caching of telerik assemblies
hello henrik, - - you can do this even now. please find attached an example of how to achieve it. - - the key point here is to provide an extmap file for each dll you will be referencing. i did this for the telerik.windows.controls.dll. the content of the extmap file is selfexplanatory. the file is located in the binaries folder of the silverlight application. please let us know if you have more questions. - - regards, - valentin.stoychev - the telerik team - - instantly find answers to your questions on the new telerik support portal. - check out the tips for optimizing your support resource searches. -
yes, assembly is required for the tv stand. deepstream-appsrc-test is the sample for appsrc and appsink

tee can be used as normal. no special code is needed. solved via email! for others reaching this thread, note that any tx you send via a defender relayer has a ttl, which defaults to 8hs. after it expires, defender will automatically invalidate it and replace it with a noop tx with a fast gas price, that will unstuck your tx queue. read more [here](https://docs.openzeppelin.com/defender/relay#valid-until)! a secure library where definitive authorized versions of all media configuration items cis are stored and protected Hi JimCreech,


It is possible to achieve a similar result in Dataiku by going through the following steps:


* run a [Window](https://knowledge.dataiku.com/latest/courses/visual-recipes/window.html) recipe using id as partitioning column, event as ordering column, and compute the lagdiff values on days
* run a [Prepare](https://knowledge.dataiku.com/latest/courses/basics/prepare-data/concept-prepare-recipe.html) recipe which will:
	+ run the assertions on the specific conditions you mentioned and create a dummy\_tmp column we'll reuse at the final step
	+ filter out the records where event == 01 (since we won't need it for the aggregation)
* run a [Groupby](https://knowledge.dataiku.com/latest/courses/basics/group-data/group-summary.html) recipe to aggregate at the id level and get the minimum of the dummy\_tmp column (which should be 1 if all conditions you described are verified, 0 otherwise).


You will find attached a sample DSS project with an example of implementation of those steps using the small example data mentioned in your post. If you are working on a regular basis with time series data, you may find this [tutorial](https://knowledge.dataiku.com/latest/courses/visual-recipes/window/window.html) on the Window recipe capabilities useful.


Hope this helps !


Best,


Harizo




[View solution in original post](/t5/General-Discussion/Using-Lag-and-Lead-in-Dataiku/m-p/19308#M1146) tiler\_src\_pad\_buffer\_probe is before the nvmultistreamtiler in the pipeline, so the ntp\_timestamp for each stream is meaningful. your appsink is after nvmultistreamtiler in the pipeline, nvmultistreamtiler will combine multiple streams into one new stream, so ntp\_timestamp in each separated stream m… seems to be solved by adding a method

find\_aac(x::base.broadcast.extruded) = x.x

not sure why it is needed, or is it enough. created a [pr](https://github.com/julialang/julia/pull/34112). grafting – layering try doing a compression test tallies are usually marked in a bunch of 4.