Developer Guide
Codebase Structure¶
The codebase is organized in a modular, datatype /
feature centric way so that adding a feature for a new
datatype is pretty straightforward and requires
isolated code changes. All the datatype specific logic
lives in the corresponding feature module all of which
are under ludwig/features/
.
Feature classes contain raw data preprocessing logic
specific to each data type. All input (output)
features implement build_input
(build_output
)
method which is used to build encodings (decode
outputs). Output features also contain
datatype-specific logic to compute output measures
such as loss, accuracy, etc.
Encoders and decoders are modularized as well (they are
under ludwig/models/modules
) so that they
can be used by multiple features. For example sequence
encoders are shared among text, sequence, and
timeseries features.
Various model architecture components which can be reused are also split into dedicated modules, for example convolutional modules, fully connected modules, etc.
Bulk of the training logic resides in ludwig/models/model.py
which initializes a tensorflow session, feeds the
data, and executes training.
Adding an Encoder¶
1. Add a new encoder class¶
Source code for encoders lives under ludwig/models/modules
.
New encoder objects should be defined in the
corresponding files, for example all new sequence
encoders should be added to ludwig/models/modules/sequence_encoders.py
.
All the encoder parameters should be provided as
arguments in the constructor with their default values
set. For example RNN
encoder takes the
following list of arguments in its constructor:
def __init__(
self,
should_embed=True,
vocab=None,
representation='dense',
embedding_size=256,
embeddings_trainable=True,
pretrained_embeddings=None,
embeddings_on_cpu=False,
num_layers=1,
state_size=256,
cell_type='rnn',
bidirectional=False,
dropout=False,
initializer=None,
regularize=True,
reduce_output='last',
**kwargs
):
Typically all the dependencies are initialized in the encoder's constructor (in the case of the RNN encoder these are EmbedSequence and RecurrentStack modules) so that at the end of the constructor call all the layers are fully described.
Actual creation of tensorflow variables takes place inside the __call__
method of the encoder. All encoders should have the following signature:
__call__(
self,
input_placeholder,
regularizer,
dropout,
is_training
)
Inputs
- input_placeholder (tf.Tensor): input tensor.
- regularizer (A (Tensor -> Tensor or None) function): regularizer function passed to
tf.get_variable
method. - dropout (tf.Tensor(dtype: tf.float32)): dropout rate.
- is_training (tf.Tensor(dtype: tf.bool), default:
True
): boolean indicating whether this is a training dataset.
Return
- hidden (tf.Tensor(dtype: tf.float32)): feature encodings.
- hidden_size (int): feature encodings size.
Encoders are initialized as class member variables in input feature object constructors and called inside build_input
methods.
2. Add the new encoder class to the corresponding encoder registry¶
Mapping between encoder keywords in the model definition and encoder classes is done by encoder registries: for example sequence encoder registry is defined in ludwig/features/sequence_feature.py
sequence_encoder_registry = {
'stacked_cnn': StackedCNN,
'parallel_cnn': ParallelCNN,
'stacked_parallel_cnn': StackedParallelCNN,
'rnn': RNN,
'cnnrnn': CNNRNN,
'embed': EmbedEncoder
}
Adding a Decoder¶
1. Add a new decoder class¶
Souce code for decoders lives under ludwig/models/modules
.
New decoder objects should be defined in the corresponding files, for example all new sequence decoders should be added to ludwig/models/modules/sequence_decoders.py
.
All the decoder parameters should be provided as arguments in the constructor with their default values set. For example Generator
decoder takes the following list of arguments in its constructor:
__init__(
self,
cell_type='rnn',
state_size=256,
embedding_size=64,
beam_width=1,
num_layers=1,
attention_mechanism=None,
tied_embeddings=None,
initializer=None,
regularize=True,
**kwargs
)
Decoders are initialized as class member variables in output feature object constructors and called inside build_output
methods.
2. Add the new decoder class to the corresponding decoder registry¶
Mapping between decoder keywords in the model definition and decoder classes is done by decoder registries: for example sequence decoder registry is defined in ludwig/features/sequence_feature.py
sequence_decoder_registry = {
'generator': Generator,
'tagger': Tagger
}
Adding a new Feature Type¶
1. Add a new feature class¶
Souce code for feature classes lives under ludwig/features
.
Input and output feature classes are defined in the same file, for example CategoryInputFeature
and CategoryOutputFeature
are defined in ludwig/features/category_feature.py
.
An input features inherit from the InputFeature
and corresponding base feature classes, for example CategoryInputFeature
inherits from CategoryBaseFeature
and InputFeature
.
Similarly, output features inherit from the OutputFeature
and corresponding base feature classes, for example CategoryOutputFeature
inherits from CategoryBaseFeature
and OutputFeature
.
Feature parameters are provided in a dictionary of key-value pairs as an argument to the input or output feature constructor which contains default parameter values as well.
All input and output features should implement build_input
and build_output
methods correspondingly with the following signatures:
build_input¶
build_input(
self,
regularizer,
dropout_rate,
is_training=False,
**kwargs
)
Inputs
- regularizer (A (Tensor -> Tensor or None) function): regularizer function passed to
tf.get_variable
method. - dropout_rate (tf.Tensor(dtype: tf.float32)): dropout rate.
- is_training (tf.Tensor(dtype: tf.bool), default:
True
): boolean indicating whether this is a training dataset.
Return
- feature_representation (dict): the following dictionary
{
'type': self.type, # str
'representation': feature_representation, # tf.Tensor(dtype: tf.float32)
'size': feature_representation_size, # int
'placeholder': placeholder # tf.Tensor(dtype: tf.float32)
}
build_output¶
build_output(
self,
hidden,
hidden_size,
regularizer=None,
**kwargs
)
Inputs
- hidden (tf.Tensor(dtype: tf.float32)): output feature representation.
- hidden_size (int): output feature representation size.
- regularizer (A (Tensor -> Tensor or None) function): regularizer function passed to
tf.get_variable
method.
Return - train_mean_loss (tf.Tensor(dtype: tf.float32)): mean loss for train dataset. - eval_loss (tf.Tensor(dtype: tf.float32)): mean loss for evaluation dataset. - output_tensors (dict): dictionary containing feature specific output tensors (predictions, probabilities, losses, etc).
2. Add the new feature class to the corresponding feature registry¶
Input and output feature registries are defined in ludwig/features/feature_registries.py
.
Adding a new Integration¶
Ludwig provides an open-ended method of third-party system integration. This makes it easy to integrate other systems or services with Ludwig without having users do anything other than adding a flag to the command line interface.
To contribute an integration, follow these steps:
- Create a Python file in
ludwig/contribs/
with an obvious name. In this example, it is calledmycontrib.py
. - Inside that file, create a class with the following structure, renaming
MyContribution
to a name that is associated with the third-party system:
class MyContribution():
@staticmethod
def import_call(argv, *args, **kwargs):
# This is called when your flag is used before any other
# imports.
def experiment(self, *args, **kwargs):
# See: ludwig/experiment.py and ludwig/cli.py
def experiment_save(self, *args, **kwargs):
# See: ludwig/experiment.py
def train_init(self, experiment_directory, experiment_name, model_name,
resume, output_directory):
# See: ludwig/train.py
def train(self, *args, **kwargs):
# See: ludwig/train.py and ludwig/cli.py
def train_model(self, *args, **kwargs):
# See: ludwig/train.py
def train_save(self, *args, **kwargs):
# See: ludwig/train.py
def train_epoch_end(self, progress_tracker):
# See: ludwig/models/model.py
def predict(self, *args, **kwargs):
# See: ludwig/predict.py and ludwig/cli.py
def predict_end(self, test_stats):
# See: ludwig/predict.py
def test(self, *args, **kwargs):
# See: ludwig/test.py and ludwig/cli.py
def visualize(self, *args, **kwargs):
# See: ludwig/visualize.py and ludwig/cli.py
def visualize_figure(self, fig):
# See ludwig/utils/visualization_utils.py
def serve(self, *args, **kwargs):
# See ludwig/utils/serve.py and ludwig/cli.py
def collect_weights(self, *args, **kwargs):
# See ludwig/collect.py and ludwig/cli.py
def collect_activations(self, *args, **kwargs):
# See ludwig/collect.py and ludwig/cli.py
If your integration does not handle a particular action, you can simply remove the method, or do nothing (e.g., pass
).
If you would like to add additional actions not already handled by the above, add them to the appropriate calling location, add the associated method to your class, and add them to this documentation. See existing calls as a pattern to follow.
- In the file
ludwig/contribs/__init__.py
add an import in this pattern, using your names:
from .mycontrib import MyContribution
- In the file
ludwig/contribs/__init__.py
in thecontrib_registry["classes"]
dictionary, add a key/value pair where the key is your flag, and the value is your class name, like:
contrib_registry = {
...
"classes": {
...,
"myflag": MyContribution,
}
}
- Submit your contribution as a pull request to the Ludwig github repository.
Style Guidelines¶
We expect contributions to mimic existing patterns in the codebase and demonstrate good practices: the code should be concise, readable, PEP8-compliant, and conforming to 80 character line length limit.
Tests¶
We are using pytest
to run tests.
Current test coverage is limited to several integration tests which ensure end-to-end functionality but we are planning to expand it.
Checklist¶
Before running tests, make sure 1. Your environment is properly setup. 2. You have write access on the machine. Some of the tests require saving data to disk.
Running tests¶
To run all tests, just run
python -m pytest
from the ludwig root directory.
Note that you don't need to have ludwig module installed and in this case
code change will take effect immediately.
To run a single test, run
python -m pytest path_to_filename -k "test_method_name"
Example¶
python -m pytest tests/integration_tests/test_experiment.py -k "test_visual_question_answering"


{% extends "base.html" %} {% block content %}
cordão de ouro seoul
Expats often arrive in Korea and feel isolated within weeks. They feel cut out of mainstream society and opportunities to grow and progress. It doesn’t matter whether the expats are English teachers or business consultants for Samsung with Wharton MBAs. All expats are immediately thrown into a battle against a shrinking social circle and opportunities for genuine bonding.
Having survived six years in Korea, I am acutely aware that I would never have been able to do it without the help of my Capoeira group. Here are five reasons why Capoeira helps expats integrate into Korea.
Capoeira is a community-oriented art. Each Capoeira group has a certain identity that its members embrace. Some Capoeira groups want to build a community of great fighters that regularly challenge each other and fight ferociously to sharpen reaction times or improve their ability to do combat with other martial artists. Other Capoeira groups focus on efficiency of movement. Yet others focus on preserving a cultural heritage that survived centuries of oppression. Irrespective of the focus of an individual Capoeira group, group members rally round each other to ensure that the organisation's goals are met. That is why Capoeiristas identify with a group and think less about the differences within the group.
Capoeira was practiced by people that were denied the rights of regular citizens. Consequently, Capoeiristas go out of their way to be inclusive as they know how terrible it is to be excluded from society. Consequently, members are actively working on breaking down cultural and language barriers. For example, in our Capoeira group, expats are encouraged to learn Korean to help bond with the Korean members of the group and vice-versa. Taking the time to study another language and open your mind to another culture that is very foreign to yours is challenging. Yet, since all members of the group are burdened with over coming language and cultural issues, it immediately becomes something everyone forms strong bonds over.
Capoeira values diversity of thought and behaviour. Capoeira is a form of bodily expression where the individual becomes an artist, and the roda where he or she plays Capoeira becomes the canvas. Capoeiristas are addicted to expressions of beauty, skill, and especially creativity. They love nothing more than seeing something new unfold in the theatre of the roda. Consequently, Capoeira values the individual and what they bring to the community, irrespective of background.
Capoeiristas take time out of their busy schedules to bond with each other. They actively seek out any occasion to be with each other. This often gives Capoeira a cultish feel. However, once you try Capoeira you’ll begin to notice that members of the community simply like spending time with each other and joyfully organise events, tours, birthday parties, and other social occasions to be able to connect more deeply.
Capoeira gives feedback right away. The minute you step up to add value to the group, the minute you get recognition. Capoeira groups rapidly embrace people from all walks of life that want to improve the group in some aspect. There are many ways to contribute. You can help by planning events or opening your home for a potluck. Even training every class or putting all your energy / passion into making your movement perfect for rodas will never go unnoticed. Almost any action with the purpose of making the group a better Capoeira community is celebrated.


/*!
* @class EnumerableThreadLocal
*
* @headerfile <seqan/parallel.h>
*
* @brief Manages thread local storage.
*
* @signature template <typename TValue, typename TManager, typename TSpec>
* class ThreadPool;
*
* @tparam TValue The type of the stored value.
* @tparam TManager A policy used to manage the thread local storage. Defaults
* to @link SimpleThreadLocalManager @endlink.
* @tparam TSpec Specialization of the <tt>EnumerableThreadLocal</tt> class.
* Defaults to <tt>void</tt>.
*
* The enumerable thread local class can be used to manage thread local storage
* using a map as internal buffer. The class offers an iterator interface, such
* that the thread specific values can be enumerated allowing to apply reduce
* operations at the end of the parallel execution. Creating thread local
* storage happens via a lazy evaluation using the <tt>local</tt> function. If a
* thread, identified by its thread id, requests
* storage for the first time a new thread specific storage will be created and
* a lvalue reference pointing to this storage is returned. If the thread id was
* already registered, then a lvalue reference to the associated storage will be
* returned. The access to the <tt>local</tt> function is thread safe and can be
* called concurrently.
*
* A thread local manager can be selected via template argument. This manager
* ensures safe access from concurrent invocations. The manager can further be
* replaced to change the behavior of storing the thread local data.
*
* @see SimpleThreadLocalManager
* @see CountingThreadLocalManager
*
* @mfn EnumerableThreadLocal#Iterator
*
* @headerfile <seqan/parallel.h>
*
* @brief Constructing an instance of this class.
*
* @signature Iterator<TEnumerableThreadLocal>::Type;
*
* @tparam TEnumerableThreadLocal The type of the enumerable thread local class.
*
* @return Type The type of the iterator.
*
* @fn EnumerableThreadLocal::EnumerableThreadLocal
*
* @brief Constructing an instance of this class.
*
* @note The class is not @link CopyConstructibleConcept copy constructible
* @endlink and not move
* constructible.
*
* @signature EnumerableThreadLocal::EnumerableThreadLocal() = default;
* @signature EnumerableThreadLocal::EnumerableThreadLocal(TValue init);
*
* @param[in] init An optional value used to initialize the newly created
* storage.
*
* @fn EnumerableThreadLocal#storageManager
*
* @headerfile <seqan/parallel.h>
*
* @brief Constructing an instance of this class.
*
* @signature TManager & storageManager(etl);
*
* @param[in] etl An instance of @link EnumerableThreadLocal @endlink.
*
* @return TManager& A lvalue reference to the associated storage manager.
*
* @datarace not thread-safe.
*
* @fn EnumerableThreadLocal#local
*
* @headerfile <seqan/parallel.h>
*
* @brief Constructing an instance of this class.
*
* @signature auto & local(etl);
* @signature auto & local(etl, b);
*
* @param[in,out] etl An instance of @link EnumerableThreadLocal @endlink.
* @param[in,out] b A boolean to indicate successful creation.
*
* @return auto& A lvalue reference to the associated thread specific storage.
*
* @datarace thread-safe. Concurrent invocations of this function are
* synchronized via the storage manager.
*
* Calls the internal <tt>local</tt> member function of the associated storage
* manager. If the thread-id was used for the first time <tt>b</tt> will be set
* to <tt>true</tt> to indicate successful creation of the storage. If the
* storage was already created for the given thread-id, then <tt>b</tt> is set
* to <tt>false</tt>.
*
* @fn EnumerableThreadLocal#begin
*
* @headerfile <seqan/parallel.h>
*
* @brief Returns a bidirectional iterator to the thread specific storage.
*
* @signature TIteator begin(etl, tag);
*
* @param[in] etl An instance of @link EnumerableThreadLocal @endlink.
* @param[in] tag A tag to choose the type of the iterator. One of @link
* ContainerIteratorTags @endlink.
*
* @return TIteator Iterator to the begin of the thread stores.
*
* @datarace thread-safe.
*
* @fn EnumerableThreadLocal#end
*
* @headerfile <seqan/parallel.h>
*
* @brief Returns a bidirectional iterator to the thread specific storage.
*
* @signature TIteator end(etl, tag);
*
* @param[in] etl An instance of @link EnumerableThreadLocal @endlink.
* @param[in] tag A tag to choose the type of the iterator. One of @link
* ContainerIteratorTags @endlink.
*
* @return TIteator Iterator to the end of the thread stores.
*
* @datarace thread-safe.
*
* @fn EnumerableThreadLocal#combineEach
*
* @headerfile <seqan/parallel.h>
*
* @brief Enumerates thread local stores and applies an unary functor for each
* store.
*
* @signature void combineEach(etl, f);
*
* @param[in] etl An instance of @link EnumerableThreadLocal @endlink.
* @param[in] f An unary functor called on each thread local storage.
*
* @datarace not thread-safe.
*
* @section Possible implementation:
*
* @code{.cpp}
* std::for_each(begin(etl), end(etl), f);
* @endcode
*
*
* @see EnumerableThreadLocal#combine
*
* @fn EnumerableThreadLocal#combine
*
* @headerfile <seqan/parallel.h>
*
* @brief Enumerates thread local stores and applies a binary functor for each
* store.
*
* @signature void combine(etl, f);
*
* @param[in] etl An instance of @link EnumerableThreadLocal @endlink.
* @param[in] f A binary combinator called on each thread local storage.
*
* @datarace not thread-safe.
*
* @section Possible implementation:
*
* @code{.cpp}
* std::accumulate(begin(etl), end(etl), TValue{}, f);
* @endcode
*
*
* @see EnumerableThreadLocal#combineEach
*/


👟 "Step" Development¶
I made a name for this development method because I don't really know what it's called. However, it's a process to development I found to be profoundly effective in a majority of my situations. Those being where I'm a solo developer, tester, and releaser all wrapped into one.
The idea behind Step Development is rather easy -- take your decomposed "easily repeatable process" and script it one bullet point at a time. Testing it one bullet point at a time. While also, essentially, releasing it one bullet point at a time.
✅ Step #1: Get the EC2 Instance's Public IPv4 Address
The first bullet point in my decomposition is to grab the running EC2 instance's public IPv4 address once it is assigned fresh off a new run state. I created and tested the function released in [pkg/func.py](https://github.com/infamousjoeg/aws-ec2-cloudflare-lambda/blob/master/pkg/func.py#L8) in my Terminal within MacOS.
✅ Step #2: Authenticate to Cloudflare
The second bullet point in my decomposition is to browse to Cloudflare, while my third is to login to Cloudflare. Since this is automation, we'll kill two birds with one stone over the REST API. Better yet, after investigating the [Cloudflare REST API Documentation](https://api.cloudflare.com/), it looks like we can authenticate _while_ also updating the A record.
SCORE! Nothing to do this step!
✅ Step #3: Update A Record in Cloudflare DNS via REST API
Finally, the meat 🥩 and potatoes 🥔 of the function!
I created [cloudflare.py](https://github.com/infamousjoeg/aws-ec2-cloudflare-lambda/blob/master/cloudflare.py) as a means to creating and testing this function released in [pkg/func.py](https://github.com/infamousjoeg/aws-ec2-cloudflare-lambda/blob/master/pkg/func.py#L8). I decided to keep it because it's a great example of how to interact with Cloudflare's REST API using Python in a raw manner.
Back to the function, though. It authenticates with the provided Cloudflare e-mail address and API key. After confirmation of successful authentication, it then updates the A record associated with the A record name provided via DNS ID in the domain managed in Cloudflare associated with the provided Zone ID.


Step 2 of test for Controllers, uses a ModelRefController.
Enter Shipping Address
Full Name:
Address Line1:
Address Line2:
City:
State:
Zipcode:
Verify the shipping address
${this.value},


Lenja Pita
While simplistic in it's approach and choice of ingredients, the serbian "Lazymans pie" interpretation of apple pie delivers an awesomely well rounded experience.
metadata
Created on 2019-02-17
Source: Simon's mom
Estimated time needed: At least 56 minutes. At most 82 minutes.
Ingredients
| 1000 | g | apples | About 8 apples | |
| 300 | g | flour | ||
| 280 | g | butter | Some people swear to using unsalted butter for this recipe, personally I like using salted butter for the flavor enhancing effect of the salt. | |
| 200 | g | egg | About 4 medium sized eggs of 50g each | |
| 175 | g | sugar | About 2 tablespoons | |
| 4 | g | vanilla powder | Optional, about one teaspoon | |
| 3.5 | g | cinnamon | Optional, about one teaspoon |
Scaling
Timeline
Directions
⌛ Active time 5 minutes
- 1000 g apples | About 8 apples
Peel and cut apples:
The apples will go into the filling. If you want a more chunky filling, cut them up roughly. If you want a really smooth apple filling, consider shredding the apples.
⌛ Active time 1 minutes
- 25 g sugar | About 2 tablespoons
- 4 g vanilla powder | Optional, about one teaspoon
- 3.5 g cinnamon | Optional, about one teaspoon
Put apples in frying pan and season them
⌛ Active time 7 minutes
- 15 g butter
Fry the apples:
This has two purposes. Firstly it will draw out water from the apples, so that the dough around the apples doesn't become soggy. Secondly, depending on how finely you chopped the apples, they will become very soft, perhaps almost a smooth mass. It also helps the sugar and spices draw a bit better in. Depending on your frying pan you might need some fat to make this go smoother, in which case I recoment using some butter. One tbsp of butter is about 15 g.
⌛ Active time 1 minutes | Passive time 1 minutes
- 250 g butter | Some people swear to using unsalted butter for this recipe, personally I like using salted butter for the flavor enhancing effect of the salt.
Soften butter:
Soften the butter so that you can easily mix it with the egg yolks and sugar. Make sure that it is not too hot when you add it, or you will be eating cake with chunks of boiled egg.
⌛ Active time 3 minutes
- 200 g egg | About 4 medium sized eggs of 50g each
Split eggs:
Save the eggwhites for later, and put the egg yolks in a larger bowl you will use for mixing the rest of the ingredients
⌛ Active time 1 minutes
- 150 g sugar
Add sugar:
Add sugar to the egg yolks and mix it in
⌛ Active time 1 minutes
Add the softened butter:
Add the softened butter to the sugar/egg yolk mixture
⌛ Active time 1 minutes
- 300 g flour
Add flour:
Mix in flour until you have a smooth dough
⌛ Active time 3 minutes
Beat eggwhites:
Beat the eggwhites as good as you can, preferrably until they are stiff (usually not achievable if you are not using a machine for the beating). This step is important, because most of the fluffyness in the resulting cake will come from the air trapped in the eggwhites in this step.
⌛ Active time 1 minutes
Fold in the eggwhites:
Carefully fold the eggwhites into the dought. Remember that the more air you squeeze out at this step, the less fluffy the resulting cake becomes.
⌛ Active time 1 minutes | Passive time 10 minutes
Pre-heat the oven:
To about 150 ℃
⌛ Active time 3 minutes
- 15 g butter
Butter cake pan:
You can also use neutral cooking oil, but I prefer the taste of butter. 15 g of butter is about 1 tbsp. A pan of about 2.5L should be a good size. Deeper pans will make the assembly easier.
⌛ Active time 3 minutes
Assemble the cake:
Put about two thirds of the dough in the bottom of th cake pan. Cover it completely with the apple filling. Place the last third of the dough on top so that it agan covers the whole of the cake.
⌛ Passive time 40 minutes
Bake the cake:
For about 40 minutes. If you want, you can turn up the heat a bit for the last 5 - 10 minutes.


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Less obvious perks of using 'live' data resources include logins and payments.
How could Twitter be used in these two scenarios?
I have seen a lot of “log in with” buttons recently. I may be getting old, but I don’t yet trust these login methods. Payments with these buttons are even scarier to me! The only solution for payments, in my mind, is to link your Twitter email account with an already trusted payment site like PayPal. That way, when you make a payment with the Twitter login and pay, you know that it is only linked through that trusted PayPal source. With all of this cybercrime we see in the news on a daily basis, I am really interested in learning a lot more about this. I feel there will be a lot of jobs in mobile that have to do with security/payments.
Visit Programmableweb.com and search for JSON-P data sources.
Why would we need JSON-P, specifically?
After viewing a few topics on JSON vs JSON-P, it seems JSON-P is a way we can get around the Same Origin Policy. So we need this to be able to external data into our apps.
Based on the JSON-P search results: what data resource(s) do you think might pair well with which types of Native device features?
There are so many resources of data. I would think making a “social networking photo album” that takes all of your social network’s photos, and serves them up in one nice location. Also, there are many social networking mashup apps, you would need jsonp to get streams from all of these different sites into one.
Please describe any design and functionality intentions you may have for your Demo app components this month.
I have been interested in the accelerometer since it came out. I am going to find a way to demo this. I also hope to be able to read/write from/to Facebook and Twitter. This is something that will be incorporated into many of our apps in the future, whether it be just to post high scores from a game, or post photos from within our apps to these social networks.
Briefly describe some past 'lessons learned' regarding workflow as it relates to research, design, development, etc.
I would have to say my lesson learned for my workflow is to start early and stay organized. I had a tough time with our first “easy” assignment. I was short on time due to unforeseen circumstances, which left me with no time to design my app how I wanted it. Most of my time was just setting it up. Now that it makes more sense, I hope to be able to spend all of the other time designing and developing!
What are some of the ways that you could change your HTML on your Demo app to be more accessible? (Remember, you can use the Section tag with jQuery, for instance)
My Html is definitely full of div after div. I know we are not supposed to do that (this month more than any) but I am having a little difficulty understanding fully. I am going to find more ways of semantically marking up my html. I looked into the section tag with jQuery. This looks interesting and I want to incorporate it throughout my code. Maybe having a few section tags with different classes will be better than having a million divs.
Visit a video service on the web (like YouTube) and search for "Screen Reader Demo" and "Mobile Screen Reader" - what are some of the common frustrations (or common support mechanisms) that you see among the examples that are given - how could you fix them (or use them)?
This was an interesting topic! One video I watched had a blind man telling his frustrations with certain websites. For instance, he was “reading” a page and finally got to what he figured was the middle. Up until that point, he had no idea what the page was about. Then in the middle it gave instructions on how to use the top! My idea is this. Why not have a meta data section that addresses accessibility for every web page we make? The same spot where we tell the browser that this was written in English…Why not have a basic description made for screen readers to tell what the page was about, and where to find certain critical things on the page? One thing blind people really can use is the mouse. I think we should incorporate an iPad sized touch surface that represents all the info to the blind audience. When our meta data tells them if they want to “log in”, then touch the top. If they want a “quick update”, touch the middle, etc.
Devices: Please list two devices (and some of their features) that are made specifically to support Accessibility.
I have to mention the first device that instantly came to my mind. Jitterbug. It is a mobile phone. I remember seeing commercials for this on tv. It’s geared towards the “less technically inclined.” It features large text, and a scaled down mobile phone. You can press a button on it and an “operator” sees your contacts. You simply tell them “call my son, Bob!” And the operator can initiate a call to Bob. Another device made to specifically support accessibility is a simple hearing aid. Hearing aids can amplify speech. It can also somewhat cancel out background noise.
Mobile Apps: Please list two apps (either Android or iOS, or both) that are made specifically to support Accessibility.
An iOS app called “Amplivision” is a great app for people with not so perfect vision. It acts as a lighted magnifying glass. Now you can read all of the fine print with ease!
One iOS built in application is SIRI. It is a voice guided assistant. You can press and hold the home button and siri comes to life. Set an alarm, take a note, mark your calendar, email or text a friend, call a loved one….all from the command of your voice!
What are the 'top selling' app categories in the U.S.? Japan? Great Britain?
U.S.- Games
Japan: Games*
Great Britain: Games
Based on your three answers above, what might you infer about each of the markets listed?
While all three countries had strong sales in games, Japan was a little different depending on what site is being researched. Some showed Japan as using more utility/productivity apps. I would say that the majority of sold apps have to do with games. A lot of the time, these games come about from social media. People want to play games or socialize during their micro-breaks!
What is the average cost and bandwidth limits per user (in general) in each of the three markets? How may this impact development?
U.S.- 4.8 mbps average speed, $3.33 for 1 mbps
Japan: 61 mbps average speed, $.27 for 1 mbps
Great Britain: 7.6 mbps average speed, $4.11 for 1 mbps
This kind of info should help us make decisions on how heavy a load we expect for each application deployed to different countries. Data intense games will be better off in places like Japan.
And, finally, what would be your suggested strategy in project topic and global market choice? Feel free to have a first and second choice.
My suggested strategy for a project topic would have to be games with an emphasis on social networking. The social networking part could be a sort of “challenge between friends” or high scores. There are many categories out there, but the most sold would have to be games. If the developer is strictly after making a big check, choose games. My second choice would be a music application. Music seems to sit right in the middle as far as “sold” apps. Music will never go away, so it’s also a safe bet, although it’s not the most lucrative.
Davenport, K (2012) The best selling iPhone apps and what they mean to us. Retrieved January 27, 2013. http://www.theproducersperspective.com/my_weblog/2012/01/the-best-selling-iphone-apps-and-what-they-mean-to-us.html
DotMobi(2012) Global mobile statistics 2012 Home: all the latest stats on mobile Web, apps, marketing, advertising, subscribers, and trends...
Retrieved January 27, 2013.
http://mobithinking.com/mobile-marketing-tools/latest-mobile-stats
Martinez-Mancotta,D(2012) Internet Speeds and Cost Around the World
Retrieved January 27, 2013.
http://dailyinfographic.com/internet-speeds-around-the-world-infographic
Ofcom(2011) The average cost of a fixed broadband connection stabilised in 2011
Retrieved January 27, 2013.
http://stakeholders.ofcom.org.uk/market-data-research/market-data/communications-market-reports/cmr12/uk/UK5.64
Skinner,C(2012) UK's average broadband speed is 7.6Mbps
Retrieved January 27, 2013.
http://www.pcadvisor.co.uk/news/broadband/3334609/uks-average-broadband-speed-is-76mbps/


My name is Marco Balsamo! Welcome to my portfolio. I am a graduate of Oakland University, having studied History and Social Studies. I am proud to say that I have a passion for teaching and love working with kids. My goal is to shape the future for the next generation of young people!
Upon completing my student teaching at Dakota High School, I was approached by administration to take over a class for a teacher on maternity leave. Presently, I am teaching her U.S. History 10 course. On a daily basis, I am responsible for executing the lesson plans I have created in advance that are complementary to the lessons she has made in the past. Using the skills I have learned in student teaching, I am instilling knowledge unto my students in units pertaining to Civil Rights, the Vietnam War, Terrorism, etc. As my major is in history, I am glad to have in-class experience teaching what I truly have a passion for. Although different from student teaching, working with slightly younger students has been something that has allowed me to expand upon my skills and continue to develop as a professional.
Working under Robert Allaire at Dakota High School, I student taught three different courses: Government, American Law, and a course titled History of Motivation. While teaching these courses under the supervision of my mentor teacher, I learned important skills in the classroom. All three courses are under the umbrella of Social Studies, my passion. Teaching government allowed me to introduce my students to how the bureaucracy works and how the American government is run. In American Legal, my students became experts in the law, including Constitutional Law and case law. Lastly, History of Motivation allowed me to introduce my students to practical psychology, including units involving personality structure, career skills, how to develop a professional resume, etc.
As part of the curriculum at Oakland University, I was responsible for participating in an internship of observing an expert teacher for thirty total hours. During this internship, I learned skills including classroom management and building relationships with students. I learned other skills that I refer to as the "nuts and bolts" of teaching - making copies, writing daily lesson plans, and being in the classroom regularly in order to establish a presence as the instructor.
As my first teaching experience within a school, the skills I developed while working at Davis Junior High School stick with me to this day. The program known as AVID is an advocational program for students that are close to or may underachieve and need the extra little push in order to turn a "D" to a "C", or a "C" into a "B". While working as a tutor in the AVID program, I was able to teach students for the first time in my professional career. In addition to teaching them instructional content, AVID tutors also teach skills such as organization, timeliness, and other academic preparedness skills.
My personal interests include reading, studying, and a constant pursuit of getting better at my craft. I have a slogan that I live my life by - I am on a constant pursuit of perfection. I feel that reading is the key to becoming a better learner, and as an instructor it is my responsibility to both myself and to my students to be the content expert in the classroom. I keep up to date with current events as I feel that is critical to being a positive member of society. I read the Detroit Free Press every single day in order to keep up with the real facts! As a philosophy, I feel that being a well-rounded citizen has allowed me to become a much better person.
On a more personal note, I think that I'm just a regular guy! I like to watch sports and play some video games. My favorite TV shows include Breaking Bad and Game of Thrones, and my favorite sports teams are the New England Patriots and the Pittsburgh Steelers. I also like to travel. I have been all over the country and to several parts of Europe. Domestically my favorite cities are Pittsburgh and San Diego, while globally my favorite cities are Milan, Italy and Berlin, Germany.
In my opinion, the two most critical aspects of teaching are Explaining and Modeling Content and providing students with Tasks, Texts, and Learning Goals. This provides students with expert-level content that they can engage with, while also providing the students with a way to execute their knowledge and show me they know what the goals of the lesson are. Below are two videos displaying my growth from early in student teaching to more present, advanced skills.
Below is the attempt from the beginning of the year. Although the students understood the content, it lacked the nuance and professional skills to have my students truly learn.
Compare what you just watched to the below video. Notice how I am focusing more specifically on the goals that the students need to be aware of as well as a more thorough derscription of what exactly it is that they are learning
Part of the responsibilities of a teacher include gauging student progress. One of the skills that I am proficient at include Eliciting Student Thinking. Listen below as I ask a series of questions to my student to elicit his feedback on the progress of the lesson we had been working on.
Another skill that every good teacher needs to possess is the skill of executing Formative Assessments in the classroom. In becoming proficient in doing so, I am able to determine what teacher moves I have made successfully, what moves I have made unsuccessfully, and how I need to adjust my teaching moving forward to better instruct my students. Watch below as I provide my students with a formative assessment.
Particularly as a Social Studies instructor, Leading Group Discussion is a critical component of a classroom setting in which everyone can be involved in a debate, a group project, or other means of collectively working together. Being the expert in the room means that I am responsible for leading the group in the right direction and ensuring that the discussions are positive. Watch below as I execute a lesson while Leading Group Discussion.


| Class | Description |
|---|---|
| AnchorPane |
AnchorPane allows the edges of child nodes to be anchored to an offset from
the anchor pane's edges.
|
| Background |
The Background of a
Region . |
| BackgroundFill |
The fill and associated properties that direct how to fill the background of a
Region . |
| BackgroundImage |
Defines properties describing how to render an image as the background to
some
Region . |
| BackgroundPosition |
Represents the position of a
BackgroundImage within the
Region 's drawing area. |
| BackgroundSize |
Defines the size of the area that a BackgroundImage should fill relative
to the Region it is styling.
|
| Border |
The Border of a
Region . |
| BorderImage |
Defines properties describing how to render an image as the border of
some Region.
|
| BorderPane |
BorderPane lays out children in top, left, right, bottom, and center positions.
|
| BorderStroke |
Defines the stroke to use on a Border for styling a Region.
|
| BorderStrokeStyle |
Defines the style of the stroke to use on one side of a BorderStroke.
|
| BorderWidths |
Defines widths for four components (top, right, bottom, and left).
|
| ColumnConstraints |
Defines optional layout constraints for a column in a
GridPane . |
| ConstraintsBase |
The base class for defining node-specific layout constraints.
|
| CornerRadii |
Defines the radii of each of the four corners of a BorderStroke.
|
| FlowPane |
FlowPane lays out its children in a flow that wraps at the flowpane's boundary.
|
| GridPane |
GridPane lays out its children within a flexible grid of rows and columns.
|
| HBox |
HBox lays out its children in a single horizontal row.
|
| Pane |
Base class for layout panes which need to expose the children list as public
so that users of the subclass can freely add/remove children.
|
| Region |
Region is the base class for all JavaFX Node-based UI Controls, and all layout containers.
|
| RowConstraints |
Defines optional layout constraints for a row in a
GridPane . |
| StackPane |
StackPane lays out its children in a back-to-front stack.
|
| TilePane |
TilePane lays out its children in a grid of uniformly sized "tiles".
|
| VBox |
VBox lays out its children in a single vertical column.
|
| Enum | Description |
|---|---|
| BackgroundRepeat |
Enumeration of options for repeating images in backgrounds
|
| BorderRepeat |
Enum indicating the repetition rules for border images.
|
| Priority |
Enumeration used to determine the grow (or shrink) priority of a given node's
layout area when its region has more (or less) space available and
multiple nodes are competing for that space.
|
Provides classes to support user interface layout. Each layout pane class supports a different layout strategy for its children and applications may nest these layout panes to achieve the needed layout structure in the user interface. Once a node is added to one of the layout panes, the pane will automatically manage the layout for the node, so the application should not position or resize the node directly; see "Node Resizability" for more details.
The scene graph layout mechanism is driven automatically by the system once
the application creates and displays a Scene
.
The scene graph detects dynamic node changes which affect layout (such as a
change in size or content) and calls requestLayout()
, which marks that
branch as needing layout so that on the next pulse, a top-down layout pass is
executed on that branch by invoking layout()
on that branch's root.
During that layout pass, the layoutChildren()
callback method will
be called on each parent to layout its children. This mechanism is designed
to maximize layout efficiency by ensuring multiple layout requests are coalesced
and processed in a single pass rather than executing re-layout on on each minute
change. Therefore, applications should not invoke layout directly on nodes.
The scene graph supports both resizable and non-resizable node classes. The
isResizable()
method on Node
returns whether a
given node is resizable or not. A resizable node class is one which supports a range
of acceptable sizes (minimum <= preferred <= maximum), allowing its parent to resize
it within that range during layout, given the parent's own layout policy and the
layout needs of sibling nodes. Node supports the following methods for layout code
to determine a node's resizable range:
public Orientation getContentBias()
public double minWidth(double height)
public double minHeight(double width)
public double prefWidth(double height)
public double prefHeight(double width)
public double maxWidth(double height)
public double maxHeight(double width)
Non-resizable node classes, on the other hand, do not have a consistent
resizing API and so are not resized by their parents during layout.
Applications must establish the size of non-resizable nodes by setting
appropriate properties on each instance. These classes return their current layout bounds for
min, pref, and max, and the resize()
method becomes a no-op.
Resizable classes: Region
, Control
, WebView
Non-Resizable classes: Group
, Shape
, Text
For example, a Button control (resizable) computes its min, pref, and max sizes which its parent will use to resize it during layout, so the application only needs to configure its content and properties:
Button button = new Button("Apply");
However, a Circle (non-resizable) cannot be resized by its parent, so the application
needs to set appropriate geometric properties which determine its size:
Circle circle = new Circle();
circle.setRadius(50);
For example, to override the preferred size of a ListView:
listview.setPrefSize(200,300);
Or, to change the max width of a button so it will resize wider to fill a space:
button.setMaxWidth(Double.MAX_VALUE);
For the inverse case, where the application needs to clamp the node's min or max size to its preferred:
listview.setMaxSize(Region.USE_PREF_SIZE, Region.USE_PREF_SIZE);
And finally, if the application needs to restore the intrinsically computed values:
listview.setPrefSize(Region.USE_COMPUTED_SIZE, Region.USE_COMPUTED_SIZE);
Node
provides the layoutBounds
property to define the 'logical' bounds
of the node for layout and boundsInParent
to define the visual bounds
once all effects, clipping, and transforms have been applied.
These two bounds properties will often differ for a given node and
layoutBounds
is computed differently depending on the node class:
| Node Type | Layout Bounds |
|---|---|
Shape ,ImageView |
Includes geometric bounds (geometry plus stroke). Does NOT include effect, clip, or any transforms. |
Text |
logical bounds based on the font height and content width, including white space.
can be configured to be tight bounds around chars glyphs by setting boundsType .
Does NOT include effect, clip, or any transforms.
|
Region , Control , WebView |
always [0,0 width x height] regardless of visual bounds,
which might be larger or smaller than layout bounds.
|
Group |
Union of all visible childrens' visual bounds (boundsInParent )
Does NOT include effect, clip, or transforms set directly on group,
however DOES include effect, clip, transforms set on individual children since
those are included in the child's boundsInParent .
|
So for example, if a DropShadow
is added to a shape,
that shadow will not be factored into layout by default. Or, if a
ScaleTransition
is used to
pulse the size of a button, that pulse animation will not disturb layout around
that button. If an application wishes to have the effect, clip, or transform
factored into the layout of a node, it should wrap that node in a Group.
Submit a bug or feature
For further API reference and developer documentation, see Java SE Documentation. That documentation contains more detailed, developer-targeted descriptions, with conceptual overviews, definitions of terms, workarounds, and working code examples.
Copyright © 2008, 2021, Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


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This is an application that works in a way when you key in your age it gives your the names of that specific group of people.
Select gender
Male Female


Zian (Zane) Liu
The content of this resume has been optimized for the web, and role descriptions have been shortened for brevity. You can e-mail me if you would like a tailored and more detailed copy of this document.
Last updated July 2017
Contact Information
E-mail<EMAIL_ADDRESS>Education
University of California, Berkeley and San Francisco (joint)
Master of Translational Medicine (M.T.M.), Jun 2017
Advanced Coursework in Regulatory Sciences, UCSF-Stanford CERSI
University of California, Berkeley
Bachelor of Science (B.S.) with High Honors in Bioengineering, May 2016
Minor Certificate in Public Policy, Goldman School of Public Policy
Certificate, Synthetic Biology Engineering Research Center (SynBERC)
Skills
Medical Device Development
FDA regulation, compliance, 510(k), IDE, reimbursement strategy, user studies, market research
Quality System Engineering
21 CFR 820, ISO 13485, ISO 14971, design controls, validation, verification, clinical evaluation
Programming and Data Analysis
Python, NumPy, Java, R, Ruby, HTML, JavaScript, COMSOL, MATLAB
Bioengineering and Laboratory
Molecular cloning, cell culture, PCR, western blot, ELISA, DNA/RNA extraction
Medical Device & Digital Health
Respira Labs
Regulatory Affairs and IP Consultant (2017). Conducted regulatory, patentability, and freedom-to-operate analysis. Advised company founders on ensuring compliance throughout product development.
Open MS BioScreen, Bove Lab, UCSF Neurology
Regulatory Lead and Graduate Researcher (2016 - 2017). Developed a web application that helps physicians and patients track and manage multiple sclerosis (MS) patients’ symptoms and unique disease presentations. Created and ensured adoption of an FDA-exempt regulatory strategy.
Drug-Delivering Contact Lens Project, UCSF Ophthalmology and Berkeley Biodesign
Biodesign Student (2015 - 2016). Designed a dual-layer drug-eluting contact lens to deliver antifungal drug natamycin to treat corneal ulcers, targeted for use in developing countries.
Policy Analysis
“Bioweapons for Dummies”, Goldman School of Public Policy
Biosecurity Researcher (2015 - 2016) advised by Stephen Maurer, JD. Studied the impacts of synthetic biology and amateur “biohacking” on national security, and proposed policies to decrease the biosecurity risk of such activities
Laboratory and Teaching
Department of Molecular and Cell Biology, UC Berkeley
Biophysical Chemistry Tutor (2014 - 2016). Presented 50+ lectures on protein structural analysis, thermodynamics, and enzyme kinetics at weekly tutoring sessions, attracting an average attendance of 30-50 students.
Undergraduate Biology Laboratory Instructor (2015). Aided in teaching general biology to 30+ undergraduates over 10+ sessions. Supervised rat, cockroach, earthworm, and crayfish dissections.
Joint Bioenergy Institute, Lawrence Berkeley National Laboratory
Undergraduate Researcher (2014 - 2015). Engineered S. cerevisiae to overproduce biofuel molecules in yeast, employing synthetic biology and CRISPR/Cas9 gene editing technology.
Publications
Articles
- Liu Z, “Bioweapons… for dummies? ” Bulletin of the Atomic Scientists, Sep 2015
Conferences and Seminars
- Haack A, Lin I, Liu Z, and Shrestha A “A Drug Eluting Contact Lens for Corneal Ulceration,” presented at Rice 360° 6th Annual Undergraduate Global Health Technologies Design Competition, Houston, Texas, USA, Apr 2016.
- Liu Z, “Bioweapons… for Dummies? Evaluating the Treat of Rogue Biohacking,” presented at Departmental Seminar, James Martin Center for Nonproliferation Studies, Middlebury Institute of International Studies, Monterey, California, USA, Feb 2016.
Professional Affiliations and Leadership
Bioengineering Honor Society at UC Berkeley
BioEHSC Co-Founder and Adviser (2014 - present); member since 2013
Former President and Academic Officer
Spearheaded new programs to promote career readiness, industrial partnerships, and educational outreach. Founded the BioEHSC, the only comprehensive bioengineering research and design competition for U.S. high schools.
Tau Beta Pi, the Engineering Honor Society, CA-A Chapter
Alumnus Advisor (2016 - present); member since 2013
Former Recording Secretary and longest-serving committee chair
Oversaw sustainability and knowledge retention efforts. Led the 501(c)3 project, coordinating with Advisory Board members and pro-bono lawyers to revise bylaws and practices to secure tax-exempt status.
Biomedical Engineering Society, UC Berkeley Chapter
Member since 2012
Directed fundraising efforts targeting local small businesses for the 2012 “Fall Reception” professional dinner and the 2013 inaugural “Legacy Night” alumni dinner.
Interests
Fountain pen calligraphy, menswear and fashion, current affairs, coffee, educational outreach, photography, public speaking


Style Tiles are a great way to solidify website colors, fonts and elements.
Responsive Style Tiles kicks it up a notch to display style responsibly so customers can get a feel how style will look at mobile, tablet and desktop levels.
Responsive Style Tiles was built using the [Exelsior] Framework (https://github.com/nys-its/excelsior) and Foundation Framework.
Other Tools used were:
View the [Responsive Style Tile] (http://courtney-jordan.github.io/responsive-styletiles/) Demo


Scots man in court after being mauled by his own bulldog
Allan Forsyth, 30 was pounced upon by Bronson his Johnson American Bulldog in April this year, and suffered multiple bites to his body and calf.
by Connor GordonA man who was attacked by his own dog ended up in the dock.
Allan Forsyth, 30 was pounced upon by Bronson his Johnson American Bulldog in April this year, and suffered multiple bites to his body and calf.
He was charged under the dangerous dogs act of being the owner of the dog which attacked him to his severe injury at his home in Rutherglen , near Glasgow .
Forsyth today admitted the charge which could result in Bronson being put down.
Sheriff Alan Findlay has ordered a report on Bronson by an animal expert to see if it is a danger to the public.
Glasgow Sheriff Court heard that Bronson mauled Forsyth after he allegedly annoyed it in the early hours of the morning.
The court heard police were contacted at 2.45am by Forysth’s partner.
When they arrived they found four dogs outside of the house and Forysth on the couch with “significant” leg and arm injuries.
Prosecutor Louise McNeill said: “Officers were told by Forsyth’s partner that he had been drinking and was antagonising the dog.
“She said Bronson lashed out and bit him several times repeatedly on the body.”
The court heard that one of the other dogs saved Forsyth from more serious injury by attacking Bronson.
Ms McNeill added: “One of the female dogs began to attack Bronson which caused him to lose his grip.”
All four dogs were taken to police kennels by the officers where they have remained since the incident.
It is open to the sheriff to have Bronson destroyed for the attack.
However, Sheriff Findlay replied: “It seems a bit unfair on Bronson if he has been harassed.”
He deferred sentence on Forsyth for background reports and a report on Bronson.
Sheriff Findlay will also determine whether Forsyth should be from owning animals.
Forsyth, now of Cambuslang, has been bailed meantime.


Midical
Clinic
home
diseases
hospitals
Appointments
doctors
contact US
Nearest medical clinic
Click the below button to see the nearest clicnic


Concatenates a list of SparseTensor
along the specified dimension. (deprecated arguments)
tf.compat.v1.sparse.concat
tf.compat.v1.sparse_concat( axis, sp_inputs, name=None, expand_nonconcat_dim=False, concat_dim=None, expand_nonconcat_dims=None )
Warning: SOME ARGUMENTS ARE DEPRECATED: (concat_dim)
. They will be removed in a future version.
Instructions for updating:
concat_dim is deprecated, use axis instead
Concatenation is with respect to the dense versions of each sparse input.
It is assumed that each inputs is a SparseTensor
whose elements are ordered
along increasing dimension number.
If expand_nonconcat_dim is False, all inputs' shapes must match, except for the concat dimension. If expand_nonconcat_dim is True, then inputs' shapes are allowed to vary among all inputs.
The indices
, values
, and shapes
lists must have the same length.
If expand_nonconcat_dim is False, then the output shape is identical to the inputs', except along the concat dimension, where it is the sum of the inputs' sizes along that dimension.
If expand_nonconcat_dim is True, then the output shape along the non-concat dimensions will be expand to be the largest among all inputs, and it is the sum of the inputs sizes along the concat dimension.
The output elements will be resorted to preserve the sort order along increasing dimension number.
This op runs in O(M log M)
time, where M
is the total number of non-empty
values across all inputs. This is due to the need for an internal sort in
order to concatenate efficiently across an arbitrary dimension.
For example, if axis = 1
and the inputs are
sp_inputs[0]: shape = [2, 3] [0, 2]: "a" [1, 0]: "b" [1, 1]: "c" sp_inputs[1]: shape = [2, 4] [0, 1]: "d" [0, 2]: "e"
then the output will be
shape = [2, 7] [0, 2]: "a" [0, 4]: "d" [0, 5]: "e" [1, 0]: "b" [1, 1]: "c"
Graphically this is equivalent to doing
[ a] concat [ d e ] = [ a d e ] [b c ] [ ] [b c ]
Another example, if 'axis = 1' and the inputs are
sp_inputs[0]: shape = [3, 3] [0, 2]: "a" [1, 0]: "b" [2, 1]: "c" sp_inputs[1]: shape = [2, 4] [0, 1]: "d" [0, 2]: "e"
if expand_nonconcat_dim = False, this will result in an error. But if expand_nonconcat_dim = True, this will result in:
shape = [3, 7] [0, 2]: "a" [0, 4]: "d" [0, 5]: "e" [1, 0]: "b" [2, 1]: "c"
Graphically this is equivalent to doing
[ a] concat [ d e ] = [ a d e ] [b ] [ ] [b ] [ c ] [ c ]
axis
: Dimension to concatenate along. Must be in range [-rank, rank),
where rank is the number of dimensions in each input SparseTensor
.sp_inputs
: List of SparseTensor
to concatenate.name
: A name prefix for the returned tensors (optional).expand_nonconcat_dim
: Whether to allow the expansion in the non-concat
dimensions. Defaulted to False.concat_dim
: The old (deprecated) name for axis.expand_nonconcat_dims
: alias for expand_nonconcat_dimA SparseTensor
with the concatenated output.
TypeError
: If sp_inputs
is not a list of SparseTensor
.


About
Ergo will have a hardening protocol upgrade on block #417,792. This upgrade introduces breaking changes where Non-Outsourceability will be switched off. After this hardfork, the new PoW (called Autolykos v2.0) will support traditional mining pools.
The following instructions will be valid since block #417,792. More details here and here. Previous instructions, which is valid until block #417,791, can be found here.
Ergo mining is based on Autolykos v.2, a Proof-of-Work algorithm designed to be ASIC resistant. Miners have to perform memory-hard computations~(at least 2.5 GB memory is needed, but the current most efficient implementation utilizes around 4 GB of vRAM) that makes Ergo friendly for GPU mining. Unlike Autolykos v.1, Autolykos v.2 does not require access to private keys, thereby allowing mining pool formation. As soon as a correct solution is found, the miner broadcasts the block along with the solution and is able to collect the block reward after a delay of 720 blocks using the secret of his mining address. The rest of the network verifies the solution which can be done very efficiently, requiring less than a kilobyte of memory.
Read more about miningMining Pools
-
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{{ if eq .Language.Lang .Site.Params.defaultLanguage }}
{{ $defaultLangPages := where .Sites.First.RegularPages.ByDate "Section" "mining" }}
{{ range $defaultLangPages }}
- {{ .Params.title }} {{end}} {{end}} {{ end }}
Miners
-
{{ range .Site.Home.AllTranslations }}
{{ if eq .Language.Lang .Site.Params.defaultLanguage }}
{{ $defaultLangPages := where .Sites.First.RegularPages.ByDate "Section" "miners" }}
{{ range $defaultLangPages }}
- {{ .Params.title }} {{end}} {{end}} {{ end }}


Moon
Round trip
Take a trip to and back from the moon.
$12.01
Trip + 1 month stay
Ticket includes a round trip plus a 1 month stay at our newest "Salllly" satellite.
$1.01
Mars
Round trip
Take a trip to and back from the Mars.
$1.001
Trip + 1 month stay
Ticket includes a round trip plus a 3-day stay at our newest "Jerrrryy" satellite.
$120.01
Mercury
Round trip
Take a trip to and back from the Mercury.
$0.109
Trip + 1 month stay
Ticket includes a round trip plus a 3 minute stay at our newest "Yelleer" satellite.
$13.01


|
Performs sprintf-like conversion, ie. puts the values in a template.
DO NOT use it instead of built-in conversions in simple cases such as
'Cost: %.2f' as it would introduce unneccessary latency oposed to
'Cost: ' + cost.toFixed(2).
|
code » |


Defined in generated file: python/ops/gen_sdca_ops.py
Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
tf.compat.v1.train.sdca_optimizer( sparse_example_indices, sparse_feature_indices, sparse_feature_values, dense_features, example_weights, example_labels, sparse_indices, sparse_weights, dense_weights, example_state_data, loss_type, l1, l2, num_loss_partitions, num_inner_iterations, adaptative=True, name=None )
linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.
Proximal Stochastic Dual Coordinate Ascent.
Shai Shalev-Shwartz, Tong Zhang. 2012
$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$
Adding vs. Averaging in Distributed Primal-Dual Optimization.
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan,
Peter Richtarik, Martin Takac. 2015
Stochastic Dual Coordinate Ascent with Adaptive Probabilities.
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
sparse_example_indices
: A list of Tensor
objects with type int64
.
a list of vectors which contain example indices.sparse_feature_indices
: A list with the same length as sparse_example_indices
of Tensor
objects with type int64
.
a list of vectors which contain feature indices.sparse_feature_values
: A list of Tensor
objects with type float32
.
a list of vectors which contains feature value
associated with each feature group.dense_features
: A list of Tensor
objects with type float32
.
a list of matrices which contains the dense feature values.example_weights
: A Tensor
of type float32
.
a vector which contains the weight associated with each
example.example_labels
: A Tensor
of type float32
.
a vector which contains the label/target associated with each
example.sparse_indices
: A list with the same length as sparse_example_indices
of Tensor
objects with type int64
.
a list of vectors where each value is the indices which has
corresponding weights in sparse_weights. This field maybe omitted for the
dense approach.sparse_weights
: A list with the same length as sparse_example_indices
of Tensor
objects with type float32
.
a list of vectors where each value is the weight associated with
a sparse feature group.dense_weights
: A list with the same length as dense_features
of Tensor
objects with type float32
.
a list of vectors where the values are the weights associated
with a dense feature group.example_state_data
: A Tensor
of type float32
.
a list of vectors containing the example state data.loss_type
: A string
from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss"
.
Type of the primal loss. Currently SdcaSolver supports logistic,
squared and hinge losses.l1
: A float
. Symmetric l1 regularization strength.l2
: A float
. Symmetric l2 regularization strength.num_loss_partitions
: An int
that is >= 1
.
Number of partitions of the global loss function.num_inner_iterations
: An int
that is >= 1
.
Number of iterations per mini-batch.adaptative
: An optional bool
. Defaults to True
.
Whether to use Adaptive SDCA for the inner loop.name
: A name for the operation (optional).A tuple of Tensor
objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).
out_example_state_data
: A Tensor
of type float32
.out_delta_sparse_weights
: A list with the same length as sparse_example_indices
of Tensor
objects with type float32
.out_delta_dense_weights
: A list with the same length as dense_features
of Tensor
objects with type float32
.


public enum LifecycleStep extends java.lang.Enum<LifecycleStep>
| Modifier and Type | Class and Description |
|---|---|
static class |
LifecycleStep.StepInfo |
| Modifier and Type | Method and Description |
|---|---|
static java.util.List<LifecycleStep> |
getSteps(java.util.List<LifecycleStep.StepInfo> infos) |
static LifecycleStep |
valueOf(java.lang.String name)
Returns the enum constant of this type with the specified name.
|
static LifecycleStep[] |
values()
Returns an array containing the constants of this enum type, in
the order they are declared.
|
public static final LifecycleStep ON_CREATE_INITIAL_STATE
public static final LifecycleStep ON_CREATE_LAYOUT
public static final LifecycleStep ON_PREPARE
public static final LifecycleStep ON_MEASURE
public static final LifecycleStep ON_BOUNDS_DEFINED
public static final LifecycleStep ON_CREATE_MOUNT_CONTENT
public static final LifecycleStep ON_MOUNT
public static final LifecycleStep ON_UNMOUNT
public static final LifecycleStep ON_BIND
public static final LifecycleStep ON_UNBIND
public static final LifecycleStep ON_ATTACHED
public static final LifecycleStep ON_BIND_DYNAMIC_VALUE
public static final LifecycleStep ON_CALCULATE_CACHED_VALUE
public static final LifecycleStep ON_CREATE_LAYOUT_WITH_SIZE_SPEC
public static final LifecycleStep ON_CREATE_MOUNT_CONTENT_POOL
public static final LifecycleStep ON_CREATE_TRANSITION
public static final LifecycleStep ON_CREATE_TREE_PROP
public static final LifecycleStep ON_FOCUSED_EVENT_VISIBLE
public static final LifecycleStep ON_ENTERED_RANGE
public static final LifecycleStep ON_ERROR
public static final LifecycleStep ON_EVENT
public static final LifecycleStep ON_EVENT_VISIBLE
public static final LifecycleStep ON_FULL_IMPRESSION_VISIBLE_EVENT
public static final LifecycleStep ON_VISIBILITY_CHANGED
public static final LifecycleStep ON_EXITED_RANGE
public static final LifecycleStep ON_LOAD_STYLE
public static final LifecycleStep ON_MEASURE_BASELINE
public static final LifecycleStep ON_POPULATE_ACCESSIBILITY_NODE
public static final LifecycleStep ON_POPULATE_EXTRA_ACCESSIBILITY_NODE
public static final LifecycleStep ON_REGISTER_RANGES
public static final LifecycleStep ON_TRIGGER
public static final LifecycleStep ON_UPDATE_STATE
public static final LifecycleStep ON_UPDATE_STATE_WITH_TRANSITION
public static final LifecycleStep ON_DETACHED
public static final LifecycleStep ON_UNFOCUSED_EVENT_VISIBLE
public static final LifecycleStep ON_EVENT_INVISIBLE
public static final LifecycleStep SHOULD_UPDATE
public static final LifecycleStep RENDER
public static LifecycleStep[] values()
for (LifecycleStep c : LifecycleStep.values()) System.out.println(c);
public static LifecycleStep valueOf(java.lang.String name)
name
- the name of the enum constant to be returned.java.lang.IllegalArgumentException
- if this enum type has no constant with the specified namejava.lang.NullPointerException
- if the argument is nullpublic static java.util.List<LifecycleStep> getSteps(java.util.List<LifecycleStep.StepInfo> infos)


Polarizer, QDEF supply capacity in China insufficient
by Rebecca Kuo, Tainan; Steve Shen, DIGITIMESTaiwan's suppliers of polarizers and quantum dot enhancement films (QDEF) still have opportunity to make gains in China where the supply capacity for these two optical products is still insufficient despite the fast expansion of its flat panel industry, according to industry sources.
The global polarizer market is currently dominated by major suppliers in Korea and Japan, with the top-three vendors - LG Chem, Nitto Denko and Sumitomo Chemical - accounting for 65% of global supply. Korea's Samsung SDI ranked fourth with a 9% share.
Taiwan's suppliers, including Cheng Mei Materials Technology, BenQ Materials, Optimax Technology and Wah Hong Industrial, together make up a 15%. Major makers in China, including Sheng Wave Optoelectronic, Sunnypol Optoelectronics and Win Polarizer Optoelectronics, contribute about 8%, the sources indicated.
Among Taiwan's makers, Wah Hong has tapped into the small- to medium-size polarizer market in China focusing on automotive and industrial-control applications, revealed the sources, adding that polarizer shipments now account for 20% of Wah Hong's total sales.
Meanwhile, Wah Hong and another Taiwan-based company Efun Technology have also begun enhancing their deployments in China's QDEF market where only Beijing-based Nano Top Electronic Technology is currently being regarded as a significant supplier for QDEF products, said the sources.


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Icons made by
Freepik
from
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is licensed by
CC 3.0 BY
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15 Biggest Losers In Sales In October 2019 – Celerio, Xcent, Aspire, WR-V
by Surendhar MHere we have listed the 15 biggest losers in terms of volumes sales on YoY basis in October 2019
The auto industry has been hampered by the sales crisis over the past year and despite the slight improvements made by the automakers in volume numbers in October, they do not reflect on the actual scenario as last month was brimmed with festive season discounts and benefits.
Maruti Suzuki’s Celerio small hatchback endured 60% YoY drop last month with 3,669 units as against 9,260 units during October 2018 and in terms of volume it was the biggest loser ahead of Hyundai Xcent and Honda WR-V.
Its sales fortunes are expected to improve following the debut of the BSVI petrol engine in the coming weeks. The Xcent, which will be replaced by Aura sedan in January 2020, saw 56% YoY de-growth in October 2019 as 1,388 units were retailed against 3,143 units.
| Model | October 2019 Sales | October 2018 Sales |
| 1. Maruti Suzuki Celerio (-60%) | 3,669 | 9,260 |
| 2. Hyundai Xcent (-56%) | 1,388 | 3,143 |
| 3. Honda WR-V (-58%) | 1,367 | 3,253 |
| 4. Maruti Suzuki S-cross (-59%) | 1,356 | 3,317 |
| 5. Tata Tigor (-61%) | 1,152 | 2,927 |
| 6. Mahindra Marazzo (-73%) | 1,044 | 3,810 |
| 7. Toyota Liva (-64%) | 612 | 1,696 |
| 8. Ford Aspire (-79%) | 519 | 2,520 |
| 9. Frd Freestyle (-74%) | 505 | 1,929 |
| 10. Datsun Go & go + (-72%) | 471 | 1,697 |
| 11. Tata Hexa (-69%) | 229 | 745 |
| 12. Mahindra Kuv 100 (-85%) | 183 | 1,244 |
| 13. Mahindra Thar (-74%) | 180 | 701 |
| 14. Toyota Corolla (-63%) | 90 | 242 |
| 15. Nissan Micra (-73%) | 66 | 249 |
The WR-V is not having a good time in the market either as only 1,367 units were sold against 3,253 units in October 2018 with YoY decline of 58%. The Maruti Suzuki S-Cross, Tata Tigor and Mahindra Marazzo were the only other models in four-digit numbers with 59, 61 and 73% YoY drop respectively.
Toyota has been struggling with the sales of the Liva for long and it managed to garner only 612 units in October 2019 as against 1,696 units with 64% YoY drop while Ford’s Aspire met with a YoY slump of 79% as 519 units were sold against 2,520 units during the same month last year.
The Blue Oval’s Freestyle is also part of the losers list in terms of volumes as only 505 units were retailed last month against 1,929 units with 74% decline. Other big losers in YoY volume sales for October include Datsun Go and Go+, Tata Hexa, Mahindra KUV100, Mahindra Thar, Toyota Corolla and Nissan Micra.


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Apply for this job- Team:
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Mozilla is a thriving global community of technologists, thinkers and builders working collaboratively and openly to keep the Internet alive and accessible for all. When you work at Mozilla, you give yourself a chance to make a difference in the lives of Web users everywhere. And you give us a chance to make a difference in your life every day. Join our team today and together we’ll make a better Web for tomorrow.


 I am a sophomore from Shanghai, China. I am going to put all my work on digital fabrication on this website.
Welcome to my webpage. I am a sophomore from Shanghai, China. I am going to put all my work on digital fabrication on this website.


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/category.20theory.20universes.html ---
universes v u w variables {C : Type u} [𝒞 : category.{v} C] variables {X : Type w} [topological_space X] def presheaf X C : Type (max u v w) := <insert maths here> def : large_category (presheaf X C) := sorry
gives me
type mismatch at application large_category (presheaf X C) term presheaf X C has type Type (max u v w) : Type (max (u+1) (v+1) (w+1)) but is expected to have type Type (?+1) : Type (?+2)
Seems to me that Lean wants to know that "something is large". The risk is that if C is a small category then there won't be a universe small enough to contain my morphisms! I guess in that case I would rather have had a small category.
I'm going to make it a small category. A better approach would be to demand u>=1, which is fine here because u=1 in all the examples I care about, butI don't know how to manage that because of the delicate universe category-theory set-up -- I'm only just learning the basics.
Oh this is a nightmare. The morphisms have type max v w
. Maybe I should just make a category :-/
I am back in hell
def XYZ: has_coe_to_fun (topological_space.presheaf X C) := by exact topological_space.presheaf.has_coe_to_fun
invalid type ascription, term has type has_coe_to_fun (presheaf ?m_1 ?m_3) : Type (max (max ? ?) (max (?+1) 1) (?+1)) but is expected to have type has_coe_to_fun (presheaf X C) : Type (max (max u v w) ?) state: X : Type w, _inst_1 : topological_space X, C : Type u, 𝒞 : category C ⊢ has_coe_to_fun (presheaf X C)
I just really want to say "switch the universes off, I honestly don't care and i think @Mario Carneiro is paranoid"
a little bit of type in type never hurt anyone.
I don't remember exactly, but this might have been one of the reasons why the category lib stopped using coercions for functors.
the computer scientists want us to be polymorphic and I just want sheaves of rings and I'll do anything they say about universes, as long as it works when stuff is Type 0 (sets) or Type 1 (classes) and the ZFC people will be happy. Why am I getting these obscure errors?
type mismatch at application presheaf.res'.{?l_1 ?l_2} ℱ term ℱ has type presheaf.{v u w} X C : Type (max u v w) but is expected to have type presheaf.{?l_1 ?l_2 ?l_1} ?m_3 ?m_5 : Type (max ?l_1 ?l_2)
I don't know how to fix it with ulift. Am I supposed to be managing these universes myself?
I think I will try again with v, the universe of the morphisms in C being equal to w, the universe of the topological space X. In reality v and w are always sets so I'd even happily set v=w=0.
Looks like swapping all your w
s to v
s might help.
I'm letting w = v and it's working much better
w=v corresponds to the fact that mathematicians know that topological spaces and sets of morphisms are sets.
so they can live in the same universe
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


the little static site engine that could
Booklit documents are basically just text files with special syntax for function calls. Aside from those, everything is either text or a comment.
Text files are bit more complicated than they sound, though, so here are the rules:
The top-level of a document is a series of paragraphs, separated by one or more blank lines.
A paragraph is a series of lines, separated by linebreaks.
A line is a series of words.
A word is either a string, an invoke, or an interpolated word.
A string is a series of characters other than {}\
, or a \
followed by one of those characters, all within one line.
An invoke is a function call.
An interpolated word is a word wrapped in curly braces ({}
). This is useful for cases where a zero-argument invoke has to be smashed in the middle of a word, just{\like}this
.
Comments are delimited by {-
and -}
. They can be multi-line, appear in between words, and they can also be nested. This makes commenting out large blocks of content easy:
Hi, I'm{- a comment -} in the middle of a sentence!
{-
I'm hanging out at the top level,
{- being nested and stuff -}
with multiple lines.
-}
Function calls are denoted by a single backslash (\
), followed by series of alphanumeric characters and hyphens (foo-bar
), forming the function name.
Following the name, there may be any number of arguments, which can come in a few different forms:
{line}
With no linebreak after the {
, the argument forms a single line.
{word wrapped
line}
As above, but the word wrapping point gets converted into a single space, as if it were written as {word wrapped line}
.
{
paragraph 1
paragraph 2
}
With a linebreak after the {
, the argument forms a block of paragraphs.
{{
paragraph 1
indented paragraph 2
\with{syntax}
}}
With doubled-up curly braces, whitespace is preserved in the content, rather than being parsed into paragraphs.
Note that the first line of the content determines an indentation level that is then skipped for all lines. It is the only whitespace that is ignored.
{{{
paragraph 1
indented {paragraph} 2
\with{no-syntax}
}}}
Tripled-up curly braces form a verbatim argument. Similar to preformatted, whitespace is preserved. In addition, there is no interpreting or parsing of Booklit syntax within. This is useful for large code blocks where the content may contain special characters that Booklit normally may interpret (e.g. \
, {
, }
).


Webpage redirection is great for automatically redirecting people visiting an old URL to a new URL where the content has moved to. It can be done using JavaScript, but there is also a convenient HTML-only solution.
To redirect visitors reaching yourwebsite.com/blog
to yourwebsite.com/posts
, add this to the <head>
section of the HTML at yourwebsite.com/blog
:
<meta http-equiv="refresh" content="0;url='/posts'" />
<link rel="canonical" href="/posts" />
walkthrough
Setting http-equiv="refresh"
in the first line performs the actual redirect. The number in the content
attribute specified in seconds tells the browser how long to wait before redirecting. In this example, 0
redirects immediately. Increasing the wait may be useful for briefly displaying a 404 page before automatically switching to the home page. The url
specfies the destination for the redirect. In this example, we wanted to switch to /posts
.
The second line is optional; it helps search engines know which the preferred version of the page is. Usually, this is the redirect destination.


Filter on the payee type field. In addition to normal type field values, ALL can be specified to retrieve all payees. If absent the assumed value is ALL
Page size to request. Default is 25 (standard pagination)
x-v
header
string
mandatory
Version of the API end point requested by the client. Must be set to a positive integer. The data holder should respond with the highest supported version between x-min-v and x-v. If the value of x-min-v is equal to or higher than the value of x-v then the x-min-v header should be treated as absent. If all versions requested are not supported then the data holder must respond with a 406 Not Acceptable. See HTTP Headers
x-min-v
header
string
optional
Minimum version of the API end point requested by the client. Must be set to a positive integer if provided. The data holder should respond with the highest supported version between x-min-v and x-v. If all versions requested are not supported then the data holder must respond with a 406 Not Acceptable.
x-fapi-interaction-id
header
string
optional
An RFC4122 UUID used as a correlation id. If provided, the data holder must play back this value in the x-fapi-interaction-id response header. If not provided a [RFC4122] UUID value is required to be provided in the response header to track the interaction.
x-fapi-auth-date
header
string
optional
The time when the customer last logged in to the Data Recipient Software Product. Required for all resource calls (customer present and unattended). Not to be included for unauthenticated calls.
x-fapi-customer-ip-address
header
string
optional
The customer's original IP address if the customer is currently logged in to the Data Recipient Software Product. The presence of this header indicates that the API is being called in a customer present context. Not to be included for unauthenticated calls.
The customer's original standard http headers Base64 encoded, including the original User Agent header, if the customer is currently logged in to the Data Recipient Software Product. Mandatory for customer present calls. Not required for unattended or unauthenticated calls.
The version of the API end point that the data holder has responded with.
200
x-fapi-interaction-id
string
An RFC4122 UUID used as a correlation id. If provided, the data holder must play back this value in the x-fapi-interaction-id response header. If not provided a [RFC4122] UUID value is required to be provided in the response header to track the interaction.
4xx
x-fapi-interaction-id
string
An RFC4122 UUID used as a correlation id. If provided, the data holder must play back this value in the x-fapi-interaction-id response header. If not provided a [RFC4122] UUID value is required to be provided in the response header to track the interaction.
ID of the payee adhering to the rules of ID permanence
nickname
string
mandatory
The short display name of the payee as provided by the customer. Where a customer has not provided a nickname, a display name derived by the bank for the payee consistent with existing digital banking channels
description
string
optional
A description of the payee provided by the customer
type
string
mandatory
The type of payee. DOMESTIC means a registered payee for domestic payments including NPP. INTERNATIONAL means a registered payee for international payments. BILLER means a registered payee for BPAY
Type of account object included. Valid values are: account A standard Australian account defined by BSB/Account Number. card A credit or charge card to pay to (note that PANs are masked). payId A PayID recognised by NPP
BPAY CRN of the Biller (if available). Where the CRN contains sensitive information, it should be masked in line with how the Data Holder currently displays account identifiers in their existing online banking channels. If the contents of the CRN match the format of a Credit Card PAN they should be masked according to the rules applicable for MaskedPANString. If the contents are are otherwise sensitive, then it should be masked using the rules applicable for the MaskedAccountString common type.


|
SuanShu, a Java numerical and statistical library | |||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
public class CreateMatrix
This class collects the utility functions to create a new matrix/vector from existing ones.
| Method Summary | |
|---|---|
static DenseMatrix |
cbind(Matrix... matrices)
Take an array of matrices and combine them by columns. |
static DenseMatrix |
cbind(MatrixList matrices)
Take a list of matrices and combine them by columns. |
static DenseMatrix |
cbind(Vector... vectors)
Take an array of vectors and combine them by columns. |
static DenseMatrix |
cbind(VectorList vectors)
Take a list of vectors and combine them by columns. |
static DenseMatrix |
columns(Matrix A,
int[] cols)
CreateMatrix a sub-matrix from the columns of a matrix. |
static DenseMatrix |
columns(Matrix A,
int begin,
int end)
CreateMatrix a sub-matrix from the columns of a matrix. |
static Matrix |
copyAndReplace(Matrix toReplace,
int rowFrom,
int rowTo,
int colFrom,
int colTo,
Matrix replacement)
Deprecated. Not supported yet. |
static DenseMatrix |
diagonalMatrix(Matrix A)
Take the diagonal of a matrix. |
static DenseMatrix |
rbind(Matrix... matrices)
Take an array of matrices and combine them by rows. |
static DenseMatrix |
rbind(MatrixList matrices)
Take a list of matrices and combine them by rows. |
static DenseMatrix |
rbind(Vector... vectors)
Take an array of vectors and combine them by rows. |
static DenseMatrix |
rbind(VectorList vectors)
Take a list of vectors and combine them by rows. |
static Matrix |
replace(Matrix toReplace,
int rowFrom,
int rowTo,
int colFrom,
int colTo,
Matrix replacement)
Replace a part of a matrix with a smaller matrix. |
static DenseMatrix |
rows(Matrix A,
int[] rows)
CreateMatrix a sub-matrix from the rows of a matrix. |
static DenseMatrix |
rows(Matrix A,
int begin,
int end)
CreateMatrix a sub-matrix from the rows of a matrix. |
static DenseMatrix |
subMatrix(Matrix A,
int[] rows,
int[] cols)
CreateMatrix a sub-matrix from the intersections of rows and columns of a matrix. |
static DenseMatrix |
subMatrix(Matrix A,
int rowFrom,
int rowTo,
int colFrom,
int colTo)
CreateMatrix a sub-matrix from the four corners of a matrix. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Method Detail |
|---|
public static DenseMatrix cbind(Vector... vectors)
vectors
- an array of vectors, e.g., v1, v2, v3, ...
[v1 v2 v3 ...]
public static DenseMatrix cbind(VectorList vectors)
vectors
- a list of vector, e.g., {v1, v2, v3, ...}
[v1 v2 v3 ...]
public static DenseMatrix cbind(Matrix... matrices)
matrices
- an array of matrices, e.g., A1, A2, A3, ...
[A1 A2 A3 ...]
public static DenseMatrix cbind(MatrixList matrices)
matrices
- a list of matrices, e.g., {A1, A2, A3, ...}
[A1 A2 A3 ...]
public static DenseMatrix rbind(Vector... vectors)
vectors
- an array of vector, e.g., v1, v2, v3, ...
| v1 |
| v2 |
| v3 |
| ... |
java.lang.IllegalArgumentException
- if the vectors form a jagged arraypublic static DenseMatrix rbind(VectorList vectors)
vectors
- an array of vector, e.g., {v1, v2, v3, ...}
| v1 |
| v2 |
| v3 |
| ... |
public static DenseMatrix rbind(Matrix... matrices)
matrices
- a list of matrices, e.g., A1, A2, A3, ...
| A1 |
| A2 |
| A3 |
| ... |
public static DenseMatrix rbind(MatrixList matrices)
matrices
- a list of matrices, e.g., {A1, A2, A3, ...}
| A1 |
| A2 |
| A3 |
| ... |
public static DenseMatrix subMatrix(Matrix A, int rowFrom, int rowTo, int colFrom, int colTo)
A
- matrixrowFrom
- the beginning row indexrowTo
- the ending row indexcolFrom
- the beginning column indexcolTo
- the ending column index
A[rowFrom:rowTo, colFrom:colTo]
public static DenseMatrix subMatrix(Matrix A, int[] rows, int[] cols)
A
- a matrixrows
- the rows to be extractedcols
- the columns to be extracted
A[rows, cols]
public static DenseMatrix rows(Matrix A, int[] rows)
A
- a matrixrows
- the rows to be extracted
A
public static DenseMatrix rows(Matrix A, int begin, int end)
A
- a matrixbegin
- the beginning row index (counting from 1)end
- the ending row index (counting from 1)
A
public static DenseMatrix columns(Matrix A, int[] cols)
A
- a matrixcols
- the columns to be extracted
A
public static DenseMatrix columns(Matrix A, int begin, int end)
A
- a matrixbegin
- the beginning column index (counting from 1)end
- the ending column index (counting from 1)
A
public static Matrix replace(Matrix toReplace, int rowFrom, int rowTo, int colFrom, int colTo, Matrix replacement)
This method is best NOT to apply to SymmetricMatrix
because it is hard to ensure the symmetry property after replacement.
Note that no new DoubleMatrixAccessor instance is created.
toReplace
- matrix whose part is to be replacedrowFrom
- the beginning row indexrowTo
- the ending row indexcolFrom
- the beginning column indexcolTo
- the ending column indexreplacement
- matrix to be inserted into 'toReplace'
toReplace
matrix, which has changed@Deprecated public static Matrix copyAndReplace(Matrix toReplace, int rowFrom, int rowTo, int colFrom, int colTo, Matrix replacement)
The two input matrices are not changed.
This method is best NOT to apply to SymmetricMatrix
because it is hard to ensure the symmetry property after replacement.
toReplace
- matrix whose part is to be replacedrowFrom
- the beginning row indexrowTo
- the ending row indexcolFrom
- the beginning column indexcolTo
- the ending column indexreplacement
- matrix to be inserted into toReplace
DenseMatrix
whose entries are the same as toReplace
, except for the replacement partpublic static DenseMatrix diagonalMatrix(Matrix A)
A
- a matrix
Ai
, zeros elsewhere
java.lang.IllegalArgumentException
- if A
is not square
|
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{{ test-ipv6.com FAQ }}
{{ Q: Can you fix my VPN? }}
{{ No, I can not fix your VPN.}}
{{ Q: How does this test work? }}
{{ The test is entirely client-side javascript. To determine reachability, a series of ajax requests are made from the
web server, using various DNS names that force the use of IPv6 only, or dual stack, or other such scenarios. The
pass/fail of such fetches, as well as how long they take, are taken into account. }}
{{ Q: Why is this web site reachable via IPv4 only? }}
{{ You're right, there are no AAAA records, intentionally. A percentage of users are unable to browse sites that are
dual-stack. If the users can't connect, then they can't be told they have a problem. This is a big problem facing
content providers today; of which, I work at one for my $dayjob. As such, the main test page requires IPv4 (either
native or translated). }}
{{ At some point, when the percentage of "broken" users has gone significantly down, I'll consider making
test-ipv6.com dual-stack. At last check, March 2017, we still see 0.2% broken
visiting test-ipv6.com. And we still want them to understand their
situation. }}
{{ Q: I run a production IPv6-only network. Without IPv4. My users
can't reach test-ipv6.com. }}
{{ Send email to<EMAIL_ADDRESS>If your IPv6-only network has
dedicated name servers, or a dedicated BGP ASN, jfesler will enable AAAA
records for test-ipv6.com for you. }}
{{ If you don't meet the above requirements, you can visit
http://ipv6.test-ipv6.com (IPv6-only) or http://ds.test-ipv6.com
(IPv4+IPv6).}}
{{ Q: What do you mean by broken? }}
{{ A percentage of users today have IPv6 enabled, but are either using a public tunnel that is currently giving poor
performance; or otherwise have a route that is installed but broken or suboptimal. However, because they have a
route at all, in many cases the address selection algorithm of RFC3484 will pick using it, and trying to use this
broken route. It can take towards 75+ seconds before the browser gives up! }}
{{ From the perspective of a user with these conditions, a web site offering both A and AAAA DNS records (ie, "dual
stack") will appear to time out; and the user will move on to another site that offers a similar product. This is
the quandary content providers have. }}
{{ If we detect that you will have problems reaching dual-stack web sites,
we recommend you see the Broken User FAQ. It
provides several steps to try and identify your root cause for being
broken; and barring that, what you can do to disable IPv6 until your ISP
offers native IPv6 connectivity. }}
{{ Q: Why did your stats say you already have IPv6-only users? }}
{{ They went to http://ipv6.test-ipv6.com . }}
{{ Q: How valid are the stats? }}
{{ They do not represent the average web consumer. Visitors to this site are self-selecting. The intent of this site
is to not provide stats, but instead to inform the user the level of readiness for the world to move to IPv6 (with
or without'em!). As such, stats found here will be completely different from an average content provider. }}
{{ Q: How does your test differ, from what the content providers are doing? }}
{{ Content providers are gathering metrics of broken vs non-broken users, in an aggregate form. They are not at this
time actually giving the user feedback as to the user's current state. Those metrics will principally drive the
business decision of when to start publishing sites over IPv6, versus remaining IPv4 only. IPv4 only for some
content sites really is an option; it has a handful of downsides, but the expectation is that everyone will always
be able to reach IPv4, in some fashion. }}
{{ This site, on the other hand, is intended to help the user understand their current state, and what that state
possibly means to them. }}
{{ Q: What are your data sources? }}
{{ The following data sources are checked daily, and are used to convert
IP addresses into Internet Service Provider names.
- University of Oregon Route Views Archive Project provides hourly views of the Internet routing tables.
- http://bgp.potaroo.net/asn.txt is a curated list of ASNs to internet service provider names. This is provided by Geoff Huston, a well respected researcher from APNIC.
{{ Q: What features have been added/removed? }}
- {{ 2017: Added TLS (https) support}}
- {{ 2022: Removed links to swap http/https. Browsers are starting to forcefully upgrade to https. }}
- {{ 2022: Removed tunnel detection by comparing ASN numbers. More service providers are using a different entity for IPv4 vs IPv6. }}
- {{ 2022: Removed direct IP checks, NAT64 detection, Teredo detection. This is for support cost reasons; too many people complain about the tests that are skipped in https mode, and we're seeing browsers forcefully upgrade to https. }}
{{ Q: Do you actually read the feedback? }}
{{ Yes, I do. Thank you! Note that I can only follow up with you if you do leave contact information. While I am still grateful for feedback without contact information, I will be unable to reply with any answers if you asked for them. }}
{{ Q: Is this open source? }}
{{ Yes. See the source page for details. }}


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