class: center, middle, inverse, title-slide # Enhancing QTL mapping with allelic series inferences ### Fred Boehm ### U. Mass. Medical School ### June 24, 2020 (updated: 2020-06-24) --- # Mtb-related QTL in CC - 52 CC lines - Measured 11 "clinical" traits - spleen CFU, lung CFU, spleen CEQ - 8 cytokines ??? Broad peaks and limited detection power --- # Mtb-related QTL in CC
cc_trait
chr
pos
lod
pvalue
spleenCFU
2
174.3
7.0
0.213
spleenCEQ
2
178.3
9.1
0.024
lungCFU
7
37.8
7.5
0.129
lungCFU
15
78.2
7.1
0.203
IL1B
17
31.6
6.8
0.280
IL2
18
49.4
7.0
0.212
IL9
2
41.5
8.6
0.047
IL10
2
174.3
6.9
0.267
IL10
17
82.5
7.2
0.179
IL17
15
74.1
7.8
0.066
KC
7
45.2
7.6
0.123
LIX
7
36.1
6.8
0.297
VEGF
3
131.0
6.9
0.294
VEGF
4
154.1
6.8
0.302
??? - Red rows denote p < 0.05 - Blue row is of interest later --- # Mtb-related QTL in DO - 763 mice - 5 traits: lung CFU, spleen CFU, percent normal lung, peak body weight, euthanized body weight - Primary focus is on BCG vaccination response --- # Mtb-related QTL in DO
trait
chr
pos
lod
pvalue
lung_mtb
17
43.4
7.2
0.115
spleen_mtb
1
127.7
7.1
0.142
spleen_mtb
11
118.7
7.1
0.141
spleen_mtb
15
78.4
7.6
0.056
spleen_mtb
17
33.8
7.3
0.094
peak_bw
5
139.6
7.1
0.097
peak_bw
9
83.1
6.9
0.123
peak_bw
11
21.0
7.2
0.075
euth_bw
1
149.2
7.4
0.063
euth_bw
5
139.3
7.0
0.117
--- # Nearby peaks in CC and DO
trait
chr
pos
lod
pvalue
study
IL17
15
74.1
7.8
0.066
cc
lungCFU
15
78.2
7.1
0.203
cc
spleen_mtb
15
78.4
7.6
0.056
do
IL1B
17
31.6
6.8
0.280
cc
spleen_mtb
17
33.8
7.3
0.094
do
lung_mtb
17
43.4
7.2
0.115
do
IL10
17
82.5
7.2
0.179
cc
??? - CC and DO both show peaks on two chromosomes: 15 and 17 - Lung CFU in CC and spleen CFU in DO map near each other on Chr 15 - Chr 17: IL-1B in CC maps near spleen CFU peak on Chr 17 --- # What are allele effects? - In CC and DO, we work with 8 founder allele probabilities `$${Trait} = p_{A}b_A + p_{B}b_B + p_{C}b_C + p_{D}b_D + p_{E}b_E + p_{F}b_F + p_{G}b_G + p_{H}b_H + \epsilon$$` ??? - Standard QTL mapping methods allow for every founder to have its own allele - However, founders often share (functional) alleles - Thus, in DO and CC, some QTL have fewer than 8 distinct alleles - QTL exhibit contrasts in allele effects - "Contrast" is a pairwise difference in mean value - How many alleles are present here? --- # TIMBR for allelic series inferences - Infers allele effects and configuration - Bayesian strategy - One "sample" from the "posterior distribution" consists of a collection of allele effects values and a single configuration of alleles - Draw thousands of samples to summarize distribution --- # TIMBR for allelic series inferences - Inferences are sensitive to prior distribution specification - We may need to consider multiple prior distributions --- # Allele effects for lung CFU in CC on Chr 15 ![](sassetti-lab-June-2020-slides_files/figure-html/unnamed-chunk-10-1.png)<!-- --> --- # Allele effects for spleen CFU in DO on Chr 15 - Two distinct priors yielded highly similar results - little apparent difference among the 8 histograms ![](sassetti-lab-June-2020-slides_files/figure-html/unnamed-chunk-11-1.png)<!-- --> Posterior mean number of alleles: 1.3041 Most probable allelic series: 0.7889 (0,0,0,0,0,0,0,0) --- # Allele effects for IL-1B in CC on Chr 17 ![](sassetti-lab-June-2020-slides_files/figure-html/unnamed-chunk-12-1.png)<!-- --> Posterior mean number of alleles: 2.9538 Most probable allelic series: 0.1948 (0,0,1,0,0,0,0,0) --- # Allele effects for spleen CFU in DO on Chr 17 ![](sassetti-lab-June-2020-slides_files/figure-html/unnamed-chunk-13-1.png)<!-- --> Posterior mean number of alleles: 3.1713 Most probable allelic series: 0.2254 (0,0,0,0,0,0,0,0) --- # Reducing models with allelic series `$${Trait} = p_{A}b_A + p_{B}b_B + p_{C}b_C + p_{D}b_D + p_{E}b_E + p_{F}b_F + p_{G}b_G + p_{H}b_H + \epsilon$$` `$${Trait} = p_{1}b_1 + p_{2}b_2 + p_{3}b_3 + \gamma$$` For example, for series (0,0,0,1,1,2,2,2) `$$p_1 = p_A + p_B + p_C$$` `$$p_2 = p_D + p_E$$` and `$$p_3 = p_F + p_G + p_H$$` --- # DO peaks with reduced models
do_trait
chr
pos
lod
cc_trait
cc_trait_pos
lung_mtb
5
140.5
3.6
IL1B
149.1
pct_normallung
5
121.7
3.1
IL1B
149.1
lung_mtb
17
44.7
3.6
IL10
82.5
peak_bw
17
32.3
3.2
IL10
82.5
euth_bw
17
32.2
3.0
IL10
82.5
pct_normallung
7
82.0
3.5
IL17
36.6
pct_normallung
15
81.6
3.8
IL17
74.1
spleen_mtb
4
153.0
3.1
VEGF
154.1
pct_normallung
5
120.7
3.4
VEGF
61.1
peak_bw
9
83.1
3.1
VEGF
123.6
euth_bw
9
53.6
3.3
VEGF
123.6
lung_mtb
17
44.7
3.6
VEGF
74.2
peak_bw
17
32.3
3.2
VEGF
74.2
euth_bw
17
32.2
3.0
VEGF
74.2
- Still waiting for permutation p-values --- # Spleen Mtb (reduced model) on Chr 4 ![](sassetti-lab-June-2020-slides_files/figure-html/unnamed-chunk-15-1.png)<!-- --> --- # Goals 1. Map CC TnSeq traits for peaks near DO peaks 1. Continue analyzing DO RNASeq data with gbrs 1. Map RNASeq-derived traits in DO --- class: inverse, center, middle # References


TransactionService
public class TransactionService
Provides access to transaction operations
-
getTransaction(budgetId:
AsynchronoustransactionId: ) Returns a single transaction.
Declaration
Swift
public func getTransaction( budgetId: String, transactionId: String ) async throws -> TransactionDetail
Parameters
budgetId
The id of the budget (last_used can also be used to specify the last used budget).
transactionId
Id of the transaction.
Return Value
A single transaction
-
Returns all transactions for a budget.
Declaration
Swift
public func getTransactions( budgetId: String, sinceDate: Date? = nil, type: TransactionType? = nil, lastKnowledgeOfServer: Int? = nil ) async throws -> [TransactionDetail]
Parameters
budgetId
The id of the budget (last_used can also be used to specify the last used budget)
sinceDate
If specified, only transactions on or after this date will be included. The date should be ISO formatted (e.g. 2016-12-30).
type
If specified, only transactions of the specified type will be included.
lastKnowledgeOfServer
The starting server knowledge. If provided, only entities that have changed since
lastKnowledgeOfServer
will be included.Return Value
A list of transactions
-
Returns all transactions for an account.
Declaration
Swift
public func getTransactions( budgetId: String, accountId: String, sinceDate: Date? = nil, type: TransactionType? = nil, lastKnowledgeOfServer: Int? = nil ) async throws -> [TransactionDetail]
Parameters
budgetId
The id of the budget (last_used can also be used to specify the last used budget)
accountId
The id of the account.
sinceDate
If specified, only transactions on or after this date will be included. The date should be ISO formatted (e.g. 2016-12-30).
type
If specified, only transactions of the specified type will be included.
lastKnowledgeOfServer
The starting server knowledge. If provided, only entities that have changed since
lastKnowledgeOfServer
will be included.Return Value
A list of transactions
-
Returns all transactions for a category.
Declaration
Swift
public func getTransactions( budgetId: String, categoryId: String, sinceDate: Date? = nil, type: TransactionType? = nil, lastKnowledgeOfServer: Int? = nil ) async throws -> [HybridTransaction]
Parameters
budgetId
The id of the budget (last_used can also be used to specify the last used budget)
categoryId
The id of the category.
sinceDate
If specified, only transactions on or after this date will be included. The date should be ISO formatted (e.g. 2016-12-30).
type
If specified, only transactions of the specified type will be included.
lastKnowledgeOfServer
The starting server knowledge. If provided, only entities that have changed since
lastKnowledgeOfServer
will be included.Return Value
A list of transactions
-
Returns all transactions for a payee.
Declaration
Swift
public func getTransactions( budgetId: String, payeeId: String, sinceDate: Date? = nil, type: TransactionType? = nil, lastKnowledgeOfServer: Int? = nil ) async throws -> [HybridTransaction]
Parameters
budgetId
The id of the budget (last_used can also be used to specify the last used budget)
payeeId
The id of the payee.
sinceDate
If specified, only transactions on or after this date will be included. The date should be ISO formatted (e.g. 2016-12-30).
type
If specified, only transactions of the specified type will be included.
lastKnowledgeOfServer
The starting server knowledge. If provided, only entities that have changed since
lastKnowledgeOfServer
will be included.Return Value
A list of transactions
-
saveTransaction(budgetId:
Asynchronoustransaction: ) Creates a new transactions.
Declaration
Swift
public func saveTransaction( budgetId: String, transaction: NewTransaction ) async throws -> TransactionDetail
Parameters
budgetId
The id of the budget (last_used can also be used to specify the last used budget)
transaction
New transaction
Return Value
The newly created transaction
-
saveTransactions(budgetId:
Asynchronoustransactions: ) Creates several new transactions.
Declaration
Swift
public func saveTransactions( budgetId: String, transactions: [NewTransaction] ) async throws -> ( transactions: [TransactionDetail], transactionIds: [String], duplicateImportIds: [String] )
Parameters
budgetId
The id of the budget (last_used can also be used to specify the last used budget)
transactions
New transactions
Return Value
The newly created transactions
-
updateTransaction(budgetId:
Asynchronoustransaction: ) Updates a single transaction.
Declaration
Swift
public func updateTransaction( budgetId: String, transaction: UpdateTransaction ) async throws -> TransactionDetail
Parameters
budgetId
The id of the budget (last_used can also be used to specify the last used budget)
transaction
Transaction to be updated
Return Value
The updated transaction
-
updateTransactions(budgetId:
Asynchronoustransactions: ) Updates several transactions.
Declaration
Swift
public func updateTransactions( budgetId: String, transactions: [UpdateTransaction] ) async throws -> ( transactions: [TransactionDetail], transactionIds: [String], duplicateImportIds: [String] )
Parameters
budgetId
The id of the budget (last_used can also be used to specify the last used budget)
transactions
Updated transactions
Return Value
The updated transactions
-
deleteTransaction(budgetId:
AsynchronoustransactionId: ) Delete a single transaction.
Declaration
Swift
public func deleteTransaction( budgetId: String, transactionId: String ) async throws -> TransactionDetail
Parameters
budgetId
The id of the budget (last_used can also be used to specify the last used budget)
transactionId
Id of the rransaction to be deleted
Return Value
The updated transaction


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5.9-Magnitude Earthquake Jolts Mindanao, Philippines - USGS
The Philippines is located in a seismically active zone, known as the Ring of Fire, and often suffers from powerful earthquakes. The archipelago lies along the border between the Philippine Sea plate and the Eurasian plate, which collide and increase the seismic activity in the area.
A 5.9-magnitude earthquake has hit 92 kilometres from Cotabato city on the island of Mindanao, the Philippines, the Unites States Geological Survey reported.
The tremor occurred at 13:22:12 (UTC) at a depth of 25.8 kilometres.
There are no immediate reports of any casualties or damage caused by the quake.
In late October, a 6.8-magnitude earthquake hit the Philippines second-largest island of Mindanao, located in the south of the country.


This is the *first* editor. ------------------------------ Just plain **Markdown**, except that the input is sanitized:
I'm the ghost from the past!
and that it implements "fenced blockquotes" via a plugin: """ Do it like this: 1. Have idea. 2. ??? 3. Profit! """


Package-level declarations
Types
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The Activity that receives Cardinal SDK result from 3DS v2 flow
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A class containing additional information for ThreeDSecure 2.
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3D Secure is a protocol that enables cardholders and issuers to add a layer of security to e-commerce transactions via password entry at checkout.
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interface ThreeDSecureListener
Implement this interface to receive 3DS verification notifications.
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Class to parse and contain 3D Secure lookup params
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A class containing 3DS information about a postal address
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interface ThreeDSecurePrepareLookupCallback
Callback for receiving result of prepareLookup.
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A class to contain 3D Secure request information used for authentication
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Class to parse and contain 3D Secure authentication responses
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interface ThreeDSecureResultCallback
Callback for receiving result of performVerification, continuePerformVerification, onBrowserSwitchResult, and onActivityResult.
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UI Customization Options for 3D Secure 1 Flows.
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Deprecated.
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Button customization options for 3D Secure 2 flows.
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Label customization options for 3D Secure 2 flows.
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Text box customization options for 3D Secure 2 flows.
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Toolbar customization options for 3D Secure 2 flows.
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UI customization options for 3D Secure 2 flows.


Assign each attribute a numbered position to determine the order in which they're assembled for your spreadsheet (e.g. 1, 2, 3)


Nigerian athletes need to plan for life after retirement — John Fashanu - Premium Times Nigeria
by Yusuf AkinpeluRecalling a horrific injury that halted his career as a football player in the English Premier League, John Fashanu, ex-England international, has said not too many Nigerian players end well after they retire from the game.
Mr Fashanu spoke at a book launch, titled “After the game, what next?” written by former Dream Team III (under-23) player, Abubakar Musa, in Abuja Saturday.
He narrated how a wrong footing into a tackle against former Manchester United midfielder, Ryan Giggs, ended his football career. Then he plied his trade with Aston Villa.
The tackle led him to the reality of retirement which many are not able to cope with due to lack of planning, he said.
“That was the last time I played football, Aston Villa against Manchester United. Then, there suddenly is a void in your life because you are not sure of what to come,” he said of his experience.
“Some go the right way. Some go into drugs. Often, the stories we would hear about how some of our well known Nigerian players are not too good because, always, transition from football to something else is not there.”
As an antidote, he recommended the lines of thought in the newly launched book.
The book details how athletes are can be psychologically ready for retirement while highlighting some of the factors that hamper a proper retirement plan.
While reviewing the book, its author, Sani Abubakar, said the book is centered around all sports and its idea was birthed from an impromptu talk he was given at an event.
After the event, he then decided to do some research and combine his experience as a former football international.
The book cited education, demand from families, wrong associations and emotional trauma as some of the factors that affect a proper retirement plan.
“There is depression after retirement if you fail to plan. We have had instances where players put pen to paper without reading the terms and conditions of the deal because they are not educated,” Mr Abubakar, a graduate of Public Administration, said.
“Education opens your horizon. Education makes you to be teachable. Education makes you adapt to the current trends of changes. Most of the challenges of our athletes today is that they are not educated. Education helps you to be receptive to information.”
Coaches of local sports academies were also charged to incorporate soft skill training into their curriculum, for which young talents can fall back to when they retire.
He, therefore, advised intending and current athletes to seek the expertise of a financial adviser who would help them put their resources together in the course of their careers.
In attendance at the event were Abdul Sule, Chairman, Kogi United and Confluence Queens; Hamza Ibrahim, Chairman, Veterans of Abuja Basketball Team; Austin Popo, General Secretary, National Association of Nigeria Professional Footballers (NANFP) and his assistant, Danladi Musa. Zainab Bagudu, wife of Kebbi State governor, was represented by Israel Adoji.


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IBM ECMA-402 Intl demo
Click a button to change the locale being formatted.
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Current Date/Time
Number
[Default Locale]
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* Rock crushes Scissors
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* Rock crushes Lizard
* Lizard Poisons Spock
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Tab-delimited files with these five fields may be uploaded:
Required Fields:
The first row of the file should label the columns, and the labels must match exactly. Several optional fields are supported:
Optional Fields
For additional information on these parameters, a page explaining each field can be found here.


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just a moment...
Set Options for WiFi access
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for access point mode
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for access point mode
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(ignore your WLAN)
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for station mode
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for station mode
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pywgrib2_xr.Wgrib2DatasetAccessor¶
-
class
pywgrib2_xr.
Wgrib2DatasetAccessor
(xarray_obj)[source]¶ Provides custom attributes and methods on xarray Dataset.
Do not instantiate this class directly, use accessor name ‘.wgrib2’ to call its method, for example:
>>> ds.wgrib2.grid()
Methods
__init__
(xarray_obj)Initialize self.
get_grid
()Returns GRIB2 grid definition as dictionary.
grid
(grid[, iptype])Remaps all variables to points specified by longitude and latitude.
location
(points[, iptype])Remaps all variables to point locations.
winds
(winds[, inplace])Changes vector coordinates to grid/Earth direction.


Introduction
Ensemble methods (random forests, gradient boosting machines) have proven to be a winning strategy on Kaggle1. The building blocks of those methods are decision trees, which are generally well understood. It can seem, however, that the ensembling of many trees can produce a sort of magic that allows it to achieve much better performance than a single tree.
When learning about these methods the discussion moves quickly from declaring them an ensemble of trees into a discussion of hyperparameter tuning, glossing over exactly how or why boosting works so well. When I was first learning the boosting algorithm I came across a blog post by Arthur Charpentier outlining and visualizing in a regression context how boosting gradually moves predictions closer to their target2. Inspired by that post, I’ve written up this post highlighting the boosting algorithm for classification.
Here I’ll highlight the algorithm, write code to fit a model, apply it to a dataset where the output is a non-linear function of the input,visualize the boosting process and associated decrease in the loss function, and briefly discuss implications for cross-validation and model selection in a larger machine learning task.
Algorithm
In An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani they lay out the boosting algorithm on page 322:3
- Set \(\hat{f}(x)\) = 0 and \(r_{i}\) = \(y_{i}\) for all \(i\) in the training set.
- For \(b\) = 1,2,…,B, repeat:
- Fit a tree \(\hat{f}^{b}\) with d splits (d+1 terminal nodes) to the training data (X,r).
- Update \(\hat{f}\) by adding in a shrunken version of the new tree: \(\hat{f}(x)\leftarrow\hat{f}(x)+\lambda\hat{f}^{b}(x)\).
- Update the residuals, \(r_{i}\leftarrow r_{i} - \lambda\hat{f}^{b}(x_{i})\).
- Output the boosted model, \(\hat{f}(x) = \sum_{b=1}^{B}\lambda \hat{f}^{b}(x)\)
Paraphrasing the Algorithm, Without Mathematical Notation
- Set all predictions to 0, and calculate the residuals (will be the value of \(y\) since all predictions are 0).
- At each of \(B\) iterations do:
- Fit a classication tree to the data, mapping the residual, \(r\), from the input, \(X\).
- Update your predictions by adding a fraction of what the model predicted.
- Update the residuals to reflect the shrunken predictions.
- The final model is the sum of sequentially adding up the predictions from all the fitted trees.
Data
I’ll create a fake dataset with a non-linear relationship between the input and the output. The output y
takes on a value of either 0 or 1 based on the value of X
.
X <- seq(from = 0, to = 100, by = 1)
y <- ifelse(X <= 15, 1,
ifelse(X <= 25, 0,
ifelse(X <= 45, 1,
ifelse(X <= 70, 0,
ifelse(X <= 95, 1, 0)))))
plot(x = X, y = y, pch = 20)
Coding the Algorithm
- Load libraries, instantiate R objects to store intermediate results, write loss function, and set hyperparameters.
library(tibble)
library(rpart)
trees <- list()
predictions <- list()
losses <- numeric()
logloss_func <- function(y, yhat){
sum(-y*log(yhat+1e-8) + (1-yhat)*log(1-yhat+1e-8)) # add stability to log calculation
}
B = 50
lambda = 0.2
maxdepth = 1
- Set \(\hat{f}(x)\) = 0 and \(r_{i}\) = \(y_{i}\) for all \(i\) in the training set.
We’ll use yhat
for predictions and r
for residuals.
data <- tibble(X, y)
data$yhat <- rep(0, times = nrow(data))
data$r <- data$y - data$yhat
- For \(b\) = 1,2,…,B, repeat:
- Fit a tree \(\hat{f}^{b}\) with d splits (d+1 terminal nodes) to the training data (X,r).
- Update \(\hat{f}\) by adding in a shrunken version of the new tree: \(\hat{f}(x)\leftarrow\hat{f}(x)+\lambda\hat{f^{b}}(x)\).
- Update the residuals, \(r_{i}\leftarrow r_{i} - \lambda\hat{f}^{b}(x_{i})\).
for(b in 1:B){
# Part a
fit <- rpart(r ~ X, data=data, maxdepth = maxdepth, method = "anova")
# Part b
data$yhat <- data$yhat + lambda*predict(fit,newdata=data)
# Part c
data$r <- data$r - lambda*predict(fit,newdata=data)
# Save intermediate results for visualization later, and remove current tree
trees[b] <- list(fit)
losses <- c(losses, logloss_func(data$y, data$yhat))
predictions[[b]] <- tibble(X = data$X,
yhat = data$yhat)
rm(fit)
}
- Output the boosted model, \(\hat{f}(x) = \sum_{b=1}^{B}\lambda \hat{f}^{b}(x)\)
boost_prediction <- function(X,num_trees){
pred <- numeric(length = length(X))
for(j in c(1:num_trees)){
pred <- pred + lambda*predict(trees[[j]], newdata = data.frame(X = X))
}
return(pred)
}
Visualize
The boosting procedure is an iterative process, which lends itself nicely to visualization. In Step 2 of the code we saved the values of the loss function after every iteration, as well as the fitted values of the model. Here we’ll construct a visualization of the process. This visualization is made possible by the fantastic gganimate
and magick
packages in R.4
library(ggplot2)
library(gganimate)
library(magick)
Building visualization datasets
loss_plot_data <- tibble(iteration = c(1:B),
loss = losses)
boost_plot_data <- do.call(rbind, predictions)
boost_plot_data$B <- rep(c(1:B), each = length(X))
boost_plot_data$y <- rep(data$y, times = B)
Create the individual plots
boost_plot <- ggplot(data = boost_plot_data,
aes(x = X, y = y),
colour = 'black') +
geom_point() +
geom_point(aes(x = X, y = yhat), colour = 'red') +
transition_states(B) +
labs(title = 'Iteration: {closest_state}', subtitle = "Boosting",x = 'X', y = 'y')
boost_gif <- animate(boost_plot, width = 400, height = 400)
loss_plot <- ggplot(data = loss_plot_data,
aes(x = iteration, y = loss),
colour = 'black') +
geom_point() +
geom_line() +
transition_reveal(along = iteration) +
shadow_trail(distance = 1/B) +
labs(title = ' ', subtitle = 'Loss')
loss_gif <- animate(loss_plot,width = 400, height = 400)
Combine the gifs and view
loss_mgif <- image_read(loss_gif)
boost_mgif <- image_read(boost_gif)
combined_gif <- image_append(c(boost_mgif[1], loss_mgif[1]))
for(i in 2:100){
combined <- image_append(c(boost_mgif[i], loss_mgif[i]))
combined_gif <- c(combined_gif, combined)
}
combined_gif
Almost all of the loss reduction occurs in the first 10 iterations even though there isn’t separation of all the six groups of \(X\) values until the 22nd iteration. Beyond the 22nd iteration we can see improvement in predictions but the changes are slow and gradual, which is also reflected in the loss reduction graph. By the time we get to the 50th iteration we’re in a position where if draw our decision boundary to be at \(\hat{y} = 0.50\) and predict a value of 1 for all values at or above that line and a value of 0 to all points below that line, that we’d achieve 100% accuracy in the classification task.
Making predictions and applications to validation and test datasets
To illustrate the sequential nature of making predictions, I’ll generate predictions for five \(X\) values, and print out the model’s predictions using 5, 10, or 50 trees. Generating predictions was Step 3 in the algorithm.
vals_to_predict <- c(5,25,35,60,99)
prediction_tibble <- tibble(x = vals_to_predict,
y = data$y[vals_to_predict + 1], # the first value of X is 0 so the index of x is x+1
five_trees = sapply(vals_to_predict, boost_prediction, num_trees = 5),
ten_trees = sapply(vals_to_predict, boost_prediction, num_trees = 10),
fifty_trees = sapply(vals_to_predict, boost_prediction, num_trees = 50))
prediction_tibble
## # A tibble: 5 x 5
## x y five_trees ten_trees fifty_trees
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 5 1 0.515 0.727 0.859
## 2 25 0 0.421 0.563 0.449
## 3 35 1 0.421 0.563 0.751
## 4 60 0 0.294 0.337 0.208
## 5 99 0 0.427 0.584 0.490
As seen above, where \(y=1\) the predictions follow an upward trajectory of continuous improvement. Where \(y=0\) however, there is actually a decrease in accuracy going from 5 trees to 10 before becoming more accurate by the time we reach fifty trees. I think this is a function of the fact that maximum tree depth is set to 1, so that any split that includes a mixture of both 0’s and 1’s in a terminal node will naturally move predictions upward, away from 0, where all predictions began. Over the course of the boosting process splits will be made where the average value in a terminal node is less than current predictions, which will bring the predictions for values where \(y=0\) closer to the true value.
Before deploying a model it is wise to have an idea of how well it will make predictions on data previously unseen by it. Best practice in this case is to split available data into training, validation, and test datasets. The training dataset is used to fit the model, performance of the model at varying values of hyperparameters is evaluated using the validation dataset, and once a single is model is chosen for deployment, an estimate of model performance on previously unseen data is estimated by using the test dataset.
It is generally true that model fit metrics strictly improve on the training dataset as model complexity grows. This is not the case with validation datasets. There comes a point the model begins to learn the idiosyncracies of the training data rather than a more general relationship between input features and the output of the data generating mechanism. The appropriate model to use in deployment would be the one that performs the best on the validation dataset, even if it doesn’t perform as well on the training dataset.
Hyperparameter tuning and model selection is not the focus of this post, but several details in this example are relevant to its application. The hyperparameters here are the number of trees (\(B\)), the value of the learning rate (\(\lambda\)), and the maximum depth of each tree (1). In this example \(\lambda\) and maximum tree depth remained fixed and we iterated our model fitting over \(B\) different trees. A fuller treatment would involve fitting multiple models to the training data at varying levels of \(\lambda\) and maximum tree depth and comparing model performance on the validation dataset using different number of trees fit (anywhere from \(1\) to \(B\)).
Session Info
sessionInfo()
## R version 3.4.4 (2018-03-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] methods stats graphics grDevices utils datasets base
##
## other attached packages:
## [1] magick_2.0 gganimate_1.0.2 ggplot2_3.1.0 rpart_4.1-13
## [5] tibble_2.0.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.0 pillar_1.3.1 compiler_3.4.4
## [4] plyr_1.8.4 prettyunits_1.0.2 tools_3.4.4
## [7] progress_1.2.0 digest_0.6.18 evaluate_0.13
## [10] gtable_0.2.0 png_0.1-7 pkgconfig_2.0.2
## [13] rlang_0.3.1 cli_1.0.1 yaml_2.2.0
## [16] blogdown_0.11 xfun_0.5 withr_2.1.2
## [19] dplyr_<IP_ADDRESS> stringr_1.4.0 knitr_1.22
## [22] hms_0.4.2 grid_3.4.4 tidyselect_0.2.5
## [25] glue_1.3.0 R6_2.4.0 fansi_0.4.0
## [28] gifski_0.8.6 rmarkdown_1.11 bookdown_0.9
## [31] farver_1.1.0 purrr_0.3.1 tweenr_1.0.1
## [34] magrittr_1.5 scales_1.0.0 htmltools_0.3.6
## [37] assertthat_0.2.0 colorspace_1.4-0 labeling_0.3
## [40] utf8_1.1.4 stringi_1.3.1 lazyeval_0.2.1
## [43] munsell_0.5.0 crayon_1.3.4
https://www.kaggle.com/antgoldbloom/what-algorithms-are-most-successful-on-kaggle↩
James, Gareth Michael., et al. An Introduction to Statistical Learning with Applications in R. Springer, 2013.↩
https://github.com/thomasp85/gganimate/wiki/Animation-Composition↩


Evaluating Heap Memory Usage
1. Open Chrome Developer tools by pressing either:
Ctrl
+ Shift
+ I
or
F12
2. Navigate to the "Performance" tab
Ensure the "Memory" checkbox is checked. Then click on the record icon.
3. Click on the button below to manually increase the memory used by this page
This works by creating 10,000 div
nodes and pushing a string consisting of 'x' repeating 1 million times
4. Click on the button below to manually increase the memory used by this page
This does the same as above, but it repeats itself over the course of 10 seconds


|
Elektra
0.9.3
|
We are proud to present Elektra 0.8.25.
Elektra serves as a universal and secure framework to access configuration settings in a global, hierarchical key database. For more information, visit https://libelektra.org.
For a small demo see here:
You can also read the news on our website
This release is dedicated to drastically improve the speed of Elektra. Two non-trivial features facilitate most of the improvement:
ksLookup (...)
for very fast access of configurationWe added a new, binary and fast storage plugin called mmapstorage
. It leverages the mmap()
syscall and supports full Elektra semantics. We provide two compile variants: mmapstorage
and mmapstorage_crc
. The mmapstorage_crc
variant enables CRC32 checksums for critical data, while the mmapstorage
variant omits the checksum for maximum performance.
We ran a synthetic benchmark with 257 iterations using 40k keys in a keyset, and compared the performance to the dump
storage plugin.
Median write time in microseconds:
| Plugin | Time |
|---|---|
dump | 71079 |
mmapstorage | 2964 |
mmapstorage_crc | 7644 |
Median read time in microseconds:
| Plugin | Time |
|---|---|
dump | 82737 |
mmapstorage | 1145 |
mmapstorage_crc | 5744 |
In our benchmark, the mmapstorage
plugin writes more than 23x faster, and reads more than 72x faster than the dump
storage plugin.
For this release the plugin is marked as experimental, even though it is already used as default storage plugin in a build job on our build server.
Thanks to Mihael Pranjić for this improvement.
The hybrid search algorithm is now implemented, this concludes the extension of the ksLookup (...)
search with the order preserving minimal perfect hash map (OPMPHM). The hybrid search combines the best properties of the binary search and the OPMPHM. The hybrid search decides dynamically which search algorithm to use.
Because of the automatic decision, usually nothing needs to be done by API users to take advantage of this improvement. Advanced API user, however, can overrule the hybrid search by passing KDB_O_OPMPHM
or KDB_O_BINSEARCH
to ksLookup (...)
. The constants are defined in kdbproposal.h. For low-memory systems the building of the hash map can be disabled altogether at build-time by disabling the CMake variable ENABLE_OPTIMIZATIONS
(by default enabled now).
The implemented randomized OPMPHM algorithm is in 99.5% of the measured random cases optimal. However the randomization property of the algorithm leaves an uncertainty.
The results made with random cases had shown that the hybrid search is, except for small keyset sizes, almost always faster compared to the standalone binary search. The performance increase strongly depended on the measured hardware. In the random cases where the hybrid search is faster, on average ~8.53% to ~20.92% of time was saved. The implemented hybrid search works only above a keyset size of 599
to exclude the small keyset sizes.
Thanks to Kurt Micheli for this improvement.
The following section lists news about the plugins we updated in this release.
We improved the performance of the directoryvalue plugin. _(René Schwaiger)_ This plugin is used for configuration file formats that do not support that directories contain values, like it is the case in JSON. A program manipulating a 13 MB JSON file which first did not succeed within 10 hours is now finished in 44 seconds.
There is a new, experimental plugin called process. This plugin utilizes the pluginprocess library in order to execute arbitrary other plugins in an own process, acting as a proxy itself. Therefore it is not required to explicitly change a plugin's implementation if it shall be executed in an own process. This plugin is not completely finished yet, as currently there is no way for it to mimic the proxied plugin's contract in Elektra. It can be used with simple plugins like dump
however, check the limitations in the readme for more details. _(Armin Wurzinger)_
The detection of the mntent
functions now also works correctly, if you use the compiler switch -Werror
. _(René Schwaiger)_
We fixed an issue with the passwd plugin not properly setting compile flags. This resolves a problem with undefined functions when building with musl. _(Lukas Winkler)_
The experimental gpgme plugin was brought into existence to provide cryptographic functions using GnuGP via the libgpgme
library. _(Peter Nirschl)_
The network
plugin now also allows for non-numerical hosts (i.e. "localhost") to be set and tries to resolve it via DNS. _(Michael Zronek)_
This new plugin parses a subset of YAML using a parser generated by Bison. _(René Schwaiger)_
The build system now disables the plugin automatically, if you use a GCC compiler (6.x
or earlier) and enable the option ENABLE_ASAN
. We updated the behavior, since otherwise the plugin will report memory leaks at runtime. _(René Schwaiger)_
now works correctly. _(René Schwaiger)_
antlr
as ANTLR executable, if antlr4
is not available. _(René Schwaiger)_6.x
or earlier) to translate Elektra. We introduced this behavior, since the code generated by ANTLR (YAML.h
) seems to contain a double free that causes a segmentation fault on systems that use the GNU C library. _(René Schwaiger)_6.x
or earlier) and enable the option ENABLE_ASAN
. _(René Schwaiger)_This new plugin parses a subset of YAML using the Earley Parser library YAEP. _(René Schwaiger)_
This new plugin can be used to validate that the value of a key is a reference to another key. _(Klemens Böswirth)_
The text below summarizes updates to the C (and C++)-based libraries of Elektra.
As always, the ABI and API of kdb.h is fully compatible, i.e. programs compiled against an older 0.8 version of Elektra will continue to work (ABI) and you will be able to recompile programs without errors (API).
This is the last release for which we have built Jessie packages:
Obviously, we will continue to update the stretch package:
Following plugins got added:
A new library got added (should be packaged privately for now):
Optimize elektraKsFilter to not duplicate keys _(Markus Raab)_
A new library which can be used to match keys against globbing patterns was introduced. _(Klemens Böswirth)_
The API is still experimental, so it should not be used externally for now.
libease
provides the function elektraArrayValidateBaseNameString
, which can be used to validate that a given string is an Elektra array name. _(Klemens Böswirth)_
Bindings allow you to utilize Elektra using various programming languages. This section keeps you up to date with the multi-language support provided by Elektra.
Do not use private Elektra headers for Ruby bindings as preparation for a Ruby libelektra
gem. _(Bernhard Denner)_
dump
and mmapstorage
. _(Mihael Pranjić)_cp
. _(Markus Raab)_mount
. _(Markus Raab)_kdb find-tools
. _(Markus Raab)_clang-format
6.0 for formatting C and C++ code. _(René Schwaiger)_reformat-source
now displays information about the installed version of clang-format
, if it is unable to locate a supported version of the tool. _(René Schwaiger)_shfmt
. _(René Schwaiger)_reformat-shfmt
reformats Shell scripts using the tool shfmt
. _(René Schwaiger)_dbus
plugin does not leak memory anymore, if it fails on macOS. _(Thomas Wahringer)_testkdb_allplugins
and testscr_check_kdb_internal_check
do not test a plugin on an ASAN enabled build anymore, if you specify the status tag memleak
in the plugin contract. _(René Schwaiger)_compare_key
now also checks if the meta values of keys are equal. _(René Schwaiger)_testscr_check_bashisms
does not print warnings about skipped files anymore. _(René Schwaiger)_shell
plugin. _(René Schwaiger)_dump
and mmapstorage
. _(Mihael Pranjić)_testcpp_contextual_basic
now compiles without warnings, if we use Clang 7 as compiler. _(René Schwaiger)_testmod_[plugin]
) now depend on the respective CMake targets for the plugins themselves (elektra-[plugin]
). _(Klemens Böswirth)_BUILD_FULL
but not BUILD_SHARED
is used. _(Klemens Böswirth)_testscr_check_formatting
now also checks the formatting of Shell code. _(René Schwaiger)_-v
. _(René Schwaiger)_--text
any more. _(René Schwaiger)_1.8.1
. _(René Schwaiger)_check_symbol_exists
that handles issues with -Werror -Wpedantic
. _(Lukas Winkler)_INCLUDE_SYSTEM_DIRECTORIES
of the function add_plugin
now supports multiple include directories. _(René Schwaiger)_ENABLE_COVERAGE=ON
) should now also work on macOS. _(René Schwaiger)_run_checkshell
to run all shell checks (testscr_check.*
). _(René Schwaiger)_run_nocheckshell
runs all tests except for shell checks. _(René Schwaiger)_run_all
now runs tests that do not modify the key database in parallel. _(René Schwaiger)_docker run --rm anoxis/registry-cli -r https://hub-public.libelektra.org
. You can search for images using --images-like
, for example: docker run --rm anoxis/registry-cli -r https://hub-public.libelektra.org --images-like alpine
. Afterwards pull your desired image as you would do from any public registry, for example: docker pull hub-public.libelektra.org/build-elektra-alpine:201811-37597a34fed4988639cdaf4d6a2c54754d09918586f53389e4fde5fd3b3a7180
. _(Lukas Winkler)_alpine
. _(René Schwaiger)_-Werror
in debian-stable-full
. _(Lukas Winkler)_-Werror
to the build jobs:alpine
,debian-stable-full-i386
,debian-stable-full-mmap-asan
,debian-stable-full-mmap
,debian-stable-full-optimizations-off
,debian-stable-full-xdg
,debian-stable-minimal
,debian-stable-multiconf
,debian-unstable-clang-asan
,debian-unstable-full-clang
,debian-unstable-full
,ubuntu-xenial
, anddebian-stable-asan
. _(René Schwaiger)_debian-stable-full-mmap
and debian-stable-full-mmap-asan
with mmapstorage
as the default storage. _(Mihael Pranjić)_🍏 Clang ASAN
. _(René Schwaiger)_🍏 Clang ASAN
and 🐧 GCC ASAN
now only translates a minimal set of plugins, since we had various timeout problems with these jobs before. We explicitly excluded plugins, to make sure that the build jobs still test newly added plugins. _(René Schwaiger)_🍏 mmap
on macOS with mmapstorage
as the default storage. _(Mihael Pranjić)_10.0
). _(René Schwaiger)_🍏 Check Shell
, which only runs shell checks such as testscr_check_oclint
. This update allows us to remove the shell checks from the jobs 🍏 MMap
and 🍏 Clang
, which sometimes hit the timeout limit for public repositories before. _(René Schwaiger)_-Werror
. _(René Schwaiger)_🍏 FULL
and the build job 🐧 FULL
build Elektra using the CMake options BUILD_FULL=ON
and BUILD_SHARED=OFF
. _(René Schwaiger)_script
stage of the build jobs print less non-relevant output. Usually the commands in this stage should now only print verbose output if a test fails. _(René Schwaiger)_The website is generated from the repository, so all information about plugins, bindings and tools are always up to date.
We are currently working on following topics:
Following authors made this release possible:
| Commits | Author |
|---|---|
| 1 | Thomas Wahringer thoma s.wa hring er@l ibele ktra .org |
| 2 | Bernhard Denner bernh ard. denne r@gm ail.c om |
| 7 | Kurt Micheli e1026 558@ stude nt.t uwien .ac. at |
| 12 | Michael Zronek micha el.z ronek @gma il.co m |
| 33 | Lukas Winkler derwi nlu+ git@g mail .com |
| 28 | Klemens Böswirth k.boe swir th+gi t@gm ail.c om |
| 30 | Armin Wurzinger e1528 532@ stude nt.t uwien .ac. at |
| 38 | Peter Nirschl peter .nir schl@ gmai l.com |
| 100 | Markus Raab marku s@li belek tra. org |
| 180 | Mihael Pranjic mpran j@li mun.o rg |
| 418 | René Schwaiger sanss ecou rs@me .com |
849 commits, 581 files changed, 18503 insertions(+), 3192 deletions(-)
We welcome new contributors!
You can download the release from here or GitHub
The hashsums are:
The release tarball is also available signed by Markus Raab using GnuPG from here or GitHub
Already built API-Docu can be found here or GitHub.
Subscribe to the RSS feed to always get the release notifications.
If you also want to participate, or for any questions and comments please contact us via the issue tracker on GitHub.
For more information, see https://libelektra.org
Best regards, Elektra Initiative


Used to delete a file resident on the SDL module in the app’s local cache. Not supported on first generation SDL vehicles
Since SmartDeviceLink 2.0
see SDLPutFile SDLListFiles
Convenience init to delete a file
- (nonnull instancetype)initWithFileName:(nonnull NSString *)fileName;
init(fileName: String)
A file reference name
An SDLDeleteFile object
a file reference name @discussion a String value representing a file reference name
@property (nonatomic, strong) NSString *_Nonnull syncFileName;
var syncFileName: String { get set }


Hello there, my name is Bianca and I enjoy collecting ties and bow ties. Other interests include long walks in the park, reading, instagramming and spending time with family. I was born, raised and currently living in New York. I graduated from Queensborough Community College with an Associates Degree in New Media Technology and will be continuing my studies at the Academy of Art University for Web Design and New Media. School only taught me the basics of programming and design, so I decided to teach myself everything I needed to know to become a web developer.
I have always had a passion for art and design. From mischeviously finger painting on walls as a toddler to creating abstract images and designs in photoshop. I am always tweaking my web design skills, filling my head with new ideas for new designs and at the same time learning new techniques to perfect my craft and make these ideas come to life.
I am currently a Senior Digital Designer at Universal Music Group. I work with the creative team to design websites that include best UX/UI practices or design promotional content for artists. I also do freelance design and development projects for many clients around the world.
I specialize in designing creative, interactive, user and search engine friendly websites. I also create responsive websites, so the site can be easily viewed on any mobile device. All of the websites I've created have valid HTML and CSS code (validated with W3C validators), comply with W3C Accessibility Guidelines and are cross-browser tested. I make sure the projects I design are engaging, user-friendly, and aesthetically pleasing.


m i n e s w e e p e r
source code
Click to reveal a square, right-click to mark as a mine
Select a difficulty to get started
easy
medium
hard


Skitari wrote:It was actually Multi Animagicus and was a level 11 spell.
I would be against both Vis Contra Magicus and White Pot Effect spell not being in the game. they were removed for good reasons.
I would also like to voice my opinion in making thief/pally/fighter multi attacks not being lvl 22+, but more like 12 tops. Nobody liked leveling hitting single mobs, and it's more grief than accomplishment having to level like that. and before someone spouts out the age-old "BUT IT WAS ALWAYS LIKE THAT" that's not an argument, just because that's how something was doesn't mean we can't improve on it.
Necromadon wrote:Skitari wrote:It was actually Multi Animagicus and was a level 11 spell.
I would be against both Vis Contra Magicus and White Pot Effect spell not being in the game. they were removed for good reasons.
I would also like to voice my opinion in making thief/pally/fighter multi attacks not being lvl 22+, but more like 12 tops. Nobody liked leveling hitting single mobs, and it's more grief than accomplishment having to level like that. and before someone spouts out the age-old "BUT IT WAS ALWAYS LIKE THAT" that's not an argument, just because that's how something was doesn't mean we can't improve on it.
If you level that high you deserve good spells.
As for spin, I dunno it really was a HUGE contributing factor to the good ol' days.
Ender wrote:to Vitriol:
Spin was added in 2001. It is now 2007. I can't see how calling an event in the first seventh of Xen's lifetime very new is justified. I have no problem with you trying to make that guy look like a complete newb, but at least do it right. Maybe some personal bashing? Or at least a reference to his mother's snatch.
And about the level 12 spin:
I'm against it because it would make even more hunting areas obsolete. Personally, I loved spin when it was 25 for paladins. It gave me motivation to level. If I had it at 12, sure I could gain xp three times as fast, but what would be the point?
Kuristopher wrote:can there be Animus Adgredi avalible to all classes around 8-10.. it was a nice way of communication (pointing)... and can all classes eventually get trans also? or will it be unnecessary? i though that was a big part of the game back then.
Vitriol wrote:level 22 will probably be a pretty high level in PV. I wouldn't mind spin being at a lower level.
Necromadon wrote:Vitriol wrote:level 22 will probably be a pretty high level in PV. I wouldn't mind spin being at a lower level.
22 won't be pretty high if spin is lowered so fighters can level wicked fast early on
thadiusofx3 wrote:Spin was in the game in november of 2000, i just confirmed.
A few more spells:
Infinitus Divinus
Lacerti Delere
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⇑
X+
Y+
⇐
↺
⇒
⇓
X-
Y-
Create
To create a function, type an equation with a space between each character, and click "Create". For example type:
"x + 2", "x ^ 0.5", "x ^ ( 0.5 * x )"
Tuka bla
Tuka bla
Tuka bla
Pener


>Back to indexIn week 1 of Digital Fabrication and Engineering, we worked through several design challenges. This challenge in particular was to build a structure out of straws, tape, and string that could hold a cup full of pennies without collapsing. Whichever group's build held the most pennies won, which happened to be my group.


Usage: qiime diversity beta-phylogenetic-alt [OPTIONS]
Computes a user-specified phylogenetic beta diversity metric for all pairs
of samples in a feature table. This implementation is recommended for
large datasets, otherwise the results are identical to beta_phylogenetic.
This method is an implementation of the Strided State UniFrac algorithm.
Multiple variants of the UniFrac metric are available, including
Generalized UniFrac (Chen et al. 2012), Variance Adjusted UniFrac (Chang
et al. 2011), as well as Weighted normalized and unnormalized UniFrac
(Lozupone et al. 2007) and unweighted UniFrac (Lozupone et al. 2005)
Options:
--i-table ARTIFACT PATH FeatureTable[Frequency]
The feature table containing the samples
over which beta diversity should be
computed. [required]
--i-phylogeny ARTIFACT PATH Phylogeny[Rooted]
Phylogenetic tree containing tip identifiers
that correspond to the feature identifiers
in the table. This tree can contain tip ids
that are not present in the table, but all
feature ids in the table must be present in
this tree. [required]
--p-metric [weighted_normalized_unifrac|weighted_unifrac|unweighted_unifrac|generalized_unifrac]
The beta diversity metric to be computed.
[required]
--p-n-jobs INTEGER The number of workers to use. [default: 1]
--p-variance-adjusted / --p-no-variance-adjusted
Perform variance adjustment based on Chang
et al. BMC Bioinformatics 2011. Weights
distances based on the proportion of the
relative abundance represented between the
samples at a given node under evaluation.
[default: False]
--p-alpha FLOAT This parameter is only used when the choice
of metric is generalized_unifrac. The value
of alpha controls importance of sample
proportions. 1.0 is weighted normalized
UniFrac. 0.0 is close to unweighted UniFrac,
but only if the sample proportions are
dichotomized. [optional]
--p-bypass-tips / --p-no-bypass-tips
In a bifurcating tree, the tips make up
about 50% of the nodes in a tree. By
ignoring them, specificity can be traded for
reduced compute time. This has the effect of
collapsing the phylogeny, and is analogous
(in concept) to moving from 99% to 97% OTUs
[default: False]
--o-distance-matrix ARTIFACT PATH DistanceMatrix % Properties(['phylogenetic'])
The resulting distance matrix. [required if
not passing --output-dir]
--output-dir DIRECTORY Output unspecified results to a directory
--cmd-config PATH Use config file for command options
--verbose Display verbose output to stdout and/or
stderr during execution of this action.
[default: False]
--quiet Silence output if execution is successful
(silence is golden). [default: False]
--citations Show citations and exit.
--help Show this message and exit.
Beta diversity (phylogenetic) - High Performance Computation
Computes a user-specified phylogenetic beta diversity metric for all pairs
of samples in a feature table. This implementation is recommended for large
datasets, otherwise the results are identical to beta_phylogenetic. This
method is an implementation of the Strided State UniFrac algorithm.
Multiple variants of the UniFrac metric are available, including
Generalized UniFrac (Chen et al. 2012), Variance Adjusted UniFrac (Chang et
al. 2011), as well as Weighted normalized and unnormalized UniFrac
(Lozupone et al. 2007) and unweighted UniFrac (Lozupone et al. 2005)
Parameters
----------
table : FeatureTable[Frequency]
The feature table containing the samples over which beta diversity
should be computed.
phylogeny : Phylogeny[Rooted]
Phylogenetic tree containing tip identifiers that correspond to the
feature identifiers in the table. This tree can contain tip ids that
are not present in the table, but all feature ids in the table must be
present in this tree.
metric : Str % Choices({'generalized_unifrac', 'unweighted_unifrac', 'weighted_normalized_unifrac', 'weighted_unifrac'})
The beta diversity metric to be computed.
n_jobs : Int, optional
The number of workers to use.
variance_adjusted : Bool, optional
Perform variance adjustment based on Chang et al. BMC Bioinformatics
2011. Weights distances based on the proportion of the relative
abundance represented between the samples at a given node under
evaluation.
alpha : Float % Range(0, 1, inclusive_end=True), optional
This parameter is only used when the choice of metric is
generalized_unifrac. The value of alpha controls importance of sample
proportions. 1.0 is weighted normalized UniFrac. 0.0 is close to
unweighted UniFrac, but only if the sample proportions are
dichotomized.
bypass_tips : Bool, optional
In a bifurcating tree, the tips make up about 50% of the nodes in a
tree. By ignoring them, specificity can be traded for reduced compute
time. This has the effect of collapsing the phylogeny, and is analogous
(in concept) to moving from 99% to 97% OTUs
Returns
-------
distance_matrix : DistanceMatrix % Properties(['phylogenetic'])
The resulting distance matrix.


Nokia SBC Auto Deployment
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Before you get started, it's worth familiarising yourself with how Worldpay Within works. We'd recommend following the pre-established flows in the order they are presented for each Thing (Machine A and Machine B). If you stray from the flows, we cannot guarantee that the SDKs will work.
We recommend following the below flows for consumer and producer devices. You can find a breakdown in each of the steps below.
The devices are not synchronised by the SDK, so you'll need to ensure that the SDKs call the Worldpay Within API in the correct order. If you follow the steps below, then the outcome should be as expected.
The consumer device should follow this flow. These steps should map to the interface either in Go, or in your chosen SDK wrapper.
| Step in Flow | What it does |
| 1. SETUP | Setups up the device |
| 2. DEVICE DISCOVERY | Discover devices that are broadcasting messages |
| 3. (CHOOSE MESSAGE) | Choose the message, then use that information in the following steps |
| 4. INIT CONSUMER | Initialises the device as a consumer |
| 5. REQUEST SERVICES | Request service messages from the device that is broadcasting, which lists what services it's offering |
| 6. GET SERVICE PRICES | Get's the prices for the service you are interested in |
| 7. SELECT SERVICE | Selects the service the device wants to consume |
| 8. MAKE PAYMENT | Makes a payment for the service that is chosen |
| 9. BEGIN SERVICE DELIVERY | Starts the process / flow to consume the service based on the service token received. |
| 10. STOP SERVICE DELIVERY | Called to tell the other thing that it has completed it's consumption of the service |
The producer device should follow this flow. These steps should map to the interface either in Go, or in your chosen SDK wrapper.
| Step in Flow | What it does |
| 1. SETUP | Setups the device |
| 2A. ADD SERVICE | Adds a service to the producer |
| 2B. REMOVE SERVICE | Removes a service from the producer |
| 3. INIT PRODUCER | Initialises the producer |
| 4A. START BROADCAST | Starts the producer broadcasting it's service message |
| 4B. STOP BROADCAST | Stops the producer from broadcasting it's service message |
For the alpha release, we made the decision to go with four different wrappers.


{@repeat times="3"} hey {/repeat} goodbye.
{@filter type="u"} {name} {value} {/filter}
{@formatEmail value="{email}"/}
this is the first iteration.
{:else}this is the {@math key="{$idx}" method="add" operand="1"/} iteration.
{:last}this was the last iteration.
{/firstAndLast} {/array}


[1]
we were we booked train tickets commission was impeccable.
[2]
shopping is great at the markets leave room in your case.


---
layout: post
title: The Gadsten Flag
categories: []
tags: []
status: publish
type: post
published: true
meta:
_original_post_id: ''
_original_import_author: John Duprey
author:
---
"She never begins an attack, nor, when once engaged, ever surrenders: She is therefore an emblem of magnanimity and true courage. ... she never wounds 'till she has generously given notice, even to her enemy, and cautioned him against the danger of treading on her."
"I confess I was wholly at a loss what to make of the rattles, 'till I went back and counted them and found them just thirteen, exactly the number of the Colonies united in America; and I recollected too that this was the only part of the Snake which increased in numbers. ..."
"'Tis curious and amazing to observe how distinct and independent of each other the rattles of this animal are, and yet how firmly they are united together, so as never to be separated but by breaking them to pieces. One of those rattles singly, is incapable of producing sound, but the ringing of thirteen together, is sufficient to alarm the boldest man living."
These words are believed to have come from Benjamin Franklin. I've often thought this flag is better suited for our nation as well. I've made this emblem my user icon for politics. You can read more about the origins
here.


This component is not yet configured, ask your Salesforce Admin to follow the set up guide!
Confirm Journey Removal
Are you sure you would like to remove the {prettyObjectName} from {journeyForRemoval.name}?
Doing so will stop the {prettyObjectName} from receiving any further communications related to this journey.
Confirm Removal From All Journeys
Are you sure you would like to remove the {prettyObjectName} from all marketing journeys?
Doing so will stop the {prettyObjectName} from receiving any further communications related to the journeys they are currently in.


Senior loses $300K in online romance scam: OPP
Norfolk County OPP say the money was sent between July and November 2019
by Mark PareA reminder from the OPP to familiarize yourself with a romance and dating scam, after someone was bilked for around $300-thousand dollars.
Norfolk County OPP were alerted to the issue last Thursday by a senior, claiming they'd established an online relationship with someone after meeting on a dating site.
Through the conversations, OPP say several emails were exchanged involving the suspect's history and employment status, and a request for money was made, claiming several emergencies came up and the money was needed.
The money was sent between July and November of this year.
"Single or recently unattached people - often females - are among the growing number of vulnerable population to be victimized by criminals through fraudulent means," OPP state in a release, "Usually this occurs through contact online via e-mail blasts, social media (Facebook, Twitter, Pinterest, etc.) or singles and dating-related 'meet' websites."
They say knowledge is power, and advise you to not give out personal information over the phone, the internet or through social media or dating sites.
OPP add to always get independent advice from either family members, a trusted friend or advisor before doing anything involving cash, personal information, time or a commitment.


Floyd Mayweather target 2 fights as he comes out of retirement in 2020
- Floyd Mayweather will come out of retirement in the year 2020
- And the American is already targeting fighting two opponents next year
- The 42-year-old has not fought any opponent since his last fight two years ago
American boxing legend Floyd Mayweather has a plan in place to face two big opponents when he returns to professional fight in the year 2020.
Since he defeated Conor McGregor two years ago, Floyd Mayweather has not fought with anyone until Friday, November 22, when he announced his decision to come out of retirement.
Floyd Mayweather revealed news of the comeback beneath a photo of him wearing boxing trunks and hand wraps on his official social media page.
He has been challenged to fight by many professional boxers since he retired from boxing which Floyd has turned down.
PAY ATTENTION: Install Pitch Football App for FREE to get the latest football News & Scores
Osinbajo mourns as presidential jet pilot of 19 years Captain Edorhe dies (photos)
According to the report onTMZ, Floyd Mayweather could face Manny Pacquiao and another UFC fighter in his second in the year 2020.
Earlier, Legit.ng had reported how Floyd Mayweather was tempted to return to the ring after being challenged to a fight by the founder of Alibaba group Jack Ma.
Mayweather has retired from boxing after more than 50 fights in his career in which he was never beaten by any opponent.

Abu Azeez: Meet the only Nigerian rated among top 50 beach soccer players worldwide | Legit TV


| Number of Classes | Line Coverage | Mutation Coverage |
|---|---|---|
| 55 | 63% | 4% |
| Name | Number of Classes | Line Coverage | Mutation Coverage |
|---|---|---|---|
| org.jsoup | 5 | 98% |
0% |
| org.jsoup.examples | 3 | 56% |
0% |
| org.jsoup.helper | 6 | 56% |
2% |
| org.jsoup.internal | 2 | 96% |
2% |
| org.jsoup.nodes | 15 | 90% |
9% |
| org.jsoup.parser | 14 | 46% |
3% |
| org.jsoup.safety | 2 | 87% |
0% |
| org.jsoup.select | 8 | 91% |
5% |


--- title: Reduce learning rate when a metric has stopped improving. type: docs repo: https://github.com/rstudio/keras menu: main: name: Reduce learning rate when a metric has stopped improving. - keras parent: keras-reference aliases: - /reference/keras/callback_reduce_lr_on_plateau.html - /keras/reference/callback_reduce_lr_on_plateau.html - /guide/keras/reference/callback_reduce_lr_on_plateau.html - /tools/tools/keras/reference/callback_reduce_lr_on_plateau.html - /installation/keras/reference/callback_reduce_lr_on_plateau.html - /tutorials/keras/reference/callback_reduce_lr_on_plateau.html - /guide/tools/keras/reference/callback_reduce_lr_on_plateau.html - /deploy/keras/reference/callback_reduce_lr_on_plateau.html - /tools/keras/reference/callback_reduce_lr_on_plateau.html - /tutorials/tools/keras/reference/callback_reduce_lr_on_plateau.html ---
Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.
callback_reduce_lr_on_plateau(
monitor = "val_loss",
factor = 0.1,
patience = 10,
verbose = 0,
mode = c("auto", "min", "max"),
min_delta = 1e-04,
cooldown = 0,
min_lr = 0
)
| monitor | quantity to be monitored. |
| factor | factor by which the learning rate will be reduced. new_lr = lr
|
| patience | number of epochs with no improvement after which learning rate will be reduced. |
| verbose | int. 0: quiet, 1: update messages. |
| mode | one of "auto", "min", "max". In min mode, lr will be reduced when the quantity monitored has stopped decreasing; in max mode it will be reduced when the quantity monitored has stopped increasing; in auto mode, the direction is automatically inferred from the name of the monitored quantity. |
| min_delta | threshold for measuring the new optimum, to only focus on significant changes. |
| cooldown | number of epochs to wait before resuming normal operation after lr has been reduced. |
| min_lr | lower bound on the learning rate. |


Utility Functions¶
Utility functions for SRSF Manipulations
moduleauthor:: J. Derek Tucker<EMAIL_ADDRESS>-
utility_functions.
SqrtMean
(gam)[source]¶ calculates the srsf of warping functions with corresponding shooting vectors
- Parameters
gam – numpy ndarray of shape (M,N) of M warping functions with N samples
- Return type
2 numpy ndarray and vector
- Return mu
Karcher mean psi function
- Return gam_mu
vector of dim N which is the Karcher mean warping function
- Return psi
numpy ndarray of shape (M,N) of M SRSF of the warping functions
- Return vec
numpy ndarray of shape (M,N) of M shooting vectors
-
utility_functions.
SqrtMeanInverse
(gam)[source]¶ finds the inverse of the mean of the set of the diffeomorphisms gamma
- Parameters
gam – numpy ndarray of shape (M,N) of M warping functions with N samples
- Return type
vector
- Return gamI
inverse of gam
-
utility_functions.
SqrtMedian
(gam)[source]¶ calculates the median srsf of warping functions with corresponding shooting vectors
- Parameters
gam – numpy ndarray of shape (M,N) of M warping functions with N samples
- Return type
2 numpy ndarray and vector
- Return gam_median
Karcher median warping function
- Return psi_meidan
vector of dim N which is the Karcher median srsf function
- Return psi
numpy ndarray of shape (M,N) of M SRSF of the warping functions
- Return vec
numpy ndarray of shape (M,N) of M shooting vectors
-
utility_functions.
cumtrapzmid
(x, y, c, mid)[source]¶ cumulative trapezoidal numerical integration taken from midpoint
- Parameters
x – vector of size N describing the time samples
y – vector of size N describing the function
c – midpoint
mid – midpiont location
- Return type
vector
- Return fa
cumulative integration
-
utility_functions.
diffop
(n, binsize=1)[source]¶ Creates a second order differential operator
- Parameters
n – dimension
binsize – dx (default = 1)
- Return type
numpy ndarray
- Return m
matrix describing differential operator
-
utility_functions.
elastic_depth
(f, time, method='DP2', lam=0.0, parallel=True)[source]¶ calculates the elastic depth between functions in matrix f
- Parameters
f – matrix of size MxN (M time points for N functions)
time – vector of size M describing the sample points
method – method to apply optimization (default=”DP2”) options are “DP”,”DP2”,”RBFGS”
lam – controls the elasticity (default = 0.0)
- Return type
scalar
- Return amp
amplitude depth
- Return phase
phase depth
-
utility_functions.
elastic_distance
(f1, f2, time, method='DP2', lam=0.0)[source]¶ ” calculates the distances between function, where f1 is aligned to f2. In other words calculates the elastic distances
- Parameters
f1 – vector of size N
f2 – vector of size N
time – vector of size N describing the sample points
method – method to apply optimization (default=”DP2”) options are “DP”,”DP2”,”RBFGS”
lam – controls the elasticity (default = 0.0)
- Return type
scalar
- Return Dy
amplitude distance
- Return Dx
phase distance
-
utility_functions.
f_K_fold
(Nobs, K=5)[source]¶ generates sample indices for K-fold cross validation
:param Nobs number of observations :param K number of folds
- Return type
numpy ndarray
- Return train
train indexes (Nobs*(K-1)/K X K)
- Return test
test indexes (Nobs*(1/K) X K)
-
utility_functions.
f_to_srsf
(f, time, smooth=False)[source]¶ converts f to a square-root slope function (SRSF)
- Parameters
f – vector of size N samples
time – vector of size N describing the sample points
- Return type
vector
- Return q
srsf of f
-
utility_functions.
geigen
(Amat, Bmat, Cmat)[source]¶ generalized eigenvalue problem of the form
max tr L’AM / sqrt(tr L’BL tr M’CM) w.r.t. L and M
:param Amat numpy ndarray of shape (M,N) :param Bmat numpy ndarray of shape (M,N) :param Bmat numpy ndarray of shape (M,N)
- Return type
numpy ndarray
- Return values
eigenvalues
- Return Lmat
left eigenvectors
- Return Mmat
right eigenvectors
-
utility_functions.
gradient_spline
(time, f, smooth=False)[source]¶ This function takes the gradient of f using b-spline smoothing
- Parameters
time – vector of size N describing the sample points
f – numpy ndarray of shape (M,N) of M functions with N samples
smooth – smooth data (default = F)
- Return type
tuple of numpy ndarray
- Return f0
smoothed functions functions
- Return g
first derivative of each function
- Return g2
second derivative of each function
-
utility_functions.
innerprod_q
(time, q1, q2)[source]¶ calculates the innerproduct between two srsfs
:param time vector descrbing time samples :param q1 vector of srsf 1 :param q2 vector of srsf 2
- Return type
scalar
- Return val
inner product value
-
utility_functions.
invertGamma
(gam)[source]¶ finds the inverse of the diffeomorphism gamma
- Parameters
gam – vector describing the warping function
- Return type
vector
- Return gamI
inverse of gam
-
utility_functions.
optimum_reparam
(q1, time, q2, method='DP2', lam=0.0, grid_dim=7)[source]¶ calculates the warping to align srsf q2 to q1
- Parameters
q1 – vector of size N or array of NxM samples of first SRSF
time – vector of size N describing the sample points
q2 – vector of size N or array of NxM samples samples of second SRSF
method – method to apply optimization (default=”DP2”) options are “DP”,”DP2”,”RBFGS”
lam – controls the amount of elasticity (default = 0.0)
grid_dim – size of the grid, for the DP2 method only (default = 7)
- Return type
vector
- Return gam
describing the warping function used to align q2 with q1
-
utility_functions.
optimum_reparam_pair
(q, time, q1, q2, lam=0.0)[source]¶ calculates the warping to align srsf pair q1 and q2 to q
- Parameters
q – vector of size N or array of NxM samples of first SRSF
time – vector of size N describing the sample points
q1 – vector of size N or array of NxM samples samples of second SRSF
q2 – vector of size N or array of NxM samples samples of second SRSF
lam – controls the amount of elasticity (default = 0.0)
- Return type
vector
- Return gam
describing the warping function used to align q2 with q1
-
utility_functions.
outlier_detection
(q, time, mq, k=1.5)[source]¶ calculates outlier’s using geodesic distances of the SRSFs from the median
- Parameters
q – numpy ndarray of N x M of M SRS functions with N samples
time – vector of size N describing the sample points
mq – median calculated using
time_warping.srsf_align()
k – cutoff threshold (default = 1.5)
- Returns
q_outlier: outlier functions
-
utility_functions.
randomGamma
(gam, num)[source]¶ generates random warping functions
- Parameters
gam – numpy ndarray of N x M of M of warping functions
num – number of random functions
- Returns
rgam: random warping functions
-
utility_functions.
resamplefunction
(x, n)[source]¶ resample function using n points
- Parameters
x – functions
n – number of points
- Return type
numpy array
- Return xn
resampled function
-
utility_functions.
rgam
(N, sigma, num)[source]¶ Generates random warping functions
- Parameters
N – length of warping function
sigma – variance of warping functions
num – number of warping functions
- Returns
gam: numpy ndarray of warping functions
-
utility_functions.
smooth_data
(f, sparam)[source]¶ This function smooths a collection of functions using a box filter
- Parameters
f – numpy ndarray of shape (M,N) of M functions with N samples
sparam – Number of times to run box filter (default = 25)
- Return type
numpy ndarray
- Return f
smoothed functions functions
-
utility_functions.
srsf_to_f
(q, time, f0=0.0)[source]¶ converts q (srsf) to a function
- Parameters
q – vector of size N samples of srsf
time – vector of size N describing time sample points
f0 – initial value
- Return type
vector
- Return f
function
-
utility_functions.
update_progress
(progress)[source]¶ This function creates a progress bar
- Parameters
progress – fraction of progress
-
utility_functions.
warp_f_gamma
(time, f, gam)[source]¶ warps a function f by gam
:param time vector describing time samples :param q vector describing srsf :param gam vector describing warping function
- Return type
numpy ndarray
- Return f_temp
warped srsf
-
utility_functions.
warp_q_gamma
(time, q, gam)[source]¶ warps a srsf q by gam
:param time vector describing time samples :param q vector describing srsf :param gam vector describing warping function
- Return type
numpy ndarray
- Return q_temp
warped srsf
-
utility_functions.
zero_crossing
(Y, q, bt, time, y_max, y_min, gmax, gmin)[source]¶ finds zero-crossing of optimal gamma, gam = s*gmax + (1-s)*gmin from elastic regression model
- Parameters
Y – response
q – predicitve function
bt – basis function
time – time samples
y_max – maximum repsonse for warping function gmax
y_min – minimum response for warping function gmin
gmax – max warping function
gmin – min warping fucntion
- Return type
numpy array
- Return gamma
optimal warping function


{{> meta}} {{> css}} {{> js}}
Better check yourself, you're not looking too good.
Please enter the email address associated with your account


XPathNodeSet Class
Definition
Line
31
of
System.XPath/XPathObject.cm
Inheritance
Constructors & Destructor
public
XPathNodeSet
()
Member Functions
public
void
Add
(
Node
* node)
public
int
Length
()
const
public
override
UniquePtr
<
Node
>
ToDom
()
const
public
Node
*
operator[]
(
int
index)
const
Member Variables
private
NodeList
nodes
Constructor& Destructor Details
XPathNodeSet Constructor
public
XPathNodeSet()
Definition
Line
33
of
System.XPath/XPathObject.cm
Member Function Details
Add Member Function
public
void
Add(
Node
* node)
Definition
Line
45
of
System.XPath/XPathObject.cm
Length Member Function
public
int
Length()
Definition
Line
41
of
System.XPath/XPathObject.cm
ToDom Member Function
public
override
UniquePtr
<
Node
> ToDom()
Definition
Line
49
of
System.XPath/XPathObject.cm
operator[] Member Function
public
Node
* operator[](
int
index)
Definition
Line
37
of
System.XPath/XPathObject.cm


HTML Summary
HTML(HyperText Markup Language) is used to define our page content,
structure and meaning.
You don't use it for styling purposes. Use CSS for
that instead!
- HTML uses "elements" to describe(annotate)content
- HTML element typically have an opening tag, content, and then a closing tag
- You can also have void (empty) elements like images
- You can also configure elements with attributes
- There's a long list of available elements but you'll gain experience over time, no worries.
Learn more about all available HTML elements on the MDN HTML element reference.
CSS Summary
CSS (Cascading Style Sheets) is used for styling your page content.
- Styles are assigned via property-value pairs
- You can assign styles via the "style" attribute
- To avoid code duplication, you typically use global styles (e.g. via the "style" element) instead
- Alternatively,you can work with external stylesheet files which you "link" to
- When working with CSS,concepts like "inheritance", "specificity" and the "box model" should be understood.
Learn more about all available CSS properties an values on the MDN CSS property reference.


Dedicated, highly-reliable employee. Quickly adapts to new training, technologies, and situations.
SUMMARY OF QUALIFICATIONS
- Knowledge of C++, C#, Swift
- Basic familiarity of Java, JavaScript, and HTML
- Experience with Mac and Windows OS
- Proficient with Microsoft Office 365 Suite and Adobe Photoshop
- Devoted to detail
- Technologically inclined
- Excellent trouble shooting skills
- Presently developing personal small-scale software applications
EDUCATION
New England Institute of Technology, East Greenwich, RI
Associate in Science Degree, Game Design and Simulation Programming, September 2018
WORK EXPERIENCE
Stop and Shop Supermarket Company, Richmond, RI May 2016 - present
Associate:
- Communicate with manager to complete daily requests and expectations.
- Assist customers with questions about the store, products, and surrounding area.
- Stock shelves and removed old product.
- Clean and organize dairy back room.
- Unload deliveries from trucks.
Chariho High School, Wood River Junction, RI Fall 20141 to 1 Help Desk Intern:
- Respond to help tickets and calls from students and teachers.
- Provide immediate hardware support in classrooms.
- Assist in setting up desktops in new and redesigned office spaces for administrators.
ACCOMPLISHMENTS AND BACKGROUND
Boy Scouts of America, May 2009 - 2016
Eagle Scout:
- Patrol Leader of the Flaming Arrows.
- Eagle project was the construction and implementation of several bat boxes for the Richmond Rural Preservation Land Trust.
- Frequent collaborative events.
- Several community service opportunities throughout the year.
Skills USA, 2016
Silver Medal in Computer Applications
- IC3 Certification
- Small group collaboration
- Strong familiarity with the Microsoft Office 365 Suite
- Written troubleshooting skills
Hobbies include camping, walking, and biking through the woods, skiing, and assembling computers.


A Utility to assign JS functions to a hash i.e. when the hash exists in the url, the JS function will execute.


Spas in Bangalore
Browse by Genres
All
Eat Out
Beauty
Adventure
Events
Health
Special title treatment
With supporting text below as a natural lead-in to additional content.


--- layout: post title: Contract First web services, yes, that's the path to redemption tags: [soa, web-services] ---
Christian Weyer gives us a very handy tool to experiment with the concept of contract first web services.
For those of you not yet hooked up on the latest WS - SOA trends, we are moving to the idea of a contract being an agreement between endpoints to establish a communication. Instead of saying:
We now say:
The shift is important for interop, and can resolve partly the biggest problems with versioning and other advanced problems. It's not a perfect solution, but it's a good step.
Indigo supports this idea, but this add-in gives you a way to create your contract first, and to get the code after. This is important because, in the SOA model, Object Oriented methodologies are only use within the boundaries of your service, which you can access from the outside world only through the contract.
Yeah, I know, boring explanation, here is the link, enjoy.


is a logical descendant of that controlled element as indicated by the aria-owns attribute.
div_text_12a
Cass
Gabriel
Cass
Gabriel


{% extends "reports/tabular.html" %} {% load hq_shared_tags %} {% load i18n %} {% block reportcontent %} {# hacky mchack - since we're inheriting from tabular, i'm just repeating myself here with the hat here #}


embeding from ypu.tube
embed only link from u.tube
embeding from website (google.com)
how to add video directly
1:fristly bring the source flie in that folder by creating a new file 2:then write (video src="") tag 3:ued (controlls autoplay loop width="") loop to replay that video automaticaly. 4:write ending tage of
this is copyright


Steps to test zmigrate
1. Stop the chain and build latest
cd ~/komodo/src
./komodo-cli -ac_name=RKT stop
git pull
make
2. Compile dapp
./cc/dapps/makedapps
3. Start the chain (don't start mining)
./komodod -ac_name=RKT -ac_supply=0 -ac_reward=25600000000 -ac_halving=77777 -ac_private=1 -addnode=<IP_ADDRESS> -addnode=<IP_ADDRESS> &
4. Create a sprout address and shield mined funds there
./komodo-cli -ac_name=RKT z_getnewaddress sprout
./komodo-cli -ac_name=RKT z_shieldcoinbase "*" zcdiaH6hVy8vuTiCwerBoGPt2fpthQo6KEPDZJwiMekqsj3mhXYLiGZ4SzvbyVZzQbqAp94fH761imGfHa4cc3tUqnuaDo7
# change to your zc address
5. Create and sapling z addr and initiate migrate
./komodo-cli -ac_name=RKT z_getnewaddress
./zmigrate RKT zs1fp7h7y348l6nm4qwwqq2ntu8vjtsgkmf7ghntsd72tef2wkzdh8ppyeegv9klaj4zzcpupfr0kd
# change to your sapling z address
6. Wait for migrate to be done. It may take a while at the end, don't worry and don't cancel.
7. Use z_listunspent call and search for and address that starts with zc. If none, that means the migrate has been successful for you.
./komodo-cli -ac_name=RKT z_listunspent
(edited)


This function approximates sqrt(abs(x))*sgn(x), such that the derivative is finite and smooth in x=0.
| Function | Approximation | Range |
|---|---|---|
| y = regRoot(x) | y ~= sqrt(abs(x))*sgn(x) | abs(x) >>delta |
| y = regRoot(x) | y ~= x/sqrt(delta) | abs(x) << delta |
With the default value of delta=0.01, the difference between sqrt(x) and regRoot(x) is 16% around x=0.01, 0.25% around x=0.1 and 0.0025% around x=1.
function regRoot extends Modelica.Icons.Function; input Real x; input Real delta = 0.01 "Range of significant deviation from sqrt(abs(x))*sgn(x)"; output Real y; end regRoot;


I am currently learning how to write code in the languages HTML and CSS from Udacity and it is pretty cool so far.
I have learned about Inline and Block tags
I have also learned about DOM Tree-Like Structures, and how to use the developer tools in Google Chrome.
I learned how to do all of this from Udacity and it was pretty fun while doing it!
I do not really understand the difference betwen span and
I guess div makes a new line, or a box around a specific point, while I am still not sure what Span does.


FREQUENTLY ASKED QUESTIONS
Which credit card statements do you support?
So far, the following credit card statements work:
Amazon Chase Visa
Chase Credit Cards
Can you please support my credit card?
I'll try. Send me an email and I'll give it a shot. My email address is socialnetapis @ gmail .com (spaces added to beat bots).


Looping in Ruby and Javascript
For loop in RubyUsed for iterating through a known number of steps. for i in 0..10 puts "the current loop number is #{i}" end # or, for element in array puts "The current element is: #{element}" end |
For loop in JavascriptUsed for iterating through a known number of steps. for (var i = 0; i < 10; i++){ console.log( "the current loop number is: " + i); } // or, for each (var element in array) { console.log( "The current element is: " + element); } |
While loop in RubyUsed for conditional looping. The
n = 1 while n < 10 puts n n = n + 1 end |
While loop in JavaScriptUsed for conditional looping. The keyword
var n = 1 while (n < 10) { console.log(n); n = n + 1; } |
Operating on each member of an Array in RubyUsed for performing some function on each element in an array. names = ["peter", "paul", "mary"] names.each {| name | puts "#{name} is in the band!"} |
Operating on each member of an Array in JavascriptUsed for performing some function on each element in an array. var names = ["peter", "paul", "mary"]; function printNames (element, index, array) { console.log(element + " is in the band!"); } names.forEach(printNames); |


MRISA
MRISA (Meta Reverse Image Search API) is a RESTful API which takes an image URL, does a reverse Google image search, and returns a JSON array with the search results.
Usage
Install the necessary dependencies:
pip install certifi flask pycurl beautifulsoup4
Start the server with:
python mrisa/server.py
API
Perform a Reverse Image Search
Performs a reverse image search using the supplied image URL as input.
URL
Arguments
- image_url - A URL to an image to use for the search input.
Request Example
curl -X POST http://localhost:5000/search
-H "Content-Type: application/json"
-d '{
"image_url":
"http://placehold.it/350x150.png"
}'
Response Example
{
"descriptions": ["test description", "..."],
"links": ["http://test_link.com", "..."],
"similar_images": ["http://test_image.jpg", "..."],
"titles": ["test title", "..."]
}
Using cURL to make a call to the API
curl -X POST -H "Content-Type: application/json" -d '{"image_url":"http://upload.wikimedia.org/wikipedia/commons/2/29/Voyager_spacecraft.jpg"}' http://localhost:5000/search
There is a client implentation in python in the repository but it should be nice and easy to create one in most languages.
Download
Find out more and download a copy from GitHub. Better yet, check out the repo and play about with the code ;).
Acknowledgments
Many thanks to everyone who has contributed, used and starred the project on github.
Special thanks to:
- Doggan for his extensive improvements in all areas
- Quantomicus for making the project v3 compatable.
- daniel1111 for adding 'best guess' and fixing parsing of links in the search results
Contributions are always welcome, so please don't hesitate to get in touch!


Every week BackPage hosts over 16,000 ads commercializing sex trafficking, in Houston alone. Using fake posts, Johns will be informed of the consequences of their actions after clicking on BackPage ads. These fake posts will be generated and posted using a natural language processing model built on the scraped ads.
BP_IMITATEThis extension will:
Scan web pages for suspicious activity
Tracks and records data to database managed by local/national Police Departments
All data will be used for investigation purposes
Processing model built will gather location information, age and phone number (if available)
The search tool we built provides organized content for investigators and compiles it into a searchable database. Houston P.D. will have access to photos, names, contact information and other text, ready to use at their fingertips.
VIEW_SEARCHAt a glance the heat map of Houston will display businesses without permits/licenses, busts and closings. User will be able to filter the data based on zip code.
Geo-mapping of Houston will display businesses without permits/licenses at a glance. User will be able to filter data based on busts, closings, and view heat maps. Map will also have a search function by zip code.
VIEW_MAP


Register
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Last Name:
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Other:
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{% extends "base.html" %}
{#
The entry point for the ReadTheDocs Theme.
Any theme customisations should override this file to redefine blocks defined in
the various templates. The custom theme should only need to define a main.html
which `{% extends "base.html" %}` and defines various blocks which will replace
the blocks defined in base.html and its included child templates.
#}
{% block extrahead %}
{% endblock %}


Tools to quickly compile taxonomic and distribution data from the Brazilian Flora 2020.
| Version: | 0.3.0 |
| Depends: | R (≥ 3.0.0) |
| Imports: | httr, dplyr, utils, jsonlite, stringdist |
| Suggests: | testthat, shiny |
| Published: | 2017-12-04 |
| Author: | Gustavo Carvalho |
| Maintainer: | Gustavo Carvalho <gustavo.bio at gmail.com> |
| BugReports: | http://www.github.com/gustavobio/flora/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://www.github.com/gustavobio/flora |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | flora results |
| Reference manual: | flora.pdf |
| Package source: | flora_0.3.0.tar.gz |
| Windows binaries: | r-devel: flora_0.3.0.zip, r-release: flora_0.3.0.zip, r-oldrel: flora_0.3.0.zip |
| OS X binaries: | r-release: flora_0.3.0.tgz, r-oldrel: flora_0.3.0.tgz |
| Old sources: | flora archive |
Please use the canonical form https://CRAN.R-project.org/package=flora to link to this page.


Marco Castelluccio
Overall navigable report of Linux + Windows code coverage:
https://marco-c.github.io/code-coverage-reports/
With a short and random usage of the report Sylvestre and I were able to remove ~50 files with ~12000 lines of code.
https://developer.mozilla.org/en-US/docs/Mozilla/Testing/Measuring_Code_Coverage_on_Firefox
# Enable code coverage
ac_add_options --enable-coverage
export CFLAGS="--coverage"
export CXXFLAGS="--coverage"
export LDFLAGS="--coverage"
# Without debug mode, content processes terminate with "_exit",
# which doesn't update coverage counters.
ac_add_options --enable-debug
# With the sandbox, content processes can't write updated coverage
# counters in the gcda files.
ac_add_options --disable-sandbox
# Enable code coverage
ac_add_options --enable-coverage
export CC="clang-cl.exe"
export CXX="clang-cl.exe"
export CFLAGS="--coverage"
export CXXFLAGS="--coverage"
# Currently need a custom Clang build
export LDFLAGS="PATH_TO_LLVM_BUILD_DIR/lib/clang/6.0.0/lib/windows/clang_rt.profile-x86_64.lib"
# Without debug mode, content processes terminate with "_exit",
# which doesn't update coverage counters.
ac_add_options --enable-debug
# With the sandbox, content processes can't write updated coverage
# counters in the gcda files.
ac_add_options --disable-sandbox
./mach try fuzzy -q 'linux64-ccov'
Windows:
./mach try fuzzy -q 'win64-ccov'


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UK expresses concern over violence in Hong Kong campus
The United Kingdom (UK) said it is concerned by the escalating violence from both police and protesters occupying university campuses in Hong Kong.
A Foreign Office spokesperson made this known in a statement yesterday.
“The UK is seriously concerned by the escalation in violence from both the protestors and the authorities around Hong Kong university campuses,” the spokesperson said.
The autonomous Chinese city’s Polytechnic University became a scene of tumult on Sunday when protesters holed up inside threw petrol bombs and fired arrows at the police.
The police responded with tear gas and water cannon.
READ ALSO: Defiant Hong Kong protesters clash with police, set fires
The police denied media reports that they had tried to enter the campus in a pre-dawn raid.
Several protesters have been arrested while trying to run from a Hong Kong university campus surrounded by police.
Around 100 people tried to leave the Polytechnic University, but were met with tear gas and rubber bullets.
In the past week, the campus has turned into a battleground as long-running anti-government protests become more violent.


A personal story
Many years ago outside a school concert, a familiar voice called out to me,
“Hey Cloud, long time no see!”
“Oh! Hey Jen. I’m here with my friends, wanna sit with us?”
“Sure, I’ll be a bit late going in, save me a seat.”
I saved the seat beside me. As the hall started to fill up and people sat down in my row, a cheerful looking girl walked over and looked intently at the empty seat.
“Sorry, I’m saving it for a friend...”
5 seconds later, the girl was still standing there. She was no longer cheerful. That’s when I realized that it was Jen.
I always thought that I was inattentive or just bad with people. While that is probably true, I didn’t learn until recently that I’m bad at something that most people take for granted: recognizing faces.
According to Ken Nakayama and Richard Russell at Harvard and Bradley Duchaine at University College London, face-blindness may affect around 2% of people. 150 million face-blind people on earth.
Many face-blind people don’t realize that they are any worse than the general population. Many find their own ways to compensate. I rely heavily on voice, clothing and hairstyle. In films, characters changing outfits is always a rollercoaster ride for me. In public, someone waving at me might cause me some confusion but I’ll usually know who they are as soon as I hear their voice.
There’s another way to compensate. According to Joseph DeGutis at Harvard, training can make a significant difference.
WooWho is my attempt to help myself and people like me.


Pouria ShirasbData Science - Machine Learning - Software Development
Portfolio
Distracted Driver Detection
There was a competition on kaggle about detecting distracted drivers from dashcam snapshot photos of drivers. The training dataset consisted of 26 drivers and 10 classes for a total of 22424 images. The task was to classify each image into one of the 10 classes as such “Holding cellphone on the right hand” or “Reaching for backseat”. I experimented with numerous state of the art deep convolutional neural networks such as Resnet, VGG-CAM, VGG16 and GoogleNet. Eventually, I used a mixture of 13 fine-tuned VGG16 CNNs implemented in Python using Keras with Theano backend, plus addition code for various tasks along the pipline such as data cleaning and visualization.
For the capstone project of my Data Science specialization, given a dataset of phrases provided by Swiftkey, we were tasked with analyzing the word sequences, creating a web application using R and RShiny to predict the next word in a sentence and finally, to create a slide promoting the web application.
Using the dataset http://groupware.les.inf.puc-rio.br/har (see the section on the Weight Lifting Exercise Dataset), we would like to predict “classe” variable given all the other measurements. After preprocessing the data, we train a random forest model to make predictions with a reasonable accuracy.
We are interested in answering two questions, (1) Across the United States, which types of events are most harmful with respect to population health? and (2) which types of events have the greatest economic consequences? We use the storm data published by NOAA to find the total cost in USD and total casualties per event type to determine the top five event types that are most harmful in terms of health and in terms of economy.
Using a dataset about personal movement using activity monitoring devices such as Fitbit, Nike Fuelband or Jawbone, we would like to answer a few questions such as "What is mean total number of steps taken per day?" or "Are there differences in activity patterns between weekdays and weekends?"


WITNESS LIST
Finance
February 5, 2015 9:00 AM
FOR:
Day, Joanne Microbiologist (Myself, TSEU, all state employees & retirees), Austin
AGAINST:
Joseph, Zenobia C. Veteran (also providing written testimony) (Self), Austin, TX
ON:
., Anumeha Interim Executive Director of PRB (also providing written testimony) (State
Pension Review Board)
Bishop, Ann S Executive Director (also providing written testimony) (ERS), Austin, TX
Blount, Elizabeth retired state employee (Retired State Employee Association)
Braden, Paul Lawyer (also providing written testimony) (State Pension Review Board
(Chair))
Butler, Daniel Residential Instructor - TSBVI (Texas State Employees Union), Austin
Cantu, Jr., Eliseo "Al" Chair (also providing written testimony) (Texas Veterans
Commission)
Conklin, Lena LBB Staff (also providing written testimony) (LBB)
Crews, Andre Correctional Office (also providing written testimony) (AFSCME)
Fallon, Katy LBB Staff (also providing written testimony) (LBB)
Hamilton, William C (Bill) retired state employee (RSEA - Retired State Employees
Association (President)), Austin, TX
Haverlah, Leroy Retired - State Protective & Regulatory Services (Texas State Employees
Union)
Hoffman, Emily LBB Staff (also providing written testimony) (LBB)
Jordan, Michelle Executive Director of the agency (also providing written testimony)
(Texas Emergency Services Retirement System)
McGeady, John LBB Staff (also providing written testimony) (LBB)
Morganti, Emily LBB Staff (also providing written testimony) (LBB)
Palladino, Thomas Executive Director (also providing written testimony) (Texas Veterans
Commission)
Perez, Kevin LBB Staff (also providing written testimony) (LBB)
Powers, Daniel Residential Instructor - TSBVI (Texas State Employees Union), Austin
Powers, Maura retired state employee (also providing written testimony) (AFSCME Tx
Retirees)
Scott, Pam Retired state employee (AFSCME), Austin, TX
Sneed, John Executive Director, State Preservation Board (also providing written
testimony) (State Preservation Board), Austin, TX
White, Suzanne retired via ERS (myself & other ERS retirees), Wimberley, TX
Wilson, Porter Executive Director Designate (also providing written testimony) (ERS),
Austin, TX
Zavodny, Don Association Director (also providing written testimony) (AFSCME)
Registering, but not testifying:
FOR:
Dally, William State Retiree (Self), Aus., TX
Hymel, Ray Lobbyist (Texas Public Employees Association (TPEA)), Austin, TX
WITNESS LIST
Finance
February 5, 2015 9:00 AM
ON:
Cebulski, Mark S. Exec. Director, AFSCME Tx Retirees (AFSCME Tx Retirees)
Crowder, Don A. Retired Teacher (AFSCME)
Delgado, Yolanda retired child care licensing rep Dept. of Family and Protective Services -
25 yrs. (Dept. of Family and Protective Services), Austin, TX
Fenlaw, Mark Actuary (Texas Emergency Services Retirement System), Austin, TX
Johnson, Judith Certified Public Accountant (Texas Emergency Services Retirement
System)
Lindner, Ray Executive Director, (National Guard Association of Texas)
Raschke, Don Retired State Employee (Retire State Employees Association)
Roberts, Wayne R. Board Member, State Pension Review Board (PRB), Austin, TX
Seely, Joseph retired (AFSCME Texas Retirees), Austin
Seely, Peggy D. Retired State Employee (AFSCME Texas Retirees), Austin, TX