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https://zh.wikipedia.org/wiki/%E6%96%87%E5%8C%96%E8%B3%87%E6%9C%AC

https://www.researchgate.net/publication/233404014_The_Influence_of_Habitus_in_the_Relationship_between_Cultural_Capital_and_Academic_Achievement

https://github.com/abulbasar/machine-learning


Hands-On Machine Learning with Scikit-Learn and TensorFlow
Concepts, Tools, and Techniques to Build Intelligent Systems

http://shop.oreilly.com/product/0636920052289.do
https://github.com/ageron/handson-ml

install.packages("BiocManager")
install.packages("devtools")
devtools::install_github("stephenturner/annotables")
BiocManager::install("SummarizedExperiment")
BiocManager::install("DESeq2")a
BiocManager::install("pathview")
BiocManager::install("gage")
BiocManager::install("genefilter")
BiocManager::install("AnnotationDbi")
BiocManager::install("org.Hs.eg.db")


library("tximport")
library("readr")
library("tximportData")
dir <- system.file("extdata", package="tximportData")
tx2gene <- read_csv(file.path(dir, "tx2gene.gencode.v27.csv"))
curpath <- "I:/06 Salmon/SRP068976-HCC"
setwd(curpath)
files = c("SRR3129887.sf","SRR3129888.sf","SRR3129889.sf","SRR3129890.sf","SRR3129891.sf","SRR3129892.sf", "SRR3129893.sf", "SRR3129894.sf", "SRR3129895.sf",
            "SRR3129913.sf","SRR3129921.sf", "SRR3129922.sf", "SRR3129923.sf")
cond = c("A","A","A","A","A","A","A","A", "B","B","B","B","B")
txi <- tximport(files, type="salmon", tx2gene=tx2gene)
samples <- read.table("samples.txt", header=TRUE)
samples$condition <-cond


library("DESeq2")
dds <- DESeqDataSetFromTximport(txi,
                                   colData = samples,
                                   design = ~ condition)
dds <- DESeq(dds)
res <- results(dds)
summary(res)

DESeq2::plotDispEsts(dds, main="Dispersion Estimates")
plotMA(res, main="Differentially Expressed Genes ", ylim=c(-2,2))

library("annotables")
library("AnnotationDbi")
library("org.Hs.eg.db")
eids=gsub("\\..*","",row.names(res))
res$symbol = mapIds(org.Hs.eg.db,
                     keys=eids,
                     column="SYMBOL",
                     keytype="ENSEMBL",
                     multiVals="first")
res$entrez = mapIds(org.Hs.eg.db,
                     keys=eids,
                     column="ENTREZID",
                     keytype="ENSEMBL",
                     multiVals="first")
res$name =   mapIds(org.Hs.eg.db,
                     keys=eids,
                     column="GENENAME",
                     keytype="ENSEMBL",
                     multiVals="first")
write.csv(as.data.frame(res),file="SRP068976-HCC-de.csv")

The 5th IEEE International Conference on Big Data Intelligence and Computing

Kaohsiung, Taiwan, Nov. 18-21, 2019

http://www.cs.ccu.edu.tw/~conference/datacom2019/

Research Article (regular track):

Paper Submission

July 20, 2019

Author Notification

August 31, 2019



Poster/Special Session:

Paper Submission

September 10, 2019

Author Notification

September 26, 2019



Registration Due:

October 10, 2019



Camera ready submission:

October 20, 2019

https://cis.ieee.org/images/files/Publications/TETCI/SI15_CFP_ALCI.pdf

CALL FOR PAPERS
IEEE Transactions on Emerging Topics in Computational Intelligence
Special Issue on Adversarial Learning in Computational Intelligence

International Journal of Computer Vision Special Issue on
Generative Adversarial Networks for Computer Vision

Guest Editors

 

Jun-Yan Zhu, Massachusetts Institute of Technology
Hongsheng Li, The Chinese University of Hong Kong
Eli Shechtman, Adobe Research
Ming-Yu Liu, NVIDIA Research
Jan Kautz, NVIDIA Research
Antonio Torralba, Massachusetts Institute of Technology

Scope

 

Generative Adversarial Networks (GANs) have been at the forefront of research on generative models in the past few years. GANs can approximate real data distribution and synthesize realistic data samples. The concept of GANs is not limited to generating samples from certain data distributions but also has inspired many other research trends, including image generation and editing, feature learning, visual domain adaptation, data generation and augmentation for visual recognition, and many other practical applications, often leading to state of the art results. While GANs have achieved substantial progress for various computer vision applications, many issues remain to be solved and new research problems emerge. For example, what are the appropriate network structures and objective functions for generating visual data (e.g., images, videos, 3D)? What are the proper metrics for evaluating deep generative models? How can we improve the photorealism and resolution of the synthesized data samples? How can the generated data help solve other computer vision tasks?


This special issue provides a significant collective contribution to this emerging field of study. Specifically, we aim to solicit original contributions that include the following three areas:

Theoretical analysis and foundations: Authors are invited to submit manuscripts on the theoretical considerations of GANs and its variants such as the convergence and the limitations of models.
Novel formulations and training methods: We would like to solicit submissions on new network architectures, robust objective functions, and better training procedures that can improve the quality, resolution, and training stability of GANs-based models.
New computer vision applications: We welcome new work that explores GANs-based approaches for computer vision applications. We encourage original research in these fields to discuss how they adopt adversarial learning to individual computer vision applications. Besides, we also encourage submissions on solving cross-disciplinary research problems through adversarial learning, such as vision and language as well as robotics and vision

http://people.csail.mit.edu/junyanz/ijcvgans.html

30 September 2019 - Submission deadline
31 December 2019 - First decision notification
28 February 2020 - Revised version deadline
30 April 2020 - Final decision notification
July 2020 - Publication
https://www.journals.elsevier.com/neural-networks/call-for-papers/special-issue-on-deep-neural-network-representation

https://www.ijcai19.org/

Abstract submission deadline: February 19, 2019 (11:59PM UTC-12)
Paper submission deadline: February 25, 2019 (11:59PM UTC-12)
Rebuttal period: April 15, 0:00 UTC-12 - April 20 23:59 UTC-12
Paper notification: May 9, 23:59 UTC -12

https://issuu.com/

Ubuntu 16.04 安裝 TensorFlow GPU GTX 1060

程式碼: [Select]

sudo dpkg -i cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb
sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub
sudo apt-get update

verify CUDA installation in 16.04

程式碼: [Select]

nvidia-smi
nvcc --version