- kubeadm will now not fail preflight checks when running on >= 5.0 Linux kernel ([#74355](https://github.com/kubernetes/kubernetes/pull/74355), [@brb](https://github.com/brb))
- Scheduler cache snapshot algorithm has been optimized to improve scheduling throughput. ([#74041](https://github.com/kubernetes/kubernetes/pull/74041), [@bsalamat](https://github.com/bsalamat))

### Server-side Apply graduates to GA

- Eliminated and prevented future use of the `md5` algorithm in favor of more appropriate hashing algorithms. ([#133511](https://github.com/kubernetes/kubernetes/pull/133511), [@BenTheElder](https://github.com/BenTheElder)) [SIG Apps, Architecture, CLI, Cluster Lifecycle, Network, Node, Security, Storage and Testing]

*   Object count quotas supported on all standard resources using count/<resource>.<group> syntax ([#54320](https://github.com/kubernetes/kubernetes/pull/54320),[ @derekwaynecarr](https://github.com/derekwaynecarr))
*   Apply algorithm in scheduler by feature gates. ([#52723](https://github.com/kubernetes/kubernetes/pull/52723),[ @k82cn](https://github.com/k82cn))

- Kube-proxy: Checked whether IPv6 was available on Linux before using it. ([#131265](https://github.com/kubernetes/kubernetes/pull/131265), [@rikatz](https://github.com/rikatz))
- Kubeadm: Added support for ECDSA-P384 as an encryption algorithm type in v1beta4. ([#131677](https://github.com/kubernetes/kubernetes/pull/131677), [@lalitc375](https://github.com/lalitc375))

import org.jspecify.annotations.NullUnmarked;

/**
 * Enumerates several algorithms providing equivalent functionality to {@link Quantiles}, for use in
 * {@link QuantilesBenchmark}. These algorithms each calculate either a single quantile or multiple
 * quantiles. All algorithms modify the dataset they are given (the cost of a copy to avoid this
 * will be constant across algorithms).
 *
 * @author Pete Gillin
 * @since 20.0
 */
@NullUnmarked

architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for...

import com.google.caliper.Param;
import com.google.caliper.api.SkipThisScenarioException;
import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

/**
 * Benchmarks for various algorithms for computing the mean and/or variance.
 *
 * @author Louis Wasserman
 */
@NullUnmarked
public class StatsBenchmark {

  enum MeanAlgorithm {
    SIMPLE {
      @Override

 *
 * <h3>Relationship to {@link Object#hashCode}</h3>
 *
 * <p>Java's baked-in concept of hash codes is constrained to 32 bits, and provides no separation
 * between hash algorithms and the data they act on, so alternate hash algorithms can't be easily
 * substituted. Also, implementations of {@code hashCode} tend to be poor-quality, in part because

 *
 * <h3>Relationship to {@link Object#hashCode}</h3>
 *
 * <p>Java's baked-in concept of hash codes is constrained to 32 bits, and provides no separation
 * between hash algorithms and the data they act on, so alternate hash algorithms can't be easily
 * substituted. Also, implementations of {@code hashCode} tend to be poor-quality, in part because