- What are the “components of learning” in Machine Learning?
- What is bias-variance tradeoff?
- How is KNN different than k-means clustering?
- What are ROC and AUC?
- What is a cost function?
- What is gradient descent?
- Why feature scaling is important before applying a learning algorithm?
- What is Linear Regression?
- What is Polynomial Regression?
- What is Logistic Regression?
- Can Logistic Regression be used for multiclass classification?
- What is overfitting? How can you avoid it?
- What is Regularization?
- Why is Bayes’ Rule is useful in spam filtering?
- Does getting more training data always improve the performance of the model?
- How do you approach a machine learning problem?
- What type of error metrics do you use to evaluate imbalanced classifications?
- What is SVM and how does it work?
- How does k-means clustering algorithm work?
- What is the “ceiling analysis” and why is it important?
- What are the different types of testing?
Q1. What are the “components of learning” in Machine Learning?
Consider the following diagram: (source: caltech)
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: The target function is the true function that connects the input to the output. Learning Algorithm’s goal is estimate this function to the best of its ability.
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Training examples is the historical data that is available to us. We will use this to come up with a function.
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Hypothesis set is the set of functions we choose to use. We are going to find a hypothesis which approximates to the real hypothesis .
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Learning algorithm is the process of figuring out the hypothesis . This is when the parameters of the candidate function is decided and fine tuned.
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Hypothesis set: Linear Regression -> Learning algorithm: Ordinary least squares
Hypothesis set: Neural Networks -> Learning algorithm: Back propagation
Q2. What is bias-variance tradeoff?
What do we mean by the variance and bias of a statistical learning method? Variance refers to the amount by which (function that defines the model) would change if we estimated it using a different training data set. Since the training data are used to fit the statistical learning method, different training data sets will result in a different . But ideally the estimate for should not vary too much between training sets. However, if a method has high variance then small changes in the training data can result in large changes in . In general, more flexible statistical methods have higher variance.
On the other hand, bias refers to the error that is introduced by approximating a real-life problem, which may be extremely complicated, by a much simpler model. For example, linear regression assumes that there is a linear relationship between Y and X1, X2,…,Xp. It is unlikely that any real-life problem truly has such a simple linear relationship, and so performing linear regression will undoubtedly result in some bias in the estimate of .
Q3. How is KNN different than k-means clustering?
KNN is a supervised classification algorithm which requires labeled output to work. K refers the number of neighbors of a point. We look at these point to decide which class the point belongs to. If K is small, it means that our different classes have sharp boundaries and resulting model is probably overfitted. If K is big, the boundaries are smoother and the model is probably more generalizable.
K-means, on the other hand, refers to an unsupervised learning algorithm. It doesn’t require labelled outputs. What we need is the number of clusters that we want the points fall into, and a distance measure. The algorithm gradually finds cluster centers and a number of points around them. And concludes that these points belong to the same cluster.
Q4. What are ROC and AUC?
In a classification problem, the confusion matrix represents the performance of the classification algorithm. Ideally, we would like to have less misclassifications. However, depending on the decision threshold we use, the correct classifications may differ from one class to other. The ROC (receiver operating characteristic) curve is a visual representation of all the confusion matrices we have for various thresholds (for binary classifications). It helps us to decide the best threshold for our problem. The AUC (area under the curve) summarizes the ROC curve with a single value. Usually, the higher the AUC the better.
Q5. What is a cost function?
Examine the following picture (credit to Andrew Ng at Coursera):
For a given training set, the learning algorithm estimates a function (hypothesis), which maps the input to the output to the best its ability. This mapping may not be perfect, but it is the closest approximation to the actual function.
Learning algorithm tries to find the best parameters. In other words, it needs to choose parameters so that will be close the for our training examples. This is essentially an optimization problem and can be formulated as:
The above is called an objective function or cost function, and can be read as the following: We have to choose and parameters that minimizes the solution to above equation. denotes the training input.
This is sometimes called “Squared difference cost function” and it is the most commonly used cost function in regression problems.
Q6. What is gradient descent?
Gradient Descent is an algorithm that is used to minimize a function. For example, can be minimized over and . Having 2 parameters, we will end up with a 3D surface function where x-axis is , y-axis is , and the z-axis is the output of the function.
Imagine it as a landscape with hills and valleys. What Gradient Descent does is, it starts from an arbitrary point in the landscape, and finds the direction of the steepest descent at that point. After finding it, it descends with a certain distance. Then, looks for the steepest descent at the new point and jumps at that direction. It repeats this process until it reaches to a local minima. The result may not be the optimal solution, since Gradient Descent doesn’t guarantee to find the global minimum.
(for and )
: learning rate (how fast the algorithm updates)
Gradient Descent is an iterative process and it is not deterministic. After each iteration, the cost function has to decrease. After a certain number of iterations, the decrease slows down and this is when you decide if the algorithm is converged or not. Usually, if you choose a very large , the algorithm can overshoot and never converge. If the , then you have a very slow convergence. A good practice is trying different values of and plotting the cost function to see if it is converged.
Rather than using Gradient Descent, the parameters can be computed numerically using the normal equation method:
Since normal equation method involves inverting matrices, if the number of features too large Gradient Descent would be the better choice.
Q7. Why feature scaling is important before applying a learning algorithm?
If there is a large difference between the ranges of the features, the Gradient Descent algorithm can take a long time to converge. There would be unnecessary oscillations due to highly skewed values. So, feature scaling is needed for the Gradient Descent to efficiently work.
Q8. What is Linear Regression?
Linear regression is an analytical technique used to model the relationship between several input variables and a continuous output variable. The key point is that we assume that the relationship is linear. This is a safe assumption because in the worst case, a linear relationship can be achieved by transforming the variables. Linear regression generates a probabilistic model, where some randomness always exists. Consider the following equation:
The model represents the relationship between the income (output) and age and education (input) of employees that work in a company. The error term captures the variations that do not fit the linearity assumption. The fitted model should minimize the overall error between the model output and the actual observations. But what parameters should we choose to achieve this? Ordinary Least Squares (OLS) is the most commonly used technique to find those parameters. For a dataset with observations:
Q9. What is Polynomial Regression?
If fitting a straight line to data is not applicable, maybe a more flexible line would work better. Polynomial regression provides this flexibility. It is similar to linear regression except that the cost model includes higher order versions of the features.
Q10. What is Logistic Regression?
Logistic Regression is used to solve classification problems and it is widely used by ML community. It is an extension of the Linear Regression method where the objective is fitting a linear line to a set of data points. However, using Linear Regression for a classification problem has many disadvantages. Thus, Logistic Regression has been developed as a remedy. The most obvious difference is that Logistic Regression forces the output to be somewhere between 0 and 1.
Logistic regression:
In order to constrain the output, we use a special function, called sigmoid or logistic function. This is where the name comes from.
Logistic regression gives us the probability of being either 1 or 0. Assume that we pick the threshold as 0.5. If the hypothesis is less than 0, logistic function leads to 0. If the hypothesis is larger than 0, the logistic function generates a 1.
Q11. Can Logistic Regression be used for multiclass classification?
Yes. Using the idea of one-vs-all, we can use standard Logistic regression to perform multiclass classification. One-vs-all treats a class as the positive class while treating all the others as negative. And repeats this for each of classes and ends up with number of classifiers.
Q12. What is overfitting? How can you avoid it?
High bias: Our opinion about the model is biased, e.g. we think that there is a linear relationship underlying the model, thus we use a simple linear regression model. However, the real relationship is more complex, and we underfit the curve.
High variance: We pick a complex model to fit to the training set. We do a good job but if the underlying mechanism has a lot of variance, it means that our model would not be able to capture all and it would not do a good job on a different set of data. In other words, it will overfit to the training set, but it won’t generalize.
There are two main ways to avoid overfitting:
- Reduce number of features:
- Either manually select the most important features and discard the noise which lead overfitting
- Or use a feature selection algorithm to automatically eliminate least significant features
- Regularization
- Keep all the features, but reduce the influence of some parameters
Q13. What is Regularization?
Most of the time, in order to decrease the variance and increase model’s generalizability we want to reduce the number of features. One method is keeping all the featues in the model while decreasing their weights by shrinking them towards zero. Sometimes they are shrunken down to exactly zero. This is called regularization. Regularization plays an important role in high-dimensional datasets. We basically penalize the loss function by adding an extra term. This extra term depends on the feature weights, the higher the weights the more we penalize the loss function. Remember the cost function for linear regression. Here is the version with added regularization term:
is called the regularization parameter
Ridge regression: Regularized linear regression model. It doesn’t enforce coefficients to be zero, it only shrinks them towards zero.
Lasso regression: Similar to Ridge but it enforces the coefficient towards zero and effectively gets rid of them. It is basically a feature selection method.
Elastic net regression: In order to get the best of both worlds, this method linearly combines L1 and L2 penalties of the lasso and ridge methods.
Q14. Why is Bayes’ Rule is useful in spam filtering?
It is estimated that 60% of the emails in the world are spam. Email clients run a program in the background to classify a new email as spam or safe. One popular method is to use the words that appear in the email content to detect if it is spam or not. I mathematical terms, we are interested in the probability of en email being spam given the words inside:
Bayes’ rule can be used to compute this probability:
: The probability of an email being spam, regardless of the words it contains. According to our experience, this number is 60%. : How often these words are seen when the email is classified as spam? : How often this word combination is used regardless of spam.
The classification algorithm used here is called Naive Bayes Classification. The “naive” part comes from the classifier’s assumption that features are independent; knowing one feature doesn’t tell us anything about the other features. This is a naive assumption because in reality, most of the time the features are dependent.
Q15. Does getting more training data always improve the performance of the model?
If you have few data points, the model will be trained easily since there is less points to be fitted. As the training data increase, fitting those data points will get progressively harder. On the other hand, the more training data you have the better chance there is for your model to generalize. There are two extremes that you have to consider:
1) High bias (simple model, underfitting)
If a learning algorithm is suffering from high bias, getting more training data will not (by itself) help much.
2) High variance (complex model, overfitting)
If a learning algorithm is suffering from high variance, getting more training data is likely to help.
Q16. How do you approach a machine learning problem?
Rather than relying on your gut feeling and randomly trying different methods to optimize your learning algorithm, you can take the following steps to make informed decisions:
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Start with a simple model. Use training, validation, and test sets to evaluate your model. Slowly build up your model to a more complex version.
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Plot learning curves and try to see if you have a high bias problem (your model is too simple) or high variance problem (your model can’t generalize to unseen data). Then, take an appropriate action to improve your model.
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Perform error analysis. Manually examine the samples your model got wrong. See if there is any pattern to it. Think what type of features is causing the confusion and which ones are helping. Try to add new features that might have caught those errors.
Q17. What type of error metrics do you use to evaluate imbalanced classifications?
We can use precision and recall. We want them to be close to 1 for high performance classifiers.
For example:
Interpretation: If we were to use the good-old misclassification metric, accuracy, the above classifier actually performed very good, i.e. 90%. However, in reality, half of the positive class is misclassified. So, this classifier does a terrible job and recall captures that. But, when it assigns a positive class its decision is correct 80% of the time, and precision captures that information. Using these two metrics, we can report the classification performance with a single numerical number, that is called F1 score:
This number should be close to 1 for us to conclude that this is a good classifier. As you can see, 0.6 is not good enough.
Q18. What is SVM and how does it work?
SVM stands for Support Vector Machines, and “it is arguably the most successful classification method in machine learning” (Prof. Abu-Mostafa).
Consider the following dataset:
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Which line best separates the two classes? H1 doesn’t separate the classes. H2 does, but only with a small margin. H3 is the best line, separates with the highest margin. But, how do we find H3, how do we decide the best line?
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The distance from the separating line corresponds to the confidence of our classification. The farther a point from the line, the more confident we are that it belong to its class. An SVM performs classification by finding the hyperplane that maximizes the margin between two classes. The vectors that define the hyperplane are the support vectors.
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SVM performs a constrained optimization problem. The optimization is to maximize the distance between the nearest points in classes and the hyperplane (margin), and the constraints are the nearest data points should not fall inside the margin.
- When using an SVM model, there are few parameters to consider:
- constant: This parameter adjusts the complexity of the model. If you pick a large C, the decision boundary would become more curvy and lead overfitting.
- Kernel trick: If the classes are not linearly separable, i.e. if there is a need for a nonlinear decision boundary. We can use the kernel trick to transform the data into a linearly separable space.
- : This parameter is the variance in the Gaussian kernel. The larger it is, the wider the gaussian curve (smooth kernel, simple model, leads high bias).
- Which one to use? Logistic regression or SVM? : number of observations, : number of features.
- is large (10,000), is small (10-1,000): Use Logistic Regression or SVM with linear kernel. (you don’t have enough data to figure out the complexity in the data)
- is small (1 - 1,000), is intermediate (1,000 - 10,000): Use SVM with Gaussian kernel. (seems a fairly complex dataset, better use Gaussian kernel)
- is small (1 - 1,000), is large (50,000+): Try to add more features and use Logistic Regression or SVM without a kernel.
Q19. How does k-means clustering algorithm work?
(From Hastie & Tibshirani). K-means clustering is a simple and elegant approach for partitioning a data set into K distinct, non-overlapping clusters, such that the total within-cluster variation, summed over all K clusters is as small as possible. The algorithm flows as follows:
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Randomly assign a number, from to , to each observations. These serve as initial cluster assignments for the observations.
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Iterate until the cluster assignments stops changing:
a) For each of the clusters, compute the cluster centroid. The cluster centroid is the vector of the feature means for the observations in the cluster.
b) Assign each observation to the cluster whose centroid is closest (where closest is defined using Euclidean distance)
Note: Because the K-means finds a local rather than a global optimum, the results depend on the initial random class assignments. For that, you should run the algorithm multiple times and select the best solution (in which the objective function is minimized). See the picture to visualize the steps.
Q20. What is the “ceiling analysis” and why is it important?
Ceiling analysis is the process of identifying the weakest (and most promising) link in your machine learning pipeline which is worth to improve. Your machine learning pipeline might compose of multiple components each performing a different task. If your pipeline is performing poorly in overall, which part of the pipeline you should work on so that its performance improves?
Let’s assume that we have the following ML pipeline for a car detection application:
- image -> 80% (overall accuracy)
- background removal -> 90% (receives 100% correct labels)
- image segmentation -> 92% (receives 100% correct labels)
- vehicle recognition -> 100% (receives 100% correct labels)
Let’s assume that we want to use ceiling analysis to identify the bottlenecks in our system. What we do is we manually label the test set for a particular component (background removal), so that that components receives 100% correct labels. Then, we check the system’s performance. If there is a large increase, for instance from 80% to 90%, then it means that correctly labelling the test set at that component is very important and we should invest our time to improve that component.
Q21. What are the different types of testing?
There are 3 test classes:
- Unit testing
- validate each piece of program
- each function is tested separately
- Regression testing
- running the unit test again after fixing bugs
- this is to ensure that you didn’t introduce new bugs
- Integration testing
- test the whole system
- make sure that interacting pieces work nicely


"x" "1" "BHIDE,SV, BHALERAO,EB, SARODE,AV AND MARU,GB, MUTAGENICITY ANDCARCINOGENICITY OF MONO AND DIACETYL HYDRAZINE, CANCER LETT. 23(2):235-240,1984"


Snapshot structure of 1.0 M LiPF 6 DEC solution obtained from MD simulation trajectories. ( a) Li + and PF 6 − are represented by yellow spheres and grey octagons, respectively. ( b) The ions in this LIB electrolyte solution form three-dimensionally extended ion network structures.


Archive for posts tagged with 'XML'
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Working with XSLT using new XML schema in Umbraco 4.1
Most of the Umbraco community are aware that the XML schema in the upcoming Umbraco 4.1 release has changed. Instead of each document being a node element, the element name is the node-type alias, same with property values; they no longer use data elements with alias attributes. Here is a quick example, comparing the old/legacy with the new: <node id="1066" parentID="-1" level="1" nodeName="Home" ... nodeTypeAlias="Homepage" path="-1,1066"> <data alias="bodyText"><![CDATA[
Welcome to my homepage.
]]></data> </node> <Homepage id="1066" parentID="-1" level="1"…
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Convert XmlReader to String
I was in the middle of developing a member look-up AJAX function for an Umbraco project, when I ran into a slight problem, (confusion rather), about how to pull the XML back from SQL Server and return it to the browser (AJAX). The SQL statement was straight-forward, very simple, does a LIKE query against the members table, no problem there. Added “FOR XML AUTO” to return the result-set back as an XML data-type ... all…
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Converting CSV to XML
There’s a bit a functionality on one of the projects that I’m working on where I need to do a lookup. The data I’ve been given is in an Excel spreadsheet – which I needed to convert to XML (as ultimately the data will be stored in a CMS that handles XML better). I’ve done a lot of projects where I need to convert XML to Excel (via CSV), but not many where I need…
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Yahoo’s Pipes
I’m reading a lot about Yahoo’s Pipes this morning… I had a quick play around with it, looks like a really easy way to mash-up data/web-services!
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Yahoo and MSN will use Google’s Sitemap Protocol
Yahoo and MSN are uniting with Google to provide support for the Sitemap Protocol! (more info here – sitemaps.org)


Laura Dern and Carrie Fisher had more things in common than we thought
When you first think of Laura Dern and Carrie Fisher, you wouldn’t think the two had much in common with each other.
However, the two can relate more than one may think as they both grew up in famous families.
Laura learned everything she knows about acting from her mother Diane Ladd who starred in Alice Doesn’t Live Here Anymore while Carrie’s mother was Singing in the Rain star Debbie Reynolds.
Not enough for you?
Both of these actresses also had famous but less involved fathers.
The singer Eddie Fisher was Carrie’s father, and the actress even revealed he had an affair with Princess Margaret.
Laura’s father, on the other hand, is Nebraska actor Bruce Dern.
Carrie had been a loud comedian who wasn’t afraid to bring humour to uncomfortable situations, the same with Laura.
She once said she enjoyed finding ‘humour in broken places’, which was what Carrie was great at.
The late actress wrote books that used comedy to explore the sexual harassment that is so prevalent in Hollywood.
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She also used to write about her struggles with addiction and mental health issues, both topics she’s been very open about.
The two talented actresses got to work together on Star Wars: The Last Jedi before Carrie died in 2016.
Carrie, of course, reprised her role as Leia Organa while Dern played Vice Admiral Amilyn Holdo, a female rebel leader.
In the movie, the Admiral and Leia had a complicated relationship as they were lifelong friends since before the events of The Last Jedi.
On working with the original Star Wars actress, Laura said: ‘She has always been one of the most extraordinary beacons, an original screen icon.’
Got a showbiz story?
If you've got a story, video or pictures get in touch with the Metro.co.uk Entertainment team by emailing us<EMAIL_ADDRESS>calling 020 3615 2145 or by visiting our Submit Stuff page - we'd love to hear from you.


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This is a template for the documentation of an Adapter.
Here you can provide a brief description of the adapter and what is the use-case covered by the implementation. It is also good to add the goal of the Adapter and what value it brings to the DAO.
An overview of the entire process executed by Adapter functions, the main interactions and routines covered/executed.
Specify which additional configurations are required to make this adapter work. For instance: needs access to the DAO members, needs access to the DAO Bank, relies on Adapter X, Y and Extension Z.
DAORegistry Access Flags: SUBMIT_PROPOSAL
, ....
Bank Extension Access Flags: WITHDRAW
, INTERNAL_TRANSFER
, ....
Describe each variable public and private of the adapter and what is the purpose of that variable.
Add the information about all the interactions that are triggered by this DAO, which contracts it depends on, and which functions it calls.
Describe the public and private functions signatures with proper documentation and clearly explain what each function does. Specify what are expected the arguments and pre-conditions to execute the functions. Also, provide what is the expected outcome.
Examples:
/**
* @notice Creates a guild kick proposal, opens it for voting, and sponsors it.
* @dev A member can not kick himself.
* @dev Only one kick per DAO can be executed at time.
* @dev Only members that have units can be kicked out.
* @dev Proposal ids can not be reused.
* @param dao The dao address.
* @param proposalId The guild kick proposal id.
* @param memberToKick The member address that should be kicked out of the DAO.
* @param data Additional information related to the kick proposal.
*/
function submitKickProposal(
DaoRegistry dao,
bytes32 proposalId,
address memberToKick,
bytes calldata data
) external override onlyMember(dao)
List all the events that are emitted by the function in this Adapter implementation.


There seems to be no clear relationship between latitude and cloudiness but there are many cities with 0%-20% and between 75%-95% of cloudiness.


In this module, we will focus on fantasy books and how they allow students to use their imagination and understand symbolism and motifs through reading.
Through reading, writing , and particapting as a class we will explore these questions.
Children’s fantasy literature: why escaping reality is good for kids Finding Fantasy: The Genre that Makes Difficult Topics Easier for Students to Discuss
Rita SoltanFairy Tale Characters Breathe New Life: A Fantasy Book Club Approach for Tweens
Complete the readings.
Annotate one of the readings.
Write an open-ended reflection about Fantasy fiction. Some questions to guide your reflection: (optional)
Have you ever read any Fantasy novels? If so, what were they? Why did you like/dislike them?
What are some of common themes you have seen in Fantasy books before?
What is your favorite type of Fantasy? Why?
Do you think using Fantasy in the classroom is beneficial for students?
What are some elements of Fantasy that you enjoy?
Your task is to create an interesting magical creature that includes a short description of how this creature came to be. What does this creature look like? What does/can it do? Does it have any special powers? You can do this through pictures or word description. Have fun with it and be creative!


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Vortex Lite ISO C++ API Reference Guide
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#include "Time.hpp"
Public Member Functions | |
| Time () | |
| Time (int64_t sec, uint32_t nanosec=0) | |
| int | compare (const Time &that) const |
| uint32_t | nanosec () const |
| void | nanosec (uint32_t ns) |
| bool | operator!= (const Time &that) const |
| Time & | operator+= (const Duration &a_ti) |
| Time & | operator-= (const Duration &a_ti) |
| bool | operator< (const Time &that) const |
| bool | operator<= (const Time &that) const |
| bool | operator== (const Time &that) const |
| bool | operator> (const Time &that) const |
| bool | operator>= (const Time &that) const |
| int64_t | sec () const |
| void | sec (int64_t s) |
| int64_t | to_microsecs () const |
| int64_t | to_millisecs () const |
| double | to_secs () const |
Static Public Member Functions | |
| static const Time | from_microsecs (int64_t microseconds) |
| static const Time | from_millisecs (int64_t milliseconds) |
| static const Time | from_secs (double seconds) |
| static const Time | invalid () |
Time represents a time value and can:
| dds::core::Time::Time | ( | ) |
Create a Time of zero seconds.
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explicit |
Create a Time elapsing a specific amount of time.
| int dds::core::Time::compare | ( | const Time & | that | ) | const |
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static |
Create a Time from a number of microseconds
| microseconds | number of microseconds |
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static |
Create a Time from a number of milliseconds
| milliseconds | number of miliseconds |
|
static |
Create a Time from a number of seconds
| seconds | number of seconds |
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static |
| uint32_t dds::core::Time::nanosec | ( | ) | const |
| void dds::core::Time::nanosec | ( | uint32_t | ns | ) |
Set number of nanoseconds
| ns | number of nanoseconds |
| bool dds::core::Time::operator!= | ( | const Time & | that | ) | const |
| bool dds::core::Time::operator< | ( | const Time & | that | ) | const |
| bool dds::core::Time::operator<= | ( | const Time & | that | ) | const |
| bool dds::core::Time::operator== | ( | const Time & | that | ) | const |
| bool dds::core::Time::operator> | ( | const Time & | that | ) | const |
| bool dds::core::Time::operator>= | ( | const Time & | that | ) | const |
| int64_t dds::core::Time::sec | ( | ) | const |
| void dds::core::Time::sec | ( | int64_t | s | ) |
Set number of seconds
| s | number of seconds |
| int64_t dds::core::Time::to_microsecs | ( | ) | const |
| int64_t dds::core::Time::to_millisecs | ( | ) | const |


EVERYONE SAYS I LOVE YOU (1996) A Film Review by Chuck Dowling Copyright 1997 Chuck Dowling
Everyone Says I Love You (1996) ** out of ***** - Cast: Alan Alda, Woody Allen, Drew Barrymore, Lukas Haas, Goldie Hawn, Gaby Hoffman, Edward Norton, Natalie Portman, Julia Roberts, Tim Roth, David Ogden Stiers, Billy Crudup. Written and Directed by: Woody Allen. Running Time: 101 minutes.
You know, there's a reason musicals aren't made anymore. They don't work. Anyone remember "Newsies"? Why Woody Allen has tried one is beyond me, as "Everyone Says I Love You" doesn't work either.
There really is no plot to the film. Edward Norton (playing a younger Woody Allen, which gets annoying since Allen himself is also in the film) wants to get married to Drew Barrymore to the delight of her parents (Alda and Hawn). That's it. To fill time, just about everyone gets to sing. And I believe everyone except Barrymore (who was overdubbed) has their own song to sing.
Usually I prefer Woody's films which he actually appears in over the ones where he doesn't. Not in this case however, because Allen's character is so unnecessary to the film. His character's subplot is absurd. How's this sound: Allen lives in Paris, and his daughter lives in New York. One day, his daughter happens to overhear Julia Roberts talking to her therapist. For no reason, she takes detailed mental notes. Now, cut to Venice. Allen has taken his daughter on vacation. They are having dinner and who happens to walk by but... Julia Roberts! Allen's daughter then tells him everything that Roberts likes, so he can be her perfect man. Allen then tries to woo her (and succeeds? What the...?) The thing is, the daughter giving him information that she heard Roberts tell her therapist. She was telling her therapist about stuff like her favorite flower. Is that what you'd talk to your therapist about? Your turn-ons and turn-offs, getting so specific as your favorite flower? Between this contrived subplot and the dozens of musical numbers, none of the characters can be developed and therefore there's no story.
I just don't like the concept of the musical. Wouldn't it perplex you if you were having dinner with someone and all of a sudden a musical number broke out? At one point it seems like one of the characters is about to acknowledge this singing oddness, but it doesn't pay off. But just because it's a musical isn't why I dislike it. There's no story to the film, and only a few laughs. Although, a dance number at the end of the film with Hawn and Allen is incredibly well staged.
You will get to see some things you've never seen in a Woody Allen film before, aside from the musical numbers. There's a few seconds of some black people! There's almost a car chase/action sequence! There's computerized special effects! But there's no story!
And not to be crude, but I have to mention something. Watch for the scene where Tim Roth and Drew Barrymore talk out on a balcony or something. It won't be hard to miss Barrymore, because either it was 30 degrees on the set that day or she's auditioning to be the model for Schumacher's next Batsuit. (The password is: massive nipple protrusions). [R]
-- Chuck Dowling --
Visit Chuck's Movie Reviews at http://users.southeast.net/~chuckd21/ Over 1,600 movies rated and/or reviewed! Movie news, film related links, and reader's reviews.
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The Grizzlies are hoping for a big season with acquisition of Jo Mo from MSU
Fan support should be up this year


{% extends 'jumpstart/base.html' %} {% block jumpstartcontent %}
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SIGUSR1 logfile rotations: Defaults to 10 or 0, dependent on system. Specifies the number of logfile rotations to make when you type 'squid -k rotate'. The default is 10, which will rotate with extensions 0 through 9. Setting logfile_rotate to 0 will disable the rotation, but the logfiles are still closed and re-opened. This will enable you to rename the logfiles yourself just before sending the rotate signal.
Note, the 'squid -k rotate' command normally sends a USR1 signal to the running squid process. In certain situations (e.g. on Linux with Async I/O), USR1 is used for other purposes, so -k rotate uses another signal. It is best to get in the habit of using 'squid -k rotate' instead of 'kill -USR1
Error messages text: HTML text to include in error messages. Make this a "mailto" URL to your admin address, or maybe just a link to your organizations Web page.
To include this in your error messages, you must rewrite the error template files (found in the "errors" directory). Wherever you want the 'err_html_text' line to appear, insert a %L tag in the error template file.
Per-client statistics: If you want to disable collecting per-client statistics, then turn off client_db here.
X-Forwarded-For header: Default is 'on'. If set, Squid will include your system's IP address or name in the HTTP requests it forwards. By default it looks like in the HTTP requests it forwards. By default it looks like this:
If you disable this, it will appear as:
Log ICP queries: Default is 'on'. If set, ICP queries are logged to access.log. You may wish do disable this if your ICP load is VERY high to speed things up or to simplify log analysis.
Minimum direct hops: Default is 4. If using the ICMP pinging stuff, do direct fetches for sites which are no more than this many hops away.
Keep memory for future use: Default is 'yes'. If set, Squid will keep pools of allocated (but unused) memory available for future use. If memory is a premium on your system and you believe your malloc library outperforms Squid routines, disable this.
Amount of memory to keep: Default is 'Unlimited'. If set to a non-zero value, Squid will keep at most the specified limit of allocated (but unused) memory in memory pools. All free() requests that exceed this limit will be handled by your malloc library. Squid does not pre-allocate any memory, just safe-keeps objects that otherwise would be free()d. Thus, it is safe to set memory_pools_limit to a reasonably high value even if your configuration will use less memory.
If not set (default) or set to zero, Squid will keep all memory it can. That is, there will be no limit on the total amount of memory used for safe-keeping.
Headers to pass through: Default is 'All headers'. This option replaces the old 'http_anonymizer' option with something that is much more configurable. You may now specify exactly which headers are to be allowed, or which are to be removed from outgoing requests.
There are two methods of using this option. You may either allow specific headers (thus denying all others), or you may deny specific headers (thus allowing all others).
Fake User-Agent: Default is 'none'. If you filter the User-Agent header with the Headers to pass through option it may cause some Web servers to refuse your request.
WCCP Router Address: Use this option to define your WCCP ``home'' router for Squid.
WCCP Incoming Address: Use this option if you require WCCP messages to be received on only one interface. Do NOT use this option if you're unsure how many interfaces you have, or if you know you have only one interface.
WCCP Outgoing Address: Use this option if you require WCCP messages to be sent out on only one interface. Do NOT use this option if you're unsure how many interfaces you have, or if you know you have only one interface.


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Boy, 5, calls 911 for Happy Meal - and officer delivers
Policeman answered call after boy hung up
by Alaa Elassar, CNN(CNN) - When 5-year-old Charlie Skabelund called 911, he had what you could call a serious emergency for a little boy: he needed a McDonald's Happy Meal.
What he didn't expect was for Officer Randolph "Scott" Valdez of the Mesa, Arizona, Police Department to come to his rescue.
"Hi, is this McDonald's?" Charlie asked the 911 operator who answered his call.
When asked whether he had an emergency, Charlie requested a Happy Meal before hanging up.
According to his mom, Kim Skabelund, the family was getting ready for a Halloween party on Oct. 27 when the phone rang.
Not realizing what Charlie had done, Skabelund said they were "shocked and kind of embarrassed" when the 911 dispatcher called back and asked to speak to Charlie's dad.
Although Charlie was safe, a police officer still had to come by to conduct a welfare check.
Afraid that he was in trouble, Charlie was shy at first. But when Valdez brought a Happy Meal, Skabelund said the two became buddies.
Charlie, a generally sweet and kind preschooler known for putting others ahead of himself, was most excited to share the Happy Meal with his sister.
"We were so appreciative of how Officer Valdez handled the situation," Skabelund told CNN. "He taught Charlie a valuable lesson that day, not only how to use 911 appropriately, but also how to treat others with love and kindness."
Skabelund said the family hoped Valdez's extra effort will be an example for people to be kinder to each other.


envoy
1.10.0-dev-e3e520
About the documentation
Introduction
Getting Started
Building and installation
Configuration reference
Overview
Listeners
Listener filters
Network filters
HTTP connection manager
HTTP filters
Thrift filters
Cluster manager
Health checkers
Access logging
Rate limit service
Runtime
Statistics
Route table check tool
Overload manager
Secret discovery service (SDS)
Well Known Dynamic Metadata
Operations and administration
Extending Envoy for custom use cases
v2 API reference
FAQ
envoy
Docs
»
Configuration reference
View page source
Configuration reference
¶
Overview
Bootstrap configuration
Example
Upgrading from v1 configuration
Management server
Aggregated Discovery Service
Management Server Unreachability
Statistics
Status
Listeners
Overview
Statistics
Listener discovery service (LDS)
Listener filters
Original Destination
Original Source
Proxy Protocol
TLS Inspector
Network filters
Client TLS authentication
Echo
External Authorization
Mongo proxy
MySQL proxy
Rate limit
Role Based Access Control (RBAC) Network Filter
Redis proxy
TCP proxy
Thrift proxy
Upstream Cluster from SNI
HTTP connection manager
Overview
Route matching
Traffic Shifting/Splitting
HTTP header manipulation
HTTP header sanitizing
Statistics
Runtime
Route discovery service (RDS)
HTTP filters
Buffer
CORS
DynamoDB
External Authorization
Fault Injection
gRPC HTTP/1.1 bridge
gRPC HTTP/1.1 reverse bridge
gRPC-JSON transcoder
gRPC-Web
Gzip
Health check
Envoy Header-To-Metadata Filter
IP Tagging
JWT Authentication
Lua
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Role Based Access Control (RBAC) Filter
Router
Squash
Tap
Thrift filters
Rate limit
Router
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Overview
Statistics
Runtime
Cluster discovery service
Health checking
Circuit breaking
Health checkers
Redis
Access logging
Configuration
Format Rules
Format Strings
Default Format String
Format Dictionaries
Command Operators
Rate limit service
gRPC service IDL
Runtime
File system layout
Comments
Updating runtime values via symbolic link swap
Using runtime overrides for deprecated features
Statistics
Statistics
Server
File system
Route table check tool
Overload manager
Resource monitors
Overload actions
Statistics
Secret discovery service (SDS)
SDS server
SDS Configuration
Examples one: static_resource
Examples two: SDS server
Statistics
Well Known Dynamic Metadata


Quite recently I decided to try something new. Having not got much off of smoking Salvia divinorum I decided to try putting the extract under my tongue. I held it for 5 minutes and then washed it down with a little water. A couple of minutes pass, then WHAM! I noticed intense visual hallucinations, nothing frightening but weird like flies or lines dancing everywhere and blurry faces. I also noticed that my body was sweating and I was light headed. I was also happy and really felt in touch with people.
I went with my friends down to their house and smoked some bud later that day I noticed that although the salvia experience only lasted about 40 minutes and this being a hour later I still felt lightheaded and my pot buzz only lasted about 15 minutes then nothing. I still wonder to this day if Salvia divinorum could effect a marijuana buzz. I recommend this over smoking salvia at least it works better for me and the euphoria from it still lingers to this day many months later.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


C Library Functions - get_next_char (3)
NAME
get_next_char(3f) - [M_io:READ] read from a file one character at a time
(LICENSE:PD)
CONTENTS
Syntax
Description
Options
Example
Author
License
SYNTAX
subroutine get_next_char(fd,c,ios)
integer,intent(in) :: fd
character,intent(out) :: c
integer,intent(out) :: ios
DESCRIPTION
This reads a file opened with stream access one character at a
time, much like ""read(fd,iostat=ios) c" but with buffering, which
I have found to be up to sixty times faster than such a plain read,
although this varies depending on how or if the programming environment
implements I/O buffering itself.
IT USES SAVED VARIABLES AND CAN ONLY BE USED ON ONE FILE AT A TIME
IN THE CURRENT FORM. A user type including the saved values and the
LUN could easily resolve this.
OPTIONS
|
FD |
A Fortran unit number of a file opened for stream access
|
|
C |
The next returned character if IOS=0
|
|
IOS |
The error status returned by the last read. It is zero (0) if
no error occurred
|
|
EXAMPLE
Sample program:
program demo_get_next_char
use,intrinsic :: iso_fortran_env, only : iostat_end
use M_io, only : get_next_char
implicit none
character(len=4096) :: filename ! filename to read
character(len=256) :: message ! returned error messages
integer :: fd ! file descriptor for input file
integer :: ios,ios1 ! hold I/O error flag
character :: c1 ! current character read
filename=test.in
open(unit=fd,file=trim(filename),access=stream,status=old,&
& iostat=ios,action=read,form=unformatted,iomsg=message)
if(ios /= 0)then
write(*,*)&
*demo_get_next_char* ERROR: could not open //&
trim(filename)
write(*,*)&
*demo_get_next_char* ERROR: //trim(message)
stop 5
endif
! loop through read of file one character at a time
ONE_CHAR_AT_A_TIME: do
! get next character from buffered read from file
call get_next_char(fd,c1,ios1)
if(ios1 == iostat_end)then
! reached end of file so stop
stop
elseif(ios1 /= 0 )then
! error on file read
write(*,*)&
*demo_get_next_char* ERROR: before end of //&
trim(filename)
stop 1
endif
! do something with the characters
write(*,(a),advance=no)c1
enddo ONE_CHAR_AT_A_TIME
end program demo_get_next_char
AUTHOR
John S. Urban
LICENSE
Public Domain
| Nemo Release 3.1 | get_next_char (3) | July 22, 2023 |
Generated by manServer 1.08 from 7cad2096-3502-4bc8-8b45-cfb4cfd15843 using man macros.


| DOSE: |
1 hit |
oral |
LSD |
(blotter / tab) |
Preamble:
When I look my first and so far only hit of LSD, I really had no idea what I was getting into. I had really only heard of the visual and auditory hallucination part of LSD, and figured all the rest was media fluff. I didn't think it did anything really profound. I thought that I'd lay in bed and watch funny pictures for 8 hours. I was totally wrong.
I'd always been curious about substances, but never had the opportunity to try any. My friend told me that he had some LSD, and made it apparent that he'd give me a hit for free. I was really interested in trying it. The only research I'd done so far on LSD was about any medical side effects or illnesses or death. After hearing it was very rare, I decided to go for it, not understanding all the effects it had on the mind. But come to think of it, even if I had understood the effects to the mind, I would still have done it, but I would have been better prepared.
So there was a psytrance party type thing that my friend was intent on going to. I wanted to go see it too. We were going to possibly drop some LSD there, but it turned out to be very disappointing, and I am very glad that we decided not to take the LSD for reasons that will become apparent as I explain my experience.
We went back to campus and he gave me the hit of LSD he promised (which I did not take yet, but wrapped up in some paper and left on my desk), and we went to hang out with some friends for the night.
A day or two passed, I can't remember, and during these days I slept very minimally. Bad sleeping habits and tiredness were not a new thing to me, and it was perfectly normal for me to walk around like a zombie for a day or two before being able to get a good night's rest.
Anyway, one afternoon, I remembered about the LSD. I decided to take it. I'd heard from my friend that if you turn out all the lights and lay in the dark, it's very intense. I thought that was a good idea, but the problem was it was daytime and my shutters didn't close entirely. What I ended up doing was taking thumbtacks and tacking up thick towels over the windows, using the wood trim to press the tacks into. Then I rolled up a towel and laid it along the bottom. Now there was no daylight entering my room, and when I turned the lights off it was pitch dark aside from the faint light that came out the bottom of my door. Perfect!
Mindset:
My mindset was, I thought, very good, but it turned out to be the wrong sort of mindset for LSD. I was ready to have a new experience, but I wasn't ready to lose my state of consciousness and have everything I know to be true torn apart at the seams. I didn't realize how attached to reality I was. Nonetheless, I was happy, excited, and wanted to have some fun.
Setting:
I was in my dorm room, where I spent 99% of the trip. It was the weekend, so the dorms were not as busy as usual, many of the students having gone home. I was comfortable in my dorm room, having spent many hours in there quite happily. The surroundings were very familiar. I had lived there for about 7 months.
Experience:
I took the acid at 1:30 PM, half an hour or so after I took my ortho tricyclen. In hindsight, that might have been a bad idea, because tricyclen (birth control) has noticable effects on certain medications and drugs. I haven't been able to find any info about its reactions with LSD, but it's possible they had something to do with how hard this hit me.
 However, within 10 minutes I started to feel very paranoid. I was staring at the towels that I had tacked up over my window, and I was extremely frightened that something was outside, trying to get into my window, even though I knew it wasn't true. There wasn't any amount of rationalization in the world that could tell me not to be scared of this. I knew for a fact that nothing could climb up 3 stories and try to get into my room through the window, and even if they could, why would they? There was no reason to be scared of this, and suddenly I was very afraid of the dark. I took the towels down, and that eased the fear away. I was still slightly worried, but being able to see outside my window really helped.
From here on, everything is really kind of a blur. I can't tell exactly what order the rest of these events came in, so I will try my best, but it's possible that one thing happened before another, so any inconsistencies in my story should be taken as evidence of the fact that the traditional notion of time had very little meaning to me.
As a result of my sleep deprivation, I think, I passed out on my bed. I came to, to the sound of my don slipping a package of papers under my door. I had forgotten that I'd taken LSD, and I didn't know how long I'd slept. It was probably only a half hour, but it wasn't something I was worried about.
I didn't realize I was tripping, and I started leafing through the papers, having nothing better to do. Gradually, little signals started going off telling me that something was wrong or different.  I was so concerned with my state of mind that I hardly noticed any visuals, which I wish now that I'd paid more attention to so I could describe them. I chatted with people on MSN for awhile, and was very concerned with trying to get them to understand that time is a fractal, recurring, repeating, etc. At this point I felt like a genius. I was in euphoria over the knowledge I had acquired. The more I thought about time, the more I agreed that it was like a fractal, for reasons that I can't remember now and could never actually put into plain english.
I remember my pattern of dialogue with the people I was chatting with was very odd, but it didn't seem odd at the time. When somebody asked me a question, I'd repeat it back to them as a statement and elaborate on some things that really did not matter but seemed extremely significant to me, 90% of the time. For example, somebody would ask me 'Did you feed your fish?' and I'd say 'Did I feed my fish.... hmm, I feed my fish often. Whether I fed my fish today is irrelevant. He can live for weeks without food.'
It was suggested by some of my friends that I lie down, so I did. What happened next is not a phenomenon that I've heard about as an effect of LSD, but my friend seemed to think that it wasn't unusual.
I laid down and I think I tried to take a nap. But what happened was I was thrown into a state where I was not sleeping, but I wasn't quite awake either, and it was much like a nightmare. I was repeatedly and recursively plagued with images of a very frightening looking face, telling me how I should not have taken LSD, that it would make me insane forever. It seemed like a madman, going on and on about the effects of LSD in a haunting, angry voice. While I was experiencing this repetitive nightmare, my soul seemed to be outside of my body. It felt like my body was an empty shell, and I was an observer, watching myself experience these things. In this not-asleep and not-awake state, I began to go into convulsions that I can only describe as seisure-like. My body twitched repeatedly, especially my legs and arms. They would jerk repeatedly toward my right side, in a manner much like the twitches that I experience during an orgasm, but stronger and obviously without the orgasm.
I got very warm and sweaty. I kept twitching and having this nightmare for about an hour to two hours, before I snapped out of it. I was trying to 'play along', the way I'd been told my trip would be easier if I did, but I was sick of this nightmare, and wanted to do something more productive. I decided to eat.
I got up, still very warm and dripping with sweat, and looked in my minifridge for some food. I got out some strawberries. This is when I think I first noticed visuals. The strawberries appeared to be VERY LARGE, and I thought it was extremely comical when I took a bite out of one. I was distinctly aware of the pattern made by a strawberry's seeds, and realized it was a pattern based on the golden ratio, that the seeds spiral out in two directions, in a circular fashion such that it wrapped around the strawberry. This seemed profound to me, and I sat for awhile just staring at the strawberry, laughing occasionally.
Eventually I ate the whole carton of strawberries and was still hungry. I ate some other stuff that I can't quite remember, and then decided I was very thirsty. I had a jug of water in my fridge and started to drink that too. I was amazed with the pouring of the water- it made a light twinkling sound and the light from my window caused playful rainbows to appear on the surface of the water. To this day I still notice these rainbows, which makes me wonder if they always existed and the LSD just let me see them.
This entire time, I was distinctly aware that my ability to concentrate on anything except the things I was conjuring up in my mind was very stunted. I remember, in particular, my trip to get some water was very slow and agonizing. I would stand up from my bed, find something to be interested in, and think about that for about 5 minutes before I remembered that I was going to get some water. So I'd take another step, and I'd get distracted by something in my mind or something that I could see or hear. It felt like it took half an hour just to get a glass of water. To me, it felt like I was 'getting lost in time' every time I tried to do something. It was like I 'couldn't remember why I would want to do anything at all'. The quotations are lines of thought that I recognize as being directly from my acid-influenced state. They are not my sober words - they are words that I remember thinking while I was under the influence.
I chatted on IRC for awhile with my friends, including the one who had given me the LSD. I thanked him a few times. I think, not quite sure anymore, that he was also on LSD. Or he might have just been on pot at the time, I can't quite remember. But we had a short discussion about the nature of reality and time, which intrigued me greatly.
I felt like I had to go to the washroom every fifteen minutes, which was probably true because I was drinking a lot of water. I seemed to be occupying my time with drinking water and going to the washroom. I was extremely aware of how much of a hard cycle it was, and that I could break it by stopping drinking water, but I didn't want to stop drinking water. I felt rather dehydrated.
I felt like I had very little control of my bladder, though I seem to have had full control. I never once wet myself, though it felt like my bladder control was very weak and every time I went to the washroom, it felt like I had to go very badly, but it turned out that there wasn't very much pent up urine, and I only tinkled a little bit each time, but it felt like a huge relief.
One time, I was talking with a friend on MSN and I was very frank about my washroom situation.  It was like I was experiencing an alternate timeline to my own, at the same time that I was experiencing my own timeline, and it felt extremely real. In the alternate timeline, I had actually peed myself, and I saw the wet spot on my pants, exactly as if I had peed myself. I looked at it in horror and told him, yes, I did pee myself. All this felt extremely, exceedingly real. I was not aware that it was not happening. If somebody asked me if what was I experiencing was real, I would have said yes.
But of course, it wasn't real. Suddenly I snapped back to my own reality, and realized I was sitting in my room, had to pee really badly, but had not peed myself. At this realization, I rushed to the washroom. But, when I opened the door of my room, I was suddenly very scared to go outside. I really didn't want to go out there. So I closed it again. This triggered a 'time spiral', as I would best describe it. I felt myself open and close the door again, and then I saw many versions of myself opening and closing the door, some versions closing the door, some opening it, all of it happening repetitively, recursively, and like a 'living fractal'. It was like I was watching a movie of myself, repeatedly opening and closing the door.
I have no idea how long this went on. I have no idea how long it appeared to go on. It was timeless. As this was happening, the concept of the passage of time as we see it was non-existent in my mind, and none of these actions actually involved time. They were timeless. To this day, I don't know how many times I actually physically opened or closed that door. I only know that eventually, time 'flipped' over, much the way you would flip a card. There was a huge heave and time flipped itself over, with much inertia, and began to spiral 'outward' until I was back to my present state, outside of the door, waiting to go to the washroom. I was glad the dorms were mostly empty, because I'm not sure what somebody might have seen if they were watching me at this time. They'd surely know something was wrong, that's for sure. I wasn't acting normally at all.
I felt like I had just been dropped into a black hole of time, and had somehow struggled my way out of it, or through it, onto the other side. I remembered that I had to go to the washroom, so I rushed down the hall, and the walls pulsated and changed colours around me. I got into the washroom and there was a girl there. I froze. I wondered how weird I looked. I had to try to act normal though, so I smiled and said 'Hi.' She did the same, and didn't seem to realize anything was up.
When I got into the stall, I was suddenly afraid to pee. I knew that I had to, but at this point, I realized that I could no longer tell imagination from reality. I couldn't tell if I was imagining something or if it was actually happening. So, if I released my bladder in the washroom, I could actually be still sitting in my room. But the release of my bladder would be real, and I would pee myself.
I had a bit of a breakdown when I realized this. I had to resolve it to continue. I made confident in my mind the idea that I had to TRUST myself. There was no other way out of it. If I couldn't tell reality from imagination, let it be so. I had to trust myself that I was actually there.
So with that, I released my bladder, and as soon as I did it, I realized that I had done the right thing. I was actually in the washroom, and I had done right to trust myself.
I came out of the washroom feeling like I had just conquered something important. I ran through the pulsing, breathing hall, back to my room, and told my friends on MSN that I had managed to go to the washroom!  They might have thought I was just acting silly for the hell of it.
Around T+6 hours, I knew that the LSD had to be wearing off sometime soon, but I still felt just as high as ever. I was tired, so I laid down and tried to take another nap, but I had completely forgotten what happened last time I did that. I fell into another state of non-awakedness and non-sleep, complete along with the nightmares and dissociation of my mind from my body, and for awhile I just rolled with it, no matter how terrifying it was. It was like pure fear embodied as a visual entity with a voice. That's what I thought it was, pure terror. I don't know why I allowed it to affect me for so long, but I know I was definitely ALLOWING it to affect me, and this time the nightmare and dissociation went on for another 1-2 hours, but it was definitely longer than last time.
Eventually I realized that I had laid down trying to fall asleep, and remembered that the same thing had happened last time. I realized that this probably meant that I couldn't sleep until the LSD wore off. I got up, once again sweaty and hot from my seisuring, and decided to take in all the beauty around me.
I looked out the window. It was evening and dark. I looked at all the stuff around me and appreciated the beauty in everything I saw. I decided to look in the mirror.
I looked like death.  My face looked 'dry', is one of the ways I can describe it. Dry and hot. My eyes were shot, and I looked very cranky and slightly insane, like I could start giggling maniacally at any moment. I realized that I was looking at an insane person. The thought occurred to me, am I ever going to be normal? I had to convince myself that I was just on a drug, and it would wear off. But the things I had seen and experienced would be with me forever. How was I going to live with everything?
By now I was tired of tripping. I wanted to be normal again.  I had to try to think of what would be 'normal' for me. 'Normal' for me would be to sit on my computer and chat on IRC, but that was far too unfulfilling. I really didn't want to do that.
My friend told me that having an orgasm on LSD was really intense, but I didn't feel at all like masturbating. I started, and tried, but I just wasn't in the mood. Nowhere close to it. I wondered how he could ever have the motivation to do such a thing while tripping. It was the last thing I wanted to do.
It was about T+8 hours, and I didn't know what to do with myself. I didn't want to go out of my room, and I didn't want to do anything that was in my room. I decided to watch some anime - Azumanga Daioh. That normally amused me. But as I watched it, I quickly realized that I could not focus on the storyline at all. It was like I was seeing small clips of the video, one at a time, out of sequence with each other, so that the storyline made no sense and the conversations the characters had were ridiculous and did not follow a sequence. I realized that my state of mind was changed so much that I could not do normal things, so I let myself do what my mind felt was natural and easy for it. Instead of watching the anime with a normal intent, I watched it admiring the artwork, watched the characters morph out of proportion to one another, change colours, etc. I found this so funny that I watched another episode or two.
By the end of these, I knew my LSD trip had to be over, but I still felt high and wired. But I was so tired. I talked to my friend who gave me the LSD, on IRC, and he seemed slightly surprised by the length of my trip, but was not really concerned. He told me to just relax and let it happen. I explained that I wanted to sleep, and he said I wouldn't be able to sleep until it was over, so just take it for granted.
At this point I had run out of things to do and I was desperately trying to find something to keep me from falling into another nightmare. I decided to draw. I became completely involved in my drawing - it felt essential that I draw what the LSD wanted me to draw. But I wasn't making any sense. At one point the pen I was using ran out, so I started using a strawberry to draw with, not realizing that this was not really drawing but smudging the remains of a strawberry all over the page.  I wrote 'The evil strawberry attacked the pen!' Or was it the pen attacked the strawberry, I really don't remember.
Eventually I found another pen to write with. I had drawn a few pages, and this new page was going to be special. This page was going to be beautiful. I wanted to make something beautiful and significant. So I began, with trembling lines that wrapped around themselves to form peaks, valleys, and eye-like structures. I only got half done before I got tired. I again desperately wanted to sleep and be rid of this altered state. I wanted to be normal again.
I started to freak out, got scared that I would never be normal again. I couldn't see any conceivable way that I could possibly go back to normal after what I had learned and knew. I pushed these thoughts out of my mind, and decided to 'roll with it' again. Without thinking, I laid on the floor, stomach down, and stared at the floor. I could see patterns. They were rumbling, but weren't quite the same patterns I had seen before. By now I knew the LSD must have worn off, but I was still altered. It was very scary, but I kept rolling with it, figuring that fighting it would only hurt me and there was nothing I could do to help myself.
This next part is extremely hard to remember, but I'll try.
I laid on the floor and spread my arms and legs. I kept shaking and shaking and felt a resonance within my shaking. I thought there was something significant about the way I shook. I thought I was vibrating the way the universe vibrated, that I had become one with the universe. That if I kept this up, I would become pure energy. I kept trembling and shaking on the floor, and felt my body burst with white light. I was no longer in my body, and I saw myself on the floor and thought I was dead. I had a conversation with 'God', some might say, though I've never believed in God. I think of it more as a conversation with the universe. It was not in spoken words, so I don't remember what it was and even if I did, I could not explain it or describe it or remember it.
I also thought that I had died, but this didn't bother me one bit. I realized that I had nothing left to live for, and there wasn't really anything I wanted to do in my life. Through all my life I was a drifting soul, pretty much just seeking pleasure and understanding. That was all. Other people had goals like having a family, becoming a master of a profession, or becoming the best at something. Me, I had nothing. I was in university with no place to go, nothing to lose. And as I realized this, I started to come back to my body. But I didn't want to. I begged the universe to let me stay in this enlightened state, but I was not allowed. I was condemned to my body, but I shouldn't be sad about it.
Nonetheless, I was, and when I woke up in my body, I was frustrated, despite not understanding most of what went on. I laid there on the floor, and once again slipped into a nightmare non-sleep non-awake state. I didn't care anymore. There was nothing left. Nothing that existed mattered. Nothing that I felt mattered. We are just arrangements of vibrating strings.
All of a sudden I realized how ridiculous this was. All the things I'd experienced. By now it was far, far, far past the time that LSD was supposed to wear out, and I was still experiencing open eye visuals and especially closed eye visuals. I didn't want to experience it anymore. I wanted to go to sleep. I got very scared, very very scared, and went back on IRC to talk to the friend who gave me the LSD.
He wasn't there, so I started yelling and screaming in my mind. I needed him. He'd been through this type of thing, maybe not exactly like it, but he'd done lsd. How did he cope? I needed to know how to cope. So I stayed there, waiting, until he came back, at which point everyone else in the channel seemed very concerned. I didnít' want to bother anyone, that was the last thing I wanted to do, but I didn't know what else I could do.
I talked to him and he told me that this was probably happening because of my sleep deprivation, and that if I slept I'd feel better. I explained that I couldn't sleep. Every time I laid down and tried to sleep, I fell into that 'nightmare'. He told me to just roll with it, but I couldn't do that anymore. It was too scary. Too much. I tried a few times, to lay down and sleep, but every time I felt the nightmare creeping up on me I had to stop. By now I was crying, with tears streaming down my cheeks, and I started looking for the emergency number. I was convinced that doctors could get me out of this. They could fix it. I wanted to know from my friends that if I told them about this, that nothing bad would happen to me. That I wouldn't get fined or go to jail or get a criminal record. Unfortunately they couldn't tell me that. It was an illegal substance that I took, so I would be mistreated and nobody would take me seriously.
My friend told me that I had to sleep before this would be over, and I cried, because I knew I couldn't sleep, so I thought it would get worse and worse and worse until I died. Plus, I couldn't function in public, so I couldn't go down to the cafeteria to eat. The food in my room would run out eventually, and when it did... I'd be screwed.
He suggested chamomile tea. Yes! I thought. Why didn't I think of that before? I have chamomile tea. With a renewed sense of 'this can work', I brewed some tea and drank it while I watched my fish. I eventually calmed down. I laid down and tried to sleep, but still the nightmares passed over me. You have to understand that by this time, nearly 24 hours had passed. I had tripped all through the night and experienced more than most people experience in their whole lives. I was trying to handle it, but it was really hard.
I had another cup of tea and suddenly tiredness took over me. It was noon the day after I had taken the LSD, so 22.5 hours by the time I fell asleep, and I slept until 10 PM. When I woke up, I barely knew who or what I was. It took a few minutes to register in me that I had just been through a really, absurdly long and violent LSD trip. I got anxious and checked my closed eyes for visuals. None. I checked the things around me for visuals. None. I waved my hand in front of me. Nothing.
I had never been so happy in my life to be normal.
I slept until about 6 in the morning the next day, to have another cup of chamomile, then went back to sleep until after noon. When I woke up I felt like a million bucks. It was amazing. Life was amazing. I had a renewed interest in life. I wanted to do everything. I was so damn happy that I was alive.
After-effects:
By now, 6 months after the trip, I have experienced a few 'flashback'-like states, where I experienced the extreme paranoia and terror that I had sometimes experienced while on LSD. I am sure they were from the experience, because I had never experienced this sort of thing before. I had always been relatively mentally healthy, if a little bit depressed at times. Despite this, I think my experience changed me for the better. I don't know what it is, but reality seems slightly different. I have learned to be more accommodating of other people, friendlier, and more accepting to different viewpoints. And this is from somebody who has never had an enemy in her life.
The main thing that has stuck with me is that now, whenever I look at some water, if I concentrate, I can see the rainbows that refract from the light entering the water. It's not exactly unpleasant at all, in fact I rather like it. I'd call it a very positive thing. It reminds me that I don't know everything. Life is more complicated than I think it is.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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This webpage illustrates the buildings.csv dataset. The visualization represents the relationship between Acquired Year and Construction Year as a scatter plot. The bar chart represents the relationship between Congress Districts and Total Square Footage. The Agency Name and Square Footage of each building can be observed by pointing at the individual points, by using click and drag selection the Sum of selected buildings can be visualized in the second plot.


Null
In JavaScript null is "nothing". It is supposed to be something that doesn't exist.
Unfortunately, in JavaScript, the data type of null is an object.
Note You can consider it a bug in JavaScript that typeof null is an object. It should be null.
You can empty an object by setting it to null:
Example
var person = null; // Value is null, but type is still an object
You can also empty an object by setting it to undefined:
Example
var person = undefined; // Value is undefined, type is undefined
Difference Between Undefined and Null
typeof undefined // undefined
typeof null // object
null === undefined // false
null == undefined // true


We are best at what we do, we want you to be the same. At Nullatech we offer courses on different high tech fields. Big data analysis, NoSql databases in practice and modern web development are just a glimpse of what we offer.


@Retention(value=RUNTIME)
@Target(value=TYPE)
public @interface DatabaseIdentityStoreDefinition
IdentityStore
that
stores caller credentials and identity attributes in a relational database,
and make that implementation available as an enabled CDI bean.
The container-provided IdentityStore
must support validating UsernamePasswordCredential
,
and may support validating other credential types.
| Modifier and Type | Optional Element and Description |
|---|---|
java.lang.String |
callerQuery
SQL query to validate the {caller, password} pair.
|
java.lang.String |
dataSourceLookup
Full JNDI name of the data source that provides access to the data base
where the caller identities are stored.
|
java.lang.String |
groupsQuery
SQL query to retrieve the groups associated with the caller when
authentication succeeds.
|
java.lang.Class<? extends PasswordHash> |
hashAlgorithm
A
PasswordHash implementation used to verify plaintext passwords
by generating a hash of the password and comparing it against the hashed
value returned from the database via the callerQuery() . |
java.lang.String[] |
hashAlgorithmParameters
Used to specify algorithm-specific parameters.
|
int |
priority
Determines the order in case multiple IdentityStores are found.
|
java.lang.String |
priorityExpression
Allow
priority to be specified as a Jakarta Expression Language expression. |
IdentityStore.ValidationType[] |
useFor
Determines what the identity store is used for
|
java.lang.String |
useForExpression
Allow
useFor to be specified as an Jakarta Expression Language expression. |
public abstract java.lang.String dataSourceLookup
public abstract java.lang.String callerQuery
useFor()
contains
IdentityStore.ValidationType.VALIDATE
.
The name of the caller that is to be authenticated has to be set as the one and only placeholder. The (hashed) password should be in the first column of the result.
Example query:
select password from callers where name = ?
public abstract java.lang.String groupsQuery
useFor()
contains
IdentityStore.ValidationType.PROVIDE_GROUPS
.
The name of the caller that has been authenticated has to be set as the one and only placeholder. The group name should be in the first column of the result.
Example query:
select group_name from caller_groups where caller_name = ?
public abstract java.lang.Class<? extends PasswordHash> hashAlgorithm
PasswordHash
implementation used to verify plaintext passwords
by generating a hash of the password and comparing it against the hashed
value returned from the database via the callerQuery()
.public abstract java.lang.String[] hashAlgorithmParameters
Parameters are specified as a list of name/value pairs, using the format below:
parameterName=parameterValue
For example:
Algorithm.param1="value" Algorithm.param2=32
This attribute supports immediate Jakarta Expression Language expressions (${} syntax) for both the
parameterValue
as well as for a full array element. If an EL
expression is used for a full array element, the expression must evaluate
to either a single string, a string array or a string Stream
where
in each case every string must adhere to the above specified format.
public abstract int priority
public abstract java.lang.String priorityExpression
priority
to be specified as a Jakarta Expression Language expression.
If set, overrides any value set with priority
.priority
Jakarta Expression Language expressionpublic abstract IdentityStore.ValidationType[] useFor


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