Gecode Modules
Here is a list of all modules:- Functionality by programming task
- Interfacing to Gecode
- Setting up scripts
- Using finite domain integers
- Integer variables
- Argument arrays
- Variable arrays
- Domain constraints
- Simple relation constraints
- Equality constraints
- Element constraints
- Distinct constraints
- Channel constraints
- Scheduling constraints
- Regular constraints
- Sorting constraints
- Cardinality constraints
- Boolean constraints
- Arithmetic constraints
- Linear constraints
- Branching
- Assigning
- Search engines
- Using finite integer sets
- Set variables
- Argument arrays
- Variable arrays
- Projector constraints
- Range and value iterators for set variables
- Domain constraints
- Relation constraints
- Set operation/relation constraints
- Convexity constraints
- Sequence constraints
- Distinctness constraints
- Connection constraints to finite domain variables
- Selection constraints
- Branching
- Direct modelling
- Programming search engines
- Programming actors
- Programming variables
- Interfacing to Gecode
- Common functionality
- Other available functionality
- Example scripts (models)


CRUD application that routes using Express to display calculated matches based on user input.
View
App that pulls data from Giphy's API to display gifs of movies that take place in San Francisco.
View


This is a toast - it will appear in the preview pane of the email client, use wisely!
<%=<EMAIL_ADDRESS>%>
<%= @mall.address %>, <%= @mall.city %>, <%= @mall.state %>, <%= @mall.zip %>


NFL star Myles Garrett admits he 'lost his cool' before hitting opposition player with helmet
Cleveland Browns defensive end Myles Garrett sparked the brawl during his side's victory over AFC North rivals Pittsburgh Steelers on Thursday
by Liam Prenderville, http://www.facebook.com/mirrorfootballCleveland Browns defensive end Myles Garrett admitted he "lost his cool" after starting the brawl which marred their 21-7 victory over AFC North rivals Pittsburgh Steelers on Thursday evening.
Cleveland's scrappy win at the FirstEnergy Stadium was overshadowed by an ugly skirmish between Garrett and Steelers quarterback Mason Rudolph with eight seconds remaining in the match.
Garrett and Rudolph wrestled on the ground well after the quarterback had passed to Trey Edmunds for an 11-yard gain.
The Cleveland player then ripped off Rudolph's helmet and swung it at the Pittsburgh man, making contact with his head.
The Steelers' offensive linemen David DeCastro and Maurkice Pouncey then tackled Garrett to the ground, with Pouncey punching and kicking the defensive end in the head before the Browns' defensive tackle Larry Ogunjobi ran in and pushed Rudolph to the floor.
Garrett, Pouncey and Ogunjobi were all ejected from the game, with the Browns defensive end expected to be given a lengthy ban by the NFL.
Speaking to NFL.com, Garrett said: "I made a mistake, I lost my cool and I regret it. It's going to come back to hurt our team. I appreciate my team having my back but it should've never gotten to that point. It's on me.
"A win's a win. I don't think it was overshadowed by what happens in eight seconds. What we did on the opening 59 minutes should not go unnoticed.
"It was embarrassing. What I did was foolish and I shouldn't allow myself to slip like that. It was out of character, but it was a situation like that, it is an emotional game, and I allowed myself to follow through those emotions.
"I've hurt the whole team with what I did and could come back to hurt us. I don't know what sort of repercussions I will face but it's got to be better."
Rudolph tried to defer from answering questions surrounding the incident in his post-match press conference but did describe Garrett's actions as "cowardly".
He said: "I thought it was pretty cowardly. I got sacked, we exchanged some words, he kept keeping on and one thing led to another.
"You can go back and watch the tape if you want and make your own assumptions."
Cleveland quarterback Baker Mayfield, who is good friends with Rudolph, could not defend his team-mate's actions.
"It's inexcusable," Mayfield told FOX Sport. "I don't care, rivalry or not, you can't do that. That's kind of the history of what's been going on here lately, hurting yourself.
"It's inexcusable. Garrett knows that. I hope he does now. It's tough."
Steelers head coach Mike Tomlin declined to comment on the incident, while opposite number Freddie Kitchens said he was "embarrassed" of his defensive end.


Reuse & Recycle
Circular Economy
A Europe-wide EU Strategy for Plastics in the Circular Economy and annex to transform the way plastics and plastics products are designed, produced, used and recycled. By 2030, all plastics packaging should be recyclable.
To achieve its ambitious vision, the Strategy foresees actions to improve the economics and quality of plastic recycling; to curb plastic waste and littering; to drive investments and innovation; and to harness global action. To reduce the leakage of plastics into the environment, the Commission has also adopted a new proposal on Port Reception Facilities, to tackle sea-based marine litter and published a report on the impact of the use of oxo-degradable plastic, including oxo-degradable plastic carrier bags, on the environment.
A Communication on options to address the interface between chemical, product and waste legislation that assesses how the rules on waste, products and chemicals relate to each other.
A Monitoring Framework on progress towards a circular economy at EU and national level. It is composed of a set of ten key indicators which cover each phase – i.e. production, consumption, waste management and secondary raw materials – as well as economic aspects – investments and jobs - and innovation.
Reuse, Regenerate, Recycle
The modern idea of regenerative and responsible product design, typified by the concept of the circular economy, stems from a number of whole system design concepts. Influences on this concept come from renowned architect Walter R Stahl, sustainability pioneer Gunter Pauli and Natural Step founder Karl-Hendrik Robért, to name a few.
Amongst the influences on the concept are the criteria outlined in the Hannover Principles, published in 1991, compiled and written by architect William McDonough and chemist Michael Braungart, the founders of Cradle to Cradle®. The principles are a set of statements about designing buildings and objects with forethought about their environmental impact, their effect on sustainable growth and their overall impact on society.
The circular economy is a relatively new system of operation, which aims to ‘close the loop’ and design waste out of the system. This means a transition from the outdated ‘take, make, dispose’ linear operating model, which is highly wasteful and detrimental to the environment, to a more responsible all-encompassing and abundant resource management system. This current system mines resources, uses them in the manufacturing of a product and then disposes of these valuable materials at the end of use; usually to landfill or incineration, meaning a large amounts of resource value is lost. On the other hand, the circular economy closes this resource loop by providing a system of operation that designs wastage out of the system, avoiding landfills and incineration altogether and keeping resources in use for as long as possible through reuse and regeneration of new products.
This circular economy is a vital system to facilitate reduction in the depletion of resources. For example, a “circular” designed phone would be one that is designed for disassembly at the end of use, made with approved high-quality materials that can be safely re-utilised, either into another phone or other suitable products. The reuse of resources include their reclamation by the original manufacturer for use in new products, allowing manufacturers substantial financial savings. This activity retains material value and dramatically reduces undesirable ecological impacts.


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Genesis 37:21
And Reuben heard it, and he delivered him out of their hands; and said, Let us not kill him.


Turbulent mixing and biogeochemical processes in the ocean can interact when occurring at similar time scales; for example, carbonate formation occurs at similar time scales as small-scale turbulence in the upper ocean, while submesoscale eddies evolve over the same time scales as plankton blooms. This project seeks to understand how these processes interact with each other, which has implications for global biogeochemical cycles. It involves accurate large-eddy simulations of upper-ocean turbulence and detailed biogeochemical kinetic models.
Most practical combustion phenomena are turbulent, but turbulent flames pose a challenging modeling problem due to the complex interactions between fluid flow, chemical kinetics, and thermodynamics. We are both studying the turbulence-chemistry interactions that occur in the combustion of practical fuels, and also developing better modeling techniques and best practices.
Smoldering combustion plays an important role in wildfires, since it can contribute significantly to carbon emissions and also transition back to the flaming mode of combustion. However, the physical and chemical processes that control the ignition, propagation, and emissions of smoldering in woody fuels are not well understood, and this project is working to fix that.
Effects of fuel content and density on the smoldering characteristics of cellulose and hemicellulose (2019)
Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures (2019)
The combustion community has generated many thousands of fundamental combustion data points using a variety of experimental apparatuses, which are extremely useful for validating and refining models for fuel oxidation. However, most such data are either represented via figures or tables, not openly accessible, or stored in a format not easily readable by people or machines. This project is designing human and machine-readable data standards for combustion measurements, and building an open, community database for them (along with tools to use the data).
ChemKED: A human- and machine-readable data standard for chemical kinetics experiments (2018)
Assessing impacts of discrepancies in model parameters on autoignition model performance: A case study using butanol (2018)
This project is investigating a concept for efficient electrical power generation using a pulse detonation engine (PDE) coupled with direct power extraction via a magnetohydrodynamic (MHD) generator. The system is particularly well-suited for pure oxygen combustion (oxy-combustion) since it doesn't have any moving interior parts, so it could offer a viable approach to producing power efficiently and capturing the carbon from the exhaust.
Typical approaches to using supercomputing systems to solve large systems of partial differential equations, like those governing fluid flow, require significant amounts of memory transfer at every computational time step. This project is developing a method to reduce the number of communication steps compared with typical approaches, with the goal of improving overall performance.
Accelerating solutions of one-dimensional unsteady PDEs with GPU-based swept time–space decomposition (2018)
Our group advocates for the open sharing of all research artifacts developed with public funds, including open access papers, open data, and open research software. We work on following best practices in software development, enabling reproducibility in computational research, and giving credit for software developments.
The case for openness in engineering research (2018)
Journal of Open Source Software (JOSS): design and first-year review (2018)
Fourth Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE4) (2018)
See more
A multi-disciplinary perspective on emergent and future innovations in peer review (2017)
Report on the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3) (2016)
The challenge and promise of software citation for credit, identification, discovery, and reuse (2016)
Software citation principles (2016)
This project is looking at ways to evaluate fuels for advanced combustion engines that operate at lower combustion temperatures than conventional gasoline or diesel engines. Traditional fuel ratings such as octane number poorly predict the behavior of fuels in the newer engine modes, so we are working on ways to better quantify fuel performance there.
FACE gasoline surrogates formulated by an enhanced multivariate optimization framework (2018)
Predicting fuel research octane number using Fourier-transform infrared absorption spectra of neat hydrocarbons (2016)
A novel fuel performance index for LTC engines based on operating envelopes in light-duty driving cycle simulations (2015)
Investigation of the LTC fuel performance index for oxygenated reference fuel blends (2015)
Simulations of reacting fluid flows can be performed with accurate models for chemical kinetics by intelligently choosing and designing appropriate solvers. For this project, we are looking at choosing the best algorithm based on local conditions in a simulation and also the available computing hardware.
Accelerating finite-rate chemical kinetics with coprocessors: Comparing vectorization methods on GPUs, MICs, and CPUs (2018)
An investigation of GPU-based stiff chemical kinetics integration methods (2017)
pyJac: Analytical Jacobian generator for chemical kinetics (2017)
Recent progress and challenges in exploiting graphics processors in computational fluid dynamics (2014)
Accelerating moderately stiff chemical kinetics in reactive-flow simulations using GPUs (2014)
Simulations of combustion and chemically reacting flows use chemical kinetic models to describe how hydrocarbon fuels break down and react. But, these models can be extremely large and complex, particularly for fuel components relevant to practical transportation fuels. This project develops tools for reducing the size and complexity of these models.
Reduced chemistry for butanol isomers at engine-relevant conditions (2017)
Development of efficient and accurate skeletal mechanisms for hydrocarbon fuels and kerosene surrogate (2015)
An automated target species selection method for dynamic adaptive chemistry simulations (2015)
Reduced chemistry for a gasoline surrogate valid at engine-relevant conditions (2015)
Mechanism reduction for multicomponent surrogates: a case study using toluene reference fuels (2014)
On the importance of graph search algorithms for DRGEP-based mechanism reduction methods (2011)
Skeletal mechanism generation for surrogate fuels using directed relation graph with error propagation and sensitivity analysis (2010)
Vacuum arc remelting (VAR) furnaces have been used for decades to produce high-quality metal ingots for demanding applications like in the aerospace and biomedical fields, but the extreme conditions inside a furnace make diagnostics and control of the process challenging. This project analyzed an approach for sensing the position of the electrical arc inside a VAR furnace using finite element simulations.
Analysis of an approach for detecting arc positions during vacuum arc remelting based on magnetic flux density measurements (2018)


public class AllphoneLinguist extends java.lang.Object implements Linguist
| Modifier and Type | Field and Description |
|---|---|
static java.lang.String |
PROP_ACOUSTIC_MODEL
The property that defines the acoustic model to use when building the search graph
|
static java.lang.String |
PROP_CD
The property that controls whether to use context dependent phones.
|
static java.lang.String |
PROP_PIP
The property that controls phone insertion probability.
|
PROP_FILLER_INSERTION_PROBABILITY, PROP_LANGUAGE_WEIGHT, PROP_SILENCE_INSERTION_PROBABILITY, PROP_UNIT_INSERTION_PROBABILITY, PROP_WORD_INSERTION_PROBABILITY
| Constructor and Description |
|---|
AllphoneLinguist() |
| Modifier and Type | Method and Description |
|---|---|
void |
allocate()
Allocates the linguist.
|
void |
deallocate()
Deallocates the linguist.
|
AcousticModel |
getAcousticModel() |
java.util.ArrayList<HMM> |
getCDSuccessors(Unit lc,
Unit base) |
java.util.ArrayList<HMM> |
getCISuccessors() |
float |
getPhoneInsertionProb() |
SearchGraph |
getSearchGraph()
Retrieves search graph.
|
java.util.ArrayList<Unit> |
getUnits(SenoneSequence senoneSeq) |
void |
newProperties(PropertySheet ps)
This method is called when this configurable component needs to be reconfigured.
|
void |
startRecognition()
Called before a recognition.
|
void |
stopRecognition()
Called after a recognition.
|
boolean |
useContextDependentPhones() |
@S4Component(type=AcousticModel.class) public static final java.lang.String PROP_ACOUSTIC_MODEL
@S4Double(defaultValue=0.05) public static final java.lang.String PROP_PIP
@S4Boolean(defaultValue=false) public static final java.lang.String PROP_CD
public void newProperties(PropertySheet ps) throws PropertyException
Configurable
newProperties
in interface Configurable
ps
- a property sheet holding the new dataPropertyException
- if there is a problem with the properties.public SearchGraph getSearchGraph()
Linguist
Implementor's note: This method is typically called at the beginning of each recognition and therefore should be
getSearchGraph
in interface Linguist
public void startRecognition()
Linguist
Implementor's Note - Some linguists (or underlying lanaguge or acoustic models) may keep caches or pools that need to be initialzed before a recognition. A linguist may implement this method to perform such initialization. Note however, that an ideal linguist will, once allocated, be state-less. This will allow the linguist to be shared by multiple simulataneous searches. Reliance on a 'startRecognition' may prevent a linguist from being used in a multi-threaded search.
startRecognition
in interface Linguist
public void stopRecognition()
Linguist
Implementor's Note - Some linguists (or underlying lanaguge or acoustic models) may keep caches or pools that need to be flushed after a recognition. A linguist may implement this method to perform such flushing. Note however, that an ideal linguist will once allocated, be state-less. This will allow the linguist to be shared by multiple simulataneous searches. Reliance on a 'stopRecognition' may prevent a linguist from being used in a multi-threaded search.
stopRecognition
in interface Linguist
public void allocate() throws java.io.IOException
Linguist
Implementor's Note - A well written linguist will allow allocate to be called multiple times without harm. This will allow a linguist to be shared by multiple search managers.
public void deallocate() throws java.io.IOException
Linguist
Implementor's Note - if the linguist is being shared by multiple searches, the deallocate should only actually deallocate things when the last call to deallocate is made. Two approaches for dealing with this:
(1) Keep an allocation counter that is incremented during allocate and decremented during deallocate. Only when the counter reaches zero should the actually deallocation be performed.
(2) Do nothing in dellocate - just the the GC take care of things
deallocate
in interface Linguist
java.io.IOException
- if an IO error occurspublic AcousticModel getAcousticModel()
public float getPhoneInsertionProb()
public boolean useContextDependentPhones()
public java.util.ArrayList<HMM> getCISuccessors()
public java.util.ArrayList<Unit> getUnits(SenoneSequence senoneSeq)


Kim Kardashian Prepared For Kanye West's Bid For The White House
by AllHipHop StaffKim Kardashian not only supports Kanye West's bid for the presidency in 2024, but she's also actually preparing to be the First Lady.
(AllHipHop News) Kim Kardashian is "ready for anything" when it comes to husband Kanye West's career ambitions - including running for the U.S. presidency in 2024.
The rapper announced his planned political venture during his appearance at the Fast Company Innovation Festival in New York earlier this month.
Speaking to U.S. TV show 'Extra" while promoting her Skims shapewear collection, reality TV star and budding lawyer Kim confessed she's totally on board with his plans.
“I would be ready for anything. I always am," she said of the prospect of being the First Lady of the United States.
Kim also spoke about her husband's recent Sunday Service gospel performance with famed pastor and televangelist Joel Osteen, calling it "amazing" and "so major,” and gushed she's proud of the Jesus Is King star for finding his spirituality.
“I think everyone has their own personal journey and growth and I'm really happy that he (has), you know, found his calling and what he really believes in," the Keeping Up with the Kardashians star gushed. “It's funny, I'll look back at interviews that he's done maybe 10 years ago where he said, 'I really want to start a church…' He really made all of his dreams come true. What he really wanted to do. And that just makes me happy.”


A Reproduction of Gapminder
In Hans Rosling's iconic TED Talk he shows us that many advances have been made since the 60s, when our notions of development were established. The engaging infographic illustrates how our ongoing perceptions of a "first world" and a "third world" are wrong, and that the world has become a spectrum of developing countries.
{{ super() }}You can skip ahead to 3m 58s to see him talking through the plot we've just made. Or sit back and enjoy a great example of statistics communication.


class: center, middle # IEMS 5780
Building and Deploying Scalable
Machine Learning Services ### Lecture 6 - Image Classification #### Albert Au Yeung
18th October, 2019 --- class: middle, center # Image Classification --- # Image Classification * In **image classification**, the task is to categorize incoming images into one of the pre-defined classes/labels * It can be a **binary classification problem** or a **multi-class classification problem** * Example: - Which of the following are images of a **star ferry**?
--- class: equal-split # Image Classification * Some well-known image classification tasks: - [MNIST Hand-written digits classification](http://yann.lecun.com/exdb/mnist/) - [ImageNet Large Scale Visual Recognition Challenge](http://www.image-net.org/challenges/LSVRC/) .column-left[
] .column-right[
] --- # Image Classification ### Challenges 1. **High dimensionality** - Images are represented digitally as a matrix of **pixels** - If we use **RGB** to represent the colour of a pixel, a 300 x 300 image has 270,000 values 2. **Representation** - Representing an image using a feature vector of all the pixels is of little use - The scale and perspective of the object inside an image can vary a lot 3. **Noise** - A picture of an object usually have something in the background - An object may appear with another irrelevant object in the same image --- class: split # What is an Image .column-left[ * An image is represented digitally by **pixels** (picture elements) * Each pixel contains the values of the colour of the corresponding component of the image * Resolution refers to the **number of pixels** used to represent the image * The values of the pixels can represent the intensity of light, the RGB values, hue, saturation, brightness, etc. ] .column-right[
An image with 48 x 48 pixels with RGB channels
] --- # What is an Image
A grey scale version of the previous image
Each pixel has a value between 0 and 255
--- # What is an Image ### RGB (Red, Green, Blue) * One of the most commonly used colour model * Each pixel is represented by **three integer values**, each of **8 bits (0 to 255)** * This allows 16,777,216 (256
3
) combinations for a pixel
--- # Image Classification ### Why is image classification difficult? * Semantics is **lost** in an image represented as a matrix of pixels * We need to bridge the **gap between pixels and meanings** * What are the **features** that can be used to represent an image?
--- # Representation and Feature Extraction * In order to perform computation on images, we need an **appropriate representation** of the images. * Some **representations** are: - Use the values of the **raw pixels** - Use **keywords (metadata)** to describe an image, and thus an image can be represented as a bag of words - Extract **visual features** from the image and use a vector of features to represent an image * Finding a **good representation** is hard! --- # Image Features * A **feature** is a piece of information relevant for solving a certain computational task * In **computer vision**, features can be pixels, points, edges, shapes, or other more complex **visual structures** * **Feature detection** – the process of identifying features in an image that is representative of the image itself * Ref: https://en.wikipedia.org/wiki/Feature_detection_(computer_vision) --- class: split # Feature Extraction .column-left[
Edge Detection
https://en.wikipedia.org/wiki/Edge_detection
Blob Detection
http://www.ipb.uni-bonn.de/projects/etrims/results/blob-detection-Dateien
] .column-right[
Corner Detection
https://dzone.com/articles/corner-detection-opencv
] --- # SIFT * SIFT stands for **Scale-Invariant Feature Transform** * Published by **David Lowe** (now at Google) in 1999/2004 * An algorithm for detecting and describing **local features** in images * One of the most widely used feature extraction algorithm in computer vision * Able to detect features when the **scales** and **rotations** are different * Ref: [Distinctive Image Features from Scale-Invariant Keypoints](https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf) --- # SIFT ## Problem Definition * What are the criteria of **good features**? - Distinctive - Easy to extract - Invariant to noise, illumination ,scaling, rotation, viewing direction - Easy to match against a large database of features --- # SIFT * SIFT is an approach for detecting (determine what is a feature) and describing local features with the following steps: 1. Scale-space extrema detection 2. Keypoint localization 3. Orientation assignment 4. Generation of keypoint descriptors * Ref: [http://docs.opencv.org/trunk/da/df5/tutorial_py_sift_intro.html](http://docs.opencv.org/trunk/da/df5/tutorial_py_sift_intro.html ) --- class: split # SIFT .column-left[ * **1. Scale-space extrema detection** - Search for stable features across different scales - An image is rescaled and smoothed by a Gaussian (blurring) filter - Find out interesting points by selecting the local maxima or minima among different scales * **2. Keypoint localization** - Remove points with low contrasts or edges (not necessary interesting features) ] .column-right[
] --- # SIFT * **3. Orientation assignment** - Assign an orientation to each keypoint, so that the features detected are invariant to image rotation * **4. Generation of keypoint descriptors** - A **16x16** neighbourhood around the keypoint is taken, and a histogram of orientations of the gradients are calculated - Finally, each keypoint is represented by **a vector of length 128**
--- class: split # SIFT in OpenCV .column-left[ ```python import cv2 import numpy as np img = cv2.imread('star-ferry.jpg') gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) sift = cv2.xfeatures2d.SIFT_create() kp = sift.detect(gray,None) flag = cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS img = cv2.drawKeypoints(gray, kp, img, flags=flag) cv2.imwrite('keypoints.jpg', img) ``` ] .column-right[
] --- # SIFT * Keypoints can be used to **match objects** across different images
--- # Visual Bag-of-Words * In text classification, a document can be represented by a **bag of words** * Similar approach can be used in image classification
* Ref: [http://www.mathworks.com/help/vision/ug/image-classification-with-bag-of-visual-words.html](http://www.mathworks.com/help/vision/ug/image-classification-with-bag-of-visual-words.html) --- # Visual Bag-of-Words ### Steps 1. Extract **keypoints** from images using SIFT 2. **Cluster** the keypoints to create a **visual dictionary** using a clustering algorithm (e.g. K-means) 3. For a given image, check the cluster to which each keypoint is in, and create a **histogram** of the distribution of clusters 4. Images are then represented by **vectors by normalizing the histograms** --- # Visual Bag-of-Words
* Ref: [http://www.olivier-augereau.com/blog/?p=358](http://www.olivier-augereau.com/blog/?p=358) --- # Limitations * SIFT aims at identifying interesting points automatically, but keypoints are still relatively **primitive** and are not usually good enough for **classification tasks** * The visual BoW approach does not consider **spatial relations** among visual words * SIFT cannot be easily adapted to **different domains** to extract distinct features, as it is not based on **machine learning** * SIFT is computationally expensive ### We need better algorithms to extract image features! --- class: center, middle # Deep Learning --- class: equal-split # Neural Networks (NN) / Artificial Neural Networks (ANN) .column-left[ * A computational model inspired by the biological neural networks in the human body * A model involving a set of **neurons** connected to one another in some specific ways * Can be used to model **non-linear** relations between inputs and outputs ] .column-right[
] --- # Basic Unit: Neurons * A **neuron** is the basic unit in a neural network (also called a **node**) * Each input is associated with a **weight** * A neuron applies a function `\(f\)` on the **weighted sum** of the inputs, and generates its **output**
--- # Activation Functions * `\(f\)` is called the **activation function** of the neuron * It is usually a **non-linear** function * Using non-linear activation functions allow neural networks to model **non-linear** relations
\(\text{sigmoid}(x) = \frac{1}{1 + e^{-x}}\) \(\text{tanh}(x) = \frac{1 - e^{-2x}}{1 + e^{-2x}} \) \(\text{ReLU}(x) = \text{max}(0, x)\)
--- class: center, middle ### TED Talk ### How We Teach Computers to Understand Pictures (Fei Fei Li)
https://www.youtube.com/watch?v=40riCqvRoMs
--- # Feed-forward Neural Networks * In a **feed-forward** neural network, computation is performed in a **single direction** * Neurons are arranged in one or more **layers** * Nodes are connected by **edges**, with **weights** associated with the edges
--- class: equal-split # Feed-forward Neural Networks .column-left[ * A feed-forward neural network has **three different types of nodes**: - **Input Nodes** Pass inputs to the neural network, no computation is performed - **Hidden Nodes** Perform computations and transfer information from the previous layer to the next layer - **Output Nodes** Represents the output of the neural network ] .column-right[
] --- class: equal-split # Multi-Layer Perceptron (MLP) .column-left[ * A neural network that is consist of **one or more** hidden layers are also called a **multi-layer perceptron (MLP)** * A multi-layer perceptron can be used to learn **non-linear functions** * Also called a **fully connected** neural network (each node in one layer is connected to all nodes in the next layer) ] .column-right[
] --- # Loss Function in Neural Networks * A neural network can be easily designed to perform **multi-class classification** * This is by creating **N output nodes** in the output layer * A commonly used **loss function** in training neural networks for classification is the **logarithmic loss function** (Log Loss) * Ref: [http://scikit-learn.org/stable/modules/model_evaluation.html#log-loss](http://scikit-learn.org/stable/modules/model_evaluation.html#log-loss) $$ \text{logloss} = -\frac{1}{N} \sum\_{i=1}^N \sum\_{c=1}^C y\_{ic} \text{log}(p\_{ic}) $$ * `\(N\)` is number of training samples, `\(C\)` is the number of classes (labels) * `\(y_{ic} = 1\)` if sample `\(i\)` belongs to class `\(c\)`, otherwise it is 0 * `\(p_{ic}\)` is the **predicted probability** that sample `\(i\)` belongs to class `\(c\)` --- # Convolutional Neural Network * A neural network can be easily adopted to handle **images** * For example, we can **resize** our images to a fixed dimension, and each **pixel** (or each channel of each pixel) can be sent to an **input node** * However, we do not use simple fully connected neural networks for image classification - Does not really extract useful features from the image - Too many parameters (weights) to learn
--- # Convolutional Neural Network * We would like the neural network to **extract useful features** and then use those features for classification * What are **useful features**? * We can design a neural network that helps us to extract features that are useful for classification * Features should be - **Localized**: basic features (corners, points, etc.) are usually found in a small regions - **Hierarchical**: more **complex features** are usually composed of basic features --- # Convolutional Neural Network * Features can be extracted using **convolutional layers** * [Convolution](https://en.wikipedia.org/wiki/Convolution): modifying one function with another function * By applying convolution on images, we pick a small area of pixels, apply a function on the values, and computes an output
--- # Convolutional Neural Network * The filter is responsible for extracting meainingful features from the raw image * Some common filters are shown below * However, in training a **convolutional neural network**, we learn the values in the filters from the data
--- # Convolutional Neural Network * In a convolutional neural network, we can have **multiple layers** of convolution operations, together with other **pooling** layers * After features are extracted, they are fed into some **fully connected layers**, which act as a classifier
https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/
--- # AlexNet * Described in the paper [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) (Krizhevsky et. al 2012) * One of the first work that attracted wide attention to applying CNN in computer vision * Achieved top-5 error rate of 18.9%
--- # Inception * Described in the paper [Going Deeper with Convolutions](https://arxiv.org/abs/1409.4842) (Szegedy et al. 2014) * A new architecture of CNN by Google with many more layers * Achieved top-5 error rate of 6.67%
--- # ResNet * Described in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) (He et al. 2015) * Introducing the idea of residual learning, allowing a neural network to have many more layers (152 layers) * Achieved top-5 error rate of 3.57%
--- # Commonly Used Deep Learning Frameworks * Many deep learning frameworks are available (especially in Python) * [Tensorflow](https://www.tensorflow.org/tutorials/) (by Google) * [PyTorch](https://pytorch.org/) (by Facebook) * [MXNet](https://mxnet.apache.org/) (adotped by AWS) * [Keras](https://keras.io/) (A high level API using Tensorflow or Theano as backend) * And many more, see [Comparison of Deep Learning Software](https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software)
--- # GPU vs. CPU * A deep neural network usually consists of a **huge number of parameters** - AlexNet: ~62,000,000 weights - GoogLeNet (Inception): ~6,9000,000 weights - ResNet 152: ~60,000,000 weights * A large number of parameters implies that we need **a lot of training data** in order to optimize their values * Deep learning is made possible by parallel computation on **graphical processing units (GPUs)** * See some comparison of the time required to train a deep learning model here: [https://dawn.cs.stanford.edu/benchmark/index.html](https://dawn.cs.stanford.edu/benchmark/index.html) * Most deep learning frameworks support model training on **multiple GPU cards** --- # Testing with Pre-trained Models * ResNet50 in Keras: [l6-example-keras-resnet50.ipynb](https://drive.google.com/open?id=1AwaKEG_vdpwhl_5C36CAGczfCTaWrGzp) * Inception V3 in PyTorch: [l6-example-pytorch-inceptionv3.ipynb](https://drive.google.com/open?id=1QjsSyG9c8JGhu-2w5koLClzB3dRND3To) --- class: center, middle # End of Lecture 6


I am a designer who works with interfaces and the best part of my work is to figure out what our user had in mind :);
I'm a person of many talents.
You can contact me by my Twitter


Tournament Schedule of Events
Friday, April 4, 2014
Registration Party
Tony P's
777 East 17th Avenue, Denver
6PM - 9PM
Saturday, April 5, 2014
Tournament Pool Play
Dive Volleyball
2335 Delgany Street, Denver
7AM – Registration Opens
7:30AM – Captains' Meeting
8AM - TBD - Pool Play (All Divisions - See Pool Play/Bracket for details)
Seeding Party
The Eagle
1475 36th St, Denver
8PM - 10PM
Sunday, April 6, 2014
Tournament Bracket Play
Dive Volleyball
2335 Delgany Street, Denver
7AM – Facility Opens
7:30AM – Captains' Meeting
8AM - 5PM - Tournament Bracket Play
Tournament Awards Banquet
Tony P's
777 East 17th Avenue, Denver
6PM - 8PM


How to Map Table to Key-value
Where to Start The Implementation
"We can solve any problem by introducing an extra level of indirection." 1. Database is one of the most complicated software. Database system engineers must jungle three balls: develop the features, improve performance, and ensure correctness. A modern database evolves a spectrum of multiple technologies: lower file system; fundamental storage engine; relational model mapper; query optimizer; concensus protocol and implementation; isolation level, monitoring etc. Luckily, abstraction is a fundamental tool to address this problem: we can separate each of these features into different layers and each layer has at least one open source choice. The era of Internet has established cornerstones for each layer.
The first part of the tutorial is to map SQL's data model to lower storage engine. When I became curious about this topic, I did some literature review. CockroachDB 2 and TiDB 3, the two NewSQL implementations, have very good documentation 4 on how do they map the relational model to lower storage engine. I recommend you reading at least CockroachDB's article on this, as the TiDB's article is in Chinese.
Mapping 101
Key-value store requires keys to be unique and thus we need to uniquely map data in SQL
into unique keys. The data here are not only rows but also indexes.
Normally, when you construct a key, the basic idea is <namespace>::<data>
.
There are three components here: <namespace>
, encoding ::
and <data>
.
<namespace>
can be many things and the straightforward choice is table/index name.
In SQL, uniqueness of columns is always within a table.
This is a good choice and we will use table name as the <namespace>
for row data and combination of table and index names for indexes.
In later chapters, the <namespace>
can be expanded to many other things.
<data>
can contain the column name or ID but we still need some uniqueness.
The default uniqueness in a table is primary key.
So a popular way 3 1 to build <data>
is table_name::column_name::primary_key_of_row
.
The third component is encoding ::
. Encoding is important as it must satisfy two requirements:
- Stable prefix
- Fast/easy parsing
Key-value databases like RocksDB and LevelDB save keys into different SST files.
Each SST file covers a range of [a, b)
(by default, byte array comparison).
When you want to check a key k
, database looks up in ranges' table and finds
a range [a_i, b_i)
that satisfy k in [a_i, b_i)
. Database won't further look into
any SST file till the range matches. This is the LST's way to handle indexes.
RocksDB even has a prefix optimization to assist these operations.
Considering a key table_name::column_name::column_value::primary_key_of_row
,
the prefix table_name::column_name::column_value
can easily pick up
all values of the column. Therefore, we need an encoding that preserves the byte order
of the prefix: there is no way for any column of any row has a different
table_name::column_name::column_value
format.
A simple solution is use the format as it is: each component is connected by double colons. However, the input is pure byte array, which means a key may contains double colons and this makes it hard to parse the encoding. One difference between human-readable and machine-parsable format is: machine can compute lengths easily. So machine-oriented format prefers recording lengths of components as metadata; human prefer stream without lengths metadata.
I am thankful to the trend of open source infrastructures: only rare company can implement from A to Z in house and barely any company wants to be locked in any non open source solutions.


Classes—Fall, 2017
- CSCI 111 - Programming and Algorithms 1
- CSCI 430 - Software Engineering
- CINS 490 - Computer Information Systems Capstone
Other courses taught recently
- CINS 110 - Intro. to Web Programming
- CSCI 431 - Usability Engineering
- CSCI 630 - Software Engineering (graduate)
Office Hours
- Tuesdays: 1:00 - 3:00
- Thursdays: 1:00 - 3:00


Query\QueryBuilderInterface
Interface defining the Query Builder class
Synopsis
interface QueryBuilderInterface
{
- // methods
- public QueryBuilderInterface select()
- public QueryBuilderInterface selectMax()
- public QueryBuilderInterface selectMin()
- public QueryBuilderInterface selectAvg()
- public QueryBuilderInterface selectSum()
- public QueryBuilderInterface distinct()
- public QueryBuilderInterface explain()
- public QueryBuilderInterface table()
- public QueryBuilderInterface from()
- public QueryBuilderInterface like()
- public QueryBuilderInterface orLike()
- public QueryBuilderInterface notLike()
- public QueryBuilderInterface orNotLike()
- public QueryBuilderInterface having()
- public QueryBuilderInterface orHaving()
- public QueryBuilderInterface where()
- public QueryBuilderInterface orWhere()
- public QueryBuilderInterface whereIn()
- public QueryBuilderInterface orWhereIn()
- public QueryBuilderInterface whereNotIn()
- public QueryBuilderInterface orWhereNotIn()
- public QueryBuilderInterface set()
- public QueryBuilderInterface join()
- public QueryBuilderInterface groupBy()
- public QueryBuilderInterface orderBy()
- public QueryBuilderInterface limit()
- public QueryBuilderInterface groupStart()
- public QueryBuilderInterface notGroupStart()
- public QueryBuilderInterface orGroupStart()
- public QueryBuilderInterface orNotGroupStart()
- public QueryBuilderInterface groupEnd()
- public PDOStatement get()
- public PDOStatement getWhere()
- public int countAll()
- public int countAllResults()
- public PDOStatement insert()
- public ?PDOStatement insertBatch()
- public PDOStatement update()
- public ?int updateBatch()
- public PDOStatement delete()
- public string getCompiledSelect()
- public string getCompiledInsert()
- public string getCompiledUpdate()
- public string getCompiledDelete()
- public void resetQuery()
Violations
PHPMessDetector
| Line | Rule | Message |
|---|---|---|
| 81 | BooleanArgumentFlag | The method selectMax has a boolean flag argument $as, which is a certain sign of a Single Responsibility Principle violation. |
| 81 | ShortVariable | Avoid variables with short names like $as. Configured minimum length is 3. |
| 90 | BooleanArgumentFlag | The method selectMin has a boolean flag argument $as, which is a certain sign of a Single Responsibility Principle violation. |
| 90 | ShortVariable | Avoid variables with short names like $as. Configured minimum length is 3. |
| 99 | BooleanArgumentFlag | The method selectAvg has a boolean flag argument $as, which is a certain sign of a Single Responsibility Principle violation. |
| 99 | ShortVariable | Avoid variables with short names like $as. Configured minimum length is 3. |
| 108 | BooleanArgumentFlag | The method selectSum has a boolean flag argument $as, which is a certain sign of a Single Responsibility Principle violation. |
| 108 | ShortVariable | Avoid variables with short names like $as. Configured minimum length is 3. |
| 402 | BooleanArgumentFlag | The method countAllResults has a boolean flag argument $reset, which is a certain sign of a Single Responsibility Principle violation. |
| 462 | BooleanArgumentFlag | The method getCompiledSelect has a boolean flag argument $reset, which is a certain sign of a Single Responsibility Principle violation. |
| 471 | BooleanArgumentFlag | The method getCompiledInsert has a boolean flag argument $reset, which is a certain sign of a Single Responsibility Principle violation. |
| 480 | BooleanArgumentFlag | The method getCompiledUpdate has a boolean flag argument $reset, which is a certain sign of a Single Responsibility Principle violation. |
| 489 | BooleanArgumentFlag | The method getCompiledDelete has a boolean flag argument $reset, which is a certain sign of a Single Responsibility Principle violation. |
Methods
public
- countAll() — Retrieve the number of rows in the selected table
- countAllResults() — Retrieve the number of results for the generated query - used in place of the get() method
- delete() — Deletes data from a table
- distinct() — Adds the 'distinct' keyword to a query
- explain() — Shows the query plan for the query
- from() — Specify the database table to select from
- get() — Select and retrieve all records from the current table, and/or execute current compiled query
- getCompiledDelete() — Returns the generated 'delete' sql query
- getCompiledInsert() — Returns the generated 'insert' sql query
- getCompiledSelect() — Returns the generated 'select' sql query
- getCompiledUpdate() — Returns the generated 'update' sql query
- getWhere() — Convenience method for get() with a where clause
- groupBy() — Group the results by the selected field(s)
- groupEnd() — Ends a query group
- groupStart() — Adds a paren to the current query for query grouping
- having() — Generates a 'Having' clause
- insert() — Creates an insert clause, and executes it
- insertBatch() — Creates and executes a batch insertion query
- join() — Creates a join phrase in a compiled query
- like() — Creates a Like clause in the sql statement
- limit() — Set a limit on the current sql statement
- notGroupStart() — Adds a paren to the current query for query grouping, prefixed with 'NOT'
- notLike() — Generates a NOT LIKE clause
- orGroupStart() — Adds a paren to the current query for query grouping, prefixed with 'OR'
- orHaving() — Generates a 'Having' clause prefixed with 'OR'
- orLike() — Generates an OR Like clause
- orNotGroupStart() — Adds a paren to the current query for query grouping, prefixed with 'OR NOT'
- orNotLike() — Generates a OR NOT LIKE clause
- orWhere() — Where clause prefixed with "OR"
- orWhereIn() — Where in statement prefixed with "or"
- orWhereNotIn() — OR WHERE NOT IN (FOO) clause
- orderBy() — Order the results by the selected field(s)
- resetQuery() — Clear out the class variables, so the next query can be run
- select() — Specifies rows to select in a query
- selectAvg() — Selects the average value of a field from a query
- selectMax() — Selects the maximum value of a field from a query
- selectMin() — Selects the minimum value of a field from a query
- selectSum() — Selects the sum of a field from a query
- set() — Sets values for inserts / updates / deletes
- table() — Specify the database table to select from
- update() — Creates an update clause, and executes it
- updateBatch() — Creates a batch update, and executes it.
- where() — Specify condition(s) in the where clause of a query Note: this function works with key / value, or a passed array with key / value pairs
- whereIn() — Where clause with 'IN' statement
- whereNotIn() — WHERE NOT IN (FOO) clause


Example: Using custom texture and 3d models for your pets¶
This example shows how to create a resource pack in order to put custom textures or 3d models on to the pets of PetBlocks.
Goal¶
All players should see and select a 3d model of a pet in the pet inventory.
When the pet is being active, a 3d model should be shown.
Way 1: Using the NBT tag CustomModelData¶
Version: Minecraft 1.14-Latest
Note
This is the recommend way to create custom models for PetBlocks
As mentioned before a powerful editor like Notepad++ in this example is recommend.
Warning
1. Make a copy of your config.yml! YAML, the configuration language this config.yml is using, takes spaces and tabs very serious, so be careful otherwise the config.yml cannot be parsed when executing the reload command.
Create a resource pack with the CustomModelData NBT tag
A good starting point is the following link: https://www.planetminecraft.com/forums/communities/texturing/new-1-14-custom-item-models-tuto-578834/
Open the config.yml of PetBlocks and enter your custom model name using the NBT tag.
Reload the config.yml, activate the resource pack and check out if your model works ingame.
Way 2: Using the damage value and unbreakable¶
Version: Minecraft 1.9-Latest
As mentioned before a powerful editor like Notepad++ in this example is recommend.
Warning
1. Make a copy of your config.yml! YAML, the configuration language this config.yml is using, takes spaces and tabs very serious, so be careful otherwise the config.yml cannot be parsed when executing the reload command.
Download the ready to use resource pack PetBlx which was created by NullBlox.
Take a look at the /assets/minecraft/models/item directory inside the downloaded .zip file. You can see a stone_hoe.json file which contains the whole configuration how minecraft should interprete the models in this resource pack.
4. (Optional) Editing the resource pack. This resource pack is already finished to work together with PetBlocks to show 2 models (we will only show the bee model). Anyway, this how you would edit it:
Note
The following section was copied from the guide https://www.spigotmc.org/wiki/custom-item-models-in-1-9-and-up/ created and owned by the spigot community. It was accessed on the 9th November 2019.
In this item folder, create a new file called wooden_hoe.json. This will be the “container” for all of your custom items. It tells the client which models to use for certain durability values of the wooden hoe.
This file will look something like this:
{
"parent": "item/handheld",
"textures": {
"layer0": "items/wood_hoe"
},
"overrides": [
{"predicate": {"damaged": 0, "damage": 0.01666666666667}, "model": "item/my_cool_custom_item"}
]
}
Ok, so let’s break this down.
We are overriding wood hoe, but extending the base version. So the “parent” and “textures” are telling the client to use the wood hoe defaults. This means that a wood hoe with zero damage will look normal.
Now for the new stuff- the “overrides” block lets you specify different models to use depending on certain properties of the item. These properties are called “predicates”, and for our purposes we will focus on the “damage” predicate.
This will vary the model based on damage, as a percentage from 0 to 1. So the value “0.016666” in there comes from the ratio “1/60” - because I want to apply this model at a damage/durability value of 1, and a wood hoe has a max durability of 60.
See the mc wiki for all the durabilities of items: http://minecraft.gamepedia.com/Hoe
You can add as many damage predicates as you want, up to the max durability of the item- just do the “x/60” math for each one.
You can also use this tool to auto-generate a template given a specific type of tool, saving you from the maths: http://accidentalgames.com/media/durabilityModels.php (If you are using 1.13 version or later, here’s the updated version of this generator: https://geenium.github.io/damage-value-generator/ )
Preserving the original items If you want to still be able to use the vanilla damageable items, this is possible! Add another entry to your model file, so it ends up looking like this: ssss
{
"parent": "item/handheld",
"textures": {
"layer0": "items/wood_hoe"
},
"overrides": [
{"predicate": {"damaged": 0, "damage": 0.01666666666667}, "model": "item/my_cool_custom_item"},
{"predicate": {"damaged": 1, "damage": 0}, "model": "item/wooden_hoe"}
]
}
This will cause damaged versions to continue to use the base model. Note that if you use the JSON generator I linked above, you’ll need to edit the final entry- it needs to refer to the model name, so wooden_hoe instead of wood_hoe for instance.
Finally, put your custom item model in the same item folder in the resource pack, in this case it would be in “my_cool_custom_item.json”. (Creating custom item models is not in the scope of this tutorial, I think there are some good ones out there already and also tools like Cubik you can use)
Zip up the root folder of your RP, add it to your <minecraft>/resourcepacks folder and you should be able to load it and see your custom item!
If in doubt, feel free to take a look at my resource pack structure on github:
https://github.com/elBukkit/MagicPlugin/tree/master/Magic/src/resource-pack/default
FINAL NOTES
Once you have this all working, there are some things to consider.
The higher-level tools will give you more item models to work with. A diamond hoe has a max durability of 1,562 (!)
(Optional) Create your own 3d models for minecraft.
NullBlox who has originally created the PetBlx resource pack has created tons of amazing and well explained videos how to setup resource packs and create 3d models. You can check out his youtube playlist:
The downloaded resource pack therefore contains a configuration for the stone_hoe item which we now need to setup in PetBlocks.
The numeric id of the stone_hoe is 291 which we set to both icon and skin.
Configure the correct damage (which model you would like to show) and set the skin nbt tag {Unbreakable:1}.
Reload the config.yml, activate the resource pack and check out if your model works ingame.
Note
If the server console now displays an error then the config.yml cannot be parsed. In this case apply your backup you have made and try the steps again being extra carefully.


The Robo Expo 3.0 is an exhibition held to provide a glimpse of the future with robot
technology incorporated in society.
Examples of robot introduction being promoted by companies, organizations and various participating schools
are displayed calling visitor’s attention to the future of robotics.
Robots that have a potential to be employed in various industries such as manufacturing, health care, service
robots and disaster management are displayed to open up the
minds of the visitors to the endless use of Bots.
For the purpose we would be inviting problem statements from participants themselves and faculties of NIT
Raipur.
After proper validation and evaluation, we would shortlist the models that would be of considerable value to
the society in modern times.
These models will then be clubbed into similar groups for the purpose of presentation.
Any and all doubts and queries regarding the models will be cleared by the club members and all possible
assistance will be provided in order to make this venture successful.
First Prize - ₹ 10000
Runner-Up - ₹ 8000
To bring out the best technologies and ideas in the field of robotics under one roof.
To widen the scope of current practices for a more varied problem set.
To provide a platform for budding ideas with technical help for their future applications.


sparknlp_jsl.annotator.AssertionDLApproach¶
- class sparknlp_jsl.annotator.AssertionDLApproach[source]¶
Bases:
sparknlp.common.AnnotatorApproach
Train a Assertion Model algorithm using deep learning. from extracted entities and text. AssertionLogRegModel requires DOCUMENT, CHUNK and WORD_EMBEDDINGS type annotator inputs, which can be obtained by e.g a
The training data should have annotations columns of type
DOCUMENT
,CHUNK
,WORD_EMBEDDINGS
, thelabel
column (The assertion status that you want to predict), thestart
(the start index for the term that has the assertion status), theend
column (the end index for the term that has the assertion status).This model use a deep learning to predict the entity.Input Annotation types
Output Annotation type
DOCUMENT, CHUNK, WORD_EMBEDDINGS
ASSERTION
- Parameters
- label
Column with one label per document. Example of possible values: “present”, “absent”, “hypothetical”, “conditional”, “associated_with_other_person”, etc.
- startCol
Column that contains the token number for the start of the target
- endCol
olumn that contains the token number for the end of the target
- batchSize
Size for each batch in the optimization process
- epochs
Number of epochs for the optimization process
- learningRate
Learning rate for the optimization process
- dropout
dropout”, “Dropout at the output of each layer
- maxSentLen
Max length for an input sentence.
- graphFolder
Folder path that contain external graph files
- configProtoBytes
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
- validationSplit
Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.
- evaluationLogExtended
Select if you want to have mode eval.
- testDataset
Path to test dataset. If set used to calculate statistic on it during training.
- includeConfidence
whether to include confidence scores in annotation metadata
- enableOutputLogs
whether or not to output logs
- outputLogsPath
Folder path to save training logs
- verbose
Level of verbosity during training
Examples
>>> import sparknlp >>> from sparknlp.base import * >>> from sparknlp.annotator import * >>> from sparknlp_jsl.annotator import * >>> from sparknlp.training import * >>> from pyspark.ml import Pipeline >>> document_assembler = DocumentAssembler() \ ... .setInputCol("text") \ ... .setOutputCol("document") >>> sentence_detector = SentenceDetector() \ ... .setInputCol("document") \ ... .setOutputCol("sentence") >>> tokenizer = Tokenizer() \ ... .setInputCols(["sentence"]) \ ... .setOutputCol("token") >>> embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models") \ ... .setInputCols(["sentence", "token"]) \ ... .setOutputCol("word_embeddings") \ ... .setCaseSensitive(False) >>> chunk = Chunker() \ ... .setInputCols([sentence]) \ ... .setChunkCol("chunk") \ ... .setOutputCol("chunk") >>> assertion = AssertionDLApproach() \ ... .setLabelCol("label") \ ... .setInputCols(["document", "chunk", "word_embeddings"]) \ ... .setOutputCol("assertion") \ ... .setOutputCol("assertion") \ ... .setBatchSize(128) \ ... .setDropout(0.012) \ ... .setLearningRate(0.015) \ ... .setEpochs(1) \ ... .setStartCol("start") \ ... .setEndCol("end") \ ... .setMaxSentLen(250) >>> assertionPipeline = Pipeline(stages=[ ... document_assembler, ... sentence_detector, ... tokenizer, ... embeddings, ... chunk, ... assertion]) >>> assertionModel = assertionPipeline.fit(dataset)
Methods
__init__
()clear
(param)Clears a param from the param map if it has been explicitly set.
copy
([extra])Creates a copy of this instance with the same uid and some extra params.
explainParam
(param)Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap
([extra])Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
fit
(dataset[, params])Fits a model to the input dataset with optional parameters.
fitMultiple
(dataset, paramMaps)Fits a model to the input dataset for each param map in paramMaps.
Gets current column names of input annotations.
Gets whether Annotator should be evaluated lazily in a RecursivePipeline.
getOrDefault
(param)Gets the value of a param in the user-supplied param map or its default value.
Gets output column name of annotations.
getParam
(paramName)Gets a param by its name.
getParamValue
(paramName)Gets the value of a parameter.
hasDefault
(param)Checks whether a param has a default value.
hasParam
(paramName)Tests whether this instance contains a param with a given (string) name.
isDefined
(param)Checks whether a param is explicitly set by user or has a default value.
isSet
(param)Checks whether a param is explicitly set by user.
load
(path)Reads an ML instance from the input path, a shortcut of read().load(path).
read
()Returns an MLReader instance for this class.
save
(path)Save this ML instance to the given path, a shortcut of 'write().save(path)'.
set
(param, value)Sets a parameter in the embedded param map.
setBatchSize
(size)Set Size for each batch in the optimization process.
Sets ConfigProto from tensorflow, serialized into byte array.
setDropout
(rate)Set a dropout at the output of each layer
setEnableOutputLogs
(value)Sets if you enable to output to annotators log folder.
setEndCol
(e)Set column that contains the token number for the end of the target.
setEpochs
(number)Sets number of epochs for the optimization process
Creates a Annotation from a Spark Row.
Sets folder path that contain external graph files.
setIncludeConfidence
(value)Sets if you waht to include confidence scores in annotation metadata.
setInputCols
(*value)Sets column names of input annotations.
setLabelCol
(label)Set a column with one label per document.
setLazyAnnotator
(value)Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
setLearningRate
(lamda)Set a learning rate for the optimization process
setMaxSentLen
(length)Set the max length for an input sentence.
setOutputCol
(value)Sets output column name of annotations.
setOutputLogsPath
(value)Sets folder path that contain external graph files.
setParamValue
(paramName)Sets the value of a parameter.
setStartCol
(s)Set a column that contains the token number for the start of the target
setTestDataset
(path[, read_as, options])Sets path to test dataset.
Set Choose the proportion of training dataset to be validated against the model on each Epoch.
setVerbose
(value)Sets level of verbosity during training.
write
()Returns an MLWriter instance for this ML instance.
Attributes
batchSize
configProtoBytes
dropout
enableOutputLogs
endCol
epochs
evaluationLogExtended
getter_attrs
graphFolder
includeConfidence
inputCols
label
lazyAnnotator
learningRate
maxSentLen
outputCol
outputLogsPath
Returns all params ordered by name.
startCol
testDataset
validationSplit
verbose
- clear(param)¶
Clears a param from the param map if it has been explicitly set.
- copy(extra=None)¶
Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.
- Parameters
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
- explainParam(param)¶
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
- explainParams()¶
Returns the documentation of all params with their optionally default values and user-supplied values.
- extractParamMap(extra=None)¶
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
- Parameters
extra – extra param values
- Returns
merged param map
- fit(dataset, params=None)¶
Fits a model to the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.
- Returns
fitted model(s)
New in version 1.3.0.
- fitMultiple(dataset, paramMaps)¶
Fits a model to the input dataset for each param map in paramMaps.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
.paramMaps – A Sequence of param maps.
- Returns
A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.
New in version 2.3.0.
- getInputCols()¶
Gets current column names of input annotations.
- getLazyAnnotator()¶
Gets whether Annotator should be evaluated lazily in a RecursivePipeline.
- getOrDefault(param)¶
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
- getOutputCol()¶
Gets output column name of annotations.
- getParam(paramName)¶
Gets a param by its name.
- getParamValue(paramName)¶
Gets the value of a parameter.
- Parameters
- paramNamestr
Name of the parameter
- hasDefault(param)¶
Checks whether a param has a default value.
- hasParam(paramName)¶
Tests whether this instance contains a param with a given (string) name.
- isDefined(param)¶
Checks whether a param is explicitly set by user or has a default value.
- isSet(param)¶
Checks whether a param is explicitly set by user.
- classmethod load(path)¶
Reads an ML instance from the input path, a shortcut of read().load(path).
- property params¶
Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
- classmethod read()¶
Returns an MLReader instance for this class.
- save(path)¶
Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
- set(param, value)¶
Sets a parameter in the embedded param map.
- setBatchSize(size)[source]¶
Set Size for each batch in the optimization process.
- Parameters
- sizeint
Size for each batch in the optimization process
- setConfigProtoBytes(b)[source]¶
Sets ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()`.
- Parameters
- bbytes
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
- setDropout(rate)[source]¶
Set a dropout at the output of each layer
- Parameters
- ratefloat
Dropout at the output of each layer
- setEnableOutputLogs(value)[source]¶
Sets if you enable to output to annotators log folder.
- Parameters
- valuesrt
Folder path that contain external graph files.
- setEndCol(e)[source]¶
Set column that contains the token number for the end of the target.
- Parameters
- rowstr
Column that contains the token number for the end of the target
- setEpochs(number)[source]¶
Sets number of epochs for the optimization process
- Parameters
- numberint
Number of epochs for the optimization process
- setEvaluationLogExtended(v)[source]¶
Creates a Annotation from a Spark Row.
- Parameters
- vbool
Evaluation log extended.
- setGraphFolder(p)[source]¶
Sets folder path that contain external graph files.
- Parameters
- psrt
Folder path that contain external graph files.
- setIncludeConfidence(value)[source]¶
Sets if you waht to include confidence scores in annotation metadata.
- Parameters
- pbool
- Value that selects if you want to use confidence scores in annotation metadata
- setInputCols(*value)¶
Sets column names of input annotations.
- Parameters
- *valuestr
Input columns for the annotator
- setLabelCol(label)[source]¶
Set a column with one label per document. Example of possible values: “present”, “absent”, “hypothetical”, “conditional”, “associated_with_other_person”, etc.
- Parameters
- labelstr
label. Column with one label per document. Example of possible values: “present”, “absent”, “hypothetical”, “conditional”, “associated_with_other_person”, etc.
- setLazyAnnotator(value)¶
Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
- Parameters
- valuebool
Whether Annotator should be evaluated lazily in a RecursivePipeline
- setLearningRate(lamda)[source]¶
Set a learning rate for the optimization process
- Parameters
- lamdafloat
Learning rate for the optimization process.
- Returns
- Annotation
The new Annotation.
- setMaxSentLen(length)[source]¶
Set the max length for an input sentence.
- Parameters
- lengthint
Max length for an input sentence.
- setOutputCol(value)¶
Sets output column name of annotations.
- Parameters
- valuestr
Name of output column
- setOutputLogsPath(value)[source]¶
Sets folder path that contain external graph files.
- Parameters
- valuesrt
Folder path that contain external graph files.
- setParamValue(paramName)¶
Sets the value of a parameter.
- Parameters
- paramNamestr
Name of the parameter
- setStartCol(s)[source]¶
Set a column that contains the token number for the start of the target
- Parameters
- sstr
Column that contains the token number for the start of the target
- setTestDataset(path, read_as='SPARK', options={'format': 'parquet'})[source]¶
Sets path to test dataset. If set used to calculate statistic on it during training.
- Parameters
- pathsrt
Path to test dataset. If set used to calculate statistic on it during training.
- setValidationSplit(v)[source]¶
- Set Choose the proportion of training dataset to be validated against the model on each Epoch.
The value should be between 0.0 and 1.0 and by default it is 0.0 and off.
- Parameters
- vfloat
Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.
- setVerbose(value)[source]¶
Sets level of verbosity during training.
- Parameters
- valueint
Level of verbosity during training.
- uid¶
A unique id for the object.
- write()¶
Returns an MLWriter instance for this ML instance.


| pmclust and pkmeans {pmclust} | R Documentation |
Parallel Model-Based Clustering and Parallel K-means Algorithm
pmclust(X = NULL, K = 2, MU = NULL, algorithm = .PMC.CT$algorithm, RndEM.iter = .PMC.CT$RndEM.iter, CONTROL = .PMC.CT$CONTROL, method.own.X = .PMC.CT$method.own.X, rank.own.X = .pbd_env$SPMD.CT$rank.source, comm = .pbd_env$SPMD.CT$comm) pkmeans(X = NULL, K = 2, MU = NULL, algorithm = c("kmeans", "kmeans.dmat"), CONTROL = .PMC.CT$CONTROL, method.own.X = .PMC.CT$method.own.X, rank.own.X = .pbd_env$SPMD.CT$rank.source, comm = .pbd_env$SPMD.CT$comm)
X |
a GBD row-major matrix or a |
K |
number of clusters. |
MU |
pre-specified centers. |
algorithm |
types of EM algorithms. |
RndEM.iter |
number of Rand-EM iterations. |
CONTROL |
a control for algorithms, see |
method.own.X |
how |
rank.own.X |
who own |
comm |
MPI communicator. |
These are high-level functions for several functions in pmclust
including: data distribution, setting global environment .pmclustEnv
,
initializations, algorithm selection, etc.
The input X
is either in ddmatrix
or gbd
. It will
be converted in gbd
row-major format and copied into
.pmclustEnv
for computation. By default, pmclust uses a
GBD row-major format (gbdr
). While common
means that
X
is identical on all processors, and single
means that
X
only exist on one processor rank.own.X
.
These functions return a list with class pmclust
or pkmeans
.
See the help page of PARAM
or PARAM.org
for details.
Wei-Chen Chen<EMAIL_ADDRESS>and George Ostrouchov.
High Performance Statistical Computing (HPSC) Website: http://thirteen-01.stat.iastate.edu/snoweye/hpsc/
Programming with Big Data in R Website: http://r-pbd.org/
set.global
,
e.step
,
m.step
.
set.global.dmat
,
e.step.dmat
,
m.step.dmat
.
## Not run: # Save code in a file "demo.r" and run in 4 processors by # > mpiexec -np 4 Rscript demo.r ### Setup environment. library(pmclust, quiet = TRUE) ### Load data X <- as.matrix(iris[, -5]) ### Distribute data jid <- get.jid(nrow(X)) X.gbd <- X[jid,] ### Standardized N <- allreduce(nrow(X.gbd)) p <- ncol(X.gbd) mu <- allreduce(colSums(X.gbd / N)) X.std <- sweep(X.gbd, 2, mu, FUN = "-") std <- sqrt(allreduce(colSums(X.std^2 / (N - 1)))) X.std <- sweep(X.std, 2, std, FUN = "/") ### Clustering library(pmclust, quiet = TRUE) comm.set.seed(123, diff = TRUE) ret.mb1 <- pmclust(X.std, K = 3) comm.print(ret.mb1) ret.kms <- pkmeans(X.std, K = 3) comm.print(ret.kms) ### Finish finalize() ## End(Not run)


HANGMAN GAME
Chinese
German
French
Spanish
Russian
Turkish
SCORE
0
Word
Question1
Question2
Question3
Question4
START / NEXT


ST NEOT AND THE FOX.
One day the holy hermit was standing in his bath chanting the Psalms, when he heard the sound of huntsmen approaching.
Whether the saint feared, ridicule or ill-treatment, we know not; but certainly he left some psalms unsung that day, and hastily gathering up his clothes, he fled to his cell.
In his haste the good man lost his shoe, and a hungry fox having escaped the hunters, came to the spring to drink. Having quenched the fever of thirst, and being hungry, he spied the saint's shoe, and presently ate it. The hermit despatched his servant to look for his shoe; and, lo, he found the fox cast into a deep sleep, and the thongs of the shoe hanging out of his vile mouth. Of course the shoe was pulled out of his stomach, and restored to the saint.


POLITICO
....having transparent election is our concern.
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{% extends "datapages/realtime_base.html" %} {% load humanize %} {% load staticfiles %} {% block js %} {{ block.super }} {% if weeklysummaries %} {% endif %} {% endblock %} {% block sitecss %} {{ block.super }} {% endblock sitecss %} {% block body_class %}{% endblock %} {% block pagetitle %}{{ title }} {% endblock %} {% block rtmain %}
This page shows the top House and Senate races by weekly outside spending totals. Weeks start on Monday and end on Sunday. Only races with more than $1,000 reported in outside spending are shown. Also see the top TV markets in terms of political ad buy documents filed during this week.


Dalla Porta claims Malaysian GP Moto3 title
SEPANG, Nov 3 — Italian Lorenzo Dalla Porta won the Moto3 race from seventh grid in the 2019 Shell Malaysia Motorcycle Grand Prix (MotoGP) at the Sepang Circuit here, today.
The 22-year-old representing Leopard Racing team finished the race in 38 minutes 01.355 seconds, 0.410s ahead of Spaniard Sergio Garcia of Estrella Galicia and 0.803s ahead of third-placed Jaume Masia of Mugen Race.
“Yesterday, I told the media I only want to enjoy and try to win the race. And today, I’m simply enjoying it and won the race as well,” said Dalla Porta.
Briton John McPhee representing Malaysia’s home team Petronas Sprinta Racing was 1.342s behind the leader to settle in seventh place while his teammate Ayumu Sasaki of Japan retired after being involved in a crash in the eighth lap. — Bernama


{{!-- TEMPORARY: Show all apps, implemented the same as "featured apps" on front page until
we have enough apps to break the front page down by genre. --}}
All Apps
{{> genreTableOther genre="All"}}
{{!--
{{#each genres}}
{{> genreTable genre=this.name singleLine=true}}
{{/each}}
--}}


| PWG Atlanta Meeting Details |
1394 Printing: September 15,16
IPP: September 17,18
Other Projects: September 19
| Meeting
Location: Atlanta Marriott Midtown Suites 35 14th Street Atlanta, Georgia 30309<PHONE_NUMBER> |
Ping List |
The hotel rate is $129 per
night.
Ask for the Lexmark rate at this hotels.
Deadline for reservations is August 29, 1997
All attendees are responsible for making their own hotel
reservations.
There will be a meeting charge in the range of $35 - $40; participants are responsible for their lunch.
YOU MUST RSVP TO Don Wright<EMAIL_ADDRESS>WITH THE FOLLOWING INFORMATION:


Personal Details
| Age: | 22 |
| DOB: | 02-09-1995 | Specialization: | Cyber Security |
| Nationality: | Nigerian |
| Place of Residence: | Tallinn, Estonia |
| Marital status: | Single |
| Hobbies: | coding, singing, dancing, swimming, horse riding |
| Age: | 22 |
| DOB: | 02-09-1995 | Specialization: | Cyber Security |
| Nationality: | Nigerian |
| Place of Residence: | Tallinn, Estonia |
| Marital status: | Single |
| Hobbies: | coding, singing, dancing, swimming, horse riding |


As a describer of life and manners, he must be allowed to stand perhaps the first.
His humour, which, as Steele observes, is peculiar to himself
He never "o'ersteps the modesty of nature," nor raises merriment.
His figures neither divert by distortion nor amaze by aggravation.
He copies life with so much fidelity that he can be hardly said to invent
This is box.This box is contain so many information.
Most frequent parasites seen in box.casts are unique to the kindny.
An acidic environment promotes.As a describer of life and manners, he must be allowed
to stand perhaps the first of the first rank. His humour, which, as Steele observes, is
peculiar to himself, is so happily diffused as to give the grace of novelty to domestic
scenes and daily occurrences. He never "o'ersteps the modesty of nature," nor raises
merriment or wonder by the violation of truth.
This is regular text,which is aligned to the left by default.
you can easily change,the aligned using the class text center and text right on each
paragraph.The crafting of clear, coherent paragraphs is the subject of considerable
stylistic debate.Forms generally vary among types of writing. For example, newspapers,
scientific journals, and fictional essays have somewhat different conventions for the
placement of paragraph breaks.


UD Validation since release 2.5
The following mechanism ensures that we can add new validation tests on one hand, and we do not
lose old treebanks on the other hand. Note that “new tests” do not really mean new guidelines.
These tests typically check guidelines that were already valid before but we were not able to
test them.
See the release checklist for general information on validation and
treebank requirements.
See the online validation report
for the current status of the dev
branch of each treebank.
New Treebanks
Treebanks that have not yet been released in UD must pass all currently required tests before they can be released for the first time.
Previously Released Treebanks
Treebanks with Unfinished New Development (UNDev)
The treebank has been valid and released under the current major version of the UD guidelines (v2 at present). Since then, data were modified or added and new errors were introduced. These are new errors, not newly discovered old errors. If the errors are not fixed before the upcoming data freeze, the treebank will be released in the same form in which it was released last time. The current contents of the dev branch will not be merged into the master branch.
Legacy Treebanks
The treebank has been successfully released under the current major version of the UD guidelines (v2 at present) and passed all mandatory tests that were available at the time of that release. Legacy treebanks are not required to pass newly added tests (although their maintainers are of course encouraged to fix the newly found errors) and they will be included in future releases as long as the major version of the guidelines has not changed. If they are being fixed, they can be also fixed incrementally. For example: The treebank passed all tests required for UD 2.3 and became part of that release. After UD 2.3, several new tests were added and the treebank does not pass two of them, labeled A and B. The maintainers manage to fix all type A errors but not type B errors; they do not introduce any new errors of other types. The partially fixed treebank will be released in UD 2.4, still as a legacy treebank, but the exception is now granted only to the type B errors. It is not allowed to reintroduce any type A error in the future.
Obsolete Treebanks
The UD Japanese KTC treebank was part of UD 1.* releases and it was never converted to the v2 guidelines. Therefore it was not part of UD 2.* releases.
If there is a new version of the guidelines in the future (v3), it must specify how the legacy treebanks are to be treated. Especially if the new version of the guidelines is not backwards-compatible.
Levels of Validity
The validation tests are organized to several levels. The validation script can be optionally invoked to only test validity up to level L (but the official on-line validation will still require passing the higher levels in order for the treebank to be released).
Level 1: Backbone CoNLL-U Format
These are the core tests that check the file integrity. Software tools can crash if their input is not valid at this level. Level 1 should be relatively stable; it is not expected that new tests will be added here. (And if they are, all treebanks including legacy treebanks must be made to pass them.)
Example tests: each token line has 10 columns, there is an empty line after each sentence. Except for token ID, values in columns can be unspecified, as in partially processed data. The file could also contain non-UD annotation and be level-1 valid.
Level 2: Basic UD Concepts
The UPOS tag must be present and it must be one of the 17 tags defined in UD. HEAD and DEPREL must be present, universal part of DEPREL must be one of the 37 known labels, the structure must be a rooted tree. Morphological features, if present, must follow the prescribed format. Enhanced dependencies in DEPS, if present, are checked as well.
The annotation in LEMMA, XPOS, FEATS, DEPS and MISC is optional and it can be unspecified. A few treebanks omit even the word FORM; this is for copyright reasons, the user must obtain a licensed copy of the underlying text and then run specialized software to merge it with the UD annotation.
Note that any feature-value pair and any relation subtype is valid on level 2 if it has the expected form (checked by a regular expression). To disallow language-specific features and relation subtypes, use the validation level 4 but set the language to “ud”.
Level 2 should be relatively stable; it is not expected that new tests will be added here. (And if they are, all treebanks including legacy treebanks must be made to pass them.)
Level 3: Universal Guidelines
Any language-independent rule that follows from the guidelines and can be reliably checked automatically will be checked here unless it was already checked at level 2. Typical rules involve (in)compatibility between particular UPOS tags, relation types and tree topology. If enhanced dependencies are present, the rules can apply to them too.
The first rule of this type was introduced between the releases 2.1 and 2.2. It verifies that certain types of relations (e.g., conj) always go left-to-right. Numerous other level-3 tests were added later; legacy treebanks can be released without passing them.
Level 4: Language-specific Labels
Morphological features (FEATS) and relation types (DEPREL and DEPS) allow language-specific extensions. Therefore the lower levels checked only the general format of these columns but not the actual labels. Level 4 assumes that we know the language of the data we are validating and we can check a list of available labels in a separate file. The current practice is that maintainers of treebanks of a given language collectively maintain the lists of labels for that language. A feature-value pair is considered valid if it is documented in the guidelines (either globally or specifically for the language) and it is specifically permitted with the given UPOS tag in the given language. A relation subtype is considered valid if it is documented and permitted (turned on) in the given language.
In addition to features and deprels, language-specific configuration may also describe the permitted types of “words with spaces”. These are currently described using regular expressions.
Note that it could make sense to check level 4 for a treebank that is valid on levels 1 and 2 but not 3. It would mean that we want to test the general file format and label inventory, but not the more intricate interplay between various pieces of the annotation. However, the current implementation of the validator assumes that a treebank valid on a level is also valid on all lower levels.
Note that language-specific guidelines can change between releases and do not have to wait until the next major UD guidelines upgrade, as long as they do not contradict the current universal guidelines.
Level 5: Language-specific Guidelines
Any other reliably testable rules that apply to one language or a group of languages but not to all languages. At present there is an experimental set of level-5 rules that lists for each language the lemmas that can be auxiliary verbs/particles, and lemmas that can be copulas.
Future rules could specify that a particular feature must occur with a particular UPOS tag (currently it is only possible to exclude features that must not occur with a UPOS tag); however, we would have to account for the fact that features in general are optional and the treebank may not have them at all.
Level 6: Treebank-specific Requirements
One may want to test that some kind of annotation, which is generally optional, is always present in their treebank. For example, all words should have the Translit and Gloss attributes in the MISC column. No such tests have been implemented so far.
Warnings
Many potential rules on levels 3-5 cannot be implemented as strict tests because there are legitimate exceptions. However, if it is still probable that a violation of the rule signals an actual annotation error, the rule could issue a warning. No such tests have been implemented so far.
Code Switching
When validating language-specific lists of morphological features, auxiliary verbs etc., the
validator can temporarily switch to a language different from the main language of the file.
This is useful in cases of code-switching, i.e., when a foreign language phrase is inserted in
the text, and the annotators decided to annotate it according to the foreign language guidelines
(instead of simply tagging everything as X
and connecting it as a flat
structure). The
alternative language is signaled for individual tokens using the Lang
attribute in the MISC
column.
Its value is the ISO 639 language code, as registered for the language in UD (either two letters
from ISO 639-1, or three letters from ISO 639-3, lowercased!)
If Lang=xx
is provided, the morphological features, their values and their applicability to
particular UPOS tags will be validated as in language xx
, with one exception: the feature
Foreign=Yes
should be present and it says that the word is foreign in the main language of
the treebank, not in language xx
.
Other Notes
We should have a section in the documentation that would list all validation tests, explain them, link to the relevant parts of the guidelines and possibly also to the relevant part of the source code of the validation script. Some newly added rules are discussed and refined; in such cases, we may want to link to the corresponding issues in the issue tracker too.
It would be nice if the validator could output the relevant CoNLL-U snippet with the errors highlighted, possibly even visualizing the tree. This would be in addition to providing the id of the problematic sentence/line, and it would be output on demand only. UDAPI already does this, so maybe we could run the validator together with UDAPI.


{% extends 'base.html' %} {% block title %}Home{% endblock title %} {% block nav-style %}mkt-nav{% endblock nav-style %} {% block content %} {# Banner Section %}
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Mukesh Kumar Gupta, Raj Kumar appointed as LIC managing directors
The two were promoted by the government through an order dated Friday
by Press Trust of IndiaLife Insurance Corporation of India on Friday said Mukesh Kumar Gupta and Raj Kumar have taken charge as managing directors of the company.
The two were promoted by the government through an order dated Friday.
Gupta was earlier executive director, personnel, for the life insurance behemoth and had also served as zonal manager in charge of the central zone, an official statement said.
Kumar was executive director for international operations and had also served as the chief executive of the insurer's asset management arm.
With their elevation, the number of managing directors at the company goes up to four, the statement said. T C Susheel Kumar and Vipin Anand are the other two MDs.


| DOSE: T+ 0:00 |
1 |
oral |
LSD |
(cookie / food) |
| T+ 2:00 |
1 line |
insufflated |
Ketamine |
(pill / tablet) |
| T+ 2:00 |
1 cig. |
smoked |
Cannabis |
(plant material) |
I started off the night by dissolving a sugar cube soaked in a rather potent mixture of LSD that my friends and I had been enjoying of late. Normally I would take two for a strong trip, like I was after tonight, but I had decided to experiment with mixing other drugs with LSD to change the psychedelic experience.
This Acid was particularly pleasant as it came on fast. After an hour I was already getting strong visual distortions, the usual warping effects that one experiences while under the influence of LSD. I waited another two hours until I was starting into the peak of the journey, where the visual distortions became full blown, open eye hallucinations, auditory hallucinations and that warped sense of thought that starts to come about. It was now that I decided to take my hit of Ketamine.
The Ketamine was in a pill, so it may have contained MDMA and other ingredients, but the main compononent was the Ketamine, and it was the K hit we were after. Myself and two of my friends crushed up a pill each and snorted a line. We then rolled a joint and shared it around. Half an hour later I was beginning on the most intense trip that I had ever experienced. First I was hit by an intense feeling of numbness, offset by euphoria, and I could hear strange sounds echoing in the background. I remember looking at one of my friends, who said something to me, but I couldn't make out what was said. Suddenly I forgot how to differentiate between sight, sound, taste, smell and touch. It all became a whirlwhind blur of intense visuals and sounds.
I remember watching a spot on the wall that seemed to be crumbling inwards, then an intense burst of light exploded from that spot. I watched the light trail and it became a dragon. It twisted and turned, split into two, then rushed towards me. I let out a frightened yelp as I was overcome by the light wave and sat up suddenly. I can't remember lying down, but obviously I had done it previously. I started to think about that, but quickly lost my train of thought. I could hear something in the distance, something just beyond my reach. I tried to grab at it, but I failed and was only rewarded by a maniacal laugh that sent a shiver running up and down my spine.
This is when the fear started to hit me. It crept up my spine slowly but surely, my heart started to race, I could hear the blood pumping through my veins... THUMP, THUMP, THUMP, THUMP... suddenly this just became another sound swept up in the whirlwhind of psychedelic colours. I watched as a rainbow myriad of patterns washed over me and I saw in them evil creatures dancing, taunting me and laughing over and over in that horrid, evil laugh.  All things became meaningless, time had lost all relevance, reality no longer existed, all that was and all that there ever would be was my soul, my being, my very existence.
At this point I got stuck in a loop where I convinced myself I was some kind of deity stuck in a pre-infantile state where I was not fully aware of my awesome powers, and that I could only tap into them slightly through dreaming up my reality. I forgot now that I was on any kind of drugs and as I watched my soul float away from my body, higher and higher into the myriad of colours I started to lose my sense of composure. For lack of a better word, I freaked out because I thought that my soul was being torn from my body and I would have no way to return to my divine essence, doomed forever to obscurity in purgatory. Obviously some other deity was responsible for this, one who knew about my infantile state where I was stuck in a dream, and now that I was awakening they were trying to dispose of me. I tride to scramble my way back to my body, but I realised that as a soul I had no arms or legs.
At this stage I was yelling and squirming on the floor, and my friends were trying to calm me down, but I still couldn't comprehend them. After a while my soul seemed to return to my body and I started to calm down, focusing on the still shifting, pulsing patterns and entrancing sounds echoing all around me. I have no idea how much time had passed, but I was still tripping intensely, and by the time I could finally comprehend that there were other Human beings in the room with me, it had been nearly nine hours since I had taken the LSD. It still took about another half hour before I could follow conversation sufficiently to respond to my friend's queries, at which point I assured them that I was ok.
Speaking with my two other friends who had combined the three, I got similar reports. It seems that the dissosciative effect of Ketamine combined with the psychedelics of Acid creates a very, very intense out of body experience that is very difficult to differentiate between reality and the effects of the drug. I have no doubt whatsoever that the sheer intensity of this was due in large to the mixing of Weed with the other two, however. While I would reccommend the experimentation of Ketamine with Acid, combining Cannabis with this as well is a very intense and disturbing experience.
While I enjoyed myself, and am pleased to have experienced such an intense journey, it is not a combination that I think is meant for Human use. It is very, very easy to panic and have a bad trip on this combination, and were it not for the fact that my friends and I are all experienced trippers who knew how to handle it, I think that things wouldn't have turned out so well. Out of the three of us, I enjoyed it the most, one said it was interesting and the other said he hated every second of the trip. We all agreed it was something that we would never combine again.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.