TRA选择的是2号预测器，也就是Loss最小的那个。...问题3，最优运输规则（Optimal Transport, OT）到底对于TRA有没有帮助?...问题4，TRA模型对于Predictor的数量（也就是假设的市场状态的数量）是否敏感？...usp=sharing 3、将数据放入以下文件夹：/qlib/examples/benchmarks/TRA/data 4、修改/qlib/examples/benchmarks/TRA/example.py...内的代码，图中红圈部分改为config_alstm_tra_init，然后运行文件。
二 VDJ数据处理 2.1 VDJ数据合并 首先将上篇推文单细胞免疫组库VDJ| 从零开始scRepertoire分析，解决真实场景中可能的问题中提到的所有VDJ文件 合并在一起，可以linux中cat...Cell_name vdj % dplyr::filter(high_confidence =="true" & chain %in% c("TRA...，TRB 前面也提到了clone一般是结合TRA 和 TRB的cdr3序列 ，因此这里先拆分TRA 和 TRB ，以备后面合并使用 vdj_a % filter(chain =="TRA...= max(reads), umis=max(umis) ) vdj_b <- data.frame(inner_join(vdj_b, test)) dim(vdj_b) 按照Cell_name合并TRA...$cdr3.TRB, sep="_") subT@meta.data = subset(subT@meta.data, productive.TRA == "true" & productive.TRB
FCFL与其他联邦学习可穿戴系统的主要区别是： (1) FCFL采用TRA，在客户选择过程中消除了网络带宽限制，从而实现了公平训练。...FCFL的核心是ThrowRightAway (TRA) 和Movement Aware Federated Learning (MAFL)。...TRA 最近有工作表明，机器学习算法可以容忍有限的数据丢失（在测试中为10%-35%）。在这项工作的启发下，我们提出TRA算法，允许服务器接受网络带宽达不到要求的客户端数据。...FCFL利用ThrowRightAway (TRA) 忽略并替换一些丢失的数据，并使用轻量级的恢复策略避免多次重传。同时，TRA使得无论网络条件如何，都能实现完全公平的客户端选择。...我们还设计并实现了一个运动监测系统，由智能手表、智能手机和Linux服务器组成，智能手表上的活动识别模型与应用一起进行训练，能够达到超过97%的准确率。
human.hg19.excl.tsv genome=/home/jianmingzeng/reference/genome/hg19/hg19.fa ~/biosoft/delly/delly_v0.7.6_linux_x86..._64bit call -t DEL -g $genome -o DEL.bcf -x $excl $bam ~/biosoft/delly/delly_v0.7.6_linux_x86_64bit call...-t DUP -g $genome -o DUP.bcf -x $excl $bam ~/biosoft/delly/delly_v0.7.6_linux_x86_64bit call -t INV...-g $genome -o INV.bcf -x $excl $bam ~/biosoft/delly/delly_v0.7.6_linux_x86_64bit call -t TRA -g $genome...-o TRA.bcf -x $excl $bam ~/biosoft/delly/delly_v0.7.6_linux_x86_64bit call -t INS -g $genome -o INS.bcf
= pd.DataFrame(ptCom) distancemat_tra['distance'] = distance_tra distancemat_tra = distancemat_tra.pivot...(pt1)] = 0 distancemat_tra = distancemat_tra.fillna(0) distancemat_tra = distancemat_tra.loc[list...(data.keys()),list(data.keys())] distancemat_tra = distancemat_tra+distancemat_tra.T distancemat_tra...= (distancemat_tra-distancemat_tra.min().min())/(distancemat_tra.max().max()-distancemat_tra.min().min...()) distancemat = w[0]*distancemat_tra return distancemat distancemat = DistanceMat(data,w=
// what the fuck is English // //- //-... //- // 他妈的是英语 // // //
self.postorder(root.left) self.postorder(root.right) self.post_res.append(root.val) tra...=traveral() tra.preorder(root) tra.inorder(root) tra.postorder(root) print(tra.pre_res) print(tra.in_res...) print(tra.post_res) 输出： 二、非递归版本 class non_recursive: def preorder(self,root): stack=[]
---- 服务端代码与之前一致 id.txt TRA_1_index TRA_2_index TRA_3_index TRA_4_index TRA_5_index ---- 压测端 package main...=TRA_2_index|0 B|0 B|| 2022-06-07 16:55:49|20.918µs|200|GET|/test/TRA_3_index|::1|name=TRA_3_index|0...B|0 B|| 2022-06-07 16:55:49|21.048µs|200|GET|/test/TRA_4_index|::1|name=TRA_4_index|0 B|0 B|| 2022-06...TRA_2_index|::1|name=TRA_2_index|0 B|0 B|| 2022-06-07 16:55:49|29.287µs|200|GET|/test/TRA_3_index|::1...|name=TRA_3_index|0 B|0 B|| 2022-06-07 16:55:49|45.1µs|200|GET|/test/TRA_4_index|::1|name=TRA_4_index
tr[6] " #en= "/html/body/div[3]/div/div[1]/div[2]/div/table/tbody/tr[6]/td[1]/strong" #tra...//td[@class="span2"]/strong/text()')) tra = get_text(tr.xpath('....//td[@class="span10"]/text()')) print(en, tra) if en: item[en] = tra
_1_index|::1|name=TRA_1_index|0 B|0 B||2022-06-07 16:55:49|20.895µs|200|GET|/test/TRA_2_index|::1|name...=TRA_2_index|0 B|0 B||2022-06-07 16:55:49|20.918µs|200|GET|/test/TRA_3_index|::1|name=TRA_3_index|0 B...|0 B||2022-06-07 16:55:49|21.048µs|200|GET|/test/TRA_4_index|::1|name=TRA_4_index|0 B|0 B||2022-06-07...|200|GET|/test/TRA_1_index|::1|name=TRA_1_index|0 B|0 B||2022-06-07 16:55:49|49.178µs|200|GET|/test/TRA...=TRA_3_index|0 B|0 B||2022-06-07 16:55:49|45.1µs|200|GET|/test/TRA_4_index|::1|name=TRA_4_index|0 B|0
public void saveTest() { Session session = HibernateUtil.openSession(); Transaction tra...add(s2); c3.getStudents().add(s3); Session session = HibernateUtil.openSession(); Transaction tra...= session.beginTransaction(); session.save(c1); session.save(c2); session.save(c3); tra.commit...给上面王五添加数学课 @Test public void manytomany(){ Session session = HibernateUtil.openSession(); Transaction tra...给王五删除数学课 @Test public void manytomany(){ Session session = HibernateUtil.openSession(); Transaction tra
tra_img_name_list = [] fg_list_name = 'image.txt' with open(fg_list_name, 'r') as reader: path_list...(line) tra_lbl_name_list = [] for img_path in tra_img_name_list: # 获取所有mask文件地址 img_name...= img_path.split(os.sep)[-1] aaa = img_name.split(".")[0] tra_lbl_name_list.append('data/' +..., lbl_name_list=tra_lbl_name_list, transform=transforms.Compose([...=tra_lbl_name_list, # transform=transforms.Compose([ # RescaleT(320),
//blog.csdn.net/darkread/article/details/8064493 4.使用批处理脚本探测局域网中存活的主机设备 描述: 开发人员常常需要使用windows系统管理众多的Linux...tra!...call,set tra=%%code:~%tra%,1%% ::echo tra=%tra% ::pause set /a var/=16 ::echo var=%var% set str=%tra%...=%var%%%2 ::echo %var% ::echo %tra% ::pause call,set tra=%%code:~%tra%,1%% ::echo tra=%tra% ::pause set...(set str=%tra%%str%) if %var% geq 2 goto again1 ::echo %var%%str% if %var% neq 0 (set binstr=0b %var
TreeNode buildTree(int[] preorder, int[] inorder) { pi = 0; ii = 0; return tra...(preorder, inorder, null); } private TreeNode tra(int[] po, int[] io, TreeNode root){...ii > io.length - 1) return null; TreeNode cur = new TreeNode(po[pi++]); cur.left = tra...(po, io, cur); ii++; cur.right = tra(po, io, root); return cur; } 106.Construct
stringsAsFactors = FALSE); genepos %>% head() cisDist<-5000 pvOutputThreshold_cis<-0.1 pvOutputThreshold_tra...errorCovariance<-numeric() useModel<-modelLINEAR output_file_name_cis = tempfile(); output_file_name_tra...Matrix_eQTL_main( snps = snps, gene = gene, cvrt = cvrt, output_file_name = output_file_name_tra..., pvOutputThreshold = pvOutputThreshold_tra, useModel = useModel, errorCovariance = errorCovariance...pvalue.hist = "qqplot", min.pv.by.genesnp = FALSE, noFDRsaveMemory = FALSE); unlink(output_file_name_tra
tra_img_name_list = [] fg_list_name = 'image.txt' with open(fg_list_name, 'r') as reader: path_list...reader.readlines() for line in path_list: line = line.replace('\n', '').replace('\\', '/') tra_img_name_list.append...(line) tra_lbl_name_list = [] for img_path in tra_img_name_list: # 获取所有mask文件地址 img_name...= img_path.split(os.sep)[-1] aaa = img_name.split(".")[0] tra_lbl_name_list.append('data/' +...DataLoader salobj_dataset = SalObjDataset( img_name_list=tra_img_name_list, lbl_name_list=tra_lbl_name_list
Session session = HibernateUtil.openSession(); dao.session = session; test(){ Transaction tra...= session.beginTransaction(); dao.insertMoney(); dao.update(); tra.commit();...TestService{ SessionFactory sf = HibernateUtil.getSessionFactory(); test(){ Transaction tra...sf.getCurrenSession().beginTransaction(); dao.insertMoney(); dao.update(); tra.commit
\in \mathbb{R}^{m*n} 3 trace operator if A∈ℝn∗nA \in \mathbb{R}^{n*n},then trA=∑ni=1AiitrA = \sum_{i=...4 trAB=trBAtr AB = tr BA trABC=trCAB=trBCAtr ABC = tr CAB = tr BCA ∇AtrAB=BT\nabla_A trAB=B^T trA...=trATtrA = trA^T ifa∈ℝ,tra=aif a \in \mathbb{R}, tra = a ∇AtrABATC=CAB+CTABT\nabla_A trABA^TC = CAB
\Local\\tesseract5\\tesseract5\\tessdata/" # # $available # [1] "chi_sim" "chi_sim_vert" "chi_tra..." "chi_tra_vert" "eng" "osd" # # $version # [1] "5.0.1" # # $configs # [1] "...109499741 # 官方语言包地址（选择更多）https://tesseract-ocr.github.io/tessdoc/Data-Files # tesseract_download("chi_tra
表示从左边开始延伸并在指定位置结束REF 列 N 相对于[1:800[和]1:800]的位置即为第一个断点1:500相对于第二个断点的位置N 可能是某一个特定序列，这取决于REF列符号表示法的易位（表示的也比较模糊，仅有标签是不够的。所以就有了在INFO列增加相应的标签（CHR2表示第二个断点的染色体，END表示具体位置）表示第二个断点的位置和方向。...所以，两种表示方法间的对应关系就有了：BND with CT INFO field1 500 . N N[1:800[1 500 . N ......N ... CHR2=1;END=800;CT='5to3'1 500 . N [1:800[N1 500 . N ......N ...
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