OUTBOUND|INBOUND|ANY
: follow outgoing, incoming, or edges pointing in either
direction in the traversal; Please note that this can't be replaced by a bind parameter.
A traversal starts at one specific document (startVertex) and follows all edges connected to this document. For all documents (vertices) that are targeted by these edges it will again follow all edges connected to them and so on. It is possible to define how many of these follow iterations should be executed at least (min depth) and at most (max depth).
For all vertices that were visited during this process in the range between min depth and max depth iterations you will get a result in form of a set with three items:
Let's take a look at a simple example to explain how it works. This is the graph that we are going to traverse:
We use the following parameters for our query:
Now it walks to one of the direct neighbors of A, say B (note: ordering is not guaranteed!):
The query will remember the state (red circle) and will emit the first result A → B (black box). This will also prevent the traverser to be trapped in cycles. Now again it will visit one of the direct neighbors of B, say E:
We have limited the query with a max depth of 2, so it will not pick any neighbor of E, as the path from A to E already requires 2 steps. Instead, we will go back one level to B and continue with any other direct neighbor there:
Again after we produced this result we will step back to B. But there is no neighbor of B left that we have not yet visited. Hence we go another step back to A and continue with any other neighbor there.
And identical to the iterations before we will visit H:
And J:
After these steps there is no further result left. So all together this query has returned the following paths:
Now let's see how we can write a query that follows this schema. You have two options here, you can either use a named graph or a set of edge collections (anonymous graph).
FOR vertex[, edge[, path]]
IN [min[..max]]
OUTBOUND|INBOUND|ANY startVertex
GRAPH graphName
[OPTIONS options]
FOR
: emits up to three variables:
vertices
: an array of all vertices on this pathedges
: an array of all edges on this pathIN
min..max
: the minimal and maximal depth for the traversal:
OUTBOUND|INBOUND|ANY
: follow outgoing, incoming, or edges pointing in either
direction in the traversal; Please note that this can't be replaced by a bind parameter.
_id
. All other values will lead to a warning and an empty
result. If the specified document does not exist, the result is empty as well
and there is no warning.
GRAPH
graphName (string): the name identifying the named graph.
Its vertex and edge collections will be looked up.
OPTIONS
options (object, optional): used to modify the execution of the
traversal. Only the following attributes have an effect, all others are ignored:
min depth > 1
a vertex that was found before
min depth
might not be returned at all (it still might be part of a path). Note:
Using this configuration the result is not deterministic any more. If there
are multiple paths from startVertex to vertex, one of those is picked.
min depth > 1
, an edge that was found before
min depth
might not be returned at all (it still might be part of a path). Note:
Using this configuration the result is not deterministic any more. If there
are multiple paths from startVertex over edge one of those is picked.
FOR vertex[, edge[, path]]
IN [min[..max]]
OUTBOUND|INBOUND|ANY startVertex
edgeCollection1, ..., edgeCollectionN
[OPTIONS options]
Instead of GRAPH graphName
you may specify a list of edge collections. Vertex
collections are determined by the edges in the edge collections. The rest of the
behavior is similar to the named version.
If the same edge collection is specified multiple times, it will behave as if it
were specified only once. Specifying the same edge collection is only allowed when
the collections do not have conflicting traversal directions.
For traversals with a list of edge collections you can optionally specify the direction for some of the edge collections. Say for example you have three edge collections edges1, edges2 and edges3, where in edges2 the direction has no relevance but in edges1 and edges3 the direction should be taken into account. In this case you can use OUTBOUND as general traversal direction and ANY specifically for edges2 as follows:
FOR vertex IN OUTBOUND
startVertex
edges1, ANY edges2, edges3
All collections in the list that do not specify their own direction will use the
direction defined after IN
. This allows to use a different direction for each
collection in your traversal.
All three variables emitted by the traversals might as well be used in filter statements. For some of these filter statements the optimizer can detect that it is possible to prune paths of traversals earlier, hence filtered results will not be emitted to the variables in the first place. This may significantly improve the performance of your query. Whenever a filter is not fulfilled, the complete set of vertex, edge and path will be skipped. All paths with a length greater than max will never be computed.
In the current state, AND
combined filters can be optimized, but OR
combined filters cannot.
Filtering on paths allows for the most powerful filtering and may have the highest impact on performance. Using the path variable you can filter on specific iteration depths. You can filter for absolute positions in the path by specifying a positive number (which then qualifies for the optimizations), or relative positions to the end of the path by specifying a negative number.
FOR v, e, p IN 1..5 OUTBOUND 'circles/A' GRAPH 'traversalGraph'
FILTER p.edges[0].theTruth == true
RETURN p
will filter all paths where the start edge (index 0) has the attribute theTruth equal to true. The resulting paths will be up to 5 items long.
Similar to filtering the edges on the path you can also filter the vertices:
FOR v, e, p IN 1..5 OUTBOUND 'circles/A' GRAPH 'traversalGraph'
FILTER p.vertices[1]._key == "G"
RETURN p
And of course you can combine these filters in any way you like:
FOR v, e, p IN 1..5 OUTBOUND 'circles/A' GRAPH 'traversalGraph'
FILTER p.edges[0].theTruth == true
AND p.edges[1].theFalse == false
FILTER p.vertices[1]._key == "G"
RETURN p
The query will filter all paths where the first edge has the attribute theTruth equal to true, the first vertex is "G" and the second edge has the attribute theFalse equal to false. The resulting paths will be up to 5 items long.
Note: Although we have defined a min of 1, we will only get results of depth 2. This is because for all results in depth 1 the second edge does not exist and hence cannot fulfill the condition here.
With the help of array comparison operators filters can also be defined on the entire path, like ALL edges should have theTruth == true:
FOR v, e, p IN 1..5 OUTBOUND 'circles/A' GRAPH 'traversalGraph'
FILTER p.edges[*].theTruth ALL == true
RETURN p
Or NONE of the edges should have theTruth == true:
FOR v, e, p IN 1..5 OUTBOUND 'circles/A' GRAPH 'traversalGraph'
FILTER p.edges[*].theTruth NONE == true
RETURN p
Both examples above are recognized by the optimizer and can potentially use other indexes than the edge index.
It is also possible to define that at least one edge on the path has to fulfill the condition:
FOR v, e, p IN 1..5 OUTBOUND 'circles/A' GRAPH 'traversalGraph'
FILTER p.edges[*].theTruth ANY == true
RETURN p
It is guaranteed that at least one, but potentially more edges fulfill the condition. All of the above filters can be defined on vertices in the exact same way.
Filtering on the path influences the Iteration on your graph. If certain conditions aren't met, the traversal may stop continuing along this path.
In contrast filters on vertex or edge only express whether you're interestet in the actual value of these
documents. Thus, it influences the list of returned documents (if you return v or e) similar
as specifying a non-null min
value. If you specify a min value of 2, the traversal over the first
two nodes of these paths has to be executed - you just won't see them in your result array.
Similar are filters on vertices or edges - the traverser has to walk along these nodes, since you may be interested in documents further down the path.
 For better overview we only return the vertex IDs:
arangosh> db._query("FOR v IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' RETURN v._key");
[
"B",
"C",
"D",
"E",
"F",
"G",
"H",
"I",
"J",
"K"
]
[object ArangoQueryCursor, count: 10, hasMore: false]
arangosh> db._query("FOR v IN 1..3 OUTBOUND 'circles/A' edges RETURN v._key");
[
"B",
"C",
"D",
"E",
"F",
"G",
"H",
"I",
"J",
"K"
]
[object ArangoQueryCursor, count: 10, hasMore: false]
arangosh> db._query("FOR v IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' RETURN v._key");
arangosh> db._query("FOR v IN 1..3 OUTBOUND 'circles/A' edges RETURN v._key");
We can nicely see that it is heading for the first outer vertex, then goes back to the branch to descend into the next tree. After that it returns to our start node, to descend again. As we can see both queries return the same result, the first one uses the named graph, the second uses the edge collections directly.
Now we only want the elements of a specific depth (min = max = 2), the ones that are right behind the fork:
arangosh> db._query("FOR v IN 2..2 OUTBOUND 'circles/A' GRAPH 'traversalGraph' return v._key");
[
"C",
"E",
"H",
"J"
]
[object ArangoQueryCursor, count: 4, hasMore: false]
arangosh> db._query("FOR v IN 2 OUTBOUND 'circles/A' GRAPH 'traversalGraph' return v._key");
[
"C",
"E",
"H",
"J"
]
[object ArangoQueryCursor, count: 4, hasMore: false]
arangosh> db._query("FOR v IN 2..2 OUTBOUND 'circles/A' GRAPH 'traversalGraph' return v._key");
arangosh> db._query("FOR v IN 2 OUTBOUND 'circles/A' GRAPH 'traversalGraph' return v._key");
As you can see, we can express this in two ways: with or without max parameter in the expression.
Now let's start to add some filters. We want to cut of the branch on the right side of the graph, we may filter in two ways:
_key
== G
label
attribute of the edge connecting A to G is
right_foo
arangosh> db._query("FOR v, e, p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' FILTER p.vertices[1]._key != 'G' RETURN v._key");
[
"B",
"C",
"D",
"E",
"F"
]
[object ArangoQueryCursor, count: 5, hasMore: false]
arangosh> db._query("FOR v, e, p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' FILTER p.edges[0].label != 'right_foo' RETURN v._key");
[
"B",
"C",
"D",
"E",
"F"
]
[object ArangoQueryCursor, count: 5, hasMore: false]
arangosh> db._query("FOR v, e, p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' FILTER p.vertices[1]._key != 'G' RETURN v._key");
arangosh> db._query("FOR v, e, p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' FILTER p.edges[0].label != 'right_foo' RETURN v._key");
As we can see all vertices behind G are skipped in both queries.
The first filters on the vertex _key
, the second on an edge label.
Note again, as soon as a filter is not fulfilled for any of the three elements
v
, e
or p
, the complete set of these will be excluded from the result.
We also may combine several filters, for instance to filter out the right branch (G), and the E branch:
arangosh> db._query("FOR v,e,p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' FILTER p.vertices[1]._key != 'G' FILTER p.edges[1].label != 'left_blub' return v._key");
[
"B",
"C",
"D"
]
[object ArangoQueryCursor, count: 3, hasMore: false]
arangosh> db._query("FOR v,e,p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' FILTER p.vertices[1]._key != 'G' AND p.edges[1].label != 'left_blub' return v._key");
[
"B",
"C",
"D"
]
[object ArangoQueryCursor, count: 3, hasMore: false]
arangosh> db._query("FOR v,e,p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' FILTER p.vertices[1]._key != 'G' FILTER p.edges[1].label != 'left_blub' return v._key");
arangosh> db._query("FOR v,e,p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' FILTER p.vertices[1]._key != 'G' AND p.edges[1].label != 'left_blub' return v._key");
As you can see, combining two FILTER
statements with an AND
has the same result.
All our previous examples traversed the graph in OUTBOUND edge direction.
You may however want to also traverse in reverse direction (INBOUND) or
both (ANY). Since circles/A
only has outbound edges, we start our queries
from circles/E
:
arangosh> db._query("FOR v IN 1..3 OUTBOUND 'circles/E' GRAPH 'traversalGraph' return v._key");
[
"F"
]
[object ArangoQueryCursor, count: 1, hasMore: false]
arangosh> db._query("FOR v IN 1..3 INBOUND 'circles/E' GRAPH 'traversalGraph' return v._key");
[
"B",
"A"
]
[object ArangoQueryCursor, count: 2, hasMore: false]
arangosh> db._query("FOR v IN 1..3 ANY 'circles/E' GRAPH 'traversalGraph' return v._key");
[
"F",
"B",
"C",
"D",
"A",
"G"
]
[object ArangoQueryCursor, count: 6, hasMore: false]
arangosh> db._query("FOR v IN 1..3 OUTBOUND 'circles/E' GRAPH 'traversalGraph' return v._key");
arangosh> db._query("FOR v IN 1..3 INBOUND 'circles/E' GRAPH 'traversalGraph' return v._key");
arangosh> db._query("FOR v IN 1..3 ANY 'circles/E' GRAPH 'traversalGraph' return v._key");
The first traversal will only walk in the forward (OUTBOUND) direction. Therefore from E we only can see F. Walking in reverse direction (INBOUND), we see the path to A: B → A.
Walking in forward and reverse direction (ANY) we can see a more diverse result. First of all, we see the simple paths to F and A. However, these vertices have edges in other directions and they will be traversed.
Note: The traverser may use identical edges multiple times. For instance, if it walks from E to F, it will continue to walk from F to E using the same edge once again. Due to this we will see duplicate nodes in the result.
Please note that the direction can't be passed in by a bind parameter.
Now let's have a look what the optimizer does behind the curtain and inspect traversal queries using the explainer:
arangosh> db._explain("FOR v,e,p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' LET localScopeVar = RAND() > 0.5 FILTER p.edges[0].theTruth != localScopeVar RETURN v._key", {}, {colors: false}); Query string: FOR v,e,p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' LET localScopeVar = RAND() > 0.5 FILTER p.edges[0].theTruth != localScopeVar RETURN v._key Execution plan: Id NodeType Est. Comment 1 SingletonNode 1 * ROOT 2 TraversalNode 1 - FOR v /* vertex */, p /* paths */ IN 1..3 /* min..maxPathDepth */ OUTBOUND 'circles/A' /* startnode */ GRAPH 'traversalGraph' 3 CalculationNode 1 - LET localScopeVar = (RAND() > 0.5) /* simple expression */ 4 CalculationNode 1 - LET #6 = (p.`edges`[0].`theTruth` != localScopeVar) /* simple expression */ 5 FilterNode 1 - FILTER #6 6 CalculationNode 1 - LET #8 = v.`_key` /* attribute expression */ 7 ReturnNode 1 - RETURN #8 Indexes used: By Type Collection Unique Sparse Selectivity Fields Ranges 2 edge edges false false 85.00 % [ `_from`, `_to` ] base OUTBOUND Traversals on graphs: Id Depth Vertex collections Edge collections Options Filter conditions 2 1..3 circles edges uniqueVertices: none, uniqueEdges: path Optimization rules applied: Id RuleName 1 move-calculations-up 2 optimize-traversals 3 move-calculations-down arangosh> db._explain("FOR v,e,p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' FILTER p.edges[0].label == 'right_foo' RETURN v._key", {}, {colors: false}); Query string: FOR v,e,p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' FILTER p.edges[0].label == 'right_foo' RETURN v._key Execution plan: Id NodeType Est. Comment 1 SingletonNode 1 * ROOT 2 TraversalNode 1 - FOR v /* vertex */, p /* paths */ IN 1..3 /* min..maxPathDepth */ OUTBOUND 'circles/A' /* startnode */ GRAPH 'traversalGraph' 3 CalculationNode 1 - LET #5 = (p.`edges`[0].`label` == "right_foo") /* simple expression */ 4 FilterNode 1 - FILTER #5 5 CalculationNode 1 - LET #7 = v.`_key` /* attribute expression */ 6 ReturnNode 1 - RETURN #7 Indexes used: By Type Collection Unique Sparse Selectivity Fields Ranges 2 edge edges false false 85.00 % [ `_from`, `_to` ] base OUTBOUND 2 edge edges false false 85.00 % [ `_from`, `_to` ] level 0 OUTBOUND Traversals on graphs: Id Depth Vertex collections Edge collections Options Filter conditions 2 1..3 circles edges uniqueVertices: none, uniqueEdges: path (#3.`label` == "right_foo") Optimization rules applied: Id RuleName 1 move-calculations-up 2 move-filters-up 3 move-calculations-up-2 4 move-filters-up-2 5 optimize-traversals
arangosh> db._explain("FOR v,e,p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' LET localScopeVar = RAND() > 0.5 FILTER p.edges[0].theTruth != localScopeVar RETURN v._key", {}, {colors: false}); arangosh> db._explain("FOR v,e,p IN 1..3 OUTBOUND 'circles/A' GRAPH 'traversalGraph' FILTER p.edges[0].label == 'right_foo' RETURN v._key", {}, {colors: false});
We now see two queries: In one we add a variable localScopeVar, which is outside the scope of the traversal itself - it is not known inside of the traverser. Therefore, this filter can only be executed after the traversal, which may be undesired in large graphs. The second query on the other hand only operates on the path, and therefore this condition can be used during the execution of the traversal. Paths that are filtered out by this condition won't be processed at all.
And finally clean it up again:
arangosh> var examples = require("@arangodb/graph-examples/example-graph.js");
arangosh> examples.dropGraph("traversalGraph");
true
If this traversal is not powerful enough for your needs, like you cannot describe your conditions as AQL filter statements, then you might want to have a look at manually crafted traversers.
Also see how to combine graph traversals.


Air-gapped environments
Copy a bundle into a compressed tarball for offline relocation, then push to a private registry for true air-gapped file transfers.
--- title: "" ---
Package, distribute, and relocate your container images and their dependent configuration files as if they were a single OCI image: a bundle. Consume bundles with confidence that their contents are unchanged after relocation.
Imgpkg packages your files into images using OCI image specifications, allowing the image to be pushed to any registry (like Docker, Harbor, ECR, GCR, …).
Images are identified with a unique SHA256 digest based on the file contents. Imgpkg uses that digest to ensure that the relocated files are identical to those originally pushed.
A bundle captures your configuration files and a list of references to images on which they depend. Copying your bundle will relocate your configuration files along with the referenced images to your destination (i.e. repository, tarball). Imgpkg treats the whole thing as one.
Copy a bundle into a compressed tarball for offline relocation, then push to a private registry for true air-gapped file transfers.
Produces a lock file that can later ensure exactly the same image versions (with digests) are used.
Imgpkg makes no assumptions about the contents of bundles. It can manage all kinds of container+configuration files: Kubernetes deployments, Machine Learning models, etc.
# Create a bundle — in a registry — with configuration and image references
$ imgpkg push --file=bundle-dir/ --bundle=example/bundle-repo-name
# Copy a bundle (w/ dependency images) to another registry
$ imgpkg copy --bundle=example/bundle-repo-name \ --to-repo=internal-registry/bundle-repo-name
# Download bundle into a tarball for air-gapped relocation
$ imgpkg copy --bundle=internal-registry/bundle-repo-name \ --to-tar=/Volumes/secure-thumb/bundle.tar
# Pull a (relocated) bundle to local machine
$ imgpkg pull --bundle=internal-registry/bundle-repo-name \ --output=/tmp/bundle-dir


Sales Information
Add a New Store
Location:
Minimum Hourly Customers:
Maximum Hourly Customers:
Average Cookies per Sale:
Add a Store


Data Scientist Intern - Pricing Strategy and Analytics - San Diego, CA 92121
As a Data Science Intern in the Pricing Strategy and Analytics team in San Diego, you will have a chance to:
-
Apply statistical methods to analyze the effect of pricing and sales decisions on business performance
-
Experience building and interpreting machine learning models on real business data
-
Visualize and interpret the results of analyses, and create strategy recommendations
-
Present the results and the corresponding recommendations to the relevant stakeholders, including senior leadership
-
Develop and implement frameworks and processes for systematic data analysis
-
Immerse yourself in very large data sets and complex problems
-
Develop tools to ingest, merge, and clean datasets
Qualifications
-
Interest in pricing, economics, and/or consumer behavior
-
Scientific thinking and ability to design and critically analyze quantitative research
-
Ability to think creatively about research problems and invent original solutions to modelling challenges
-
Coursework and/or hands-on experience in quantitative research, with diverse academic backgrounds (Economics, Social Sciences, Life Sciences, etc.) considered
-
Proficiency with R or Python is required
-
Ability to build and interpret linear regression models, with machine learning experience a plus
-
Strong analytical skills, with a high level of attention to detail
-
Proficiency in Microsoft Excel and Microsoft PowerPoint
-
Excellent written and verbal communication skills
Nice-To-Haves:
-
Experience working with large data sets
-
Experience in data engineering
-
Exposure to digital marketing
-
Experience working in or collaborating with industry
-
Experience creating data visualizations
-
Ability to build dashboards and other tools using front-end development skills
-
Experience in software development
Scope & Impact
-
Collaborates with peers, junior engineers, data scientists and project team
Education & Experience
-
Current Bachelor's, Master's or PHD candidate, with advanced degree students preferred


Research
Core Research Area in Computer Vision
Computer vision is the study of images and video in order to extract meaningful information (e.g., "Is there a person in this photograph?"), make measurements (e.g., "How tall is the person in this photograph?"), and ultimately achieve a task-specific understanding of what is being seen. Robust computer vision techniques have many important potential applications, and research at the University of Delaware covers several such areas. Prominent among these include scene understanding, computational photography, camera systems, biometrics, medical image analysis, mobile robot perception, remotely sensed image analysis, and metric reconstruction of indoor/outdoor scenes.
» Learn about Dynamic Vision Labs
Current Faculty
Chandra Kambhamettu, Professor: Computer vision, Visualization, Computer graphics.
Christopher Rasmussen, Associate Professor: Intersection of computer vision and robotics; Perception for mobility in semi-structured and outdoor environments.
Jingyi Yu, Associate Professor: Computational imaging; Rendering; Geometric modeling; Mobile computer vision; Surveillance; Image understanding for bioinformatics.
Courses
CISC 442/642 Introduction to Computer Vision
CISC 829 Computational Geometry
CISC 849 Advanced Topics: Computational Photography and Videos
CISC 849 Robot Navigation and Autonomy
CISC 849 Deformable Bodies
CISC 849 Advanced topics in Computer Vision
CISC 849 Computational Photography and Videos


Copyright (c) 2002 Jorge Gomez Arrausi
Unlur is played in an empty hexagonal board:
|
This ingenious PIE rule is necessary, because White has a much easier task than Black.
| An example Players dropped 4 black stones. Then, one player passes and becomes Black. The usual alternating turns start with a White move. |
Unlur was the 2002 Game Design Competition winner (check the other finalists). Check the game's website.


Our simulations reveal substantial inequality in the global-mean temperature response to identical total annual aerosol emissions in each region (Fig. 1b, y-axis). The largest global mean temperature change (−0.29 ± 0.01 °C, induced in our simulations by emissions from Western Europe) is approximately 14 times larger than the smallest global mean temperature changes (−0.02 ± 0.01 °C induced by emissions from India). Global-mean temperature effects correlate roughly with latitude, with higher latitude emitting regions typically generating stronger temperature effects than lower latitude regions, a behavior also seen in previous studies of the response to aerosol forcing in different latitude bands 19. However, substantial differentiation occurs within latitude bands. For example, emissions sourced from East Africa generate approximately four times more cooling (−0.06 ± 0.01 °C) than emissions from India (−0.02 ± 0.02 °C) at similar latitudes—demonstrating the importance of our region-by-region focus.
There are also important regional distinctions in the degree to which the temperature changes due to aerosol emissions are likely to be concentrated in the emitting region versus being felt globally (Fig. 1b, placement relative to diagonals). Because aerosols generally remain concentrated near their source and most strongly influence surface temperature by attenuating incoming solar radiation to the regions they overlie, they cool the emitting region more strongly than the global mean in all cases (Figs. 1b, 2). However, although Indian emissions produce the smallest global-mean cooling effects, their impacts are much more strongly concentrated within the emitting region than for other regions. India experiences cooling from its own emissions at a level ~21 times greater than the global mean (−0.42 ± 0.03 K versus −0.02 ± 0.01 K), while Western European, and Indonesian emissions generate localized cooling (−0.50 ± 0.06, and −0.14 ± 0.05 K, respectively) that is less than two times as great as the respective global-mean cooling (−0.29 ± 0.01, and −0.07 ± 0.01 K, respectively). As a result, even though Western European emissions produce ~14 times the global mean cooling effect of Indian emissions, India and Western Europe experience comparable regional-mean cooling from their own emissions. In other words, regions like Western Europe, Indonesia, and (to a slightly lesser extent) the United States strongly export the climate impacts of their emissions, while regions like India more strongly experience the cooling effects of their own emissions.
Patterns of surface air temperature response to identical aerosol emissions from eight regions. Spatial patterns of surface air temperature change in response to identical aerosol emission in each the eight regions are shown. Global mean temperature change (K) with standard error is shown in bottom left. Grid markings indicate regions that are not statistically significant at the 95% confidence level via t-test
We find that differences in global-mean temperature response resulting from identical emissions emerge at each of several steps between initial emission and eventual temperature effect. Aerosol emissions impact temperature when their resulting atmospheric concentrations interact with atmospheric radiation, both directly and through aerosols’ rapid effects on cloud radiative properties and amount. These radiative interactions then modify the top-of-atmosphere energy balance, which creates a global-mean temperature change mediated by a series of feedback processes. Differences in the temperature change induced by each regional emission can emerge at each step in this process: from emission to atmospheric burden, from burden to top-of-atmosphere radiative forcing, and from top-of-atmosphere radiative forcing to temperature change.
The total atmospheric aerosol burdens generated by the identical regional emissions are spread between 128–233 Gg of sulfate aerosol, 9.29–15.7 Gg of black carbon aerosol, and 23.1–54.2 Gg of organic carbon aerosol (Supplementary Table 1; spatial distributions shown in Supplementary Figs. 1, 2, and 3, respectively). However, the atmospheric burdens of the individual aerosol species generated by each regional emission are largely uncorrelated with the regional emissions’ relative potency at changing global-mean temperature (Supplementary Fig. 4). Thus, the disparity in temperature effects does not arise solely from a disparity in the atmospheric lifetime and resulting atmospheric burden of aerosols emitted from each region, but also through the differential generation of climate forcing and feedbacks by those burdens.
How do the aerosol burdens from each region’s emissions translate into radiative forcing, which in turn drives the global mean temperature change? The radiative effects of sulfate (a global-mean cooling agent), black carbon (a global-mean warming agent), and organic carbon (a global-mean cooling agent, though with minor shortwave absorbing properties) will counteract each other in driving the radiative forcing. This cancellation can be accommodated by using an aggregate measure of aerosol optical properties, such as aerosol optical depth. In order to capture the cancellation between the absorbing and scattering aerosol burdens, we calculate this as the ratio between the change in global-mean total aerosol optical depth and global-mean absorbing aerosol optical depth caused by each regional emission.
The ratio of total to absorbing aerosol optical depth explains approximately 60% of the variance ( R = 0.60, 0.44–0.69) in global-mean top-of-atmosphere effective radiative forcing (ERF) from each regional emission (Fig. 3a). We calculate the ERF as the top-of-atmosphere radiative imbalance induced by the regional emission after the atmosphere and land surface has been allowed to respond (see Methods). The variance in ERF is thus largely explained by the rate of global-mean cancellation between the absorbing and scattering aerosol burdens resulting from each regional emission, arising from differences in aerosol mixing rates, relative altitudes, and other microphysical and radiative factors 30, 31. The remaining variance may be explained by (1) the radiative environment in which the aerosol population is interacting, created by regional differences in climatological cloud cover or background aerosol; or (2) the particular cloud/convective environment in which the aerosols are placed and the influence this has on the manifestation of the aerosols’ indirect and semidirect effects on clouds 32, 33. We find that the ratio of total aerosol optical depth to absorbing aerosol optical depth is approximately as well correlated with clear-sky ERF ( R = 0.58, 0.31–0.61) as it is with the total ERF, indicating that the presence or absence of climatological cloud does not substantially impact the translation of the atmospheric aerosol burdens into radiative forcing.
Relationships between aerosol burden, radiative forcing, and temperature change. The ratio of aerosol optical depth produced by the atmospheric burden of sulfate, black carbon, and organic carbon to its absorbing component (AOD/AAOD) ( a, y-axis) explains 60% of variance in effective radiative forcing ( a, b, x-axis), which in turn has differing efficacy ( b, diagonals) at producing global mean temperature changes ( b, y-axis). Error bars capture the standard error
Although the rate of global-mean cancellation between the absorbing and scattering aerosol burdens is somewhat correlated with the top-of-atmosphere radiative forcing generated by each emitting region, the relative top-of-atmosphere effective radiative forcings are not well correlated with the relative global mean temperature change (Fig. 3b), indicating a substantial divergence in forcing efficacy depending on emitting region. The forcing efficacy of each regional emission—i.e., the global-mean temperature change per unit top-of-atmosphere effective radiative forcing (captured in Fig. 4a and by placement relative to the diagonal lines in Fig. 3b)—range from 0.24 ± 0.14 K(Wm −2) −1 to 1.3 ± 0.06 K(Wm −2) −1. This constitutes a factor of five range in forcing efficacy between emitting regions, roughly twice the relative range in estimated forcing efficacy between global historical aerosols and greenhouse gases 7, 34. Emissions from the U.S. and Western Europe have the largest forcing efficacies (1.32 ± 0.06 and 1.09 ± 0.07 K(Wm −2) −1, respectively), producing outsized temperature responses for the effective radiative forcing they generate. Emissions from regions like Brazil, meanwhile, produce a comparable effective radiative forcing to emissions from Western Europe or the U.S., but generate much smaller global-mean temperature change (Fig. 3b). These efficacy differences highlight the shortcomings of using global-mean radiative forcing to estimate the climate effects of highly regionalized forcings.
Radiative feedbacks versus forcing efficacy. Differences in forcing efficacy ( a) are largely explained by variance in the global-mean radiative gain from surface albedo and cloud feedbacks ( b)—i.e., the additional top-of-atmosphere flux change from the feedback due to a unit of effective radiative forcing—across emissions regions. Error bars capture the standard error
Differing forcing efficacy is fundamentally driven by differences in how the particular spatiotemporal distribution of global- and annual-mean top-of-atmosphere radiative forcing excites feedback processes that contribute to eventual temperature change. This forcing-feedback framework can be represented mathematically as:
The climate system balances an initial radiative forcing (d F) through the radiative effects of a change in temperature (
We find that the differences in forcing efficacy across emitting region can be largely explained by differences in the degree to which the top-of-atmosphere radiative forcing from each regional emission excites top-of-atmosphere surface albedo radiative feedbacks and cloud radiative feedbacks. For each feedback process, we characterize its relative rate of excitement by a given region’s emissions as the radiative gain: the ratio of radiative perturbation from the feedback (
The differences in radiative gain from surface albedo feedbacks and the associated forcing efficacies sort roughly by latitude of emissions. The surface albedo feedback is dominantly driven by sea ice changes (Supplementary Fig. 5) in both the Arctic and Antarctic, and manifests in increased spatial extent and temporal duration of the sea ice. Forcing from Western European and Chinese emissions strongly increases sea ice in both the Arctic and Antarctic, inducing a strong surface albedo feedback radiative gain, while forcing induced by Indian and Brazilian emissions has relatively little effect. The sea ice responses are not strongly spatially collocated with the respective radiative forcings (Supplementary Fig. 5) or atmospheric aerosol burdens (Supplementary Figs. 1, 2, and 3), indicating that the polar effects primarily result from changes in atmospheric circulations that control the energy balance of and/or sea ice dynamics in the polar regions, rather than in situ forcing or aerosol deposition onto the ice. This aligns well with previous studies that show strong dependence of sea-ice albedo feedbacks on the meridional placement of forcings 34, 35.
The inter-regional differences in radiative gain associated with cloud feedbacks also help to explain the inter-regional differences in forcing efficacy, and are partially driven by the climatological cloud environment with which each region’s aerosol emissions interact. Aerosols emitted in India, Indonesia, and Brazil produce large localized cloud changes (Supplementary Fig. 7), associated with the dynamical and thermodynamical effects of the localized aerosol forcing acting on the strongly convective cloud environment in these regions 36– 39. The cloud changes in response to all regional emissions are primarily dynamical or thermodynamical—rather than microphysical—in nature, as aerosol indirect effects (captured by the change in cloud droplet number concentration, Supplementary Fig. 7) are locally confined and relatively uncorrelated with the maxima in cloud change. India’s strong cloud feedback gain and weak surface albedo feedback gain counteract each other in setting the relative overall efficacy of the radiative forcing from Indian emissions.
In our simulations, the cloud feedbacks generated in several regions largely manifest through a north–south shift in tropical cloud cover (Supplementary Fig. 7) associated with the intertropical convergence zone (ITCZ). This meridional ITCZ shift results from the large-scale atmospheric circulation adjusting to compensate for the hemispheric radiative imbalance induced by the localized aerosol forcing and its climate effects 40– 43. This is likely amplified by the surface albedo feedback to each regional emission, which will generate its own hemispheric energy imbalance 44. Even in the presence of Southern Hemispheric emissions, Arctic sea ice increases more strongly than Antarctic sea ice in all cases (Supplementary Fig. 8). This is likely attributable to the stronger overall regional climate sensitivity of the Arctic relative to the Antarctic 45– 47. This common hemispheric imbalance in sea ice response contributes to the comparable ITCZ shifts seen in response to many of the regional aerosol emissions.


{%- include head.html -%}
James Kitchens | Projects
{%- include navbar.html -%}
{%- include project-cards-new.html -%}


This is a simple game of tic tac toe using JavaScript and SVG.
Known issues: Doesn't work in Firefox yet.


The vehicle is an autonomous agent defined and used accordingly to the class Vehicle.
The class consists of several methods:
-Update that modifies parameteres of the vehicle,
-ApplyForce, which applies new vectors(forces) to the vehicle,
-steering behaviours (listed in the left column).
In the box on the right you can see basic methods that are used throughout simulations thus, they won't appear on other pages.
-Update that modifies parameteres of the vehicle,
-ApplyForce, which applies new vectors(forces) to the vehicle,
-steering behaviours (listed in the left column).
In the box on the right you can see basic methods that are used throughout simulations thus, they won't appear on other pages.


Essentail Supplies
-
Essential supplies focal persons
posted on August 11
-
Fuel and LPG distibutor
posted on August 11
-
Vegetable
delivery service
posted on August 12
Home Services
-
Cable TV
services
posted on August 12
General Health Services
-
Focals
for Essential services (Central Region)
posted on August 14
-
Focals
for Essential services (Central Region) - DZO
posted on August 14
-
Essential
health services numbers
posted on August 11
Local Leaders
-
Contact
details of Dzongdags
posted on August 11
-
Details
of Focal Officers in Dzongkhags
posted on August 11
Dzongkhag Webpage
-
Dzongkhag Website
posted on
-
Dzongkhag Website
posted on
-
Dzongkhag Facebook
posted on
Task Force
-
Dzongkhag Covid-19 Response Team
posted on August 11
Toll Free Number
-
Update
and instructions for calling Hotline and other numbers [see end of post]
posted on August 17


A project at Stanford University to develop domain-specific languages for physical simulation. Liszt is focused on performance portability. Performance portable programs are programs that can be run efficiently on a variety of different parallel systems/platforms/architectures. (e.g. CPU, GPU, Multi-core, Clusters, Supercomputers)
Documentation and artifacts for the original Liszt project/language can be found online.
Ebb is the primary DSL for the Liszt project, with specialized DSLs for collision detection and other problems in the works.
A Lua-embedded DSL for writing simulations. Porting an Ebb program from CPU to GPU only requires one command line flag. We think that's pretty cool.
Ebb was designed with a flexible data model that allows for encoding a range of different domains. As a non-exhaustive list, Ebb supports triangle meshes, grids, tetrahedral meshes, and particles. Furthermore, domain libraries are user-authorable and can be coupled together in user code. For example, Ebb seamlessly supports coupling particles to a grid, or coupling the vertices of a mesh to a grid.
For the rest of this manual, we assume a passing familiarity with the Lua language. Specifically, Ebb is embedded in Lua 5.1, though the Lua language is largely feature-stable today. You can find a number of good tutorials, manuals and documentation online.
See the README for installation instructions.
Since Ebb doesn't support string values, let's do some arithmetic instead
import 'ebb'
local L = require 'ebblib'
local GridLibrary = require 'ebb.domains.grid'
local grid = GridLibrary.NewGrid2d {
size = {2,2},
origin = {0,0},
width = {2,2},
}
local ebb printsum( c : grid.cells )
L.print(21 + 21)
end
grid.cells:foreach(printsum)
Save this in a file hello42.t
. Then, execute the command ./ebb hello42.t
to run the Ebb program. This will print out 42
4 times, once for each of the 4 grid cells in the 2x2 grid we created.
If you want to be able to call Ebb from anywhere on your system, you'll want to add the LISZT_EBB_ROOT/bin
directory to your system path.
C
codeCurrently the Ebb interpreter cannot be called from C code as a library, though this is relatively easy to do. Please contact the developers if this use case is important to you.
See the tutorials file for a sequence of example code.
Ebb consists of two parts: an embedded language, and a Lua API. The language proper is used to define Ebb functions, while the Lua API is used to construct and interrogate the data structures, as well as launch functions via foreach
calls. For instance, in the hello42
sample program above, the printsum()
function is written in the Ebb language, while the rest of the program makes calls to the API.
In addition to these two parts, a set of standard domain and support libraries are provided, which this documentation will also discuss.
The remainder of the manual will assume a passing familiarity with the structure of Ebb programs. For a more intuitive introduction to the language, please see the tutorials.
The Ebb language is used to define Ebb functions, which can either be used in other Ebb functions, or executed for each element of some relation.
Ebb functions can be declared anonymously in place of a Lua expression just like Lua and Terra functions
local foo = ebb() ... end
or syntactic sugar can be used to name and bind a function to a local or global/pre-existing name
ebb foo() ... end
local ebb bar() ... end
Arguments to an Ebb function can be supplied typed or un-typed
ebb foo ( x : L.double, y : L.int ) ... end
ebb bar ( z, another_arg) ... end
ebb baz ( x : L.double, y ) ... end
If no type is supplied for an argument, then the argument type will be inferred when the function is called. A single function can be specialized multiple different ways, so if a second call produces a different type signature, then the function will be typechecked and compiled a second time.
Functions are processed at three distinct points. When the Lua thread of control hits a definition, the function is defined, capturing any values from the enclosing Lua context. Then, whenever a function is called for the first time, it is typechecked and compiled. Finally, both the first and every subsequent time a function is called, it is executed. As mentioned above, attempting to execute a function in a new context may cause a repetition of typechecking and compilation.
One important note here is that unless a function is used, it will not be type-checked. This is unlike static languages, where all code is checked for errors at compile time, but also unlike dynamic languages in that the code is all type-checked on the first function execution, even those parts of the function that fail to execute.
Ebb functions consist of a sequence of statements, and possibly a final return statement
ebb foo( x : L.double, y : L.double )
var z = 32.0
return x + y * z
end
If a function takes exactly one argument, which is a key from some relation and returns no values, then that function can be executed for each element of the relation. These functions are the primary (data-parallel) computations of Ebb
ebb dilate_vertex( v : vertices )
var magnitude = L.length(v.pos)
v.pos = v.pos * magnitude
end
vertices:foreach(dilate_vertex)
Ebb types (detailed below) include
L.int L.float L.double L.bool L.uint64
as well as vectors and matrices of these primitive types and key types representing references to rows of a given relation. Whenever a relation rel
is used in place of a type, it will be automatically promoted to the type L.key(rel)
.
Literals use the same format conventions as C and Terra. 0
is assumed to be an integer, 0.0
a double, and 0.0f
a float. 0ULL
is a uint64.
In order to cast an expression in Ebb to a different type, that type can be invoked as if it were a function call. For example, casting a double to an integer
ebb round( x : L.double )
return L.int(x)
end
Ebb supports the following built in binary logic/arithmetic operations with their usual behavior:
== ~= < > <= >=
not or and
+ - * / %
In addition the unary-prefix operations -
and not
are also supported. Matching sized vectors or matrices can be added or subtracted, as well as being scaled or divided by a scalar. However, matrix-vector multiplication and other matrix/vector operations are not built in.
Vectors can be constructed using curly braces, such as in this example function that constructs a vector with 3 copies of the argument
ebb vec_of_3 ( x )
return { x, x, x }
end
Matrices can likewise be constructed by placing curly braces around 3 vectors of the same length
ebb mat_of_2 ( x )
return { { x, x },
{ x, x } }
end
Subsequent vectors are assumed to be subsequent rows of the matrix.
Square brackets are used to access elements of vectors and matrices.
ebb matvec3 ( A, x )
return { A[0,0] * x[0] + A[0,1] * x[1] + A[0,2] * x[2],
A[1,0] * x[0] + A[1,1] * x[1] + A[1,2] * x[2],
A[2,0] * x[0] + A[2,1] * x[1] + A[2,2] * x[2] }
end
Other Ebb functions are called like you would expect
ebb mat_of_3 ( x )
return { vec_of_3(x), vec_of_3(x), vec_of_3(x) }
end
Likewise, Terra or C functions can be directly inlined into an Ebb function, though Ebb may or may not be able to port the resulting function between CPU and GPU without using a different version of the function.
terra sum_vals( x : double, y : double )
return x + y
end
ebb double_val ( x : L.double )
return sum_vals( x, x )
end
Ebb variables can be declared with a type annotation and/or initialized. If no explicit type annotation is provided, then the type is inferred from the intialization expression.
ebb foo()
var x : L.double
var y : L.int = 42
var z = 3.2
end
Variables can have their values re-assigned
ebb foo()
var x : L.double
x = 3.5
x = x * x
return x -- will return 7.0
end
Given a key from some relation, we can access the value of a field of the relation at a given element as if it were member data
ebb tet_mass ( t : tetrahedra )
return t.volume * t.density
end
When accessed in this way, we say a field is being READ.
Alternatively, we can write functions that WRITE to fields, as we demonstrate here by computing triangle normals
ebb compute_normal ( t : triangles )
var n = L.cross( t.v[1].pos - t.v[0].pos,
t.v[2].pos - t.v[0].pos )
t.normal = n / L.length(n)
end
Finally, functions can _REDUCE_ into fields using any of the reduction operators `+=`, `*=`, `min=`, or `max=`:
ebb newton_step ( v : vertices ) v.pos += 0.2 * v.velocity + 0.2 * 0.2 * v.acceleration v.velocity += 0.2 * v.acceleration end
Whenever an Ebb global variable is used on the right-hand-side of an an assignment, we say it is _READ_.
local uniform_density = L.Global(L.double, 0.2) ebb tet_mass ( t : tetrahedra ) return t.volume * uniform_density end
Like fields, we can _REDUCE_ into Ebb globals, though Ebb functions are not allowed to write Ebb globals.
local max_vel = L.Global(L.double, 0.0) ebb measure_max_vel ( v : vertices ) max_vel max= L.length(v.vel) end
-------------------------------------------------------------------------
## Phase Checking
Besides usual typechecking, Ebb implements a special kind of typechecking that we call _phase-checking_. Phase-checking ensures that an Ebb function can be concurrently executed for each element of a relation without the possibility of data-races. It consists of three simple rules
* If a field is _exclusively_ accessed through the _centered_ key (defined below) then any combination of _READs_, _WRITEs_, and _REDUCEs_ are allowed.
* If a field is accessed dependently (through a key-field, query-loop, or affine-indexing) then either the field must only be _READ_, or only _REDUCED_ with a consistent reduction operation.
* If a global is accessed, it must only be _READ_, or only be _REDUCED_ with a consistent reduction operation.
The _centered_ key is the key passed in from the relation on which `rel:foreach(func)` is called. For instance, in the following execution `v` is centered, while `nv` is not, even though `nv` is also from `vertices`. Because `vertices.t` is not _exclusively_ accessed through the centered key, we can only _READ_ from it, not reduce or write, even via the centered key.
local ebb compute_update( v : vertices ) var avg = 0.0 for e in v.edges do var nv = e.head avg += e.weight * (nv.t - v.t) end -- the following line would cause a phase-checking error -- v.t += avg v.t_change = avg end
vertices:foreach(compute_update)
-------------------------------------------------------------------------
## Control Flow
Ebb supports `do ... end` blocks in order to hide variables in local scopes.
ebb foo( x : L.double ) x = 2.0 var y = 3.0 do x = 4.0 y = x end L.print(x, y) -- will output 2.0, 4.0 end
Ebb supports standard `if` statements as well.
ebb sgn( x : L.double ) var val : L.double if x > 0.0 then val = 1.0 elseif x < 0.0 then val = -1.0 else val = 0.0 end return val end
Ebb supports `for` loops with numeric bounds. Unlike Lua, but like Terra, Ebb for loops count over the range exclusive of the upper bound. That is, the loop below will count `2, 3, ..., n-1, n` but not `n+1`.
ebb fact( n : L.int ) var prod = 1 for k=2,n+1 do prod *= k end return prod end
Ebb supports `while` loops as well
ebb fact( n : L.int ) var prod = 1 while n > 1 do prod *= n n = n - 1 end return prod end
And Ebb supports the Lua/Terra `repeat ... until <cond>` loop
ebb double_to_above( n : L.int, lower : L.int ) repeat n *= 2 until n >= lower return n end
Finally, Ebb supports the query loop, which usually will look something like the following
ebb compute_local_avg( v : vertices ) var sum = 0.0 var n = 0 for e in v.edges do sum += e.head.t n += 1 end v.avg = sum / n end
Though, as explained in the tutorials, `v.edges` is the query expression `L.Where(edges.tail, v)` hidden behind a macro.
-------------------------------------------------------------------------
-------------------------------------------------------------------------
# The Ebb API
In the following, we assume that the Ebb library has been loaded into a local variable `L`, e.g. via the command `local L = require 'ebblib'`.
## Types
Ebb types are Lua objects, available via the standard library. We can test whether a Lua value is an Ebb type by calling
L.is_type(val)
There are 5 primitive types
L.bool L.int L.float L.double L.uint64
We can test whether a type is primitive or not using
typ:isprimitive()
-------------------------------
Small matrices or vectors of primitives are given types via the constructors
L.vector(primitive_type, n_entries) L.matrix(primitive_type, n_rows, n_columns)
We can test whether a type is a vector, matrix, or neither via the calls
typ:isvector() typ:ismatrix() typ:isscalar() -- neither of the above
If a type is a vector, then we can get the number of entries as `typ.N`; if a type is a matrix, then we can get the number of rows as `typ.Nrow` and number of columns as `typ.Ncol`. We can retrieve the primitive type by calling
typ:basetype()
which can also be called on primitives, returning the original type.
As a convenience, several standard vector and matrix types are given shorthands by the standard library.
L.vec2d -- is L.vector(L.double, 2) L.mat3f -- is L.matrix(L.float, 3, 3) L.mat2x3i -- is L.matrix(L.int, 2, 3)
These shorthands will work for 2, 3, and 4-dimensional types, and use the primitive type coding that d=double, f=float, i=int, and b=bool.
We can also apply the following tests irrespective of whether a type is scalar, vector or matrix
typ:isintegral() -- true for L.int and L.uint64 typ:isnumeric() -- true for all but L.bool typ:islogical() -- true for only L.bool
-------------------------------
Lastly, we can construct _key types_ to talk about references between relations.
L.key(relation)
To inquire for the specific relation, we can access `typ.relation`.
As a convenience, the Ebb API will automatically promote a relation `rel` into the type `L.key(rel)` anywhere it expects to get a type. For instance, we can construct vectors and matrices of keys, and pass a relation directly in place of the primitive.
L.vector(L.key(vertices), 3) -- equivalently L.vector(vertices, 3)
We can test whether a type is a scalar key using the function
typ:isscalarkey()
Alternatively, we can test whether it is any sort of key type: scalar, vector or matrix
typ:iskey()
### Summary of Types
Scalar types are either one of 5 primitives or a key-type. All of these scalar types can be organized into vectors and matrices.
If type-casting of values in code can be done without any loss of precision, Ebb will insert coercions as necessary.
--------------------------------------------------------------------------
## Builtins
Ebb defines built-ins for two reasons: 1) some special functionality cannot be defined as user-level libraries; 2) to allow portability of the standard math routines. (Note: We hope to move the math functions into user-level libraries in the future)
Given some arbitrary Lua value, we can test whether it's an Ebb builtin using the function
L.is_builtin(val)
Ebb provides assert and print functions that are portable across CPU and GPU execution to help with debugging. The `L.print()` built-in supports a small subset of the standard printf functionality. If you find the print function inadequate for your debugging needs, please let the Ebb developers know.
Note that both `L.assert()` and `L.print()` may cause side-effects. When executed in parallel, no guarantees are made about the order in which these side-effects take place. Therefore, these functions should only be used for debugging. Field dumping should be used when reproducible output is desired.
L.assert(booltest) L.print(...)
While the internal representation of key values is hidden from Ebb programmers, we allow users to extract stable identifiers for rows, which can also be helpful for debugging:
L.id(keyval) -- return an identifier for an element of a non-grid relation L.xid(keyval) L.yid(keyval) L.zid(keyval) -- return coordinates for an element of a grid relation
The "topological query" functions (`L.Affine()` and `L.Where()`) are also builtins, although they are discussed [elsewhere in this manual](BROKEN)
TODO: WHERE?
We provide the 3 following convenience functions for numeric vectors
L.length(vec) L.cross(vec_a, vec_b) L.dot(vec_a, vec_b)
and a GPU portable random number generator that returns a double in the range 0 to 1.
L.rand()
We also provide a set of functions from _math.h_. When executed on a GPU, standard CUDA implmentations or special GPU instructions will be used in their place. If you need access to standard math functions you don't see here, please contact the Ebb developers.
L.acos(x) L.asin(x) L.atan(x) L.cbrt(x) L.ceil(x) L.cos(x) L.fabs(x) L.floor(x) L.fmax(x,y) L.fmin(x,y) L.fmod(x,denom) L.log(x) L.pow(base,exp) L.sin(x) L.sqrt(x) L.tan(x)
### External C functions in Ebb code
Lastly, any C-functions imported via Terra can be used in Ebb code as if they were a built-in. (For instance, all of the C `math.h` library can be imported this way.) However, functions imported this way will usually not be portable to GPU.
--------------------------------------------------------------------------
## Globals
Global variables are used to represent non-spatial values (i.e. not defined per-element) that change over the course of a simulation. If you're instead confident that the value will remain fixed, using a `Constant` instead will result in better performance.
Global variables are created using the following function call
local glob = L.Global( typ, init_value )
A lua value can be tested for whether or not it's an Ebb global using
L.is_global(val)
Besides using a global inside Ebb functions, we can set or get its value from Lua using
glob:set(new_value) local lookup_value = glob:get()
And finally, we can inquire for a Global's type using
local typ = glob:Type()
Inside an Ebb function, a global can either be read from, or reduced into.
--------------------------------------------------------------------------
## Constants and Literals
Constants work very similarly to globals, except the value of a constant cannot be modified from Lua or from Ebb.
local const = L.Constant( typ, init_value )
L.is_constant(val)
local lookup_value = const:get()
local typ = const:Type()
When used in Ebb code, a constant will be assigned the specified type.
Literals in Ebb code can arise from a literal in the code, such as the `1.0` in the following example. In this case, Ebb interprets `1.0` as a literal of type `double` and so coerces `x` into a double before taking the sum.
local ebb literal_example_1( x : int ) return x + 1.0 end
However, literals can also arise from inlined Lua variables, such as `inc` in the following example. However, because the untyped value `1` is inlined into the code (all Lua numbers are of the single Lua type _number_) Ebb chooses the most conservative possible type for the literal, which is `int` here. Consequently, the function returns an `int` rather than a `double`.
local inc = 1.0 local ebb literal_example_2( x : int ) return x + inc end
Constants can solve this problem with literals by allowing the programmer to provide an explicit type for a value declared in Lua.
--------------------------------------------------------------------------
## Relations
Apart from globals, (and data stored only at the Lua level) all Ebb data is stored in _relations_, which are like database/spreadsheet tables. Usually a domain library will set up relations for programmers, though domain libraries do not have any kind of privilleged API access.
The following call creates a new relation with `n_size` rows/elements. We specify the `name` as well for debugging output.
local relname = L.NewRelation{ name = 'relname', size = n_size, }
If we want to instead declare a 2d or 3d grid-structured relation, then we instead call the following. `dims_list` is a Lua list of 2 or 3 numbers; `periodic_list` is a list of 2 or 3 booleans. If the `periodic` argument is ommitted, (it's optional) then by default no dimensions are periodic.
local relname = L.NewRelation{ name = 'relname', dims = dims_list, periodic = periodic_list, }
We can test whether a lua value is a relation using the function
L.is_relation(val)
and given a relation, we can test whether it's a grid using
rel:isGrid()
We can retrieve any of the arguments we used to create the relation using the following functions
rel:Name() rel:Size() rel:Dims() rel:Periodic()
If a relation is grid-structured, then `rel:Size()` returns the total number of elements. If a relation is not grid-structured, then `rel:Dims()` returns a list with one number in it: the size of the relation.
The following call will print information about the relation and all the fields defined on it. This function is not optimized for efficiency. It should only be used for debugging and on relatively small relations.
rel:Print()
----------------------------
The following call will execute an Ebb function `efunc` for each element of a relation `rel`.
rel:foreach(efunc)
Besides defining fields (detailed below), macros or functions can be installed in place of fields. A field-macro can be installed via the following call.
rel:NewFieldMacro(field_name, macro)
Field macros will only be passed a single argument: the key on which they get invoked.
There is one special exception to this rule. If a field-macro is installed with the name `__apply_macro`, then it will get invoked whenever the syntax `k(arg1, arg2, ...)` is used. The macro will get passed `k` as its first argument, followed by all of the arguments within the parentheses.
Besides field-macros, field-functions can be installed using one of the following three calls. All calls take a field name, and an ebb function. The reduce call also takes a (string) argument to specify which reduction operation is being overloaded. Ebb functions installed as read-functions should take a single argument: the key on which they are invoked. Ebb functions installed as write-functions or reduce-functions should take two arguments: the key on which they're invoked and the right-hand-side value that should be written or reduced.
rel:NewFieldReadFunction(field_name, efunc) rel:NewFieldReduceFunction(field_name, reduce_op, efunc) rel:NewFieldWriteFunction(field_name, efunc)
----------------------------
### Grouping and Query-Loops
If a relation `rel` is not grid structured, and has a field `kf` of scalar key type `L.key(r_target)`, then we can group `rel` by `kf` using the call
rel:GroupBy(rel.kf)
or equivalently
rel:GroupBy('kf')
Strings will be automatically converted into full fields.
Once `rel` is grouped by `kf`, and given a key `rt` from `r_target`, then we can execute the query-loop
for r in L.Where(rel.kf, r_t) do ... end
inside of ebb functions.
We can test whether a relation is grouped using the call
rel:isGrouped()
And given that a relation is grouped, we can query for the field it's grouped on using
rel:GroupedKeyField()
which will return the field object (not name string) of the key field `rel` is grouped by.
If a relation is neither grid-structured nor grouped, then we say that it's in _plain_ mode. We can test for whether a relation is plain using
rel:isPlain()
--------------------------------------------------------------------------
## Fields
New fields can be defined on relations using the following call
rel:NewField(name, typ)
which returns the new field object.
Given an arbitrary Lua value, we can test whether it's a field using the call
L.is_field(val)
We can retrieve the field's name, parent relation, full name (parent relation's name concatenated with field name), type and size (i.e. the size of the parent relation) all using a set of functions
field:Name() field:FullName() field:Relation() field:Size() field:Type()
The contents of the field can be printed, though the `Print()` function is not optimized for efficiency. It should only be used for debugging and on relatively small relations.
field:Print()
Given two fields with names `f1` and `f2` on the same relation `rel` and with the same type, we can swap their contents using the function
rel:Swap('f1', 'f2')
If we would like to copy the contents of `f1` to `f2` instead, we can call
rel:Copy{ from = 'f1', to = 'f2' }
-------------------------
In order to get data into and out of fields, we use the functions
field:Load(...) field:Dump(...)
which are polymorphic based on their arguments.
When passed a value of the same type as the field, `Load()` will load that same value in for the field's value at every element. For instance, the following load a double, and vector of 3 doubles respectively
f1:Load(3.2) f2:Load({0,0,0})
If another field of the same type is passed to `Load()`, then the values are copied over from that field to initialize the first one.
f1:Load(f2)
If a Lua function is passed in as the argument, then the lua function will be invoked once per-argument being passed the id of the element and returning the value to be assigned for that element. For instance, the following call will assign values equal to the log of the element's id to each element.
f1:Load(function(id) return math.log(id) end)
If the relation is a grid, then 2 or 3 identifiers are supplied instead.
If a Lua list of `field:Size()` values is passed to `Load()`, then those values will be used to initialize the field. In the case of a grid, this should be a list-of-lists, reflecting the grid structure.
field:Load( { val1, val2, ... } ) grid_field:Load( { { val1, val2, ... }, ... })
Finally, loading can be perfomed using C or Terra functions, as detailed below in the section on File I/O
If `Dump()` is called with an empty table passed in as the argument, then `Dump()` will return a Lua list containing the values of the field.
local vals = field:Dump({})
Otherwise, we recommend using the File I/O facilities to dump the contents of a field to file.
--------------------------------------------------------------------------
## Subsets
To define a subset on a relation, we call the new subset function.
Currently, the API supports two ways of defining subsets of grids. If you need other ways to specify subsets, please contact the developers.
The first form of `NewSubset()` takes a list of ranges specifying a rectangle, such as
rel:NewSubset(name, { {x_lo, x_hi}, {y_lo, y_hi}, {z_lo,z_hi} })
where `x_lo` and `x_hi` specify an inclusive range. In the case of a 2d grid, the z-bounds should be omitted.
The second form of `NewSubset()` works similarly, but takes a whole list of rectangles in the form given for the first call
rel:NewSubset(name, { rectangles = { rect_1, rect_2, ... } })
The subset is then defined as the union of these rectangles.
We can test whether a relation has any subsets defined using
rel:hasSubsets()
We can test whether a given Lua value is a subset object using
L.is_subset(val)
Given a subset, we can interrogate it for its name and parent _relation_
subset:Name() subset:FullName() subset:Relation()
`FullName()` will return a string that concatenates the parent relation's name and the subset's name with a '.' in-between.
Most importantly, ebb functions can be executed for each element in a subset using the following call
subset:foreach(efunc)
--------------------------------------------------------------------------
## Data Layout Descriptors
In order to support interoperability with externally defined code, Ebb defines a kind of metadata called a _data layout descriptor_ (DLD) to specify exactly where and how a field of data is laid out. To get immediate access to the backing data regardless of where it is, call
local dld = field:GetDLD()
This will return a Lua table reflecting the C-struct format. When passing this descriptor into a C or Terra function called from Lua, you should convert the descriptor into a C-struct by calling
dld:toTerra()
To get access to the DLD type via Terra, you can require the standard library
local DLD = require 'ebb.lib.dld'
which will then expose the Terra type as
DLD.C_DLD
Alternatively, if you are separately compiling C-code to link against an Ebb program, you can use the provided header file at [include/ebb/lib/dld.h](../include/ebb/lib/dld.h). Both the `.t` and `.h` file include full documentation inline of the format.
A DLD is laid out as
typedef struct DLD { uint8_t version[2]; /* This is version 1,0 / uint16_t base_type; / enumeration / flags / uint8_t location; / enumeration / uint8_t type_stride; / in bytes / uint8_t type_dims[2]; / 1,1 scalar; n,1 vector; ... */
uint64_t address; /* void* / uint64_t dim_size[3]; / size in each dimension / uint64_t dim_stride[3]; / 1 = 1 element, not 1 byte */ } DLD;
The `address` field holds a pointer to the start of the data.
To illustrate how the different parameters of the description work, suppose we have a 3d grid of 3-by-3 small-matrices of doubles. To access the `i,j`-th entry in the small matrix at grid cell `x,y,z`, we would compute the following address.
uint elem_index = x * dld.dim_stride[1] + y * dld.dim_stride[2] + z * dld.dim_stride[3] ; double * elem_addr = (double)( (uint)(dld.address) + elem_index * dld.type_stride ); double * entry_addr = elem_addr + i*3 + j;
Note the following. The `dim_stride` values specify strides in elements not bytes. Elements are assumed to be stored `type_stride` bytes apart. And small matrices are always stored in row-major order, densely packed. Likewise vectors are densely packed, though the `type_stride` may specify that there should be some number of unused padding bytes at the end of a vector or matrix element to improve alignment, e.g. for SIMD-vectorization.
The `location` field either assumes the value `CPU` or `GPU` (defined as constants in both the `.t` and `.h` files).
The `base_type` enumeration is a bit more complicated, but constants are defined for all of the following:
UINT_8 UINT_16 UINT_32 UINT_64
SINT_8 SINT_16 SINT_32 SINT_64
FLOAT DOUBLE
Additionally, since Ebb maintains specialized key encodings, `dld.t` defines constants for all possible key encodings, e.g.
KEY_32 KEY_64_32 KEY_16_32_64
In `dld.h` there are instructions for how to compose similar constants in a C file.
--------------------------------------------------------------------------
## Load and Dump (File I/O)
Ebb uses DLDs to support efficient, custom file I/O libraries. This behavior is triggered by passing a Terra or C function `tfunc` to `Load()` or `Dump()`
field:Load( tfunc, ... ) field:Dump( tfunc, ... )
The first argument of `tfunc` should always have type `&DLD.C_DLD`. Any remaining arguments passed into `Load()`/`Dump()` after `tfunc` will be passed into `tfunc` as additional arguments. Similarly, any value returned from `tfunc` will be returned by the `Load()`/`Dump()` call.
For instance in the following example, we can pass in a filename and return an error code if the file doesn't exist.
local terra demo_load ( dld : &DLD.C_DLD, filename : &int8 ) if not file_exists(filename) then return 1 else return 0 end end
local errcode = field:Load(demo_load, "some_file.data")
Sometimes file formats require simultaneous access to multiple fields of a relation. Ebb supports multi-field `Load()` and `Dump()` calls for these cases. To load or dump fields `f1`, `f2`, etc. call
rel:Load( { f1, f2, ... }, tfunc, ... ) rel:Dump( { f1, f2, ... }, tfunc, ... )
`f1`, `f2`, etc. can be either field objects from `rel` or strings specifying fields of `rel`. Just like the `field:Load()`/`field:Dump()` forms, any additional arguments are passed through to the Terra/C function, and any return value is returned. When using this form the first DLD pointer argument will point to an array of DLDs: one for each field requested.
---------------------
Using Terra's metaprogramming facilities, it's possible to generic, optimized file I/O routines. In order to support the necessary introspection to make this happen, Ebb allows for the creation of `Loader` and `Dumper` objects.
local loader = L.NewLoader(lua_func) local dumper = L.NewDumper(lua_func)
These loader/dumper objects can be passed to field load/dump calls
field:Load(loader, ...) field:Dump(dumper, ...)
When called like this, the Lua function used to create the loader or dumper is called with `field` as the first argument, and the remaining arguments passed through. Any returned values, will be returned similarly.
Loader/Dumpers are used to implement the standard CSV library, which then allows simulation code to simply write
local CSV = require 'ebb.io.csv'
field:Load(CSV.Load) field:Dump(CSV.Dump)
without having to worry about what type of field is being processed.
Like with other Ebb objects, we can test whether arbitrary Lua values are loader or dumper objects using the functions
L.is_loader(val) L.is_dumper(val)
--------------------------------------------------------------------------
## Macros and Quotes
Ebb macros work similarly to Terra macros, though they are less developed. Macros consist of Lua functions that are executed during typechecking/compilation. These macro functions are passed in Ebb code as arguments, and expected to return a piece of quoted Ebb code.
local macro = L.Macro(lua_func)
We can test whether a given lua value is an Ebb macro using
L.is_macro(val)
Ebb quotes can be constructed in two forms.
local q = ebb `some_ebb_expression_here local q = ebb quote some_ebb_statement_1 some_ebb_statement_2 ... in some_ebb_expression_here end ``The second form is converted to a let-expression internally, while the first form is substituted in directly. For instance, the first form allows writing
ebb L.Where(...)
even though L.Where(...)
is neither an expression nor statement in the Ebb language. (It can only ever occur in a query-loop)
WARNING: Like macros in most languages, if you use the argument to a macro more than once, you will create duplications of the subtrees. If you do not intend to do this, you need to assign the value of a macro argument to a temporary using the ebb quote ... in ... end
form. In general, please rely on Ebb functions in preference to macros where possible.
If you find the current macro system insufficient or buggy for something you want to accomplish, please contact the developers about possible changes.


--- layout: post title: Losk TVC date: '2015-04-03T07:30:32+02:00' categories: - Klare Lijn — Ligne Claire - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/115381742864 ---


In 1992, Burke recorded the solo album Open House with Mark Graham (harmonica, clarinet, vocals), Paul Kotapish (guitar, mandolin, cittern, bass), and Sandy Silva (percussion). These three comprised the core of his band and together, as the group Open House, they recorded two other albums, Second Story (1994) and Hoof and Mouth (1997).
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
Sandy Silva was among the first to combine body percussion techniques andcontemporary movement with percussive dance - her work stands at the vanguard ofcontemporary artistry in traditional music and dance. Over the past three decades, Sandy has developed a personal vocabulary of movement and sound based on the percussive dancepractices of Hungary, Appalachia, Cape Breton and Andalusia, and the idioms of circus,contemporary dance, and theatre. Silva’s choreography weaves a dialogue between thesegestural and rhythmic vocabularies, blending the local and the post-modern in an organic,bodily response to her world. The result is an incredibly dynamic and emotive expression at the crossroads of movement and sound.
Born at an early age in Takoma Park, Maryland, Paul Kotapish spent most of his youth in Virginia near Washington D.C. with one ear glued to a portable record player and the other attached to a transistor radio. His folks, unable to endure his relentless pestering, bought him a guitar in 1963. Paul learned two chords and promptly started a band.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.


11. Practice Client-Server Features
Let's do some Coding Practice
In the previous concept, you learned how to setup a simple web service in Node.js. Now let's test those skills! We will be integrating a new concept with the hashing concept you practiced before.
To do this quiz, you will follow a few simple steps:
Step 1 Clone the project repository in this Github repository Step 2 Open the terminal and install the packages: npm install
Step 3 Open the file app.js and start coding - See additional info below Step 4 Run your application node app.js
Step 5 Go to your browser and type: http://localhost:8080/
Step 6 Get the hash generated in your browser and answer the quiz in your classroom
Additional info for Step 3
Review the boilerplate code
Once you have cloned Github project, and installed the packages dependencies with npm install
, open the app.js file:
// Http library
const http = require('http');
//Step 1. Import crypto-js/sha256
// Http port
const port = 8080;
//Mock Data
var blocks = [];
let block_1 = {"height":"0","body":"Udacity Blockchain Developer", "time":<PHONE_NUMBER>};
let block_2 = {"height":"1","body":"Udacity Blockchain Developer Rock!", "time":<PHONE_NUMBER>};
blocks.push(block_1);
blocks.push(block_2);
//Step 2. Configure web service
/**
* Take the block_2 data from the array "blocks" and generate the hash to be written into the response.
*/
//Add your code here
// Notify console
console.log("Web Server started on port 8080\nhttp://localhost:"+port);
// Start server with http port
app.listen(port);
As you can see, the tasks you have to complete are:
- Import crypto-js/sha256
- Create the variable
app
:
const app = http.createServer(function (request, response){ // your code go here}
You need to add the hash generated from the data of the block_2 object (which is in the array blocks) to the response of this service.
After that, get the hash generated in your browser and come back to answer the quiz.
Select the correct Hash


Instructions:
Perform DeleteMin three times, and after each deletion, restore heap property. For each record stored into heap you can see a key that corresponds to the priority. "Decrement heap size" -button will delete the last key of the heap. In addition, by first clicking one key and then another, you can swap the records.


There is not a holiday or vacation that is complete without having an authentic meal of that place. Kanyakumari is a perfect destination that will not only feast your eyes with its amazing beauty, but also fill your stomach with amazing food. It is a hub of delicious south Indian food and is widely available throughout the town. From popular south Indian dishes such dosa and idli, it is also famous for seafood delicacies.
The restaurants in Kanyakumari offer amazing dishes that are prepared using fresh produce, flavorful spices, and an extra serving of love. While it is easy to find a restaurant at every nook and corner in this beautiful town, we have listed some of the best and must-visit restaurants for you to get a taste of authentic Kanyakumari food.
Hotel Seaview is one of the top restaurants in Kanyakumari serving a variety of delicious dishes. If you are planning to visit seaview then don’t forget to try their chilli chicken and the famous South Indian thali. This multi cuisine restaurant has a pleasant ambience and is known for its impeccable service.
Hotel Seashore is among the famous restaurants in Kanyakumari known for serving amazing quality of food. The restaurant in Hotel is located on the sixth floor from where you can enjoy enchanting views of Kanyakumari. If you are heading here, then you must surely try their pongal and kesaribath.
Hotel Saravana is a pure veg restaurant serving mouth-watering North Indian as well as South Indian dishes. You must surely taste their South-Indian thali if you want to experience the authentic taste of South Indian food. Located in the main market area it is a favorite food joint of many tourists.
Hotel Triveni is another famous vegetarian restaurant in Kanyakumari. Located on the main road, the place is often crowded with food lovers looking for delightful options. Here you get tasty and affordable local thali meals. They also have other thali options like Rajasthani, Gujarati and North Indian Thalis.
Hotel Sri Annapoorna is one of the most popular restaurants in Kanyakumari that offers amazing hospitality, delicious food, and quick service. This is one of the best veg restaurants you can choose from when visiting Kanyakumari. The extensive vegetarian menu of the restaurant is bound to offer some delicious delicacies to its guests. The ambience of the restaurant is really decent, and sophisticated. The restaurant is budget friendly and offers food that will leave you with a full stomach and smiley face.
Sangam restaurant has carved itself a niche in Kanyakumari as one of the best restaurants in the region. Sangam restaurant is a name that was once synonyms with Kashmir but now it has made its place in the people of Kanyakumari owing to the delicious food it offers. The hospitality, quick service, and mouth watering dishes makes this one of the most popular restaurants.
Looking for amazing South Indian dishes while you are travelling in Kanyakumari? Look no further than Archana restaurant. This restaurant is one of the most popular restaurants in Kanyakumari which is known for their amazing food and quick service. They serve typical Tamil cuisine such as Idli, Vada, and Masala Dosa. For everyone who loves south indian foods should go there and try their amazing food.
The Ocean is a multi-cuisine restaurant in Kanyakumari that serves food that is just mouth-wateringly delicious. The restaurant is known for a beautiful ambience and a view that will make you want to stay here all day long. They use a fresh produce of the ingredients to prepare their food and that is why the food here tastes so amazing. Visit this restaurant and enjoy flavorful meals.
Tired of eating south Indian cuisines day in and day out? The Curry in Kanyakumari is an amazing multi-cuisine restaurant that serves cuisines of China, Japan, Mexico, and Thailand along with South India and North India. Their excellent service, authentic flavors, and delicious food offers a fine dining experience that cannot be replicated.
Kanyakumari |
Sights to |
History |
Delicious Dishes |


Puzzle 6
On the edge of town,
on a brownfield site,
a new housing estate has been laid out consisting of sixteen
plots of land.
The Planning Committee have specified the following rules:
- Only one building per plot
- The building must be constructed in one of the four quadrants
of the plot
- top left
- top right
- bottom left
- bottom right
- In the other three quadrants of a plot, there must be gardens.
- When a building has been constructed in a quadrant,
all plots due north, south, east and west of this plot
must have a garden in that particular quadrant
(that is, on any row of plots (north-south or east-west)
there mustn’t be two plots having buildings in the same quadrant).
- Furthermore, the corner plots must have buildings in their
inner quadrants
- There must be a building on each plot.
The task of constructing the buildings has been given to the
Gung-Ho Construction Company
and the task of planting the gardens has been given to the
Meticulous Landscaping Company.
Unfortunately, before the Landscaping Company arrive, the
Construction Company already started work on two buildings
(marked with solid rectangles on the map below)
The CEO of the Meticulous Landscaping Company
is concerned that the Gung-Ho Construction Company are going to
construct buildings randomly and discover that it won’t be possible
to have a building in each plot.
“We will just knock some buildings down and build them in a
different quadrant until we have got it right.” said Gung-Ho’s
CEO.
The CEO of the Meticulous Landscaping Company
thought this was a bad idea, so suggested,
“For each building constructed, let us plant the gardens
on the same plot, and in the same quadrant of the other plots
in the same north-south row and in the same east-west row.”
So the Landscaping Company did that (marked with circles on the map
below).
The CEO of the Meticulous Landscaping Company then suggested,
“Now construct a building in the top-left plot, because we know which
quadrant it has to be in, and we will plant the corresponding gardens in the
top-most and left-most plots.”
After that was done, the map of the estate looked like this:
“Now construct buildings in the other corner plots, because we know which
quadrants they have to be in, and we will plant the corresponding gardens.”
“Now, if a plot has three gardens, then the building must go in the
remaining quadrant; and as you build we will plant the corresponding gardens.”
You should now be able to complete the puzzle.
Puzzle 6 part 2
A farmer has a small orchard containing two apples trees (A), a cherry tree (C),
a damson tree (D) and a beehive (B).
A
D
B
C
A
The farmer wants to plant more trees and get more beehives so that:
- The orchard has four rows and four columns
- Each row and each column contains exactly one each of an apple tree, a cherry tree, a damson tree and a beehive.
For each square in the orchard, the farmer makes a mark on the ground
to indicate which item
can’t go there, based on what items
are already in the row and column of that square.
That is, the farmer is
eliminating the impossible.
- If the square can’t contain an apple tree, make a mark in the top-left quadrant.
- If the square can’t contain a beehive, make a mark in the top-right quadrant.
- If the square can’t contain a cherry tree, make a mark in the bottom-left quadrant.
- If the square can’t contain a damson tree, make a mark in the bottom-right quadrant.
The orchard now looks like this:
A
D
B
C
A
If a square contains three marks,
then the farmer knows which item goes there
because it is the only one possible.
The farmer plants the right tree or places a beehive in that square
then marks the other squares in the same row and column with a corresponding
mark.
There are three squares on our map with three marks,
so let’s put the correct letter in that square and
mark the other squares in the same row and column.
A
D
A
D
C
B
C
A
There are now two more squares with three marks.
Write the correct letter in those squares, mark the corresponding squares
in the same row and column, and repeat until you have filled the grid.
This sort of puzzle (where you must place one of each item in every row
and column) is known as a Latin Square.
The most common variant of this is the sudoku.
But all Latin Square puzzles can be solved in the same way —
by marking in the cell which items/numbers can’t go there.
The marks need only be a small dot placed in a particular location
within the cell. Marking the dots is like filling in the dead ends of a
maze – it is all part of the meditative application of some simple
rules that frees our brain from having to do any hard thinking.


(519) - 875 - 9822
Emailus<EMAIL_ADDRESS>admin
Logout
Toggle navigation
GOVERNMENT SCHEMES
Choose Best Options
GOVERNMENT SCHEMES
Home
Edit Schemes
Add Schemes
Update Schemes
Delete Schemes
Our Functionality
DELETE SCHEME
Scheme ID


-Google Home Hardware, Python and Flask Backend, Heroku Web Hosting
-Api.ai Conversational Agent for User Experience
-Twilio REST Api to handle phone calls / text messages
Galaxy Defenders
-Inspired by retro arcade game: Space Invaders
-Made with Python and external module: Pygame
-Features include: multiple levels, power-ups, boss battle, and game statistics


Base API and schedulers
The main worker functions are wrapped in an R6
class with the name of QSys
. This provides a standardized API to the lower-level messages that are sent via ZeroMQ
.
The base class itself is derived in scheduler classes that add the required functions for submitting and cleaning up jobs:
+ QSys
|- Multicore
|- LSF
+ SGE
|- PBS
|- Torque
|- etc.
A pool of workers can be created using the workers()
function, which instantiates an object of the corresponding QSys
-derived scheduler class. See ?workers
for details.
# start up a pool of three workers using the default scheduler
w = workers(n_jobs=3)
# if we make an unclean exit for whatever reason, clean up the jobs
on.exit(w$finalize())
Worker startup
For workers that are started up via a scheduler, we do not know which machine they will run on. This is why we start up every worker with a TCP/IP address of the master socket that will distribute work.
This is achieved by the call to R common to all schedulers:
R --no-save --no-restore -e 'clustermq:::worker("{{ master }}")'
On the master’s side, we wait until a worker connects:
# this will block until a worker is ready
msg = w$receive_data()
Common data and exports
Workers will start up without any knowledge of what they should process or how. In order to transfer initial data to the worker, we first create and serialize a list object with the following fields:
fun
- the function to call with iterated data
const
- the constant data each function call should receive
export
- objects that will be exported to the workers’ .GlobalEnv
rettype
- character string which data type to return; e.g. list
, logical
common_seed
- random seed for function calls; will be offset by job ID
token
- character string to identify this data set; this is optional, if an automatically generated token will be returned if none is given
# create a reusable, serialized ZeroMQ object with the common data on the master
w$set_common_data(fun, const, export, rettype, common_seed, token)
Workers that connect to the master will send a list with a field token
. This can be used to check if the worker already received the common data it is supposed to work on.
if (msg$token != <token>)
w$send_common_data()
Iterated data
If the worker has already received the common data, we can send it a chunk of iterated arguments to work on. These are passed as a list of iterables, e.g. a data.frame
with a column for each iterated argument.
It also needs to have a column with name <space>id<space>
, which will be used to identify each call.
chunk = data.frame(arg1=1:5, arg2=5:1, ` id `=1:5)
w$send_job_data(chunk)
If the worker has finished processing, it will send a message with the field result
that is a list, containing:
result
- a named rettype with results
warnings
- a list with warning messages of individual calls
errors
- a list with error messages of individual calls
msg = w$receive_data()
if (!is.null(msg$result)) {
# store result here, handle errors/warnings if required
}
Custom calls
Apart from sending common and iterated data that the worker will process in chunks, it is also possible to send arbitrary calls that it will evaluate. It needs the following fields:
expr
- the expression to be evaluated
env
- list with all additional objects required to perform the call
ref
- an identifier for the call; will default to the expression itself
w$send_call(expr, env=list(...), ref="mycall1")
Main event loop
Putting the above together in an event loop, we get what is essentially implemented in master
.
w = workers(3)
on.exit(w$finalize())
while (we have new work to send) {
msg = w$receive_data()
if (!is.null(msg$result))
# handle result
if (msg$token != <token>)
w$send_common_data()
else
w$send_job_data(...)
}
# if proper cleanup is successful, cancel kill-on-exit
if (w$cleanup())
on.exit()
A loop of a similar structure can be used to extend clustermq
. As an example, this was done by drake
using common data and custom calls only (no iterated chunks).


Collector Yumi:
Go, then, and investigate the east wing of the lab. You recall Atlanta's directions for going there, yes?
"I remember."
"I don't remember."


Request a Protein
General Information
Request Name
Nurix Alias
Description
Description
Description
Description
Target Forms
Avi-NTRK1(1-89/116-796)
ELONB (1-112)
ELONB (1-112)
Avi-NTRK1(1-89/116-796)
Avi-NTRK1(1-89/116-796)
Avi-NTRK1(1-89/116-796)
Avi-NTRK1(1-796)-DP
Additional Information
Amount (mg)
Final Concentration
Biotinylated
Biotinylated
Modifications
icon
None
icon
Phosphorylated
icon
Dephosphorylated
Description
Send Request
cancel


The Doctor for your crops and plants.
Created with ❤ by console.log("Hackers").
Upload the picture (jpg/png) of the plant you wish to examine for diseases.
(Only the following plants can be examined due to limitations in the dataset, we will be adding more plants in the latter future .)
An AI web based application to help the farmers make sure their crops and plants are healthy & fit, detect the disease from the plant's pictures and recommend natural remedies and prevention.
The application is developed using PyTorch Used Transfer Learning technique and developed the model on pretrained Densnet121 model Currently the application predicts the disease with upto 93% accuracy on following plants


E A T A L I A N O
Note: Due To Current COVID-19 Situation, We Aren't Accepting Cash On Delivery(COD).


© 2015 libertyernie
http://lakora.us/brawl/rawtexturemanager
https://github.com/libertyernie/rawtexturemanager
Permission is hereby granted, free of charge, to any person
obtaining a copy of this software and associated documentation
files (the "Software"), to deal in the Software without
restriction, including without limitation the rights to use, copy,
modify, merge, publish, distribute, sublicense, and/or sell copies
of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.
Raw Texture Manager is designed to help you extract and
replace the textures inside Super Smash Bros. Melee's uncompressed
data files. It doesn't analyze the files or their structure in any
way; rather, it tries to match the filename of the .dat file
you're trying to open with one or more definition files
in the Definitions folder. Each definition file lists the
locations, formats, and sizes of each texture in the .dat file it
is describing.
Unlike Melee
Toolkit, this program does not find textures in a file on
its own; it needs definition files. As such, it's essentially just
a tool to automate the conversion and hex-editing process. In the
future, Melee Toolkit (or a program like it) will likely make this
program obsolete; until then, hopefully this will help fill some
gaps (missing textures, etc.)
Definition files are in JSON, a
data-interchange format derived from JavaScript and commonly used
to transmit data on the web from one site to another.
Each definition file contains an object with three attributes:
This version of Raw Texture Manager contains definitions for:
When opening a custom texture, make sure the filename ends with
the original Melee filename; i.e. MyTexture][PlMrNr.dat.


By default, defining a theme does not make its colors available for applying to non AngularJS
Material elements. The $mdColors
service is used by the md-color
directive to convert a
set of color expressions to RGBA values and then apply those values to the element as CSS
property values.
Getting a color based on a theme
Applying a color from a palette to an element
$mdColors.applyThemeColors(element, colorExpression);
Lookup a set of colors by hue, theme, and palette, then apply those colors
with the provided opacity (via rgba()
) to the specified CSS property.
| Parameter | Type | Description |
|---|---|---|
* element
angular.element
|
angular.element |
the element to apply the styles to |
* colorExpression
Object
|
Object |
Keys are CSS properties and values are strings representing
the |
$mdColors.getThemeColor(expression);
Get a parsed RGBA color using a string representing the theme-palette-hue-opacity
of the
desired color.
| Parameter | Type | Description |
|---|---|---|
* expression
string
|
string |
color expression like |
| Returns | Description |
|---|---|
string |
a CSS color value like |