Gecode::VarImp< VIC > Class Template Reference
[Programming variables]
Base-class for variable implementations. More...
#include <core.hpp>
Public Member Functions | |
| VarImp (Space &home) | |
| Creation. | |
| VarImp (void) | |
| Creation of static instances. | |
Protected Member Functions | |
| void | cancel (Space &home) |
| Cancel all subscriptions when variable implementation is assigned. | |
| bool | advise (Space &home, ModEvent me, Delta &d) |
| Run advisors when variable implementation has been modified with modification event me and domain change d. | |
| ModEvent | fail (Space &home) |
| Run advisors to be run on failure and returns ME_GEN_FAILED. | |
| void | schedule (Space &home, PropCond pc1, PropCond pc2, ModEvent me) |
| Schedule subscribed propagators. | |
Dependencies | |
|
| |
| void | subscribe (Space &home, Propagator &p, PropCond pc, bool assigned, ModEvent me, bool schedule) |
| Subscribe propagator p with propagation condition pc. | |
| void | cancel (Space &home, Propagator &p, PropCond pc) |
| Cancel subscription of propagator p with propagation condition pc. | |
| void | subscribe (Space &home, Advisor &a, bool assigned, bool fail) |
| Subscribe advisor a to variable. | |
| void | cancel (Space &home, Advisor &a, bool fail) |
| Cancel subscription of advisor a. | |
| unsigned int | degree (void) const |
| Return degree (number of subscribed propagators and advisors). | |
| double | afc (void) const |
| Return accumulated failure count (plus degree). | |
Cloning variables | |
|
| |
| VarImp (Space &home, VarImp &x) | |
| Constructor for cloning. | |
| bool | copied (void) const |
| Is variable already copied. | |
| VarImp * | forward (void) const |
| Use forward pointer if variable already copied. | |
| VarImp * | next (void) const |
| Return next copied variable. | |
Bit management | |
|
| |
| unsigned int | bits (void) const |
| Provide access to free bits. | |
| unsigned int & | bits (void) |
| Provide access to free bits. | |
Variable implementation-dependent propagator support | |
|
| |
| static void | schedule (Space &home, Propagator &p, ModEvent me, bool force=false) |
| Schedule propagator p with modification event me. | |
| static void | reschedule (Space &home, Propagator &p, PropCond pc, bool assigned, ModEvent me) |
| Schedule propagator p. | |
| static ModEvent | me (const ModEventDelta &med) |
| Project modification event for this variable type from med. | |
| static ModEventDelta | med (ModEvent me) |
| Translate modification event me into modification event delta. | |
| static ModEvent | me_combine (ModEvent me1, ModEvent me2) |
| Combine modifications events me1 and me2. | |
Delta information for advisors | |
|
| |
| static ModEvent | modevent (const Delta &d) |
| Return modification event. | |
Memory management | |
|
| |
| static void * | operator new (size_t, Space &) |
| Allocate memory from space. | |
| static void | operator delete (void *, Space &) |
| Return memory to space. | |
| static void | operator delete (void *) |
| Needed for exceptions. |
Detailed Description
template<class VIC>
class Gecode::VarImp< VIC >
Base-class for variable implementations.
Implements variable implementation for variable implementation configuration of type VIC.
Definition at line 219 of file core.hpp.
Constructor & Destructor Documentation
| Gecode::VarImp< VIC >::VarImp | ( | Space & | home | ) | [inline] |
| Gecode::VarImp< VIC >::VarImp | ( | void | ) | [inline] |
| Gecode::VarImp< VIC >::VarImp | ( | Space & | home, | |
| VarImp< VIC > & | x | |||
| ) | [inline] |
Member Function Documentation
| void Gecode::VarImp< VIC >::cancel | ( | Space & | home | ) | [inline, protected] |
| bool Gecode::VarImp< VIC >::advise | ( | Space & | home, | |
| ModEvent | me, | |||
| Delta & | d | |||
| ) | [inline, protected] |
| ModEvent Gecode::VarImp< VIC >::fail | ( | Space & | home | ) | [inline, protected] |
| void Gecode::VarImp< VIC >::subscribe | ( | Space & | home, | |
| Propagator & | p, | |||
| PropCond | pc, | |||
| bool | assigned, | |||
| ModEvent | me, | |||
| bool | schedule | |||
| ) | [inline] |
Subscribe propagator p with propagation condition pc.
In case schedule is false, the propagator is just subscribed but not scheduled for execution (this must be used when creating subscriptions during propagation).
In case the variable is assigned (that is, assigned is true), the subscribing propagator is scheduled for execution. Otherwise, the propagator subscribes and is scheduled for execution with modification event me provided that pc is different from PC_GEN_ASSIGNED.
| void Gecode::VarImp< VIC >::cancel | ( | Space & | home, | |
| Propagator & | p, | |||
| PropCond | pc | |||
| ) | [inline] |
Cancel subscription of propagator p with propagation condition pc.
Reimplemented in Gecode::Int::BoolVarImp.
| void Gecode::VarImp< VIC >::subscribe | ( | Space & | home, | |
| Advisor & | a, | |||
| bool | assigned, | |||
| bool | fail | |||
| ) | [inline] |
Subscribe advisor a to variable.
The advisor a is only subscribed if assigned is false.
If fail is true, the advisor a is also run when a variable operation triggers failure. This feature is undocumented.
Reimplemented in Gecode::Int::IntVarImpBase, Gecode::Int::BoolVarImpBase, Gecode::Set::SetVarImpBase, and Gecode::Float::FloatVarImpBase.
| void Gecode::VarImp< VIC >::cancel | ( | Space & | home, | |
| Advisor & | a, | |||
| bool | fail | |||
| ) | [inline] |
Cancel subscription of advisor a.
Reimplemented in Gecode::Int::BoolVarImp.
| unsigned int Gecode::VarImp< VIC >::degree | ( | void | ) | const [inline] |
| double Gecode::VarImp< VIC >::afc | ( | void | ) | const [inline] |
| bool Gecode::VarImp< VIC >::copied | ( | void | ) | const [inline] |
| VarImp< VIC > * Gecode::VarImp< VIC >::forward | ( | void | ) | const [inline] |
| VarImp* Gecode::VarImp< VIC >::next | ( | void | ) | const |
Return next copied variable.
| void Gecode::VarImp< VIC >::schedule | ( | Space & | home, | |
| Propagator & | p, | |||
| ModEvent | me, | |||
| bool | force = false | |||
| ) | [inline, static] |
| void Gecode::VarImp< VIC >::reschedule | ( | Space & | home, | |
| Propagator & | p, | |||
| PropCond | pc, | |||
| bool | assigned, | |||
| ModEvent | me | |||
| ) | [inline, static] |
| ModEvent Gecode::VarImp< VIC >::me | ( | const ModEventDelta & | med | ) | [inline, static] |
| ModEventDelta Gecode::VarImp< VIC >::med | ( | ModEvent | me | ) | [inline, static] |
Translate modification event me into modification event delta.
Reimplemented in Gecode::Float::FloatVarImp, Gecode::Int::IntVarImp, and Gecode::Int::BoolVarImp.
| ModEvent Gecode::VarImp< VIC >::me_combine | ( | ModEvent | me1, | |
| ModEvent | me2 | |||
| ) | [inline, static] |
| ModEvent Gecode::VarImp< VIC >::modevent | ( | const Delta & | d | ) | [inline, static] |
Return modification event.
Reimplemented in Gecode::Int::BoolVarImp.
| unsigned int Gecode::VarImp< VIC >::bits | ( | void | ) | const [inline] |
| unsigned int & Gecode::VarImp< VIC >::bits | ( | void | ) | [inline] |
| void Gecode::VarImp< VIC >::schedule | ( | Space & | home, | |
| PropCond | pc1, | |||
| PropCond | pc2, | |||
| ModEvent | me | |||
| ) | [inline, protected] |
| void * Gecode::VarImp< VIC >::operator new | ( | size_t | s, | |
| Space & | home | |||
| ) | [inline, static] |
| void Gecode::VarImp< VIC >::operator delete | ( | void * | , | |
| Space & | ||||
| ) | [inline, static] |
| void Gecode::VarImp< VIC >::operator delete | ( | void * | ) | [inline, static] |
Member Data Documentation
| ActorLink** Gecode::VarImp< VIC >::base |
| VarImp<VIC>* Gecode::VarImp< VIC >::fwd |
unsigned int Gecode::VarImp< VIC >::idx [inline] |
Indices of subscribed actors.
The entries from base[0] to base[idx[pc_max]] are propagators, where the entries between base[idx[pc-1]] and base[idx[pc]] are the propagators that have subscribed with propagation condition pc.
The entries between base[idx[pc_max]] and base[idx[pc_max+1]] are the advisors subscribed to the variable implementation.
VarImp< VIC > * Gecode::VarImp< VIC >::next [inline] |
The documentation for this class was generated from the following file:
- gecode/kernel/core.hpp


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= "A step by step recipe to make bolognese pasta:" prompt
What’s going on in a HuggingFace Tokenizer?
You’ve probably seen this sort of sample code on the HF website, for example on the FLAN LLM page:
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
= AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")
model = AutoTokenizer.from_pretrained("google/flan-t5-small", use_fast=False)
tokenizer
= tokenizer("A step by step recipe to make bolognese pasta:", return_tensors="pt")
inputs = model.generate(**inputs)
outputs print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
['Pour a cup of bolognese into a large bowl and add the pasta']
The LLM output is impressive, but I was curious what was going on under the hood of the tokenizer(...)
line. What’s going on there, and how is the library designed to support all sorts of different encoding styles and parameters?
I chose to investigate the use_fast=False
option, although it looks like fast
currently isn’t even an option for T5 (FLAN) tokenizers
As we’ll see, it mostly delegates to the SentencePiece library, but there’s a few layers of Python that reveals some nice helper functions, and showed me a bit more about how tokenization is handled by HuggingFace.
When we call tokenizer(...)
, it’s really calling tokenizer.__call__(...)
under the hood – this is a typical Python syntactic sugar.
Using Jupyter’s nice ??
we can inspect the source code and start digging in:
Here’s the call stack – most of these functions pretty trivially delegate to the next one in the list, with lots of error checking and batching and extra-argument passing.
__call__ # delegates to _call_one...
tokenizer.# delegates to encode_plus...
tokenizer._call_one # delegates to _encode_plus...
tokenizer.encode_plus tokenizer._encode_plus
_encode_plus
splits into three separate calls of interest (I am adding some additional variable names for legibility here):
= self.tokenize(text, **kwargs)
tokens = self.convert_tokens_to_ids(tokens)
ids self.prepare_for_model(ids, ...)
Let’s look at them one by one:
tokenize
tokenizer.tokenize(prompt) # delegates to _tokenize...
tokenizer._tokenize(prompt) # delegates to sp_model.encode...
tokenizer.sp_model.encode(prompt, out_type=str)
Okay, we’re down to the sp_model
, aka the SentencePiece module that the tokenizer holds inside of it. This calls _EncodeAsPieces
, which returns a list of strings – our first transformation! We’ve gone from a single prompt to a list of tokens. You can see how the more typical words (by, step, make) were not split up, but bolognese
was. Maybe next time I’ll dig more into what words are part of the vocab and what aren’t, and why.
tokenizer.sp_model._EncodeAsPieces(=prompt,
text=tokenizer.sp_model._enable_sampling,
enable_sampling=tokenizer.sp_model._nbest_size,
nbest_size=tokenizer.sp_model._alpha,
alpha=tokenizer.sp_model._add_bos,
add_bos=tokenizer.sp_model._add_eos,
add_eos=tokenizer.sp_model._reverse,
reverse=tokenizer.sp_model._emit_unk_piece,
emit_unk_piece )
['▁A',
'▁step',
'▁by',
'▁step',
'▁recipe',
'▁to',
'▁make',
'▁',
'b',
'ologne',
's',
'e',
'▁pasta',
':']
Ok, _EncodeAsPieces
is now at the _sentencepiece.so
layer, no longer pure Python, which I’ll leave as an adventure for another day.
That was tokenization – let’s move on to…
convert_tokens_to_ids
tokenizer.convert_tokens_to_ids# basically a list comprehension, operates on each str, not the full list
tokenizer._convert_token_to_id_with_added_voc
tokenizer._convert_token_to_id tokenizer.sp_model.piece_to_id
tokenizer.sp_model.piece_to_id??
Signature: tokenizer.sp_model.piece_to_id(arg) Docstring: <no docstring> Source: def _batched_func(self, arg): if type(arg) is list: return [_func(self, n) for n in arg] else: return _func(self, arg) File: ~/miniconda3/lib/python3.9/site-packages/sentencepiece/__init__.py Type: method
'▁A') tokenizer.sp_model.piece_to_id(
71
tokenizer.convert_tokens_to_ids(tokenizer.tokenize(prompt))
[71, 1147, 57, 1147, 2696, 12, 143, 3, 115, 23443, 7, 15, 13732, 10]
Okay, so now we’ve converted each human-readable token to ids! Notice step
is 1147, occurring both at the 1st and 3rd index.
Once again it looks like we’re hitting the compiled layer (the source of sp_model.piece_to_id
is wrong, IPython is getting confused), but it’s interesting how many Python layers you have to push through to get there.
Let’s move on to the third and final call:
prepare_for_model
tokenizer.prepare_for_model(=tokenizer.convert_tokens_to_ids(tokenizer.tokenize(prompt)),
ids )
{'input_ids': [71, 1147, 57, 1147, 2696, 12, 143, 3, 115, 23443, 7, 15, 13732, 10, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
This returns a dictionary with an attention mask, which isn’t that interesting in this specific example, and the input_ids
, which is almost the same as the previous output of convert_tokens_to_ids
. The only difference is a trailing 1
, which is the eos_token
, aka end-of-sequence token:
tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
1
Sure enough, when I looked at the source for prepare_for_model
, it does a bunch of different things (padding, truncation, backwards compatibility, etc) – but most importantly here, adds a EOS token.
The specific function that does it is build_inputs_with_special_tokens
:
tokenizer.build_inputs_with_special_tokens(tokenizer.convert_tokens_to_ids(tokenizer.tokenize(prompt)))
[71, 1147, 57, 1147, 2696, 12, 143, 3, 115, 23443, 7, 15, 13732, 10, 1]
'eos_token'] tokenizer.special_tokens_map[
'</s>'
Summary
So – to wrap it all up – for this simple example copied from the HuggingFace website, you can get the same functionality from the tokenizer by breaking down to these smaller helper methods vs the higher level __call__
tokenizer.prepare_for_model(=tokenizer.convert_tokens_to_ids(tokenizer.tokenize(prompt)),
ids )
{'input_ids': [71, 1147, 57, 1147, 2696, 12, 143, 3, 115, 23443, 7, 15, 13732, 10, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
tokenizer(prompt)
{'input_ids': [71, 1147, 57, 1147, 2696, 12, 143, 3, 115, 23443, 7, 15, 13732, 10, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
This was mostly an educational exercise to see what’s going on under the hood of the HuggingFace – I definitely wouldn’t recommend doing this sort of thing in production – but is a great way to get a bit deeper understanding of the library!


This is an example of a basic Teacher Virus (TV) payload. You can see what it's made up of at its GitHub repository: github.com/harrylongworth/tv-helloworld
To create your own first payload why don't you fork this payload and give it a go? You'll then be able to install your forked version by simply entering in your GitHub username and the name you give the forked repository.
However - if you want to start from scratch; all it takes to create a working payload (our word for apps that run on the TV platform) is at least an index.html (or index.php) file in the root of a GitHub repository.
You should also include an icon.png (or icon.svg) file to become the button image for your payload. If you don't provide an image TV will use the default OATSEA logo image (which this payload uses)
GitHub will suggest (and we agree) that you also create a README.md file to explain your payload - and an open source license file (Apache 2.0 please).
If you have any questions please register at teachervirus.org and join the community.


|
AssemblerEmulator - Nikola Bebic
Assembler and emulator used for systems software class
|
Class representing the math expression. More...
#include <Expression.h>
Public Member Functions | |
| virtual int32_t | value () const =0 |
| Evaluates the expression. More... | |
| virtual bool | setValue (std::string symbol, std::shared_ptr< Expression > value) noexcept |
| Traverses the subtree and sets the value for the symbol. More... | |
| virtual bool | validate () const noexcept |
| Validates the expression. More... | |
| virtual bool | containsSymbol () const noexcept |
| Tests whether the expression contains a Symbol. More... | |
| virtual int | symbolCount () const noexcept |
| Counts the symbols in the expression. More... | |
| virtual void | pushChildren (std::stack< std::reference_wrapper< std::shared_ptr< Expression >>> &stack) const noexcept |
| Pushes the children to the stack. More... | |
| virtual void | resolveSymbolTable (const SymbolTable &symbol_table) noexcept |
| Resolves the symbols from the symbol table and sets the relocation info. More... | |
| virtual void | resolveImports (std::unordered_set< std::string > imported_symbols) noexcept |
| Resolves the imported symbols and sets the relocation info. More... | |
| virtual void | resolveCurrentPcSymbol (size_t section_index, size_t offset) noexcept |
| Resolves the current PC symbol and sets the relocation info. More... | |
| virtual std::list< RelocationRecord > | generateRelocations () const |
| Generates the relocation records for the subtree. More... | |
| virtual | ~Expression ()=default |
Class representing the math expression.
Definition at line 16 of file Expression.h.
|
virtualdefault |
|
virtualnoexcept |
Tests whether the expression contains a Symbol.
Reimplemented in bnssassembler::Operation, bnssassembler::Symbol, and bnssassembler::SubtractOperation.
Definition at line 14 of file Expression.cpp.
|
virtual |
Generates the relocation records for the subtree.
Reimplemented in bnssassembler::Operation, bnssassembler::Symbol, bnssassembler::SubtractOperation, and bnssassembler::AddOperation.
Definition at line 40 of file Expression.cpp.
|
virtualnoexcept |
Pushes the children to the stack.
| stack | Reference to the stack |
Reimplemented in bnssassembler::Operation.
Definition at line 24 of file Expression.cpp.
|
virtualnoexcept |
Resolves the current PC symbol and sets the relocation info.
| section_index | Current PC section |
| offset | PC address in relation to the current section beginning |
Reimplemented in bnssassembler::Operation, and bnssassembler::Symbol.
Definition at line 36 of file Expression.cpp.
|
virtualnoexcept |
Resolves the imported symbols and sets the relocation info.
| imported_symbols | Collection of imported symbols |
Reimplemented in bnssassembler::Operation, and bnssassembler::Symbol.
Definition at line 32 of file Expression.cpp.
|
virtualnoexcept |
Resolves the symbols from the symbol table and sets the relocation info.
| symbol_table | Symbol table |
Reimplemented in bnssassembler::Operation, and bnssassembler::Symbol.
Definition at line 28 of file Expression.cpp.
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virtualnoexcept |
Traverses the subtree and sets the value for the symbol.
| symbol | Name of the symbol |
| value | New value of the symbol |
Reimplemented in bnssassembler::Operation, and bnssassembler::Symbol.
Definition at line 4 of file Expression.cpp.
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virtualnoexcept |
Counts the symbols in the expression.
Reimplemented in bnssassembler::Operation, bnssassembler::Symbol, and bnssassembler::SubtractOperation.
Definition at line 19 of file Expression.cpp.
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virtualnoexcept |
Validates the expression.
Reimplemented in bnssassembler::DivideOperation, and bnssassembler::MultiplyOperation.
Definition at line 9 of file Expression.cpp.
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pure virtual |
Evaluates the expression.
| Throws | if the expression has variables or could not be evaluated (for example, division by zero) |
Implemented in bnssassembler::Literal, bnssassembler::Operation, and bnssassembler::Symbol.


This topic describes the UserJvmOptionsService
API, which can be used in self-contained Java and JavaFX applications to alter the settings for the Java Virtual Machine (JVM). The new settings are used the next time that the application is started.
This topic contains the following sections:
JVM options are used to configure the JVM. For example, options are available for managing memory, setting the class path, and turning on debugging. When you use the Java Packaging tools to package your self-contained application, you can pass in the JVM options that you want your application to use (-BjvmOptions
), and provide default values for options that the user can set (-BuserJvmOptions
).
The UserJvmOptionsService
API enables you to provide a way to manage JVM options through the application, for example, by providing a preferences or settings feature. The settings specified by the user are used each time the application is started. If a user changes the settings, the application must be restarted to use the new settings.
When an application is launched, the values for JVM options are determined in the following order:
Options set through the UserJvmOptionsService
API take precedence.
For options that are not set through the API, values from the -BuserJvmOptions
arguments that were specified when the application package was created are used.
For options that are not set through the API or the -BuserJvmOptions
arguments, values from the -BjvmOptions
arguments that were specified when the application package was created are used.
For options that are not specified in any of the above locations, system values are used.
The UserJvmOptionsService
API is available in the packager.jar
file, which is in the java-home/lib
directory. To use the UserJvmOptionsService
API, include the packager.jar
file in the bundle for your self-contained application.
The UserJvmOptionsService
API includes the following methods:
getUserJVMDefaults
. See Section 15.2.1, "Instantiating UserJvmOptionsService" for details.
getUserJVMOptions
. See Section 15.2.2, "Getting Current Values of JVM Options" for details.
getUserJVMOptionDefaults
. See Section 15.2.3, "Getting Default Values of JVM Options" for details.
setUserJVMOptions
. See Section 15.2.4, "Setting New Values for JVM Options" for details.
The first step in using the UserJvmOptionsService
API is to get an instance of the service to use. The instance is determined by the packager and the launcher. To get the correct instance, call UserJvmOptionsService.getUserJVMDefaults()
in a command similar to the following example:
UserJvmOptionsService ujo=UserJvmOptionsService.getUserJVMDefaults();
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Note: Do not attempt to directly instantiate an instance ofUserJvmOptions . Use only what is provided by the UserJvmOptions.getUserJVMDefaults() method. |
Use the getUserJVMOptions()
method to get the current JVM options for the application. This method returns the JVM options and values that were set either using the setUserJVMOptions()
method or using the -BuserJvmOptions
arguments when the application package was created. If changes have been made to the settings since the application was started, the values returned show the latest settings, not the settings used when the application was started.
The following example shows a sample call to the getUserJVMOptions()
method using the instance of the UserJvmOptionsService
that was obtained in Section 15.2.1, "Instantiating UserJvmOptionsService":
Map<String, String> userOptions = ujo.getUserJVMOptions();
JVM options are returned as key-value pairs. Each key starts with a dash(-). The key ends with an equals sign (=) if one is needed between the key and value.
Use the getUserJVMOptionDefaults()
method to get the default JVM options for the application. This method returns the JVM options and values that were set using the -BuserJvmOptions
arguments when the application package was created.
The following example shows a sample call to the getUserJVMOptionDefaults()
method using the instance of the UserJvmOptionsService
that was obtained in Section 15.2.1, "Instantiating UserJvmOptionsService":
Map<String, String> defaults = ujo.getUserJVMOptionsDefaults();
JVM options are returned as key-value pairs. Each key starts with a dash(-). The key ends with an equals sign (=) if one is needed between the key and value.
Use the setUserJVMOptions()
method to store changes that the user makes to the JVM options. The application must be restarted to use the new values. If the user does not set a value for an option that was specified in a -BuserJvmOptions
argument when the application package was created, the value from the -BuserJvmOptions
argument is used when the application is started.
JVM options are passed as key-value pairs, which are concatenated when passed to the JVM. Start the key with a dash(-). If an equals sign (=) is needed between the key and value, it must be the last character in the key. For example, to set the system property applicationName
to SampleApp, use the key -DapplicationName=
and the value SampleApp
. To set the maximum size of the memory allocation pool to 80 megabytes, use the key -Xmx
and the value 80M
.
The following example shows a sample call to the setUserJVMOptions() methid using the instance of the UserJvmOptionsService
that was obtained in Section 15.2.1, "Instantiating UserJvmOptionsService" and a set of options contained in the userOptions
variable:
ujo.setUserJVMOptions(userOptions);
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Note: No validation or error checking is performed. If invalid values are provided, your application might not start. |
The following sample code shows how the UserJvmOptionsService
API can be used to track the first and last time an application is run. The sample also generates a table to show the current value and default value for the options.
Example 15-1
// get the helper instance UserJvmOptionsService ujo = UserJvmOptionsService.getUserJVMDefaults(); Map<String, String> userOptions = ujo.getUserJVMOptions(); // print out all the options currently set for (Map.Entry <String, String> entry : userOptions.entrySet()) { System.out.println("key:" + entry.getKey() + " value:" + entry.getValue()); } // if we haven't marked the first run, do so now if (!userOptions.containsKey("-DfirstRunMs=")) { userOptions.put("-DfirstRunMs=", Long.toString(System.currentTimeMillis())); } // add the last run userOptions.put("-DlastRunMs=", Long.toString(System.currentTimeMillis())); // save the changes ujo.setUserJVMOptions(userOptions); ... // create a table row with Key, Current Value, and Default Value DefaultTableModel model = new DefaultTableModel(); model.addColumn("Key"); model.addColumn("Effective"); model.addColumn("Default"); Map<String, String> defaults = ujo.getUserJVMOptionDefaults(); for (Map.Entry <String, String> entry : userOptions.entrySet()) { // get the default, it may be null String def = defaults.get(entry.getKey()); model.addRow(new Object[] {entry.getKey(), entry.getValue(), def == null ? "<no default>" : def}); }


Hey,hi,one of my friend recently participated in a tagline competition.He got through the first stage but now he needs your help to go ahead. Just a few clicks from your side might decide if he wins or not. He is trailing right now but I am sure you can help him.So please help out and like this: http://www.facebook.com/photo.php?fbid=138280692920014&set=a.138280322920051.36169.107331189348298&type=1&theater Also,for liking the photo,you might have to like the page first. http://www.facebook.com/dducmun PS:You can alwys unlike the page after liking that photo.I am sure you will help like the photo,thanks. :)


MehrBarzCTO of MehrBarz ON OUR KNOWLEDGE OF GENERAL PRINCIPLES UNIVERSALS The shape of the table is no better. We are all in the habit of judging as to the 'real' shapes of things, and we do this so unreflectingly that we come to think we actually see the real shapes. But, in fact, as we all have to learn if we try to draw, a given thing looks different in shape from every different point of view. If our table is 'really' rectangular, it will look, from almost all points of view, as if it had two acute angles and two obtuse angles. If opposite sides are parallel, they will look as if they converged to a point away from the spectator; if they are of equal length, they will look as if the nearer side were longer. |
They are as follows:
| Date | Experience |
|---|---|
| 2012 | I wanted to ask you about that operation. |
| 2018-2019 | You may have them. | Totals | 7444 |
| App development | ⭐⭐⭐⭐⭐ |
| Python development | ⭐⭐⭐⭐ |
| Web development | ⭐⭐⭐⭐⭐ |


| 1 | package fr.sii.ogham.testing.assertion.internal; | |
| 2 | ||
| 3 | import static fr.sii.ogham.testing.assertion.internal.helper.ImplementationFinder.findSender; | |
| 4 | import static fr.sii.ogham.testing.assertion.util.AssertionHelper.assertThat; | |
| 5 | import static org.apache.commons.lang3.reflect.FieldUtils.readField; | |
| 6 | ||
| 7 | import org.hamcrest.Matcher; | |
| 8 | ||
| 9 | import fr.sii.ogham.core.service.MessagingService; | |
| 10 | import fr.sii.ogham.sms.sender.impl.CloudhopperSMPPSender; | |
| 11 | import fr.sii.ogham.sms.sender.impl.cloudhopper.ExtendedSmppSessionConfiguration; | |
| 12 | import fr.sii.ogham.testing.util.HasParent; | |
| 13 | ||
| 14 | /** | |
| 15 | * Helper class to make assertions on Cloudhopper instance created by Ogham. | |
| 16 | * | |
| 17 | * For example, to ensure that particular configuration is used for emails: | |
| 18 | * | |
| 19 | * <pre> | |
| 20 | * {@code | |
| 21 | * cloudhopper() | |
| 22 | * .host(is("localhost") | |
| 23 | * .splitter() | |
| 24 | * .enabled(is(true)) | |
| 25 | * } | |
| 26 | * </pre> | |
| 27 | * | |
| 28 | * @author Aurélien Baudet | |
| 29 | * | |
| 30 | */ | |
| 31 | public class CloudhopperAssertions extends HasParent<MessagingServiceAssertions> { | |
| 32 | private final CloudhopperSMPPSender cloudhopperSender; | |
| 33 | ||
| 34 | public CloudhopperAssertions(MessagingServiceAssertions parent, CloudhopperSMPPSender cloudhopperSender) { | |
| 35 | super(parent); | |
| 36 | this.cloudhopperSender = cloudhopperSender; | |
| 37 | } | |
| 38 | ||
| 39 | /** | |
| 40 | * Ensures that configured host is correct. | |
| 41 | * | |
| 42 | * @param matcher | |
| 43 | * the matcher to ensure that host is correct. | |
| 44 | * @return this instance for fluent chaining | |
| 45 | */ | |
| 46 | public CloudhopperAssertions host(Matcher<String> matcher) { | |
| 47 |
1
1. host : removed call to fr/sii/ogham/testing/assertion/util/AssertionHelper::assertThat → NO_COVERAGE |
assertThat(getHost(cloudhopperSender), matcher); |
| 48 |
1
1. host : replaced return value with null for fr/sii/ogham/testing/assertion/internal/CloudhopperAssertions::host → NO_COVERAGE |
return this; |
| 49 | } | |
| 50 | ||
| 51 | /** | |
| 52 | * Ensures that configured port is correct. | |
| 53 | * | |
| 54 | * @param matcher | |
| 55 | * the matcher to ensure that port is correct. | |
| 56 | * @return this instance for fluent chaining | |
| 57 | */ | |
| 58 | public CloudhopperAssertions port(Matcher<Integer> matcher) { | |
| 59 |
1
1. port : removed call to fr/sii/ogham/testing/assertion/util/AssertionHelper::assertThat → NO_COVERAGE |
assertThat(getPort(cloudhopperSender), matcher); |
| 60 |
1
1. port : replaced return value with null for fr/sii/ogham/testing/assertion/internal/CloudhopperAssertions::port → NO_COVERAGE |
return this; |
| 61 | } | |
| 62 | ||
| 63 | /** | |
| 64 | * Ensures that configured SMPP version is correct. | |
| 65 | * | |
| 66 | * @param matcher | |
| 67 | * the matcher to ensure that SMPP version in is correct. | |
| 68 | * @return this instance for fluent chaining | |
| 69 | */ | |
| 70 | public CloudhopperAssertions interfaceVersion(Matcher<Byte> matcher) { | |
| 71 |
1
1. interfaceVersion : removed call to fr/sii/ogham/testing/assertion/util/AssertionHelper::assertThat → NO_COVERAGE |
assertThat(getConfiguration(cloudhopperSender).getInterfaceVersion(), matcher); |
| 72 |
1
1. interfaceVersion : replaced return value with null for fr/sii/ogham/testing/assertion/internal/CloudhopperAssertions::interfaceVersion → NO_COVERAGE |
return this; |
| 73 | } | |
| 74 | ||
| 75 | /** | |
| 76 | * Make assertions on "User Data" to ensure that it is correctly configured. | |
| 77 | * | |
| 78 | * For example: | |
| 79 | * | |
| 80 | * <pre> | |
| 81 | * {@code | |
| 82 | * userData() | |
| 83 | * .useShortMessage(is(true)) | |
| 84 | * } | |
| 85 | * </pre> | |
| 86 | * | |
| 87 | * @return builder for fluent chaining | |
| 88 | */ | |
| 89 | public CloudhopperUserDataAssertions userData() { | |
| 90 |
1
1. userData : replaced return value with null for fr/sii/ogham/testing/assertion/internal/CloudhopperAssertions::userData → NO_COVERAGE |
return new CloudhopperUserDataAssertions(this, cloudhopperSender); |
| 91 | } | |
| 92 | ||
| 93 | /** | |
| 94 | * Make assertions on splitter instance to ensure that it is correctly | |
| 95 | * configured. | |
| 96 | * | |
| 97 | * For example: | |
| 98 | * | |
| 99 | * <pre> | |
| 100 | * {@code | |
| 101 | * splitter() | |
| 102 | * .enabled(is(true)) | |
| 103 | * } | |
| 104 | * </pre> | |
| 105 | * | |
| 106 | * @return builder for fluent chaining | |
| 107 | */ | |
| 108 | public CloudhopperSplitterAssertions splitter() { | |
| 109 |
1
1. splitter : replaced return value with null for fr/sii/ogham/testing/assertion/internal/CloudhopperAssertions::splitter → NO_COVERAGE |
return new CloudhopperSplitterAssertions(this, cloudhopperSender); |
| 110 | } | |
| 111 | ||
| 112 | /** | |
| 113 | * Find instance of {@link CloudhopperSMPPSender}. | |
| 114 | * | |
| 115 | * @param messagingService | |
| 116 | * the messaging service | |
| 117 | * @return the found instance | |
| 118 | */ | |
| 119 | public static CloudhopperSMPPSender getCloudhopperSender(MessagingService messagingService) { | |
| 120 |
1
1. getCloudhopperSender : replaced return value with null for fr/sii/ogham/testing/assertion/internal/CloudhopperAssertions::getCloudhopperSender → NO_COVERAGE |
return findSender(messagingService, CloudhopperSMPPSender.class); |
| 121 | } | |
| 122 | ||
| 123 | private static ExtendedSmppSessionConfiguration getConfiguration(CloudhopperSMPPSender cloudhopperSender) { | |
| 124 | try { | |
| 125 |
1
1. getConfiguration : replaced return value with null for fr/sii/ogham/testing/assertion/internal/CloudhopperAssertions::getConfiguration → NO_COVERAGE |
return (ExtendedSmppSessionConfiguration) readField(cloudhopperSender, "configuration", true); |
| 126 | } catch (IllegalAccessException e) { | |
| 127 | throw new IllegalStateException("Failed to read 'configuration' of CloudhopperSMPPSender", e); | |
| 128 | } | |
| 129 | } | |
| 130 | | |
| 131 | private static String getHost(CloudhopperSMPPSender cloudhopperSender) { | |
| 132 |
1
1. getHost : replaced return value with "" for fr/sii/ogham/testing/assertion/internal/CloudhopperAssertions::getHost → NO_COVERAGE |
return getConfiguration(cloudhopperSender).getHost(); |
| 133 | } | |
| 134 | ||
| 135 | private static int getPort(CloudhopperSMPPSender cloudhopperSender) { | |
| 136 |
1
1. getPort : replaced int return with 0 for fr/sii/ogham/testing/assertion/internal/CloudhopperAssertions::getPort → NO_COVERAGE |
return getConfiguration(cloudhopperSender).getPort(); |
| 137 | } | |
| 138 | } | |
Mutations | ||
| 47 |
1.1 |
|
| 48 |
1.1 |
|
| 59 |
1.1 |
|
| 60 |
1.1 |
|
| 71 |
1.1 |
|
| 72 |
1.1 |
|
| 90 |
1.1 |
|
| 109 |
1.1 |
|
| 120 |
1.1 |
|
| 125 |
1.1 |
|
| 132 |
1.1 |
|
| 136 |
1.1 |


Stretch Goals
I love setting goals and adding them but the issue is tend to do so on a daily, weekly, seasonally-ish, and yearly patterns. And I do so directly to my Notes. Which at this point (9/4/20) is still my main PKB. I have several shortcuts setup that allow me to do it in a particular format automatically for each of those lengths of time. Which is useful - but Notes is still just a placeholder for something better.
Something that can manage my goals in all the time formats. Will remind about stale goals and set them in a place where extra details can be added on them if necessary.
Stale goals also inherently means time needs to be attributed to them (which is necessary for a good goal anyway).
And it needs to be easily cross referenceable with my daily activities, studies, and regimen in general.
I added these Pandemic Goals at the beginning of April and have added some since. But I don’t know where to put them now on the site. A piece of me wants to leave them here until they’re all finished and then move them down to be replaced by more recent goals. Another piece of me feels like this isn’t the right medium for it and that I have to update this collection method before i try and think of a more complex system.
Which makes me think I’ve got a new goal.


|
Mackey
V3
A C++ library for computing RO(G) homology
|
Contains the multiplication graph and the methods for factorizing generators. More...
#include "../Factorization.hpp"
Namespaces | |
| Mackey | |
| The namespace for everything that is group-independent. | |
Functions | |
| template<typename group_t > | |
| std::ostream & | Mackey::operator<< (std::ostream &os, const FactorizationPrinter< group_t > &F) |
| Prints Factorization. More... | |
Contains the multiplication graph and the methods for factorizing generators.


WITNESS LIST
Licensing & Administrative Procedures Committee
April 4, 2011 - 8:00 AM
For:
Owen, Bob (Texas Society of CPAs)
Against:
Gibson, Richard (Self; Trusted Tax LTD)
Melcer, M Richard "Rick" (Texas Society of Enrolled Agents)
Rivas, Ricardo (Texas Society of Enrolled Agents)
On:
Barber, Kathy (NFIB)
Treacy, William "Bill" (Tx State BD. of Public Accountancy)
Registering, but not testifying:
Against:
Hall, Darrin (City of Houston)
For:
McCluskey, John (Professional Bondsmen of Texas)
Registering, but not testifying:
For:
Dillard, Wynn (Professional Bondsmen of Texas)
Walstad, Scott (Professional Bondsman of Texas)
For:
Dillard, Wynn (Professional Bondsmen of Texas)
Registering, but not testifying:
For:
McCluskey, John (Professional Bondsmen of Texas)
Walstad, Scott (Professional Bondsman of Texas)
On:
Steen, Alan (TABC Resource)
Registering, but not testifying:
For:
Lively, Lance (TX. Package Stores Association)
McKinney, Mike (Wholesale Beer Distributors of Texas)
Short, Jim (Spec's Wines Spirits & Finer Foods)
1
WITNESS LIST
For:
Brown, Bryan (Retama Development Corp.)
Shubeck, Drew (Lone Star Park)
Against:
Hooper, David (Texas Thoroughbred Association)
Rountree, Marsha (Texas Horsemen's Partnership)
On:
Fenner, Mark (Texas Racing Commission)
Forrest, David (Texas Equine Research Account Advisory Committee)
Lunt, David (Texas AgriLife Research)
Ogle, Steven (Sunset Advisory Commission (Project Manager))
Trout, Chuck (Texas Racing Commission)
For:
Briers, Stanley (Self; TX. Plumb. & Mech. Cont. Asson.)
On:
Koebele, Steve (Greater Houston Retailers Cooperative Association)
Registering, but not testifying:
For:
Shelley, Dan (Associated Plumbing, Heating, and Cooling Contractors APHCC)
Against:
Brown, Jay (Valero)
DuBois, Doug (Texas Petroleum Marketers and Convenience Store Association)
On:
Maxwell, Robert (Board of Plumbing Examiners)
For:
Aranda, David (Labor Apprenticeship for elevator constructors)
Crumley, Nancy (NEII- National Elevator Industry Inc)
Registering, but not testifying:
For:
Burke, Roland (Thyssen Krupp Elevator CO. 3615 Willow Spring RD, Austin, TX. 78704)
On:
Kuntz, William (Texas Department of Licensing & Regulation)
2
WITNESS LIST
Registering, but not testifying:
For:
Horton, Jess (Southwest Tow Operators Association)
On:
Kuntz, William (Texas Department of Licensing & Regulation)
For:
Horton, Jess (Southwest Tow Operators Association)
Rash, Jeanette (Texas Towing & Storage Assoc.)
Wilson, Wendy (Texas Apartment Association)
Registering, but not testifying:
For:
Goode, Rose (Texas Towing & Storage Association)
Tredway, CJ (Texas Towing and Storage Association)
On:
Kuntz, William (Texas Department of Licensing & Regulation)
3


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See all tags.


Why I contribute to OSS projects
I spent quite some time contributing patches to the Elixir project lately, and I am quite proud of it:
Some people have been asking me why I spend so many time on contributing patches to a relatively unknown OSS project, and others - coming from a non-ICT-background asked me why I bother to work on someone else’s product for free, while I can charge thousands of Euros to other companies.
 Well, my friends, the answer is pretty straightforward:
TL;DR: I learn new things to get better, and I think the best way to learn a new thing is to get involved with the team conceiving the new thing.
… but here is a more thorough explanation:
Why work for free?
I have written a detailed blog post where I explain in great detail why I work for free, but I will make a short recap here:
- My income comes in bursts; I deliberately take time off every n months to study something I consider to be emerging and interesting. An excerpt of things I studied in the
past over several ‘sabatticals’:
- BDD (first post/)
- CQRS (first post/)
- NancyFx/Simple.Data (first post/)
- Lean Start-up (first post/)
- Now Erlang/Elixir (a post, another post/)
- I study new things all the time to grow as both a person and a consultant
- Being in an emerging field allows you to connect with thought leaders and people who matter in the community
- Expanding your horizon allows you to can contribute more value to organisations
- Last - but not least - my work - /life balance
Why contribute to OSS
I am a developer. As I said, I consider the best way to learn a new platform, is to get involved and talk with the people that conceived the platform. This is hard in an established community, as people tend to flock around the thought leaders, they are not really accessible any more. In newer communities, this is not the fact.
Also, I personally get a kick out of finding “the next big thing”(tm) and following it’s evolution from a concept or an idea with some early adopters, into a big established community with lots of members and a profound vision and established reputation.
During the last week, I have had the honour to discuss with José Valim (the creator of Elixir and founder of Platformatec, and also one of the main contributors to the Rails core ) using newgroups, github issues/pull requests, Skype chats and some screen sharing sessions. While watching a video, reading blog posts or a book might actually teach you a big deal about something new, I think there is a tremendous amount of value in interacting with the incrowd, as you can find out what motivates them, what their product and vision is about, and what the essence is.
But why do they even bother to teach you?
In essence, we are just exchanging assets; I fix bugs, he coaches and corrects where necessary.
That, my friend, is where the catch is: it works in both ways, i.e. there ain’t no such thing as free lunch.
The reason José is taking all this time and helping me out personally, is because I am also helping him out with his vision. As the Elixir platform is developed in a non-windows environment, he has no way to verify whether everything works in a windows environment.
Hence, when I saw that problem, I decided to take ownership, and help him out. I started with a small thing , and slowly got more and more into the essence of Elixir, where I got some really helpful tips on how to achieve things. I am still a novice, but at least I am making some progress here.
Conclusion
What it all comes down to, is that I love to learn new things, and I do consider Elixir to become one of the next big things - and I am not alone on this -.
It is natural for people to get stuck in a constrained environment, and opening up this world every once in a while allows us to grow and evolve into better persons.
Which is why I would advise anyone to start contributing to an OSS project you like; even if it does not become the next big thing, you will get a tremendous amount of value and sense of achievement by contributing to it.
Signing off,
Tom


Our mandate: dramatically improve site performance on Gawker Media sites (Gawker, Jezebel, Deadspin...). Try to deliver readable pages to most users within 1 second.
A great way to begin any web performance effort is by analyzing waterfall charts. (Webpagetest.org is one fantastic free tool for viewing these)
Let's take a look at one!
144 in total!
Total load time: 23.5s
Scripts • Images • Fonts/Styles
Ad-related requests, mostly. 114 of our 144 requests...
Like most media companies, a large chunk of our revenue comes from ads.
Revenue is used to pay software developers as well as some others involved in the business.
So we need some other way of making money.
So, we can't eliminate these 114 requests right away. Let's focus on our first-party content...
Now we're looking at 30 requests, much more manageable.
(loaded in 18.8s)
23 requests (loaded in 10.4s)
Our script loader, require.js, followed by a 0.94s gap before the scripts it loads start to come in. 1.67s total delay.
3 requests (loaded in 8.4s)
<script src="dependency1.js"></script>
<script src="dependency2.js"></script>
<script src="combined-scripts.js"></script>
<script src="require.js" data-main="combined-scripts.js"></script>
We used the power of waterfall charts to identify some key performance issues to investigate further:
Tools used for this presentation:


New Customer
By creating an account you will be able to shop faster, be up to date on an order's status, and keep track of the orders you have previously made.
{% extends 'index.html' %} {% load static %} {% block content %}
By creating an account you will be able to shop faster, be up to date on an order's status, and keep track of the orders you have previously made.


I'm Rachel, a late 20-something Australian living in Sunny California, USA.
My love affair with the web began when my dad first brought home a dial-up modem in '97 but I only started building it 5 years ago. I got my start making Flash banners for an advertising agency and have been obsessed with animation/motion on the web ever since. These days I find myself spending half of my dev time animating interfaces with CSS and JavaScript and the other half building APIs and backend applications with Node.js. I'm currently building all the websites for the awesome dudes over at Active Theory.
Even better than coding is spending time with my amazing husband, going to the beach, buying leopard print clothing and obsessing over eyebrow makeup.
If you are not a recruiter or a creep and would like to contact me, I'm pretty chatty over at Twitter, or you can send me an email at contact@rachsmith.com.


The Land of Stuff
The Land of Stuff is an online multiplayer role-playing game. You can explore a vast world solo or with friends, battling monsters, gaining loot and completing story quests.
The full game is available for download here: The Land of Stuff. (Requires Windows)
Check out the project page on GitHub here.


Anim pariatur cliche reprehenderit, enim eiusmod high life accusamus terry richardson ad squid. 3 wolf
moon officia aute, non cupidatat skateboard dolor brunch. Food truck quinoa nesciunt laborum eiusmod.
Upload File
{{$index+1}}
{{file.name}}
NOTE! Attached image thumbnail is supported in Latest Firefox, Chrome, Opera, Safari and Internet Explorer 10 only


#include <outputdataprinter.h>
Definition at line 15 of file outputdataprinter.h.
Static Public Member Functions | |
| static void | displayEngineOutputDataFromNet (EngineOutputData &engine_output_data, const InetAddress &from) |
| template<typename SpecificFaceState> | |
| static void | displayFaceStateFromNet (const SpecificFaceState &face_state, const InetAddress &from) |
| static void displayFaceStateFromNet | ( | const SpecificFaceState & | face_state, | |
| const InetAddress & | from | |||
| ) | [static] |
Display face states from a given network host.
60 Hz data is displayed by continuously overwriting previous frame data. NOTE: this method is not intended to be used for more than one host at the same time, since it is a static method.
Definition at line 24 of file outputdataprinter.h.


| |
Methods defined here:
- init_(self, c=0, s='')
- Initialize self. See help(type(self)) for accurate signature.
- add(self, v)
- div(self, e=0)
- mid(self, mode=0, most=-1)
Data descriptors defined here:
- _dict_
- dictionary for instance variables (if defined)
- _weakref_
- list of weak references to the object (if defined)
|


Trust and Sincerity
I haven't had an opportunity yet to try the exercise since reading this chapter, but I have reflected on the points made. It is important to remove judgement or bias from someone before we have been proven otherwise. This exercise is good for treating everybody we meet as good, it makes us less judgemental and would most likely give us the opportunity to see aspects of people we wouldn't have previously allowed ourselves too.
Empathetic Listening
This was a good exercise to do with my girlfriend. This was good for us as we had our normal conversations in a way but with more emphasis on how we felt. I imagine this task would be a lot tougher with someone whom I did not already know very well but it would most likely enhance the power of empathetic listening when done with someone you don't know very well.
Loving/Kindness Meditation
I found this exercise to have the biggest effect on me of this weeks tasks. This really made me understand how empathy is a crucial trait for humankind. It makes us forget any grudges or misgivings we may have about someone when we remind ourselves they feel the same things we believe may be exclusive to ourselves.
Political Empathy
In this situation I had found out that my flatmate had borrowed my car
and whilst it was parked someone had dinged the front bumper. He had borrowed the car
to pick up somebody from the airport. The conflict arose because they did not
tell me until I mentioned the bump in the car.
In my mind, I felt that they had been dishonest in not telling me that there
had been an accident. I did not know really why they did not tell me that this had happened, as I have insurance and it would have been easy enough to sort out. Accidents
happen. From their perspective I imagine they thought they didn't need to tell me because
it wasn't a big dent and they hadn't caused the accident. I believe that is why they did not tell me about it.


A coding witch is an individual who identifies as a witch in her mundane life, and integrates the same logic, morality, and laws to her coding work as she does her physical workings.
A traditional coding witch is someone who abides by older traditions and laws within her coding fields. She respects the history that led to the development of her craft, and honors it within her coding style.
A kitchen coding witch is someone who likes to creatively invent, test, and integrate many types of coding styles and rules, with the goal of making something different as a whole out of separate parts.
A hedge coding witch is someone who avidly seeks out mentorship and apprenticeship both with modern programmers, as well as programmers past. She utilizes lessons from all she interacts with to fine tune her craft.
An elemental coding witch is someone who partakes in projects that requires certain structure, or ritual, if you will. She thrives on using familiar elements in her coding consistently.
A green coding witch is someone who adjusts to the environment she is in. She is focused on bringing equity, balance, and completion to her projects.
A solitary coding witch is someone who thrives working and learning on her own. She avoids typical academic pathways and rarely works in a teamwork setting.
A secular coding witch is someone who is eager to learn all different types of coding and programming. She does not adhere to a particular school of thought, and, instead, focuses on a wide breadth of knowledge.
An ecelectic coding witch is someone who thrives on chaos. She made adhere or borrow from any of the above types.
Spirituality in a witches coding work is a concept that may seem strange to apply to something that thrives on structure, code, formulas, and logic. For a coding witch, it is important to balance the structure/individualism with the world around her. She seeks to apply justice in ways that are often overlooked. In any culture, systems still reflect the eniviroment around them. A coding witch keeps this in mind, even as she implements that which would be considered scientific.
logic= x
justice= y
x+y=balance
apprenticeship= x
tradition= y
x+y=hedge
practice= x
application= y
x+y=elemental
divination= x
intention= y
x+y=solution
html= x
focus= y
x+y=certification


| reverting_id | reverting_user_text | rev_user_text | reverting_comment | diff | |
|---|---|---|---|---|---|
| 79879 | 74336844 | JL-Bot | Addbot | removing non-applicable orphan template | --- \n +++ \n @@ -1,4 +1,3 @@\n -{{Orphan|date=February 2009}} The '''Monuments Men Foundation for the Preservation of Art''' is an [[United States|American]] [[Foundation (nonprofit organization)|foundation]], with the objective to preserve the legacy of people that served in the [[Monuments, Fine Arts, and Archives program]] ("the Monuments Men"),{{cite web |url=http://www.monumentsmenfoundation.org/about/ |title=Monuments Men Foundation For the Preservation of Art – About The Foundation |accessdate=2008-02-07}} during and after [[World War II]]. The Monuments Men Foundation for the Preservation of Art is one of the recipients of the 2007 [[National Humanities Medal]]s "6 Academics Receive National Honors in Arts and Humanities" Chronicle of Higher Education Nov. 16, 2007 [http://chronicle.com/daily/2007/11/733n.htm?at summary] |
| 88400 | 81692765 | JL-Bot | Addbot | removing non-applicable orphan template | --- \n +++ \n @@ -1,4 +1,3 @@\n -{{Orphan|date=February 2009}} '''Chrysler UK''' was the [[United Kingdom|British]] division of the [[United States|American]] car-maker [[Chrysler]]. It was founded in 1970 when the Chrysler-owned [[Rootes Group]] was renamed to reflect its ownership by Chrysler. The initial British Chrysler range consisted of Rootes developed [[Hillman]], [[Sunbeam]] and [[Humber]] vehicles. The first British Chrysler to wear a Chrysler badge was the 1975 [[Chrysler Alpine|Alpine]], and a year later the remaining Hillman range was rebadged as Chryslers, while Chrysler's French range retained the [[Simca]] badge. The French version of the Chrysler Alpine was the '''Simca 1307/1308'''. |
| 120707 | 111230440 | JL-Bot | Addbot | removing non-applicable orphan template | --- \n +++ \n @@ -1,4 +1,3 @@\n -{{Orphan|date=February 2009}} Sister '''Anne Carr''' (1934 - 11 Feb. 2008) was a [[feminist]] [[theologian]] at the [[University of Chicago Divinity School]], where she was the first female permanent member of the faculty. She was a member of the [[Sisters of Charity of the Blessed Virgin Mary]], having taken her final vows in 1966. |
| 135409 | 137743329 | Xenobot | SmackBot | [[User:Xenobot|Bot]]) rm ibid template per discussion ([[User talk:Xeno|report errors?]] | --- \n +++ \n @@ -1,4 +1,3 @@\n -{{Ibid|date=January 2009}} {{Jews and Judaism sidebar}} '''Torah reading''' ({{Hebrew Name 1|קריאת התורה|K'riat HaTorah|"Reading [of] the Torah"}}) is a [[Judaism|Jewish]] [[religion|religious]] [[ritual]] that involves the public reading of a set of passages from a [[Sefer Torah|Torah scroll]]. The term often refers to the entire ceremony of removing the [[Torah scroll]] (or scrolls) from the [[ark (synagogue)|ark]], chanting the appropriate excerpt with special [[cantillation]], and returning the scroll(s) to the ark. It is distinct from academic [[Torah study]] and hatorah.{{clarify}} |
| 136126 | 139143126 | JL-Bot | Addbot | removing non-applicable orphan template | --- \n +++ \n @@ -1,4 +1,3 @@\n -{{Orphan|date=February 2009}} [[Image:Tanenuiarangi.jpg|thumb|right|460px|Tānenuiarangi, the ''wharenui'' at Waipapa marae, University of Auckland, New Zealand. Participants in a weekend-long hui (meeting), having slept overnight in the house, relax in the morning before the proceedings of the day begin.]]A '''whare whakairo''' (literally "carved house"), is described in English as a [[Māori]] 'meeting house'. ==Whare Whakairo== |


Preparation time
30 mins to 1 hour
Cooking time
10 to 30 mins
Serves
Makes 24 large biscuits
Dietary
Bara brith is a traditional Welsh cake laced with gentle spices and dried fruit. The flavours are lovely in these cute biscuits - quite similar to Shrewsbury biscuits.
Equipment: for this recipe you will need a pestle and mortar and a sheep-shaped biscuit cutter.
Preheat the oven to 190C/170C Fan/Gas 5. Line a large baking tray with baking parchment.
Put the teabags in a bowl, cover with boiling water and brew for 5 minutes, until very strong. Remove the teabags and add a splash of orange liqueur. Soak the mixed fruit in the tea for 10-15 minutes, then drain well and set aside.
When cool, chop the dried fruit very finely, add both citrus zests and transfer to a pestle and mortar. Grind the mixture to a paste.
Put the flour, sugar, spices and salt in a large bowl. Rub in the butter using your fingertips. Add 3 teaspoons of the ingredients from the pestle and mortar. Mix and combine with your fingertips until the mixture resembles fine breadcrumbs. Add just enough buttermilk to bring the mixture together as a dough.
Roll out on lightly floured work surface until the dough is about ½cm/¼ in thick. Cut out shapes using a sheep shaped biscuit cutter and transfer to the prepared tray. Prick all over with a fork and bake for 10-12 minutes or until pale golden-brown. Leave to cool slightly, then transfer to a wire rack to cool completely.
For the icing, sift two-thirds of the icing sugar into a bowl and mix in enough water to make a thick but pourable icing. Stir in a little pearlescent food colour. Spoon into a squeezy bottle and cover the bodies of the sheep evenly to make the fleeces. Set aside in a cool place to allow the icing to set.
Sift remaining icing sugar and mix with the almond extract and enough water to make a thick icing. Transfer half the icing to a separate bowl. Add the black food colour to one and the silver to the other. Put into two disposable piping bags with small plain nozzles attached. Use the black icing to decorate the heads and legs of the sheep and the silver to pipe swirls on their fleeces. Allow the icing to set, then serve.


--- layout: default nav: tutorial ---
This example shows how to implement a client side job management UI using intercooler
Note that the "server side" implementation is mocked out using mockjax, so you can see the entire implementation. Click the "Source Code" tab to see the code.


public class TestArmaElementWise
extends java.lang.Object
Arma
.| Modifier and Type | Field and Description |
|---|---|
Mat |
_testElements
A matrix composed of elements to be used for this test.
|
| Constructor and Description |
|---|
TestArmaElementWise() |
| Modifier and Type | Method and Description |
|---|---|
static java.util.Collection<java.lang.Object[]> |
getTestElementWise()
Returns all test matrices used for this test.
|
void |
testAbs()
Tests
Arma.abs(Mat) by comparing it per-element with Math.abs(double) . |
void |
testFloor()
Tests
Arma.abs(Mat) by comparing it per-element with Math.floor(double) . |
void |
testSquare()
Tests
Arma.abs(Mat) by comparing it per-element with Math.pow(double, 2) . |
public Mat _testElements
public static java.util.Collection<java.lang.Object[]> getTestElementWise()
public void testAbs()
Arma.abs(Mat)
by comparing it per-element with Math.abs(double)
.public void testFloor()
Arma.abs(Mat)
by comparing it per-element with Math.floor(double)
.public void testSquare()
Arma.abs(Mat)
by comparing it per-element with Math.pow(double, 2)
.


High Priest Maximilian:
Pleased to meet you. I am High Priest Maximilian of this temple, may Einhasad's light comfort your soul's distress. Why have you come here, to the heart of our faith?
Listen to information about second class transfer.
Sub Class.
Clan.
Alliance.
Quest.


--- layout: post current: post cover: assets/images/posts/2020-07-11_14-40-00/gnuboard_title.png navigation: True title: How to start devspoon-startup-cloud-tizen solution date: 2020-07-11 14:40:00 tags: [Build web service for start-up] class: post-template subclass: 'post' author: bluebamus ---
This page is reserved preview posting. It'll be fill by bluebamus author later.


A good research project relies on strong theoretical foundation. Theories are a summary of prior research findings and present predictions you can test. In accounting and finance, a lot of theories are mathematical theories. Sometimes, research articles will give a good explanation of the arguments in a theory but sometimes you will have to put in some extra effort to understand the theory. In this section, I will give you two techniques that can help to understand the theory better. The first is making the theory simpler and less general and redo the derivations. The second is to simulate data based on the theory and to visualise the theory with plots. Computers are very good at doing calculations. Whenever possible, you should let computers do the work for you. Simulate and visualise is a technique, we will use a lot more in the rest of the notes.
Let us introduce a new theory how the size of the company is related to the compensation of the CEO. In Chapter 2 I presented a basic model where talented CEOs hire more people and attract more capital and thus grow the company. This theory ignored that companies and CEOs can choose to work with each other. In this section, we introduce a new theory about matching firms and CEOs (Edmans and Gabaix 2016; Tervio 2008).
The theory assumes that the increase in \(V\)alue of a firm from time \(0\) to time \(1\) is given by the following equation.
\[\begin{equation} \tag{5.1} V_1(n) - V_0(n) = C V_0(n) T(m) \end{equation}\]
The increase in value depends on the \(T\)alent of the CEO, the inital \(V\)alue of the firm, and a scaling factor \(C\).30 The scaling factor is not crucial to the theory. If performance is measured in millions of dollars or in billions of dollars, the scaling factor will differ but that is not telling us anything about the economic mechanism. \(n\) is the rank of the size of the firm and \(m\) is the rank of the talent of the CEO. \(n = 1\) is the largest firm, \(m = 1\) is the most talented CEO. The question the theory is trying to answer which CEO, \(m\), is going to work for firm, \(n\).
The theory assumes that firms will make a decision about which CEO to hire and CEOs will only accept to work for a firm if they cannot do better in another firm. The firms have to compensate a manager with a \(w\)age and will maximise the residual value of the firm, \(V_1(n) - V_0(n) - w(m)\). The model assumes that the managers will need a compensation above \(w_0\).
Tervio (2008) and Edmans and Gabaix (2016) show that when \(m = n\), no firm or CEO can improve themselves by switching. This means that the most talented CEO works for the largest firm, the second most talented CEO works for the second largest firm, until we reach the least talented CEO and the smallest firm. The intuition is that CEOs have a larger impact in larger firms.31 If a CEO is good at managing people, the impact of the CEO will be better if they are managing more people. Therefor, most value is created when the largest firms are managed by the best CEOs. The difficulty is to determine how much each firm should pay the CEO. I first go over a simplified mathematical model that gets some of the intuition across, then I explain how you can simulate from the more complicated model
Let us assume that there are only three CEOs and only three firms. In equilibrium, we want to make sure that the largest firm (\(n=1\)) cannot do better than hiring the most talented CEO (\(m=1\)). In other words, the performance of the most talented CEO after paying their compensation, should be higher than the residual performance of the other two CEOs.
\[\begin{equation} \tag{5.2} \begin{aligned} CV_0(1) T(1) - w(1) \geq CV_0(1) T(2) - w(2) \\ CV_0(1) T(1) - w(1) \geq CV_0(1) T(3) - w(3) \end{aligned} \end{equation}\]
Next, the second largest firm has to be better off hiring the the second best CEO.
\[\begin{equation} \tag{5.3} CV_0(2) T(2) - w(2) \geq CV_0(2) T(3) - w(3) \end{equation}\]
If we add the first condition of (5.2) to condition (5.3). We can rewrite everything and get the second condition of (5.3).
\[\begin{align*} CV_0(1) T(1) - w(1) + CV_0(2) T(2) - {\color{blue}{w(2)}} \geq CV_0(1) T(2) - {\color{blue}{w(2)}} + CV_0(2) T(3) - w(3) \\ CV_0(1) T(1) - w(1) \geq C(V_0(1) - V_0(2))T(2) + C(V_0(2) - {\color{blue}{V_0(1)}})T(3) + {\color{blue}{CV_0(1)T(3)}}- w(3) \\ CV_0(1) T(1) - w(1) \geq {\color{blue} {C(V_0(1) - V_0(2))(T(2) - T(3))}} + CV_0(1)T(3) - w(3) \end{align*}\]
Because \(V_0(1) > V_0(2)\) and \(T_0(2) > T_0(3)\), we can delete the first term on the right hand side. So, the two inequalities give us the third inequality from the previous slide for free.
Because firms will prefer to paying a lower compensation than more compensation, firms pay their CEO just enough so that smaller firms are not willing to pay the same amount of money to poach the CEO away. For each firm, we have to make sure that the advantage of having a more talented CEO is smaller than the extra wage of hiring the CEO.
\[\begin{align*} CV_0(2) (T(1) - T(2)) \geq w(1) - w(2) \\ CV_0(3) (T(2) - T(3)) \geq w(2) - w(3) \end{align*}\]
Because firm \(n=1\) and \(2\) will set the wage just high enough to make sure that a smaller firm does not poach their CEO, they will set the compensation \(w(1)\) and \(w(2)\) just high enough but not higher. We can simplify the resulting conditions than to equalities.
\[\begin{equation} \tag{5.4} \begin{aligned} CV_0(2) (T(1) - T(2)) + w(2) = w(1) \\ CV_0(3) (T(2) - T(3)) + w(3) = w(2) \end{aligned} \end{equation}\]
The deriviations are a bit tedious and I do not necessarily want you to be able to do this entirely on your own. However, it shows that with some small calculations and with some economic intuition about what we want to calculate, we can again derive the relation between firm value and CEO compensation.
If the literature points you to a mathematical model and you want to understand it better. Breaking it down to a simpler model with only 2 or 3 firms can be very illuminating. I hope the three firm model helped you to get some of the intuition behind the model without resorting to too complicated maths. It is relatively easy to see that we can write the inequalities in (5.4) in a more general form.
\[\begin{equation} \tag{5.5} \begin{aligned} CV_0(n + 1) (T(n) - T(n+1)) + w(n + 1) = w(n) \\ \forall n = 1,.., N-1 \end{aligned} \end{equation}\]
The basic idea is that a larger firm will pay more for a CEO than a smaller firm. If the larger firm wants to make sure that a the more talented CEO works for them, they need to pay the CEO a high enough wage. The difference between the two wages for a talented CEO will be the surplus that the CEO would create in the smaller firm. So the smaller firm will not be willing to poach the more talented CEO because the costs (higher wage) would outweight the benefit (higher surplus).
The original papers go further with the derivations
(Edmans and Gabaix 2016; Tervio 2008). However,
this is an algorithm we can give to R
when we add some further
assumptions. So instead of going over all the math, we are going
to simulate wages and firm values based on the algorithm.
The original theoretical papers also need the extra
assumptions. The goal of the simulation is to show that
sometimes you do not need all the fancy maths when you can write a
computer program to do the work for you. We will use similar
assumptions as the original papers but implement them in an R
program.
First, let us load the tidyverse
package.
Next, we simulate data for obs=500
observations. size_rate
is a parameter that controls the size of firms. A value of 1
means that firms have constant returns to size, the same
assumption as in Chapter 2. talent_rate
is something similar for the Talent of the CEOs. A larger number
for both rate parameters implies that differences between sizes
or CEOs become larger at the top. C
is the \(C\) constant in the
model. scale
is an additional parameter that helps me scale the
size of the firms so that I get similar numbers as the real data.32 This is not necessarily a fudge. The theory does not say
anything about whether we should measure firm size in USD, in AUD
or in CNY. So the scale we use is arbitrary. w0
is the base
wage for the least talented CEO.
obs <- 500
size_rate <- 1; talent_rate <- 2/3;
C <- 10; scale <- 600; w0 <- 0;
n <- c(1:obs)
size <- scale * n ^ (-size_rate)
talent <- - 1/talent_rate * n ^ (talent_rate)
n
is an R
vector of length obs
(i.e. 500) with values from
1 to obs
. So it is the rank in size and talent for each firm
and CEO. You can see what n
look like by just typing n
and
enter in the R console.
Size and talent follow an exponential distribution which has some theoretical motivation given in Tervio (2008) and Edmans and Gabaix (2016). If that interests you, please go have a look but it goes beyond what we need today. What we do is give a value for the size of each firm from 1 to 500 and for the talent of each CEO from 1 to 500.
We can also calculate the wage of each CEO-firm combination from
equation (5.5). We start by creating
a wage vector with 500 NA
values.33 The rep
function creates a vector of obs
repititions of
NA
At the last (obs = 500
) position of the vector, we set the wage
equal to w0
for the least talented CEO and the smallest firm.
Than for each firm (we go from i = 499
to i = 1
), we set the
wage as the wage of the smaller firm (i + 1
) and subtract the
surplus our CEO would generate in the smaller firm over what
their CEO is now generating.34 Again scale
and C
just scale
some of the values so that they are easier to interpret. These
parameters are less important for the economic intuition.
wage <- rep(NA, obs)
wage[obs] <- w0
for (i in (obs - 1):1){
wage[i] <- wage[i + 1] + 1/scale * C * size[i + 1] *
(talent[i] - talent[i + 1])
}
Now we can put all our variables in a dataset. The tidyverse
calls datasets tibbles
and they are the main object that
tidyverse
functions work on.
With the data we simulated, we can visualise the theory and see whether our theory matches our intuition. Visulising a theory is one way to understand the assumptions and to check whether it matches the data. Even if you do not have your data yet, it will give you an idea of what the data should look like.
Figure 5.1 shows the relation between the
simulated CEO wage and simulated firm size. It’s not
perfect but the plot does follow a similar non-linear pattern as
what we found in Chapter 2 (see Figure
5.2).35 qplot
is a funtion to make quick plots and it is part of the
ggplot
package which is part of the tidyverse
.
Figure 5.1: Relation between simulated CEO wage and firm size.
Figure 5.2: (ref:us-comp-value-plot)
To better understand the assumptions in the theory, we can also plot how talent and size change as a function of the rank of respectively the CEO and the firm. I glossed over the details before but that does not mean that we cannot check whether those functions make sense.
Figure 5.3 shows us which assumptions were
necessary for the theory to work. We see that the difference in
talent at the top of the distribution (n = 1
) is not that
large, the difference in firm sizes is much more pronounced and
is driving the difference in wages according to this theory.
Figure 5.3: Relation between talent and rank of CEO (A) and between size and rank of the firm (B).
p_talent <- qplot(data = simulated_data, y = talent, x = n)
p_size <- qplot(data = simulated_data, y = size, x = n)
cowplot::plot_grid(p_talent, p_size, ncol = 1, labels = "AUTO")
In the code, I use a function from the cowplot
package to plot
different plots (p_talent
and p_size
) in 1 column. The
automatic labels will add the A and B labels for the plots.
R
One of the most valuable aspects of R
is that you can write
new functions. Functions allow you to create your own verbs to
apply to objects. In the previous section, we simulated data with
a number of parameters in the theory set at a fixed value. If we
want to compare the sensitivity of the theory to changes in the
parameters, we want to simulate new datasets with different
parameter values. A function to simulate data is what we need.
Functions are created with the function
function. In between
brackets, you define the parameter you want to use in your
functions and their default values. In between the curly braces
{}
you tell R
what it should do with those parameters.
Ideally, you should not rely on any parameter or data that is not
defined in your function. R
has some liberal defaults which
might give you unexpected results if you do that. You can rely on
external functions like I do to create the tibble
.
I do nothing in the function that I have not done before. The
only addition is that at the end, the function return
s the
simulated data. You can then use the function to create a new
simulated dataset.36 I call the function create_fake_data
because I want to
emphasise that there is nothing special about simulated data.
Some people prefer more dignified names. If you prefer that, you
can use any other name for the function. Just make sure that it
is clear what your function is doing.
create_fake_data <- function(obs = 500, size_rate = 1,
talent_rate = 1,
w0 = .001, C = 1){
scale = 600
n = 1:obs
size = scale * n ^ (-size_rate)
talent = -1/talent_rate * n ^ talent_rate
wage = rep(NA, obs)
wage[obs] = w0
for (i in (obs - 1):1){
wage[i] = wage[i + 1] + 1/scale * C * size[i + 1] *
(talent[i] - talent[i + 1])
}
fake_data = dplyr::tibble(n = n, size = size, talent = talent,
wage = wage)
return(fake_data)
}
fake_data <- create_fake_data(talent_rate = 2/3, C = 0.01)
We can create new datasets where the talent_rate is increased to 1 (high talent) and the effect of CEOs on the surplus is increased to .015 instead of .10. I choose .10 in this simulation instead of 10 so that all values can be interpreted in billion USD to mimic the real data. Again, that is a rather arbitrary scaling issue.
data1 <- create_fake_data(obs = 500, talent_rate = 2/3, C = .01) %>%
mutate(talent_rate = "low talent", C = "low effect")
data2 <- create_fake_data(obs = 500, talent_rate = 1, C = .01) %>%
mutate(talent_rate = "high talent", C = "low effect")
data3 <- create_fake_data(obs = 500, talent_rate = 2/3, C = .015) %>%
mutate(talent_rate = "low talent", C = "high effect")
data4 <- create_fake_data(obs = 500, talent_rate = 1, C = .015) %>%
mutate(talent_rate = "high talent", C = "high effect")
data_exp <- bind_rows(data1, data2, data3, data4)
data_exp
## # A tibble: 2,000 x 6
## n size talent wage talent_rate C
## <int> <dbl> <dbl> <dbl> <chr> <chr>
## 1 1 600 -1.5 0.0241 low talent low effect
## 2 2 300 -2.38 0.0197 low talent low effect
## 3 3 200 -3.12 0.0172 low talent low effect
## 4 4 150 -3.78 0.0156 low talent low effect
## 5 5 120 -4.39 0.0143 low talent low effect
## 6 6 100 -4.95 0.0134 low talent low effect
## 7 7 85.7 -5.49 0.0126 low talent low effect
## 8 8 75 -6.00 0.0120 low talent low effect
## 9 9 66.7 -6.49 0.0114 low talent low effect
## 10 10 60 -6.96 0.0110 low talent low effect
## # … with 1,990 more rows
The bind_rows
function allows you to combine the four datasets
in one big dataset data_exp
. This will help us to plot the data
in Figure 5.4 with the more complicated but
also more flexible ggplot
function. In the function, we first
define the data we want to use. Within the aes()
specification,
we define the variables that are going to be plotted. We tell
ggplot that we want the data to be plotted as the following
geometric elements a point and a line.37 Obviously, you do not
need both. This is just to show you what you can do. I make the line
colour grey. The facet_grid
creates different subplots depending on
whether an observation has a different value for the talent_rate
variable and the C
variable.
Figure 5.4: (ref:sim-combined)
plot_exp <- ggplot(data_exp, aes(x = size, y = wage)) +
geom_point() +
geom_line(colour = "gray") +
facet_grid(talent_rate ~ C)
print(plot_exp)
It looks like our theory is much more sensitive to changes in the talent rate than in changes to the scaling effect.
In these notes, I will come back to the idea of simulation over and over. For three big reasons:
Simulating data from a theory and visualising the theory helps you sharpen your intuition for your theory and for which values are reasonable and which ones are not. If you know upfront which values are not reasonable that will help you to interpret your findings in your statistical analysis. If you estimate parameters in your model that are too big or too small, it might be an indication that something went wrong with your analysis.
When you simulate data you can simulate variables that you cannot observe (e.g. CEO talent). Sometimes these variables need to be included in your statistical analysis to get unbiased parameter estimates. With simulated datasets, you can run the analysis with and without the unobservable variables to investigate the impact of including and excluding the variable.
If there are different statistical tests you can use to investigate your research question, you do not want to test the different tests on your real data. If you pick the statistical test that gives you the “right” answer, you are likely to delude yourself. The right way to compare different statistical tests is to see whether they can estimate parameters in simulated data where you know what the right value of the parameter is.
In short, the advantage of being able to generete simulated data is that it sharpens your understanding of your theory and of your statistical test.
Edmans, Alex, and Xavier Gabaix. 2016. “Executive Compensation: A Modern Primer.” Journal of Economic Literature 54 (4): 1232–87.
Tervio, Marko. 2008. “The Difference That CEOs Make: An Assignment Model Approach.” American Economic Review 98 (3): 642–68. https://doi.org/10.1257/aer.98.3.642.
Page built: 2021-02-24


| Network Working Group | R. Quattlebaum |
| Internet-Draft | j. woodyatt, Ed. |
| Intended status: Standards Track | Nest Labs, Inc. |
| Expires: May 5, 2018 | November 1, 2017 |
Spinel Thread Support
draft-rquattle-spinel-thread-caf7411-dirty
This document specifies a specialization of the Spinel protocol to support Thread(R) networks.
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Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet-Drafts is at http://datatracker.ietf.org/drafts/current/.
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This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (http://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License.
TODO: Writeme.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in [RFC2119].
The layout diagrams in this document that show encodings of data in byte sequences use the convention described in Guide for Internet Standards Writers [RFC2360] section 3.1, Packet Diagrams. Specifically, bits in bytes are indexed from most significant to least significant, and the least significant bit in a field representing an integer value more than one bit in length is the least significant bit of a the signed or unsigned number, as the case requires.
Thread(R) NCPs which adhere to this specification MUST also satisfy the following requirements:
All serious implementations of an NCP SHOULD also support the network save feature (See draft-rquattle-spinel-network-save).
The Thread(R) technology defines the following capabilities:
Properties for Thread(R) are allocated out of Spinel's Tech property section (see (draft-rquattle-spinel-core#property-sections)).
The IPv6 address of the leader. (Note: May change to long and short address of leader)
The long address and short address of the parent of this node.
Table containing the long and short addresses of all the children of this node.
The router-id of the current leader.
The leader weight of the current leader.
The leader weight for this node.
The local network data.
The local stable network data.
Data per item is:
Data per item is:
Set to true before changing local net data. Set to false when finished. This allows changes to be aggregated into single events.
This property contains the value of the mode TLV for this node. The meaning of the bits in this bitfield are defined by section 4.5.2 of the Thread(R) specification.
Used when operating in the Child role.
Allows you to get or set the Thread(R) NETWORK_ID_TIMEOUT constant, as defined by the Thread(R) specification.
Note that some implementations may not support CMD_GET_VALUE router ids, but may support CMD_REMOVE_VALUE when the node is a leader.
Allow the HOST to directly observe all IPv6 packets received by the NCP, including ones sent to the RLOC16 address.
Default value is false.
Allow the HOST to indicate whether or not the router role is enabled. If current role is a router, setting this property to false starts a re-attach process as an end-device.
Specifies the self imposed random delay in seconds a REED waits before registering to become an Active Router.
Specifies the preferred Router Id. Upon becoming a router/leader the node attempts to use this Router Id. If the preferred Router Id is not set or if it can not be used, a randomly generated router id is picked. This property can be set only when the device role is either detached or disabled.
Data per item is:
Specifies the maximum number of children currently allowed. This parameter can only be set when Thread(R) protocol operation has been stopped.
The leader network data.
The stable leader network data.
Data per item is:
Passess Pre-Shared Key for the Device to the NCP in the commissioning process. When the Extended address is ommited all Devices which provided a valid PSKd are allowed to join the Thread(R) Network.
Set to true to enable the native commissioner. It is mandatory before adding the joiner to the network.
Set to true to enable the TMF proxy.
Data per item is:
| Octects: | 2 | n | 2 | 2 |
|---|---|---|---|---|
| Fields: | Length | CoAP | locator | port |
This property allows the host to send and receive TMF messages from the NCP's RLOC address and support Thread-specific border router functions.
This property specifies the value used in Thread(R) MLE Discovery Request TLV during discovery scan operation. Default value is false.
This property is used to enable/disable EUI64 filtering during discovery scan operation. Default value is false.
This property specifies the PANID used for filtering during discovery scan operation. Default value is 0xffff (broadcast PANID) which disables PANID filtering.
This property can be used to set the steering data for MLE Discovery Response messages.
EDITOR: Insert verbiage here conforming to RFC Editor guidelines.
EDITOR: insert verbiage here: registry creation forms, et cetera.
| [RFC2119] | Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997. |
| [RFC2360] | Scott, G., "Guide for Internet Standards Writers", BCP 22, RFC 2360, DOI 10.17487/RFC2360, June 1998. |
Thread is a registered trademark of The Thread Group, Inc.
Special thanks to Nick Banks, Jonathan Hui, Abtin Keshavarzian, Yakun Xu, Piotr Szkotak, Arjuna Sivasithambaresan and Martin Turon for their substantial contributions and feedback related to this document.
This document was prepared using mmark by (Miek Gieben) and xml2rfc (version 2).


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Resizes the object to the width and height of its container.
resize()
Returns number of milliseconds it took to resize.
Description
The .resize() method is often called internally, but it is not limited to internal use only. You
do not need to call this method when window is resized by the user, because the library already has an event listener for it. But
you need to call it when you change the size of the container.


Convience mapping of render_widget for hepExplorer.
hepExplorer(df = safetyData::adam_adlbc, mapping = NULL, ...)
| df | data frame containing lab data used to render for hepExplorer. Default is safetyData::adam_adlbc. |
|---|---|
| mapping | named list with the current data mappings. See details for default mapping. |
| ... | additional options to be added to mapping. Will overwrite mapping. |
The data and mapping should match the specs described in the hepExplorer javascript library. Items passed in ... are added to mapping, and then the list is converted to json via jsonlite::toJSON(mapping, auto_unbox=TRUE, null="null")
.
The default mapping shown below is designed to work with data in the CDISC ADaM format (like safetydata::adam_adlbc
).
mapping <- list( measure_col = "PARAM", measure_values = list( ALT = "Alanine Aminotransferase (U/L)", AST = "Aspartate Aminotransferase (U/L)", TB = "Bilirubin (umol/L)", ALP = "Alkaline Phosphatase (U/L)" ), id_col = "USUBJID", value_col = "AVAL", normal_col_low = "A1LO", normal_col_high = "A1HI", studyday_col = "ADY", visit_col = "VISIT", visitn_col = "VISITNUM" )
Parameters that are not included in the default mapping can be accessed via ...; Key options and defaults for safetyData::adam_adlbc shown below:
filters
: list of columns to be included as data filters (e.g. `filters=c("SEX","AGEGR1")``)
group_cols
: list of columns used to define grouping and set point color (e.g. `filters=c("SEX","AGEGR1")``)
x_options
and y_options
- specify which labs can be used for x and y axis dropdowns. By default, all options are included on x-axis, but only Bilirubin is shown on y-axis. To allow an interactive y-axis, use y_options="all"
.
baseline
- flag defining the baseline visit for each participant. baseline
must be provided to enable the mDish view on the hep-explorer chart. Define as a list with value_col
and values
(e.g. baseline=list(value_col="ABLFL",values="Y")
)
title
and warningText
- Strings used to define the header text shown above the filters.
For more options see the full specs in the javascript library.
if (FALSE) { # Render widget with defaults hepExplorer() # Add age group to default hepExplorer(group_cols=c("SEX","AGEGR1")) # Enable interactive y-axis hepExplorer(y_options='all') # Use custom mapping for SDTM data hepExplorer( df=safetyData::sdtm_lb, measure_col = "LBTEST", measure_values = list( ALT = "Alanine Aminotransferase", AST = "Aspartate Aminotransferase", TB = "Bilirubin", ALP = "Alkaline Phosphatase" ), id_col = "USUBJID", value_col = "LBSTRESN", normal_col_low = "LBORNRLO", normal_col_high = "LBORNRHI", studyday_col = "LBDY", visit_col = "VISIT", visitn_col = "VISITNUM" ) }


硬件¶
USB Interface¶
The DT5495 is equipped with a USB2.0 interface. The USB physical layer is managed by a high-speed transceiver controlled by the Main FPGA.
Ethernet Interface¶
In addition to the USB, the DT5495 provides a 10/100T Ethernet interface controlled by the Main FPGA.
Main FPGA¶
The MFPGA (Altera Cyclone V E) manages the Ethernet and USB interfaces and the connection with the UFPGA through a proprietary 16-bit 50 MHz local bus. The MFPGA has a dedicated external flash memory for configuration purposes.
It also pilots the flash memories dedicated to loading the firmware on the UFPGA and on the GDG.
User FPGA¶
The User FPGA (Altera Cyclone V GX) manages the I/O peripherals (A/D, B/E, G, C/F ports) and communicates with the GDG. A dedicated external flash memory can store a set of firmware images to be loaded on the User FPGA. A dedicated JTAG connector allows to program the UFPGA “on-the-fly” for fast firmware prototyping and debugging.
Gate and Delay Generator¶
The DT5495 hosts a Gate and Delay Generator able to provide up to 32 gated and delayed signals(“delayed signals”) triggered by 32 inputs (“start signals”). The gate width and delay value are user programmable. The GDG is an external component implemented in a Xilinx Spartan-6 FPGA. It is connected through a serial bus (SPI) to the User FPGA for gate and delay register programming (refer to Sect. Gate and Delay Controller for detailed information).
The GDG configuration is stored in a dedicated flash memory. The GDG firmware cannot be modified by the user.
Clock Distribution¶
Each FPGA receives the same 50-MHz system clock generated by a common on-board oscillator.


Ploror688.github.io
Online math calculators and solvers to help calculate and solve problems are included Sir , please solve following program ,which I am unable to solve.The program is : Write a program in java to accept two date in dd/mm/yyyy format and display. Fulfillment by Amazon (FBA) is a service we offer sellers that lets them store their products in Amazon's fulfillment centers, and we directly pack, ship, and provide.
Sql server - SQL Functions - factorial - Stack Overflow. Zero factorial or 0! (video) Permutations Khan Academy. Java coding interview questions are mostly some programming exercises which is asked to be solved in java programming language in limited time during. Factorial and counting seat arrangements (video). I am a beginner in SQL Functions. What is the best way to create a function for factorial in SQL Server- Бытовые счетчики учета расхода холодной и горячей воды. Газовые котлы и колонки различных.
- If you've been paying particularly close attention to our use of the factorial operator in the videos on permutations and combinations Ask Math Questions you want answered Share your favorite Solution to a math problem Share a Story about your experiences with Math which could inspire. Sal explains a tricky factorial problem about counting seat arrangements. College Algebra Tutor, Help and Practice Online StudyPug.
Новости Счетчики газа, Счетчики воды - ООО "Факториал. Top 10 Java Programming Coding Interview Questions Answers. Amazon.com: Basic Statistics: Tales of Distributions.
Chapter 1 The Certified Quality Engineer Exam 3 THE CERTIFIED QUALITY ENGINEER EXAM 1.0 THE EXAM The first ASQ certified quality engineer (CQE) examination. What is Factorial Design? - Definition Example - Video. Your complete College Algebra help that gets you better marks! Learn with step-by-step video help, instant College Algebra practice and a personal study. Which of these two methods of getting a factorial (loop vs recursive) is more efficient/faster? and if that one can be improved, how so? Language: Java private static. The main causes of chronic neck pain disorders. It is, at least was until very recently, generally accepted that posture will affect both general function
The simplest studies involve one independent and one dependent variable. But what happens when a researcher wants to study ISC Important Programs – Guide For School. Cracking the HackerRank Test: 100% score How to Calculate the Probability of Combinations - Video. To calculate the probability of a combination, you will need to consider the number of favorable outcomes over the number of total outcomes. It’s the company who decides on the duration, the questions, the unit tests and the input (stdin or not). The correcter can see how long you spent. THE CERTIFIED QUALITY ENGINEER. Online Math Calculators and Solvers - analyzemath.com. Math Questions Math Answers - Solving Math Problems. The multifactorial causes and solutions to chronic. No. Date Asked Question # 1050 :: 12/24/13: Suppose a sample of farmers is to be selected for estimating the cost of cultivation of maize per hectare.
UsableStats: View Answered Stats Questions. Recursion vs For loops - Factorials, Java - Stack Overflow.


{% extends "layout.html" %} {% block body %} {% raw %}
{{ meta.unique_visitors.length }} unique IP-address {{ meta.unique_visitors.length > 1 ? "es have" : "has" }} visited this page. The table below shows every visit that fetched the encrypted message. This means they knew the unique identifier, but it does not necessarily mean they had the password to decrypt the message.


A JavaScript project containing standalone javascript widgets based on Bootstrap, jQuery UI and other third-party widgets, styled with LESS.
In the RichWidgets project we are curating a set of OSS responsive javascript widgets. Existing OSS widgets are leveraged wherever possible, either consuming them directly as widgets, or by composing them into new widgets. These widgets are then made available through this project with a consistent API and Look and Feel - making it easier for you to add rich functionality to your web apps (or web frameworks!).
The current RichWidget architecture is centered around the jQuery UI widget factory with LESS & Bootstrap for styling and theming. Candidate widgets that do not use the widget factory will be wrapped with the widget factory to provide a consistent widget API.
Preview the widgets and see some sample use cases on our demo site: http://richwidgets.io.
To consume Richwidgets in your application/framework, you will currently have to build the widget assets.
(Note: In the time-frame of our 0.1 release we will provide RichWidgets as a Bower package.)
RichWidgets are jQuery plugins (built using the jQuery UI Widget Factory). Executing a plugin to include the widget's functionality is as simple as:
$('#myDiv').widgetName(options);
For a list of available widgets and their associated options check out the API Docs.
A widget's public methods are available through the jQuery UI Widget factory API. Accessing a public API method in this way looks like:
$('#myDiv').widgetName('methodName', param1, parma2, ....);
Accessing private methods is discouraged, but can be done so as follows:
var widget = $('myDiv').data('widgetName');
widget._privateMethod(params);
Acessing private methods can be handy for testing purposes, or for extending a widget. However the private method API has not contract of stability. If you find yourself needing to access a private method consider filing an issue requesting that the functionality be exposed via a public method.
The options used to initialize a widget via the options
parameter can be mutated using the option
public API method of the jQuery UI widget factory:
$('#myDiv').widgetName('option', 'optionName', param1, parma2, ....);
All widgets support the destroy
method. Invoking this method will remove the widget and restore your DOM to it's initial state:
$('#myDiv').widgetName('destroy');
Along with cloning the project you will need to install the following tools:
Once installed, go to the project root and run the following commands:
npm install
grunt bower
grunt
This should drop a bunch of logs in your shell and use up some bandwith. Once it's completed, you will have built the
richwidgets dist
folder which contains both demos
of the widgets, as well as all the compiled widgets' assets
.
Now that widgets and demo have been built with the grunt
command, you can view the widgets by either:
dist/demo
foldergrunt connect:demo:keepalive
and navigate your browser to http://localhost:9000/Have a problem with one of our widgets? Is there a particular widget you'd like to see incorporated in the project? File an issue in our issue tracker.
RichWidgets is very much an open source project, your contributions are valued! Learn how to get involved with the project in our CONTRIBUTING.md guide.
Read about our approach to testing in the TESTS.md guide.


NEW
CENTURY
Wellness Group
Portal Home
Medical Records
Appointments
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My Profile
Services
Sign Out
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Patient Number:
00002
First name:
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Last name:
Social Security #:
Insurance:
Date of Birth:
Gender:
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Address:
Phone Number:
Email:


Back
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CryMa
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