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- 来自专栏Apache IoTDB
PAX（Partition Attributes Across）
今天分享一篇文章：《Weaving Relations for Cache Performance》，来自 2001 年 VLDB。看这篇之前需要先了解一下 NSM 和 DSM：列式存储的起源：DSM
1.7K10发布于 2020-09-27 - 来自专栏医学和生信笔记
dplyr中的across操作
dplyr中的across函数取代了之前的xx_if/xx_at/xx_all，用法更加灵活，初学时觉得不如xx_if/xx_at/xx_all简单易懂，用习惯后真是利器！ 主要是介绍across函数的用法，这是dplyr1.0才出来的一个函数，大大简化了代码 可用于对多列做同一个操作。 一般用法 陷阱 across其他连用 和filter()连用 一般用法 library(dplyr, warn.conflicts = FALSE) across()有两个基本参数： .cols：选择你想操作的列 .fn：你想进行的操作，可以使一个函数或者多个函数组成的列表 可以替代_if()，at_()，all_() starwars %>% summarise(across(where(is.character 和filter()连用 across()不能直接和filter()连用，和filter()连用的是if_any()和if_all()。
1.1K30编辑于 2022-11-15 - 来自专栏CreateAMind
大脑间的同步性 Synchrony Across Brains
大脑间的同步性 Synchrony Across Brains https://www.annualreviews.org/docserver/fulltext/psych/76/1/annurev-psych 更具体而言，我们着重探讨脑间同步（synchrony across brains）在促进社会互动与沟通、塑造社会协调与学习中所发挥的作用，并进一步探讨脑间同步减弱可能构成精神病理的核心特征之一。
14910编辑于 2026-03-11 - 来自专栏火星娃统计
dplyr_new version-across
5月29日，dplyr迎来来大更新，作为大版本的更新，还是有必要进行学习一下 安装 # remove.packages("dplyr") # install.packages("dplyr") across FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot. across 和summarise合用 实现按照鸢尾花种类分组，并按照组输出包含Petal的变量的均值 iris %>% group_by(Species) %>% summarise(across(contains virginica 5.55 2.03 #多列多个结果汇总 iris_wide<-iris %>% group_by(Species) %>% summarise(across 2.2 ## 11 virginica Petal.Length 278. 5.55 4.5 ## 12 virginica Petal.Width 101. 2.03 1.4 across
52530发布于 2020-09-15 - 来自专栏Hongten
S2 - Lesson 36 - Across the Channel
Words Record train strong anxiously swimmer intend success solid Content Across the channel Debbie Hart is going to swim across the English Channel tomorrow.
30010编辑于 2023-03-06 - 来自专栏bye漫漫求学路
Sequential regulatory activity prediction across chromosomes with convolutional neural networks
Sequential regulatory activity prediction across chromosomes with convolutional neural networks 基于卷积神经网络的染色体序列调控活动预测
1K50发布于 2021-03-22 - 来自专栏landv
Peer-to-Peer (P2P) communication across middleboxes
Srisuresh & Kegel [Page 1] Internet-Draft P2P applications across both of these sessions, ensuring that the "identity" of the client's port is maintained across of S1 and S2) differ, even as the endpoint identity of the client application is lost across incoming traffic that a symmetric NAT does, while maintaining a private port's identity across In this situation there is no hope of generic, transparent peer-to-peer connectivity across the
1.2K30发布于 2018-12-14 - 来自专栏蓝里小窝
阅读笔记 | Distributed Inference with Deep Learning Models across Heterogeneous Edge Devices
Li, “Distributed Inference with Deep Learning Models across Heterogeneous Edge Devices,” in IEEE INFOCOM
37350编辑于 2023-10-15 - 来自专栏饶文津的专栏
「POJ-3608」Bridge Across Islands （旋转卡壳--求两凸包距离）
题目链接 POJ-3608 Bridge Across Islands 题意 依次按逆时针方向给出凸包，在两个凸包小岛之间造桥，求最小距离。 题解 旋转卡壳的应用之一：求两凸包的最近距离。
64110发布于 2020-06-02 - 来自专栏优雅R
「R」dplyr 列式计算
() 函数，它可以帮助你以更加简洁的方式重写上述代码： df %>% group_by(g1, g2) %>% summarise(across(a:d, mean)) 我们将从讨论 across 下面是联合 across() 和它最喜欢的动词函数 summarise()的一些例子。但你也可以联合 across() 和任意其他的 「dplyr」 动词函数，我们后面会提及。 为什么我们喜欢 across()？ 为什么我们决定从上面的函数迁移到 across()？ 例如，你现在可以转换以 x 开头的数值列：across(where(is.numeric) & starts_with("x")). across() 不需要使用 vars()。 为什么过了这么久才发现 across()？
3.4K10编辑于 2022-01-21 - 来自专栏Java那些事
每天一道leetcode-最大子序和
left_across_mid_sum += nums[i]; left_across_mid_max = max(left_across_mid_max, left_across_mid_sum ); } int right_across_mid_max = MIN; int right_across_mid_sum = 0; for ( int i = mid + 1; i <= end; i++ ){ right_across_mid_sum += nums[i]; right_across_mid_max = max(right_across_mid_max, right_across_mid_sum); } int across_mid_max = left_across_mid_max + right_across_mid_max; return max(left_max, max(right_max, across_mid_max)); } }; 3.1.2
65310发布于 2019-01-09 - 来自专栏北野茶缸子的专栏
37. R 茶话会（七：高效的处理数据框的列）
across test2 %>% summarise(across(-any_of("id"), mean)) across 必须要在mutate 或summarise 这类函数内部，对数据框的列进行类似 和summarise_all 一样，其本质也可以接受list 传递函数： test2 %>% summarise( across(-where(is.character), list(avg = ~ >% summarise(n = n(), across(where(is.numeric), sd)) #> n x y #> 1 NA 1 4.041452 因为across 3 df %>% summarise(n = n(), across(where(is.numeric) & ! 取反的方法只对across 的.cols 参数生效。 > test2 %>% group_by(id) %>% summarise_at( + colnames(test2,) & !
2.4K20编辑于 2021-12-17 - 来自专栏云云众生s
Kubernetes v1.31的跨核心分配CPU
在 Kubernetes v1.31 中，我们很高兴地宣布对 CPU 管理功能进行重大改进：CPUManager 静态策略的 distribute-cpus-across-cores 选项。 或 distribute-cpus-across-cores=true。 需要注意的是，此策略选项当前不能与 full-pcpus-only 或 distribute-cpus-across-numa 选项结合使用。 目前的一个重大局限性是 distribute-cpus-across-cores 无法与其他在 CPU 分配策略方面可能冲突的策略选项结合使用。 结论 在 Kubernetes CPU 管理器中引入 distribute-cpus-across-cores 策略，是我们不断改进资源管理和增强应用程序性能所做的一种持续努力。
36210编辑于 2024-08-25 - 来自专栏数据库相关
通过 esrally 压测elasticsearch
shards | | 50.6006 | min | | Min cumulative indexing time across shards | | 0 | min | | Median cumulative indexing time across shards | | 0 | min | | Max cumulative indexing time across shards | | 0 | min | | Min cumulative indexing throttle time across shards | | 0 | min | | Median cumulative indexing throttle time across
1.4K10发布于 2019-12-10 - 来自专栏超级架构师
Designing your SaaS Database for Scale with Postgres
As these tables grow however, you need to think about ways to scale your multi-tenant database across same table(s) The option you pick has implications on scalability, how you handle data that varies across And these implications have been discussed in detail across many StackOverflow questions and database the three design options -with enough effort- can address questions around scale, data that varies across What about data that varies across tenants?
92940发布于 2018-04-09 - 来自专栏大数据生态
Elasticsearch 6.0.0本地单机16核32G压测报告
shards | | 31.9571 | min | | Min cumulative indexing time across shards | | 6.318 | min | | Median cumulative indexing time across shards | | 6.3739 | min | | Max cumulative indexing time across shards | | 0 | min | | Min cumulative indexing throttle time across shards | | 0 | min | | Median cumulative indexing throttle time across
2.1K2111发布于 2021-10-20 - 来自专栏数据处理
proc-tabulate-report
proc report data=parks missing; column type, (region, (museums camping)); define region /across ; define type / across; run; ? missing; column region type, (museums camping); * group 行; define region /group; * across 列; define type / across; run; ? ; title 'Statistics with a Group and Across Variable'; run; ?
2.7K40发布于 2018-06-01 The Value Blox Creates for Both Businesses and Users
Industry-wide event aggregation and calendarsPersistent exposure beyond one-off promotionsContinuous visibility across execution.2.2 Making marketing measurable and verifiableBlox enables businesses to:Track event performance across to:Identify high-quality contributorsBuild long-term relationship mapsReuse verified participation across Turning participation into opportunityWith verifiable participation history, users can:Build credibility across
9310编辑于 2025-12-22- 来自专栏皮皮星球
Deep Dive into Elasticsearch's Distributed Architecture
When data is indexed, it gets distributed across various shards in the cluster. This ensures effective load-balancing across multiple nodes, offering high availability and fault tolerance Sharding and Replication Elasticsearch spreads data in an index across multiple shards, which can be distributed across multiple nodes. This process, known as sharding, enables data to be distributed and parallelized across a cluster, leading
45520编辑于 2023-11-18 - 来自专栏大数据生态
Elasticsearch 7.10.1集群压测报告（4核16G*3，AMD）
indexing time across primary shards 5.89373 min Cumulative indexing throttle time of primary shards 0 min Min cumulative indexing throttle time across primary shards 0 min Median cumulative indexing throttle time across primary shards 0 min Max cumulative indexing throttle time across primary min Min cumulative merge throttle time across primary shards 0 min Median cumulative merge throttle time across primary shards 0 min Max cumulative merge throttle time across primary shards 0.367133
2.3K2510编辑于 2022-05-16
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