File Renamer Turbo Full Crack
Chara Dagres
and this is/was working fine, the whole frame gets replaced by the contents of the partial.
I decided to get rid of this partial and moved the content of this partial to create.turbo_stream.erb but I noticed that upon a successful request, the content of the create.turbo_stream.erb was getting appended to end of the body.
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from the main MolecularIntegrals.jl installation. Currently I just add the @turbo commands to the relevant for-loops and re-time, since the vrr! routine is several calls down in the integral code. But if you think a clear comparison is important, I can make _turbo versions of all these routines. Let me know.
Yes, I was going to ask if the vrr! call you were benchmarking was really representative of the typical call.
For one thing, I noticed that in your actual code it gets called with SVector instead of Vector arguments.
@turbo speeds things up be evaluating multiple loop iterations in parallel using SIMD instructions.
For this to speed things up, we need multiple loop iterations.
Of course, @simd works the same way, but both macros make different trade offs in terms of performance as a function of the number of loop iterations.
Here is a very simple example, just using a sum:
I've run into this weird problem when applying turbo smooth. I created a simple box , applied a chamfer modifier and then applied turbo smooth on top. As you can see some of the edges have become black and this shading issue is also visible when rendered. Any idea why is this happening?
I think ive figured it out. it was a shading problem. when viewport is set to standard I get that display glitch, once it's set to Performance or other options the model is displayed correctly. weird.
Drivetrain is an application developed by the TURBO group that provides core informatics components to load relational data into a fully ontologized RDF triple store. It reads data that have been mapped to the TURBO ontology (via Karma) and performs the following steps to load the data into a GraphDB RDF triplestore.
During the data import step, the input data are written to an isolated section of the graph. The triples are not expected to have globally unique identifiers and so must be sectioned off from all other data in the triple store.
The shortcut expansion phase takes all triples in the input data that use shortcut relations and expands them to fully ontologized forms. A single shortcut triple will likely expand to many ontologized triples.
After this phase is complete, the data in the isolated import graph have globally unique identifiers and are fully ontologized, though they may not yet be ready to be incorporated into the rest of the triple store.
Data integrity rules are applied to all triples in the isolated import graph to assure that the data meet the minimum level of integrity required by the Drivetrain application. Several conditions must be met to pass, including the following:
If the data do not pass all integrity checks, then the process is halted. Data that do not meet the minimum requirements are not incorporated into the rest of the RDF triple store. If all integrity checks have passed, then the data are ready to be connected to the rest of the graph.
After this phase is complete, the RDF data are normalized such that all entities in reality can be identified by a single unique identifier that is independent yet connected to the source relational data.
Since our data comes from many sources, it is possible that the same Biobank Consenter may appear in multiple data sources, each of which may contain different or contradicting information. It is the goal of the Referent Tracker to apply custom rules in order to determine when two Consenters must be combined into one. Likewise, the same Encounter may also appear in multiple data sources.
Entity Linking is a generic term used here to mean the process of attaching Consenters to their Encounters based on data provided by a Join table. This process is necessary because Consenters and their Encounters may be received in separate files. Drivetrain can make matches by comparing the Literal values of Encounter Symbols and Consenter Symbols, and the values of the respective registries.
It should be evident that our model creates an instance of a consenter CRID and an encounter CRID from each row in the Relational join data, but these are not currently attached to any Consenters or Encounters, and no Consenters or Encounters are created. This breaks the typical paradigm of treating Identifiers as dependents of Consenters or Encounters, but this route avoids the creation of extraneous Consenters and Encounters which would add an undesirable level of complexity as well as potentially corrupt the results of queries. The turbo:sharesRowWithpredicate is what binds the two identifier nodes.
Upon data instantiation, the Join triples are entered into their own shortcut expansion graphs and are expanded into the EntityLinkData graph. The actual Join process takes place after Referent Tracking and before Conclusionating. For each pair of identifiers linked by the turbo:sharesRowWith predicate, Drivetrain searches for Referent Tracked Consenters and Encounters with identifiers and registries which match their respective identifiers and registries in the join table. In the case that a Referent Tracked Consenter is found with a symbol and registry which matches the literals in the join data, and a Referent Tracked Encounter is found with a symbol and registry which matches the literals in the join data, and those literals in the join data are linked with the turbo:sharesRowWith predicate, Drivetrain can confidently declare that based on the information available, the Consenter participated in this Encounter.
Once again, this process will only work if both the Consenter and Encounter in question are Referent Tracked. Drivetrain does not accept non-Referent Tracked nodes because they contain only incomplete information. In addition to the creation of a link between the Consenter and the Encounter, for each Join a new node is created of type ConsenterUnderInvestigation, designating the role of the Consenter.
During the conclusionating phase, rules are applied to the data to collapse potentially conflicting data to single conclusions, which can be used for querying purposes. The potentially conflicting data derived from the sources remain in the graph and can be queried. To facilitate easy querying, the conclusions, which are RDF triples, are placed in a separate named graph.
It is not guaranteed that the source data required to calculate BMI at date of recruitment will be both available and of sufficient quality. It may be that height and weight measurements were recorded at the healthcare encounter, the study recruitment encounter, neither, or both. Further, the data may have been recorded improperly, which would result in a calculated BMI that is outside the acceptable range.
The triples above show that a female Gender Identity Datum isAbout consenter1. The Conclusionator will interpret such a graph pattern as an argument for the BiologicalSex of consenter 1 as type Female. However, there may be more GIDs associated with this consenter which represent a conflicting view, so it is important for the program to look at the whole picture before making a conclusion.
Since more than 50% of GIDs which are about consenter pmbb:consenter1 are representative of a female Biological Sex, the Gender Identity Conclusionator will create the following triples in a named graph:
Something you may have noticed is that Biological Sex instance pmbb:biologicalSex1 now has two types. If we sent the following SPARQL query to all graphs in the database, we would get a redundant result:
In OBO Foundry, FemaleBiologicalSex is a subclass of BiologicalSex, so it could be seen as unnecessary to additionally specify this triple. However, as a general rule we like to avoid changing an instance from one type to another, and this approach also allows us to have a named graph specifying the output of each conclusion process without overwriting any information which might be useful if we need to retrace our steps.
Date of Birth Conclusionating occurs in a similar vein to Biological Sex Conclusionating, in that one or more datums are considered for each instance of a birth in order to draw a conclusion. The biggest differences between the two Conclusionating processes are caused by the fact that while Biological Sex Conclusionating involves manipulating the type of a Biological Sex node, Date of Birth Conclusions are represented by a Literal date value.
Date of Birth Datums are not connected to the Consenter in our instantiation model. Instead, they are connected directly to the Birth of a Consenter. Unlike Gender Identity Datums, Date of Birth Datums do not have a type which reflects the actual Date of Birth value; the type is always the same. The Literal value associated with the Date of Birth Datum is what the Date of Birth Conclusionator will use to make its inferences.
In this case, we have 2 Datums in agreement and one contradictory, likely because the month and the date were accidentally flipped prior to instantiation. With our current rule, we will see the following triples created in a named graph:
Unlike in the Biological Sex Conclusionator, where the Biological Sex instance is pre-existent and only has its type updated, we are creating a new instance of obo:DateOfBirth in the conclusionated graph, which holds our computed value.
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