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This assumes you have a separate file that contains a key value and latitude/longitude
and the source file has a key value. For example, you might have a list of precincts, e.g.:
precinct,latitude,longitude
4171107648,40.197055,-105.100351
2171207308,39.996986,-105.084938
2173307410,40.039912,-105.081393
The source file is a list of voters, e.g.:
name,precinct
John Doe,4171107648
Jane Doe,4171107648
Jim Smith,2171207308
To add the lat/lon:
seesv:seesv -geocodejoin precinct precincts.csv precinct latitude longitude


For New York's 112th Assembly District
Issues
The Environment
"If the environment were a bank it would have been saved already"
-Bernie Sanders
-
Fight Climate Change
- Subsidize renewable sources of energy
- Implement a carbon tax
- Incentivize citizen adoption of green technologies
-
Protect our Environment
- Fund cleanups of our rivers, forests, and farmlands
- Increase fines for pollution
Healthcare
"The test of a civilization is the way that it cares for its helpless members."
-Pearl Buck
-
Everyone Has a Right to Healthcare
-
Addiction is a Disease, Not a Crime
- Fight the addiction epidemic with rehabilitation, not jail
- Protect a Woman's Right to Choose
Technology
"ISPs should not be able to engage in any sort of fuckery that limits or manipulates the choices you make online."
-John Oliver
-
Restore Net Neutrality
- A free and open Internet has been essential to the technological explosion
- Ensure small businesses can compete with massive corporations
-
Expand Rural Broadband Access
- A fast and reliable Internet connection is essential for modern life
- Allow municipal broadband networks
- Incentivize mobile carriers for better service
Education
"Education is the most powerful investment in our future."
-UNICEF
-
Support our Public Schools
- Provide greater funding for academics
- Provide greater funding for sports, the arts, and other activities, all of which have been proven to promote academic growth
-
Expand the Excelsior Scholarship
- Remove the arbitrary income cap
- Stop restricting post-graduation movement
- Include room and board
Balancing Firearm Safety and Rights
"Everyone is in favor of gun control. The debate is to what extent."
-Unknown
- Protect the Rights of Hunters and Farmers to Feed their Families and Guard their Livelihood
- Ensure People can Adequately Defend Themselves and their Families
-
Reform the NY SAFE ACT
- Require background checks for all firearm transfers, public and private
- Ensure only people who are mentally fit can have access to firearms
- Introduce a mandatory 3-day waiting period on all firearm and ammunition sales
Political Reform
"The present system has clearly broken down. The results produced are not fair to any party, nor to any section of the community."
-Winston Churchill
-
Fight Corruption in Albany
- Limit campaign contributions from lobbyists and Super PACS
- 5 year moratorium on lobbying after leaving office
-
Ensure Fair Elections
- Provide public funding to all candidates
- Open party primaries to all registered voters
- Expand early and absentee voting
- Allow same-day registration
- Implement ranked choice voting
PAID FOR BY ZACHARY CLARK


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[1]
16:9, Low Light; Focusing problems, lack of manual override
[2]
sony quality - the best for the price; still expensive dont like the LCD viewfinder


TI-73 Link Protocol Guide v1.0 - Manual Variable Transfers (to do...)
The TI-73 uses manual transfer protocols when it is in Link mode. On the sending unit a user must choose the variables to be sent, and the receiving calculator must be put into a special reception mode.
The protocol for all manual variable transfers is shown below. For information on packet formats, click here.
A computer can be either the sending unit or the receiving unit.
| Step | Direction | Packet | ||
|---|---|---|---|---|
|
1 |
|
|
|
VAR |
|
2 |
|
ACK | ||
|
3 |
|
CTS or SKIP/EXIT. Do step 4 (send the ACK). Then if skip, skip to step 7, or if exit then stop. | ||
|
4 |
|
ACK | ||
|
5 |
|
DATA | ||
|
6 |
|
ACK | ||
|
7 |
|
EOT or another VAR. If var, go to step 2. | ||
|
8 |
|
ACK |
The data packet contains the variable data. For information on variable formats, click here.
Example (PC sending variables to TI):
| PC: | 73 06 0D 00 09 00 00 41 00 00 00 00 00 00 00 00 00 4A 00 | Variable Header: Real Number "A" |
| TI: | 73 56 00 00 | Acknowledgement of header |
| 73 09 00 00 | Clear to send variable | |
| PC: | 73 56 00 00 | Acknowledgement of CTS |
| 73 15 09 00 00 80 12 56 34 78 85 25 55 93 02 | Variable data: "1.2563478852555" | |
| TI: | 73 56 00 00 | Acknowledgement of data |
| PC: | 73 06 0D 00 09 00 00 42 00 00 00 00 00 00 00 00 00 4B 00 | Variable Header: Real Number "B" |
| TI: | 73 56 00 00 | Acknowledgement of header |
| 73 36 01 00 02 02 00 | Skip variable | |
| PC: | 73 56 00 00 | Acknowledgement of variable skip |
| 73 92 00 00 | No more variables | |
| TI: | 73 56 00 00 | Acknowledgement of EOT |


#include <ParticleUniverseSystemManager.h>
typedef Ogre::map<Ogre::String, ParticleEmitterFactory*>::type ParticleUniverse::ParticleSystemManager::EmitterFactoryMap [protected] |
typedef Ogre::map<Ogre::String, ParticleAffectorFactory*>::type ParticleUniverse::ParticleSystemManager::AffectorFactoryMap [protected] |
typedef Ogre::map<Ogre::String, ParticleObserverFactory*>::type ParticleUniverse::ParticleSystemManager::ObserverFactoryMap [protected] |
typedef Ogre::map<Ogre::String, ParticleEventHandlerFactory*>::type ParticleUniverse::ParticleSystemManager::EventHandlerFactoryMap [protected] |
typedef Ogre::map<Ogre::String, ParticleRendererFactory*>::type ParticleUniverse::ParticleSystemManager::RendererFactoryMap [protected] |
typedef Ogre::map<Ogre::String, ExternFactory*>::type ParticleUniverse::ParticleSystemManager::ExternFactoryMap [protected] |
typedef Ogre::map<Ogre::String, ParticleBehaviourFactory*>::type ParticleUniverse::ParticleSystemManager::BehaviourFactoryMap [protected] |
typedef Ogre::map<Ogre::String, IAlias*>::type ParticleUniverse::ParticleSystemManager::AliasMap [protected] |
typedef Ogre::map<Ogre::String, ParticleSystem*>::type ParticleUniverse::ParticleSystemManager::ParticleSystemTemplateMap [protected] |
typedef Ogre::map<Ogre::String, ParticleSystem*>::type ParticleUniverse::ParticleSystemManager::ParticleSystemMap [protected] |
| ParticleUniverse::ParticleSystemManager::ParticleSystemManager | ( | void | ) |
| ParticleUniverse::ParticleSystemManager::~ParticleSystemManager | ( | void | ) |
Clear the aliasmap and destroy the objects in it
| ParticleSystem * ParticleUniverse::ParticleSystemManager::_createSystemImpl | ( | const Ogre::String & | name | ) | [protected] |
Implementation of creating a particle system used by the MovableObject factory.
| ParticleSystem * ParticleUniverse::ParticleSystemManager::_createSystemImpl | ( | const Ogre::String & | name, | |
| const Ogre::String & | templateName | |||
| ) | [protected] |
Implementation of creating a particle system used by the MovableObject factory.
| void ParticleUniverse::ParticleSystemManager::_destroySystemImpl | ( | ParticleSystem * | particleSystem | ) | [protected] |
Delete a particle system.
| void ParticleUniverse::ParticleSystemManager::removeAndDestroyDanglingSceneNodes | ( | Ogre::SceneNode * | scenNode | ) |
Removes and deletes any SceneNode was created by a Particle System
| void ParticleUniverse::ParticleSystemManager::destroyAllParticleSystemTemplates | ( | void | ) |
Remove all registered templates
| BoxSet * ParticleUniverse::ParticleSystemManager::createBoxSet | ( | const Ogre::String & | name, | |
| Ogre::SceneManager * | sceneManager, | |||
| const Ogre::uint | poolSize = 20 | |||
| ) |
Create a BoxSet. This is similar to a BillboardSet, instead it uses boxes.
| void ParticleUniverse::ParticleSystemManager::destroyBoxSet | ( | BoxSet * | boxSet, | |
| Ogre::SceneManager * | sceneManager | |||
| ) |
Destroy the BoxSet.
| SphereSet * ParticleUniverse::ParticleSystemManager::createSphereSet | ( | const Ogre::String & | name, | |
| Ogre::SceneManager * | sceneManager, | |||
| const Ogre::uint | poolSize = 20 | |||
| ) |
Create a SphereSet. This is similar to a BillboardSet, instead it uses spheres.
| void ParticleUniverse::ParticleSystemManager::destroySphereSet | ( | SphereSet * | sphereSet, | |
| Ogre::SceneManager * | sceneManager | |||
| ) |
Destroy the SphereSet.
| void ParticleUniverse::ParticleSystemManager::registerAttachable | ( | Attachable * | attachable, | |
| Ogre::SceneManager * | sceneManager | |||
| ) |
Registers a previously created Attachable. This is needed, because the scenemanager has to know.
| void ParticleUniverse::ParticleSystemManager::unregisterAttachable | ( | Attachable * | attachable, | |
| Ogre::SceneManager * | sceneManager | |||
| ) |
Unregisters a created Attachable.
| void ParticleUniverse::ParticleSystemManager::addEmitterFactory | ( | ParticleEmitterFactory * | factory | ) |
Adds a new 'factory' object for emitters to the list of available emitter types.
| factory | Pointer to a ParticleEmitterFactory subclass created by the plugin or application code. |
| ParticleEmitterFactory * ParticleUniverse::ParticleSystemManager::getEmitterFactory | ( | const Ogre::String & | emitterType | ) |
Searches a ParticleEmitterFactory.
| void ParticleUniverse::ParticleSystemManager::removeEmitterFactory | ( | const Ogre::String & | emitterType | ) |
Remove an ParticleEmitterFactory, but doesn't delete it. Search the factory by its type.
| void ParticleUniverse::ParticleSystemManager::destroyEmitterFactory | ( | const Ogre::String & | emitterType | ) |
Delete an ParticleEmitterFactory. Search the factory by its type.
| ParticleEmitter * ParticleUniverse::ParticleSystemManager::createEmitter | ( | const Ogre::String & | emitterType | ) |
Method for creating a new emitter from a factory.
| emitterType | String name of the emitter type to be created. A factory of this type must have been registered. |
| ParticleEmitter * ParticleUniverse::ParticleSystemManager::cloneEmitter | ( | ParticleEmitter * | emitter | ) |
Clone a ParticleEmitter.
| void ParticleUniverse::ParticleSystemManager::destroyEmitter | ( | ParticleEmitter * | emitter | ) |
Delete a ParticleEmitter.
| void ParticleUniverse::ParticleSystemManager::addAffectorFactory | ( | ParticleAffectorFactory * | factory | ) |
Add a ParticleAffectorFactory to this ParticleSystemManager.
| factory | Pointer to a ParticleAffectorFactory subclass created by the plugin or application code. |
| ParticleAffectorFactory * ParticleUniverse::ParticleSystemManager::getAffectorFactory | ( | const Ogre::String & | affectorType | ) |
Searches a ParticleAffectorFactory.
| void ParticleUniverse::ParticleSystemManager::removeAffectorFactory | ( | const Ogre::String & | affectorType | ) |
Remove an ParticleAffectorFactory, but doesn't delete it. Search the factory by its type.
| void ParticleUniverse::ParticleSystemManager::destroyAffectorFactory | ( | const Ogre::String & | affectorType | ) |
Delete a ParticleAffectorFactory. Search by its type.
| ParticleAffector * ParticleUniverse::ParticleSystemManager::createAffector | ( | const Ogre::String & | affectorType | ) |
Create a ParticleAffector given a type.
| ParticleAffector * ParticleUniverse::ParticleSystemManager::cloneAffector | ( | ParticleAffector * | affector | ) |
Clone a ParticleAffector.
| void ParticleUniverse::ParticleSystemManager::destroyAffector | ( | ParticleAffector * | affector | ) |
Delete a ParticleAffector.
| ParticleTechnique * ParticleUniverse::ParticleSystemManager::createTechnique | ( | void | ) |
Create a ParticleTechnique.
| ParticleTechnique * ParticleUniverse::ParticleSystemManager::cloneTechnique | ( | ParticleTechnique * | technique | ) |
Clone a ParticleTechnique.
| void ParticleUniverse::ParticleSystemManager::destroyTechnique | ( | ParticleTechnique * | technique | ) |
Delete a ParticleTechnique.
| void ParticleUniverse::ParticleSystemManager::addObserverFactory | ( | ParticleObserverFactory * | factory | ) |
Add a ParticleObserverFactory to this ParticleSystemManager.
| factory | Pointer to a ParticleObserverFactory subclass created by the plugin or application code. |
| ParticleObserverFactory * ParticleUniverse::ParticleSystemManager::getObserverFactory | ( | const Ogre::String & | observerType | ) |
Searches a ParticleObserverFactory.
| void ParticleUniverse::ParticleSystemManager::removeObserverFactory | ( | const Ogre::String & | affectorType | ) |
Remove an ParticleObserverFactory, but doesn't delete it. Search the factory by its type.
| void ParticleUniverse::ParticleSystemManager::destroyObserverFactory | ( | const Ogre::String & | observerType | ) |
Delete a ParticleObserverFactory given its type.
| ParticleObserver * ParticleUniverse::ParticleSystemManager::createObserver | ( | const Ogre::String & | observerType | ) |
Create a ParticleObserver given a certain type.
| ParticleObserver * ParticleUniverse::ParticleSystemManager::cloneObserver | ( | ParticleObserver * | observer | ) |
Clone a ParticleObserver.
| void ParticleUniverse::ParticleSystemManager::destroyObserver | ( | ParticleObserver * | observer | ) |
Destroye a ParticleObserver.
| void ParticleUniverse::ParticleSystemManager::addEventHandlerFactory | ( | ParticleEventHandlerFactory * | factory | ) |
Add a ParticleEventHandlerFactory to this ParticleSystemManager.
| factory | Pointer to a ParticleEventHandlerFactory subclass created by the plugin or application code. |
| ParticleEventHandlerFactory * ParticleUniverse::ParticleSystemManager::getEventHandlerFactory | ( | const Ogre::String & | eventHandlerType | ) |
Searches a ParticleEventHandlerFactory.
| void ParticleUniverse::ParticleSystemManager::removeEventHandlerFactory | ( | const Ogre::String & | affectorType | ) |
Remove an ParticleEventHandlerFactory, but doesn't delete it. Search the factory by its type.
| void ParticleUniverse::ParticleSystemManager::destroyEventHandlerFactory | ( | const Ogre::String & | eventHandlerType | ) |
Delete a ParticleEventHandlerFactory given a certain type.
| ParticleEventHandler * ParticleUniverse::ParticleSystemManager::createEventHandler | ( | const Ogre::String & | eventHandlerType | ) |
Create a ParticleEventHandler given a certain type.
| ParticleEventHandler * ParticleUniverse::ParticleSystemManager::cloneEventHandler | ( | ParticleEventHandler * | eventHandler | ) |
Clone a ParticleEventHandler.
| void ParticleUniverse::ParticleSystemManager::destroyEventHandler | ( | ParticleEventHandler * | eventHandler | ) |
Delete a ParticleEventHandler.
| void ParticleUniverse::ParticleSystemManager::addRendererFactory | ( | ParticleRendererFactory * | factory | ) |
Add a ParticleRendererFactory to this ParticleSystemManager.
| ParticleRendererFactory * ParticleUniverse::ParticleSystemManager::getRendererFactory | ( | const Ogre::String & | rendererType | ) |
Searches a ParticleRendererFactory.
| void ParticleUniverse::ParticleSystemManager::removeRendererFactory | ( | const Ogre::String & | affectorType | ) |
Remove an ParticleRendererFactory, but doesn't delete it. Search the factory by its type.
| void ParticleUniverse::ParticleSystemManager::destroyRendererFactory | ( | const Ogre::String & | rendererType | ) |
Delete a ParticleRendererFactory given a certain type.
| ParticleRenderer * ParticleUniverse::ParticleSystemManager::createRenderer | ( | const Ogre::String & | rendererType | ) |
Create a ParticleRenderer.
| ParticleRenderer * ParticleUniverse::ParticleSystemManager::cloneRenderer | ( | ParticleRenderer * | renderer | ) |
Clone a ParticleRenderer.
| void ParticleUniverse::ParticleSystemManager::destroyRenderer | ( | ParticleRenderer * | renderer | ) |
Delete a ParticleRenderer.
| void ParticleUniverse::ParticleSystemManager::addExternFactory | ( | ExternFactory * | factory | ) |
Add a ExternFactory to this ParticleSystemManager.
| ExternFactory * ParticleUniverse::ParticleSystemManager::getExternFactory | ( | const Ogre::String & | externType | ) |
Searches an ExternFactory.
| void ParticleUniverse::ParticleSystemManager::removeExternFactory | ( | const Ogre::String & | affectorType | ) |
Remove an ParticleExternFactory, but doesn't delete it. Search the factory by its type.
| void ParticleUniverse::ParticleSystemManager::destroyExternFactory | ( | const Ogre::String & | externType | ) |
Delete a ExternFactory given a certain type.
| Extern * ParticleUniverse::ParticleSystemManager::createExtern | ( | const Ogre::String & | externType | ) |
Create an Extern object.
Clone an Extern object.
| void ParticleUniverse::ParticleSystemManager::destroyExtern | ( | Extern * | externObject | ) |
Delete an Extern object.
| void ParticleUniverse::ParticleSystemManager::addBehaviourFactory | ( | ParticleBehaviourFactory * | factory | ) |
Add a BehaviourFactory to this ParticleSystemManager.
| ParticleBehaviourFactory * ParticleUniverse::ParticleSystemManager::getBehaviourFactory | ( | const Ogre::String & | behaviourType | ) |
Searches a ParticleBehaviourFactory.
| void ParticleUniverse::ParticleSystemManager::removeBehaviourFactory | ( | const Ogre::String & | affectorType | ) |
Remove an ParticleBehaviourFactory, but doesn't delete it. Search the factory by its type.
| void ParticleUniverse::ParticleSystemManager::destroyBehaviourFactory | ( | const Ogre::String & | behaviourType | ) |
Delete a BehaviourFactory given a certain type.
| ParticleBehaviour * ParticleUniverse::ParticleSystemManager::createBehaviour | ( | const Ogre::String & | behaviourType | ) |
Create a Behaviour object.
| ParticleBehaviour * ParticleUniverse::ParticleSystemManager::cloneBehaviour | ( | ParticleBehaviour * | behaviour | ) |
Clone a Behaviour object.
| void ParticleUniverse::ParticleSystemManager::destroyBehaviour | ( | ParticleBehaviour * | behaviour | ) |
Delete a Behaviour object.
| ParticleSystem * ParticleUniverse::ParticleSystemManager::createParticleSystemTemplate | ( | const Ogre::String & | name, | |
| const Ogre::String & | resourceGroupName | |||
| ) |
Create a ParticleSystemTemplate.
| void ParticleUniverse::ParticleSystemManager::replaceParticleSystemTemplate | ( | const Ogre::String & | name, | |
| ParticleSystem * | system | |||
| ) |
| const Ogre::String & ParticleUniverse::ParticleSystemManager::getLastCreatedParticleSystemTemplateName | ( | void | ) |
Returns the name of the last created template. Because the templates are often created by means of a script, it is not straightforward to get the template name. It is embedded in the script. Only afterwards it is possible to get the name of the created template, by using this function.
| void ParticleUniverse::ParticleSystemManager::addParticleSystemTemplate | ( | const Ogre::String & | name, | |
| ParticleSystem * | systemTemplate | |||
| ) |
Add a ParticleSystem template to this ParticleSystemManager.
| ParticleSystem * ParticleUniverse::ParticleSystemManager::getParticleSystemTemplate | ( | const Ogre::String & | templateName | ) |
Retrieves a particle system template.
| void ParticleUniverse::ParticleSystemManager::destroyParticleSystemTemplate | ( | const Ogre::String & | templateName | ) |
Remove a particle system template and delete it.
| void ParticleUniverse::ParticleSystemManager::particleSystemTemplateNames | ( | Ogre::vector< Ogre::String >::type & | v | ) |
Fill a list of template names in the vector.
| ParticleSystem * ParticleUniverse::ParticleSystemManager::createParticleSystem | ( | const Ogre::String & | name, | |
| const Ogre::String & | templateName, | |||
| Ogre::SceneManager * | sceneManager | |||
| ) |
Creates a ParticleSystem, using a template as a blueprint.
| ParticleSystem * ParticleUniverse::ParticleSystemManager::createParticleSystem | ( | const Ogre::String & | name, | |
| Ogre::SceneManager * | sceneManager | |||
| ) |
Creates a default ParticleSystem.
| ParticleSystem * ParticleUniverse::ParticleSystemManager::getParticleSystem | ( | const Ogre::String & | name | ) |
Get a ParticleSystem by name.
| void ParticleUniverse::ParticleSystemManager::destroyParticleSystem | ( | ParticleSystem * | particleSystem, | |
| Ogre::SceneManager * | sceneManager | |||
| ) |
This is the function that must be used by the client application that wants to delete a ParticleSystem.
| void ParticleUniverse::ParticleSystemManager::destroyParticleSystem | ( | const Ogre::String & | particleSystemName, | |
| Ogre::SceneManager * | sceneManager | |||
| ) |
Delete a ParticleSystem.
| void ParticleUniverse::ParticleSystemManager::destroyAllParticleSystems | ( | Ogre::SceneManager * | sceneManager | ) |
Destroy all registered particle systems.
| void ParticleUniverse::ParticleSystemManager::addAlias | ( | IAlias * | alias | ) |
Add an alias to this ParticleSystemManager.
| IAlias * ParticleUniverse::ParticleSystemManager::getAlias | ( | const Ogre::String & | aliasName | ) |
Returns an alias.
| void ParticleUniverse::ParticleSystemManager::destroyAlias | ( | IAlias * | alias | ) |
Delete an alias from the ParticleSystemManager.
| void ParticleUniverse::ParticleSystemManager::destroyAllAliasses | ( | void | ) |
Delete all aliasses.
| ParticleSystemManager::AliasMap * ParticleUniverse::ParticleSystemManager::_getAliasMap | ( | void | ) |
Returns the alias map. Not for general purposes; only use it if you really need it.
| ParticleSystemManager & ParticleUniverse::ParticleSystemManager::getSingleton | ( | void | ) | [static] |
Override standard Singleton retrieval.
| ParticleSystemManager * ParticleUniverse::ParticleSystemManager::getSingletonPtr | ( | void | ) | [static] |
Override standard Singleton retrieval.
| void ParticleUniverse::ParticleSystemManager::writeScript | ( | ParticleSystem * | particleSystem, | |
| const Ogre::String & | fileName | |||
| ) |
Writes a ParticleSystem to a file in the script format.
| const Ogre::String & ParticleUniverse::ParticleSystemManager::writeScript | ( | ParticleSystem * | particleSystem | ) |
Writes a ParticleSystem to string in the script format.
| ParticleScriptSerializer * ParticleUniverse::ParticleSystemManager::getParticleScriptSerializer | ( | void | ) | const |
Returns the ParticleScriptSerializer.
| virtual void ParticleUniverse::ParticleSystemManager::write | ( | ParticleScriptSerializer * | serializer, | |
| const IElement * | element | |||
| ) | [inline, virtual] |
| CameraDependency * ParticleUniverse::ParticleSystemManager::createCameraDependency | ( | void | ) |
Returns Camera Dependency.
| void ParticleUniverse::ParticleSystemManager::createDepthMap | ( | Ogre::Camera * | camera, | |
| Ogre::SceneManager * | sceneManager | |||
| ) |
Create a depth map (texture) of all visible scene objects, except overlays and particle systems that render soft particles.
| void ParticleUniverse::ParticleSystemManager::destroyDepthMap | ( | void | ) |
Perform cleanup activities.
| bool ParticleUniverse::ParticleSystemManager::notifyDepthMapNeeded | ( | Ogre::Camera * | camera, | |
| Ogre::SceneManager * | sceneManager | |||
| ) |
Notify the Particle System Manager that is depthmap is needed.
Use the first camera that is passed. Although in theory, other camera's may be passed, they are ignored. The depth map is created for this camera viewport only.
| void ParticleUniverse::ParticleSystemManager::registerSoftParticlesRenderer | ( | ParticleRenderer * | renderer | ) |
Register the renderer, because it renders soft particles.
| void ParticleUniverse::ParticleSystemManager::unregisterSoftParticlesRenderer | ( | ParticleRenderer * | renderer | ) |
Unregister the renderer, because it will not use soft particles anymore.
| Ogre::Real ParticleUniverse::ParticleSystemManager::getDepthScale | ( | void | ) | const |
Used to scale the values of the generated depth map
| void ParticleUniverse::ParticleSystemManager::setDepthScale | ( | Ogre::Real | depthScale | ) |
| const Ogre::String & ParticleUniverse::ParticleSystemManager::getDepthTextureName | ( | void | ) |
Returns the name of the depth texture.
| void ParticleUniverse::ParticleSystemManager::setExternDepthTextureName | ( | const Ogre::String & | depthTextureName | ) |
| void ParticleUniverse::ParticleSystemManager::resetExternDepthTextureName | ( | void | ) |
Reset the external depth texture name, so it is not used anymore.
| DynamicAttribute * ParticleUniverse::ParticleSystemManager::createDynamicAttribute | ( | DynamicAttribute::DynamicAttributeType | type | ) |
Create a DynamicAttribute.
| bool ParticleUniverse::ParticleSystemManager::isAutoLoadMaterials | ( | void | ) | const |
Determines whether materials must be loaded dynamically or not. If the function returns 'false', the materials must be loaded manually.
| void ParticleUniverse::ParticleSystemManager::setAutoLoadMaterials | ( | bool | autoLoadMaterials | ) |
Determines whether materials must be loaded dynamically or not.
friend class ParticleSystemFactory [friend] |
ParticleScriptSerializer* ParticleUniverse::ParticleSystemManager::mParticleScriptSerializer [protected] |
ParticleTechniqueWriter ParticleUniverse::ParticleSystemManager::mParticleTechniqueWriter [protected] |
ParticleSystemTemplateMap ParticleUniverse::ParticleSystemManager::mParticleSystemTemplates [protected] |
ParticleSystemManagerWriter ParticleUniverse::ParticleSystemManager::mParticleSystemManagerWriter [protected] |
bool ParticleUniverse::ParticleSystemManager::mAutoLoadMaterials [protected] |
Ogre::String ParticleUniverse::ParticleSystemManager::mDepthTextureName [protected] |
Ogre::String ParticleUniverse::ParticleSystemManager::mDepthMaterialName [protected] |
Ogre::String ParticleUniverse::ParticleSystemManager::mDepthVertexName [protected] |
Ogre::String ParticleUniverse::ParticleSystemManager::mDepthFragmentName [protected] |
Ogre::RenderTexture* ParticleUniverse::ParticleSystemManager::mDepthMap [protected] |
Ogre::Technique* ParticleUniverse::ParticleSystemManager::mDepthTechnique [protected] |
Ogre::Pass* ParticleUniverse::ParticleSystemManager::mDepthPass [protected] |
bool ParticleUniverse::ParticleSystemManager::mDepthMapExtern [protected] |
Ogre::OverlayContainer* ParticleUniverse::ParticleSystemManager::mDebugPanel [protected] |
Ogre::Overlay* ParticleUniverse::ParticleSystemManager::mDebugOverlay [protected] |
Ogre::Real ParticleUniverse::ParticleSystemManager::mDepthScale [protected] |
Ogre::String ParticleUniverse::ParticleSystemManager::mLastCreatedParticleSystemTemplateName [protected] |
BuiltinScriptTranslatorManager* ParticleUniverse::ParticleSystemManager::mBuiltinScriptTranslatorManager [protected] |


Software Engineer Return Path / Context.IO
There are a lot of misconceptions of what the command line can do.
PC users, I will cover you in a bit!
The application / GUI
Program processes the commands
There are different types of shells!
Developed by Stephen Bourne in 1977!
Not the first shell ever, but the one that most other shells are "based" on.
bash = Bourne Again Shell (1989)
Get it? Because the inventor of sh is Stephen Bourne?
We'll come back to those!
$ pwd
Print Working Directory
Don't be afraid to use it! Use it all the time!
$ ls
LiSt files in a directory
$ ls -a
LiSt files and hidden 👻 files in a directory
$ pwd
$ ls
$ cd
REPEAT
$mkdir
MaKe a DIRectory
$ touch
Create a file
$ mkdir test && cd test && pwd && touch test.txt && ls
defaults write com.apple.finder CreateDesktop -bool false;killall Finder
defaults write com.apple.finder CreateDesktop -bool true;killall Finder
Use aliases for that!
Like custom "autocorrect" in iOS
alias hideDesktop="defaults write com.apple.finder AppleShowAllFiles NO; killall Finder /System/Library/CoreServices/Finder.app"
Set this in a .bash_profile
http://brew.sh
Install and manage tons of stuff!
$ brew cask install google-chrome
Create a simple bash script that installs applications
setup.sh
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
brew cask install google-chrome
brew cask install spotify
brew cask install iterm2
brew cask install sublime-text
cecy.co/cli-for-n00bs


---
layout: post
title: Why I really dislike Microsoft
tags:
- Personal Opinion
status: publish
type: post
published: true
meta: {}
---
Andrew suggests that people like me who dislike Microsoft are doing it for reasons of juvenile jealousy. Speaking for myself that's actually completely wrong, I dislike Microsoft because:
- Their attempt to force Windows adoption by releasing compilers with no MS DOS support long before Windows became a standard PC platform and there were still many, many, DOS users.
- The way they treated Stac Electronics in the '90s
- The poor quality of MS operating systems, IMHO it was not until Windows 2000 that Windows became of just about usable quality
- The fact that so much data is locked up in secret MS Office file formats
- and so on...
Once upon Bill Gates was someone I admired, but then I grew up. BTW, I am neither a graduate or young (unfortunately)


Open Dialog
Repaint Dialog
Destroy Dialog
Open Dialog 2
×
Dialog header
One fine body…
The input will be empty after repaint.
×
Dialog 2 header
One fine body…
Type some words


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|
|
1997 |
| |
|
2017 |
|
|
? |
Whether? When? How?
|
- There is no world champion for mathematics - it is a collaborative endevaour.
- As is the quest to automate mathematics.
- Indeed, much of the progress is a by-product of people working towards other
goals.
- Converely, progress towards automating mathematics should lead to other
benefits.
- Especially helping mathematicians, students of mathematics and users of
mathematics.
- And making trains safer.
A proof: Sum of Angles in a Triangle
- Consider a triangle $ABC$.
- Draw a line through $A$ parallel to $BC$.
- Use equality of interior opposite angles.
- Use angles on a line add up to 180 degrees.
Another proof: infinitely many primes
- For any $n$, we show that there exists a prime $p$ such that $p > n$.
- Consider the number $n!+1$, where $n! = 1 \times 2 \times \dots \times n$.
- Let $p$ be the smallest prime factor of $n!+1$.
- If $p \leq n$ then $p | n!$ so $p \nmid n!+1$.
- Hence $p$ is a prime greater than $n$.
What went into the proofs
-
Auxiliary Constructions:
- the parallel line
- the term $n! + 1$.
-
Theorems/Definitions to use.
-
Deductions and Computations.
-
The theorems and definitions needed both knowledge of the precise statement
and the ability to recognize relevance.
Two systems for Thinking
Our thinking is based on two systems:
- System 1: Intuitive, Associative, Automatic.
- handles language, vision, other sensorimotor tasks.
- fast, but error prone.
- System 2: Analytical, Logical, Controlled.
- handles reasoning, planning, problem solving.
- slow, but accurate.
Moravec Paradox
- System 1 needs far more raw computational power than System 2.
- Our brain (mostly System 1) performs about 100 trillion operations per second (about 100
Teraflops) according
to Moravec.
- Moravec predicted (in 1997) that computers will reach parity with this in 2040.
- Further, our brain's storage is about 100 terabytes.
- Another way to view this is that our accumulated knowledge, memory, experience,
learning etc is
stored in 100 trillion weights.
Why aren’t we supercomputers?
- A neuron completes between 1 and 200 operations in a second (laptop ≈ 3
billion).
- The 100 trillion operations per second the brain performs are massively
parallel.
- Logic/Algorithms often cannot be parallelized, e.g. long division.
- The clock speeds of computers have not increased much in the last 15 years,
speedups are by
parallelization,
especially with Graphics Processing Units (GPUs).
Computers today
- The NVIDIA A100 GPU delivers 312 Teraflops of performance.
- Renting this on Google cloud with this for an year (40 hours a week)
costs about the salary of a mathematician in India.
- On the other hand, the RAM on such a GPU is 40 GB; though we can scale this by
parallelization.
- The “accumulated knowledge” in the largest AI system (Google’s PaLM) is about
540 billion
weights, much less than our brain.
Ingredients for doing mathematics
- Accumulation of knowledge:
precise (System 2);
associations (System 1).
- Learning to learn (System 1/System 2).
- Computations and Deductions (System 2).
- Recognition of relevance (System 1).
- Constructions based on Analogy (System 1).
- Originality: going beyond the known (can be from System 1/System 2).
A concrete prediction
-
Open bet by Christian Szegedy, Google Research.
-
By the year 2029:
"A diverse set of 100 graduate text books are automatically formalized/verified in a popular
proof
assistant (eg Lean). 10% of problems from a preselected 100 open human conjectures is proved
completely autonomously."
Some computer-assisted proofs
- Four-colour problem: Any map can be coloured with at most
$4$
colours.
- Kepler conjecture: The most efficient way to pack spheres
is
the hexagonal close packing.
- Smale conjecture for hyperbolic $3$-manifolds.
- These proofs involved essentially a huge number of calculations of a single
type.
Robbins conjecture
- Robbins conjecture was a conjectural characterization of
Boolean algebras in
terms of
associativity and commutativity of $\vee$ and the Robbins equation
$\neg(\neg(a\vee b)\vee \neg(a \vee \neg b)) = a$.
- This was conjectured in the 1930s, and finally proved in 1996 using the
automated theorem prover EQP.
- This was a non-trivial proof by deduction, but where we knew the axioms with
which we start.
Boolean Pythagorean triples problem
- Problem: Is it possible to
colour
the
positive integers either red or blue, so that if three integers $a$, $b$, $c$,
satisfy $a^{2}+b^{2}=c^{2}$, they are not all the same colour?
- This was asked in the 1980s by Ronald Graham who offered a $100 prize.
- This was solved by Marijn Heule, Oliver Kullmann and Victor W. Marek in May
2016 using a SAT Solver,
a powerful deduction engine for finite problems.
Interactive Theorem Provers
- Interactive Theorem Provers are software systems where proofs are
obtained by human-machine
collaboration.
- The computer both finds (parts of) proofs and verifies
correctness.
- With interactive theorem provers, mathematics on a large scale can be digitied
(formalized) in a modular fashion, with the automation greatly facilitating this.
- Indeed the Lean Mathematical Library has most undergraduate
mathematics in it, and some advanced topics.
- As more mathematics is in such libraries, it becomes easier and more enjoyable to formalize even
more.
- Automation is also improving, which also facilitates formalization.
- Lean 4 is a programming language seamlessly integrated with an interactive
theorem prover, which (as we will see) helps with formalization and makes it useful in new ways.
- The main current application of Lean is to ensure a super-human
level of correctness.
- One hopes this is without super-human
effort.
Who guards the guards?
-
A computer verified proof is only as trustworthy as the system that verified the proof.
-
Following the de Bruijn principle, proofs are verified by a small
trusted
kernel, which can be thoroughly checked.
-
For example, the Lean theorem prover has four (small) proof checkers written in
four languages.
-
In a specific case, one only has to check that the definitions and the
statement are correct, which is typically not hard especially if they use a
library.
The Liquid Tensor experiment
- In December 2020, Peter Scholze challenged the formalization community to
computer verify a foundational theorem of Condensed
Mathematics.
— with this theorem, the hope that the condensed formalism can be fruitfully applied to real
functional analysis stands or falls. I think the theorem is of utmost foundational importance,
so being 99.9% sure is not enough.
- This was taken up by the Lean community led by Johann Commelin, with active
collaboration from Scholze.
-
In about six months a major step, Theorem 9.4 of Scholze's notes, was proved.
-
Scholze said this was the part where he was worried about, and that he was now satisfied
that
the correctness of his result was checked.
-
About an year later, the origin goal, Theorem 9.1 was proved in Lean.
- Scholze said he understood aspects of his proof after the formalization which
he hadn't earlier.
Sums of reciprocals
-
In December 2021, Thomas Bloom proved that any set of natural numbers of positive upper
density
contains a finite subset whose reciprocals add up to $1$, answering a question of Erdős and
Graham.
-
While his paper was still under review, Thomas Bloom
learnt Lean, and,
along with Bhavik Mehta, formalized this theorem in Lean.
Kaplansky's Unit Conjecture
-
About an year ago, Giles Gardam disproved the long-standing Kaplansky's Unit
Conjecture, which asserted that group rings of torsion-free groups over fields
do
not have non-trivial units.
-
Gardam used a SAT solver to find such non-trivial units for a specific group and the field
$\mathbb{F}_2$.
-
Anand Rao Tadipatri and I formalized the proof in Lean 4 using its integration of programs and
proofs.
- As these examples show, the effort for formalization of mathematics is rapidly decreasing.
- Mathematics in Lean is much better structured than the human literature.
- Lean also has powerful automation, which helps formalization.
- All this paves the way (one hopes) for facilitating mathematical discovery.
Formal methods
Mathematical proofs elsewhere
Formal methods
- We specify (describe) software, hardware etc. in precise
mathematical terms.
- We give mathematical proofs of correct behavior, which are
computer
verified.
- This gives a much greater certainty of correctness.
- However, proofs are much harder than tests.
- Formal proofs use interactive theorem provers; with better provers we can
prove more often.
Do we need completely correct always?
| |
Pentium FDIV Bug |
Fixing an error is very costly |
| |
Therac 25 radiation machine |
Safety critical |
| |
WhatsApp Pegasus attack |
A bug is a vulnerability |
Some users of formal methods
| |
Intel Chips |
Fixing an error is very costly |
| |
Paris driverless metro |
Safety critical |
| |
Scala dotty compiler |
A bug is a vulnerability |
Steps towards
Artificial Intelligence
The common sense barrier
- We know a lot of things, on which we base common sense (according to
Marvin Minsky).
- Further, these pieces of knowledge are organized in a way that they can be
combined.
- Expert Systems have a lot of knowledge encoded as structured
data, functions,
algorithms.
- These are purely System 2, i.e., logical/algorithmic/rule-based systems.
- Besides knowledge and fast algorithms, we also need enough raw power.
Kasparov vs Deep Blue
- In 1997, a computer Deep Blue defeated the Chess world
champion Gary Kasparov.
- Deep Blue was an Expert system, based on elaborate rules for judging
strengths of positions.
- However, Deep Blue was very limited in certain
capabilities.
- Kasparov was far better at evaluating strengths of positions than Deep Blue,
and in knowing what moves are worth considering (System 1).
- Deep Blue compensated for this by being able to consider far more move
sequences.
The tacit knowledge barrier
- Tacit knowledge is the kind of knowledge that is difficult to
transfer to another person by
means of writing it down or verbalizing it.
- Examples: riding a bicycle, speaking a language; evaluating positions in chess.
- Tacit knowledge is much more important in the chinese game Go compared to Chess.
- Further, in Go the number of legal moves is much larger so brute force is less
effective.
- As a result, DeepBlue style expert systems are hopeless at Go.
Deep learning
- Computers can acquire knowledge by
learning.
- The idea of computers learning goes back at least to Turing, or even regression
in statistics.
-
What was needed was hardware and a system that could learn efficiently: Deep neural
networks.
- In a deep neural network the input is transformed into the output through a
series of
layers.
- Each layer is typically a linear transformation, depending on weights, followed
by a simple
non-linear function.
- Starting about 2012, deep neural networks performed very well in image
processing tasks, far
above rule-based systems.
-
It turns out that as the image is processed by successive layers, the raw data gets modified
to capture abstraction/meaning.
Limitations (≈ 10 years ago)
- Needed labelled data for training.
- Narrow: training specific to the task.
- Had difficulty with long range dependencies, in particular language.
- Hard to scale up, parallelize.
- Could not go beyond training data.
Representation Learning
-
Word2Vec considered the problem of predicting a word given its neighbours.
-
This was trained on solutions of this problem that involve mapping words into space, and predicting
from neighbours using the points.
-
Only the mapping of words into space was retained, so geometry captures
associations.
-
Another lesson is that it is useful to solve synthetic problems: play, solving exercises.
Transfer learning
- A set of common layers was used for many tasks.
- Some of the tasks could be synthetic (exercises).
- This resulted in networks trained for one task needing much less training for a
related task.
AlphaGo vs Lee Sedol
- In March 2016, a Go playing system AlphaGo defeated 18-time world
champion Lee Sedol.
- In January 2017, AlphaGo defeated the world number one Ke Jie
comprehensively.
- The strategy of AlphaGo was based on
- A policy function - what moves to consider.
- A value function - how good a position is.
- Given these, Go was played using a search through possible moves suggested by
policy.
AlphaGo and Learning
- The policy and value
functions of AlphaGo are deep neural networks
that were trained.
- The policy network was initially trained by learning to predict the next
move from games of expert players (behaviour cloning).
- The value network was trained by AlphaGo playing against
itself: Reinforcement Learning.
- AlphaGo considered fewer sequences of moves than Deep Blue.
- AlphaGo came up with unexpected moves.
AlphaGo Zero and Alpha Zero
- AlphaGo was succeeded (and defeated) by AlphaGo Zero, which learnt
purely by
self play.
- Its successor, AlphaZero, could master a variety of similar games
starting with
just the rules.
- AlphaZero took just 4 hours to become the strongest chess player on the
planet
(beating a traditional chess program, Stockfish).
- AlphaZero “had a dynamic, open style”, and “prioritizes
piece
activity over material, preferring positions that looked risky and aggressive.”
“Attention is all you need”
- The meaning of a word depends on the context, i.e., other words surrounding it.
- In the transformer architecture, this is captured by learning
to which other
words to pay attention.
- This allowed parallelization and captured long-range dependencies.
- Transformers are used in AlphaFold 2 which predicts protein
structures on par with experiments.
Foundation Models
- With the transformer architecture training could be scaled up enormously.
- It was found that models kept improving as the number of parameters grew (when trained on huge datasets).
- Huge models such as GPT-3 and PaLM have been trained on essentially all of the
internet, either by trying to predict the next word or by trying to fill in masked out parts.
- Applications: Dall-E 2, ChatGPT etc., have become sensations.
-
We have computer systems highly capable in:
|
Intuition |
Precise Reasoning |
| Scalability |
ChatGPT |
Lean Prover |
| Originality |
AlphaZero |
SAT Solvers |
- Yet they can get much better.
-
More importantly, must make them work together,
- while doing mathematics and while learning,
- with each other and with us.


Find the Doctor that will help you!
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| Screen Size |
65" |
65" |
65" |
| Display type |
LCD |
LCD |
LCD |
| Display resolution (H x V, pixels) |
3840x2160 |
3840x2160 |
3840x2160 |
| 3D capability |
No |
No |
No |
| Backlight type |
Direct LED |
Edge LED |
Edge LED |
| HDR (High Dynamic Range) compatibility |
Yes (HDR10, HLG, Dolby Vision) |
Yes (HDR10, HLG) |
Yes |
| Picture processor |
Picture Processor X1™ Ultimate |
4K HDR Processor X1 |
4K HDR Processor X1 |
| Clarity enhancement |
- 4K X-Reality™ PRO
- Dual database processing
- Object-based Super Resolution
|
4K X-Reality™ PRO |
4K X-Reality™ PRO |
| Contrast enhancement |
- Live Colour™ Technology
- Precision Colour Mapping
- Super bit mapping™ 4K HDR
|
- Live Colour™ Technology
- Super bit mapping™ 4K HDR
- TRILUMINOS™ Display
|
- Live Colour™ Technology
- Super bit mapping™ 4K HDR
- TRILUMINOS™ Display
|
| Motion enhancer (Native Hz) |
X-Motion Clarity |
Motionflow™ XR 960 (Native 120 Hz) |
Motionflow™ XR 960 (Native 120 Hz) |
| Speaker Type |
Bass Reflex Speaker |
Bass Reflex Speaker |
Bass Reflex Speaker |
| Sound processing |
No |
ClearAudio+ |
ClearAudio+ |
| Operating System |
Android™ |
Android™ |
Android™ |
| Transmission type |
Electronic 6-speed automatic transmission |
Electronic 6-speed automatic transmission |
Electronic 6-speed automatic transmission |
| HDMI inputs total |
4 (1 side/3 bottom) |
4 (1 side/3 bottom) |
4 (1 side/3 bottom) |
| USB ports |
2 (side), 1 (bottom) |
3 (Side) |
3 (Side) |


module type SYMBOLS =sig
..end
The signature for the set of symbols appearing in a grammar.
type
t
The set of symbols.
val empty : t
The empty set.
val def_term : string -> t -> t
Add a terminal to the symbols.
val def_non_term : string -> t -> t
Add a non terminal to the symbols
val def_fun : string -> string list -> t -> t
Add a functional non terminal along with its parameters to the symbols.
val terminals : t -> string list
Get the list of terminals.
val non_terminals : t -> string list
Get the list of non terminals.
val functionals : t -> string list
Get the list of functional non terminals.
val defined : t -> string list
Get the list of "defined" symbols, that is both non terminals and functional non terminals.
val is_term : string -> t -> bool
Test if the given symbol is a terminal.
val is_non_term : string -> t -> bool
Test if the given symbol is a non terminal.
val is_defined : string -> t -> string list option
Test if the given symbol is "defined" and returns its parameters.
See Common.SYMBOLS.defined
.


9
You walk west admiring the enviroment. A light breeze rustles through the patches of grass. A few short trees dot the landscape, but as you move farther west, they become more numerous and larger. Eventually they shade your path and traveling slows down. Beams of light break through the canopy and dance along the ground.
There are other changes among the trees as well. They begin to gain an almost liquid appearance. You move closer to one of the trees and gently lay your hand on the surface expecting it part like water. Instead you feel just a solid mass. You press harder against the hard surface. It is as solid as rock.
Alfred does a deeper scan of the tree you are pressing against, “It appears to not be completely organic. I am picking up small traces of nanobots intertwined within the fibers of the tree.” Data from Alfred’s scans feed to your visor. You continue to walk as Alfred continues to scan the trees and relaying the information.Then you stop.
Before you a clearing opens up with a lone tree in the center. It isn’t natural though. It stretches twice as high as any other tree, is five times the diameter of the other trees, and it is shifting. It twists and curls around in the strangest patterns, never resting. The surface of the tree is like oil upon water as it reflects a rainbow of colors.
As you watch a door forms with stairs traveling upward before it vanishes again. Another then appears a short distance from the first with stairs traveling downward before vanishing. The doors continue to appear in an alternating fashion.
This is the source of the power I was reading Dave
You move closer to admire the living structure. Then you her rustling from behind you in the woods. Life. You then turn towards the structure again. You can't inspect all of it right now. You have to choose what to inspect first.


Cited from Coffee: A drink for the devil
Legend has it that Kaldi, a lonely goat herder in ninth-century Ethiopia, discovered the energising and invigorating effects of coffee when he saw his goats getting excited after eating some berries from a tree. Kaldi told the abbot of the local monastery about this and the abbot came up with the idea of drying and boiling the berries to make a beverage.
An alternative story says coffee was first discovered by a sheik named Omar, while in exile from Mocha (Arabia Felix in present-day Yemen). Somewhat hungry, Omar one day chewed some berries only to find them bitter. He tried boiling them, resulting in a fragrant brown liquid which gave him unnatural and exceptional energy and allowed him to stay awake for days on end.
Coffee, like alcohol, has a long history of prohibition, attracting fear and suspicion and religious disquiet and hypocrisy. Had the zealots (of all religions) got their way then there would not be very many coffee houses open today.
Initially, coffee was met with suspicion and religious prejudice in the Middle East and Turkey. Rumor has it, it was a mysterious, exotic and intoxicating liquor. To Catholics it was the ‘bitter invention of Satan’, carrying the whiff of Islam, and it seemed suspiciously like a substitute for wine as used in the Eucharist; in any event, it was outlawed.
Coffee was significantly drunk in the ubiquitous public coffee houses which sprang up in villages, towns and cities across the Middle East and east Africa. Coffee drinking and conversation were complemented by all manner of entertainment: musical performances, dancing, games of chess and, most crucially, gossiping, arguing and discussing the breaking news of the day (or night). These coffee houses soon became known as ‘schools of the wise’, the place you went to if you wanted to know what was going on in your world.
In the 1674 The Women’s Petition Against Coffee, wives argued that coffee “made men as unfruitful as the deserts whence that unhappy berry is said to be brought, so much so that the offspring of our mighty ancestors would dwindle into a succession of apes and pigmies”. Men's Answer to The Women’s Petition Against Coffee protested that it was “base adulterate wine” and “muddy ale” that made men impotent. Coffee, on the other hand, was the Viagra of the day, making “the erection more vigorous, the ejaculation more full, add[ing] a spiritualascendency to the sperm.”


Example Code: Arrays. Clusters and Data Bundling¶
Overview¶
This example VI demonstrates basics of arrays, clusters, and how to bundle data in a cluster with or without name(label).
Behavior¶
1-D Array
If array elements are initialized in the 1-D array control, they are pssed to the 1-D array indicator.
2-D Array
Specify the inital value, rows, and columns, the 2-D array indicator should show the initialized array correspondingly.
Extract Array Subset
Specify the starting index and the range for the row and column in the predefined array, the array indcator should show extracted subset/elemensts.
Bundle a numerical value, a boolean value and a string into a cluster without labels.
Bundle a numerical value, a boolean value and a string into a cluster with labels, and extract the elements in the cluster by its label.


The data is encoded using NCIt where possible (with mappings possible to others, such as SNOMED, etc.). For the data set 1 (eligibility data) format, an example is for HIV: dataset1_example_struct_elig_criteria_hiv_format.xlsx For data set 2 (participant data) format, please see dataset2_example_participant_format.xlsx. For data set 3 (clinician-based match) data, please see dataset3_example_match_format.xlsx.


A short conclusion
Born in Mars in 2088, Master Alcy went on becomes the top Mars governer and a 5-star general in star ship commanding. In 2100 he was elected President of the Union Earch, becoming the first top powered governer on Earth that are from other planet. He served 4 terms and expand the Union Earch's colony to the Center of the Galaxy.


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WITNESS LIST Subcommittee on Technology & Business Growth Committee March 18, 1999-8:30A SB 106 FOR: Chandler, Mickey (Forum for Responsible & Ethical E-mail), Austin ON: LeQuay Parker, Tracy (Self), Austin Registering, but not testifying: For: Schmehl, Paul (University of Texas at Dallas), Richardson Shields, Joe (Self), Friendswood Wahala, Karl (City of San Antonio), San Antonio Written materials submitted: For: Meyer, Albert (Austin Internet), Austin Rathbun, Michael (Internet America), Dallas SB 456 Registering, but not testifying: For: Bandy, Susan (Houston 2012), Houston Blackwood, Susan (San Antonio Sports Foundation), San Antonio Hansen, Don (Texas Hotel & Motel Association), Austin Richie Jackson (Texas Restaurant Association), Austin On: Barton, Karey (State Comptroller), Austin Hamilton, Billy (State Comptroller), Austin LeBas, James (State Comptroller), Austin SB 614 FOR: Cornelius, Rebecca (Pat Griffin Realty, Inc.), Pearland Pinkus, David (Small Business United of Texas), Austin Rich, Paul (Self), Austin Smith, David (Self), Midland Waldrop, Alan (TABCC), Austin AGAINST: Ayres, Barbara (We Will Not Forget SAJE), Austin Hampton, Hartley (Texas Trial Lawyers Association), Austin Korioth, Tony (TML Intergovernmental Risk Pool), Austin Levy, Rick (Texas AFL-CIO), Austin Registering, but not testifying: For: Alexander, Tom (NFIB), Austin Boyd, J. Patrick (Ray Boyd Plaster & Drywall, Inc.), Garland Bruns, Richard (Texas Construction Association), Austin Criss, Ralph (NFIB), Austin Dipprey, Ron (Dow Chemical Co.), Austin Fletcher, Courtney (Extraordinary Events, Inc.), Bastrop Galloway, Carolyn (Self), Dallas Glenn, Eric (Texas Association of Business & Chambers of Commerce), Austin Hammond, Bill (Texas Association of Business & Chambers of Commerce), Austin Hansen, Don (Texas Hotel & Motel Association), Austin Howden, Robert (NFIB/TX), Austin Ingram, Keith (Attorney for Paul Rich), Austin Keene, Brad (Westover Capitol, LP), Austin Mintz, David (Texas Apartment Association), Austin Newton, Chris (Texas Petroleum Marketers & Convenience), Austin Reynolds, Jim (Mechanical Contractors of Texas), Austin Rigsbee, Ken (Phillips Petroleum Co.), Austin Risk, Raymond (Texas Construction Association), Austin Rowe, Ben (Minyard Food Stores), Coppel Salazar, Rossanna (ROSS Communications), Austin Schulle, Gerhardt Jr. (Texas Society of Professional Engineers), Austin Sewell, Jim (Assoicated General Contractors - Building Branch), Austin Spicer, Donald (LCR Contractors, Inc.), Dallas Toth, Chris (CD Lone Star & TX Const. Assoc.), Pflugerville Tryon, Bill (Texas Civil Justice League), Austin Venable, Peggy (Citizens for a Sound Economy), Austin Wayne, Ralph (Texas Civil Justice League), Austin Wells, Katy (American Electronics Association), Austin Against: Dillon, Lana K. (Self), Portland McDaniel, Gay (Texas Association Against Sexual Assault), Austin Murray, Mary Cay (We Will Not Forget S.A.J.E.), Austin Written materials submitted: For: Wise, Andrew (AMerican Electronics Association), Austin Against: Aaron, Ron, M.A., J.D. (Jewish Family Service), San Antonio Lambe, Dan (Texas Watch), Austin


Wow, what a hard week. But we are nearly ready with defining the most important details for our launch event on November 22nd in Vienna. Currently we have planned two tracks for .NET developers and web developers, one track for database developers (SQL Server 2005 CLR, XML...) and 3 tracks covering SQL Server 2005 (DBA, BI and Upgrade, Migration and Integration) ... and of course a special track for VSTS.
Now it's just about finalizing the things. I am really looking forward to one of the most interesting launch events we ever had:)


Super lightweight without dependencies
A few kilobytes of pure JavaScript.
Dynamic sizing
Image-elements are dynamically sized and adjusted relative to the pv-speed.
Media type independence
The parallax effect applies not only on images but on videos as well.
Performance is key
Maximize your parallax effects with hardware acceleration and
zero
external libraries.
Viewport-only animations
Parallax elements are only animated within the current viewport, saving enormous resources.


S
- the type of messages this RemoteMessenger
will send
to the remote server. The corresponding
MessageService
on the remote server should receive
messages as S
or some superclass thereof.R
- the type of messages this RemoteMessenger
will
receive from the remote server. The corresponding
MessageService
on the remote server should send
messages as R
or some subclass thereof.public interface RemoteMessenger<S,R>
MessageService<R, S>
running on that remote server.| Modifier and Type | Interface and Description |
|---|---|
static interface |
RemoteMessenger.Callback<M>
Callback interface for
RemoteMessenger . |
static class |
RemoteMessenger.Factory |
| Modifier and Type | Method and Description |
|---|---|
void |
disconnect()
Disconnects from the remote server.
|
boolean |
isConnected()
Checks if this
RemoteMessenger is connected to a remote
server |
void |
requestConnect()
Attempts to connect to a remote server.
|
void |
sendMessage(S message)
Sends a
S message to the remote server. |
void |
setCallback(RemoteMessenger.Callback<? super R> callback)
Sets callback destination.
|
void setCallback(RemoteMessenger.Callback<? super R> callback)
WeakReferences
to ensure the
RemoteMessenger
does not inadvertently keep the
Callback
from being GCedcallback
- boolean isConnected()
RemoteMessenger
is connected to a remote
servertrue
iff this RemoteMessenger
can send
and receive messages from a remote servervoid requestConnect()
onConnected()
callback if
successful or onConnectionFailed()
if unsuccessfulvoid disconnect()
onDisconnected()
when
finishedvoid sendMessage(S message) throws IllegalStateException
S
message to the remote server. May be called from
the main threadmessage
- the S
message to sendIllegalStateException
- If the RemoteMessenger
is not connected


Roll a D1000!
Seed:
Roll!
Result of Roll:
Seed used is:
Time of Roll:
Validate Roll
Seed:
Time of Roll:
Validate!
Outcome of Roll Was:


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{% extends 'layouts/layout.html' %} {% block pageTitle %} We’re really busy at the moment {% endblock %} {% block content %}
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--- layout: post title: My Cloud Journey Audience With Architects and Engineers date: '2012-02-10T13:17:09-08:00' tags: tumblr_url: http://alexhwilliams.tumblr.com/post/17386011037/my-cloud-journey-audience-with-architects-and-engineers ---
http://bit.ly/wsHM8D


| Date perm_contact_calendar | Amount Donated |
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| Date perm_contact_calendar | Amount Donated |


| 1 |
Shahid_2009 |
Comparison of distance measures in spatial analytical modeling for health service planning |
Shahid, Rizwan; Bertazzon, Stefania; Knudtson, Merril L; Ghali, William A |
2009 |
2009-11-06 |
COMM-USE |
N |
PMC2781002 |
19895692.0 |
10.1186/1472-6963-9-200 |
eabho73n |
0.832002 |
Chang_2011 |
|
| 2 |
Smith_2012 |
ciliaFA: a research tool for automated, high-throughput measurement of ciliary beat frequency using freely available software |
Smith, Claire M; Djakow, Jana; Free, Robert C; Djakow, Petr; Lonnen, Rana; Williams, Gwyneth; Pohunek, Petr; Hirst, Robert A; Easton, Andrew J; Andrew, Peter W; O’Callaghan, Christopher |
2012 |
2012-08-01 |
COMM-USE |
N |
PMC3607980 |
23351276.0 |
10.1186/2046-2530-1-14 |
pj330wn4 |
0.783753 |
Montgomery_2004 |
|
| 3 |
Quintana_1998 |
Optimization of phosphorus localization by EFTEM of nucleic acid containing structures |
Quintana, C.; Marco, S.; Bonnet, N.; Risco, C.; Gutiérrez, M.L.; Guerrero, A.; Carrascosa, J.L. |
1998 |
1998-08-31 |
PMC |
N |
PMC7135716 |
9744088.0 |
10.1016/s0968-4328(98)00011-0 |
wsgb5s6z |
0.700367 |
Kylberg_2009, Qi_Z_2018, Liao_2006, Dai_Q_2012 |
|
| 4 |
Koning_2013 |
Nanofabrication of a gold fiducial array on specimen support for electron tomography |
Koning, Roman I.; Kutchoukov, Vladimir G.; Hagen, Cornelis W.; Koster, Abraham J. |
2013 |
2013-12-31 |
PMC |
N |
PMC7111319 |
23954496.0 |
10.1016/j.ultramic.2013.06.015 |
m1i5m19t |
0.667819 |
Kylberg_2009 |
|
| 5 |
Cheng_2010 |
Manifold construction based on local distance invariance |
Cheng, Wei-Chen; Liou, Cheng-Yuan |
2010 |
2010-02-11 |
PMC |
N |
PMC7091362 |
|
10.1007/s12293-009-0030-y |
3m1zet9t |
0.625902 |
Liao_2006, Chang_2011 |
Bakker_2018, Liu_K_2007, Saad_2005, Feng_2019 |
| 6 |
Moon_2019 |
Development of a Single Leg Knee Exoskeleton and Sensing Knee Center of Rotation Change for Intention Detection |
Moon, Dae-Hoon; Kim, Donghan; Hong, Young-Dae |
2019 |
2019-09-13 |
COMM-USE |
N |
PMC6767670 |
31540298.0 |
10.3390/s19183960 |
6fthj1op |
0.595834 |
Qi_Z_2018 |
|
| 7 |
Kylberg_2009 |
Towards Automated TEM for Virus Diagnostics: Segmentation of Grid Squares and Detection of Regions of Interest |
Kylberg, Gustaf; Sintorn, Ida-Maria; Borgefors, Gunilla |
2009 |
2009-01-01 |
PMC |
N |
PMC7120801 |
|
10.1007/978-3-642-02230-2_18 |
43igjn6x |
0.555992 |
Dai_Q_2012, Chang_2011, Qi_Z_2018 |
|
| 8 |
Budzan_2013 |
Face and eyes localization algorithm in thermal images for temperature measurement of the inner canthus of the eyes |
Budzan, Sebastian; Wyżgolik, Roman |
2013 |
2013-09-30 |
PMC |
N |
PMC7110491 |
|
10.1016/j.infrared.2013.05.007 |
flgc5l04 |
0.538085 |
Qi_Z_2018, Dai_Q_2012, Mendizabal-Ruiz_2018 |
Dwith_Chenna_2018 |
| 9 |
Kim_D_2018 |
Mapping Oil Spills from Dual-Polarized SAR Images Using an Artificial Neural Network: Application to Oil Spill in the Kerch Strait in November 2007 |
Kim, Daeseong; Jung, Hyung-Sup |
2018 |
2018-07-11 |
COMM-USE |
N |
PMC6069476 |
29997367.0 |
10.3390/s18072237 |
4ylv92td |
0.534557 |
Dai_Q_2012, Qi_Z_2018, Saw_A_2019, Saw_A_2019 |
Acar_2016 |
| 10 |
Li_W_2016 |
Determination of total organic carbon and soluble solids contents in Tanreqing injection intermediates with NIR spectroscopy and chemometrics |
Li, Wenlong; Qu, Haibin |
2016 |
2016-03-15 |
PMC |
N |
PMC7114577 |
|
10.1016/j.chemolab.2015.12.018 |
86srlles |
0.510982 |
Saw_A_2019, Dai_Q_2012, Saw_A_2019, Hoang_2015, Feng_2010 |
|
| 11 |
Dwith_Chenna_2018 |
Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening † |
Dwith Chenna, Yedukondala Narendra; Ghassemi, Pejhman; Pfefer, T. Joshua; Casamento, Jon; Wang, Quanzeng |
2018 |
2018-01-04 |
COMM-USE |
N |
PMC5795541 |
29300320.0 |
10.3390/s18010125 |
uqvjx98j |
0.505850 |
Chang_2011, Dai_Q_2012, Kylberg_2009, Lee_C_2017 |
Budzan_2013 |
| 12 |
Jonić_2016 |
Versatility of Approximating Single-Particle Electron Microscopy Density Maps Using Pseudoatoms and Approximation-Accuracy Control |
Jonić, Slavica; Sorzano, Carlos Oscar S. |
2016 |
2016-12-21 |
COMM-USE |
N |
PMC5209604 |
28097146.0 |
10.1155/2016/7060348 |
wvw6q7px |
0.489616 |
Qi_Z_2018, Liao_2006, Saw_A_2019, Dai_Q_2012 |
Quintana_1998, Punjani_2019, Zhong_2020 |
| 13 |
Acar_2016 |
Adaptive multiresolution method for MAP reconstruction in electron tomography |
Acar, Erman; Peltonen, Sari; Ruotsalainen, Ulla |
2016 |
2016-11-30 |
PMC |
N |
PMC7115799 |
27522477.0 |
10.1016/j.ultramic.2016.08.002 |
3u81ntle |
0.480301 |
Qi_Z_2018, Dai_Q_2012 |
Saad_2005, Punjani_2019 |
| 14 |
Saad_2005 |
Orientation determination by wavelets matching for 3D reconstruction of very noisy electron microscopic virus images |
Saad, Ali Samir |
2005 |
2005-03-02 |
COMM-USE |
N |
PMC555555 |
15743519.0 |
10.1186/1472-6807-5-5 |
89xnnvuv |
0.463506 |
Qi_Z_2018, Chang_2011, Dai_Q_2012, Feng_2010 |
Acar_2016 |
| 15 |
Niethammer_2013 |
Segmentation with area constraints |
Niethammer, Marc; Zach, Christopher |
2013 |
2013-01-31 |
None |
N |
PMC3656501 |
23084504.0 |
10.1016/j.media.2012.09.002 |
61va8q92 |
0.435151 |
Qi_Z_2018 |
Acar_2016, Punjani_2019, Saad_2005, Punjani_2020 |
| 16 |
Dogantekin_2013 |
Automatic RNA virus classification using the Entropy-ANFIS method |
Dogantekin, Esin; Avci, Engin; Erkus, Oznur |
2013 |
2013-07-31 |
PMC |
N |
|
|
10.1016/j.dsp.2013.01.011 |
xoepiscc |
0.424744 |
Qi_Z_2018, Saw_A_2019, Dai_Q_2012, Chang_2011 |
|
| 17 |
Maday_2020 |
An adaptive parareal algorithm |
Maday, Y.; Mula, O. |
2020 |
2020-04-09 |
PMC |
N |
|
|
10.1016/j.cam.2020.112915 |
ejaditj5 |
0.396622 |
Liou_2013, Qi_Z_2018 |
Acar_2016 |
| 18 |
Du_X_2019 |
Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis |
Du, Xiaokun; Zhu, Rongbo; Li, Yanhong; Anjum, Ashiq |
2019 |
2019-09-30 |
PMC |
N |
PMC7103364 |
|
10.1016/j.future.2019.01.016 |
ak33vta6 |
0.389235 |
Lee_C_2017 |
Joshi_2020, McCrae_2008, Yu_A_2011 |
| 19 |
Thi_T_2014 |
Recognizing flu-like symptoms from videos |
Thi, Tuan Hue; Wang, Li; Ye, Ning; Zhang, Jian; Maurer-Stroh, Sebastian; Cheng, Li |
2014 |
2014-09-12 |
COMM-USE |
N |
PMC4180141 |
25217118.0 |
10.1186/1471-2105-15-300 |
gm1e9j80 |
0.380548 |
|
Fu_M_2020, Guo_S_2020, Zhou_2020 |
| 20 |
Punjani_2020 |
3D Variability Analysis: Directly resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM images |
Ali Punjani; David J. Fleet |
2020 |
2020-04-09 |
BioRxiv |
N |
|
|
10.1101/2<IP_ADDRESS>466 |
yxo8oanc |
0.361497 |
Qi_Z_2018, Chang_2011, Dawson_2007, Dai_Q_2012 |
Zhong_2020, Punjani_2019 |
| 21 |
Weber_2017 |
Novel algorithm for accelerated electroanatomic mapping and prediction of earliest activation of focal cardiac arrhythmias using mathematical optimization |
Weber, Tobias; Katus, Hugo A.; Sager, Sebastian; Scholz, Eberhard P. |
2017 |
2017-06-23 |
None |
N |
PMC5446561 |
28279745.0 |
10.1016/j.hrthm.2017.03.001 |
q3vrzlz1 |
0.349942 |
Chang_2011, Qi_Z_2018, Feng_2010 |
|
| 22 |
Li_W_2011 |
Cross-Crystal Averaging with Search Models to Improve Molecular Replacement Phases |
Li, Weikai; Li, Fang |
2011 |
2011-02-09 |
None |
N |
PMC3037595 |
21300285.0 |
10.1016/j.str.2010.12.007 |
in8mbp7c |
0.302651 |
Qi_Z_2018, Chang_2011, Dai_Q_2012 |
Frenz_2019 |


As data scientists, we are not immune from cognitive bias. We do a lot to minimize it, we may even try to quantify it. But it is a part of our protoplasm and, especially, the protoplasm of our untrained clients.
A good place to start is the Monty Hall Problem. The pre-historic Let’s Make a Deal game show on broadcast television featured a host, Monty Hall, and contestants who were offered the chance to win fabulous prizes if they picked the right curtain, 1, 2, or 3. No clues were provided, it was pure guesswork. The possibilities were either a fabulous all expense paid Caribbean cruise, sometimes a consolation microwave and there was always a joke prize, such as a goat.
When the contestant made her choice, Monty did not open the curtain, but made an offer: Give up the chance to see what was behind your curtain? Or choose between the two curtains? And then he opened one curtain to show the goat or the microwave. Keep your curtain or switch?
This put the contestant in an emotional race state between greed and risk aversion. How bad would I feel if I gave up the fabulous prize for either the joke or the consolation prize. As any data scientist would expect, one-third of the time the contestant suffered regret by discovering that she had selected either the goat or the microwave and lost the chance at the dream of a lifetime.
After a while, the popular media asked a woman, reputed to be a super-genius, for the solution, to which she replied that statistically it made sense to take the offer to give up your initial choice and switch. No! The consensus was that there was a 50-50 chance that the contestant already had the winner, which had to be behind one of the two curtains. The concensus, sadly, included many statisticians who should have known better.
Parts of the statistical community, in common with the comman man, raised the irrefutable objection that as between the two curtains there was a 50-50 chance that the prize was hidden behind one of them. Which, of course, was a valid assertation as far as it went, which was not far enough.
Ab initio as those fond of showing obsolete erudition might say, or, more appropriately, at the beginning, the contestant had odds of 1:3 to choose the right door. Meaning, she also had odds of 2:3 of choosing the wrong door. She chose sides, as she was compelled to do, as to which was the right door. She knew that the joint probability of the other two doors containing the prize were 2:3.
Now, comes the hard part. She already knew that only one of the other doors could have the real prize if she didn’t, and since there were two of them, the chances were 50-50, between them. Gaining the knowledge of which one of the doors did not have the winning 50% chance changed her estimate of the odds of the remaining door relative to the open door, but it did nothing (or should have done nothing) to change the estimate of her door relative to the joint probability of the other two doors.
More simply, she started with a 1:3 chance of winning. She still has a 1:3 chance of winning, but she now knows that the remaining alternative has a 2:3 chance of winning. Therefore, she doubles her chances by switching.
Do the math. It’s not hard, except emotionally. A lot of data science problems have that potential.
In the words of Paul Simon
A man sees what he wants to see and disregards the rest.


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“Ideas are responsible for the progression and prosperity of humans – without them we would still be living in prehistoric times.”
Do you have an idea that could take the world way ahead of its time? Are you looking for the means to get the prototype of such an idea off the ground? Look no further.
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Register, participate and stand a chance to earn an internship opportunity or get your work incubated by pioneers!


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Hartz IV - every drink 1 eur

Tricky Error Message

You can really do some interesting stuff on the web, one of which is creating pages/images that look like error messages and pop up in your browser. Like so:

Thankfully there was the url at the top, not that I would have believed it otherwise, but like this it was sure. Not bad if it weren’t so mean, then again, they were nice enough to add "advertisement" on the bottom right … that seems to make this kind of stuff all right ;)

Cool Offices
Here is a great story about a visit at Pixar and it holds some very nice photos. In general, the office space they created is amazing, very thought provoking, free thinking kind of system. Take a look at what the animators created for themselves. These shots moved me so much that I didn’t even care too much about the server pics, and that means a lot ;)
Ourmedia.org Launched
I just found out via Hugo E. Martin that Ourmedia has launched. A good history and short backgrounder is here. The site actually explains itself very well in short words:
Welcome to Ourmedia.org
Share your videos, audio files, photos, text or software - for free - with a global community of creative individuals.
Get your works noticed and recognized. Make your voice heard. Register now - here’s why.
Bloglines to Be Bought by InterActiveCorp?

Ok, not really, but it seems that InterActivecorp, who own Hotwire.com, Expedia, match.com, Citysearch, LendingTree and more … will buy Ask Jeeves for close to $2 billion. As Ask Jeeves has just bought Bloglines, not Bloglines belongs to InterActiveCorp, unless they have some clause that they can get out again which I doubt. InterActiveCorp does seem to be a bad mother of a company though.

Update: John wrote up his thoughts and he includes an interview with Barry Diller, Chairman and CEO of IAC. Very nice post and some very nice quotes especially this one about the border between the web and traditional media getting blurry:

Yes. There’s no question but that the Internet is going to change the way entertainment products are distributed. The first popular piece of this is the digital video recorder. That’s a profound change. It proves that the 500-channel universe was interim technology.

Big… big … big … true. Point. Done. Thank you. Please carry on.

My Mac Just Got Better

Sometimes it’s the small things that change the way you are working and there are two apps that I downloaded and installed that just changed the way I am working with my mac and I do love it. One is SizzlingKeys by Yellow Mug Software, allowing me to easily control iTunes without always having to get the window to the front. A few keys and iTunes stops, starts, skips or simply goes a little bit louder. The other is Quicksilver which means I hit two keys, type Cam… hit enter and Camino starts. And all that works with any app on your mac and a lot more stuff. My Dock just got cleared fully and I am only using Quicksilver to start things. Oh, and Azureus is a great Bittorrent application in Java!

Still Time to Buy YHOO?
Ok, Yahoo! is getting scary. They now bought Flickr, which was rumored for some time but now is public. Technorati is already lighting up and this will surely be the talk of the day. First rumors I heard are around $40 million on very light revenue but I will likely change this around when I have the time to read more.
All in all I will have to see if I should not invest in YHOO. They bought Oddpost, my then eMail client, and then opened up their entire search system via an API. They going full force on blogging and now bought Flickr. If anything, they are getting cool for the hip and that means the hip go to Yahoo! and that means Yahoo! will possibly do good. More thinking needed but Yahoo! is starting to be one cool company looking at this from this side.
Internet 2.0 Waiting List Mode

 There we go, the Internet 2.0 conference in Paris that Loic initiated is now full and has a waiting list. No wonder if you consider the lineup and the fact that it will take place in the french Senat! I would have so much loved to go, but sadly it’s on the same day as the last exam from my MBA. I could probably fly over really quickly to at least grab the party but that does seem a little bit nuts ;) I am very happy to know that two friends from Cologne are going though and I will have to make them film it or something. Congratulations to Loic for getting this going. Looking forward to being there the second time around.

Some 25 Wines to Try
Handelsblatt Going Full Force on Blogging
As Nico reported some time ago the german newspaper Handelsblatt has launched their Handelsblatt global reporting where they outfitted 28 global correspondants with a blog where they can report live, personal, stories that might or might not get reprinted in the press. Very nice indeed and it’s good to see that Handelsblatt is pushing it this far and as professional.


--- layout: presentation title: "Hands-on Workshop - DevOps Meetup" --- DevOps Hands-on Workshop: CI/CD Pipeline Creation Lansing DevOps Meetup February 5th, 2019 Brendon Thiede DevOps Engineer at the Michigan Supreme Court Focused on Automation Repeatable deployments are a must... Our Journey... Code Change → Unit Test/Build → Deploy/Functional Test Code Change Git Unit Test Testing Strategy Environment Configuration Release Pipeline
Lansing DevOps Meetup February 5th, 2019


@*
* Copyright 2023 HM Revenue & Customs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*@
@import models.ApprovalProcessReview
@this(layout: main_layout)
@(review: ApprovalProcessReview)(implicit request: Request[_], messages: Messages)
@reviewStatus() = @{
messages("processReview.status", review.pages.count(_.status == PageReviewStatus.Complete), review.pages.length)
}
@layout(s"${review.title} - ${messages("factCheck.heading")}",
Some((routes.AdminController.approvalSummaries.url, "backlink.label"))) {
@review.title
@reviewStatus()
@messages("factCheck.sendConfirmationComment")
@messages("factCheck.sendConfirmation") } @{ //$COVERAGE-OFF$ }


- Jenkins - Class in org.incendo.jenkins
-
Jenkins4J instance class.
- JenkinsAPIType - Enum in org.incendo.jenkins
-
The enum Jenkins api type.
- JenkinsAuthentication - Class in org.incendo.jenkins
-
Class used to authenticate the Jenkins session
- JenkinsAuthentication() - Constructor for class org.incendo.jenkins.JenkinsAuthentication
-
- JenkinsBasicAuthentication - Class in org.incendo.jenkins
-
Jenkins authentication using the BASIC authentication protocol
Class used to authenticate the Jenkins session
- JenkinsBasicAuthentication(String, String) - Constructor for class org.incendo.jenkins.JenkinsBasicAuthentication
-
- JenkinsBuilder - Class in org.incendo.jenkins
-
Builder class for
Jenkins
instances
- JenkinsBuildNotFoundException - Exception in org.incendo.jenkins.exception
-
The type Jenkins build not found exception.
- JenkinsBuildNotFoundException(String, int) - Constructor for exception org.incendo.jenkins.exception.JenkinsBuildNotFoundException
-
Instantiates a new Jenkins build not found exception.
- JenkinsJobNotFoundException - Exception in org.incendo.jenkins.exception
-
The type Jenkins job not found exception.
- JenkinsJobNotFoundException(String) - Constructor for exception org.incendo.jenkins.exception.JenkinsJobNotFoundException
-
Instantiates a new Jenkins job not found exception.
- JenkinsNodeReadException - Exception in org.incendo.jenkins.exception
-
Exception indicating that the library failed to parse a jenkins node
- JenkinsNodeReadException(String, Throwable) - Constructor for exception org.incendo.jenkins.exception.JenkinsNodeReadException
-
Instantiates a new Jenkins node read exception.
- JenkinsNotAuthenticatedException - Exception in org.incendo.jenkins.exception
-
Thrown when the client lacks read permissions
- JenkinsNotAuthenticatedException(String) - Constructor for exception org.incendo.jenkins.exception.JenkinsNotAuthenticatedException
-
Instantiates a new Jenkins job not found exception.
- JenkinsPathProvider - Class in org.incendo.jenkins
-
Simple holder class for Jenkins path(s)
- JenkinsReader - Class in org.incendo.jenkins
-
Reader that reads data from a JenkinsService
and then
parses it into Jenkins objects
- JenkinsReader(Jenkins, JenkinsPathProvider, JenkinsAPIType) - Constructor for class org.incendo.jenkins.JenkinsReader
-
Instantiates a new Jenkins reader.
- JobDescription - Class in org.incendo.jenkins.objects
-
The type Job description.
- JobDescription(String, String, String, String) - Constructor for class org.incendo.jenkins.objects.JobDescription
-
Instantiates a new Job description.
- JobInfo - Class in org.incendo.jenkins.objects
-
The type Job info.
- JobInfo(Jenkins, String, String, String, String, String, String, Collection<BuildDescription>, BuildDescription, BuildDescription, BuildDescription, BuildDescription, int) - Constructor for class org.incendo.jenkins.objects.JobInfo
-
Instantiates a new Job info.
- JsonJenkinsReader - Class in org.incendo.jenkins.json
-
JenkinsReader
using Google Gson
Reader that reads data from a
JenkinsService
and then
parses it into Jenkins objects
- JsonJenkinsReader(Jenkins, JenkinsPathProvider) - Constructor for class org.incendo.jenkins.json.JsonJenkinsReader
-
Instantiates a new Json jenkins reader.


Birthday nachos for everyone
Tri-tip frankfurter cupim shank alcatra. Tail filet mignon shankle ribeye fatback, jerky sirloin shoulder ham t-bone kielbasa leberkas chuck. Pancetta bresaola tenderloin bacon pork, chuck t-bone hamburger beef.
Fatback sirloin jerky leberkas biltong t-bone bresaola strip steak boudin alcatra ribeye pork chop jowl.
Meatball pork chop t-bone tail. Beef ribs boudin tongue porchetta kevin capicola tri-tip andouille strip steak pork pork loin pastrami bresaola shankle pork belly.
Chuck pork beef turducken sausage spare ribs ground round pancetta strip steak biltong t-bone. Frankfurter jerky salami, hamburger beef chicken doner alcatra pork belly kevin chuck shoulder turkey pancetta. Chuck pastrami landjaeger frankfurter pig meatball shoulder short loin tail shank pork belly. Shank pork belly hamburger beef ribs doner.
Shankle corned beef flank, landjaeger hamburger chicken ball tip shoulder capicola meatloaf tail pork loin leberkas. Turkey corned beef pork belly porchetta, sausage pork bresaola short ribs flank ball tip tenderloin frankfurter pastrami.
Bacon ipsum dolor amet tenderloin ball tip frankfurter, sausage chicken strip steak flank hamburger. Meatloaf biltong ham hock, shoulder kielbasa doner ham pastrami meatball capicola.
Shankle alcatra sausage, chuck picanha tenderloin tongue flank kevin corned beef shank. Kielbasa corned beef beef ribs jowl biltong, chicken beef. Frankfurter brisket swine pork t-bone rump beef ribs venison, capicola shoulder tenderloin shankle. Shankle filet mignon shank, t-bone frankfurter spare ribs ground round short loin.


IT Worker
Report
About
Schools
Me
My work
Duck
Hello, hola, नमस्ते !
I grind HTML and CSS and then weld them with PHP into beautiful and efficient websites.
menu
Section 1
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public class Sudoku
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
static int |
COLUMNS |
static Variable[][] |
grid |
static int |
ROWS |
| Constructor and Description |
|---|
Sudoku()
Sudoku Puzzle constructor;
initializes a sudoku puzzle with an empty 9x9 grid
|
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<Variable> |
getNeighbours(Variable X)
return the neighbours of a variable
a neighbour is a variable that forms an arc this variable
|
void |
initConstraints()
Initialize binary constraints of all variables.
|
boolean |
isComplete(Variable[][] assignment)
Check if assignment is complete
Assignment is complete if all squares have assigned values that are consistent with
original gird.
|
boolean |
isConsistent(Variable var,
Variable[][] assignment)
Check if variable's value is consistent with assignment.
|
boolean |
isSolved()
A csp sudoku puzzle is solved if for each Xi, Di == 1
and value != 0
|
void |
populateGrid(java.lang.String fname)
Opens input file and reads its content.
|
void |
printAllConstraints()
print constraints of all 81 variables
|
void |
printDomains()
print domains of all 81 variables
|
void |
printGrid()
Print the sudoku puzzle gird in a nice format
0 indicates an empty unit.
|
void |
updateGrid()
Update the state of the grid after AC3 algorithm is applied.
|
public static final int ROWS
public static final int COLUMNS
public static Variable[][] grid
public Sudoku()
public void populateGrid(java.lang.String fname)
fname
- - name of the input filepublic void updateGrid()
public boolean isSolved()
public boolean isComplete(Variable[][] assignment)
assignment
- - an assignment to be checkedpublic boolean isConsistent(Variable var, Variable[][] assignment)
var
- - variable whose value to be checked for consistency with assignmentassignment
- - assignment that value will be checked for consistency againstpublic java.util.ArrayList<Variable> getNeighbours(Variable X)
X
- - variable whose neighbours to be foundpublic void initConstraints()
public void printGrid()
public void printDomains()
public void printAllConstraints()


| Department | RequestedBy Dr. | AssignedTo Dr. | ItemName | Qty | Price | Total | ||
|---|---|---|---|---|---|---|---|---|
| X | ||||||||
| Total : {{billingTransaction.TotalAmount}} | ||||||||
| In Words : {{billingTransaction.TotalAmount | NumberInWords}} Only. |
| SubTotal: | ||
| Total Amount: | ||
| Remarks: | ||
| In Words : {{billingTransaction.TotalAmount | NumberInWords}} Only. | ||


To all application owners,
Security is the top-most priority of any organization. To ensure continued success in our drive to design and architect a secure infrastructure, we are implementing a 90-day automatic rotation of all AWS IAM User Access Keys. This will begin on [DATE HERE].
Why is this important and how does fixing this help you?
Rotating Identity and Access Management (IAM) credentials periodically will significantly reduce the chances that a compromised set of access keys can be used without your knowledge to access certain components within your AWS account.
What this means for you:
You will need to update your applications every 90-days with the new Access Key pair. These can be found in AWS Secrets Manager in a secret accessible by your application’s AWS IAM User only. You will have a grace period of 10 days after rotation before old Access Keys are disabled. There will be another 10-day grace period before old Access Keys are deleted. Until they are disabled, old Access Keys can be used to programmatically retrieve the new Access Keys from AWS Secrets Manager.
Enabling this setting will also help you align with the following compliance standards:
• The Center of Internet Security AWS Foundations Benchmark
• APRA
• MAS
• National Institute of Standards and Technology (NIST)
• AWS Well-Architected Framework
AWS IAM Access Keys have been rotated for the below:
[insert-violations-here]
Should you have any questions, please reach out to [EMAIL HERE]. As always, thank you for continuing to strive for a secure work environment.
Sincerely,
[SIGNED BY];


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- enable web-enabled eyeballs


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Upload a file. Only .pkl files are allowed.
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{{ form.pickle_file }}
{{ form.submit() }}