benchmark helps // us figure out which is faster: four rejections of ML-DSA-44, or three of // ML-DSA-65. (It's the former, but only barely.) b.Run("ML-DSA-44", func(b *testing.B) { // Same as TestACVPRejectionKAT/Test/Path/ML-DSA-44/1. seed := fromHex("5C624FCC1862452452D0") μ := fromHex("2ad1c72bb0fcbe28099c" + "19327fa57818ee4e3718") skHash := fromHex("29374951cb2bc3cda731") sigHash := fromHex("dcc71a421bc6ffafb7df") for b.Loop() { priv, err := NewPrivateKey44(seed) if err != nil { b.Fatalf("NewPrivateKey:...

- Kubeadm now distinguishes between generated and user supplied component configs, regenerating the former ones if a config upgrade is required ([#86070](https://github.com/kubernetes/kubernetes/pull/86070), [@rosti](https://github.com/rosti)) [SIG Cluster Lifecycle]

            calling this op is typically not necessary, as it is automatically
            used when computing the gradient of `tf.sparse.segment_sum`.
    *   Renaming of tensorflow::int64 to int_64_t in numerous places (the former
        is an alias for the latter) which could result in needing to regenerate
        selective op registration headers else execution would fail with
        unregistered kernels error.

diceRoll < 10: // Generate a high scalar in [2^252, 2^252 + 2^124). s[31] = 1 << 4 rand.Read(s[:16]) s[15] &= (1 << 4) - 1 default: // Generate a valid scalar in [0, l) by returning [0, 2^252) which has a // negligibly different distribution (the former has a 2^-127.6 chance // of being out of the latter range). rand.Read(s[:]) s[31] &= (1 << 4) - 1 } val := Scalar{} fiatScalarFromBytes((*[4]uint64)(&val.s), &s) fiatScalarToMontgome(&val.s, (*fiatScalarNonMontgom)(&val.s)) return reflect.ValueOf(val)...