-
correlation
public double correlation(java.lang.String strVariate1Name, java.lang.String strVariate2Name) throws java.lang.ExceptionRetrieve the Correlation between the Named Variate Pair- Parameters:
strVariate1Name
- The Named Variate #1strVariate2Name
- The Named Variate #2- Returns:
- Correlation between the Named Variate Pair
- Throws:
java.lang.Exception
- Thrown if the Named Variate Correlation cannot be retrieved
-


 If you want to share your time and experience with social initiatives you are a potential participant of volunteer movement.
 In this case you are a potential consumer of volunteer services.
Meetup place — Digital Lab
The Digital Lab is a Falanster initiative. Its goal is to wake up the IT volunteer movement in Belarus. This is the place where:
- Social initiatives can ask for some digital help.
- IT volunteers can choose an interesting task for themselves, meet new people and get new experience.
- The Digital Lab Team can consult you on your digital issues.
Statistics
volunteering
projects
volunteers
History of organizations
League of Youth
Voluntary Service
Organisation’s team prepares programs for protecting traditions of student groups and growing of the volunteer movement among the youth of Belarus.
Club house
"Kryly nadzei"
Platform for the rehabilitation of people with psycho-physical disabilities. Here participants practice social inclusion and mutual help.
Komnata 3/0/2
Place that connects several public organizations: NGO Fialta, Association of Belarusian students and Minsk Urban Platform.
History of volunteers
Dmitri Buimistrov,
frontend developer
"Digital Lab is a place where you can get experience. Here people get skills that can be useful on the real projects.”
Anastasia Frost,
designer
“IT volunteering is a good opportunity to reveal yourself, find like-minded people and do something useful for those who don’t have all necessary resources."
Svetlana Kotikova,
business analyst
"Success of the volunteer project depends on personal responsibility. Everyone should be ready to work and be included in the process."
Why is it important?
It will increase social connections and knowledge exchange between specialists and social activists.
The importance of digital values and rights (like privacy, right for development, overcoming of digital discrimination with gaining of digital knowledge) will be widen through exchange of values and culture code among netizens, geeks, nerds and social pioneers.
It will lead to a sustainable development of our country. If you want to learn more look through the project page on Falanster site.