public function AssetLoader(... args)
As mentioned above, the AssetLoader loads objects according to the URL and the settings object.
The Settings Object can receive the following:
- onComplete - when loading is complete, this event is fired
- extra - an object that will be passed for each call back - useful for passing an id for example
- onInit - call back fired when the loading has commenced
- onUpdate - useful for preloaders, in the object that is received with the callback function, the event "e" contains bytesTotal and loadedBytes
- onError - for any type of error that prevents it from loading, this callback is called.
- ... and many more... please consult each handler for specific call backs
- Remember that the extra is passed with each call back, so you can really use this to do anything
How to setup your callback function:
function handleCallBack( obj:Object )
{
trace(obj.asset);
trace(obj.extra);
trace(obj.e); // Only for onError, onUpdate... or other event dependent callbacks. Consult the individual handlers for more information.
}
Example Usage:
new AssetLoader("images.xml", {onComplete: handleXMLFile});
new AssetLoader("imagesXMLGenerator.cgi", {onComplete: handleXMLFile, filetype: AssetLoaderTypes.XML});
// Notice that you can immediately tell AssetLoader what type a file is, so it does not need to figure it out on its own.
var loaderObj:Object = new Object();
loaderObj["image1.jpg"] = {onComplete: handleImageLoad};
loaderObj["image2.jpg"] = {onComplete: handleImageLoad};
loaderObj["image3.jpg"] = {onComplete: handleImageLoad};
new AssetLoader(loaderObj);
Tips:
- If you are loading multiple assets at a time, create an object, and only call the AssetLoader once. This makes it better on performance
- Combine multiple AssetLoaders for best use, i.e. use AssetLoader to load an xml list full of images, then loop through the XML to create an object to pass to the AssetLoader again.
Parameters
| ... args — You can pass either an object containing URLS:String and the SETTINGS:Object in the form of {url1:SettingsObj1, url2:SettingsObj2...} OR you can pass two variables: 1 url and 1 settings object
|
public static function stopQueued():void
Removes all items awaiting loading


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EUR/USD could rebound to the 1.1080/95 band – Commerzbank
by Pablo PiovanoIn opinion of Karen Jones, Team Head FICC Technical Analysis Research at Commerzbank, EUR/USD could attempt to regain the 1.1080/95 area in the very near term.
Key Quotes
“EUR/USD has managed to stabilise around the 61.8% retracement at 1.0994. Thursdays price action constituted a key day reversal and we would allow for a rebound into the 1.1080/1.1095 band very near term. This is the location of the 55 day ma and we will need to regain this for a viable retest of the 1.1180 recent high. While capped by the 55 day ma, the market is regarded as under pressure and capable of extending the decline to the next Fibonacci support at 1.0943”.
“It is possible that we will see one more final leg down to the base of the channel at 1.0865 and the 1.0814 Fibo retracement before a sustained recovery is seen”.


This is the first website I am going to make. It will teach me the basics of
html and css
CSS Learning Goals
- What Properties Are
- How you use elements, and element attributes to change styles
- How CSS styles cascade
- How to include these styles
Feel free to email me with any questions or advice


Filter by Category: Tutorials - Projects - Informative
Category: Tutorials Posted by Riddhish Bhalodia on March 7, 2016


We are team of creatively diverse, driven, innovative individuals working in various locations from the world.


There five ways to create Services in AngularJS:
- {{ type.name }} - {{ type.description }}
Other important notes:
- Services are designed to be injected in the other components of your application. Therefore, their
creation is very much a part of Angular's dependency injection system. Objects that know how to create
injectable services are called providers. In order to create a service, we must first create the provider
that will build it with the built-in service $provider.
- The basic process to create a service: $provide creates the provider, and the provider creates the service.


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Result - Firefox 51.0a2 Aurora (Developer Edition) Testday
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Invite for Womoz TN Featured Add-ons e10s Compatibility sprint.
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MozActivate India.
Result - Firefox 50.0 Aurora Testday
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Result - Firefox 50.0b3 BetaTestday
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SFD2016 at coimbatore.


Portfolio styled with Bulma - source code
We are constantly looking for new partners to migrate onto the Piper Net. The future of the internet is here - venture towards it with Pied Piper!


Before Change
def main(depth=2, width=512, nb_epoch=30):
if CupyOps.xp != None:
Model.ops = CupyOps()
Model.Ops = CupyOps
// Configuration here isn"t especially good. But, for demo..
with Model.define_operators({"**": clone, ">>": chain}):
model = ReLu(width) >> ReLu(width) >> Softmax()
train_data, dev_data, _ = datasets.mnist()
After Change
def main(depth=2, width=512, nb_epoch=30):
prefer_gpu()
// Configuration here isn"t especially good. But, for demo..
with Model.define_operators({"**": clone, ">>": chain}):
model = ReLu(width) >> ReLu(width) >> Softmax()


{% load i18n %} {% comment %} Table headers for the page listing, when in 'explore' mode. Expects the following variables: sortable: if true, headings are links to wagtailadmin_explore with sort parameters applied. show_ordering_column: if true, an 'ordering' column is added. orderable: if true, the 'ordering' column is populated (again with links to wagtailadmin_explore). If either sortable or orderable is true, the following variables are also required: parent_page: The page instance currently being browsed (used to generate the correct wagtailadmin_explore urls) ordering: the current sort parameter {% endcomment %}


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The NullExecutionModel doesn't place any trades.
NullExecutionModel
SetExecution(new NullExecutionModel());
self.SetExecution(NullExecutionModel())
To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.


List of Publications
Ordered list of papers:
- Bladder cancers in Senegal: clinical and histological epidemiological particularities with Diao B. Prog Urol. 2008,18: 445 - 481
- Urological emergencies in a university hospital in Dakar with Fall B. Prog Urol. 2008, 18 (10): 650 - 653.
- Trauma to the penis: About 23 cases FWith Sow Y. Andrologie, 2008,18 (3): 210-215
- Elephantiasis of the external genital organs: clinical and therapeutic aspects. e-memoirs of the National Academy of Surgery, 2003,2 (2): 39 - 42
- Arterial segmentation of the kidney (About 92 pieces treated by injection corrosion. . Thesis medicine university cheikh anta Diop Dakar 1993, n ° 20
- Iatrogenic strictures of the urethra: experience of a Senegalese hospital. . With Ze Ondo C. African Journal of Urology, 2015, 21. 144 - 147
- EPeyronie's disease: Clinical and therapeutic aspects. with Ze Ondo C., African Journal of Urology, 2014, 20, 38 - 41.


Try out our Premium Member services: Virtual Legal Assistant, Query Alert Service and an ad-free experience. Free for one month and pay only if you like it.
1 / 3 904-905-WPL-3098-&-3099-18.odt IN THE HIGH COURT OF JUDICATURE AT BOMBAY ORDINARY ORIGINAL CIVIL JURISDICTION WRIT PETITION (L) NO.3098 OF 2018 WITH WRIT PETITION (L) NO.3099 OF 2018 M/s Nuvoco Vistas Corporation Limited & Anr. .... Petitioners versus State of Maharashtra & Ors. ... Respondents ....... • Mr.Ameya Gokhale a/w Mr.Vaibhav Singh, Ms.Radhika Indapurkar, i/b. M/s. Shardul Amarchand Mangaldas, Advocate for Petitioners. • Mr.S.B. Gore, AGP, for State Respondent No.1. • Ms.S.U. Deshmukh, Advocate for Respondent No.2. • Mr.Satish Kamat, Advocate for Respondent No.3. CORAM : SHANTANU S. KEMKAR & SARANG V. KOTWAL, JJ.
DATE : 19th SEPTEMBER, 2018.
P.C. :
Parties through their Counsel. With consent, heard finally.
2. Challenging the order dated 01/08/2018, Ex.'A', passed by Respondent No.2 Maharashtra Pollution Control Nesarikar ::: Uploaded on - 24/09/2018 ::: Downloaded on - 24/09/2018 23:54:50 ::: 2 / 3 904-905-WPL-3098-&-3099-18.odt Board u/s 33 (A) of the Water (Prevention & Control of Pollution) Act, 1974 and u/s 31(A) of the Air (Prevention & Control of Pollution) Act, 1981, whereby directions have been issued to the Petitioner to stop its activities forthwith and also directing Respondent No.3 to disconnect the electricity and water supply to the Petitioner's Unit, the Petitioner has approached this Court by filing this Petition under Article 226 of the Constitution of India.
3. According to Petitioner Consent to Operate is valid upto 31/03/2020. It is the case of the Petitioner that without issuing any show cause notice and without giving any opportunity of hearing, impugned order has been passed which clearly violates the principles of natural justice.
4. On the other hand, the learned Counsel appearing for the Respondents have supported the impugned order.
5. Having considered the submissions by the learned counsel for the parties and having gone through the impugned order, we find that before recording the aforesaid findings ::: Uploaded on - 24/09/2018 ::: Downloaded on - 24/09/2018 23:54:50 ::: 3 / 3 904-905-WPL-3098-&-3099-18.odt against the Petitioner, no show cause notice or opportunity of hearing was ever afforded to the Petitioner. In the circumstances, in our considered view, the impugned order being violation of the principles of natural justice, the same cannot be sustained. We therefore quash the impugned order. However with liberty to Respondent No.2 to initiate fresh proceedings, if so advised, in accordance with law after issuing show cause notice to the Petitioner.
6. With the aforesaid directions and liberty, the Petition is disposed of.
7. Parties to act on the authenticated copy of this order.
8. We also make it clear that in case the electricity connection has already been disconnected, the same be now reconnected by the Respondent No.3 forthwith. (SARANG V. KOTWAL, J.) (SHANTANU S. KEMKAR, J.) ::: Uploaded on - 24/09/2018 ::: Downloaded on - 24/09/2018 23:54:50 :::


Beijing is a metropolis in northern China and the capital of the People's Republic of China. It is one of the four municipalities of the PRC, which are equivalent to provinces in China's administrative structure. Beijing is one of the Four Great Ancient Capitals of China.
love itShanghai is a dynamic growing prosperity of the city, is China's largest commercial and financial center, is also the Western Pacific region important international port city. Its extensive contacts within and outside the transport, communications more developed, is attracting more and more attention to the eyes.
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ExpressionMatrix2 Python API
Software for analysis, visualization, and clustering of gene expression data from single-cell RNA sequencing.
This page contains reference information for the Python API. See here for the complete documentation.
CellGraphVertexInfo
-
class
ExpressionMatrix2.
CellGraphVertexInfo
This class is used to give the Python code access to information about the vertices of a cell similarity graph. An object of this type contains information for a single vertex.
-
__init__
(self: ExpressionMatrix2.CellGraphVertexInfo) → None This constructor creates an uninitialized CellGraphVertexInfo object.
-
cellId
This data member contains the cell id of the cell corresponding to the vertex.
-
x
(self: ExpressionMatrix2.CellGraphVertexInfo) → float Returns the x coordinate of the vertex in the two-dimensional layout of the graph the vertex belongs to.
-
y
(self: ExpressionMatrix2.CellGraphVertexInfo) → float Returns the y coordinate of the vertex in the two-dimensional layout of the graph the vertex belongs to.
-
ExpressionMatrix
-
class
ExpressionMatrix2.
ExpressionMatrix
This is the top level class in the ExpressionMatrix2 module. Most high level functionality is provided by this class. Binary data files for an instance of this class are stored in a single directory on disk. They are accessed as memory mapped files.
-
__init__
(self: ExpressionMatrix2.ExpressionMatrix, directoryName: str, allowReadOnly: bool=False) → None Construct a new expression matrix or access an existing one. The argument is the name of the directory that contains, or will contain, binary data for the ExpressionMatrix object. If the directory does not exist, it will be created, and initialized to a new empty ExpressionMatrix without any genes and cells. If the directory already exists, it must be a directory containing a previously created ExpressionMatrix. In this case, the constructor will access the existing ExpressionMatrix.
-
addCell
(self: ExpressionMatrix2.ExpressionMatrix, metaData: List[Tuple[str, str]], expressionCounts: List[Tuple[str, float]]) → int Adds a cell to the system. The cell expression counts and meta data are given in Python lists. metadata is a list of tuples with meta data (name, value) pairs. expressionCounts is a list of tuples (geneName, count). See here for an example. Returns the cell id of the cell that was just added. Cell ids begin at zero and increment by one each time a cell is added.
-
addCellFromJson
(self: ExpressionMatrix2.ExpressionMatrix, jsonString: str) → int Adds a cell to the system. The cell expression counts and meta data are given in a JSON string. See here for information on the expected format of the JSON string. Returns the cell id of the cell that was just added. Cell ids begin at zero and increment by one each time a cell is added.
-
addCellMetaData
(self: ExpressionMatrix2.ExpressionMatrix, cellMetaDataFileName: str) → None Add cell meta data reading it from a csv file. Each line of the file contains cell meta data values for one cell. Cell meta data names are in the first line. The first column of the file contain cell names. All cell names must already be present.
-
addCells
(self: ExpressionMatrix2.ExpressionMatrix, expressionCountsFileName: str, expressionCountsFileSeparators: str=', ', cellMetaDataFileName: str, cellMetaDataFileSeparators: str=', ', additionalCellMetaData: List[Tuple[str, str]]=[]) → None Adds cells to the system, reading expression counts and cell meta data from files. See here for more information. .
-
addCellsFromBioHub1
(self: ExpressionMatrix2.ExpressionMatrix, expressionCountsFileName: str, initialMetaDataCount: int, finalMetaDataCount: int, plateMetaDataFileName: str) → None Add to the system cells created by the BioHub pipeline (July 2017 version, for Illumina data). See here for more information.
-
addCellsFromBioHub2
(self: ExpressionMatrix2.ExpressionMatrix, plateFileName: str, totalExpressionCountThreshold: float) → None Add to the system cells created by the BioHub pipeline (September 2017 version, for 10X data). See here for more information. This functon is only available in a build that includes HDF5 support.
-
addCellsFromBioHub3
(self: ExpressionMatrix2.ExpressionMatrix, expressionCountsFileName: str, expressionCountsFileSeparators: str=',', plateMetaData: List[Tuple[str, str]]) → None Add to the system cells created by the BioHub pipeline (November 2017 version, for Illumina data). See here for more information.
-
addCellsFromHdf5
(self: ExpressionMatrix2.ExpressionMatrix, fileName: str, cellNamePrefix: str, cellMetaData: List[Tuple[str, str]], totalExpressionCountThreshold: float) → None Adds cells to the system, reading expression counts from a file in HDF5 format as created by the 10X Genomics pipeline. See here for more information. This functon is only available in a build that includes HDF5 support.
-
addGene
(self: ExpressionMatrix2.ExpressionMatrix, geneName: str) → bool Adds a gene with the specified name. Returns True if the gene was added and False if the gene already existed.
-
analyzeLsh
(self: ExpressionMatrix2.ExpressionMatrix, arg0: str, arg1: str, arg2: int, arg3: int, arg4: float) → None Only intended to be used for testing. See the source code in the ExpressionMatrix2/src directory for more information.
-
analyzeLshSignatures
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes', cellSetName: str='AllCells', lshCount: int=1024, seed: int=231) → None Only intended to be used for testing. See the source code in the ExpressionMatrix2/src directory for more information.
-
analyzeSimilarPairs
(self: ExpressionMatrix2.ExpressionMatrix, arg0: str, arg1: float) → None Only intended to be used for testing. See the source code in the ExpressionMatrix2/src directory for more information.
-
cellCount
(self: ExpressionMatrix2.ExpressionMatrix) → int Returns the total number of cells.
-
cellIdFromString
(self: ExpressionMatrix2.ExpressionMatrix, cellString: str) → int Returns the cell id corresponding to a given name, or invalidCellId if no cell with the given name exists.
-
compareSimilarPairs
(self: ExpressionMatrix2.ExpressionMatrix, arg0: str, arg1: str) → None Only intended to be used for testing. See the source code in the ExpressionMatrix2/src directory for more information.
-
computeCellGraphLayout
(self: ExpressionMatrix2.ExpressionMatrix, graphName: str) → None Computes the two-dimensional layout for the graph with the given name. The graph must have been previously created with a call to
ExpressionMatrix2.ExpressionMatrix.createCellGraph()
. This function must be called once before the first call toExpressionMatrix2.ExpressionMatrix.getCellGraphVertices()
for the graph.
-
computeCellSimilarity
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes', cellId0: int, cellId1: int) → float Returns the similarity between the expression vectors of two cells, specified by their cell ids. The similarity is computed taking into account only genes in the specifified gene set. It is computed as the Pearson correlation coefficient of the expression vectors of the two cells. The computed similarity is always between -1 and 1, and it is rarely negative.
-
computeLshSignatures
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes', cellSetName: str='AllCells', lshName: str, lshCount: int=1024, seed: int=231) → None Compute cell LSH signatures and store them.
-
computeMetaDataRandIndex
(self: ExpressionMatrix2.ExpressionMatrix, cellSetName: str='AllCells', metaDataName0: str, metaDataName1: str) → Tuple[float, float] Returns the Rand Index and Adjusted Rand Index of the contingency table created using two given meta data fields.
-
createCellGraph
(self: ExpressionMatrix2.ExpressionMatrix, graphName: str, cellSetName: str='AllCells', similarPairsName: str, similarityThreshold: float=0.5, k: int=20, keepIsolatedVertices: bool=False) → None Create a new cell similarity graph. similarityThreshold is the minimum cell similarity required for an edge to be created. k controls the maximum connectivity of the graph. A cell graph or cell similarity graph is an undirected graph in which each vertex represents a cell. An edge between two vertices is created if the corresponding cells have similarity that exceeds a chosen threshold. In areas of high connectivity, only the edges with the highest similarity for each cell are kept.
-
createCellSet
(self: ExpressionMatrix2.ExpressionMatrix, cellSetName: str, cellIds: List[int]) → None Creates a new cell set cellSetName including the cells with the specified ids.
-
createCellSetDifference
(self: ExpressionMatrix2.ExpressionMatrix, inputSetName0: str, inputSetName1: str, outputSetName: str) → None Creates a new cell set as the difference between two cell sets. The newly created cell set consists of all cells that are in inputCellSetName0 but not in inputCellSetName1. Returns False if the operation failed because one of the input cell sets does not exist or the output cell set already exists, True otherwise.
-
createCellSetIntersection
(self: ExpressionMatrix2.ExpressionMatrix, inputSetsNames: str, outputSetName: str) → None Creates a new cell set as the intersection of two or more cell sets. The names of the input cell sets specified as the first argument must be separated by commas. Returns False if the operation failed because one of the input cell sets does not exist or the output cell set already exists, True otherwise.
-
createCellSetUnion
(self: ExpressionMatrix2.ExpressionMatrix, inputSetsNames: str, outputSetName: str) → None Creates a new cell set as the union of two or more cell sets. The names of the input cell sets specified as the first argument must be separated by commas. Returns False if the operation failed because one of the input cell sets does not exist or the output cell set already exists, True otherwise.
-
createCellSetUsingMetaData
(self: ExpressionMatrix2.ExpressionMatrix, cellSetName: str, metaDataFieldName: str, matchString: str, useRegex: bool) → None Creates a new cell set cellSetName consisting of all cells for which the specified metaDataName matches the given matchTarget. If useRegularExpression is False, this uses a simple string matching. Otherwise, matchTarget is treated as a regular expression to be matched. As a simple application example, suppose all cells contain a meta data name Tissue that gives the type of tissue from which the cell was obtained. Calling createCellSetUsingMetaData with metaDataName=’Tissue’, matchTarget=’Brain’, and useRegularExpression=’False’ will create a new cell set consisting of the cells for which the value of the Tissue meta data field is Brain. Regular expressions can be used to select values that satisfy a variety of criteria.
-
createCellSetUsingNumericMetaDataBetween
(self: ExpressionMatrix2.ExpressionMatrix, cellSetName: str, metaDataFieldName: str, lowerBound: float, upperBound: float) → None Creates a new cell set cellSetName consisting of all cells for which the specified metaDataName is numeric, greater than lowerBound, and less than upperBound.
-
createCellSetUsingNumericMetaDataGreaterThan
(self: ExpressionMatrix2.ExpressionMatrix, cellSetName: str, metaDataFieldName: str, lowerBound: float) → None Creates a new cell set cellSetName consisting of all cells for which the specified metaDataName is numeric and greater than lowerBound.
-
createCellSetUsingNumericMetaDataLessThan
(self: ExpressionMatrix2.ExpressionMatrix, cellSetName: str, metaDataFieldName: str, upperBound: float) → None Creates a new cell set cellSetName consisting of all cells for which the specified metaDataName is numeric and less than upperBound.
-
createClusterGraph
(self: ExpressionMatrix2.ExpressionMatrix, cellGraphName: str, clusterGraphName: str, stableIterationCount: int=3, maxIterationCount: int=100, seed: int=231, minClusterSize: int=100, k: int=3, similarityThreshold: float=0.5, similarityThresholdForMerge: float=0.9) → None Creates a new cluster graph by running label propagation clustering on an existing cell graph. A cluster graph is an undirected graph in which each vertex represents a cluster found in a cell graph. An edge between two vertices is created if the corresponding clusters have average expression vectors that are sufficiently similar. In areas of high connectivity, only the edges with the highest similarity for each cluster are kept.
-
createGeneGraph
(self: ExpressionMatrix2.ExpressionMatrix, geneGraphName: str, geneSetName: str='AllGenes', similarGenePairsName: str, k: int, similarityThreshold: float) → None Creates a gene similarity graph.
-
createGeneSetDifference
(self: ExpressionMatrix2.ExpressionMatrix, inputSetName0: str, inputSetName1: str, oututSetName: str) → bool Creates a new gene set as the difference between two gene sets. The newly created gene set consists of all genes that are in inputGeneSetName0 but not in inputGeneSetName1. Returns False if the operation failed because one of the input gene sets does not exist or the output gene set already exists, True otherwise.
-
createGeneSetFromGeneNames
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str, geneNames: List[str]) → None Creates a new gene set given a list of gene names.
-
createGeneSetIntersection
(self: ExpressionMatrix2.ExpressionMatrix, inputSetsNames: str, outputSetName: str) → bool Creates a new gene set as the intersection of two or more gene sets. The names of the input gene sets specified as the first argument must be separated by commas. Returns False if the operation failed because one of the input gene sets does not exist or the output gene set already exists, True otherwise.
-
createGeneSetUnion
(self: ExpressionMatrix2.ExpressionMatrix, inputSetsNames: str, outputSetName: str) → bool Creates a new gene set as the union of two or more gene sets. The names of the input gene sets specified as the first argument must be separated by commas. Returns False if the operation failed because one of the input gene sets does not exist or the output gene set already exists, True otherwise.
-
createGeneSetUsingInformationContent
(self: ExpressionMatrix2.ExpressionMatrix, existingGeneSetName: str='AllGenes', cellSetName: str='AllCells', normalizationMethod: ExpressionMatrix2.NormalizationMethod, geneInformationContentThreshold: float, newGeneSetName: str) → None Creates a new gene set consisting of genes that pass an information content test. The information content, in bits, of each gene in existingGeneSetName is computed using cellSetName and the specified NormalizationMethod. The new gene set newGeneSetName is created, consisting of genes whose computed information content (in bits) exceeds the specified geneInformationContentThreshold. The information content is roughly related to the variability of the expression of that gene in the specified cell set. Genes that are widely expressed have a low information content. Genes that are very selectively expressed have a high information content. A gene that is equally expressed in all cells has zero information content. A gene that is expressed only in one cell has log2N bits of information content, where N is the number of cells in the cell set used to compute information content. A geneInformationContentThreshold of 2 bits is often effective in filtering out widely expressed genes.
-
createMetaDataFromClusterGraph
(self: ExpressionMatrix2.ExpressionMatrix, clusterGraphName: str, metaDataName: str) → None Creates meta data from the cluster ids stored in a cluster graph by storing the cluster ids in the specified cluster graph into a cell meta data field.
-
createSignatureGraph
(self: ExpressionMatrix2.ExpressionMatrix, signatureGraphName: str, cellSetName: str='AllCells', lshName: str, minCellCount: int) → None Prototype code.
-
createWellExpressedGeneSet
(self: ExpressionMatrix2.ExpressionMatrix, inputGeneSetName: str='AllGenes', inputCellSetName: str='AllCells', outputGeneSetName: str, minCellCount: int) → None Create a gene set consisting of genes that are expressed in at least a minimum number of cells. The resulting gene set will contain all of the genes in the specified existing gene set that are expressed in at least the specified minimum number of cells, counting only cells in the specified cell set.
-
downsampleCellSet
(self: ExpressionMatrix2.ExpressionMatrix, inputCellSetName: str='AllCells', newCellSetName: str, probability: float, seed: int) → None Create a new cell set by downsampling an existing cell set. Return true if successful, false if the input cell set does not exist.
-
explore
(self: ExpressionMatrix2.ExpressionMatrix, port: int=17100, docDirectory: str='', localOnly: bool=False) → None Starts an http server that can be used, in conjunction with a Web browser, to interact with the ExpressionMatrix object. The localOnly argument can be used to control whether the server will accept connections from everywhere (default) or only from the same machine on which the server is running.
-
findSimilarGenePairs0
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes', cellSetName: str='AllCells', normalizationMethod: ExpressionMatrix2.NormalizationMethod=NormalizationMethod.L2, similarGenePairsName: str, k: int=100, similarityThreshold: float=0.2) → None
-
findSimilarPairs0
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes', cellSetName: str='AllCells', similarPairsName: str, k: int=100, similarityThreshold: float=0.2) → None Creates and stores a new object similarPairsName containing pairs of similar cells from cellSetName. The computation is done by directly looping over all possible cell pairs with both cells in cellSetName and performing for each pair an exact computation of the cell similarity, taking into account only genes in geneSetName. For each cell, only the best (most similar) k or fewer similar cells are stored, among those that exceed the specified similarityThreshold. This computational cost of this function grows with the square of the number of cells in cellSetName.  When the number of cells exceeds a few thousands, it is more practical to perform an approximate computation using findSimilarPairs4.
-
findSimilarPairs4
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes', cellSetName: str='AllCells', similarPairsName: str, k: int=100, similarityThreshold: float=0.2, lshCount: int=1024, seed: int=231) → None Like findSimilarPairs0, but uses Locality-Sensitive Hashing (LSH) with lshCount LSH hashes to speed up the computation of the similar pairs. Like findSimilarPairs0, this still loops over all possible cell pairs with both cells in cellSetName and therefore the computational cost still grows asymptotically with the square of the number of cells. However the computation is orders of magnitudes faster. The computation is approximate, and the error decreases as lshCount increases. For the suggested value lshCount=1024, the standard deviation of the pair similarity computed in this way is 0.05 or less.
-
findSimilarPairs4Gpu
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes', cellSetName: str='AllCells', lshName: str, similarPairsName: str, k: int=100, similarityThreshold: float=0.2, lshCount: int=1024, seed: int=231, kernel: int=1, blockSize: int=16) → None Only available in a build with GPU functionality enabled.Like findSimilarPairs0, but uses Locality-Sensitive Hashing (LSH) with lshCount LSH hashes to speed up the computation of the similar pairs. Like findSimilarPairs0, this still loops over all possible cell pairs with both cells in cellSetName and therefore the computational cost still grows asymptotically with the square of the number of cells. However the computation is orders of magnitudes faster. The computation is approximate, and the error decreases as lshCount increases. For the suggested value lshCount=1024, the standard deviation of the pair similarity computed in this way is 0.05 or less.
-
findSimilarPairs5
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes', cellSetName: str='AllCells', lshName: str, similarPairsName: str, k: int=100, similarityThreshold: float=0.2, lshSliceLength: int, bucketOverflow: int=1000) → None LSH-based computation of similar cell pairs without looping over all possible pairs of cells.Prototype code. Use findSimilarPairs4 instead.
-
findSimilarPairs6
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes', cellSetName: str='AllCells', lshName: str, similarPairsName: str, k: int=100, similarityThreshold: float=0.2, permutationCount: int, searchCount: int, permutedBitCount: int=64, seed: int=231) → None Computation of similar cell pairs using LSH and the Charikar algorithm to avoid looping over all possible pairs of cells.Prototype code, see the code for details. Use findSimilarPairs4 instead.
-
findSimilarPairs7
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes', cellSetName: str='AllCells', lshName: str, similarPairsName: str, k: int=100, similarityThreshold: float=0.2, lshSliceLengths: List[int], maxCheck: int, log2BucketCount: int) → None LSH-based computation of similar cell pairs without looping over all possible pairs of cells.Prototype code. Use findSimilarPairs4 instead.
-
findSimilarPairs7Gpu
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes', cellSetName: str='AllCells', lshName: str, similarPairsName: str, k: int=100, similarityThreshold: float=0.2, lshSliceLengths: List[int], maxCheck: int, log2BucketCount: int, blockSize: int) → None LSH-based computation of similar cell pairs without looping over all possible pairs of cells.Prototype code. Use findSimilarPairs4 instead.
-
geneCount
(self: ExpressionMatrix2.ExpressionMatrix) → int Returns the total number of genes.
-
geneIdFromName
(self: ExpressionMatrix2.ExpressionMatrix, geneName: str) → int Return the numeric id of a gene given its name. Return invalidGeneId if a gene with the given name does not exist.
-
geneName
(self: ExpressionMatrix2.ExpressionMatrix, geneId: int) → str Return the name of a gene given its numeric id.
-
getCellExpressionCount
(self: ExpressionMatrix2.ExpressionMatrix, cellId: int, geneId: int) → float Returns the expression count for a given cell and gene. The returned value can be zero.
-
getCellExpressionCountFromGeneName
(self: ExpressionMatrix2.ExpressionMatrix, cellId: int, geneName: str) → float Returns the expression count for a given cell and gene. The returned value can be zero.
-
getCellExpressionCounts
(self: ExpressionMatrix2.ExpressionMatrix, cellId: int) → List[Tuple[int, float]] Returns the non-zero expression counts for a given cell. The returned list contains pairs of gene ids and the corresponding expression counts for the given cell.
-
getCellGraphEdges
(self: ExpressionMatrix2.ExpressionMatrix, graphName: str) → List[Tuple[int, int]] Returns the cell ids corresponding to the two vertices of each edge of the cell graph with the given name. The graph must have been previously created with a call to
ExpressionMatrix2.ExpressionMatrix.createCellGraph()
.
-
getCellGraphNames
(self: ExpressionMatrix2.ExpressionMatrix) → List[str] Return a list containing the names of all currently defined cell similarity graphs.
-
getCellGraphVertices
(self: ExpressionMatrix2.ExpressionMatrix, graphName: str) → List[ChanZuckerberg::ExpressionMatrix2::CellGraphVertexInfo] Returns information about the vertices of the cell graph with the given name. The graph must have been previously created with a call to
ExpressionMatrix2.ExpressionMatrix.createCellGraph()
and its two-dimensional layout must have been computed with a call toExpressionMatrix2.ExpressionMatrix.computeCellGraphLayout()
.
-
getCellMetaData
(self: ExpressionMatrix2.ExpressionMatrix, cellId: int) → List[Tuple[str, str]] Returns all meta data pairs (name, value) for a given cell.
-
getCellMetaDataValue
(self: ExpressionMatrix2.ExpressionMatrix, cellId: int, metaDataName: str) → str Returns the value of the specified meta data name for the given cell. Returns an empty string if the cell does not have that meta data name. The only meta data field that all cells are guaranteed to have is CellName.
-
getCellSet
(self: ExpressionMatrix2.ExpressionMatrix, cellSetName: str) → List[int] Returns the cell ids of the cells in the cell set with the specified name. If the cell set does not exists, returns an empty container.
-
getCellSetNames
(self: ExpressionMatrix2.ExpressionMatrix) → List[str] Returns a list containing the names of all currently defined cell sets.
-
getCellsExpressionCount
(self: ExpressionMatrix2.ExpressionMatrix, cellIds: List[int], geneId: int) → List[float] Returns the expression counts for a set of cells and for a given gene. Some or all of the returned values can be zero.
-
getCellsExpressionCountFromGeneName
(self: ExpressionMatrix2.ExpressionMatrix, cellIds: List[int], geneName: str) → List[float] Returns the expression counts for a set of cells and for a given gene. Some or all of the returned values can be zero.
-
getCellsExpressionCounts
(self: ExpressionMatrix2.ExpressionMatrix, cellIds: List[int]) → List[List[Tuple[int, float]]] Returns the non-zero expression counts for a given set of cells. The returned list contains, for each cell, a list of pairs of gene ids and the corresponding expression counts.
-
getCellsExpressionCountsForGenes
(self: ExpressionMatrix2.ExpressionMatrix, cellIds: List[int], geneIds: List[int]) → List[List[Tuple[int, float]]] Returns the non-zero expression counts for given sets of cells and genes. The returned list contains, for each cell, pairs of gene ids and the corresponding expression counts.
-
getCellsMetaData
(self: ExpressionMatrix2.ExpressionMatrix, cellIds: List[int]) → List[List[Tuple[str, str]]] Returns all meta data pairs (name, value) for a set of cells specified by list cellIds. Each element in the returned list corresponds to the cell id at the same position in list cellIds.
-
getClusterAverageExpression
(self: ExpressionMatrix2.ExpressionMatrix, clusterGraphName: str, clusterId: int) → List[float] Return the average, L2-normalized, gene expression of a cluster. The cluster is specified using the name of the cluster graph it belongs to, and its cluster id within that cluster graph. The values returned correspond one on one to the gene ids returned by
ExpressionMatrix2.ExpressionMatrix.getClusterGraphGenes()
for the same cluster graph. .
-
getClusterCells
(self: ExpressionMatrix2.ExpressionMatrix, clusterGraphName: str, clusterId: int) → List[int] Returns a list of the cell ids for the cells a cluster. The cluster is specified using the name of the cluster graph it belongs to, and its cluster id within that cluster graph.
-
getClusterGraphGenes
(self: ExpressionMatrix2.ExpressionMatrix, clusterGraphName: str) → List[int] Returns a list of the ids of the genes used to create a cluster graph.
-
getClusterGraphVertices
(self: ExpressionMatrix2.ExpressionMatrix, clusterGraphName: str) → List[int] Returns a list of the cluster ids for the vertices of an existing cluster graph. Each vertex of a cluster graph corresponds to a cluster.
-
getDenseExpressionMatrix
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes', cellSetName: str='AllCells', normalizationMethod: ExpressionMatrix2.NormalizationMethod=NormalizationMethod.none) → array Get a dense representation of a subset of the expression matrix corresponding to a given gene set and cell set. The returned matrix is a numpy array with row-major memory layout (C-style), with a number of rows equal to the number of cells in the specified cell set, and a number of columns equal to the number of genes in the specified gene set. This means that, because of C-style layout, the returned matrix is indexed in python as m[cellId][geneId], where cellId and geneId are ids local to the cell set and gene set respectively (that is, they only equal global cell ids and gene ids if the function is called for the AllCells and AllGenes sets). Gene sets and cell sets store their genes and cells in order of increasing id.
-
getGeneGraphConnectivity
(self: ExpressionMatrix2.ExpressionMatrix, geneGraphName: str) → List[List[Tuple[int, float]]] Return the connectivity of the specified gene graph. The return vector is indexed by the local GeneId in the gene set that was used to create the gene graph. It contain pairs (local GeneId, similarity).
-
getGeneSetGenes
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str='AllGenes') → List[int] Get the global gene ids of the genes in a gene set.
-
removeCellMetaData
(self: ExpressionMatrix2.ExpressionMatrix, cellSetName: str, metaDataName: str) → None Removes a meta data field for all cells of a given cell set.
-
removeCellSet
(self: ExpressionMatrix2.ExpressionMatrix, cellSetName: str) → None Removes the cell set with the specified name. If the cell set does not exists, raises an exception.
-
removeGeneSet
(self: ExpressionMatrix2.ExpressionMatrix, geneSetName: str) → None Removes an existing gene set.
-
removeSignatureGraph
(self: ExpressionMatrix2.ExpressionMatrix, signatureGraphName: str) → None Prototype code.
-
removeSimilarPairs
(self: ExpressionMatrix2.ExpressionMatrix, similarPairsName: str) → None Remove the similar cell pairs object with the specified name.
-
setGeneMetaData
(self: ExpressionMatrix2.ExpressionMatrix, geneName: str, name: str, value: str) → None Set a meta data field for a gene.
-
testExpressionMatrixSubset
(self: ExpressionMatrix2.ExpressionMatrix, arg0: int, arg1: int) → None For debugging/testing use only.
-
writeSimilarPairs
(self: ExpressionMatrix2.ExpressionMatrix, arg0: str) → None Only intended to be used for testing. See the source code in the ExpressionMatrix2/src directory for more information. For debugging/testing use only.
-
NormalizationMethod
-
class
ExpressionMatrix2.
NormalizationMethod
Various ways to normalize gene expressions.
- none: no normalization.
- L1: L1 normalization (sum of values is 1).
- L2: L2 normalization (sum of squares of values is 1).
- Invalid: invalid normalization.


Using Svelte GL
You’ll need a bundler plugin that recognises .glsl files and turns them into JavaScript strings, e.g. https://github.com/glslify/rollup-plugin-glslify.
Creating a basic scene
<script>
import * as GL from '@sveltejs/gl';
</script>
<GL.Scene>
<GL.PerspectiveCamera location={[4,4,5]} lookAt={[0,0,0]} near={0.01} far={1000}/>
<!-- light coming from above and to the right -->
<GL.DirectionalLight direction={[-3,-2,-1]} intensity={1}/>
<!-- floor -->
<GL.Mesh
geometry={GL.plane()}
rotation={[-90,0,0]}
scale={10}
uniforms={{
color: 0xffffff
}}
/>
<!-- box -->
<GL.Mesh
geometry={GL.box()}
location={[0,0.5,0]}
uniforms={{
color: from_hex(color)
}}
/>
</GL.Scene>
Each scene needs:
- a top-level
<GL.Scene>
component - one or more lights — currently we have
<GL.AmbientLight>
,<GL.PointLight>
and<GL.DirectionalLight>
- one or more
<GL.Mesh>
components — the objects in the scene


A couple of weeks ago, I was having lunch with my wife at a little Chinese restaurant. My fortune was the title of this spout piece. I immediately associated it with my recent job interview at Microsoft and it made me smile. I had already pretty much decided to take the job, but it was nice that the Universe agreed with me. : )
Right now I'm sitting in NEO (New Employee Orientation) for Microsoft employees. Not only is it a cool name, but the nice folks at Microsoft provide a wireless network for new folks that can't spent hours w/o one (although I'm the only one that's geeky enough to have his laptop out at the moment : ). I start today as a Content Strategist on the MSDN content team at Microsoft, Corp. I'll be in charge of the Longhorn DevCenter. A DevCenter is a section of the MSDN web site that focuses on a specific technology area, like the XML/Web Services DevCenter.
Longhorn is Microsoft's next major operation system (and different than Windows Server 2003, which is the OS that Microsoft is launching this month). I can't (yet) say anything more about Longhorn except that it's cool enough that I took a job at MS so that I could get my hands on it. If you are dying for more info on Longhorn, but aren't quite ready to change your employment arrangements to get it, check out the PDC in October. And, if you stop by and say "hi," I'll show you the implant scars... : )


10.2. Hash Function Principles (WORK IN PROGRESS)¶
10.2.1. Hash Function Principles¶
Hashing generally takes records whose key values come from a large range and stores those records in a table with a relatively small number of slots. Collisions occur when two records hash to the same slot in the table. If we are careful—or lucky—when selecting a hash function, then the actual number of collisions will be few. Unfortunately, even under the best of circumstances, collisions are nearly unavoidable. To illustrate, consider a classroom full of students. What is the probability that some pair of students shares the same birthday (i.e., the same day of the year, not necessarily the same year)? If there are 23 students, then the odds are about even that two will share a birthday. This is despite the fact that there are 365 days in which students can have birthdays (ignoring leap years). On most days, no student in the class has a birthday. With more students, the probability of a shared birthday increases. The mapping of students to days based on their birthday is similar to assigning records to slots in a table (of size 365) using the birthday as a hash function. Note that this observation tells us nothing about which students share a birthday, or on which days of the year shared birthdays fall.
Try it for yourself. You can use the calculator to see the probability of a collision. The default values are set to show the number of people in a room such that the chance of a duplicate is just over 50%. But you can set any table size and any number of records to determine the probability of a collision under those conditions.
Use the calculator to answer the following questions.
To be practical, a database organized by hashing must store records in a hash table that is not so large that it wastes space. To balance time and space efficiency, this means that the hash table should be around half full. Because collisions are extremely likely to occur under these conditions (by chance, any record inserted into a table that is half full should have a collision half of the time), does this mean that we need not worry about how well a hash function does at avoiding collisions? Absolutely not. The difference between using a good hash function and a bad hash function makes a big difference in practice in the number of records that must be examined when searching or inserting to the table. Technically, any function that maps all possible key values to a slot in the hash table is a hash function. In the extreme case, even a function that maps all records to the same slot in the array is a hash function, but it does nothing to help us find records during a search operation.
We would like to pick a hash function that maps keys to slots in a way that makes each slot in the hash table have equal probablility of being filled for the actual set keys being used. Unfortunately, we normally have no control over the distribution of key values for the actual records in a given database or collection. So how well any particular hash function does depends on the actual distribution of the keys used within the allowable key range. In some cases, incoming data are well distributed across their key range. For example, if the input is a set of random numbers selected uniformly from the key range, any hash function that assigns the key range so that each slot in the hash table receives an equal share of the range will likely also distribute the input records uniformly within the table. However, in many applications the incoming records are highly clustered or otherwise poorly distributed. When input records are not well distributed throughout the key range it can be difficult to devise a hash function that does a good job of distributing the records throughout the table, especially if the input distribution is not known in advance.
There are many reasons why data values might be poorly distributed.
Natural frequency distributions tend to follow a common pattern where a few of the entities occur frequently while most entities occur relatively rarely. For example, consider the populations of the 100 largest cities in the United States. If you plot these populations on a numberline, most of them will be clustered toward the low side, with a few outliers on the high side. This is an example of a Zipf distribution. Viewed the other way, the home town for a given person is far more likely to be a particular large city than a particular small town.
Collected data are likely to be skewed in some way. Field samples might be rounded to, say, the nearest 5 (i.e., all numbers end in 5 or 0).
If the input is a collection of common English words, the beginning letter will be poorly distributed.
Note that for items 2 and 3 on this list, either high- or low-order bits of the key are poorly distributed.
When designing hash functions, we are generally faced with one of two situations:
We know nothing about the distribution of the incoming keys. In this case, we wish to select a hash function that evenly distributes the key range across the hash table, while avoiding obvious opportunities for clustering such as hash functions that are sensitive to the high- or low-order bits of the key value.
We know something about the distribution of the incoming keys. In this case, we should use a distribution-dependent hash function that avoids assigning clusters of related key values to the same hash table slot. For example, if hashing English words, we should not hash on the value of the first character because this is likely to be unevenly distributed.
In the next module, you will see several examples of hash functions that illustrate these points.


Team zaitra is a winner of Copernicus Hackathon Brno and participant of the Copernicus Accelerator programme. We are part of TechNest technological centre in Brno.
Our software skills +
experts from S.A.B. Aerospace =
powerful solutions for your company.
Let's find out if your
software product or service could work better with the help of our neural network.
We are interested in public and global blockchain projects. We would love to work on one with you and find the potential.
It is always great fun to create software which is capable to find objects in an image.
Do you need one?


The 10 edgiest innovation ideas of 2016
How NASA’s planetary defense officer will protect Earth from asteroids
Fitness fads that should fade in 2016


|
DESERT
3.0
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Files | |
| file | data_link/uwUFetch/initlib.cpp [code] |
| file | uwUFetch_AUV.cpp [code] |
| file | uwUFetch_AUV.h [code] |
| file | uwUFetch_AUV_mod_1.cpp [code] |
| file | uwUFetch_AUV_mod_2.cpp [code] |
| file | uwUFetch_cmn_hdr.h [code] |
| file | uwUFetch_NODE.h [code] |
| file | uwUFetch_NODE_HEADNODE.cpp [code] |
| file | uwUFetch_NODE_HEADNODE_mod_1.cpp [code] |
| file | uwUFetch_NODE_HEADNODE_mod_2.cpp [code] |
| file | uwUFetch_NODE_SENSOR.cpp [code] |


{include file="home/user/user_nav"} {include file="home/user/report"} {include file="home/user/record"}


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 You used more unique words than 78% of Grammarly users.
Your vocabulary was quite voluminous last week. You used more unique words than 78% of Grammarly users.
Your vocabulary was quite voluminous last week. You used more unique words than 78% of Grammarly users.
Your vocabulary was quite voluminous last week. You used more unique words than 78% of Grammarly users.
Your vocabulary was quite voluminous last week.   Missing comma in compound sentence
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Michael Chad Bauman
Unafffiliated
I am currently employed as the Estimating Manager for San Juan Construction
B.S. in Mathematics, A.S. with emphasis on Mathematics, A.A. in Outdoor Education
81401
I have no previous political involvement. I have not made any donations or contributions to any campaign or political party. Being on this commission will be the first time I have participated in politics beyond voting every November.
My employer, San Juan Construction, is an employee owned company. I served 4 years on the Company Communication Committee. The goal of the committee was to promote an ownership culture within the company.
I have participated in and helped to coordinate/organize community service projects in the Montrose area through the Church I attend.
Government by the people, for the people is a revolutionary idea, but is subverted by gerrymandering. Serving on the commission provides an opportunity for me, one of more than three hundred million Americans, the ability to help government function as intended. When those in power use their position to marginalize others, it slowly destroys part of who we are as a Nation. I hope that by serving on the commission, I can help to draw congressional districts that ensure all who live in Colorado can have an equal vote and can elect representatives who actually represent the people who live in their district. Gerrymandering is an abhorrent practice, and I am excited to be part of the solution to end it.
Every person selected to be a member of this commission will have their own political views and will have strong opinions about how the new districts should be drawn. As a member of the commission, my first goal would be to participate in some basic team building exercises. This will establish a foundation on which the members can begin to understand the root cause of each individual’s perspective. For the commission to succeed in drawing fair districts, while accounting for 21 different peoples concerns, it is going to require tremendous patience and a commitment to understanding the concerns of the other members. Two people can view the same event and arrive at wildly different conclusions. Too often in politics, people take a binary view that only one person can be correct. This results in further division and typically the only solution is a compromise that falls short of both parties needs. To successfully represent all of Colorado, the commission members will need to accept, and find solutions too, issues that may not appear to be a problem to them, but are very real to others. I believe the lack of understanding of the realities of how other people live contributes to political gridlock and partisan politics. I do not have any magical tools that will eliminate this, but I do feel that my awareness of this issue helps me to remain fair and impartial and I am committed to bring this to the commission. I am excited to help return the power to the people of Colorado and appreciate your consideration for the commission.
As the estimating manager of an industrial construction company, I spend most of my workday analyzing project estimates, financial budgets, insurance calculations, and construction schedules. I also must determine the reasons we were not successful on a project and make adjustments for the next bid proposal. In order to be successful, this all must be done with data driven analytics. I have a B.S. degree in mathematics. Math is one large series of logic stepping-stones. You build a base at the most simple idea, then use logic and reasoning to prove something new is true. Once that truth is established, you can use it to prove something else.  If selected to be a member of the commission, I plan to use my math background to develop and set of criteria that an algorithm can follow to draw the districts. Hopefully, this will set a standard for how future commissions approach redistricting in Colorado and the rest of the nation.
Male


- Leave the page by navigating to the ABOUT page or hitting the Save button, the current grid state will be stored in the localStorage object.
- To restore the previous grid state, use the refresh Restore Grid State button.
- To restore a specific feature state, click on the corresponding restore action button.
- Use the switch buttons to stop saving state for the corresponding feature.
Features to include in the grid state persistance:
Cell Selection
Filtering
Advanced Filtering
Paging
Sorting
Grouping
Columns
Serialize
Restore actions:
Grid state actions:


ISL 2019, Jamshedpur FC vs Chennaiyin FC Live Score Streaming: When and where to watch
by Sports DeskBy: Sports Desk | Updated: December 9, 2019 5:46:01 pm
Jamshedpur FC vs Chennaiyin FC Live Score Streaming: Jamshedpur will hope for another three easy points. (Source: ISL)
Jamshedpur FC vs Chennaiyin FC Live Score Streaming, ISL 2019 Live Score Streaming Online: Jamshedpur will be vying for the three entire points against Chennaiyin at home, as they hope to leapfrog Bengaluru to move upto the second spot in the table. Chennaiyin will have new manager Owen Coyle in the dugout for the first time.
Jamshedpur, sitting fourth are currently on 11 points while Chennaiyin are on 5 points, where the latter has the oppportunity to make an impressive start uner the new manager and move upto sixth in the table. But, considering Sergio Castel’s form and Antonio Iriondo’s astute tactics, that task will prove to be difficult for the 2017/18 champions. While Jamshedpur have lost just one of their last 13 home matches, Chennaiyin are yet to score on the road this season.
When is Jamshedpur FC vs Chennaiyin FC in the Indian Super League?
Jamshedpur FC vs Chennaiyin FC will be played on Monday, December 9.
What time is the kickoff between Jamshedpur FC vs Chennaiyin FC in the Indian Super League?
Jamshedpur FC vs Chennaiyin FC will kick off at 7:30 PM IST.
Where is Jamshedpur FC vs Chennaiyin FC in the Indian Super League being played?
The Indian Super League match between Jamshedpur FC vs Chennaiyin FC will be played at the JRD Tata Sports Complex in Jamshedpur.
Which channel will air Jamshedpur FC vs Chennaiyin FC in the Indian Super League?
The Indian Super League match between Jamshedpur FC vs Chennaiyin FC will broadcast on Star Sports 2 and Star Sports 2 HD.
Where can I watch the live streaming match between Jamshedpur FC vs Chennaiyin FC?
The live streaming match of Jamshedpur FC vs Chennaiyin FC will be available on Hotstar and Jio TV.
For all the latest Sports News, download Indian Express App
© IE Online Media Services Pvt Ltd
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- Indian Super League


| Figure 1 |
Study region, climate and sites sampled across the Northern ... |
| Figure 2 |
Hypothetical interrelations among climate, soil, community s ... |
| Figure 3 |
The estimated smoothers for ANPP ( a, b, solid lines) and PU ... |
| Figure 4 |
Principal component analyses (PCA). ( a)-based on plant spec ... |
| Figure 5 |
Structural equation models examining effects of environmenta ... |


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Twitter Word Cloud
I recently had a conversation with Srini (aka Mike) Vasan (CEO at Quantum Ventura) on the subject of semantic analysis that led to this fun little project. The idea is to take a Twitter stream (using the wonderful open source Tweetinvi C# API) and marrying the output to a word cloud, which I've actually implemented as a Force Directed Graph using, as a starting point, the code that Bradley Smith blogged about in 2010. I looked at a few word cloud generators but none were suited for real-time updating, however, a force directed graph is a perfect way of creating a living, dynamic view of tweets as they are happening in real time.
A (silent) video shows this best, so I've posted one here: https://www.youtube.com/watch?v=vEH_1h0jrZY
Wait until the 10 second mark for fun stuff to start happening.
The salient points of this applet are:
The source code is on GitHub, here: https://github.com/cliftonm/TwitterWordCloud-WinForm
This is very simple. You'll need to first obtain a consumer key and consumer secret from Twitter here: https://apps.twitter.com/. Then, get an access token and access token secret from here: https://api.twitter.com/oauth/request_token
Once you've done that, the credentials are set up in the API with the call:
// Setup your credentials TwitterCredentials.SetCredentials("Access_Token", "Access_Token_Secret", "Consumer_Key", "Consumer_Secret");
To get this working in the app, you'll need to place these keys into a file called "twitterauth.txt" in the bin\Debug (or bin\Release) folder. The format should be:
[Access Token] [Access Token Secret] [Consumer Key] [Consumer Secret]
For example (made up numbers):
bl6NVMpfD bxrhfA8v svdaQ86mNTE lvGwXzG3MJnN
The values you get from Twitter will be much longer.
I read these four lines and initialize the credentials with:
protected void TwitterAuth() { string[] keys = File.ReadAllLines("twitterauth.txt"); TwitterCredentials.SetCredentials(keys[0], keys[1], keys[2], keys[3]); }
The code gracefully handles starting and stopping a stream. By graceful, I mean that, if a stream exists, we shut down the current stream, wait for the event that indicates it has stopped, and then start a new stream.
/// <summary> /// If a stream hasn't been started, just start it. /// If a stream has been started, shut it down, and when it's stopped, start the new stream. /// </summary> protected void RestartStream(string keyword) { if (stream != null) { Clear(); stream.StreamStopped += (sender, args) => StartStream(keyword); stream.StopStream(); } else { StartStream(keyword); } } /// <summary> /// Start a stream, filtering ony the keyword and only English language tweets. /// </summary> protected void StartStream(string keyword) { stream = Stream.CreateFilteredStream(); stream.AddTrack(keyword); stream.MatchingTweetReceived += (sender, args) => { if (args.Tweet.Language == Language.English) { UpdateFdg(args.Tweet.Text); } }; stream.StartStreamMatchingAllConditionsAsync(); } /// <summary> /// User wants to stop the stream. /// </summary> protected void OnStop(object sender, EventArgs e) { if (stream != null) { stream.StreamStopped += (s, args) => stream = null; stream.StopStream(); } } /// <summary> /// Clear the word cloud. /// </summary> protected void Clear() { wordNodeMap.ForEach(kvp => kvp.Value.Diagram = null); wordNodeMap.Clear(); }
There's lots of parts of speech that needs to be parsed out of a tweet. At the moment, the dictionary of words to exclude is harcoded:
protected List<string> skipWords = new List<string>(new string[] { "a", "an", "and", "the", "it", ... etc ...
you get the idea.
We also want to remove punctuation (a somewhat brute force approach):
protected List<string> punctuation = new List<string>(new string[] { ".", ",", ";", "?", "!" }); public static class Extensions { // TODO: This is probably painfully slow. public static string StripPunctuation(this string s) { var sb = new StringBuilder(); foreach (char c in s) { if (!char.IsPunctuation(c)) { sb.Append(c); } } return sb.ToString(); } }
and filter out specific components of the tweet and the words in our dictionary:
/// <summary> /// Return true if the word should be eliminated. /// The word should be in lowercase! /// </summary> protected bool EliminateWord(string word) { bool ret = false; int n; if (int.TryParse(word, out n)) { ret = true; } else if (word.StartsWith("#")) { ret = true; } else if (word.StartsWith("http")) { ret = true; } else { ret = skipWords.Contains(word); } return ret; }
As mentioned earlier, once we reach the limit of 100 words, we remove stale words to make room for new words:
/// <summary> /// Remove the stalest 1 hit count word from the list -- this is the word that has not been updated the longest. /// We do this only when the word count exceends MaxWords /// </summary> protected void RemoveAStaleWord() { if (wordNodeMap.Count > MaxWords) { // TODO: Might be more efficient to maintain a sorted list to begin with! DateTime now = DateTime.Now; KeyValuePair<string, TextNode> tnode = wordNodeMap.Where(w => w.Value.Count==1). OrderByDescending(w => (now - w.Value.UpdatedOn).TotalMilliseconds).First(); // Do not call RemoveNode, as this results in a stack overflow because the property setter has this side effect. tnode.Value.Diagram = null; // THIS REMOVES THE NODE FROM THE DIAGRAM. wordNodeMap.Remove(tnode.Key); wordTweetMap.Remove(tnode.Key); } }
The above algorithm applies only to words with one hit count. If we don't do this, high volume streams, like "Obama" result in words never gaining any traction because of the huge volume of tweets coming in. By eliminating only the oldest "rabble", we get a nice word cloud of the concerns around President Obama:
Saturation is avoided by reducing all word counts over time, based on the number of tweets (iterations) modulus some saturation value, currently set to 20 - in other words, every 20 tweets, all word counts are decremented:
/// <summary> /// Prevent saturation by decrementing all word counts every 20 tweets. /// </summary> protected void ReduceCounts() { // Every 20 iterations (the default for SaturationCount), decrement the word count on all non-1 count words. // This allows us to eventually replace old words no longer comning up in new tweets. if (iteration % SaturationCount == 0) { iteration = 0; wordNodeMap.Where(wc => wc.Value.Count > 1).Select(wc => wc.Key).ForEach(w=>wordNodeMap[w].DecrementCount()); } }
The tweets are received asynchronously, so we put a lock around adding them to the queue:
protected void UpdateFdg(string text) { lock (this) { tweetQueue.Enqueue(text); } }
The entire process of updating the FDG is done in the application's main thread, specifically in the OnPaint method, which is called 20 times a second by invalidating the owner-draw panel:
timer = new System.Windows.Forms.Timer(); timer.Interval = 1000 / 20; // 20 times a second, in milliseconds. timer.Tick += (sender, args) => pnlCloud.Invalidate(true);
In the Paint event handler, we dequeue the tweet, update the nodes in the graph, execute a single iteration cycle of the FDG and draw the results:
pnlCloud.Paint += (sender, args) => { Graphics gr = args.Graphics; gr.SmoothingMode = System.Drawing.Drawing2D.SmoothingMode.HighQuality; ++paintIteration; if (!overrun) { overrun = true; int maxTweets = 20; // We assume here that we can parse the data faster than the incoming stream hands it to us. // But we put in a safety check to handle only 20 tweets. while (tweetQueue.Count > 0 && (--maxTweets > 0)) { string tweet; lock (this) { tweet = tweetQueue.Dequeue(); } SynchronousUpdate(tweet); } // gr.Clear(Color.White); diagram.Iterate(Diagram.DEFAULT_DAMPING, Diagram.DEFAULT_SPRING_LENGTH, Diagram.DEFAULT_MAX_ITERATIONS); diagram.Draw(gr, Rectangle.FromLTRB(12, 24, pnlCloud.Width - 12, pnlCloud.Height - 36)); overrun = false; } else { gr.DrawString("overrun", font, brushBlack, new Point(3, 3)); } };
I've never seen the application emit an overrun message, so I assume that everything processes fast enough that that's not an issue. Also, the processing of the incoming tweets, filtering the words, etc., could all be done in separate threads, but for simplicity, and because a lot of the existing code that I used for the FDG would need refactoring to be more thread-friendly, I decided to keep it simple and perform all the processing synchronously.
The workhorse function is really SynchronousUpdate. Here, we remove any punctuation, eliminate words we don't care about, replace stale words with any new words in the tweet, and update the word hit counters. We also record up to "MaxTweet" tweets for each word, which (as I'll show next) on mouse-over, you can then see the tweet text. Here's the method:
protected void SynchronousUpdate(string tweet) { string[] words = tweet.Split(' '); ++iteration; ReduceCounts(); foreach (string w in words) { string word = w.StripPunctuation(); string lcword = word.ToLower(); TextNode node; if (!EliminateWord(lcword)) { if (!wordNodeMap.TryGetValue(lcword, out node)) { ++totalWordCount; PointF p = rootNode.Location; RemoveAStaleWord(); TextNode n = new TextNode(word, p); rootNode.AddChild(n); wordNodeMap[lcword] = n; wordTweetMap[lcword] = new Queue<string>(new string[] { tweet }); } else { wordNodeMap[lcword].IncrementCount(); Queue<string> tweets = wordTweetMap[lcword]; // Throw away the oldest tweet if we have more than 20 associated with this word. if (tweets.Count > MaxTweets) { tweets.Dequeue(); } tweets.Enqueue(tweet); } } } }
Mouse-overs are handled by two events:
pnlCloud.MouseMove += OnMouseMove; pnlCloud.MouseLeave += (sender, args) => { if (tweetForm != null) { tweetForm.Close(); tweetForm=null; mouseWord=String.Empty; } };
When the mouse leaves the owner-draw panel, we close the form displaying the tweets and reset everything to a "not showing tweets" state.
When the user moves the mouse over the owner-draw panel, we check for whether the mouse coordinates are inside the rectangle displaying a word. There's some logic to update the existing tweet form or create a new one if one isn't displayed:
/// <summary> /// Display tweets for the word the user is hovering over. /// If a tweet popup is currently displayed, move popup window until the mouse is over a different word. /// </summary> protected void OnMouseMove(object sender, MouseEventArgs args) { var hits = wordNodeMap.Where(w => w.Value.Region.Contains(args.Location)); Point windowPos = PointToScreen(args.Location); windowPos.Offset(50, 70); if (hits.Count() > 0) { string word = hits.First().Key; TextNode node = hits.First().Value; if (mouseWord == word) { tweetForm.Location = windowPos; } else { if (tweetForm == null) { tweetForm = new TweetForm(); tweetForm.Location = windowPos; tweetForm.Show(); tweetForm.TopMost = true; } // We have a new word. tweetForm.tbTweets.Clear(); ShowTweets(word); mouseWord = word; } } else { // Just move the window. if (tweetForm != null) { tweetForm.Location = windowPos; tweetForm.TopMost = true; } } }
The result is popup window that moves with the mouse as the user moves around the owner-draw panel:
If you look at Bradley Smith's original FDG code, you'll notice that I've changed a few things. For one, I'm not drawing the force lines, only the nodes:
foreach (Node node in nodes) { PointF destination = ScalePoint(node.Location, scale); Size nodeSize = node.Size; RectangleF nodeBounds = new RectangleF(center.X + destination.X - (nodeSize.Width / 2), center.Y + destination.Y - (nodeSize.Height / 2), nodeSize.Width, nodeSize.Height); node.DrawNode(graphics, nodeBounds); }
The original code was also simply drawing spots, so I extended the SpotNote
class to be able draw text as well:
public class TextNode : SpotNode { protected int count; public int Count { get { return count; } } public Rectangle Region { get; set; } public DateTime CreatedOn { get; set; } public DateTime UpdatedOn { get; set; } public static Dictionary<int, Font> fontSizeMap = new Dictionary<int, Font>(); protected string text; public TextNode(string text, PointF location) : base() { this.text = text; Location = location; count = 1; CreatedOn = DateTime.Now; UpdatedOn = CreatedOn; } /// <summary> /// Update the UpdatedOn timestamp when incrementing the count. /// </summary> public void IncrementCount() { ++count; UpdatedOn = DateTime.Now; } /// <summary> /// Do NOT update the UpdatedOn timestamp when decrementing the count. /// Also, do not allow the count to go 0 or negative. /// </summary> public void DecrementCount() { if (count > 1) { --count; } } public override void DrawNode(Graphics gr, RectangleF bounds) { // base.DrawNode(gr, bounds); Font font; int fontSize = Math.Min(8 + Count, 36); if (!fontSizeMap.TryGetValue(fontSize, out font)) { font = new Font(FontFamily.GenericSansSerif, fontSize); fontSizeMap[fontSize] = font; } // Create a color based on count, from 1 to a max of 24 // Count (or count) is the true count. Here we limit the count to be between 1 and 24. int count2 = Math.Min(count, 24); if (count2 >= twitterWordCloud.AppForm.CountThreshold) { int blue = 255 * (24 - count2) / 24; int red = 255 - blue; Brush brush = new SolidBrush(Color.FromArgb(red, 0, blue)); SizeF strSize = gr.MeasureString(text, font); PointF textCenter = PointF.Subtract(bounds.Location, new Size((int)strSize.Width / 2 - 5, (int)strSize.Height / 2 - 5)); Region = Rectangle.FromLTRB((int)textCenter.X, (int)textCenter.Y, (int)(textCenter.X + strSize.Width), (int)(textCenter.Y + strSize.Height)); gr.DrawString(text, font, brush, textCenter); brush.Dispose(); } } }
This class also colorizes the text and each node, being a unique word, keeps track of the hit count and created/updated date.
I also removed the asynchronous behavior of the FDG that Bradley had originally implemented. Also removed was the detection for when to stop iterating -- the graph iterates forever, which is evident in the constant wiggle of the central spot. Various tweaks were also made to better support adding / removing nodes.
This was a fun little project to throw together using some great existing work and just writing a bit of logic around the Twitter and FDG pieces to glue them together. I found some disappointing things:
I also found some interesting things:
There's also some other fun things that could be done, such as plotting the tweets on a map, filtering tweets by geolocation, extending the filtering mechanism for "and" vs. "or" filter tracks, and so forth. This applet really just touches the surface of what can be done.


You are viewing an historical archive of past issues. Please
report new issues to the appropriate project issue tracker on
GitHub.
Comments
-
Greg Hurrell
If the
TERM
type is anything like xterm, you'll probably need to make a special mapping to make the ESC key work. For an example, see this post. -
asgeo1
Ah, thanks Wincent. This worked a treat:
if &term =~ "xterm" || &term =~ "screen"
let g:CommandTCancelMap = ['<ESC>', '<C-c>']
endif
Add a comment
Comments are now closed for this issue.


Paeda444.github.io
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Tagging
Experimenters and researchers often desire to generate manually tagged data to support the creation of supervised machine learning models. For example, if one were interested in generating accelerometry data while walking in order to create a walking recognition model, then each AccelerometerDatum would need to be tagged with ground truth labels (walking and not walking) in order to be used for supervised machine learning. Furthermore, the experimenter would need many replications of the activity to obtain representative data. Sensus supports this tagging process in a straightforward way that alleviates most of the tedious work associated with keeping track of the readings taken during such activities. Configure tagging as follows:
- Allow tagging for the protocol by enabling AllowTagging.
- Enter a list of tags in the AvailableTags field. These tags should describe the target activities. Ultimately, these tags will likely become the class labels to be predicted using supervised machine learning.
- Start the protocol.
- Tap the protocol and select "Tag Data".
- Select the tags you wish to apply (optionally adding new tags if desired).
- Press the Start button.
- Engage in the target activity.
- Press the Stop button and confirm the tagging.
Immediately upon pressing the Start button, each Datum that is stored will have the following two fields set:
- TaggedEventId: A unique identifier for the tagging session that begins with tapping the Start button and ends with tapping the Stop button. All Datum objects collected during this interval will have the same TaggedEventId.
- TaggedEventTags: The tags selected above.
Immediately upon pressing the Stop button, data will cease to be tagged and the above two fields will have empty values for all subsequent Datum objects. Note that the protocol will continuously collect data before the tagging starts and after the tagging ends. The only effect of tagging is to set the two fields above for all data within the start-stop interval.
After you have generated one or more taggings, tap the export button to send yourself a CSV file summarizing the tagged data. The format of the CSV file is as follows:
tagged-event-id start end tags
XXXX XXXX XXXX A|B
The example above shows a single tagged event with placeholder values for the identifier and start/end times. This event indicates that two tags (A and B) were applied to all data generated during this interval. The CSV file does not contain the actual raw data, which will be stored either locally (if you have configured local-only storage) or in your RemoteDataStore.


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| export var name = "scrollreveal";
export var version = "4.0.0-beta.9";
export var description = "Easy scroll animations for web and mobile browsers.";
export var homepage = "https://scrollrevealjs.org";
export var main = "dist/scrollreveal.js";
export var scripts = {"prebuild":"rm -rf dist/*","build":"npm run bundle && npm run bundle:min","bundle":"./node_modules/rollup/bin/rollup -c rollup.conf.js","bundle:min":"./node_modules/rollup/bin/rollup -c rollup.conf.min.js","lint":"./node_modules/eslint/bin/eslint.js src test","pretest":"rm -rf .ignore/coverage/**/ && npm run lint","test":"./node_modules/karma/bin/karma start karma.conf.js","testing":"npm test -- --no-single-run","coverage":"COVERAGE=true ./node_modules/karma/bin/karma start karma.conf.js","sandbox:bundle":"./node_modules/rollup/bin/rollup -w -c ./.ignore/sandbox/rollup.conf.sandbox.js","sandbox:server":"node ./.ignore/sandbox/server.sandbox.js","coverage:server":"node ./.ignore/coverage/server.coverage.js"};
export var repository = {"type":"git","url":"https://github.com/jlmakes/scrollreveal.git"};
export var bugs = {"url":"https://github.com/jlmakes/scrollreveal/issues"};
export var dependencies = {"rematrix":"^0.1.0"};
export var devDependencies = {"chai":"^3.5.0","eslint":"^3.11.0","karma":"^1.3.0","karma-chrome-launcher":"^2.0.0","karma-coverage":"^1.1.1","karma-coveralls":"^1.1.2","karma-mocha":"^1.3.0","karma-mocha-reporter":"^2.2.0","karma-phantomjs-launcher":"^1.0.2","karma-rollup-preprocessor":"^3.0.0","karma-sauce-launcher":"^1.1.0","karma-sinon-chai":"^1.2.4","live-server":"jlmakes/live-server","mocha":"^3.1.2","rollup":"^0.x","rollup-plugin-buble":"^0.x","rollup-plugin-istanbul":"^1.1.0","rollup-plugin-json":"^2.1.0","rollup-plugin-node-resolve":"^2.0.0","rollup-plugin-strip":"^1.1.1","rollup-plugin-uglify":"^1.0.1","rollup-watch":"^3.2.2","sinon":"^1.17.6","sinon-chai":"^2.8.0"};
export var author = "Julian Lloyd";
export var license = "GPL-3.0";
export default {
name: name,
version: version,
description: description,
homepage: homepage,
main: main,
scripts: scripts,
repository: repository,
bugs: bugs,
dependencies: dependencies,
devDependencies: devDependencies,
author: author,
license: license
};
|


java.lang.Object SpriteSheetManager
public final class SpriteSheetManager
This class manages all the game sprites.
| Constructor Summary | |
|---|---|
SpriteSheetManager()
|
| Method Summary | |
|---|---|
static greenfoot.GreenfootImage |
getSprite(java.lang.String spriteSheetFile,
int id,
int blockWidth)
This uses an ID where 0 will be the first image on the sheet. |
| Methods inherited from class |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public SpriteSheetManager()
| Method Detail |
|---|
public static greenfoot.GreenfootImage getSprite(java.lang.String spriteSheetFile, int id, int blockWidth)
spriteSheetFile
- The sprite sheet image file.id
- The sprite id
The sprite is returned by the following algorithm.
int cols = spriteSheet.getWidth() / blockWidth;
int x = (id % cols) * blockWidth;
int y = (id / cols) * blockWidth;
blockWidth
- The sprite block width. Ex.: if the block width was 32 the sprite size is 32x32 pixels.


gannett
Businessmodel of Gannett
Customer Segments
Gannett delivers content through multiple channels, serving a broad range of customers. These customers principally comprise:
- General Consumers, including the audiences of numerous local, national and regional publications online and in print; and
- Advertisers, including advertising agencies, brand marketers, and commercial entities. Gannett’s publications attract advertisers of varying sizes, selling advertising space to large businesses such as Audi, PGA Tour, and Schoology, as well as with to smaller, local and regional businesses.
Gannet’s portfolio of publications and brands principally serves a US customer base. It also publishes content in the UK for local audiences, with its online media content available to customers worldwide.
Value Propositions
Gannett Provides value to its customers in the following ways:
- The quality of its content, with the Company publishing content across a wide range of well-regarded publications, with a track record for quality and reliable reporting;
- The diversity of its publications, with the Company publishing a broad range of local and national newspapers, as well as offering content on specialist subjects, particularly sports, including boxing, baseball, and mixed martial arts;
- Its extensive readership and audience, with the Company serving local, regional and national audiences across the US, as well as in the UK, with its online content available worldwide; and
- Its accessibility and distribution network, with the Company distributing print media across the US and abroad, and providing digital content across various online and mobile channels. ### Channels
Gannett operates a website at www.gannett.com , through which it provides information on its various brands, services, and activities. The Company publishes numerous local and national brands, all of which operate their own online channels through which they distribute content. The Company’s various publications are available via a number of mobile channels, including iOS and Android applications, as well as through social media accounts which comprise a significant part of its publication infrastructure. The Company’s online content is available to consumers both on a subscription and free-to-use basis.
Gannett also circulates print versions of many of its publications, which it sells through retailers and distributors, as well as via mail subscriptions. The Company operates its own plants and equipment, as part of its distribution networks. Gannett provides its advertising solutions through its own in-house sales and marketing teams, with each of the Company’s publications offering advertising solutions on an individual basis, often serving client in the same locality or related to the theme of the publication.
Customer Relationships
Much of Gannett’s content is available on a self-service process, through the Company’s numerous digital channels. Customers are able to access content from the Company’s publications, manage their subscriptions, and change their account details without interacting directly with members of the Company’s sales and marketing teams. Customers can also purchase physical subscriptions autonomously.
Gannett also establishes close relationships with customers, with its sales and marketing teams dealing directly with retail and distribution customers, and establishing long-term relationships. Similarly, the Company works closely with its advertising customers so as to establish a detailed brief covering their budget and requirements.
Gannett provides support to its existing and potential customers, who are able to submit enquiries to the Company’s support staff by post, over the phone and via email. Each of the Company’s individual publications also operates its own contact channels, as well as social media accounts, including with Facebook, Twitter, LinkedIn, Instagram, Pinterest, and YouTube.
Key Activities
Gannett operates as an international, multi-platform news and information publisher. It provides local and national content, as well as niche content, in physical form, as well as online and via mobile.
The Company’s portfolio comprises around approximately 110 daily publications, more than 400 non-daily publications, as well as digital platforms, primarily in the US – where it has operations in more than 30 states nationwide, as well as in Guam – and the UK. The Company’s portfolio includes high-profile outlets such as USA TODAY, which has a total daily circulation of approximately four million and Sunday circulation of over 3.9 million.
Gannett’s UK operations are organized under the Newsquest umbrella, which manages the activities of more than 20 daily paid-for publications. The Company’s operations also include commercial printing, marketing and data services operations.
Key Partners
Gannett collaborates with various companies and organisations in order to efficiently serve its customers. These partners include:
- Advertising and Branding Partners, principally comprising advertising agencies which assist in matching advertisers with appropriate publications from the Company’s portfolio;
- Supplier and Vendor Partners, comprising suppliers of resources, tools, and services, which support the publication and distribution of the Company’s content across multiple platforms;
- Channel and Distribution Partners, comprising distributors which assist in extending the Company’s own sales and marketing reach; and
- Discovery and technology Partners, comprising various technology companies and platforms that assist in identifying and disseminating content for the Company’s various publications. Gannett has agreed a number of partnerships in recent years. This includes an investment in news aggregator Digg, which will help Gannett’s USA Today Network reach more people; a partnership with discovery platform Taboola, which will integrate content recommendations across Gannett’s network of publications; and a deal with Kimbia to raise funds for non-profits across the US.
Key Resources
Gannett’s key resources are its content and intellectual properties, its various proprietary publications, its online and mobile platforms and its IT infrastructure, its publication infrastructure and networks – including its raw material supply chain, its sales and distribution channels, its partnerships, and its personnel.
Gannett owns and leases a number of properties as part of its operations. This includes a number of publishing centres and plants, in the US and in the UK, that are key to the publication process. The Company also owns a number of intellectual properties – including trademarks, domain names, and copyrights – that are material to the success of the Company’s operations and brand identities.
Cost Structure
Gannett incurs costs in relation to the management of its intellectual properties, the generation and acquisition of content, the maintenance of its digital platforms and IT infrastructure, the operation and maintenance of its physical publishing infrastructure – including the procurement of raw materials, the operation of its sales and marketing channels, the management of its partnerships, and the retention of it personnel.
In 2015 Gannett’s cost of sales and operating expenses amounted to $1.87 billion, and selling, general and administrative costs totaling $707.02 million. The bulk of the Company’s selling, general and administrative costs related to payroll and employee benefit costs – including the payment of salaries and benefits to its workforce of 18,700 employees – which totaled $515 million for the year.
Revenue Streams
Gannett generates revenue through the publication and distribution of various local and national publications across the US and UK. The Company’s revenue is principally categorised as follows:
- Advertising Revenue, comprising revenue derived from the Company’s provision of multi-platform advertising solutions across its various publications; and
- Circulation Revenue, comprising revenue derived from the sale of premium content and the collection of fees for both digital and physical subscriptions. In 2015 Gannett generated operating revenue in the amount of $2.89 billion, down on the $3.17 billion recorded by the Company in 2014. The majority of the Company’s revenue was derived from its advertising solutions, which accounted for $1.61 billion in annual revenue. Revenue derived from circulation totalled $1.06 billion, with the Company’ remaining revenue attributed to other activities, such as commercial printing services.


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https://github.com/pbeshai/d3-interpolate-path
This example page shows an older version, v1.1.1. See the latest version.
d3-interpolate-path is a D3 plugin that adds an interpolator optimized for SVG <path> elements. See the GitHub page for details on usage.


Type Definitions
onErrorCallback(error)
Function called after an error.
Using
this
inside it will return the current AddressSearch
Parameters:
| Name | Type | Description |
|---|---|---|
error |
Object | The error |
- Source:
onPredictCallback(value, predictions)
Function called after a prediction.
Using
this
inside it will return the current AddressSearch
Parameters:
| Name | Type | Description |
|---|---|---|
value |
String | The user's input |
predictions |
HTMLUListElement | The predictions UL element |
- Source:
onSelectCallback(address)
Function called after a selection.
Using
this
inside it will return the current AddressSearch
Parameters:
| Name | Type | Description |
|---|---|---|
address |
PlaceResult | The selected address |
- Source:


| spark.glm {SparkR} | R Documentation |
Fits a generalized linear model against a Spark DataFrame.
spark.glm(data, formula, ...) ## S4 method for signature 'SparkDataFrame,formula' spark.glm(data, formula, family = gaussian, epsilon = 1e-06, maxit = 25)
data |
SparkDataFrame for training. |
formula |
A symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', '.', ':', '+', and '-'. |
family |
A description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. Refer R family at https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html. |
epsilon |
Positive convergence tolerance of iterations. |
maxit |
Integer giving the maximal number of IRLS iterations. |
a fitted generalized linear model
## Not run:
##D sc <- sparkR.init()
##D sqlContext <- sparkRSQL.init(sc)
##D data(iris)
##D df <- createDataFrame(sqlContext, iris)
##D model <- spark.glm(df, Sepal_Length ~ Sepal_Width, family="gaussian")
##D summary(model)
## End(Not run)