APIs and databases
Scraping isn't the only method for retrieving data; sometimes data resources on the internet — even one maintained by government agencies — actually want to give it away in a usable format. Web application rogramming interfaces exist to make this happen. More often than not, you pass a query through an API's URL and get a response in JSON (JavaScript Object Notation) format, which is highly structured and can look a lot like a Python list or dictionary.
We're going to query a Socrata API maintained by the city of Chicago. This particular data source is where crime incidents are reported by the Chicago Police Department; the lag time for reporting is about one week.
Our initial goal is to grab the most recent week of crimes in the system, which means pinging the API to figure out the date of the last crime and then grabbing every reported crime on that day and the previous six. This will involve some work with datetime
and date math in Python.
We're also going to inspect the incoming stream of JSON from the API to make sure we can tell if any are missing fields. This becomes important because we are going to feed each crime event into an SQLite database.
SQLite may not be as robust as some other database managers, but it's part of the standard library in later versions of Python 2.7 and creates lightweight database files that are easy to connect to and query.
We'll write some automated queries for crime trends, including which beats in Chicago had the most reported narcotics crimes during the week and what the violent crime rate was in different communities.
This particular task uses these files:
-
API.py: The main script that fetches data from the crime API, puts it in a SQLite database and runs queries on the data.
-
crime.db: A SQLite database that only has a table with population data for Chicago's various community areas. We'll add to it from the crime API.
-
fun_with_datetime.py: A more focused look at datetime objects and how they work in Python.
-
fun_with_sqlite.py: The fundamentals of connecting to, modifying and querying a database with Python.
-
crime_backup.json.zip: Shoddy internet or a downed website won't ruin this exercise; this is compressed JSON that represents a week of Chicago crime.
Finished versions are in the completed folder.


--- layout: post title: "[UPDATE] It's Alive!" date: 2020-03-01 01:45:00 -0400 background: '/img/posts/06-electricalshelf.jpg' ---
Hello again! It's been a while since the last post, but alas 4B is like that.
We have been making good progress! The electrical shelf is completed as seen in the header! The exterior casing, motion systems and gantry have also all been completed! Pictures are included below.
Embedded software and the user interface are also in a very good position right now! Moving forward the main focus is getting the heater operational and tested as well as installing the pinbed. The heater installations have been a little delayed as our simulations did not match up with reality. The heat gun's flow is not turbulent enough to evenly heat the plastic all across, as such a new heater has been obtained. Testing is in progress to ensure that the new heater has the ability to evenly heat the plastic. Once this is complete, it will be installed and more testing will be conducted.
As for the pinbed, it is also almost close to completion! We were able to outsource the rest of the cutting to the Engineering Student Machine Shop (EMS) and we got them all back recently. Below is a picture of less than half of them. There's too many pins to count to be honest...
We have started inserting the pins into the pinbed and we're about halfway there. Soon it will be ready to install and be tested! Our dreams are finally becoming a reality and it's amazing to see.
Here's a video of our system thus far!
Please look forward to more updates and a video of the full system operation soon!
Yours Truly,
Formagine


[Erowid Note: While it may be that the substance described below is 2C-B, Ecstasy tablets are notoriously impure or misrepresented, often containing chemicals other than those they are presumed to contain. This is more of a general Ecstasy report.]
My experience on 2C-B was one I will honestly never forget. It started out at the club that night, I had taken 2 tabs on an empty stomach at 9:30 pm. I rolled extremely hard all night until about 4 when I came down, and decided I needed one more to get me through the day without feeling terrible.
At the time I really cared less about what I was getting, the guy was selling purple clubs and I assumed it was mescaline so I purchased it. The tab was a lighter purple, pretty thick and a little chalky. I took it at 6:30 and didnít begin to feel effects until the car ride home at 8 in the morning.
I could tell I was tripping in the car. When my friend and I arrived back at the apartment I felt way better than I did leaving the club. I had an extreme energy boost even though I hadnít eaten in 12 hours or slept in 24. I felt like it was going to be a great day. My friend and I both took a bong hit and took a beer to the balcony to smoke a cigarette.
Going outside set my trip way off, thatís when the visuals began to kick in. Looking at the grass turning purple, I could see every blade grow, breathe, and swirl. I felt like I had microscopic vision, and was talking my friendís ear off about the detail of the visuals I was having. I took a tiny sip of beer and an intense surge made my entire body spasm hard. I knew that was a mistake because the visuals had taken a stronger turn now. If I stared at anything for more than a few seconds my eyes began to burn and nothing looked familiar until I blinked. This is when I began to get scared of the intensity. I stood up and headed for the door quickly, and as soon as I opened the door the true of effects of this drug kicked in instantly.
I looked in the mirror and my pupils were bigger than I had ever seen them in my life. The entire apartment looked as if someone had drawn it. I began to sweat profusely and immediately laid down on the couch. I started to panic because I did not know what drug I was on, and this was way more intense than any mescaline trip I ever had. I tried closing my eyes but the visuals were so much more intense than the open eye visuals. My friend watched in horror as I started to freak out. The drug had taken a dark turn in my head.
I began peaking hard. My friend said my pupils were getting larger and smaller every second, and I was feeling the same thing in my ears. The entire time I felt extremely uncomfortable, no euphoria or enlightenment. For a long time I thought I was dying, and the visuals were very dark, but my friend would say something like ďyouíre alright man,Ē and it helped lighten up the visuals. My body was extremely sensitive to yawns or popping my ears, both made me trip hard and I avoided them if possible. The more I wondered what I was on the more I freaked out, and I needed to know what I was tripping so hard on, so I contacted my good friend who was very into the tab business. I told her what I took and she was sad to say that it was 2C-B. This eased my mind because I couldnít help think I was on something ridiculous like crystal meth or just dying in general. At this point I was much more relaxed and tried to enjoy the hardcore hallucinations I was having.
Listening to music wasnít anything special. Sounds were very distorted and not pleasant as they are on MDMA or 2C-E. The beat of the music changed the visuals somewhat like a music visualizer but it still sounded as it does sober. I had a hard time enjoying the experience as much as I tried, the drug is not nearly as glorious as 2C-E. I feel 2C-B is the drug for a talented artist. I can still remember all visuals I had and would paint everything I saw that day. The trip wore off around 6 that evening and I didnít stop having hallucinations for days after.
Being an experienced LSD, Mescaline, and 2C-E user, this was still the most intense drug I have ever come across. Fortunately I was in a controlled environment while I freaked out for the majority of my trip. Overall I would not try this drug again, but I look back and laugh at the experience.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Tweets Classification
Home
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Kaggle
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This project is the result of an extensive group collaboration. The seven teams depended on one another by providing data and metadata, maintaing delivery deadlines and helping eachother out in the field. The following section provides short insight into the individial team's work.
The project management group had the responsibility to coordinate, evaluate, monitor and provide assistance to the other groups. This included the initial task of evaluating the groups' needs, time management to fulfill the course deadlines and the final report submission. The organization of communication platforms like Trello, Slack and Google Drive helped ensure deadlines were maintained and communication among the different groups ran smoothly
Sensor and platform group was responsible for all the tasks required for the collection of field data using UAV. The main tasks included the selection of appropriate sensors and their integration with the chosen platform, flight planning, UAV setup, sensor setup, establishment of control points, etc. Besides this there were some intermediate tasks such as 3D printing to mount the sensors to UAV gimbal, data management and data distribution.
LiDAR team's main goal was to develop a strategy to deal with the available equipment to generate highly accurate 3D models (DEM and DSM) for the project’s study area. The focus lay mostly on an approach to work with a low-cost LiDAR scanner (provided by Sweep) on the outdoor environment. Since the scanner was designed for indoor data capturing, its capabilities were pushed forward to generate a solution for integrating it in the landscape monitoring process. The final products are: merged and classified LAS point clouds and georeferenced DEM and DSM. The products were passed on to the ESRI cloud group for processing and storing.
The senseBox group takes its name from the SenseBox project developed by the Institute for Geoinformatics in the University of Münster. “The senseBox is a do-it-yourself kit for stationary and mobile sensor stations”. The SenseBox group used the senseBox to build different kind of sensors to monitor the environmental quality of the river Aa study area. The sensors built were: a stationary weather logger, a stationary water level logger, a mobile air quality logger mounted on a drone, and a mobile pH logger and water temperature logger.
Provision of meaningful spatial products and services is integral in process of monitoring the conservation and renaturation of the environment. This project group aimed to analyze cost efficient and high spatial resolution images acquired by using unmanned aerial systems to understand the process of renaturation of Aa river near Münster, Germany. Among the generated products were: DSM, Orthorectified multiespectral image, orthophoto, NDVI, cloud points, 3d-mesh and image classification. This set of products was shared with Esri and Web group. Click here for more details about this group's work.
The ESRI Cloud Group's main goal was to create web applications to visualize 2D and 3D data using the ESRI Commercial Platform’s products, namely ArcGIS Pro, ArcMap, Collector for ArcGIS and ArcGIS Online. Tasks were closely related to the data acquirement by other groups using a geodatabase, producing vector and raster data visualizations (both 2D and 3D), sharing the outputs in the Web Portal, sharing feature classes with field collaborators to collect and store data using the app Collector for ArcGIS, giving data access to the Webteam according to the WMS protocol, and designing and customizing the 2D and 3D ArcGIS web applications.
Contact:The purpose of the Web Portal group was to develop this web-platform in which the obtained data of the project and related information is presented to the public. Allowing the exploratory visualization, interaction with several products. There were no restrictions concerning the technologies used and the features implemented as long as the generated data was displayed and accessible in a user friendly, efficient way. The web technologies included in the portal development were mainly Javascript, HTML, CSS, AngularJS, Leaflet. The data displayed on the map are mostly WMS layers stored in the ESRI cloud, which was managed by the ESRI cloud group.


| Elements' Summary |
| Name |
specifies the exact name of the font.
|
| Height |
specifies the height of the font in the measure of the
destination.
|
| Width |
specifies the width of the font in the measure of the
destination.
|
| StyleName |
specifies the style name of the font.
|
| Family |
specifies the general style of the font.
|
| CharSet |
specifies the character set which is supported by the font.
|
| Pitch |
specifies the pitch of the font.
|
| CharacterWidth |
specifies the character width.
|
| Weight |
specifies the thickness of the line.
|
| Slant |
specifies the slant of the font.
|
| Underline |
specifies the kind of underlining.
|
| Strikeout |
specifies the kind of strikeout.
|
| Orientation |
specifies the rotation of the font.
|
| Kerning |
For requesting, it specifies if there is a kerning table available.
For selecting, it specifies if the kerning table is to be used.
|
| WordLineMode |
specifies if only words get underlined.
|
| Type |
specifies the technology of the font representation.
|
| Elements' Details |
Name
string Name;
- Description
- specifies the exact name of the font.
|
|
Height
short Height;
- Description
- specifies the height of the font in the measure of the
destination.
|
|
Width
short Width;
- Description
- specifies the width of the font in the measure of the
destination.
|
|
StyleName
string StyleName;
- Description
- specifies the style name of the font.
|
|
Family
short Family;
- Description
- specifies the general style of the font.
Use one value out of the constant group
FontFamily.
|
|
CharSet
short CharSet;
- Description
- specifies the character set which is supported by the font.
Use one value out of the constant group
CharSet.
|
|
Pitch
short Pitch;
- Description
- specifies the pitch of the font.
Use one value out of the constant group
FontPitch.
|
|
CharacterWidth
float CharacterWidth;
- Description
- specifies the character width.
Depending on the specified width, a font that supports this
width may be selected.
The value is expressed as a percentage.
|
|
Weight
float Weight;
- Description
- specifies the thickness of the line.
Depending on the specified weight, a font that supports this
thickness may be selected.
The value is expressed as a percentage.
|
|
Slant
FontSlant Slant;
- Description
- specifies the slant of the font.
|
|
Underline
short Underline;
- Description
- specifies the kind of underlining.
Use one value out of the constant group
FontUnderline.
|
|
Strikeout
short Strikeout;
- Description
- specifies the kind of strikeout.
Use one value out of the constant group
FontStrikeout.
|
|
Orientation
float Orientation;
- Description
- specifies the rotation of the font.
The unit of measure is degrees; 0 is the baseline.
|
|
Kerning
boolean Kerning;
- Description
- For requesting, it specifies if there is a kerning table available.
For selecting, it specifies if the kerning table is to be used.
|
|
WordLineMode
boolean WordLineMode;
- Description
- specifies if only words get underlined.
true means that only non-space characters get underlined,
false means that the spacing also gets underlined.
This property is only valid if the property
FontDescriptor::Underline
is not FontUnderline::NONE.
|
|
Type
short Type;
- Description
- specifies the technology of the font representation.
One or more values out of the constant group
FontType can be combined by
an arithmetical or-operation.
|
|
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


angular.mock.module
ng
angular.mock.module(fns);
| Param | Type | Details |
|---|---|---|
| fns | ...(stringFunctionObject | any number of modules which are represented as string aliases or as anonymous module initialization functions. The modules are used to configure the injector. The 'ng' and 'ngMock' modules are automatically loaded. If an object literal is passed they will be register as values in the module, the key being the module name and the value being what is returned. |


Beck - Sea Change
This album fills me with a type of melancholy that I can't really describe. Written after his break up with his longtime girlfriend, the album is full of songs of heartache and loneliness. But somehow he does put a positive, hopeful spin on things as the album progresses. I love it when personal happenings in artists lives inspire them to such a degree to make amazing music like this.
Beck is a great artist. He fully deserved his Grammy that he won for his next album, Morning Phase.
Listen to it here.


In this project, you'll define and train a DCGAN on a dataset of faces. Your goal is to get a generator network to generate new images of faces that look as realistic as possible!
The project will be broken down into a series of tasks from loading in data to defining and training adversarial networks.
You'll be using the CelebFaces Attributes Dataset (CelebA) to train your adversarial networks.
This dataset is more complex than the number datasets (like MNIST or SVHN) you've been working with, and so, you should prepare to define deeper networks and train them for a longer time to get good results. It is suggested that you utilize a GPU for training.
Since the project's main focus is on building the GANs, we've done some of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. Some sample data is show below.
If you are working locally, you can download this data by clicking here
This is a zip file that you'll need to extract in the home directory of this notebook for further loading and processing. After extracting the data, you should be left with a directory of data processed_celeba_small/
Run following cell if you're using Colab.
!wget https://raw.githubusercontent.com/VVKot/dlnd-face-generation/master/problem_unittests.py -qq
!wget https://s3.amazonaws.com/video.udacity-data.com/topher/2018/November/5be7eb6f_processed-celeba-small/processed-celeba-small.zip -qq
# can comment out after executing
!unzip -qq processed-celeba-small.zip
data_dir = 'processed_celeba_small/'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
import problem_unittests as tests
#import helper
%matplotlib inline
The CelebA dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations, you'll only need the images. Note that these are color images with 3 color channels (RGB)#RGB_Images) each.
Since the project's main focus is on building the GANs, we've done some of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. This pre-processed dataset is a smaller subset of the very large CelebA data.
There are a few other steps that you'll need to transform this data and create a DataLoader.
get_dataloader
function, such that it satisfies these requirements:¶image_size x image_size
in the x and y dimension.To create a dataset given a directory of images, it's recommended that you use PyTorch's ImageFolder wrapper, with a root directory processed_celeba_small/
and data transformation passed in.
# necessary imports
import torch
from torchvision import datasets
from torchvision import transforms
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import TensorDataset, DataLoader
from torch.autograd import Variable
import numpy as np
import torch.optim as optim
def get_dataloader(batch_size, image_size, data_dir='processed_celeba_small/'):
"""
Batch the neural network data using DataLoader
:param batch_size: The size of each batch; the number of images in a batch
:param img_size: The square size of the image data (x, y)
:param data_dir: Directory where image data is located
:return: DataLoader with batched data
"""
# TODO: Implement function and return a dataloader
transform = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor()
])
dataset = datasets.ImageFolder(data_dir, transform)
loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
return loader
celeba_train_loader
with appropriate hyperparameters.¶Call the above function and create a dataloader to view images.
batch_size
parameterimage_size
must be 32
. Resizing the data to a smaller size will make for faster training, while still creating convincing images of faces!#  You should seen square images of somewhat-centered faces.
Note: You'll need to convert the Tensor images into a NumPy type and transpose the dimensions to correctly display an image, suggested imshow
code is below, but it may not be perfect.
# helper display function
def imshow(img):
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
# obtain one batch of training images
dataiter = iter(celeba_train_loader)
images, _ = dataiter.next() # _ for no labels
# plot the images in the batch, along with the corresponding labels
fig = plt.figure(figsize=(20, 4))
plot_size=20
for idx in np.arange(plot_size):
ax = fig.add_subplot(2, plot_size/2, idx+1, xticks=[], yticks=[])
imshow(images[idx])
You need to do a bit of pre-processing; you know that the output of a tanh
activated generator will contain pixel values in a range from -1 to 1, and so, we need to rescale our training images to a range of -1 to 1. (Right now, they are in a range from 0-1.)
# TODO: Complete the scale function
def scale(x, feature_range=(-1, 1)):
''' Scale takes in an image x and returns that image, scaled
with a feature_range of pixel values from -1 to 1.
This function assumes that the input x is already scaled from 0-1.'''
# assume x is scaled to (0, 1)
# scale to feature_range and return scaled x
min_range, max_range = feature_range
return (max_range - min_range) * x + min_range
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
# check scaled range
# should be close to -1 to 1
img = images[0]
scaled_img = scale(img)
print('Min: ', scaled_img.min())
print('Max: ', scaled_img.max())
A GAN is comprised of two adversarial networks, a discriminator and a generator.
Your first task will be to define the discriminator. This is a convolutional classifier like you've built before, only without any maxpooling layers. To deal with this complex data, it's suggested you use a deep network with normalization. You are also allowed to create any helper functions that may be useful.
def create_conv_layer(inputs, outputs, kernel_size=4, stride=2, padding=1, do_batch_norm=True):
nn_layers = []
layer = nn.Conv2d(inputs, outputs, kernel_size, stride, padding, bias=False)
nn_layers.append(layer)
if do_batch_norm:
nn_layers.append(nn.BatchNorm2d(outputs))
return nn.Sequential(*nn_layers)
def create_deconv_layer(inputs, outputs, kernel_size=4, stride=2, padding=1, do_batch_norm=True):
nn_layers = []
layer = nn.ConvTranspose2d(inputs, outputs, kernel_size, stride, padding, bias=False)
nn_layers.append(layer)
if do_batch_norm:
nn_layers.append(nn.BatchNorm2d(outputs))
return nn.Sequential(*nn_layers)
class Discriminator(nn.Module):
def __init__(self, conv_dim):
"""
Initialize the Discriminator Module
:param conv_dim: The depth of the first convolutional layer
"""
super(Discriminator, self).__init__()
# complete init function
self.conv_dim = conv_dim
self.leaky_relu_slope = 0.2
self.conv1 = create_conv_layer(3, conv_dim, do_batch_norm=False)
self.conv2 = create_conv_layer(conv_dim, conv_dim*2)
self.conv3 = create_conv_layer(conv_dim*2, conv_dim*4)
self.conv4 = create_conv_layer(conv_dim*4, conv_dim*8)
self.convolutions = [
self.conv1,
self.conv2,
self.conv3,
self.conv4
]
self.fc = nn.Linear(conv_dim * 32, 1)
def forward(self, x):
"""
Forward propagation of the neural network
:param x: The input to the neural network
:return: Discriminator logits; the output of the neural network
"""
# define feedforward behavior
for i, conv in enumerate(self.convolutions):
x = F.leaky_relu(conv(x), self.leaky_relu_slope)
x = x.view(-1, self.conv_dim*32)
x = self.fc(x)
return x
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(Discriminator)
The generator should upsample an input and generate a new image of the same size as our training data 32x32x3
. This should be mostly transpose convolutional layers with normalization applied to the outputs.
z_size
32x32x3
class Generator(nn.Module):
def __init__(self, z_size, conv_dim):
"""
Initialize the Generator Module
:param z_size: The length of the input latent vector, z
:param conv_dim: The depth of the inputs to the *last* transpose convolutional layer
"""
super(Generator, self).__init__()
self.conv_dim = conv_dim
self.deconv1 = create_deconv_layer(conv_dim*8, conv_dim*4)
self.deconv2 = create_deconv_layer(conv_dim*4, conv_dim*2)
self.deconv3 = create_deconv_layer(conv_dim*2, conv_dim)
self.deconv4 = create_deconv_layer(conv_dim, 3, do_batch_norm=False)
self.deconvolutions = [
self.deconv1,
self.deconv2,
self.deconv3,
self.deconv4
]
self.fc = nn.Linear(z_size, conv_dim * 32)
def forward(self, x):
"""
Forward propagation of the neural network
:param x: The input to the neural network
:return: A 32x32x3 Tensor image as output
"""
# define feedforward behavior
x = self.fc(x)
batch_size = x.shape[0]
x = x.view(batch_size, self.conv_dim*8, 2, 2)
for i, deconv in enumerate(self.deconvolutions):
if i != len(self.deconvolutions) - 1:
x = F.relu(deconv(x))
else:
x = deconv(x)
x = torch.tanh(x)
return x
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(Generator)
To help your models converge, you should initialize the weights of the convolutional and linear layers in your model. From reading the original DCGAN paper, they say:
All weights were initialized from a zero-centered Normal distribution with standard deviation 0.02.
So, your next task will be to define a weight initialization function that does just this!
You can refer back to the lesson on weight initialization or even consult existing model code, such as that from the networks.py
file in CycleGAN Github repository to help you complete this function.
def weights_init_normal(m):
"""
Applies initial weights to certain layers in a model .
The weights are taken from a normal distribution
with mean = 0, std dev = 0.02.
:param m: A module or layer in a network
"""
# classname will be something like:
# `Conv`, `BatchNorm2d`, `Linear`, etc.
classname = m.__class__.__name__
# TODO: Apply initial weights to convolutional and linear layers
print(classname)
if 'Conv' in classname:
print('Found conv layer!')
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif 'Linear' in classname:
print('Found linear layer!')
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
Define your models' hyperparameters and instantiate the discriminator and generator from the classes defined above. Make sure you've passed in the correct input arguments.
 Here, we'll set this as a boolean variable train_on_gpu
. Later, you'll be responsible for making sure that
- Models,
- Model inputs, and
- Loss function arguments
Are moved to GPU, where appropriate.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import torch
# Check for a GPU
device = 'cuda' if torch.cuda.is_available() else 'cpu'
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('No GPU found. Please use a GPU to train your neural network.')
else:
print('Training on GPU!')
D = D.to(device)
G = G.to(device)
Now we need to calculate the losses for both types of adversarial networks.
- For the discriminator, the total loss is the sum of the losses for real and fake images,
d_loss = d_real_loss + d_fake_loss
.- Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.
The generator loss will look similar only with flipped labels. The generator's goal is to get the discriminator to think its generated images are real.
You may choose to use either cross entropy or a least squares error loss to complete the following real_loss
and fake_loss
functions.
def real_loss(D_out, smooth=False):
'''Calculates how close discriminator outputs are to being real.
param, D_out: discriminator logits
return: real loss'''
batch_size = D_out.size(0)
if smooth:
labels = torch.ones(batch_size)*0.9
else:
labels = torch.ones(batch_size)
labels = labels.to(device)
criterion = nn.BCEWithLogitsLoss()
loss = criterion(D_out.squeeze(), labels)
return loss
def fake_loss(D_out):
'''Calculates how close discriminator outputs are to being fake.
param, D_out: discriminator logits
return: fake loss'''
batch_size = D_out.size(0)
labels = torch.zeros(batch_size)
labels = labels.to(device)
criterion = nn.BCEWithLogitsLoss()
loss = criterion(D_out.squeeze(), labels)
return loss
lr = 0.0005
beta1 = 0.3
beta2 = 0.999 # default value
d_optimizer = optim.Adam(D.parameters(), lr, betas=(beta1, beta2))
g_optimizer = optim.Adam(G.parameters(), lr, betas=(beta1, beta2))
Training will involve alternating between training the discriminator and the generator. You'll use your functions real_loss
and fake_loss
to help you calculate the discriminator losses.
You've been given some code to print out some loss statistics and save some generated "fake" samples.
def train(D, G, n_epochs, print_every=50):
'''Trains adversarial networks for some number of epochs
param, D: the discriminator network
param, G: the generator network
param, n_epochs: number of epochs to train for
param, print_every: when to print and record the models' losses
return: D and G losses'''
# move models to GPU
if train_on_gpu:
D.cuda()
G.cuda()
# keep track of loss and generated, "fake" samples
samples = []
losses = []
# Get some fixed data for sampling. These are images that are held
# constant throughout training, and allow us to inspect the model's performance
sample_size=16
fixed_z = np.random.uniform(-1, 1, size=(sample_size, z_size))
fixed_z = torch.from_numpy(fixed_z).float()
# move z to GPU if available
if train_on_gpu:
fixed_z = fixed_z.cuda()
# epoch training loop
for epoch in range(n_epochs):
# batch training loop
for batch_i, (real_images, _) in enumerate(celeba_train_loader):
batch_size = real_images.size(0)
real_images = scale(real_images)
# ===============================================
# YOUR CODE HERE: TRAIN THE NETWORKS
# ===============================================
# ============================================
# TRAIN THE DISCRIMINATOR
# ============================================
d_optimizer.zero_grad()
# 1. Train with real images
# Compute the discriminator losses on real images
real_images = real_images.to(device)
D_real = D(real_images)
d_real_loss = real_loss(D_real)
# 2.
D_fake = D(fake_images)
g_loss = real_loss(D_fake, True) # use real loss to flip labels
# perform backprop
g_loss.backward()
g_optimizer.step()
# ===============================================
# END OF YOUR CODE
# ===============================================
# Print some loss stats
if batch_i % print_every == 0:
# append discriminator loss and generator loss
losses.append((d_loss.item(), g_loss.item()))
# print discriminator and generator loss
print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(
epoch+1, n_epochs, d_loss.item(), g_loss.item()))
## AFTER EACH EPOCH##
# this code assumes your generator is named G, feel free to change the name
# generate and save sample, fake images
G.eval() # for generating samples
samples_z = G(fixed_z)
samples.append(samples_z)
G.train() # back to training mode
# Save training generator samples
with open('train_samples.pkl', 'wb') as f:
pkl.dump(samples, f)
# finally return losses
return losses
Set your number of training epochs and train your GAN!
# set number of epochs
n_epochs = 10
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# call training function
losses = train(D, G, n_epochs=n_epochs)
Plot the training losses for the generator and discriminator, recorded after each epoch.
fig, ax = plt.subplots()
losses = np.array(losses)
plt.plot(losses.T[0], label='Discriminator', alpha=0.5)
plt.plot(losses.T[1], label='Generator', alpha=0.5)
plt.title("Training Losses")
plt.legend()
View samples of images from the generator, and answer a question about the strengths and weaknesses of your trained models.
# helper function for viewing a list of passed in sample images
def view_samples(epoch, samples):
fig, axes = plt.subplots(figsize=(16,4), nrows=2, ncols=8, sharey=True, sharex=True)
for ax, img in zip(axes.flatten(), samples[epoch]):
img = img.detach().cpu().numpy()
img = np.transpose(img, (1, 2, 0))
img = ((img + 1)*255 / (2)).astype(np.uint8)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
im = ax.imshow(img.reshape((32,32,3)))
# Load samples from generator, taken while training
with open('train_samples.pkl', 'rb') as f:
samples = pkl.load(f)
_ = view_samples(-1, samples)
When you answer this question, consider the following factors:
Answer: To improve the performance we need to diversify the dataset. We need to add more non-white celebrities. Also, it will probably help if we normalize background(say, make it white for all faces) and train for more epochs.
For the tasks of the face generation, we might consider a bigger model with a bigger output size. Some images look really pixelated, and model can't generate eyes in high detail in some cases.
I used Adam optimizer, which is standard and considered one of the best for the GANs. I also applied the trick with decreasing batch size so that period of when most of the learning happens, e.g. when both losses are comparably high, is longer.
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "problem_unittests.py" files in your submission.


CAVERYNTH
DOWNLOAD & PLAY ONLINE: ITCH.IO
Short 2D skill-based platformer. At the time of making this game I was obsessed with the game Celeste. I wanted to make a platformer that, like Celeste, featured levels challenging both the player's mechanical andproblem-solving skills.
The game features 12 levels all built over the previous one. Starting from a simple walk, in each level a new obstacle is added to the course.
This creates the feeling of repeating the same level, while progressively adding challenge, either via skill checks in which the player needs to be precise, or "puzzle" obstacles, which require thinking to find the path to the end of the level.
This game was an excellent chance for exploring 2D level design, and playing with the new Unity 2D lighting system and the particle system.
Caverynth acquired some popularity in GameJolt.com, occupying one of the top positions in the Platformer - Hot category. It was also one of the games selected to be featured in Cuarentena Perpetua, an online showcase of Spanish videogames ran by IndieMad.


Introduction¶
This is the documentation for CIVET [1]—Contentious Incident Variable Entry Template—customizable data entry system. CIVET was developed by the NSF-sponsored project titled “A Method for Leveraging Public Information Sources for Social Science Research” which is creating tools to improve the efficiency of data generation in the social sciences. The project had an initial focus on coding event data in the domain of contentious politics, but we expect that these tools will be relevant in a number of data-generation domains.
The core objective of CIVET is to provide a reasonably simple—yes, simple—set of commands that will allow a user to set up a web-based coding environment without the need to master the likes of HTML, CSS and Javascript. As currently implemented, the system is a rather ugly prototype; it will also be evolving as we add additional elements. Nonetheless, the system should now be useable for coding.
CIVET is implemented in the widely-used and well documented Python-based Django system [2] which is widely available on various cloud platforms: a rather extended list of “Django-friendly” hosting services can be found at
The complete CIVET code is licensed as open source under the MIT license and provided on GitHub at https://github.com/civet-software .
CIVET currently has two modes:
- Coding form template:
- This is a template-based for setting up a web-based coding form which implements several of the common HTML data entry formats and exports the resulting data as a tab-delimited text file. This is fully functional and should be useable for small projects.
- Text annotation/extraction:
- This uses CIVET “workspaces” which combine related texts, their metadata, and the coding form. Workspaces allow for manual and automated text annotation, then the ability to extract various types of information into the fields of a coding form.
Status of the Program: 31 August 2015¶
The NSF funding for the project ended on this date. At this point, all of the documented features of the program should be working except as noted above. However, we are just beginning the process of operational field testing and it is likely—which is to say, inevitable— that some additional bugs will be found, hence this is still considered “Beta-0.9” rather than “1.0.” We currently have two field tests underway, and are hoping to get some additional ones going, and will be posting bug fixes to GitHub promptly as these appear and are resolved.
At present, we have not developed any software for generating the workspace files, though we expect to have at least a couple programs available in the next few months. The problem here is that identifying the various metadata and components in a set of texts is highly specific to the text source, and to date we’ve not found general solutions for this. As noted in the final chapter of this document, over the next year or so we will be seeking additional funding for tool development for these “front-end” tasks, though given the very slow pace of the public funding cycle, this is unlikely to occur until late in 2016 at the earliest. In the meantime, we will be leveraging tools developed in existing projects and, of course, would very much appreciate the contribution of any ancillary tools that the user community develops, particularly for common sources such as Lexis-Nexis, Factiva, ProQuest, LDC Gigaword, and various news feeds and social media.
Status of the Program: August 2016¶
Over the past year, CIVET has been used in two projects, though both with my assistance in generating the YAML files and with some additional customization, much of which has been incorporated into the general system.
To my knowledge, however—and if you know of some project using this, please let me know—it has not been used for the originally intended purpose of providing a platform which would allow someone with relatively limited programming knowledge to create a web-based form. I suspect this is due to one or more of the following factors:
- The process of getting Django installed, while thoroughly documented, apparently can require some experimentation and tweaking. That said, one of the projects decided to install Django on laptops used by the coders rather than through a server: this allowed the coders to work anywhere.
- When using workspaces, which allow access to the most sophisticated parts of the system, the texts must be converted to the YAML format, a task for which I’ve yet to see a general solution and almost certainly requires Python, perl or Java programming skills
- Realistically, there are only a small number of new projects starting in any given year which are sufficiently large that existing tools such as spreadsheets or Google Forms are inadequate. And many of those, of course, will have resources to directly develop customized pages rather than working within the constraints of CIVET
So, this is essentially just an open-source version of a codebase that I can customize to generate coding forms. Which I’m realizing is probably pretty much what about 95% of open-source projects are, though that still gives the client a whole lot more knowledge and power than they have with a proprietary system, even if they never change a line of code. Whatever.
Some random observations from those two deployments:
- Additional customization of the code has gone quite smoothly, even allowing for the inevitable decay of my comprehension of the system. Granted, even when I’ve forgotten the details of the code I’m still working with my programming idioms, but it does seem like as a base, this is quite solid. The same can be said for the YAML workspace format.
- Neither of the two installations made any use of the manual
annotation, and in the second, the
NEVER_ANNOTATE
option was added to bypass this entirely: annotation is either handled automatically using vocabulary files, or directly putting the HTML into the YAML files. - More generally, though completely expected, the deployment have generated a number of interesting ideas for new features that did not emerge in the abstract design phase. I’ve also left in some custom code—commented-out or otherwise deactivated— that could be used for examples of further possible extensions.
- On two occasions over the past year there were changes to Django that required minor changes—one or two lines of code— to CIVET in order to keep it running. Unsurprisingly, we have also found that Django is more likely to be fully compatible with up-to-date hardware and operating systems: in particular, one project ran into some issues running it on some old copies of Windows. Once again, this is less of a turn-key system than I’d hoped.
Documentation¶
Documentation is maintained using the Sphinx system, which provides both an
on-line version and a reasonably-well-formatted PDF version. There
are links to both of these compiled versions on the home page; the .rst
source texts for the documentation are in the
directory djcivet_site/docs. That directory contains a Sphinx Makefile so revisions can be compiled using the standard
command make html latexpdf
.
The on-line documentation currently resides at the site http://civet.parusanalytics.com/civetdocs/; [3] a PDF version can
be downloaded by clicking the Download PDF
link on the home page. [4]
Footnotes
| [1] | http://en.wikipedia.org/wiki/CIVET |
| [2] | An earlier prototype was implemented in the Flask framework: see
Appendix 4 |
| [3] | This is a bug, not a feature: there is presumably a way of accessing these at djcivet_site/docs/_build/html/, or somewhere else within the djcivet_site/ directory in a manner that has them correctly rendered, but I haven’t figured it out yet. Fixes are welcome. |
| [4] | This is handled in views.download_pdfdocs() : it first looks for the PDF version of the documentation in
docs/_build/latex/civetdoc.pdf, which is where the most current version is likely to be located when the
documentation was produced using the make latexpdf command in the docs/ directory. If that isn’t present,
it checks the /static/ directory: this can be used in deployments in order to avoid uploading docs/. If neither
is available, it gets the copy posted at http://civet.parusanalytics.com/, which may or may not correspond exactly to the
version being used depending on what modifications have been made. The command make movepdf will copy civetdoc.pdf from
_build/latex to /static/ |


SSL Examples
You need to run the following command in "client" folder to generate a self-signed SSL certificate.(You do not need to enter any information in the wizard for the command below, just enter through)
$ openssl req -new -x509 -keyout server.pem -out server.pem -days 365 -nodes
Then run the server.py file in "client" folder to run static file server with SSL certificate
python3 server.py
You also need to run the argos with webviz with ssl, edit your .argos file like,
Then you can access the server IP in browser https://localhost:8000/examples Chrome might ask you to "go back to safety" as the certificate is "self-signed", just proceed anyways.


Welcome the simple information center of Covid 19
WHO supports the Smithsonian Science Education Center’s new COVID-19 guide for youth.
How WHO is supporting refugees and migrants during the COVID-19 pandemic.
Online seminar targets primary health care workers to manage COVID-19 in Indonesia.


Basic IDBKeyRange example
These are a few of my favourite things
Don't filter values at all
Return
only
one value
Key value for single value selection
Return a
range
of values
Lower limit for range
Upper limit for range
Return values with a
lower
bound
Lower bound limit for results
Return values with an
upper
bound
Upper bound limit for results
Filter by name
Filter by rating
Run query


{% load static %}
Speed download
Navbar
Home
(current)
Features
Pricing
Disabled
Click to see your speedtest
click and wait a moment to see your speed connection
Click
Speed download
Speed upload
Ping
City
Organisation
{{download}}
{{upload}}
{{ping}}
{{ request.ipinfo.region }}
{{ request.ipinfo.org }}


Skills
- Critical Thinking
- Using logic and reasoning to identify the strengths and weaknesses of alternative solutions, conclusions or approaches to problems.
- Programming
- Writing computer programs for various purposes.
- Complex Problem Solving
- Identifying complex problems and reviewing related information to develop and evaluate options and implement solutions.
- Active Learning
- Understanding the implications of new information for both current and future problem-solving and decision-making.
- Systems Analysis
- Determining how a system should work and how changes in conditions, operations, and the environment will affect outcomes.
HTML
95%
CSS
85%
JavaScript
30%


We're sorry but mall doesn't work properly without JavaScript enabled. Please enable it to continue.


It’s time for the financial industry to begin putting the client’s interests above their own.
Done are the days of defining a client's worth based upon the amount of income they earn, the businesses they own, or worse yet, the amount of money they have to invest.
Truvize is defined by one fundamental principle, Transparency.
Jonathan is a founding partner of Truvize. Nothing is more important to him than his faith in Jesus Christ, his family, and his friends. He is a firm believer in the adage, “work to live, don’t live to work.”
He is happily married to his beautiful wife Fallon. They both love to travel, eat good food and attend various sporting events.
Over the past few years, Jonathan has also become an avid golfer and continues to try to convert as many of his friends as possible over to the sport.
Prior to founding Truvize, Jonathan spent the first seven years of his career working for traditional financial services companies. Over the course of that time, it became clear to him there is a desperate need in the industry for unbiased, fiduciary advice. With Truvize, his ultimate goal is to change the way people think about financial planning. To be viewed as a true, unbiased advisor, with only the success of his clients as the basis for every recommended strategy. The knowledge and experience he has accumulated through working on hundreds of individual financial plans, each with their own intricacies, will provide his clients the expertise necessary to accomplish their financial goals.
Drew is a founding partner of Truvize. He believes that how a person chooses to define themselves effects every decision that they make. His desire is to be defined by His relationship with Jesus Christ.
As a family man Drew spends the majority of his time with his beautiful wife Sammie and his two children Taylor and Jackson. They are his greatest investment and he hopes to instill in each of them the same love that Christ has shown him.
Drew has been a financial planner since 2009 and has spent his career servicing clients with a wide range of goals. He has always believed that a financial plan is the key to a client realizing success. In the midst of his career he began to identify a major area of concern within the traditional financial services model, conflict of interest.
And so, he founded Truvize with his partner and co-visionary Jonathan Lopez. Truvize is what clients truly need. Transparent, holistic, financial advice without conflicts of interest.
We work with people from all walks of life.
We believe the quality of service you receive should not be based on your income, your net worth, or your ability to purchase a financial product.
 This can be our focus, because our compensation model is not based on the products you purchase or the investments we manage.
We believe that the best approach to providing our clients sound financial advice is through a comprehensive financial plan. We understand that every client has different goals and these goals will have varying levels of complexity.
Our pricing model directly correlates to the amount of time it will take us to execute and maintain your financial plan.
A financial plan must have a strong anchor to keep clients from drifting off course.
Anchor is about accountability, it is designed to focus on short-term goals such as building an emergency fund, budgeting, and debt management.
This is a temporary stop meant to build the necessary foundation for our Endeavor and Cultivate platforms.
A flexible business planning solution designed to tailor 401(k) and other retirement plan services to your needs.
Email<EMAIL_ADDRESS>for more details.
Every journey requires the execution of a detailed plan to achieve success. Endeavor begins with a rigorous analysis of your current financial position followed by ongoing, unbiased advice.
While Anchor focuses on the foundation of a sound financial plan, Endeavor branches out into every aspect of your financial livelihood.
For example, review of the benefits offered by your employer, professional 401(k) management, and access to our state of the art financial planning technology through your Personal Client Portal. Endeavor is about guiding you towards your dreams in the most efficient and transparent manner possible.
Our Anchor platform without the ongoing service.
While all the traditional aspects of a financial plan are still addressed Cultivate is designed for clients that seek access to our financial expertise and our network of professionals to achieve goals outside the nine dots.
This is the pinnacle of personalized financial service. Whether you are looking to purchase an investment property, start a business, or need guidance concerning estate taxes a partner will be your financial “quarterback”, coordinating meetings, facilitating relationships, and anything else deemed pertinent. If you can dream it, Cultivate will help you grow it.
Unsure about the advice you’ve been receiving? Put it to the test…
***Truvize has built a robust financial planning model that includes initial and ongoing services. Not all of these services are mentioned above. Please schedule a free 30 minute consultation for more details.
Truvize believes that insurance is an important part of a sound financial plan, but too often, it is oversold. Insurance is a solution, not the only solution.
We have seen countless examples of insurance products being sold for the benefit of the agent and not in the best interest of the client.
We have put into place several safeguards to ensure that our insurance recommendations are in the best interest of our clients.
Address: 500 N. Oregon Second Floor El Paso, Texas 79907
Phone Numbers:
Office:<PHONE_NUMBER> Fax:<PHONE_NUMBER>
Contact Email:
<EMAIL_ADDRESS>Our pricing is completely transparent. There are no hidden fees. Our goal is to establish a trustworthy relationship with you, our clients.
We put your life, and your goals as our top priority, leveraging the best technology the industry has to offer to save you time while planning for your future.
Absolutely. Throughout our time in the industry, we have had the privilege of establishing relationships with some of the best professionals in their specific industries.
Whether you need help with your taxes, a will, or looking to buy a home or car, we can help direct you to people who have proven to us they are not only competent but also trustworthy.
We never receive a kickback or referral fee from any of these relationships, we simply want our clients to be the most successful they can be.
Most clients do not know the difference between a fiduciary standard and a sustainability standard. Truvize operates under a fiduciary standard.
A fiduciary has a legal and ethical obligation to put the other party's interests above their own. For a financial planner/advisor, that means helping a client make decision in his/her best interest, even if it means reduced compensation - or no compensation - for the advisor.
Under a suitability standard the advisor is only required to recommend products and investments that are "suitable" and may not be in the best interest of the client. Every client should ask their advisor if they a operating as their fiduciary.
We believe history has shown that a globally diversified, low-cost portfolio based on a client’s risk tolerance gives you the best chance of success.
Active management and individual stock picking bring higher expenses, more transaction costs, and higher taxes, all of which can hurt a portfolio significantly in the long run.
Using some of the most robust risk planning software available, we work with our clients to decide on an allocation that fits their desire, willingness and need to take the risk and build a portfolio accordingly.
Once a portfolio is set we monitor it daily, rebalancing when needed, to ensure the client's risk score matches their allocation.
$0. While we certainly work with clients that would be considered "high net worth", we founded Truvize with the principal of being able to provide the highest quality financial advice, regardless of assets.
Requiring a specific amount of assets to receive financial advice seems counterintuitive to us.
Truvize is here to help whether you are just getting started or already have a significant investment portfolio.
Click on the link below to schedule a meeting and you will receive an email containing our financial documents checklist.
Contact Us


Sussex Staff in Mind is a virtual wellbeing hub that is accessible to all health and local authority social care staff in the Sussex area (and NHS staff in Hampshire CAMHS). Mindfulness courses were offered as part of the Staff in Mind programme. After the current eight-week mindfulness course that starts Wednesday, 24 May there are no more Staff in Mind courses on offer right now.
You can access the hub if you are experiencing emotional or psychological difficulties. You do not have to inform your manager or GP when contacting the hub and it is a confidential service. The hub offers a space for you to talk about what is going on and to think through possible options if needed (including support to access further mental health services if required). You can book an appointment through the Sussex Staff in Mind website. The first part of the process involves completing a self-assessment about how you are feeling and then there is the option of booking a confidential follow up appointment with a member of the team. Sussex Staff in Mind has recently commissioned a number of 8-week mindfulness courses that can be accessed by staff who use their service.
What exactly is mindfulness and how can it help?
Mindfulness is about being present with our experience with an attitude of kindness and curiosity. This enables us to relate to our experience, rather than run from it, and that gives us some freedom not to be blown around by strong emotions or sensations, or by compelling thoughts. In that space that we then create, we can see more clearly what we are experiencing and we can decide how to respond in ways that are most beneficial for ourselves and those around us.
There is a lot of data to suggest there are high levels of healthcare and social care staff reporting stress and feeling unwell. There is evidence to suggest that healthcare staff attending mindfulness courses are significantly less stressed at the end of the course. In addition, the effects on stress are maintained at follow-up time periods. It is hoped that a number of staff members can benefit from these courses and that it may help reduce stress levels. Anyone who works in the Integrated Care Systems in Sussex can apply for a place on one of these courses. They will be available on a first come first served basis. It is possible to self-refer without having to be seen by Sussex Staff in Mind service first.
What are mindfulness courses?
Mindfulness is about being present with our experience with an attitude of kindness and curiosity. The sessions focus mainly on teaching skills that develop awareness of body sensations, feelings and thoughts, which allow you to pause and step back from a situation before you react. You will begin tolearn to live in the present and avoid ruminating on the past or worrying about the future. In each session there will be a mixture of ‘skills practice’, ‘conversation’ about this practice and exercises from cognitive therapy. This is not a therapy group so you will not be expected to share details of your personal life. However, you will be in a group of up to 15 colleagues, some of whom you may know. At the heart of each group will be a guided mindfulness practice and an opportunity to talk about it. Everything is ‘invitational’ and you will not be required to speak in the group if you would prefer not to.
Sussex Health and Care Partnership has commissioned 8-week mindfulness courses for the following organisations:
- Brighton & Hove CCG
- Brighton & Hove City Council
- University Hospitals Sussex (East)
- East Sussex County Council
- East Sussex Healthcare NHS Trust
- NHS East Sussex CCG, which has now combined:
- Eastbourne, Hailsham and Seaford CCG
- Hastings and Rother CCG
- High Weald Lewes Havens CCG
- NHS West Sussex CCG, which has now combined:
- Coastal West Sussex CCG
- Crawley CCG
- Horsham and Mid Sussex CCG
- Queen Victoria Hospital
- South East Coast Ambulance NHS Foundation Trust
- Sussex Community NHS Foundation Trust
- West Sussex County Council
- University Hospitals Sussex (West)
Sussex Staff in Mind also covers the GPs and their practice staff in Sussex.
The next Mindfulness Based Cognitive Therapy course
The next course is fully booked and there are no more courses planned for the moment.
If you are employed by Sussex Partnership, please apply for the courses for SPFT staff at:
https://sussexmindfulnesscentre.nhs.uk/practice/groups-for-staff/


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AttrOrVariable Class
Definition at line
12
of
Rule.hpp
Constructors
AttrOrVariable
(
const
std::u32string& typeName_,
const
std::u32string& name_)
Member Functions
const
std::u32string&
Name
()
const
const
std::u32string&
TypeName
()
const
Member Variables
std::u32string
name
std::u32string
typeName
Constructor Details
AttrOrVariable Constructor
cmajor::parsing::AttrOrVariable::AttrOrVariable(
const
std::u32string& typeName_,
const
std::u32string& name_)
Definition at line
24
of
Rule.cpp
:
24
:
typeName
(
typeName_
)
,
name
(
name_
)
25
{
26
}
Declaration at line
15
of
Rule.hpp
Member Function Details
Name Member Function
const
std::u32string& cmajor::parsing::AttrOrVariable::Name()
const
Definition at line
17
of
Rule.hpp
:
17
{
return
name
;
}
Called by:
cmajor::parsing::Rule::ExpandCode
TypeName Member Function
const
std::u32string& cmajor::parsing::AttrOrVariable::TypeName()
const
Definition at line
16
of
Rule.hpp
:
16
{
return
typeName
;
}
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 Hero image block --}} {{!--Callout block --}} Did You Forget Your Password? Don't stress Hi {{firstName}}, We heard that you forgot your password. To reset it, click this link: Reset Password If someone other than you sent this request, simply ignore this email.
Hi {{firstName}},
We heard that you forgot your password. To reset it, click this link:
If someone other than you sent this request, simply ignore this email.


{% extends 'base.html' %} {% block head_title %}Create a table by file upload{% endblock %} {% block body_title %}Create a table by file upload{% endblock %} {% block main %}


Marsden has studied improv at DSI (now The PIT) in North Carolina, Second City Chicago, iO, and with the Neo-Futurists. She is currently completing Second City’s Conservatory program. Marsden won two competitions at Second City’s Training Center with her troupe, Improv Murder, and subsequently completed two successful s how-runs in the De Maat Studio Theatre; Improv Murder and New Year Who Dis? Marsden’s improv troupe, Thicc & Emotional, has performed self-titled runs at The Playground Theatre and in Second City’s Donny’s Skybox Theatre. Marsden has also performed as a guest improviser with some of Chicago’s best troupes, including Matt Damon Improv, Girl Gang, and Gay Co.
For details about all of her upcoming shows, check out the calendar on the homepage!


A E F G M P
A
- addFlight(AbstractFlight) - Method in class edu.pdx.cs410J.shraddha.Airline
-
- Airline - Class in edu.pdx.cs410J.shraddha
-
The class for the airline Project to accept Airline extended from the Abstract class AbstractAirline
- Airline() - Constructor for class edu.pdx.cs410J.shraddha.Airline
-
E
- edu.pdx.cs410J.shraddha - package edu.pdx.cs410J.shraddha
-
Contains the application classes for the CS410J Airline project.
F
- Flight - Class in edu.pdx.cs410J.shraddha
-
The class for the airline Project to accept Flight details extended from the Abstract class AbstractFlight
- Flight() - Constructor for class edu.pdx.cs410J.shraddha.Flight
-
Constructor as the setter function for setting the elements as null for all the elements
G
- getArrivalString() - Method in class edu.pdx.cs410J.shraddha.Flight
-
- getDepartureString() - Method in class edu.pdx.cs410J.shraddha.Flight
-
- getDestination() - Method in class edu.pdx.cs410J.shraddha.Flight
-
- getFlights() - Method in class edu.pdx.cs410J.shraddha.Airline
-
- getName() - Method in class edu.pdx.cs410J.shraddha.Airline
-
- getNumber() - Method in class edu.pdx.cs410J.shraddha.Flight
-
- getSource() - Method in class edu.pdx.cs410J.shraddha.Flight
-
M
- main(String[]) - Static method in class edu.pdx.cs410J.shraddha.Project1
-
P
- Project1 - Class in edu.pdx.cs410J.shraddha
-
The main class for the CS410J airline Project
- Project1() - Constructor for class edu.pdx.cs410J.shraddha.Project1
-
A E F G M P
Copyright © 2000–2017 Portland State University. All rights reserved.


|
UMI3D Unity SDK
2.5.b.221125
A UMI3D sdk implementation using Unity
|
Manager for client user tracking related events More...
Classes | |
| class | AvatarEvent |
| class | BoardedVehicleEvent |
| class | BodyPoseEvent |
| class | EmoteEvent |
| class | EmotesConfigEvent |
| class | HandPoseEvent |
Public Member Functions | |
| virtual bool | RegisterEmbd (ulong id, UserAvatar u) |
| Register the UserAvatar instance of a user in the concerned dictionary More... | |
| virtual bool | UnregisterEmbd (ulong id) |
| Unregister the UserAvatar instance of a user in the concerned dictionary More... | |
| virtual bool | TryGetValue (ulong id, out UserAvatar embd) |
| Try to get the UserAvatar instance of a user from the concerned dictionary More... | |
| void | SetFPSTarget (int newFPSTarget) |
| Set the number of tracked frame per second that are sent to the server. More... | |
| void | SetStreamedBones (List< uint > bonesToStream) |
| Set the list of streamed bones. More... | |
| void | SetCameraPropertiesSending (bool activeSending) |
| Setter for sendCameraProperties. More... | |
| void | SetTrackingSending (bool activeSending) |
| Setter for sendTracking. More... | |
| void | EmbarkVehicle (BoardedVehicleDto vehicleDto) |
| Make a user board in in a vehicle that supports boarded users. More... | |
Public Attributes | |
| Transform | skeletonContainer |
| Transform of the gameobject containing the user's skeleton. More... | |
| Transform | viewpoint |
| Transform associated with the viewpoint of the user. More... | |
| uint | viewpointBonetype |
| BoneType associated with the viewpoint of the user. More... | |
| Dictionary< ulong, UserAvatar > | embodimentDict = new Dictionary<ulong, UserAvatar>() |
| Collection of instanciated UserAvatar by the user's id. More... | |
| UnityEvent | skeletonParsedEvent |
| This event is raised after each analysis of the skeleton. More... | |
| UnityEvent | sendingCameraProperties |
| This event has to be raised to send a CameraPropertiesDto. By default, it is raised at the beginning of Play Mode. More... | |
| UnityEvent | startingSendingTracking |
| This event has to be raised to start sending tracking data. The sending will stop if the Boolean "sendTracking" is false. By default, it is raised at the beginning of Play Mode. More... | |
| bool | alwaysSendTrackingFrame = false |
| If true, always send tracking frames (according to "targetTrackingFPS"), otherwise, frames will be sent only if user has moved More... | |
| ulong | avatarSceneId = 0 |
| HandPoseEvent | handPoseEvent = new HandPoseEvent() |
| BodyPoseEvent | bodyPoseEvent = new BodyPoseEvent() |
| BoardedVehicleEvent | boardedVehicleEvent = new BoardedVehicleEvent() |
| EmotesConfigEvent | EmotesLoadedEvent = new EmotesConfigEvent() |
| Triggered when an EmoteConfig file have been loaded More... | |
| EmoteEvent | EmoteChangedEvent = new EmoteEvent() |
| Trigered when an emote changed on availability More... | |
| UnityEvent | EmotePlayedSelfEvent = new UnityEvent() |
| Trigered when an emote is played by the user More... | |
| UnityEvent | EmoteEndedSelfEvent = new UnityEvent() |
| Trigered when an emote is finished to be played by the user More... | |
| AvatarEvent | avatarEvent = new AvatarEvent() |
Protected Member Functions | |
| override void | Awake () |
| virtual void | Start () |
| virtual IEnumerator | DispatchTracking () |
| Dispatch User Tracking data through Tracking Channel More... | |
| virtual IEnumerator | DispatchCamera () |
| Dispatch Camera data through Tracking Channel More... | |
| void | BonesIterator () |
| Iterate through the bones of the browser's skeleton to create BoneDto More... | |
| bool | HasPlayerMoved (Vector3 position, Quaternion rotation, List< BoneDto > bones) |
| Defines if user has moved since last time. More... | |
Protected Attributes | |
| bool | sendTracking = true |
| If true the avatar tracking is sent. More... | |
| UMI3DEmotesConfigDto | emoteConfig |
| Emote configuration on the server, with al available emotes for the user. More... | |
| UserTrackingFrameDto | LastFrameDto = new UserTrackingFrameDto() |
| UserCameraPropertiesDto | CameraPropertiesDto = null |
| bool | sendCameraProperties = false |
| Should the client send UserCameraPropertiesDto? More... | |
Properties | |
| bool | trackingReception [get, protected set] |
| True when the user's tracking has started. More... | |
| bool | SendTracking [get] |
| If true the avatar tracking is sent. More... | |
| float | targetTrackingFPS = 15 [get, protected set] |
| Frequency indicating the number tracked frames send to the server per seconds. More... | |
| bool | IsEmotePlaying = false [get, protected set] |
| True when an emote is currently playing More... | |
| bool | IgnoreBones = false [get, protected set] |
| If true, disable the sending of bones rotations in frame dtos. More... | |
Manager for client user tracking related events
|
protected |
|
protected |
Iterate through the bones of the browser's skeleton to create BoneDto
| forceNotNullDto | If true, LastFrameDto won't be null. |
|
protectedvirtual |
Dispatch Camera data through Tracking Channel
Reimplemented in umi3d.cdk.collaboration.UMI3DCollaborationClientUserTracking.
|
protectedvirtual |
Dispatch User Tracking data through Tracking Channel
Reimplemented in umi3d.cdk.collaboration.UMI3DCollaborationClientUserTracking.
| void umi3d.cdk.userCapture.UMI3DClientUserTracking.EmbarkVehicle | ( | BoardedVehicleDto | vehicleDto | ) |
Make a user board in in a vehicle that supports boarded users.
| vehicleDto |
|
protected |
Defines if user has moved since last time.
| position | User position |
| rotation | User rotation |
| bones | List of all bones |
|
virtual |
Register the UserAvatar instance of a user in the concerned dictionary
| id | the id of the user |
| u | the UserAvatar instance to register |
| void umi3d.cdk.userCapture.UMI3DClientUserTracking.SetCameraPropertiesSending | ( | bool | activeSending | ) |
Setter for sendCameraProperties.
| activeSending |
| void umi3d.cdk.userCapture.UMI3DClientUserTracking.SetFPSTarget | ( | int | newFPSTarget | ) |
Set the number of tracked frame per second that are sent to the server.
| newFPSTarget |
| void umi3d.cdk.userCapture.UMI3DClientUserTracking.SetStreamedBones | ( | List< uint > | bonesToStream | ) |
Set the list of streamed bones.
| bonesToStream |
| void umi3d.cdk.userCapture.UMI3DClientUserTracking.SetTrackingSending | ( | bool | activeSending | ) |
Setter for sendTracking.
| activeSending |
|
protectedvirtual |
Reimplemented in umi3d.cdk.collaboration.UMI3DCollaborationClientUserTracking.
|
virtual |
Try to get the UserAvatar instance of a user from the concerned dictionary
| id | the id of the user |
| embd | the UserAvatar instance if found |
|
virtual |
Unregister the UserAvatar instance of a user in the concerned dictionary
| id | the id of the user |
| bool umi3d.cdk.userCapture.UMI3DClientUserTracking.alwaysSendTrackingFrame = false |
If true, always send tracking frames (according to "targetTrackingFPS"), otherwise, frames will be sent only if user has moved
| AvatarEvent umi3d.cdk.userCapture.UMI3DClientUserTracking.avatarEvent = new AvatarEvent() |
| ulong umi3d.cdk.userCapture.UMI3DClientUserTracking.avatarSceneId = 0 |
| BoardedVehicleEvent umi3d.cdk.userCapture.UMI3DClientUserTracking.boardedVehicleEvent = new BoardedVehicleEvent() |
| BodyPoseEvent umi3d.cdk.userCapture.UMI3DClientUserTracking.bodyPoseEvent = new BodyPoseEvent() |
|
protected |
| Dictionary<ulong, UserAvatar> umi3d.cdk.userCapture.UMI3DClientUserTracking.embodimentDict = new Dictionary<ulong, UserAvatar>() |
Collection of instanciated UserAvatar by the user's id.
This also includes avatars form other users in the environment.
| EmoteEvent umi3d.cdk.userCapture.UMI3DClientUserTracking.EmoteChangedEvent = new EmoteEvent() |
Trigered when an emote changed on availability
|
protected |
Emote configuration on the server, with al available emotes for the user.
| UnityEvent umi3d.cdk.userCapture.UMI3DClientUserTracking.EmoteEndedSelfEvent = new UnityEvent() |
Trigered when an emote is finished to be played by the user
| UnityEvent umi3d.cdk.userCapture.UMI3DClientUserTracking.EmotePlayedSelfEvent = new UnityEvent() |
Trigered when an emote is played by the user
| EmotesConfigEvent umi3d.cdk.userCapture.UMI3DClientUserTracking.EmotesLoadedEvent = new EmotesConfigEvent() |
Triggered when an EmoteConfig file have been loaded
| HandPoseEvent umi3d.cdk.userCapture.UMI3DClientUserTracking.handPoseEvent = new HandPoseEvent() |
|
protected |
|
protected |
Should the client send UserCameraPropertiesDto?
| UnityEvent umi3d.cdk.userCapture.UMI3DClientUserTracking.sendingCameraProperties |
This event has to be raised to send a CameraPropertiesDto. By default, it is raised at the beginning of Play Mode.
|
protected |
If true the avatar tracking is sent.
| Transform umi3d.cdk.userCapture.UMI3DClientUserTracking.skeletonContainer |
Transform of the gameobject containing the user's skeleton.
| UnityEvent umi3d.cdk.userCapture.UMI3DClientUserTracking.skeletonParsedEvent |
This event is raised after each analysis of the skeleton.
| UnityEvent umi3d.cdk.userCapture.UMI3DClientUserTracking.startingSendingTracking |
This event has to be raised to start sending tracking data. The sending will stop if the Boolean "sendTracking" is false. By default, it is raised at the beginning of Play Mode.
| Transform umi3d.cdk.userCapture.UMI3DClientUserTracking.viewpoint |
Transform associated with the viewpoint of the user.
| uint umi3d.cdk.userCapture.UMI3DClientUserTracking.viewpointBonetype |
BoneType associated with the viewpoint of the user.
|
getprotected set |
If true, disable the sending of bones rotations in frame dtos.
Frame dtos withouth bones are much lighter.
|
getprotected set |
True when an emote is currently playing
|
get |
If true the avatar tracking is sent.
|
getprotected set |
Frequency indicating the number tracked frames send to the server per seconds.
|
getprotected set |
True when the user's tracking has started.


ARTICLE 1
DEFINITIONS AND FIDUCIARY DUTIES
[§557A-101] Short title. This chapter may be cited as the Uniform Principal and Income Act. [L 2000, c 191, pt of §1]


BY EDWIN JAHIEL
MY BEST FRIEND'S WEDDING ** maximum. Directed by P.J. Hogan. Written by Ronald Bass. Produced by Jerry Zucker and Bass. Photography, Laszlo Kovacs.Editing, Garth Craven & Lisa Fruchtman. Production design, Richard Sylbert. Music,James Newton Howard. Cast: Julia Roberts (Julianne Potter), Dermot Mulroney (Michael O'Neal), Cameron Diaz (Kimmy Wallace), Rupert Everett (George Downes), Philip Bosco (Walter Wallace), M.Emmet Walsh (Joe O'Neal), Rachel Griffiths (Samantha Newhouse), Carrie Preston (Amanda Newhouse), Chris Masterson (Scott O'Neal), et al. A Sony Tri-Star release. 112 minutes. PG-13.
My Best Friend's Wedding is another marriage picture by Australian P.J. Hogan whose warm, funny, original Muriel's Wedding was a hit, starting at the 1994 Cannes Festival. This time Hogan is not so lucky.
I saw this film on Friday. As the end credits rolled by, I thought :"TGIF" -- Thank God It's Finished. MBFW seems to be an attempt at a 90s screwball romantic comedy, vaguely along the lines of much earlier ones. But instead of a crazy, outlandish structure, minutely plotted, diversified and with strong ensemble playing (think of Cary Grant gems such as His Girl Friday and My Favorite Wife, both of 1940), this picture is made to showcase Julia Roberts at the expense of everything and everyone else. Almost, that is.
The film's fourth banana, English actor Rupert Everett, salvages the show, steals every scene he's in, even those in which he speaks to Roberts on long-distance calls. Roberts plays Julianne, a New York food critic who refers to "creme brulee" as "kraym broolay." Everett is George, her editor and confidant. He is handsome, sexy, smart. He is also gay,which you certainly wouldn't know if you hadn't been told from the start.
Nine years ago Julianne had a torrid but short affair with college-mate Michael. Julianne is no long-distance runner when it comes to relationships, but the reasons why the twosome stopped are still not clear. They switched to being best friends and such perfect soulmates that they made a pact: if by the time they reached age 28 they had found no true love, they would marry each other.
The time has come. Michael, now a sportswriter in Chicago, calls Julianne, but instead of popping the question he announces feverishly that in four days he will marry young college girl Kimmy, the daughter of Chicago tycoon. He has such prenuptual jitters that Julianne simply must come and lend support.
For Julianne this comes as an IRA bomb throw. It triggers in her the absolute notion that Michael is the man of her life. She flies in, ostensibly to help him, but in reality to get him away from Kimmy.
The film, always focused on Roberts, is the string of Julianne's machiavellian machinations. She charms the eager fiancee ("You're my new best friend" says naive Kimmy), worms her way into and works on everybody, systematically diminishes Kimmy in Michael's eyes. A few of her strategies are amusing, most are too cruel to be entertaining, as when she manoeuvers tin-eared Kimmy into singing karaoke in a truly atrocious voice.The story becomes increasingly phony, nonsensical and often verges on hysteria.
So-feminine Julianne is called Jules by her friends. It is a significant detail. I am sure that the scriptwriter had His Girl Friday (1940) in mind. That movie was a remake of The Front Page (1931), where two battling Chicago newsmen were named respectively Walter Burns and Hildy Johnson. In His Girl Friday Cary Grant plays editor Walter Burns and reporter Hildy Johnson now becomes a woman (Rosalind Russell) and Walter's ex-wife. She's currently engaged to Ralph Bellamy, whom Grant undermines and ridicules so as to get Russell back, both as a star reporter and as his wife. There are other parallels between MBFD and His Girl.
Julia Roberts is considered blessed by the tooth-fairy by many, and no doubt beloved by dentists. She throws herself into her role body and soul, but the script is incomplete about her character, and makes her barefaced tactics for the reconquest of Michael unappealing --even nasty.
Michael is namby-pamby, Kimmy is painfully immature, both lack personality, both are credulous dim-bulbs and lambs too easily led to slaughter. Script and performers seem to grope as to what might come next. There are hardly any sparkling lines of dialogue, except for Rupert Everett's. Even then, when he shows up in Chicago and tells a group that Jules went with so many men "that she couldn't sit down for seven years," this indelicacy is out of character for George. Still, far and away, the best part is when he poses dashingly, convincingly and funnily as the fiance of Jules, and enchants one and all.
The film becomes exponentially cynical, confused and incredible, including many psychological as well as time-and-space impossibilities in the last reel. The finale is a non-closure cop-out. The only touching scene is in a corridor of the Drake Hotel, a quiet hiatus in which a passing bellman called Richard gently tries to boost the morale of a despondent Julianne. I think that the actor is Paul Giamatti.
That old movie subject --love-stricken people using strategies that will lead to marriage-- is not very applicable today. In 1997 such romanticism does not jibe with the rate of 54.8% marriages ending in divorce. You don't need statistics either to know how increasingly short-lived so many marriages are. Julia Roberts and Lyle Lovett were not an isolated case. Sad but true,"'til death do us part" in movies has become a hard sell.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


A rootkit is a malware that is designed to hid itself from being detected by the user and the system process. There are two modes in the system the user mode and the kernel mode, the user mode consistent of applications, user mode drivers and windows API, so mainly the executables and DLL files. The kernel mode are the kernel mode drivers, file system drivers and operating system kernel so mainly the .sys files and the rootkits mainly resides and act on these files. It is here in the kernel mode that the rootkit can act as a filter and hide files from being shown.
Bootkit does not need to hide since they modify the MBR (master boot record). The MBR are first read when we boot up the system so if the malicious code are there, it will be executed without us knowing and will already be in the processes. So the main difference between the two is that rootkit needs to hide while bootkit does not hide.
In the lecture we learned how we can do kernel debugging to find memory patterns and from following the pointers in the memory we can see if
they have been modified to point to another memory address.
Kernel debugging is the same as using debugging for any other program that you are
going through and looking at the code, but in our case, we are looking at the assembly code which will show us how the machine instructions are being
called and we can trace all the instruction of the operating system. So we start from the address that Tuluka has shown as suspicious and set
a breakpoint and when it hits we can look at the call and see how it was called and we will notice an extra entry that intercepts the windows API and
we can look into the assembly code and see the instructions and by following through we will eventually see that it will take us back to the original
call and using that we can find the difference address and get the offset.
In the lecture, Tuluka is introduced as a tool that is used for checking if a file's address is being redirected or changed. It is a very useful tool for you to see if a file is suspicious and it will highlight those files and list its original address and the address that it is currently pointing to as well as showing you the location of the suspicious file. However, we know that since those files are being redirected, we won't be able to see those file if we were go to that file location. We would need to patch the address back to the original to see the file.
A process can be seen as an object that contains one or more threads, it has an object table what lists and handles all other objects that is
relates or uses by this process.
A thread can be seen as the smallest unit of execution within the operating system. So when a code is run, it is first converted into assembly and then machine
code then placed into a memory and executed in cpu. Each thread has their own stack that has user mode and kernel stack and it is within this kernel stack that
the rootkit takes control and use it to its purpose. Because the rootkit is able to go into the kernel and change the pointer of the original address that the
kernel is supposed to point to and have it run the rootkit malware and then go back to the original address.
So understanding the rootkit we can either find the thread in the process that it is running and terminate it or we can use debugger and find the address in memory
that is changed and patch it with the original address so it is not being rerouted, this way the malware is still in memory but it will not be called.
In lab 1, we are using tuluka to see what files are suspicious. From that we just want to see a simple memory manipulation by the malware.
In lab 2, we go into looking at the process memory and look at which process is hooked and is running the malware.
In lab 3, we are using kernal debugging to the thread that is responsible for modifying the kernal and by looking into the assembly code instructions we can trace the step by step and find the malicious code afterwards we can find the original address and patch it.
In lab 4, we went over file content forging, which means that the malware can overwrite existing files and provide a clean view of the file for the anti-virus software and avoid getting detection.
In lab 5, we are analying the brazilian banker rootkit using winDBG and we see that it prevents security tools from loading.
In conclusion, I learned more detail regarding the windows system internals, how the windows x86 system internal work and why they are target for the rootkits. In addition to understanding how rootkits work, I also learned how processes work and the basics of threads and understand why how that leads to the development of multi-core cpus since one cpu with one instruction pipeline would not be able to process more than one thread and hyperthreading does not work out when both threads needs the same math processor so multi-core is developed. Even though this lecture is a bit dry but it taught me a lot of information and basics of windows system internals.


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Every now and then, when I think I may be getting too big for my boots, I like to take a lesson in humility. I find that by contemplating the absurd Mr and Mrs Fripp, those paragons of self-effacement, I am able to regain a more judicious perspective on the world.
Mr Fripp is well known as the bespectacled guitar noodler who had the gall to take a technique common in experimental music of the 1950s and 60s (a tape delay and looped signal, basically) and call it ‘Frippertronics'. That's like me writing a sonnet and saying it's a new type of poem called a ‘Keyet’.
Meanwhile, I am indebted to the current edition of Private Eye for this splendid quotation from Mrs Fripp, in answer to a question posed by Now magazine:
Q—What have you done with all your punky stage outfits?
A—I have a house to keep my press cuttings, costumes, head-dresses, stage make-up and books that refer to me. There's a room of recordings of every TV show I've done and another full of fan mail.
John & Yoko only needed an apartment for the fur coats. Toyah requires a whole house.
And thanks to Max Décharné for alerting me to the existence of the town of Toyah in Texas. The photograph below is captioned thus: Three wooden grave markers stand as testament to the desolation of West Texas. Toyah was a crew change point on the Texas & Pacific in the early 20th century. Like the hot wind, the trains still blow through Toyah, but most of the citizenry moved elsewhere. Who can blame them?


SMD (3025) 2 Pin Pushbutton (Tactile) - BT3025
Summary
Name: SMD (3025) 2 Pin Pushbutton (Tactile)
ID: BUTA-3025-X-02PI-01
Hex ID: BT3025
WebPage: https://github.com/oomlout/oomlout-OOMP/wiki/BUTA-3025-X-02PI-01
Short URL: http://oom.lt/BT3025
Revision History: https://github.com/oomlout/oomlout-OOMP/blob/master/parts/BUTA-3025-X-02PI-01/
| Type |
Size |
Color |
Description |
Index |
BUTA Pushbutton (Tactile) |
3025 SMD (3025) |
X
|
02PI 2 Pin |
01
|
Images
About
This part is awaiting a description.
Specifications
| Info |
Value |
| Type |
Pushbutton (Tactile) |
| Size |
SMD (3025) |
| Description |
2 Pin |
| Number of Pins |
2 |
Extra Details
Spotted a mistake, want to add more? Let us know<EMAIL_ADDRESS>All images and resources are licensed [CC BY-SA] unless otherwise stated (ie. the datasheets)


women of the american west
Women of the West | The Gilder Lehrman Institute of American History
Justice of the Plains: The Movement Westward, John S. Curry (LC- Women are like water to Western history. Both have flowed through the terrain we have come .
http://www.gilderlehrman.org/history-by-era/development-west/essays/women-west
Encyclopedia of Women in the American West - Sage Publications
A SAGE Publications book: American History, Women's Studies, American Government & Politics, Encyclopedia of Women in the American WestGordon Moris .
http://www.sagepub.com/books/Book220824
Women in the American West (Cultures in the ... - Amazon.com
"[O]ne could read the book from cover to cover, which is a lively and informative experience, or use the index to go directly to a subject
Even taking into .
http://www.amazon.com/Women-American-West-Cultures/dp/1598840509
Things You'll Need
- Certified copies of public records evidence in your case
Instructions
-
-
1
Teresa Jordan | Cowgirls: Women of the American West
by Teresa Jordan University of Nebraska Press. Just out of college in the late 1970s, Teresa Jordan crisscrossed the rural West, driving over 60,000 miles from .
http://www.teresajordan.com/writing/books/cowgirls-women-of-the-american-west/ -
2
Western America: Pioneers, Cowgirls, Wives, Etc. - Women's History ...
The history of women in the Western US, as pioneers, cowgirls, wives, and all their other roles.
http://womenshistory.about.com/od/westernamerica/US_American_West_Pioneers_Cowgirls_Wives.htmLegendary Women of the American West - CyberSoup's Wild West
Women of the American wild west had to be a resourceful lot to cope with the elements that surrounded them: the harsh conditions, lawlessness and living in an .
http://thewildwest.org/cowboys/wildwestlegendarywomen -
-
3
Role of Women in the American West | eHow.com
Role of Women in the American West. In the 1800s, women were expected to stay in the "women's sphere" of society, caring for home and family under the .
http://www.ehow.com/facts_5664092_role-women-american-west.htmlThis Shall Be the Land for Women - WOW Museum: Western ...
Women of the American West led the nation and the world into the struggle for female voting rights, known as the "suffrage movement." This remarkable suffrage .
http://theautry.org/explore/exhibits/suffrage/ -
4
Encyclopedia of Women in the American West - Gordon Moris ...
The Encyclopedia of Women in the American West captures the lives of more than 150 women who made their mark from the mid1800s to the present, .
http://books.google.com/books/about/Encyclopedia_of_Women_in_the_American_We.html?id=U9zIeyugktEC
-
1
References
Resources
- Photo Credit Justice image by MVit from civil air patrol airplanes


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title: About
layout: page
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I like financial markets, coding, machine learning and algebraic math. Viewing the world as numbers is interesting to me.

I try my best to avoid instant gratification and try to involve myself in delayed gratification pursuits.
I attempt to avoid:
- [] Movies and TV Shows
- [] Eating Junk Food
- [WIP] More
I attempt to include:
- [] Adequate Sleep (at 7-8hrs regularly)
- [] Resistance Training (a way of life now)
- [] Walking - no intent/goals/measurement yet - need to add intention
- [] Meditation - still need to start
I like to have less, but high-quality friends. I believe men should grow together to become successful together.
Email me, if you have something interesting to share.
Skills
- Concurrent Code Design and Architecture
- Machine Learning
- Competitive Coding
- RTOS Concepts
- Writing Hardware Drivers
- Agile Software Development
Languages and Frameworks
- C/C++
- Python
- PyTorch
- Numpy
- Pandas
- Scripting
- JavaScript
- ReactNative
- AngularJS
- ReactJS
- Git


[theqoo] TVXQ's Tokyo Dome Concert's Seating Status Today
by Suspiria77Source: Theqoo (1), Theqoo (2)
1.Even on a week day the seats are all full
2.They are the best
3.Pearl red never dies
4.TVXQ is the tour king
5.Wow, it's a week day. Great!
6.What's TVXQ's charm? (I'm not trying to downgrade them, I just wonder how a group can still have a lot of loyal fans in a foreign country for over 10 years)
7.No group can match TVXQ's stage presence
8.Wow, even the most popular J-pop artists can't fill the venue in a week day
9.TVXQ is really a big idol for their juniors
10.Honestly their stage performances are much better than the album. They are sweeping all the money away
11.It's a week day, most of people in Japan work until night
12.They even sold the limited-view seats. How can people watch them?
13.They're performing there for 3 week days, all of them were sold out, even limited view seats.
14.Wow, is this possible?
15.165,000 spectators in 3 days.
16.Let's sweep the YEN away~
17.They've been doing this only with 2 people...


First Post! Introduction
Posted May 26, 2015
Hello! Can you hear me? Testing!
Ahem. Now that we're all set, here's a brief explanation:
A long time ago, in a galaxy far away...
No! That's a different story. This one really only started about a month ago, when I actively started coding with HTML and JavaScript. I learned HTML in middle school, but at that time HTML was pretty much the only thing used in web design. Since then so much more has been added! While working in a project management capacity for a software company, I got an up-close look at the way web work is done in the modern age of HTML5, CSS3, and cool JavaScript interactivity.
I started the Khan Academy Intro to JS course months back out of curiosity, but my work schedule caused it to fall by the wayside. After some developments at that company, I suddenly had some time to sit down and learn! In the Khan HTML course, one of the assignements was to make an HTML blog format. I thought that continuing the may be a good way to improve my web design skills. I've been experimenting with CSS and JS, and I plan to continue implementing new design updates to this page.


Lift Off
Round 1
It is a model Making Event where the participants will be provided all the needed materials and they will be asked to make a mechanism which can lift specific load to a certain height and you have to make a hydraulic ramp by using syringes, pipes, candy sticks etc.
Judgement Criteria:
-Your Concept behind the design and the way you have implemented your design.
-The amount of load your mechanism can lift.
-The height upto which your mechanism can lift the load.
Event Fee – 50 Rs (per team)
Participants per team- 3
Event manager- Kedar Dave
Mobile no.-8141717770


2
A Living Stone and a Holy People
1 So put away all malice and all deceit and hypocrisy and envy and all slander.
2 Like newborn infants, long for the pure spiritual milk, that by it you may grow up to salvation—
3 if indeed you have tasted that the Lord is good.
4 As you come to him, a living stone rejected by men but in the sight of God chosen and precious,
5 you yourselves like living stones are being built up as a spiritual house, to be a holy priesthood, to offer spiritual sacrifices acceptable to God through Jesus Christ.
6 For it stands in Scripture:
“Behold, I am laying in Zion a stone,
a cornerstone chosen and precious,
and whoever believes in him will not be put to shame.”
7 So the honor is for you who believe, but for those who do not believe,
“The stone that the builders rejected
has become the cornerstone,”
8 and
“A stone of stumbling,
and a rock of offense.”
They stumble because they disobey the word, as they were destined to do.
9 But you are a chosen race, a royal priesthood, a holy nation, a people for his own possession, that you may proclaim the excellencies of him who called you out of darkness into his marvelous light.
10 Once you were not a people, but now you are God's people; once you had not received mercy, but now you have received mercy.
11 Beloved, I urge you as sojourners and exiles to abstain from the passions of the flesh, which wage war against your soul.
12 Keep your conduct among the Gentiles honorable, so that when they speak against you as evil doers, they may see your good deeds and glorify God on the day of visitation.
Submission to Authority
13 Be subject for the Lord's sake to every human institution, whether it be to the emperor as supreme,
14 or to governors as sent by him to punish those who do evil and to praise those who do good.
15 For this is the will of God, that by doing good you should put to silence the ignorance of foolish people.
16 Live as people who are free, not using your freedom as a cover-up for evil, but living as servants of God.
17 Honor everyone. Love the brotherhood. Fear God. Honor the emperor.
18 Servants, be subject to your masters with all respect, not only to the good and gentle but also to the unjust.
19 For this is a gracious thing, when, mindful of God, one endures sorrows while suffering unjustly.
20 For what credit is it if, when you sin and are beaten for it, you endure? But if when you do good and suffer for it you endure, this is a gracious thing in the sight of God.
21 For to this you have been called, because Christ also suffered for you, leaving you an example, so that you might follow in his steps.
22 He committed no sin, neither was deceit found in his mouth.
23 When he was reviled, he did not revile in return; when he suffered, he did not threaten, but continued entrusting himself to him who judges justly.
24 He himself bore our sins in his body on the tree, that we might die to sin and live to righteousness. By his wounds you have been healed.
25 For you were straying like sheep, but have now returned to the Shepherd and Overseer of your souls.