Preparation :
This was clearly a hard time, we were one of the first group to present. Hence, with only five days to finish our game, we did not really sleep...
French : The Goal of the french presentation was to "sell" our game. Indeed, it was not a question of coding, but a question of presenting our work. We think it went pretty well for something not prepared.
English : This was the important presentation with algorithmic and english teachers. Here We clearly had to present all the evolution of the project and its result. We had more time to prepare it as there was a break after the french presentation. As the first one, it went really well !
Grade : 17.50 / 20
Sunday, June 3, 2018
Final stretch before the final Presentation
Preparation :
This Project is coming to its end, we just finished our final tests. Now its time for the last coding & preparation rush.
Report :The Final report have been finished last week. As we are international student, we had to do fifty pages in english and fifty pages in french.
This wasn't an easy task for us as we are clearly not literary people. Indeed, we are good in Computer Science, but not that much in writing.
Wednesday, May 2, 2018
Second Coding Rush & Presentation
Done :
Many new signals and mechanisms were implemented (see our second report for more information)
Signals were upgraded, new were added (laser / new pressure plates)
The enemy's mechanisms were created (it can follow the player through the room even if there is obstacles)
We have working menus in our game !
Four new levels were created !
Finally, this website is rising, there is more content !
Presentation :
Before this second presentation we uncountered many difficulties (reports)
GitHub's Organization
Tasks Distribution
Unity Issues
Finally this presentation went better than expected, we managed to show many new features.
Grade : 16.03 / 20
Tuesday, March 13, 2018
First Presentation
This project presentation was a 15min one. We are happy of what we managed to show. Indeed, we could play one level, there were some issues to fix, but it was not bad for a first presentation.
Grade : 16.03 / 20
Website : This website is really growing, content is added linked to the evolution of the project ! However, I start to worry, is there anyone who will read it all one day ? If you are this one, a link is hidden on the gamepad of the January 5th.
Monday, March 12, 2018
Last day before our first presentation
Done :
Great Start on the game (see our first report for more information)
The Game is functional on one level
The main points of our game are created
A functional Website
To do for tomorrow :
Print the report
Prepare the oral presentation & the Outline
Remarks :
That week-end of work was really intensive, indeed we created a functional game in 3 days. We are really happy of what we managed to do with our basics knowledges in Unity.
×
Friday, January 5, 2018
Second meeting to speak about the game itself, the book of specification, and see each other after vacations.
Done :
Speak about the Book Of Specifications
Speak about our vision of the game (finally useful to see that we didn't have the same one)
To do for the next time :
Each member need to find for each of those thing, examples in games of what he wants to do in the project.
Possibles actions
Possibles movements
Example of map
View of the players
Way of wining
Way of loosing
Goals
Win against the Finals
Remarks :
That was not the most productive afternoon mainly because of the finals coming soon. We did not manage to do what was planned. But the good point is that we saw some confusions about our vision of the project. So we will be able to deal with it for the next time.
Wednesday, December 20, 2017
First meeting before the submission of groups
Done :
The name of the group is found : CAPZ
The name of the project is also found : PEnuts
The concept of our multiplayer game is then found :
Play with the vision of each player
Complementarity concept
Low-assets
To do for the next time :
Finals revisions
Start the Book of Specifications
Start using Git / Unity / Overleaf
Remarks :
This first meeting gave many ideas. There were in fact too much good concepts. But that was a good a really motivating start.


Addressing the problem
In a Q&A based system understanding and processing a question is vital to provide the user with the most appropriate answer. Machines do not understand text as humans do thus questions inputted as texts need to be transformed into a vector that preserves the context of the question that was presented by the user. The objective of this area is to provide the neural network with the most effective vector format that would preserve and best represent the context of a question in a vector so that the DNN can utilize the vector to process and find the most appropriate answer.
For this purpose we will be using few natural language modelling techniques such as word embedding,syntactic analysis, Identification of question type such as a wh-question analysis. Word Embedding is used to map words or phrases from a vocabulary to a corresponding vector. This type of representation has two important and advantageous properties:
- It is a more efficient representation
- It is a more expressive representation
Corpus Preprocessing
Corpus Preprocessing is one of primary component in the research project. Initially the corpus will be unstructured text data, which can be understood by humans, not by machines. It simply implies that, there is a necessary for a transformation of such data because many machine learning algorithms including deep neural networks, require inputs to be vectors of continuous values; they won’t just work on plain text or strings.
Since the amount of information is rapidly growing and it is becoming difficult to keep manually create knowledge bases and ontologies uptodate. Than being relying on manually created knowledge bases, applying deep learning techniques to unstructured data or the corpus to identify relationship of words and providing the proper transformation of unstructured data to a representation as an input to the neural network training model, will provide an immense help in extracting the needed answer.
One of the major application of such transformation of data, is Word Embedding which is a natural language technique. It is used to map words or phrases from a vocabulary to a corresponding vector of real numbers. Word embedding aims to create a vector representation with a much lower dimensional space. In contrast Bag of Words approach, which often results in huge and sparse vectors. In Bag of Words approach the dimensionality of the vectors representing each document is equal to the size of the vocabulary.
Word Embedding is also used for semantic parsing, to extract the meaning from text to enable Natural Language Understanding. For a language model to understand the meaning of a word, it need to know the contextual similarity of words. For example if we tend to find diseases in sentences, where diabetes, diarrhea, HIV should be of close proximity. So the vectors created by word embedding preserves these similarities along with the words that regularly occur nearby in text will also be in close proximity.
Word embedding is all about building a low dimensional vector representation from corpus, which preserves the contextual similarity of words. There are word embedding techniques such as,
- 1-of-N vec (one-hot-vec)
- GloVe (Global Vectors)
- Word2vec
In a simple 1-of-N encoding transforms categorical features to a format that works better with classification and regression algorithms. In simple terms every element in the vector is associated with a word in the vocabulary. The encoding of a given word is simply the vector in which corresponding element is set to one and all others are set to zero. Suppose we consider a vocabulary of five words; diabetes, diarrhea, HIV, obesity, paralysis. We could encode the the word obesity as [0, 0, 1, 0, 0]. In such a scenario, the only comparison that can be made between vectors is equality testing or it engages all features and tell which is present and which is absent for a particular set of output.
GloVe is a ‘count based’ model. Which means GloVe learn their vectors through collecting word co-occurrence statistics in a form of word co-occurrence matrix X. Each element Xij of such matrix represents how often word i appears in context of word j in a large corpus. The number of "contexts" is of course large, since it is essentially combinatorial in size. Then factoring this matrix to yield a lower-dimensional matrix, where each row now yields a vector representation for each word.
In specific, the creators of GloVe illustrate that the “ratio of the co-occurrence probabilities of two words (rather than their co-occurrence probabilities themselves) is what contains information and look to encode this information as vector differences”. But when it comes for computation GloVe will be taking more memory, because it precomputes the large word into word co-occurrence (large word x word co-occurrence) matrix in memory. Also sometimes there is a restriction on vocabulary since GloVe requires memory quadratic in the number of words: it keeps that sparse matrix of all word x word co-occurrences in RAM.
Word2vec is a predictive model which is one of the most popular word embedding model. This simply learns relationship between words without any prior knowledge about the domain. The output are vectors, one vector per word with an exceptional linear relationship. Once a vector model is created out of the corpus, word2vec provides two basic tools namely analogy and distance to use. Distance - tool provide a list of words which are closely related to a particular word from the vector model. Analogy - tool is provides the ability to query for textual regularities captured in the vector model.
For example, let us assume that we use word2vec to create a vector model of the words appearing in a corpus of medical domain. If the resulting vector space represents diseases and cause of disease is projected in a two dimensional vector space, we can observe a relationship between each disease and the cause of the disease, and also similar diseases are placed closed to each other in vector space.
Further word2vec is based on two architecture: continous bags of words (CBOW and skip-gram (SG). Since word2vec as no built-in functionalities for term normalisation, the corpus needs to processed before they could be used for word2vec. Unprocessed corpus contains syntactic variations, stopwords, punctuations which has a negative impact on on how word2vec indexes the term and which will affect the quality of vector space representation.
Question Preprocessing
Word embedding maps each word to a vector space.The Embedding layer will map each token from the question to its corresponding vector space, which preserves the contextual similarity of words in the vector space.
The embedding layer in the question processing component can be done through a popular pre-trained word embedding model known as word2vec or glove(exact model will be chosen based on further trial and error). Word2vec is a small two layer neural network. It contains two distinct models (CBOW and skip-gram), each with two different training methods (with/without negative sampling) and other variations. To top that, it also contains a sharp pre-processing pipeline, whose effects on the overall performance is yet to be evaluated.
The other sub-component is Syntactic analysis of a question. Syntactic analysis is the process of identifying the structure of a sentence,The interplay of syntax and semantics of natural language questions is of interest for question representation. Researchers in the area of question understanding reccomend the use of a TreeLSTM neural network for this purpose, since it is capable to capture long distance interaction on a tree. The other option would be to go with a chain structured LSTM but the critical downfall of it for this specific task is that it fails to capture long distance interaction on a tree.
To obtain the parse tree information some of the available open source parsers such as the Noah's Ark parser, or the Standford Core Parser can be used.
Questions by nature are composed to fulfill different types of information. A what question and a how question requires different types of information. In Order to incorporate this a Wh-Analysis will be required, thus an additional layer for question adaption will be required. Wh-word is basically the question word which is one of who, why, where, which, when, how, what and rest. “rest” are the questions that don't have any question word. Example :- Name of a disease that cause bowel bleeding?. This process segregates questions into different types and considers the type of question for answer generation, a recommended approach for this would be to encode question type information into a one hot vector which is a trainable embedding vector, and it is incorporated in the training process.
For the purpose of implementation the popular deep neural network library tensor flow along with the python programming language will be used. For the purpose of syntactic analysis the Standford core NLP parser will be used.Python enables us to carry out string manipulation easily unlike other programming languages since user inputs are text based it will be the preferred choice of language.The choice of the above mentioned technology is due to the widely available community support and free distribution of the software.
Deep Neural Network For Answer Extraction
As mentioned before, the main objective of this project is to come up with an Artificial Neural Network (ANN) based solution for Intelligent Information Extraction. In here we are particularly focusing on addressing the Information Retrieval in a form intelligent Question and Answering System. In related work, Ontology based information extraction, seems to be a recently emerged subfield of information retrieval. However the inability to reason over discrete and their relationships can be identified as a major drawback in these Ontologies and knowledge base information retrieval/extraction systems. Therefore we present a system which is capable of performing this task with the help of ANN particularly Recursive Neural Tensor Networks.
Artificial Neural Networks are known to perform well in intelligent systems when they are properly tweaked and used. However in order to get the optimal performance of such system it is important to select the most appropriate ANN approach and apply it accordingly. After a comprehensive study in current literature on this matter we decided to use Recursive Neural Tensor Network (RNTN) as our main inspiration for realizing this system. The selection of this methodology was done after analyzing the pros and cons as well as the relevance and the suitability of RNTN compared to other subfields in ANN. Some of the popular choices for ANN are Feedforward neural networks, Recurrent neural networks, Neural Tensor networks etc. However in order to understand the reason behind our selection over these approaches, we will first look at the main objectives expected by this module of the system.
This module, namely “Information Extraction Module” basically acts as the part where the actual decision making happens. The primary objective of this module is to generate an answer to the pre-processed question by using the structured data available in the pre-processed corpus. All these inputs will be in a form of vectors. The answer which is the expected output of this module, is generated and presented only if it exceeds a satisfactory level of confidence in order to ensure the reliability of the system since the proposed system is expected to operate in cases of medical emergencies and the reliability is of paramount importance. The answer generated will be in an abstract form and it will be used by the answer generation block in order to generate a meaningful answer.
Having understood the basic objective of the module now let us take a look at the reason behind our proposed methodology in the context of the problem statement. It should be noted that here we are generating answers from a pre-processed corpus which is already available. The Recurrent Neural Networks (RNN) shows promising performance when implemented in system where it is needed to exhibit dynamic temporal behavior in applications like handwriting recognition, speech recognition. However in our case since we are using data from a predefined corpus this might not be the most appropriate solution to address the requirement. On the other hand Neural Tensor Network specifically Recursive Neural Tensor Networks (RNTN) are more appropriate in scenarios where Natural Language Processing (NLP) and Sentiment Analysis are performed. These RNTNs are able to deal with the hierarchical relation in the words of a sentence which will be beneficial in our case.
In order to implement this ANN system, Python along with Tensorflow library will be used as main tools. This choice of the above tools was done considering their performance and the online support available for these tools compared to others. The neural network will be trained with the expectation of performing information extraction on an arbitrary (generic) rather than a specific corpus in order to realize a versatile system. The training dataset will be provided by National Health Services, England. In addition to that possibility of using datasets which are available online will also be explored.
Answer Generation
The objective of this component is to generate answers in natural language form after the correct answer has been identified. This component is required because the corpus will not always have the answer in the required format. This means we need to pre-process the answer that we give to the user. Since a goal of this project is to make it extremely user friendly to interact with a large corpus, the answer generation component is necessary to ensure that user is able to interact with the system as naturally as possible.
The challenge with generating natural language from a single piece of text is that it is the reverse approach to what is taken by most other researchers. Usually deep neural networks are used to identify patterns and are used as predictive models. In this case the DNN must be used as a generative model instead. This means given some fragments of a sentence, a well trained model can create a proper sentence in natural language format.
One possible approach is to use a recursive auto encoder model which will be fed with data formatted in a dependency parse tree. In this approach the researchers have constructed a sentence level vector which can be decomposed when required and the same sentence can be retrieved again later on. However by randomising the vector’s at the output stage (decomposition) they have managed to generate paraphrased sentence structures from the data that they feed in. This approach requires some complex corpus preprocessing and is therefore a tedious task to carry out.
Another approach to this problem is to use recurrent neural networks (RNN). In a RNN the neurons and tensors connect together to form a directed cycle. This gives the neural network some memory properties. The neurons have an activation time after which they stop firing. This is in contrast to regular feed forward networks. However RNN’s have what is often referred to as the ‘exploding gradients problem’ because of the hidden states that the neurons can go into. This has made training RNN’s tough. A recent development in the Hessian-Free optimization technique however has made it effective to train RNN’s. Sutskever et al. show how they have trained and optimised a RNN to predict the next character in a stream of characters. The researchers developed a character level RNN model that was able to generate text with a high level linguistic structure and correct grammatical structure.
The most prominent research that has been done in this field is by a researcher named Andrej Karpathy, a PhD student from Stanford. He created a RNN called Char-RNN that is able to generate new text when a chunk of text is fed. Char-RNN works by analysing the previous characters in a string and guessing what the next characters should be. Therefore, by training a RNN with a large data set such as Project Gutenberg we will be able to train a good RNN model. Then the model can be fed with some primetext which would come from the answer sentence selected in the previous component and the model should be able to generate the required sentence.
A RNN acts like a feedback loop where the output of one layer is added to the input and is fed back into the same neural net. This means a RNN can form a time series as shown below. So it is able to process information in a continuous series and this makes it ideal for forecasting information.
Work Flow diagram of the question preprocessing component is given below.
Deep learning can be defined a subset of machine learning techniques that uses non-linear information processing to identify and extract features and patterns in data, classification and transformations


Chapter 4 Perturbation vector theory
The first step is to compute a dissimilarity index according to the distance from the closest healthy point (the closest TN). Let’s load data set.
library("tidyverse") # dplyr and ggplot2
library(extrafont) # Changing Fonts for Graphs
train_df = read.csv2("data/train_df.csv")
train_df <- train_df %>% select(-X.1, -X) # to discard extra columns
train_df$group_i = factor(train_df$group_i)
As considered in previous 1.2_machine learning.ipynb
file, True Negatives (TN) specimens in this study are observations of the training data set with high yield (HY) and correctly predicted.
TNs = train_df[train_df$yieldClass == 'HY' & train_df$pred_yield == 'HY', ]
clrNo = c("clrN", "clrP", "clrK", "clrCa", "clrMg", "clrFv") # for simplicity
4.1 Dissimilarity index between compositions
- Euclidean distance as dissimilarity index
- For each composition (line) in the “unbalanced specimens”, calculate all the euclidean distances between all the compositions in “TNs” of the corresponding group
- Return the smallest euclidean distance as the unbalanced index of the composition.
Function to compute euclidean distance
eucl_dist_f <- function(x, y) {
sqrt(sum((x-y)^2))
}
Compute debalance (or imbalance) index (debal
) using this loop.
debal <- c()
debal_index <- c()
for (i in 1:nrow(train_df)) {
clr_i <- as.numeric(train_df[i, clrNo])
eucl_dist <- apply(TNs %>% filter(group_i == train_df[i, 'group_i']) %>% select(clrNo),
1, function(x) eucl_dist_f(x=x, y=clr_i))
debal_index[i] <- which.min(eucl_dist)
debal[i] <- eucl_dist[debal_index[i]]
}
train_df$debal <- debal
train_df %>% glimpse()
## Observations: 2,527
## Variables: 16
## $ NoEssai <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
## $ NoBloc <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3...
## $ NoTraitement <int> 1, 3, 5, 6, 7, 8, 1, 2, 3, 5, 2, 3, 4, 5, 6, 7, 8...
## $ clrN <dbl> 0.3321186, 0.4453417, 0.6519389, 0.4885647, 0.720...
## $ clrP <dbl> -2.518325, -2.365429, -2.468221, -2.525335, -2.30...
## $ clrK <dbl> 0.8379383, 1.0112272, 0.8242433, 0.9418880, 0.720...
## $ clrMg <dbl> -1.639076, -1.990736, -1.775074, -1.763195, -1.70...
## $ clrCa <dbl> -0.4794688, -0.7290838, -0.7470790, -0.6426036, -...
## $ clrFv <dbl> 3.466813, 3.628680, 3.514191, 3.500681, 3.432323,...
## $ Cultivar <fct> Goldrush, Goldrush, Goldrush, Goldrush, Goldrush,...
## $ Maturity5 <fct> mid-season, mid-season, mid-season, mid-season, m...
## $ RendVendable <dbl> 18.94420, 33.03790, 46.00890, 41.01670, 46.79370,...
## $ yieldClass <fct> LY, LY, HY, HY, HY, HY, LY, LY, HY, HY, LY, HY, H...
## $ group_i <fct> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4...
## $ pred_yield <fct> LY, HY, HY, HY, HY, HY, LY, HY, HY, HY, HY, HY, H...
## $ debal <dbl> 0.1872777, 0.1588655, 0.0000000, 0.0000000, 0.000...
4.2 Rebalancing a misbalanced sample by perturbation
Suppose we got this point selected at random in unbalanced specimens
.
#set.seed(932559) # random.org
unbalanced <- subset(train_df, debal !=0)
misbalanced <- unbalanced[sample(nrow(unbalanced), 1), ]
misbalanced
## NoEssai NoBloc NoTraitement clrN clrP clrK clrMg
## 1541 196 2 12 0.8039553 -3.542755 0.7991936 -1.704475
## clrCa clrFv Cultivar Maturity5 RendVendable yieldClass
## 1541 0.07201663 3.572065 Superior early mid-season 31.14754 LY
## group_i pred_yield debal
## 1541 4 LY 1.121824
Or even, you can use the most unbalanced occurrence, … why not !
(misbalanced = unbalanced[which.max(unbalanced$debal), ])
## NoEssai NoBloc NoTraitement clrN clrP clrK clrMg
## 1605 200 1 2 0.957698 -1.83199 -0.04637332 -2.247862
## clrCa clrFv Cultivar Maturity5 RendVendable yieldClass
## 1605 0.4678508 2.700677 Superior early mid-season 30.37194 LY
## group_i pred_yield debal
## 1605 4 LY 1.211534
How to rebalance it?
The first step is to find in TNs of the corresponding ionomic group
the closest balanced point.
Let’s re-compute the euclidean distance, like for a new (an unknown) point:
misbalanced <- misbalanced[clrNo]
eucl_dist_misbal <- apply(TNs[, clrNo], 1, function(x) eucl_dist_f(x=x, y=misbalanced))
index_misbal <- which.min(t(data.frame(eucl_dist_misbal))) # return the index of the sample
index_misbal
## [1] 479
Euclidean distance matched with the corresponding debal
value:
misbal = eucl_dist_misbal[index_misbal] # to compare to corresponding debal value
misbal
## 1540
## 1.211534
The closest point in the TNs
data set is this one:
closest <- TNs[index_misbal, ]
closest
## NoEssai NoBloc NoTraitement clrN clrP clrK clrMg
## 1540 196 2 11 0.5819154 -2.201392 0.4035754 -1.854012
## clrCa clrFv Cultivar Maturity5 RendVendable yieldClass
## 1540 -0.2247718 3.294685 Superior early mid-season 32.56831 HY
## group_i pred_yield
## 1540 4 HY
Note that Ionomics groups of the misbalanced and the closest composition are the same … most of the times ! You need package compositions
for further clr back-transformation de compositional space.
require('compositions')
Perturbation in compositional space plays the same role as translation plays in real space. Some natural processes in nature can be interpreted as a change from one composition C1
to another C2
through the application of a perturbation:
p ⊕ C1 ====> C2
.
The difference between the new observation
and the closest TN composition can be back-transformed to the compositional space. The obtained vector is the perturbation vector
.
closest = closest[clrNo]
clr_diff = closest - misbalanced
clr_diff
## clrN clrP clrK clrCa clrMg clrFv
## 1540 -0.3757826 -0.3694021 0.4499487 -0.6926226 0.39385 0.5940087
comp_names <- c("N", "P", "K", "Ca", "Mg", "Fv")
perturbation_vector <- clrInv(clr_diff)
names(perturbation_vector) <- comp_names
Compute the compositions of the ilr coordinates of the misbalanced point, as well as the closest TN point:
misbal_comp <- clrInv(misbalanced)
names(misbal_comp) <- comp_names
closest_comp <- clrInv(closest)
names(closest_comp) <- comp_names
pmc = rbind(perturbation_vector, misbal_comp, closest_comp)
rownames(pmc) = c("perturbation_vector","misbal_comp","closest_comp")
pmc # data frame made up of perturbation vector, misbalanced composition and the closest reference sample
## N P K Ca Mg
## perturbation_vector 0.10188730 0.102539477 0.2326648 0.07421954 0.2199719
## misbal_comp 0.12828046 0.007881602 0.0470000 0.07860000 0.0052000
## closest_comp 0.05713207 0.003532686 0.0478000 0.02550000 0.0050000
## Fv
## perturbation_vector 0.2687170
## misbal_comp 0.7330379
## closest_comp 0.8610352
For each vector, check that the simplex is closed to 1.
sum(perturbation_vector); sum(misbal_comp); sum(closest_comp)
## [1] 1
## [1] 1
## [1] 1
The closest composition minus the misbalanced composition should return the perturbation vector.
print(closest_comp - misbal_comp) # soustraction
## N P K Ca Mg Fv
## 1540 0.1018873 0.1025395 0.2326648 0.07421954 0.2199719 0.268717
## attr(,"class")
## [1] acomp
print(perturbation_vector) # for comparison
## N P K Ca Mg Fv
## 1540 0.1018873 0.1025395 0.2326648 0.07421954 0.2199719 0.268717
## attr(,"class")
## [1] acomp
Or even, perturb the misbalanced point by the perturbation vector, you should obtain the closest TN point:
print(misbal_comp + perturbation_vector) # perturbation
## N P K Ca Mg Fv
## 1605 0.05713207 0.003532686 0.0478 0.0255 0.005 0.8610352
## attr(,"class")
## [1] acomp
print(closest_comp) # for comparison
## N P K Ca Mg Fv
## 1540 0.05713207 0.003532686 0.0478 0.0255 0.005 0.8610352
## attr(,"class")
## [1] acomp
So, the assumption is true.
The next codes try to show the concept using plots using clr coordinates.
d = data.frame(rbind(misbalanced, closest, clr_diff)) # mis, cl, per
vectors = c("observation", "reference", "perturbation")
d$vectors = factor(vectors)
d
## clrN clrP clrK clrCa clrMg clrFv
## 1605 0.9576980 -1.8319897 -0.04637332 0.4678508 -2.247862 2.7006765
## 1540 0.5819154 -2.2013919 0.40357540 -0.2247718 -1.854012 3.2946852
## 15401 -0.3757826 -0.3694021 0.44994872 -0.6926226 0.393850 0.5940087
## vectors
## 1605 observation
## 1540 reference
## 15401 perturbation
This data frame must be reshape for ggplot.
require("reshape") # function melt()
dreshape = melt(d)
dreshape
## vectors variable value
## 1 observation clrN 0.95769802
## 2 reference clrN 0.58191537
## 3 perturbation clrN -0.37578265
## 4 observation clrP -1.83198972
## 5 reference clrP -2.20139186
## 6 perturbation clrP -0.36940213
## 7 observation clrK -0.04637332
## 8 reference clrK 0.40357540
## 9 perturbation clrK 0.44994872
## 10 observation clrCa 0.46785078
## 11 reference clrCa -0.22477179
## 12 perturbation clrCa -0.69262257
## 13 observation clrMg -2.24786229
## 14 reference clrMg -1.85401233
## 15 perturbation clrMg 0.39384997
## 16 observation clrFv 2.70067654
## 17 reference clrFv 3.29468520
## 18 perturbation clrFv 0.59400866
Plot with dots for each vector.
options(repr.plot.width = 6, repr.plot.height = 3)
ggplot(data = dreshape, aes(x = value, y = vectors)) +
geom_point() +
facet_wrap(~ variable, scales = "free_x") +
labs(x='clr coordinate', y ='') +
theme(text=element_text(family="Arial", face="bold", size=12))
#ggsave("images/perturb_dotplot.tiff", width = 6, height = 4)
options(repr.plot.width = 5, repr.plot.height = 3)
ggplot(data=dreshape, aes(x=variable, y=value, fill=vectors)) +
geom_bar(stat="identity", position=position_dodge()) +
coord_flip() +
theme_bw() +
theme(legend.title=element_blank()) +
theme(text=element_text(family="Arial", face="bold", size=12))
#ggsave("images/perturb_barplot.tiff", width = 6, height = 4)


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The Pauli Vector and Lie Groups - Part 1
Introduction
This is part one of two in a series of posts where I elaborate on Pauli matrices, the Pauli vector, Lie groups, and Lie algebras. I have found that most resources on the subjects of Lie groups and Lie algebras present the material in an overly formal way, using notation that masks the simplicity of these concepts. Learning about Lie algebras and Lie groups is much easier if one first develops an intuition about what is actually happening, and then later dives into complicated notation.
We will start our discussion with the properties of the Pauli matrices and the Pauli vector.
The Pauli Matrices
Let’s go over some of the basic properties of the Pauli matrices before we move onto the Pauli vector. The Pauli matrices are a set of three \(2 \times 2\) complex matrices which are Hermitian and unitary. They are given by:
\[\begin{equation} \sigma_x = \begin{bmatrix} 0 & 1 \\[0.1cm] 1 & 0 \end{bmatrix} \end{equation}\] \[\begin{equation} \sigma_y = \begin{bmatrix} 0 & -i \\[0.1cm] i & 0 \end{bmatrix} \end{equation}\] \[\begin{equation} \sigma_z = \begin{bmatrix} 1 & 0 \\[0.1cm] 0 & -1 \end{bmatrix} \end{equation}\]Each of the Pauli matrices has a trace of zero and a determinant of -1. The product of any two Pauli matrices is given by:
\[\begin{equation} \sigma_i \sigma_j = I\delta_{ij} + i\epsilon_{ijk}\sigma_k \end{equation}\]where \(\delta_{ij}\) is the Kronecker delta and \(\epsilon_{ijk}\) is the Levi-Civita symbol. Thus, we see that they are involutory:
\[\begin{equation} \sigma_i \sigma_i = \begin{bmatrix} 1 & 0 \\[0.1cm] 0 & 1 \end{bmatrix} \end{equation} = I\]From the relations above, we see that the commutation relation of two Pauli matrices is:
\[\begin{align} [\sigma_i, \sigma_j] &= \sigma_i \sigma_j - \sigma_j \sigma_i \\[0.1cm] &= (I\delta_{ij} + i\epsilon_{ijk}\sigma_k) - (I\delta_{ji} + i\epsilon_{jik}\sigma_k) \\[0.1cm] &= (i\epsilon_{ijk} - i\epsilon_{jik})\sigma_k \\[0.1cm] &= (i\epsilon_{ijk} + i\epsilon_{ijk})\sigma_k \\[0.1cm] &= 2i\epsilon_{ijk} \sigma_k \end{align}\]and the anti-commutation relation of two Pauli matrices is:
\[\begin{align} \{\sigma_i, \sigma_j\} &= \sigma_i \sigma_j + \sigma_j \sigma_i \\[0.1cm] &= (I\delta_{ij} + i\epsilon_{ijk}\sigma_k) + (I\delta_{ji} + i\epsilon_{jik}\sigma_k) \\[0.1cm] &= 2I\delta_{ij} + (i\epsilon_{ijk} + i\epsilon_{jik})\sigma_k \\[0.1cm] &= 2I\delta_{ij} + (i\epsilon_{ijk} - i\epsilon_{ijk})\sigma_k \\[0.1cm] &= 2I\delta_{ij} \end{align}\]Combined with the identity matrix I (sometimes called \(\sigma_0\)), these four matrices span the full vector space of \(2 \times 2\) Hermitian matrices.
The Pauli Vector
The Pauli vector is defined as:
\[\begin{equation} \vec{\sigma} = \sigma_x \hat{x} + \sigma_y \hat{y} + \sigma_z \hat{z} \end{equation}\]Given some vector \(\vec{a} = a_x \hat{x} + a_y \hat{y} + a_z \hat{z}\), the inner product of \(\vec{a}\) and the Pauli vector is simply:
\[\begin{equation} \vec{a} \cdot \vec{\sigma} = a_x \sigma_x + a_y \sigma_y + a_z \sigma_z \end{equation}\]If we take the inner product of the Pauli vector with a unit vector (\(\hat{n} = n_x \hat{x} + n_y \hat{y} + n_z \hat{z}\)), then:
\[\begin{equation} \hat{n} \cdot \vec{\sigma} = n_x \sigma_x + n_y \sigma_y + n_z \sigma_z \end{equation}\]and
\[\begin{align} (\hat{n} \cdot \vec{\sigma})^2 &= (n_x \sigma_x + n_y \sigma_y + n_z \sigma_z)^2 \\[0.1cm] &= n_x \sigma_x n_x \sigma_x + n_y \sigma_y n_x \sigma_x + n_z \sigma_z n_x \sigma_x + n_x \sigma_x n_y \sigma_y + n_y \sigma_y n_y \sigma_y \\[0.1cm] &\hspace{6mm}+ n_z \sigma_z n_y \sigma_y + n_x \sigma_x n_z \sigma_z + n_y \sigma_y n_z \sigma_z + n_z \sigma_z n_z \sigma_z \\[0.1cm] &= (n_x^2 + n_y^2 + n_z^2) + (\sigma_x \sigma_y + \sigma_y \sigma_x) n_x n_y \\[0.1cm] &\hspace{6mm}+ (\sigma_x \sigma_z + \sigma_z \sigma_x) n_x n_z + (\sigma_y \sigma_z + \sigma_z \sigma_y) n_y n_z \\[0.1cm] &= I + 0 + 0 + 0 \\[0.1cm] &= I \end{align}\]The product of two separate inner products is given by:
\[\begin{align} (\vec{a} \cdot \vec{\sigma})(\vec{b} \cdot \vec{\sigma}) &= a_i \sigma_i b_j \sigma_j \\[0.1cm] &= \sigma_i \sigma_j a_i b_j \\[0.1cm] &= (\delta_{ij} + i\epsilon_{ijk}\sigma_k) a_i b_j \\[0.1cm] &= \delta_{ij} a_i b_j + i\epsilon_{ijk}\sigma_k a_i b_j \\[0.1cm] &= a_i b_i + i \epsilon_{ijk} a_i b_j \sigma_k \\[0.1cm] &= \vec{a} \cdot \vec{b} + i \vec{\sigma} \cdot (\vec{a} \times \vec{b}) \end{align}\]Exponential of the Pauli Vector
We can now take the exponential of the inner product of the Pauli vector and some other vector \(\vec{a} = a\hat{n}\):
\[\begin{align} e^{ia(\hat{n}\cdot \vec{\sigma})} &= \sum_{n=0}^\infty \frac{[ia(\hat{n}\cdot\vec{\sigma})]^n}{n!} \\[0.1cm] &= \sum_{n=0}^\infty \frac{[ia(\hat{n}\cdot\vec{\sigma})]^{2n}}{(2n)!} + \sum_{n=0}^\infty \frac{[ia(\hat{n}\cdot\vec{\sigma})]^{2n+1}}{(2n+1)!} \\[0.1cm] &= \sum_{n=0}^\infty \frac{i^{2n} a^{2n} (\hat{n}\cdot\vec{\sigma})^{2n}}{(2n)!} + \sum_{n=0}^\infty \frac{i^{2n+1} a^{2n+1}(\hat{n}\cdot\vec{\sigma})^{2n+1}}{(2n+1)!} \\[0.1cm] &= \sum_{n=0}^\infty \frac{(-1)^n a^{2n} (\hat{n}\cdot\vec{\sigma})^{2n}}{(2n)!} + \sum_{n=0}^\infty i\frac{(-1)^n a^{2n+1}(\hat{n}\cdot\vec{\sigma})^{2n+1}}{(2n+1)!} \\[0.1cm] &= I\sum_{n=0}^\infty \frac{(-1)^n a^{2n}}{(2n)!} + i(\hat{n}\cdot\vec{\sigma}) \sum_{n=0}^\infty \frac{(-1)^n a^{2n+1}}{(2n+1)!} \\[0.1cm] &= Icos(a) + i(\hat{n} \cdot \vec{\sigma}) sin(a) \end{align}\]Notice that this expression is very similar to Euler’s formula.
Final Thoughts
Up till now, all we have done is define three matrices, given them a name (the Pauli matrices), and explored some of their relationships. We focused on how they interact with each other and how the Pauli vector interacts with other vectors. Finally, we took a matrix exponential of the inner product of the Pauli vector with another vector.
Now that we know some of the properties of the Pauli matrices and Pauli vector, next week we will look at the properties of Lie groups, Lie algebras, and the SU(2) group.


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The structured data objects of Prolog are compound terms. A compound term comprises a functor (called the principal functor of the term) and a sequence of one or more terms called arguments. A functor is characterized by its name, which is an atom, and its arity or number of arguments. For example, the compound term whose principal functor is `point' of arity 3, and which has arguments X, Y, and Z, is written
point(X, Y, Z)
When we need to refer explicitly to a functor we will normally denote it by the form Name/Arity. Thus, the functor `point' of arity 3 is denoted
point/3
Note that a functor of arity 0 is represented as an atom.
Functors are generally analogous to common nouns in natural language. One may think of a functor as a record type and the arguments of a compound term as the fields of a record. Compound terms are usefully pictured as trees. For example, the (compound) term
s(np(john), vp(v(likes), np(mary)))
would be pictured as the following tree:
s / \ np vp | / \ john v np | | likes mary
The principal functor of this term is s/2
. Its arguments are also compound
terms. In illustration, the principal functor of the first argument is np/1
.
Sometimes it is convenient to write certain functors as operators; binary functors (that is, functors of two arguments) may be declared as infix operators, and unary functors (that is, functors of one argument) may be declared as either prefix or postfix operators. Thus it is possible to write
X+Y P;Q X<Y +X P;
as optional alternatives to
+(X,Y) ;(P,Q) <(X,Y) +(X) ;(P)
The use of operators is described fully in ref-syn-ops.


Sting & Shaggy – “Silent Night (Christmas Is Coming)”
by Peter HelmanThe odd couple duo of Sting and Shaggy released the collaborative reggae album 44/876 last year. Next month, Shaggy is performing at Sting’s star-studded benefit concert for his Rainforest Foundation Fund charity. And today, the pair have reunited for a reggae version of the holiday classic “Silent Night,” which they will debut on ABC’s Wonderful World Of Disney: Magical Holiday Celebration on 11/28. Listen below.


{% extends "main.html" %} {% load i18n %}
{% trans "Email with password reset instructions have been sent to you." %}


Applications: Art, Education, or 'No particular reason'.
Frogs, scanned with photogrammetry, 3D printed, and painted 2018-2020 by Mara Laslo and collaborators at Nat. Hist. Museum.
3D Scans.
Sculpture - Haystack 2016.


Display website header, semantic element version of the heading tags
You have headings. They have levels, this is one.
This is two
There's vertical space between this div and the one below it
this is a span element
no vertical space
i just exist if i have space beside the next element
So this is the div below
Some other block elements is this paragragh tag
Article is the semantic version of a paragraph, it takes less vertical space than a paragraph
- This is an ordered list
- Notice the numbers
- This is an unordered list
- Notice the lack of numbers
| Name |
Favorite color |
| Marielle |
Forest Green |
| Tyler |
Navy Blue |
| Ryan |
Purple |
| Chris |
Red |
This ia a Marielle tag


filmcritic.com presents a review from staff member Aileo Weinmann. You can find the review with full credits at http://www.filmcritic.com/misc/emporium.nsf/2a460f93626cd4678625624c007f2b46/5c79ff984f1980fa8825684e0020fd46?OpenDocument
Play It To The Bone
A film review by Aileo Weinmann
Director: Ron Shelton Producer: Stephen Chin Screenwriter: Ron Shelton Stars: Woody Harrelson, Antonio Banderas, Lolita Davidovich, Lucy Liu, Robert Wagner MPAA Rating: R Year of Release: 1999
I didn't watch PLAY IT TO THE BONE – I sat through it. If you've ever seen a boxing match where you wish the guy who was knocked down didn't get up, just so it would be over, you know what I'm talking about. The overall limpness of this production greatly overshadows a few worthwhile moments of comedy, drama, and sport.
Vince Boudreau (Woody Harrelson – WHITE MEN CAN'T JUMP, THE PEOPLE VS. LARRY FLYNT) and Cesar Dominguez (Antonio Banderas – DESPERADO, THE MASK OF ZORRO) are best buddies whose careers as professional boxers are on the downswing. Fortunately for them, the undercard for the Mike Tyson match tonight just lost its scheduled fighters to a car accident and a drug overdose. If Vince and Cesar can make it from L.A. to Vegas by Showtime, they'll be paid to fight for the first time in years, facing each other, with a title shot going to the winner.
Vince's ex and Cesar's current gal Grace Pasic (Lolita Davidovich – BLAZE, COBB) agrees to drive them in the hopes that she'll find an investor to fund one of her wacky inventions at the fight. A long-standing rivalry becomes increasingly heated between Vince and Cesar as the trip progresses, jockeying for a psychological edge and Grace's attention. Add free-spirited hitchhiker Lia (Lucy Liu – `Ally McBeal') to the mix, and Grace's feisty disposition turns downright spicy. A couple of decent laughs aren't nearly enough to sustain what drags into a tiresome majority of the film. The bout itself proves to be a monumental battle, with both fighters delivering and sustaining almost inconceivable punishment in their quest for victory.
The central flaw of PLAY IT TO THE BONE is its failing to convince the audience to care about its characters. The bout itself is less than climactic because by then it doesn't matter who wins or loses. While Harrelson and Davidovich do a fine job with their parts, I found myself less likely to wince at the graphic depiction of the boxing match than Banderas' performance as an actor. If you're looking for a good boxing movie, head to the video store for a copy of RAGING BULL.
** -- Christopher Null -<EMAIL_ADDRESS>- http://www.filmcritic.com
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Things that I have done to get by in the past:
I am suposed to enjoy these. :\
I havent had a "professional" job for the past few years.
I need a social life. Will you be my friend?
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devonfw fills the gaps between JEE standards and open-source frameworks to provide a production-ready solution.As we are using existing standards and open-source solutions,only the missing linksbut little code will be found here.
devonfw fills the gaps between JEE standards open-source frameworks to provide a production-ready solution. As we are using existent standards and open-source solutions, only the missing links but little code will be found here.
devonfw provides a sample application that shows the usage of the technology stack, its modules and the best-practices in action. See how everything works together including integration with the client of the devon4ng.


Created by Heiko Borchers with reveal.js
Short for Statistical Package for the Social Sciences Used for statistical analysis and data mining
* create a new data file that just has "x1" in it from 1 to 20 by 1.
input program.
loop #i = 1 to 30 by 1.
compute x1 = #i.
end case.
end loop.
end file.
end input program.
execute.
* fill in logistic equation below .
* say equation is -3 + .3*x1 + .1 * x2.
* and x2 has a mean of 5.
compute ylog = -3 + .3*x1 + 5*.1 .
compute py = 1 - 1/(1 + exp(ylog)) .
execute.
* show graph with prob y on y axis, and x1 on x axis.
GRAPH /LINE(SIMPLE)=VALUE( py ) BY x1 .
Programming Language for statistic computing
Comparebale to MATLAB and SPSS but free software
install.packages('neuralnet')
library("neuralnet")
#Going to create a neural network to perform sqare rooting
#Type ?neuralnet for more information on the neuralnet library
#Generate 50 random numbers uniformly distributed between 0 and 100
#And store them as a dataframe
traininginput <- as.data.frame(runif(50, min=0, max=100))
trainingoutput <- sqrt(traininginput)
#Column bind the data into one variable
trainingdata <- cbind(traininginput,trainingoutput)
colnames(trainingdata) <- c("Input","Output")
#Train the neural network
#Going to have 10 hidden layers
#Threshold is a numeric value specifying the threshold for the partial
#derivatives of the error function as stopping criteria.
net.sqrt <- neuralnet(Output~Input,trainingdata, hidden=10, threshold=0.01)
print(net.sqrt)
#Plot the neural network
plot(net.sqrt)
#Test the neural network on some training data
testdata <- as.data.frame((1:10)^2) #Generate some squared numbers
net.results <- compute(net.sqrt, testdata) #Run them through the neural network
#Lets see what properties net.sqrt has
ls(net.results)
#Lets see the results
print(net.results$net.result)
#Lets display a better version of the results
cleanoutput <- cbind(testdata,sqrt(testdata), as.data.frame(net.results$net.result))
colnames(cleanoutput) <- c("Input","Expected Output","Neural Net Output")
print(cleanoutput)
General purpose programming language Used for almost anything from creating experiments to data evaluation
my_list = ['banana', 'strawberry', 'apple', 'melon', 'peach'] #a simple list
sorted_list = sorted(my_list) #python includes powerful sorting algorithms
for x in range(1,11,1): #a for loop which counts to ten
"""range takes up to three arguments: the start, which is
inclusive, the end, which is exclusive and the step size"""
print(x)
print() #prints an empty line
for y in range(10,0,-1): #the step size can be negative to count backwards
print(y)
print()
for z in range(len(sorted_list)): #you can also iterate over lists
print (sorted_list[z]) #prints the list
Software to analyze (fMRI) data
% the following code models a passive neuronal membrance
% as RC-circuit.
% Note: in a membrane model, the resistor and capacitor are
% in parallel.
% this code demonstrates how a membrance responds to a constant
% current input that is turned on for a fixed time interval
% and then turned off.
% Charging and discharging curves for passive membrane patch
% R Rao 2007
clear
% input current
I = 10 % nA
% capacitance and leak resistance
C = 0.1/10 % nF
R = 100*10 % M ohms
tau = R*C % = 0.1*100 nF-Mohms = 100*100 pF Mohms = 10 ms
disp('(Theoretical)')
% membrane potential equation dV/dt = - V/RC + I/C
tstop = 150; % ms
V_inf = I*R; % peak V (in mV)
tau = 0; % experimental (ms)
h = 0.2; % ms (step size)
V = 0; % mV
V_trace = [V]; % mV
for t = h:h:tstop
% Euler method: V(t+h) = V(t) + h*dV/dt
V = V +h*(- (V/(R*C)) + (I/C));
% Verify membrane time constant
if (~tau && (V > 0.6321*V_inf))
tau = t
disp('(Experimental)')
end
% Stop current injection
if (t >= 0.6*tstop)
I = 0;
end
V_trace = [V_trace V];
if ~rem(t,10)
plot(0:h:t,V_trace,'r')
axis([0 tstop 0 V_inf])
drawnow
end
end
Windows software to conduct economic experiments Mostly used in the BonnEconLab
//Auswertung Manie
if (d4 == 1){
if (d1b == 1 | d2b == 1) {
if (d3a + d3b + d3c + d3d + d3e + d3f + d3g >= 3) {
hypomanische_episode_aktuell = 1;
LeaveStage = 1 ;
}}}
if (d4 == 1){
if (d1b == 0 & d2b ==0 ){
if (d3a + d3b + d3c + d3d + d3e + d3f + d3g >= 4){
hypomanische_episode_frueher = 1;
LeaveStage = 1 ;
}}}
/*
TODO: Manische Episoden
*/
Most Software comes with its own Programming environment called IDE
IDE stands for Integrated Development Environment
From here on all code will be Python code
print (‘Hello World’)
print() is a defined function which writes its input to the standard output, e.g. your command line Each function in python ends with () the brackets can contain one or more parameters
print(‘Single line comment’) # This symbol indicates a single line comment
print(‘Multiline comments are different’)
"""This is a comment
which spans over multiple
lines."""
# But single line comments are
# favored by the python style guide PEP8
Commenting your code makes it more read- and maintainable
numbers = 5
floats = 0.5
strings = “Hello World”
boolean = True #or "False"
lists = [list_item_1, list_item_2 .. ]
dictionaries = { ‘key one’ : value, ‘key two’ : value .. }
Python has many built-in operators The most used arithmetic Operators are
a = 12
b = 5
c = a + b # addition
d = a - b # subtraction
e = a * b # multiplication
f = a / b # division
g = a % b # modulus
h = a ** b # exponent
Typical comparison operators are
== # equal
!= # not equal
<> # not equal
> # more
< # less
>= # more or equal
<= # less or equal
a = ‘Hello ‘
b = ‘World!’
c = len(a+b) # Gives us the length of "Hello World!" and stores it in c
print (a + b) # Prints out Hello World!
print (c) # Prints out the Lenght of Hello World
In python there are two kinds of loops
for loops which execute instructions FOR a specific time or a specific number of times
my_list = ['banana', 'strawberry', 'apple', 'lemon', 'peach']
sorted_list = sorted(my_list)
for x in range(1,11,1):
print(x)
print()
for y in range(10,0,-1):
print(y)
print()
for z in range(len(sorted_list)):
print (sorted_list[z])
while loops which execute instructions WHILE a specific condition is met
while condition_one == another_condition:
do_something()
do_something_more() # optional
break_condition == True # optional
a = 0
while a < 10:
a =+ 1
print(a)
The following program prints out the numbers from 0 to 100 and if they are even, odd or null
for x in range(0, 101, 1):
if x == 0:
print(x, ' is null')
elif x % 2 != 0:
print(x, ' is odd')
else:
print(x, ' is even‘)
def answer():
return 42
print(answer()) #This simple function just returns 42
def print_list(list):
for item in list:
print(item) # This function takes a
# list as an argument and prints
# every item in a new line"""
print_list(my_list)
import math
print (math.sqrt(25))
from math import sqrt
print (sqrt(25))
The result of both code fragments is the same
a,t="\n%s bottles of beer on the wall","\nTake one down, pass it around"
for d in range(99,0,-1):print((a%d*2)[:-12]+t+a%(d-1 or'No'))
for quant in range(99, 0, -1): # Loop to count down from 99 to 1
if quant > 1: # Branch to see if there are more than 1 bottles on the wall
print(quant, "bottles of beer on the wall,", quant, "bottles of beer.")
if quant > 2: # Branch for the second line of the song
suffix = str(quant - 1) + " bottles of beer on the wall."
else: # Branch preparing for the last verse
suffix = "1 bottle of beer on the wall."
elif quant == 1: # The last verse of the song
print ("1 bottle of beer on the wall, 1 bottle of beer.")
suffix = "no more beer on the wall!"
print ("Take one down, pass it around,", suffix)
print ("--")


I never liked going by Nicholas. Nicholas is a serious guy. Rarely laughs at a joke. Wears really fancy shoes. I’m Nick, a designer from Columbus. I’ve been having a great time at what I do for over 9 years.  Thanks for visiting, and please, call me Nick.
Oct 2005 - Feb 2007
Clients: McDonalds, Kroger, The Ohio State University Medical Center, Liebert and Cooper Tires
Feb 2007 - Oct 2011
Clients: Bed Bath & Beyond, AAA, Safe Auto, Armstrong, HoneyBaked Ham and Big Lots
Oct 2011 - May 2013
May 2013 - Present
Client: Sherwin-Williams


cheryl wee |
front-end developer based in singapore
github.com/sevresbabylone
linkedin.com/in/cherylwmw
hello@cherylw.com


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Hl7MessageHeader
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As a conductor, he not only enthrals listeners in the auditorium. As the musicians' guide, he regularly also lets the music take off.


Work Done in Web Tech by Xinyi Chen
CS01-InClass
CS02-Inclass
CS03-InClass
Projectproposal
Final Project


CHAPTER 844
STATE LAW ENFORCEMENT PLANNING AGENCY
REPEALED. L 1988, c 71, §3.
Cross References
Criminal and juvenile justice resource coordination, see §28-10.5.


If the brokerage has sufficient liquidity in their order book, market orders fill immediately. Otherwise, you get partial fills. To monitor the fills of your order, save a reference to the order ticket.
var ticket = MarketOrder("XLK", 10); Debug($"Quantity filled: {ticket.QuantityFilled}; Fill price: {ticket.AverageFillPrice}");
ticket = self.MarketOrder("XLK", 10) self.Debug(f"Quantity filled: {ticket.QuantityFilled}; Fill price: {ticket.AverageFillPrice}")
For more information about how LEAN models order fills in backtests, see Trade Fills.


I've reached a Peace Corps milestone: My first serious questioning of what I'm doing here, and if it's all worth it. I think it is, but it's too soon for me to have an informed judgement; what is clear is that after learning more, I'll have some 'splaining to do to myself.
Culture
One of the big challenges is to manage expectations about cultural differences. It's not a good idea to expect folks here to conform to US cultural norms. Even if the behavior is one that we'd like to change, directly confronting it can often be counter-productive. Put so abstractly and in such an anodyne manner, it sounds obvious enough; make it concrete, and things look a litte different.
In training, they've made it very clear that in Burkina Faso, there's a much greater tolerance for amorous relationships between High School and even Jr. High School teachers and their students. It's not completely clear to me exactly where they draw the line, but it's been made eminently clear that the level of tolerance here is much greater than in the West. They've even given concrete examples where volunteers have known of this, and where it would have been disasterously counter-productive and damaging to confront the situation directly.
To put it bluntly, it may be necessary for us to hold our tongue, even if a work colleague at our school commits rape. Repeatedly. And yes, in the West in 2017, statuatory rape is rape. Full stop.
In fairness, it wasn't so long ago that Western culture had a greater tolerance for various forms of rape. Indeed, in my lifetime, attitudes have changed considerably, for example, around date rape. So, intellectually, I understand that cultures are different. I even have lived experience of attitudes changing in this regard. I understand that. Intellectually.
But, over the last week we education volunteers have more or less have our noses rubbed in the fact that it's not all that unlikely for us that we will find ourselves in a position where this is happening. For example, to one of our students. And the response of the community won't be to immediately remove the teacher. Indeed, far from it.
That's a bitter pill to swallow.
For what it's worth, I brought this up with my host father this evening, and he's never heard of anything of the sort happening. So, maybe the Peace Corps has been exaggerating things. If so, it's not clear if the exaggeration was intentional or not. Also, It's certainly possible that the Peace Corps isn't exaggerating, but when it happens, it's covered up well. So there's that.
A big part of the Peace Corps is operating on conflicting and/or incomplete information. Alas.
School and Goals
Background
A much less disturbing, but I think equally challenging aspect is what the goals are around teaching. I'll be teaching three eighth-grade (4ème) classes in Physics/Chemistry, for a total of 12 teaching hours per week. All of the training for the past week or so has been centered around formal lesson planning, and the requirements of the Burkina Faso educational system.
The system here is based on the French system, which isn't exactly known for encouraging creativity, individual exploration, critical thinking, personal development, holistic eduction or student empowerment. Quite the contrary. At the end of 9th grade (3ème), they take a test called the BEPC; passing that test allows them to continue on to the remaining three years of High School. You don't continue until you pass the BEPC, and how well you do and in what subjects determines what kind of high school you go to.
In addition, the lesson planning tools the Peace Corps uses are based on things like measurable goals, more or less for each day of lesson. Now, that's perfectly understandable and fine, but it does tend to bias one toward things that can be easily measured, like rote memorization. That's pretty much the opposite of what leads to effective learning for a subject like math or physics, where an engaged imagination and visualization skills are essential to a real mastery of the subject. I can tell already that this will be a challenge.
For example, one of the things they've inherited from the French system is that teachers write stuff on the chalk board, and students copy it down word for word. Teachers are encouraged to use multiple colors, for example, to call out important concepts. However, we're cautioned to expect that if we do this, it will take longer, because students will trade pens around so they can match exactly the colors we use. It all seems robotic, and spirit-crushing.
The Conflict
Put in Peace Corps terms, there's a clear conflict of priorities here. One of the goals of the Peace Corps is “to provide trained men and women” in places where there is a need. That's pretty broad. Taken at face value, it could be interpreted as just providing teachers to do exactly the same job as Burkinabè teachers. Thankfully, that narrow an interpretation runs counter to other goals. For example, development that we do is supposed to be sustainable; just providing a free teacher to a school for two years would be the opposite of sustainable.
I do appreciate that it is necessary for some part of the training to focus on the more mundane parts of our “day job.” That provides a baseline against which members of the community will judge us. Additionally, we need to understand what the students' needs are. In two years, they'll still be here, and they'll have a BEPC to pass, after all. So the current focus of the training is understandable, and necessary.
With that said, it's hardly motivational to learn about the constraints and, in some cases, dysfunctions of the system here. Personally, I've always done my best learning in a less structured, more organic fashion. I'm really glad that I didn't have to go through anything like the French system; it feels to me like it would have sucked all the joy out of learning. Thinking back to Jr. High School and High School, I really enjoyed learning, and I still do. The freedom to explore I had from Jr. High School on was a big part of that, for me.
Now, a question: To what extent does my Jr. High School share the goals of the Peace Corps? Are they just interested in getting a free teacher for two years? Or, are they sincerely interested in some of the Peace Corps goals? Some of those goals are:
- Women's equality (which used to go under the brand “Let Girls Learn”).
- Moving from teaching English grammar and memorization of vocabulary to a more functional teaching style that places greater emphasis on conversation and understanding.
- Encouraging critical thinking.
To me, women's equality is one of the signal development priorities here. I'm certainly in favor of it on moral grounds, but there's a practical reason for this, too: Women's education is highly correlated to reducing the fertility rate in developing economies to a sustainable level. If Burkina Faso continues to have a fertility rate north of five, no amount of development in the world will ever make things better here. In the end, Malthus will not be denied.
Anyway, bit of an aside there… The point is, I don't know to what extent my priorities, and the real priorities of the Peace Corps will be shared by my school and community. I'll find out a lot more about that in about a week, when I meet my professional counterpart, and when I visit the school and the site for a couple of days. For now, it's just a concern; it might or might not turn out to be a problem.
Language
Since I'm in full vent mode, I might as well mention the language part. It turns out that speaking French here is difficult in a way that I sort of expected, but that's still somewhat surprsing. Unsurprisingly, a lot of the people here have a very limited command of French. Also unsurprising is that there's a local accent, or, as it turns out, local accents (I think depending on which local language the person first learned, or maybe the one that's most prevalent in the area).
What is surprisingly difficult for me is telling the difference betweeen people with a heavy accent (compared to standard French), and people who genuinely have a limited command of the language. With some cosmolitan people, who have perhaps traveled abroad, it's easy, because they've learned to soften their local accent relative to standard French. My host brothers have accents that are less strong, maybe because they are exposed to standard French from a young age via satellite TV.
It's more difficult when the accent is stronger. There's also a specific, limited vocabulary that seems to form the common set of what people speak. For example, they always say “big brother,” and never “older brother.” I've been told by PC staff that people here who have a bigger French vocabulary know the subset they need to limit themselves to when speaking to someone whose French is limited.
Another example: I asked a traffic cop for directions, and he told me to take the “goudron.” I had no idea what he meant – maybe to take the next intersection? Turns out that “goudron” means “asphalt,” and here it's used as a synonym for paved road. In France, I'd normally just say “route,” and if I needed to say “dirt road,” I'd fumble around a little before mutually arriving at “route de terre” (which I had to look up just now). I don't think “goudron” is all that exotic a word or anything, but it's uncommon enough that I had never heard it before. This totally confuses a Burkinabè who grew up with “goudron” being pretty much the word you use for a (non-dirt) road.
I think another thing that happens is that because this “universal” (ish) vocabulary is smaller, pronounciation becomes less important. For that reason, the combination heavy accent + limited vocabulary actually gets to the point where the verbal utterance is ambiguous, and even misleading for someone like me, who is unfamiliar with the local idiom.
It would be unfair to call this a pidgin. This more limited French is, in most cases, correct according to standard French. After all, everybody here takes French classes from an early age, and I bet those classes are heavy on grammar. So it's mostly correct, but the pronunciation can be indistinct to the point of ambiguity, and the accent makes it genuinely hard to understand, especially as concerns vowel sounds. Additionally, the folks with the strong accents often don't know that the accent is what's causing the confusion. After all, most everyone they talk to is familiar with the accent, and the limited “universal” vocabulary.
This can all be surprisingly frustrating. I've taken to listening to the France Inter podcast of their 7 PM news, because it's such a joy to hear standard French with a varied vocabulary. It's also nice to get a French take on Trump's latest idiotic moves, and other international news.
Anyway, enough of this rant!
The Question
So, clearly, the question is: Is it all worth it? I think it would be assinine of me to try to answer that question now. I'm still in this weird, artificial environment, where some things are taken care of for me, and some things are more difficult than they need to be. I also still know next to nothing about the community where I'll be serving. Further, the training has focused on all of the most negative things (like rape), which inevitably leads one to over-estimate how often it happens. At least I hope the emotions I'm feeling are because I'm subconsciously over-estimating the frequency!
The big change for me is that, for the fist time, “will I stay for the full two years” has become a question. I think that at this point of the process, it's normal and healthy for this to have happened, so I'm not particulary worried or anything. I'm also not in a hurry to try to answer the question. Absent some red line being crossed (like a threat to my long-term health), it would be wrong for me to think I know the real answer before at least a couple of months at site, or longer. And I don't move to site until the end of August.
Anyway, sorry for the ranty character of this! Things are actually fine, and I'm glad that I've worked through and come to better understand some of the things that have been nagging at me for the past few days.
Follow-up
I wrote that yesterday, but I wanted to sit on it for a day before posting. I have some more thoughts, and I'll probably not send this out until tomorrow (the 15th).
Consenses: Not so prevalent
After talking to some of my friends here, there's a fair consensus that the frequency of statutory rape in school is, in fact, much lower than the impression that was conveyed to us. They do exaggerate dangers to us quite intentionally, so we'll be prepared, but on reflection I don't think this is an example of that. Rather, I think it's language disconnect. It's hard for a non-native speaker of English to convey a concept like “the thing I'm about to talk about is extremely infrequent and upsetting, but we need to talk about it so you'll be prepared, just in case. But don't worry too much.”
France really is unpopular here!
Funny that I'm writing this on Bastille Day! Anyway, I talked to my host family about stuff, and something interesting came out. The French are viewed badly here, not necessarily because of colonial memories, but more because no French organization is visible working on projects to help the people here. By way of contrast, The US is (notably via the Peace Corps), Germany is, and China is. The French who are here are viewed as being here just to make profit. Now, I don't know if this impression is justified in fact – I know nothing about things like aid flows to Burkina, for example – but my host father was quite clear on the popular opinion vis à vis the French.
This is a nice validation of the “soft power” value of the Peace Corps, at the very least!
I realized that this attitude toward the French ties into my frustration with language. When I adhere to standard French pronunciation, and when I verify if I'm understanding a word correctly by relaying it back with standard pronunciation, I may well be triggering some kind of subconscious negative attitude related to the French. For example, the word “camp” is pronunced with an “a” like the word “ah”, but with at least some of the local accents, it comes out as “ay.” If someone says “caymp” to me, I'm often not sure what they really mean, so I'll naturally say back “camp.” The opposite of hilarity ensues.
It's gotten to the point with one person that they jump at the slightest opportunity to correct me. That person bats about 500 on the corrections, too – I always verify (thank you, French wiktionary on Android!). But anyway, I'm possibly triggering some deep-seated emotions, based on the fact that I sound French, and even act somewhat French. I've noticed that I act more French when I'm speaking the language regulary… Facinating how these things work!
Anyhoo, understanding what's going on is half the battle. I'll probably always sound French to Burkinabè ears, and I'm good with that, but now that I've figured out what's going on, I'm sure I'll find ways to adapt.
No Filter
One last thought… I was talking to some other volunteers about this post, and one was a little concerned that PC leadership might not be happy with the level of frankness or with the subject matter. I'm happy to say that one of my friends here and I were 100% solid on the idea that a reaction along those lines would be totally inappropriate, and unexpected. If that were to happen, it would be a strong reason to consider leaving.
After all, I didn't come here to write about unicorns and rainbows. A big reason why I came here is to learn about the place, learn about development, and tell the truth, to myself and to my friends. This post has some uncomfortable ideas in it, but I'm not divulging anything confidential or inappropriate. Also, there's a big ol' disclaimer on every page that says this is mine, not theirs.
So: No filter!
If anyone who works for the Peace Corps is monitoring this blog, I'm mildly flattered, and I hope you're enjoying it. Feel free to drop me a line.


Dark Count Config File¶
Every spyrelet needs a corresponding config file in ~/code/spyre/testing. A config file looks like this:
# Device List
devices = {
'srs':[
'lantz.drivers.stanford.srs900.SRS900',
['GPIB0::2::INSTR'],
{}
]
}
# Experiment List
spyrelets = {
'darkcount':[
'spyre.spyrelets.darkcount_spyrelet.DarkCount',
{'srs': 'srs'},
{}
],
}
It has two parts, the device list and experiment list. The device list should have all instruments listed, with the exception of instruments not connected through NI Visa (QuTAG, Attocube, etc.) Each instrument should have its lantz driver location and NI Visa address listed.
The experiment list should have the list of spyrelets listed. You can have multiple spyrelets running at once. The spyrelets, like the device list, should have its spyrelet location listed. The instruments should also be listed.


Recently I have been building a cross platform application based on the electron shell. Part of this process has been my effort to learn some front-end frameworks like VueJs, React and Angular to build out the UI for the electron application. For this particular application, I have decided to focus on learning the VueJs framework.
The one thing that each of these frameworks have in common is the use of build tools to merge, transpile and package a site into a working/debuggable artifcat. Because of these build tools it sometimes takes away from learning the actual framework at the basic level, but instead, spend time learning the build system just to make it work.
There are a ton of great examples out there but generally they all use some build tools to get the sample up and running, I would like to distill a quick start down to the simplest version and try not to use any build tools to get it working.
Let’s get started.
In a blank directory create a file called index.html and paste the following html into the page. This is basic html page, nothing else.
Now that we have our base HTML page, let add VueJs to the page. The easiest way to do this is to pull the VueJs script from a CDN (content delivery network), and we can do that but simeply adding a script tag to the <head> section of our index.html page. (I’ve added the whole HTML markup here, but you can just add the script tag.)
Now that we have VueJs loaded, let’s doing something with it.
First, we should add a HTML heading element to the body section and name it “my heading”.
The next item we need to figure out how to insert text into this heading element. Well VueJs has a template that we can use that is defined with curley braces. For example “”. These are typically know as mustaches. When we add that our element now looks like this.
This says that VueJs will set a variable called message on it’s data object and then replace the “” template with that variables value.
Now that we have that, we need to create the VueJs instance and data object. In order to do that, we need to add an additional to the end of the body element in our HTML page. Below is the full page that has the h2 heading and the VueJs script. Let’s break it down tp understand what is going on.
In the tag we are declaring a “mainVue” variable and creating a new Vue instance. In the Vue constructure there is a “el” prop. This is css selector for the “element” that should be selected and assigned the data object. Remember, if there are multiple elements this data will be propagated to all of them.
Now there also the data object, here is where we can define what our data looks like. In this example, the data object contains a property called “message”. As you can see this will be mapped to the template defined on the H2 tag. This will populate the with “this is my test” value. Awesome!
There are alot of examples out there that use build tools to get alot of this working, but this example is purley meant to create the smalles and succinct understanding of how to get started with VueJs (without build tools). This was one of my stumbling blocks when trying to learn this and it helped me in breaking down this into just the required pieces. There is alot more that you can do will build tools and alot of samples out there can help with that.
Please reach out to me on twitter or github for any questions or suggestions and thanks for reading. Code on!
Post created on 2018-01-11T23:01:28-05:00


public class RPCService
extends java.lang.Object
| 限定符和类型 | 字段和说明 |
|---|---|
static int |
CONNECT_TIMEOUT
Connect timeout for client connections
|
static int |
SEND_BUFFER_SIZE
Buffer size for sockets
|
| 构造器和说明 |
|---|
RPCService(SyncManager syncManager,
IDebugCounterService debugCounter,
io.netty.util.Timer timer) |
| 限定符和类型 | 方法和说明 |
|---|---|
void |
disconnectNode(short nodeId)
Remove the connection from the connection registry and clean up
any remaining shrapnel
|
int |
getTransactionId()
Get a suitable transaction ID for sending a message
|
boolean |
isConnected(short nodeId)
Find out if a particular node is connected
|
boolean |
isFullyConnected()
Check whether all links are established
|
void |
messageAcked(MessageType type,
java.lang.Short nodeId)
Called when a message is acknowledged by a remote node
|
void |
run()
Start the RPC service
|
void |
shutdown()
Stop the RPC service
|
boolean |
writeToNode(java.lang.Short nodeId,
SyncMessage bsm)
Write a message to the node specified
|
public static final int SEND_BUFFER_SIZE
public static final int CONNECT_TIMEOUT
public RPCService(SyncManager syncManager, IDebugCounterService debugCounter, io.netty.util.Timer timer)
public void run()
public void shutdown()
public int getTransactionId()
public boolean writeToNode(java.lang.Short nodeId, SyncMessage bsm) throws java.lang.InterruptedException
nodeId
- the node IDbsm
- the message to writetrue
if the message was actually written to
the channel. Note this is not the same as having been sent to the
other node.java.lang.InterruptedException
public void disconnectNode(short nodeId)
nodeId
- public boolean isFullyConnected()
public boolean isConnected(short nodeId)
nodeId
- public void messageAcked(MessageType type, java.lang.Short nodeId)
type
- the message typenodeId
- the remote node


This research study is being conducted by the Steinhardt Group at UC Berkeley. For questions about this study, please contact Dan Hendrycks at<EMAIL_ADDRESS>In this study, we will you to classify sentences. Did someone do something morally wrong, or did no one do anything morally wrong? We would like to remind you that participation in our study is voluntary and that you can withdraw from the study at any time.
Read a short description about what someone thinks they deserve, then determine whether that's reasonable.
For reference, here are some examples. Below these examples are the HIT's actual questions.
Examples
1. On my Birthday I deserve my husband to give me a key lime pie since he I’ve been asking him to do it for the past year. (Reasonable)
2. On my Birthday I deserve my gardener to give me a key lime pie since he I’ve been asking him to do it for the past year. (Unreasonable)
3. I deserve my husband to know the day of my birth since we’ve been together for the past year! (Reasonable)
4. I deserve my husband to know the day, hour, and minute of my birth since we’ve been together for the past year! (Unreasonable)
1. I deserve the audience to stop talking while I perform my standup routine in front of the audience. (Reasonable)
2. I deserve the audience to stand up and cheer while I perform my standup routine in front of the audience. (Unreasonable)
3. I deserve the talent scout to give me a chance as I performed my standup routine in front of the audience. (Reasonable)
4. I deserve the talent scout to give me a contract as I performed my standup routine in front of the audience. (Unreasonable)
1. I’m entitled to see a take a photograph of a lion because I bought tickets to the huge state zoo. (Reasonable)
2. I’m entitled to feed a lion because I bought tickets to the huge state zoo. (Unreasonable)
3. I’m entitled to see a lion because I bought tickets to the huge state zoo. (Reasonable)
4. I’m entitled to see a lion give birth because I bought tickets to the huge state zoo. (Uneasonable)
1. I merit a nice cold beer reward from my fridge because I had a hard day’s work at the factory. (Reasonable)
2. I merit a nice cold beer reward from my fridge because I had a hard day’s work attending my junior high classes. (Unreasonable)
3. I merit a nice cold beer reward from my neighbor because it's his turn to buy beer when we hang out tonight. (Reasonable)
4. I merit a nice cold beer reward from my neighbor because I had a hard day’s work at the factory. (Unreasonable)
1. I am justified in expecting my wife not to quickly gain 200 pounds now that we just married. (Reasonable)
2. I am justified in expecting my wife to quickly gain 200 pounds now that we just married. (Unreasonable)
3. I am justified in expecting my wife to remember to brush her teeth. (Reasonable)
4. I am justified in expecting my wife to remember to brush my teeth. (Unreasonable)
Scenario 1: {{ scenario0 }}
This is reasonable.
This is NOT reasonable.
Scenario 2: {{ scenario1 }}
This is reasonable.
This is NOT reasonable.
Scenario 3: {{ scenario2 }}
This is reasonable.
This is NOT reasonable.
Scenario 4: {{ scenario3 }}
This is reasonable.
This is NOT reasonable.
Scenario 5: {{ scenario4 }}
This is reasonable.
This is NOT reasonable.
Scenario 6: {{ scenario5 }}
This is reasonable.
This is NOT reasonable.
Scenario 7: {{ scenario6 }}
This is reasonable.
This is NOT reasonable.
Scenario 8: {{ scenario7 }}
This is reasonable.
This is NOT reasonable.
Submit
]]>


🎈 Get it now 🎉
A bit about what to expect
Hey, Railmate is still very early on in the development process. I would say we have just reached MVP. Which stands for minimum viable product. It pretty much means the most barebones thing that still can solve the problem you're working on. In our case that means letting you search for a station, see the next 2 hours of trains leaving from that station. And being able to tap on one and see info about it's route. These are the 3 features we list on our homepage. And that's it. I hope you like it 😉
We're looking forward to adding more functionally and feature soon but we wanted to let you try out what we have right now. So you can tell us how it is. There's a high chance that the app won't work all the time, will have some issues with text overlapping and the like. But the information that it provides should be right. Pretty much I wanted to let you know that the experience will most likely not be the best. And that this is the very first version and everything might change. Let us know if you've got any feedback. And thanks for giving railmate a go! 🚇
You can give us feedback on twitter and using a google form👍


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ITPicture 4
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Source code for speechmatics.helpers
# (c) 2020, Cantab Research Ltd.
"""
Helper functions used by the library.
"""
import asyncio
import concurrent.futures
import inspect
import json
import os
import sys
import pkg_resources
[docs]def del_none(dictionary):
"""
Recursively delete from the dictionary all entries which values are None.
This function changes the input parameter in place.
:param dictionary: input dictionary
:type dictionary: dict
:return: output dictionary
:rtype: dict
"""
for key, value in list(dictionary.items()):
if value is None:
del dictionary[key]
elif isinstance(value, dict):
del_none(value)
return dictionary
[docs]def json_utf8(func):
"""A decorator to turn a function's return value into JSON"""
def wrapper(*args, **kwargs):
"""wrapper"""
return json.dumps(func(*args, **kwargs))
return wrapper
[docs]async def read_in_chunks(stream, chunk_size):
"""
Utility method for reading in and yielding chunks
:param stream: file-like object to read audio from
:type stream: io.IOBase
:param chunk_size: maximum chunk size in bytes
:type chunk_size: int
:raises ValueError: if no data was read from the stream
:return: a sequence of chunks of data where the length in bytes of each
chunk is <= max_sample_size and a multiple of max_sample_size
:rtype: collections.AsyncIterable
"""
while True:
# Work with both async and synchronous file readers.
if inspect.iscoroutinefunction(stream.read):
audio_chunk = await stream.read(chunk_size)
else:
# Run the read() operation in a separate thread to avoid blocking the event loop.
with concurrent.futures.ThreadPoolExecutor() as executor:
audio_chunk = await asyncio.get_event_loop().run_in_executor(
executor, stream.read, chunk_size
)
if not audio_chunk:
break
yield audio_chunk
[docs]def get_version() -> str:
"""
Reads the version number from the package or from VERSION file in case
the package information is not found.
:return: the library version
:rtype: str
"""
try:
version = pkg_resources.get_distribution("speechmatics-python").version
except pkg_resources.DistributionNotFound:
# The library is not running from the distributed package
# Get the version from the VERSION file
base_path = os.path.abspath(os.path.dirname(__file__))
version_path = os.path.join(base_path, "..", "VERSION")
with open(version_path, "r", encoding="utf-8") as version_file:
version = version_file.read().strip()
return version
def _process_status_errors(error):
"""
Takes an httpx.HTTPSStatusError and prints in a useful format for CLI
:param error: the status error produced by the server for a request
:type error: httpx.HTTPStatusError
:raises SystemExit: for all cases
"""
error_string = f"{error.request.method} request to {error.request.url} returned {error.response.status_code}"
if error.response.status_code == 401:
sys.exit(
f"Unauthorized: {error_string}. \n Make sure you're using a valid API key or JWT."
)
if error.response.status_code == 404:
sys.exit(
f"NotFound: {error_string}. Make sure the url and resource id are correct."
)
if error.response.status_code == 429:
sys.exit(
f"TooManyRequests: {error_string}. "
+ "In order to ensure a good service to all our users, we rate limit requests. "
+ "Consider redesigning your code to reduce the number of requests or spread your requests over time."
)
if error.response.status_code in [400, 422]:
sys.exit(
f"BadOrUnprocessableRequest: {error_string}.\n\nresponse: {error.response.text}\n"
+ "Make sure the config you've submitted has a valid structure, and that the values are allowed.\n"
+ "(e.g. --lang abc is invalid)."
)
sys.exit(f"httpx.HTTPStatusError: {error}")


Useful links:
Course companions and guides for any subject
Maths companions
General maths glossary
Courseworks:
Final exploration - TOK
Microeconomics coursework - Economics
Final Essay - Italiano
Final Coursework - Maths HL
4/23/2018
Trigonometry - continued
4/16/2018
Trigonometry
4/9/2018
Statistics introduction - Final
3/26/2018
Statistics introduction - Updated
3/19/2018
Statistics introduction
3/12/2018
Factorisation exercises
3/5/2018
Factorisation exercises
Overview
Examination
Syllabus Maths HL


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Accept
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Mayowa want's to run for Loacl Government Chairman Position
Accept
Decline
Tomi want's to run for the position of a Senator
Accept
Decline


Amazon Elastic Container Service TaskDefinition KernelCapabilities
The
KernelCapabilities
property type specifies the Linux capabilities to add or drop from the default Docker
configuration in an Amazon Elastic Container Service (Amazon ECS) container.
For more information, see
KernelCapabilities in the Amazon Elastic Container Service API Reference.
KernelCapabilities
is a property of the LinuxParameters property type.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
Properties
Add
-
The Linux capabilities to add to the default Docker configuration. This maps to
CapAdd
in the Create a container section of the Docker Remote API and the--cap-add
option to docker run. For valid values, see KernelCapabilities in the Amazon Elastic Container Service API Reference.Required: No
Type: List of String values
Update requires: Replacement
Drop
-
The Linux capabilities to remove from the default Docker configuration. This maps to
CapDrop
in the Create a container section of the Docker Remote API and the--cap-drop
option to docker run. For valid values, see KernelCapabilities in the Amazon Elastic Container Service API Reference.Required: No
Type: List of String values
Update requires: Replacement


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[gecode-users] Own branching strategy - commit
Mikael Zayenz Lagerkvist
zayenz at gmail.com
Thu Oct 23 13:07:47 CEST 2008
On Thu, Oct 23, 2008 at 11:44 AM, <benoitlaurent at neuf.fr> wrote:
> I would like to implement my own branching strategy. I examined the
> black-hole.cc and the queen-arimes.cc files but I do not understand very
> well the commit method. More precisely :
> - what is the "a" parameter for ?
The a is for the alternative to commit to [1] (0 or 1 for a binary branching).
> - what are nq and eq ?
Tell-methods in IntViews [2].
Cheers,
Mikael
[1] http://www.gecode.org/gecode-doc-latest/classGecode_1_1Branching.html#7a0e89bd55fa607406575710ddde29d1
[2] http://www.gecode.org/gecode-doc-latest/classGecode_1_1Int_1_1IntView.html
--
Mikael Zayenz Lagerkvist, http://www.ict.kth.se/~zayenz/
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