Welcome to the world of evidence! Evidence-based teaching is effective teaching, and we bring you the most effective methods. Read on to find out more.
/**
* Called when a null model is about to be retrieved in order to allow a subclass to provide an
* initial model.
* <p>
* By default this implementation looks components in the parent chain owning a
* {@link IComponentInheritedModel} to provide a model for this component via
* {@link IComponentInheritedModel#wrapOnInheritance(Component)}.
* <p>
* For example a {@link FormComponent} has the opportunity to instantiate a model on the fly
* using its {@code id} and the containing {@link Form}'s model, if the form holds a
* {@link CompoundPropertyModel}.
*
* @return The model
*/
protected IModel<?> initModel()
{
IModel<?> foundModel = null;
// Search parents for IComponentInheritedModel (i.e. CompoundPropertyModel)
for (Component current = getParent(); current != null; current = current.getParent())
{
// Get model
// Don't call the getModel() that could initialize many in between
// completely useless models.
// IModel model = current.getDefaultModel();
IModel<?> model = current.getModelImpl();
if (model instanceof IWrapModel && !(model instanceof IComponentInheritedModel))
{
model = ((IWrapModel<?>)model).getWrappedModel();
}
if (model instanceof IComponentInheritedModel)
{
// return the shared inherited
foundModel = ((IComponentInheritedModel<?>)model).wrapOnInheritance(this);
setFlag(FLAG_INHERITABLE_MODEL, true);
break;
}
}
// No model for this component!
return foundModel;
}
MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
At the core of the platform lies a common data model to enable interoperability between the different components. In order to interact with the platform, a component should know how to interact with at least a subset of the CDM. The model describes all the commonly used data that is dealt with in the platform, and therefore covers at least taxonomic names and concepts; literature references; authors; (type) specimen; structured descriptive data; and species related content of any kind like economic use or conservation status. Nearly all this data has already been described by existing or upcoming TDWG standards. Unfortunately, there are still major gaps in compatibility, so a new integrated data model has to be developed in order to quickly yield results.
Term: a word or phrase used to describe a thing or to express a concept [1]
Concept: an abstract idea. Concepts are expressed by terms. [1]
Fact: Facts relate terms [2]
Fact Type: a fact type is an association between two or more concepts
Vocabulary: the body of words used in a particular language or in a particular sphere [1]
Terminology: the body of terms used in a subject of study, profession, etc. [1]
Fact Model: A fact model establishes the basis for shared operational business knowledge. [2] This model is usually graphical and can also be called the business object model or BOM.
We implemented the N-services model through computer simulations. In this paper, we present insights developed into the growth and formation of a referral network of agents and how efficiently services were produced through one of our simulations.
As the name implies, AMDD is the agile version of Model Driven Development (MDD). MDD is an approach to software development where extensive models are created before source code is written. A primary example of MDD is the Object Management Group (OMG)’s Model Driven Architecture (MDA) standard. With MDD a serial approach to development is often taken, MDD is quite popular with traditionalists, although as the RUP/EUP shows it is possible to take an iterative approach with MDD. The difference with AMDD is that instead of creating extensive models before writing source code you instead create agile models which are just barely good enough that drive your overall development efforts. AMDD is a critical strategy for scaling agile software development beyond the small, co-located team approach that we saw during the first stage of agile adoption.
The team found that students remembered the pairs much better when they first tried to retrieve the answer before it was shown to them. In a way this pretesting effect is counterintuitive: Studying a pair for 13 seconds produces worse recall than studying the pair for 5 seconds, if students in the latter condition spent the previous 8 seconds trying to retrieve or guess the answer. But the effect averaged about 10 percent better recall, and occurred both immediately after study and after a delay averaging 38 hours.
This planet was by far the hardest for me to render properly. I've put together two different models of Earth; one is designed to be photo-realistic, and the other is designed after NASA's Blue Marble. Both models include optional night and city lights sections. These Earth models have been used in various productions by hobbyists and film groups. I've written a tutorial demonstrating how to build the models - you may find it here.
Column, index, and stored routine names are not case sensitive on any platform, nor are column aliases. Trigger names are case sensitive, which differs from standard SQL.
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome - GitHub - jerryji1993/DNABERT: DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome