text:
Italy PM says 'determined' to avoid EU debt procedure
Rome (AFP)
Italian Prime Minister Giuseppe Conte said Tuesday his government is determined to avoid triggering an EU procedure for excessive debt levels.
The European Commission last week put Italy on notice about its snowballing debt, as well as its deteriorating deficit position, reopening a political battle with Rome.
The move by the EU's executive arm against Italy's bloated budget begins a complicated process that -- if approved by eurozone ministers -- could result in an unprecedented fine of more than three billion euros ($3.4 billion), a prospect Conte said he hoped to avoid.
Italy's far right Deputy Prime Minister Matteo Salvini has vowed not to yield to the EU.
But Conte said that, when he met both Salvini and Deputy Prime Minister Luigi di Maio on Monday, both were "in complete agreement" that any procedure should be avoided.
"The negotiation with the European Commission and with our European Union partners will see the government absolutely determined to avoid, in the coming months, a procedure for excessive debt," Conte told a gathering of business leaders in Rome.
However, the coalition government is under pressure. Conte threatened last week to resign unless the two parties in the governing populist coalition -- Salvini's League and di Maio's Five Star Movement -- stopped squabbling.
"I want a clear, unequivocal and speedy response," Conte said, calling for "loyal collaboration" from all ministers.
Italy's debt ratio, at 132 percent of gross domestic product, is the second-biggest in the eurozone after Greece.


Trader and manufacturer of cosmetic colors, synthetic food dyes, beverage flavours, ice cream flavours, cosmetics dyes, synthetic food colors, tower rich chocolate flavour cake, flavours, concentrate.
Wholeseller and trading of flavours and aromatics for bakery, chocolate candies, muska butter, dairy products, eclairs, toffees, flavoured candy, essential oils, biscuits & other bakery products.
We are leading manufacturer and exporter of chemical supplier special in fine chemical, flavor, food additive, pharmaceutical and intermediates.
Grabees is a company where you can place orders on groceries.food,bakery and sweet. We also providing deivery service.
Cinnamic aldehyde Name: Cinnamic aldehyde Synonyms: cinnamaldehyde�C��-phenylacrolein CAS Number: 104-55-2 Assay:99% Molecular Formula: C9H8O Molecular Weight: 132.16 Packing: 200kg/barrel Melting Point:-8�� Appearance: Colorless or light yellow liquid Uses: It is also an important raw material of medicine, one commonly used in external medicine, compound classes.One is also an important spice commonly used in soap flavor, modulation gardenia, jasmine, lily of the valley, rose and other essences. It is used for fruit preservation, recent studies have shown that cinnamic aldehyde is used chewing gum on oral bacteria and deodorant can play a dual effect.
Bake Tech Enterprises is a high-tech bakery machine and bakery equipment manufacturer in Delhi India,established in 1990.Call:09990767811.
Manufacturing and exporting of bakery products, candies, alcoholic beverages, bakery, confectionary, dairy, sweet candies, caramel candies, plain rusks, flavored rusks, namkeen.
Supplier and exporter of badam cookies, macaroon, cream biscuits, glucose biscuits, milk rusks, plain rice puffs, flavors, preservatives, sweeteners, emulsifiers, stabilizers.
Exporters and suppliers of milk flavours, dairy flavours, food flavours and food colors, cake mixes, bread mixes, cake gels, flavors, pharmaceuticals flavours, liquid flavour, pure natural perfumes, floral fragrances.
We are manufacturer & suppliers of cinnamon range, flavor, essence, food additive, vpharmaceutical raw material and intermediate etc.
We are manufacture and exporter of chemical antisepsis, flavor & essence and food additive, pharmaceutical intermediates.
We manufacture trader & suppliers of bakery products etc.
We are one of the leading manufacturer, supplier and exporters of grill oven, pantry area products, steam cooking equipments, food preparation equipment, burner and stoves, bakery equipments, kitchen accessories.


Here's a shed that Marty is building using my 12x16 barn with porch shed plans.
The following is some information Marty provided along with the pictures he sent me.
My wife and I decided to use your plans to build a shed. We also decided that we would not build the porch, So we just put up 4 walls. We also put in 4 windows and a door with a storm door, We received our wood first week in September and 7 weeks later finished the shed all except for the ramp. Now mind you we are both retired- I am 64 and my wife is 63. We worked pretty well together, she was the master of the rule and the square and the level. No slam bam git -r-done with her around.
Every thing was going as planed- we built our first truss and the first thing we said was uhh o we are in deep trouble. we finished installing all of the trusses.I borrowed some cross buck scaffolding and ladder standoffs to finish the job. Before that point in time we thought at first there is no way we could handle the rest of this job.
Marty Looks Like He's Enjoying This!
Just getting the plywood on the roof was a trick. By myself, I thru the plywood up on the scaffolding. After nailing a 2x4 across the trusses to hold the plywood until nailed --the one side went real smooth but about killed this ole man LOL. The other side was a little more strenuous since it was about 2 ft higher but it worked out.
The roofing was a breeze. I had done commercial and residential roofing for about 18 years, just one of my 3 professions. Struck one line on whole roof. Came out perfect.
Fly rafters were a breeze. No one should be intimidated by them. I actually put one side of them on by myself. The gable rafters were a bit tricky but with the scaffolding no problem.
We absolutely love this building---doing it our self made it all worth our time and effort.--- Will wait until spring to build the ramp and the one step needed to get in the front door.
Did Your Wife Take This Picture Marty!
Everytime my wife looks out the back door- I hear her say--I cannot believe we built that. She was afraid of heights until we done this project.
This is not a project for the faint of heart.
Thank you so much Marty for sending in pictures of your shed building project! I look forward to getting more from you and seeing the finished views of your neat shed.


text:
package com.amazonaws.services.machinelearning.model;

import java.io.Serializable;

/**
 *

 * The data specification of an Amazon Relational Database Service (Amazon RDS)
 * DataSource.
 *

 */
public class RDSDataSpec implements Serializable, Cloneable {

    /**
     *

     * Describes the DatabaseName and
     * InstanceIdentifier of an an Amazon RDS database.
     *

     */
    private RDSDatabase databaseInformation;
    /**
     *

     * The query that is used to retrieve the observation data for the
     * DataSource.
     *

     */
    private String selectSqlQuery;
    /**
     *

     * The AWS Identity and Access Management (IAM) credentials that are used
     * connect to the Amazon RDS database.
     *

     */
    private RDSDatabaseCredentials databaseCredentials;
    /**
     *

     * The Amazon S3 location for staging Amazon RDS data. The data retrieved
     * from Amazon RDS using SelectSqlQuery is stored in this
     * location.
     *

     */
    private String s3StagingLocation;
    /**
     *

     * A JSON string that represents the splitting and rearrangement processing
     * to be applied to a DataSource. If the
     * DataRearrangement parameter is not provided, all of the
     * input data is used to create the Datasource.
     *

     *

     * There are multiple parameters that control what data is used to create a
     * datasource:
     *

     *

     *

     *

     * percentBegin
     *

     *

     * Use percentBegin to indicate the beginning of the range of
     * the data used to create the Datasource. If you do not include
     * percentBegin and percentEnd, Amazon ML includes
     * all of the data when creating the datasource.
     *

     *

     *

     *

     * percentEnd
     *

     *

     * Use percentEnd to indicate the end of the range of the data
     * used to create the Datasource. If you do not include
     * percentBegin and percentEnd, Amazon ML includes
     * all of the data when creating the datasource.
     *

     *

     *

     *

     * complement
     *

     *

     * The complement parameter instructs Amazon ML to use the data
     * that is not included in the range of percentBegin to
     * percentEnd to create a datasource. The
     * complement parameter is useful if you need to create
     * complementary datasources for training and evaluation. To create a
     * complementary datasource, use the same values for
     * percentBegin and percentEnd, along with the
     * complement parameter.
     *

     *

     * For example, the following two datasources do not share any data, and can
     * be used to train and evaluate a model. The first datasource has 25
     * percent of the data, and the second one has 75 percent of the data.
     *

     *

     * Datasource for evaluation:
     * {"splitting":{"percentBegin":0, "percentEnd":25}}
     *

     *

     * Datasource for training:
     * {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
     *

     *

     *

     *

     * strategy
     *

     *

     * To change how Amazon ML splits the data for a datasource, use the
     * strategy parameter.
     *

     *

     * The default value for the strategy parameter is
     * sequential, meaning that Amazon ML takes all of the data
     * records between the percentBegin and percentEnd
     * parameters for the datasource, in the order that the records appear in
     * the input data.
     *

     *

     * The following two DataRearrangement lines are examples of
     * sequentially ordered training and evaluation datasources:
     *

     *

     * Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
     *

     *

     * Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
     *

     *

     * To randomly split the input data into the proportions indicated by the
     * percentBegin and percentEnd parameters, set the strategy
     * parameter to random and provide a string that is used as the
     * seed value for the random data splitting (for example, you can use the S3
     * path to your data as the random seed string). If you choose the random
     * split strategy, Amazon ML assigns each row of data a pseudo-random number
     * between 0 and 100, and then selects the rows that have an assigned number
     * between percentBegin and percentEnd.
     * Pseudo-random numbers are assigned using both the input seed string value
     * and the byte offset as a seed, so changing the data results in a
     * different split. Any existing ordering is preserved. The random splitting
     * strategy ensures that variables in the training and evaluation data are
     * distributed similarly. It is useful in the cases where the input data may
     * have an implicit sort order, which would otherwise result in training and
     * evaluation datasources containing non-similar data records.
     *

     *

     * The following two DataRearrangement lines are examples of
     * non-sequentially ordered training and evaluation datasources:
     *

     *

     * Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
     *

     *

     * Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
     *

     *

     *

     */
    private String dataRearrangement;
    /**
     *

     * A JSON string that represents the schema for an Amazon RDS
     * DataSource. The DataSchema defines the
     * structure of the observation data in the data file(s) referenced in the
     * DataSource.
     *

     *

     * A DataSchema is not required if you specify a
     * DataSchemaUri
     *

     *

     * Define your DataSchema as a series of key-value pairs.
     * attributes and excludedVariableNames have an
     * array of key-value pairs for their value. Use the following format to
     * define your DataSchema.
     *

     *

     * { "version": "1.0",
     *

     *

     * "recordAnnotationFieldName": "F1",
     *

     *

     * "recordWeightFieldName": "F2",
     *

     *

     * "targetFieldName": "F3",
     *

     *

     * "dataFormat": "CSV",
     *

     *

     * "dataFileContainsHeader": true,
     *

     *

     * "attributes": [
     *

     *

     * { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2",
     * "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL"
     * }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5",
     * "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" },
     * { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, {
     * "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
     *

     *

     * "excludedVariableNames": [ "F6" ] }
     *

     * <?oxy_insert_end>
     */
    private String dataSchema;
    /**
     *

     * The Amazon S3 location of the DataSchema.
     *

     */
    private String dataSchemaUri;
    /**
     *

     * The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic
     * Compute Cloud (Amazon EC2) instance to carry out the copy operation from
     * Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.
     *

     */
    private String resourceRole;
    /**
     *

     * The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service
     * to monitor the progress of the copy task from Amazon RDS to Amazon S3.
     * For more information, see Role templates for data pipelines.
     *

     */
    private String serviceRole;
    /**
     *

     * The subnet ID to be used to access a VPC-based RDS DB instance. This
     * attribute is used by Data Pipeline to carry out the copy task from Amazon
     * RDS to Amazon S3.
     *

     */
    private String subnetId;
    /**
     *

     * The security group IDs to be used to access a VPC-based RDS DB instance.
     * Ensure that there are appropriate ingress rules set up to allow access to
     * the RDS DB instance. This attribute is used by Data Pipeline to carry out
     * the copy operation from Amazon RDS to an Amazon S3 task.
     *

     */
    private com.amazonaws.internal.SdkInternalList<String> securityGroupIds;

    /**
     *

     * Describes the DatabaseName and
     * InstanceIdentifier of an an Amazon RDS database.
     *

     *
     * @param databaseInformation
     *        Describes the DatabaseName and
     *        InstanceIdentifier of an an Amazon RDS database.
     */

    public void setDatabaseInformation(RDSDatabase databaseInformation) {
        this.databaseInformation = databaseInformation;
    }

    /**
     *

     * Describes the DatabaseName and
     * InstanceIdentifier of an an Amazon RDS database.
     *

     *
     * @return Describes the DatabaseName and
     *         InstanceIdentifier of an an Amazon RDS database.
     */

    public RDSDatabase getDatabaseInformation() {
        return this.databaseInformation;
    }

    /**
     *

     * Describes the DatabaseName and
     * InstanceIdentifier of an an Amazon RDS database.
     *

     *
     * @param databaseInformation
     *        Describes the DatabaseName and
     *        InstanceIdentifier of an an Amazon RDS database.
     * @return Returns a reference to this object so that method calls can be
     *         chained together.
     */

    public RDSDataSpec withDatabaseInformation(RDSDatabase databaseInformation) {
        setDatabaseInformation(databaseInformation);
        return this;
    }

    /**
     *

     * The query that is used to retrieve the observation data for the
     * DataSource.
     *

     *
     * @param selectSqlQuery
     *        The query that is used to retrieve the observation data for the
     *        DataSource.
     */

    public void setSelectSqlQuery(String selectSqlQuery) {
        this.selectSqlQuery = selectSqlQuery;
    }

    /**
     *

     * The query that is used to retrieve the observation data for the
     * DataSource.
     *

     *
     * @return The query that is used to retrieve the observation data for the
     *         DataSource.
     */

    public String getSelectSqlQuery() {
        return this.selectSqlQuery;
    }

    /**
     *

     * The query that is used to retrieve the observation data for the
     * DataSource.
     *

     *
     * @param selectSqlQuery
     *        The query that is used to retrieve the observation data for the
     *        DataSource.
     * @return Returns a reference to this object so that method calls can be
     *         chained together.
     */

    public RDSDataSpec withSelectSqlQuery(String selectSqlQuery) {
        setSelectSqlQuery(selectSqlQuery);
        return this;
    }

    /**
     *

     * The AWS Identity and Access Management (IAM) credentials that are used
     * connect to the Amazon RDS database.
     *

     *
     * @param databaseCredentials
     *        The AWS Identity and Access Management (IAM) credentials that are
     *        used connect to the Amazon RDS database.
     */

    public void setDatabaseCredentials(
            RDSDatabaseCredentials databaseCredentials) {
        this.databaseCredentials = databaseCredentials;
    }

    /**
     *

     * The AWS Identity and Access Management (IAM) credentials that are used
     * connect to the Amazon RDS database.
     *

     *
     * @return The AWS Identity and Access Management (IAM) credentials that are
     *         used connect to the Amazon RDS database.
     */

    public RDSDatabaseCredentials getDatabaseCredentials() {
        return this.databaseCredentials;
    }

    /**
     *

     * The AWS Identity and Access Management (IAM) credentials that are used
     * connect to the Amazon RDS database.
     *

     *
     * @param databaseCredentials
     *        The AWS Identity and Access Management (IAM) credentials that are
     *        used connect to the Amazon RDS database.
     * @return Returns a reference to this object so that method calls can be
     *         chained together.
     */

    public RDSDataSpec withDatabaseCredentials(
            RDSDatabaseCredentials databaseCredentials) {
        setDatabaseCredentials(databaseCredentials);
        return this;
    }

    /**
     *

     * The Amazon S3 location for staging Amazon RDS data. The data retrieved
     * from Amazon RDS using SelectSqlQuery is stored in this
     * location.
     *

     *
     * @param s3StagingLocation
     *        The Amazon S3 location for staging Amazon RDS data. The data
     *        retrieved from Amazon RDS using SelectSqlQuery is
     *        stored in this location.
     */

    public void setS3StagingLocation(String s3StagingLocation) {
        this.s3StagingLocation = s3StagingLocation;
    }

    /**
     *

     * The Amazon S3 location for staging Amazon RDS data. The data retrieved
     * from Amazon RDS using SelectSqlQuery is stored in this
     * location.
     *

     *
     * @return The Amazon S3 location for staging Amazon RDS data. The data
     *         retrieved from Amazon RDS using SelectSqlQuery is
     *         stored in this location.
     */

    public String getS3StagingLocation() {
        return this.s3StagingLocation;
    }

    /**
     *

     * The Amazon S3 location for staging Amazon RDS data. The data retrieved
     * from Amazon RDS using SelectSqlQuery is stored in this
     * location.
     *

     *
     * @param s3StagingLocation
     *        The Amazon S3 location for staging Amazon RDS data. The data
     *        retrieved from Amazon RDS using SelectSqlQuery is
     *        stored in this location.
     * @return Returns a reference to this object so that method calls can be
     *         chained together.
     */

    public RDSDataSpec withS3StagingLocation(String s3StagingLocation) {
        setS3StagingLocation(s3StagingLocation);
        return this;
    }

    /**
     *

     * A JSON string that represents the splitting and rearrangement processing
     * to be applied to a DataSource. If the
     * DataRearrangement parameter is not provided, all of the
     * input data is used to create the Datasource.
     *

     *

     * There are multiple parameters that control what data is used to create a
     * datasource:
     *

     *

     *

     *

     * percentBegin
     *

     *

     * Use percentBegin to indicate the beginning of the range of
     * the data used to create the Datasource. If you do not include
     * percentBegin and percentEnd, Amazon ML includes
     * all of the data when creating the datasource.
     *

     *

     *

     *

     * percentEnd
     *

     *

     * Use percentEnd to indicate the end of the range of the data
     * used to create the Datasource. If you do not include
     * percentBegin and percentEnd, Amazon ML includes
     * all of the data when creating the datasource.
     *

     *

     *

     *

     * complement
     *

     *

     * The complement parameter instructs Amazon ML to use the data
     * that is not included in the range of percentBegin to
     * percentEnd to create a datasource. The
     * complement parameter is useful if you need to create
     * complementary datasources for training and evaluation. To create a
     * complementary datasource, use the same values for
     * percentBegin and percentEnd, along with the
     * complement parameter.
     *

     *

     * For example, the following two datasources do not share any data, and can
     * be used to train and evaluate a model. The first datasource has 25
     * percent of the data, and the second one has 75 percent of the data.
     *

     *

     * Datasource for evaluation:
     * {"splitting":{"percentBegin":0, "percentEnd":25}}
     *

     *

     * Datasource for training:
     * {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
     *

     *

     *

     *

     * strategy
     *

     *

     * To change how Amazon ML splits the data for a datasource, use the
     * strategy parameter.
     *

     *

     * The default value for the strategy parameter is
     * sequential, meaning that Amazon ML takes all of the data
     * records between the percentBegin and percentEnd
     * parameters for the datasource, in the order that the records appear in
     * the input data.
     *

     *

     * The following two DataRearrangement lines are examples of
     * sequentially ordered training and evaluation datasources:
     *

     *

     * Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
     *

     *

     * Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
     *

     *

     * To randomly split the input data into the proportions indicated by the
     * percentBegin and percentEnd parameters, set the strategy
     * parameter to random and provide a string that is used as the
     * seed value for the random data splitting (for example, you can use the S3
     * path to your data as the random seed string). If you choose the random
     * split strategy, Amazon ML assigns each row of data a pseudo-random number
     * between 0 and 100, and then selects the rows that have an assigned number
     * between percentBegin and percentEnd.
     * Pseudo-random numbers are assigned using both the input seed string value
     * and the byte offset as a seed, so changing the data results in a
     * different split. Any existing ordering is preserved. The random splitting
     * strategy ensures that variables in the training and evaluation data are
     * distributed similarly. It is useful in the cases where the input data may
     * have an implicit sort order, which would otherwise result in training and
     * evaluation datasources containing non-similar data records.
     *

     *

     * The following two DataRearrangement lines are examples of
     * non-sequentially ordered training and evaluation datasources:
     *

     *

     * Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
     *

     *

     * Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
     *

     *

     *

     *
     * @param dataRearrangement
     *        A JSON string that represents the splitting and rearrangement
     *        processing to be applied to a DataSource. If the
     *        DataRearrangement parameter is not provided, all of
     *        the input data is used to create the Datasource.

     *

     *        There are multiple parameters that control what data is used to
     *        create a datasource:
     *

     *

     *

     *

     *        percentBegin
     *

     *

     *        Use percentBegin to indicate the beginning of the
     *        range of the data used to create the Datasource. If you do not
     *        include percentBegin and percentEnd,
     *        Amazon ML includes all of the data when creating the datasource.
     *

     *

     *

     *

     *        percentEnd
     *

     *

     *        Use percentEnd to indicate the end of the range of
     *        the data used to create the Datasource. If you do not include
     *        percentBegin and percentEnd, Amazon ML
     *        includes all of the data when creating the datasource.
     *

     *

     *

     *

     *        complement
     *

     *

     *        The complement parameter instructs Amazon ML to use
     *        the data that is not included in the range of
     *        percentBegin to percentEnd to create a
     *        datasource. The complement parameter is useful if you
     *        need to create complementary datasources for training and
     *        evaluation. To create a complementary datasource, use the same
     *        values for percentBegin and percentEnd,
     *        along with the complement parameter.
     *

     *

     *        For example, the following two datasources do not share any data,
     *        and can be used to train and evaluate a model. The first
     *        datasource has 25 percent of the data, and the second one has 75
     *        percent of the data.
     *

     *

     *        Datasource for evaluation:
     *        {"splitting":{"percentBegin":0, "percentEnd":25}}
     *

     *

     *        Datasource for training:
     *        {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
     *

     *

     *

     *

     *        strategy
     *

     *

     *        To change how Amazon ML splits the data for a datasource, use the
     *        strategy parameter.
     *

     *

     *        The default value for the strategy parameter is
     *        sequential, meaning that Amazon ML takes all of the
     *        data records between the percentBegin and
     *        percentEnd parameters for the datasource, in the
     *        order that the records appear in the input data.
     *

     *

     *        The following two DataRearrangement lines are
     *        examples of sequentially ordered training and evaluation
     *        datasources:
     *

     *

     *        Datasource for evaluation:
     *        {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
     *

     *

     *        Datasource for training:
     *        {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
     *

     *

     *        To randomly split the input data into the proportions indicated by
     *        the percentBegin and percentEnd parameters, set the
     *        strategy parameter to random and provide
     *        a string that is used as the seed value for the random data
     *        splitting (for example, you can use the S3 path to your data as
     *        the random seed string). If you choose the random split strategy,
     *        Amazon ML assigns each row of data a pseudo-random number between
     *        0 and 100, and then selects the rows that have an assigned number
     *        between percentBegin and percentEnd.
     *        Pseudo-random numbers are assigned using both the input seed
     *        string value and the byte offset as a seed, so changing the data
     *        results in a different split. Any existing ordering is preserved.
     *        The random splitting strategy ensures that variables in the
     *        training and evaluation data are distributed similarly. It is
     *        useful in the cases where the input data may have an implicit sort
     *        order, which would otherwise result in training and evaluation
     *        datasources containing non-similar data records.
     *

     *

     *        The following two DataRearrangement lines are
     *        examples of non-sequentially ordered training and evaluation
     *        datasources:
     *

     *

     *        Datasource for evaluation:
     *        {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
     *

     *

     *        Datasource for training:
     *        {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
     *

     *

     */

    public void setDataRearrangement(String dataRearrangement) {
        this.dataRearrangement = dataRearrangement;
    }

    /**
     *

     * A JSON string that represents the splitting and rearrangement processing
     * to be applied to a DataSource. If the
     * DataRearrangement parameter is not provided, all of the
     * input data is used to create the Datasource.
     *

     *

     * There are multiple parameters that control what data is used to create a
     * datasource:
     *

     *

     *

     *

     * percentBegin
     *

     *

     * Use percentBegin to indicate the beginning of the range of
     * the data used to create the Datasource. If you do not include
     * percentBegin and percentEnd, Amazon ML includes
     * all of the data when creating the datasource.
     *

     *

     *

     *

     * percentEnd
     *

     *

     * Use percentEnd to indicate the end of the range of the data
     * used to create the Datasource. If you do not include
     * percentBegin and percentEnd, Amazon ML includes
     * all of the data when creating the datasource.
     *

     *

     *

     *

     * complement
     *

     *

     * The complement parameter instructs Amazon ML to use the data
     * that is not included in the range of percentBegin to
     * percentEnd to create a datasource. The
     * complement parameter is useful if you need to create
     * complementary datasources for training and evaluation. To create a
     * complementary datasource, use the same values for
     * percentBegin and percentEnd, along with the
     * complement parameter.
     *

     *

     * For example, the following two datasources do not share any data, and can
     * be used to train and evaluate a model. The first datasource has 25
     * percent of the data, and the second one has 75 percent of the data.
     *

     *

     * Datasource for evaluation:
     * {"splitting":{"percentBegin":0, "percentEnd":25}}
     *

     *

     * Datasource for training:
     * {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
     *

     *

     *

     *

     * strategy
     *

     *

     * To change how Amazon ML splits the data for a datasource, use the
     * strategy parameter.
     *

     *

     * The default value for the strategy parameter is
     * sequential, meaning that Amazon ML takes all of the data
     * records between the percentBegin and percentEnd
     * parameters for the datasource, in the order that the records appear in
     * the input data.
     *

     *

     * The following two DataRearrangement lines are examples of
     * sequentially ordered training and evaluation datasources:
     *

     *

     * Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
     *

     *

     * Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
     *

     *

     * To randomly split the input data into the proportions indicated by the
     * percentBegin and percentEnd parameters, set the strategy
     * parameter to random and provide a string that is used as the
     * seed value for the random data splitting (for example, you can use the S3
     * path to your data as the random seed string). If you choose the random
     * split strategy, Amazon ML assigns each row of data a pseudo-random number
     * between 0 and 100, and then selects the rows that have an assigned number
     * between percentBegin and percentEnd.
     * Pseudo-random numbers are assigned using both the input seed string value
     * and the byte offset as a seed, so changing the data results in a
     * different split. Any existing ordering is preserved. The random splitting
     * strategy ensures that variables in the training and evaluation data are
     * distributed similarly. It is useful in the cases where the input data may
     * have an implicit sort order, which would otherwise result in training and
     * evaluation datasources containing non-similar data records.
     *

     *

     * The following two DataRearrangement lines are examples of
     * non-sequentially ordered training and evaluation datasources:
     *

     *

     * Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
     *

     *

     * Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
     *

     *

     *

     *
     * @return A JSON string that represents the splitting and rearrangement
     *         processing to be applied to a DataSource. If the
     *         DataRearrangement parameter is not provided, all of
     *         the input data is used to create the Datasource.

     *

     *         There are multiple parameters that control what data is used to
     *         create a datasource:
     *

     *

     *

     *

     *         percentBegin
     *

     *

     *         Use percentBegin to indicate the beginning of the
     *         range of the data used to create the Datasource. If you do not
     *         include percentBegin and percentEnd,
     *         Amazon ML includes all of the data when creating the datasource.
     *

     *

     *

     *

     *         percentEnd
     *

     *

     *         Use percentEnd to indicate the end of the range of
     *         the data used to create the Datasource. If you do not include
     *         percentBegin and percentEnd, Amazon ML
     *         includes all of the data when creating the datasource.
     *

     *

     *

     *

     *         complement
     *

     *

     *         The complement parameter instructs Amazon ML to use
     *         the data that is not included in the range of
     *         percentBegin to percentEnd to create a
     *         datasource. The complement parameter is useful if
     *         you need to create complementary datasources for training and
     *         evaluation. To create a complementary datasource, use the same
     *         values for percentBegin and percentEnd,
     *         along with the complement parameter.
     *

     *

     *         For example, the following two datasources do not share any data,
     *         and can be used to train and evaluate a model. The first
     *         datasource has 25 percent of the data, and the second one has 75
     *         percent of the data.
     *

     *

     *         Datasource for evaluation:
     *         {"splitting":{"percentBegin":0, "percentEnd":25}}
     *

     *

     *         Datasource for training:
     *         {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
     *

     *

     *

     *

     *         strategy
     *

     *

     *         To change how Amazon ML splits the data for a datasource, use the
     *         strategy parameter.
     *

     *

     *         The default value for the strategy parameter is
     *         sequential, meaning that Amazon ML takes all of the
     *         data records between the percentBegin and
     *         percentEnd parameters for the datasource, in the
     *         order that the records appear in the input data.
     *

     *

     *         The following two DataRearrangement lines are
     *         examples of sequentially ordered training and evaluation
     *         datasources:
     *

     *

     *         Datasource for evaluation:
     *         {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
     *

     *

     *         Datasource for training:
     *         {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
     *

     *

     *         To randomly split the input data into the proportions indicated
     *         by the percentBegin and percentEnd parameters, set the
     *         strategy parameter to random and
     *         provide a string that is used as the seed value for the random
     *         data splitting (for example, you can use the S3 path to your data
     *         as the random seed string). If you choose the random split
     *         strategy, Amazon ML assigns each row of data a pseudo-random
     *         number between 0 and 100, and then selects the rows that have an
     *         assigned number between percentBegin and
     *         percentEnd. Pseudo-random numbers are assigned using
     *         both the input seed string value and the byte offset as a seed,
     *         so changing the data results in a different split. Any existing
     *         ordering is preserved. The random splitting strategy ensures that
     *         variables in the training and evaluation data are distributed
     *         similarly. It is useful in the cases where the input data may
     *         have an implicit sort order, which would otherwise result in
     *         training and evaluation datasources containing non-similar data
     *         records.
     *

     *

     *         The following two DataRearrangement lines are
     *         examples of non-sequentially ordered training and evaluation
     *         datasources:
     *

     *

     *         Datasource for evaluation:
     *         {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
     *

     *

     *         Datasource for training:
     *         {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
     *

     *

     */

    public String getDataRearrangement() {
        return this.dataRearrangement;
    }

    /**
     *

     * A JSON string that represents the splitting and rearrangement processing
     * to be applied to a DataSource. If the
     * DataRearrangement parameter is not provided, all of the
     * input data is used to create the Datasource.
     *

     *

     * There are multiple parameters that control what data is used to create a
     * datasource:
     *

     *

     *

     *

     * percentBegin
     *

     *

     * Use percentBegin to indicate the beginning of the range of
     * the data used to create the Datasource. If you do not include
     * percentBegin and percentEnd, Amazon ML includes
     * all of the data when creating the datasource.
     *

     *

     *

     *

     * percentEnd
     *

     *

     * Use percentEnd to indicate the end of the range of the data
     * used to create the Datasource. If you do not include
     * percentBegin and percentEnd, Amazon ML includes
     * all of the data when creating the datasource.
     *

     *

     *

     *

     * complement
     *

     *

     * The complement parameter instructs Amazon ML to use the data
     * that is not included in the range of percentBegin to
     * percentEnd to create a datasource. The
     * complement parameter is useful if you need to create
     * complementary datasources for training and evaluation. To create a
     * complementary datasource, use the same values for
     * percentBegin and percentEnd, along with the
     * complement parameter.
     *

     *

     * For example, the following two datasources do not share any data, and can
     * be used to train and evaluate a model. The first datasource has 25
     * percent of the data, and the second one has 75 percent of the data.
     *

     *

     * Datasource for evaluation:
     * {"splitting":{"percentBegin":0, "percentEnd":25}}
     *

     *

     * Datasource for training:
     * {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
     *

     *

     *

     *

     * strategy
     *

     *

     * To change how Amazon ML splits the data for a datasource, use the
     * strategy parameter.
     *

     *

     * The default value for the strategy parameter is
     * sequential, meaning that Amazon ML takes all of the data
     * records between the percentBegin and percentEnd
     * parameters for the datasource, in the order that the records appear in
     * the input data.
     *

     *

     * The following two DataRearrangement lines are examples of
     * sequentially ordered training and evaluation datasources:
     *

     *

     * Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
     *

     *

     * Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
     *

     *

     * To randomly split the input data into the proportions indicated by the
     * percentBegin and percentEnd parameters, set the strategy
     * parameter to random and provide a string that is used as the
     * seed value for the random data splitting (for example, you can use the S3
     * path to your data as the random seed string). If you choose the random
     * split strategy, Amazon ML assigns each row of data a pseudo-random number
     * between 0 and 100, and then selects the rows that have an assigned number
     * between percentBegin and percentEnd.
     * Pseudo-random numbers are assigned using both the input seed string value
     * and the byte offset as a seed, so changing the data results in a
     * different split. Any existing ordering is preserved. The random splitting
     * strategy ensures that variables in the training and evaluation data are
     * distributed similarly. It is useful in the cases where the input data may
     * have an implicit sort order, which would otherwise result in training and
     * evaluation datasources containing non-similar data records.
     *

     *

     * The following two DataRearrangement lines are examples of
     * non-sequentially ordered training and evaluation datasources:
     *

     *

     * Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
     *

     *

     * Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
     *

     *

     *

     *
     * @param dataRearrangement
     *        A JSON string that represents the splitting and rearrangement
     *        processing to be applied to a DataSource. If the
     *        DataRearrangement parameter is not provided, all of
     *        the input data is used to create the Datasource.

     *

     *        There are multiple parameters that control what data is used to
     *        create a datasource:
     *

     *

     *

     *

     *        percentBegin
     *

     *

     *        Use percentBegin to indicate the beginning of the
     *        range of the data used to create the Datasource. If you do not
     *        include percentBegin and percentEnd,
     *        Amazon ML includes all of the data when creating the datasource.
     *

     *

     *

     *

     *        percentEnd
     *

     *

     *        Use percentEnd to indicate the end of the range of
     *        the data used to create the Datasource. If you do not include
     *        percentBegin and percentEnd, Amazon ML
     *        includes all of the data when creating the datasource.
     *

     *

     *

     *

     *        complement
     *

     *

     *        The complement parameter instructs Amazon ML to use
     *        the data that is not included in the range of
     *        percentBegin to percentEnd to create a
     *        datasource. The complement parameter is useful if you
     *        need to create complementary datasources for training and
     *        evaluation. To create a complementary datasource, use the same
     *        values for percentBegin and percentEnd,
     *        along with the complement parameter.
     *

     *

     *        For example, the following two datasources do not share any data,
     *        and can be used to train and evaluate a model. The first
     *        datasource has 25 percent of the data, and the second one has 75
     *        percent of the data.
     *

     *

     *        Datasource for evaluation:
     *        {"splitting":{"percentBegin":0, "percentEnd":25}}
     *

     *

     *        Datasource for training:
     *        {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
     *

     *

     *

     *

     *        strategy
     *

     *

     *        To change how Amazon ML splits the data for a datasource, use the
     *        strategy parameter.
     *

     *

     *        The default value for the strategy parameter is
     *        sequential, meaning that Amazon ML takes all of the
     *        data records between the percentBegin and
     *        percentEnd parameters for the datasource, in the
     *        order that the records appear in the input data.
     *

     *

     *        The following two DataRearrangement lines are
     *        examples of sequentially ordered training and evaluation
     *        datasources:
     *

     *

     *        Datasource for evaluation:
     *        {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
     *

     *

     *        Datasource for training:
     *        {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
     *

     *

     *        To randomly split the input data into the proportions indicated by
     *        the percentBegin and percentEnd parameters, set the
     *        strategy parameter to random and provide
     *        a string that is used as the seed value for the random data
     *        splitting (for example, you can use the S3 path to your data as
     *        the random seed string). If you choose the random split strategy,
     *        Amazon ML assigns each row of data a pseudo-random number between
     *        0 and 100, and then selects the rows that have an assigned number
     *        between percentBegin and percentEnd.
     *        Pseudo-random numbers are assigned using both the input seed
     *        string value and the byte offset as a seed, so changing the data
     *        results in a different split. Any existing ordering is preserved.
     *        The random splitting strategy ensures that variables in the
     *        training and evaluation data are distributed similarly. It is
     *        useful in the cases where the input data may have an implicit sort
     *        order, which would otherwise result in training and evaluation
     *        datasources containing non-similar data records.
     *

     *

     *        The following two DataRearrangement lines are
     *        examples of non-sequentially ordered training and evaluation
     *        datasources:
     *

     *

     *        Datasource for evaluation:
     *        {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
     *

     *

     *        Datasource for training:
     *        {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
     *

     *

     * @return Returns a reference to this object so that method calls can be
     *         chained together.
     */

    public RDSDataSpec withDataRearrangement(String dataRearrangement) {
        setDataRearrangement(dataRearrangement);
        return this;
    }

    /**
     *

     * A JSON string that represents the schema for an Amazon RDS
     * DataSource. The DataSchema defines the
     * structure of the observation data in the data file(s) referenced in the
     * DataSource.
     *

     *

     * A DataSchema is not required if you specify a
     * DataSchemaUri
     *

     *

     * Define your DataSchema as a series of key-value pairs.
     * attributes and excludedVariableNames have an
     * array of key-value pairs for their value. Use the following format to
     * define your DataSchema.
     *

     *

     * { "version": "1.0",
     *

     *

     * "recordAnnotationFieldName": "F1",
     *

     *

     * "recordWeightFieldName": "F2",
     *

     *

     * "targetFieldName": "F3",
     *

     *

     * "dataFormat": "CSV",
     *

     *

     * "dataFileContainsHeader": true,
     *

     *

     * "attributes": [
     *

     *

     * { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2",
     * "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL"
     * }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5",
     * "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" },
     * { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, {
     * "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
     *

     *

     * "excludedVariableNames": [ "F6" ] }
     *

     * <?oxy_insert_end>
     *
     * @param dataSchema
     *        A JSON string that represents the schema for an Amazon RDS
     *        DataSource. The DataSchema defines the
     *        structure of the observation data in the data file(s) referenced
     *        in the DataSource.

     *

     *        A DataSchema is not required if you specify a
     *        DataSchemaUri
     *

     *

     *        Define your DataSchema as a series of key-value
     *        pairs. attributes and
     *        excludedVariableNames have an array of key-value
     *        pairs for their value. Use the following format to define your
     *        DataSchema.
     *

     *

     *        { "version": "1.0",
     *

     *

     *        "recordAnnotationFieldName": "F1",
     *

     *

     *        "recordWeightFieldName": "F2",
     *

     *

     *        "targetFieldName": "F3",
     *

     *

     *        "dataFormat": "CSV",
     *

     *

     *        "dataFileContainsHeader": true,
     *

     *

     *        "attributes": [
     *

     *

     *        { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2",
     *        "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType":
     *        "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, {
     *        "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName":
     *        "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
     *        "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType":
     *        "WEIGHTED_STRING_SEQUENCE" } ],
     *

     *

     *        "excludedVariableNames": [ "F6" ] }
     *

     */

    public void setDataSchema(String dataSchema) {
        this.dataSchema = dataSchema;
    }

    /**
     *

     * A JSON string that represents the schema for an Amazon RDS
     * DataSource. The DataSchema defines the
     * structure of the observation data in the data file(s) referenced in the
     * DataSource.
     *

     *

     * A DataSchema is not required if you specify a
     * DataSchemaUri
     *

     *

     * Define your DataSchema as a series of key-value pairs.
     * attributes and excludedVariableNames have an
     * array of key-value pairs for their value. Use the following format to
     * define your DataSchema.
     *

     *

     * { "version": "1.0",
     *

     *

     * "recordAnnotationFieldName": "F1",
     *

     *

     * "recordWeightFieldName": "F2",
     *

     *

     * "targetFieldName": "F3",
     *

     *

     * "dataFormat": "CSV",
     *

     *

     * "dataFileContainsHeader": true,
     *

     *

     * "attributes": [
     *

     *

     * { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2",
     * "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL"
     * }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5",
     * "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" },
     * { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, {
     * "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
     *

     *

     * "excludedVariableNames": [ "F6" ] }
     *

     * <?oxy_insert_end>
     *
     * @return A JSON string that represents the schema for an Amazon RDS
     *         DataSource. The DataSchema defines the
     *         structure of the observation data in the data file(s) referenced
     *         in the DataSource.

     *

     *         A DataSchema is not required if you specify a
     *         DataSchemaUri
     *

     *

     *         Define your DataSchema as a series of key-value
     *         pairs. attributes and
     *         excludedVariableNames have an array of key-value
     *         pairs for their value. Use the following format to define your
     *         DataSchema.
     *

     *

     *         { "version": "1.0",
     *

     *

     *         "recordAnnotationFieldName": "F1",
     *

     *

     *         "recordWeightFieldName": "F2",
     *

     *

     *         "targetFieldName": "F3",
     *

     *

     *         "dataFormat": "CSV",
     *

     *

     *         "dataFileContainsHeader": true,
     *

     *

     *         "attributes": [
     *

     *

     *         { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2",
     *         "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType":
     *         "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, {
     *         "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName":
     *         "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
     *         "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType":
     *         "WEIGHTED_STRING_SEQUENCE" } ],
     *

     *

     *         "excludedVariableNames": [ "F6" ] }
     *

     */

    public String getDataSchema() {
        return this.dataSchema;
    }

    /**
     *

     * A JSON string that represents the schema for an Amazon RDS
     * DataSource. The DataSchema defines the
     * structure of the observation data in the data file(s) referenced in the
     * DataSource.
     *

     *

     * A DataSchema is not required if you specify a
     * DataSchemaUri
     *

     *

     * Define your DataSchema as a series of key-value pairs.
     * attributes and excludedVariableNames have an
     * array of key-value pairs for their value. Use the following format to
     * define your DataSchema.
     *

     *

     * { "version": "1.0",
     *

     *

     * "recordAnnotationFieldName": "F1",
     *

     *

     * "recordWeightFieldName": "F2",
     *

     *

     * "targetFieldName": "F3",
     *

     *

     * "dataFormat": "CSV",
     *

     *

     * "dataFileContainsHeader": true,
     *

     *

     * "attributes": [
     *

     *

     * { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2",
     * "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL"
     * }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5",
     * "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" },
     * { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, {
     * "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
     *

     *

     * "excludedVariableNames": [ "F6" ] }
     *

     * <?oxy_insert_end>
     *
     * @param dataSchema
     *        A JSON string that represents the schema for an Amazon RDS
     *        DataSource. The DataSchema defines the
     *        structure of the observation data in the data file(s) referenced
     *        in the DataSource.

     *

     *        A DataSchema is not required if you specify a
     *        DataSchemaUri
     *

     *

     *        Define your DataSchema as a series of key-value
     *        pairs. attributes and
     *        excludedVariableNames have an array of key-value
     *        pairs for their value. Use the following format to define your
     *        DataSchema.
     *

     *

     *        { "version": "1.0",
     *

     *

     *        "recordAnnotationFieldName": "F1",
     *

     *

     *        "recordWeightFieldName": "F2",
     *

     *

     *        "targetFieldName": "F3",
     *

     *

     *        "dataFormat": "CSV",
     *

     *

     *        "dataFileContainsHeader": true,
     *

     *

     *        "attributes": [
     *

     *

     *        { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2",
     *        "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType":
     *        "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, {
     *        "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName":
     *        "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
     *        "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType":
     *        "WEIGHTED_STRING_SEQUENCE" } ],
     *

     *

     *        "excludedVariableNames": [ "F6" ] }
     *

     * @return Returns a reference to this object so that method calls can be
     *         chained together.
     */

    public RDSDataSpec withDataSchema(String dataSchema) {
        setDataSchema(dataSchema);
        return this;
    }

    /**
     *

     * The Amazon S3 location of the DataSchema.
     *

     *
     * @param dataSchemaUri
     *        The Amazon S3 location of the DataSchema.
     */

    public void setDataSchemaUri(String dataSchemaUri) {
        this.dataSchemaUri = dataSchemaUri;
    }

    /**
     *

     * The Amazon S3 location of the DataSchema.
     *

     *
     * @return The Amazon S3 location of the DataSchema.
     */

    public String getDataSchemaUri() {
        return this.dataSchemaUri;
    }

    /**
     *

     * The Amazon S3 location of the DataSchema.
     *

     *
     * @param dataSchemaUri
     *        The Amazon S3 location of the DataSchema.
     * @return Returns a reference to this object so that method calls can be
     *         chained together.
     */

    public RDSDataSpec withDataSchemaUri(String dataSchemaUri) {
        setDataSchemaUri(dataSchemaUri);
        return this;
    }

    /**
     *

     * The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic
     * Compute Cloud (Amazon EC2) instance to carry out the copy operation from
     * Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.
     *

     *
     * @param resourceRole
     *        The role (DataPipelineDefaultResourceRole) assumed by an Amazon
     *        Elastic Compute Cloud (Amazon EC2) instance to carry out the copy
     *        operation from Amazon RDS to an Amazon S3 task. For more
     *        information, see Role templates for data pipelines.
     */

    public void setResourceRole(String resourceRole) {
        this.resourceRole = resourceRole;
    }

    /**
     *

     * The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic
     * Compute Cloud (Amazon EC2) instance to carry out the copy operation from
     * Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.
     *

     *
     * @return The role (DataPipelineDefaultResourceRole) assumed by an Amazon
     *         Elastic Compute Cloud (Amazon EC2) instance to carry out the copy
     *         operation from Amazon RDS to an Amazon S3 task. For more
     *         information, see Role templates for data pipelines.
     */

    public String getResourceRole() {
        return this.resourceRole;
    }

    /**
     *

     * The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic
     * Compute Cloud (Amazon EC2) instance to carry out the copy operation from
     * Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.
     *

     *
     * @param resourceRole
     *        The role (DataPipelineDefaultResourceRole) assumed by an Amazon
     *        Elastic Compute Cloud (Amazon EC2) instance to carry out the copy
     *        operation from Amazon RDS to an Amazon S3 task. For more
     *        information, see Role templates for data pipelines.
     * @return Returns a reference to this object so that method calls can be
     *         chained together.
     */

    public RDSDataSpec withResourceRole(String resourceRole) {
        setResourceRole(resourceRole);
        return this;
    }

    /**
     *

     * The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service
     * to monitor the progress of the copy task from Amazon RDS to Amazon S3.
     * For more information, see Role templates for data pipelines.
     *

     *
     * @param serviceRole
     *        The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline
     *        service to monitor the progress of the copy task from Amazon RDS
     *        to Amazon S3. For more information, see Role templates for data pipelines.
     */

    public void setServiceRole(String serviceRole) {
        this.serviceRole = serviceRole;
    }

    /**
     *

     * The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service
     * to monitor the progress of the copy task from Amazon RDS to Amazon S3.
     * For more information, see Role templates for data pipelines.
     *

     *
     * @return The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline
     *         service to monitor the progress of the copy task from Amazon RDS
     *         to Amazon S3. For more information, see Role templates for data pipelines.
     */

    public String getServiceRole() {
        return this.serviceRole;
    }

    /**
     *

     * The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service
     * to monitor the progress of the copy task from Amazon RDS to Amazon S3.
     * For more information, see Role templates for data pipelines.
     *

     *
     * @param serviceRole
     *        The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline
     *        service to monitor the progress of the copy task from Amazon RDS
     *        to Amazon S3. For more information, see Role templates for data pipelines.
     * @return Returns a reference to this object so that method calls can be
     *         chained together.
     */

    public RDSDataSpec withServiceRole(String serviceRole) {
        setServiceRole(serviceRole);
        return this;
    }

    /**
     *

     * The subnet ID to be used to access a VPC-based RDS DB instance. This
     * attribute is used by Data Pipeline to carry out the copy task from Amazon
     * RDS to Amazon S3.
     *

     *
     * @param subnetId
     *        The subnet ID to be used to access a VPC-based RDS DB instance.
     *        This attribute is used by Data Pipeline to carry out the copy task
     *        from Amazon RDS to Amazon S3.
     */

    public void setSubnetId(String subnetId) {
        this.subnetId = subnetId;
    }

    /**
     *

     * The subnet ID to be used to access a VPC-based RDS DB instance. This
     * attribute is used by Data Pipeline to carry out the copy task from Amazon
     * RDS to Amazon S3.
     *

     *
     * @return The subnet ID to be used to access a VPC-based RDS DB instance.
     *         This attribute is used by Data Pipeline to carry out the copy
     *         task from Amazon RDS to Amazon S3.
     */

    public String getSubnetId() {
        return this.subnetId;
    }

    /**
     *

     * The subnet ID to be used to access a VPC-based RDS DB instance. This
     * attribute is used by Data Pipeline to carry out the copy task from Amazon
     * RDS to Amazon S3.
     *

     *
     * @param subnetId
     *        The subnet ID to be used to access a VPC-based RDS DB instance.
     *        This attribute is used by Data Pipeline to carry out the copy task
     *        from Amazon RDS to Amazon S3.
     * @return Returns a reference to this object so that method calls can be
     *         chained together.
     */

    public RDSDataSpec withSubnetId(String subnetId) {
        setSubnetId(subnetId);
        return this;
    }

    /**
     *

     * The security group IDs to be used to access a VPC-based RDS DB instance.
     * Ensure that there are appropriate ingress rules set up to allow access to
     * the RDS DB instance. This attribute is used by Data Pipeline to carry out
     * the copy operation from Amazon RDS to an Amazon S3 task.
     *

     *
     * @return The security group IDs to be used to access a VPC-based RDS DB
     *         instance. Ensure that there are appropriate ingress rules set up
     *         to allow access to the RDS DB instance. This attribute is used by
     *         Data Pipeline to carry out the copy operation from Amazon RDS to
     *         an Amazon S3 task.
     */

    public java.util.List<String> getSecurityGroupIds() {
        if (securityGroupIds == null) {
            securityGroupIds = new com.amazonaws.internal.SdkInternalList<String>();
        }
        return securityGroupIds;
    }

    /**
     *

     * The security group IDs to be used to access a VPC-based RDS DB instance.
     * Ensure that there are appropriate ingress rules set up to allow access to
     * the RDS DB instance. This attribute is used by Data Pipeline to carry out
     * the copy operation from Amazon RDS to an Amazon S3 task.
     *

     *
     * @param securityGroupIds
     *        The security group IDs to be used to access a VPC-based RDS DB
     *        instance. Ensure that there are appropriate ingress rules set up
     *        to allow access to the RDS DB instance. This attribute is used by
     *        Data Pipeline to carry out the copy operation from Amazon RDS to
     *        an Amazon S3 task.
     */

    public void setSecurityGroupIds(
            java.util.Collection<String> securityGroupIds) {
        if (securityGroupIds == null) {
            this.securityGroupIds = null;
            return;
        }

        this.securityGroupIds = new com.amazonaws.internal.SdkInternalList<String>(
                securityGroupIds);
    }

    /**
     *

     * The security group IDs to be used to access a VPC-based RDS DB instance.
     * Ensure that there are appropriate ingress rules set up to allow access to
     * the RDS DB instance. This attribute is used by Data Pipeline to carry out
     * the copy operation from Amazon RDS to an Amazon S3 task.
     *

     *

     * NOTE: This method appends the values to the existing list (if
     * any). Use {@link #setSecurityGroupIds(java.util.Collection)} or
     * {@link #withSecurityGroupIds(java.util.Collection)} if you want to
     * override the existing values.
     *

     *
     * @param securityGroupIds
     *        The security group IDs to be used to access a VPC-based RDS DB
     *        instance. Ensure that there are appropriate ingress rules set up
     *        to allow access to the RDS DB instance. This attribute is used by
     *        Data Pipeline to carry out the copy operation from Amazon RDS to
     *        an Amazon S3 task.
     * @return Returns a reference to this object so that method calls can be
     *         chained together.
     */

    public RDSDataSpec withSecurityGroupIds(String... securityGroupIds) {
        if (this.securityGroupIds == null) {
            setSecurityGroupIds(new com.amazonaws.internal.SdkInternalList<String>(
                    securityGroupIds.length));
        }
        for (String ele : securityGroupIds) {
            this.securityGroupIds.add(ele);
        }
        return this;
    }

    /**
     *

     * The security group IDs to be used to access a VPC-based RDS DB instance.
     * Ensure that there are appropriate ingress rules set up to allow access to
     * the RDS DB instance. This attribute is used by Data Pipeline to carry out
     * the copy operation from Amazon RDS to an Amazon S3 task.
     *

     *
     * @param securityGroupIds
     *        The security group IDs to be used to access a VPC-based RDS DB
     *        instance. Ensure that there are appropriate ingress rules set up
     *        to allow access to the RDS DB instance. This attribute is used by
     *        Data Pipeline to carry out the copy operation from Amazon RDS to
     *        an Amazon S3 task.
     * @return Returns a reference to this object so that method calls can be
     *         chained together.
     */

    public RDSDataSpec withSecurityGroupIds(
            java.util.Collection<String> securityGroupIds) {
        setSecurityGroupIds(securityGroupIds);
        return this;
    }

    /**
     * Returns a string representation of this object; useful for testing and
     * debugging.
     *
     * @return A string representation of this object.
     *
     * @see java.lang.Object#toString()
     */
    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        sb.append("{");
        if (getDatabaseInformation() != null)
            sb.append("DatabaseInformation: " + getDatabaseInformation() + ",");
        if (getSelectSqlQuery() != null)
            sb.append("SelectSqlQuery: " + getSelectSqlQuery() + ",");
        if (getDatabaseCredentials() != null)
            sb.append("DatabaseCredentials: " + getDatabaseCredentials() + ",");
        if (getS3StagingLocation() != null)
            sb.append("S3StagingLocation: " + getS3StagingLocation() + ",");
        if (getDataRearrangement() != null)
            sb.append("DataRearrangement: " + getDataRearrangement() + ",");
        if (getDataSchema() != null)
            sb.append("DataSchema: " + getDataSchema() + ",");
        if (getDataSchemaUri() != null)
            sb.append("DataSchemaUri: " + getDataSchemaUri() + ",");
        if (getResourceRole() != null)
            sb.append("ResourceRole: " + getResourceRole() + ",");
        if (getServiceRole() != null)
            sb.append("ServiceRole: " + getServiceRole() + ",");
        if (getSubnetId() != null)
            sb.append("SubnetId: " + getSubnetId() + ",");
        if (getSecurityGroupIds() != null)
            sb.append("SecurityGroupIds: " + getSecurityGroupIds());
        sb.append("}");
        return sb.toString();
    }

    @Override
    public boolean equals(Object obj) {
        if (this == obj)
            return true;
        if (obj == null)
            return false;

        if (obj instanceof RDSDataSpec == false)
            return false;
        RDSDataSpec other = (RDSDataSpec) obj;
        if (other.getDatabaseInformation() == null
                ^ this.getDatabaseInformation() == null)
            return false;
        if (other.getDatabaseInformation() != null
                && other.getDatabaseInformation().equals(
                        this.getDatabaseInformation()) == false)
            return false;
        if (other.getSelectSqlQuery() == null
                ^ this.getSelectSqlQuery() == null)
            return false;
        if (other.getSelectSqlQuery() != null
                && other.getSelectSqlQuery().equals(this.getSelectSqlQuery()) == false)
            return false;
        if (other.getDatabaseCredentials() == null
                ^ this.getDatabaseCredentials() == null)
            return false;
        if (other.getDatabaseCredentials() != null
                && other.getDatabaseCredentials().equals(
                        this.getDatabaseCredentials()) == false)
            return false;
        if (other.getS3StagingLocation() == null
                ^ this.getS3StagingLocation() == null)
            return false;
        if (other.getS3StagingLocation() != null
                && other.getS3StagingLocation().equals(
                        this.getS3StagingLocation()) == false)
            return false;
        if (other.getDataRearrangement() == null
                ^ this.getDataRearrangement() == null)
            return false;
        if (other.getDataRearrangement() != null
                && other.getDataRearrangement().equals(
                        this.getDataRearrangement()) == false)
            return false;
        if (other.getDataSchema() == null ^ this.getDataSchema() == null)
            return false;
        if (other.getDataSchema() != null
                && other.getDataSchema().equals(this.getDataSchema()) == false)
            return false;
        if (other.getDataSchemaUri() == null ^ this.getDataSchemaUri() == null)
            return false;
        if (other.getDataSchemaUri() != null
                && other.getDataSchemaUri().equals(this.getDataSchemaUri()) == false)
            return false;
        if (other.getResourceRole() == null ^ this.getResourceRole() == null)
            return false;
        if (other.getResourceRole() != null
                && other.getResourceRole().equals(this.getResourceRole()) == false)
            return false;
        if (other.getServiceRole() == null ^ this.getServiceRole() == null)
            return false;
        if (other.getServiceRole() != null
                && other.getServiceRole().equals(this.getServiceRole()) == false)
            return false;
        if (other.getSubnetId() == null ^ this.getSubnetId() == null)
            return false;
        if (other.getSubnetId() != null
                && other.getSubnetId().equals(this.getSubnetId()) == false)
            return false;
        if (other.getSecurityGroupIds() == null
                ^ this.getSecurityGroupIds() == null)
            return false;
        if (other.getSecurityGroupIds() != null
                && other.getSecurityGroupIds().equals(
                        this.getSecurityGroupIds()) == false)
            return false;
        return true;
    }

    @Override
    public int hashCode() {
        final int prime = 31;
        int hashCode = 1;

        hashCode = prime
                * hashCode
                + ((getDatabaseInformation() == null) ? 0
                        : getDatabaseInformation().hashCode());
        hashCode = prime
                * hashCode
                + ((getSelectSqlQuery() == null) ? 0 : getSelectSqlQuery()
                        .hashCode());
        hashCode = prime
                * hashCode
                + ((getDatabaseCredentials() == null) ? 0
                        : getDatabaseCredentials().hashCode());
        hashCode = prime
                * hashCode
                + ((getS3StagingLocation() == null) ? 0
                        : getS3StagingLocation().hashCode());
        hashCode = prime
                * hashCode
                + ((getDataRearrangement() == null) ? 0
                        : getDataRearrangement().hashCode());
        hashCode = prime * hashCode
                + ((getDataSchema() == null) ? 0 : getDataSchema().hashCode());
        hashCode = prime
                * hashCode
                + ((getDataSchemaUri() == null) ? 0 : getDataSchemaUri()
                        .hashCode());
        hashCode = prime
                * hashCode
                + ((getResourceRole() == null) ? 0 : getResourceRole()
                        .hashCode());
        hashCode = prime
                * hashCode
                + ((getServiceRole() == null) ? 0 : getServiceRole().hashCode());
        hashCode = prime * hashCode
                + ((getSubnetId() == null) ? 0 : getSubnetId().hashCode());
        hashCode = prime
                * hashCode
                + ((getSecurityGroupIds() == null) ? 0 : getSecurityGroupIds()
                        .hashCode());
        return hashCode;
    }

    @Override
    public RDSDataSpec clone() {
        try {
            return (RDSDataSpec) super.clone();
        } catch (CloneNotSupportedException e) {
            throw new IllegalStateException(
                    "Got a CloneNotSupportedException from Object.clone() "
                            + "even though we're Cloneable!", e);
        }
    }
}

redpajama_set_name: RedPajamaGithub


text:
Susan G. Komen, Orange County #OCTutuTour
Home/Case Study/Susan G. Komen, Orange County #OCTutuTour
Susan G. Komen, Orange County #OCTutuTourmraymond2019-02-27T20:52:14+00:00
HKA has served as the marketing communications agency for prominent nonprofit client Susan G. Komen Orange County since 2007, supporting its many signature initiatives, including the annual Race for the Cure, Pink Tie Ball and Pink Tie Guy ambassador program. Komen Orange County is a highly recognized 501(c)(3) public charity that operates under the charter of the international organization headquartered in Dallas.
Each year, Komen Orange County hosts a Race for the Cure in Newport Beach to welcome thousands of breast cancer survivors, friends, family members and the community in support of a world without breast cancer. In 2014, Komen Orange County sought an out-of-the-box approach to increase Race attendance beyond its existing target audience. HKA was tasked with elevating the nonprofit's visibility to generate additional support for the organization and ultimately increase Race registrants.
The nonprofit launched a signature "tutu tour" in Orange County. The initiative sought to collect as many pictures of men, women and children donning pink tutus with signs displaying why they Race for the Cure. The signs ranged from "I tutu for impact," "I tutu for breakthroughs" and "I tutu for futures," which aligned with the 2014 Race theme.
To support the tutu tour, HKA executed a three-pronged marketing communications approach focusing on traditional media outreach, celebrity relations and social media.
First, HKA invited targeted media to cover the kick-off event featuring more than 30 tutu-clad Orange County community leaders, volunteers, breast cancer survivors and Race team members with "I tutu for…" signs to raise awareness about the importance of breast health and encourage Race participation.
Next, HKA reached out to a dozen local personalities to garner support for the campaign and engaged the interested celebrities by sending tutus and "I tutu for…" signs for them to join the fun.
Finally, HKA took the campaign to the social media community, which joined by posting photos with a unique hashtag #OCTutuTour on social media channels. HKA retweeted, and reshared on Komen Orange County's Facebook and Twitter to optimize engagement.
The OC Tutu Tour kickoff generated broadcast media coverage on various local broadcast affiliates, and feature stories appeared in prominent local print publications.
The social media campaign resulted in nearly 100 photos with people donning tutus with the unique hashtag. Influential personalities, including Ali Fedotowsky, Carmelita Jeter and Peggy Tanous, all shared tutu photos, yielding more than 800,000 impressions. The Mommy Blogger community also got involved and shared photos and Race information on their social media channels and blogs, resulting in nearly 50,000 target audience impressions.
The 23rd Annual Race for the Cure welcomed more than 16,000 registrants and more than 20,000 event attendees with Komen Orange County exceeding its fundraising goal of $2 million.
Carothers DiSante & Freudenberger LLP & Wounded Warrior Project
Haskell & White LLP & Susan G. Komen, OC
Fairhaven Memorial Park & Mortuary
Tredway Lumsdaine & Doyle LLP
Susan G. Komen, OC

redpajama_set_name: RedPajamaCommonCrawl


In this scenario we have three VMs deployed on a NSX vSwitch with VNI 5002. Two are running on the same hosts and they can ping each other, the other one is running on a separated host and cannot ping the other VMs.
1572 is the size required by VXLAN. If it doesn't work, check physical switch for configured MTU.


Before you bet with your bookie, you should analyze the match using H2H stats for Savona vs Pistoiese .
Do you predict Savona or Pistoiese to win? Submit your soccer prediction based on 1x2, Asian Handicap, Total Goals Over/Under, HT/FT, DNB, Double Chance or Correct Score to Tipsters Competition and you could win a prize if you are the top 10 winners.
Savona v Pistoiese predictions can be derived from the H2H stats analysis.
You could check the H2H stats based on Savona home ground. Alternatively, you could view the past results based on Pistoiese home ground.
The graph compares the results of Savona and Pistoiese in Serie C.
See also: Savona vs Pistoiese live score for In-Play soccer results. Hear sound alert whenever there is a goal or red card.
Pistoiese are in the 12th position of Serie C.


text:
Game Companies: HIPSTER WHALE
Companies by Alpha
Developed and Published Games
Platform: All PlatformsAndroid iOS (iPhone/iPad) Linux Macintosh Nintendo Switch PC PlayStation 4 Xbox One Year: All Years2014 2015 2016 2018 2020 Region: All RegionsNorth America Europe Australia Japan Asia Korea South America Role: Developer or PublisherDeveloper Publisher
iOS (iPhone/iPad) Crossy Road - Endless Arcade Hopper 11/20/14 North America
Android Crossy Road 01/08/15 North America
Android Pac-Man 256: Endless Maze 08/18/15 North America
iOS (iPhone/iPad) Pac-Man 256: Endless Arcade Maze 08/19/15 North America
Android Disney Crossy Road 04/06/16 North America
Xbox One Pac-Man 256 06/21/16 North America
PC Pac-Man 256 06/21/16 North America
PlayStation 4 Pac-Man 256 06/21/16 North America
Macintosh Pac-Man 256 06/21/16 North America
Linux Pac-Man 256 06/21/16 North America
iOS (iPhone/iPad) Piffle 10/03/18 North America
iOS (iPhone/iPad) Crossy Road Castle 02/27/20 North America
Nintendo Switch Piffle 09/02/20 North America
Also on GameFAQs...
Help - Answers to the most commonly asked questions about GameFAQs.
FAQ Bookmarks - Access and manage the bookmarks you have added to different guides.
Bounty - Write a guide for a Most Wanted game, get cash.
Game Companies - A list of all the companies that have developed and published games.
Game Credits - A list of all the people and groups credited for all the games we know of.
Most Wanted - The Top 100 popular games without full Guides on GameFAQs.
My Games - Build your game collection, track and rate games.
Rankings - A list of games ranked by rating, difficulty, and length as chosen by our users.
Top 100 - The Top 100 most popular games on GameFAQs today.
What's New - New games, guides, reviews, and more.


text:
About BirdLife International
BirdLife Partners
Forests of Hope
Sites & Habitats (IBAs and...
Local Engagement and Empowerment
Migratory Birds & Flyways
Preventing Extinctions
A YEAR IN THE LIFE OF AN ALBATROSS
World Bird Club Gift Membership Pack!
Donate to BirdLife International
Join BirdLife International
Fundraise for BirdLife
Species Champions
10 ways we've saved the world's rarest birds in the last 10 years
A decade ago, BirdLife created a program to stop birds from going extinct as a result of human activity. Here are some of its successes.
The Hooded grebe is just one bird that might have gone extinct without conservation © Juan María Raggio
By James Lowen
At its 2008 World Congress, BirdLife launched the Preventing Extinctions Programme, bringing together the whole Partnership's species conservation efforts. The underlying principle was simple: "BirdLife couldn't, with a clear conscience, stand by while bird extinctions continue as a result of human activity," says Jim Lawrence, BirdLife Global Marketing Manager. "While extinctions will sadly happen, we can't and won't accept that they are inevitable, and will strive to take action, either directly or through advocacy." Underpinned by BirdLife's science, often supported by 'Species Champions' who provide funding, and working through local 'Species Guardians' (frequently BirdLife Partners), the Programme has helped at least 483 threatened species, many Critically Endangered. That's a mighty list to choose from – but what are the Programme's greatest success stories to date?
1. BACK FROM JUST 19 BIRDS
In relation to land area, nowhere has more threatened birds than the Pacific. Many evolved on tiny islands, thriving without predators or competitors. Then Man arrived, introducing non-native plants and animals, with devastating consequences. Twenty years ago, just 19 Tahiti Monarchs Pomarea nigra could be located on Tahiti. These final survivors had to contend with not one, but nine invasive species. SOP Manu (BirdLife in French Polynesia) has literally rescued the Monarchs from the jaws of defeat, controlling predators as diverse as cats, mynas, rats and ants, and in 2017, 70 monarchs were counted. The next stage will be to translocate birds to a location where these predators are absent.
2. FINDING NEW PLACES TO BREED
It seems apt that a spectacular bird once mummified to accompany ancient Egyptian royalty into the afterlife should count European royalty amongst its present-day supporters. Alongside ZEISS, HSH Prince Albert II of Monaco is a Species Champion for the Northern Bald Ibis Geronticus eremita. Once widespread through the Mediterranean, this species now breeds in the wild only in Morocco, and is considered Critically Endangered. However, following two decades of colony protection, with support from GREPOM (BirdLife in Morocco), Morocco's population has soared from 59 pairs in 1997 to 600 birds in 2016. Even better, pairs bred at two new sites last year.
3. FROM FOE TO FRIEND
In 2012, some 100,000 migrating Amur Falcons Falco amurensis were trapped in Nagaland, India – then slaughtered for food. Alerted by a video of the massacre that subsequently went viral, Bombay Natural History Society (BNHS, BirdLife in India) marshalled an emergency response that saw trappers swiftly arrested and their nets destroyed. BNHS then engineered an engagement programme that has transformed local attitudes: Nagaland's Chief Minister calls the falcons "esteemed guests". After five years of conservation action, the safe passage of a million Amur Falcons is again assured: not a single falcon is known to have been killed for the plate since 2012. Instead, the migrating birds now provide a feast for the eyes.
4. SAVING ISLAND BIRDS
Two Indian Ocean island states famously illustrate how dedicated action can reverse the fortunes of the world's rarest birds. Thanks to the Mauritian Wildlife Foundation (BirdLife Partner), the legendary salvation of the Mauritius Kestrel Falco punctatus has been used as a model to save other species, including ongoing population increases since 2008 for Echo Parakeet Psittacula eques, Rodrigues Warbler Acrocephalus rodericanus, Mauritius Fody Foudia rubra (pictured) and Rodrigues Fody Foudia flavicans. In Seychelles, several species have seen similar recoveries through innovative conservation action by Nature Seychelles (BirdLife Partner). Most remarkably, in 2015, Seychelles Warbler Acrocephalus sechellensis was re-categorised as Near Threatened – an astonishing turnaround from the nadir of 30 birds precisely 50 years ago.
5. ONCE PRESUMED EXTINCT, NOW BACK FROM THE BRINK
As the Millennium dawned, Chinese Crested Tern Thalasseus bernsteini was presumed long extinct. Fifteen years later, 16 chicks fledged at a colony in eastern China, after conservationists including BirdLife attracted birds to Tiedun Dao island through decoy models and audio systems playing tern calls. In 2016, Burung Indonesia (BirdLife Partner) confirmed a new wintering site in eastern Indonesia. In 2017, things improved further when birds were discovered breeding in South Korea, where BirdLife is now supporting the Government to safeguard the new nesting site. The Chinese Crested Tern may remain Critically Endangered – the highest possible threat category – but at least it has been spared an entry in the log of extinct species.
6. CREATING A REFUGE FOR THE BIRD THAT WENT MISSING FOR OVER A CENTURY
Sometimes, Roger Safford (BirdLife Senior Programme Manager, Preventing Extinctions) suggests, successful conservation can involve a simple concept: "If a forest occupied by a Critically Endangered species is large enough, properly protected and well-managed, the bird involved should survive." The Cherry-throated Tanager Nemosia rourei illustrates Safford's point. This striking Brazilian endemic was an enigma for 120 years until its 1998 rediscovery. There may be as few as 50 birds left, all in Espírito Santo's remnant Atlantic Forest. In 2017, thanks to support from BirdLife Species Champion Urs-Peter Stäuble, SAVE Brasil (BirdLife Partner) helped Grupo Águia Branca (a major Brazilian company) to create a 1,688-hectare private reserve. Work now continues to secure protection for another patch of habitat known to harbour the species.
7. SUPPORTING A MIGRATING BIRD ACROSS 14 COUNTRIES
"One reason for the Programme's success," says Lawrence, "is the 'Power of Many' afforded by the BirdLife Partnership." This is particularly apparent in wide-ranging migratory species such as the Sociable Lapwing Vanellus gregarius (Critically Endangered), which breeds in Russia and Kazakhstan, then migrates through 14 countries to winter in north-east Africa, the Middle East and South Asia. Supported by Species Champion Swarovski Optik, BirdLife Partners collaborated on a satellite-tagging project, revealing unknown wintering grounds and pinpointing key staging posts along the wader's route. "At one location we discovered a flock of 3,200 lapwings – more than we thought existed worldwide!" says Lawrence.
8. GOING VIRAL FOR CONSERVATION
At the 2008 BirdLife World Congress in Argentina, where the programme was launched, corridors hummed with concerns about the perilous status of a remarkable local waterbird. Hooded Grebe Podiceps gallardoi numbers had slumped by 80% in 25 years. In response, BirdLife upped its threat status to Critically Endangered and Aves Argentinas (BirdLife in Argentina) initiated a conservation programme that has catapulted this stunning creature to international stardom (via an internet video reaching 20 million viewers). Aves Argentinas has identified and combatted numerous threats to the grebe's existence, including securing the designation of a new national park. Although populations have now stabilised, Aves Argentinas is increasingly worried about a new threat from a hydroelectric dam.
9. POLICY IN ACTION
South Asia's formerly super-abundant vultures were brought to near-extinction by a drug administered to a ubiquitous domestic animal. Treating cattle with diclofenac killed many millions of White-rumped Vultures Gyps bengalensis (whose numbers plummeted by 99.9% in a frighteningly short time) and fellow carcass-feeders. BNHS (BirdLife in India), Bird Conservation Nepal (BirdLife Partner) and authorities in Pakistan and Bangladesh (with support from UK BirdLife Partner the RSPB, who also contributed significantly to several of the other successes described here, especially concerning Northern Bald Ibis, Spoon-billed Sandpiper and Sociable Lapwing) blew the whistle on diclofenac, and the painkiller was banned for veterinary use in all four countries. But our work is not yet done. "BNHS later discovered a loophole – the use of large vials of human formulations of diclofenac – and persuaded the Indian Government to ban these in 2015," says Lawrence.
10. A TASK FORCE TO SAVE 'SPOONIE'
In the 1970s, 2,000 Spoon-billed Sandpiper Calidris pygmaea pairs bred in north-east Russia. Thirty years on, there were 90% fewer. Extinction seemed imminent. BirdLife is part of the Spoon-billed Sandpiper Task Force, an international partnership addressing threats across the wader's range, which extends south to wintering grounds in Thailand and Myanmar. One Species Champion, Heritage Expeditions, is supporting action by transporting conservationists and their equipment, helping them discover new breeding sites and release locally captive-raised birds into the wild. The Chinese Government, meanwhile, halted coastal land reclamation in 2018, a boost to 'Spoonies' and millions of other migratory shorebirds using the East Asian-Australasian Flyway.
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