Functions and types that the GLSL specification doesn't define, but useful to have for a C++ program. More...
Modules | |
| GLM_GTX_associated_min_max: Associated Min/Max | |
Min and max functions that return associated values not the compared onces. <glm/gtx/associated_min_max.hpp> need to be included to use these functionalities. | |
| GLM_GTX_bit: Extended bitwise operations | |
Allow to perform bit operations on integer values. | |
| GLM_GTX_closest_point: Find closest point | |
Find the point on a straight line which is the closet of a point. | |
| GLM_GTX_color_cast: Color conversion | |
Conversion between two color types. | |
| GLM_GTX_color_space: RGB to HSV conversion | |
Related to RGB to HSV conversions and operations. | |
| GLM_GTX_color_space_YCoCg: RGB to YCoCg conversion | |
RGB to YCoCg conversions and operations. | |
| GLM_GTX_compatibility: Cg and HLSL compatibility | |
Provide functions to increase the compatibility with Cg and HLSL languages. | |
| GLM_GTX_component_wise: Component wise | |
Operations between components of a type. | |
| GLM_GTX_epsilon: Epsilon comparison | |
Comparison functions for a user defined epsilon values. | |
| GLM_GTX_euler_angles: Matrix from euler angles | |
Build matrices from Euler angles. | |
| GLM_GTX_extend: Position extending | |
Extend a position from a source to a position at a defined length. | |
| GLM_GTX_extented_min_max: Extended min max | |
Min and max functions for 3 to 4 parameters. | |
| GLM_GTX_fast_exponential: Fast exponentiation functions | |
Fast but less accurate implementations of exponential based functions. | |
| GLM_GTX_fast_square_root: Fast square root functions | |
Fast but less accurate implementations of square root based functions. | |
| GLM_GTX_fast_trigonometry: Fast trigonometric functions | |
Fast but less accurate implementations of trigonometric functions. | |
| GLM_GTX_handed_coordinate_space: Space Handedness | |
To know if a set of three basis vectors defines a right or left-handed coordinate system. | |
| GLM_GTX_inertia: Intertial matrix | |
Create inertia matrices. | |
| GLM_GTX_int_10_10_10_2: Packed integer | |
Pack vector to 1010102 integers. Storage only. | |
| GLM_GTX_integer: Extended integer functions | |
Add support for integer for core functions. | |
| GLM_GTX_intersect: Intersection tests | |
Add intersection functions. | |
| GLM_GTX_log_base: Log with base | |
Logarithm for any base. base can be a vector or a scalar. | |
| GLM_GTX_matrix_cross_product: Cross product matrix form | |
Build cross product matrices. | |
| GLM_GTX_matrix_major_storage: Build matrix | |
Build matrices with specific matrix order, row or column. | |
| GLM_GTX_matrix_operation: Extended matrix operations | |
Build diagonal matrices from vectors. | |
| GLM_GTX_matrix_query: Query matrix properties | |
Query to evaluate matrix properties. | |
| GLM_GTX_mixed_producte: Mixed product | |
Mixed product of 3 vectors. | |
| GLM_GTX_multiple: Multiples | |
Find the closest number of a number multiple of other number. | |
| GLM_GTX_norm: Vector norm calculations | |
Various way to compute vector norms. | |
| GLM_GTX_normal: Compute normals | |
Compute the normal of a triangle. | |
| GLM_GTX_normalize_dot: Normalize dot product | |
Dot product of vectors that need to be normalize with a single square root. | |
| GLM_GTX_number_precision: Number precision | |
Defined size types. | |
| GLM_GTX_ocl_type: OpenCL types | |
OpenCL types. | |
| GLM_GTX_optimum_pow: Optimum pow | |
Integer exponentiation of power functions. | |
| GLM_GTX_orthonormalize: Orthonormalize | |
Orthonormalize matrices. | |
| GLM_GTX_perpendicular: Perpendicular | |
Perpendicular of a vector from other one. | |
| GLM_GTX_polar_coordinates: Polar coordinates | |
Conversion from Euclidean space to polar space and revert. | |
| GLM_GTX_projection: Projection | |
Projection of a vector to other one. | |
| GLM_GTX_quaternion: Extented quaternion types and functions | |
Extented quaternion types and functions. | |
| GLM_GTX_random: Random | |
Generate random number from various distribution methods. | |
| GLM_GTX_raw_data: Raw data | |
Projection of a vector to other one. | |
| GLM_GTX_reciprocal: Reciprocal | |
Define secant, cosecant and cotangent functions. | |
| GLM_GTX_rotate_vector: Rotate vector | |
Function to directly rotate a vector. | |
| GLM_GTX_simd_mat4: SIMD mat4 type and functions | |
SIMD implementation of mat4 type. | |
| GLM_GTX_simd_vec4: SIMD vec4 type and functions | |
SIMD implementation of vec4 type. | |
| GLM_GTX_spline: Spline | |
Spline functions. | |
| GLM_GTX_string_cast: String cast | |
Setup strings for GLM type values. | |
| GLM_GTX_transform: Extented transformation matrices | |
Add transformation matrices. | |
| GLM_GTX_transform2: Extra transformation matrices | |
Add extra transformation matrices. | |
| GLM_GTX_unsigned_int: Unsigned int | |
Add support for unsigned integer for core functions. | |
| GLM_GTX_vector_angle: Vector access | |
Function to set values to vectors. | |
| GLM_GTX_vector_angle: Vector angle | |
Compute angle between vectors. | |
| GLM_GTX_vector_query: Vector query | |
Query informations of vector types. | |
| GLM_GTX_verbose_operator: Verbose operator | |
Use words to replace operators. | |
| GLM_GTX_wrap: Texture coordinate wrap modes | |
Wrapping mode of texture coordinates. |
Functions and types that the GLSL specification doesn't define, but useful to have for a C++ program.
Experimental extensions are useful functions and types, but the development of their API and functionality is not necessarily stable. They can change substantially between versions. Backwards compatibility is not much of an issue for them.
Even if it's highly unrecommended, it's possible to include all the extensions at once by including <glm/ext.hpp>. Otherwise, each extension needs to be included a specific file.


Tulsi: India’s herbal Deity
The ancient texts of Vedas and Puranas state that the number of gods and
goddesses in the Hindu pantheon is about 330 million. A sizeable collection combined with the mythological aspect is bound to broaden one's perspective with
regards to the existence of deities in various forms. A divinity being worshipped in various forms is not simply a localized tradition in Hindu culture,
similar practices are observed in Japan with respect to Shinto believes about Kami, spirits residing in plants, animals or effigies.
One such example is Tulsi, a plant local to Southeast Asia and worshipped all across India as a deity. Tulsi’s mythological lore involves
it being the bride of Lord Vishnu when he is in the Shaligram form. This might be fitting mythological tale to show reverence to this valuable plant but it does
not shed any light on the cultural or scientific properties and influences this herb possesses. Tulsi is a culturally profound plant, known all across the world as Holy Basil.
Tulsi’s scientific nomenclate is Ocimum tenuiflorum or its synonym Ocimum sanctum and it belongs to the Lamiaceae family of flora. Astonishingly Tulsi is a close relative to mint,
despite the difference in the profiles of both herbs. While Tulsi can also be classified as a subshrub due to its anatomical morphology and phyllotaxy, in simpler words Tulsi is
a multi-branched plant with green or purple oval-shaped leaves which are slightly toothed at the margin. Tulsi also possesses a strong, distinct and characteristic smell. There
are broadly three varieties of Tulsi, namely Rama Tulsi, Krishna Tulsi and the Vana Tulsi. Rama tulsi’s characteristics are broad green leaves with slightly sweet taste while
Krishna Tulsi can be identified by the purplish green leaves and Vana Tulsi is the one that grows in the wild. Tulsi is one of the very few deities who have a major role in
the diet, pun totally intended. Tulsi is involved in medicine, culinary arts and cultural ceremonies for ages. In culinary and cuisine sphere, Tulsi is a versatile ingredient
added in tea and also in Thai cookery alike. From a medical standpoint, Tulsi has been an age-old part of Ayurveda and homoeopathy, it has also established its dominance in
household remedies especially in pulmonary issues. Tulsi has been a quintessential part of Indian household for ages owning a to it's versatility as a herb. It finds applications
as a bug repellent, a source of essential oils and a distinct fragrance that is slightly sweet. Along with all these properties it is a very convenient plant to grow in closures
as well. Apart from the cultural aspect, due to its merits, Tulsi has earned its godhood in every sense.


class: center, titleslide
# Python Workshop: # Natural Language Processing
##
Ties de Kok
## Tilburg University --- layout: true class: mainlayout --- class: tocslide .left-column[ ## Agenda ] .right-column[ ### What are we going to discuss today?
1. Positioning session 2. Terminology 3. Python for NLP 4. NLP Python tools 5. Topics: - Process and Clean text - Direct feature extraction - Represent text numerically - Machine learning ] --- class: tocslide .left-column[ ## Agenda ## Positioning ] .right-column[ ### Where does this session fit into the bigger scheme of NLP?
- Determining relevance textual data - Finding sources textual data - Gathering textual data
.emphasized[Processing textual data]
.emphasized[Analyzing textual data] ] --- class: tocslide .left-column[ ## Agenda ## Positioning ## Terminology ] .right-column[ ### Many inter-related names and terms:
- Computational Linguistics - Textual Analysis - Text Mining
.emphasized[Natural Language Processing] ] --- class: tocslide .left-column[ ## Agenda ## Positioning ## Terminology ## Language ] .right-column[ ### Which programming language / software to use for NLP?
.emphasized[Python] - R - PERL ] --- class: tocslide .left-column[ ## Agenda ## Positioning ## Terminology ## Language ## NLP Python ] .right-column[ ### NLP Python libraries: my recommendations
**Standard NLP libraries**: 1. [`NLTK`](http://www.nltk.org/) and the higher-level wrapper [`TextBlob`](https://textblob.readthedocs.io/en/dev/) 2. [`Spacy`](https://spacy.io/) and the higher-level wrapper [`Textacy`](https://github.com/chartbeat-labs/textacy)
**Standard machine learning library**: 1. [`scikit learn`](http://scikit-learn.org/stable/)
**Topic modelling library** *(not covered)*: 1. [`Gensim`](https://github.com/RaRe-Technologies/gensim) ] --- class: tocslide .left-column[ ## Agenda ## Positioning ## Terminology ## Language ## NLP Python ## NLP Space ] .right-column[
] --- class: tocslide .left-column[ ## Process
& Clean ] .right-column[
] --- exclude: false class: tocslide .left-column[ ## Process
& Clean ] .right-column[ ### Text normalization - Sentence segmentation > i.e. split text up into sentences - Word tokenization > i.e. split sentence up into tokens (i.e. words) ] -- exclude: true .right-column-next[ - Entity normalization > i.e. "http://www.google.com" → "URL" - Lemmatization & Stemming > Convert tokens to a base representation ] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ] .right-column[ ### Language modelling Text has a complex underlying structure that you can tap into. - Part-of-Speech tagging > Identify the "Word Class" of a token (e.g. noun, verb) - Remove stop words > Remove words that don't carry any informational value - Uni-Gram vs. N-Grams > Multi-word token: retain some of the sequential nature ] --- exclude: false class: tocslide .left-column[ ## Process
& Clean ] .right-column[ ###
Uni-Gram vs. N-Grams
> Multi-word token: retain some of the sequential nature
"Tilburg University is located in Noord Brabant"
Unigram
Bigram
Trigram
Tilburg
Tilburg-University
Tilburg-University-is
University
University-is
University-is-located
is
is-located
is-located-in
located
located-in
located-in-Noord
in
in-Noord
in-Noord-Brabant
Noord
Noord-Brabant
Brabant
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[
] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[ ### Feature search
* Entity extraction > e.g. extract PEOPLE / EVENTS / DATES / MONETARY VALUES * Pattern search (`RE`) > i.e. use [`Regular Expressions`](https://scotch.io/tutorials/an-introduction-to-regex-in-python) to look for patterns * Term (Dictionary) counting > i.e. count the number of times a term occurs ] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[ ### Pattern search (`RE`)
**TIP**: Use [Pythex.org](https://pythex.org/) or [Regex101.com](https://regex101.com) to try out your regular expression
Example on Pythex:
click here
Example on Regex101:
click here
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[ ### Term (Dictionary) counting
] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[ ### **Accounting Research:** Term (Dictionary) counting 1. Loughran and McDonald (2011, JF)
Positive / Negative dictionaries for financial texts 2. Garcia and Norli (2012, JFE)
Geographic dispersion based on state name mentions 3. Brochet, Loumioti, and Serafeim (2015, RAST)
Count horizon related words in conference calls ] -- exclude: true .right-column-next[
References:
Loughran, T., & McDonald, B. (2011).
When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of Finance, 66(1), 35-65.
Garcia, D., & Norli, Ø. (2012).
Geographic dispersion and stock returns. Journal of Financial Economics, 106(3), 547-565.
Brochet, F., Loumioti, M., & Serafeim, G. (2015).
Speaking of the short-term: Disclosure horizon and managerial myopia. Review of Accounting Studies, 20(3), 1122-1163.
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[ ### Text evaluation * Language > i.e. detect whether text is English * Readability > i.e. use the [`TextStat`](https://github.com/shivam5992/textstat) package to calculate text statistics * Text similarity
See the awesome [`FuzzyWuzzy`](https://github.com/seatgeek/fuzzywuzzy) package for details. ] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[ ### **Accounting Research:** Readability measures 1) Li (2008, JAE)
Basic readability metrics (Fog etc.) and earnings
2) Bonsall, Leone, Miller, Rennekamp (2017, JAE)
Proprietary "Plain English" measure ] -- exclude: true .right-column-next[
References:
Li, F. (2008).
Annual report readability, current earnings, and earnings persistence. Journal of Accounting and economics, 45(2-3)
Bonsall IV, S. B., Leone, A. J., Miller, B. P., & Rennekamp, K. (2017).
A plain English measure of financial reporting readability. Journal of Accounting and Economics, 63(2-3)
] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[ ### **Accounting Research:** Similarity measures 1) Merkley (2013, TAR)
Identify amount of repetitive R&D information based on similarity
2) Lang and Stice-Lawrence (2015, JAE)
Similarity of financial narratives based on cosine similarity ] -- exclude: true .right-column-next[
References:
Merkley, K. J. (2013).
Narrative disclosure and earnings performance: Evidence from R&D disclosures. The Accounting Review, 89(2), 725-757.
Lang, M., & Stice-Lawrence, L. (2015).
Textual analysis and international financial reporting: Large sample evidence. Journal of Accounting and Economics, 60(2-3), 110-135.
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ] .right-column[
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ] .right-column[ ### Bag of Words Also labelled: *frequency based representation* Term frequency (TF)
(Figure taken from: https://web.stanford.edu/~jurafsky/slp3/6.pdf)
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ] .right-column[ ### Term frequency (TF) example:
>
[1] "The sky is blue." > [2] "The sun is bright today." > [3] "The sun in the sky is bright." > [4] "We can see the shining sun, the bright sun."
Note: the collection of all text documents is called the *corpus*
(Example taken from: http://ethen8181.github.io/machine-learning/clustering_old/tf_idf/tf_idf.html)
] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ] .right-column[
] -- exclude: true .right-column-next[
(Figure taken from: and https://moz.com/blog/7-advanced-seo-concepts)
] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ] .right-column[ ### TF-IDF example:
>
[1] "The sky is blue." > [2] "The sun is bright today." > [3] "The sun in the sky is bright." > [4] "We can see the shining sun, the bright sun."
(Example taken from: http://ethen8181.github.io/machine-learning/clustering_old/tf_idf/tf_idf.html)
] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ] .right-column[ ### Word Embeddings Are there alternatives to the frequency based representation?
Yes, meet the new "secret sauce": **word embeddings**! ] -- exclude: true .right-column-next[
Word embeddings are based on a "prediction based representation". Basic idea: > A word is characterized by the company it keeps:
> 1. A **Ferrari** is a fast car > 2. A **Lamborgini** is a fast car
Notes: the most well-known adaptation is `Word2Vec`. Word embeddings are sometimes called *Continous Bag of Words*
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### What is Machine Learning?
> A machine learning algorithm is not explicitly programmed.
Instead, the algorithm is trained based on the input + output data. Does this sound familiar? ] -- .right-column-next[
A linear regression is also machine learning! ] -- .right-column-next[ ### Example: sentiment analysis Traditional method:
manually create pos/neg word lists Machine learning method:
manually classify sentence pos/neg score
pos/neg word lists determined by algorithm ] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### Supervised Machine Learning
> Supervised ML algorithms are trained on classified training data. ] -- .right-column-next[
### Where to get training data? 1. Use a naturally classified training set - News categories - Movie reviews - Text books for different levels of English 2. Create your own training set - Manually classify text - Crowdsource a training set
Amazon Mechanical Turk is a great way to get training data! ] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### Supervised Machine Learning: models
Three often used models for Supervised ML: 1. Naive Bayes classifier ([sklearn link](http://scikit-learn.org/stable/modules/naive_bayes.html)) 2. SVM: Support Vector Machines ([sklearn link](http://scikit-learn.org/stable/modules/svm.html)) 3. Decision Trees ([sklearn link](http://scikit-learn.org/stable/modules/tree.html#classification)) ] -- exclude: true .right-column-next[ **My recommendation?** Always try multiple models to see which gives you the best results. * Naive Bayes is good for small samples and quick testing. * SVM is more sophisticated, generally better for more complex models. * Decision Trees are more intuitive but harder to train. Regardless of the model:
hyperparameter optimization is very important! ] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[
(Slide taken from: https://www.slideshare.net/sparktc/hyperparameter-optimization-sven-hafeneger)
] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### Model Selection and Evaluation
> i.e. how to select the model and hyperparameters? ####There are two essential metrics in ML: 1. Precision > High precision --> low false positive rate 2. Recall > High recall --> low false negative rate
For details see: [Precision-Recall](http://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html)
] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### **Accounting Research:** Supervised Machine Learning 1) Li (2010, JAR)
Classify tone and content of FL statements using Naïve Bayes
2) Jegadeesh and Wu (2013, JAE)
Term weights for tone words by "training" on abnormal returns ] -- exclude: true .right-column-next[
References:
Li, F. (2010).
The information content of forward‐looking statements in corporate filings—A naïve Bayesian machine learning approach. Journal of Accounting Research, 48(5), 1049-1102.
Jegadeesh, N., & Wu, D. (2013).
Word power: A new approach for content analysis. Journal of Financial Economics, 110(3), 712-729.
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### Unsupervised Machine Learning
> Unsupervised ML algorithms are trained using only input data. Do unsupervised ML models work for all problems?
No! Usually only for clustering / topic modelling. ] -- .right-column-next[
Examples of unsupervised models: 1. Principal Component Analysis / Factor Analysis 2. **Latent Dirichlet Allocation (LDA)** > Unsupervised topic model technique to discover abstract topics from a collection of documents. ] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### Latent Dirichlet Allocation (LDA)
> Unsupervised topic model technique to discover abstract topics from a collection of documents. ] -- exclude: true .right-column-next[
### LDA procedure You define the number of topics (*N*) and the other hyperparameters. LDA then assigns each document a vector with *N* topic probabilities. **Important:** topics are not labeled and there is a degree of randomness > i.e. running the same model twice can result in different topic labels! ] --- class: tocslide
Made with the awesome
pyLDAvis
package
--- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### **Accounting Research:** Unsupervised Machine Learning 1) Dyer, Lang, and Stice-Lawrence (2017, JAE)
Use LDA to evaluate how the topics of 10-Ks have changed over time 2) Huang et al. (2017, MS)
Thematic content of a large sample of analyst reports using LDA 3) Bird, Karolyi, Ma (2018, SSRN)
Use LDA on 8-K documents to detect strategic misclassification ] -- exclude: true .right-column-next[
References:
Dyer, T., Lang, M., & Stice-Lawrence, L. (2017).
The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation. Journal of Accounting and Economics
Huang, A. H., Lehavy, R., Zang, A. Y., & Zheng, R. (2017).
Analyst information discovery and interpretation roles: A topic modeling approach. Management Science.
Bird, A., Karolyi, S. A., & Ma, P. (2018).
Strategic disclosure misclassification. SSRN
] --- exclude: true class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ## Neural Networks ] .right-column[ ### Neural Networks for NLP Natural Language is very complex, NLP is hard: 1. The Pope's baby steps on gays 2. Scientists study whales from space 3. Boy paralyzed after tumor fights back to gain black belt
(Examples from: http://web.stanford.edu/class/cs224n/lectures/cs224n-2017-lecture1.pdf)
] -- exclude: true .right-column-next[ ### Deep Neural Networks DNN allow to model complex phenomenon, promising progress for NLP! Interested? Check out the Stanford course CS224n ([Syllabus](http://web.stanford.edu/class/cs224n/syllabus.html))! ] --- exclude: true class: tocslide .left-column[ ## Notebook ] .right-column[ ### How to get started with NLP and Python?
Take a look at my NLP repository!
Github.com/TiesdeKok/Python_NLP_Tutorial
] --- class: tocslide .left-column[ ## Closing remarks ] .right-column[ ### Closing remarks Getting started with NLP can be overwhelming.
This is normal!
**General tips:** 1. Remember, Google is your friend 2. Having a hard time determining your next step?
Try to explicitly formulate what your (sub-)goal is 3. Asking for help?
Avoid the XY problem: [xyproblem.info/](http://xyproblem.info/) 4.  Download the session 4 materials from GitHub 2. Make sure you use the `uw-python-camp` environment ## Mini-task: **Goal:** Solve tasks in the "natural_language_processing.ipynb" notebook. 1. Open a Jupyter Notebook in the `UW_python_2018` folder 2. Solve the natural language processing tasks
Find them in `Session_4 > natural_language_processing.ipynb` For help: -
Python Basics Notebook
-
NLP Python Notebook
]


|
HonokaMiku
5.0.3
Universal LL!SIF decrypter
|
Base class of Version 2 decrypter. More...
#include <DecrypterContext.h>
Public Member Functions | |
| void | decrypt_block (void *buffer, uint32_t len) |
| XOR block of memory. More... | |
| void | decrypt_block (void *dest, const void *src, uint32_t len) |
| XOR block of memory and write the result to different buffer. More... | |
| void | goto_offset (uint32_t offset) |
| Recalculate decrypter context to decrypt at specific position. More... | |
| void | goto_offset_relative (int32_t offset) |
| Recalculate decrypter context to decrypt at specific position. More... | |
| void | final_setup (const char *, const void *, int32_t) |
| Finalize decrypter context (Version 3 only). Does nothing in other decryption version. More... | |
| Public Member Functions inherited from HonokaMiku::DecrypterContext | |
| virtual uint32_t | get_id ()=0 |
| Gets the game property of the current decrypter context. More... | |
Protected Member Functions | |
| V2_Dctx () | |
| V2_Dctx (const char *prefix, const void *header, const char *filename) | |
| void | update () |
| The key update function. Used to update the key. Used internally and protected. More... | |
| Protected Member Functions inherited from HonokaMiku::DecrypterContext | |
| DecrypterContext () | |
Additional Inherited Members | |
| Public Attributes inherited from HonokaMiku::DecrypterContext | |
| uint32_t | init_key |
| Key used at pos 0. Used when the decrypter needs to jump to specific-position. More... | |
| uint32_t | update_key |
Current key at pos More... | |
| uint32_t | pos |
| Variable to track current position. Needed to allow jump to specific-position. More... | |
| uint32_t | xor_key |
| Values to use when XOR-ing bytes. More... | |
| uint8_t | version |
Decrypter version. JP Decrypt sets this to 3 while others sets this to 2 . More... | |
Base class of Version 2 decrypter.
|
inlineprotected |
|
protected |
|
virtual |
XOR block of memory.
| buffer | Buffer to be decrypted |
| len | Size of buffer |
| std::runtime_error | The current decrypter context is not currently finalized (Version 3 only) |
Implements HonokaMiku::DecrypterContext.
|
virtual |
XOR block of memory and write the result to different buffer.
| dest | Destination buffer that will contain decrypted bytes |
| src | Source buffer that contains encrypted bytes |
| len | Size of src |
| std::runtime_error | The current decrypter context is not currently finalized (Version 3 only) |
Implements HonokaMiku::DecrypterContext.
|
inlinevirtual |
Finalize decrypter context (Version 3 only). Does nothing in other decryption version.
| filename | File name that want to be decrypted. This affects the key calculation. |
| block_rest | The next 12-bytes header of Version 3 encrypted file. |
Implements HonokaMiku::DecrypterContext.
|
virtual |
Recalculate decrypter context to decrypt at specific position.
| offset | Absolute position (starts at 0) |
| std::runtime_error | The current decrypter context is not currently finalized (Version 3 only) |
Implements HonokaMiku::DecrypterContext.
|
virtual |
Recalculate decrypter context to decrypt at specific position.
| offset | Position relative to current HonokaMiku::DecrypterContext::pos |
| std::runtime_error | The current decrypter context is not currently finalized (Version 3 only) |
Implements HonokaMiku::DecrypterContext.
|
protectedvirtual |
The key update function. Used to update the key. Used internally and protected.
Implements HonokaMiku::DecrypterContext.


|
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@ApiAudience.Public @ApiStability.Experimental @Inheritance.Sealed public interface KijiTableAnnotator
Manages setting, retrieving, and searching key value annotations for a KijiTable.
Operations performed on multiple key value pairs are not guaranteed atomic.
| Field Summary | |
|---|---|
static String |
ALLOWED_ANNOTATION_KEY_PATTERN
Regex describing restrictions on KijiTableAnnotator keys. |
| Method Summary | |
|---|---|
Map<KijiColumnName,Map<String,String>> |
getAllColumnAnnotations()
Get the value of all column annotations for columns in the table served by this annotator. |
Map<String,String> |
getAllColumnAnnotations(KijiColumnName column)
Get the value of all annotations on the given column. |
Map<KijiColumnName,Map<String,String>> |
getAllColumnAnnotationsInFamily(String family)
Get the value of all annotations on columns in the given family. |
Map<String,String> |
getAllTableAnnotations()
Get the value of all annotations on the table served by this annotator. |
String |
getColumnAnnotation(KijiColumnName column,
String exactKey)
Get the value of an exact key from the given column. |
Map<String,String> |
getColumnAnnotationsContaining(KijiColumnName column,
String substring)
Get the value of all annotations on the given column whose keys contain the given substring. |
Map<KijiColumnName,String> |
getColumnAnnotationsInFamily(String family,
String exactKey)
Get the value of all annotations on columns in the given family whose keys match the given exact key. |
Map<KijiColumnName,Map<String,String>> |
getColumnAnnotationsInFamilyContaining(String family,
String substring)
Get the value of all annotations on columns in the given family whose keys contain the given substring. |
Map<KijiColumnName,Map<String,String>> |
getColumnAnnotationsInFamilyMatching(String family,
String pattern)
Get the value of all annotations on columns in the given family whose keys match the given regex pattern. |
Map<KijiColumnName,Map<String,String>> |
getColumnAnnotationsInFamilyStartingWith(String family,
String prefix)
Get the value of all annotations on columns in the given family whose keys start with the given prefix. |
Map<String,String> |
getColumnAnnotationsMatching(KijiColumnName column,
String pattern)
Get the value of all annotations on the given column whose keys match the given regex pattern. |
Map<String,String> |
getColumnAnnotationsStartingWith(KijiColumnName column,
String prefix)
Get the value of all annotations on the given column whose keys start with the given prefix. |
KijiTable |
getTable()
Get the table served by this annotator. |
String |
getTableAnnotation(String exactKey)
Get the value of an exact key from the table served by this annotator. |
Map<String,String> |
getTableAnnotationsContaining(String substring)
Get the value of all annotations on the table served by this annotator whose keys contain the given substring. |
Map<String,String> |
getTableAnnotationsMatching(String pattern)
Get the value of all annotations on the table served by this annotator whose keys match the given regex pattern. |
Map<String,String> |
getTableAnnotationsStartingWith(String prefix)
Get the value of all annotations on the table served by this annotator whose keys start with the given prefix. |
Map<KijiColumnName,Set<String>> |
removeAllColumnAnnotations()
Remove all annotations from all column in the table served by this annotator. |
Set<String> |
removeAllColumnAnnotations(KijiColumnName column)
Remove all annotations from the given column. |
Map<KijiColumnName,Set<String>> |
removeAllColumnAnnotationsInFamily(String family)
Remove annotations from all columns in the given family. |
Set<String> |
removeAllTableAnnotations()
Remove all annotations from the table served by this annotator. |
void |
removeColumnAnnotation(KijiColumnName column,
String exactKey)
Remove the value of the given exact key from the given column. |
Set<String> |
removeColumnAnnotationsContaining(KijiColumnName column,
String substring)
Remove all annotations from the given column whose keys contain the given substring. |
Set<KijiColumnName> |
removeColumnAnnotationsInFamily(String family,
String exactKey)
Remove annotations with the given exact key from all columns in the given family. |
Map<KijiColumnName,Set<String>> |
removeColumnAnnotationsInFamilyContaining(String family,
String substring)
Remove annotations from all columns in the given family whose keys contain the given substring. |
Map<KijiColumnName,Set<String>> |
removeColumnAnnotationsInFamilyMatching(String family,
String pattern)
Remove annotations from all columns in the given family whose keys match the given regex pattern. |
Map<KijiColumnName,Set<String>> |
removeColumnAnnotationsInFamilyStartingWith(String family,
String prefix)
Remove annotations from all columns in the given family whose keys start with the given prefix. |
Set<String> |
removeColumnAnnotationsMatching(KijiColumnName column,
String pattern)
Remove all annotations from the given column whose keys match t he given regex pattern. |
Set<String> |
removeColumnAnnotationsStartingWith(KijiColumnName column,
String prefix)
Remove all annotations from the given column whose keys start with the given prefix. |
void |
removeTableAnnotation(String exactKey)
Remove the annotation for the given key from the table served by this annotator. |
Set<String> |
removeTableAnnotationsContaining(String substring)
Remove all annotations from the table served by this annotator whose keys contain the given substring. |
Set<String> |
removeTableAnnotationsMatching(String pattern)
Remove all annotations from the table served by this annotator whose keys match the given regex pattern. |
Set<String> |
removeTableAnnotationsStartingWith(String prefix)
Remove all annotations from the table served by this annotator whose keys start with the given prefix. |
void |
setColumnAnnotation(KijiColumnName column,
String key,
String value)
Set a single key value pair for the given column. |
void |
setColumnAnnotations(KijiColumnName column,
Map<String,String> kvs)
Set the given keys and values on the given column. |
void |
setTableAnnotation(String key,
String value)
Set a single key value pair for the table served by this annotator. |
void |
setTableAnnotations(Map<String,String> kvs)
Set the given keys and values on the table served by this annotator. |
| Methods inherited from interface java.io.Closeable |
|---|
close |
| Field Detail |
|---|
static final String ALLOWED_ANNOTATION_KEY_PATTERN
| Method Detail |
|---|
void setTableAnnotation(String key, String value) throws IOException
key
- annotation key to set. Must conform to ALLOWED_ANNOTATION_KEY_PATTERN
.value
- annotation value to set.
IOException
- in case of an error writing to the meta table.void setColumnAnnotation(KijiColumnName column, String key, String value) throws IOException
column
- column on which to set the key value pair.key
- annotation key to set. Must conform to ALLOWED_ANNOTATION_KEY_PATTERN
.value
- annotation value to set.
IOException
- in case of an error writing to the meta table.void setTableAnnotations(Map<String,String> kvs) throws IOException
kvs
- key value mapping to set for the table served by this annotator. Keys must conform
to ALLOWED_ANNOTATION_KEY_PATTERN
.
IOException
- in case of an error writing to the meta table.void setColumnAnnotations(KijiColumnName column, Map<String,String> kvs) throws IOException
column
- column on which to set the given keys and values.kvs
- key value mapping to set for the given column. Keys must conform to
ALLOWED_ANNOTATION_KEY_PATTERN
.
IOException
- in case of an error writing to the meta table.void removeTableAnnotation(String exactKey) throws IOException
exactKey
- key to remove from the table served by this annotator.
IOException
- in case of an error writing to the meta table.Set<String> removeTableAnnotationsStartingWith(String prefix) throws IOException
prefix
- key prefix to match.
IOException
- in case of an error reading or writing to the meta table.Set<String> removeTableAnnotationsContaining(String substring) throws IOException
substring
- key substring to match.
IOException
- in case of an error reading or writing to the meta table.Set<String> removeTableAnnotationsMatching(String pattern) throws IOException
pattern
- regex pattern to match.
IOException
- in case of an error reading or writing to the meta table.Set<String> removeAllTableAnnotations() throws IOException
IOException
- in case of an error reading or writing to the meta table.void removeColumnAnnotation(KijiColumnName column, String exactKey) throws IOException
column
- column from which to remove an annotation.exactKey
- key to remove from the given column.
IOException
- in case of an error writing to the meta table.Set<String> removeColumnAnnotationsStartingWith(KijiColumnName column, String prefix) throws IOException
column
- column from which to remove annotations.prefix
- key prefix to match.
IOException
- in case of an error reading or writing to the meta table.Set<String> removeColumnAnnotationsContaining(KijiColumnName column, String substring) throws IOException
column
- column from which to remove annotations.substring
- key substring to match.
IOException
- in case of an error reading or writing to the meta table.Set<String> removeColumnAnnotationsMatching(KijiColumnName column, String pattern) throws IOException
column
- column from which to remove annotations.pattern
- regex pattern to match.
IOException
- in case of an error reading or writing to the meta table.Set<String> removeAllColumnAnnotations(KijiColumnName column) throws IOException
column
- column from which to remove annotations.
IOException
- in case of an error reading or writing to the meta table.Set<KijiColumnName> removeColumnAnnotationsInFamily(String family, String exactKey) throws IOException
family
- column family from which to remove annotations.exactKey
- key to remove from all columns in the given family.
IOException
- in case of an error reading or writing to the meta table.Map<KijiColumnName,Set<String>> removeColumnAnnotationsInFamilyStartingWith(String family, String prefix) throws IOException
family
- column family from which to remove annotations.prefix
- key prefix to match.
IOException
- in case of an error reading or writing to the meta table.Map<KijiColumnName,Set<String>> removeColumnAnnotationsInFamilyContaining(String family, String substring) throws IOException
family
- column family from which to remove annotations.substring
- key substring to match.
IOException
- in case of an error reading or writing to the meta table.Map<KijiColumnName,Set<String>> removeColumnAnnotationsInFamilyMatching(String family, String pattern) throws IOException
family
- column family from which to remove annotations.pattern
- regex pattern to match.
IOException
- in case of an error reading or writing to the meta table.Map<KijiColumnName,Set<String>> removeAllColumnAnnotationsInFamily(String family) throws IOException
family
- column family from which to remove annotations.
IOException
- in case of an error reading or writing to the meta table.Map<KijiColumnName,Set<String>> removeAllColumnAnnotations() throws IOException
IOException
- in case of an error reading or writing to the meta table.String getTableAnnotation(String exactKey) throws IOException
exactKey
- exact key to retrieve.
IOException
- in case of an error reading from the meta table.Map<String,String> getTableAnnotationsStartingWith(String prefix) throws IOException
prefix
- key prefix to match.
IOException
- in case of an error reading from the meta table.Map<String,String> getTableAnnotationsContaining(String substring) throws IOException
substring
- key substring to match.
IOException
- in case of an error reading from the meta table.Map<String,String> getTableAnnotationsMatching(String pattern) throws IOException
pattern
- regex pattern to match.
IOException
- in case of an error reading from the meta table.Map<String,String> getAllTableAnnotations() throws IOException
IOException
- in case of an error reading from the meta table.String getColumnAnnotation(KijiColumnName column, String exactKey) throws IOException
column
- column from which to get the value of an annotation.exactKey
- exact key to retrieve.
IOException
- in case of an error reading from the meta table.Map<String,String> getColumnAnnotationsStartingWith(KijiColumnName column, String prefix) throws IOException
column
- column from which to get annotations.prefix
- key prefix to match.
IOException
- in case of an error reading from the meta table.Map<String,String> getColumnAnnotationsContaining(KijiColumnName column, String substring) throws IOException
column
- column from which to get annotations.substring
- key substring to match.
IOException
- in case of an error reading from the meta table.Map<String,String> getColumnAnnotationsMatching(KijiColumnName column, String pattern) throws IOException
column
- column from which to get annotations.pattern
- regex pattern to match.
IOException
- in case of an error reading from the meta table.Map<String,String> getAllColumnAnnotations(KijiColumnName column) throws IOException
column
- column from which to get annotations.
IOException
- in case of an error reading from the meta table.Map<KijiColumnName,String> getColumnAnnotationsInFamily(String family, String exactKey) throws IOException
family
- column family from which to get annotations.exactKey
- exact key to retrieve.
IOException
- in case of an error reading from the meta table.Map<KijiColumnName,Map<String,String>> getColumnAnnotationsInFamilyStartingWith(String family, String prefix) throws IOException
family
- column family from which to get annotations.prefix
- key prefix to match.
IOException
- in case of an error reading from the meta table.Map<KijiColumnName,Map<String,String>> getColumnAnnotationsInFamilyContaining(String family, String substring) throws IOException
family
- column family from which to get annotations.substring
- key substring to match.
IOException
- in case of an error reading from the meta table.Map<KijiColumnName,Map<String,String>> getColumnAnnotationsInFamilyMatching(String family, String pattern) throws IOException
family
- column family from which to get annotations.pattern
- regex pattern to match.
IOException
- in case of an error reading from the meta table.Map<KijiColumnName,Map<String,String>> getAllColumnAnnotationsInFamily(String family) throws IOException
family
- column family from which to get annotations.
IOException
- in case of an error reading from the meta table.Map<KijiColumnName,Map<String,String>> getAllColumnAnnotations() throws IOException
IOException
- in case of an error reading from the meta table.KijiTable getTable()
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{{$header := dict "Title" "Login"}}
{{template "header" (merge $header .)}}
{{ if not .User }}
{{ $userid := dict "name" "UserID" "placeholder" "Email or username" "required" true "type" "text" "pattern" ".{3,}" "title" "Must have atleast 3 characters" }}
{{ $password := dict "name" "Password" "placeholder" "Password" "required" true "type" "password" "pattern" ".{8,}" "title" "Must have atleast 8 charcaters"}}
{{ template "form" (dict "action" "/api/auth/login" "method" "post" "input" (list $userid $password) "redirect" "/dashboard" "errors" (dict "404" "Invalid credenitals" "400" "Bad request"))}}
OR you could register through this link
{{ else }}


About Me
I’m a cyber geek from Puebla, México. I love make programs.
I’m a graduate of the faculty of “Computer Science” at the University of Puebla BUAP.
My specialty is the development of applications for iPhone and iPad with Swift and Objetive-C, i love that, but also i love make apps for Android and Web Apps.
I have a team with which we make great systems, they are "Nut Systems Developers", and we are more than partners we are a family with more than 6 years of experience making the greatest apps.
We love design, we love programing, we love tecnology, we love teamwork, we love minimalism, we love Scrum, and we love do it with passion.
We are available as a freelancers and would love to take up challenging projects. If you would like to talk about a project, opportunity, consult about something or would simply like to chat, feel free to leave a message :)
My Portfolio
Project 1
Festival de Fotografía Puebla 2016
Project 2
BUSSA aplicación prevenir feminicidios DITCO 2016
Project 3
WisdromBird consulta tu trending topic local


How to Enable Automatic Logon in Windows XP
WinBook Tech Article For more information visit www.winbookcorp.com. Subject: How to Enable Automatic Logon in Windows XP. Keywords: winxp, auto log on, .
http://www.winbookcorp.com/_technote/WBTA20000896.htm
Treatment for Herniated or Bulging Discs
How to automatically log on to a user account in Windows XP
Explains how to log on to a user account automatically during the Windows XP startup process.
http://support.microsoft.com/kb/282866
Automatic logon for Windows XP Professional (Windows XP ...
Nov 11, 2005 . Set a computer running Microsoft Windows XP Professional (Not Windows XP Home) to automatically log on. This bypasses the Welcome .
http://www.tacktech.com/display.cfm?ttid=358
automatically logon to windows zp Herniated or Bulging Discs
6 Tips on How to Enable Automatic Logon in Windows XP - wikiHow
2 days ago . How to Enable Automatic Logon in Windows XP. For a home PC, needing to log on every time your PC starts up may not be necessary.
http://www.wikihow.com/Enable-Automatic-Logon-in-Windows-XP
Configure Windows XP to Automatically Login
Configure Windows XP to Automatically Login. After downloading fixes from Windows Updates page or via Automatic Updates, you may notice that Windows XP .
http://windowsxp.mvps.org/Autologon.htm
Automatically login to Windows XP with a user account - YouTube
Jan 18, 2010 . If you have multiple user accounts in Windows XP you can automatically bypass the XP Welcome Screen and automatically login to the .
http://www.youtube.com/watch?v=d6PQnxHzmNk
Official Impairment Listing for Spinal Disorders
How to log on to Windows XP automatically
Nov 18, 2008 . Auto Login Windows XP While in in many cases it would be a security risk, having your Windows XP computer log in automatically can be .
http://downloadsquad.switched.com/2008/11/18/how-to-log-on-to-windows-xp-automatically/
- crumbly hoof disease donkey (narrowing of the spine)
Set up an automatic logon to Windows XP | TechRepublic
May 4, 2004 . TechRepublic member dusty1one is seeking a way to bypass the Microsoft Windows XP logon/welcome screen. Here's how to do it.
http://www.techrepublic.com/article/set-up-an-automatic-logon-to-windows-xp/5280112Automatic Logon to Windows NT, 2000 and XP at Registry Guide for ...
Jul 4, 2003 . Windows includes a feature that allows you to configure the computer to automatically logon to the network, bypassing the Winlogon dialog .
http://www.pctools.com/guides/registry/detail/13/
Login Automatically on Windows XP Home / Professional - How-To ...
Nov 8, 2006 . If your Windows XP installation forces you to login every time you reboot, you can automate the login process easily so that you won't have to .
http://www.howtogeek.com/howto/windows/login-automatically-on-windows-xp-home-professional/
TIP: Windows XP: automatic logon
How to turn on automatic logon in Windows XP A. When joined to a domain: You can use Registry Editor to add your log on information.
http://setiathome.berkeley.edu/forum_thread.php?id=1074&sort=6
Medical-Vocational Allowance for Herniated Disc
How to turn on automatic logon in Windows XP
This article describes how to configure Windows XP to automate the logon process by storing your password and other pertinent information in the registry .
http://support.microsoft.com/kb/315231
How do I make Windows XP automatically login?
How do I make Windows XP automatically login? Default behavior of Windows XP is that it automatically logs in if there is only one user account in the system .
http://www.winxptutor.com/autologon.htm
How to Enable Auto Logon to Windows XP and Vista Joined as ...
Jun 15, 2008 . In standalone computer which does not join any Active Directory (AD) or Windows NT domain as a member, user can configure in such as way .
http://www.mydigitallife.info/how-to-enable-auto-logon-to-windows-xp-and-vista-joined-as-domain-member/
Appealing a Denial of Benefits
How to log in automatically with Windows XP
Logging in automatically with Windows XP may not be possible after the Windows XP operating system has been updated with the Automatic Updates.
http://en.kioskea.net/faq/514-how-to-log-in-automatically-with-windows-xp
Configuring Auto Logon
Jan 7, 2009 . Can I configure Windows 2000/XP/2003 to log me on automatically? You sure can! Old but nevertheless good. The auto logon can be .
http://www.petri.co.il/configuring_autologon.htm


KanbanFlow ========== A pattern for controlling dataflow in concurrent producer-consumer scenarios version 1.0, Sept. 17th 2011 version 1.1, Oct. 7th 2020 (move location, add usages) by Dierk König As to my best knowledge, such a pattern has not been published before. Anyone who comes across an earlier publication please send me note. The KanbanFlow pattern offers a coordination scheme for the classical producer-consumer scenario where one or many producers create products that are to be consumed by one or many consumers. Producers and consumers work concurrently and have no direct dependency. Their sole mean of interaction is through dataflows. Context ------- An object-oriented software development environment with support for concurrent execution such as Java or Groovy. The availability of multiple independent processing resources such as multiple cores makes the pattern even more appealing but is not required. The pattern also applies when using other programming approaches such as dataflow programming, functional programming, and actor-based programming. Problem statement ----------------- In order to coordinate producers and consumers, we require that a) consumers wait for products to be delivered, b) producers don't overuse resources in case of production being faster than consumption, c) we get simple means of controlling the amount of concurrency, and d) products can be consumed in a steady flow at an optimal rate. Requirement a) is automatically met when producers send their products to the consumers via dataflow queues since they block consumers until a product is available. Requirement b) is more difficult. Imagine that producers are a 100 times faster than consumers. Such a situation can easily arise if production means fast calculation and consumption means writing calculated results (the "product") to a comparably slow device. If producers and consumers run uncoordinated at maximum speed, for every consumed product there are 99 products waiting in the dataflow queue. The queue will rapidly fill up and will eventually exhaust all memory. Furthermore, producers exhaust all calculation capacity very early in the process, unnecessarily compromising the machine's responsiveness. We overuse both, memory and processing time. In general, we overuse system resources. Requirement c) is the trickiest one since one cannot give hard numbers for "simplicity" and not even for the "amount of concurrency". We have no choice but leaving the valuation of simplicity to everyone's personal taste. For ourselves, we will judge a system simpler that can be controlled by a single number over one that requires reorganizing datastructures and dependencies. Requirement d) suggests that we prefer continuous delivery over first waiting before finally getting all products at once. But this feature shall not compromise the overall throughput. Therefore we require an optimal rate of products being consumed per time unit. Forces ------ Classical means of synchronizing producers and consumers involve data structures that block producers when they try to put a product in the dataflow queue but the queue has reached its maximum capacity. One possible example is a synchronous dataflow queue, which essentially has a blocking queue that can hold at most one product. This covers requirements a) and b). Such an explicit synchronization requires careful setup and parametrization as soon as you have multiple producers and consumers and possibly a whole chain of those. You can take an industrial assembly line as an analogy. You either need to ensure that all operations happen at the exact same rate (heartbeat) or you need carefully dimensioned buffers to serve as intermediate stores to cope for variations in processing time (the so-called jitter). Without these considerations, the throughput suffers since waiting time is propagated back "upstream" blocking multiple processing units. You are not always making the best progress towards the next consumed product. For "optimizing the whole" (as the lean production movement calls it) or simply adapting to new production steps or more/less production units one has to carefully re-wire the units with their queues along with reconsidering the buffer sizes. Synchronization also generally opens the possibility for deadlocks and a single processing unit can block the whole system. Solution idea ------------- The manufacturing industry has widely adopted the principles of "lean" production. One of the core principles is to limit the "work in progress" (WIP), i.e. avoid unfinished products in the system, especially in buffers. Without buffers, a product is made "just in time" for consumption. The goal is to optimize the "lead time", which maps in our context to the mean time for producing and consuming a product. To this end, manufacturers (and since recently also software developers) make use of Kanban cards. The rules as adapted to our needs are as follows: - Whenever a consumer is ready to receive a product he sends such a card to the producer. - Whenever a producer receives a card, he produces and sends the product along with the card to the consumer. - The number of cards is limited. In fact, this number is the maximum WIP. Figure 1 shows such a scenario with one producer and one consumer. The producer sends cards and products "downstream" to the consumer. The consumer sends cards back "upstream". Both "upstream" and "downstream" are dataflow queues. Figure 1, KanbanFlow with in single-producer single-consumer scenario: You may have observed that there is only one queue from producer to consumer, even though two pieces of information are sent (card and product). Using two different dataflow queues works perfectly fine in this simple scenario but it is more convenient to only have one queue when it comes to multiple producers and consumers as we will see in a minute. Meanwhile, we send cards and products by putting them in trays as used when going through airport security. The card is permanently attached to the tray while products are placed inside the tray by the producer and emptied by the consumer. As suggested by figure 2, the consumer sends empty trays back upstream. Figure 2, Empty trays are brought back upstream: The construction can easily be generalized into longer chains or multiple producers and consumers. Figure 3 depicts the situation with three producers and three consumers. Three producers can mean three different objects each running in its own thread or more conveniently one producer object being used by three concurrent threads. The same applies to consumers. Figure 3, KanbanFlow with multiple producers and consumers: An animated movie (30s) of this scenario is under kanban-3-3-6.mov (currently unavailable) and a more extensive screencast (4:40) showing many different options is under kanban-simulation.mov (currently unavailable). Example implementation ---------------------- Here is an example implementation using Groovy and dataflow abstractions from the excellent GPars library by Vaclav Pech. (source also at github, groovy 1.8.2, gpars 0.11)
When running, you will see output like ... [0:983] [1:984] [5:985] 0:983 [2:986] [4:987] [3:988] [0:989] 1:984 2:986 5:985 4:987 3:988 [1:990] [2:991] 0:989 [5:992] 1:990 5:992 2:991 [4:993] 4:993 [1:994] [3:995] [0:996] [5:997] 1:994 3:995 [4:998] [2:999] 0:996 [1:1000] 5:997 4:998 1:1000 2:999 telling you that a producer got the tray with card #0 and placed product number 983 in it. Then trays #1 and #5 were filled before a consumer got tray #0 and consumed product 983. Notice that the association of card and products always remains stable since they ride in the same tray. In fact, this feature is optional. The system would work fine with products and cards being mixed but then the output becomes harder to understand. However, trays are not necessarily used for production or consumption in any foreseeable order. The only guarantee is that once a product is put inside a tray, this tray cannot be filled a second time before it is emptied by a consumer. The heart of the example is // producer operator prodWiring, { tray -> tray.product = prod downstream << tray } def consumer = operator consWiring, { tray -> tray.product == null // optional, just for the sake of clarity upstream << tray } Producer and consumer being dataflow operators they exclusively receive one tray at a time. The rest of the example code is for setting up, making products concurrently, and stopping after soMany products making sure they have all been consumed. One can easily play around with various scenarios by changing the maxForks parameters or the wip. Resulting context ----------------- Since the WIP, i.e. the number of trays/cards in the system, never changes, the upper limit for any queue in the system is the WIP. In our example, no queue will ever become larger than 6 entries. As a direct consequence, queues never overflow their capacity and we never block when writing to a queue. The coordination scheme cannot produce deadlocks and allows efficient implementations. Every processing unit can make progress when it has a tray available. So in average, we do not need more trays than we have processing units (number of producers plus number of consumers). In a system without bottlenecks, the average size of any queue is 0.5 since any tray is either in processing or about to be fetched next. When there are bottlenecks in the system, trays pile up before the bottleneck and downstream queues empty out. That means no waiting time downstream and we are still progressing at maximum speed that the system allows. Any given system is easily controlled by only one parameter: the WIP. Let's take the single-producer, single-consumer scenario as an example. - WIP = 0 : no progress, system halts - WIP = 1 : sequential execution, no concurrency - WIP = 2 : producer produces while consumer consumes, maximum throughput - WIP > 2 : does not enhance throughput but wastes memory Now let's consider a slow consumer that takes twice as long as a producer. The consumer becomes the bottleneck and we proceed at his maximum speed since whenever he is finished with a tray, the next one is readily available. Increasing the WIP does not help. The network does not allow a higher throughput. In that case we need to modify the system to include two consumers and a WIP of three to be balanced. Even this modification is easily achieved by changing the maxForks parameter. In order to balance the system let #p be the number of producers > 0, let #c be the number of consumers > 0, let tp the average time for production, and let tc the average time for consumption. The system is balanced when #c / #p = tc / tp wip = #p + #c assuming that all processing units can proceed concurrently without resource restrictions beyond the KanbanFlow coordination scheme. In a multiple-producer, multiple-consumer scenario the system will never stop making progress because of a single processing unit being stalled. Only if all producers or all consumers stall at the same time, the flow is stopped. KanbanFlow is a composable pattern. KanbanFlows can be chained. If the system is made up of dataflow queues and dataflow operators, access to a tray is always safe. Access to any shared mutable state outside the items being passed through dataflows needs the same consideration as with any concurrent access. Examples are implicit synchronization when printing to System.out and incrementing the product number through an AtomicInteger variable. Known usages ------------ KanbanFlow has been used in experimental settings in academia and for the OpenDolphin JavaFX and JavaScript clients. See for example the kanbanflow animation project. Related work ------------ KanbanFlow makes use of various dataflow abstractions. Its automatic throttling of the producer can be compared to the "back-pressure" as used in reactive streams.


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| 3 | | Since version 0.9, Trac supports being run through the [http://www.fastcgi.com/ FastCGI] interface. Like [wiki:TracModPython mod_python], this allows Trac to remain resident, and is faster than external CGI interfaces which must start a new process for each request. However, unlike mod_python, it is able to support [http://httpd.apache.org/docs/suexec.html SuEXEC]. Additionally, it is supported by much wider variety of web servers. |
| | 3 | [http://www.fastcgi.com/ FastCGI] interface allows Trac to remain resident much like with [wiki:TracModPython mod_python]. It is faster than external CGI interfaces which must start a new process for each request. However, unlike mod_python, FastCGI supports [http://httpd.apache.org/docs/suexec.html Apache SuEXEC], i.e. run with different permissions than web server. Additionally, it is supported by much wider variety of web servers. |
| | 4 | |
| | 5 | '''Note for Windows:''' Trac's FastCGI does not run under Windows, as Windows does not implement `Socket.fromfd`, which is used by `_fcgi.py`. If you want to connect to IIS, you may want to try [trac:TracOnWindowsIisAjp AJP]. |
| 39 | | But neither of these will work for `mod_fcgid`. A similar but partial |
| 40 | | solution for `mod_fcgid` is: |
| | 39 | ==== setup with `mod_fcgid` ==== |
| | 40 | Configure `ScriptAlias` (see TracCgi for details), but call `trac.fcgi` |
| | 41 | instead of `trac.cgi`. Note that slash at the end - it is important. |
| | 42 | {{{ |
| | 43 | ScriptAlias /trac /path/to/www/trac/cgi-bin/trac.fcgi/ |
| | 44 | }}} |
| | 45 | |
| | 46 | To setup Trac environment for `mod_fcgid` it is necessary to use |
| | 47 | `DefaultInitEnv` directive. It cannot be used in `Directory` or |
| | 48 | `Location` context, so if you need to support multiple projects, try |
| | 49 | alternative environment setup below. |
| | 50 | |
| 44 | | But this cannot be used in `Directory` or `Location` context, which makes it |
| 45 | | difficult to support multiple projects. |
| 46 | | |
| 47 | | A better method which works for both of these modules (and for [http://www.lighttpd.net/ lighttpd] and CGI as well), because it involves |
| 48 | | no server configuration settings for environment variables, is to set one |
| 49 | | of the variables in `trac.fcgi`, e.g.: |
| | 54 | |
| | 55 | ==== alternative environment setup ==== |
| | 56 | A better method to specify path to Trac environment it to embed the path |
| | 57 | into `trac.fcgi` script itself. That doesn't require configuration of server |
| | 58 | environment variables, works for both FastCgi modules |
| | 59 | (and for [http://www.lighttpd.net/ lighttpd] and CGI as well): |
| 60 | | Using this method, different projects can be supported by using different |
| 61 | | `.fcgi` scripts with different `ScriptAliases`, copying and appropriately |
| 62 | | renaming `trac.fcgi` and adding the above code to create each such script. |
| | 70 | With this method different projects can be supported by using different |
| | 71 | `.fcgi` scripts with different `ScriptAliases`. |
| | 72 | |
| | 73 | See [https://coderanger.net/~coderanger/httpd/fcgi_example.conf this fcgid example config] which uses a !ScriptAlias directive with trac.fcgi with a trailing / like this: |
| | 74 | {{{ |
| | 75 | ScriptAlias / /srv/tracsite/cgi-bin/trac.fcgi/ |
| | 76 | }}} |
| | 77 | |
| | 78 | == Simple Cherokee Configuration == |
| | 79 | |
| | 80 | The configuration on Cherokee's side is quite simple. You will only need to know that you can spawn Trac as an SCGI process. |
| | 81 | You can either start it manually, or better yet, automatically by letting Cherokee spawn the server whenever it is down. |
| | 82 | First set up an information source in cherokee-admin with a local interpreter. |
| | 83 | |
| | 84 | {{{ |
| | 85 | Host: |
| | 86 | localhost:4433 |
| | 87 | |
| | 88 | Interpreter: |
| | 89 | /usr/bin/tracd —single-env —daemonize —protocol=scgi —hostname=localhost —port=4433 /path/to/project/ |
| | 90 | }}} |
| | 91 | |
| | 92 | If the port was not reachable, the interpreter command would be launched. Note that, in the definition of the information source, you will have to manually launch the spawner if you use a ''Remote host'' as ''Information source'' instead of a ''Local interpreter''. |
| | 93 | |
| | 94 | After doing this, we will just have to create a new rule managed by the SCGI handler to access Trac. It can be created in a new virtual server, trac.example.net for instance, and will only need two rules. The '''default''' one will use the SCGI handler associated to the previously created information source. |
| | 95 | The second rule will be there to serve the few static files needed to correctly display the Trac interface. Create it as ''Directory rule'' for ''/chrome/common'' and just set it to the ''Static files'' handler and with a ''Document root'' that points to the appropriate files: ''/usr/share/trac/htdocs/'' |
| 250 | | LiteSpeed web server is an event-driven asynchronous Apache replacement designed from the ground-up to be secure, scalable, and operate with minimal resources. LiteSpeed can operate directly from an Apache config file and is targeted for business-critical environments. |
| 251 | | |
| 252 | | Setup |
| 253 | | |
| 254 | | 1) Please make sure you have first have a working install of a Trac project. Test install with “tracd” first. |
| 255 | | |
| 256 | | 2) Create a Virtual Host for this setup. From now on we will refer to this vhost as TracVhost. For this tutorial we will be assuming that your trac project will be accessible via: |
| | 286 | !LiteSpeed web server is an event-driven asynchronous Apache replacement designed from the ground-up to be secure, scalable, and operate with minimal resources. !LiteSpeed can operate directly from an Apache config file and is targeted for business-critical environments. |
| | 287 | |
| | 288 | === Setup === |
| | 289 | |
| | 290 | 1. Please make sure you have first have a working install of a Trac project. Test install with “tracd” first. |
| | 291 | |
| | 292 | 2. Create a Virtual Host for this setup. From now on we will refer to this vhost as !TracVhost. For this tutorial we will be assuming that your trac project will be accessible via: |
| | 349 | == Simple Nginx Configuration == |
| | 350 | |
| | 351 | 1. Nginx configuration snippet - confirmed to work on 0.6.32 |
| | 352 | {{{ |
| | 353 | server { |
| | 354 | listen <IP_ADDRESS>:443; |
| | 355 | server_name trac.example; |
| | 356 | |
| | 357 | ssl on; |
| | 358 | ssl_certificate /etc/ssl/trac.example.crt; |
| | 359 | ssl_certificate_key /etc/ssl/trac.example.key; |
| | 360 | |
| | 361 | ssl_session_timeout 5m; |
| | 362 | |
| | 363 | ssl_protocols SSLv2 SSLv3 TLSv1; |
| | 364 | ssl_ciphers ALL:!ADH:!EXPORT56:RC4+RSA:+HIGH:+MEDIUM:+LOW:+SSLv2:+EXP; |
| | 365 | ssl_prefer_server_ciphers on; |
| | 366 | |
| | 367 | # (Or ``^/some/prefix/(.*)``. |
| | 368 | if ($uri ~ ^/(.*)) { |
| | 369 | set $path_info /$1; |
| | 370 | } |
| | 371 | |
| | 372 | # You can copy this whole location to ``location [/some/prefix]/login`` |
| | 373 | # and remove the auth entries below if you want Trac to enforce |
| | 374 | # authorization where appropriate instead of needing to authenticate |
| | 375 | # for accessing the whole site. |
| | 376 | # (Or ``location /some/prefix``.) |
| | 377 | location / { |
| | 378 | auth_basic "trac realm"; |
| | 379 | auth_basic_user_file /home/trac/htpasswd; |
| | 380 | |
| | 381 | # socket address |
| | 382 | fastcgi_pass unix:/home/trac/run/instance.sock; |
| | 383 | |
| | 384 | # python - wsgi specific |
| | 385 | fastcgi_param HTTPS on; |
| | 386 | |
| | 387 | ## WSGI REQUIRED VARIABLES |
| | 388 | # WSGI application name - trac instance prefix. |
| | 389 | # (Or ``fastcgi_param SCRIPT_NAME /some/prefix``.) |
| | 390 | fastcgi_param SCRIPT_NAME ""; |
| | 391 | fastcgi_param PATH_INFO $path_info; |
| | 392 | |
| | 393 | ## WSGI NEEDED VARIABLES - trac warns about them |
| | 394 | fastcgi_param REQUEST_METHOD $request_method; |
| | 395 | fastcgi_param SERVER_NAME $server_name; |
| | 396 | fastcgi_param SERVER_PORT $server_port; |
| | 397 | fastcgi_param SERVER_PROTOCOL $server_protocol; |
| | 398 | fastcgi_param QUERY_STRING $query_string; |
| | 399 | |
| | 400 | # for authentication to work |
| | 401 | fastcgi_param AUTH_USER $remote_user; |
| | 402 | fastcgi_param REMOTE_USER $remote_user; |
| | 403 | } |
| | 404 | } |
| | 405 | }}} |
| | 406 | |
| | 407 | 2. Modified trac.fcgi: |
| | 408 | |
| | 409 | {{{ |
| | 410 | #!/usr/bin/env python |
| | 411 | import os |
| | 412 | sockaddr = '/home/trac/run/instance.sock' |
| | 413 | os.environ['TRAC_ENV'] = '/home/trac/instance' |
| | 414 | |
| | 415 | try: |
| | 416 | from trac.web.main import dispatch_request |
| | 417 | import trac.web._fcgi |
| | 418 | |
| | 419 | fcgiserv = trac.web._fcgi.WSGIServer(dispatch_request, |
| | 420 | bindAddress = sockaddr, umask = 7) |
| | 421 | fcgiserv.run() |
| | 422 | |
| | 423 | except SystemExit: |
| | 424 | raise |
| | 425 | except Exception, e: |
| | 426 | print 'Content-Type: text/plain\r\n\r\n', |
| | 427 | print 'Oops...' |
| | 428 | print |
| | 429 | print 'Trac detected an internal error:' |
| | 430 | print |
| | 431 | print e |
| | 432 | print |
| | 433 | import traceback |
| | 434 | import StringIO |
| | 435 | tb = StringIO.StringIO() |
| | 436 | traceback.print_exc(file=tb) |
| | 437 | print tb.getvalue() |
| | 438 | |
| | 439 | }}} |
| | 440 | |
| | 441 | 3. reload nginx and launch trac.fcgi like that: |
| | 442 | |
| | 443 | {{{ |
| | 444 |<EMAIL_ADDRESS>~ $ ./trac-standalone-fcgi.py |
| | 445 | }}} |
| | 446 | |
| | 447 | The above assumes that: |
| | 448 | * There is a user named 'trac' for running trac instances and keeping trac environments in its home directory. |
| | 449 | * `/home/trac/instance` contains a trac environment |
| | 450 | * `/home/trac/htpasswd` contains authentication information |
| | 451 | * `/home/trac/run` is owned by the same group the nginx runs under |
| | 452 | * and if your system is Linux the `/home/trac/run` has setgid bit set (`chmod g+s run`) |
| | 453 | * and patch from ticket #T7239 is applied, or you'll have to fix the socket file permissions every time |
| | 454 | |
| | 455 | Unfortunately nginx does not support variable expansion in fastcgi_pass directive. |
| | 456 | Thus it is not possible to serve multiple trac instances from one server block. |
| | 457 | |
| | 458 | If you worry enough about security, run trac instances under separate users. |
| | 459 | |
| | 460 | Another way to run trac as a FCGI external application is offered in ticket #T6224 |
| | 461 | |


Huskies rebound from loss, rout Black Bears
Washington bounces back from its first defeat of the season to beat Maine 72-53.
Associated Press
SEATTLE — Isaiah Stewart dominated on the interior and scored 16 points, Quade Green added 11 points on 5-for-5 shooting, and No. 25 Washington rebounded from its first loss of the season with a 72-53 win over Maine on Tuesday night.
The Huskies (3-1) were handed their first setback over the weekend in a 75-62 loss to No. 20 Tennessee on a neutral court in Toronto, but began a stretch of three games in six days with a convincing victory where Washington finally found a rhythm at the offensive end.
The Huskies shot a season-best 46% despite going just 3 of 15 on 3-point attempts. Stewart, the Huskies’ bruising 6-foot-9 freshman, overpowered the Bears in the paint. But getting Green going offensively was an important development as he is Washington’s most experienced guard. The Kentucky transfer was shooting just 30% from the field through the first three games.
Andrew Fleming and Nedeljko Prijovic led the Black Bears (1-3) with 18 points each. Maine was unable to overcome shooting just 33% and committing 14 turnovers in the first half.
Washington started sluggishly for the fourth straight game, but quickly got its offense ignited thanks to Nahziah Carter. Washington scored 15 straight points midway through the first half, including a trio of highlight dunks from the junior. Carter’s first came off a lob from Jaden McDaniels. He added another lob dunk from Green and capped his five minutes of highlights with a windmill dunk on a breakaway that gave Washington a 32-13 lead.
Peter Stumer finally snapped the run with a 3 for the Black Bears, but any chance Maine had of keeping the game close was gone. Washington led 36-18 at halftime and by as many as 24 in the second half.


-
Slides day 1
Exercise 1 - Parameter estimation
Exercise 2 - Tree topologies
Exercise 3 - Model comparison
Exercise 4 - Branch support
Exercise 5 - Command line
Exercise 6 - Inferring ML phylogenies with codon models
Exercise 7 - Inferring ML phylogenies using real datasets
Exercise 8 - Re-Analyze published datasets
Solution 3 - Model comparison
Inferring phylogenies using maximum likelihood
Observing the effect of substitution models the final inferred tree topology.
Goals
In this exercise you are asked to compare different substitution models (and their variations) on the same dataset.
Datasets
Dataset file:
Execution
| Model | Log-likelihood | Parameters | AIC |
|---|---|---|---|
| JC | -6379.66383 | 37 | 12833.32766 |
| JC_I | -6311.4165 | 38 | 12698.833 |
| JC_G | -6304.38084 | 38 | 12684.76168 |
| JC_G_I | -6304.35263 | 39 | 12686.70526 |
| HKY | -6251.43051 | 41 | 12584.86102 |
| HKY_I | -6180.09689 | 42 | 12444.19378 |
| HKY_G | -6172.58045 | 42 | 12429.1609 |
| HKY_G_I | -6172.52896 | 43 | 12431.05792 |
| GTR | -6241.48788 | 45 | 12572.97576 |
| GTR_I | -6171.17472 | 46 | 12434.34944 |
| GTR_G | -6163.87291 | 46 | 12419.74582 |
| GTR_G_I | -6172.52896 | 47 | 12439.05792 |
The number of parameters are computed in the following way for a rooted
tree:
substitution models: JC = 0 parameters | HKY = 4 parameters | GTR = 8
parameters
invariant sites = +1 parameter
gamma rates = +1 parameter
We compute the AIC value as follows:
Questions
1. Which model is the best (including HKY+Gamma), based on the AIC criterion?
We select the model with the lowest AIC value. In this case, GTR + Gamma
This exercise can also be solved using jmodeltest which performs automatically phylogenetic tree reconstructions with every model and extra parameters (i.e. invariant sites, gamma).
Tasks
- Which model is the best (including HKY+Gamma), based on the AIC criterion?
- What about adding invariant sites (+I)?
- Previous
- Next


ElasticSearchLogger is logger that dumps execution logs to ElasticSearch service. ElasticSearch is a popular search index. It is often used to store and index execution logs by itself or as a part of ELK (ElasticSearch - Logstash - Kibana) stack.
Authentication is not supported in this version.
Configuration parameters:
- level: maximum log level to capture - source: source (context) name - connection(s):
- discovery_key: (optional) a key to retrieve the connection from IDiscovery - protocol: connection protocol: http or https - host: host name or IP address - port: port int - uri: resource URI or connection string with all parameters in it
- options:
- interval: interval in milliseconds to save log messages (default: 10 seconds) - max_cache_size: maximum int of messages stored in this cache (default: 100) - index: ElasticSearch index name (default: "log") - daily: true to create a new index every day by adding date suffix to the index name (default: false) - reconnect: reconnect timeout in milliseconds (default: 60 sec) - timeout: invocation timeout in milliseconds (default: 30 sec) - max_retries: maximum int of retries (default: 3) - index_message: true to enable indexing for message object (default: false)
References:
- *:context-info:*:*:1.0 (optional) ContextInfo to detect the context id and specify counters source - *:discovery:*:*:1.0 (optional) IDiscovery services to resolve connection
Example:
logger := NewElasticSearchLogger(); logger.Configure(cconf.NewConfigParamsFromTuples( "connection.protocol", "http", "connection.host", "localhost", "connection.port", "9200" )); logger.Open("123") logger.Error("123", ex, "Error occured: %s", ex.message); logger.Debug("123", "Everything is OK.");
type ElasticSearchLogger struct { *clog.CachedLogger // contains filtered or unexported fields }
func NewElasticSearchLogger() *ElasticSearchLogger
NewElasticSearchLogger method creates a new instance of the logger. Retruns *ElasticSearchLogger pointer on new ElasticSearchLogger
func (c *ElasticSearchLogger) Close(correlationId string) (err error)
Close method are closes component and frees used resources. Parameters:
- correlationId string (optional) transaction id to trace execution through call chain.
Returns error or nil, if no errors occured.
func (c *ElasticSearchLogger) Configure(config *cconf.ConfigParams)
Configure are configures component by passing configuration parameters. Parameters:
- config *cconf.ConfigParams configuration parameters to be set.
func (c *ElasticSearchLogger) IsOpen() bool
IsOpen method are checks if the component is opened. Returns true if the component has been opened and false otherwise.
func (c *ElasticSearchLogger) Open(correlationId string) (err error)
Open method are ppens the component. Parameters:
- correlationId string (optional) transaction id to trace execution through call chain.
Returns error or nil, if no errors occured.
func (c *ElasticSearchLogger) Save(messages []*clog.LogMessage) (err error)
Save method are saves log messages from the cache. Parameters:
- messages []*clog.LogMessage a list with log messages
Retruns error or nil for success.
func (c *ElasticSearchLogger) SetReferences(references cref.IReferences)
SetReferences method are sets references to dependent components. Parameters:
- references cref.IReferences references to locate the component dependencies.


This map was created to supplement the subject of a Tweet from Brownsburg's Town Manager, Grant Kleinhenz.
One of the main goals of the Brownsburg Map Project is to visualize and add greater spatial context to the undertakings of the Town of Brownsburg's government. Through this map we hope to communicate the locations and dates for the street resurfacing projects in a way that is easy to understand and accessible to the citizens of Brownsburg.Tentative schedule for 2014 Street Resurfacing released. http://t.co/qxR4TJF4dR Work to start as early as Thursday, August 14th.
— Grant A. Kleinhenz (@BrownsburgTM) August 6, 2014
This map was made on top of BootLeaf, a web mapping template by Bryan McBride featuring Bootstrap and Leaflet. Mapping of planned resurfaced streets layer was carried out using geojson.io.
| Data Layer | geoJSON | KML |
|---|---|---|
| 2014 Fall Street Resurfacing data | Download | Download |
| Data Layer | Source | Producer |
|---|---|---|
| 2014 Fall Street Resurfacing data | 2014 Street Overlay Program | Town of Brownsburg |
The data provided on this site is for informational and planning purposes only.
Absolutely no accuracy or completeness guarantee is implied or intended. All information on this map is subject to such variations and corrections as might result from a complete title search and/or accurate field survey.
|<EMAIL_ADDRESS>| |
| @brownsburgmap |


Zamfara: Why Yari May Be Arrested – Matawalle
by George OgboluGovernor Bello Matawalle of Zamfara State has said he could order the arrest of his predecessor Abdulaziz Yari if killings continue in his state.
The governor made the accusation at government house, Gusau, while receiving some firearms that some “repentant bandits” surrendered, the Punch reports.
Matawalle reportedly blamed recent attacks on Yari, saying the former governor is inciting violence in the state.
The governor said attacks occur in Zamfara State whenever Abdulaziz Yari visits the state.
According to Matawalle, “I have decided to take this action against the former governor because I observed that whenever he visited the state, there would be attacks and killing of innocent people immediately he left.”
Naija News reports that fourteen persons were killed last Sunday in an attack on Karaye village in Gummi local government area.
The attack is the first major breach of the peace agreement between the state government and suspected bandits.
“Security is the responsibility of all and I wonder why some people such as the former governor and his followers are not happy with the current development in which the security of the state has significantly improved,” Governor Matawalle added.
He urged charged security operatives to arrest and prosecute anybody found to be responsible for the killings.


What we’ll ask you
We need to know how your condition affects you, so you’ll be asked about the following:
About you
- help with your application
- nationality and where you live
- payments from abroad
You and your condition
- your conditions and disabilities
- your medication
- your treatments and therapy
- side effects
- changes in your condition
- monitoring your condition
- professionals who know about your condition
- information you can send
- any aids and adaptations you have
- seeing, speaking and hearing
You and your home
- moving around your home
- using the toilet
- washing yourself
- getting dressed
- making a hot meal from fresh ingredients
- eating and drinking
Getting out and about
- getting out and walking about
- mixing with people
Making sense of things
- planning a local journey
- getting somewhere you have never been
- getting somewhere you know
- understanding written information
- dealing with money
You'll have chance to check and change your answers at the end.
Include as much information as you can in your answers.


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TXT's Soobin Voted The Best Rookie IDOL 2019
by kfan1234TXT Soobin was announced as 'The Best Rookie IDOL 2019' after he snatched first place in KingChoice's '2019 Best Rookie KPOP IDOL 2019' voting Poll. KingChoice is well known as a popular international KPop & Celebrity voting website.
Soobin is a South Korean singer under Big Hit Entertainment. He is the leader of the boy group TXT.
On January 13, Soobin was revealed as the second member of Big Hit Entertainment's newest boy group TXT. He made his official debut with the group on March 4, 2019 with their debut mini album, The Dream Chapter: Star.
The debut album Dream Chapter: Star according to Billboard 200 was at No. 140 in the United States, making it the highest-charting debut album by an all-male KPOP group.


...
Ateneo de Manila University
December 13–15, 2017
OFFICIAL SCOREBOARD [DEMO]
...
...
Legend:
Solved First
Solved
Tried, Incorrect
Pending Judgement


IMPORTANT: If your experience deviates from this document, please document the changes to keep it up-to-date.
As part of the release process, two of the weekly developer calls will be co-opted as “release meetings.”
The first developer call after a release will be used as the release meeting to start the next release cycle. During this meeting, the following items must be identified:
All of this information should be added to the GitHub milestone for the given release. This should give the community and maintainers a clear set of guidelines to follow when choosing whether or not to add issues and PRs to a given release.
The developer call closest to two weeks before the scheduled release date will be used to review any remaining PRs that should be pulled into the release. This is the place to debate whether or not we should wait before cutting a release and any other concerns. At the end of this meeting, if the release date has not been pushed out, the first RC should be cut. Subsequent developer calls in between this meeting and the release date should have some time set aside to see if any bugs were found. Once the release date is reached, the final release can be cut
So you’re in charge of a new release for Helm? Cool. Here’s what to do…
Just kidding! :trollface:
All releases will be of the form vX.Y.Z where X is the major version number, Y is the minor version number and Z is the patch release number. This project strictly follows semantic versioning so following this step is critical.
It is important to note that this document assumes that the git remote in your repository that corresponds to “https://github.com/kubernetes/helm” is named “upstream”. If yours is not (for example, if you’ve chosen to name it “origin” or something similar instead), be sure to adjust the listed snippets for your local environment accordingly. If you are not sure what your upstream remote is named, use a command like git remote -v
to find out.
If you don’t have an upstream remote, you can add one easily using something like:
git remote add upstream git@github.com:kubernetes/helm.git
In this doc, we are going to reference a few environment variables as well, which you may want to set for convenience. For major/minor releases, use the following:
export RELEASE_NAME=vX.Y.0
export RELEASE_BRANCH_NAME="release-X.Y"
export RELEASE_CANDIDATE_NAME="$RELEASE_NAME-rc.1"
If you are creating a patch release, you may want to use the following instead:
export PREVIOUS_PATCH_RELEASE=vX.Y.Z
export RELEASE_NAME=vX.Y.Z+1
export RELEASE_BRANCH_NAME="release-X.Y"
export RELEASE_CANDIDATE_NAME="$RELEASE_NAME-rc.1"
Major releases are for new feature additions and behavioral changes that break backwards compatibility. Minor releases are for new feature additions that do not break backwards compatibility. To create a major or minor release, start by creating a release-vX.Y.0
branch from master.
git fetch upstream
git checkout upstream/master
git checkout -b $RELEASE_BRANCH_NAME
This new branch is going to be the base for the release, which we are going to iterate upon later.
Patch releases are a few critical cherry-picked fixes to existing releases. Start by creating a release-vX.Y.Z
branch from the latest patch release.
git fetch upstream --tags
git checkout $PREVIOUS_PATCH_RELEASE
git checkout -b $RELEASE_BRANCH_NAME
From here, we can cherry-pick the commits we want to bring into the patch release:
# get the commits ids we want to cherry-pick
git log --oneline
# cherry-pick the commits starting from the oldest one, without including merge commits
git cherry-pick -x <commit-id>
git cherry-pick -x <commit-id>
This new branch is going to be the base for the release, which we are going to iterate upon later.
When doing a minor release, make sure to update pkg/version/version.go with the new release version.
$ git diff pkg/version/version.go
diff --git a/pkg/version/version.go b/pkg/version/version.go
index 2109a0a..6f5a1a4 100644
--- a/pkg/version/version.go
+++ b/pkg/version/version.go
@@ -26,7 +26,7 @@ var (
// Increment major number for new feature additions and behavioral changes.
// Increment minor number for bug fixes and performance enhancements.
// Increment patch number for critical fixes to existing releases.
- Version = "v2.6"
+ Version = "v2.7"
// BuildMetadata is extra build time data
BuildMetadata = "unreleased"
git add .
git commit -m "bump version to $RELEASE_CANDIDATE_NAME"
In order for others to start testing, we can now push the release branch upstream and start the test process.
git push upstream $RELEASE_BRANCH_NAME
Make sure to check helm on CircleCI and make sure the release passed CI before proceeding.
If anyone is available, let others peer-review the branch before continuing to ensure that all the proper changes have been made and all of the commits for the release are there.
Now that the release branch is out and ready, it is time to start creating and iterating on release candidates.
git tag --sign --annotate "${RELEASE_CANDIDATE_NAME}" --message "Helm release ${RELEASE_CANDIDATE_NAME}"
git push upstream $RELEASE_CANDIDATE_NAME
CircleCI will automatically create a tagged release image and client binary to test with.
For testers, the process to start testing after CircleCI finishes building the artifacts involves the following steps to grab the client from Google Cloud Storage:
linux/amd64, using /bin/bash:
wget https://kubernetes-helm.storage.googleapis.com/helm-$RELEASE_CANDIDATE_NAME-linux-amd64.tar.gz
darwin/amd64, using Terminal.app:
wget https://kubernetes-helm.storage.googleapis.com/helm-$RELEASE_CANDIDATE_NAME-darwin-amd64.tar.gz
windows/amd64, using PowerShell:
PS C:\> Invoke-WebRequest -Uri "https://kubernetes-helm.storage.googleapis.com/helm-$RELEASE_CANDIDATE_NAME-windows-amd64.zip" -OutFile "helm-$ReleaseCandidateName-windows-amd64.zip"
Then, unpack and move the binary to somewhere on your $PATH, or move it somewhere and add it to your $PATH (e.g. /usr/local/bin/helm for linux/macOS, C:Files.exe for Windows).
Spend several days explicitly investing time and resources to try and break helm in every possible way, documenting any findings pertinent to the release. This time should be spent testing and finding ways in which the release might have caused various features or upgrade environments to have issues, not coding. During this time, the release is in code freeze, and any additional code changes will be pushed out to the next release.
During this phase, the $RELEASE_BRANCH_NAME branch will keep evolving as you will produce new release candidates. The frequency of new candidates is up to the release manager: use your best judgement taking into account the severity of reported issues, testers’ availability, and the release deadline date. Generally speaking, it is better to let a release roll over the deadline than to ship a broken release.
Each time you’ll want to produce a new release candidate, you will start by adding commits to the branch by cherry-picking from master:
git cherry-pick -x <commit_id>
You will also want to update the release version number and the CHANGELOG as we did in steps 2 and 3 as separate commits.
After that, tag it and notify users of the new release candidate:
export RELEASE_CANDIDATE_NAME="$RELEASE_NAME-rc.2"
git tag --sign --annotate "${RELEASE_CANDIDATE_NAME}" --message "Helm release ${RELEASE_CANDIDATE_NAME}"
git push upstream $RELEASE_CANDIDATE_NAME
From here on just repeat this process, continuously testing until you’re happy with the release candidate.
When you’re finally happy with the quality of a release candidate, you can move on and create the real thing. Double-check one last time to make sure everything is in order, then finally push the release tag.
git checkout $RELEASE_BRANCH_NAME
git tag --sign --annotate "${RELEASE_NAME}" --message "Helm release ${RELEASE_NAME}"
git push upstream $RELEASE_NAME
We will auto-generate a changelog based on the commits that occurred during a release cycle, but it is usually more beneficial to the end-user if the release notes are hand-written by a human being/marketing team/dog.
If you’re releasing a major/minor release, listing notable user-facing features is usually sufficient. For patch releases, do the same, but make note of the symptoms and who is affected.
An example release note for a minor release would look like this:
## vX.Y.Z
Helm vX.Y.Z is a feature release. This release, we focused on <insert focal point>. Users are encouraged to upgrade for the best experience.
The community keeps growing, and we'd love to see you there!
- Join the discussion in [Kubernetes Slack](https://slack.k8s.io/):
- `#helm-users` for questions and just to hang out
- `#helm-dev` for discussing PRs, code, and bugs
- Hang out at the Public Developer Call: Thursday, 9:30 Pacific via [Zoom](https://zoom.us/j/696660622)
- Test, debug, and contribute charts: [GitHub/kubernetes/charts](https://github.com/kubernetes/charts)
## Installation and Upgrading
Download Helm X.Y. The common platform binaries are here:
- [MacOS amd64](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-darwin-amd64.tar.gz) ([checksum](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-darwin-amd64.tar.gz.sha256))
- [Linux amd64](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-linux-amd64.tar.gz) ([checksum](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-linux-amd64.tar.gz.sha256))
- [Linux arm](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-linux-arm.tar.gz) ([checksum](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-linux-arm.tar.gz.sha256))
- [Linux arm64](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-linux-arm64.tar.gz) ([checksum](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-linux-arm64.tar.gz.sha256))
- [Linux i386](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-linux-386.tar.gz) ([checksum](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-linux-386.tar.gz.sha256))
- [Linux ppc64le](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-linux-ppc64le.tar.gz) ([checksum](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-linux-ppc64le.tar.gz.sha256))
- [Windows amd64](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-windows-amd64.zip) ([checksum](https://storage.googleapis.com/kubernetes-helm/helm-vX.Y.Z-windows-amd64.zip.sha256))
Once you have the client installed, upgrade Tiller with `helm init --upgrade`.
The [Quickstart Guide](https://docs.helm.sh/using_helm/#quickstart-guide) will get you going from there. For **upgrade instructions** or detailed installation notes, check the [install guide](https://docs.helm.sh/using_helm/#installing-helm). You can also use a [script to install](https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get) on any system with `bash`.
## What's Next
- vX.Y.Z+1 will contain only bug fixes.
- vX.Y+1.Z is the next feature release. This release will focus on ...
## Changelog
- chore(*): bump version to v2.7.0 08c1144f5eb3e3b636d9775617287cc26e53dba4 (Adam Reese)
- fix circle not building tags f4f932fabd197f7e6d608c8672b33a483b4b76fa (Matthew Fisher)
The changelog at the bottom of the release notes can be generated with this command:
PREVIOUS_RELEASE=vX.Y.Z
git log --no-merges --pretty=format:'- %s %H (%aN)' $RELEASE_NAME $PREVIOUS_RELEASE
Once finished, go into GitHub and edit the release notes for the tagged release with the notes written here.
Congratulations! You’re done.  You’ve earned it.
After enjoying a nice $DRINK_OF_CHOICE, go forth and announce the glad tidings of the new release in Slack and on Twitter. You should also notify any key partners in the helm community such as the homebrew formula maintainers, the owners of incubator projects (e.g. ChartMuseum) and any other interested parties.
Optionally, write a blog post about the new release and showcase some of the new features on there!


Project
Quick Array
Description
A fast C++ resizable array library with small object optimization
Built from the ground up with performance in mind, quick array
is up to six times faster than GCC and Clang's
std::vector
implementation. It employs small object optimization, the copy and swap idiom, and
other techniques to avoid heap allocations as well as saving memory when storing less than 23 bytes of data on the
x86_64 platform.
Features
High performance and memory efficient data structures for the most demanding tasks
91 Lines of Code
High Performance
Simple Integration
Configurable
Memory Efficient
Usage
Setup Guide
To use quick array
, simply include quickarray.h
in your source file.
You can adjust certain parameters like growth factor with the macros defined at the start of
the header file. Be sure to set the debug
macro to false in production builds for
increased performance.
License
MIT License
A short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code.