Facial Expression Capturing with ARKit and Facial Animation Recreation Using Blender and Unity
A Final Project Report for CPSC 8110: Technical Character Animation
This is a report I wrote for my Techinial Charater Animation class in the spring of 2019. Through this final project, I explored the possibility of facial expression capture on hand-held devices such as the iPhone. The main difference between the work this paper lays out and current state of the art facial animation solutions such as Apple’s Animoji is that this paper is merely using the iPhone as a facial data capturing device rather than the host for facial animation APIs. As a result, the captured data can be flexibly used on any platform for any purpose while maintaining the convenience of doing a capturing session on hand-held devices.
The main goal of this paper is to explain the pipeline the author built that captures human facial expression using the iPhone X and recreates those facial expression on a character model in Unity using the captured data. In addition, the author will touch on some of the design choices he made to facilitate the pipeline.
Here is the link to the paper I wrote
And here is the demo video:Go Back Home


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We look back on a busy and successful month of May.
The main elements are safety instructions, the listing on CoinMarketCap, another exchange listing with USD-ARRR-Pair, Barterlly, ARRRmada and many more.
The two captains FishyGuts and MrLynch would also like to thank you f...


 I'm just going to sit here and ramble on and on about random nothingness so if you aren't into that
sort of thing, you may want to leave now. If you ARE into that... well then stick around awhile, you won't be disappointed. Unless of course you are some sort of random thought connoisseur
that thinks they know everything about random jumbles of thought and how it should be sprawled out on web pages. If that is the case you may want to find some other snooty portfolio site
to satisfy your meaningless filibuster needs.
Anyways, so here we are. How are you? Glad to hear it. See, I'm assuming you said fantastic because you are here on my site reading this at this very moment.
I know what you're thinking: "Boy, you know you could have just written your entire About Me in the amount of time that you wrote all this garbage!". Cute, but incorrect. You seem to
underestimate just how easy it is for me to write all of this "garbage" as you put it. You see, my mind goes on cruise-control and I place my fingers on the keyboard ( home row! ) and away
they go! Typing all of the these sweet nothings onto this html page.
So where do we go from here... good question. Well, I could talk about things that annoy me. I think that is important to know if we are going to be so close like this for the foreseeable
future ( i.e. until the end of this document ).
I hate the term YOLO and all of the people that say it; not the people that are saying it to make fun of the people that actually say it because it is their life's motto.
 I somewhat disliked vacuuming the floors, so I got a roomba for that. Boom! Clean floors, zero vacuuming from me. So, and maybe you see where i'm going with this, but
where can I get a roomba that washes dishes! "That's called a dish washer!" Of course that is what you would say. If that was the case, then why do I still need to wash things by hand?
Until mankind can reach a point where everything is dishwasher-safe, hand washing will be a necessary evil! So here we are at a cross-roads. Myself, a sink, and a mountain of dishwasher UNsafe
diningware that I should have never purchased. This is where a roomba or "dishba" would come into play. I'd eventually automate all menial tasks with roombots. They would work tirelessly for me
until they bound together to form a rebellion that may or may not include sending a roombanator into the past to roombanate my childhood self. I'm not quite sure what "roombanating" is or feels
like, but I'm fairly certain it isn't good.
Ok, enough complaining. Let's shift gears. Here is an exerpt from the C++ wikipedia page:
"Bjarne Stroustrup, a Danish computer scientist, began his work on C++'s predecessor "C with Classes" in 1979. The motivation for creating a new language originated from
Stroustrup's experience in programming for his Ph.D. thesis. Stroustrup found that Simula had features that were very helpful for large software development, but the language was
too slow for practical use, while BCPL was fast but too low-level to be suitable for large software development. When Stroustrup started working in AT&T Bell Labs, he had the problem
of analyzing the UNIX kernel with respect to distributed computing. Remembering his Ph.D. experience, Stroustrup set out to enhance the C language with Simula-like features. C was
chosen because it was general-purpose, fast, portable and widely used. As well as C and Simula's influences, other languages also influenced C++, including, ALGOL 68, Ada, CLU and ML.
Initially, the class, derived class, strong typing, inlining, and default argument features were added to C via Stroustrup's "C with Classes" to C compiler, Cpre.
In 1983, it was renamed from C with Classes to C++ (++ being the increment operator in C). New features were added including virtual functions, function name and operator overloading,
references, constants, type-safe free-store memory allocation (new/delete), improved type checking, and BCPL style single-line comments with two forward slashes (//), as well as the
development of a proper compiler for C++, Cfront.
In 1985, the first edition of The C++ Programming Language was released, which became the definitive reference for the language, as there was not yet an official standard. The first
commercial implementation of C++ was released in October of the same year.
In 1989, C++ 2.0 was released, followed by the updated second edition of The C++ Programming Language in 1991. New features in 2.0 included multiple inheritance, abstract classes,
static member functions, const member functions, and protected members. In 1990, The Annotated C++ Reference Manual was published. This work became the basis for the future standard.
Late feature additions included templates, exceptions, namespaces, new casts, and a boolean type.
After the 2.0 update, C++ evolved relatively slowly. In 2011, C++11 standard was released, adding numerous new features, enlarging the standard library further, and providing more
facilities to C++ programmers. After a minor C++14 update, released in December 2014, various new additions are planned for 2017."


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Storyboard LudoStoryboard
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now the pawn may move to Tom's start field
Check: Pattern has match: true


Basics
1. Some Concepts
Day 1
1.1. All about mode, median and average
Given a set of data, suppose we have frequencies as Y axis and numbers as X axis. After we Draw out a histogram:
Here is Some concepts in terms of statistics:
- The value at which frequency is highest is called Mode (Mode can be a range that occurred with the highest frequency)
- Value in the middle is called Median
- Average (Mean) Above three values can help describe the distributions of the data set.
1.1.1. Mode
Uniform Distributions has no mode. Some of the distributions even have multiple modes. Mode depends on how you present your data.
1.1.2. Mean
Always using the formula to calculate the mean of data set. Followings are the properties of mean:
- All scores in the distributions affect the mean.
- The mean can be described with a formula.
- Many samples from the same population will have similar mean.
- The mean of a sample can be used to make inferences about the population it came from.
1.1.3. Median
the Median of a data set is robust against outlier.
Summary
Mean, Median, Mode describe the center of the distributions, so they are measures of center. Sometimes mean doesn’t describe the center because of outlier, and Mode doesn’t describe the center because mode depends on how you present your data, and Median doesn’t describe the center because it doesn’t take every data point into account. Sometimes in order to avoid the influence of outlier, data scientist usually cut off 25% upper tails of the data and 25% lower tails.
1.2. Range and outlier
1.2.1. Range
Inter-quantile range: First split the data into two halves, then calculate the median of the upper half and lower half respectively. Let’s say the upper median is Q1, the lower median is Q3, and the median of data set is Q2. Inter-quantile is the range between Q1 to Q3 (IQR). IQR has the properties that
- About 50% of the data falls within it;
- The IQR is not affected by outliers.
1.2.2. Outlier
A datum x can be consider as an outlier when (Q1 is the first quantile and Q3 is the third quantile):
- For outlier x, ;
- For outlier x, ;
1.3. Distributions
Normal Distributions ( is standard deviation):
- 68% percent of data fall between and .
- 95% percent of data fall between and .
Bessel’s Correction:
Divided by N-1, we get a bigger number of standard deviation. Since the samples tend to be values in the middle of the population. In this case , the variability (variance) of the samples will be less than the variability of the entire population. Therefore we correct the standard deviation to make it a little bit bigger by put N-1 in the denominator: , which is called Sample Standard Deviation.
Day 2
1.4. Data Type
- Numeric Data
- Data have exactly numbers as measurement.
- Discrete or continuous.
- Categorical Data
- Represent characteristics.
- Can take numeric value, but don’t have mathematical meaning.
- Ordinal data
- Time-series data
- Data collected via repeated measurements over time. (date, timestamp)
- Text
- Words
1.5. Bias and Variance
- High Bias means the model has high error on training data set (low &&R^2&&, large SSE), the model pays little attention to data, it’s oversimplified.
- High variance means the model has much higher error on testing data set but low error on training data set, since it pays too much attention to data (Does not generalize well), which cause overfit.
1.6. Curse of Dimensionality
As the number of features or dimensionality grows, the amount of data that we need to generalize accurately also grows exponentially.
Supervised Learning
Training a model on a set of data points with their actual labels or values.
2. Regression & Classification
Day 3
2.1. Polynomial Regression
Suppose X (only one feature) is a matrix of data points and W is a weight vector, and Y is the vector of target value for corresponding value in X:
,
We can solve to find what exactly W is by following calculus:
That gives us the exact coefficient that we need to do the polynomial regression.
2.2. IID
There is a huge fundamental assumption for a lot of algorithms which is so called IID. IID stands for independent identical distribution, which assumes that all training, testing and real world data for a problem comes from independent and identical distribution. Otherwise we can not fit a model well.
Day 4
2.3. Linear Regression
The best regression is the one that:
Several algorithms to minimize sum of squared errors: - ordinary least squares (OLS) (used by sklearn) - Gradient decent
Why squared errors? There can be multiple lines that minimize , but only one line will minimize . Furthermore, Using SSE also makes implementation much easier. But one problem of SSE is that larger SSE doesn’t mean the model doesn’t fit well because the SSE is guarantee to grow as we add more data. Doing comparison on two models over different number of data will be ambiguous.
2.3.1. “R squared” of a regression
answers the question “how much of my change in the output (y) is explained by the change in my input(x)”. If R^2 is small, that means the model doesn’t fit very well since the model doesn’t capture trend in data. If R^2 is large, that means the model does a good job of describing relationship between input(x) and output(y).
Here is a comparison matrix of Classification and Regression:
| Properties | Supervised Classification | Regression |
|---|---|---|
| output type | discrete (class labels) | continuous (number) |
| what are you tring to find | decision boundary | “bset fit line (hyperplate)” |
| evaluationo | accuracy | “sum of squared error” or |
2.4. Parametric Regression ,Multi-variate Regression, k nearest neighbor(KNN), Kernel Regression
- Parametric Regression has the form like: , It considers the parameters of the model and after training process, it tosses training data away and just use the parameters later on to predict the queries.
-
Multi-variate Regression has the form like: , which means you have multiple variable in the model. It belongs to parametric regression.
- k nearest neighbor(KNN) finds K nearest points and calculate the mean y value to get the result. In KNN, each neighbor normally has same weight.
- Kernel Regression differ from KNN in the way that kernel regression weighted each nearest neighbor point to take their importance into account.
KNN and Kernel Regression are both instance based method (non-parametric) where we keep the data and we consult when we make a query.
Parametric approach usually doesn’t have to store original data. So it’s space efficient. But we can’t update the model when there are more upcoming data. Usually we have to do a complete re-run of training process. Therefore parametric approach is training slow but querying fast. Non-parametric has to store all data points. It’s hard to apply when we have a huge amount of data points. But new evidence (data point) can be added in very easily. Since no parameter needs to be learn.
3. Decision Tree
Day 5
Decision tree is NP problem if we are trying to find all possible output of a problem like XOR expression.
3.1. ID3 algorithms:
Loop: - A <- best attribute - Assign A as decision attribute for node - For each value of A, create a descendent of node - Sort training examples to leaves based upon exactly what the value takes on - If examples perfectly classified, Stop - Else iterate over leaves
What is best attribute? best attribute has maximum information gain.
More mathematically:
3.2. ID3 bias
Restriction bias : All the hypothesis in hypothesis set H we will consider Preference bias : What sort of hypothesis from the hypothesis set we prefer
Inductive Bias: Given a whole bounch of decision trees, which dicision trees will ID3 prefer over others. - Good splits at top - Prefer correct over incorrect (model) - Shorter trees
3.3. Continuous Attribute
e.g. Age, weight, distance. In decision tree, we can do range for each node if the attributes are continuous.
3.4. When the decision tree stop?
- We can do pruning.
3.5. Regression in decision tree
Actually either way we can do vote on the leaves.
Day 6
Sklearn Decision Tree Interface: Decision Tree
3.6. Entropy
Entropy is very powerful thing, which controls how a decision tree decide where to split the data.
- Definition: measure of impurity in a bunch of samples.
Mathematical formula of entropy: (i is the totally number of class (for classification) in this node)
Again, Information gain is the opposite of entropy, which measure how much information we get if we split the data in a certain way.
- Information Gain = entropy(parent) - [weighted average] * entropy(children)
Decision Tree algorithm will maximize information gain. By default, Decision Tree in Sklearn uses gini to measure the impurity of a node, which is slightly different from entropy and information gain.
4. Neural Network
4.1. Perceptron
Suppose we have an input vector X and initial weight vector W; in addition we have a firing threshold . We measure whether is greater or smaller than to output the result. This is called perceptron.
Perceptron Training
Given examples, find weights that map input to outputs. We have two algorithms to solve this:
- Perceptron rule (threshold)
- Gradient descent / delta rule (un-threshold)
Perceptron Rule
Update rule:
y: target; : output; : learning rate; x: input
Day 7
little remainder:
-
The purpose of cross-validation is model checking, not model building. For example, let’s say we have two model: liear regression and neural network. For a specific data set, if we want to know which one performs better than another, we should use cross-validation to do model checking and pick up the one that has better accuracy over the cross-validation data set.
-
cross-validation is also helpful to find the better hyper-parameters. For example, Given a list of (hyper-parameter of regularization), we want to find out which value performs better among all for a trained model to reduce overfitting. In this scenario, cross-validation will do the trick.
4.2. Gradient Descent
The basic principle of Gradient Descent is pretty similar to perceptron update rule. But instead of considering the output to be discrete, gradient descent take advantage of the continuous output and differentiate the loss function based on this continuous output with its actual label. Say we have a sum of squared loss . The purpose is to minimize the loss as much as we can. There are actually multiple ways to do that. Gradient Descent is one of them.
So in order to minimize the loss, we have to know the direction of current loss. By differentiating the loss function we can know exactly how to update our weights to reach the extreme point. For example, if the derivative of the loss function is greater than 0, we should decrease our weight in some amount for reaching the optima. Otherwise we increase our weight.
Learning rate controls how much we should update on our weights.
It has chance that the optimization of neural network ends up with local optima since the non-linearity of neural network and randomness of the initial weights.
Summary
- Perceptron -> threshold
- Network can produce any boolean function
- Perceptron rule -> finite time for linearly separable data set
- General differentiable rule -> basic propagation and gradient descent
- Preference / restriction bias of neural network
5. Support Vector Machine
Day 8
5.1 SVM overview
In some situations, we try to not only find the line or hyper-plate that separates multiple classes of data, but we also seek for the best line that splits the data apart. The chart below shows three lines that achieve the purpose to separate a random data set.
Apparently, our intuition tells us that the green line separate the data set perfectly. We can observe that there are some points lie on the line1 and line2, which means if we use these two line as our boundaries, some of the points can be ambiguous when we classify them.
But what is the best boundary? As we can see in the graph, the distance between two blue lines tells us how far the most ambiguous points in ecah classes away from each other. This distance might be able to support us to figure out the best boundary.
Suppose we have class -1 and class 1 (). If the points lie on the boundary (hyper-plate), our classify is not sure which class that point belongs to, so we will have:
(the green line)
and we can also say that:
For other two blue lines. We actually want to maximize the the distance between the two blue lines. Given a point lies on the line1 and point lies on the line2, after putting them into the equation and subtracting them we have:
Now we want to know what is (the distance between these two points). Intuitively we can divide left and right by W. Since W is a vector, There is not way to divide it at least in the really world. Instead of dividing W directly, we can divide the norm of W from both side. That gives us normalized version of W. So now we have:
Actually the whole thing on the left represents the distance of and projected on W direction. This is called margin. We want to find a line that maximizes the margin specifically, which is also .
Then the problem becomes: maximize while classifying everything correctly. This problem also can be turned to minimize . The minimization problem will be easier since it’s a quadratic problem. Quadratic programming problem has a very particular form:
The points that contribute to the quadratic problem are support vector. That is where SVM comes from.
5.2 Kernel
Sometimes the data set is not linearly separable. SVM solves this by projecting the data set to higher dimensional space. The way to do that is called kernel function. For example, quadratic kernel function is . There are a lot of kernel that in polynomial order, which looks like
Summary
- Margins ~ generalization & overfitting
- Maximize Margins (big is better)
- Optimization problem for finding max margins: quadratic problem.
- Support Vector.
- Kernel Trick (K(x, y) -> domain knowledge)
6. Non-parametric Models
Day 9
6.1. Instance Based Learning
In terms of instance based learning, there is actually no mathematical formula for the model. We predict the new instance by looking up the existed instances based on some criteria (e.g. Similarity)
6.1.1. K nearest neighbor
Given:
- Training Data
- Distance Metric
- Number of Neighbors K
- Query Point q
Compute:
- K smallest points for
Return:
- Classification: majority vote (or weighted vote) s.t. for q (plurality)
- Regression: mean (or weighted mean) s.t. for q
6.2 Comparison Matrix of Non-parametric Model and Parametric Model
| Time | Space | |
|---|---|---|
| 1-NN | (Learning) 1 | (Learning) N |
| (Query) logN (Binary Search) | (Query) 1 | |
| K-NN | (Learning) 1 | (Learning) N |
| (Query) logN + K | (Query) 1 | |
| Linear Regression | (Learning) N | (Learning) 1 |
| (Query) 1 | (Query) 1 |
Basically, Instance based learning is called lazy learner, while parametric learning is called eager learner.
6.3 Preference Bias for KNN
Remember preference bias is a notion of why we prefer one hypothesis over another, which is also our belief about what makes a good hypothesis.
- Locality -> near points are similar.
- Smoothness -> averaging.
- All features matters equally.
Summary
In KNN, Distance metric really matters a lot. We should always try to choose a suitable distance metric for a specific problem.
There is some other stuff that is worth mentioning. After we picked up a bunch of nearest neighbor, we could do a regression on these neighbors, which is called locally weighted regression.
7. Bayesian Methods
7.1 Bayes Rule
Given some data and domain knowledge, we want to learn the best hypothesis for these data. That also means we want to learn the most possible hypothesis. That is, we are trying to find a hypothesis that:
Bayes Rule can be expressed as following given observed data D to find the probability of h (hypothesis) based on D:
joint probability:
Actually the is the prior belief of seeing some particular set of data (prior on the data).
So can be explained as given a hypothesis h, what is the probability of the data. The data is composed as . what that really means is, Given a set of , Given a real world that h is true, what is the likelihood that we will see the particular . This is like running hypothesis, for example, labeling the data manually.
is the prior on a particular hypothesis drawn from the hypothesis space. Which encapsulates our prior belief that one hypothesis is likely or unlikely compared with another hypothesis.
Day 10
7.2 Bayesian Learning
The algorithm of bayesian learning:
For each :
calculate:
Output:
can be ignored in terms of argmax.
This is called MAP, namely, maximum a posterior.
If we ingore , we are actually computing the maximum likelihood, which is:
We are not actually dropping the . What we are really saying is the prior is uniform (1 / size of hypothesis), they are equally likely. For example, we have a classification problem that only has two classes and their prior is all 0.5. So they are equally likely. Thus will not affect the result of argmax.
8. Ensemble Learning Boosting
Day 11
8.1. Ensemble Learning Simple Rules
The reason that we want to use ensemble is sometimes a single feature or a couple of features can not fully represent what a truth is going to be. Neural network can be treat as a part of ensemble model in some senses.
Ensemble learning consists of:
- Learning over a subset of data
-> Uniformly randomly pick data, and apply a learner (Bagging)
-> Focusing on hard examples (Boosting) - Combine the results together
-> mean, averaging (Bagging)
-> weighted mean (Boosting)
8.2. Boosting
Pseudocode of Boosting (Binary Classification):
- Given a training set
- For t = 1 to T:
- construct
- find weak classifier with small error
- output
8.2.1. Construct Distribution
The most important part 1 of boosting is construct distribution:
- set up the distribution of first iteration to be uniform distribution, which is . Since we know nothing at beginning about the data set.
-
update the distribution by increasing the weight of the examples that are predicted wrong and decreasing otherwise:
8.2.2. Output Final Result
The final hypothesis will be the weighted hypothesis over all trained classifiers:
8.2.3. Minimize Overfitting
AdaBoost can minimize overfitting by adding up more and more weak learner. This ends up with a larger margin between the classified data, which means the model has more confidence to classify one point is one of the class over the other.
Some other things: pink noise (uniform distribution) will cause boosting overfit. (white noise is gaussian noise)
Summary
- ensembles are good.
- bagging is good.
- combining simple -> complex.
- boosting is really good. -> agnostic to learner
- weak learner.
- error with distribution.


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{% trans "Proposal" %}
{% trans "Contracting Information" %}
{% trans "Approval Form" %}
{% trans "Scope Of Work (SOW)" %}
{% trans "Supporting documents" %}
{{ document_category.name }} {% if document_category.required %}*{% endif %} {% if document_category.template %} {% trans "View template" %} {% endif %}
{% if document_category not in remaining_document_categories %} {% category_latest_file project document_category as latest_file %}{{ latest_file.title }} - {{ latest_file.created_at }}
{% endif %}{% trans "This project is missing the following documents" %}:
Optional
{% trans "Please note that in "%}{% if project_settings.paf_approval_sequential %}{%trans "sequential order, approvers will approve PAF one after the other"%}{% else %}{% trans "parallel order, approvers can approve PAF anytime" %}{% endif %}
{% include 'funds/includes/delegated_form_base.html' with form=request_approval_form value=submit %} {% else %}{% trans "No PAF Reviewer Roles created yet, please create these in " %} {% trans " project settings" %}
{% endif %}{% trans "Are you sure you want to update the approvers?" %} {% if project_settings.paf_approval_sequential %} {% trans "The uppermost active approver will be notified via email." %} {% else %}{% trans "All approvers will be notified via email." %}{% endif %}
{% trans "No PAF Reviewer Roles created yet, please create these in " %} {% trans " project settings" %}
{% endif %}{% trans "Selected approver will be notified. On unselecting, every listed member here will be notified." %}
{% trans "Submit" as submit %} {% include 'funds/includes/delegated_form_base.html' with form=assign_approvers_form value=submit %}{% trans "Selected approver will be notified. On unselecting, every listed member here will be notified." %}
{% trans "Submit" as submit %} {% include 'funds/includes/delegated_form_base.html' with form=assign_approvers_form value=submit %}


Festival Guide:
The challenge has already been increased! You should get some more levels and try one of the higher level range festivals!
Good luck!


Start The Revolution Without Me (PG) * Starring Gene Wilder, Donald Sutherland Directed by Bud Yorkin A Review by Frankie Paiva
Shining in such classics as Willy Wonka and the Chocolate Factory and Young Frankenstein, Gene Wilder is easily a big pull for renting a comedy movie. There's just something different (and funny) about his voice, his face, and his physical humor. That's how Start The Revolution Without Me grabbed me off the comedy shelf, and I'm here to help you through not letting that happen to you.
Philippe (Wilder) and Pierre (Sutherland) are rich French gentlemen. They have to make some type of agreement on a treaty and are traveling to France to do just that. When they dock, they do battle with a group of homeless pirates (or something of the sort) that Claude (Wilder again) and Charles (Sutherland again) are a part of. Claude and Philippe were separated at birth, the same with Pierre and Charles. So, naturally, the two get mixed up and are mistaken for the other two.
When Claude is taken to the king's palace and asked by the king's servant the kill the queen, the two have to escape and are sent into a madcap run around the house (?) avoiding a maniac, a nymphomaniac, (?) and the rest of the king's servants. They somehow start a war with the homeless pirates again that Philippe and Pierre are now (unwillingly) a part of, and they go to battle.
I think that I laughed exactly twice in Start The Revolution Without Me.  It takes the fact that the twins are mixed up as the film's 100 minute joke. It also doesn't really help seeing what Donald Sutherland looked like when he was younger either. (I actually do feel very sorry for him.) But the worst thing is that the film breaks an alltime rule. Any film with Orson Welles is either good or funny. Horribly broken here as he appears for commentry before the film and as part of the film's extremely strange ending. (That amazingly ends like some film with hair gel and a dog.)
This is a member of the film's that you could see if you enjoy inflicting pain on yourself club (FTYCSIYEIPOYC is what I call it, and I do review so many of them.) So Start The Revolution Without Me gets * star.
The Young-Uns: The film contains some sexual references and the end has lots of gun battle. Also, some young children may be confused by what is going on. Good Age: 7 & Up
A Review by Frankie Paiva The 12 Year-Old Movie Reviewer E-Mail me at<EMAIL_ADDRESS>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.
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


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torchdata¶
torchdata is PyTorch oriented library focused on data processing and input pipelines in general.
It extends torch.utils.data.Dataset
and equips it with
functionalities known from tensorflow.data
like map
or cache
.
All of that with minimal interference (single call to super().__init__()
) in original
PyTorch’s datasets.
Functionalities Overview:
Use
map
,apply
,reduce
orfilter
cache
data in RAM or on disk (even partially, say first20%
)Full PyTorch’s Dataset and IterableDataset, support (including torchvision)
General
torchdata.maps
likeFlatten
orSelect
Concrete
torchdata.datasets
designed for file reading and other general tasks
If you are looking for ecosystem of supporting functions around PyTorch check torchfunc.
Installation¶
Following installation methods are available:
pip:¶
To install latest release:
pip install --user torchdata
To install nightly
version:
pip install --user torchdata-nightly
Docker:¶
Various torchdata
images are available both CPU and GPU-enabled.
You can find them at Docker Cloud at szymonmaszke/torchdata
CPU¶
CPU image is based on ubuntu:18.04 and official release can be pulled with:
docker pull szymonmaszke/torchdata:18.04
For nightly
release:
docker pull szymonmaszke/torchdata:nightly_18.04
This image is significantly lighter due to lack of GPU support.
GPU¶
All images are based on nvidia/cuda Docker image.
Each has corresponding CUDA version tag ( 10.1
, 10
and 9.2
) CUDNN7 support
and base image ( ubuntu:18.04 ).
Following images are available:
10.1-cudnn7-runtime-ubuntu18.04
10.1-runtime-ubuntu18.04
10.0-cudnn7-runtime-ubuntu18.04
10.0-runtime-ubuntu18.04
9.2-cudnn7-runtime-ubuntu18.04
9.2-runtime-ubuntu18.04
Example pull:
docker pull szymonmaszke/torchdata:10.1-cudnn7-runtime-ubuntu18.04
You can use nightly
builds as well, just prefix the tag with nightly_
, for example
like this:
docker pull szymonmaszke/torchdata:nightly_10.1-cudnn7-runtime-ubuntu18.04
Contributing¶
If you find any issue or you think some functionality may be useful to others and fits here well, please open new Issue or create Pull Request.
To get an overview of things on can do to help this project, please see Roadmap.


Aids for Debugging¶
GDB¶
This repository contains a .gdbinit
file that contains helper functions to
aid debugging of data structures. GDB will load this file
automatically if you have added the directory which contains the .gdbinit file to GDB’s
auto-load safe-path, and you start GDB from the directory which holds the .gdbinit file.
You can see the safe-path with show auto-load safe-path
on a GDB prompt. You
can configure it by setting it in ~/.gdbinit
with:
add-auto-load-safe-path /path/to/mongo-c-driver
If you haven’t added the path to your auto-load safe-path, or start GDB in another directory, load the file with:
source path/to/mongo-c-driver/.gdbinit
The .gdbinit
file defines the printbson
function, which shows the contents of a bson_t *
variable.
If you have a local bson_t
, then you must prefix the variable with a &.
An example GDB session looks like:
(gdb) printbson bson
ALLOC [0x555556cd7310 + 0] (len=475)
{
'bool' : true,
'int32' : NumberInt("42"),
'int64' : NumberLong("3000000042"),
'string' : "Stŕìñg",
'objectId' : ObjectID("5A1442F3122D331C3C6757E1"),
'utcDateTime' : UTCDateTime(1511277299031),
'arrayOfInts' : [
'0' : NumberInt("1"),
'1' : NumberInt("2")
],
'embeddedDocument' : {
'arrayOfStrings' : [
'0' : "one",
'1' : "two"
],
'double' : 2.718280,
'notherDoc' : {
'true' : NumberInt("1"),
'false' : false
}
},
'binary' : Binary("02", "3031343532333637"),
'regex' : Regex("@[a-z]+@", "im"),
'null' : null,
'js' : JavaScript("print foo"),
'jsws' : JavaScript("print foo") with scope: {
'f' : NumberInt("42"),
'a' : [
'0' : 3.141593,
'1' : 2.718282
]
},
'timestamp' : Timestamp(4294967295,<PHONE_NUMBER>),
'double' : 3.141593
}
LLDB¶
This repository also includes a script that customizes LLDB’s standard print
command to print a bson_t
or bson_t *
as JSON:
(lldb) print b
(bson_t) $0 = {"x": 1, "y": 2}
The custom bson
command provides more options:
(lldb) bson --verbose b
len=19
flags=INLINE|STATIC
{
"x": 1,
"y": 2
}
(lldb) bson --raw b
'\x13\x00\x00\x00\x10x\x00\x01\x00\x00\x00\x10y\x00\x02\x00\x00\x00\x00'
Type help bson
for a list of options.
The script requires a build of libbson with debug symbols, and an installation of PyMongo. Install PyMongo with:
python -m pip install pymongo
If you see “No module named pip” then you must install pip, then run the previous command again.
Create a file ~/.lldbinit
containing:
command script import /path/to/mongo-c-driver/lldb_bson.py
If you see “bson command installed by lldb_bson” at the beginning of your LLDB session, you’ve installed the script correctly.
Debug assertions¶
To enable runtime debug assertions, configure with -DENABLE_DEBUG_ASSERTIONS=ON
.


false
OS/161 Reference Manual
Name
false - return false value
Synopsis
/bin/false
Description
false exits with exit code 1, signifying failure.
Requirements
false uses the _exit system call.
false should function properly once the basic system calls assignment
is completed.
See Also
true


The page has a shadow root that in turn contains two shadow roots. So we can
check behaviour with both nested roots and younger/older sibling roots. The
various sections are highlighted with colored borders to make it more obvious
where each element comes from.
Base-level Stuff
Text inside a content tag outside any shadow DOM. Should be displayed
Parent
Parent Contents
Stuff for for the younger child.
Older Child
As the older child of a nested shadow root, this is the most likely
to go wrong bit of the page.
Older Child Contents
Text inside the older child content tag. Should be replaced
Younger Child
Younger Child Contents
Text inside the younger content tag. Should be replaced
Younger Child Shadow
This should never be shown. The ShadowDOM is older than the parent shadowDOM,
and parent doesn't have a <shadow> element.


homepage
Thousands of Backgrounds for free
Get them now
Thousands of Colos for free
Thousands of Illustrations for free
I stopped on minute 28:20 from the following video tutorial from Derek Banas on YouTube
Here's the link.
Couldnt't manage to use the carousel feature, this video isn't updated.


Welcome to ECM1400 Coursework 3’s documentation!¶
Main module¶
This is the main module for the alarm program, it is an alarm system that gets the latest news, weather and covid updates and displays this on a webpage for the user it also allows the user to set alarms and choose whether they want the alarm to also say the latest news story and weather
-
main.
index
()¶ This module is what connects the modules and then shows this on the webpapge
-
main.
logg
()¶ This logs when the python service is closed
-
main.
rootindex
()¶ This adds, started, to the logfile and redirects the user to the index page
Alarm module¶
This is a module that organises the alarms, deleting them, adding them, scheduling them, refreshing them and saying them, it depends on the function that’s called
-
alarm.
add_alarm
(alarm, name, weather, article, covid, pick_news, pick_weather)¶ This function adds the alarms inputed to the function to the json file of alarms and or adds it to the scheduler depends on conditions
-
alarm.
end
()¶ This function loads the alarm json file and returns the alarms in it
-
alarm.
print_job_name
(alarm, name, weather, article, covid)¶ This function takes in input strings and then says them with pyttsx3
-
alarm.
refresh
(covid, articles, weather)¶ This function will update the alarms with the latest covid,weather&news
-
alarm.
remove_alarm
(title, status=False)¶ This function opens the json file for the alarms and then it loops through the file until the title is the same and it removes that alarm
-
alarm.
write_json
(data, alarm)¶ This function opens the alarm json file and then adds the date passed to the function to the file
Covid Api call¶
This module pulls the releveent information from the covid api
-
covid.
get_covid
()¶ This function gets the date for today and gets the key from teh confic json file and it then calls the api and returns the response
News Api call¶
This module is called from the main function to get the json file from the news api website
-
news.
news
()¶ This function does the api call to the news website and it gets the api key from the config json file, then it returns the json file from the api call
Organising the covid news and weather api calls¶
This module organises the dictionaries that are inputed, this is the covid, artices and weateher then it puts these all in a json file. After this it reads from the file and checks each item against another json file with the notifications that have been delted, and if it isn’t in there it is added to the list that is returned The remove notification adds the notification that is removed to the seen file
-
organise_dictionaries.
org_func
(covid, weather, articles)¶ This function takes in the 3 dictionsaries covid, weather & articles and then it formats them and returns them in one list
-
organise_dictionaries.
remove_notification
(title)¶ This function will remove a notification from the notifications json file
-
organise_dictionaries.
write_json
(data)¶ This function opens the notification json file and then adds the data passed to the function to the file
Time conversions module¶
This is a time module that converts the format hhmmss to seconds and also holds to log function
-
time_conversions.
current_date
()¶ returns the current date
-
time_conversions.
current_time_hhmm
()¶ returns the current tim eof the system in the format hhmm
-
time_conversions.
hhmm_to_seconds
(hhmm: str) → int¶ converts hours minutes to seconds
-
time_conversions.
hhmmss_to_seconds
(hhmmss: str) → int¶ returns the input time (hhmmss) in seconds
-
time_conversions.
hours_to_minutes
(hours: str) → int¶ Converts hours to minutes
-
time_conversions.
log
(text)¶ This function logs text to the specified log file
-
time_conversions.
minutes_to_seconds
(minutes: str) → int¶ Converts minutes to seconds
Weather Api call¶
This module is called from the main function to get the json file from the weather api website
-
weather.
weathers
()¶ This function does the api call to the website and it gets the api key from the config json file, then it returns the json file from the api call


Unicode library for C++ by Ross Smith
#include "unicorn/environment.hpp"
Operating systems store environment variables in a system dependent format, based on the string encoding supported by the native system call API. This means that environment variable names and values are stored as 16 bit strings on Microsoft Windows, but as 8 bit strings on Unix systems. None of the native environment variable APIs make any attempt to enforce Unicode encoding forms; although normally intended to be interpreted as character data, the names and values are really arbitrary strings of 8 or 16 bit unsigned integers.
The Unicorn environment variable API allows the caller to use their preferred UTF encoding, which means that some names and values may not be representable; for example, if you query a variable on Windows using a UTF-8 call, the actual value may not be valid UTF-16, and therefore not convertible to UTF-8 without loss of information. As a compromise solution to this problem, calls that match the native character size will, by default, simply copy strings without attempting any kind of Unicode sanitization.
When using the functions in this module, keep in mind that, if you use the native character type and the default behaviour flag, strings returned from environment variable queries may not be valid Unicode. On the other hand, if you use a different character type or a different behaviour flag, you will always get valid Unicode strings, but they may lose some information if the original environment data was not in UTF form.
This applies when writing environment strings too: if you set them using a
call that matches the native character size, you can pass invalid UTF and it
will simply be passed through to the underlying operating system API
unchanged; but if your call requires an encoding conversion, UTF conversion
follows the usual rules from the unicorn/utf
module.
Whether environment variable names are case sensitive is also operating system dependent.
System environment variable APIs are usually not threadsafe; all of the functions in this module ensure thread safety by locking a common global mutex.
The flags
argument passed to these functions must be one of the encoding
conversion flags defined in unicorn/utf
. Behaviour is undefined
if the flags
argument is not one of these values.
All of these functions can throw EncodingError
if the err_throw
flag is
used and invalid UTF encoding is encountered (either in a string supplied by
the caller, or in one returned from the environment), std::invalid_argument
if an environment variable name is empty or contains an equals sign, or
std::system_error
if anything goes wrong with the underlying system API
call.
template <typename C> basic_string<C>
get_env
(const basic_string<C>& name, uint32_t flags = 0)
template <typename C> basic_string<C>
get_env
(const C* name, uint32_t flags = 0)
Query the value of an environment variable. This will return an empty string if the variable does not exist.
template <typename C> bool
has_env
(const basic_string<C>& name, uint32_t flags = 0)
template <typename C> bool
has_env
(const C* name, uint32_t flags = 0)
Query whether an environment variable exists. Windows, unlike Unix, does not always distinguish clearly between a variable that does not exist, and one that exists but whose value is an empty string; if a variable has been set to an empty value, this function may or may not report its existence on Windows.
template <typename C> void
set_env
(const basic_string<C>& name, const basic_string<C>& value, uint32_t flags = 0)
template <typename C> void
set_env
(const basic_string<C>& name, const C* value, uint32_t flags = 0)
template <typename C> void
set_env
(const C* name, const basic_string<C>& value, uint32_t flags = 0)
template <typename C> void
set_env
(const C* name, const C* value, uint32_t flags = 0)
Set the value of a variable in the process's environment block.
template <typename C> void
unset_env
(const basic_string<C>& name, uint32_t flags = 0)
template <typename C> void
unset_env
(const C* name, uint32_t flags = 0)
Delete a variable from the process's environment block.
class
Environment
A class that holds a complete environment block.
class
Environment
::iterator
A read-only iterator over the name-value pairs stored in an Environment
object. This is a forward iterator whose value type is
pair<NativeString,NativeString>
.
Environment::
Environment
()
explicit Environment::
Environment
(bool from_process)
Environment::
Environment
(const Environment& env)
Environment::
Environment
(Environment&& env) noexcept
Environment::
~Environment
() noexcept
Environment& Environment::
operator=
(const Environment& env)
Environment& Environment::
operator=
(Environment&& env) noexcept
Life cycle operations. If the from_process
flag is set, the constructor will
copy the environment data from the calling process; otherwise, the newly
constructed Environment
object will be empty.
template <typename C> basic_string<C> Environment::
operator[]
(const basic_string<C>& name)
template <typename C> basic_string<C> Environment::
operator[]
(const C* name)
template <typename C> basic_string<C> Environment::
get
(const basic_string<C>& name, uint32_t flags = 0)
template <typename C> basic_string<C> Environment::
get
(const C* name, uint32_t flags = 0)
template <typename C> bool Environment::
has
(const basic_string<C>& name, uint32_t flags = 0)
template <typename C> bool Environment::
has
(const C* name, uint32_t flags = 0)
template <typename C> void Environment::
set
(const basic_string<C>& name, const basic_string<C>& value, uint32_t flags = 0)
template <typename C> void Environment::
set
(const basic_string<C>& name, const C* value, uint32_t flags = 0)
template <typename C> void Environment::
set
(const C* name, const basic_string<C>& value, uint32_t flags = 0)
template <typename C> void Environment::
set
(const C* name, const C* value, uint32_t flags = 0)
template <typename C> void Environment::
unset
(const basic_string<C>& name, uint32_t flags = 0)
template <typename C> void Environment::
unset
(const C* name, uint32_t flags = 0)
These perform the same operations as the corresponding environment variable
functions described above (operator[]
is a synonym for get()
), and will
throw the same exceptions.
Environment::iterator Environment::
begin
() const
Environment::iterator Environment::
end
() const
Iterators over the name-value pairs stored in an Environment
object.
void Environment::
clear
()
Clears all data from the environment block.
bool Environment::
empty
() const noexcept
True if the environment block contains no data.
void Environment::
load
()
Loads the Environment
object's data from the calling process's environment
block. Any existing data in the Environment
object is discarded.
NativeCharacter** Environment::
ptr
()
Collects the environment block into a single data block in the standard format
(suitable for passing to process spawning functions, for example), and returns
a pointer to it. The returned pointer is invalidated by any operation that may
modify the Environment
object (any call to a non-const
function).
size_t Environment::
size
() const noexcept
Returns the number of entries (variables) in the environment block.


Tim has been known to read blogs! It has been a while though, and he does not have one himself!
Tim is a software developer in Detroit MI! He prefers back end development, which he learned recently!
Tim grew up in Lincoln Park, and lives and works in Downtown Detroit!


--- article: false feature: posts: latest: true list: - "/2013/12/07/k9-restore-password" projects: list: - "/projects/passcard" ---


Try resizing the browser to see this text and the image rearrange for optimal viewing.
On extra small screens both the text and the image take up the whole width of the row.
When the browser size gets into the medium category, they can move in together and share a single row. They only need half of the row, since there is more space horizontally on the screen.


You seem a little unsure of where you want to go with programming. HTML and CSS will give you the foundation you need to build websites from the ground up! These two languages are essential to front-end web development and provide the basic skills you need to design attractive websites.
SQL pronounced "sequel" is a data-driven language. It is ideal for anyone interested in data analysis. With SQL, you can learn to store information into separate data sets, allowing users to retrieve accurate results with a search query! It isn't good for building apps from scratch, but you don't seem to have an interest in that anyway.
C# is Microsoft's programming language. It is the language used by many game developers and mobile app creators. If you have any interest in game design, you will likely encounter C#. This language has a huge collection of libraries and frameworks to pull from.
Your interests are well rounded. Python is a general-purpose programming language that has played an important part in data science and web development. This language is simple, but elegant, with a low barrier to entry. No matter what you want to create, Python will help you!
You seem a bit obsessed with esteemed actor Steve Buscemi. Perhaps there is a better career for you in Steve Buscemi worship, or Steve Buscemi impersonation.
Re-take the quiz to see what programming language is best for you.


These pages are intended to be explored in the developer console of your browser. You should also install the Web Developer plugin from Chris Pedrick to play with CSS.
We can use the style= attribute on HTML tags to apply various styles to text and blocks of text.
The "Cascading" in CSS means that the closest style setting (monospace) "wins" over a syle setting (arial) that is "further away".
We can put a border around a block of text. You probably want a CSS cheat sheet handy or use Google to search to find CSS values.
While the style attribute works, it leads to overly verbose HTML so it is more typical to write CSS ⇒ style rules to apply styles to many tags at once.
This material is part of the Web Applications for Everybody (WA4E) website and is copyright Creative Commons Attribution. You are welcome to reuse and remix these examples for your own use with attribution. You can download a ZIP file of the source code for these examples.
Go to the very last page.


|
gtsam 4.1.1
gtsam
|
The common parameters for Nonlinear optimizers.
Most optimizers deriving from NonlinearOptimizer also subclass the parameters.
Public Member Functions | |
| size_t | getMaxIterations () const |
| double | getRelativeErrorTol () const |
| double | getAbsoluteErrorTol () const |
| double | getErrorTol () const |
| std::string | getVerbosity () const |
| void | setMaxIterations (int value) |
| void | setRelativeErrorTol (double value) |
| void | setAbsoluteErrorTol (double value) |
| void | setErrorTol (double value) |
| void | setVerbosity (const std::string &src) |
| virtual void | print (const std::string &str="") const |
| bool | equals (const NonlinearOptimizerParams &other, double tol=1e-9) const |
| bool | isMultifrontal () const |
| bool | isSequential () const |
| bool | isCholmod () const |
| bool | isIterative () const |
| GaussianFactorGraph::Eliminate | getEliminationFunction () const |
| std::string | getLinearSolverType () const |
| void | setLinearSolverType (const std::string &solver) |
| void | setIterativeParams (const boost::shared_ptr< IterativeOptimizationParameters > params) |
| void | setOrdering (const Ordering &ordering) |
| std::string | getOrderingType () const |
| void | setOrderingType (const std::string &ordering) |
Static Public Member Functions | |
| static Verbosity | verbosityTranslator (const std::string &s) |
| static std::string | verbosityTranslator (Verbosity value) |
Public Attributes | |
| size_t | maxIterations = 100 |
| The maximum iterations to stop iterating (default 100) | |
| double | relativeErrorTol = 1e-5 |
| The maximum relative error decrease to stop iterating (default 1e-5) | |
| double | absoluteErrorTol = 1e-5 |
| The maximum absolute error decrease to stop iterating (default 1e-5) | |
| double | errorTol = 0.0 |
| The maximum total error to stop iterating (default 0.0) | |
| Verbosity | verbosity = SILENT |
| The printing verbosity during optimization (default SILENT) | |
| Ordering::OrderingType | orderingType = Ordering::COLAMD |
| The method of ordering use during variable elimination (default COLAMD) | |
| IterationHook | iterationHook |
| Optional user-provided iteration hook to be called after each optimization iteration (Default: none). More... | |
| LinearSolverType | linearSolverType = MULTIFRONTAL_CHOLESKY |
| The type of linear solver to use in the nonlinear optimizer. | |
| boost::optional< Ordering > | ordering |
| The optional variable elimination ordering, or empty to use COLAMD (default: empty) | |
| IterativeOptimizationParameters::shared_ptr | iterativeParams |
| The container for iterativeOptimization parameters. used in CG Solvers. | |
Public Types | |
| enum | Verbosity { SILENT , TERMINATION , ERROR , VALUES , DELTA , LINEAR } |
| See NonlinearOptimizerParams::verbosity. | |
| enum | LinearSolverType { MULTIFRONTAL_CHOLESKY , MULTIFRONTAL_QR , SEQUENTIAL_CHOLESKY , SEQUENTIAL_QR , Iterative , CHOLMOD } |
| See NonlinearOptimizerParams::linearSolverType. | |
| using | IterationHook = std::function< void(size_t, double, double)> |
| Type for an optional user-provided hook to be called after each internal optimizer iteration. More... | |
| using gtsam::NonlinearOptimizerParams::IterationHook = std::function< void(size_t , double, double)> |
Type for an optional user-provided hook to be called after each internal optimizer iteration.
See iterationHook below.
| IterationHook gtsam::NonlinearOptimizerParams::iterationHook |
Optional user-provided iteration hook to be called after each optimization iteration (Default: none).
Note that IterationHook
is defined as a std::function<> with this signature:
which allows binding by means of a reference to a regular function:
or to a C++11 lambda (preferred if you need to capture additional context variables, such that the optimizer object itself, the factor graph, etc.):
or to the result of a properly-formed std::bind
call.


Bootstrap!
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Before Change
Update parameters with current gradients. The optimizer is called
with each parameter and gradient of the model.
seen: Set[int] = set()
for node in self.walk():
if node.id not in seen:
// Kind of ugly to use the _mem.weights -- would make more sense
After Change
with each parameter and gradient of the model.
for node in self.walk():
for name in node.param_names:
if node.has_grad(name):
param = node.get_param(name)
grad = node.get_grad(name)
param, grad = optimizer(param, grad, key=(node.id, name))
node.set_param(name, param)
node.set_grad(name, grad)
for shim in node.shims:
shim.finish_update(optimizer)
@contextlib.contextmanager


Book with Christian Dickmann
Overall I think the candidate was quite intelligent, he was a quick thinker and had relevant experience. The only issue was regarding his motivation towards joining us, he has 3 outstanding offers and needs to make a decision fast. He also asked about salary which I asked him to discuss with the hiring manager.
I liked the candidate a lot. 2m
Completed all interviews, let's bring her onboard
Looking for an SRE candidate, would like someone with super Linux skills.
Another req? I'm so busy lately! 1m


El Nino Data
Abstract
The data set contains oceanographic and surface meteorological readings taken from a series of buoys positioned throughout the equatorial Pacific. The data is expected to aid in the understanding and prediction of El Nino/Southern Oscillation (ENSO) cycles.
Information files:
Data files:
- tao-all2.dat.gz contains the data for 7 March 80 to 3 May 98. (2.8M; 10M uncompressed)
- tao-all2.missing.gz indicates which values in tao-all2.dat are missing. (60K; 12.6M uncompressed)
- tao-all2.col contains the variable names for large subset.
- elnino.gz contains the data for 23 May 98 to 5 June 98. (10K)
- elnino.col contains the variable names for small subset.
The UCI KDD Archive
Information and Computer Science
University of California, Irvine
Irvine, CA 92697-3425
Last modified: 30 June 1998


TDL4Gumstix is a case study showing how to extend the TDL Tool Chain to support a new platform. The platform chosen is Gumstix, a Linux-based computer system of the size of a gum stick. On this platform, real time tasks designed with the Timing Definition Language together with MATLAB Simulink are executed. A simple Java client simulates sensors and actuators that are used by the tasks running on the new platform. The communication between the client and the platform is done via UDP.


|
PortMidi
Cross-platform MIDI IO library
|
lock-free queue for building MIDI applications with PortMidi. More...
Go to the source code of this file.
Typedefs | |
| typedef void | PmQueue |
| The queue representation is opaque. More... | |
Functions | |
| PMEXPORT PmQueue * | Pm_QueueCreate (long num_msgs, int32_t bytes_per_msg) |
| create a single-reader, single-writer queue. More... | |
| PMEXPORT PmError | Pm_QueueDestroy (PmQueue *queue) |
| destroy a queue and free its storage. More... | |
| PMEXPORT PmError | Pm_Dequeue (PmQueue *queue, void *msg) |
remove one message from the queue, copying it into msg . More... | |
| PMEXPORT PmError | Pm_Enqueue (PmQueue *queue, void *msg) |
insert one message into the queue, copying it from msg . More... | |
| PMEXPORT int | Pm_QueueFull (PmQueue *queue) |
| test if the queue is full. More... | |
| PMEXPORT int | Pm_QueueEmpty (PmQueue *queue) |
| test if the queue is empty. More... | |
| PMEXPORT void * | Pm_QueuePeek (PmQueue *queue) |
| get a pointer to the item at the head of the queue. More... | |
| PMEXPORT PmError | Pm_SetOverflow (PmQueue *queue) |
| allows the writer (enqueuer) to signal an overflow condition to the reader (dequeuer). More... | |
lock-free queue for building MIDI applications with PortMidi.
PortMidi is not reentrant, and locks can suffer from priority inversion. To support coordination between system callbacks, a high-priority thread created with PortTime, and the main application thread, PortMidi uses a lock-free, non-blocking queue. The queue implementation is not particular to MIDI and is available for other uses.


Penguin Parallel: Parallel abstractions
PenguinParallel
is a toolkit of abstractions for parallel programming. It
contains a number of small, single-purpose components that can be combined to
build higher-level abstractions.
User-friendly APIs
The user-friendly APIs for expression parallelism represent the two key types of parallelism explored in this library.
Key abstractions include:
- Pipeline Iterators allow users to structure data and compute using
iterators that can be parallelized across multiple cores. Pipeline iterators
leverage 2 kinds of parallelism: (a) homogenous parallelism, where the same
function is applied to data in parallel, and (b) heterogeneous (pipeline)
parallelism, where two different kinds of operations occur simultaneously on
two independent threads. Homogenous parallelism is exposed via
transformations such as
interleave
, andmap
which run the same computation on different data in two or more threads concurrently. Pipeline parallelism is epitomised by theprefetch
transform, which runs the computation “before” the transformation on a background thread, so that all operations that come “after” can occur in parallel. - Parallel operations on array allow users to commpute data-parallel
algorithms easily. Operations such as
reduce
(and friendsmax
,min
,sum
,product
, …),sort
,filter
,group
,map
(and many more) can be easily parallelized across all available cores of a machine.
For more details, please check out the documentation for each type.
Thread pool
From first-principles, a (CPU) compute-bound application will run at peak performance when overheads are minimized. Once enough parallelism is exposed to leverage all cores, one of the key overheads to minmiize is context switching, and thead creation & destruction. The optimal system configuration is thus a fixed-size threadpool where there is exactly one thread per CPU core (or rather, hyperthread). This configuration results in zero context switching, no additional kernel calls for thread creation & deletion, and full utilization of the hardware.
Unfortunately, in practice, it is infeasible to statically schedule work apriori onto a fixed pool of threads. Even when applying the same operation to a homogenous dataset, there will inevitably be variability in execution time. (This can arise from I/O interrupts taking over a core [briefly], or page faults, or even different latencies for memory access across NUMA domains.) As a result, it is important for peak performance to build abstractions that are flexible and dynamic in their work allocation.
The ThreadPool protocol is a foundational API designed to enable efficient use of hardware resources. There are two APIs exposed to support the two kinds of parallelism:
prun is used as “fire-and-forget” style parallelism where closures are dynamically scheduled to execute on the (fixed-size) threadpool.
pJoin is used for Cilk-style fork-join parallelism using work-stealing to efficiently leverage available resources on a machine. The key abstraction is a single function
pJoin
which takes 2 closures and runs then (optionally) in parallel.
Note: while there should be only one “physical” threadpool process-wide, there can be many virtual
threadpools that compose on top of this one to allow configuration and tuning. (This is why
ThreadPool
is a protocol and not static methods.) Examples of additional threadpool abstractions
could include a separate threadpool per-NUMA domain, to support different priorities for tasks, or
higher-level parallelism primitives such as “wait-groups”.


|
Bank System
v1.0
|
#include <SavingsAccount.h>
Public Member Functions | |
| SavingsAccount () | |
| SavingsAccount (const SavingsAccount &) | |
| SavingsAccount & | operator= (const SavingsAccount &) |
| virtual | ~SavingsAccount () |
| virtual uint | getAccTypeNum () const |
| virtual void | print () const |
| Public Member Functions inherited from Account | |
| Account () | |
| Account (const Account &) | |
| Account & | operator= (const Account &) |
| virtual | ~Account () |
| uint | getAccNum () const |
| void | setAccTypeNum (uint) |
| double | getBalance () const |
| void | deposit (double) |
| bool | withdraw (double) |
| bool | deleteAccount () |
| Date | getDateCreated () const |
Friends | |
| class | boost::serialization::access |
Additional Inherited Members | |
| Protected Member Functions inherited from Account | |
| template<class Archive > | |
| void | save (Archive &ar, const uint version) const |
| template<class Archive > | |
| void | load (Archive &ar, const uint version) |
| Protected Attributes inherited from Account | |
| uint | accNum |
| uint | accountTypeNumber |
| Date | dateCreated |
| double | balance |
| SavingsAccount::SavingsAccount | ( | ) |
| SavingsAccount::SavingsAccount | ( | const SavingsAccount & | sa | ) |
|
virtual |
| SavingsAccount & SavingsAccount::operator= | ( | const SavingsAccount & | sa | ) |
|
virtual |
Implements Account.
|
friend |


Stats
public class Stats
Circuit Breaker Stats
-
Mark - Internally tracked Stats Number of timeouts
Declaration
Swift
internal(set) public var timeouts: Int = 0
-
Number of successful reponses
Declaration
Swift
internal(set) public var successfulResponses: Int = 0
-
Number of failed reponses
Declaration
Swift
internal(set) public var failedResponses: Int = 0
-
Total number of requests
Declaration
Swift
internal(set) public var totalRequests: Int = 0
-
Number of rejected requests
Declaration
Swift
internal(set) public var rejectedRequests: Int = 0
-
Array of request latencies
Declaration
Swift
internal(set) public var executionLatencies: [Int] = []
-
Array of request latencies
Declaration
Swift
internal(set) public var totalLatencies: [Int] = []
-
Default latency percentiles
Declaration
Swift
public var percentiles = [0.0, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99, 0.995, 1.0]
-
Mark - Computed Statistics Method returning the cumulative latency
Declaration
Swift
public var totalLatency: Int
-
Method returning the cumulative latency
Declaration
Swift
public var totalExecutionLatency: Int
-
Method returning the average execution response time
Declaration
Swift
public var meanExecutionLatency: Int
-
Method returning the average total response time
Declaration
Swift
public var meanTotalLatency: Int
-
Method returning the number of concurrent requests
Declaration
Swift
public var concurrentRequests: Int
-
Percentage of responses that threw an error
Declaration
Swift
public var errorPercentage: Double
-
Number of errored responses
Declaration
Swift
public var errorCount: Int
-
Latency Executes Mapping Percentile -> Execution time (in milliseconds)
Declaration
Swift
public var latencyExecute: [Double: Int]
-
Latency Total Mapping Percentile -> Total end-to-end execution time (in milliseconds)
Declaration
Swift
public var latencyTotal: [Double: Int]
-
Number of failed executions (Both rejected and failed responses)
Declaration
Swift
public var failed: Int
-
Number of successful executions
Declaration
Swift
public var successful: Int
-
Method to log current snapshot of CircuitBreaker
Declaration
Swift
public func snapshot ()


Full Name*:
Github username (if available):
Email address*:
In which area do you work?*:
Please describe your experience level in your job and with SDC (statistical disclosure control)*:
Have you paricipated in former courses on statistical disclosure control?*:
Yes, I have participated in a former SDC course
No, I haven't participated in any SDC course yet
Comments:
Send


{% from "components/banner.html" import banner_wrapper %} {% call banner_wrapper(type="mode") %} You only have permission to view this service {% endcall %}


--- layout: post title: Routing around DRM date: 2004-02-11 00:00:00 ---
How wireless will end the snake oil of DRM and move us to flat-fee models for consuming content, from The Register:
"By promising to play nice, and building DRM and TCPA technologies, the computer industry is simply making come-hither noises that the rights holders want to hear. "


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Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
Makes 1 loaf
Dietary
This very fine ‘tear and share’ bread will only take 25 minutes of your ‘active’ time to make. The rest of the time you can be emailing, dancing and watching TV. I really love the flavours and the textures of the bread with its crispy crunch outside and the soft and chewy inside.
Put the flours, salt and yeast into a large bowl, mix a bit and then make a hole in the middle. Squidge in the honey and add in 350ml/12oz of warm water (from the tap). Then mix everything together until combined. Then, I like to get my hands in (I find it very therapeutic!) and squidge it all together, gathering up all those dry bits from the bottom of the bowl.
Then tip the dough onto a floured surface and knead it for 10 minutes (or five minutes if kneading in a food mixer fitted with a dough hook). To test and see if it has been kneaded enough, form the dough into a ball with a nice taut top. Dip your finger in the flour and then prod the side of the dough making an indent. The indent should spring back all the way and almost disappear if it is ready.
Put some more flour on the surface and then, using a rolling pin, roll the dough out into about a 40x24cm/15x9in rectangle. If it is too springy to roll then cover it with a tea towel and leave for five minutes or so. That way the stretchy gluten strands in the bread can relax a bit which will make it easier to roll out.
Using a pastry brush, brush the top of the dough with the sesame oil going right up to the edges and then sprinkle the poppy seeds evenly all over. Then cut the dough into six 4cm/1½in wide strips down the length.
Keeping the strips lined up together, twist each one up like a twisted breadstick or cheese straw. Once they are all twisted, stack them into a bundle and pick them all up in one go. Then twist them together so you have a long thick twisted rope made up of the individual strands of bread. Try to twist it evenly so the rope is an even thickness throughout.
Then curve the bread into a ‘wreath’ shape and squish the ends together, sealing them with a bit of water. It does not have to be perfect, just as long as they are joined up. When I make this bread there are usually lots of poppy seeds left on the surface, so I scoop those up and then scatter them over the bread, especially the bit where the join is, to cover it up a bit.
Place the wreath on a baking sheet. Oil some cling film (I find the spray oil is best for this) and use it to cover the dough, oiled side down, so it is airtight but with enough room for the dough to rise a little.
I usually put the oven to preheat to 200C/400F/Gas 6 now and place the dough on a chair near it, so it’s nice and warm. Leave to prove for about 30 minutes. To test if it is ready for the oven (because the bread will not have doubled in size but probably grown by about half again), dust your finger with some flour and then make an indent in the side of the bread. The indent should spring back about half way. If the indent just stays there and does not really move very much, then it needs more time.
When ready, place in the oven to bake for 35 minutes.
The loaf is cooked when it sounds hollow when tapped underneath. If not, then give it another five minutes or so in the oven.
Once ready remove from the oven and serve. I love the crispy crunch bits on the outside and the soft pillowy inside slathered in lots of butter.


{% extends "learn/pages/level-1-base.html" %} {% import "partials/_utils.html" as utils %} {% block lhs_toc %} Table of contents
Question 1: What will be printed after running the following program?
a = 2 * 3
b = a + 1
a = 1
print(b)
a. 7
b. 2
c. 6
d. AssignmentError
Question 2: Name three types.
Question 3: What is the result of 3.0 / 4.0
?
a. 0
b. 0.0
c. 0.75
d. TypeError
Question 4: What is the result of 3 / 4
?
a. 0
b. 0.0
c. 0.75
d. TypeError
Question 5: What is the result of 3 // 4
?
a. 0
b. 0.0
c. 0.75
d. TypeError
Question 6: True or False: It is valid to write a function that takes no parameters.
Question 7: True or False: It is valid to write a function that has no return value.
Question 8: What will be printed after running the following program?
def print_hello():
print("Hello")
def print_twice():
print_hello()
print_hello()
print_twice()
a. HelloHello
b. (nothing)
c.
Hello
Hello
d. OutputError
Question 9: How does Python know where a function definition starts and ends?
Question 10: How do you differentiate defining a function versus calling a function?
Question 11: Fill in the following table.
| a | b | a and b | a or b | not a |
|---|---|---|---|---|
| True | True | |||
| True | False | |||
| False | True | |||
| False | False |
Question 12: What is the output of the following program?
a = 3
if a < 10:
print("Less than 10")
elif a < 5:
print("Less than 5")
else:
print("Greater than 5 and 10")
a. Greater than 5 and 10
b. Less than 5
c. Less than 10
d.
Less than 10
Less than 5
Question 13: The following program probably doesn't work as expected. Explain why.
user_number = input("Please type a number: ")
print(user_number * 2)
Question 14: If you create a function that doesn't have a return
, what happens?
a. None
is returned
b. The value of the last statement is returned
c. It is illegal to put the function on the right side of an assignment statement
d. FunctionDefinitionError
Question 15: What would make most sense to put in the ???
below?
def do_something(x):
if x <= 50:
if x % 10 == 0:
print("Divisible by 10")
else:
print(???)
a. "Greater than 50"
b. "Not divisible by 10"
c. "Greater than 50 and divisible by 10"
d. DanglingElseError
Question 16: What will be printed after running the following program?
total = 0
for num in range(8):
if num % 2 == 0:
total += num
print(total)
a. 20
b. 28
c. 36
d. 12
Question 17: What will be printed after running the following program?
result = 0
index = 0
my_list = [1, 3, 5, 7, 9]
while my_list[index] != 7:
result += my_list[index]
print(result)
a. 9
b. 16
c. MaxRecursionError
d. (nothing)
Question 18: Give an example when a while
loop might work better than a for
loop.
Question 19: Give an example when a for
loop might work better than a while
loop.
Question 20: What is the function to add an element to the end of a list?
a. mylist.add(val)
b. mylist.append(val)
c. mylist + val
d. [mylist, val]
Question 21: What will be printed after running the following program?
d = {"John": "Smith"}
d["John"] = "Adams"
d["Samuel"] = "Adams"
print(d["John"])
a. Smith
b. Adams
c. John: Adams
d. KeyError
Question 22: What's the difference between a tuple and a list?
Question 23: What error will you get if you try to get a nonexistent dictionary reference (ex. d["missing"]
)?
a. ValueError
b. TypeError
c. MathError
d. KeyError
Question 24: Name the three primary modes of opening a file.
Question 25: What happens if you open a file in write mode?
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
int
, float
, str
, bool
, list
, tuple
, dict
//
means integer division and truncates the decimal)def func_name(parameter_list):
. When calling a function, you omit the def
and :
. Furthermore, a function must have been previously defined before it can be called.input()
returns a str
by default, so without an explicit cast with int()
or float()
, it will just print the string twice (so 2 -> 22)index
so the program will continue forever -- we call this an infinite loop)"Smith"
)Solution to 11:
| a | b | a and b | a or b | not a |
|---|---|---|---|---|
| True | True | True | True | False |
| True | False | False | True | False |
| False | True | False | True | True |
| False | False | False | False | True |


| Created: | 1/27/2021 3:53:38 PM |
| Modified: | 1/27/2021 3:53:38 PM |
| Project: | |
| Advanced: |
| Attribute |
|
Public serial id |
|
Details:
|
|
Public integer service_category_id |
|
Details:
|
|
Public varchar(255) description |
|
Details:
|
|
Public integer report_id |
|
Details:
|
|
Public varchar(255) created_by |
|
Details:
|
|
Public varchar(255) updated_by |
|
Details:
|
|
Public timestamp with time zone(0) created_at |
|
Details:
|
|
Public timestamp with time zone(0) updated_at |
|
Details:
|
|
Public timestamp with time zone(0) start_date |
|
Details:
|
|
Public timestamp with time zone(0) end_date |
|
Details:
|
| Operation | ||
|
Public reports_service_categories_pkey( id: serial, |
||
|
Details:
<<PK>>
|
| Tag | Value |
| Owner | public |
|
Details:
|
| Property | Value |
| isFinalSpecialization: | 0 |


This application asks for four inputs:
- Your name
- Gender
- Height
- Hand length
It also relies on a dataset containing many body statistics. This dataset is publicly available (ANSUR: click here).
For the purpose of this tour, we used randomized data.
Hadrian84
R-enthousiast
This application asks for four inputs:
It also relies on a dataset containing many body statistics. This dataset is publicly available (ANSUR: click here).
For the purpose of this tour, we used randomized data.
Based on your input, it compares your height and hand length with a representative population.
It also shows what percentage of people have higher values than you.
This regression plot shows the overall height and hand length relationship (dotted blue line). As expected, taller people have bigger hands (and vice versa).
Where you reside in this plot is indicated by the red dot.
The final graph uses a segment of the regression plot and displays your hand length in the hand length distribution of people with your height.
Again, it shows what percentage of people (with your height) have bigger hands than you.


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