machine learning
Briefly describe the flow of a complete machine learning project.
- Formulate it into a mathmatic problem
- Collect samples
- Feature preprocessing and feature selection
- Model training and tuning
- Determine the direction and idea of model tuning: Common methods such as cross-validation, drawing learning curves, etc. overfitting, underfitting, Error analysis: whether it is a problem of parameters or algorithm selection, whether it is a problem of features or of the data itself…
- Model fusion:
- Model deployment: Engineering is result-oriented, and the effect of the model running online directly determines the success or failure of the model. It not only includes its accuracy, error, etc., but also its running speed (time complexity), resource consumption (space complexity), and whether the stability is acceptable.
What’s the trade-off between bias and variance?
In statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters. The bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set:
-
What is bias?
Bias is the difference between the average prediction of our model and the correct value which we are trying to predict. Model with high bias pays very little attention to the training data and oversimplifies the model. It always leads to high error on training and test data (underfitting). -
What is variance?
Variance is the variability of model prediction for a given data point or a value which tells us spread of our data. Model with high variance pays a lot of attention to training data and does not generalize on the data which it hasn’t seen before. As a result, such models perform very well on training data but has high error rates on test data (overfitting). -
Mathematically
Let the variable we are trying to predict as Y and other covariates as X. We assume there is a relationship between the two such that
\[Y=f(X) + e\]Where $e$ is the error term and it’s normally distributed with a mean of $0$.
We will make a model \(\hat{f}(X)\) of \(f(X)\) using linear regression or any other modeling technique. So the expected squared error at a point $x$ is
\[Err(x)=E[(Y-\hat{f}(x))^2]\]The \(Err(x)\) can be further decomposed as
\[Err(x)=(E[\hat{f}(x)]-f(x))^2+E[(\hat{f}(x)-E[\hat{f}(x)])^2]+\sigma^2_e \\ =Bias^2+Variance+Irreducible Error\]\(Err(x)\) is the sum of \(Bias^2\), variance and the irreducible error.
Irreducible error is the error that can’t be reduced by creating good models. It is a measure of the amount of noise in our data. Here it is important to understand that no matter how good we make our model, our data will have certain amount of noise or irreducible error that can not be removed.
The bias–variance decomposition is a way of analyzing a learning algorithm’s expected generalization error with respect to a particular problem as a sum of three terms, the bias, variance, and a quantity called the irreducible error, resulting from noise in the problem itself.
What is the difference between supervised and unsupervised machine learning?
-
Supervised Learning In supervised machine learning, a model makes predictions or decisions based on past or labeled data. Labeled data refers to sets of data that are given tags or labels, and thus made more meaningful.
-
Unsupervised Learning In unsupervised learning, we don’t have labeled data. A model can identify patterns, anomalies, and relationships in the input data.
What is Overfitting, and How Can You Avoid It?
The Overfitting is a situation that occurs when a model learns the training set too well, taking up random fluctuations in the training data as concepts. These impact the model’s ability to generalize and don’t apply to new data.
When a model is given the training data, it shows 100 percent accuracy—technically a slight loss. But, when we use the test data, there may be an error and low efficiency. This condition is known as overfitting.
There are multiple ways of avoiding overfitting, such as:
- Data normalization, imput missing values, increase sampls, and add noise
- Regularization. It involves a cost term for the features involved with the objective function
- Making a simple model. With lesser variables and parameters, the variance can be reduced
- Cross-validation methods like k-folds can also be used
- If some model parameters are likely to cause overfitting, techniques for regularization like LASSO can be used that penalize these parameters
- dropout、regularization、batch normalization
Why normalization?
- After normalization, the speed of gradient descent to find the optimal solution is accelerated;
- Normalization may improve the accuracy because of distance calculation
Normalization methods
- minmax
- standand
- non-linear: log
How Do You Handle Missing or Corrupted Data in a Dataset?
One of the easiest ways to handle missing or corrupted data is to drop those rows or columns or replace them entirely with some other value.
There are two useful methods in Pandas:
- IsNull() and dropna() will help to find the columns/rows with missing data and drop them
- Fillna() will replace the wrong values with a placeholder value
How Can You Choose a Classifier Based on a Training Set Data Size? When the training set is small, a model that has a right bias and low variance seems to work better because they are less likely to overfit.
For example, Naive Bayes works best when the training set is large. Models with low bias and high variance tend to perform better as they work fine with complex relationships.
Explain the Confusion Matrix with Respect to Machine Learning Algorithms.
| Actual:Positive | Actual:Negative | ||
|---|---|---|---|
| Predicted:Positive | True Positive (TP) | False Positive (FP) (Type I) | Positive Predictive Value(PPV), Precision \(\frac{TP}{TP+FP}\) |
| Predicted:Negative | False Negative (FN) (Type II) | True Negative (TN) | |
| True Positive Rate(TPR), Recall, Sensitivity \(\frac{TP}{TP+FN}\) | False Positive Rate(FPR) \(\frac{FP}{FP+TN}\) |
For a model to be accurate, the values across the diagonals should be high. The total sum of all the values in the matrix equals the total observations in the test data set.
The F1-score combines the precision and recall of a classifier into a single metric by taking their harmonic mean.
\[F1-score = \frac{2(precision * recall)}{precision+recall}\]- precision: A low precision can also indicate a large number of False Positives(Type I).
- recall: A low recall indicates many False Negatives(Type II).
For any system to be able to achieve maximum Precision (no false positive) and maximum Recall (no false negative) there needs to be an absence of type I and II errors.
Explain how a ROC curve works.
An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters:
- True Positive Rate
- False Positive Rate An ROC curve plots TPR vs. FPR at different classification thresholds. Lowering the classification threshold classifies more items as positive, thus increasing both False Positives and True Positives. The following figure shows a typical ROC curve.
AUC stands for “Area under the ROC Curve.” That is, AUC measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0,0) to (1,1).
How is KNN different from k-means clustering?
| K-means | KNN |
|---|---|
| K-Means is unsupervised | KNN is supervised in nature |
| K-Means is a clustering algorithm | KNN is a classification algorithm |
| The points in each cluster are similar to each other, and each cluster is different from its neighboring clusters | It classifies an unlabeled observation based on its K (can be any number) surrounding neighbors |
What is Bayes’ Theorem? How is it useful in a machine learning context?
Bayes Theorem is a method to determine conditional probabilities – that is, the probability of one event occurring given that another event has already occurred. Because a conditional probability includes additional conditions – in other words, more data – it can contribute to more accurate results.
Say a bookstore manager has information about his customers’ age and income. He wants to know how book sales are distributed across three age-classes of customers: youth (18-35), middle-aged (35-60), and seniors (60+).
Let us term our data X. In Bayesian terminology, X is called evidence. We have some hypothesis H, where we have some X that belongs to a certain class C.
| Our goal is to determine the conditional probability of our hypothesis H given X, i.e., $$P(H | X)$$. |
| In simple terms, by determining $$P(H | X)$$, we get the probability of X belonging to class C, given X. X has attributes of age and income – let’s say, for instance, 26 years old with an income of $2000. H is our hypothesis that the customer will buy the book. |
Pay close attention to the following four terms:
-
Evidence – As discussed earlier, \(P(X)\) is known as evidence. It is simply the probability that the customer will, in this case, be of age 26, earning $2000.
-
Prior Probability – \(P(H)\), known as the prior probability, is the simple probability of our hypothesis – namely, that the customer will buy a book. This probability will not be provided with any extra input based on age and income. Since the calculation is done with lesser information, the result is less accurate.
-
Posterior Probability – $$P(H X)$$ is known as the posterior probability. Here, P(H X) is the probability of the customer buying a book (H) given X (that he is 26 years old and earns $2000). -
Likelihood – $$P(X H)$$ is the likelihood probability. In this case, given that we know the customer will buy the book, the likelihood probability is the probability that the customer is of age 26 and has an income of $2000.
Given these, Bayes Theorem states:
\[P(H | X) = \frac{P(X | H) * P(H)}{P(X)}\]What’s the difference between probability and likelihood?
- Probability refers to the chance that a particular outcome occurs based on the values of parameters in a model.
- Likelihood refers to how well a sample provides support for particular values of a parameter in a model.
When calculating the probability of some outcome, we assume the parameters in a model are trustworthy.
However, when we calculate likelihood we’re trying to determine if we can trust the parameters in a model based on the sample data that we’ve observed.
Why is “Naive” Bayes naive?
Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption and unrealistic for real data; however, the technique is very effective on a large range of complex problems.
How is a decision tree pruned?
Pruning is a technique in machine learning that reduces the size of decision trees. It reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.
Pruning can occur in:
- Top-down fashion. It will traverse nodes and trim subtrees starting at the root
- Bottom-up fashion. It will begin at the leaf nodes
What is a Random Forest?
A ‘random forest’ is a supervised machine learning algorithm that is generally used for classification problems. It operates by constructing multiple decision trees during the training phase. The random forest chooses the decision of the majority of the trees as the final decision.
How does random forest generate the forest? Additionally, why would we use it over other algorithms such as alogistic regression? Random forest generates the forest by essentially bootstrapping your dataset to create many different decision tree functions and then aggregating them together to create a superior model that has a lower variance than the individual decision trees have. Because the bootstrapped samples used to create the individual decision tree models are not truly independent, a random subset of the features from the dataset are used to split nodes for purity. This reduces the correlation between individual trees.
The advantage that Random Forest has over models such as logistic regression is that it can capture non-linear relationships in the data and does not make assumptions about continuity in the feature values that models such as logistic regression make.
Let’s say you have a categorical variable with thousands of distinct values, how would you encode it?
- One-hot encoding:
- pros - two levels numeric encoding, naive
- cons - given the cardinality, dimensions increase, curse of dimensionlity
- label encoding:
- pros - numeric vector given the categories
- cons - does not add any predictive power, confusion around the interpretability
- count encoding: replacing categories with their counts computed on the train set.
- pros - indicative of the frequency, predictive power
- cons - sensitive to outliers, may result in collision - encoding two categories as the same value.
- Target encoding - proportion/avg of Y for this categorical level
- pros - predictive power, better performance, learning from the labels
- cons - chances of target leakage if not implemented properly
We apply One-Hot Encoding when:
The categorical feature is not ordinal (like the countries above) The number of categorical features is less so one-hot encoding can be effectively applied We apply Label Encoding when:
The categorical feature is ordinal (like Jr. kg, Sr. kg, Primary school, high school) The number of categories is quite large as one-hot encoding can lead to high memory consumption reference
Which is more important to you? Model accuracy, or model performance? Well, you must know that model accuracy is only a subset of model performance. The accuracy of the model and performance of the model are directly proportional and hence better the performance of the model, more accurate are the predictions.
How would you handle an imbalanced dataset?
-
- Choose Proper Evaluation Metric F1 score keeps the balance between precision and recall and improves the score only if the classifier identifies more of a certain class correctly.
-
- Resampling (Oversampling and Undersampling)
-
- SMOTE Synthetic Minority Oversampling Technique or SMOTE is another technique to oversample the minority class. Simply adding duplicate records of minority class often don’t add any new information to the model. In SMOTE new instances are synthesized from the existing data. If we explain it in simple words, SMOTE looks into minority class instances and use k nearest neighbor to select a random nearest neighbor, and a synthetic instance is created randomly in feature space.
-
- BalancedBaggingClassifier When we try to use a usual classifier to classify an imbalanced dataset, the model favors the majority class due to its larger volume presence. A BalancedBaggingClassifier is the same as a sklearn classifier but with additional balancing. It includes an additional step to balance the training set at the time of fit for a given sampler. This classifier takes two special parameters “sampling_strategy” and “replacement”. The sampling_strategy decides the type of resampling required (e.g. ‘majority’ – resample only the majority class, ‘all’ – resample all classes, etc) and replacement decides whether it is going to be a sample with replacement or not.
-
- Threshold moving
When should you use classification over regression?
Classification is about identifying group membership while regression technique involves predicting a response. Both techniques are related to prediction, where classification predicts the belonging to a class whereas regression predicts the value from a continuous set. Classification technique is preferred over regression when the results of the model need to return the belongingness of data points in a dataset to specific explicit categories. (For instance, when you want to find out whether a name is male or female instead of just finding it how correlated they are with male and female names.
Name an example where ensemble techniques might be useful.
Ensemble Technique is a machine learning method or technique that combines various base models in order to produce one optimal and predictive model. reduce overfitting and more robust
A good example of how ensemble methods are commonly used to solve data science problems is the random forest algorithm (having multiple CART models). It performs better compared to individual CART model by classifying a new object where each tree gives “votes” for that class and the forest chooses the classification having the most votes (over all the trees in the forest). In case of regression, it takes the average of outputs of different trees.
How do you ensure you’re not overfitting with a model?
- 1- Keep the model simpler: remove some of the noise in the training data.
- 2- Use cross-validation techniques such as k-folds cross-validation.
- 3- Use regularization techniques such as LASSO
What evaluation approaches would you work to gauge the effectiveness of a machine learning model?
Use the kernel function to ensure that there is no data in the high-dimensional space to calculate the coordinates of the point, and the inner product in the space can be calculated. Many algorithms can express such an inner product form, using kernel energy to ensure that low-dimensional data is calculated in a high-dimensional space.
What is Principal Component Analysis?
Principal Component Analysis or PCA is a multivariate statistical technique that is used for analyzing quantitative data. The objective of PCA is to reduce higher dimensional data to lower dimensions, remove noise, and extract crucial information such as features and attributes from large amounts of data.
What Are Some Methods of Reducing Dimensionality?
You can reduce dimensionality by combining features with feature engineering, removing collinear features, or using algorithmic dimensionality reduction.
How would you evaluate a logistic regression model?
- Accuracy Score
- Recall: Recall can be thought of as a measure of a classifiers completeness. A low recall indicates many False Negatives.
- Precision: Precision can be thought of as a measure of a classifiers exactness. A low precision can also indicate a large number of False Positives.
- F1 Score, if you are dealing with a imbalanced dataset this is a must. If you are looking to select a model that is balanced between precision and recall F measure should be used.
What’s the “kernel trick” and how is it useful?
Kernel trick allows the inner product of mapping function instead of the data points. The trick is to identify the kernel functions which can be represented in place of the inner product of mapping functions. Kernel functions allow easy computation. In essence, what the kernel trick does for us is to offer a more efficient and less expensive way to transform data into higher dimensions.
Explain the difference between L1 and L2 regularization.
- L1 regularization penalizes the sum of absolute values of the weights, whereas L2 regularization penalizes the sum of squares of the weights.
- The L1 regularization solution is sparse. The L2 regularization solution is non-sparse.
- L2 regularization doesn’t perform feature selection, since weights are only reduced to values near 0 instead of 0. L1 regularization has built-in feature selection.
- L1 regularization is robust to outliers, L2 regularization is not.
- L1 is a Laplace distribution and L2 is a Gaussian distribution.
What’s a Fourier transform?
The Fourier transform is a mathematical function that decomposes a waveform, which is a function of time, into the frequencies that make it up. The result produced by the Fourier transform is a complex valued function of frequency. The absolute value of the Fourier transform represents the frequency value present in the original function and its complex argument represents the phase offset of the basic sinusoidal in that frequency.
Q9: What’s your favorite algorithm, and can you explain it to me in less than a minute?
Q13: What is deep learning, and how does it contrast with other machine learning algorithms?
Q14: What’s the difference between a generative and discriminative model? Core Idea: Discriminative models draw boundaries in the data space, while generative models try to model how data is placed throughout the space. A generative model focuses on explaining how the data was generated, while a discriminative model focuses on predicting the labels of the data.
| Mathematical Intuition: In mathematical terms, a discriminative machine learning trains a model which is done by learning parameters that maximize the conditional probability P(Y | X), while on the other hand, a generative model learns parameters by maximizing the joint probability of P(X, Y). |
Outliers: Generative models have more impact on outliers than discriminative models.
Computational Cost: Discriminative models are computationally cheap as compared to generative models.
generative models:
- Naive Bayes, Hidden Markov Models, Gaussian Mixture Models, Document Topic Generation (LDA), Restricted Boltzmann Machines
discriminative models:
- K-Nearest Neighbors, SVM, Decision Trees, Perceptrons, Linear Discriminant Analysis (LDA), Linear Regression, Traditional Neural Networks, Logistic Regression, Boosting, Conditional Random Fields
What cross-validation technique would you use on a time series dataset?
 In order to accurately simulate the “real world forecasting environment, in which we stand in the present and forecast the future” (Tashman 2000), the forecaster must withhold all data about events that occur chronologically after the events used for fitting the model. So, rather than use k-fold cross-validation, for time series data we utilize hold-out cross-validation where a subset of the data (split temporally) is reserved for validating the model performance. For example, see following figure where the test set data comes chronologically after the training set. Similarly, the validation set comes chronologically after the training subset.
What is bagging?
Bagging, also known as bootstrap aggregation, is the ensemble learning method that is commonly used to reduce variance within a noisy dataset. In bagging, a random sample of data in a training set is selected with replacement—meaning that the individual data points can be chosen more than once. After several data samples are generated, these weak models are then trained independently, and depending on the type of task—regression or classification, for example—the average or majority of those predictions yield a more accurate estimate.
As a note, the random forest algorithm is considered an extension of the bagging method, using both bagging and feature randomness to create an uncorrelated forest of decision trees.
Bagging vs. boosting
Bagging and boosting are two main types of ensemble learning methods. As highlighted in this study (PDF, 248 KB) (link resides outside IBM), the main difference between these learning methods is the way in which they are trained. In bagging, weak learners are trained in parallel, but in boosting, they learn sequentially. This means that a series of models are constructed and with each new model iteration, the weights of the misclassified data in the previous model are increased. This redistribution of weights helps the algorithm identify the parameters that it needs to focus on to improve its performance. AdaBoost, which stands for “adaptative boosting algorithm,” is one of the most popular boosting algorithms as it was one of the first of its kind. Other types of boosting algorithms include XGBoost, GradientBoost, and BrownBoost.
Another difference in which bagging and boosting differ are the scenarios in which they are used. For example, bagging methods are typically used on weak learners which exhibit high variance and low bias, whereas boosting methods are leveraged when low variance and high bias is observed.
What does S (stochastic) in SGD stand for?
- Optimization algorithms that use the entire training set are called batch or deterministic gradient algorithms because all samples are processed simultaneously in one large batch.
- Optimization algorithms that use only a single sample at a time are sometimes called stochastic or online algorithms.
- Most algorithms for deep learning fall somewhere in between, using more than one rather than all training samples. They are now commonly referred to simply as stochastic methods.
LSTM structure derivation, why is it better than RNN?
Derive the changes of forget gate, input gate, cell state, hidden information, etc.; because LSTM has in and out and the current cell informaton is superimposed after being controlled by the input gate, RNN is multiplication, so LSTM can prevent the gradient from vanishing or exploding.
Gradients vanishing and exploding
- Gradient clipping, regularization: WGAN
- relu、leakrelu、elu
- batchnorm
- residual connection
- LSTM
covariance and correlation
covariance(A, C) cannot compare with covariance(B, C) because of the different ranges of A and B Correlation is the standard covariance. reference


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[gecode-users] Reification
Mikael Zayenz Lagerkvist
zayenz at gmail.com
Mon Apr 27 17:59:09 CEST 2009
2009/4/27 Joe Porter <joe.porter at gmail.com>:
> I have a few questions about reification (I'm still using Gecode 2.2.0, but
> 1. Can I do open-ended reification?
> For example, if I post a linear constraint ((x=y+c) <=> ~b) and no alternate
> (for b true) will this either assert the linear constraint or assert b, or
> do I have to have another clause for the b case?
I'm not sure what you mean, but I'll try. If you post a reified
constraint c with a control-variable b, but otherwise no constraints
on b, then the constraint won't really "mean" anything. The only point
at which the constraint will do something is to set b to one or zero
if the constraint is found to hold or not hold.
> 2. Can I use the same BoolVar to reify groups of contraints?
> For example, if I have (xi=yi+c) for i=1..n, can I post them all with the
> same b?
Sure, there is nothing special about the control variable, it is just
like any other BoolVar.
Cheers,
Mikael
--
Mikael Zayenz Lagerkvist, http://www.ict.kth.se/~zayenz/
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Lazugh Cad Processing Metadata
Metadata
This document is formatted using MarkDown
This work was done by Anthony Beck, OrcID: 0000-0002-2991-811X.
This work was done for free because I want to see it in Open Street Map.
You can download the files from my anonymous ftp sites here: ftp://<IP_ADDRESS>/FTPBeck/Lazugh.zip.
Things for others to do and be aware of
- Sort out the projection
- Check that these file reflect the original data (i.e. validate this stuff)
- Specifically check to see if the number of trees output represent the number of trees in the oroginal
- There are, inevitable, weirdnesses in the data. If you see the csv file you will find co-ordinates that are not in Luzugh. Not sure if I can do anything about this - I think it's an artifact of the way they did there CAD work.
- There are no attributes in the polygon shapefiles. I'm not that bothered. You may be
- The falaj system is currently as polylines (in Structure) you can export it as an individual thing. Do note that it does connect through to other landscape features (wells, cisterns, generators etc. ) - this connectivity has not been fully articulated (you are in a better position to do this than you were). BTW some of the falaj original survey is pretty poor).
- I'm happy to chat about this.
I will put this into Open Street Map once I've had initial feedback from you all. In OSM hopefully other people will sort out some of the problems/layering issues which means you will have a better base-map to work with :-)
Processing the Lazugh CAD survey
- Done in AutoCAD MAP
- All saved in Lazugh/CAD
- Open file "Survey drawing".dwg
- Document layer types and details (stored in WhatTheLayersMean.xlsx)
- Cleaned out extraneous rubbish (hatch layers etc) saved the layer as SurveydrawingARB_clean.dwg
- Created an autoCAD map query template linking to SurveydrawingARB_clean.dwg called LazughTemplate.dwg
- Pull out the groups as defined in the excell spreadsheet and process independently
- All further work saved in Lazugh/CAD/Derivatives
Processing the 3d data
Completed = Yes
Purpose
This data represents 3d point and breakline data suitable for producing a Digital Elevation Model within a GIS package.
Outputs
- 3d_Lazugh.dwg
- 3d_points.csv
- 3d_breaklines.shp
- 3d points.shp
Layers
- 3D breakline Line
- b break line Line
- SRF-FLT Line
- SURVEY DATA Point
Processing
- Template saved as 3d_Lazugh.dwg
- A Map query was run as 'Report'on the layer 'SURVEY DATA' reporting X, Y, Z to the file 3d_points.csv
- This was saved as LazughReportTemplate.dwg
- A Map query was run as 'draw' on the following layers
- 3D breakline Line
- b break line Line
- SRF-FLT Line
- SURVEY DATA Point
- A visual inspection showed that SRF-FLT contained most of the breakline data (layers '3d breakline' and 'b break line' were deleted)
- The data was exported into the subfolder ShapeFiles:
- Line geometry as 3d_breaklines.shp
- Point geometry as 3d points.shp
Processing the Control data
Completed = Yes
Purpose
The survey company established 3d control points in the study area. These will be useful for future survey work.
Outputs
- Control_Lazugh.dwg
- Control_points.csv
- Control_points.shp
Layers
- BENCH MARK Point
Processing
- Using LazughReportTemplate.dwg
- A Map query was run as 'Report' on the layer 'BENCH MARK' reporting X, Y, Z to the file Control_points.csv
- a copy of LazughTemplate.dwg was made and called Control_Lazugh.dwg
- A Map query was run as 'draw' on the following layers
- BENCH MARK Point
- The data was exported into the subfolder ShapeFiles:
- Point geometry as 3d points.shp
Processing the Falaj data
Completed = No - it has been incorporated into the SURFACE output (you can extract it from the LINE shapefile). I'd have liked tis to be prettier.... ho hum.
Purpose
The Falaj network in Lazugh.
Outputs
Layers
- 3d falaj centre line Line Falaj
- 3d falaj line Line Falaj
- NEW FALAJ 3d CENTRE line Line Falaj
- NEW FALAJ 3d CENTRE line Line Falaj
- Wash box Polygon Falaj
- Water pole Mixed Falaj
- Water tank Polygon Falaj
- Well Polygon Falaj
- CW Line Falaj?
Processing
- a copy of LazughTemplate.dwg was made and called Falaj_Lazugh.dwg
- A Map query was run as 'draw' on the following layers
- The data was exported into the subfolder ShapeFiles:
- Point geometry as
Processing the Network data
Completed = Yes
Purpose
Road and track network data in Lazurgh
Outputs
- Network_Lazugh.dwg
- Network_Polygon.shp
- Network_Line.shp
Layers
- Asphalt Road Line
- TRACK ROAD Line
Processing
- A copy of LazughTemplate.dwg was made and called Network_Lazugh.dwg
- A Map query was run as 'draw' on the following layers
- Asphalt Road Line
- TRACK ROAD Line
- The polygon segments were closed
- Drawing cleanup was used to create topologically clean line segments
- Topology was created
- Closed polygons were created on the layer 'RoadPolygon'
- The data was exported into the subfolder ShapeFiles:
- Polygon geometry as Network_Polygon.shp
- Line geometry as Network_line.shp
Processing the Street Furniture data
Completed = Yes
Purpose
Street Furniture in Lazurgh
Outputs
- StreetFurniture_Lazugh.dwg
- StreetFurniture_Points.shp
- StreetFurniture_Points.csv
Layers
- BOLLARDS point
- EP Point
- FH Point
- LP Point
- PIPE point
- POLE point
- TP Point
- UGC Point
- VALVE02 Point
- Road Sign Board point
Processing
- A copy of LazughTemplate.dwg was made and called StreetFurniture_Lazugh.dwg
- A Map query was run as 'draw' on the following layers
- BOLLARDS point
- EP Point
- FH Point
- LP Point
- PIPE point
- POLE point
- TP Point
- UGC Point
- VALVE02 Point
- Road Sign Board point
- The layers were renamed to something sensible
- The data was exported into the subfolder ShapeFiles (layers was exported as an attribute):
- Point geometry as StreetFurniture_Point.shp
- The X,Y, Z and Layer value were then exported as a re[port into StreetFurniture_Points.csv
Processing the Vegetation (including trees) data
Completed = Yes
Purpose
Exporting al the the vegetation areas as recorded in Luzugh
Outputs
- Vegetation_Lazugh.dwg
- Vegetation_Point.shp
- Vegetation_Point.csv
Layers
- BUSH Mixed
- DATES Point
- DATES DRY Point
- DATES MEDIUM Point
- DATES SMALL Point
- FLOWER Point
- LEMON TREE DRY Point
- TREE Point
- TREE ALLEE Point
- TREE ANAR Point
- TREE BANANA Point
- TREE COCONUT Point
- TREE GUVA Point
- TREE NEEM Point
- TREE ORANGE Point
- TREE PAPPAYA Point
- TREE SMALL Point
Processing
- A Map query was run as 'report' on the following layers exporting (X, Y, Z and Layer) into Vegetation_point.csv
- BUSH Mixed
- DATES Point
- DATES DRY Point
- DATES MEDIUM Point
- DATES SMALL Point
- FLOWER Point
- LEMON TREE DRY Point
- TREE Point
- TREE ALLEE Point
- TREE ANAR Point
- TREE BANANA Point
- TREE COCONUT Point
- TREE GUVA Point
- TREE NEEM Point
- TREE ORANGE Point
- TREE PAPPAYA Point
- TREE SMALL Point
- This layer was edited in Open Office Calc. The following structural changes occured:
- Z field removed
- LAYER field renamed to ORIGINAL_LAYER
- Fields PLANT_GROUP, SPECIES and CHARACTERISTICS added
- The following data was added (the first vale represents the value in OIRINGAL_LAYER)
ORIGINALLAYER|PLANTGROUP|SPECIES|CHARACTERISTICS ----|----|----|---- BUSH|Bush|Unspecified|Unspecified Dates|Tree|Date|Unspecified DATES DRY|Tree|Date|Dry DATES MEDIUM|Tree|Date|Medium DATES SMALL|Tree|Date|Small FLOWER|Flower|Unspecified|Unspecified Lemon Tree dry|Tree|Lemon|Unspecified TREE ALLEE|Tree|Allee|Unspecified TREE ANAR|Tree|Anar|Unspecified TREE COCONUT|Tree|Coconut|Unspecified TREE GUVA|Tree|Guva|Unspecified TREE LEMON|Tree|Lemon|Unspecified TREE MANGO|Tree|Mango|Unspecified TREE NEEM|Tree|Neem|Unspecified TREE ORANGE|Tree|Orange|Unspecified TREE UNSPECIFIED|Tree|Unspecified|Unspecified TREE UNSPECIFIED SMALL|Tree|Unspecified|Small
- The csv data was imported into QGIS and then saved as a shapefile:
- Point geometry as Vegetation_Point.shp
Processing the Structure data
Completed = Yes
Purpose
Exporting all the structure data recorded in Luzugh. The aim is to export this as polygons with real topology and lines for the non-polygon components. This may also include all the Falaj bits.
Essenitally this is a messy nightmare
Outputs
- Structure_Lazugh.dwg
- StructureLazughCleaned.dwg
- Structure_Line.shp
- Structure_Polygon.shp
Layers
- All that I deem relevant
Processing
- A Map query was run as 'draw' on the following layers
- All that I deem relevant
- Line only layers were seperated (and their layers prefixed with line)
- A blanket drawing cleanup (overlaps, duplicates, intersections etc.) was used to remove initial cruft
- Hand editing and closing of polygons (FALAJ in particular)
- Gave up on the FALAJ as it was just too much
- put falaj into the lines component
- All saved as StructureLazughCleaned.dwg
- Created topology
- Saved the version with a topology file
- All saved as StructureLazughCleaned_withTopology.dwg
- Closed polyline topology layers exported as shapefile
- Structure_Polygon.shp
- Polyline data exportde as shapefile with Layer as attribute
- Structure_Line.shp


"Deathdream" Also Known As: "Dead of Night" (1972) "Night Andy Came Home, The" (1972) "Night Walk" (1972) "Veteran, The" (1972) Distributed by: Entertainment International Pictures Produced by: Impact Films / Quadrant Films
Cinematography by Jack McGowan . Music by Carl Zittrer. Costume Design by Dyke Davis. Written by Alan Ormsby. Make-up Effects by Tom Savini & Alan Ormsby. Edited by Ronald Sinclair. Executive Producers: Gerald Flint-Shipman, Geoffrey Nethercott Produced by Bob Clark, John Trent, Peter James. Directed by Bob Clark John Marley, Richard Backus, Lynn Carlin, Henderson Forsyth, Anya Ormsby, Jane Daly, Michael Mazes
Review by R.L.Strong )1997 R.L. Strong Bob Clark's "DEATHDREAM" is without a doubt one of the Seventies most important and effective horror films. Taking it's basic premise from the classic tale of "The Monkey's Paw", the film combines vampirism with not only the political climate of the Seventies and the war in Vietnam, but also the youth movement. After years (decades?) of visceral horror being the basic means by which terror is explored, "Deathdream" manages to do the impossible in a horror film. It creates a mood and an atmosphere of dread that is palpable. Just watching any scene from the first 50 minutes of the film, will give any viewer a sense of unease that they will be unlikely to shake off immediately. "Deathdream" deals in a disenfranchised Americana, where family values are being questioned and faithfully held beliefs in family and honor are laid to rest.
The plot of the film is pretty straight forward. Andy is a lieutenant in the U.S. Army serving in Vietnam. He and his Captain are killed in a firefight. As Andy dies, he hears the pleas of his Mother, telling him not to die, that he must come home. We immediately switch to a small Midwestern town, Anywhere USA. A truck driver picks up a soldier. He stops off at a dinner, getting coffee and mentioning how strange the Soldier is. "He hasn't said a word". He tells the waitress. At the Brooks' home, the family is awakened in the middle of the night by the arrival of the local enlistment office. He tells the family that their son, Andy has been killed in the line of duty. The family is distraught. Andy's Mother refuses to accept the idea that her only son is not coming home. Later that night, the family is awakened again by the sound of someone breaking into the house. Rushing downstairs, Mr. Brooks is startled by his son, Andy, standing behind the front door in full military uniform.
Celebrations start immediately. Andy's Mother is overjoyed. Plans are made to tell all of Andy's friends. Andy says not to. He just wants time to rest. His Father asks Andy if the situation in the War was bad. Andy replies.. "Over where, Dad?" The next morning, news reports come in telling of the murder of a truck driver. His throat is slashed, but he also has hypodermic punctures in his arm. Andy isolates himself in his room. He refuses to see any of his friends, or let his family acknowledge that he is back. The tension between the family members grows as Andy becomes more mysterious and threatening. As the Brooks family begins to abandon their faiths and love for one another, Andy becomes more treacherous, murdering the family Doctor, using his blood (in one of the films most disturbing scenes) as a FIX.
The performances are adequate for the most part. The standouts are John Marley, one of the industries most underrated character actors as the Father of the besieged Brooks family. His character becomes more and more ineffectual as the film progresses, finally giving in to complete depression. This performance alone makes the film worth viewing. Richard Backus as ANDY gives a chilling interpretation of the walking dead. His character is pure evil. This is a soulless and totally uncalculating creature. Andy's soul purpose now is to inflict terror and pain on all those around him. But mostly on his own family. Mr. Backus creates a feeling of dread and fear that I haven't seen on screen until Anthony Hopkins started asking for more wine Director Bob Clark has created one of the most intelligent and emotional horror films ever made. The film pays more tribute to the style of Val Lewton than it does to the gore-miesters of it's decade. This is not to say that the film does not contain scenes of visceral horror. The murder of the town Doctor (played by Henderson Forsyth) is both frightening and revolting. But Mr. Clark does not wallow in these scenes, he uses them as a release from the tension that the film has been building too. "Deathdream" in my mind should be included with the other classics of the genre ("Exorcist", "Night of the Living Dead", "The Haunting") as a classic. The script by Alan Orsmby ('Children Shouldn't Play With Dead Things', 'My, Bodyguard') is marvelously structured. The dialogue is honest and doesn't give into the standard type of one-liner mentality so prevalent in horror films today. The cinematography by Jack McGowan is wonderful in it's simplicity, recalling the best work of such good cinematographers (but poor Directors) as T. V. Mikels and Ray Dennis Steckler.
This is not a film for children! The movie deals with issues that are too adult and emotional for youngsters to grasp. Also, the violence in the film is not romanticized. This is not "SCREAM" or any other film of that type, that tries to compromise it's horror with a leveling spoonful of comedy. It's not a cult film (though it could be if it had the exposure it deserves). It is without a doubt, one of the 10 best horror films of the seventies. And I would go on to say, of all time!
-- _________________________________________________ |"It's not enought to tell us what a man did. | |You've have to tell us who he was.." Citizen Kane| |_________________________________________________|
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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{% extends "jumbotron.html" %} {% block jumbo %} {% if messages %}
{% endif %} {% endfor %}We're working to collaboratively develop and deploy a concept inventory for {{ CI_COURSE }}. If you're an educator who works on courses related to {{ CI_COURSE }}, we need your help!
{% endblock %} {% block body %}A concept inventory (CI) is an assessment that aims to evaluate high-level student learning goals for a particular subject area. CIs elevate the level discourse within the educational community by providing a mechanism for quantitative evaluation across pedagogical methods, instructors, and institutions.
Conceptum is an open source web platform for collaboratively developing and deploying concept inventories.


Use our menu to place an order online, or phone our store to place a pickup order. Fast and fresh food.
Use our menu to place an order online and our staff will have your food ready ASAP!


Domino Development - Back to Basics - Part 1: The NSF
Over the last five years I have trained many developers on XPages. A good portion of them came from a non-Notes development background: Java, dotNet, PHP and others. Most of them had a better grasp on the necessities of web development than most of their die-hard-is-there-anything-other-than-the-Notes-client LotusScript colleagues. However they struggled to fully appreciate the finer points of the NSF based nature of Domino development. The little article series is for them.
In short: Notes is a distributed application platform with an integrated NoSQL document database featuring a rich security and event model. It can be accessed in many different languages and protocols. While it is written in C, the most prominent languages used to develop applications in Notes are LotusScript (a Basic dialect, now de emphasised), JavaScript and Java. The most used protocols are HTTP(s), NRPC and SMTP.
A NSF contains properties, an Access Control List, View indexes and a collection of Note elements, hence the name Note(s). The file name is not a property stored inside the NSF, however the title and the ReplicaID are. The ReplicaID has a special meaning: two databases sharing the same ReplicaID can synchronise their content in a process called replication, typically between a client and a server or multiple servers. When a server contains two or more NSF with the same replicaID replication can happen with any of them. A typical mistake: copy a NSF on the file system "as backup" creating not a copy but a replica. There is a menu action that creates a real copy for that (File - Application - New Copy).
Inside the NSF all data is stored in Note elements: both the design of the database, as well as the actual content. A data note (a.k.a Document) can be listed in a view (with the exception of a profile document), while a design note lists only in Domino Designer (unless you hack it). Design and data note elements share the same inner structure.
NSF do not contain any schema or data definitions. It is all Note and Item.
The contained items follow the same pattern: they have a name, a set of properties and a collection of values. Two important properties are name and type. The name of an item doesn't need to be unique. Quite regular, especially when looking at the type RichText you have more than one item with the same name (for RichText the unwritten convention is to use the name Body). Items that start with the $ sign are used for system properties (e.g. $HasNativeMime indicates that the note contains a MIME document rather than RichText). Items also contain a serial number for conflict resolution in replication. The values in an item share the same data type. Items are always multi-value (a Vector in Java) even if the count is just one.
Next up: Forms and Documents
In short: Notes is a distributed application platform with an integrated NoSQL document database featuring a rich security and event model. It can be accessed in many different languages and protocols. While it is written in C, the most prominent languages used to develop applications in Notes are LotusScript (a Basic dialect, now de emphasised), JavaScript and Java. The most used protocols are HTTP(s), NRPC and SMTP.
In the beginning was the NSF
Almost everything in Notes is stored in NSF databases (NSF = Notes Storage Facility).A NSF contains properties, an Access Control List, View indexes and a collection of Note elements, hence the name Note(s). The file name is not a property stored inside the NSF, however the title and the ReplicaID are. The ReplicaID has a special meaning: two databases sharing the same ReplicaID can synchronise their content in a process called replication, typically between a client and a server or multiple servers. When a server contains two or more NSF with the same replicaID replication can happen with any of them. A typical mistake: copy a NSF on the file system "as backup" creating not a copy but a replica. There is a menu action that creates a real copy for that (File - Application - New Copy).
Inside the NSF all data is stored in Note elements: both the design of the database, as well as the actual content. A data note (a.k.a Document) can be listed in a view (with the exception of a profile document), while a design note lists only in Domino Designer (unless you hack it). Design and data note elements share the same inner structure.
NSF do not contain any schema or data definitions. It is all Note and Item.
A Note is where you find anything
An individual note contains a series of properties and a collection of items (not to confuse with a field in an RDBMS or a field in a form). Besides data values for creation and update a note carries a UniversalId, a NoteId and a serial number. The NoteId is unique inside one specific NSF, the UniversalId is the identifier to link two note elements in two NSF (that have the same ReplicaId) together, so they synchronise on replication. The UniversalId is unique inside the NSF. Two different NSF can have documents with the same UniversalId. This is not an exception, but happens e.g. on mail routing. The serial number counts the times that note has been updated and is used in conflict resolution while replicating.The contained items follow the same pattern: they have a name, a set of properties and a collection of values. Two important properties are name and type. The name of an item doesn't need to be unique. Quite regular, especially when looking at the type RichText you have more than one item with the same name (for RichText the unwritten convention is to use the name Body). Items that start with the $ sign are used for system properties (e.g. $HasNativeMime indicates that the note contains a MIME document rather than RichText). Items also contain a serial number for conflict resolution in replication. The values in an item share the same data type. Items are always multi-value (a Vector in Java) even if the count is just one.
Next up: Forms and Documents
Posted by Stephan H Wissel on 23 December 2013 | Comments (6) | categories: IBM Notes XPages


Implementation Overview
This page only works in modern browsers (e.g. firefox, chrome, ms edge) with javascript enabled.
Site:
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Location:
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Map of HCSRN organizations


#include <math.h>
#include "slu_zdefs.h"
Defines | |
| #define | ITMAX 5 |
Functions | |
| void | zgsrfs (trans_t trans, SuperMatrix *A, SuperMatrix *L, SuperMatrix *U, int *perm_c, int *perm_r, char *equed, double *R, double *C, SuperMatrix *B, SuperMatrix *X, double *ferr, double *berr, SuperLUStat_t *stat, int *info) |
-- SuperLU routine (version 3.0) -- Univ. of California Berkeley, Xerox Palo Alto Research Center, and Lawrence Berkeley National Lab. October 15, 2003
Modified from lapack routine ZGERFS
| #define ITMAX 5 |
| void zgsrfs | ( | trans_t | trans, | |
| SuperMatrix * | A, | |||
| SuperMatrix * | L, | |||
| SuperMatrix * | U, | |||
| int * | perm_c, | |||
| int * | perm_r, | |||
| char * | equed, | |||
| double * | R, | |||
| double * | C, | |||
| SuperMatrix * | B, | |||
| SuperMatrix * | X, | |||
| double * | ferr, | |||
| double * | berr, | |||
| SuperLUStat_t * | stat, | |||
| int * | info | |||
| ) |
Purpose =======
ZGSRFS improves the computed solution to a system of linear equations and provides error bounds and backward error estimates for the solution.
If equilibration was performed, the system becomes: (diag(R)*A_original*diag(C)) * X = diag(R)*B_original.
See supermatrix.h for the definition of 'SuperMatrix' structure.
Arguments =========
trans (input) trans_t Specifies the form of the system of equations: = NOTRANS: A * X = B (No transpose) = TRANS: A'* X = B (Transpose) = CONJ: A**H * X = B (Conjugate transpose)
A (input) SuperMatrix* The original matrix A in the system, or the scaled A if equilibration was done. The type of A can be: Stype = SLU_NC, Dtype = SLU_Z, Mtype = SLU_GE.
L (input) SuperMatrix* The factor L from the factorization Pr*A*Pc=L*U. Use compressed row subscripts storage for supernodes, i.e., L has types: Stype = SLU_SC, Dtype = SLU_Z, Mtype = SLU_TRLU.
U (input) SuperMatrix* The factor U from the factorization Pr*A*Pc=L*U as computed by zgstrf(). Use column-wise storage scheme, i.e., U has types: Stype = SLU_NC, Dtype = SLU_Z, Mtype = SLU_TRU.
perm_c (input) int*, dimension (A->ncol) Column permutation vector, which defines the permutation matrix Pc; perm_c[i] = j means column i of A is in position j in A*Pc.
perm_r (input) int*, dimension (A->nrow) Row permutation vector, which defines the permutation matrix Pr; perm_r[i] = j means row i of A is in position j in Pr*A.
equed (input) Specifies the form of equilibration that was done. = 'N': No equilibration. = 'R': Row equilibration, i.e., A was premultiplied by diag(R). = 'C': Column equilibration, i.e., A was postmultiplied by diag(C). = 'B': Both row and column equilibration, i.e., A was replaced by diag(R)*A*diag(C).
R (input) double*, dimension (A->nrow) The row scale factors for A. If equed = 'R' or 'B', A is premultiplied by diag(R). If equed = 'N' or 'C', R is not accessed.
C (input) double*, dimension (A->ncol) The column scale factors for A. If equed = 'C' or 'B', A is postmultiplied by diag(C). If equed = 'N' or 'R', C is not accessed.
B (input) SuperMatrix* B has types: Stype = SLU_DN, Dtype = SLU_Z, Mtype = SLU_GE. The right hand side matrix B. if equed = 'R' or 'B', B is premultiplied by diag(R).
X (input/output) SuperMatrix* X has types: Stype = SLU_DN, Dtype = SLU_Z, Mtype = SLU_GE. On entry, the solution matrix X, as computed by zgstrs(). On exit, the improved solution matrix X. if *equed = 'C' or 'B', X should be premultiplied by diag(C) in order to obtain the solution to the original system.
FERR (output) double*, dimension (B->ncol) The estimated forward error bound for each solution vector X(j) (the j-th column of the solution matrix X). If XTRUE is the true solution corresponding to X(j), FERR(j) is an estimated upper bound for the magnitude of the largest element in (X(j) - XTRUE) divided by the magnitude of the largest element in X(j). The estimate is as reliable as the estimate for RCOND, and is almost always a slight overestimate of the true error.
BERR (output) double*, dimension (B->ncol) The componentwise relative backward error of each solution vector X(j) (i.e., the smallest relative change in any element of A or B that makes X(j) an exact solution).
stat (output) SuperLUStat_t* Record the statistics on runtime and floating-point operation count. See util.h for the definition of 'SuperLUStat_t'.
info (output) int* = 0: successful exit < 0: if INFO = -i, the i-th argument had an illegal value
Internal Parameters ===================
ITMAX is the maximum number of steps of iterative refinement.


PDC Pre-Con Tutorial: Windows Forms
Here. I don't think I've mentioned it here, but I'm really looking forward to the Windows Forms pre-conference tutorial I'm giving with Rocky Lhotka at the PDC. I've never spoken at a PDC before but it'll be just like old times with Don, Tim and Martin down the hall.
Also, I've never spoken with Rocky before, but he's also a big Midwesterner with a strong opinion, so it should be fun. Plus, he prefers semi-colonless languages, so all kinds of sparks should fly as I attempt to convert him to the one, true way and he does the same to me. We may have trouble remembering there's an audience. : )


Segment 3D cases by splitting each 3D case into 2D planes and segmenting those 2D planes using residual UNet.
Count Vehicle using density map regression.
Django server for auto-labeling tool based on CVAT
Django application server for a distributed cloud disk storage.


MailChimp Form Code for Lead Magnet Automations Created Today
MailChimp Form Code for Lead Magnet Automations Created Before
Choose Date:
MailChimp Form Code for an Interest
Script Ideas
Test MailChimp Form for a Lead Magnet Automation Created Today


In this tutorial, you learned how to encode rests with MEI. The elements you've learned are:
These elements are almost always empty and have no child elements. With the space element, you already learned about a fairly advanced mechanism of aligning multiple voices sharing a staff in MEI. If you're interested in that, you may also want to investigate into the@next
and
@prev
attributes from MEI's
att.linking
attribute class (available on <note>
and other events). That attribute class allows to
lay "breadcrumbs" through an MEI file that make it possible to follow voices across multiple layers
(and staves, if necessary). This would facilitate analytical use of your MEI file, and is not required in
most situations, though.


{% load static from staticfiles %}
This tool lets you quickly configure GroupVPN groups using our XMPP server. Guest users are limited to groups with only two machines. Registered users can create groups with up to 50 machines. {% block main %} {% endblock %}


Improve this Doc
Badges are small components that typically communicate a numerical value to the user. They are typically used within an item.
_For more information, Check out the [API docs](../api/components/badge/Badge)._
Basic Usage
Demo
Demo Source
```html
Followers
260k
```
Badges can also be given any color attribute:
```html
```


HEAD
Test
Hello world testdev 3
Hello world testdev 4 - ito
Are we there yet?
Change the js
test line
=======
Test
Hello world testdev 3
Are we there yet?
test line
>>>>>>> bafb3fce71a7eba4e0c9eb6e08bad6d888703d9b


THE PLEDGE A film review by Jonathan Curiel Copyright 2001 filmcritic.com filmcritic.com
An early note to parents with young, blonde daughters: Think twice about seeing The Pledge, and if you must go -- if you're eager to see Jack Nicholson give one of his best cinematic performances ever -- then take a Valium before entering the theater and practice saying, "This won't happen to my family! This won't happen to my family!"
>From the opening shot, where we see the top of Nicholson's half-bald, hair-transplanted head, The Pledge is an exercise in stomaching an ugly truth. Body parts, pony-tailed girls splotched with blood and bruises -- this isn't a film about happy endings and human triumph.
Sean Penn has directed a dark thriller that encrusts Jerry Black (Nicholson) with a Sisyphean task: Find the person who savaged a child in the snow near Reno, Nevada. Bloated and baggy-eyed, Black enters the case on the night of his retirement party, then volunteers to work on it for the next year and a half, after pledging to the dead girl's mother that he'll bring the killer to justice. (Hence the movie's title.)
Trailers for The Pledge have played up the intensity of Nicholson's character, but those expecting a bigger-than-life caricature a la A Few Good Men or The Shining (or any number of other Nicholson vehicles) will be disappointed. Nicholson, whose character looks like he'd be perfectly at home in a bowling alley, is relatively understated in The Pledge, and that's what makes it so riveting and believable. There are no over-the-top moments that call for lines like "You can't handle the truth!" Instead of yelling his way past obstacles, Jerry Black navigates through clues like an old, frustrated man. Nicholson told a Hollywood reporter that he was "more of myself" in The Pledge, and moviegoers have Penn to thank for this real-life performance. Penn and Nicholson are good friends and apparently share a love of storytelling that doesn't rely on gimmicks or pyrotechnics. In fact, Penn based The Pledge on a 1958 novel by Swiss writer Friedrich Durrenmatt: "A German existentialist," according to a man at the screening I attended.
Existential terror is just one of the many layers that give The Pledge a sheen that is sickening and yet satisfying to watch. There is beauty in The Pledge, represented by the snowy, green outdoors and the face of the young girl who Jerry Black strongly believes could be the next victim. An ensemble cast of veteran actors and actresses (led by Helen Mirren, who plays a psychologist) lets Penn add scenes that flush out the edges of Nicholson's memorable character.
 Wright Penn has improved her acting since She's So Lovely, the 1997 flick that showcased her, her husband, and John Travolta, but that's like saying Monica Lewinsky has gotten smarter and more articulate since first emerging into the international spotlight from the shadows of the White House. Wright Penn is a blight on the Penn name. For her to appear in the same movie as Nicholson, let alone kiss him on screen and consort with him, is a testament to the credo, "Anything is possible in America."
And anything is. The Pledge is a reminder that evil is alive and well on this planet. Getting rid of it is harder than it looks.

The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


My name is Jordan Soh and I am 25 years old. I spent 6 years in the U.S Army and now I currently live in Frisco, Tx. This is my first week here at SMU taking the full time Coding Bootcamp!


Unique wood fired pizzas or be tempted by our pasta, anipasto,foccasias, salads and Brushchetta dishes.Relax in the friendly ambience as you enjoy the aroma of the wood ovens or sit al fresco and sock up the Mildura sunshine
Food description that is followed after food tittle
Food description that is followed after food tittle
Food description that is followed after food tittle
We bring the session's best mix of organic produce and hand-
crafted farm products convenietly to your door by growing and
partnering with local farms and artisans in your area.
Moringa
Copyright moringa restaurant


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Class CompositeResolverOverride
A ResolverOverride that composites other ResolverOverride objects. The GetResolver operation then returns the resolver from the first child override that matches the current context and request.
Inherited Members
Namespace: Unity.Resolution
Assembly: Unity.Abstractions.dll
Syntax
public class CompositeResolverOverride : ResolverOverride, IEnumerable<ResolverOverride>, IEnumerable
Constructors
| Improve this Doc View SourceCompositeResolverOverride()
Declaration
public CompositeResolverOverride()
CompositeResolverOverride(ResolverOverride[])
Declaration
public CompositeResolverOverride(params ResolverOverride[] resolverOverrides)
Parameters
| Type | Name | Description |
|---|---|---|
| ResolverOverride[] | resolverOverrides |
Methods
| Improve this Doc View SourceAdd(ResolverOverride)
Add a new ResolverOverride to the collection that is checked.
Declaration
public void Add(ResolverOverride newOverride)
Parameters
| Type | Name | Description |
|---|---|---|
| ResolverOverride | newOverride | item to add. |
AddRange(IEnumerable<ResolverOverride>)
Add a set of ResolverOverrides to the collection.
Declaration
public void AddRange(IEnumerable<ResolverOverride> newOverrides)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Collections.Generic.IEnumerable<ResolverOverride> | newOverrides | items to add. |
GetEnumerator()
Returns an enumerator that iterates through the collection.
Declaration
public IEnumerator<ResolverOverride> GetEnumerator()
Returns
| Type | Description |
|---|---|
| System.Collections.Generic.IEnumerator<ResolverOverride> | A System.Collections.Generic.IEnumerator<T> that can be used to iterate through the collection. |
GetResolver(IBuilderContext, Type)
Return a IDependencyResolverPolicy that can be used to give a value for the given desired dependency.
Declaration
public override IDependencyResolverPolicy GetResolver(IBuilderContext context, Type dependencyType)
Parameters
| Type | Name | Description |
|---|---|---|
| IBuilderContext | context | Current build context. |
| System.Type | dependencyType | Type of dependency desired. |
Returns
| Type | Description |
|---|---|
| IDependencyResolverPolicy | a IDependencyResolverPolicy object if this override applies, null if not. |
Overrides
Explicit Interface Implementations
| Improve this Doc View SourceIEnumerable.GetEnumerator()
Returns an enumerator that iterates through a collection.
Declaration
IEnumerator IEnumerable.GetEnumerator()
Returns
| Type | Description |
|---|---|
| System.Collections.IEnumerator | An System.Collections.IEnumerator object that can be used to iterate through the collection. |


new Fragment(name, domain, tasklist)
Represents a single, named workflow, where all activities and decisions are defined.
Parameters:
| Name | Type | Argument | Default | Description |
|---|---|---|---|---|
name |
string | The name of the workflow. | ||
domain |
string | The AWS SWF domain name to execute this workflow in. | ||
tasklist |
string |
<optional> |
<name>-tasklist | The name of the tasklist to listen for tasks on. |
- Source:
Methods
-
accumulator(name, deps, fragment, doneFn, options) → {Fragment}
-
Add a accumulator execution to the workflow The accumulator executes a fragment until the `doneFn` returns a truthy value, taking the results of each iteration and making them available to any dependents
Parameters:
Name Type Argument Description name
string The unique name of the accumulator fragment. deps
Array <optional>
The names of the dependencies that must be met before this activity can execute. fragment
Fragment The workflow fragment to iterate over. Generated by `usher.fragment()` doneFn
function Indicates when the accumulator is complete by returning a truthy value. options
Object <optional>
Custom options for this activity. - Source:
Returns:
This workflow so you can chain commands.- Type
- Fragment
-
activity(name, deps, options) → {Fragment}
-
Add an activity to the workflow
Parameters:
Name Type Argument Description name
string The unique name of the activity. deps
Array <optional>
The names of the dependencies that must be met before this activity can execute. options
Object <optional>
Custom options for this activity. - Source:
Returns:
This workflow so you can chain commands.- Type
- Fragment
-
activityDefaults(options) → {Fragment}
-
Set global activity options for the entire workflow
Parameters:
Name Type Description options
Object Custom options to use for all defined activities. - Source:
Returns:
This workflow so you can chain commands.- Type
- Fragment
-
child(name, deps, workflowName, workflowVersion, options) → {Fragment}
-
Add an child workflow execution to the workflow
Parameters:
Name Type Argument Description name
string The unique name of the child workflow. deps
Array <optional>
The names of the dependencies that must be met before this activity can execute. workflowName
string The name of the workflow to execute. workflowVersion
string The version of the workflow to execute. options
Object <optional>
Custom options for this activity. - Source:
Returns:
This workflow so you can chain commands.- Type
- Fragment
-
decision(name, deps, decisionFn) → {Fragment}
-
Add a decision to the workflow
Parameters:
Name Type Argument Description name
string The unique name of the decision. deps
Array <optional>
The names of the dependencies that must be met before this decision can execute. decisionFn
Fragment~decisionLogic The logic for this decision. - Source:
Returns:
This workflow so you can chain commands.- Type
- Fragment
-
loop(name, deps, fragment, loopFn, options) → {Fragment}
-
Add a looping workflow execution to the workflow The loop executes in batches to help alleviate rate limit exceptions. The number of items to proccess per batch and the delay between batches are both configurable.
Parameters:
Name Type Argument Description name
string The unique name of the loop fragment. deps
Array <optional>
The names of the dependencies that must be met before this activity can execute. fragment
Fragment The workflow fragment to loop over. Generated by `usher.fragment()` loopFn
function A function given the taks's input that returns an array. For every item in the Array an execution of the `fragment` workflow will execute. options
Object <optional>
Custom options for this activity. [itemsPerBatch, batchDelay] - Source:
Returns:
This workflow so you can chain commands.- Type
- Fragment
-
result(name, deps, transformFn) → {Fragment}
-
Add a result transformation to the workflow. This sets the fragments results based on the `transformFn`
Parameters:
Name Type Argument Description name
string The unique name of the result task. deps
Array <optional>
The names of the dependencies that must be met before this decision can execute. transformFn
Fragment~transformationLogic <optional>
The funtion that will perform the transformation. - Source:
Returns:
This workflow so you can chain commands.- Type
- Fragment
-
sequencedTasks() → {Array}
-
Get the defined tasks for the fragment in execution order
- Source:
Returns:
An array of defined tasks- Type
- Array
-
tasks() → {Array}
-
Get the defined tasks for the fragment
- Source:
Returns:
An array of defined tasks- Type
- Array
-
terminate(name, deps) → {Fragment}
-
Add a termination point to the workflow
Parameters:
Name Type Argument Description name
string The unique name that represents this termination point. deps
Array <optional>
The names of the dependencies that must be met before the workflow can terminate. - Source:
Returns:
This workflow so you can chain commands.- Type
- Fragment
-
transform(name, deps, transformFn) → {Fragment}
-
Add a transformation to the workflow. Transformations are good for manipulating the results of prior activities into new representations for future dependents.
Parameters:
Name Type Argument Description name
string The unique name of the transformation. deps
Array <optional>
The names of the dependencies that must be met before this decision can execute. transformFn
Fragment~transformationLogic <optional>
The funtion that will perform the transformation. - Source:
Returns:
This workflow so you can chain commands.- Type
- Fragment
-
variable(name, deps, valueFn) → {Fragment}
-
Set an variable accessable to all future scheduled activities
Parameters:
Name Type Argument Description name
string The unique name of the variable. deps
Array <optional>
The names of the dependencies that must be met before this variable can be set. valueFn
Fragment~variableValueFunction <optional>
The funtion that will calculate the variable name. - Source:
Returns:
This workflow so you can chain commands.- Type
- Fragment
-
whileLoop(name, deps, fragment, doneFn, options) → {Fragment}
-
Add a while loop workflow execution to the workflow The loop executes until the `doneFn` returns a truthy value
Parameters:
Name Type Argument Description name
string The unique name of the while loop fragment. deps
Array <optional>
The names of the dependencies that must be met before this activity can execute. fragment
Fragment The workflow fragment to loop over. Generated by `usher.fragment()` doneFn
function Indicates when the loop is complete by returning a truthy value. options
Object <optional>
Custom options for this activity. - Source:
Returns:
This workflow so you can chain commands.- Type
- Fragment
Type Definitions
-
decisionLogic(input) → {Boolean}
-
The decision logic to execute when evaluating the given named decision
Parameters:
Name Type Description input
Object The results of all dependencies for this decision - Source:
Returns:
Should dependents of this decision execute- Type
- Boolean
-
transformationLogic(input) → {*}
-
The tranformation to execute
Parameters:
Name Type Description input
Object The results of all dependencies for this transformation - Source:
Returns:
The transformed input- Type
- *
-
variableValueFunction(input) → {*}
-
A function to determine the value of the variable
Parameters:
Name Type Description input
Object The results of all dependencies for this variable - Source:
Returns:
The variable value- Type
- *


HOME
SALES
CONTACT
SHOP
Add a Restaurant!
Restaurant Info
Location
Min Hourly Customers
Max Hourly Customers
Average Cookie Per Customer
Submit


2 Kings 12:6
But it was so, that in the three and twentieth year of king Jehoash the priests had not repaired the breaches of the house.


All students and recent graduates are welcome.
CodeRED is a 24 hours hackathon, organized by CougarCS, the largest computer science organization at the University of Houston. It's an event for people to come together and innovate by pushing their limits to create something amazing!
Want to learn something new? Come join us at CodeRED Artemis to solve real-life problems, explore new possibilities, and shoot for the moon.
All students and recent graduates are welcome.
Don't worry! CodeRED is designed to be beginner friendly. We will be having plenty of resources, workshops and mentors to point you in the right direction.
Not a dollar! It's completely free. We'll be providing snacks, meals and drinks as well.
That's pretty common, you'll have some time before and at the beginning of the event to meet other participants who are looking for a team as well. We highly recommend that you team up with someone and make new friends.
A team may have a maximum of 4 people, though you can go solo too.
We know Hackathons and swag go hand in hand like peanut butter and jelly so, of course there will be swag!!!
The moon duh... it can stay out all night. (Okay, we'll stop with the Artemis jokes now.)
Feel free to reach out to us at<EMAIL_ADDRESS>or on Twitter @CodeRED_HOU
Without the support and incredible interaction between students, mentors, and our partners, CodeRED would not be possible.
Join us in accelerating the future of Houston's innovators
and
cultivating our world-class maker community.
Henry Hite
Communications Officer
Jeff Onyemachi
Logistics Officer
Abdullah
Logistics Officer
Jordan Miles
Logistics Officer


Our overarching goal is to explore and understand new quantum states of electronic matter on the atomic scale. To do so, we use and develop novel spectroscopic-imaging scanning tunneling microscopy (SI-STM) tools to visualize the relevant quantum mechanical degrees of freedom.
Questions of interest include: (i), How does the Mott state collapse upon doping and how is this related to the complex phase diagram of high-temperature superconductors? (ii), What is the strange metal phase seen in correlated electron systems? Is this an exotic long-range entangled state? What is the mechanism of dissipation in that state? (iii), Why is the transition temperature in high-temperature superconductors so high?
Currently, our instrument of choice is SI-STM. State-of-the-art SI-STM measures an array of tunneling spectra on a given sample, registered to the atomic sites with picometer precision. Each is proportional to the local density of states at a given location. Ideally, the recorded spectra are so tightly packed that the measurement yields a three-dimensional mapping of the local density of states as a function of locations and energy. This is shown on the image on the right-hand side (10x10 nm2), and its Fourier transform, below.
The quantum materials which we will investigate encapsulate some of the great unsolved mysteries of physics. They include high-temperature superconductors, quantum-critical compounds, graphene, and topological electronic matter that can be used for error-resistant quantum computing.
A main goal is to use modern technology to build the new instrumentation needed to understand these quantum materials. I learned my trade in Seamus Davis’ SI-STM lab and with Felix Baumberger, and later moved as an ETH fellow to Andreas Wallraff’s qudev lab where we investigated coupled cavity arrays in circuit QED. This allowed me to learn new techniques such as high frequency measurements, low temperature noise-free amplification, and quantum-limited measurements. The goal is to combine these with SI-STM.
This will enable the instrumental capabilities to visualize the different quantum mechanical degrees of freedom needed to understand next-generation quantum materials. STM will be the main method, but we use different spectroscopic-imaging techniques to visualize not only the topography, but also the density of states, spins, and other degrees of freedom hidden below the surface.


|
cppkafka
0.2
|
Helper to perform pattern matching when consuming messages. More...
#include <consumer_dispatcher.h>
Classes | |
| struct | EndOfFile |
| struct | Event |
| struct | Throttle |
| struct | Timeout |
Public Member Functions | |
| BasicConsumerDispatcher (ConsumerType &consumer) | |
| template<typename... Args> | |
| void | run (const Args &... args) |
| Consumes messages dispatching events to the appropriate callack. More... | |
| void | stop () |
| Stops consumption. More... | |
Helper to perform pattern matching when consuming messages.
As the way to consume messages requires you to:
This class introduces a pattern matching based approach to consuming messages so the usual loop is simplified away and you can process messages without having to check for all those cases.
When calling BasicConsumerDispatcher::run, a list of callbacks has to be provided. These will handle each case (message, timeout, error, eof), allowing you to only provide what you need. The only callback that is required is the message one. For the rest, the following actions will be performed as defaults:
The signature for each callback should be as following (or compatible)
| cppkafka::BasicConsumerDispatcher< ConsumerType >::BasicConsumerDispatcher | ( | ConsumerType & | consumer | ) |
Constructs a consumer dispatcher over the given consumer
| consumer | The consumer to be used |
| void cppkafka::BasicConsumerDispatcher< ConsumerType >::run | ( | const Args &... | args | ) |
Consumes messages dispatching events to the appropriate callack.
This will loop until BasicConsumerDispatcher::stop is called
| args | The list of callbacks to be executed |
| void cppkafka::BasicConsumerDispatcher< ConsumerType >::stop | ( | ) |
Stops consumption.
Note that as this is synchronous, if there's any poll operations currently in progress, then this will stop after the current call returns


umount, umount2 - unmount filesystem
#include <sys/mount.h>
int umount(const char *target);
int umount2(const char *target, int flags);
umount() and umount2() remove the attachment of the (topmost)
filesystem mounted on target.
Appropriate privilege (Linux: the CAP_SYS_ADMIN capability)
is required to unmount filesystems.
Linux 2.1.116 added the umount2() system call, which, like
umount(), unmounts a target, but allows additional flags
controlling the behavior of the operation:
- MNT_FORCE (since Linux 2.1.116)
- Ask the filesystem to abort pending requests before attempting the
unmount. This may allow the unmount to complete without waiting for an
inaccessible server, but could cause data loss. If, after aborting
requests, some processes still have active references to the filesystem,
the unmount will still fail. As at Linux 4.12, MNT_FORCE is
supported only on the following filesystems: 9p (since Linux 2.6.16), ceph
(since Linux 2.6.34), cifs (since Linux 2.6.12), fuse (since Linux
2.6.16), lustre (since Linux 3.11), and NFS (since Linux 2.1.116).
- MNT_DETACH (since Linux 2.4.11)
- Perform a lazy unmount: make the mount point unavailable for new accesses,
immediately disconnect the filesystem and all filesystems mounted below it
from each other and from the mount table, and actually perform the unmount
when the mount point ceases to be busy.
- MNT_EXPIRE (since Linux 2.6.8)
- Mark the mount point as expired. If a mount point is not currently in use,
then an initial call to umount2() with this flag fails with the
error EAGAIN, but marks the mount point as expired. The mount point
remains expired as long as it isn't accessed by any process. A second
umount2() call specifying MNT_EXPIRE unmounts an expired
mount point. This flag cannot be specified with either MNT_FORCE or
MNT_DETACH.
- UMOUNT_NOFOLLOW (since Linux 2.6.34)
- Don't dereference target if it is a symbolic link. This flag allows
security problems to be avoided in set-user-ID-root programs that
allow unprivileged users to unmount filesystems.
On success, zero is returned. On error, -1 is returned, and errno is set
appropriately.
The error values given below result from filesystem type independent errors.
Each filesystem type may have its own special errors and its own special
behavior. See the Linux kernel source code for details.
- EAGAIN
- A call to umount2() specifying MNT_EXPIRE successfully
marked an unbusy filesystem as expired.
- EBUSY
- target could not be unmounted because it is busy.
- EFAULT
- target points outside the user address space.
- EINVAL
- target is not a mount point.
- EINVAL
- umount2() was called with MNT_EXPIRE and either
MNT_DETACH or MNT_FORCE.
- EINVAL (since Linux 2.6.34)
- umount2() was called with an invalid flag value in
flags.
- ENAMETOOLONG
- A pathname was longer than MAXPATHLEN.
- ENOENT
- A pathname was empty or had a nonexistent component.
- ENOMEM
- The kernel could not allocate a free page to copy filenames or data
into.
- EPERM
- The caller does not have the required privileges.
MNT_DETACH and MNT_EXPIRE are available in glibc since version
2.11.
These functions are Linux-specific and should not be used in programs intended
to be portable.
Shared mount points cause any mount activity on a mount point, including
umount() operations, to be forwarded to every shared mount point in the
peer group and every slave mount of that peer group. This means that
umount() of any peer in a set of shared mounts will cause all of its
peers to be unmounted and all of their slaves to be unmounted as well.
This propagation of unmount activity can be particularly
surprising on systems where every mount point is shared by default. On such
systems, recursively bind mounting the root directory of the filesystem onto
a subdirectory and then later unmounting that subdirectory with
MNT_DETACH will cause every mount in the mount namespace to be lazily
unmounted.
To ensure umount() does not propagate in this fashion, the
mount point may be remounted using a mount() call with a
mount_flags argument that includes both MS_REC and
MS_PRIVATE prior to umount() being called.
The original umount() function was called as umount(device) and
would return ENOTBLK when called with something other than a block
device. In Linux 0.98p4, a call umount(dir) was added, in order to
support anonymous devices. In Linux 2.3.99-pre7, the call
umount(device) was removed, leaving only umount(dir) (since now
devices can be mounted in more than one place, so specifying the device does
not suffice).
mount(2), mount_namespaces(7), path_resolution(7),
mount(8), umount(8)
This page is part of release 5.05 of the Linux man-pages project. A
description of the project, information about reporting bugs, and the latest
version of this page, can be found at https://www.kernel.org/doc/man-pages/.


Broken Heart Museum
“Broken Heart Museum” is a mixed reality museum that brings the visitor to a room of a broken heart. It digs into possibilities of mixing the physical world and the virtual world, ultimately blending both of them together.
#WebVR #a-frame #Javascript #Museum


controllers, kind of
1. introduce event listeners
Yield an array of listeners like this:
var evts = [\{
key: 'click',
callback: function (e) \{ /* do something cool */ }
}]
yield \{ listeners: evts }
If you're familier with deferred concept, you can resolve the promise inside the listener.
var deferred = new Deferred()
var evts = [\{
key: 'click',
callback: function (e) \{ deferred.resolve(\{ message: hello() }) }
}]
yield \{ listeners: evts }
yield deferred.promise
2. combinations
OK, we have generators and routers. Then, combine them.
-
#hour/10
{ msg }
-
#hour/14
{ msg }
-
#hour/18
{ msg }


Here's a vega-lite visualization of the building inventory dataset.
This is a bar chart displaying the Total Million Square Footage in each Congressional District.


Yellowstone National Park spans an area of 3,468.4 square miles (8,983 km2), comprising lakes, canyons, rivers and mountain ranges. Yellowstone Lake is one of the largest high-elevation lakes in North America and is centered over the Yellowstone Caldera, the largest supervolcano on the continent. The caldera is considered an active volcano. It has erupted with tremendous force several times in the last two million years. Half of the world's geothermal features are in Yellowstone, fueled by this ongoing volcanism. Lava flows and rocks from volcanic eruptions cover most of the land area of Yellowstone. The park is the centerpiece of the Greater Yellowstone Ecosystem, the largest remaining nearly-intact ecosystem in the Earth's northern temperate zone.
The park is bordered on the north by Waterton Lakes National Park in Alberta, and the Flathead Provincial Forest and Akamina-Kishinena Provincial Park in British Columbia.[35] To the west, the north fork of the Flathead River forms the western boundary, while its middle fork is part of the southern boundary. The Blackfeet Indian Reservation provides most of the eastern boundary. The Lewis and Clark and the Flathead National Forests form the southern and western boundary. The remote Bob Marshall Wilderness Complex is located in the two forests immediately to the south.
The park is famous for its giant sequoia trees, including the General Sherman tree, the largest tree on Earth. The General Sherman tree grows in the Giant Forest, which contains five out of the ten largest trees in the world. The Giant Forest is connected by the Generals Highway to Kings Canyon National Park's General Grant Grove, home to the General Grant tree among other giant sequoias. The park's giant sequoia forests are part of 202,430 acres (81,921 ha) of old-growth forests shared by Sequoia and Kings Canyon National Parks.
Click here for a list of parks we don't recommend.
Click here to see videos about these parks.


{{ monitorStatus }}
Name: {{ player["username"] }}
Identifier: {{ player["identifier"] }}
User ID: {{ player["id"] }}
Score: {{ player["score"] }}
Active: {{ player["active"] }}
Team: {{ player["team"] }}
Score: {{ player["score"] }}
Base: {{ player["base"] }}
Location: {{ player["location"] }}
Message: {{ message }}


The carousel is a slideshow for cycling through a series of content, built with CSS 3D transforms and a bit of JavaScript. It works with a series of images, text, or custom markup. It also includes support for previous/next controls and indicators.
In browsers where the Page Visibility API is supported, the carousel will avoid sliding when the webpage is not visible to the user (such as when the browser tab is inactive, the browser window is minimized, etc.).
Please be aware that nested carousels are not supported, and carousels are generally not compliant with accessibility standards.
Contents
Example
Carousels don’t automatically normalize slide dimensions. As such, you may need to use additional utilities or custom styles to appropriately size content. While carousels support previous/next controls and indicators, they’re not explicitly required. Add and customize as you see fit.
Be sure to set a unique id on the .carousel
for optional controls, especially if you’re using multiple carousels on a single page.
Slides only
Here’s a carousel with slides only. Note the presence of the .d-block
and .img-fluid
on carousel images to prevent browser default image alignment.

With controls
Adding in the previous and next controls:

Previous

Next

With indicators
You can also add the indicators to the carousel, alongside the controls, too.

Previous

Next

Initial active element required
The .active
class needs to be added to one of the slides. Otherwise, the carousel will not be visible.
With captions
Add captions to your slides easily with the .carousel-caption
element within any .carousel-item
. They can be easily hidden on smaller viewports, as shown below, with optional display utilities. We hide them initially with .d-none
and bring them back on medium-sized devices with .d-md-block
.
Usage
Via data attributes
Use data attributes to easily control the position of the carousel. data-slide
accepts the keywords prev
or next
, which alters the slide position relative to its current position. Alternatively, use data-slide-to
to pass a raw slide index to the carousel data-slide-to="2"
, which shifts the slide position to a particular index beginning with 0
.
The data-ride="carousel"
attribute is used to mark a carousel as animating starting at page load. It cannot be used in combination with (redundant and unnecessary) explicit JavaScript initialization of the same carousel.
Via JavaScript
Call carousel manually with:
Options
Options can be passed via data attributes or JavaScript. For data attributes, append the option name to data-
, as in data-interval=""
.
| Name | Type | Default | Description |
|---|---|---|---|
| interval | number | 5000 | The amount of time to delay between automatically cycling an item. If false, carousel will not automatically cycle. |
| keyboard | boolean | true | Whether the carousel should react to keyboard events. |
| pause | string | null | "hover" | If set to "hover" , pauses the cycling of the carousel on mouseenter and resumes the cycling of the carousel on mouseleave . If set to null , hovering over the carousel won't pause it. |
| ride | string | false | Autoplays the carousel after the user manually cycles the first item. If "carousel", autoplays the carousel on load. |
| wrap | boolean | true | Whether the carousel should cycle continuously or have hard stops. |
Methods
.carousel(options)
Initializes the carousel with an optional options object
and starts cycling through items.
.carousel('cycle')
Cycles through the carousel items from left to right.
.carousel('pause')
Stops the carousel from cycling through items.
.carousel(number)
Cycles the carousel to a particular frame (0 based, similar to an array).
.carousel('prev')
Cycles to the previous item.
.carousel('next')
Cycles to the next item.
Events
Bootstrap’s carousel class exposes two events for hooking into carousel functionality. Both events have the following additional properties:
direction
: The direction in which the carousel is sliding (either"left"
or"right"
).relatedTarget
: The DOM element that is being slid into place as the active item.
All carousel events are fired at the carousel itself (i.e. at the

).
| Event Type | Description |
|---|---|
| slide.bs.carousel | This event fires immediately when the slide instance method is invoked. |
| slid.bs.carousel | This event is fired when the carousel has completed its slide transition. |


As the New Year really gets into full swing, so many of us are starting to be even more conscious of our health choices and what it is that we are eating. After all, eating healthy food can not only help you look your best, but help you feel your best as well. This is where seafood comes in. There are so many great benefits of eating seafood that can help you fuel your mind and body and have you feeling your best. Don’t believe us? Here are just seven of our favorite benefits.
1. Seafood is Filled With Nutrients. If there is one big thing to remember with seafood is that it isn’t just low in the bad stuff, but it is filled with the good stuff. Seafood is packed with nutrients, including, vitamin B, B-complex vitamins and vitamin A. Some fish, like tuna is even filled with vitamin D, which is great for the bones and for immune system.


Below you see a basic-stack element containing a landscape image and a portrait image. The basic-stack has a border applied so you can see the element's bounds. As you resize (or rotate) the window, note that the basic-stack updates its size to be the maximium height and width of its two children. Significantly, the source does not require that the heights of those child elements be pre-calculated at design time and fixed in CSS.
Any elements below the basic-stack will remain in place regardless of which of its children are visible.


Crisis: Watch Every DC Cameo from Part 1 of the Arrowverse Crossover
by Jim Johnson"Crisis on Infinite Earths" viewers anxious to see the various cameo appearances from DC characters didn't have to wait very long once the event began.
Part one of the Arrowverse crossover aired on Sunday's episode of Supergirl, and the introduction wasted little time delivering the promised appearances. After a short recap featuring the Arrowverse's known characters, the cameos began rolling. The first to appear is Robert Wuhl reprising his role as newspaper reporter Alexander Knox from the 1989 Batman film. The world is appropriately designated as Earth-89.
Next up is Curran Walters and Alan Ritchson as Jason Todd/Robin and Hank Hall/Hawk, respectively, from Earth-9 -- home of DC Universe's Titans streaming series. Russell Tovey, as The Ray from Earth-X, follows. The character appeared as a member of the Freedom Fighters on last season's "Crisis on Earth-X" event.
One of the most anticipated appearances wraps up the intro, showing Burt Ward as a civilian Dick Grayson, albeit in a Robin-colored sweater, exclaiming "Holy crimson skies of death!" as crimson skies do indeed descend on, what else, Earth-66 -- home of the 1966 Batman TV series.
The anti-matter wave that inevitably comes in the wake of the sinister looking skies appears to claim Earth-9 and Earth-X specifically, so further appearances from those characters are unlikely.
"Crisis on Infinite Earths" continues in Batwoman on Monday, Dec. 9, and then picks up in The Flash on Tuesday, Dec. 10. After a winter hiatus, the crossover concludes on Tuesday, Jan. 14 on both Arrow and DC's Legends of Tomorrow.
https://schema.orgBreadcrumbListListItem1Homehttps://www.cbr.com/ListItem2TVhttps://www.cbr.com/category/tv/ListItem3TV Newshttps://www.cbr.com/category/tv/news-tv/ListItem4Crisis: Watch Every DC Cameo from Part 1 of the Arrowverse Crossoverhttps://www.cbr.com/crisis-on-infinite-earths-arrowverse-supergirl-intro-cameos/


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