6 LDA Inference
6.1 General Overview
We have talked about LDA as a generative model, but now it is time to flip the problem around. What if I have a bunch of documents and I want to infer topics? To figure out a problem like this we are first going to assume the documents were generated using a generative model similar to the ones we created in the previous section.
Now it is time to flip the problem around. What happens when I have a bunch of documents and I want to know what topics are present in each document and what words are present (most probable) in each topic? To answer the previous questions, we first need to be able to attain the answer for Equation (6.1). (NOTE: The derivation for LDA inference via Gibbs Sampling is taken from (Darling 2011), (Heinrich 2008) and (Steyvers and Griffiths 2007).)
\[
\begin{equation}
p(\theta, \phi, z|w, \alpha, \beta) = {p(\theta, \phi, z, w|\alpha, \beta) \over p(w|\alpha, \beta)}
\tag{6.1}
\end{equation}
\] Equation (6.1) says the following:
The probability of the document topic distribution, the word distribution of each topic, and the topic labels of all words (in all documents) and the hyperparameters \(\alpha\) and \(\beta\). In particular we are interested in estimating the probability of topic (z) for a given word (w) (and our prior assumptions, i.e. hyperparameters) for all words and topics. From this we can infer \(\phi\) and \(\theta\). (NOTE - COME BACK TO THIS AS THIS NEEDS TO BE MORE CLEAR)
Quick notes:
* Equation (6.1) is based on the following statistical property
\[ \begin{equation} p(A, B | C) = {p(A,B,C) \over p(C)} \tag{6.2} \end{equation} \]
- All the variables used in this section were outlined at the beginning of Chapter 5 if you need a refresher.
Let’s take a step from the math and just map out ‘things we know’ versus the ‘things we don’t know’ in regards to an inference problem:
Known Parameters
- Documents (d in D): We have a set number of documents we want to identify the topic strucutres in.
- Words (w in W): We have a collection of words and word counts for each document which we place in a document word matrix.
- Vocabulary (W): The unique list of words across all documents. This means our document word matrix has one row for each document and one column for each word in our vocabulary.
- Hyperparemeters:
- \(\overrightarrow{\alpha}\): Our prior assumption about the topic distribution of our documents. This book will only use symmetric \(\alpha\) values, in other words we assume all topics are equally as probably in any given document (similar to the naive assumption of a fair die). We will be supplying the \(\alpha\) value for inference.
- Higher \(\overrightarrow{\alpha}\) - We assume documents will have a similar and close to uniform distribution of topics.
- Lower \(\overrightarrow{\alpha}\) - We assume document topic distributions vary more drastically.
- \(\overrightarrow{\beta}\): Our prior assumption about the word distribution of each topic.
- Higher \(\overrightarrow{\beta}\): Word distributions in each topic are closer to uniform, i.e. each word is equally as likely in each topic.
- Lower \(\overrightarrow{\beta}\): Word distributions vary more from topic to topic.
- \(\overrightarrow{\alpha}\): Our prior assumption about the topic distribution of our documents. This book will only use symmetric \(\alpha\) values, in other words we assume all topics are equally as probably in any given document (similar to the naive assumption of a fair die). We will be supplying the \(\alpha\) value for inference.
And on to the parts we don’t know….
** Unknown (Latent) Parameters **
- Number of Topics (k): Disclaimer - We need to specify the number of topics we assume are present in the documents. However we don’t know the real number of topics in the corpus. For the purposes of this book I’m going to skip how to estimate the number of topics. If you are interested in learning more about how to estimate the number of topics present in a document see REFERENCE LIST.
- Document Topic Mixture (\(\theta\)): We need to determine the topic distribution in each document.
- Word Distribution of Each Topic (\(\phi\)): We need to know the distribution of words in each topic. Obviously some words are going to occur very often in a topic while others may have zero probability of occurring in a topic.
- Word topic assignment (z): This is actually the main thing we need to infer. To be clear, if we know the topic assignment of every word in every document, then we can derive the document topic mixture, \(\theta\), and the word distribution, \(\phi\), of each topic.
6.2 Mathematical Derivations for Inference
Back to the math…
The derivation connecting equation (6.1) to the actual Gibbs sampling solution to determine z for each word in each document, \(\overrightarrow{\theta}\), and \(\overrightarrow{\phi}\) is very complicated and I’m going to gloss over a few steps. For complete derivations see (Heinrich 2008) and (Carpenter 2010).
As stated previously, the main goal of inference in LDA is to determine the topic of each word, \(z_{i}\) (topic of word i), in each document.
\[ \begin{equation} p(z_{i}|z_{\neg i}, \alpha, \beta, w) \tag{6.3} \end{equation} \]
Notice that we are interested in identifying the topic of the current word, \(z_{i}\), based on the topic assignments of all other words (not including the current word i), which is signified as \(z_{\neg i}\).
\[ \begin{equation} \begin{aligned} p(z_{i}|z_{\neg i}, \alpha, \beta, w) &= {p(z_{i},z_{\neg i}, w, | \alpha, \beta) \over p(z_{\neg i},w | \alpha, \beta)}\\ &\propto p(z_{i}, z_{\neg i}, w | \alpha, \beta)\\ &\propto p(z,w|\alpha, \beta) \end{aligned} \tag{6.4} \end{equation} \]
You may notice \(p(z,w|\alpha, \beta)\) looks very similar to the definition of the generative process of LDA from the previous chapter (equation (5.1)). The only difference is the absence of \(\theta\) and \(\phi\). This means we can swap in equation (5.1) and integrate out \(\theta\) and \(\phi\).
\[ \begin{equation} \begin{aligned} p(w,z|\alpha, \beta) &= \int \int p(z, w, \theta, \phi|\alpha, \beta)d\theta d\phi\\ &= \int \int p(\phi|\beta)p(\theta|\alpha)p(z|\theta)p(w|\phi_{z})d\theta d\phi \\ &= \int p(z|\theta)p(\theta|\alpha)d \theta \int p(w|\phi_{z})p(\phi|\beta)d\phi \end{aligned} \tag{6.5} \end{equation} \]
As with the previous Gibbs sampling examples in this book we are going to expand equation (6.4), plug in our conjugate priors, and get to a point where we can use a Gibbs sampler to estimate our solution.
Below we continue to solve for the first term of equation (6.5) utilizing the conjugate prior relationship between the multinomial and Dirichlet distribution. The result is a Dirichlet distribution with the parameters comprised of the sum of the number of words assigned to each topic and the alpha value for each topic in the current document d.
\[ \begin{equation} \begin{aligned} \int p(z|\theta)p(\theta|\alpha)d \theta &= \int \prod_{i}{\theta_{d_{i},z_{i}}{1\over B(\alpha)}}\prod_{k}\theta_{d,k}^{\alpha k}\theta_{d} \\ &={1\over B(\alpha)} \int \prod_{k}\theta_{d,k}^{n_{d,k} + \alpha k} \\ &={B(n_{d,.} + \alpha) \over B(\alpha)} \end{aligned} \tag{6.6} \end{equation} \]
Similarly we can expand the second term of Equation (6.5) and we find a solution with a similar form. The result is a Dirichlet distribution with the parameter comprised of the sum of the number of words assigned to each topic across all documents and the alpha value for that topic.
\[ \begin{equation} \begin{aligned} \int p(w|\phi_{z})p(\phi|\beta)d\phi &= \int \prod_{d}\prod_{i}\phi_{z_{d,i},w_{d,i}} \prod_{k}{1 \over B(\beta)}\prod_{w}\phi^{B_{w}}_{k,w}d\phi_{k}\\ &= \prod_{k}{1\over B(\beta)} \int \prod_{w}\phi_{k,w}^{B_{w} + n_{k,w}}d\phi_{k}\\ &=\prod_{k}{B(n_{k,.} + \beta) \over B(\beta)} \end{aligned} \tag{6.7} \end{equation} \]
This leaves us with the following:
\[ \begin{equation} \begin{aligned} p(w,z|\alpha, \beta) &= \prod_{d}{B(n_{d,.} + \alpha) \over B(\alpha)} \prod_{k}{B(n_{k,.} + \beta) \over B(\beta)} \end{aligned} \tag{6.8} \end{equation} \]
The equation necessary for Gibbs sampling can be derived by utilizing (6.8). This is accomplished via the chain rule and the definition of conditional probability.
Quick Note: The chain rule is outlined in Equation (6.9)
\[ \begin{equation} p(A,B,C,D) = P(A)P(B|A)P(C|A,B)P(D|A,B,C) \tag{6.9} \end{equation} \] The conditional probability property utilized is shown in (6.10) \[ \begin{equation} P(B|A) = {P(A,B) \over P(A)} \tag{6.10} \end{equation} \]
CHANGE THE -i’s TO THE NEGATION SIGN FOR CONSISTENCY
\[ \begin{equation} \begin{aligned} p(z_{i}|z_{\neg i}, w) &= {p(w,z)\over {p(w,z_{\neg i})}} = {p(z)\over p(z_{\neg i})}{p(w|z)\over p(w_{\neg i}|z_{\neg i})p(w_{i})}\\ \\ &\propto \prod_{d}{B(n_{d,.} + \alpha) \over B(n_{d,\neg i}\alpha)} \prod_{k}{B(n_{k,.} + \beta) \over B(n_{k,\neg i} + \beta)}\\ \\ &\propto {\Gamma(n_{d,k} + \alpha_{k}) \Gamma(\sum_{k=1}^{K} n_{d,\neg i}^{k} + \alpha_{k}) \over \Gamma(n_{d,\neg i}^{k} + \alpha_{k}) \Gamma(\sum_{k=1}^{K} n_{d,k}+ \alpha_{k})} {\Gamma(n_{k,w} + \beta_{w}) \Gamma(\sum_{w=1}^{W} n_{k,\neg i}^{w} + \beta_{w}) \over \Gamma(n_{k,\neg i}^{w} + \beta_{w}) \Gamma(\sum_{w=1}^{W} n_{k,w}+ \beta_{w})}\\ \\ &\propto (n_{d,\neg i}^{k} + \alpha_{k}) {n_{k,\neg i}^{w} + \beta_{w} \over \sum_{w} n_{k,\neg i}^{w} + \beta_{w}} \end{aligned} \tag{6.11} \end{equation} \]
We will now use Equation (6.11) in the example below to complete the LDA Inference task on a random sample of documents. To calculate our word distributions in each topic we will use Equation (6.12).
\[ \begin{equation} \phi_{k,w} = { n^{(w)}_{k} + \beta_{w} \over \sum_{w=1}^{W} n^{(w)}_{k} + \beta_{w}} \tag{6.12} \end{equation} \]
The topic distribution in each document is calcuated using Equation (6.13).
\[ \begin{equation} \theta_{d,k} = {n^{(k)}_{d} + \alpha_{k} \over \sum_{k=1}^{K}n_{d}^{k} + \alpha_{k}} \tag{6.13} \end{equation} \]
What if I don’t want to generate docuements. What if my goal is to infer what topics are present in each document and what words belong to each topic? This is were LDA for inference comes into play.
Before going through any derivations of how we infer the document topic distributions and the word distributions of each topic, I want to go over the process of inference more generally.
The General Idea of the Inference Process
- Initialization: Randomly select a topic for each word in each document from a multinomial distribution.
- Gibbs Sampling:
- For i iterations
- For document d in documents:
- For each word in document d:
- assign a topic to the current word based on probability of the topic given the topic of all other words (except the current word) as shown in Equation (6.11)
- For each word in document d:
6.3 Animal Farm - Code Example
Now let’s revisit the animal example from the first section of the book and break down what we see. This time we will also be taking a look at the code used to generate the example documents as well as the inference code.
rm(list = ls())
library(MCMCpack)
library(tidyverse)
library(Rcpp)
library(knitr)
library(kableExtra)
library(lsa)
get_topic <- function(k){
which(rmultinom(1,size = 1,rep(1/k,k))[,1] == 1)
}
get_word <- function(theta, phi){
topic <- which(rmultinom(1,1,theta)==1)
# sample word from topic
new_word <- which(rmultinom(1,1,phi[topic, ])==1)
return(c(new_word, topic))
}
cppFunction(
'List gibbsLda( NumericVector topic, NumericVector doc_id, NumericVector word,
NumericMatrix n_doc_topic_count,NumericMatrix n_topic_term_count,
NumericVector n_topic_sum, NumericVector n_doc_word_count){
int alpha = 1;
int beta = 1;
int cs_topic,cs_doc, cs_word, new_topic;
int n_topics = max(topic)+1;
int vocab_length = n_topic_term_count.ncol();
double p_sum = 0,num_doc, denom_doc, denom_term, num_term;
NumericVector p_new(n_topics);
IntegerVector topic_sample(n_topics);
for (int iter = 0; iter < 100; iter++){
for (int j = 0; j < word.size(); ++j){
// change values outside of function to prevent confusion
cs_topic = topic[j];
cs_doc = doc_id[j];
cs_word = word[j];
// decrement counts
n_doc_topic_count(cs_doc,cs_topic) = n_doc_topic_count(cs_doc,cs_topic) - 1;
n_topic_term_count(cs_topic , cs_word) = n_topic_term_count(cs_topic , cs_word) - 1;
n_topic_sum[cs_topic] = n_topic_sum[cs_topic] -1;
// get probability for each topic, select topic with highest prob
for(int tpc = 0; tpc < n_topics; tpc++){
// word cs_word topic tpc + beta
num_term = n_topic_term_count(tpc, cs_word) + beta;
// sum of all word counts w/ topic tpc + vocab length*beta
denom_term = n_topic_sum[tpc] + vocab_length*beta;
// count of topic tpc in cs_doc + alpha
num_doc = n_doc_topic_count(cs_doc,tpc) + alpha;
// total word count in cs_doc + n_topics*alpha
denom_doc = n_doc_word_count[cs_doc] + n_topics*alpha;
p_new[tpc] = (num_term/denom_term) * (num_doc/denom_doc);
}
// normalize the posteriors
p_sum = std::accumulate(p_new.begin(), p_new.end(), 0.0);
for(int tpc = 0; tpc < n_topics; tpc++){
p_new[tpc] = p_new[tpc]/p_sum;
}
// sample new topic based on the posterior distribution
R::rmultinom(1, p_new.begin(), n_topics, topic_sample.begin());
for(int tpc = 0; tpc < n_topics; tpc++){
if(topic_sample[tpc]==1){
new_topic = tpc;
}
}
// print(new_topic)
// update counts
n_doc_topic_count(cs_doc,new_topic) = n_doc_topic_count(cs_doc,new_topic) + 1;
n_topic_term_count(new_topic , cs_word) = n_topic_term_count(new_topic , cs_word) + 1;
n_topic_sum[new_topic] = n_topic_sum[new_topic] + 1;
// update current_state
topic[j] = new_topic;
}
}
return List::create(
n_topic_term_count,
n_doc_topic_count);
}
')
# 3 topics - land sea & air
# birds and amphibious have cross over
# fish - sea 100
# land animals - 100 land
beta <- 1
k <- 3 # number of topics
M <- 100 # let's create 10 documents
alphas <- rep(1,k) # topic document dirichlet parameters
xi <- 100 # average document length
N <- rpois(M, xi) #words in each document
# whale1, whale2, FISH1, FISH2,OCTO
sea_animals <- c('\U1F40B', '\U1F433','\U1F41F', '\U1F420', '\U1F419')
# crab, alligator, TURTLE,SNAKE
amphibious <- c('\U1F980', '\U1F40A', '\U1F422', '\U1F40D')
# CHICKEN, TURKEY, DUCK, PENGUIN
birds <- c('\U1F413','\U1F983','\U1F426','\U1F427')
# SQUIRREL, ELEPHANT, COW, RAM, CAMEL
land_animals<- c('\U1F43F','\U1F418','\U1F402','\U1F411','\U1F42A')
vocab <- c(sea_animals, amphibious, birds, land_animals)
# equal probability 1/18
# 0 - animals that are not possible
# 1 - for shared
# 4 - non-shared
shared <- 2
non_shared <- 4
not_present <- 0
land_phi <- c(rep(not_present, length(sea_animals)),
rep(shared, length(amphibious)),
rep(non_shared, 2), # turkey and chicken can't fly
rep(shared, 2), # regular bird and pengiun
rep(non_shared, length(land_animals)))
land_phi <- land_phi/sum(land_phi)
sea_phi <- c(rep(non_shared, length(sea_animals)),
rep(shared, length(amphibious)),
rep(not_present, 2), # turkey and chicken can't fly
rep(shared, 2), # regular bird and pengiun
rep(not_present, length(land_animals)))
sea_phi <- sea_phi/sum(sea_phi)
air_phi <- c(rep(not_present, length(sea_animals)),
rep(not_present, length(amphibious)),
rep(not_present, 2), # turkey and chicken can't fly
non_shared, # regular bird
not_present, # penguins can't fly
rep(not_present, length(land_animals)))
air_phi <- air_phi/sum(air_phi)
# calculate topic word distributions
phi <- matrix(c(land_phi, sea_phi, air_phi), nrow = k, ncol = length(vocab),
byrow = TRUE, dimnames = list(c('land', 'sea', 'air')))
theta_samples <- rdirichlet(M, alphas)
thetas <- theta_samples[rep(1:nrow(theta_samples), times = N), ]
new_words <- t(apply(thetas, 1, function(x) get_word(x,phi)))
ds <-tibble(doc_id = rep(1:length(N), times = N),
word = new_words[,1],
topic = new_words[,2],
theta_a = thetas[,1],
theta_b = thetas[,2],
theta_c = thetas[,3]
)
ds %>% filter(doc_id < 3) %>% group_by(doc_id) %>% summarise(
tokens = paste(vocab[word], collapse = ' ')
) %>% kable(col.names = c('Document', 'Animals'),
caption ="Animals at the First Two Locations")
| Document | Animals |
|---|---|
| 1 | 🐑 🐋 🐂 🐓 🐑 🦃 🦃 🐪 🦃 🐓 🐑 🐂 🐋 🐪 🐢 🐂 🐑 🐓 🐍 🐑 🐟 🐍 🐙 🐿 🐪 🐘 🐑 🐙 🦃 🐘 🐑 🐂 🐂 🐊 🐂 🐘 🐿 🦃 🦃 🐧 🐪 🐍 🐿 🐘 🐍 🐂 🐙 🐦 🐂 🐟 🐍 🐠 🐑 🦃 🐓 🦃 🐂 🐘 🐠 🐓 🐢 🐘 🦃 🐙 🐘 🐍 🐢 🐑 🐍 🐘 🐦 🐧 🐙 🐦 🐂 🐋 🐘 🐠 🐓 🐿 🐘 🐿 🦀 🐓 🐿 🐊 🐓 🦀 🐂 🐪 🐓 🐠 🐪 🐦 🐙 🐠 🐘 🦃 🐘 🐑 🐊 🐑 🐳 |
| 2 | 🐠 🐟 🐋 🐋 🐢 🐳 🦀 🐦 🐘 🐦 🐓 🐦 🐦 🦃 🐦 🐦 🐂 🦀 🐢 🦀 🐊 🐿 🦃 🐳 🐦 🐠 🐪 🐍 🐠 🐪 🐧 🐂 🐿 🐋 🐦 🐦 🐦 🐿 🐋 🐿 🐠 🐦 🐳 🐂 🐦 🐙 🐟 🐑 🐪 🐪 🦀 🐑 🐊 🐦 🐦 🐿 🦃 🐙 🐿 🦃 🐓 🐊 🐦 🐢 🐙 🐊 🦀 🐙 🐍 🐓 🐍 🐳 🐋 🐙 🐧 🐢 🐋 🐘 🐑 🐍 🐑 🐳 🐦 🐠 🐂 🐧 🐙 🐍 |
The habitat (topic) distributions for the first couple of documents:
| Document | Land | Sea | Air |
|---|---|---|---|
| 1 | 0.8168897 | 0.1688578 | 0.0142525 |
| 2 | 0.3860697 | 0.4442854 | 0.1696449 |
With the help of LDA we can go through all of our documents and estimate the topic/word distributions and the topic/document distributions.
This is our estimated values and our resulting values:
######### Inference ###############
current_state <- ds %>% dplyr::select(doc_id, word, topic)
current_state$topic <- NA
t <- length(unique(current_state$word))
# n_doc_topic_count
n_doc_topic_count <- matrix(0, nrow = M, ncol = k)
# document_topic_sum
n_doc_topic_sum <- rep(0,M)
# topic_term_count
n_topic_term_count <- matrix(0, nrow = k, ncol = t)
# colnames(n_topic_term_count) <- unique(current_state$word)
# topic_term_sum
n_topic_sum <- rep(0,k)
p <- rep(0, k)
# initialize topics
current_state$topic <- replicate(nrow(current_state),get_topic(k))
# get word, topic, and document counts (used during inference process)
n_doc_topic_count <- current_state %>% group_by(doc_id, topic) %>%
summarise(
count = n()
) %>% spread(key = topic, value = count) %>% as.matrix()
n_topic_sum <- current_state %>% group_by(topic) %>%
summarise(
count = n()
) %>% select(count) %>% as.matrix() %>% as.vector()
n_topic_term_count <- current_state %>% group_by(topic, word) %>%
summarise(
count = n()
) %>% spread(word, count) %>% as.matrix()
# minus 1 in, add 1 out
lda_counts <- gibbsLda( current_state$topic-1 , current_state$doc_id-1, current_state$word-1,
n_doc_topic_count[,-1], n_topic_term_count[,-1], n_topic_sum, N)
# calculate estimates for phi and theta
# phi - row apply to lda_counts[[1]]
# rewrite this function and normalize by row so that they sum to 1
phi_est <- apply(lda_counts[[1]], 1, function(x) (x + beta)/(sum(x)+length(vocab)*beta) )
rownames(phi_est) <- vocab
colnames(phi) <- vocab
theta_est <- apply(lda_counts[[2]],2, function(x)(x+alphas[1])/(sum(x) + k*alphas[1]))
theta_est <- t(apply(theta_est, 1, function(x) x/sum(x)))
# rewrite this function and normalize by row so that they sum to 1
phi_est <- apply(lda_counts[[1]], 1, function(x) (x + beta)/(sum(x)+length(vocab)*beta) )
rownames(phi_est) <- vocab
colnames(phi) <- vocab
theta_est <- apply(lda_counts[[2]],2, function(x)(x+alphas[1])/(sum(x) + k*alphas[1]))
theta_est <- t(apply(theta_est, 1, function(x) x/sum(x)))
colnames(theta_samples) <- c('land', 'sea', 'air')
vector_angles <- cosine(cbind(theta_samples,theta_est))[4:6, 1:3]
estimated_topic_names <- apply(vector_angles, 1, function(x)colnames(vector_angles)[which.max(x)])
phi_table <- as.tibble(t(round(phi,2))[,estimated_topic_names])
phi_table <- cbind(phi_table, as.tibble(round(phi_est, 2)))
# names(theta_table)[4:6] <- paste0(estimated_topic_names, ' estimated')
# theta_table <- theta_table[, c(4,1,5,2,6,3)]
names(phi_table)[4:6] <- paste0(estimated_topic_names, ' estimated')
phi_table <- phi_table[, c(4,1,5,2,6,3)]
row.names(phi_table) <- colnames(phi)
kable(round(phi_table, 2), caption = 'True and Estimated Word Distribution for Each Topic')
| air estimated | air | land estimated | land | sea estimated | sea | |
|---|---|---|---|---|---|---|
| 🐋 | 0.00 | 0 | 0.00 | 0.00 | 0.12 | 0.12 |
| 🐳 | 0.00 | 0 | 0.02 | 0.00 | 0.12 | 0.12 |
| 🐟 | 0.00 | 0 | 0.01 | 0.00 | 0.12 | 0.12 |
| 🐠 | 0.00 | 0 | 0.01 | 0.00 | 0.12 | 0.12 |
| 🐙 | 0.00 | 0 | 0.01 | 0.00 | 0.13 | 0.12 |
| 🦀 | 0.00 | 0 | 0.05 | 0.05 | 0.06 | 0.06 |
| 🐊 | 0.01 | 0 | 0.04 | 0.05 | 0.06 | 0.06 |
| 🐢 | 0.00 | 0 | 0.05 | 0.05 | 0.07 | 0.06 |
| 🐍 | 0.01 | 0 | 0.06 | 0.05 | 0.05 | 0.06 |
| 🐓 | 0.01 | 0 | 0.09 | 0.10 | 0.00 | 0.00 |
| 🦃 | 0.00 | 0 | 0.11 | 0.10 | 0.00 | 0.00 |
| 🐦 | 0.92 | 1 | 0.01 | 0.05 | 0.08 | 0.06 |
| 🐧 | 0.01 | 0 | 0.05 | 0.05 | 0.06 | 0.06 |
| 🐿 | 0.01 | 0 | 0.09 | 0.10 | 0.01 | 0.00 |
| 🐘 | 0.00 | 0 | 0.10 | 0.10 | 0.00 | 0.00 |
| 🐂 | 0.00 | 0 | 0.11 | 0.10 | 0.00 | 0.00 |
| 🐑 | 0.00 | 0 | 0.10 | 0.10 | 0.00 | 0.00 |
| 🐪 | 0.00 | 0 | 0.11 | 0.10 | 0.00 | 0.00 |
The document topic mixture estimates are shown below for the first 5 documents:
| Location | air estimated | air | land estimated | land | sea estimated | sea |
|---|---|---|---|---|---|---|
| 1 | 0.04 | 0.01 | 0.90 | 0.82 | 0.06 | 0.17 |
| 2 | 0.19 | 0.17 | 0.48 | 0.39 | 0.33 | 0.44 |
| 3 | 0.31 | 0.40 | 0.13 | 0.18 | 0.56 | 0.42 |
| 4 | 0.82 | 0.64 | 0.17 | 0.33 | 0.01 | 0.03 |
| 5 | 0.31 | 0.29 | 0.31 | 0.41 | 0.39 | 0.30 |
| 6 | 0.35 | 0.32 | 0.11 | 0.15 | 0.54 | 0.53 |


{# # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -#} {% extends "base.html" %} {% block title %}Manage{% endblock %} {# Hide mobile search on manager pages #} {% block mobile_search %}{% endblock %} {% block session_notifications %} {% if not request.user.primary_email.verified %}
{% endif %} {% endblock %} {% block content %}


 Your new project space is
waiting for you!
{% endblocktrans %}
{% endblock %}
{% block secondary_text %}
{% if request.user_agent.is_mobile %}
{% blocktrans %}
Please make sure to activate your account from your desktop computer,
as App Building is not optimized for mobile devices.
{% endblocktrans %}
{% else %}
{% blocktrans %}
Simply activate your account to start collecting better data
anywhere in the world.
{% endblocktrans %}
{% endif %}
{% endblock %}
{% block reason_for_email %}
{% blocktrans %}
You received this message because we received a new user request
{% endblocktrans %}
{% endblock %}


{{/*
--
-- PARTIAL -> Banner Services
-- Render services that have the .Param.serviceType of "Primary" in a list format
--
*/}}
{{ range .Params.banner }} {{/* Start RANGE .service_list */}}
{{ $service := .service }}
{{ with $.Site.GetPage "section" "services" }} {{/* Start WITH section */}}
{{ range where .Data.Pages "Section" "services" }} {{/* Start RANGE section */}}
{{/* For matching clients, enter it's scope and render some data */}}
{{ if eq .Title $service }}


George Reddy Review – Only for Few Bloody Good Moments
by Siddartha ToletiBOTTOM LINE
Only for Few Bloody Good Moments
OUR RATING
2.5/5
CENSOR
‘U/A’ Certified, 2 hrs 33 mins.
What Is the Film About?
George Reddy (Sandeep Madhav), a brilliant student enters Osmania University but revolts against the disparities in the campus in the name of Caste, Religion, and Money. He stands by the oppressed and becomes their voice entering Student Politics. The rest is all about how George Reddy fought on various and how he is eliminated.
How Is Sandeep Madhav’s Performance?
Sandeep Madhav excelled in the role of George Reddy. He pulled off George Reddy’s character perfectly by following very little data available about him.
Critically, he should have done more in some areas but that is mostly due to poor-etching of his character.
Direction by Jeevan Reddy?
Selecting a subject like George Reddy poses confusion. You have to strike a balance between what he actually stood for and how he did that.
The first part has various versions. People look at George Reddy from several points of view. So the director concentrated on the second part and tried to commercialize the heroism in George Reddy. But is George Reddy’s Story complete without showing what he stood for? No. The director failed here. There are several moments in the movie which are excellent but at the end of the day, a few good moments do not make a good film.
There is no strong motive and conflict point in the story. There are conflict points in George Reddy’s story but the director may have feared to put them across the table. When you see a man fighting for a cause that you do not see, you do not connect to the story. That is what exactly wrong with George Reddy. The Fire Ball fight, the Interval Speech, and the Blade Fight in the second half are superb and bring most of the worth the movie gets. The director rushed the things in the second half and everything happens in a hurry. The climax is also not effectively dealt with.
On the whole, George Reddy lives up to the hype only for a few moments and let down the audience with too much focus on those moments in the name of commercial elements. You may try it if you can sit through the film only for those moments.
Others?
There are numerous new actors and they bring the necessary raw and native vibe to the Period Story. Chaitanya Krishna and Satyadev are decent. The guys who played the characters of Lallan and Rajanna are good as well. The heroine is okay as well. The rest is okay in whatever they are offered.
Music and Other Departments?
The Music composed by Suresh Bobbili is terrific. The background score is the major highlight of the film and elevates several scenes. The songs are okay as well. Sudhakar Yakkanti did a terrific job with the camera work taking us to the time of the story. The Production design of Osmania University is authentic. The editing looks patchy probably there movie has got several censor cuts. The writing should have been good but some of the dialogues worked very well.
Highlights?
Background Score (BGM)
Sandeep Madhav’s Performance
Fire Ball Fight and Blade Fight
Some Dialogues
Drawbacks?
Poor Writing
Lack of Proper Motives and Conflicts
Boring Second Half
Rushed Climax
Alternative Take
Student Politics and the real issues which prompted a Gold Medalist resort to Violence should have been showcased well. Along with the creation of commercially viable content, the script should have been loyal to George Reddy’s Story.
Did I Enjoy It?
Yes in Parts
Will You Recommend It?
Try if you can sit through only for a few bloody good moments
George Reddy Review by Siddartha Toleti
| Summary | |
|---|---|
| Reviewer | Siddartha Toleti |
| Review Date | 2019-11-22 |
| Reviewed Item | George Reddy Movie Review |
| Author Rating |
|


Match Play. Red leads 2-0 to 7. Should Red double? If doubled, should White take?
pips Red=121 White=122
(CorrectAnswer= 0/0, ErrorScore= 0)
A classic middle game double. Many people underestimate the strength of Red’s broken prime and hold off doubling positions like this one. However, it is very volatile so the time to double is precisely now.
Despite leading 2-0 Red should still double. White has an anchor and many routes to victory, so this is a very easy take. For money this is a stronger double but still a comfortable take.
Despite leading 2-0 Red should still double. White has an anchor and many routes to victory, so this is a very easy take. For money this is a stronger double but still a comfortable take.
Cube analysis 2-ply cubeless equity +0.531 (Money: +0.540) 0.705 0.210 0.011 - 0.295 0.090 0.002 Cubeful equities: 1. Double, take +0.694 2. Double, pass +1.000 (+0.306) 3. No double +0.628 (-0.066) Proper cube action: Double, take


{% extends "base.html" %} {% set title = "404 - Not Found" %} {% block content %}
Looks like we couldn't find what you're looking for. Maybe you'd prefer to go on an adventure? Or maybe you'd like to return to browse.


|
Bitstream Interpretation Library (BIL)
0.1
|
Contains declaration of Packet class. More...
Go to the source code of this file.
Classes | |
| class | bil::Packet |
| Abstract base class for packets in a bitstream. More... |
Namespaces | |
| namespace | bil |
Macros | |
| #define | BIL_PACKET_HPP |
Functions | |
| Packet * | bil::new_clone (const Packet &src) |
| Creates a polymorphic copy of a packet. |
Contains declaration of Packet class.
Definition in file Packet.hpp.
| #define BIL_PACKET_HPP |
Definition at line 8 of file Packet.hpp.


| Modifier and Type | Class and Description |
|---|---|
class |
Apriori
Class implementing an Apriori-type algorithm.
|
class |
FPGrowth
Class implementing the FP-growth algorithm for
finding large item sets without candidate generation.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CfsSubsetEval
CfsSubsetEval :
Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. |
class |
ReliefFAttributeEval
ReliefFAttributeEval :
Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class. |
class |
WrapperSubsetEval
WrapperSubsetEval:
Evaluates attribute sets by using a learning scheme. |
| Modifier and Type | Class and Description |
|---|---|
class |
BVDecompose
Class for performing a Bias-Variance decomposition on any classifier using the method specified in:
Ron Kohavi, David H. |
class |
BVDecomposeSegCVSub
This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1).
|
| Modifier and Type | Class and Description |
|---|---|
class |
NaiveBayes
Class for a Naive Bayes classifier using estimator
classes.
|
class |
NaiveBayesMultinomial
Class for building and using a multinomial Naive Bayes classifier.
|
class |
NaiveBayesMultinomialUpdateable
Class for building and using an updateable multinomial Naive Bayes classifier.
|
class |
NaiveBayesUpdateable
Class for a Naive Bayes classifier using estimator classes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ADNode
The ADNode class implements the ADTree datastructure which increases the
speed with which sub-contingency tables can be constructed from a data set in
an Instances object.
|
class |
BIFReader
Builds a description of a Bayes Net classifier
stored in XML BIF 0.3 format.
|
| Modifier and Type | Class and Description |
|---|---|
class |
K2
This Bayes Network learning algorithm uses a hill
climbing algorithm restricted by an order on the variables.
|
class |
SimulatedAnnealing
This Bayes Network learning algorithm uses the
general purpose search method of simulated annealing to find a well scoring
network structure.
|
class |
TabuSearch
This Bayes Network learning algorithm uses tabu
search for finding a well scoring Bayes network structure.
|
class |
TAN
This Bayes Network learning algorithm determines
the maximum weight spanning tree and returns a Naive Bayes network augmented
with a tree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GaussianProcesses
* Implements Gaussian processes for regression without hyperparameter-tuning.
|
class |
Logistic
Class for building and using a multinomial logistic
regression model with a ridge estimator.
|
class |
SimpleLogistic
Classifier for building linear logistic regression
models.
|
class |
SMO
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
|
class |
SMOreg
SMOreg implements the support vector machine for regression.
|
class |
VotedPerceptron
Implementation of the voted perceptron algorithm by Freund and Schapire.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Puk
The Pearson VII function-based universal kernel.
|
class |
RegSMO
Implementation of SMO for support vector regression
as described in :
A.J. |
class |
RegSMOImproved
Learn SVM for regression using SMO with Shevade,
Keerthi, et al. adaption of the stopping criterion.
|
class |
StringKernel
Implementation of the subsequence kernel (SSK) as
described in [1] and of the subsequence kernel with lambda pruning (SSK-LP)
as described in [2].
|
| Modifier and Type | Class and Description |
|---|---|
class |
IBk
K-nearest neighbours classifier.
|
class |
KStar
K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
|
class |
LWL
Locally weighted learning.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ALVOT |
class |
CRplus |
class |
KORAomega |
| Modifier and Type | Class and Description |
|---|---|
class |
AdaBoostM1
Class for boosting a nominal class classifier using
the Adaboost M1 method.
|
class |
AdditiveRegression
Meta classifier that enhances the performance of a regression base classifier.
|
class |
Bagging
Class for bagging a classifier to reduce variance.
|
class |
ClassificationViaRegression
Class for doing classification using regression methods.
|
class |
CVParameterSelection
Class for performing parameter selection by cross-validation for any classifier.
|
class |
LogitBoost
Class for performing additive logistic regression.
|
class |
RandomSubSpace
This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
|
class |
Stacking
Combines several classifiers using the stacking method.
|
class |
Vote
Class for combining classifiers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DecisionTable
Class for building and using a simple decision
table majority classifier.
|
class |
JRip
This class implements a propositional rule learner,
Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was
proposed by William W.
|
class |
M5Rules
Generates a decision list for regression problems using separate-and-conquer.
|
class |
OneR
Class for building and using a 1R classifier; in
other words, uses the minimum-error attribute for prediction, discretizing
numeric attributes.
|
class |
PART
Class for generating a PART decision list.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HoeffdingTree
A Hoeffding tree (VFDT) is an incremental, anytime
decision tree induction algorithm that is capable of learning from massive
data streams, assuming that the distribution generating examples does not
change over time.
|
class |
J48
Class for generating a pruned or unpruned C4.5
decision tree.
|
class |
LMT
Classifier for building 'logistic model trees',
which are classification trees with logistic regression functions at the
leaves.
|
class |
M5P
M5Base.
|
class |
RandomForest
Class for constructing a forest of random trees.
|
| Modifier and Type | Class and Description |
|---|---|
class |
M5Base
M5Base.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Canopy
Cluster data using the capopy clustering algorithm, which requires just one pass over the data.
|
class |
Cobweb
Class implementing the Cobweb and Classit clustering algorithms.
|
class |
FarthestFirst
Cluster data using the FarthestFirst algorithm.
|
class |
SimpleKMeans
Cluster data using the k means algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ChebyshevDistance
Implements the Chebyshev distance.
|
class |
ConjugateGradientOptimization
This subclass of Optimization.java implements conjugate gradient descent
rather than BFGS updates, by overriding findArgmin(), with the same tests for
convergence, and applies the same line search code.
|
class |
EuclideanDistance
Implementing Euclidean distance (or similarity) function.
|
class |
HVDM1 |
class |
HVDM2 |
class |
HVDM3 |
class |
ManhattanDistance
Implements the Manhattan distance (or Taxicab geometry).
|
class |
MinkowskiDistance
Implementing Minkowski distance (or similarity)
function.
|
class |
Optimization
Implementation of Active-sets method with BFGS update to solve optimization
problem with only bounds constraints in multi-dimensions.
|
class |
SimilarityMeasure
Implementing Euclidean distance (or similarity) function.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BallTree
Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search.
|
class |
CoverTree
Class implementing the CoverTree datastructure.
|
class |
KDTree
Class implementing the KDTree search algorithm for nearest neighbour search.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BottomUpConstructor
The class that constructs a ball tree bottom up.
|
class |
MedianDistanceFromArbitraryPoint
Class that splits a BallNode of a ball tree using
Uhlmann's described method.
|
class |
MiddleOutConstructor
The class that builds a BallTree middle out.
|
class |
PointsClosestToFurthestChildren
Implements the Moore's method to split a node of a
ball tree.
|
class |
TopDownConstructor
The class implementing the TopDown construction
method of ball trees.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KMeansInpiredMethod
The class that splits a node into two such that the
overall sum of squared distances of points to their centres on both sides of
the (axis-parallel) splitting plane is minimum.
|
class |
MedianOfWidestDimension
The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread.
|
class |
MidPointOfWidestDimension
The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread.
|
class |
SlidingMidPointOfWidestSide
The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.
|
| Modifier and Type | Class and Description |
|---|---|
class |
IteratedLovinsStemmer
An iterated version of the Lovins stemmer.
|
class |
LovinsStemmer
A stemmer based on the Lovins stemmer, described here:
Julie Beth Lovins (1968). |
| Modifier and Type | Class and Description |
|---|---|
class |
Agrawal
Generates a people database and is based on the
paper by Agrawal et al.
|
class |
LED24
This generator produces data for a display with 7
LEDs.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BIRCHCluster
Cluster data generator designed for the BIRCH
System
Dataset is generated with instances in K clusters. |
| Modifier and Type | Class and Description |
|---|---|
class |
PairedCorrectedTTester
Behaves the same as PairedTTester, only it uses the corrected resampled
t-test statistic.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Discretize
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
|
class |
MergeNominalValues
Merges values of all nominal attributes among the
specified attributes, excluding the class attribute, using the CHAID method,
but without considering re-splitting of merged subsets.
|
class |
NominalToBinary
Converts all nominal attributes into binary numeric
attributes.
|
class |
PartitionMembership
* A filter that uses a PartitionGenerator to generate partition membership values; filtered instances are composed of these values plus the class attribute (if set in the input data) and rendered as sparse instances.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KernelFilter
Converts the given set of data into
a kernel matrix.
|
class |
PKIDiscretize
Discretizes numeric attributes using equal
frequency binning and forces the number of bins to be equal to the square root of
the number of values of the numeric attribute.
|
class |
RandomProjection
Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BoundaryVisualizer
BoundaryVisualizer.
|
Copyright © 2018 University of Waikato, Hamilton, NZ. All Rights Reserved.


Font used is Montserrat. SIL Open Font License
Social media icons (Facebook, Twitter) are from https://paulrobertlloyd.com/2009/06/social_media_icons
UI icons are from http://google.github.io/material-design-icons/ under the Apache License Version 2.0.
phaser.js
Copyright © 2017 Richard Davey, Photon Storm Ltd.
Phaser is distributed under the MIT License.
https://phaser.io/download/license
pixi.js - v2.2.3
Copyright (c) 2012-2014, Mat Groves
http://goodboydigital.com/
pixi.js is licensed under the MIT License.
http://www.opensource.org/licenses/mit-license.php
Mat Groves http://matgroves.com/ @Doormat23
Class object based on John Resig's code; inspired by base2 and Prototype
http://ejohn.org/blog/simple-javascript-inheritance/
Add to Home screen, MIT License:
http://cubiq.org/add-to-home-screen
Android is a trademark of Google Inc.


Getting started¶
Prerequisites¶
Installation of Docker¶
Running PCGR requires that Docker is set up on your host. Docker has very complete installation instructions for different platforms:
- installing Docker on Linux
- installing Docker on Mac OS
- NOTE: We have not yet been able to perform enough testing on the Windows platform, and we have received feedback that particular versions of Docker/Windows do not work with PCGR (an example being mounting of data volumes)
Check that Docker is running, e.g by typing
docker ps
ordocker images
in your terminal windowIMPORTANT: The following represent the minimal computing resources that must be assigned to the Docker virtual machine:
- Memory: 5GB
- CPUs: 4
For Docker version 1.13 on Mac OS X there is an option to change CPU’s and RAM from the UI and restart Docker. This can be found through Docker Preferences (Advanced) in the toolbar. Similarly for Docker for Windows.
Python¶
An installation of Python (version 2.7.13) is required to run PCGR.
Check that Python is installed by typing python --version
in your
terminal window. In addition, a Python
library for parsing configuration
files encoded with TOML is
needed. To install, simply run the following command:
pip install toml
Download PCGR¶
Download and unpack the latest software release (0.5.3)
Download and unpack the data bundle (approx. 16Gb) in the PCGR directory
- Download the accompanying data
bundle
from Google Drive to
~/pcgr-X.X
(replace X.X with the version number, e.g.~/pcgr-0.5.3
) - Decompress and untar the bundle, e.g. through the following Unix
command:
gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -
A data/ folder within the pcgr-X.X software folder should now have been produced
- Download the accompanying data
bundle
from Google Drive to
Pull the PCGR Docker image - 0.5.3 from DockerHub (approx 4.2Gb) :
docker pull sigven/pcgr:0.5.3
(PCGR annotation engine)
Run test - generation of clinical report for a cancer genome¶
A tumor sample report is generated by calling the Python script pcgr.py, which takes the following arguments and options:
usage: pcgr.py [-h] [--input_vcf INPUT_VCF] [--input_cna INPUT_CNA]
[--force_overwrite] [--version]
pcgr_dir output_dir configuration_file sample_id
Personal Cancer Genome Reporter (PCGR) workflow for clinical interpretation of
somatic nucleotide variants and copy number aberration segments
positional arguments:
pcgr_dir PCGR base directory with accompanying data directory,
e.g. ~/pcgr-0.5.3
output_dir Output directory
configuration_file PCGR configuration file (TOML format)
sample_id Tumor sample/cancer genome identifier - prefix for
output files
optional arguments:
-h, --help show this help message and exit
--input_vcf INPUT_VCF
VCF input file with somatic query variants
(SNVs/InDels). Note: GRCh37 is currently the only
reference genome build supported (default: None)
--input_cna INPUT_CNA
Somatic copy number alteration segments (tab-separated
values) (default: None)
--force_overwrite By default, the script will fail with an error if any
output file already exists. You can force the
overwrite of existing result files by using this flag
(default: False)
--version show program's version number and exit
The configuration file, formatted using TOML (an easy to read file format) enables the user to configure a number of options in the PCGR workflow, related to the following:
- MSI prediction
- Mutational signatures analysis
- Coding target size - for mutational burden analysis
- Tumor-only analysis options (i.e. exclusion of germline variants/enrichment for somatic calls)
- VEP/vcfanno options
- Specification of INFO tags in VCF that denote sequencing depth/allelic support of variants
- Log-ratio thresholds for gains/losses in CNA analysis
The examples folder contain input files from two tumor samples sequenced within TCGA. It also contains a PCGR configuration file. A report for a colorectal tumor case can be generated by running the following command in your terminal window:
python pcgr.py --input_vcf ~/pcgr-0.5.3/examples/tumor_sample.COAD.vcf.gz
--input_cna ~/pcgr-0.5.3/examples/tumor_sample.COAD.cna.tsv
~/pcgr-0.5.3 ~/pcgr-0.5.3/examples
`~/pcgr-0.5.3/examples/pcgr_configuration_examples.toml tumor_sample.COAD
This command will run the Docker-based PCGR workflow and produce the following output files in the examples folder:
- tumor_sample.COAD.pcgr.html - An interactive HTML report for clinical interpretation
- tumor_sample.COAD.pcgr.vcf.gz - VCF file with rich set of annotations for precision oncology
- tumor_sample.COAD.pcgr.maf - A basic MAF file for use as input in downstream analyses with other tools (e.g. 2020plus, MutSigCV)
- tumor_sample.COAD.pcgr.snvs_indels.tiers.tsv - Tab-separated values file with variants organized according to tiers of functional relevance
- tumor_sample.COAD.pcgr.mutational_signatures.tsv - Tab-separated values file with estimated contributions by known mutational signatures and associated underlying etiologies
- tumor_sample.COAD.pcgr.snvs_indels.biomarkers.tsv - Tab-separated values file with clinical evidence items associated with biomarkers for diagnosis, prognosis or drug sensitivity/resistance
- tumor_sample.COAD.pcgr.cna_segments.tsv.gz - Tab-separated values file with annotations of gene transcripts that overlap with somatic copy number aberrations


Basic Web Accessibility - Accessible Anchor
Try and reach the element(s) below using just your keyboard. If you can't, use your mouse.
||
Broken Anchor
||
Accessible Anchor
||
Broken Inputs
||
Accessible Inputs
||
Broken Button
||
Accessible Button
||
Tabindex
||
This is a link...
...with a valid href!
This is a link...


--- layout: page title: They came, they saw, they entered Metadata date: 2013-05-01 01:42:07.000000000 -04:00 type: page parent_id: '0' published: true password: '' status: publish categories: [] tags: [] meta: _edit_last: '43578620' _wp_page_template: default _publicize_pending: '1' author: login: openelections email<EMAIL_ADDRESS>display_name: openelections first_name: '' last_name: '' ---
We had a great turnout last Saturday for our first Metadata Sprint. Volunteers joined us from cities around the country including Brooklyn, Chicago, Louisville, Milwaukee, Minneapolis, Philadelphia, and Phoenix - plus our bi-coastal OpenElections team hosting from D.C., San Francisco and L.A..
We worked diligently for three hours and between 13 of us collectively entered 193 elections into our system, making progress on 15 states, and almost doubling the number of elections entered so far.
Ahead of the pack were Milwaukee Journal Sentinel news app developer Allan James Vestal, with 29 elections entered, and MIT Electronics and Computer Science student and news app developer Joanna Kao, with 22 elections.
A big thank you to both of your for being OpenElections Rock Stars!
@allanjvestal @joannaskao
We also had lively discussions about the quirks in electoral processes in various states including stumbling across a 2011 race in Arkansas in which one person voted from the Hot Springs airport, all sorts of unusual candidate names (real and otherwise) including 'Grand-Pa' Goshorn in Arizona in 2010, and various colorful stories about candidates and races from the beats of reporters in the group.
We also got great feedback on our Metadata admin, and flushed out some great edge cases for the nuanced and wildly varied ways that people vote from state to state that we hadn't accounted for in our admin. Surveying local electoral processes across the country is a very colorful and telling process. While we have a better idea of the scale of the task in front of us, we also learned that we can do it. A reminder: we still have several states up for adoption, so if you're interested in joining the effort, let us know.
Thank you to all of you who came out! Let's do it again soon!


This page uses window.onresize = resize; in the JS code to execute the resize function. Try to change the size of your window now!
window.onresize = resize;
Curent page size:
Screen size:


--- layout: post status: publish published: true title: BizTalk Server 2006 R2 author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 140 wordpress_url: http://winterdom.com/2006/11/biztalkserver2006r2 date: '2006-11-28 13:25:05 +0000' date_gmt: '2006-11-28 13:25:05 +0000' categories: - BizTalk - WCF tags: [] comments: [] ---
Richard Seroter has posted a quick guide on the BizTalk 2006 R2 Beta 1 installation, showing a few of the new features included. Pretty cool stuff. Meanwhile, Jesus Rodriguez posted a cool overview on how to use the WCF adapter in R2 to invoke a WCF service using the NetTcpBinding.
I'm very excited about the new features announced in R2, particularly about the WCF adapter and the WCF adapter framework, and I'm hoping to get my hands dirty with it in the future.
BTW, I've been giving these past few weeks short BizTalk Clinics with Microsoft costumers as well as longer trainings with developers, and WCF integration with BizTalk has been one of the topics I was most often asked about, so there's definitely some healthy interest on these topics!


--- ---
IISC MATHEMATICS
INITIATIVE
Department of Mathematics
Indian Institute of Science
Bangalore- 560 012
*********************************************
Course :- "Optimal Control of Turbulence" (Nine lectures)
Speaker :- Prof. S. S. Sritharan,
University of Wyoming, USA
Venue: - Lecture Hall I, Department of Mathematics Indian Institute of Science
Time
:- 3.30 - 5.00 pm
PROGRAMME:-
|
NO |
TITLE |
DAY |
DATE |
|
1 |
Introduction to Deterministic and Stochastic Navier-Stokes Equations |
Monday |
December 20,2004 |
|
2 |
Existence Theory for Optimal Controls | Wednesday |
December 22, 2004 |
|
3 |
The Pontryagin Maximum Principle and Adjoint Equations | Friday |
December 24, 2004 |
|
4 |
Dynamic Programming and Feedback Analysis | Monday | December 27, 2004 |
|
5 |
Robust (H-infinity) Control and Differential Game Approach | Wednesday | December 29, 2004 |
|
6 |
Controllability and Observability | Friday | December 31, 2004 |
|
7 |
Constraint Preserving Feedback Controls | Monday | January 03, 2005 |
|
8 |
Optimal Stopping and Impulse Controls for Stochastic Navier-Stokes Equations | Wednesday | January 05, 2005 |
|
9 |
Nonlinear Filtering Theory for Stochastic Navier-Stokes Equations | Friday | January 08, 2005 |


I'm a web developer. I've immersed myself into a world of engineering, problem-solving, teamwork, and independent time management. Always wanting to build, I like to keep a number of projects going on that help me improve upon my skillset.
When it's time to take a break from coding, I like to remind myself that I am human with my other interests. I played as Principal Cellist in the Illinois Wesleyan Civic Orchestra, went on tour with my band, and later went on to be a bike mechanic at a local shop. I now have about 6 bikes. I like meeting fellow nerds that are also passionate about nerdy things.


Loading Spinner
Under Review Available since 1.0.0
A loading spinner informs the user of an ongoing operation. Only one busy indicator should be shown at a time.
The aria-hidden attribute is used to hide and show the element.
Loading indicators are not visible all the time, only when needed. To show and hide the loading indicator the aria-hidden
attribute is used to hide/show the element.
Loader element
The loading element is used to display the loading indicator animation.
Usage with other elements
The loading indicator is most often used within another component to indicate the loading state. A container (even ui-
, app-
, and page-level) can be “activated” by setting aria-busy="true"
.
The spinner should be included inside the container. Visibility can be toggled in relation to the aria-busy attribute. They should always be opposites, i.e, if currently loading, section[aria-busy="true"]
, .fd-spinner[aria-hidden="false"]
, once the content is loaded, toggle to false and true respectively.


Fill the form and know your future
Select all the unlucky events that have happened this month.
Select all the lucky events that have happened this month.


1:00
Click to zoom, click and drag to move.
Click to draw pixel, click and drag to move.
Loading...
+
-


--- layout: post status: publish published: true title: Podcast 4.45 - Hobo Hitting Coach author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 7993 wordpress_url: http://ivyenvy.com/?p=7993 date: '2012-11-27 22:03:56 +0000' date_gmt: '2012-11-28 04:03:56 +0000' categories: - Podcast tags: - Rob Deer - Scott Feldman comments: - id: 5554 author: Bob Hall author_email: '' author_url: '' date: '2012-12-01 09:10:47 +0000' date_gmt: '2012-12-01 15:10:47 +0000' content: Good reading of The Haka... Congratulations! ---
Andy, Corey and Kurt discuss a few of the moves the Cubs made in the last week. Rob Deer was hired to fill the position of Assistant Hitting Coach. We talk about the idea of a career .220 hitter that was legendary for striking out coaching the young players of the organization on hitting. The Cubs add another piece to the rotation by signing Scott Feldman.
We answer some listener-submitted questions, read a humorous Cubs.com Internet Muscle Flex and pick a winner for this week's photo caption contest.
If you plan to do any shopping on Amazon.com during the holidays, we'd love your support. All you have to do is go to http://ivyenvy.com/amazon. Your shopping experience is the same. If you have family or friends that shop on Amazon, we'd really appreciate it if you let them know of our affiliation with Amazon.


enum class BaseArity : Arity
A base arity implementation that provides arity for instructions with one or two arguments.
|
Represents the arity of an instruction that has one argument. |
|
|
Represents the arity of an instruction that has two arguments. |
val number: Int
An integer that represents how many arguments the arity describes. |


Before Change
def make_client(project_id=None, context=None):
context = context or datalab.context.Context.default()
project_id = project_id or context.project_id
return google.cloud.monitoring.Client(
project=project_id,
credentials=context.credentials,
)
After Change
def make_client(project_id=None, context=None):
context = context or datalab.context.Context.default()
project_id = project_id or context.project_id
client = google.cloud.monitoring.Client(
project=project_id,
credentials=context.credentials,
)
client._connection_class.USER_AGENT = "pydatalab/v0"
return client


--- layout: comedian permalink: /comedians/tori-burton.html comedian: id: tori-burton name: Tori Burton img-align: 50% 50% ---
Originating from the UK, stand-up comedian and MC Tori Burton has found herself finally making some sense of the hot mess that is her life in Prague. From immigration to lap dance, paralysis to the paranormal, problem solving to pooping, Tori speaks on her experiences with conviction and honesty.


Before Change
"passage": "One time I was writing a unit test, and it succeeded on the first attempt."
}
with open("tests/fixtures/bidaf/experiment.json") as f:
config = json.loads(f.read())
bidaf_config = Params(replace_none(config))
model = BidafPredictor.from_config(bidaf_config)
result = model.predict_json(inputs)
After Change
"passage": "One time I was writing a unit test, and it succeeded on the first attempt."
}
bidaf_config = Params.from_file("tests/fixtures/bidaf/experiment.json")
model = BidafPredictor.from_config(bidaf_config)
result = model.predict_json(inputs)


 Sunt ut voluptatum eius sapiente, totam reiciendis temporibus qui quibusdam, recusandae sit vero unde, sed, incidunt et ea quo dolore laudantium consectetur!
The ultimate objective for which we work is to attain maximum client satisfaction. We have employed experienced client servicing agents so as to gain better clarity on their exact requirements. Furthermore, the vendors assure timely delivery of the finest range of products as per the client requirements.
We are very particular about the product quality that we bring to the clients. The quality policies are designed by keeping in mind the prevailing market standards. Furthermore, these policies are revised on a regular basis so as to remain at a competitive market position.
We are very particular about the product quality that we bring to the clients. The quality policies are designed by keeping in mind the prevailing market standards. Furthermore, these policies are revised on a regular basis so as to remain at a competitive market position.


febrile records infection seizure (.%) seizures virus viral []. influenza using enterovirus {border-style associated px;} seasonal #ffffff;} children study HIRA Korean distribution respiratory [], infections number (,, codes data defined medical database patients years South included winter Influenza Table px; reported summer {font-family Health analysis diagnosed font-size frequent high studies virus, viruses .%), Enterovirus Insurance Neurol RSV, Review based cases central common diagnostic double; frequently health incidence information insurance nervous prevalent total (Table April Assessment Elsevier However, Pediatr Rotavirus according annual border-color border-width cause correlation enteric highest monthly population recorded seizure, seizure. seizures. solid; spring {font-style {font-weight .%). Adenovirus COVID- Febrile KCD- Korea Korea, Number RSV Yi, adenovirus bold centre code conducted courier; database. diagnoses distributions em; events exhibited identified infections. investigated late major male manuscript. mean national patterns prevalence pt;} records, relationship research resource rotavirus, seizures: series significant summer. syncytial year younger


VenoBox Lightbox Plugin Examples
Image Gallery
Image gallery (possibility of navigation with keyboard arrows)
Various Links
iFrame
Gmaps
Inline
Vimeo
YouTube
Hello world!
this is my inline content


{% load static %} {% load crispy_forms_tags %}
Your account doesn't have access to this page. To proceed, please login with an account that has access.
Please login to see this page.
Informe login e senha para começar
- OU -
Esqueci minha senha
Registre-se


Note: This documents is only a mirror, if you want to see newest documents please goto: http://xmake.io/#/home/
xmake is a cross-platform build utility based on lua.
The project focuses on making development and building easier and provides many features (.e.g package, install, plugin, macro, action, option, task …), so that any developer can quickly pick it up and enjoy the productivity boost when developing and building project.
bash <(curl -fsSL https://raw.githubusercontent.com/tboox/xmake/master/scripts/get.sh)
bash <(wget https://raw.githubusercontent.com/tboox/xmake/master/scripts/get.sh -O -)
Invoke-Expression (Invoke-Webrequest 'https://raw.githubusercontent.com/tboox/xmake/master/scripts/get.ps1' -UseBasicParsing).Content
$ brew install xmake
On Archlinux:
$ yaourt xmake
On Ubuntu:
$ sudo add-apt-repository ppa:tboox/xmake
$ sudo apt update
$ sudo apt install xmake
Or add xmake package source manually:
deb http://ppa.launchpad.net/tboox/xmake/ubuntu yakkety main
deb-src http://ppa.launchpad.net/tboox/xmake/ubuntu yakkety main
Then we run:
$ sudo apt update
$ sudo apt install xmake
Or download deb package to install it:
.deb
install package from Releasesdpkg -i xmake-xxxx.deb
Compile and install:
$ git clone https://github.com/xmake-io/xmake.git
$ cd ./xmake
$ ./scripts/get.sh __local__
Only install and update lua scripts:
$ ./scripts/get.sh __local__ __install_only__
Uninstall:
$ ./scripts/get.sh __uninstall__
Or compile and install via make:
$ make build; sudo make install
Install to other given directory:
$ sudo make install prefix=/usr/local
Uninstall:
$ sudo make uninstall
$ xmake create -l c -P ./hello
And xmake will generate some files for c language project:
hello
├── src
│ └── main.c
└── xmake.lua
It is a simple console program only for printing hello xmake!
The content of xmake.lua
is very simple:
target("hello")
set_kind("binary")
add_files("src/*.c")
Support languages:
If you want to known more options, please run: `xmake create --help`
$ xmake
$ xmake run hello
$ xmake run -d hello
It will start the debugger (.e.g lldb, gdb, windbg, vsjitdebugger, ollydbg ..) to load our program.
[lldb]$target create "build/hello"
Current executable set to 'build/hello' (x86_64).
[lldb]$b main
Breakpoint 1: where = hello`main, address = 0x0000000100000f50
[lldb]$r
Process 7509 launched: '/private/tmp/hello/build/hello' (x86_64)
Process 7509 stopped
* thread #1: tid = 0x435a2, 0x0000000100000f50 hello`main, queue = 'com.apple.main-thread', stop reason = breakpoint 1.1
frame #0: 0x0000000100000f50 hello`main
hello`main:
-> 0x100000f50 <+0>: pushq %rbp
0x100000f51 <+1>: movq %rsp, %rbp
0x100000f54 <+4>: leaq 0x2b(%rip), %rdi ; "hello world!"
0x100000f5b <+11>: callq 0x100000f64 ; symbol stub for: puts
[lldb]$
You can also use short command option, for exmaple: `xmake r` or `xmake run`
target("test")
set_kind("binary")
add_files("src/*c")
target("library")
set_kind("static")
add_files("src/library/*.c")
target("test")
set_kind("binary")
add_files("src/*c")
add_deps("library")
We use add_deps
to link a static library to test target.
target("library")
set_kind("shared")
add_files("src/library/*.c")
target("test")
set_kind("binary")
add_files("src/*c")
add_deps("library")
We use add_deps
to link a share library to test target.
Set compilation configuration before building project with command xmake f|config
.
And if you want to known more options, please run: xmake f --help
。
You can use short or long command option, for exmaple:
`xmake f` or `xmake config`.
`xmake f -p linux` or `xmake config --plat=linux`.
$ xmake
XMake will detect the current host platform automatically and build project.
$ xmake f -p linux [-a i386|x86_64]
$ xmake
$ xmake f -p android --ndk=~/files/android-ndk-r10e/ [-a armv5te|armv6|armv7-a|armv8-a|arm64-v8a]
$ xmake
If you want to set the other android toolchains, you can use –toolchains option.
For example:
$ xmake f -p android --ndk=~/files/android-ndk-r10e/ -a arm64-v8a --toolchains=~/files/android-ndk-r10e/toolchains/aarch64-linux-android-4.9/prebuilt/darwin-x86_64/bin
The –toolchains option is used to set bin
directory of toolchains.
Please attempt to set `--arch=` option if it had failed to check compiler.
$ xmake f -p iphoneos [-a armv7|armv7s|arm64|i386|x86_64]
$ xmake
$ xmake f -p windows [-a x86|x64]
$ xmake
$ xmake f -p mingw --sdk=/usr/local/i386-mingw32-4.3.0/ [-a i386|x86_64]
$ xmake
$ xmake f -p watchos [-a i386|armv7k]
$ xmake
For linux platform:
$ xmake f -p linux --sdk=/usr/local/arm-linux-gcc/ [--toolchains=/sdk/bin] [--cross=arm-linux-]
$ xmake
Fro other cross platform:
$ xmake f -p cross --sdk=/usr/local/arm-xxx-gcc/ [--toolchains=/sdk/bin] [--cross=arm-linux-]
$ xmake
For custem cross platform (is_plat("myplat")
):
$ xmake f -p myplat --sdk=/usr/local/arm-xxx-gcc/ [--toolchains=/sdk/bin] [--cross=arm-linux-]
$ xmake
| Configuration Option | Description |
|---|---|
| –sdk | Set the sdk root directory of toolchains |
| –toolchains | Set the bin directory of toolchains |
| –cross | Set the prefix of compilation tools |
| –as | Set asm assembler |
| –cc | Set c compiler |
| –cxx | Set c++ compiler |
| –mm | Set objc compiler |
| –mxx | Set objc++ compiler |
| –sc | Set swift compiler |
| –gc | Set golang compiler |
| –dc | Set dlang compiler |
| –rc | Set rust compiler |
| –ld | Set c/c++/objc/asm linker |
| –sh | Set c/c++/objc/asm shared library linker |
| –ar | Set c/c++/objc/asm static library archiver |
| –sc-ld | Set swift linker |
| –sc-sh | Set swift shared library linker |
| –gc-ld | Set golang linker |
| –gc-ar | Set golang static library archiver |
| –dc-ld | Set dlang linker |
| –dc-sh | Set dlang shared library linker |
| –dc-ar | Set dlang static library archiver |
| –rc-ld | Set rust linker |
| –rc-sh | Set rust shared library linker |
| –rc-ar | Set rust static library archiver |
| –asflags | Set asm assembler option |
| –cflags | Set c compiler option |
| –cxflags | Set c/c++ compiler option |
| –cxxflags | Set c++ compiler option |
| –mflags | Set objc compiler option |
| –mxflags | Set objc/c++ compiler option |
| –mxxflags | Set objc++ compiler option |
| –scflags | Set swift compiler option |
| –gcflags | Set golang compiler option |
| –dcflags | Set dlang compiler option |
| –rcflags | Set rust compiler option |
| –ldflags | Set linker option |
| –shflags | Set shared library linker option |
| –arflags | Set static library archiver option |
if you want to known more options, please run: `xmake f --help`。
xmake provides a convenient and flexible cross-compiling support.
In most cases, we need not to configure complex toolchains prefix, for example: arm-linux-
As long as this toolchains meet the following directory structure:
/home/toolchains_sdkdir
- bin
- arm-linux-gcc
- arm-linux-ld
- ...
- lib
- libxxx.a
- include
- xxx.h
Then，we can only configure the sdk directory and build it.
$ xmake f -p linux --sdk=/home/toolchains_sdkdir
$ xmake
xmake will detect the prefix: arm-linux- and add the include and library search directory automatically.
-I/home/toolchains_sdkdir/include -L/home/toolchains_sdkdir/lib
bin
directory of toolchainsWe need set it manually if the toolchains /bin directory is in other places, for example:
$ xmake f -p linux --sdk=/home/toolchains_sdkdir --toolchains=/usr/opt/bin
$ xmake
For example, under the same toolchains directory at the same time, there are two different compilers:
/opt/bin
- armv7-linux-gcc
- aarch64-linux-gcc
If we want to use the armv7-linux-gcc
compiler, we can run the following command:
$ xmake f -p linux --sdk=/usr/toolsdk --toolchains=/opt/bin --cross=armv7-linux-
asm
assembler$ xmake f -p linux --sdk=/user/toolsdk --as=armv7-linux-as
If the ‘AS’ environment variable exists, it will use the values specified in the current environment variables.
We can set a unknown compiler as like-gcc/clang compiler, .e.g `xmake f<EMAIL_ADDRESS>$ xmake f -p linux --sdk=/user/toolsdk --cc=armv7-linux-clang
If the ‘CC’ environment variable exists, it will use the values specified in the current environment variables.
We can set a unknown compiler as like-gcc/clang compiler, .e.g `xmake f<EMAIL_ADDRESS>c++
compiler$ xmake f -p linux --sdk=/user/toolsdk --cxx=armv7-linux-clang++
If the ‘CXX’ environment variable exists, it will use the values specified in the current environment variables.
We can set a unknown compiler as like-gcc/clang compiler, .e.g `xmake f<EMAIL_ADDRESS>c/c++/objc/asm
linker$ xmake f -p linux --sdk=/user/toolsdk --ld=armv7-linux-clang++
If the ‘LD’ environment variable exists, it will use the values specified in the current environment variables.
We can set a unknown compiler as like-gcc/clang linker, .e.g `xmake f<EMAIL_ADDRESS>c/c++/objc/asm
shared library linker$ xmake f -p linux --sdk=/user/toolsdk --sh=armv7-linux-clang++
If the ‘SH’ environment variable exists, it will use the values specified in the current environment variables.
We can set a unknown compiler as like-gcc/clang linker, .e.g `xmake f<EMAIL_ADDRESS>c/c++/objc/asm
static library archiver$ xmake f -p linux --sdk=/user/toolsdk --ar=armv7-linux-ar
If the ‘AR’ environment variable exists, it will use the values specified in the current environment variables.
We can set a unknown compiler as like-ar archiver, .e.g `xmake f<EMAIL_ADDRESS>You can save to the global configuration for simplfying operation.
For example:
$ xmake g --ndk=~/files/android-ndk-r10e/
Now, we config and build project for android again.
$ xmake f -p android
$ xmake
You can use short or long command option, for exmaple: `xmake g` or `xmake global`.
We can clean all cached configuration and re-configure projecct.
$ xmake f -c
$ xmake
or
$ xmake f -p iphoneos -c
$ xmake
Get the help info of the main command.
$ xmake [-h|--help]
Get the help info of the configuration command.
$ xmake f [-h|--help]
Get the help info of the givent action or plugin command.
$ xmake [action|plugin] [-h|--help]
For example:
$ xmake run --help
$ xmake [-q|--quiet]
Please attempt to clean configuration and rebuild it first.
$ xmake f -c
$ xmake
If it fails again, please add -v
or --verbose
options to get more verbose info.
For exmaple:
$ xmake [-v|--verbose]
And add --backtrace
to get the verbose backtrace info, then you can submit these infos to issues.
$ xmake -v --backtrace
$ xmake [-w|--warning]
You only need run the following command:
$ xmake
xmake will scan all source code in current directory and build it automaticlly.
And we can run it directly.
$ xmake run
If we only want to generate xmake.lua file, we can run:
$ xmake f -y
If you want to known more information please see Scan source codes and build project without makefile
Thank you to all our backers! 🙏 [Become a backer]
Support this project by becoming a sponsor.  [Become a sponsor]


Treebank Statistics: UD_Naija-NSC: Features: Aspect
This feature is universal but the values Cons
, Prosp
are language-specific.
It occurs with 4 different values: Cons
, Imp
, Perf
, Prosp
.
7577 tokens (5%) have a non-empty value of Aspect
.
9 types (0%) occur at least once with a non-empty value of Aspect
.
6 lemmas (0%) occur at least once with a non-empty value of Aspect
.
The feature is used with 3 part-of-speech tags: AUX (7571; 5% instances), VERB (5; 0% instances), PRON (1; 0% instances).
AUX
7571 AUX tokens (55% of all AUX
tokens) have a non-empty value of Aspect
.
The most frequent other feature values with which AUX
and Aspect
co-occurred: Mood=EMPTY (7571; 100%), PartType=EMPTY (7571; 100%), Polarity=EMPTY (7498; 99%).
AUX
tokens may have the following values of Aspect
:
Cons
(1351; 18% of non-emptyAspect
): con, comeImp
(3123; 41% of non-emptyAspect
): deyPerf
(887; 12% of non-emptyAspect
): don, never, dey, doneProsp
(2210; 29% of non-emptyAspect
): goEMPTY
(6151): na, no, be, make, fit, meh, is, bin, will, are
| Paradigm dey | Imp | Perf |
|---|---|---|
| _ | dey | dey |
| VerbType=Cop | dey |
VERB
5 VERB tokens (0% of all VERB
tokens) have a non-empty value of Aspect
.
VERB
tokens may have the following values of Aspect
:
Imp
(5; 100% of non-emptyAspect
): deyEMPTY
(17753): go, dey, do, get, say, come, know, see, tell, wan
PRON
1 PRON tokens (0% of all PRON
tokens) have a non-empty value of Aspect
.
The most frequent other feature values with which PRON
and Aspect
co-occurred: Case=Nom (1; 100%), Number=Plur (1; 100%), Person=1 (1; 100%), Poss=EMPTY (1; 100%), PronType=Prs (1; 100%).
PRON
tokens may have the following values of Aspect
:
Perf
(1; 100% of non-emptyAspect
): WeEMPTY
(18363): I, you, e, we, am, de, me, my, dem, im
Relations with Agreement in Aspect
The 10 most frequent relations where parent and child node agree in Aspect
:
AUX –[conj]–> AUX (3; 60%),
AUX –[reparandum]–> AUX (3; 75%),
AUX –[parataxis:conj]–> AUX (1; 100%),
PRON –[flat]–> AUX (1; 100%).


レスポンシブなレイアウトパターン
３列等幅 → 1列（画像左、テキスト右）
display: table;を使用なので1段のみ。IE6は非対応
Designing Web Accessibility
How using a modern workflow can eliminate common accessibility problems.
Available on Amazon.
Designing Web Accessibility
How using a modern workflow can eliminate common accessibility problems.
Available on Amazon.
見出しが入ります
Designing Web Accessibility
How using a modern workflow can eliminate common accessibility problems.
Available on Amazon.
Designing Web Accessibility
How using a modern workflow can eliminate common accessibility problems.
Available on Amazon.
Designing Web Accessibility
How using a modern workflow can eliminate common accessibility problems.
Available on Amazon.
Designing Web Accessibility
How using a modern workflow can eliminate common accessibility problems.
Available on Amazon.


{% extends 'searchBarApp/base.html' %} {% load staticfiles %} {% block body_block %}
Your feedback has been recorded. Thank you for sparing your time. You will be redirected to our Homepage in
0
seconds
{% endblock body_block %}


Apache Flink 0.6 available
August 26, 2014 -We are happy to announce the availability of Flink 0.6. This is the first release of the system inside the Apache Incubator and under the name Flink. Releases up to 0.5 were under the name Stratosphere, the academic and open source project that Flink originates from.
What is Flink? #
Apache Flink is a general-purpose data processing engine for clusters. It runs on YARN clusters on top of data stored in Hadoop, as well as stand-alone. Flink currently has programming APIs in Java and Scala. Jobs are executed via Flink’s own runtime engine. Flink features:
Robust in-memory and out-of-core processing: once read, data stays in memory as much as possible, and is gracefully de-staged to disk in the presence of memory pressure from limited memory or other applications. The runtime is designed to perform very well both in setups with abundant memory and in setups where memory is scarce.
POJO-based APIs: when programming, you do not have to pack your data into key-value pairs or some other framework-specific data model. Rather, you can use arbitrary Java and Scala types to model your data.
Efficient iterative processing: Flink contains explicit “iterate” operators that enable very efficient loops over data sets, e.g., for machine learning and graph applications.
A modular system stack: Flink is not a direct implementation of its APIs but a layered system. All programming APIs are translated to an intermediate program representation that is compiled and optimized via a cost-based optimizer. Lower-level layers of Flink also expose programming APIs for extending the system.
Data pipelining/streaming: Flink’s runtime is designed as a pipelined data processing engine rather than a batch processing engine. Operators do not wait for their predecessors to finish in order to start processing data. This results to very efficient handling of large data sets.
Release 0.6 #
Flink 0.6 builds on the latest Stratosphere 0.5 release. It includes many bug fixes and improvements that make the system more stable and robust, as well as breaking API changes.
The full release notes are available here.
Download the release here.
Contributors #
- Wilson Cao
- Ufuk Celebi
- Stephan Ewen
- Jonathan Hasenburg
- Markus Holzemer
- Fabian Hueske
- Sebastian Kunert
- Vikhyat Korrapati
- Aljoscha Krettek
- Sebastian Kruse
- Raymond Liu
- Robert Metzger
- Mingliang Qi
- Till Rohrmann
- Henry Saputra
- Chesnay Schepler
- Kostas Tzoumas
- Robert Waury
- Timo Walther
- Daniel Warneke
- Tobias Wiens


{% extends 'base.html' %} {% load crispy_forms_tags %} {% block main %}
Forget password?
Don't have an account? Create one here
{{message}}


We extend the analysis by including nouns, increasing the total number of words explored. By adding the nouns, we can explore the bias also in terms of topics. Using the same model as we mentioned before, the accuracy score achieved in this case is much higher. It means that the differences in that case are not that subtle and start to be easier to detect. The result shows us those words that are more predictive for the two different genders. In this case … explain the words that the model output. If we associate these words to topics, we see that women are actually related to X, X, X while men are more related to Y, Y, Y.
We use a logistic regression model that takes as input a bag of words and outputs the probability that the encoded overview belongs to a woman. The encoding is done as a bag of words, it means a binary vector that signals if a word is present in the text or not. The vocabulary is build with the set of 100 most common words for men and the top 100 for women.
54.7% of the observations were correctly predicted, confirming that the bias does exist.
It is. After 50 iterations, the model's results variated only 0.001.
For the military and religion fields the confidence interval of the predictions is much widder and we can't afirm the existance of a bias.


Generate a SMART Health Card, step by step, from an initial FHIR Bundle and Key
The Verifier Portal takes as input, a QR-Code image of a SMART Health Card.
After the image is scanned, it is decoded, in stages, into a FHIR Bundle.
Each step in the process does some amount of validation.


"ADJURATION "a solemn appeal whereby one person imposes on another the obligation of speaking or acting as if under an oath (1 Samuel 14:24; Joshua 6:26; 1 Kings 22:16). We have in the New Testament a striking example of this (Matthew 26:63; Mark 5:7), where the high priest calls upon Christ to avow his true character. It would seem that in such a case the person so adjured could not refuse to give an answer. The word “adjure”, i.e., cause to swear is used with reference to the casting out of demons (Acts 19:13).
"ADJURATION "a solemn appeal whereby one person imposes on another the obligation of speaking or acting as if under an oath (1 Samuel 14:24; Joshua 6:26; 1 Kings 22:16).
We have in the New Testament a striking example of this (Matthew 26:63; Mark 5:7), where the high priest calls upon Christ to avow his true character. It would seem that in such a case the person so adjured could not refuse to give an answer.
The word “adjure”, i.e., cause to swear is used with reference to the casting out of demons (Acts 19:13).


{{ partial "header.html" . }} {{ partial "navigation.html" . }}
Below you will find articles with a focus on application development and workplace operations
⭢
{{ .Description }}


Publish databases using disconnected publishing¶
If you want to publish databases on a server that does not have a network connection to your Google Earth Enterprise Fusion workstations, you can create a database that can be output to portable media, which can then be pushed and published to GEE Server.
A large database can be copied to portable media and loaded onto
the server using geserveradmin
commands, saving the time and
bandwidth required to send terabytes of data over the network. In
addition, you can also create deltas for the same databases, which
can then be sent over the network.
Before you begin
To use the disconnected publishing service, you need to install the GEE Server - Disconnected Add-on, which you can add by running the installation process and choosing Custom as the installation type.
Tip
Disconnected production servers require X11 libraries, including Mesa. Google Earth Enterprise Server does not require an X server to be running or installed, however; only the libraries are required.
Prepare a disconnected database
Before creating a disconnected database, you can estimate the
space required to store the disconnected database by running
gedisconnectedsend
with the --report_size_only
option.
To estimate the size of a disconnected database:
From the command line, specify the database that you want to publish with the
--report_size_only
flag:gedisconnectedsend --report_size_only --sendpath \ /gevol/assets/Databases/test.kdatabase/gedb.kda/ver001/gedb
To create a disconnected database:
Create an output directory on your portable media, which you will specify when you create the disconnected database.
rm -rf /media/ddb/test_ver001/ mkdir -p /media/ddb/test_ver001/
Create the disconnected database and output the file to your portable media:
gedisconnectedsend --output /media/ddb/test_ver001/ \
--sendpath /gevol/assets/Databases/test.kdatabase/gedb.kda/ver001/gedb
In this example,
/gevol/assets/Databases/test.kdatabase/gedb.kda/ver001/gedb/
is the path to the gedb
directory of the disconnected
database.
Prepare delta disconnected databases
The disconnected database command includes the option
--havepath
or --havepathfile
, to create a delta
disconnected database, based on different versions of the same
database. To create the delta, the assetroot of the GEE Fusion
machine must store the versions of databases, as reported by
geserveradmin --listdbs
on the GEE Server machine, from which
the delta disconnected database is created.
When publishing from a GEE Fusion machine (in this example,
machine_one
) to a disconnected GEE Server machine (in this
example, machine_two
), verify the list of versions of the
disconnected database for which you want to create the delta:
For example, on machine_two
:
geserveradmin --fusion_host machine_one --listdbs > /home/user_name/ddb/dblist_server_host
On machine_one
, create the delta disconnected database using
the --havepathfile
option:
gedisconnectedsend --output /media/ddb/test_ver001/ \
--havepathfile /home/user_name/ddb/dblist_server_host \
--sendpath /gevol/assets/Databases/test.kdatabase/gedb.kda/ver001/gedb
Publish on the remote server host
Connect the portable media to the Server host and mount the drive.
In this example,
/media/ddb/test_ver001/gevol/assets/Databases/test.kdatabase/gedb.kda/ver001/gedb/
is the path to the gedb
directory of the disconnected
database.
To publish on the remote server host:
Set ownership and permissions for the
gedb
directory:sudo chown -R gefusionuser:gegroup ddb/ sudo chmod -R 755 ddb/
Register the database on the Server. Specifying the –stream_server_url is optional.
geserveradmin --fusion_host fusion_host.company.com \ --stream_server_url http://your_stream_server \ --adddb \ /media/ddb/test_ver001/gevol/assets/Databases/test.kdatabase/gedb.kda/ver001/gedb/
Push the files to the Server:
geserveradmin --fusion_host fusion_host.company.com \ --stream_server_url http://your_stream_server \ --pushdb \ /media/ddb/test_ver001/gevol/assets/Databases/test.kdatabase/gedb.kda/ver001/gedb/
Publish the database on the Server either using the GEE Server Admin console or on the command line. The GEE Server Admin console Publish dialog includes options to
To publish on the command line:
geserveradmin --fusion_host fusion_host.company.com \ --stream_server_url http://your_stream_server \ --publishdb \ /media/ddb/test_ver001/gevol/assets/Databases/test.kdatabase/gedb.kda/ver001/gedb/
Verify that the database manifests:
geserveradmin --stream_server_url http://your_stream_server \ --listdbs geserveradmin --stream_server_url http://your_stream_server \ --dbdetails \ /media/ddb/test_ver001/gevol/assets/Databases/test.kdatabase/gedb.kda/ver001/gedb
Verify the integrity of files in the published database:
ssh your_server /opt/google/bin/geindexcheck --database \ /gevol/published_dbs/stream_space/your_fusion_host/gevol/assets/Databases/test.kdatabase/gedb.kda/ver001/gedb /opt/google/bin/geindexcheck --mode all --database \ /gevol/published_dbs/stream_space/your_fusion_host/gevol/assets/Databases/test.kdatabase/gedb.kda/ver001/gedb
Tip
The paths in the examples above were split onto multiple lines for documentation using backslashes. They should be entered on one line when completing your commands.
Symbolic links
Symbolic links are turned on by default in the published databases
configuration file, AllowSymLinks=Y
in the
/gevol/published_dbs/.config
, resulting in hard or soft
symbolic links being created, depending on the logical volumes of
the relevant directories:
- hard-link: the disconnected database folder and published_dbs folder are on the same logical volume.
- soft-link: the disconnected database folder and published_dbs folder are on different logical volumes.
- If the disconnected database folder is in
/tmp
, the directory files will be copied.
Rewrite KML Layer URLs
If the disconnected database contains KML layers, the KML layers’s
URLs may require adjustments in order to resolve the KML content
in a disconnected environment. (See Add KML layers). Use the gerewritedbroot
command to update the disconnected database KML links with new
hostname, ports, paths, or filenames.
Publish KML content
Copy, publish, and modify the KML files on the disconnected GEE Server machine’s network as needed. Validate that the referenced KML files are accessible on the disconnected network via HTTP or HTTPS using a web browser or command line tools. Note the differences in hostnames, paths, and ports between their origninal locations and their locations on the disconnected network environment.
KML files can be served from the
/opt/google/gehttpd/htdocs/
folder or a subfolder, or on a separate web server.Identify which dbroot file needs to be updated, and store in an environment variable for later use:
export TARGET_DBROOT='/media/ddb/test_ver001/gevol/assets/Databases/test.kdatabase/gedb.kda/ver001/gedb/targets/new_target/dbroots/dbroot.v5p.DEFAULT'
Rewrite the KML layer links in the database on the Server.
/opt/google/bin/gerewritedbroot --source http://your_stream_server/new_target \ --dbroot_file $TARGET_DBROOT \ --kml_server your_stream_server --kml_url_path your_htdoc_path/ \ --preserve_kml_filenames
Changes are not refected until the server is restarted.
sudo service geserver restart
Validate that the server has the correct database information by dumping the dbroot as text:
wget -O dbroot.v5p.DEFAULT http://you_stream_server/new_target/dbRoot.v5 gedumpdbroot dbroot.v5p.DEFAULT
Delete disconnected databases
To delete disconnected databases at their end of life use
geserveradmin --deletedb
, the same procedure used for
databases that are published normally.


GIVE is run by a group of super passionate and enthusiastic high school and college students. We pride ourselves on being completely youth-run; GIVE is a non-profit created by kids in the hopes of helping other kids.
Kevin Q. Cao
Co-Founder
Kartik Gupta
Co-Founder
Kartik Gupta
Kartik Gupta
Kartik Gupta
Kartik Gupta
Rachel Alexander
Rashel Bajaj
Abhi Chadha
Matt Huo
Sruthi Kodali
Adarsh Kulkarni
Christina Wei


Charles Osoro Onkoba
Portfolio
Professional skills
I am an IT professional and a skilled Fullstack Developer from Moringa School with sheer determination and commitment, I am proficient in Javascript, HTML5 CSS3 and Bootstrap and Python. I am very much interested and willing to work in an IT or developer position , I have confident that my past experience and my abilities to adoption of performance of assignment, working with minimal supervision, Good communication and interpersona
My hobbies
- Swimming
- Rugby
- Golf


Useful Code snippets
Below is a collection of useful code snippets and explanations after you work through the slides.
Receiving data from your UI
To send data from UI to a Redis topic, you can do this in the VueJS module:
You can also bind the above to a Vue UI form, e.g. like this:
Registering your corporation with the exchange
To register your corporation, you would need to use request-response pattern over a pub-sub.
Following needs to happen:
- Subscribe to "exchange.logs.superhero" and log responses to watch for errors
- Generate a unique secret request ID for a one-time response topic, e.g. 12345
- Subscribe to "exchange.registry.12345"
- Post to "exchange.registry" a JSON of:
{slot:'superhero', name:'Some Corp.', reqId:12345}
- Receive on "exchange.registry.12345" a message in form
{
slot: 'superhero',
name: 'Some Corp.',
balance: 10000,
units: 0,
lastSold: 0,
lastPrice: 0,
key: 'abc12def'
}
Note, that to place bids, you will need a key from the message above, and the signaure looks like this:
Processing market bids
Once you have your key and signature, this is how you bid:
- Subscribe to "exchange.logs.superhero" and log responses
- Subscribe to "exchange.market" as in the slide in the presentation
- Post your bids to "exchange.bids" in form
{ offerId: market.offerId, slot: 'superhero', qty: 10, price: 70, signed: signature }
Highest bids are processed first. Bids under market price are not processed. There is a limited amount of units available. Signature is required.
It also makes sense to subscribe and console.log "exchange.balances.superhero" (or use otherwise)
More is coming...


BBNaija: Is Khadoni Next Bamteddy? (Video)
by Oyindamola OlubajoIn what appears to be a journey to the next level, former BBNaija housemate, Gedoni has stated that his relationship with Khafi has got to a permanent site.
He made this known during an interview with Broadway Tv during ‘The Future’ awards held on November 24.
When asked about his relationship with Khafi, Gedoni excitingly said “It’s already in the permanent site”
Perhaps, the Khadoni relationship is heading towards marriage, just like that of former housemates in the 2018 edition, Bambam and Teddy A who recently sealed their union with a white wedding ceremony in Dubai.
Even though there have always been rumours that Gedoni and Khafi’s relationship will never stand a test of time outside Big brother house, the lovebirds, in different scenarios proved people wrong.
An instance was while the duo were in the gathering of other housemates and the UK metropolitan police officer described Gedoni as the ‘salt of her life’.
Hearing this, the fashion designer went emotional as he burst into tears.
Another lovely moment was when Gedoni penned down a mind-blowing message to celebrate Khafi on her birthday, earlier this month.
In an Instagram post, the fashion designer wrote “I went into BBNaija for something I thought I wanted, and came out with someone I needed (a partner, a support system, a friend, a sister; everything I didn’t even know I needed).”
Recall that Gedoni and Khafi’s in-house relationship stirred much controversies on social media platforms, with their consequent raunchy moments.


{%extends "registration/base.html"%} {% load bootstrap5 %} {% block content %}
Some Information Might be Incorrect
Get the app


Hi, I'm
Ian Wanjira,
a Software Developer
Contact
About
I am an aspiring software developer currently studying at moringa school as a full stack web developer. My passion for programming sparked an intrest in me to purse the software development career path. I love reading books, swimming and playing chess.
Projects
Below are some of the projects that I have undertaken.


Fukui Marathon
Fukui JPN
Race Winners
(41 runnings)
22 Mar
1999
2:31:22
1998
---
13 Apr
1997
2:33:00
14 Apr
1996
2:28:42
09 Apr 1995
2:25:39
10 Apr
1994
2:19:14
11 Apr
1993
2:23:23
12 Apr
1992
2:27:19
14 Apr
1991
2:22:21
15 Apr
1990
2:27:25
09 Apr
1989
2:30:19
10 Apr
1988
2:29:24
05 Apr
1987
2:30:18
06 Apr
1986
2:24:50
07 Apr
1985
2:25:02
08 Apr
1984
2:22:08
03 Apr
1983
2:25:32
04 Apr
1982
2:26:32
05 Apr
1981
2:23:15
06 Apr
1980
2:27:41
08 Apr
1979
2:32:07
09 Apr
1978
2:26:27.4
Apr
1977
2:25:08
Apr
1976
2:28:31
06 Apr
1975
2:22:58.2
Apr
1974
2:27:36.6
Apr
1973
2:32:31.8
Apr
1972
2:24:47
11 Apr
1971
2:19:28
05 Apr
1970
2:25:20
1969
2:38:35
29 Apr
1968
2:26:03
09 Apr
1967
2:24:15
1966
2:29:18
1965
2:24:46
10 Apr
1964
2:26:03
1963
2:35:12
1962
2:37:16
1961
2:30:19
1960
2:31:35
1959
2:45:07
Notes: 1959 was the inaugural year. The venue was Ono JPN for 1997-1999. The distance was changed to a half marathon
starting in 2000 (results not available).
Kiminari Kondo-3
n/a
Kiminari Kondo-2
Shu Matsumoto (JPN)
Yutaka Ezaki (KPN)
Eiji Nakagawa (JPN)
Kiminari Kondo (JPN)
Koichi Kimura (JPN)
Tadayoshi Kametaka (JPN)
Hiroshi Kawarada (JPN)
Takemitsi Kawamoto (JPN)
Koichi Aratsu (JPN)
Seizo Onoe (JPN)
Yoshihiko Morioka (JPN)
Nobuyasu Fukuyama (JPN)
Tomio Yoshida (JPN)
Nobuaki Takaoka (JPN)
Hiroshi Nishimiya-3
Hiroshi Nishimiya-2
Hiroshi Nishimiya (JPN)
Kenshi Kondo-4
Yoshio Yamamoto (JPN)
Kenshi Kondo-3
Kenshi Kondo-2
Haruo Yamada (JPN)
Kenshi Kondo (JPN)
Katsunori Takemoto (JPN)
Mitsuo Rachi (JPN)
Yoshiaki Unetani (JPN)
Hiroki Maeda (JPN)
Minoru Aijima (JPN)
Masaharu Sagawa (JPN)
Makoto Nishimura (JPN)
Masaaki Komura (JPN)
Sadanobu Saito (JPN)
Yoshiaki Unetani (JPN)
Yoshinori Tojo-2
Yoshinori Tojo (JPN)
Mataichi Nasu (JPN)
Hiroshi Uwa (JPN)
Shogo Suzuki (JPN)
Most Wins
(1959-1999)
Men
4
Kenshi Kondo (JPN)
1974,1976-77,1979
Prize Money
(ADR qualified runners)
Prize money is not permitted in Japanese road races due to legal restrictions.
Race Time Bias
(sec/km)
Insufficient data to calculate the race time bias.
Competitive Level
(1990-date)
Insufficient data to calculate the competitive level.
Quality Performances
Men <2:20 2
Last Updated on 11 Nov 2006
Contributor: Shigenobu Ota (50)


Cyborg (StupidlyFunny)
StupidlyFunny's Page
Ebotobot
StupidlyFunny
Helios
Savage Nate
Home
StupidlyFunny
StupidlyFunny's Channel
No Posts Yet


We provide CMU undergrads with a tool that allows for task prioritization, based off of a strategy recommended by Academic Development.


Deuteronomy 18:11
Or a charmer, or a consulter with familiar spirits, or a wizard, or a necromancer.


Searcher
Base class of all searcher class This class is the base class of all searcher class, every searcher class can override its search function to implements its strategy
Attributes
n_classes: number of classification
input_shape: Arbitrary, although all dimensions in the input shaped must be fixed. Use the keyword argument input_shape (tuple of integers, does not include the batch axis) when using this layer as the first layer in a model.
verbose: verbosity mode
history_configs: a list that stores all historical configuration
history: a list that stores the performance of model
path: place that store searcher
model_count: the id of model
init
Init Searcher class with n_classes, input_shape, path, verbose The Searcher will be loaded from file if it has been saved before.
search
an search strategy that will be overridden by children classes
load_best_model
return model with best accuracy
add_model
add one model while will be trained to history list
RandomSearcher
Random Searcher class inherited from ClassifierBase class RandomSearcher implements its search function with random strategy
init
Init RandomSearcher with n_classes, input_shape, path, verbose
search
Override parent's search function. First model is randomly generated
HillClimbingSearcher
HillClimbing Searcher class inherited from ClassifierBase class HillClimbing Searcher implements its search function with hill climbing strategy
init
Init HillClimbing Searcher with n_classes, input_shape, path, verbose
_remove_duplicate
Remove the duplicate in the history_models
search
Override parent's search function. First model is randomly generated


Syntax:
member item list &key key test test-not => tail
member-if predicate list &key key => tail
member-if-not predicate list &key key => tail
Arguments and Values:
item---an object.
list---a proper list.
predicate---a designator for a function of one argument that returns a generalized boolean.
test---a designator for a function of two arguments that returns a generalized boolean.
test-not---a designator for a function of two arguments that returns a generalized boolean.
key---a designator for a function of one argument, or nil.
tail---a list.
Description:
member, member-if, and member-if-not each search list for item or for a top-level element that satisfies the test. The argument to the predicate function is an element of list.
If some element satisfies the test, the tail of list beginning with this element is returned; otherwise nil is returned.
list is searched on the top level only.
Examples:
(member 2 '(1 2 3)) => (2 3) (member 2 '((1 . 2) (3 . 4)) :test-not #'= :key #'cdr) => ((3 . 4)) (member 'e '(a b c d)) => NIL
(member-if #'listp '(a b nil c d)) => (NIL C D) (member-if #'numberp '(a #\Space 5/3 foo)) => (5/3 FOO) (member-if-not #'zerop '(3 6 9 11 . 12) :key #'(lambda (x) (mod x 3))) => (11 . 12)
Side Effects: None.
Affected By: None.
Exceptional Situations:
Should be prepared to signal an error of type type-error if list is not a proper list.
See Also:
find, position, Section 3.6 (Traversal Rules and Side Effects)
Notes:
The :test-not parameter is deprecated.
The function member-if-not is deprecated.
In the following
(member 'a '(g (a y) c a d e a f)) => (A D E A F)
the value returned by member is identical to the portion of the list beginning with a. Thus rplaca on the result of member can be used to alter the part of the list where a was found (assuming a check has been made that member did not return nil).


Our mission is to provide long-term statistical and probabilistic solutions to users. Predicter is the initial stage of this process. We sincerely believe that, you will find the best suitable product for yourself in Predicter. In other words, the Predicter is an online tool which maps your answers to optimal products.


“Custard, that noble cooling Food, / So toothsome, wholsome and so good, / That Dainty so approv'd of old, / Whose yellow surface shines like Gold ..” Ned Ward, British Wonders : Or, A Poetical Description of the Several Prodigies and Most Remarkable Accidents That have happen'd in Britain since the Death of Queen Anne (1717)