*   <li>{@linkplain State#RUNNING RUNNING} -&gt;
 *   <li>{@linkplain State#STOPPING STOPPING} -&gt;
 *   <li>{@linkplain State#TERMINATED TERMINATED}
 * </ul>
 *
 * <p>There are deviations from this if there are failures or if {@link Service#stopAsync} is called
 * before the {@link Service} reaches the {@linkplain State#RUNNING RUNNING} state. The set of legal

      for (i in rangePos until rangeLimit step 4) {
        rangeStarts += compactTable.ranges[i].code
      }
      assertThat(rangeStarts).isEqualTo(rangeStarts.sorted())
    }
  }

  @Test fun deviations() {
    assertThat("ß".map()).isEqualTo("ß")
    assertThat("ς".map()).isEqualTo("ς")
    assertThat("\u200c".map()).isEqualTo("\u200c")
    assertThat("\u200d".map()).isEqualTo("\u200d")
  }

 *   <li>{@linkplain State#RUNNING RUNNING} -&gt;
 *   <li>{@linkplain State#STOPPING STOPPING} -&gt;
 *   <li>{@linkplain State#TERMINATED TERMINATED}
 * </ul>
 *
 * <p>There are deviations from this if there are failures or if {@link Service#stopAsync} is called
 * before the {@link Service} reaches the {@linkplain State#RUNNING RUNNING} state. The set of legal

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

/**
 * A decoded [mapping table] that can perform the [mapping step] of IDNA processing.
 *
 * This implementation is optimized for readability over efficiency.
 *
 * This implements non-transitional processing by preserving deviation characters.
 *
 * This implementation's STD3 rules are configured to `UseSTD3ASCIIRules=false`. This is
 * permissive and permits the `_` character.
 *

  /**
   * Returns the <a
   * href="http://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation">
   * corrected sample standard deviation</a> of the values. If this dataset is a sample drawn from a
   * population, this is an estimator of the population standard deviation of the population which
   * is less biased than {@link #populationStandardDeviation()} (the unbiased estimator depends on

  /**
   * Returns an output {@code Future} to use in place of the given {@code input}. The output {@code
   * Future} need not be {@linkplain Future#isDone done}, making {@code AsyncFunction} suitable for
   * asynchronous derivations.
   *
   * <p>Throwing an exception from this method is equivalent to returning a failing {@code Future}.
   */
  ListenableFuture<O> apply(@ParametricNullness I input) throws Exception;

public interface AsyncCallable<V extends @Nullable Object> {
  /**
   * Computes a result {@code Future}. The output {@code Future} need not be {@linkplain
   * Future#isDone done}, making {@code AsyncCallable} suitable for asynchronous derivations.
   *
   * <p>Throwing an exception from this method is equivalent to returning a failing {@link
   * ListenableFuture}.
   */
  ListenableFuture<V> call() throws Exception;

  /**
   * Returns the <a
   * href="http://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation">
   * corrected sample standard deviation</a> of the values. If this dataset is a sample drawn from a
   * population, this is an estimator of the population standard deviation of the population which
   * is less biased than {@link #populationStandardDeviation()} (the unbiased estimator depends on