Software engineer, sound designer, conference speaker and open source maintainer.
@dubreuia
(in 10 minutes)
"Generative art is an artwork partially or completely created by an autonomous system."
Maybe you don't know how to improvise, maybe you need help to compose.
It is okay to make art without taking ourselves seriously. Generative music is a good way of doing that, because those kind of systems can be interacted with easily.
To make generative art, you need autonomous systems that makes it possible. As software developers, this is our role to provide that, whether it is for an art exhibit, a generative radio, or a fun website.
Generative systems makes tons of mistakes (also humans), but mistakes are good.
Hand crafting the rules of a painting or the rules of a music style might be a hard task. That's why Machine Learning is so interesting in arts: it can learn complex functions.
MIDI is a musical representation analogous to sheet music, where note has a pitch, velocity, and time.
Working with MIDI shows the underlying structure of the music, but doesn't define the actual sound, you'll need to use instruments (numeric or analogic).
Working with audio is harder because you have to handle 16000 samples per seconds (at least) and keep track of the general structure. Generating audio is more direct than MIDI.
Recurrent Neural Networks (RNNs) solves two important properties for music generation: they operate on sequences for the inputs and outputs and they can remember past events.
Most RNN uses Long Short-Term Memory (LSTM) cells, since by themselves, RNNs are hard to train because of the problems of vanishing and exploding gradient, making long-term dependencies hard to learn.
By using input, output and forget gates in the cell, LSTMs can learn mechanisms to keep or forget information as they go.
Variational Autoencoders (VAEs) are a pair of networks where an encoder reduces the input to a lower dimensionality (latent space), from which a decoder tries to reproduce the input.
The latent space is continuous and follows a probability distribution, meaning it is possible to sample from it. VAEs are inherently generative models: they can sample and interpolate (smoothly move in the latent space) between two points.
WaveNet is a convolutional neural network (CNN) taking raw signal as an input and synthesizing output audio sample by sample.
The WaveNet Autoencoder present in Magenta is a Wavenet-style AE network capable of learning its own temporal embedding, resulting in a latent space from which is it possible to sample and mix elements.
| Model | Network | Repr. | Encoding |
|---|---|---|---|
| DrumsRNN | LSTM | MIDI | polyphonic-ish |
| MelodyRNN | LSTM | MIDI | monophonic |
| PolyphonyRNN | LSTM | MIDI | polyphonic |
| PerformanceRNN | LSTM | MIDI | polyphonic, groove |
| MusicVAE | VAE | MIDI | multiple |
| NSynth | Wavenet AE | Audio | - |
| GANSynth | GAN | Audio | - |
(in 20 minutes)
We'll use NSynth to mix cat sounds with other sounds.
See "code/nsynth.py" and method app
in this repo.
def app(unused_argv):
encoding1, encoding2 = encode([FLAGS.wav1, FLAGS.wav2],
sample_length=FLAGS.sample_length,
sample_rate=FLAGS.sample_rate,
checkpoint=FLAGS.checkpoint)
encoding_mix = mix(encoding1, encoding2)
synthesize(encoding_mix, checkpoint=FLAGS.checkpoint)
See "code/nsynth.py" and method encode
in this repo.
def encode(paths: List[str],
sample_length: int = 16000,
sample_rate: int = 16000,
checkpoint: str = "checkpoints/wavenet-ckpt/model.ckpt-200000") \
-> np.ndarray:
audios = []
for path in paths:
audio = utils.load_audio(path,
sample_length=sample_length,
sr=sample_rate)
audios.append(audio)
audios = np.array(audios)
encodings = fastgen.encode(audios, checkpoint, sample_length)
return encodings
See "code/nsynth.py" and method mix
in this repo.
def mix(encoding1: np.ndarray,
encoding2: np.ndarray) \
-> np.ndarray:
encoding_mix = (encoding1 + encoding2) / 2.0
return encoding_mix
See "code/nsynth.py" and method synthesize
in this repo.
def synthesize(encoding_mix: np.ndarray,
checkpoint: str = "checkpoints/wavenet-ckpt/model.ckpt-200000"):
os.makedirs(os.path.join("output", "synth"), exist_ok=True)
date_and_time = time.strftime("%Y-%m-%d_%H%M%S")
output = os.path.join("output", "synth", f"{date_and_time}.wav")
encoding_mix = np.array([encoding_mix])
fastgen.synthesize(encoding_mix,
checkpoint_path=checkpoint,
save_paths=[output])
As shown, the NSynth instrument is nice, but really slow for the audio synthesis. You should use GANSynth.
We'll use DrumsRNN and MelodyRNN to generate MIDI to play the samples.
See "code/sequences.py" and method reset
in this repo.
def reset(loop_start_time: float,
loop_end_time: float,
seconds_per_loop: float):
sequence = music_pb2.NoteSequence()
sequence = loop(sequence,
loop_start_time,
loop_end_time,
seconds_per_loop)
return sequence
See "code/sequences.py" and method loop
in this repo.
def loop(sequence: NoteSequence,
loop_start_time: float,
loop_end_time: float,
seconds_per_loop: float):
sequence = ss.trim_note_sequence(sequence,
loop_start_time,
loop_end_time)
sequence = ss.shift_sequence_times(sequence,
seconds_per_loop)
return sequence
See "code/sequences.py" and method generate
in this repo.
def generate(sequence: NoteSequence,
name: str,
bundle_filename: str,
config_name: str,
generation_start_time: float,
generation_end_time: float):
generator_options = generator_pb2.GeneratorOptions()
generator_options.args['temperature'].float_value = 1
generator_options.generate_sections.add(
start_time=generation_start_time,
end_time=generation_end_time)
sequence_generator = get_sequence_generator(name,
bundle_filename,
config_name)
sequence = sequence_generator.generate(sequence,
generator_options)
sequence = ss.trim_note_sequence(sequence,
generation_start_time,
generation_end_time)
return sequence
See "code/sequences.py" and method
get_sequence_generator
in this repo.
def get_sequence_generator(name: str,
bundle_filename: str,
config_name: str):
if name == "drums":
generator = drums_rnn_sequence_generator
elif name == "melody":
generator = melody_rnn_sequence_generator
else:
raise Exception(f"Unknown sequence generator {name}")
mm.notebook_utils.download_bundle(bundle_filename, "bundles")
bundle = mm.sequence_generator_bundle.read_bundle_file(
os.path.join("bundles", bundle_filename))
generator_map = generator.get_generator_map()
sequence_generator = generator_map[config_name](
checkpoint=None, bundle=bundle)
sequence_generator.initialize()
return sequence_generator
This generative music demo helped us improvise and compose around an idea: a track composed of a percussion, a melody, and a cat 😺. We could interact with the system and it helped us improvise around a theme.
Was it perfect? No, we had little happy accidents.
Maybe we could have had more control over the sequences. Maybe we wanted to improvise around a musical style, or a specific structure.
(in 5 minutes)
The pre-trained models in Magenta are good, but if for example you want to generate a specific style, generate a specific time signature (3/4 for example), or a specific instrument (cello for example) you'll need to train your own.

A large-scale and high-quality dataset of annotated musical notes. Training audio requires lots of resources, but can be achieved using GANSynth.
From MIDI, ABCNotation, MusicXML files to NoteSequence
.
convert_dir_to_note_sequences \
--input_dir="/path/to/dataset/jazz_midi/drums/v1" \
--output_file="/tmp/notesequences.tfrecord" \
--recursive
...
Converted MIDI file /path/to/dataset/jazz_midi/drums/v1/TRVUCSW12903CF536D.mid.
Converted MIDI file /path/to/dataset/jazz_midi/drums/v1/TRWSJLM128F92DF651.mid.
...
The sequence examples are fed into the model, it contains a sequence of inputs and a sequence of labels that represents the drum track. Those are split to eval and training sets.
drums_rnn_create_dataset \
--config="drum_kit" \
--input="/tmp/notesequences.tfrecord" \
--output_dir="/tmp/drums_rnn/sequence_examples" \
--eval_ratio=0.10
...
DAGPipeline_DrumsExtractor_training_drum_track_lengths_in_bars:
[8,10): 1
[10,20): 8
[20,30): 8
[30,40): 29
[40,50): 1
[50,100): 2
...
Launch the training, using a specific configuration and hyperparameters.
drums_rnn_train \
--config="drum_kit" \
--run_dir="/tmp/drums_rnn/logdir/run1" \
--sequence_example_file="/tmp/drums_rnn/sequence_examples/training_drum_tracks.tfrecord" \
--hparams="batch_size=64,rnn_layer_sizes=[64,64]" \
--num_training_steps=20000
...
Saving checkpoints for 0 into /tmp/drums_rnn/logdir/run1/train/model.ckpt.
Accuracy = 0.013341025, Global Step = 1, Loss = 6.2323294, Perplexity = 508.9396
Accuracy = 0.43837976, Global Step = 11, Loss = 5.1239195, Perplexity = 167.99252 (50.009 sec)
global_step/sec: 0.199963
Saving checkpoints for 12 into /tmp/drums_rnn/logdir/run1/train/model.ckpt.
...
You can launch TensorBoard and go to http://localhost:6006 to view the TensorBoard dashboard.
tensorboard --logdir="/tmp/drums_rnn/logdir"
(in 5 minutes)
Magenta can send MIDI, which is understood by basically everything that makes sound: DAWs (like Ableton Live), software synthesizers (like fluidsynth), hardware synthesizers (though USB or MIDI cable), etc.
You can use Magenta and most of its models in the browser, using Magenta.js (which in turns uses Tensorflow.js).
Amazing project to share generative music applications. Harder to use the audio and MIDI in other softwares like DAWs.

<button onclick="play()">
Play Trio
</button>

Using Magenta.js and Max4Live (MaxMSP) process, Magenta Studio can be used directly in Ableton Live.
Generative music using Magenta and Ableton Live, exhibit by Claire Malrieux.
Upcoming book on Packt Publishing, expected publication date in January 2020. Easy to read for non-musicians.
Slides and source code of this presentation, blog articles on Magenta, book references, etc.


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{{ data.firstName or 'Bob' }} {{ data.lastName or 'Smith' }} has accepted your invite to act on your behalf in {{ data.service or 'myHealth' }}.
Go to {{ data.service or 'myHealth' }}’s settings to manage what {{ data.firstName or 'Bob' }} can see and do. You can cancel and remove their access at any time by visiting your NHS login account management area.


You are approached by a business developer to get advice on where to build a new store in Phoenix, AZ. There are currently six candidates. Use the Yelp dataset and come up with your own scoring scheme to identify the best location to recommend.
As a team, choose one of the following business types to work on:
For this hackathon, you will use a scoring method that is based on a series of 20 Yes/No questions that can be applied to each candidate location. The candidate location that has the most number of Yes’s is the most viable location.
Each Yes/No question generally should take on the form of
Does the location have X nearby?
where X is a feature that you think would be useful for the business to be viable.
Two examples of such questions are:
Implement scoring functions and visualize how these candidate locations are scored. The skeleton code is provided for you in report.html.
Each person must implement at least two questions to receive credits for this hackathon.
Our team chose to analyze candidate locations for (fill in the business type).
The team members who contributed to this hackathon are:
Our team came up with the following relevant questions:
Does the location have ….. ? contributed by (Name)
(one sentence justification why this question is important)
Does the location have ….. ? contributed by (Name)
(one sentence justification why this question is important)
Does the location have ….. ? contributed by (Name)
(one sentence justification why this question is important)
(add more until you hit 20)
Our team collectively has implemented (N) scoring functions. Based on the scores, our team recommends location (1, 2, 3, 4, 5, or 6 from west to east), because it receives (m) out of (N) possible scores.


Alex Iwobi: I learned about Everton's interests while on holiday in Dubai
- Alex Iwobi has revealed that he never planned on leaving Arsenal in the summer
- The winger revealed he was in Dubai for holiday when he was called to return and sign on deadline day
- The Super Eagles star admits he is happy with his present club
Nigerian football star Alex Iwobi made a deadline switch from Arsenal to Everton on a five-year deal valued around €32 million.
The move was least expected for the Super Eagles winger who was on holiday after helping his national team win bronze at the 2019 Africa Cup of Nations championship in Egypt this summer.
Since his move to the Goodison Park this campaign, the 23-year-old has managed just one goal in ten Premier League appearances but, he has expressed satisfaction with the move
Iwobi revealed that he was in the United Arab Emirates on holiday with his family when he was contacted about the Toffees interests'.
Red hot Super Eagles star set to miss AFCON qualifiers due to injury in league match
“Everything was last minute but I don’t regret the decision. I am happy to be here,” Iwobi told LiverpoolEcho.
PAY ATTENTION: Install Pitch Football App for FREE to get the latest football News & Scores
"Honestly, do you know where I was? I was in Dubai on a family holiday and I’d been there for four or five days before deadline day.
"I got the call saying Everton were interested. It was literally mad for the last two or three days.”
Reports also revealed that Arsenal rejected Everton's first bid but could not resist a second from the Merseyside outfit.
After both clubs had agreed on a deal, there was no time for Iwobi to fly back for medicals, so they took his details from the Ginners.
"I wasn’t able to,” he explains. They were able to get the medicals off Arsenal and Arsenal were able to hand them in. It was literally a crazy holiday.
Iwobi opens up on what Everton manager did to improve his game against West Ham
READ ALSO: NAIJ.com upgrades to Legit.ng
"I was on a boat and had to rush back to the sign everything on time, it was crazy. There was one part of me thinking: ‘This might not go through’.”
Legit.ng earlier reported that Alex Iwobi made the day of a young Lesotho fan who asked for his jersey after helping Super Eagles claim 4-2 win away from home.
The Everton forward went to the stands to hand it over top over to the supporter and the gesture left the young fan emotional.
The boy was also said to have gone to the airport to welcome the Super Eagles on their arrival in Lesotho.
Abu Azeez: Meet the only Nigerian rated among top 50 beach soccer players worldwide | Legit TV


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Estimation is building based on the requirements of the technologies shortage field of estimation.
Our core values are building the website for users estimating and collecting data for software evaluation as well as creating storages for user assessment results.
The fact that there are just a few websites that are able to calculate the size and number lines of code in the software project. However, they just have one or few features integrating in the program only. Furthermore, they have such an old interface that users will get bored when they proceed every step to evaluate their software weight and points.
That is the reason why we create this website to integrate a variety of estimation features which can help users have a better scenario of their project quality and quantity. In addition, we provide a website which has a more modern and perspicuous user interface that can improve user experience when using the website as well.
Building the application which helps users can estimate the software application easier, such as student, teacher, employee in the company and especially HR of the company.
From that, they can have some information about the application which they want to estimate including the size and source lines of code, use case point, function point.
On top of that, users can recognize the number of members working on that project and the amount of time that they spend on.


Backorders are one of the major problem in supply chain and logistics. Backorders are products that are temporarily out of stock, however a customer is permitted to place an order against future stock. Back orders are both good and bad: Strong demand can drive back orders, but so can suboptimal planning. The problem is when a product is not immediately available, customers may not have the extravagance or patience to wait. This results in lost sales and low customer satisfaction.
Inventory management is a risky business. Too much product on hand increases carrying costs. Too little increases the chance of back order. A review of data done by the FedEx representatives suggest that a single backorder costs the company around \$11 - \$15. For example, an average company processing 1 million orders per year with a 20% backorder rate would experience 200,000 backorders during a year. At cost of \$13.28 per back order, a total increased cost due to backorders is approximately \$2,656,000. Thus, predicting the backorders is an essential task in inventory control planning. Implementing machine learning to complete the task is the best way to go about solving this issue. Using the previous data, we are trying to predict the Backorders to prevent losses for a company.
We have taken the kaggle dataset for backorder prediction which is a large dataset with over a million SKUs. We further import an study the dataset for better understanding of the correlations and the distributions of various features.
# Importing dependencies
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import KFold
from sklearn.utils import resample
from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, confusion_matrix, accuracy_score
from sklearn import svm
from sklearn import linear_model
# Dictionary containing values for representing NaNs
na_other = {'perf_6_month_avg':-99, 'perf_12_month_avg':-99}
#reading the data
train_data = pd.read_csv("Kaggle_Training_Dataset_v2.csv", na_values = na_other)
test_data = pd.read_csv("Kaggle_Test_Dataset_v2.csv", na_values = na_other)
#The warning is due to the large size of the dataset.
train_data.head().transpose() #exploring the variables.
train_data.tail(2).transpose()
test_data.tail(2).transpose()
We see that the last rows of test and train data are null. Thus, we remove the row from our dataset.
# Drop the last row
train_data = train_data[:-1]
test_data = test_data[:-1]
#Summarise the numerical values in training data
train_data.describe().transpose()
#summarise non-numerical values in training data
train_data.describe(include = ['O']).transpose()
#Drop SKU column
train_data = train_data.drop('sku', axis = 1)
test_data = test_data.drop('sku', axis = 1)
#change categorical to boolean
cat_vars = ['potential_issue', 'deck_risk', 'oe_constraint', 'ppap_risk','stop_auto_buy', 'rev_stop', 'went_on_backorder']
for var in cat_vars:
test_data[var] = test_data[var].map({'No':0,'Yes':1})
train_data[var] = train_data[var].map({'No':0,'Yes':1})
#Looking at the distribution of perf_6_month_avg
x = train_data.perf_6_month_avg.dropna()
plt.hist(x, bins = 30)
plt.xlim(0,1)
plt.show()
#Looking at the distribution of perf_12_month_avg
x = train_data.perf_12_month_avg.dropna()
plt.hist(x, bins = 30)
plt.xlim(0,1)
plt.show()
#Looking at the distribution of the lead_time
x = train_data.lead_time.dropna()
plt.hist(x)
plt.show()
observing the plots, we can see that the distribution is either right or left skewed. Thus, replacing the missing values using the mean seems like a better option rather than using the mean.
# Replace NaNs in the dataset
# perf_6_month_avg
train_data.perf_6_month_avg = train_data.perf_6_month_avg.fillna(train_data.perf_6_month_avg.median())
test_data.perf_6_month_avg = test_data.perf_6_month_avg.fillna(test_data.perf_6_month_avg.median())
# perf_12_month_avg
train_data.perf_12_month_avg = train_data.perf_6_month_avg.fillna(train_data.perf_12_month_avg.median())
test_data.perf_12_month_avg = test_data.perf_6_month_avg.fillna(test_data.perf_12_month_avg.median())
# lead_time
train_data.lead_time = train_data.lead_time.fillna(train_data.lead_time.median())
test_data.lead_time = test_data.lead_time.fillna(test_data.lead_time.median())
forecasts = ['forecast_3_month','forecast_6_month', 'forecast_9_month']
sns.pairplot(train_data, hue = 'went_on_backorder', vars = forecasts, size = 3 )
plt.show()
sales = ['sales_1_month', 'sales_3_month', 'sales_6_month', 'sales_9_month']
sns.pairplot(train_data, vars=sales, hue='went_on_backorder', size=3)
plt.show()
We see that the relationship between the variables here is linear and they are highly correlated. Also, we observe that the backorders happen only when the value of sales and forecast is very low.
# Separate data by going on backorder or not
no_bo = train_data.loc[train_data['went_on_backorder'] == 0]
is_bo = train_data.loc[train_data['went_on_backorder'] == 1]
# Make scatter plots of the 3-month forecast against each of the sales
for col in sales:
fig = plt.figure(figsize=(6, 6))
ax = fig.gca()
no_bo.plot(kind='scatter', x=col, y='forecast_3_month', ax=ax, color='DarkBlue', legend=True)
is_bo.plot(kind='scatter', x=col, y='forecast_3_month', ax=ax, color='Red')
plt.show()
Here we can see that the relation between the forecast and the sales follow a linear pattern and have a relatively high correlation between them
# Look at forecast, sales, in transit and recommended stock level in a pair-wise scatter plot
feature_set_1 = ['forecast_3_month', 'sales_1_month', 'in_transit_qty', 'min_bank']
sns.pairplot(train_data, vars=feature_set_1, hue='went_on_backorder', size=3)
plt.show()
The scatter plots show okay linear relationships between forecast, sales, in transit and recommended stock level. All the features range from 0 to over 300,000. Backorders only occur when the features are at low values.
Due to the good correlations and sufficiently linear relationships between these features, they will all be represented by a single feature in the machine learning models. The feature chosen is sales_1_month. This is because past sales is measured, whereas the quantity in transit, recommended minimum stock and forecasts are likely derived from past sales.
# Filter out the data that will be used
# Features chosen
features = ['national_inv', 'lead_time', 'sales_1_month', 'pieces_past_due', 'perf_6_month_avg',
'local_bo_qty', 'deck_risk', 'oe_constraint', 'ppap_risk', 'stop_auto_buy', 'rev_stop']
train_df = train_data[features]
test_df = test_data[features]
# Set labels
train_label = train_data['went_on_backorder']
test_label = test_data['went_on_backorder']
# Change scale of data
# Use MinMaxScaler to convert features to range 0-1
# The label is already in the range 0-1, so it won't be affected by this.
pp_method = MinMaxScaler()
pp_method.fit(train_df)
reduced_train_df = pp_method.transform(train_df)
reduced_train_df = pd.DataFrame(reduced_train_df, columns=features)
reduced_test_df = pp_method.transform(test_df)
reduced_test_df = pd.DataFrame(reduced_test_df, columns=features)
train_complete = pd.concat([reduced_train_df,train_label], axis = 1)
test_complete = pd.concat([reduced_test_df,test_label], axis = 1)
While running the models of KNN, Logistic Regression, SVM on data, the runningtime for them were more than 3 hours. Thus, given the time constraint we plan to stick to the Random Forest for our modeling. Random Forest is computationally less expensive and gives a better result as compared to the other algorithams on an unbalanced dataset. The statement is based on a research article on Elite Data Science.
To observe the effect of undersampling on our results we apply the Random Forest algorithm bothways and then compare the results.We then use the K-fold cross validation to validate our results.
#Merging the train and test data:
total_data = pd.concat([train_complete, test_complete])
# the unbalance in the data:
total_data.went_on_backorder.value_counts()
#create a blank dataframe to fill
merged_pred = pd.DataFrame(data=None,index=total_data.index)
#Define folds for 2-fold Cross Validation
kf = KFold(n_splits=2,shuffle=True,random_state=123)
#Define index of dataset (to help in data sepparations within folds)
ind=total_data.index
#----------fit models and product predictions in each fold----------#
for train_index, test_index in kf.split(total_data):
#Define Training data
merged_train=total_data[ind.isin(train_index)]
y_train=merged_train['went_on_backorder']
X_train=merged_train.drop(['went_on_backorder'],axis=1)
#Define Test data
merged_test=total_data[ind.isin(test_index)]
y_test=merged_test['went_on_backorder']
X_test=merged_test.drop(['went_on_backorder'],axis=1)
#Define down-sampled training data
train_majority = merged_train[y_train==0]
train_minority = merged_train[y_train==1]
n_minority = len(train_minority)
train_majority_downsampled = resample(train_majority,
replace=False,
n_samples=n_minority,
random_state=123)
train_downsampled = pd.concat([train_majority_downsampled, train_minority])
y_train_downsampled = train_downsampled['went_on_backorder']
X_train_downsampled = train_downsampled.drop(['went_on_backorder'],axis=1)
#---------------------------------------------------------------#
#Function to fit models
def fitrandomforests(n_est,maxfeat,minleaf):
#names of model predictions based on tuning parameter inputs
varname= "pred_nest%s_feat%s_leaf%s" % (n_est,maxfeat,minleaf)
varname2= "pred_down_nest%s_feat%s_leaf%s" % (n_est,maxfeat,minleaf)
#Fit a Random Forest model
rf=RandomForestClassifier(n_estimators=n_est,
max_features=maxfeat,
min_samples_leaf=minleaf)
rf.fit(X_train,y_train)
preds=rf.predict_proba(X_test)[:,1]
merged_test[varname]=preds
#Fit a Random Forest model on downsampled data
rfd=RandomForestClassifier(n_estimators=n_est,
max_features=maxfeat,
min_samples_leaf=minleaf)
rfd.fit(X_train_downsampled,y_train_downsampled)
predsd=rfd.predict_proba(X_test)[:,1]
merged_test[varname2]=predsd
#---------------------------------------------------------------#
#Tuning parameter grids
#number of trees (more is better for prediction but slower)
n_est=50
#maximum features tried
maxfeatgrid=[3,5,7]
#Minimum samples per leaf
minleafgrid=[5,10,30]
#fit models
for feat in maxfeatgrid:
for leaf in minleafgrid:
fitrandomforests(n_est,feat,leaf)
#Combine predictions for this fold with previous folds
merged_pred = pd.concat([merged_pred,merged_test])
#drop NA's from dataframe caused by the method for combining datasets from each loop iteration
merged_pred=merged_pred.dropna()
#View AUC for each model and each tuning parameter specification
for feat in maxfeatgrid:
for leaf in minleafgrid:
#Random forest for given tuning parameters
varname1="pred_nest50_feat%s_leaf%s" % (feat,leaf)
rocscore1=roc_auc_score(merged_pred['went_on_backorder'],merged_pred[varname1])
print( round(rocscore1,4 ) , varname1 )
#Down Sampled Random Forest for given tuning parameters
varname2="pred_down_nest50_feat%s_leaf%s" % (feat,leaf)
rocscore2=roc_auc_score(merged_pred['went_on_backorder'],merged_pred[varname2])
print( round(rocscore2,4) , varname2 )
#ROC Curves for top performing models
#Define false positive rates/true positive rates / thresholds
#Best random forest model
fpr, tpr, thresholds = roc_curve(merged_pred['went_on_backorder'],
merged_pred['pred_nest50_feat3_leaf5'])
#Best down sampled random forest model
fpr2, tpr2, thresholds2 = roc_curve(merged_pred['went_on_backorder'],
merged_pred['pred_down_nest50_feat7_leaf5'])
#AUC for best Random Forest and Random Forest Down sampled Models
roc_auc = roc_auc_score(merged_pred['went_on_backorder'],
merged_pred['pred_nest50_feat3_leaf5'])
roc_auc2 = roc_auc_score(merged_pred['went_on_backorder'],
merged_pred['pred_down_nest50_feat7_leaf5'])
#plot ROC Curve
plt.title('ROC Curve')
plt.plot(fpr, tpr, 'b', label='RF (AUC = %0.3f)'% roc_auc)
plt.plot(fpr2, tpr2, 'g', label='RF Downsampled (AUC = %0.3f)'% roc_auc2)
plt.plot([0,1],[0,1],'r--', label='Random Guess')
plt.legend(loc='lower right')
plt.xlim([0,1])
plt.ylim([0,1])
plt.show()
#define precision, recall, and corresponding threshold for model with highest AUC
precision, recall, threshold = precision_recall_curve(merged_pred['went_on_backorder'],
merged_pred['pred_nest50_feat3_leaf5'])
#plot Precision and Recall for a given threshold.
plt.title('Precision and Recall')
plt.plot(threshold,precision[1:],'purple',label='Precision')
plt.plot(threshold,recall[1:],'orange', label='Recall')
plt.axvline(x=.085,linestyle=":")
plt.legend(loc=2,bbox_to_anchor=(1.05, 1))
plt.xlim([0,1])
plt.ylim([0,1])
plt.ylabel('Precision and Recall Values')
plt.xlabel('Threshold')
plt.show()
#Confusion Matrix
merged_pred['optimal_classification']=merged_pred['pred_nest50_feat3_leaf5']>.4
pd.crosstab(merged_pred['went_on_backorder'],
merged_pred['optimal_classification'],
rownames=['Went on Backorder'],
colnames=['Predicted going on Backorder?'])
#Accuracy of model
accuracy_score(merged_pred['went_on_backorder'],merged_pred['optimal_classification'])
#Accuracy of "naive" (never-predict-backorder) model
merged_pred['naive_estimator']=0
accuracy_score(merged_pred['went_on_backorder'],merged_pred['naive_estimator'])
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
x = total_data.drop(['went_on_backorder'],axis=1)
y = total_data['went_on_backorder']
x_train, x_val, y_train, y_val = train_test_split(x,y, test_size = 0.3, random_state = 123)
sm = SMOTE(random_state=12, ratio = 1.0)
x_train_res, y_train_res = sm.fit_sample(x_train, y_train)
rfd=RandomForestClassifier(n_estimators=50,
max_features=5,
min_samples_leaf=5)
rfd.fit(x_train_res, y_train_res)
pred = rfd.predict_proba(x_val)[:,1]
fpr, tpr, thresholds = roc_curve(y_val, pred)
#AUC for best Random Forest and Random Forest Down sampled Models
roc_auc = roc_auc_score(y_val,pred)
#plot ROC Curve
plt.title('ROC Curve')
plt.plot(fpr, tpr, 'b', label='RF (AUC = %0.3f)'% roc_auc)
plt.plot([0,1],[0,1],'r--', label='Random Guess')
plt.legend(loc='lower right')
plt.xlim([0,1])
plt.ylim([0,1])
plt.show()
#define precision, recall, and corresponding threshold for model with highest AUC
precision, recall, threshold = precision_recall_curve(y_val,pred)
#plot Precision and Recall for a given threshold.
plt.title('Precision and Recall')
plt.plot(threshold,precision[1:],'purple',label='Precision')
plt.plot(threshold,recall[1:],'orange', label='Recall')
plt.axvline(x=.66,linestyle=":")
plt.legend(loc=2,bbox_to_anchor=(1.05, 1))
plt.xlim([0,1])
plt.ylim([0,1])
plt.ylabel('Precision and Recall Values')
plt.xlabel('Threshold')
plt.show()
#Confusion Matrix
pd.crosstab(y_val,
pred>0.66,
rownames=['Went on Backorder'],
colnames=['Predicted going on Backorder?'])
#Accuracy of model
accuracy_score(y_val,pred>0.66)


Step 1:
Select Hyperparameters
Step 2: Perform Clustering
Step 2a: Relate Clusting to Labels
Step 3: T-Test Features by Cluster
Step 4: Visualize Significant Features
Step 4: Visualize Significant Features
Step 4: Visualize Significant Features
Step 4: Visualize Significant Features
Step 4: Visualize Significant Features
Step 4:
Visualize Significant Features


This is an introductory tutorial to show you some of the things you can do with coding.
Right click and click on "Inspect Element".
This shows the webpage inspector. On the left you see all elements this webpage consists of. On the right you see the styling of this webpage.
Click on the small arrow in front of < body >...< /body >.
This will show all elements of the webpage that belong to the body. If you hover over the lines you see which line corresponds to which area on the webpage.
Click on the class="jumbotron vertical-center".
We are going to change the background of this part of the webpage.
On the right side, scroll to the part that starts with .jumbotron { and click on the purple block next to background-color.
You should see a colour panel in which you can select a new colour. Go ahead, change the backbround colour to any colour you like! Make sure to scroll to the top of the page to see your changes. Now let's change something else!
Click on the arrow in front of class="container" and select the class="text-center"
On the right side, scroll to the part that starts with .jumbotron h1 { and has a part that says "color:inherit"
Change to word inherit to a colour name, for example blue. Scroll up again to see the change you made.
The last change we are going to make is changing the font. For this change we need to add a line of code.
Click next to the line that says "color:inherit". This should create a new line where you can type underneath.
Type the word "font-family", then press ENTER and type the word "times".
This changes the font to the font type "times". Just like with the colour, you can change the font to any font you want.
You have now finished the introduction tutorial, well done! If you enjoyed it, and would like to learn more about coding click here.


-senderFullname- made you co-host of the -sport- game
-description-
-location-
-datetime-
View Game on Web Browser
Copyright
2012
Active Now, Inc.
all rights reserved.


This is my projects page. This is a developing list of things that I have created with my full stack development tool box. I have learned so much in my short time at the WeCanCodeIT bootcamp. I am eager to continue to learn more about coding and to develop my portfolio as I grow.


What is Remixable Space Invaders?
This is a remixable simplified version of the classic arcade game Space Invaders. Defend your planet against the group of invaders who have appeared in the sky!
Directions:
Move with the arrow keys or a mouse/touch (turn on mouse controls below). Shoot with the S key, spacebar or with click/tap. The game on the left (or at the top on mobile) updates live as you change the sliders below. Click the 'Activate Controls!' button to get started.


fn() reAlignmentPerform realignment on a FragmentStore object.
| Defined in | <seqan/realign.h> |
|---|---|
| Signature |
void reAlignment(store, contigID, realignmentMethod, bandwidth, includeReference[,
windowBegin, windowEnd][, debug][, printTiming]);
|
Parameters
store
|
The FragmentStore to perform realignment for. |
|---|---|
realignmentMethod
|
The realignment algorithm to use, 1 for affine gap costs, 0 for linear gap costs, affine gap costs are recommended, unsigned. |
contigID
|
Identifier of the contig to realign, unsigned |
bandwidth
|
Bandwidth to use in the pairwise DP alignment algorithms unsigned. |
includeReference
|
A bool, if true then the reference will be included as a pseudo-read to increase stability in case of indels. See section "Including the Reference" for details. |
windowBegin
|
Optional unsigned window begin for windowed alignment, default: 0, i.e. no windowing. Also see section "Windowed Realignment" below. |
windowEnd
|
Optional unsigned window end for windowed alignment, default: 0, i.e. no windowing. Also see section "Windowed Realignment" below. |
debug
|
Optional bool to enable verbose logging, default: false. |
printTiming
|
Optional bool to enable printing of times, default: false. |
Detailed Description
This function implements a variant of the Anson-Myers algorithm to refine the multi-read alignments. The algorithm works in a round-robin fashion:
First, the profile sequence is created for the multi-read alignment. The algorithm then works in round.
In each round, each read alignment is selected, removed from the profile and then aligned against the profile using a DP algorithm. For this alignment, a region of bandwidth / 2 positions around the previous alignment locus is chosen and aligned using a bandwidth of bandwidth in the DP alignment algorithm. The profile is then updated with the resulting pairwise alignment. This is iterated until the global alignment score does not improve.
In the end, the reference sequence of the given contig is replaced by the consensus of the multi-read alignment.
Including the Reference
You can choose to include the reference sequence of the selected contig as a pseudo-read. In this case, the reference sequence of the selected contig will be added as an additional read and also a read alignment will be created for it. The appended pseudo-read will have the highest read ID in the store.
Windowed Realignment
Sometimes, it is useful to just realign a part of the fragment store. This can be done by using the parameters windowBegin and windowEnd. This refers to view positions in the current multi-read alignment. Set both to 0 (also the default) to disalbe windowed realignment.
References
- Anson, Eric L., and Eugene W. Myers. "ReAligner: a program for refining DNA sequence multi-alignments." Journal of Computational Biology 4.3 (1997): 369-383.


public class Tsurgeon
extends java.lang.Object
java edu.stanford.nlp.trees.tregex.tsurgeon.Tsurgeon -treeFile atree exciseNP renameVerbThe file
atree
has Penn Treebank (S-expression) format trees.
The other (here, two) files have Tsurgeon operations. These consist of
a list of pairs of a tregex expression on one or more
lines, a blank line, and then some number of lines of Tsurgeon operations and then
another blank line.
Tsurgeon uses the Tregex engine to match tree patterns on trees;
for more information on Tregex's tree-matching functionality,
syntax, and semantics, please see the documentation for the
TregexPattern
class.
If you want to use Tsurgeon as an API, the relevant method is
processPattern(edu.stanford.nlp.trees.tregex.TregexPattern, edu.stanford.nlp.trees.tregex.tsurgeon.TsurgeonPattern, edu.stanford.nlp.trees.Tree)
. You will also need to look at the
TsurgeonPattern
class and the parseOperation(java.lang.String)
method.
Here's the simplest form of invocation on a single Tree:
Tree t = Tree.valueOf("(ROOT (S (NP (NP (NNP Bank)) (PP (IN of) (NP (NNP America)))) (VP (VBD called)) (. .)))"); TregexPattern pat = TregexPattern.compile("NP <1 (NP << Bank) <2 PP=remove"); TsurgeonPattern surgery = Tsurgeon.parseOperation("excise remove remove"); Tsurgeon.processPattern(pat, surgery, t).pennPrint();
Here is another sample invocation:
TregexPattern matchPattern = TregexPattern.compile("SQ=sq < (/^WH/ $++ VP)"); List<TsurgeonPattern> ps = new ArrayList<TsurgeonPattern>(); TsurgeonPattern p = Tsurgeon.parseOperation("relabel sq S"); ps.add(p); Treebank lTrees; List<Tree> result = Tsurgeon.processPatternOnTrees(matchPattern,Tsurgeon.collectOperations(ps),lTrees);
Note: If you want to apply multiple surgery patterns, you
will not want to call processPatternOnTrees, for each individual
pattern. Rather, you should either call processPatternsOnTree and
loop through the trees yourself, or, as above, use
collectOperations
to collect all the surgery patterns
into one TsurgeonPattern, and then to call processPatternOnTrees.
Either of these latter methods is much faster.
The parser also has the ability to collect multiple
TsurgeonPatterns into one pattern by itself by enclosing each
pattern in [ ... ]
. For example,
Tsurgeon.parseOperation("[relabel foo BAR] [prune bar]")
For more information on using Tsurgeon from the command line,
see the main(java.lang.String[])
method and the package Javadoc.
| Modifier and Type | Method and Description |
|---|---|
static TsurgeonPattern |
collectOperations(java.util.List<TsurgeonPattern> patterns)
Collects a list of operation patterns into a sequence of operations to be applied.
|
static Pair<TregexPattern,TsurgeonPattern> |
getOperationFromReader(java.io.BufferedReader reader,
TregexPatternCompiler compiler)
Parses a tsurgeon script text input and compiles a tregex pattern and a list
of tsurgeon operations into a pair.
|
static java.util.List<Pair<TregexPattern,TsurgeonPattern>> |
getOperationsFromFile(java.lang.String filename,
java.lang.String encoding,
TregexPatternCompiler compiler)
Parses a tsurgeon script file and compiles all operations in the file into a list
of pairs of tregex and tsurgeon patterns.
|
static java.util.List<Pair<TregexPattern,TsurgeonPattern>> |
getOperationsFromReader(java.io.BufferedReader reader,
TregexPatternCompiler compiler)
Parses and compiles all operations from a BufferedReader into a list
of pairs of tregex and tsurgeon patterns.
|
static java.lang.String |
getTregexPatternFromReader(java.io.BufferedReader reader)
Assumes that we are at the beginning of a tsurgeon script file and gets the string for the
tregex pattern leading the file.
|
static TsurgeonPattern |
getTsurgeonOperationsFromReader(java.io.BufferedReader reader)
Assumes the given reader has only tsurgeon operations (not a tregex pattern), and parses
these out, collecting them into one operation.
|
static java.lang.String |
getTsurgeonTextFromReader(java.io.BufferedReader reader)
Assumes the given reader has only tsurgeon operations (not a tregex pattern), and returns
them as a String, mirroring the way the strings appear in the file.
|
static void |
main(java.lang.String[] args)
Usage: java edu.stanford.nlp.trees.tregex.tsurgeon.Tsurgeon [-s] -treeFile file-with-trees [-po matching-pattern operation] operation-file-1 operation-file-2 ...
|
static TsurgeonPattern |
parseOperation(java.lang.String operationString)
Parses an operation string into a
TsurgeonPattern . |
static Tree |
processPattern(TregexPattern matchPattern,
TsurgeonPattern p,
Tree t)
Tries to match a pattern against a tree.
|
static java.util.List<Tree> |
processPatternOnTrees(TregexPattern matchPattern,
TsurgeonPattern p,
java.util.Collection<Tree> inputTrees)
Applies {#processPattern} to a collection of trees.
|
static Tree |
processPatternsOnTree(java.util.List<Pair<TregexPattern,TsurgeonPattern>> ops,
Tree t) |
public static void main(java.lang.String[] args) throws java.lang.Exception
TregexPattern
pattern on one or more lines, then a
blank line (empty or whitespace), then a list of transformation operations one per line
(as specified by Legal operation syntax below) to apply when the pattern is matched,
and then another blank line (empty or whitespace).
Note the need for blank lines: The code crashes if they are not present as separators
(although the blank line at the end of the file can be omitted).
The script file can include comment lines, either whole comment lines or
trailing comments introduced by %, which extend to the end of line. A needed percent
mark can be escaped by a preceding backslash.
For example, if you want to excise an SBARQ node whenever it is the parent of an SQ node, and relabel the SQ node to S, your transformation file would look like this:
SBARQ=n1 < SQ=n2
excise n1 n1
relabel n2 S
-treeFile <filename>
specify the name of the file that has the trees you want to transform.
-po <matchPattern> <operation>
Apply a single operation to every tree using the specified match pattern and the specified operation. Use this option
when you want to quickly try the effect of one pattern/surgery combination, and are too lazy to write a transformation file.
-s
Print each output tree on one line (default is pretty-printing).
-m
For every tree that had a matching pattern, print "before" (prepended as "Operated on:") and "after" (prepended as "Result:"). Unoperated on trees just pass through the transducer as usual.
-encoding X
Uses character set X for input and output of trees.
-macros <filename>
A file of macros to use on the tregex pattern. Macros should be one per line, with original and replacement separated by tabs.
-hf <headfinder-class-name>
use the specified HeadFinder
class to determine headship relations.
-hfArg <string>
pass a string argument in to the HeadFinder
class's constructor. -hfArg
can be used multiple times to pass in multiple arguments.
-trf <TreeReaderFactory-class-name>
use the specified TreeReaderFactory
class to read trees from files.
delete <name>
deletes the node and everything below it.
prune <name>
Like delete, but if, after the pruning, the parent has no children anymore, the parent is pruned too. Pruning continues to affect all ancestors until one is found with remaining children. This may result in a null tree.
excise <name1> <name2>
The name1 node should either dominate or be the same as the name2 node. This excises out everything from
name1 to name2. All the children of name2 go into the parent of name1, where name1 was.
relabel <name> <new-label>
Relabels the node to have the new label. relabel nodeX VP
- for changing a node label to an
alphanumeric string relabel nodeX /''/
- for relabeling a node to
something that isn't a valid identifier without quoting relabel nodeX /^VB(.*)$/verb\\/$1/
- for regular
expression based relabeling. In this case, all matches of the
regular expression against the node label are replaced with the
replacement String. This has the semantics of Java/Perl's
replaceAll: you may use capturing groups and put them in
replacements with $n. For example, if the pattern is /foo/bar/
and the node matched is "foo", the replaceAll semantics result in
"barbar". If the pattern is /^foo(.*)$/bar$1/ and node matched is
"foofoo", relabel will result in "barfoo". insert <name> <position>
or insert <tree> <position>
inserts the named node or tree into the position specified.
move <name> <position>
moves the named node into the specified position.
Right now the only ways to specify position are:
$+ <name>
the left sister of the named node
$- <name>
the right sister of the named node
>i <name>
the i_th daughter of the named node
>-i <name>
the i_th daughter, counting from the right, of the named node.
replace <name1> <name2>
deletes name1 and inserts a copy of name2 in its place.
replace <name> <tree> <tree2>...
deletes name and inserts the new tree(s) in its place. If
more than one replacement tree is given, each of the new
subtrees will be added in order where the old tree was.
Multiple subtrees at the root is an illegal operation and
will throw an exception.
createSubtree <auxiliary-tree-or-label> <name1> [<name2>]
Create a subtree out of all the nodes from <name1>
through
<name2>
. The subtree is moved to the foot of the given
auxiliary tree, and the tree is inserted where the nodes of
the subtree used to reside. If a simple label is provided as
the first argument, the subtree is given a single parent with
a name corresponding to the label. To limit the operation to
just one node, elide <name2>
.
adjoin <auxiliary_tree> <name>
Adjoins the specified auxiliary tree into the named node.
The daughters of the target node will become the daughters of the foot of the auxiliary tree.
adjoinH <auxiliary_tree> <name>
Similar to adjoin, but preserves the target node
and makes it the root of <tree>. (It is still accessible as name
. The root of the
auxiliary tree is ignored.)
adjoinF <auxiliary_tree> <name>
Similar to adjoin,
but preserves the target node and makes it the foot of <tree>.
(It is still accessible as name
, and retains its status as parent of its children.
The root of the auxiliary tree is ignored.)
coindex <name1> <name2> ... <nameM>
Puts a (Penn Treebank style)
coindexation suffix of the form "-N" on each of nodes name_1 through name_m. The value of N will be
automatically generated in reference to the existing coindexations in the tree, so that there is never
an accidental clash of indices across things that are not meant to be coindexed.
In the context of adjoin
, adjoinH
,
adjoinF
, and createSubtree
, an auxiliary
tree is a tree in Penn Treebank format with @
on
exactly one of the leaves denoting the foot of the tree.
The operations which use the foot use the labeled node.
For example:
Tsurgeon: adjoin (FOO (BAR@)) foo
Tregex: B=foo
Input: (A (B 1 2))
Output: (A (FOO (BAR 1 2)))
Tsurgeon applies the same operation to the same tree for as long as the given tregex operation matches. This means that infinite loops are very easy to cause. One common situation where this comes up is with an insert operation will repeats infinitely many times unless you add an expression to the tregex that matches against the inserted pattern. For example, this pattern will infinite loop:
TregexPattern tregex = TregexPattern.compile("S=node << NP");
TsurgeonPattern tsurgeon = Tsurgeon.parseOperation("insert (NP foo) >-1 node");
This pattern, though, will terminate:
TregexPattern tregex = TregexPattern.compile("S=node << NP !<< foo");
TsurgeonPattern tsurgeon = Tsurgeon.parseOperation("insert (NP foo) >-1 node");
Tsurgeon has (very) limited support for conditional statements.
If a pattern is prefaced with
if exists <name>
,
the rest of the pattern will only execute if
the named node was found in the corresponding TregexMatcher.
args
- a list of names of files each of which contains a single tregex matching pattern plus a list, one per line,
of transformation operations to apply to the matched pattern.java.lang.Exception
- If an I/O or pattern syntax errorpublic static Pair<TregexPattern,TsurgeonPattern> getOperationFromReader(java.io.BufferedReader reader, TregexPatternCompiler compiler) throws java.io.IOException
reader
- Reader to read patterns fromnull
when the operations present in the Reader have been exhaustedjava.io.IOException
- If any IO problempublic static java.lang.String getTregexPatternFromReader(java.io.BufferedReader reader) throws java.io.IOException
java.io.IOException
- If the usual kinds of IO errors occurpublic static TsurgeonPattern getTsurgeonOperationsFromReader(java.io.BufferedReader reader) throws java.io.IOException
java.io.IOException
- If the usual kinds of IO errors occurpublic static java.lang.String getTsurgeonTextFromReader(java.io.BufferedReader reader) throws java.io.IOException
java.io.IOException
public static java.util.List<Pair<TregexPattern,TsurgeonPattern>> getOperationsFromFile(java.lang.String filename, java.lang.String encoding, TregexPatternCompiler compiler) throws java.io.IOException
filename
- A file, classpath resource or URL (perhaps gzipped) containing the tsurgeon scriptjava.io.IOException
- If there is any I/O problempublic static java.util.List<Pair<TregexPattern,TsurgeonPattern>> getOperationsFromReader(java.io.BufferedReader reader, TregexPatternCompiler compiler) throws java.io.IOException
reader
- A BufferedReader to read the operationsjava.io.IOException
- If there is any I/O problempublic static java.util.List<Tree> processPatternOnTrees(TregexPattern matchPattern, TsurgeonPattern p, java.util.Collection<Tree> inputTrees)
matchPattern
- A TregexPattern
to be matched against a Tree
.p
- A TsurgeonPattern
to apply.inputTrees
- The input trees to be processedpublic static Tree processPattern(TregexPattern matchPattern, TsurgeonPattern p, Tree t)
TsurgeonPattern
.matchPattern
- A TregexPattern
to be matched against a Tree
.p
- A TsurgeonPattern
to apply.t
- the Tree
to match against and perform surgery on.public static Tree processPatternsOnTree(java.util.List<Pair<TregexPattern,TsurgeonPattern>> ops, Tree t)
public static TsurgeonPattern parseOperation(java.lang.String operationString)
TsurgeonPattern
. Throws an TsurgeonParseException
if
the operation string is ill-formed.
Example of use:
TsurgeonPattern p = Tsurgeon.parseOperation("prune ed");
operationString
- The operation to perform, as a text stringpublic static TsurgeonPattern collectOperations(java.util.List<TsurgeonPattern> patterns)
patterns
- a list of TsurgeonPattern
operations that you want to collect together into a single compound operationTsurgeonPattern
that performs all the operations in the sequence of the patterns
argument


Send A Gift For Christmas
In these end of the year holidays send a gift
to your loved one and share the happiness
at Christmas.
In these end of the year holidays send a gift
to your loved one and share the happiness
at Christmas.
Accessory
Accessory
Accessory
Accessory
Accessory
Accessory
Start giving away before the holydays are
over. Write the home address of the person
who will senty the gift
© 2021 Bedimcode. All right reserved


EPA plans to relax coal power plant waste rules
Proposals scale back Obama administration rules
by Rene Marsh, CNNWASHINGTON, D.C. - The Environmental Protection Agency is planning Monday to relax rules regulating how power plants store waste from coal power plants and the release of water containing toxic chemicals into nearby waterways, according to a source familiar with the plans.
The Washington Post was first to report on the planned announcement.
The proposals scale back two Obama administration rules from 2015 that affect the disposal of fine powder and sludge, otherwise known as coal ash.
Coal ash can contain mercury, arsenic and other metals that could negatively affect both human health and the environment at large.
The new rules would allow extensions that could keep unlined coal ash waste ponds open for up to eight additional years and allow plants to discharge wastewater that captures the waste through a membrane system, though many plants would be exempt.
The rule relaxation comes as companies in the coal industry claimed in court that the current rules were unaffordable and as the Trump administration has taken a "pro coal" stance under the President.


section-1
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{% extends "bootstrap_layout.html" %} {% set active_page = "home" %} {% block content %}
Request a Yelp food itinerary for your vacation:


const dnd = require('../index');
This guide shows typical use cases and how to simulate DnD combat encounters using this library. We will construct a fight between several groups of combatants.
const dnd = require(‘dnd-combat-simulator’);
const dnd = require('../index');
First we create a new combat object.
let combat = new dnd.Combat();
Our first group of combatants is a pair of ogres.
let ogres = new dnd.Party();
ogres.addMember(new dnd.Combatant('ogre1', 59, 16, 0, 6, 10, 2, 4));
ogres.addMember(new dnd.Combatant('ogre2', 59, 16, 0, 6, 10, 2, 4));
The combat strategy of the ogres will be to target the strongest enemy on the battlefield.
ogres.combatStrategy = ogres.combatStrategies.highest_hp;
Finally we need to add the party to the combat and give it a name.
combat.addParty(ogres, 'ogres');
The second group are three owlbears.
let owlbears = new dnd.Party();
let owlbear1 = new dnd.Combatant('owlbear1', 59, 13, 3, 7, 10, 1, 5);
Owlbears have multiple attacks each turn. We will add their second attack here.
owlbear1.addAttack(new dnd.Attack(8, 2, 5));
let owlbear2 = new dnd.Combatant('owlbear2', 59, 13, 3, 7, 10, 1, 5);
owlbear2.addAttack(new dnd.Attack(8, 2, 5));
let owlbear3 = new dnd.Combatant('owlbear3', 59, 13, 3, 7, 10, 1, 5);
owlbear3.addAttack(new dnd.Attack(8, 2, 5));
owlbears.addMember(owlbear1);
owlbears.addMember(owlbear2);
owlbears.addMember(owlbear3);
combat.addParty(owlbears, 'owlbears');
The third group of fighters are three knights.
let knights = new dnd.Party();
let knight1 = new dnd.Combatant('knight1', 52, 18, 0, 5, 6, 2, 3);
Knights also have a second attack.
knight1.addAttack(new dnd.Attack(6, 2, 3));
Knights have a parry reaction, which lets them increase their armour class by two once per round.
knight1.addReaction(new dnd.reaction.Reaction(dnd.reaction.reactions.parry));
let knight2 = new dnd.Combatant('knight2', 52, 18, 0, 5, 6, 2, 3);
knight2.addAttack(new dnd.Attack(6, 2, 3));
knight2.addReaction(new dnd.reaction.Reaction(dnd.reaction.reactions.parry));
let knight3 = new dnd.Combatant('knight3', 52, 18, 0, 5, 6, 2, 3);
knight3.addAttack(new dnd.Attack(6, 2, 3));
knight3.addReaction(new dnd.reaction.Reaction(dnd.reaction.reactions.parry));
knights.addMember(knight1);
knights.addMember(knight2);
knights.addMember(knight3);
Knights aim to strategically remove the most dangerous enemies from the fight first.
knights.combatStrategy = knights.combatStrategies.highest_dmg;
combat.addParty(knights, 'knights');
There is a single wereboar fighting.
let wereboars = new dnd.Party();
let wereboar1 = new dnd.Combatant('wereboar1', 78, 11, 0, 5, 6, 2, 3);
wereboar1.addAttack(new dnd.Attack(6, 2, 3));
wereboars.addMember(wereboar1);
combat.addParty(wereboars, 'wereboars');
In the next step we simulate the fight 10000 times and save the winners of each group. After each simulation, we need to reset the state of the combat.
let winners = [];
for (let i = 0; i < 10000; i++){
winners.push(combat.runFight());
combat.reset();
}
After finishing the simulations we calculate the win ratios for each party and print those out.
let parties = {};
for (let party_id in combat.parties){
parties[party_id] = 0;
}
let count = 1;
winners.forEach(function(winner_party){
count++;
parties[winner_party[0].party_id] += 1;
});
console.log('\nWin Ratios');
for (let p in parties){
console.log(p + ': ' + parties[p]/100 + '%');
}


The sample demonstrates how to navigate through the numeral system options using the Keyboard.
-
Tab - Like other widgets, the jqxFormattedInput widget receives focus by
tabbing into it. Once focus is received, users will be able to use the keyboard
to change the selection. A second tab will take the user out of the widget.
-
Shift+Tab - reverses the direction of the tab order. Once in the widget,
a Shift+Tab will take the user to the previous focusable element in the tab order.
- Up/Down arrow keys - select previous or next displayFormat option.
- Alt Up/Down arrow keys - opens or closes the popup.
- Esc - closes the popup.
- Enter - selects an item.


I decided to split this into parts, because I had a great time at I/O and there was a lot to learn and experience over 3 days.
(Additional disclaimer: I do not present myself as a representative of Amazon nor does anything I say here confirm, deny or relate to any business strategy, priorities or initiatives currently being undertaken at Amazon. Any mention of Amazon products are presented as my personal opinion as a technology consumer, not as an employee. Phew.)
Keynote
The undisputed overarching theme of this year’s I/O keynote was machine learning, and how far Google’s machine learning capabilities have come. This flowed right into a narrative of how strong Google’s machine learning capabilities are, how much they have improved, and how that technology is driving the quality of the consumer-facing products it would present throughout the keynote.
Machine Learning
For Google to come out and say that there have been tremendous advances in machine learning is somewhat disingenuous. That’s not to say that they don’t have better prediction models today than they probably did 5-10 years ago, but the thing about models is the quality of predictions generated is largely based on the quality of the data they are trained on.
Saying that we have better machine learning today than we did last year is akin to saying “we have unprecedented amounts of very good, high quality data on our users that we are able to use to generate very accurate predictions in real time”, which made the follow-up presentation of Google Home more than a little unsettling.
Google Assistant
The keynote also spent significant time making a hard sell for Google assistant technology. Sundar Pichai came out and did this staged demonstration of an interaction with the assistan that displayed his words before he got to that point in the presentation. He wrapped up with a comment that this is the sort of interaction that demonstrates what Google assistant technology “aspires to be” (paraphrased).
This was extremely similar to last year’s Google Now demonstration. For me, contrived demos of technology that is not quite ready for prime time should be reserved for truly game-changing, never before seen products or technologies. We forgave the buggy Moto 360 demos two years ago because they were attempting to produce a circular smartwatch form factor, which was hard.
Demonstrating what is effectively a chat bot in this way feels like a major step backwards in what the audience has come to expect in terms of game-changing technology getting announced at I/O. At this point in the keynote, it started to feel like those bi-weekly sprint demos in which no one but marketing wants to present what they’re working on.
Home
Continuing the narrative of new applications of advanced machine learning and the assistant technology, the first (and only physical) consumer product presented was Google Home. In the weeks leading up to I/O, there were several news articles that Google was going to present its Echo competitor at I/O.
I’m not sure if my disappointment is biased because of the pre-I/O spoilers or from the presentation itself. I’m skeptical of voice-based assistant technology for the non-blind population in general for the simple fact that no presentation has ever conclusively answered this question for me:
- What is the compelling use case that voice UI is uniquely better suited for than other UIs?
I read faster than I type, I type faster than I think (sometimes), and I do both of those things much faster than talking. Using a voice assistant feels like a giant step backwards because I can gesture and swipe in the time it takes me to initialize the interaction with a voice assistant, get an acknowledgement, say my command, confirm that this is the command I intended to provide, and then complete my original goal.
Look, I use Gmail and I continue to do so because it provides a compelling product at no monetary cost to me in exchange for my data and this is an trade-off I am comfortable with. I don’t consider myself data paranoid. What I am not about to do is to run out and purchase a product that has no killer app or user story in order to provide Google with more data. It’s also a speaker?
My home is my sanctuary. When I’m home, I get comfort from the hum of domestic life – the dog down the hall barking, the rumble of the washing machine, the crackle of ice cubes falling out of the ice cube tray. I rarely if ever listen to music when I’m home, so the speaker itself is not a compelling product for me to begin with.
Personal feelings aside, the inclusion of Home in the I/O keynote felt strange not only because of the lack of compelling use case, but also because it was announced to be made available “later this year”.
Allo and Duo
If spending keynote minutes on a voice assistant demonstration felt weird, then


This view displays the history of your wallet.
All entries are grouped by the currency, which can be selected with the drop-down menu. Further information about an entry are available if you click it.


The phone and email address used to contact your establishment:
A picnic area endorsement is an outdoor area, where patrons may consume products that are purchased from the manufacturer or obtained from the sampling area. The picnic area must be approved by the Branch. Applicants must have zoning confirmation and approval of their proposed capacity by LG/FN. The following applies to the endorsement for a picnic area:
Licensees are expected to understand all relevant regulatory requirements established throughout the Liquor Control and Liquor Act, Liquor Control and Licensing Regulation, and relevant Terms and Conditions handbook.
As part of this application, you are required to confirm:| {{licenseToRemove?.name}} | |
|
{{licenseToRemove?.streetaddress}}
{{licenseToRemove?.postalCode}} {{licenseToRemove?.city}} {{licenseToRemove?.country}} |
|
| {{licenseToRemove?.licensee}} |
Prior to payment, your application will be submitted to your local government/Indigenous Nation for their comment and assessment confirmation of zoning. Please ensure you have reviewed their policy ( {{this.form.get('indigenousNation').value.websiteUrl}} ) to ensure your successful submission. If an LG/IN is the applicant, the Branch will gather community input and consider the regulatory criteria; the LG/IN is not permitted to conduct public input or provide comments on their own application. This is to prevent conflicts of interest. The applicant must pay any costs incurred to obtain the views of residents.
If you have any questions about this application, contact the Liquor and Cannabis Regulation Branch (LCRB) at<EMAIL_ADDRESS>If you have any questions about this application, contact the Liquor and Cannabis Regulation Branch (LCRB) at<EMAIL_ADDRESS>Provide a list of all shareholders, class of shares, number of voting and non-voting per shareholder, and date shares issued/transferred/cancelled. See example:
If one of the new shareholders is a private corporation, a public corporation, a society or a partnership, forms to support the applicable business type must be submitted, even if the shareholder holds less than 10% interest in the applicant corporation:
Provide supporting business forms if one or more of the shareholders is a company:
Provide the Notice of Articles for your SocietyCorporation. Alternatively, you can provide list of all current directors/officers by full legal name, position and date of appointment.
Directors and Officers are not required to submit a Personal History Summary form for liquor-related applications.
Provide a copy of your partnership agreement.
Please upload official forms that describes your organization's ownership and leadership structure.
Entities incorporated under federal or provincial legislation; example: local governments, First Nation, colleges, universities, etc. may have unique requirements. Please contact the Licensing Help Desk at 1-866-209-2111 to discuss further forms requirements
Each person involved in the applicant’s legal entity structure must submit a Personal History Summary form and a copy of your Criminal Record Search completed by the RCMP or local Police Department. The form may also require you to submit copy of your driver’s abstract or a statutory declaration. See the form for full details.
Each liquor affiliate must provide a complete Personal History Summary Form .
Sole proprietors and associates of applicants for a retail licence or authorization are required to submit forms for security screenings and financial integrity checks. {{ application?.applicationType?.name === ApplicationTypeNames.Marketer ? 'Cannabis Marketing' : 'Retail store licence or authorization' }} applications cannot be processed until all required materials have been received.
Cannabis Associate Security Screening forms are not required if you submit liquor related applications. Missing forms or documents will be requested once your application is received.
Please provide the documents as outlined on the website.
Each associate must provide a complete Cannabis Associate Security Screening form.
Shareholders (individuals) holding less than 10% interest, or shareholders with only non-voting shares, in the applicant corporation must provide their full legal name and date of birth. They do not need to provide a Personal History Summary or Cannabis Associate Security Screening form.
The special event area endorsement allows for the hosting of special events such as weddings, concerts, private parties or promotional events open to the public within a designated area.
The following applies to the endorsement of a Special Event Area:
Licensees are expected to review and understand all relevant regulatory requirements established in the Liquor Control and Licensing Act, Liquor Control and Licensing Regulation, and the relevant Terms and Conditions Handbook.
As part of this application, you are required to confirm:
The lounge endorsement permits the sale and service of liquor for on-site consumption in a designated lounge area on the manufacturing site.
The following applies to the endorsement of a Lounge Area:
Licensees are expected to review and understand all relevant regulatory requirements established in the Liquor Control and Licensing Act, Liquor Control and Licensing Regulation, and the relevant Terms and Conditions Handbook.
As part of this application, you are required to confirm:
A name cannot mislead the public about what kind of business the licensee or authorization holder operates. The name cannot imply a licensee or authorization holder is selling medical cannabis, and cannot include words in any form or combination or manner the words "apothecary", "pharmacy", "medicines", "drugs", "drug store", "drug department", "dispensary" or any other words of similar meaning that imply licensing under the Pharmacy Operations and Drug Scheduling Act. Store names cannot also use language that encourages intoxication.
Additionally, a name cannot imply an association with another business. This applies to signs, trademarks and any type of branding.
Establishment names will be reviewed for approval during the application process. For more information, please see the Cannabis Store Name Approval Process. Check the LCRB’s website for a list of all currently licensed cannabis stores in British Columbia.
Establishment names are subject to approval by LCRB
As part of the application, you can change the name of the establishment. If the name of the establishment is changing, you must provide a drawing or a photo of your proposed establishment sign below.
The establishment is currently located at the following address:
The proposed location of the establishment:
Include any information necessary for finding the site (i.e. route number, mile marker, etc.).
By submitting this application to LCRB, you are confirming that your licenced establishment will adhere to the requirements laid out in the Agricultural Land Reserve (ALR) Use Regulation .
The location of the federal production site:
Enter the local government or Indigenous Nation and police jurisdiction where the establishment will be located. Suggestions will be provided after you type the first three characters of the name.
Please review the information at {{form.get('indigenousNation').value.websiteUrl}} to ensure you meet the requirements of this application with your local government. You may need to contact them prior to submitting to ensure your successful submission.
Please ensure you have made yourself familiar with the application requirements for this local government (or Indigenous Nation) prior to submitting your application. You may need to contact them prior to submitting to ensure your successful submission.
By submitting this application to LCRB, you are confirming that your licenced establishment will adhere to the requirements laid out in the Agricultural Land Reserve (ALR) Use Regulation .
The phone and email address used to contact your establishment:
Identify the establishment type that best fits your proposed LP business, for example:
|
|
|
|
Note: the type of establishment may affect licensing conditions such as whether or not minors are permitted or when liquor may be served (see guide).
The signage submitted must show the name of the establishment and all words, images and symbols used in the signage.
If the name of the establishment is changing, you must provide a drawing or a photo of your proposed establishment sign. Your name and signage must be approved by the Branch to ensure it is appropriate, accurately reflecting the nature of the business. Do not order signs until you have received this approval.
Signage must not contain any pictures, images or symbols that imply the sale of medical cannabis (e.g. a green cross). It must not imply an association with another business (other than another non-medical cannabis retail store).
Note: If submitting renderings, renderings may be either printed or hand-drawn (as long as they are legible)
Upload renderings of the planned changes to the store's exterior:
Please confirm:
The floor plans of the proposed establishment, which must meet the following requirements:
The below floor plan is on file.
A scaled site plan must show the following:
If the proposed store is located inside a building with other businesses (e.g. a mall), submit two site plans:
1) One site plan showing location of any other buildings/businesses on the same site, including:
2) A second site plan showing the location of the proposed store inside the building’s footprint:
Full height walls are required for separation and no connecting doors between the cannabis store and the neighbouring business are permitted.
The cannabis store may share a vestibule with another type of business (e.g. a liquor store or grocery store) provided each business has its own controlled entrance within the vestibule. The cannabis store cannot share a vestibule with a business that would be contrary to public interest (i.e. business aimed at minors or a liquor consumption business).
Please download and print the f" Gro cery Store Declaration and have it be completed by the owner of an eligible grocery store.
An eligible grocery store is defined as:
To maintain eligibility, the conditions above must continue to be met, and the sales revenue from food and non-liquor liquor beverages must:
The standard service hours for a Food Primary are between 9:00AM and midnight. You may extend these hours by submitting an Extend Hours of Service Beyond Standard Service Hours application from the application dashboard after you submit your Food Primary application. LG/IN approval is required.
Indicate the proposed hours of sales below.
| Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | |
|---|---|---|---|---|---|---|---|
| Open | |||||||
| Close |
Hours of Sale are required
Which federally licensed producer(s) will the applicant represent in B.C.?
Upload the completed form below.
Please upload documents below.
Please download, print and have signed the Property Ownership Acknowledgement and Declaration Statements form.
Please download the Document Checklist Document Checklist Document Checklist Document Checklist Document Checklist Document Checklist Document Checklist Document Checklist Document Checklist Document Checklist and assemble all required documents. and assemble all required documents listed in the 'Documents Related to the Proposed Store' section.
Please upload the society’s Statement of Directors and Registered Office.
Please upload the Partnership Agreement.
Please upload:
Please provide the following details about your application:
If the applicant currently owns or leases the proposed store location, submit Option 1. If the applicant doesn't currently own or lease the proposed store location, submit Option 2.
Leases or other arrangements must not expire for at least 12 months from the date the licence or authorization is issued. Option 1 will be required before the licence or authorization is issued.
Option 1: Submit a copy of the lease, sub-lease, or proof of ownership of the proposed store location.
Option 2: Submit a copy of the offer to purchase, lease, or sub-lease the proposed store location. Note,
Any one of the following documents can be used to show valid interest in the property:
Note: Valid interest must be maintained at the current and proposed site until the relocation is finalized. LCRB may request a copy of valid interest at the current site during the relocation application process.
The below offer or proof of ownership, lease or sub-lease is on file.
Please provide contact information for the contact that the LCRB should communicate with regarding this application. This contact must be authorized to represent the applicant and have the authority to bind the applicant.
Please provide contact information for the contact that the LCRB should communicate with regarding the information submission.
The Rural Agency Store Information Submission must only be submitted by an individual with the authority to bind the Rural Agency Store. The branch relies on the Rural Agency Store Owner to ensure that the individual who submits this information is authorized to do so. Typically, an appropriate individual in a corporation will be a duly authorized signatory who will usually be an officer, or, in some cases, a director. Note: A lawyer or consultant may NOT submit information on behalf of the Rural Agency Store.
This application should be submitted the sole proprietor.
This application must only be submitted by an individual with the authority to bind the applicant. The branch relies on the applicant to ensure that the individual who submits this application is authorized to do so. Typically, an appropriate individual for a society will be a director or a senior manager (as defined in the Societies Act).
This application must only be submitted by an individual with the authority to bind the applicant. The branch relies on the applicant to ensure that the individual who submits this application is authorized to do so. Typically, an appropriate individual in a corporation will be a duly authorized signatory who will usually be an officer or, in some cases, a director.
This application must only be submitted by an individual with the authority to bind the applicant. The branch relies on the applicant to ensure that the individual who submits this application is authorized to do so. Typically, an appropriate individual will be as follows:
The application must only be submitted by an individual with the authority to bind the applicant. The branch relies on the applicant to ensure that the individual who submits this application is authorized to do so. Typically, an appropriate individual in a corporation will be a duly authorized signatory who will usually be an officer or, in some cases, a director.
Note: A lawyer or consultant, may NOT submit this application on behalf of the applicant.
Note: A lawyer or consultant, may NOT submit this application on behalf of the applicant.
Note: A lawyer, licensee representative or third party operator may NOT submit this application on behalf of the applicant.
Section 22(2) of the Cannabis Control and Licensing Act states: “A person must not submit to the general manager an application, or information or a record included as part of the application, that contains false or misleading information, or fails to disclose a material fact”.
Section 20 (1) of the Liquor Control and Licensing Act states: "The general manager may refuse to issue, renew, transfer or amend a licence if the applicant fails to disclose a material fact required by the application or makes a false or misleading statement in the application."
The application must only be submitted by an individual with the authority to bind the applicant. The branch relies on the applicant to ensure that the individual who submits this application is authorized to do so. Typically, an appropriate individual in a corporation will be a duly authorized signatory who will usually be an officer or, in some cases, a director. Note: A lawyer or consultant, may NOT submit this application on behalf of the applicant.
Section 22(2) of the Cannabis Control and Licensing Act states: “A person must not submit to the general manager an application, or information or a record included as part of the application, that contains false or misleading information, or fails to disclose a material fact”. Section 20(1) of the Liquor Control and Licensing Act states: “The general manager may refuse to issue, renew, transfer or amend a licence if the applicant fails to disclose a material fact required by the application or makes a false or misleading statement in the application.”
Proceed to pay online using a credit card through the secure web portal.
Please note: Applications which are submitted without all required information or documents will not be processed. Applicants will be contacted and provided the opportunity to submit missing information. Once applicants provide all required documents to the LCRB, the application will be processed.
The Province does not know how many applications for a cannabis retail store licence or authorization will be received. Therefore, it is difficult to predict how long applications will take to process. To minimize delays in processing, applicants must make sure the application is complete before submitting it.
The Province does not know how many applications for a cannabis marketing licence will be received. Therefore, it is difficult to predict how long applications will take to process. To minimize delays in processing, applicants must make sure the application is complete before submitting it.
Upload the completed form below.
Upload the completed form below.
Please download the Document Checklist Document Checklist Document Checklist Document Checklist Document Checklist Document Checklist Document Checklist Document Checklist Document Checklist Document Checklist and assemble all required documents. and assemble all required documents listed in the 'Documents Related to the Proposed Store' section.
Please upload the society’s Statement of Directors and Registered Office.
Please upload the Partnership Agreement.
Please upload:
Please add additional supporting documents as directed by LCRB.
Your {{application?.applicationType?.name}} submission (Job No. {{application?.jobNumber}}) has been reviewed by the Branch and is missing information that is required for it to be processed. Please see the attached letter that details the missing information.
Note: Your application will not be processed while required information is still outstanding.
Use the upload box to provide the information requested in the letter above.


This includes functions for creating 3D and 4D images using 'WebGL', 'rgl', and 'JavaScript' commands. This package relies on the X toolkit ('XTK', <https://github.com/xtk/X#readme>).
| Version: | 1.5.1 |
| Depends: | rgl, misc3d, oro.nifti |
| Imports: | grDevices |
| Published: | 2017-10-12 |
| Author: | John Muschelli [aut, cre] |
| Maintainer: | John Muschelli <muschellij2 at gmail.com> |
| License: | GPL-2 |
| Copyright: | see file COPYRIGHTS |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| In views: | MedicalImaging |
| CRAN checks: | brainR results |
| Reference manual: | brainR.pdf |
| Package source: | brainR_1.5.1.tar.gz |
| Windows binaries: | r-devel: brainR_1.5.1.zip, r-release: brainR_1.5.1.zip, r-oldrel: brainR_1.5.1.zip |
| OS X binaries: | r-release: brainR_1.5.1.tgz, r-oldrel: brainR_1.5.1.tgz |
| Old sources: | brainR archive |
| Reverse imports: | brainKCCA |
Please use the canonical form https://CRAN.R-project.org/package=brainR to link to this page.


Offer available Sunday to Friday
40% off Any Starter & 40% off Any Main Course
T&C's 40% off any starter and any main coursed only on a la Carte Menu. This offer is not available on set menus. Offer not available on Kids menu. Offer not in conjunction with any other o after a discount. Offers can be withdrawn anytime without notice.


Our Vision
To be the favoured destination for quality education in electronics and communication engineering.
1983
Establishment Year
60
seats per year
About Us
The department of Electronics & Telecommunication Engineering was established in the year 1983. Presently, it offers Bachelor of Engineering (B.E.) course in Electronics & Telecommunication Engineering with an annual intake capacity of 60. The syllabi of the B.E. course cover all major thrust areas in electronics and communication technology and also in the allied fields. The department has been always attracting quality students through the Common Entrance Examination conducted by Government of Assam. It has been always an endeavour for the teachers of this department to impart quality knowledge to the students such that as an engineer they can contribute accountably to the technological world as well as for the betterment of the society.
Our Missions
- Mission 1: To impart quality education for producing skilled manpower in Electronics and Telecommunication engineering to cater for the need of the country and the society as a whole.
- Mission 2: To encourage research and innovation in Electronics and Telecommunication with industry- academia interaction.
- Mission 3: To maintain an environment for shared learning, team work and professional development.
- Mission 4: To promote the human values and ethics for sustainable environment and society.
PEO
- PEO 1: To provide the theoretical knowledge and the practical experience of electronics and communication to make the students competent for pursuing a career in an industry or a research-and-development organization or higher studies.
- PEO 2: To facilitate innovation and entrepreneurship as well as to generate awareness for creation of intellectual property.
- PEO 3: To imbibe human values, team work and social and professional ethics for building a better environment and society.
- Mission 4: To promote the human values and ethics for sustainable environment and society.
PSO
- PSO 1: Students will be able to demonstrate software skills with competence to work and deliver in industry or research.
- PSO 2: Students will be competent to design applications and automation by using modern engineering tools with multi-disciplinary concepts


Travel Layer Visible:
Crash Layer Visible:
This layered map shows the data derived form the community bikes that are used through the city, showing the main thorough fairs of traffic on bicycles and as well as the crash data from the community bicycles highlighting hotspots of potential danger. Click on the button in the top corner to transition between the layers.


These sample applications are not anything you haven't seen before.
The interesting thing is that these demos are not done with templates, CSS and custom Javascript but
are only configured with UI-models.
By changing the ui-model, Evolutility-UI-jQuery set of views can behave as any of the following:
... or anything you can imagine by making new ui-models.
The demo apps are configured with these UI models: Course Evaluation
The data for these demos is stored in your browser's local storage (using Backbone.localStorage) but Evolutility-UI-jQuery can be configured for a REST API. For the server-side of Evolutility, use Evolutility-Server-Node with Node.js, Express.js and PostgreSQL.


New Wave
Country
Metal
New Wave Bands
Depeche Mode
Erasure
The Cure
Yazoo
Country Bands
Johnny Cash
Nancy Griffith
Patsy Cline
Hank Williams
Metal Bands
King Diamond
Iron Maiden
Def Leppard
Queensryche


Today is the feast day of St Scholastica, the patron saint of convulsive children. This is good news for Pang Hill Orphanage, where the tinies are often convulsed by wild enthusiasms for exciting games such as Pin The Paper To The Hardboard and Put The Detritus In The Waste Basket. St Scholastica can also be invoked against rainstorms, so her feast day is doubly welcome, as Pang Hill is almost invariably lashed by ferocious teeming rain no matter what the weather is like elsewhere.
This morning the orphans will have gathered in the big pantry behind the canteen to sing their special song:
O Scholastica please stop the rains / So we can concentrate our fuming brains /
On playing Watch The Orphans Faint / O Scholastica our patron saint!


Before Change
def handle(self, *args, **options):
slice_size = 1000
num_sounds = Sound.objects.filter(processing_state="OK", moderation_state="OK").count()
for i in range(0, num_sounds, slice_size):
console_logger.info("Adding %i sounds to solr, slice %i", slice_size, i)
try:
// Get all sounds moderated and processed ok
where = "sound.moderation_state = "OK" AND sound.processing_state = "OK" AND sound.id > %s"
order_by = "sound.id ASC"
sounds_qs = Sound.objects.bulk_query_solr(where, order_by, slice_size, (i, ))
add_sounds_to_solr(sounds_qs)
except SolrException as e:
console_logger.error("failed to add sound batch to solr index, reason: %s", str(e))
raise
// Get all sounds that should not be in solr and remove them if they are
sound_qs = Sound.objects.exclude(processing_state="OK", moderation_state="OK")
for sound in sound_qs:
delete_sound_from_solr(sound.id) // Will only do something if sound in fact exists in solr
After Change
def handle(self, *args, **options):
// Get all sounds moderated and processed ok
sounds_to_index = Sound.objects.filter(processing_state="OK", moderation_state="OK")
console_logger.info("Reindexing %d sounds to solr", sounds_to_index.count())
add_all_sounds_to_solr(sounds_to_index)
// Get all sounds that should not be in solr and remove them if they are


Digital agency focused on being the technological partner of different companies (from insurance companies to rapidly growing startups)
Team/Tech Lead (Jun 2013 – Apr 2015)
- The customer's technological partner focused on helping them grow by collaborating on the product strategy and finding growing opportunities
- Focused on the team's career path evolution
- Coaching and onboarding team members
Tech Lead (Feb 2012 – Jan 2014)
- Technical design of the solutions requested by customers aligned with their product strategy
- Development of high complexity solutions
- Led the adoption of good practices (such as agile programming, testing, package managers, etc) and technology modernization (moving from a proprietary framework to Laravel, CSS processors to simplify the dev experience, etc)
Engineer (Jul 2009 – Feb 2012)


Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Serves 8
The caramelised apple, thyme and crispy chorizo run through the middle of each slice of pork, with a drizzle of the dark balsamic sauce on top. It's absolutely delicious with greens such as curly kale and nutty brown basmati rice.
Preheat the oven to 160C/325F/Gas 3.
Season the pork on both sides with sea salt and freshly ground black pepper and set aside.
Heat the butter in a large frying pan and add the apple pieces. Cook without stirring for a few minutes until the apples start to caramelise. Toss and cook for a few minutes more until caramelised all over. Remove the apple from the pan and set aside.
Add the chorizo to the same pan and fry for 3-4 minutes. Add the onion and gently fry with the chorizo until it is soft and the chorizo is nicely coloured and crumbling. Add half the garlic and half the thyme, and cook for a further 30 seconds.
Drain the chorizo fat away from the mixture in the pan and mix in the caramelised apple. Spread this chorizo mixture onto the cut side of the pork(not the skin side), roll up and tie with string.
Heat the olive oil in a frying pan and brown the pork on all sides. Smear the rest of the garlic and thyme into the crackling, seasoning it with salt and pepper. Place the pork in the oven and cook for about 1 hour 50 minutes-2 hours, basting the joint in its juices from time to time. Twenty minutes before the end of the cooking time, increase the heat to 210C/410F/Gas 6-7, to crisp the crackling.
For the balsamic reduction, heat the balsamic vinegar in a small pan and simmer to reduce to about 50ml/2fl oz. It will have a syrupy consistency.
When the pork is cooked, remove the meat from the pan and rest in a warm place for 15-20 minutes, covered with foil. Add the balsamic reduction to the same pan in which you cooked the pork. Add the chipotle en adobo or paprika, redcurrant jelly and chicken stock. Heat through and season with salt and freshly ground black pepper.
Serve the pork in slightly overlapping slices arranged on a long plate, with a little of the sauce poured over along with the rest of the sauce served in a warm jug.


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The mode parameter has one of the following values:
| picCopy | This draws the picture on top of what was underneath, obscuring it completely. | |
| picXor | This draws the picture XORing it with the background. In DOS, you can use this function to do animation. Drawing an object on top of itself with XOR erases it and restores the background. | |
| picMerge | This draws the picture like picCopy except that any occurrence of the background color in the picture is not drawn to the screen. This allows you to draw an irregularly-shaped object and draw it to the screen. | |
| picUnderMerge | This draws the picture, but only where the background color was displayed underneath it. The effect of this is to make the picture appear to be displayed behind the background. |
var picID: int
var x, y : int
Draw.FillBox (50, 50, 150, 150, red)
Draw.FillStar (50, 50, 150, 150, green)
Draw.FillOval (100, 100, 30, 30, blue)
picID := Pic.New (50, 50, 150, 150)
for i : 1 .. 50
x := Rand.Int (0, maxx) % Random x
y := Rand.Int (0, maxy) % Random y
Pic.Draw (picID, x, y, picCopy)
end for
Pic.Free (picID)
This means that you can only call the function by calling Pic.Draw, not by calling Draw.
Pic.DrawSpecial for information on how to make pictures appear using special effects such as wipes and slides.
Pic.DrawFrames for information on how to display multiple frame images such as are obtained from GIF files.


Rivaldo Talks Bafana Future After AFCON Debacle
by Mazola MolefeRivaldo Coetzee says he is eager to return to the national setup following a debacle that could have very nearly ended his international career.
The Mamelodi Sundowns versatile player had initially been called up for Bafana Bafana’s provisional squad for the Africa Cup of Nations tournament in June, but did not report for duty, with then coach Stuart Baxter suggesting ‘Riva’ had gone AWOL.
Read: Downs Give Zwane & Sirino Update
Official word from his agent was that his mother was gravely ill at the time and the player had opted to look after him instead.
This week, Coetzee was unwilling to comment on what exactly transpired, but did confirm a meeting with Baxter’s successor.
“I am focused on the club now. SAFA make the decisions and it’s up to them. If I comment on what happened in the past it might just jeopardise my chances of going back. But we have sorted things out and I have spoken to the new coach, Molefi Ntseki. He said some positives things to me and I can only take that into my game and get better,” said Coetzee.
“This is my country, who wouldn’t want to play for their country? It is up to me now, whenever I get the chance to perform to the best of my ability and hopefully that will earn me a call-up. But I am focused on the club right now.”


ERASER A film review by Michael John Legeros Copyright 1996 Michael John Legeros
(WB) Directed by Charles Russell Written by Tony Puryear and Walon Green Cast Arnold Schwarzenegger, Vanessa Williams, James Caan, James Coburn, Robert Pastorelli MPAA Rating "R" (presumably for language and violence) Running Time 112 minutes Reviewed at The Wynnsong 10, Durham, NC (21JUN96)
==
The new Arnold Schwarzenegger movie is plotted like a series of old serials that have been combined and compressed, such that every ten minutes (or so) our hero must beat the clock to avoid another peril. Sirens! The cops are coming! Shots fired! The bad guys are coming! Parachute is tangled! The ground is coming! This methodical (and intentional?) approach is actually a *good* thing, given the patchwork of implausibilities, improbabilities, and other ignored concerns contained in the script. Arnie stars a steel-jawed U.S. Marshall who works for the Witness Protection Program and who has been assigned to protect-- at any cost-- a young woman (Vanessa Williams) who is blowing the whistle on her treasonous employer. The chutes and ladders involve a political conspiracy, some very high-tech weaponry, and, heaven help us, another mole inside of a secretive government agency.
Arnie's character is actually a variation on the Energizer Bunny. He gets stabbed, spiked, shot, crushed, blown-up, *and* dropped from a couple of different heights. None of the many writers (seven, I hear) thought to worry about his seeming invulnerability, any more than they thought to worry about describing a weapon as one that fires projectiles at "just below the speed of light." Help me. Granted, such head- scratchers are a trademark of the action genre, but too much of ERASER is carried too far. Or, rater, the action scenes which are supposed to *distract* us from such logic problems aren't carried far *enough*. Director Charles Russell (THE MASK) is new to the big-budget game and his set pieces are mostly modest in scope. That is, with two exceptions. Both a skydiving sequence and a roomful of escaped alligators are pure thrills. Bring a tissue, though, as you may get a nosebleed from going so far over the top.
Too erratic to be a "must see," ERASER is still recommendable for the smaller details. When Arnie is disguised as a delivery person, for example, the back of his jumpsuit reads "Let's Party."  Or, even better, the mobster who points to Arnie and asks: "Who's the tree trunk?" Other notable cast members include Babe's dad James Cromwell in one scene, James Coburn as the agency head, and Robert Pastorelli as Arnie's colorful, eleventh-hour sidekick. No one as dead-on as, say, either Tom Arnold or Charleton Heston in TRUE LIES, but they're amusing enough. (Nor is it every day that we get James Coburn stare down James Caan!) Finally, where would Schwarzenegger be without a handful of one-liners? The Terminator gets off a couple great ones that you're sure to hear elsewhere. There's also a classic exchange with two young kids, after Arnie drops from the sky into a junkyard. Very funny.
Grade: C+
--
Mike Legeros - Raleigh, NC
<EMAIL_ADDRESS>(h) -<EMAIL_ADDRESS>(w)
Visit me in MOVIE HELL
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