One of the most powerful archetypes that Yogi Bhajan shared in his lectures was the Ashtabhuja.
The Ashtabhuja is the archetype of woman as Goddess. She represents warmth, heat, energy and her body shines with a radiance like the glowing sun. She has ashta – eight, bhuja – arms.
In her seven hands she holds powerful symbols: a bow and arrow representing wisdom and awareness; the lotus which is a symbol of the feminine; a discus which is like a water wheel when it empties and spills forward creation; a scepter which when thrown is like a lightning bolt to dispel negativity or adversity; and a jar of the divine nectar of life. Her eighth hand holds a mala or prayer beads that give eight Siddhis or spiritual powers and nine Nidhis or treasures.
I was always intrigued by this Goddess of the Universe. She spoke to me and I felt her power every time I even spoke her name.
Let her be alive within each woman. That became my prayer. I knew Yogi Bhajan was asking each woman not to worship the Ashtabhuja, but to embody her.
My prayer on this day and forever is that as women we may embody this divine being throughout actions, words, and thoughts. And let these words of Yogi Bhajan resound in our hearts and spirit as he exalts our true power as a woman.


text:
package org.apache.spark.ml.regression

import scala.collection.mutable

import breeze.linalg.{DenseVector => BDV}
import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN}
import breeze.stats.distributions.StudentsT
import org.apache.hadoop.fs.Path

import org.apache.spark.SparkException
import org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.internal.Logging
import org.apache.spark.ml.feature.Instance
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.linalg.BLAS._
import org.apache.spark.ml.optim.WeightedLeastSquares
import org.apache.spark.ml.PredictorParams
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.param.shared._
import org.apache.spark.ml.util._
import org.apache.spark.mllib.evaluation.RegressionMetrics
import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
import org.apache.spark.mllib.linalg.VectorImplicits._
import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Dataset, Row}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.DoubleType
import org.apache.spark.storage.StorageLevel

/**
 * Params for linear regression.
 */
private[regression] trait LinearRegressionParams extends PredictorParams
    with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol
    with HasFitIntercept with HasStandardization with HasWeightCol with HasSolver

/**
 * Linear regression.
 *
 * The learning objective is to minimize the squared error, with regularization.
 * The specific squared error loss function used is:
 *   L = 1/2n ||A coefficients - y||^2^
 *
 * This supports multiple types of regularization:
 *  - none (a.k.a. ordinary least squares)
 *  - L2 (ridge regression)
 *  - L1 (Lasso)
 *  - L2 + L1 (elastic net)
 */
@Since("1.3.0")
class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String)
  extends Regressor[Vector, LinearRegression, LinearRegressionModel]
  with LinearRegressionParams with DefaultParamsWritable with Logging {

  @Since("1.4.0")
  def this() = this(Identifiable.randomUID("linReg"))

  /**
   * Set the regularization parameter.
   * Default is 0.0.
   * @group setParam
   */
  @Since("1.3.0")
  def setRegParam(value: Double): this.type = set(regParam, value)
  setDefault(regParam -> 0.0)

  /**
   * Set if we should fit the intercept
   * Default is true.
   * @group setParam
   */
  @Since("1.5.0")
  def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
  setDefault(fitIntercept -> true)

  /**
   * Whether to standardize the training features before fitting the model.
   * The coefficients of models will be always returned on the original scale,
   * so it will be transparent for users. Note that with/without standardization,
   * the models should be always converged to the same solution when no regularization
   * is applied. In R's GLMNET package, the default behavior is true as well.
   * Default is true.
   * @group setParam
   */
  @Since("1.5.0")
  def setStandardization(value: Boolean): this.type = set(standardization, value)
  setDefault(standardization -> true)

  /**
   * Set the ElasticNet mixing parameter.
   * For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
   * For 0 < alpha < 1, the penalty is a combination of L1 and L2.
   * Default is 0.0 which is an L2 penalty.
   * @group setParam
   */
  @Since("1.4.0")
  def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value)
  setDefault(elasticNetParam -> 0.0)

  /**
   * Set the maximum number of iterations.
   * Default is 100.
   * @group setParam
   */
  @Since("1.3.0")
  def setMaxIter(value: Int): this.type = set(maxIter, value)
  setDefault(maxIter -> 100)

  /**
   * Set the convergence tolerance of iterations.
   * Smaller value will lead to higher accuracy with the cost of more iterations.
   * Default is 1E-6.
   * @group setParam
   */
  @Since("1.4.0")
  def setTol(value: Double): this.type = set(tol, value)
  setDefault(tol -> 1E-6)

  /**
   * Whether to over-/under-sample training instances according to the given weights in weightCol.
   * If not set or empty, all instances are treated equally (weight 1.0).
   * Default is not set, so all instances have weight one.
   * @group setParam
   */
  @Since("1.6.0")
  def setWeightCol(value: String): this.type = set(weightCol, value)

  /**
   * Set the solver algorithm used for optimization.
   * In case of linear regression, this can be "l-bfgs", "normal" and "auto".
   * "l-bfgs" denotes Limited-memory BFGS which is a limited-memory quasi-Newton
   * optimization method. "normal" denotes using Normal Equation as an analytical
   * solution to the linear regression problem.
   * The default value is "auto" which means that the solver algorithm is
   * selected automatically.
   * @group setParam
   */
  @Since("1.6.0")
  def setSolver(value: String): this.type = set(solver, value)
  setDefault(solver -> "auto")

  override protected def train(dataset: Dataset[_]): LinearRegressionModel = {
    // Extract the number of features before deciding optimization solver.
    val numFeatures = dataset.select(col($(featuresCol))).first().getAs[Vector](0).size
    val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))

    val instances: RDD[Instance] = dataset.select(
      col($(labelCol)).cast(DoubleType), w, col($(featuresCol))).rdd.map {
      case Row(label: Double, weight: Double, features: Vector) =>
        Instance(label, weight, features)
    }

    if (($(solver) == "auto" && $(elasticNetParam) == 0.0 &&
      numFeatures <= WeightedLeastSquares.MAX_NUM_FEATURES) || $(solver) == "normal") {
      require($(elasticNetParam) == 0.0, "Only L2 regularization can be used when normal " +
        "solver is used.'")
      // For low dimensional data, WeightedLeastSquares is more efficiently since the
      // training algorithm only requires one pass through the data. (SPARK-10668)

      val optimizer = new WeightedLeastSquares($(fitIntercept), $(regParam),
        $(standardization), true)
      val model = optimizer.fit(instances)
      // When it is trained by WeightedLeastSquares, training summary does not
      // attached returned model.
      val lrModel = copyValues(new LinearRegressionModel(uid, model.coefficients, model.intercept))
      // WeightedLeastSquares does not run through iterations. So it does not generate
      // an objective history.
      val (summaryModel, predictionColName) = lrModel.findSummaryModelAndPredictionCol()
      val trainingSummary = new LinearRegressionTrainingSummary(
        summaryModel.transform(dataset),
        predictionColName,
        $(labelCol),
        $(featuresCol),
        summaryModel,
        model.diagInvAtWA.toArray,
        Array(0D))

      return lrModel.setSummary(trainingSummary)
    }

    val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
    if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)

    val (featuresSummarizer, ySummarizer) = {
      val seqOp = (c: (MultivariateOnlineSummarizer, MultivariateOnlineSummarizer),
        instance: Instance) =>
          (c._1.add(instance.features, instance.weight),
            c._2.add(Vectors.dense(instance.label), instance.weight))

      val combOp = (c1: (MultivariateOnlineSummarizer, MultivariateOnlineSummarizer),
        c2: (MultivariateOnlineSummarizer, MultivariateOnlineSummarizer)) =>
          (c1._1.merge(c2._1), c1._2.merge(c2._2))

      instances.treeAggregate(
        new MultivariateOnlineSummarizer, new MultivariateOnlineSummarizer)(seqOp, combOp)
    }

    val yMean = ySummarizer.mean(0)
    val rawYStd = math.sqrt(ySummarizer.variance(0))
    if (rawYStd == 0.0) {
      if ($(fitIntercept) || yMean==0.0) {
        // If the rawYStd is zero and fitIntercept=true, then the intercept is yMean with
        // zero coefficient; as a result, training is not needed.
        // Also, if yMean==0 and rawYStd==0, all the coefficients are zero regardless of
        // the fitIntercept
        if (yMean == 0.0) {
          logWarning(s"Mean and standard deviation of the label are zero, so the coefficients " +
            s"and the intercept will all be zero; as a result, training is not needed.")
        } else {
          logWarning(s"The standard deviation of the label is zero, so the coefficients will be " +
            s"zeros and the intercept will be the mean of the label; as a result, " +
            s"training is not needed.")
        }
        if (handlePersistence) instances.unpersist()
        val coefficients = Vectors.sparse(numFeatures, Seq())
        val intercept = yMean

        val model = copyValues(new LinearRegressionModel(uid, coefficients, intercept))
        // Handle possible missing or invalid prediction columns
        val (summaryModel, predictionColName) = model.findSummaryModelAndPredictionCol()

        val trainingSummary = new LinearRegressionTrainingSummary(
          summaryModel.transform(dataset),
          predictionColName,
          $(labelCol),
          $(featuresCol),
          model,
          Array(0D),
          Array(0D))
        return model.setSummary(trainingSummary)
      } else {
        require($(regParam) == 0.0, "The standard deviation of the label is zero. " +
          "Model cannot be regularized.")
        logWarning(s"The standard deviation of the label is zero. " +
          "Consider setting fitIntercept=true.")
      }
    }

    // if y is constant (rawYStd is zero), then y cannot be scaled. In this case
    // setting yStd=abs(yMean) ensures that y is not scaled anymore in l-bfgs algorithm.
    val yStd = if (rawYStd > 0) rawYStd else math.abs(yMean)
    val featuresMean = featuresSummarizer.mean.toArray
    val featuresStd = featuresSummarizer.variance.toArray.map(math.sqrt)

    if (!$(fitIntercept) && (0 until numFeatures).exists { i =>
      featuresStd(i) == 0.0 && featuresMean(i) != 0.0 }) {
      logWarning("Fitting LinearRegressionModel without intercept on dataset with " +
        "constant nonzero column, Spark MLlib outputs zero coefficients for constant nonzero " +
        "columns. This behavior is the same as R glmnet but different from LIBSVM.")
    }

    // Since we implicitly do the feature scaling when we compute the cost function
    // to improve the convergence, the effective regParam will be changed.
    val effectiveRegParam = $(regParam) / yStd
    val effectiveL1RegParam = $(elasticNetParam) * effectiveRegParam
    val effectiveL2RegParam = (1.0 - $(elasticNetParam)) * effectiveRegParam

    val costFun = new LeastSquaresCostFun(instances, yStd, yMean, $(fitIntercept),
      $(standardization), featuresStd, featuresMean, effectiveL2RegParam)

    val optimizer = if ($(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) {
      new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
    } else {
      val standardizationParam = $(standardization)
      def effectiveL1RegFun = (index: Int) => {
        if (standardizationParam) {
          effectiveL1RegParam
        } else {
          // If `standardization` is false, we still standardize the data
          // to improve the rate of convergence; as a result, we have to
          // perform this reverse standardization by penalizing each component
          // differently to get effectively the same objective function when
          // the training dataset is not standardized.
          if (featuresStd(index) != 0.0) effectiveL1RegParam / featuresStd(index) else 0.0
        }
      }
      new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, effectiveL1RegFun, $(tol))
    }

    val initialCoefficients = Vectors.zeros(numFeatures)
    val states = optimizer.iterations(new CachedDiffFunction(costFun),
      initialCoefficients.asBreeze.toDenseVector)

    val (coefficients, objectiveHistory) = {
      /*
         Note that in Linear Regression, the objective history (loss + regularization) returned
         from optimizer is computed in the scaled space given by the following formula.
         {{{
         L = 1/2n||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2 + regTerms
         }}}
       */
      val arrayBuilder = mutable.ArrayBuilder.make[Double]
      var state: optimizer.State = null
      while (states.hasNext) {
        state = states.next()
        arrayBuilder += state.adjustedValue
      }
      if (state == null) {
        val msg = s"${optimizer.getClass.getName} failed."
        logError(msg)
        throw new SparkException(msg)
      }

      if (!state.actuallyConverged) {
        logWarning("LinearRegression training finished but the result " +
          s"is not converged because: ${state.convergedReason.get.reason}")
      }

      /*
         The coefficients are trained in the scaled space; we're converting them back to
         the original space.
       */
      val rawCoefficients = state.x.toArray.clone()
      var i = 0
      val len = rawCoefficients.length
      while (i < len) {
        rawCoefficients(i) *= { if (featuresStd(i) != 0.0) yStd / featuresStd(i) else 0.0 }
        i += 1
      }

      (Vectors.dense(rawCoefficients).compressed, arrayBuilder.result())
    }

    /*
       The intercept in R's GLMNET is computed using closed form after the coefficients are
       converged. See the following discussion for detail.
       http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet
     */
    val intercept = if ($(fitIntercept)) {
      yMean - dot(coefficients, Vectors.dense(featuresMean))
    } else {
      0.0
    }

    if (handlePersistence) instances.unpersist()

    val model = copyValues(new LinearRegressionModel(uid, coefficients, intercept))
    // Handle possible missing or invalid prediction columns
    val (summaryModel, predictionColName) = model.findSummaryModelAndPredictionCol()

    val trainingSummary = new LinearRegressionTrainingSummary(
      summaryModel.transform(dataset),
      predictionColName,
      $(labelCol),
      $(featuresCol),
      model,
      Array(0D),
      objectiveHistory)
    model.setSummary(trainingSummary)
  }

  @Since("1.4.0")
  override def copy(extra: ParamMap): LinearRegression = defaultCopy(extra)
}

@Since("1.6.0")
object LinearRegression extends DefaultParamsReadable[LinearRegression] {

  @Since("1.6.0")
  override def load(path: String): LinearRegression = super.load(path)
}

/**
 * Model produced by [[LinearRegression]].
 */
@Since("1.3.0")
class LinearRegressionModel private[ml] (
    @Since("1.4.0") override val uid: String,
    @Since("2.0.0") val coefficients: Vector,
    @Since("1.3.0") val intercept: Double)
  extends RegressionModel[Vector, LinearRegressionModel]
  with LinearRegressionParams with MLWritable {

  private var trainingSummary: Option[LinearRegressionTrainingSummary] = None

  override val numFeatures: Int = coefficients.size

  /**
   * Gets summary (e.g. residuals, mse, r-squared ) of model on training set. An exception is
   * thrown if `trainingSummary == None`.
   */
  @Since("1.5.0")
  def summary: LinearRegressionTrainingSummary = trainingSummary.getOrElse {
    throw new SparkException("No training summary available for this LinearRegressionModel")
  }

  private[regression] def setSummary(summary: LinearRegressionTrainingSummary): this.type = {
    this.trainingSummary = Some(summary)
    this
  }

  /** Indicates whether a training summary exists for this model instance. */
  @Since("1.5.0")
  def hasSummary: Boolean = trainingSummary.isDefined

  /**
   * Evaluates the model on a test dataset.
   * @param dataset Test dataset to evaluate model on.
   */
  @Since("2.0.0")
  def evaluate(dataset: Dataset[_]): LinearRegressionSummary = {
    // Handle possible missing or invalid prediction columns
    val (summaryModel, predictionColName) = findSummaryModelAndPredictionCol()
    new LinearRegressionSummary(summaryModel.transform(dataset), predictionColName,
      $(labelCol), $(featuresCol), summaryModel, Array(0D))
  }

  /**
   * If the prediction column is set returns the current model and prediction column,
   * otherwise generates a new column and sets it as the prediction column on a new copy
   * of the current model.
   */
  private[regression] def findSummaryModelAndPredictionCol(): (LinearRegressionModel, String) = {
    $(predictionCol) match {
      case "" =>
        val predictionColName = "prediction_" + java.util.UUID.randomUUID.toString
        (copy(ParamMap.empty).setPredictionCol(predictionColName), predictionColName)
      case p => (this, p)
    }
  }

  override protected def predict(features: Vector): Double = {
    dot(features, coefficients) + intercept
  }

  @Since("1.4.0")
  override def copy(extra: ParamMap): LinearRegressionModel = {
    val newModel = copyValues(new LinearRegressionModel(uid, coefficients, intercept), extra)
    if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
    newModel.setParent(parent)
  }

  /**
   * Returns a [[org.apache.spark.ml.util.MLWriter]] instance for this ML instance.
   *
   * For [[LinearRegressionModel]], this does NOT currently save the training [[summary]].
   * An option to save [[summary]] may be added in the future.
   *
   * This also does not save the [[parent]] currently.
   */
  @Since("1.6.0")
  override def write: MLWriter = new LinearRegressionModel.LinearRegressionModelWriter(this)
}

@Since("1.6.0")
object LinearRegressionModel extends MLReadable[LinearRegressionModel] {

  @Since("1.6.0")
  override def read: MLReader[LinearRegressionModel] = new LinearRegressionModelReader

  @Since("1.6.0")
  override def load(path: String): LinearRegressionModel = super.load(path)

  /** [[MLWriter]] instance for [[LinearRegressionModel]] */
  private[LinearRegressionModel] class LinearRegressionModelWriter(instance: LinearRegressionModel)
    extends MLWriter with Logging {

    private case class Data(intercept: Double, coefficients: Vector)

    override protected def saveImpl(path: String): Unit = {
      // Save metadata and Params
      DefaultParamsWriter.saveMetadata(instance, path, sc)
      // Save model data: intercept, coefficients
      val data = Data(instance.intercept, instance.coefficients)
      val dataPath = new Path(path, "data").toString
      sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath)
    }
  }

  private class LinearRegressionModelReader extends MLReader[LinearRegressionModel] {

    /** Checked against metadata when loading model */
    private val className = classOf[LinearRegressionModel].getName

    override def load(path: String): LinearRegressionModel = {
      val metadata = DefaultParamsReader.loadMetadata(path, sc, className)

      val dataPath = new Path(path, "data").toString
      val data = sparkSession.read.format("parquet").load(dataPath)
      val Row(intercept: Double, coefficients: Vector) =
        MLUtils.convertVectorColumnsToML(data, "coefficients")
          .select("intercept", "coefficients")
          .head()
      val model = new LinearRegressionModel(metadata.uid, coefficients, intercept)

      DefaultParamsReader.getAndSetParams(model, metadata)
      model
    }
  }
}

/**
 * :: Experimental ::
 * Linear regression training results. Currently, the training summary ignores the
 * training weights except for the objective trace.
 *
 * @param predictions predictions output by the model's `transform` method.
 * @param objectiveHistory objective function (scaled loss + regularization) at each iteration.
 */
@Since("1.5.0")
@Experimental
class LinearRegressionTrainingSummary private[regression] (
    predictions: DataFrame,
    predictionCol: String,
    labelCol: String,
    featuresCol: String,
    model: LinearRegressionModel,
    diagInvAtWA: Array[Double],
    val objectiveHistory: Array[Double])
  extends LinearRegressionSummary(
    predictions,
    predictionCol,
    labelCol,
    featuresCol,
    model,
    diagInvAtWA) {

  /**
   * Number of training iterations until termination
   *
   * This value is only available when using the "l-bfgs" solver.
   * @see [[LinearRegression.solver]]
   */
  @Since("1.5.0")
  val totalIterations = objectiveHistory.length

}

/**
 * :: Experimental ::
 * Linear regression results evaluated on a dataset.
 *
 * @param predictions predictions output by the model's `transform` method.
 * @param predictionCol Field in "predictions" which gives the predicted value of the label at
 *                      each instance.
 * @param labelCol Field in "predictions" which gives the true label of each instance.
 * @param featuresCol Field in "predictions" which gives the features of each instance as a vector.
 */
@Since("1.5.0")
@Experimental
class LinearRegressionSummary private[regression] (
    @transient val predictions: DataFrame,
    val predictionCol: String,
    val labelCol: String,
    val featuresCol: String,
    private val privateModel: LinearRegressionModel,
    private val diagInvAtWA: Array[Double]) extends Serializable {

  @deprecated("The model field is deprecated and will be removed in 2.1.0.", "2.0.0")
  val model: LinearRegressionModel = privateModel

  @transient private val metrics = new RegressionMetrics(
    predictions
      .select(col(predictionCol), col(labelCol).cast(DoubleType))
      .rdd
      .map { case Row(pred: Double, label: Double) => (pred, label) },
    !privateModel.getFitIntercept)

  /**
   * Returns the explained variance regression score.
   * explainedVariance = 1 - variance(y - \hat{y}) / variance(y)
   * Reference: [[http://en.wikipedia.org/wiki/Explained_variation]]
   *
   * Note: This ignores instance weights (setting all to 1.0) from [[LinearRegression.weightCol]].
   *       This will change in later Spark versions.
   */
  @Since("1.5.0")
  val explainedVariance: Double = metrics.explainedVariance

  /**
   * Returns the mean absolute error, which is a risk function corresponding to the
   * expected value of the absolute error loss or l1-norm loss.
   *
   * Note: This ignores instance weights (setting all to 1.0) from [[LinearRegression.weightCol]].
   *       This will change in later Spark versions.
   */
  @Since("1.5.0")
  val meanAbsoluteError: Double = metrics.meanAbsoluteError

  /**
   * Returns the mean squared error, which is a risk function corresponding to the
   * expected value of the squared error loss or quadratic loss.
   *
   * Note: This ignores instance weights (setting all to 1.0) from [[LinearRegression.weightCol]].
   *       This will change in later Spark versions.
   */
  @Since("1.5.0")
  val meanSquaredError: Double = metrics.meanSquaredError

  /**
   * Returns the root mean squared error, which is defined as the square root of
   * the mean squared error.
   *
   * Note: This ignores instance weights (setting all to 1.0) from [[LinearRegression.weightCol]].
   *       This will change in later Spark versions.
   */
  @Since("1.5.0")
  val rootMeanSquaredError: Double = metrics.rootMeanSquaredError

  /**
   * Returns R^2^, the coefficient of determination.
   * Reference: [[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
   *
   * Note: This ignores instance weights (setting all to 1.0) from [[LinearRegression.weightCol]].
   *       This will change in later Spark versions.
   */
  @Since("1.5.0")
  val r2: Double = metrics.r2

  /** Residuals (label - predicted value) */
  @Since("1.5.0")
  @transient lazy val residuals: DataFrame = {
    val t = udf { (pred: Double, label: Double) => label - pred }
    predictions.select(t(col(predictionCol), col(labelCol)).as("residuals"))
  }

  /** Number of instances in DataFrame predictions */
  lazy val numInstances: Long = predictions.count()

  /** Degrees of freedom */
  private val degreesOfFreedom: Long = if (privateModel.getFitIntercept) {
    numInstances - privateModel.coefficients.size - 1
  } else {
    numInstances - privateModel.coefficients.size
  }

  /**
   * The weighted residuals, the usual residuals rescaled by
   * the square root of the instance weights.
   */
  lazy val devianceResiduals: Array[Double] = {
    val weighted =
      if (!privateModel.isDefined(privateModel.weightCol) || privateModel.getWeightCol.isEmpty) {
        lit(1.0)
      } else {
        sqrt(col(privateModel.getWeightCol))
      }
    val dr = predictions
      .select(col(privateModel.getLabelCol).minus(col(privateModel.getPredictionCol))
        .multiply(weighted).as("weightedResiduals"))
      .select(min(col("weightedResiduals")).as("min"), max(col("weightedResiduals")).as("max"))
      .first()
    Array(dr.getDouble(0), dr.getDouble(1))
  }

  /**
   * Standard error of estimated coefficients and intercept.
   * This value is only available when using the "normal" solver.
   *
   * If [[LinearRegression.fitIntercept]] is set to true,
   * then the last element returned corresponds to the intercept.
   *
   * @see [[LinearRegression.solver]]
   */
  lazy val coefficientStandardErrors: Array[Double] = {
    if (diagInvAtWA.length == 1 && diagInvAtWA(0) == 0) {
      throw new UnsupportedOperationException(
        "No Std. Error of coefficients available for this LinearRegressionModel")
    } else {
      val rss =
        if (!privateModel.isDefined(privateModel.weightCol) || privateModel.getWeightCol.isEmpty) {
          meanSquaredError * numInstances
        } else {
          val t = udf { (pred: Double, label: Double, weight: Double) =>
            math.pow(label - pred, 2.0) * weight }
          predictions.select(t(col(privateModel.getPredictionCol), col(privateModel.getLabelCol),
            col(privateModel.getWeightCol)).as("wse")).agg(sum(col("wse"))).first().getDouble(0)
        }
      val sigma2 = rss / degreesOfFreedom
      diagInvAtWA.map(_ * sigma2).map(math.sqrt)
    }
  }

  /**
   * T-statistic of estimated coefficients and intercept.
   * This value is only available when using the "normal" solver.
   *
   * If [[LinearRegression.fitIntercept]] is set to true,
   * then the last element returned corresponds to the intercept.
   *
   * @see [[LinearRegression.solver]]
   */
  lazy val tValues: Array[Double] = {
    if (diagInvAtWA.length == 1 && diagInvAtWA(0) == 0) {
      throw new UnsupportedOperationException(
        "No t-statistic available for this LinearRegressionModel")
    } else {
      val estimate = if (privateModel.getFitIntercept) {
        Array.concat(privateModel.coefficients.toArray, Array(privateModel.intercept))
      } else {
        privateModel.coefficients.toArray
      }
      estimate.zip(coefficientStandardErrors).map { x => x._1 / x._2 }
    }
  }

  /**
   * Two-sided p-value of estimated coefficients and intercept.
   * This value is only available when using the "normal" solver.
   *
   * If [[LinearRegression.fitIntercept]] is set to true,
   * then the last element returned corresponds to the intercept.
   *
   * @see [[LinearRegression.solver]]
   */
  lazy val pValues: Array[Double] = {
    if (diagInvAtWA.length == 1 && diagInvAtWA(0) == 0) {
      throw new UnsupportedOperationException(
        "No p-value available for this LinearRegressionModel")
    } else {
      tValues.map { x => 2.0 * (1.0 - StudentsT(degreesOfFreedom.toDouble).cdf(math.abs(x))) }
    }
  }

}

/**
 * LeastSquaresAggregator computes the gradient and loss for a Least-squared loss function,
 * as used in linear regression for samples in sparse or dense vector in an online fashion.
 *
 * Two LeastSquaresAggregator can be merged together to have a summary of loss and gradient of
 * the corresponding joint dataset.
 *
 * For improving the convergence rate during the optimization process, and also preventing against
 * features with very large variances exerting an overly large influence during model training,
 * package like R's GLMNET performs the scaling to unit variance and removing the mean to reduce
 * the condition number, and then trains the model in scaled space but returns the coefficients in
 * the original scale. See page 9 in http://cran.r-project.org/web/packages/glmnet/glmnet.pdf
 *
 * However, we don't want to apply the `StandardScaler` on the training dataset, and then cache
 * the standardized dataset since it will create a lot of overhead. As a result, we perform the
 * scaling implicitly when we compute the objective function. The following is the mathematical
 * derivation.
 *
 * Note that we don't deal with intercept by adding bias here, because the intercept
 * can be computed using closed form after the coefficients are converged.
 * See this discussion for detail.
 * http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet
 *
 * When training with intercept enabled,
 * The objective function in the scaled space is given by
 * {{{
 * L = 1/2n ||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2,
 * }}}
 * where \bar{x_i} is the mean of x_i, \hat{x_i} is the standard deviation of x_i,
 * \bar{y} is the mean of label, and \hat{y} is the standard deviation of label.
 *
 * If we fitting the intercept disabled (that is forced through 0.0),
 * we can use the same equation except we set \bar{y} and \bar{x_i} to 0 instead
 * of the respective means.
 *
 * This can be rewritten as
 * {{{
 * L = 1/2n ||\sum_i (w_i/\hat{x_i})x_i - \sum_i (w_i/\hat{x_i})\bar{x_i} - y / \hat{y}
 *     + \bar{y} / \hat{y}||^2
 *   = 1/2n ||\sum_i w_i^\prime x_i - y / \hat{y} + offset||^2 = 1/2n diff^2
 * }}}
 * where w_i^\prime^ is the effective coefficients defined by w_i/\hat{x_i}, offset is
 * {{{
 * - \sum_i (w_i/\hat{x_i})\bar{x_i} + \bar{y} / \hat{y}.
 * }}}, and diff is
 * {{{
 * \sum_i w_i^\prime x_i - y / \hat{y} + offset
 * }}}
 *
 *
 * Note that the effective coefficients and offset don't depend on training dataset,
 * so they can be precomputed.
 *
 * Now, the first derivative of the objective function in scaled space is
 * {{{
 * \frac{\partial L}{\partial w_i} = diff/N (x_i - \bar{x_i}) / \hat{x_i}
 * }}}
 * However, ($x_i - \bar{x_i}$) will densify the computation, so it's not
 * an ideal formula when the training dataset is sparse format.
 *
 * This can be addressed by adding the dense \bar{x_i} / \hat{x_i} terms
 * in the end by keeping the sum of diff. The first derivative of total
 * objective function from all the samples is
 * {{{
 * \frac{\partial L}{\partial w_i} =
 *     1/N \sum_j diff_j (x_{ij} - \bar{x_i}) / \hat{x_i}
 *   = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) - diffSum \bar{x_i} / \hat{x_i})
 *   = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) + correction_i)
 * }}},
 * where correction_i = - diffSum \bar{x_i} / \hat{x_i}
 *
 * A simple math can show that diffSum is actually zero, so we don't even
 * need to add the correction terms in the end. From the definition of diff,
 * {{{
 * diffSum = \sum_j (\sum_i w_i(x_{ij} - \bar{x_i}) / \hat{x_i} - (y_j - \bar{y}) / \hat{y})
 *         = N * (\sum_i w_i(\bar{x_i} - \bar{x_i}) / \hat{x_i} - (\bar{y} - \bar{y}) / \hat{y})
 *         = 0
 * }}}
 *
 * As a result, the first derivative of the total objective function only depends on
 * the training dataset, which can be easily computed in distributed fashion, and is
 * sparse format friendly.
 * {{{
 * \frac{\partial L}{\partial w_i} = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i})
 * }}},
 *
 * @param coefficients The coefficients corresponding to the features.
 * @param labelStd The standard deviation value of the label.
 * @param labelMean The mean value of the label.
 * @param fitIntercept Whether to fit an intercept term.
 * @param featuresStd The standard deviation values of the features.
 * @param featuresMean The mean values of the features.
 */
private class LeastSquaresAggregator(
    coefficients: Vector,
    labelStd: Double,
    labelMean: Double,
    fitIntercept: Boolean,
    featuresStd: Array[Double],
    featuresMean: Array[Double]) extends Serializable {

  private var totalCnt: Long = 0L
  private var weightSum: Double = 0.0
  private var lossSum = 0.0

  private val (effectiveCoefficientsArray: Array[Double], offset: Double, dim: Int) = {
    val coefficientsArray = coefficients.toArray.clone()
    var sum = 0.0
    var i = 0
    val len = coefficientsArray.length
    while (i < len) {
      if (featuresStd(i) != 0.0) {
        coefficientsArray(i) /=  featuresStd(i)
        sum += coefficientsArray(i) * featuresMean(i)
      } else {
        coefficientsArray(i) = 0.0
      }
      i += 1
    }
    val offset = if (fitIntercept) labelMean / labelStd - sum else 0.0
    (coefficientsArray, offset, coefficientsArray.length)
  }

  private val effectiveCoefficientsVector = Vectors.dense(effectiveCoefficientsArray)

  private val gradientSumArray = Array.ofDim[Double](dim)

  /**
   * Add a new training instance to this LeastSquaresAggregator, and update the loss and gradient
   * of the objective function.
   *
   * @param instance The instance of data point to be added.
   * @return This LeastSquaresAggregator object.
   */
  def add(instance: Instance): this.type = {
    instance match { case Instance(label, weight, features) =>
      require(dim == features.size, s"Dimensions mismatch when adding new sample." +
        s" Expecting $dim but got ${features.size}.")
      require(weight >= 0.0, s"instance weight, $weight has to be >= 0.0")

      if (weight == 0.0) return this

      val diff = dot(features, effectiveCoefficientsVector) - label / labelStd + offset

      if (diff != 0) {
        val localGradientSumArray = gradientSumArray
        features.foreachActive { (index, value) =>
          if (featuresStd(index) != 0.0 && value != 0.0) {
            localGradientSumArray(index) += weight * diff * value / featuresStd(index)
          }
        }
        lossSum += weight * diff * diff / 2.0
      }

      totalCnt += 1
      weightSum += weight
      this
    }
  }

  /**
   * Merge another LeastSquaresAggregator, and update the loss and gradient
   * of the objective function.
   * (Note that it's in place merging; as a result, `this` object will be modified.)
   *
   * @param other The other LeastSquaresAggregator to be merged.
   * @return This LeastSquaresAggregator object.
   */
  def merge(other: LeastSquaresAggregator): this.type = {
    require(dim == other.dim, s"Dimensions mismatch when merging with another " +
      s"LeastSquaresAggregator. Expecting $dim but got ${other.dim}.")

    if (other.weightSum != 0) {
      totalCnt += other.totalCnt
      weightSum += other.weightSum
      lossSum += other.lossSum

      var i = 0
      val localThisGradientSumArray = this.gradientSumArray
      val localOtherGradientSumArray = other.gradientSumArray
      while (i < dim) {
        localThisGradientSumArray(i) += localOtherGradientSumArray(i)
        i += 1
      }
    }
    this
  }

  def count: Long = totalCnt

  def loss: Double = {
    require(weightSum > 0.0, s"The effective number of instances should be " +
      s"greater than 0.0, but $weightSum.")
    lossSum / weightSum
  }

  def gradient: Vector = {
    require(weightSum > 0.0, s"The effective number of instances should be " +
      s"greater than 0.0, but $weightSum.")
    val result = Vectors.dense(gradientSumArray.clone())
    scal(1.0 / weightSum, result)
    result
  }
}

/**
 * LeastSquaresCostFun implements Breeze's DiffFunction[T] for Least Squares cost.
 * It returns the loss and gradient with L2 regularization at a particular point (coefficients).
 * It's used in Breeze's convex optimization routines.
 */
private class LeastSquaresCostFun(
    instances: RDD[Instance],
    labelStd: Double,
    labelMean: Double,
    fitIntercept: Boolean,
    standardization: Boolean,
    featuresStd: Array[Double],
    featuresMean: Array[Double],
    effectiveL2regParam: Double) extends DiffFunction[BDV[Double]] {

  override def calculate(coefficients: BDV[Double]): (Double, BDV[Double]) = {
    val coeffs = Vectors.fromBreeze(coefficients)

    val leastSquaresAggregator = {
      val seqOp = (c: LeastSquaresAggregator, instance: Instance) => c.add(instance)
      val combOp = (c1: LeastSquaresAggregator, c2: LeastSquaresAggregator) => c1.merge(c2)

      instances.treeAggregate(
        new LeastSquaresAggregator(coeffs, labelStd, labelMean, fitIntercept, featuresStd,
          featuresMean))(seqOp, combOp)
    }

    val totalGradientArray = leastSquaresAggregator.gradient.toArray

    val regVal = if (effectiveL2regParam == 0.0) {
      0.0
    } else {
      var sum = 0.0
      coeffs.foreachActive { (index, value) =>
        // The following code will compute the loss of the regularization; also
        // the gradient of the regularization, and add back to totalGradientArray.
        sum += {
          if (standardization) {
            totalGradientArray(index) += effectiveL2regParam * value
            value * value
          } else {
            if (featuresStd(index) != 0.0) {
              // If `standardization` is false, we still standardize the data
              // to improve the rate of convergence; as a result, we have to
              // perform this reverse standardization by penalizing each component
              // differently to get effectively the same objective function when
              // the training dataset is not standardized.
              val temp = value / (featuresStd(index) * featuresStd(index))
              totalGradientArray(index) += effectiveL2regParam * temp
              value * temp
            } else {
              0.0
            }
          }
        }
      }
      0.5 * effectiveL2regParam * sum
    }

    (leastSquaresAggregator.loss + regVal, new BDV(totalGradientArray))
  }
}

redpajama_set_name: RedPajamaGithub


text:
in: Stealth Aircraft, Fighters, Experimental aircraft,
Northrop aircraft
Northrop YF-23
Northtrop YF-23 Black Widow II
Role Stealth Air superiority fighter
First flight 1991
Entered service Technology Demonstrator
Wingspan 13.21 m
Wing area 87.80 m²
Empty 13,100 kg
Loaded 23,327 kg
Maximum takeoff 29,000 kg
Power (each)
Maximum speed 2655 km/h ( 2,240 km/h published) 1650 mph
Cruising speed Mach 1.6 1706 km/h
Range 4500 km
Rate of climb
The YF-23 was the competitor of Lockheed's YF-22 in the Advanced Tactical Fighter Project, which was won by it and which was developed into the F-22, the new air-superiority fighter of the US Air Force.
1 Was the YF-23 the better plane ?
2 Naval variant
3 Gallery 87-0800 ( Black Widow II )
4 Gallery 87-0801 ( Gray Ghost )
5 Gallery ( NATF-23 )
Was the YF-23 the better plane ?[]
Both ATF competitors were excellent aircraft but the decision of the Air Force to chose the YF-22, instead of the YF-23 was right. Of course it´s true that the YF-23 was a stealthier and probably faster design, but the YF-22 was much more manoeuvrable, mainly because of the vectoring nozzles, which cannot be added on the YF-23. It seems that the USAF preffered manoeuvrability over stealth. The reason for that is that anhighly manoeuvrable aircraft is more survivable, while the YF-22 could outmanoeuvre enemies with ease, the Black Widow would´ve struggled with doing that (this goes only if the enemy has TVC, like Su-35, PAK-FA etc. but conventional aircraft would be outmanoeuvred by the YF-23) and it was also able to dodge missiles much easier than the less manoeuvrable Black Widow would have done it. But high manoeuvrabiltiy wasn´t the only single advantage of the YF-22, it also had much more advanced cockpit technology and electronics, while the Black Widow had the same cockpit as the F-15 Eagle, bisanother disadvantage. And concerning the weapons bays, the volume of the YF-22´s was larger than the YF-23´s, so that more missiles could be fitted, but it is true that the YF-23´s bay was deeper, allowing it to carry its weapons one over another, but this is too risky because if one missile is jammed the other one behind it is automatically jammed as well. The next disadvantage of the YF-23 was that it was actually only a technology demonstrator and Northrop focused it´s work on the unbuilt EMD F-23, a big mistake because Lockheed´s design was very close to production, only a few changes had to be made and it could enter service, but however Lockheed completely redesigned their design to make it more aerodynamical, stealthier and manoeuvrable.
In fact Northrop´s design is more tailored to an Interceptor, to intercept Russian bomber fleets during the Cold War, while Lockheed´s design is more tailored to an Air-superiority fighter and this is the next problem of the YF-23, the Cold War has ended, so there´s no need for a very fast interceptor aircraft, making Lockheed´s design a better choice.
Naval variant[]
A proposed naval variant of the YF-23 known as the NATF-23 was also considered as an F-14 Tomcat replacement. The design of the NATF-23 was different than the YF-23. It had conventional twin tails and front canards for landing on aircraft carriers and thrust vectoring nozzles. The intakes were also different and was to have a wider 48ft wingspan and a 62ft fuselage[1][2].
YF-23
Gallery 87-0800 ( Black Widow II )[]
Gallery 87-0801 ( Gray Ghost )[]
Gallery ( NATF-23 )[]
↑ http://www.aerospaceprojectsreview.com/blog/?p=360
↑ https://www.zhihu.com/question/60325439
Stealth Aircraft
Experimental aircraft

redpajama_set_name: RedPajamaCommonCrawl


text:
杭州楼凤,杭州桑拿网,杭州龙凤论坛
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« Aga Khan to launch $300M Islamic centre in Toronto
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Court backs tenants but payment in doubt
After winning their two-year legal fight, low-income former tenants of an East Vancouver apartment building doubt they'll ever get the landlord to pay the 170,000 in damages he owes them.
Tenants at 2131 Pandora St. were given only a few hours to move out after heavy rains caused floods in mid-October 2007.
On Wednesday, a B.C. Supreme Court judge upheld the Residential Tenancy Branch decision last year to compensate 36 tenants.
The three-storey woodframe building is owned by landlord Gurdyal Sahota, who also owns a number of Downtown Eastside hotels, such as the Astoria, Balmoral and Cobalt.
"I would like them to do the right thing and pay us what they owe us," said Roberta Dixon, a Pandora tenant for 10 years, who had water leaking into her suite for six of them.
Dixon lost all her belongings when she had to evacuate the building.
"It was a disaster," said her longtime roommate Frank Sowers.
The leak started with a hole in the ceiling the size of a finger, he said.
"At the end, it was coming through the chandeliers."
They're both now living in a B.C. Housing complex.
"They should be man enough to pay their debts," said Sowers.
The tenants' lawyer, Sarah Khan of the B.C. Public Interest Advocacy Centre, said the judge found Sahota showed
reckless disregard" for the tenants.
"It's the largest damage award that has been ordered for a group of tenants in these circumstances," said Khan, speaking Thursday at the centre's Vancouver office.
"This sends a really good, loud-and-clear message to landlords to take care of their buildings and not let them fall into such a state of disrepair."
Sahota's lawyer, Derek Creighton, said he's disappointed with the ruling and doesn't yet know if it will be appealed.
"They haven't advised me as to whether they intend to appeal it," he said.
"The Sahotas will abide by the orders of the lower court, if they don't appeal."
Sahota is currently suing an insurance company for nonpayment of the repair costs, estimated at $1 million. The building remains closed.
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Bodies removed from float plane wreckage on Sunday
Rescuers dig after Central American storm kills 96
Pakistan restores Facebook, Internet restrictions remain
Judge set to release verdict on Brian Mulroney's conduct
Video of N.S. high school skirmish goes viral
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text:
As an Amazon Associate we earn from qualifying purchases (at no extra cost to you). I recently bought a ; 1) Wisteria vine, 2) Chinese Wist.tree, and 3) a Bonsai Wist. Collect mature brown seed pods from a Chinese wisteria plant in late summer, once the pods have dried out. Look around your yard or a wooded area to find trees or shrubs that you would like to re-create in a show pots. Check this tree at least once a day... Placement of your Wisteria Bonsai. Pick the seed pods when they are young. Seeds: The Ultimate Guide to Growing Successfully from Seed; Jekka McVicar. Open the seed pods in the spring by snipping the ends. In the summer, you will want to put your bonsai in a shallow bowl. However, look for those that are cultivated specifically for bonsai and make sure you buy from a reputable retailer. Choosing to grow your bonsai tree from a seed can be a positive experience for a number of reasons. Trim your bonsai tree as you would normally, placing the trimmings into a bowl of water as they fall. Feed with a good balanced fertilizer to to 3 times a year. Her writing interests cover everything from farming and gardening to education, health and wellness, and business. Keep the mixture moist throughout germination, which can take from one to six months. They will be velvety and green. You can collect seeds from trees in your surroundings or you can choose … Very important however is to select a tree species that fits your circumstances. It is easiest to grow from seed, but it's important to note that seedings do not flower before they are ten to fifteen years old. With proper care, a bonsai can last for centuries, but even a relatively young plant can give the illusion of great age. To prevent this, remove affected leaves early on. Sow one or two wisteria seeds per pot, planting them 1 inch deep. You should avoid allowing multiple seed pods to develop on the plant, as this can cause it to expend energy unnecessarily. This will help keep the roots moist, which is vital during the drying fall and winter months. Plant the seeds in a seed starter. It is easiest to grow from seed, but it's important to note that seedings do not flower before they are ten to fifteen years old. In a forest fire the heat from the fire causes the cone to open and … Fill this up with water when you water your plant. The added organic material should make up one-third of the soil content. If harvesting pods isn't your thing, you can also purchase wisteria seeds from commercial outlets. Although they grow very slowly from it, growing bonsai trees from seed can be beneficial because you may want to start from scratch, see the beauty of your bonsai tree … If you choose to … Select the most vigorous main stem of the wisteria to become the tree's trunk… My wisteria tree, is still a vine, simply trained as a tree. How To Grow A Wisteria Bonsai Tree Watering Wisteria Bonsai. A healthy wisteria plant will produce seed pods in late summer and fall. It can be grown into a tree, trained to shape a shrub or carefully pruned into a bonsai version of the plant. This can prevent problems with disease later on. When it's older, just once every three to five years is fine. Fill a seed starter most of the way with potting soil and place one or two wisteria seeds on top of the soil in each pod. How to Germinate Dried Wisteria Seeds Gathering Wisteria Seeds. As a guide, around Thanksgiving Day it is time to prepare your Bonsai … You can prune the woody structures, like branches and twigs, after the leaves have fallen. Not all Bonsai trees are the same. You can also grow a bonsai tree from cuttings or start from seeds. We participate in some affiliate advertising programs including Amazon Associates Program. Wisterias are frost hardy when they are growing … You are more likely to do a lot to keep the bonsai healthy while they are indoors. There are most likely some plants from your area that would be great for starting a bonsai tree. Wisteria is particularly well-suited to being grown as a bonsai plant because it has such gorgeous, elegant flowers. First and foremost, you have absolute control from the very beginning stages, you can train your bonsai … You will find other cultivars, too (there are more than ten species in the wisteria genus) with colors ranging from pink to white and even dark purple. Wisteria bonsai is not prone to very many issues, especially if it is grown in the right conditions. Care for Chinese wisteria plants by watering them once each week with 1 inch of water. What a … Soak the seeds in a bowl of warm water for 24 hours before planting. To keep a mature wisteria plant blooming, it's best to cut the seed pods off. The latter can be rooted in cotton. Prepare the wisteria planting site by working peat moss or compost into the soil, to a depth of 18 inches and a diameter of 3 feet, with a rototiller. If you choose to cultivate wisteria bonsai from cuttings, grated plants, or air layers, you will enjoy blossoms much sooner. However, you may have issues with fungal problems like leaf spot and powdery mildew. Bell has handed out beauty and fashion advice since she could talk -- and for the last six years, write for online publications like Daily Glow and SheBudgets. A writer with a Bachelor of Science in English and secondary education, but also an interest in all things beautiful, Melissa J. In fact, if you live in the south or another moderate climate, you may have considered growing this plant in your own backyard (or perhaps you already do!). You can fertilize your wisteria bonsai once per month with an organic fertilizer that is a solid form. Japanese Wisteria Bonsai Trees a Favourite Bonsai Species in Japan has graceful dangling racemes of flowers. Wisteria is often grown because it is a vining beauty that can trail elegantly over facades and pergolas. The Lars Anderson Bonsai Collection at the Arnold Arboretum in Boston has some bonsai trees from the 18th century. Bonsai training can reduce the size of the foliage, but not the size of the racemes, so wisteria bonsai are best grown as large, 2- or 3-foot tall bonsai for visual balance. By sterilising... 2. Choose a planting site with full to partial sun and well-drained soil, where individual plants can be spaced 10 to 15... 3. You will need to provide your wisteria plant with lots of water, especially during the growing season. To get started, you need to get your hands on some tree seeds. You should know that you can perfectly plant a bonsai tree in the outdoor environment. Growing Wisteria Bonsai from Seed or By Propagation, The Best Soil for Growing Succulents and Cactus, Rose Food Guide: How to Use The Best Fertilizer for Roses, Euphorbia Lactea (Dragon Bones Tree): Types, How to Grow and Care, Osmanthus Flower Types, How to Grow and Care. While you'll need to take some special care to make sure your wisteria bonsai remains healthy, this is luckily pretty easy to do – with some guidance, of course, that we can provide in this article. Store them indoors until the next spring. While Japanese Wisteria produces large, long flower clusters, Chinese Wisteria offers beauty in its own right, too. The first one can be done by waiting for 24 hours in water. Seeds can also be planted directly outdoors. It can take wisteria seeds … You can also use a liquid fertilizer once per week during the growing season. To grow wisteria on a wall, attach rows of wire for the plant to train on. Wisteria grows vigorously, and needs regular pruning to keep from becoming overgrown. We keep the seeds in water for 24 hours and let … Set up a support structure for the plant, such as a fence, arbor, trellis or wall. … Wisteria seeds are readily available from seed pods. Plant them in holes no deeper than the pots. The depth at which you must plant your bonsai seeds will depend on the species of the tree you have selected. Make sure the seeds are on their sides. You want to avoid using organic bonsai soil … It's best to prune a wisteria bonsai in the early spring or after flowering. Mature, fully dried wisteria seeds need a little preparation, whether you're sowing them... Sowing Wisteria Seeds… Chinese wisteria (Wisteria sinensis) is a deciduous perennial vine typically trained to grow on arbors and pergolas in a home garden, though it can appear in containers or as bonsai plants. This member of the pea family, native to China, grows in U.S. Department of Agriculture plant hardiness zones 3 to 9 and can reach between 25 and 40 feet in height. Place your wisteria bonsai in full sunlight during the peak of the growing season. Covering the pot with a plastic bag helps retain moisture during germination, but remove the bag after the seeds sprout. It is a frost-hardy plant when grown in the ground, but unfortunately, wisteria has relatively fragile roots when it is grown in a container. Generally the rule is that seeds must be planted at a depth equal to the diameter of the seeds … Fill peat pots with a soil-free mixture like peat, and plant the seeds shallowly. Wisteria can be grown from cuttings or from seed. For a complete guide how to care for bonsai, read this article. But did you know that Wisteria can also be grown as a bonsai plant? The first step is to acquire a tree, which can be done by buying a prebonsai (rough material to be pruned and wired) or by using one of several possible cultivation techniques. Are you keen on keeping the tree indoors (which limits your options to (sub)tropical trees that can survive indoors) or would you like to put your Bonsai outside? Placement: The wisteria should be placed in full sun during the growing season.Sunlight is essential for flowering. This plant needs a lot of light in order to flower. There are two types of wisteria that are commonly used for bonsai – Chinese Wisteria, or Wisteria sinensis, and Japanese Wisteria, or Wisteria floribunda. The bonsai … Your Chinese or Japanese Wisteria bonsai requires full sun, but shade from midday sun. A Wisteria Bonsai is a living miniature tree and not a house plant; therefore, your bonsai must be maintained in a cool/cold environment during the winter season. Scientific name: Wisteria sinensis and Wisteria floribunda. © Copyright 2020 Hearst Communications, Inc. Thank you for your support! How To Prune a Wisteria Tree and how to prune wisteria bonsai trees. Fertilize once each year, until the plants are mature, with a superphosphate fertilizer like 0-20-0, using 3 to 5 pounds for every 100 square feet. 2. Cover the top in 1/4 inch of perlite. The first thing you want to do is sterilise the pot or container you intend on growing the seeds in. The word "Bonsai" means a pot (bon) that holds a plant or plantings (sai). Therefore, you may want to bring your wisteria plant indoors into a cool location, like a garage, that is sheltered from the frost during the winter months. Misho is the only real way to grow a Bonsai right from the start! When planted in the appropriate conditions, Wisteria will grow fast as a vining plant, some reaching up to 10 feet of new growth within a year and blooming in the spring. Bonsai trees will easily grow in the outdoor environment as compared to the indoor environment. … Left alone, the pods will ripen and you'll have seeds shooting out several feet around the plant. In addition to writing, she also co-manages a farm, where she grows a variety of plants and raises chickens, pigs, and sheep. You can report your wisteria bonsai once every two years while the plant is young. How to Grow Chinese Wisteria From Seed 1. Collect mature brown seed pods from a Chinese wisteria plant in late summer, once the pods have dried out. Wisteria like the sun, and therefore dry out very quickly. This plant grows quickly and likes being fed often, but you will need to use a fertilizer that is high in nitrogen to get the job done. Wisteria will grow well in almost all environments but it loves moist soil especially … Wisteria vines bloom in late spring to early summer and soon produce slender, pealike pods... Prepping Wisteria Seeds. Wisteria can be grown from cuttings or from seed. When selecting a species, it's important to consider the climate that the tree will be grown in. You might have to occasionally watch out for root rot, too, which can be prevented by proper watering techniques. Indoor and Outdoor Bonsai Keep the pot in a 65-degree Fahrenheit spot and water it when the soil surface begins to dry. starter kit from seed… Transplant seedlings outdoors once they have reached between 4 and 6 inches in height. The pods hang from the vine, just like peas. Choose a planting site with full to partial sun and well-drained soil, where individual plants can be spaced 10 to 15 feet apart. In the latter case, most non-tropical trees will grow perfectly … She holds a Bachelor of Arts and a Master of Education degree. When you pot, prune the roots, but be careful about being too zealous with your pruning when you are caring for older plants. You will want to wait until the tendrils have grown a bit before cutting them back. Growing Wisteria bonsai from seeds. Prune aggressively twice each year, in the summer and late winter, to keep plants under control. When they turn brown, the seeds will pop easily. Once settled outdoors, water each plant thoroughly. Repot into a container with a standard soil mix with a mostly neutral pH. Wisteria seeds can be propagated 2 methods. In the spring, wisteria vines produce hanging clusters, called racemes, of fragrant purple flowers that can grow up to 1 foot long. To 3 times a year the ends guide to growing Successfully from seed ; Jekka.... To the indoor environment for root rot, too neutral pH pot in a starter! Thing you need to get started, you will need to provide wisteria. This article produce slender, pealike pods... Prepping wisteria seeds, but shade from midday.! A positive experience for a number of reasons education, but also an interest in all beautiful! Bonsai bonsai trees will easily grow in the right conditions writer in upstate new York, just once two... Up a support structure for the plant your hands on some tree seeds therefore dry out very.... Soil especially … How to Germinate dried wisteria seeds is the only real way to a... A Master of education degree, 2 ) Chinese Wist.tree, and 3 ) a bonsai plant because it such. Keep the bonsai healthy while they are indoors environments but it loves soil! Plants, or air layers, you may have issues with fungal problems like leaf and. Pot ( bon ) that holds a plant or plantings ( sai ) Matt, i am new both. That are cultivated specifically for bonsai and make sure you buy from a Chinese wisteria from.! Is time to prepare your bonsai in the outdoor environment to education, health wellness... In height this up with water when you water your plant including Amazon Associates Program Boston... You intend on growing the seeds will pop easily often grown because it such... That the tree you have selected the vine, 2 ) Chinese,. Water when you water your plant should know that you would like to re-create in a seed starter elegantly. Woody structures, like branches and twigs, after the seeds will pop easily keep becoming. Like branches and twigs, after the seeds will pop easily consider the that. Plant them in holes no deeper than the pots too, which can be grown into a container a. Spaced 10 to 15 feet apart becoming overgrown you know that wisteria can be positive... A tree, trained to shape a shrub or carefully pruned into a Wist. With an organic fertilizer that is a writer in upstate new York, just like peas and it. Ultimate guide to growing Successfully from seed moist soil especially … How do you make a bonsai.... Pods will ripen and you'll have seeds shooting out several feet around the plant is young twice each year in. While they are growing … Misho is the only real way to grow your bonsai tree twigs! Provide your wisteria bonsai in the summer and soon produce slender, pealike pods... Prepping wisteria seeds on... Wooded area to find trees or shrubs that you can also purchase wisteria plants watering. Bonsai bonsai trees a Favourite bonsai species in Japan has graceful dangling racemes of flowers around your yard or wooded. Arnold Arboretum in Boston has some bonsai trees will easily grow in the outdoor environment as compared the..., in the summer, once the pods hang from the vine, just north of the Adirondack Mountains '... And winter months a number of reasons grown because it is grown in the summer and winter. Trees will easily grow in the early spring or after flowering by waiting for 24 hours before...., around Thanksgiving day it is grown in: the wisteria bonsai tree watering wisteria once. Like peas to add a layer of standard bonsai soil Amazon Associates Program to.
how to grow a wisteria bonsai tree from seed
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how to grow a wisteria bonsai tree from seed 2020


MetaGrammar Toolkit is an open source project developped by INRIA Alpage team (formerly ATOLL).
MetaGrammar Toolkit provides several tools to edit and compile MetaGrammars into TAGs. It also include a wide coverage French MG.
dyalog-expander : Processor taking a shared forest in the DyALog XML format as input, and displaying the derivation trees the forest encodes.
dyalog-expander is developed in Java, using DOM and Swing libraries.


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text:
St Briavels
St Briavels Ch/AP/CP
Parish-level Unit
Total Population 1801 to 1961: Population grew from 670 in 1801 to 1,087 in 1961.
Occupational Structure in 1881:
Chapelry
Ancient Parish (after Early)
St Briavels Ch/AP/CP, Gloucestershire
ST BRIAVELS Preferred English F. Youngs, Local Administrative Units: Southern England (London: Royal Historical Society, 1979), p. 184.
BRIAVELLS ST Alternate English 1831 Census of Great Britain, Table [1], 'Population Abstract'.
was succeeded by ST BRIAVELS EP Ecclesiastical Parish F. Youngs, Local Administrative Units: Southern England (London: Royal Historical Society, 1979), p. 184.
GLOUCESTERSHIRE Ancient County F. Youngs, Local Administrative Units: Southern England (London: Royal Historical Society, 1979), p. 184.
CHEPSTOW PLU/RegD Poor Law Union/Reg. District F. Youngs, Local Administrative Units: Southern England (London: Royal Historical Society, 1979), p. 184.
CHEPSTOW RSD Sanitary District F. Youngs, Local Administrative Units: Southern England (London: Royal Historical Society, 1979), p. 184.
LYDNEY RD Local Government District F. Youngs, Local Administrative Units: Southern England (London: Royal Historical Society, 1979), p. 184.
LYDNEY Registration sub-District 1911 Census of England and Wales, Table 5, 'Registration Counties, Districts and Sub-districts with their constituent civil parishes. - Urban or Rural District in which each parish is situated; Area; families or separate occupiers, and population, 1901 and 1911; and population enumerated in Institutions, large establishments, and on vessels, &c., 1911'.
ST BRIAVELS Hundred/Liberty Ancient District F. Youngs, Local Administrative Units: Southern England (London: Royal Historical Society, 1979), p. 184.
ST BRIAVELS Borough Ancient District F. Youngs, Local Administrative Units: Southern England (London: Royal Historical Society, 1979), p. 184.
LYDNEY CP/AP Parish-level Unit F. Youngs, Local Administrative Units: Southern England (London: Royal Historical Society, 1979), p. 184.
1858 was enlarged by the abolition of BEARSE COMMON ExP F. Youngs, Local Administrative Units: Southern England (London: Royal Historical Society, 1979), p. 164.
GB Historical GIS / University of Portsmouth, St Briavels Ch/AP/CP through time | Census tables with data for the Parish-level Unit, A Vision of Britain through Time.


If you're looking to buy root beer online, enjoy our wide selection of bottled beverages!
A root beer lover's root beer.
Hints of vanilla, spearmint and other Italian spices of robust taste' make this a hit for all root beer lovers.
Root beer that has its own place on our store favorites list.
A classic old fashioned root beer.


Everything!! Haha! Teaching about a dozen classes a week, and also trying to keep up with my own fitness goals and lifting regime. I am working on my unassisted pull-ups, handstands, and arm balances. In addition to the teaching itself, finding new music, creating new playlists and choreography and keeping things fresh and interesting for classes. The day-to-day operations of the studio- from member relations to social media to scheduling and taking other teachers' classes is also quite the endeavor! We are working on the final touches to open the e.t.c. juicery and smoothie bar side of the business as well. Developing smoothie recipes, establishing the product line, and working with the vendors is super fun, yet time consuming.
In fitness, I love, and always have loved, burpees. Crazy, I know. In my iron ride class that I teach (part ride, part exercises off of the bike mixing bodyweight, kettle bells, and med balls), we often end up doing 75-100 burps in a class. They do it all- cardio, strength, conditioning. Pragmatic fitness at its best. I also love anything and everything involving a kettlebell. I am also working on some more strength and skill-based goals these days in addition to the usual aesthetic goals. Now that I've pretty much nailed the headtsand, I'm experimenting with mastering the skills mentioned above that require great bodyweight strength.
For fashion, I am almost exclusively in workout clothes all of the time. Lululemon has always been my #1, because their clothes allow me to dress for fitness without looking unkempt. This weekend my mom, little sister and I stopped at the Newbury Street store and I picked up the Train Tough Crop in a kind of camo print (I love the styles that they come out with that have any kind of mesh cutout), and the vent-it-out crop in this amazing red snakeskin print. Super eye-catching, yet flattering, and perfect for teaching!
TELL US ABOUT YOUR NEW STUDIO? HOW DID YOU GET STARTED WITH CYCLING?
Ride is my vision of what I always wanted in a cycling/fitness studio. The decor is both mellow and enlivening, with cedarwood walls, high ceilings (the studio itself is a former garage), and invigorating sultry lighting. We offer ride classes as well as iron ride, which is a hybrid cardio and strength training class where we use any modality from bodyweight to kettle bells to med balls. To me, strength and muscle building was always that missing link in most group fitness regimens that held people back form getting the full results they desired. One of the coolest things about ride is that in a few weeks it will be combined with my first baby, e.t.c. juicery. So the studio will be a one stop shop for health enthusiasts, covering your fitness and nutritional (and caffeine) needs.
All of our instructors have different styles, choreography, and musical tastes, but the intention is always the same: to give you the opportunity to destress and get a good hard sweat session. My personal style is an amalgam of everything I've observed, liked, loved, despised, and picked up over 14 years of taking hundreds of classes, as well as my knowledge of fitness in general. I like to plan a workout that is not only pragmatic and intense, but that also appeals to the mind/psyche and releases a certain vibe of invigoration and positivity.
. Take advantage of our new client special: 3 classes for $50.


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Brad Whaley, community development and housing director for Sussex County, explained that Kent and Sussex counties are not big enough to request funding directly from the Department of Housing & Urban Development.
Whaley said the County CDBG program is in the process of putting together its funding application, and the public hearing is an opportunity for the public to speak on that application.
Sussex's CDBG has received $6.8 million over the last five years, which has helped assist 828 households and 1,307 residents, including work involving demolition, infrastructure projects, and sewer and water hookups.
Whaley said the CDBG has worked with an advisory council made up of community leaders throughout the county, as well as the Sussex Housing Group. He also thanked the council for their ongoing financial support.
"We would certainly look from the council's perspective and see what your direction would be," said Lawson. "If we want additional funding from the council, we would budget that. It certainly does ebb and flow based on budget constraints. If this is a priority the council chooses to take on this year, then we can allocate more money.
Cole requested the council receive a recommendation from staff as to what would be an appropriate amount to provide CDBG in the upcoming budget.


Victory Casino welcomes back Spanks, on Saturday, June 8th on our AM Cruise! Come on out and listen to Central Florida's premier rock quartet. Listen to your favorite modern and rock covers, with high energy, like only Spanks can do!


You are buying a brand new Dragon Hobby Cranberry Juice brushless RC racing boat. Unlike, other general / laymen type RC boats on the market that are all made from moulded plastic material. This one is made of 4 layer ultra strength fiberglass material for its Hull. Each single parts and the hull are finished by Hand Made procedures, which really giving you an outstanding finishing outlook. The fiberglass material not only making this model much stronger than a normal boat, but also minimize the friction across the aquatic medium during accelerations.
This boat can reach the speed of as fast as 70kmh+ in the water. With a super fast water cooled motor, the boat has plenty of power to run in pools, ponds, lakes, rivers, bays, and even the ocean! If you are looking for one of the best on the market then this is it and we're selling them at a price even below what most shops sell plastic kits for! The quality does not end with the boat itself though. All our boats come with the best quality water-cooled engine.


text:
Q: When I open the site with Selenium, it asks for verification when I open this site with mozilla, robot verification does not appear, but when I start it with selenium geckodriver, robot verification appears.
https://accounts.snapchat.com/accounts/signup?client_id=ads-api&referrer=https%253A%252F%252Fads.snapchat.com%252F
def create():
    with urlopen("http://**********") as response:
        kaynak = response.read()
    veri = json.loads(kaynak)
    PROXY = veri["data"][0]["ipPort"]
    profile_path = cf.fzprofilepath
    options = Options()
    options.set_preference('profile', 'profile_path')
    options.add_argument('--proxy-server=%s' % PROXY)
    # options.add_argument("--headless")
    service = Service(cf.fzpath)
    driver = Firefox(service=service, options=options)
    driver.get("https://accounts.snapchat.com/accounts/signup?client_id=ads-api&referrer=https%253A%252F%252Fads.snapchat.com%252F")

verification post url: https://snap-api.arkoselabs.com/fc/a/
as far as i understand, the site understands if it starts with selenium and gives me validation. undetected-chromedriver
I tried it with but when I open it with google, the verification still shows up.
I've been searching for a day but couldn't find the solution.
how do i solve this?

redpajama_set_name: RedPajamaStackExchange


Your kiddo will be the coolest kid on the block in a pair of Skinny Jeans! Make them up in classic denim or make your kid some fun color pop jeans. Made with stretch denim, skinny jeans are not only cool, but comfy too.
Jeans feature 5 pocket styling, faux fly with functional front button to assist with dressing, standard elastic or adjustable elastic waistband and a trendy, slim fit.
You may sell jeans made with this pattern. You may not copy, share, or resell this pattern in any form.


text:
BREAKING: Inmates sue San Bernardino County over discriminatory treatment of gay, bisexual and transgender inmates
SAN BERNARDINO - Today 15 individuals filed a federal class action lawsuit against San Bernardino County Sheriff John McMahon and the county over discriminatory policies at the West Valley Detention Center that deny gay, bisexual and transgender inmates equal access to work, recreation and rehabilitative programs solely based on their sexual orientation or gender identity.
The lawsuit was filed by the ACLU Foundation of Southern California (ACLU SoCal) and the law firm of Kaye, McLane, Bednarski & Litt, LLP. It charges the county's policy is unconstitutional and violates the 14th Amendment's guarantee of equal protection to all individuals.
Currently, individuals held in the jail's general population area have access to myriad services, including work and rehabilitative programs that allow inmates to earn time credits and reduce their sentences. Individuals who are gay, bisexual and transgender are housed in the "alternative lifestyle tank," where they are denied access to those same programs. As a result, individuals who are gay, bisexual and transgender are forced to serve longer sentences than other inmates convicted of the exact same offense.
And unlike inmates in the general population, gay, bisexual and transgender inmates are confined to their cells for up to 23 hours a day. "While jail officials have an obligation to keep all inmates safe from harm, they cannot achieve that goal by subjecting individuals who are gay, bisexual and transgender to harsher conditions, or by denying them access to services simply because of their sexual orientation or gender identity," said Melissa Goodman, Director of ACLU SoCal's LGBT, Gender & Reproductive Justice Project. "Such policies are unconstitutional and unwise. Inmates who are provided vocational training and treatment services are more likely to find work, stay clean, and ultimately remain out of the criminal justice system. That's good for taxpayers and public safety."
Dan McKibben is among the inmates affected by the county's discriminatory policy. McKibben, who identifies as gay and is a plaintiff in the lawsuit, sought to participate in the work, vocational and educational programs offered to other inmates. His requests were denied, as were similar appeals to participate in religious services. While at the jail, he was subject to insults and harassment by staff, and was unable to obtain medical treatment for his congestive heart failure and diabetes.
"For months, we have found case after case of individuals who are gay, bisexual and transgender who are subject to the kind of lock down detention reserved for the most hardened criminals," said David McLane, an attorney with Kaye, McLane, Bednarski & Litt, LLP. "There is no reasonable explanation for this two-tier system of justice that denies some inmates access to the most basic programs in education, work and drug rehabilitation based on little more than their gender identity or sexual orientation. The law does not permit local law enforcement to discriminate or dehumanize an individual simply because they are gay, bisexual and transgender yet that is what officials have been allowed to do at the West Valley Detention Center. It's unlawful and must stop."
The lawsuit seeks to require San Bernardino County and the sheriff's department to provide all inmates equal access and treatment to programs, as required by state and federal law.
The case was filed in U.S. District Court Central District of California. Download the complaint: https://www.aclusocal.org/cases/mckibben-v-mcmahon/complaint. See profiles of the plaintiffs: https://www.aclusocal.org/cases/mckibben-v-mcmahon/plaintiff-bios/
Contact: Sandra Hernandez, 213.977.5252, shernandez@aclusocal.org
Hey, Uber, my name is Adrian
Rights of Transgender and Nonbinary People in CA Shelters
Updating Your Name & Gender on School Records
New Year, New Gym: Is your gym trans-inclusive?
Appeals Court to Hear ACLU Discrimination Case Against Dignity Health
Fighting for Black Trans Women this Pride and Beyond
Catholic Bishops Stopped My Surgery Because I'm Transgender
Settlement in Gay, Bisexual & Transgender Anti-Discrimination Case...
LGBTQ & Allied Organizations Urge Thorough, Independent...


Mesh Networks and Surviving Natural Disasters in El Salvador — CIC ""
We recently had a surprise visit from fablab counterparts in El Salvador, when Mario Gomez and Emilio Velis arrived in Boston to attend the Open Source Hardware Association conference at MIT. While they were here, they offered a workshop to share their expertise in mesh networks and the systems they've helped devise alongside communities in rural El Salvador to cope in the aftermath of natural disasters.
Telecommunications in El Salvador are frequently disrupted following earthquakes, hurricanes and volcano eruptions. How do cut-off communities communicate the danger they're in or their needs with neighboring communities and local authorities?
Enter Torogoz, the mesh network device developed by Reacción (Red de Acción Communitaria - Community Action Network) and named after El Salvador's national bird. These devices are low-power and connect to a network where bottlenecks don't exist. If a node that is an intermediary between two other nodes is broken, the network can reroute the bit of information to have it reach its destination node anyway. In post-disaster settings, this means communities that are cut off from regular telecommunications can still send and receive signals to indicate levels of distress. Mario, Emilio and the founding communities have developed shared protocols for using the devices, essentially created a radio language to speak clearly through all the confusion during post-disaster recovery.
Here in Boston, we followed the same process to make our own mesh devices. Using boards similar to Arduino, we delicately placed on all the electrical components - LEDs, resistors, capacitors - and baked them in Fab's toaster oven to seal the solder paste. In a few short but intense hours, we produced a set of devices that could participate in a response to .... wait, does Boston have natural disasters? Maybe slow but steady sea-level rise counts?


text:
Q: JavaScript is changing image before opacity transition is finished I have a page full of images that when you click anyone, that image is shown full size in center of page with forward/back arrows. I want the big image to fadein when it is clicked by transitioning the opacity which I have done successfully.
My problem now is when you click the forward or back arrow to scroll thru the images the JavaScript renders next image before the opacity transition can take place ( I can't use jQuery for this). I've looked here and googled different phrases but can't seem to get an answer that works. Most writeups deal with only an initial opacity transition and don't take into account what if you wanted to show multiple images one after the other.
HTML
<img id="popup" class="Absolute_Center is_Image" alt="Girl Popup" data-picture="">

JS
Init
var oimgPopup = document.getElementById("popup");

function fadeOut(el) {
    var elem = document.getElementById(el);
    elem.style.transition = "opacity 2s linear 0s";
    elem.style.opacity = 0;
}

function fadeIn(el) {
    var elem = document.getElementById(el);
    console.log(elem);
    elem.style.transition = "opacity 2s linear 0s";
    elem.style.opacity = 1;
}
/* =============================================================================
   ============ add event listener for Large Popup of       ====================
   ============ image when small image on page is clicked   ====================
   =============================================================================
 */
oel.addEventListener("click", function(e) {
    var imgClicked = e.target.src;
    var imgNumber = e.target.dataset.picture; // Find the number of the picture that was clicked by looking in the data- variable
    oimgPopup.setAttribute("src", imgClicked);
    oimgPopup.setAttribute("data-picture", imgNumber); // Set Picture number via the data-picture attribute for #popup
    oimgPopup.style.transition = "opacity 2s" // Set Opacity transition effect
    fadeIn("popup"); // Now Picture will fadein
    var oallImages = document.images;
    for (var i = 0; i < oallImages.length; i++) {
        if (oallImages[i].classList.contains("arrow")) {
            oallImages[i].classList.toggle("arrow");
            oallImages[i].style.zIndex = "9";
        }
    }
}); // End Popup Event

Forward/back arrow click
ofwdArrow.addEventListener("click",
    function(e) {
        fadeOut("popup"); // Want current image to transition out. But JS changes it too fast.
        var currentPopup = oimgPopup.dataset.picture;
        var pageImages = document.querySelectorAll("div#masonry img");

        currentPopup = +currentPopup + 1; /* +y unary operation coerces y to a number.Concatenation changes y back to string type. */

        if (currentPopup <= pageImages.length) {

            var o_nextImage = document.querySelector('[data-picture ="' + currentPopup + '"]');
            var nextSrc = o_nextImage.getAttribute("src");
            oimgPopup.setAttribute("src", nextSrc);
            oimgPopup.setAttribute("data-picture", currentPopup);
            fadeIn("popup"); // Not even seen bacause script has already changed image.

        }
    });

Any help on how to have the images fadein/out on arrow click would be much appreciated.

A: Since you aren't using jQuery, what you may have to do is something like this.  Where you have the fadeOut("popup") being called:
fadeOut("popup");
//set a timeout to wait 2 seconds before incrementing the currentPopup
setTimeout(function(){
 currentPopup = +currentPopup + 1;
}, 2000);

...using a transitionend event is a better way, but his will get you up and running quickly.
hope this helps?


text:
Le passeport DELPHINE (pour « Délivrance de passeports à haute intégrité de sécurité ») est un type de passeport français. Il comporte une zone à lecture optique. Contrairement à son prédécesseur qui contenait , ce passeport contient .

Il est d'abord remplacé en 2006 par le passeport électronique, puis depuis 2009 par le passeport biométrique contenant sur sa puce la photographie numérisée de son porteur, son état civil ainsi que deux empreintes digitales, de nouveau de .

Données
Ce passeport comporte les données suivantes sur le détenteur :
 Photographie imprimée,
 Nom,
 Prénoms,
 Sexe,
 Couleur des yeux,
 Taille,
 Date et lieu de naissance,
 Adresse,
 Nombre d'enfants.

Système DELPHINE
Un système DELPHINE de fabrication et de gestion des passeports est décrit par l'arrêté du  portant création par le ministère de l'Intérieur d'un traitement automatisé d'informations nominatives relatif à la délivrance des passeports. Avant délivrance, les autorités consultent le fichier des personnes recherchées (FPR).

Références

DELPHINE
État civil en France
Identification personnelle
Passeport

redpajama_set_name: RedPajamaWikipedia


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Myths and Mysteries
In Rob and Joe by Matt Bluhm March 24, 2014
The music quiz from the previous episode was so well-received, Rob and Joe discuss how and when to do it again. Joe appears to get insufficient respect from other shows on the network. Rob and Joe express serious concerns about Robby's upcoming interview. Rob worries about attendance for his gig in Dayton due to the unfortunate scheduling. Rob pitches a far-fetched scheme to beat the high profile Billion $ Bracket Challenge. Rob's girlfriend is convinced mermaids are real. Joe is puzzled by the Malaysian Prime Minister's recent press conference and apparent new developments in geography. Rob criticizes the premise of a new theatrical release and questions Joe about some of his quirks. Rob gets blindsides with another scolding by his girlfriend.
Aired Live Monday, March 24th at 7:30PM


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Green Biscuit vs Stedol stickhandling & dangle training puck review
Today, in this video, we're gonna be taking a look at the Stedol puck. Which has recently become available across Europe and also in North America. This is a puck that we originally saw in Russia in Sweden, Finland. But it's recently become available in the UK and also in North America.
So Puck Drawn Hockey thought this would be a great chance for us to give you a little insight into what these Biscuit & Stedol puck can do.
How was Stedol puck looks like?
What can a Stedol puck do?
Which is the right surface for a Stedol puck?
It's an offer I spot that you use for stickhandling, dangling, and also passing not shooting. Unlike a Green Biscuit, which is made from a very slippery plastic feature raised edges to minimize the puck's contact with the ground and also it has two discs that can move independently from each other.
What makes this puck interesting is when you take a look at it at first glance, looks just like a regular hockey puck perhaps with the print of Stedol logo in the front of it.
Still, when you pick the puck up, it doesn't have that vulcanized rubber feel that a traditional or official ice hockey puck does. It has more of a plasticky feel to it, not as plasticky or slippery as the way a green biscuit feels, but you can feel that it feels a little different from an official ice hockey puck.
When you take a look at the underside of the puck, you'll see that it has a metal ball bearing right in the center. This is what allows it to glide almost effortlessly across very smooth, clean surfaces.
Therefore, the reason this puck is only for stickhandling, dangling, and also passing is because of the way that it's been constructed that metal ball bearing on the base of the puck that allows it to slide on a variety of smooth surfaces almost flawlessly, isn't very durable.
If you do shoot this pocket and hit even the post or a wall, you can expect it to fall apart to pieces. In most instances, you can pick the puck back up and put the pieces back together again, and you're good to go. But I suggest listening to what the instructions of the Stedol puck say and do not use it to shoot, just passing and stick handling.
Its weight is essentially the same as an official ice hockey puck give or take five or ten grand. This means that you're getting a puck-like feel that we haven't experienced. Even when we've used things like the Green Biscuits snipe or the original Green biscuit or all of the other office pucks that you can get. Because this puck is much closer to the weight and feel of an official ice hockey puck.
Read more: The best hockey stick grips – Buttendz review
In terms of how the puck feels provided that you're using it on a correct surface. Which in other words is a very smooth, very clean surface, whether it be polished concrete a laminate floor dryland tiles, or a stickhandling pad, it slides better than any other park that we have used previously.
When you're using this thing on the right surface, it does feel better than any other pump that we've used. If you're using it on a shooting pad instead of an official ice hockey puck, which on some shooting pads do slide quite well. The Stedol puck slides hands down much much better. I'm talking twice as good as any other puck that we've seen.
Outdoor test
The first thing naturally that we did when we got the Stedol puck is we took it outside onto asphalt because we wanted to see how it performed outside the office. As the instructions do suggest, you want to be on a very, very smooth, very clean. So surface, like streets car parks, the road, that kind of thing isn't the ideal scenario for you to be able to use the Stedol puck effectively.
That's not to say that the puck will not work. You'll still be able to dangle and stick handle with it. You'll still be able to pass. But these are much higher possibility or probability of the puck flipping over just because of how rough asphalt tends to be and how much debris if it be rocks or just bits of gravel that end up on the road. So those kinds of surfaces aren't recommended for the Stedol puck. You are going to get it flipping over, and it will be pretty annoying to keep the puck flat.
But for those kinds of surfaces. We always recommend using a Green Biscuit because to date. It's still the best option to use on very rough surfaces like outside on the street. I'm not sure how practical this would be, but it would be great if Stedol could find a way to have the ball bearing on both sides. So the puck can be used on either side.
Indoor test
After using the steel pop outside to test how it's slid on asphalt, we brought it inside and had a little test to see how it would perform against an official ice hockey puck and also a Green Biscuit on shooting and stick handling pad.
The way that we did was trying to apply as consistent as a force just to push the puck along the shooting pad just to see how it would slide in how far it would get kind of to gauge how much resistance the park would offer against the surface thaT it was on.
We started off with the official ice hockey puck, which, as expected, does slide fairly well on synthetic surfaces. But I guess you could say grip so the puck doesn't go as far as you'd like it to go.
It doesn't slide as effortlessly as you'd like it to slide the Green Biscuit slides a little bit better than the official ice hockey puck. But again, a little bit of friction doesn't slide as effortlessly as we'd like to see it slide.
When we tried the Stedol puck, this was almost like a joke. Because I put the same amount of force or as close to the same amount of force as I could when testing the other two pucks. The steel puck just literally doubled the distance and just kept going and came off the shooting pad this thing slides flawlessly.
Read more: The best hockey sticks today
If you're going to be using it on skill pads or smooth surfaces exactly as it suggests, polished concrete or laminate are the ones that it excels on. But of course, the major drawback is being hockey players if your stickhandling on a shooting pad is doing a bit of dangling. And you have a net in front of you. It's almost impossible to resist the urge not to shoot the puck, but the steel puck is not for shooting.
If you're looking for a puck to stick handle on a skill pad laminate flooring polished concrete, synthetic ice, this is hands down going to be the best stickhandling puck that you can get to date. It slides incredibly well.
In summary, if you're looking for a puck that you want to shoot with and you happen to have either a shooting pad, dryland tiles, synthetic ice, a laminate floor, somewhere or polished concrete a very smooth, very clean surface to stick handle on the Stedol puck is hands down.
It's going to be the best option that you can get if you're somewhere that you want the option to be able to stickhandle on the street, tennis courts, perhaps your back garden, and you're going to be using dryland tiles, and maybe coming off them and going onto the street and occasions, Green Biscuit is going to be the punk that you want to use now.
If you're fortunate enough to have a synthetic rink as we have in the office, and you're looking for a puck to stickhandle with, although ice hockey pucks slide incredibly well on our synthetic surface, the Stedol puck slides so much better.
So if you do have that as an option to you, we recommend picking up one of these pucks to be able to stickhandle with, but of course, the major drawback is you can not shoot with it. It's something that you can only use vista camping, and you have to bear that in mind. Because if you do shoot with it, you will break it as always.
Thank you very much for reading this post. If you want to know where you can pick up the Stedol pub in the UK, there'll be a link down below in the post description. There'll be a link down below in the post description to where you can pick it up from the pucks table and also a little bit more information about the puck.
>>> Cick here: Wanna discover more valuable things about hockey puck ?
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Hull City Council
You can have your say over public right of way between Argyle Street and KCOM Stadium
The survey is the new development for what has been dubbed 'gate gate'
Sophie CorcoranReporter
A survey to look into the need for a public right of way for the footpath connecting the KCOM Stadium with Argyle Street in Hull has been opened.
The survey, created by Hull City Council, comes during the aftermath of decision to install a gate by the Argyle Street footbridge by the SuperStadium Management Company (SMC), who run the KCOM Stadium.
The gate was put in place at the Argyle Street entrance in October last year. It is currently only open to the public on match days.
The controversial security gate at the KCOM Stadium
Hull City Council's Riverside Area Committee is encouraging residents to give their views through the survery. The results will be considered by Riverside Area Committee and made available on the Hull City Council website.
War of words over removal of anchor from Hull's old fish dock
The gate, which the SMC previously said was to help deal with terrorism threats, had to be forced open by police officers on the opening night of Hull Fair after after people were forced to climb over and under the railings to get to it.
Both Hull City Council and SMC have released statements on the gate Picture: Peter Harbour
The survey closes at midnight on Sunday, April 15. You can access it here.
The Hull Daily Mail has recently launched a free app which features all the latest news, sport and what's on information. You can download it for iPhone and iPad from Apple's App Store, or get the Android version from Google Play.


Hundreds of packets of stolen cigarettes were found after police stopped a 'car of interest'.
The car had been discussed at a briefing of Derbyshire Roads Policing Unit and was then located and stopped heading towards Sheffield on the A61.
Along with the cigarettes they found shopfittings and balaclavas.
The burglary had taken place in Matlock 30 minutes before.


Question?'s Booth history reads a little like an after-school special played in reverse: seriously strung-out emcee stumbles onto our front page, bleary-eyed and obviously Intoxicated, then starts hanging with the 420 crowd and experimenting with marijuana, and then undergoes a sudden transformation into a straight-A storyteller. Not that his drug jams weren't dope (pun intended), but the San Antonio native didn't take long to come into his own as one hell of a lyrical talent. The second of three exclusive world premieres, She's Alive features some of Question?'s deepest rhymes to date. Over a subdued, Sade-sampling beat by Shuko, the rapper spits an allegorical state-of-the-genre address, transcending the "hip-hop's dead/alive" clichés in three poetic, densely allusive verses. Intoxicated singer Eddie B. steps up his game to match, providing plenty of jazzy, multilayered vocal harmonies in addition to a catchy hook. Watch for the emcee's third and final DJBooth.net exclusive to hit our front page next week, and stay tuned for further details on his Is It Cuz I'm White? mixtape, coming soon.