---
layout: post
title: 'Silence of the Domes: 1964-2014'
date: '2014-04-21T11:56:40-07:00'
tags:
- fromtheinternet
tumblr_url: http://beekley.xyz/post/83433436758/silence-of-the-domes-1964-2014
---
Silence of the Domes: 1964-2014
In 1963, a prominent local architect named Vincent G. Raney agreed to build a theater on his family’s land in San Jose, California, right next to their world-famous Winchester Mystery House. He’d…


TLM-2.0
2.0.5
Accellera TLM-2.0 proof-of-concept library
tlm_utils
tlm_utils Directory Reference
Directory dependency graph for tlm_utils:
Files
file
convenience_socket_bases.h
[code]
file
instance_specific_extensions.h
[code]
file
instance_specific_extensions_int.h
[code]
file
multi_passthrough_initiator_socket.h
[code]
file
multi_passthrough_target_socket.h
[code]
file
multi_socket_bases.h
[code]
file
passthrough_target_socket.h
[code]
file
peq_with_cb_and_phase.h
[code]
file
peq_with_get.h
[code]
file
simple_initiator_socket.h
[code]
file
simple_target_socket.h
[code]
file
tlm_quantumkeeper.h
[code]
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1.8.13


Welcome to my Data Visualization!
Here you will be able to choose an opening and play through either move by move, or you can skip ahead 5 turns!
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| Packages that use Field | |
|---|---|
| org.apache.commons.math | Common classes used throughout the commons-math library. |
| org.apache.commons.math.complex | Complex number type and implementations of complex transcendental functions. |
| org.apache.commons.math.dfp | Decimal floating point library for Java |
| org.apache.commons.math.fraction | Fraction number type and fraction number formatting. |
| org.apache.commons.math.linear | Linear algebra support. |
| org.apache.commons.math.util | Convenience routines and common data structures used throughout the commons-math library. |
| Uses of Field in org.apache.commons.math |
|---|
| Methods in org.apache.commons.math that return Field | |
|---|---|
Field<T> |
FieldElement.getField()
Get the Field to which the instance belongs. |
| Uses of Field in org.apache.commons.math.complex |
|---|
| Classes in org.apache.commons.math.complex that implement Field | |
|---|---|
class |
ComplexField
Representation of the complex numbers field. |
| Uses of Field in org.apache.commons.math.dfp |
|---|
| Classes in org.apache.commons.math.dfp that implement Field | |
|---|---|
class |
DfpField
Field for Decimal floating point instances. |
| Uses of Field in org.apache.commons.math.fraction |
|---|
| Classes in org.apache.commons.math.fraction that implement Field | |
|---|---|
class |
BigFractionField
Representation of the fractional numbers without any overflow field. |
class |
FractionField
Representation of the fractional numbers field. |
| Uses of Field in org.apache.commons.math.linear |
|---|
| Methods in org.apache.commons.math.linear that return Field | ||
|---|---|---|
protected static
|
AbstractFieldMatrix.extractField(T[] d)
Get the elements type from an array. |
|
protected static
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AbstractFieldMatrix.extractField(T[][] d)
Get the elements type from an array. |
|
Field<T> |
FieldMatrix.getField()
Get the type of field elements of the matrix. |
|
Field<T> |
SparseFieldVector.getField()
Get the type of field elements of the vector. |
|
Field<T> |
FieldVector.getField()
Get the type of field elements of the vector. |
|
Field<T> |
ArrayFieldVector.getField()
Get the type of field elements of the vector. |
|
Field<T> |
AbstractFieldMatrix.getField()
Get the type of field elements of the matrix. |
| Methods in org.apache.commons.math.linear with parameters of type Field | ||
|---|---|---|
protected static
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AbstractFieldMatrix.buildArray(Field<T> field,
int length)
Build an array of elements. |
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protected static
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AbstractFieldMatrix.buildArray(Field<T> field,
int rows,
int columns)
Build an array of elements. |
|
static
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BlockFieldMatrix.createBlocksLayout(Field<T> field,
int rows,
int columns)
Create a data array in blocks layout. |
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static
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MatrixUtils.createFieldIdentityMatrix(Field<T> field,
int dimension)
Returns dimension x dimension identity matrix. |
|
static
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MatrixUtils.createFieldMatrix(Field<T> field,
int rows,
int columns)
Returns a FieldMatrix with specified dimensions. |
| Constructors in org.apache.commons.math.linear with parameters of type Field | |
|---|---|
AbstractFieldMatrix(Field<T> field)
Creates a matrix with no data |
|
AbstractFieldMatrix(Field<T> field,
int rowDimension,
int columnDimension)
Create a new FieldMatrix |
|
Array2DRowFieldMatrix(Field<T> field)
Creates a matrix with no data |
|
Array2DRowFieldMatrix(Field<T> field,
int rowDimension,
int columnDimension)
Create a new FieldMatrix |
|
ArrayFieldVector(Field<T> field)
Build a 0-length vector. |
|
ArrayFieldVector(Field<T> field,
int size)
Construct a (size)-length vector of zeros. |
|
ArrayFieldVector(Field<T> field,
T[] d)
Construct a vector from an array, copying the input array. |
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ArrayFieldVector(Field<T> field,
T[] d,
boolean copyArray)
Create a new ArrayFieldVector using the input array as the underlying data array. |
|
ArrayFieldVector(Field<T> field,
T[] v1,
T[] v2)
Construct a vector by appending one vector to another vector. |
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BlockFieldMatrix(Field<T> field,
int rows,
int columns)
Create a new matrix with the supplied row and column dimensions. |
|
SparseFieldMatrix(Field<T> field)
Creates a matrix with no data. |
|
SparseFieldMatrix(Field<T> field,
int rowDimension,
int columnDimension)
Create a new SparseFieldMatrix |
|
SparseFieldVector(Field<T> field)
Build a 0-length vector. |
|
SparseFieldVector(Field<T> field,
int dimension)
Construct a (dimension)-length vector of zeros. |
|
SparseFieldVector(Field<T> field,
int dimension,
int expectedSize)
Build a vector with known the sparseness (for advanced use only). |
|
SparseFieldVector(Field<T> field,
T[] values)
Create from a Field array. |
| Uses of Field in org.apache.commons.math.util |
|---|
| Classes in org.apache.commons.math.util that implement Field | |
|---|---|
class |
BigRealField
Representation of real numbers with arbitrary precision field. |
| Methods in org.apache.commons.math.util that return Field | |
|---|---|
Field<BigReal> |
BigReal.getField()
Get the Field to which the instance belongs. |
| Constructors in org.apache.commons.math.util with parameters of type Field | |
|---|---|
OpenIntToFieldHashMap(Field<T> field)
Build an empty map with default size and using zero for missing entries. |
|
OpenIntToFieldHashMap(Field<T> field,
int expectedSize)
Build an empty map with specified size and using zero for missing entries. |
|
OpenIntToFieldHashMap(Field<T> field,
int expectedSize,
T missingEntries)
Build an empty map with specified size. |
|
OpenIntToFieldHashMap(Field<T> field,
T missingEntries)
Build an empty map with default size |
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FOR
Statement
Executes the provided statements while incrementing or decrementing a counter variable at each iteration.
The loop
may be terminated without regard to the counter by using the BREAK
statement.
The current iteration of the loop can be abandoned and the next iteration of the loop started by using the
CONTINUE
statement.Syntax
Arguments
- variable-name (variable name)
The name of the counter variable. The variable
name must begin with an at sign (@
), dollar sign ($
), or colon (:
). The variable
need not be previously declared; the FOR
statement acts as a variable declaration if needed. This variable
will receive an integer value that changes with each iteration of the loop.
- first-number (integer)
The value of the counter variable on the first
iteration of the loop.
- last-number (integer)
The loop stops if the counter steps past this value.
- step (integer, optional)
The number to add to the counter variable on
each iteration. If first-number ≤ last-number
then the default step is 1, otherwise the default step is -1.
- stmt (statement)
The statements to execute on each iteration of the loop. If
more than one stmt is desired, the BEGIN
and END
keywords must be used.
Examples
FOR @counter = 1 TO 10
PRINT @counter;
Prints the numbers 1 to 10 (inclusive)
in increasing order.
FOR @counter = 10 TO 1
PRINT @counter;
Prints the numbers 10 to 1 (inclusive)
in decreasing order.
FOR @counter = 1 TO 10 STEP 2
PRINT @counter;
Prints the odd numbers 1 to
9 (inclusive) in increasing order.


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In the Home tool of My Workspace, you will see messages from your system administrator under "Message of the Day". You can control how these messages appear as follows:
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Simple toggle function between 2 classes based on scroll-positioning.
If the element is in the viewable area, it gets the class options.classStop
(defaults to snh-stop
)
Otherwise, if it's even a pixel out of view, it gets the class options.classHover
(defaults to snh-hover
)
Useful for mostly CSS designs that need a hovering menu, that starts fixed to the top.
Theoretically compatible back to IE5, although I'm unsure about the JSON type object instanciation.
{}
)
:
#hover
Triggers the element to hover.
Removes the oOptions.classStop
class (default snh-stop
) and replaces it with oOptions.classHover
(default snh-hover
).
Triggers the element to hover.
Removes the oOptions.classStop
class (default snh-stop
) and replaces it with oOptions.classHover
(default snh-hover
).
#stop
Triggers the element to stop.
Replace the oOptions.classHover
class (default snh-hover
) with the oOptions.classStop
(default snh-stop
)
Triggers the element to stop.
Replace the oOptions.classHover
class (default snh-hover
) with the oOptions.classStop
(default snh-stop
)


Understanding LSTM Networks
Posted on August 27, 2015
Recurrent Neural Networks
Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.
Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It’s unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones.
Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.
In the above diagram, a chunk of neural network, \(A\), looks at some input \(x_t\) and outputs a value \(h_t\). A loop allows information to be passed from one step of the network to the next.
These loops make recurrent neural networks seem kind of mysterious. However, if you think a bit more, it turns out that they aren’t all that different than a normal neural network. A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor. Consider what happens if we unroll the loop:
This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists. They’re the natural architecture of neural network to use for such data.
And they certainly are used! In the last few years, there have been incredible success applying RNNs to a variety of problems: speech recognition, language modeling, translation, image captioning… The list goes on. I’ll leave discussion of the amazing feats one can achieve with RNNs to Andrej Karpathy’s excellent blog post, The Unreasonable Effectiveness of Recurrent Neural Networks. But they really are pretty amazing.
Essential to these successes is the use of “LSTMs,” a very special kind of recurrent neural network which works, for many tasks, much much better than the standard version. Almost all exciting results based on recurrent neural networks are achieved with them. It’s these LSTMs that this essay will explore.
The Problem of Long-Term Dependencies
One of the appeals of RNNs is the idea that they might be able to connect previous information to the present task, such as using previous video frames might inform the understanding of the present frame. If RNNs could do this, they’d be extremely useful. But can they? It depends.
Sometimes, we only need to look at recent information to perform the present task. For example, consider a language model trying to predict the next word based on the previous ones. If we are trying to predict the last word in “the clouds are in the sky,” we don’t need any further context – it’s pretty obvious the next word is going to be sky. In such cases, where the gap between the relevant information and the place that it’s needed is small, RNNs can learn to use the past information.
But there are also cases where we need more context. Consider trying to predict the last word in the text “I grew up in France… I speak fluent French.” Recent information suggests that the next word is probably the name of a language, but if we want to narrow down which language, we need the context of France, from further back. It’s entirely possible for the gap between the relevant information and the point where it is needed to become very large.
Unfortunately, as that gap grows, RNNs become unable to learn to connect the information.
In theory, RNNs are absolutely capable of handling such “long-term dependencies.” A human could carefully pick parameters for them to solve toy problems of this form. Sadly, in practice, RNNs don’t seem to be able to learn them. The problem was explored in depth by Hochreiter (1991) [German] and Bengio, et al. (1994), who found some pretty fundamental reasons why it might be difficult.
Thankfully, LSTMs don’t have this problem!
LSTM Networks
Long Short Term Memory networks – usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. They were introduced by Hochreiter & Schmidhuber (1997), and were refined and popularized by many people in following work.1 They work tremendously well on a large variety of problems, and are now widely used.
LSTMs are explicitly designed to avoid the long-term dependency problem. Remembering information for long periods of time is practically their default behavior, not something they struggle to learn!
All recurrent neural networks have the form of a chain of repeating modules of neural network. In standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer.
LSTMs also have this chain like structure, but the repeating module has a different structure. Instead of having a single neural network layer, there are four, interacting in a very special way.
Don’t worry about the details of what’s going on. We’ll walk through the LSTM diagram step by step later. For now, let’s just try to get comfortable with the notation we’ll be using.
In the above diagram, each line carries an entire vector, from the output of one node to the inputs of others. The pink circles represent pointwise operations, like vector addition, while the yellow boxes are learned neural network layers. Lines merging denote concatenation, while a line forking denote its content being copied and the copies going to different locations.
The Core Idea Behind LSTMs
The key to LSTMs is the cell state, the horizontal line running through the top of the diagram.
The cell state is kind of like a conveyor belt. It runs straight down the entire chain, with only some minor linear interactions. It’s very easy for information to just flow along it unchanged.
The LSTM does have the ability to remove or add information to the cell state, carefully regulated by structures called gates.
Gates are a way to optionally let information through. They are composed out of a sigmoid neural net layer and a pointwise multiplication operation.
The sigmoid layer outputs numbers between zero and one, describing how much of each component should be let through. A value of zero means “let nothing through,” while a value of one means “let everything through!”
An LSTM has three of these gates, to protect and control the cell state.
Step-by-Step LSTM Walk Through
The first step in our LSTM is to decide what information we’re going to throw away from the cell state. This decision is made by a sigmoid layer called the “forget gate layer.” It looks at \(h_{t-1}\) and \(x_t\), and outputs a number between \(0\) and \(1\) for each number in the cell state \(C_{t-1}\). A \(1\) represents “completely keep this” while a \(0\) represents “completely get rid of this.”
Let’s go back to our example of a language model trying to predict the next word based on all the previous ones. In such a problem, the cell state might include the gender of the present subject, so that the correct pronouns can be used. When we see a new subject, we want to forget the gender of the old subject.
The next step is to decide what new information we’re going to store in the cell state. This has two parts. First, a sigmoid layer called the “input gate layer” decides which values we’ll update. Next, a tanh layer creates a vector of new candidate values, \(\tilde{C}_t\), that could be added to the state. In the next step, we’ll combine these two to create an update to the state.
In the example of our language model, we’d want to add the gender of the new subject to the cell state, to replace the old one we’re forgetting.
It’s now time to update the old cell state, \(C_{t-1}\), into the new cell state \(C_t\). The previous steps already decided what to do, we just need to actually do it.
We multiply the old state by \(f_t\), forgetting the things we decided to forget earlier. Then we add \(i_t*\tilde{C}_t\). This is the new candidate values, scaled by how much we decided to update each state value.
In the case of the language model, this is where we’d actually drop the information about the old subject’s gender and add the new information, as we decided in the previous steps.
Finally, we need to decide what we’re going to output. This output will be based on our cell state, but will be a filtered version. First, we run a sigmoid layer which decides what parts of the cell state we’re going to output. Then, we put the cell state through \(\tanh\) (to push the values to be between \(-1\) and \(1\)) and multiply it by the output of the sigmoid gate, so that we only output the parts we decided to.
For the language model example, since it just saw a subject, it might want to output information relevant to a verb, in case that’s what is coming next. For example, it might output whether the subject is singular or plural, so that we know what form a verb should be conjugated into if that’s what follows next.
Variants on Long Short Term Memory
What I’ve described so far is a pretty normal LSTM. But not all LSTMs are the same as the above. In fact, it seems like almost every paper involving LSTMs uses a slightly different version. The differences are minor, but it’s worth mentioning some of them.
One popular LSTM variant, introduced by Gers & Schmidhuber (2000), is adding “peephole connections.” This means that we let the gate layers look at the cell state.
The above diagram adds peepholes to all the gates, but many papers will give some peepholes and not others.
Another variation is to use coupled forget and input gates. Instead of separately deciding what to forget and what we should add new information to, we make those decisions together. We only forget when we’re going to input something in its place. We only input new values to the state when we forget something older.
A slightly more dramatic variation on the LSTM is the Gated Recurrent Unit, or GRU, introduced by Cho, et al. (2014). It combines the forget and input gates into a single “update gate.” It also merges the cell state and hidden state, and makes some other changes. The resulting model is simpler than standard LSTM models, and has been growing increasingly popular.
These are only a few of the most notable LSTM variants. There are lots of others, like Depth Gated RNNs by Yao, et al. (2015). There’s also some completely different approach to tackling long-term dependencies, like Clockwork RNNs by Koutnik, et al. (2014).
Which of these variants is best? Do the differences matter? Greff, et al. (2015) do a nice comparison of popular variants, finding that they’re all about the same. Jozefowicz, et al. (2015) tested more than ten thousand RNN architectures, finding some that worked better than LSTMs on certain tasks.
Conclusion
Earlier, I mentioned the remarkable results people are achieving with RNNs. Essentially all of these are achieved using LSTMs. They really work a lot better for most tasks!
Written down as a set of equations, LSTMs look pretty intimidating. Hopefully, walking through them step by step in this essay has made them a bit more approachable.
LSTMs were a big step in what we can accomplish with RNNs. It’s natural to wonder: is there another big step? A common opinion among researchers is: “Yes! There is a next step and it’s attention!” The idea is to let every step of an RNN pick information to look at from some larger collection of information. For example, if you are using an RNN to create a caption describing an image, it might pick a part of the image to look at for every word it outputs. In fact, Xu, et al. (2015) do exactly this – it might be a fun starting point if you want to explore attention! There’s been a number of really exciting results using attention, and it seems like a lot more are around the corner…
Attention isn’t the only exciting thread in RNN research. For example, Grid LSTMs by Kalchbrenner, et al. (2015) seem extremely promising. Work using RNNs in generative models – such as Gregor, et al. (2015), Chung, et al. (2015), or Bayer & Osendorfer (2015) – also seems very interesting. The last few years have been an exciting time for recurrent neural networks, and the coming ones promise to only be more so!
Acknowledgments
I’m grateful to a number of people for helping me better understand LSTMs, commenting on the visualizations, and providing feedback on this post.
I’m very grateful to my colleagues at Google for their helpful feedback, especially Oriol Vinyals, Greg Corrado, Jon Shlens, Luke Vilnis, and Ilya Sutskever. I’m also thankful to many other friends and colleagues for taking the time to help me, including Dario Amodei, and Jacob Steinhardt. I’m especially thankful to Kyunghyun Cho for extremely thoughtful correspondence about my diagrams.
Before this post, I practiced explaining LSTMs during two seminar series I taught on neural networks. Thanks to everyone who participated in those for their patience with me, and for their feedback.
In addition to the original authors, a lot of people contributed to the modern LSTM. A non-comprehensive list is: Felix Gers, Fred Cummins, Santiago Fernandez, Justin Bayer, Daan Wierstra, Julian Togelius, Faustian Gomez, Matteo Gagliolo, and Alex Graves.↩


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The active areas are rectangles or polygons we can define on the scenes which a section of the background is marked in order to interactuate with it. They are useful tools when we have riched backgrounds with objects or characters already drawed, or in photo-realism tasks
Basically, an active area behaves like an object belonging to one single scene.
General explanation of the panel of edition usage
General explanation of the tables usage


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Point


TITLE: ED ADMISSION NOTE DATE OF NOTE: AUG 28,2008@14:02 ENTRY DATE: AUG 8,2009@23:07:24 AUTHOR: KEEP,NOLAN EXP COSIGNER: URGENCY: STATUS: COMPLETED TITLE: ER ADMISSION NOTE DATE OF NOTE: AUG 28,2008@14:22 ENTRY DATE: AUG 28,2008@14:22:59 AUTHOR: MANAGER,SYSTEM EXP COSIGNER: URGENCY: STATUS: COMPLETED CC: "My chest hurts" HPI: 55 year-old male presents to the emergency department after resolution of a 20-minute episode of chest pain, which began while he was raking leaves. The pain was described as a "pressure" sensation. The pain radiated down his arms and into his jaw and was associated with palpitations and dyspnea. The pain resolved shortly after resting. The patient had 3 prior episodes of similar pain today, lasting approximately 10 minutes each, and all with activity. He has complained of diaphoresis, mild nausea, and shortness of breath intermittently the last 3 weeks along with lightheadedness when ambulating up stairs. He denies muscle pain, pain with inspiration or movement, cough, or other associated chest complaints. PMHx: His past medical history is significant for diet controlled diabetes and hypertension. He presently is prescribed HCTZ but states that he occasionally misses. Wife states she thinks he is supposed to be taking some other medications but he is usually non-compliant. FHx: Family history is significant for a sister who had an acute MI at age 55. He has no known allergies. SHx: Patient works as a cab company dispatcher. Lives with wife. He currently smokes 1 pack per day. Denies illegal drug use. REVIEW OF SYSTEMS: [POS] General Patient looks uncomfortable [NEG] Eyes [NEG] ENT [POS] Cardiovascular "skipped beats" [NEG] Respiratory [POS] GI discomfort in upper stomach area [NEG] Genito-urological [NEG] Musculoskeletal [NEG] Sexual Function [POS] Skin Patient has felt sweaty [POS] Neurologic Patient has felt light headed: [NEG] Psych [NEG] Endocrine [POS] Hematologic/Lymphatic [POS] Allergy/Immunological PHYSICAL EXAM VS: BP: 150/85 Pulse: 80/min Respir: 24/min O2 Sat: 94% Temp: 37.3C GEN: On physical examination he is an anxious, mildly obese male who looks appropriate for age. HEENT: PERRLA. O/P Normal. His external jugular veins are not distended. CHEST: He has basilar crackles � up bilaterally and his chest is nontender to palpation. Cardiovascular exam reveals no murmurs, gallops or rubs. ABD: His abdomen is soft and non-tender. No bruits are noted EXT: He has equal bounding pulses in all four extremities. SKIN: His skin is dry and pink. NEURO: Patient A/O X 3 TESTS Labs: Chem 7, CK-MB, Myoglobin, and full troponin pending cTnI ELISA test POSITIVE EKG: + for ST elevations in all anterior leads CXR: Portable CXR negative ASSESSMENT/PLAN 55 year old male with classics symptoms of Myocardial Infarction. Should be a candidate for revascularization given onset, symptoms, past medical history, and clinical situation. 1. Initiate ACS Protocol: a. Admit to CCU b. Consult Cardiology for immediate revascularization evaluation c. Consider thrombolytics in ED /es/ SYSTEM MANAGER BSN Signed: 08/28/2008 14:23 /es/ NOLAN KEEP MD Signed: 08/08/2009 23:07


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Our e-commerce web application is created using the latest technologies which are available in the front end world at the moment. We are using ReactJS and it is declarative nature to easily handle and scale our app!
The usage of optimization technologies in React and NodeJS along with GraphQL makes our app feel snappy and enjoyable. Progressive web app compatibility makes it even more comfortable to use on any device!
Clean UI gives you the feeling of freedom and lightness while preserving all functionality that you expect from an e-commerce application


 Any amount would help support and continue development on this project and is greatly appreciated.
SEO Guest Post Outreach


Compressor Test
This test shows 4 waveforms:
- Sum of A3 (220 Hz) and C#4 (277.18 Hz) in Black
- Signal scaled by 1/2 (common "compression" approach) in Grey
- Signal capped with audioLib.Capper in Red
- Signal compressed with audioLib.Compressor (scale by 2, standard gain=0.5) in Blue
audioLib.Compressor scales the waveform and boosts lower volume levels without hard corners


You can drag the picture to a new window to see, you will find that you can not get the correct picture, because the strict parameter is set to true
you will find it's ok by http://localhost:8000/
But on http://<IP_ADDRESS>:8000/ the pictrue could not be load, cause it's not on the white list.


OpenEJB supports stateless failover. Specifically, the ability for an EJB client to failover from one server to the next if a request cannot be completed. No application state information is communicated between the servers, so this functionality should be used only with applications that are inherently stateless. A common term for this sort of setup is a server farm.
The basic design assumption is that all servers in the same group have the same applications deployed and are capable of doing the same job. Servers can be brought online and offline while clients are running. As members join/leave this information is sent to the client as part of normal EJB request/response communication so active clients always have the most current information on servers that can process their request should communication with a particular server fail.
On each request to the server, the client will send the version number associated with the list of servers in the cluster it is aware of. Initially this version will be zero and the list will be empty. Only when the server sees the client has an old list will the server send the updated list. This is an important distinction as the list is not transmitted back and forth on every request, only on change. If the membership of the cluster is stable there is essentially no clustering overhead to the protocol -- 8 byte overhead to each request and 1 byte on each response -- so you will not see an exponential slowdown in response times the more members are added to the cluster. This new list takes affect for all proxies that share the same connection.
When a server shuts down, more connections are refused, existing
connections not in mid-request are closed, any remaining connections are
closed immediately after completion of the request in progress and clients
can failover gracefully to the next server in the list. If a server
crashes requests are retried on the next server in the list (or depending
on the ConnectionStrategy
). This failover pattern is followed until there
are no more servers in the list at which point the client attempts a final
multicast search (if it was created with a PROVIDER_URL
starting with
multicast://
) before abandoning the request and throwing an exception to
the caller.
By default, the failover is ordered but random selection is supported. The multicast discovery aspect of the client adds a nice randomness to the selection of the first server.
Each discoverable service has a URI which is broadcast as a heartbeat to other servers in the cluster. This URI advertises the service's type, its cluster group, and its location in the format of 'group:type:location'. Say for example "cluster1:ejb:ejbd://thehost:4201". The URI is sent out repeatedly in a pulse and its presence on the network indicates its availability and its absence indicates the service is no longer available.
The sending of this pulse (the heartbeat) can be done via UDP or TCP: multicast and "multipoint" respectively. More on that in the following section. The rate at which the heartbeat is pulsed to the network can be specified via the 'heart_rate' property. The default is 500 milliseconds. This rate is also used when listening for services on the network. If a service goes missing for the duration of 'heart_rate' multiplied by 'max_missed_heartbeats', then the service is considered dead.
The 'group' property, cluster1 in the example, is used to dissect the servers on the network into smaller logical clusters. A given server will broadcast all it's services with the group prefixed in the URI, as well it will ignore any services it sees broadcast if they do not share the same group name.
Multicast
Multipoint
Logging


Twitter is a good way to get up-to-date information from employers and organisations in your field of interest. In some sectors, it can be an excellent self-promotional tool. Many graduate recruiters and some SMEs will use their Twitter feed to share news, updates, deadlines and job opportunities.
Create a bio
- If you want to ‘go public’ with your Twitter feed, then you need to think about your bio. Make sure your profile and avatar are both professional.
- Think about your brand. It’s important to project a professional front but without sounding bland. Use your Twitter profile to encapsulate and reflect your aspirations, interests and personality.
Start following and searching
- Follow recruiters and organisations to make sure you hear about job postings, news and initiatives.
- Retweet relevant information, but don’t forget to add your own voice.
- Use hashtags to help organise your job search.
Tweet!
- This is one of the most immediate and powerful ways to connect with people and raise your profile. Get the balance right though – aim for quality, not quantity.
- Be professional and think before you tweet.


How to contribute
There are multiple ways you can contribute to the project. And help is always welcome!
All details can be found on the contributing page.
Keep reading for a quick overview
Issue Tracker
Apache YuniKorn (Incubating) uses JIRA as an Issue Tracker. Don’t hesitate to report new bugs and improvements ideas.
Contribute to the source code
We like to get code contributions through Pull Requests on our various repositories:
But before starting, please read our Contribution guidelines, it will give you important information about our review process, and pointers on how to make a good code contribution.
You can visit our Issue Tracker to find issues to resolve.
Other contributions
Not much of a coder? There are other ways to help out:
- Documentation and website improvements are always welcome
- Helping each other by answering questions on the Mailing List
- Participating in reviewing contributions.
Mailing list
Get help using Apache YuniKorn (Incubating) or contribute to the project on our mailing lists:
- Dev : subscribe, unsubscribe, archives
for people wanting to contribute to the project. - Issues : subscribe, unsubscribe, archives
for all messsages regarding issues in jira. - Reviews : subscribe, unsubscribe, archives
for review messages and patches in GitHub pull requests.


{% extends "ems/base.html" %} {% block sidebar_content %}
Event Home
Event Setup
Manage Participants
Judge Event


Persik, Persita and Persiraja win promotion to next season’s Indonesia Liga 1
Persik Kediri defeated Persita 3-2 at the Kapten I Wayan Dipta Stadium in Bali to lift their first-ever Indonesia Liga 2 title and book a promotion to next season’s Liga 1, the country’s top division.
Persik and Persita will be joined by Persiraja who defeated Sriwijaya FC 1-0 in the third-place match held at the same venue earlier on Monday.
In the Liga 2 final, Persik had taken a 2-0 lead by the 14th minute through Risna Prahalabenta and Sandrian Wijaya, but Persita struck back to level the score at 2-2 through former Indonesia international defender Muhammad Roby and Sirvi Arfani by the 38th minute.
However, it was Persik who ultimately took home the title thanks to a winning goal from the boots of Wimba Sutan Fanosa in the second-half injury time.
Meanwhile, Assanur Rijal scored the only goal of the game for Persiraja as they broke the hearts of Sriwijaya to win promotion as the third and final team.
Meanwhile, the relegation race is still neck-and-neck in Indonesia’s top flight with Badak Lampung and Semen Padang, both on 27 points, and Kalteng Putra, with a point less, occupying the three relegation spots with six more matches left to play.
Persela Lamongan are above the bottom three at 15th with 28 points while Barito Putera are 14th with 31 points.
(Photo credit: Bola.net)


by Cat Fact Files
Posted on August 16, 201
      There is really only one reason why owners should be wary of offering a cat human food scraps. This is because a lot such as sugar or salt that could disturb the cat’s gastric balance, to the detriment of the cat. Some meats and poultry are high in protein, and if offered in large quantities…
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