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gcc链接参数--whole-archive的作用

liba.so g++ -g -o $@ $^ liba.so: liba.a g++ -g -fPIC -shared -o $@ $^ #g++ -g -fPIC -shared -o $@ -Wl,--whole-archive  $^ -Wl,-no-whole-archive liba.a: a.o ar cru $@ $^ a.o: a.cpp g++ -g -c $^ clean: rm -f x a.o liba.a   liba.a -Wl,-no-whole-archive g++ -g -o x x.cpp liba.so tmpcc6UYIAF.o: In function `main: datajayyildx.cpp 这个时候,可以启用链接参数“--whole-archive”来告诉链接器,将后面库中所有符号都链接进来,参数“-no-whole-archive”则是重置,以避免后面库的所有符号被链接进来。    $^ -Wl,-no-whole-archive liba.a: a.o ar cru $@ $^ a.o: a.cpp g++ -g -c $^ clean: rm -f x a.o liba.a

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The whole Fiori application is wrapped in a big shell xml view

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    CSS卡通小汽车

    hidden; display: flex; justify-content: center; align-items: center; background: #ccdde2; } .base .whole bug { width: calc(800 * var(--unit)); height: calc(350 * var(--unit)); position: relative; } .base .whole #2f494f 0% 30%, transparent), radial-gradient(#2f494f 0% 20%, transparent); filter: blur(3px); } .base .whole .bug__tire__shadow__ground:nth-child(2n) { left: 1%; } .base .whole .bug__whole__shadow { position: 25% 0% 0%; overflow: hidden; display: flex; justify-content: center; align-items: center; } .base .whole

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    R语言关联挖掘实例(购物篮分析)

    frequentItems items support #> 1 {other vegetables,whole milk} 0.07483477#> 2 {whole milk} 0.25551601 ,rice} => {whole milk} 0.001016777 1 3.913649#> 1705 {root vegetables,whippedsour cream,flour} => {whole list(default=rhs,lhs=whole milk # #> lhs rhs support confidence lift #> 6 {whole milk} => {other vegetables whole milk} => {yogurt} 0.05602440 0.2192598 1.5717351#> 2 {whole milk} => {root vegetables} 0.04890696 0.1914047 1.7560310#> 1 {whole milk} => {tropical fruit} 0.04229792 0.1655392 1.5775950#> 3 {whole milk

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    R语言关联挖掘实例(购物篮分析)

    frequentItems items support#> 1 {other vegetables,whole milk} 0.07483477#> 2 {whole milk} 0.25551601# ,rice} => {whole milk} 0.001016777 1 3.913649#> 1705 {root vegetables,whippedsour cream,flour} => {whole list(default=rhs,lhs=whole milk ##> lhs rhs support confidence lift#> 6 {whole milk} => {other vegetables whole milk} => {yogurt} 0.05602440 0.2192598 1.5717351#> 2 {whole milk} => {root vegetables} 0.04890696 0.1914047 1.7560310#> 1 {whole milk} => {tropical fruit} 0.04229792 0.1655392 1.5775950#> 3 {whole milk

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    java 温习之字符串(统计字串…

    =abcnabcmabceswdbca abc dfr afc; String son = abc; int ct = count1(whole, son); System.out.println(ct ); int ct2 = count2(whole, son); System.out.println(ct2); } 这种方法是通过获得一次后截取字符串 public   static int count1 (String whole,String son) { int index = 0; int count = 0; while((index=whole.indexOf(son))! =-1) { whole=whole.substring(index+son.length()); ++count; } return count; } 这种方法是通过移动检索的角标(个人建议用这种, 尤其是大篇幅的统计出现次数时候) public   static int count2(String whole,String son) { int index = 0; int count = 0;

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    全基因组癌症分析(PCAWG)文献精读

    全基因组癌症分析(Pan-Cancer Analysis of Whole Genomes 缩写:PCAWG)项目旨在对38种不同肿瘤类型的2600多种原发癌及其配对正常组织进行了全基因组测序和综合分析 1.Pan-cancer analysis of whole genomes. 5 FEB 2020, Nature----2.Patterns of somatic structural variation alterations in cancer. 5 FEB 2020, Nature----6.Analyses of non-coding somatic drivers in 2,658 cancer whole somatic genomic rearrangements in human cancer. 5 FEB 2020, Nature Genetics----9.Pan-cancer analysis of whole omics data. 5 FEB 2020, Nature Communications----15.Pathway and network analysis of more than 2500 whole

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    我的工作日志 - 2020年11月11日星期三 阴

    one row has 2 states:(1) the whole row is highlighted (2) only the arrow is highlightedbut the problem is with the second point:when we want to highlight arrow,the whole row is still highlighted我的任务 Figure boundary conditions & guidance it doesn’t mean that while highlighting the arrow, we cannot highlight the whole

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    编程小知识之时间显示

    year display, e.g. 2018(y|Y)(y|Y)(y|Y)* : short year display, e.g. 18M : whole month display, e.g. 5MMM * : whole month display with leading zero(if necessary), e.g. 05d|D : whole day display, e.g. 9(d|D)( d|D)(d|D)* : whole day display with leading zero(if necessary), e.g. 09h|H : whole hour display, e.g. 8(h|H)(h|H)(hH)* : whole hour display with leading zero(if necessary), e.g. 08m : whole minute display , e.g. 6mmm* : whole minute display with leading zero(if necessary), e.g. 06s|S : whole second display

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    Pytorch-Transformers 1.0发布,支持六个预训练框架,含27个预训练模型

    -layer, 1024-hidden, 16-heads, 340M parameters Trained on lower-cased English text using Whole-Word-Masking (see details)bert-large-cased-whole-word-masking24-layer, 1024-hidden, 16-heads, 340M parameters Trained on cased English text using Whole-Word-Masking (see details)bert-large-uncased-whole-word-masking-finetuned-squad24 -layer, 1024-hidden, 16-heads, 340M parameters The bert-large-uncased-whole-word-masking model fine-tuned -layer, 1024-hidden, 16-heads, 340M parameters The bert-large-cased-whole-word-maskingmodel fine-tuned

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    Linux-Shell脚本

    binbashecho Total parameter number is ==> $#echo Your whole parameter is ==> $@shiftecho Total parameter number is ==> $#echo Yore whole paramter is ==> $@shiftecho Total parameter number is ==> $#echo Yore whole paramter is ==> $@shiftecho Total parameter number is ==> $#echo Yore whole paramter is ==> $@ fiveTotal parameter number is ==> 3Yore whole paramter is ==> three four fiveTotal parameter number is ==> 2Yore whole paramter is ==> four five3.

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    CMake和静态库顺序

    附3:gcc链接参数--whole-archive的作用  a.h extern void foo();  a.cpp #includevoid foo(){printf(foon);}  x.cpp  $^ -Wl,-no-whole-archiveliba.a: a.oar cru $@ $^a.o: a.cppg++ -g -c $^clean:rm -f x a.o liba.a liba.so  liba.a -Wl,-no-whole-archiveg++ -g -o x x.cpp liba.sotmpcc6UYIAF.o: In function `main:dataldx.cpp:5: 这个时候,可以启用链接参数“--whole-archive”来告诉链接器,将后面库中所有符号都链接进来,参数“-no-whole-archive”则是重置,以避免后面库的所有符号被链接进来。    $^ -Wl,-no-whole-archiveliba.a: a.oar cru $@ $^a.o: a.cppg++ -g -c $^clean:rm -f x a.o liba.a liba.so

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    监督整个有向无环图因果发现(CS LG)

    原文题目:Supervised Whole DAG Causal Discovery原文:We propose to address the task of causal structure learning directions by supervised learning is restricted to learning pairwise relation, and not well suited for whole We propose a novel approach of modeling the whole DAG structure discovery as a supervised learning.

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    EpiDISH-根据甲基化信号值推断样品的细胞成分

    correcting cell-type heterogeneity in Epigenome-Wide Association Studies 》,里面有详细介绍这4个参考数据集如何制作的two whole data(package = EpiDISH)Data sets in package ‘EpiDISH’: DummyBeta.m Dummy beta value matrixLiuDataSub.m Whole blood example beta value matrixcentBloodSub.m Whole blood reference of 188 tsDHS-DMCs and 7 blood cell subtypes centDHSbloodDMC.m Whole blood reference of 333 tsDHS-DMCs and 7 blood cell subtypescentEpiFibFatIC.m

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    读 Java Arrays 源码 笔记

    下面简单介绍一下它各个功能的实现:排序----这里的排序实现有两种一种是为基本类型数组设计的,它的对外接口有两种,如下:whole arraypublic static void sort(primitive 它提供的接口如下: whole array, default asecpublic static void sort(Object a, int fromIndex, int toIndex);还有带泛型参数的接口 whole array with comparatorpublic static void sort(T[] a, Comparator

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    读 Java Arrays 源码 笔记

    下面简单介绍一下它各个功能的实现:排序这里的排序实现有两种一种是为基本类型数组设计的,它的对外接口有两种,如下:whole arraypublic static void sort(primitive 它提供的接口如下: whole array, default asecpublic static void sort(Object a, int fromIndex, int toIndex); 还有带泛型参数的接口 whole array with comparatorpublic static void sort(T[] a, Comparator

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    HDUOJ--------(1198)Farm Irrigation

    has a map of his farm, which is an array of marks denoting the distribution of water pipes over the whole If water flow crosses one square, the whole farm land in this square is irrigated and will have a good Now Benny wants to know at least how many wellsprings should be found to have the whole farm land irrigated

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    leetcode-581-Shortest Unsorted Continuous Subarray

    need to find one continuous subarray that if you only sort this subarray in ascending order, then the whole its length.Example 1:Input: Output: 5Explanation: You need to sort in ascending order to make the whole

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    leetcode-581-Shortest Unsorted Continuous Subarray

    need to find one continuous subarray that if you only sort this subarray in ascending order, then the whole its length.Example 1:Input: Output: 5Explanation: You need to sort in ascending order to make the whole

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    transformer预训练模型

    在小写英语文本上使用Whole-Word-Masking训练(见细节:https:github.comgoogle-researchbert#bert).bert-large-cased-whole-word-masking24 在区分大小写的英语文本上使用Whole-Word-Masking训练(见细节:https:github.comgoogle-researchbert#bert).bert-large-uncased-whole-word-masking-finetuned-squad24 ).bert-large-cased-whole-word-masking-finetuned-squad24个层,1024个隐藏节点,16个heads,340M参数量。 使用Whole-Word-Masking在日语上的训练,文字用MeCab和WordPiece来标记。 使用Whole-Word-Masking在日语上字符级的训练。

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