Yan Jin jyfeather

	Precentral_L	Precentral_R	Frontal_Sup_L	Frontal_Sup_R	Frontal_Sup_Orb_L	Frontal_Sup_Orb_R	Frontal_Mid_L	Frontal_Mid_R	Frontal_Mid_Orb_L	Frontal_Mid_Orb_R	Frontal_Inf_Oper_L	Frontal_Inf_Oper_R	Frontal_Inf_Tri_L	Frontal_Inf_Tri_R	Frontal_Inf_Orb_L	Frontal_Inf_Orb_R	Rolandic_Oper_L	Rolandic_Oper_R	Supp_Motor_Area_L	Supp_Motor_Area_R	Olfactory_L	Olfactory_R	Frontal_Sup_Medial_L	Frontal_Sup_Medial_R	Frontal_Mid_Orb_L2	Frontal_Mid_Orb_R2	Rectus_L	Rectus_R	Insula_L	Insula_R	Cingulum_Ant_L	Cingulum_Ant_R	Cingulum_Mid_L	Cingulum_Mid_R	Cingulum_Post_L	Cingulum_Post_R	Hippocampus_L	Hippocampus_R	ParaHippocampal_L	ParaHippocampal_R	Amygdala_L	Amygdala_R	Calcarine_L	Calcarine_R	Cuneus_L	Cuneus_R	Lingual_L	Lingual_R	Occipital_Sup_L	Occipital_Sup_R	Occipital_Mid_L	Occipital_Mid_R	Occipital_Inf_L	Occipital_Inf_R	Fusiform_L	Fusiform_R	Postcentral_L	Postcentral_R	Parietal_Sup_L	Parietal_Sup_R	Parietal_Inf_L

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slide 1

Now I will talk about the current progress of our project.

slide 2

These are two objectives of this project. First, we applied SICE method on new dataset, I call it NACC dataset, because it is from prof. Zhou, SICE is used to construct brain connectivity network based on resting fMRI data. After network is built, we compute several network statistics and do the reliability and reproducibility analysis, and compare the result with threshold method, to see if there is advantage for SICE in terms of reliability and reproducibility.

However, this work looks not so nice since we just applied existing methods and made a comparison, we want to improve more, especially in methodology part. And I have another question, what could be our target journal to submit? I will lease these in the end.

slide 3

This is a big picture of brain network analysis in general. To guide our study, we follow this flow chart to do the analysis. There are five components in this big picture, dataset, ..., connection

slide 1

Good morning, my name is Yan, and this is my teammate Juan. We will talk about this paper, name is emerging market characteristics and supply network adjustments in internationalizing food supply chains.

slide 2

This is the outline. First is the introduction to the backgrounds of the problem, then is their framwork to show how they consider this problem. And then Juan will talk the analysis and conclusion parts.

slide 3

The big background is globalization in food supply network. Globalization makes international food supplier have more oppotunities, for example, Starbucks now is very welcome in China, even Chinese people do not always drink coffee. However, Globalization also makes the design of food supply network more complicated, because some environments in developed countries are different from in developing counties, like the availability of infrastructure and resource.

In this paper, they use EM (emerging markets) to represent developing countries, like BRIC economies, so which

slide 1

Good morning, everyone. My name is Yan Jin, I am a phd student from industrial engineering department at university of washington. Today I will introduce our project, we call it insight project. My advisor, Dr. Shuai Huang and Dr. Guan Wang from LinkdedIn, and I are working on insight project together.

slide 2

The insight project is web-based, this is a screen shot of it. It is from a old version, because the current version is under development, I could not get screenshot of newer one.

slide 3

This is the outline for today. First, I will talk about what this project can do, and then how to do this work, so basically it has two main functionalities, one is from given dataset to answers that users might be interested in, and the other is from natural language questions that users ask to answers, and some tenical details in the end.

slide 4

% Finding the minimum of a submodular function using Wolfe's min norm point
% algorithm [Fujishige '91]
% Implementation by Andreas Krause ([email protected])
%
% function A = sfo_min_norm_point(F,V, opt)
% F: Submodular function
% V: index set
% opt (optional): option struct of parameters, referencing:
%

	# corrplot

	The R package corrplot package is an elegant visualization tool for correlation matrix and confidence interval. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc.


	## Installation
	You can install the stable version on [CRAN](http://cran.r-project.org/package=corrplot):

	```r
	install.packages('corrplot', dependencies = TRUE)

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	696c 652c 2050 6c61 7466 6f72 6d3a 204d
	4143 4936 342c 2043 7265 6174 6564 206f
	6e3a 2053 6174 204f 6374 2032 3420 3133
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	2020 2020 2020 2020 2020 2020 2020 2020
	2020 2020 2020 2020 2020 2020 2020 2020
	2020 2020 0000 0000 0000 0000 0001 494d
	0f00 0000 ba00 0000 789c ed57 6d0a 8020
	0c9d 44d1 affe 7893 eed6 1d3a 52b7 3325

	Our project is prognostics of Alzheimer's Disease using multimodality data. The purpose is that we want to predict when the AD will happen to the subject, and multimodality data means that we use different data sources including fMRI, PET-scan, demographcis and etc., some of them are static and some of them are time-series.

	Thus the input of our project is like this, X matrix. Here X is a spatial temporal matrix. We have P features, and for each subject, each feature is measured at four different time points, baseline, after 12 month, after 24 month, and after 36 month. And we use a linear model to predict the time of AD onset, y, which is our output. The parameter W is in matrix form, since it is multiplied by the spatial temporal data of each subject.

	This is our formulation. The first two l2 norm terms are to measure the goodness of fit, especially the second term, it is for censored samples, we prefer that the predicted y should at least be greater than the given y. And the penalty term, nuclear norm i