of items N: number of items in the stream fe: true frequency of the item e in the input stream f: estimated frequency of item δ: user-defined threshold, so that freq(x)≥ δ*N,δ∈(0,1) ε: user-defined 3 1 2 0 1 1 3 5 input stream ε=0.2 w1 w4 w3 w2 1 2 2 3 5 w1 1 2 3 5 1 0 2 0 1 0 1 0 f1 ε1 f2 ε2 f3 ε3 f5 ε5 ??? Vasiliki Kalavri | Boston University 2020 Example 8 1 2 2 3 5 5 1 1 2 3 3 3 3 1 ε=0.2 w1 w4 w3 w2 1 2 2 3 5 w1 Delete items with fx + εx ≤ 1 1 2 3 5 1 0 2 0 1 0 1 0 f1 ε1 f2 ε2 f3 ε3 f5 ε5 ??? Vasiliki Kalavri | Boston University 2020 Example 8 1 2 2 3 5 5 1 1 2 3 3 3 3 1

H a r d e n i n g G u i d e - R a n c h e r v 2 . 3 . 3 + C o nt e nt s Har d e n i n g G u i d e f or R an c h e r 2. 3. 3+ w i t h K u b e r n e t e s 1. 16 . . . 2 O v e r v i e w . . . . . . . . r n e t e s c l u s t e r h os t c on fi gu r at i on . . . . . . . 3 1. 1. 1 - C on fi gu r e d e f au l t s y s c t l s e t t i n gs on al l h os t s . . . . . . . . 3 1. 4. 11 E n s u r e t h at t - C h an ge t h e l oc al ad m i n p as s w or d f r om t h e d e f au l t v al u e . 17 3. 2. 2 - C on fi gu r e an I d e n t i t y P r ov i d e r f or Au t h e n t i c at i on . . . . . 17 3. 3 - R

” , 7: ” Sneaker ” , 8: ”Bag” , 9: ”Ankle␣Boot” , } import matplotlib . pyplot as plt f i g u r e = plt . f i g u r e () # 抽 取 索 引 为 100 的 数 据 来 显 示 img , l a b e l = training_data [ 1 0 0 ] plt 迭 代 取 出 的 数 据 量 # s h u f f l e ： 洗 牌 的 意 思， 先 把 数 据 打 乱， 然 后 再 分 为 不 同 的 batch Chapter 1. 准备章节 9 train_dataloader = DataLoader ( training_data , batch_size =64, s h u f f l e= True ) test_dataloader h u f f l e=True ) 我们写点程序检测一下 DataLoader： train_features , train_labels = next ( i t e r ( train_dataloader ) ) print ( f ” Feature ␣batch␣shape : ␣{ train_features . s i z e () }” ) print ( f ” Labels

import torchvision from PIL import Image from torch import nn from torch.nn import functional as F from torch.utils import data from torchvision import transforms 目标受众 本书面向学生（本科生或研究生）、工程师和研究人员，他 Ra×b: 包含a行和b列的实数矩阵集合 • A ∪ B: 集合A和B的并集 13 • A ∩ B：集合A和B的交集 • A \ B：集合A与集合B相减，B关于A的相对补集 函数和运算符 • f(·)：函数 • log(·)：自然对数 • exp(·): 指数函数 • 1X : 指示函数 • (·)⊤: 向量或矩阵的转置 • X−1: 矩阵的逆 • ⊙: 按元素相乘 • [·, ∇xy：y关于x的梯度 • � b a f(x) dx: f在a到b区间上关于x的定积分 • � f(x) dx: f关于x的不定积分 14 目录 概率与信息论 • P(·)：概率分布 • z ∼ P: 随机变量z具有概率分布P • P(X | Y )：X | Y 的条件概率 • p(x): 概率密度函数 • Ex[f(x)]: 函数f对x的数学期望 • X ⊥ Y : 随机变量X和Y

https://github.com/istio/istio/blob/6 5478ea81272c0ceaab568974aff7 00aef907312/security/pkg/pki/ca/f uzz_test.go#L24 5 FuzzValidateCSR istio.io/istio/security/pkg/ pki/ra https://github.com/istio/istio/blob/6 81 82 83 84 85 86 87 88 89 90 func (f *URLFetcher) Fetch() error { if _, _, err := URLToDirname(f.url); err != nil { return err } saved, err := DownloadTo(f.url, f.destDirRoot) if err != nil { return err := os.Open(saved) if err != nil { return err } defer reader.Close() return tgz.Extract(reader, f.destDirRoot) } Case 2 This will run out of memory before disk space. See issue 5 case 1. 92 // DownloadTo

timestamp '2018-11-17'; 2021-10-28 13:56:27.509 INFO operation.ExecuteStatement: Processing kent's query[1f619182-20ad-4733-995b-a5e43b80d998]: INITIALIZED_STATE - > PENDING_STATE, statement: select timestamp timestamp '2018-11-17' 2021-10-28 13:56:27.547 INFO operation.ExecuteStatement: Processing kent's query[1f619182-20ad-4733-995b-a5e43b80d998]: PENDING_STATE -> RUNNING_STATE, statement: select timestamp '2018-11-17' ExecuteStatement: Spark application name: kyuubi_USER_kent_7ad055d0-3eca-4b78- 87e8-94b22f3bade9 application ID: local-1635400506190 application web UI: http://10

timestamp '2018-11-17'; 2021-10-28 13:56:27.509 INFO operation.ExecuteStatement: Processing kent's query[1f619182-20ad-4733-995b-a5e43b80d998]: INITIALIZED_STATE - > PENDING_STATE, statement: select timestamp timestamp '2018-11-17' 2021-10-28 13:56:27.547 INFO operation.ExecuteStatement: Processing kent's query[1f619182-20ad-4733-995b-a5e43b80d998]: PENDING_STATE -> RUNNING_STATE, statement: select timestamp '2018-11-17' ExecuteStatement: Spark application name: kyuubi_USER_kent_7ad055d0-3eca-4b78- 87e8-94b22f3bade9 application ID: local-1635400506190 application web UI: http://10

secret | grep thanos-token | head -n 1 | awk '{ print $1 }') $ oc process TOKEN="$secret" -f - <f - apiVersion: template.openshift.io/v1 kind: Template parameters: - name: TOKEN objects: get - list - watch - apiGroups: - "" resources: - namespaces verbs: - get $ oc apply -f thanos-metrics-reader.yaml $ oc adm policy add-role-to-user thanos-metrics-reader -z thanos -- rol 以满足路由性能或可用性要求，如提高吞吐量的要求。oc 命令用于扩展 IngressController 资源。以下流程提供了扩展默认 IngressController 的示例。 注意 $ oc apply -f ingress-autoscaler.yaml $ oc get ingresscontroller/default -o yaml | grep replicas: replicas: 3 $

function (PDF) f (x, y) P(a1 ≤ X ≤ b1, a2 ≤ Y ≤ b2) = � b1 a1 � b2 a2 f (x, y)dxdy Marginal probability density functions fX(x) = � ∞ −∞ f (x, y)dy for − ∞ < x < ∞ fY (x) = � ∞ −∞ f (x, y)dx for to more than two random variables P(a1 ≤ X1 ≤ b1, · · · , an ≤ Xn ≤ bn) = � b1 a1 · · · � bn an f (x1, · · · , xn)dx1 · · · dxn Feng Li (SDU) GDA, NB and EM September 27, 2023 12 / 122 Independent and Y Joint PDF f (x, y) Marginal PDF fX(x) = � y f (x, y)dy The Conditional probability density function of Y given X = x fY |X(y | x) = f (x, y) fX(x) , ∀y or fY |X=x(y) = f (x, y) fX(x) , ∀y