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[慶賀]恭喜亞大獲《泰晤士報》亞洲最佳大學排名第83名,國內排名第十名-20170201

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1  Top Category / E-Learn / Cultural Capital and Academic Achievement 於: 七月 18, 2019, 01:30:15 am
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

2  Top Category / GitHub / Machine Learning 於: 七月 18, 2019, 01:16:49 am
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
3  105學年(下)課程2017Spring / R packages / Salmon+DeSeq2 於: 六月 24, 2019, 01:13:59 am
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")
4  學術活動 / 研討會資訊 / IEEE DataCom 2019-20191118 於: 六月 11, 2019, 11:34:32 pm
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
5  學術活動 / 期刊資訊 / Special Issue on Adversarial Learning-20190515 於: 五月 12, 2019, 01:43:58 am
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
6  學術活動 / 期刊資訊 / Special Issue on Generative Adversarial Networks-20190515 於: 五月 12, 2019, 01:41:47 am
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
7  學術活動 / 期刊資訊 / Special Issue on Deep Neural Network-20190930 於: 五月 12, 2019, 01:39:24 am
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
8  學術活動 / 研討會資訊 / IJCAI2019, the 28th Int. Joint Conference on Artificial Intelligence-20190810 於: 五月 12, 2019, 01:37:33 am
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


9  107學年(下)課程 2019 Spring / 107-2 電子書標準及製作 Ebooks / issuu 於: 三月 07, 2019, 12:41:59 am
https://issuu.com/
10  程式語言摘要 / Cuda+TF / CUDA installation in 16.04 於: 二月 13, 2019, 02:00:28 pm
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
11  學術活動 / 研討會資訊 / IC3-The Second International Cognitive Cities Conference-20190903 於: 二月 12, 2019, 11:34:38 pm
THE SECOND INTERNATIONAL COGNITIVE CITIES CONFERENCE (IC3)
SEPTEMBER 3-6, 2019, KYOTO, JAPAN.



http://iscie.org/ic3/

Paper submission due: May 9, 2019

Notification of acceptance: June 21, 2019

Registration due: July 20, 2019

Final manuscript due: August 1, 2019
12  學術活動 / 前端設計 / HTTPS 於: 二月 05, 2019, 12:15:45 am
https://support.google.com/domains/answer/7630973?hl=zh-Hant
https://github.com/rmbolger/Posh-ACME
13  107學年(下)課程 2019 Spring / 107-2 資料科學 Data Science / [DB Tools] 於: 一月 27, 2019, 01:46:21 am
https://sqlitebrowser.org/
14  學術活動 / 研討會資訊 / IETAC2019-資訊教育與科技應用研討會 於: 一月 27, 2019, 01:14:56 am
會議重要日期規劃
研討會與專題競賽投稿說明:
  • 論文投稿方式採線上投稿,投稿網址及論文格式說明請參閱http://ietac.hust.edu.tw
    論文徵稿開始日期:108年01月20日
    論文截稿日期:108年03月18日
    論文接受通知:108年04月12日
    論文定稿截止日期:108年04月19日

會議時間與舉辦地點
會議時間:中華民國108年5月17日(星期五)
會議地點:中臺科技大學 勤學樓 B1 國際會議廳
聯絡人:中臺科技大學資訊管理系 謝嘉芳小姐
電話:(04)22391647轉7701
E-mail:106995@ctust.edu.tw
15  107學年(下)課程 2019 Spring / 107-2 機器學習 Machine Learning / Azure Machine Learning Studio 於: 一月 14, 2019, 02:02:23 pm
https://azure.microsoft.com/zh-tw/services/machine-learning-studio/
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