Facts are identifiable pieces of data, collected by agents or loaded when the server starts. A source is a collection of facts. Rules are boundaries to ensure specific traits cannot be used.
| Origin | Fact Trait | Value | Score | ||||
|---|---|---|---|---|---|---|---|
| No facts; click 'Add Fact' to get started. | |||||||
| Order | Match | Fact Trait | Action | ||||
|---|---|---|---|---|---|---|---|
| No rules; click 'Add Rule' to get started. | |||||||
|
|
|||||||
|
|
| Source | Edge | Target | ||
|---|---|---|---|---|
| No relationships; click 'Add Relationship' to get started. | ||||


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Dr. Norman Borlaug
Here's a time line of Dr. Borlaug's life:
- 1914 - Born in Cresco, Iowa
- 1933 - Leaves his family's farm to attend the University of Minnesota, thanks to a Depression era program known as the "National Youth Administration"
- 1935 - Has to stop school and save up more money. Works in the Civilian Conservation Corps, helping starving Americans. "I saw how food changed them", he said. "All of this left scars on me."
This is a refference to the Doctor here.


package main import "fmt" func main() { fmt.Println("Hello, world!") }
"Don't communicate by sharing memory. Share memory by communicating."
Return variables you declare with a name in function signatures will be available in function body
func fooBar(numbers []float64) (processedNumbers []float64){ // Do some stuff to numbers // fill the processedNumbers array // but no need to declare it, b/c it's in the signature return processedNumbers }
func caller(){ someNums := []float64{1.2, 3.14, 2.71} result, err := calledFooBar(someNums) if err != nil { // do some error stuff } for _, x := range result { fmt.Println("The numbers are:", x) } } func calledFooBar(numbers []float64) (ezNum int, err error){ // Do some stuff to numbers return ezNum, err }
Go includes a great deal of documentation, all of which you can see from the documentation server that comes with it:
godoc -http:=8080


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- Collaborative Web Browsing - Present and Future
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- Scalable Realtime Chat (Text, Audio, Video) - using XMPP
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- Video streaming - Video Streaming Analytics Using Jwplayer and Videojs
- Behavioural Testing Ruby/Rails Apps @ Scale with Rspec and Cucumber
- TimeZone - Handling requests in client's time zone in a rails app
- JavaScript - Pro JavaScript Tips
- Ruby/Rails - Capistrano 3 - SVN support
- JavaScript - ring.js (Multiple inheritance in js)
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- Handling requests in browser time zone in a rails app
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- JavaScript - Templating with embedded js (no more ugly HTML inside js)
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- Behavioural Testing Ruby/Rails Apps @ Scale with Rspec and Cucumber
- Ruby/Rails - Capistrano 3 - SVN support
- JavaScript - ring.js (Multiple inheritance in js)
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- Ruby - Ruby Beyond Rails - Libraries and Frameworks
- Ejabberd - Issues faced and tips
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Steps
问题描述 :
One steps through integer points of the straight line. The length of a step must be nonnegative and can be by one bigger than, equal to, or by one smaller than the length of the previous step.
What is the minimum number of steps in order to get from x to y? The length of the first and the last step must be 1.
What is the minimum number of steps in order to get from x to y? The length of the first and the last step must be 1.
输入:
Input consists of a line containing n, the number of test cases.
输出:
For each test case, a line follows with two integers: 0 <= x <= y < 2^31. For each test case, print a line giving the minimum number of steps to get from x to y.
样例输入:
3 45 48 45 49 45 50
样例输出:
3 3 4
解题代码:
//* @author<EMAIL_ADDRESS>import java.util.Scanner; public class Main { public static void main(String[] args) { Scanner in=new Scanner(System.in); int a=in.nextInt(); for(int i=0;i< a;i++) { long x=in.nextLong(); x=in.nextLong()-x; long b=(long)Math.sqrt(x); long ans=-1; if(b==0) ans=0; else if(b*b==x)ans=2*b-1; else if(x<=b*b+b)ans=2*b; else ans=2*b+1; System.out.println(ans); } } }
(adsbygoogle = window.adsbygoogle || []).push({});
其实就是DFS搜索，数据量较小，穷举了所有情况
理解错了。。。。。谢谢楼主~~
这个系列很不错
妈呀！这个Dijkstra模板好长OwQ！
多谢指出，已改正
信息不全
#pragma pack(2) 这个用法太坑爹
Good task for the group. Hold it up for every yeara??s winner. This is a excellent oppotunity for a lot more enhancement. Indeed, obtaining far better and much better is constantly the crucial. Just like my pal suggests on the truth about ab muscles, he just keeps obtaining much better.
题目和代码都不一样好吗！！！搞错了
很好的方法，学习了
原来的主机不行，目前更换到了国内主机
a是根先忽略掉，递归子树。剩下前缀bejkcfghid和后缀jkebfghicd，分拆的原则的是每个子树前缀和后缀的节点个数是一样的，根节点出现在前缀的第一个，后缀的最后一个。根节点b出现后缀的第四个位置，则第一部分为四个节点，前缀bejk，后缀jkeb，剩下的c出现在后缀的倒数第2个，就划分为cfghi和 fghic，第3部分就为c、c
[…] 答案：http://www.acmerblog.com/interview-2-1549/ […]


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What I think when reading a job posting for a "rockstar" dev. https://www.youtube.com/watch?v=PVGbnPzQ0do
10/3/2014, 9:29:39 AM
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What I think when reading a job posting for a "rockstar" dev. https://www.youtube.com/watch?v=PVGbnPzQ0do
10/3/2014, 9:29:39 AM
Favs: 0
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This page displays measures for the network Semantic Network [Threat Groups x Threat Groups].
Network images have nodes colored by group and sized by betweenness centrality.
Threat Groups-Level Measures
These measures were run on the specified network, and produced a vector of values, one value for each Threat Groups node.
Input network: Semantic Network [Threat Groups x Threat Groups]
Measure Min Mean Max Std.Dev Centrality, Authority 0 0.018 0.137 0.042 Centrality, Authority [Unscaled] 0 0.013 0.097 0.029 Centrality, Betweenness 0 0.001 0.027 0.004 Centrality, Betweenness [Unscaled] 0 1.143 31 5.018 Centrality, Bonacich Power 0 0.006 0.043 0.012 Centrality, Bonacich Power [Unscaled] 0 0.144 1 0.282 Centrality, Closeness 0.007 0.007 0.009 7.181e-04 Centrality, Closeness [Unscaled] 1.417e-04 1.505e-04 1.785e-04 1.496e-05 Centrality, Contribution 0 0.023 0.216 0.043 Centrality, Contribution [Unscaled] 0 0.016 0.153 0.031 Centrality, Eccentricity 0 1.327 6 2.054 Centrality, Ego Betweenness 0 0.045 1 0.182 Centrality, Eigenvector 0 0.026 0.137 0.041 Centrality, Eigenvector [Unscaled] 0 0.019 0.097 0.029 Centrality, Exponential Rank 0.929 0.940 1 0.018 Centrality, Exponential Rank [Unscaled] 0.020 0.020 0.022 4.008e-04 Centrality, Hub 0 0.018 0.137 0.042 Centrality, Hub [Unscaled] 0 0.013 0.097 0.029 Centrality, In-Closeness 0.002 0.002 0.002 3.751e-05 Centrality, In-Closeness [Unscaled] 3.543e-05 3.590e-05 3.736e-05 7.815e-07 Centrality, In-Degree 0 0.008 0.049 0.013 Centrality, In-Degree [Unscaled] 0 1.143 7 1.818 Centrality, In-Inverse Closeness 0 0.032 0.172 0.052 Centrality, In-Inverse Closeness [Unscaled] 0 1.535 8.250 2.514 Centrality, Inverse Closeness 0 0.032 0.172 0.052 Centrality, Inverse Closeness [Unscaled] 0 1.535 8.250 2.514 Centrality, Katz 0 0.044 0.303 0.086 Centrality, Katz [Unscaled] 0 0.144 1 0.282 Centrality, Out-Degree 0 0.008 0.049 0.013 Centrality, Out-Degree [Unscaled] 0 1.143 7 1.818 Centrality, PageRank 6.463e-04 0.020 0.079 0.024 Centrality, Radiality 0 0.051 0.208 0.084 Centrality, Radiality [Unscaled] 0 354.449 1,455 590.119 Centrality, Total-Degree 0 0.008 0.049 0.013 Centrality, Total-Degree [Unscaled] 0 1.143 7 1.818 Overall Top-Ranked Threat Groups
This chart shows the Threat Groups nodes that are top-ranked overall using the node-level measures listed below. The value shown for a node is the average of its measure values, each of which is first converted into a z-score. The final scores are then normalized to be between zero and one.
There are 49 nodes in the nodeset.
Overall Score Measure List
Centrality, Authority [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Betweenness [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Bonacich Power [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Closeness [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Contribution [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Eccentricity [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Ego Betweenness [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Eigenvector [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Exponential Rank [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Hub [Semantic Network [Threat Groups x Threat Groups]]
Centrality, In-Closeness [Semantic Network [Threat Groups x Threat Groups]]
Centrality, In-Degree [Semantic Network [Threat Groups x Threat Groups]]
Centrality, In-Inverse Closeness [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Inverse Closeness [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Katz [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Out-Degree [Semantic Network [Threat Groups x Threat Groups]]
Centrality, PageRank [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Radiality [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Total-Degree [Semantic Network [Threat Groups x Threat Groups]]
Centrality, Authority
A node is authority-central to the extent that its in-links are from nodes that have many out-links. Individuals or organizations that act as authorities are receiving information from a wide range of others each of whom sends information to a large number of others.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.137 0.097 2 careto 0.125 0.089 3 energetic_bear 0.125 0.089 4 ghostnet 0.125 0.089 5 ice_fog 0.125 0.089 6 red_october 0.125 0.089 7 sofacy 0.041 0.029 8 ixeshe 0.030 0.021 9 apt_29 0.024 0.017 10 mirage 0.007 0.005 11 taidoor 0.007 0.005 12 _acnmedia 0 0 13 anchor_panda 0 0 14 anunak 0 0 15 aurora_panda 0 0 16 axiom 0 0 17 brisach 0 0 18 covellite 0 0 19 cyber_caliphate_army 0 0 20 darkhotel 0 0 21 deadeye_jackal 0 0 22 denis 0 0 23 dependent 0 0 24 desde 0 0 25 dropping_elephant 0 0 26 equation_group 0 0 27 flying_kitten 0 0 28 hacking_team 0 0 29 kimsuky 0 0 30 leviathan 0 0 31 lotus_panda 0 0 32 madi 0 0 33 martisen 0 0 34 molerats 0 0 35 muddywater 0 0 36 qaeda 0 0 37 sandworm 0 0 38 shabaab 0 0 39 shell_crew 0 0 40 silence_group 0 0 41 silent_chollima 0 0 42 stone_panda 0 0 43 terrorism 0 0 44 terrorist 0 0 45 threat 0 0 46 turla_group 0 0 47 unit_8200 0 0 48 vigilance 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.018 Lower quartile: 0 Max: 0.137 Std.dev: 0.042 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.013 Lower quartile: 0 Max: 0.097 Std.dev: 0.029 Median: 0 Upper quartile: 0 Centrality, Betweenness
The Betweenness Centrality of node v in a network is defined as: across all node pairs that have a shortest path containing v, the percentage that pass through v. When the data is weighted, the higher the weight the more value the link has. Individuals or organizations that are potentially influential are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups. This agent occurs on many of the shortest paths between other agents. The scientific name of this measure is betweenness centrality and it is calculated on agent by agent matrices.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.027 31 2 ixeshe 0.014 16 3 sofacy 0.008 9 4 _acnmedia 0 0 5 anchor_panda 0 0 6 anunak 0 0 7 apt_29 0 0 8 aurora_panda 0 0 9 axiom 0 0 10 brisach 0 0 11 careto 0 0 12 covellite 0 0 13 cyber_caliphate_army 0 0 14 darkhotel 0 0 15 deadeye_jackal 0 0 16 denis 0 0 17 dependent 0 0 18 desde 0 0 19 dropping_elephant 0 0 20 energetic_bear 0 0 21 equation_group 0 0 22 flying_kitten 0 0 23 ghostnet 0 0 24 hacking_team 0 0 25 ice_fog 0 0 26 kimsuky 0 0 27 leviathan 0 0 28 lotus_panda 0 0 29 madi 0 0 30 martisen 0 0 31 mirage 0 0 32 molerats 0 0 33 muddywater 0 0 34 qaeda 0 0 35 red_october 0 0 36 sandworm 0 0 37 shabaab 0 0 38 shell_crew 0 0 39 silence_group 0 0 40 silent_chollima 0 0 41 stone_panda 0 0 42 taidoor 0 0 43 terrorism 0 0 44 terrorist 0 0 45 threat 0 0 46 turla_group 0 0 47 unit_8200 0 0 48 vigilance 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.001 Lower quartile: 0 Max: 0.027 Std.dev: 0.004 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 1.143 Lower quartile: 0 Max: 31 Std.dev: 5.018 Median: 0 Upper quartile: 0 Centrality, Bonacich Power
This computes the centrality of each entity based on the centrality of its neighbors. Beta should be chosen such that its absolute value is less than the reciprocal of the largest eigenvalue of N.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.043 1 2 careto 0.036 0.829 3 energetic_bear 0.036 0.829 4 ghostnet 0.036 0.829 5 ice_fog 0.036 0.829 6 red_october 0.036 0.829 7 sofacy 0.019 0.452 8 apt_29 0.014 0.322 9 ixeshe 0.014 0.315 10 mirage 0.007 0.153 11 taidoor 0.007 0.153 12 covellite 0.002 0.050 13 darkhotel 0.002 0.050 14 deadeye_jackal 0.002 0.050 15 hacking_team 0.002 0.050 16 leviathan 0.002 0.050 17 muddywater 0.002 0.050 18 silence_group 0.002 0.050 19 stone_panda 0.002 0.050 20 unit_8200 0.002 0.050 21 vigilance 0.002 0.050 22 _acnmedia 0 0 23 anchor_panda 0 0 24 anunak 0 0 25 aurora_panda 0 0 26 axiom 0 0 27 brisach 0 0 28 cyber_caliphate_army 0 0 29 denis 0 0 30 dependent 0 0 31 desde 0 0 32 dropping_elephant 0 0 33 equation_group 0 0 34 flying_kitten 0 0 35 kimsuky 0 0 36 lotus_panda 0 0 37 madi 0 0 38 martisen 0 0 39 molerats 0 0 40 qaeda 0 0 41 sandworm 0 0 42 shabaab 0 0 43 shell_crew 0 0 44 silent_chollima 0 0 45 terrorism 0 0 46 terrorist 0 0 47 threat 0 0 48 turla_group 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.006 Lower quartile: 0 Max: 0.043 Std.dev: 0.012 Median: 0 Upper quartile: 0.002
Unscaled value statistics Min: 0 Mean: 0.144 Lower quartile: 0 Max: 1 Std.dev: 0.282 Median: 0 Upper quartile: 0.050 Centrality, Closeness
The closeness of a node to the other nodes in a network (also called out-closeness). Loosely, Closeness is the inverse of the sum of distances in the network from a node to all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.009 1.785e-04 2 careto 0.009 1.784e-04 3 energetic_bear 0.009 1.784e-04 4 ghostnet 0.009 1.784e-04 5 ice_fog 0.009 1.784e-04 6 ixeshe 0.009 1.784e-04 7 red_october 0.009 1.784e-04 8 sofacy 0.009 1.783e-04 9 mirage 0.009 1.781e-04 10 taidoor 0.009 1.781e-04 11 apt_29 0.009 1.775e-04 12 covellite 0.007 1.447e-04 13 darkhotel 0.007 1.447e-04 14 deadeye_jackal 0.007 1.447e-04 15 hacking_team 0.007 1.447e-04 16 leviathan 0.007 1.447e-04 17 muddywater 0.007 1.447e-04 18 silence_group 0.007 1.447e-04 19 stone_panda 0.007 1.447e-04 20 unit_8200 0.007 1.447e-04 21 vigilance 0.007 1.447e-04 22 _acnmedia 0.007 1.417e-04 23 anchor_panda 0.007 1.417e-04 24 anunak 0.007 1.417e-04 25 aurora_panda 0.007 1.417e-04 26 axiom 0.007 1.417e-04 27 brisach 0.007 1.417e-04 28 cyber_caliphate_army 0.007 1.417e-04 29 denis 0.007 1.417e-04 30 dependent 0.007 1.417e-04 31 desde 0.007 1.417e-04 32 dropping_elephant 0.007 1.417e-04 33 equation_group 0.007 1.417e-04 34 flying_kitten 0.007 1.417e-04 35 kimsuky 0.007 1.417e-04 36 lotus_panda 0.007 1.417e-04 37 madi 0.007 1.417e-04 38 martisen 0.007 1.417e-04 39 molerats 0.007 1.417e-04 40 qaeda 0.007 1.417e-04 41 sandworm 0.007 1.417e-04 42 shabaab 0.007 1.417e-04 43 shell_crew 0.007 1.417e-04 44 silent_chollima 0.007 1.417e-04 45 terrorism 0.007 1.417e-04 46 terrorist 0.007 1.417e-04 47 threat 0.007 1.417e-04 48 turla_group 0.007 1.417e-04 49 wing 0.007 1.417e-04
Value statistics Min: 0.007 Mean: 0.007 Lower quartile: 0.007 Max: 0.009 Std.dev: 7.181e-04 Median: 0.007 Upper quartile: 0.007
Unscaled value statistics Min: 1.417e-04 Mean: 1.505e-04 Lower quartile: 1.417e-04 Max: 1.785e-04 Std.dev: 1.496e-05 Median: 1.417e-04 Upper quartile: 1.447e-04 Centrality, Contribution
This computes Eigenvector Centrality on a transformation of the input network. Link values are transformed to be proportional to the dissimilarity of the nodes they connect. The intuition is that a link between two nodes with the same neighbors is not an important link since neither node gains new neighbors by the connection. Specifically, each link is weighted by the inverse of the Jaccard similarity of its nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 sofacy 0.216 0.153 2 apt_29 0.200 0.142 3 comment_crew 0.097 0.069 4 covellite 0.041 0.029 5 darkhotel 0.041 0.029 6 deadeye_jackal 0.041 0.029 7 hacking_team 0.041 0.029 8 leviathan 0.041 0.029 9 muddywater 0.041 0.029 10 silence_group 0.041 0.029 11 stone_panda 0.041 0.029 12 unit_8200 0.041 0.029 13 vigilance 0.041 0.029 14 ixeshe 0.035 0.025 15 careto 0.026 0.018 16 energetic_bear 0.026 0.018 17 ghostnet 0.026 0.018 18 ice_fog 0.026 0.018 19 red_october 0.026 0.018 20 mirage 0.010 0.007 21 taidoor 0.010 0.007 22 _acnmedia 0 0 23 anchor_panda 0 0 24 anunak 0 0 25 aurora_panda 0 0 26 axiom 0 0 27 brisach 0 0 28 cyber_caliphate_army 0 0 29 denis 0 0 30 dependent 0 0 31 desde 0 0 32 dropping_elephant 0 0 33 equation_group 0 0 34 flying_kitten 0 0 35 kimsuky 0 0 36 lotus_panda 0 0 37 madi 0 0 38 martisen 0 0 39 molerats 0 0 40 qaeda 0 0 41 sandworm 0 0 42 shabaab 0 0 43 shell_crew 0 0 44 silent_chollima 0 0 45 terrorism 0 0 46 terrorist 0 0 47 threat 0 0 48 turla_group 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.023 Lower quartile: 0 Max: 0.216 Std.dev: 0.043 Median: 0 Upper quartile: 0.041
Unscaled value statistics Min: 0 Mean: 0.016 Lower quartile: 0 Max: 0.153 Std.dev: 0.031 Median: 0 Upper quartile: 0.029 Centrality, Eccentricity
The Eccentricity Centrality of a node is the normalized inverse of its maximum distance to any other node. It is highest when the distance to all other nodes is small.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value 1 apt_29 6 2 mirage 6 3 taidoor 6 4 careto 5 5 energetic_bear 5 6 ghostnet 5 7 ice_fog 5 8 ixeshe 5 9 red_october 5 10 comment_crew 4 11 sofacy 3 12 covellite 1 13 darkhotel 1 14 deadeye_jackal 1 15 hacking_team 1 16 leviathan 1 17 muddywater 1 18 silence_group 1 19 stone_panda 1 20 unit_8200 1 21 vigilance 1 22 _acnmedia 0 23 anchor_panda 0 24 anunak 0 25 aurora_panda 0 26 axiom 0 27 brisach 0 28 cyber_caliphate_army 0 29 denis 0 30 dependent 0 31 desde 0 32 dropping_elephant 0 33 equation_group 0 34 flying_kitten 0 35 kimsuky 0 36 lotus_panda 0 37 madi 0 38 martisen 0 39 molerats 0 40 qaeda 0 41 sandworm 0 42 shabaab 0 43 shell_crew 0 44 silent_chollima 0 45 terrorism 0 46 terrorist 0 47 threat 0 48 turla_group 0 49 wing 0
Unscaled value statistics Min: 0 Mean: 1.327 Lower quartile: 0 Max: 6 Std.dev: 2.054 Median: 0 Upper quartile: 1 Centrality, Ego Betweenness
The Ego (Network) Betweenness Centrality of node in a network is its betweenness score within its own ego network. The ego network contains the following: the node itself, its immediate neighbor nodes, and all links between them.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value 1 sofacy 1 2 ixeshe 0.667 3 comment_crew 0.524 4 _acnmedia 0 5 anchor_panda 0 6 anunak 0 7 apt_29 0 8 aurora_panda 0 9 axiom 0 10 brisach 0 11 careto 0 12 covellite 0 13 cyber_caliphate_army 0 14 darkhotel 0 15 deadeye_jackal 0 16 denis 0 17 dependent 0 18 desde 0 19 dropping_elephant 0 20 energetic_bear 0 21 equation_group 0 22 flying_kitten 0 23 ghostnet 0 24 hacking_team 0 25 ice_fog 0 26 kimsuky 0 27 leviathan 0 28 lotus_panda 0 29 madi 0 30 martisen 0 31 mirage 0 32 molerats 0 33 muddywater 0 34 qaeda 0 35 red_october 0 36 sandworm 0 37 shabaab 0 38 shell_crew 0 39 silence_group 0 40 silent_chollima 0 41 stone_panda 0 42 taidoor 0 43 terrorism 0 44 terrorist 0 45 threat 0 46 turla_group 0 47 unit_8200 0 48 vigilance 0 49 wing 0
Unscaled value statistics Min: 0 Mean: 0.045 Lower quartile: 0 Max: 1 Std.dev: 0.182 Median: 0 Upper quartile: 0 Centrality, Eigenvector
Leaders of strong cliques are individuals or organizations who are collected to others that are themselves highly connected to each other. In other words, if you have a clique then the individual most connected to others in the clique and other cliques, is the leader of the clique. Individuals or organizations who are connected to many otherwise isolated individuals or organizations will have a much lower score in this measure then those that are connected to groups that have many connections themselves. The scientific name of this measure is eigenvector centrality and it is calculated on agent by agent or organization by organization matrices.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.137 0.097 2 careto 0.125 0.089 3 energetic_bear 0.125 0.089 4 ghostnet 0.125 0.089 5 ice_fog 0.125 0.089 6 red_october 0.125 0.089 7 sofacy 0.041 0.029 8 covellite 0.041 0.029 9 darkhotel 0.041 0.029 10 deadeye_jackal 0.041 0.029 11 hacking_team 0.041 0.029 12 leviathan 0.041 0.029 13 muddywater 0.041 0.029 14 silence_group 0.041 0.029 15 stone_panda 0.041 0.029 16 unit_8200 0.041 0.029 17 vigilance 0.041 0.029 18 ixeshe 0.030 0.021 19 apt_29 0.024 0.017 20 mirage 0.007 0.005 21 taidoor 0.007 0.005 22 _acnmedia 0 0 23 anchor_panda 0 0 24 anunak 0 0 25 aurora_panda 0 0 26 axiom 0 0 27 brisach 0 0 28 cyber_caliphate_army 0 0 29 denis 0 0 30 dependent 0 0 31 desde 0 0 32 dropping_elephant 0 0 33 equation_group 0 0 34 flying_kitten 0 0 35 kimsuky 0 0 36 lotus_panda 0 0 37 madi 0 0 38 martisen 0 0 39 molerats 0 0 40 qaeda 0 0 41 sandworm 0 0 42 shabaab 0 0 43 shell_crew 0 0 44 silent_chollima 0 0 45 terrorism 0 0 46 terrorist 0 0 47 threat 0 0 48 turla_group 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.026 Lower quartile: 0 Max: 0.137 Std.dev: 0.041 Median: 0 Upper quartile: 0.041
Unscaled value statistics Min: 0 Mean: 0.019 Lower quartile: 0 Max: 0.097 Std.dev: 0.029 Median: 0 Upper quartile: 0.029 Centrality, Exponential Rank
Computes the Exponential Ranking Centrality that defines the centrality of a node as its trustworthiness, which is based on the degree to which other nodes trust it. The link values define initial trust between nodes: positive values indicate trust and negative values distrust (negative link values can be used). This measure is similar to eigenvector centrality.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 1 0.022 2 careto 0.980 0.021 3 energetic_bear 0.980 0.021 4 ghostnet 0.980 0.021 5 ice_fog 0.980 0.021 6 red_october 0.980 0.021 7 sofacy 0.968 0.021 8 apt_29 0.958 0.021 9 ixeshe 0.958 0.021 10 mirage 0.948 0.021 11 taidoor 0.948 0.021 12 covellite 0.938 0.020 13 darkhotel 0.938 0.020 14 deadeye_jackal 0.938 0.020 15 hacking_team 0.938 0.020 16 leviathan 0.938 0.020 17 muddywater 0.938 0.020 18 silence_group 0.938 0.020 19 stone_panda 0.938 0.020 20 unit_8200 0.938 0.020 21 vigilance 0.938 0.020 22 _acnmedia 0.929 0.020 23 anchor_panda 0.929 0.020 24 anunak 0.929 0.020 25 aurora_panda 0.929 0.020 26 axiom 0.929 0.020 27 brisach 0.929 0.020 28 cyber_caliphate_army 0.929 0.020 29 denis 0.929 0.020 30 dependent 0.929 0.020 31 desde 0.929 0.020 32 dropping_elephant 0.929 0.020 33 equation_group 0.929 0.020 34 flying_kitten 0.929 0.020 35 kimsuky 0.929 0.020 36 lotus_panda 0.929 0.020 37 madi 0.929 0.020 38 martisen 0.929 0.020 39 molerats 0.929 0.020 40 qaeda 0.929 0.020 41 sandworm 0.929 0.020 42 shabaab 0.929 0.020 43 shell_crew 0.929 0.020 44 silent_chollima 0.929 0.020 45 terrorism 0.929 0.020 46 terrorist 0.929 0.020 47 threat 0.929 0.020 48 turla_group 0.929 0.020 49 wing 0.929 0.020
Value statistics Min: 0.929 Mean: 0.940 Lower quartile: 0.929 Max: 1 Std.dev: 0.018 Median: 0.929 Upper quartile: 0.938
Unscaled value statistics Min: 0.020 Mean: 0.020 Lower quartile: 0.020 Max: 0.022 Std.dev: 4.008e-04 Median: 0.020 Upper quartile: 0.020 Centrality, Hub
A node is hub-central to the extent that its out-links are to nodes that have many in-links. Individuals or organizations that act as hubs are sending information to a wide range of others each of whom has many others reporting to them. Technically, an agent is hub-central if its out-links are to agents that have many other agents sending links to them. The scientific name of this measure is hub centrality and it is calculated on agent by agent matrices.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.137 0.097 2 careto 0.125 0.089 3 energetic_bear 0.125 0.089 4 ghostnet 0.125 0.089 5 ice_fog 0.125 0.089 6 red_october 0.125 0.089 7 sofacy 0.041 0.029 8 ixeshe 0.030 0.021 9 apt_29 0.024 0.017 10 mirage 0.007 0.005 11 taidoor 0.007 0.005 12 _acnmedia 0 0 13 anchor_panda 0 0 14 anunak 0 0 15 aurora_panda 0 0 16 axiom 0 0 17 brisach 0 0 18 covellite 0 0 19 cyber_caliphate_army 0 0 20 darkhotel 0 0 21 deadeye_jackal 0 0 22 denis 0 0 23 dependent 0 0 24 desde 0 0 25 dropping_elephant 0 0 26 equation_group 0 0 27 flying_kitten 0 0 28 hacking_team 0 0 29 kimsuky 0 0 30 leviathan 0 0 31 lotus_panda 0 0 32 madi 0 0 33 martisen 0 0 34 molerats 0 0 35 muddywater 0 0 36 qaeda 0 0 37 sandworm 0 0 38 shabaab 0 0 39 shell_crew 0 0 40 silence_group 0 0 41 silent_chollima 0 0 42 stone_panda 0 0 43 terrorism 0 0 44 terrorist 0 0 45 threat 0 0 46 turla_group 0 0 47 unit_8200 0 0 48 vigilance 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.018 Lower quartile: 0 Max: 0.137 Std.dev: 0.042 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.013 Lower quartile: 0 Max: 0.097 Std.dev: 0.029 Median: 0 Upper quartile: 0 Centrality, In-Closeness
The closeness of all other nodes to a node in the network. Loosely, In-Closeness Centrality is the inverse of the sum of distances in the network to a node and from all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.002 3.736e-05 2 careto 0.002 3.735e-05 3 energetic_bear 0.002 3.735e-05 4 ghostnet 0.002 3.735e-05 5 ice_fog 0.002 3.735e-05 6 ixeshe 0.002 3.735e-05 7 red_october 0.002 3.735e-05 8 sofacy 0.002 3.735e-05 9 mirage 0.002 3.734e-05 10 taidoor 0.002 3.734e-05 11 apt_29 0.002 3.731e-05 12 covellite 0.002 3.562e-05 13 darkhotel 0.002 3.562e-05 14 deadeye_jackal 0.002 3.562e-05 15 hacking_team 0.002 3.562e-05 16 leviathan 0.002 3.562e-05 17 muddywater 0.002 3.562e-05 18 silence_group 0.002 3.562e-05 19 stone_panda 0.002 3.562e-05 20 unit_8200 0.002 3.562e-05 21 vigilance 0.002 3.562e-05 22 _acnmedia 0.002 3.543e-05 23 anchor_panda 0.002 3.543e-05 24 anunak 0.002 3.543e-05 25 aurora_panda 0.002 3.543e-05 26 axiom 0.002 3.543e-05 27 brisach 0.002 3.543e-05 28 cyber_caliphate_army 0.002 3.543e-05 29 denis 0.002 3.543e-05 30 dependent 0.002 3.543e-05 31 desde 0.002 3.543e-05 32 dropping_elephant 0.002 3.543e-05 33 equation_group 0.002 3.543e-05 34 flying_kitten 0.002 3.543e-05 35 kimsuky 0.002 3.543e-05 36 lotus_panda 0.002 3.543e-05 37 madi 0.002 3.543e-05 38 martisen 0.002 3.543e-05 39 molerats 0.002 3.543e-05 40 qaeda 0.002 3.543e-05 41 sandworm 0.002 3.543e-05 42 shabaab 0.002 3.543e-05 43 shell_crew 0.002 3.543e-05 44 silent_chollima 0.002 3.543e-05 45 terrorism 0.002 3.543e-05 46 terrorist 0.002 3.543e-05 47 threat 0.002 3.543e-05 48 turla_group 0.002 3.543e-05 49 wing 0.002 3.543e-05
Value statistics Min: 0.002 Mean: 0.002 Lower quartile: 0.002 Max: 0.002 Std.dev: 3.751e-05 Median: 0.002 Upper quartile: 0.002
Unscaled value statistics Min: 3.543e-05 Mean: 3.590e-05 Lower quartile: 3.543e-05 Max: 3.736e-05 Std.dev: 7.815e-07 Median: 3.543e-05 Upper quartile: 3.562e-05 Centrality, In-Degree
The number of links directed into a node normalized by the maximum number of such links. This measure is also called Column Degree Centrality because it is computed by taking the sum of the column values in the input network.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.049 7 2 careto 0.035 5 3 energetic_bear 0.035 5 4 ghostnet 0.035 5 5 ice_fog 0.035 5 6 red_october 0.035 5 7 sofacy 0.028 4 8 apt_29 0.021 3 9 ixeshe 0.021 3 10 mirage 0.014 2 11 taidoor 0.014 2 12 covellite 0.007 1 13 darkhotel 0.007 1 14 deadeye_jackal 0.007 1 15 hacking_team 0.007 1 16 leviathan 0.007 1 17 muddywater 0.007 1 18 silence_group 0.007 1 19 stone_panda 0.007 1 20 unit_8200 0.007 1 21 vigilance 0.007 1 22 _acnmedia 0 0 23 anchor_panda 0 0 24 anunak 0 0 25 aurora_panda 0 0 26 axiom 0 0 27 brisach 0 0 28 cyber_caliphate_army 0 0 29 denis 0 0 30 dependent 0 0 31 desde 0 0 32 dropping_elephant 0 0 33 equation_group 0 0 34 flying_kitten 0 0 35 kimsuky 0 0 36 lotus_panda 0 0 37 madi 0 0 38 martisen 0 0 39 molerats 0 0 40 qaeda 0 0 41 sandworm 0 0 42 shabaab 0 0 43 shell_crew 0 0 44 silent_chollima 0 0 45 terrorism 0 0 46 terrorist 0 0 47 threat 0 0 48 turla_group 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.008 Lower quartile: 0 Max: 0.049 Std.dev: 0.013 Median: 0 Upper quartile: 0.007
Unscaled value statistics Min: 0 Mean: 1.143 Lower quartile: 0 Max: 7 Std.dev: 1.818 Median: 0 Upper quartile: 1 Centrality, In-Inverse Closeness
The average closeness of a node to the other nodes in a network considering only paths emanating out from a node. Inverse Closeness is the sum of the inverse distances between a node and all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.172 8.250 2 careto 0.143 6.867 3 energetic_bear 0.143 6.867 4 ghostnet 0.143 6.867 5 ice_fog 0.143 6.867 6 red_october 0.143 6.867 7 ixeshe 0.129 6.200 8 sofacy 0.104 5 9 mirage 0.097 4.667 10 taidoor 0.097 4.667 11 apt_29 0.044 2.117 12 covellite 0.021 1 13 darkhotel 0.021 1 14 deadeye_jackal 0.021 1 15 hacking_team 0.021 1 16 leviathan 0.021 1 17 muddywater 0.021 1 18 silence_group 0.021 1 19 stone_panda 0.021 1 20 unit_8200 0.021 1 21 vigilance 0.021 1 22 _acnmedia 0 0 23 anchor_panda 0 0 24 anunak 0 0 25 aurora_panda 0 0 26 axiom 0 0 27 brisach 0 0 28 cyber_caliphate_army 0 0 29 denis 0 0 30 dependent 0 0 31 desde 0 0 32 dropping_elephant 0 0 33 equation_group 0 0 34 flying_kitten 0 0 35 kimsuky 0 0 36 lotus_panda 0 0 37 madi 0 0 38 martisen 0 0 39 molerats 0 0 40 qaeda 0 0 41 sandworm 0 0 42 shabaab 0 0 43 shell_crew 0 0 44 silent_chollima 0 0 45 terrorism 0 0 46 terrorist 0 0 47 threat 0 0 48 turla_group 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.032 Lower quartile: 0 Max: 0.172 Std.dev: 0.052 Median: 0 Upper quartile: 0.021
Unscaled value statistics Min: 0 Mean: 1.535 Lower quartile: 0 Max: 8.250 Std.dev: 2.514 Median: 0 Upper quartile: 1 Centrality, Inverse Closeness
The average closeness of a node to the other nodes in a network (also called out-inverse closeness centrality). Inverse Closeness is the average inverse distance from a node and to all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.172 8.250 2 careto 0.143 6.867 3 energetic_bear 0.143 6.867 4 ghostnet 0.143 6.867 5 ice_fog 0.143 6.867 6 red_october 0.143 6.867 7 ixeshe 0.129 6.200 8 sofacy 0.104 5 9 mirage 0.097 4.667 10 taidoor 0.097 4.667 11 apt_29 0.044 2.117 12 covellite 0.021 1 13 darkhotel 0.021 1 14 deadeye_jackal 0.021 1 15 hacking_team 0.021 1 16 leviathan 0.021 1 17 muddywater 0.021 1 18 silence_group 0.021 1 19 stone_panda 0.021 1 20 unit_8200 0.021 1 21 vigilance 0.021 1 22 _acnmedia 0 0 23 anchor_panda 0 0 24 anunak 0 0 25 aurora_panda 0 0 26 axiom 0 0 27 brisach 0 0 28 cyber_caliphate_army 0 0 29 denis 0 0 30 dependent 0 0 31 desde 0 0 32 dropping_elephant 0 0 33 equation_group 0 0 34 flying_kitten 0 0 35 kimsuky 0 0 36 lotus_panda 0 0 37 madi 0 0 38 martisen 0 0 39 molerats 0 0 40 qaeda 0 0 41 sandworm 0 0 42 shabaab 0 0 43 shell_crew 0 0 44 silent_chollima 0 0 45 terrorism 0 0 46 terrorist 0 0 47 threat 0 0 48 turla_group 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.032 Lower quartile: 0 Max: 0.172 Std.dev: 0.052 Median: 0 Upper quartile: 0.021
Unscaled value statistics Min: 0 Mean: 1.535 Lower quartile: 0 Max: 8.250 Std.dev: 2.514 Median: 0 Upper quartile: 1 Centrality, Katz
This computes the centrality of each entity based on the centrality of its neighbors. Alpha should be chosen such that its absolute value is less than the reciprocal of the largest eigenvalue of N.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.303 1 2 careto 0.251 0.829 3 energetic_bear 0.251 0.829 4 ghostnet 0.251 0.829 5 ice_fog 0.251 0.829 6 red_october 0.251 0.829 7 sofacy 0.137 0.452 8 apt_29 0.098 0.322 9 ixeshe 0.095 0.315 10 mirage 0.046 0.153 11 taidoor 0.046 0.153 12 covellite 0.015 0.050 13 darkhotel 0.015 0.050 14 deadeye_jackal 0.015 0.050 15 hacking_team 0.015 0.050 16 leviathan 0.015 0.050 17 muddywater 0.015 0.050 18 silence_group 0.015 0.050 19 stone_panda 0.015 0.050 20 unit_8200 0.015 0.050 21 vigilance 0.015 0.050 22 _acnmedia 0 0 23 anchor_panda 0 0 24 anunak 0 0 25 aurora_panda 0 0 26 axiom 0 0 27 brisach 0 0 28 cyber_caliphate_army 0 0 29 denis 0 0 30 dependent 0 0 31 desde 0 0 32 dropping_elephant 0 0 33 equation_group 0 0 34 flying_kitten 0 0 35 kimsuky 0 0 36 lotus_panda 0 0 37 madi 0 0 38 martisen 0 0 39 molerats 0 0 40 qaeda 0 0 41 sandworm 0 0 42 shabaab 0 0 43 shell_crew 0 0 44 silent_chollima 0 0 45 terrorism 0 0 46 terrorist 0 0 47 threat 0 0 48 turla_group 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.044 Lower quartile: 0 Max: 0.303 Std.dev: 0.086 Median: 0 Upper quartile: 0.015
Unscaled value statistics Min: 0 Mean: 0.144 Lower quartile: 0 Max: 1 Std.dev: 0.282 Median: 0 Upper quartile: 0.050 Centrality, Out-Degree
For any node, e.g. an individual or a resource, the out-links are the connections that the node of interest has to other nodes. For example, imagine an agent by knowledge network where the number of out-links an agent would have is the number of pieces of knowledge it is connected to. The scientific name of this measure is out-degree and it can be calculated on any network.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.049 7 2 careto 0.035 5 3 energetic_bear 0.035 5 4 ghostnet 0.035 5 5 ice_fog 0.035 5 6 red_october 0.035 5 7 sofacy 0.028 4 8 apt_29 0.021 3 9 ixeshe 0.021 3 10 mirage 0.014 2 11 taidoor 0.014 2 12 covellite 0.007 1 13 darkhotel 0.007 1 14 deadeye_jackal 0.007 1 15 hacking_team 0.007 1 16 leviathan 0.007 1 17 muddywater 0.007 1 18 silence_group 0.007 1 19 stone_panda 0.007 1 20 unit_8200 0.007 1 21 vigilance 0.007 1 22 _acnmedia 0 0 23 anchor_panda 0 0 24 anunak 0 0 25 aurora_panda 0 0 26 axiom 0 0 27 brisach 0 0 28 cyber_caliphate_army 0 0 29 denis 0 0 30 dependent 0 0 31 desde 0 0 32 dropping_elephant 0 0 33 equation_group 0 0 34 flying_kitten 0 0 35 kimsuky 0 0 36 lotus_panda 0 0 37 madi 0 0 38 martisen 0 0 39 molerats 0 0 40 qaeda 0 0 41 sandworm 0 0 42 shabaab 0 0 43 shell_crew 0 0 44 silent_chollima 0 0 45 terrorism 0 0 46 terrorist 0 0 47 threat 0 0 48 turla_group 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.008 Lower quartile: 0 Max: 0.049 Std.dev: 0.013 Median: 0 Upper quartile: 0.007
Unscaled value statistics Min: 0 Mean: 1.143 Lower quartile: 0 Max: 7 Std.dev: 1.818 Median: 0 Upper quartile: 1 Centrality, PageRank
Calculates the importance of a node based on the importance of its in-coming neighbors. The input network links are normalized and interpreted as the probability of a transition from node i to node j. The PageRank of a node can be interpreted as the fraction of times a node would be visitied when traversing the network according to the network of probabilities.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value 1 comment_crew 0.079 2 careto 0.056 3 energetic_bear 0.056 4 ghostnet 0.056 5 ice_fog 0.056 6 red_october 0.056 7 muddywater 0.049 8 stone_panda 0.049 9 darkhotel 0.048 10 deadeye_jackal 0.048 11 sofacy 0.046 12 silence_group 0.045 13 unit_8200 0.045 14 covellite 0.043 15 hacking_team 0.043 16 leviathan 0.042 17 vigilance 0.042 18 ixeshe 0.036 19 apt_29 0.035 20 mirage 0.025 21 taidoor 0.025 22 _acnmedia 6.463e-04 23 anchor_panda 6.463e-04 24 anunak 6.463e-04 25 aurora_panda 6.463e-04 26 axiom 6.463e-04 27 brisach 6.463e-04 28 cyber_caliphate_army 6.463e-04 29 denis 6.463e-04 30 dependent 6.463e-04 31 desde 6.463e-04 32 dropping_elephant 6.463e-04 33 equation_group 6.463e-04 34 flying_kitten 6.463e-04 35 kimsuky 6.463e-04 36 lotus_panda 6.463e-04 37 madi 6.463e-04 38 martisen 6.463e-04 39 molerats 6.463e-04 40 qaeda 6.463e-04 41 sandworm 6.463e-04 42 shabaab 6.463e-04 43 shell_crew 6.463e-04 44 silent_chollima 6.463e-04 45 terrorism 6.463e-04 46 terrorist 6.463e-04 47 threat 6.463e-04 48 turla_group 6.463e-04 49 wing 6.463e-04
Unscaled value statistics Min: 6.463e-04 Mean: 0.020 Lower quartile: 6.463e-04 Max: 0.079 Std.dev: 0.024 Median: 6.463e-04 Upper quartile: 0.045 Centrality, Radiality
The Radiality Centrality of a node is the normalized sum of its closeness to all other nodes. The closeness of a node u to another node v is the network diameter minus the shortest path distance from u to v.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.208 1,455 2 careto 0.207 1,450 3 energetic_bear 0.207 1,450 4 ghostnet 0.207 1,450 5 ice_fog 0.207 1,450 6 ixeshe 0.207 1,450 7 red_october 0.207 1,450 8 sofacy 0.207 1,448 9 mirage 0.206 1,442 10 taidoor 0.206 1,442 11 apt_29 0.203 1,421 12 covellite 0.021 146 13 darkhotel 0.021 146 14 deadeye_jackal 0.021 146 15 hacking_team 0.021 146 16 leviathan 0.021 146 17 muddywater 0.021 146 18 silence_group 0.021 146 19 stone_panda 0.021 146 20 unit_8200 0.021 146 21 vigilance 0.021 146 22 _acnmedia 0 0 23 anchor_panda 0 0 24 anunak 0 0 25 aurora_panda 0 0 26 axiom 0 0 27 brisach 0 0 28 cyber_caliphate_army 0 0 29 denis 0 0 30 dependent 0 0 31 desde 0 0 32 dropping_elephant 0 0 33 equation_group 0 0 34 flying_kitten 0 0 35 kimsuky 0 0 36 lotus_panda 0 0 37 madi 0 0 38 martisen 0 0 39 molerats 0 0 40 qaeda 0 0 41 sandworm 0 0 42 shabaab 0 0 43 shell_crew 0 0 44 silent_chollima 0 0 45 terrorism 0 0 46 terrorist 0 0 47 threat 0 0 48 turla_group 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.051 Lower quartile: 0 Max: 0.208 Std.dev: 0.084 Median: 0 Upper quartile: 0.021
Unscaled value statistics Min: 0 Mean: 354.449 Lower quartile: 0 Max: 1,455 Std.dev: 590.119 Median: 0 Upper quartile: 146 Centrality, Total-Degree
Individuals or organizations who are 'in the know' are those who are linked to many others and so, by virtue of their position have access to the ideas, thoughts, beliefs of many others. Individuals who are 'in the know' are identified by degree centrality in the relevant social network. Those who are ranked high on this metrics have more connections to others in the same network. The scientific name of this measure is total degree centrality and it is calculated on the agent by agent matrices.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Threat Groups x Threat Groups]
Rank Threat Groups Value Unscaled 1 comment_crew 0.049 7 2 careto 0.035 5 3 energetic_bear 0.035 5 4 ghostnet 0.035 5 5 ice_fog 0.035 5 6 red_october 0.035 5 7 sofacy 0.028 4 8 apt_29 0.021 3 9 ixeshe 0.021 3 10 mirage 0.014 2 11 taidoor 0.014 2 12 covellite 0.007 1 13 darkhotel 0.007 1 14 deadeye_jackal 0.007 1 15 hacking_team 0.007 1 16 leviathan 0.007 1 17 muddywater 0.007 1 18 silence_group 0.007 1 19 stone_panda 0.007 1 20 unit_8200 0.007 1 21 vigilance 0.007 1 22 _acnmedia 0 0 23 anchor_panda 0 0 24 anunak 0 0 25 aurora_panda 0 0 26 axiom 0 0 27 brisach 0 0 28 cyber_caliphate_army 0 0 29 denis 0 0 30 dependent 0 0 31 desde 0 0 32 dropping_elephant 0 0 33 equation_group 0 0 34 flying_kitten 0 0 35 kimsuky 0 0 36 lotus_panda 0 0 37 madi 0 0 38 martisen 0 0 39 molerats 0 0 40 qaeda 0 0 41 sandworm 0 0 42 shabaab 0 0 43 shell_crew 0 0 44 silent_chollima 0 0 45 terrorism 0 0 46 terrorist 0 0 47 threat 0 0 48 turla_group 0 0 49 wing 0 0
Value statistics Min: 0 Mean: 0.008 Lower quartile: 0 Max: 0.049 Std.dev: 0.013 Median: 0 Upper quartile: 0.007
Unscaled value statistics Min: 0 Mean: 1.143 Lower quartile: 0 Max: 7 Std.dev: 1.818 Median: 0 Upper quartile: 1


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Contribute Code
You can report bugs, submit a new feature enhancement recommendation, or submit an improved patch directly to the above.
Reporting Bugs
This section guides you through submitting a bug report for Sharding-JDBC.
Before Submitting A Bug Report
- Please make sure this is not a repeated bug.
Check Issue Page list，search if the bug you want to submit has been reported.
- Please make sure the bug exists in the latest version.We will not continue to maintain all release versions, and all changes are based only on the current version.
- Please make sure the bug can be reproduced，Please try to provide a complete reproduction step，and provide a project demo code that can be duplicated on github.
How Do I Submit A (Good) Bug Report?
Bugs are tracked as Issue Page.
- Use a clear and descriptive title for the issue to identify the problem.
- A detailed description of the steps for a recurring bug. This includes the SQL you use, the configuration, the expected results, and the actual results. Additional TRACE logs are attached.
- In GitHub, the project demo code is provided for the reoccurrence of the problem.
- If the program throws an exception, attach the complete stack log.
- If possible,include screenshots and animated GIFs which show you following the described steps and clearly demonstrate the problem.
- If performance issues are involved, attach a Profile screenshot of the CPU, memory, or network disk IO.
- Explain the applicable version, only the release version of the bug can be submitted, and should be the latest version.
- What’s the name and version of the OS you’re using
- Use the bug Label (Label) to mark the issue.
The following is the markdown template for bug,please fill in the issue according to the template.
[[Simple description of the problem]
**Problem reiteration steps:**
1. [The first step]]
2. [The Second step]
3. [Other steps...]
**Expected performance:**
[Please describe expected performance here]
**Observed performance:**
[Please describe Observed performance here]
**Screen screenshot and animated GIFs**
![Screen screenshot and animated GIFs of reiteration steps](the url of picture)
**the version of Sharding-JDBC:** [please input the version of Sharding-JDBC]
**Operating system and version:** [please input the operating system and version]
Suggesting Enhancements
This section guides you through submitting an enhancement suggestion for Sharding-JDBC.
Before Submitting An Enhancement Suggestion
- Please first check the detailed list of features。
- Please make sure this is not a repeated feature enhancement recommendation.
Check Issue Page list，to see if the enhancement has already been suggested. If it has, add a comment to the existing issue instead of opening a new one.
How Do I Submit A (Good) Enhancement Suggestion?
Enhancement suggestions are tracked as Issue Page.
- Use a clear and descriptive title for the issue to identify the suggestion.
- Provide a step-by-step description of the suggested enhancement in as many details as possible.
- Explain why this enhancement would be useful to most Sharding-jdbc users.New functions should be widely applicable.
- If possible，list similar features already available in other databases.Both commercial and open source software are available.
- Use the enhancement Label (Label) to mark the issue.
The following is the markdown template for enhancement suggestion,please fill in the issue according to the template.
[Simple suggestion description]
**Proposed new functional behavior**
[Describe the behavior pattern of the new function]
**Why is this new feature useful to most users**
[Explain why is this new feature useful to most users]
[List whether other database middleware contains the function and how it is implemented]
Contribute Patch
This section introduces development specifications, environments, samples, and documentation to contributors.
Development Concept
- Write code carefully to extract real non-functional requirements.
- the code is neat and clean to the extreme，please refer to Refactoring-Imporving Design of Existing Code and Clean Code
- Minimalist code, high reuse, no duplication of code and configuration
- Code should be at the same level of abstraction.
- When modifying the function, consider the influence surface more, and do not leave the incomplete part.
- When there is only one requirement, there is no extensibility. When two similar requirements are used, the scalability is refined.
Development Behavior Specification
- Determine the module’s test suite before submitting it, and use the test coverage tool to check that coverage cannot be lower than the master branch coverage.
- Check the code with Checkstyle,violating the validation rules needs special reasons.The template location is in sharding-jdbc/src/resources/dd_checks.xml.
- mvn clean install can be tested and compiled
- Delete unused code in time.
Code Conventions
- Look at the system’s existing code before you write it, keeping the style consistent with the way you use it.
- Variable naming is meaningful. If the method has only one return value, it uses result to name the return value. In the loop, each is used to name the loop variable, and entry is used instead of each in map.
- The nested loop is refined as method as far as possible.
- Replace Nested Conditional with Guard Clauses.
- Configuration named by Camel-Case,the first letter of file is lowercase.
- The access permissions for classes and methods are minimal, for example, if you can set it private, you don’t need public.
- The private method used in this method should follow this method closely,if there are multiple private methods,The private method should be written in the same order as the private method in the original method.
- Use guava instead of apache Commons, for example: use Strings instead of StringUtils.
- Use lombok instead of the constructor, get, set, and log methods.
- use linux line feeds.
- Indent (including blank lines) is consistent with the previous line.
- There should be no meaningless space.
- Method entry and return values are not allowed to be null, if there is a special case to be annotated.
- The code that needs to be annotated is as small as possible, explained in the method name, and the annotations should contain only javadoc and todo, fixme, and so on.
- static import is forbidden.
- The No-public class should be put in the internal package,and the class in the package is private as far as possible.
- All logs are in English.
- Use the annotation to get the spring’s business bean.
- If there is a common entry point in the module, the pointcut package should be created at the module first level path.
- Attribute configuration items need to be added to the constant enumeration of each module.
Unit Test Specification
- Test code and production code follow the same code specification.
- If there is no special reason, the test needs to be fully covered.
- Separation of code and test code for environment preparation.
- Single data assert, should use assertTrue, assertFalse, assertNull, assertNotNull.
- Multi data assert, should use assertThat.
- Exact assert, Try not to use not, containsString assert.
- The variable that invokes the business method., should named as actualXXX, The expected value should be called expectedXXX.
- Only junit assertXXX, hamcrest, mocktio related can use static import.
Compiling Code
The Sharding-JDBC code compilation requires Maven，please ensure that it is properly configured in the IDE. All dependencies used in the code can be downloaded from the public network. Please choose a reasonable mirror according to your network situation.
The code uses Lombok to generate access methods for class attributes, constructors, and so on. Therefore, please use the above link content to get the appropriate solution for your IDE.
Document Generation
The document USES the blog generation engine HUGO，please install the environment according to the document. All documents are in the directory sharding-jdbc/sharding-jdbc-doc/public.
Contribution Method
Please follow the specification to contribute code, examples and documents.
- All the problems and new features are managed with Issue Page.
- Anyone wants to develop any function, please reply to the Issue associated with that function first, indicating that you are currently working on this Issue. And in response, set a deadline for yourself, and add in the reply content.
- At the core contributors you should find a mentor(shepherd), who gives immediate feedback on design and functional implementation.
- You should create a new branch to start your work, which is the name of the current dev/issue eid. The function name is determined after discussion with the shepherd.
- When finished, send a pull request to the dev version of the current development of shardingjdbc, please do not submit PR to the master branch. Then the shepherd makes CodeReview, and then he will discuss some details with you (including design, implementation, performance, etc.). When all the team members are satisfied with this change, the mentor will submit the submission to the branch of the current development version.
- Finally, congratulations that you have become the official contributor to Sharding-JDBC!


CanonnED3D
Guardians
Brain Trees
Guardian Beacons
Guardian Ruins
Combo Maps
Guardian Data Maps
Alien Data Maps
Thargoids
Thargoid Barnacles
Thargoid Structures
Hyperdictions
Non-Human Signal Sources
Thargoid Link Messages: 3305 Survey
Combo Maps
Thargoid Data Maps
Alien Data Maps
Cartographics
Generation Ships
Lagrange Clouds
Permit Locked Regions
Canonn Challenge Route
Gnosis Route Map
Adamastor Route
Prison Network (+250ly > Sol)
Unknown Insterstellar Anomaly
Listening Posts
Combo Maps
Cartographics Data Maps
Canonn Faction
Canonn (States)
Canonn (Resources)
Canonn Deep Space Research (States)
Canonn Deep Space Research (Resources)
Biology
Horizons Biology
Amphora Plant
Anemone
Bark Mounds
Brain Tree
Shards
Thargoid Barnacles
Tubers
Odyssey Biology
Aleoids
Bacterial
Cactoid
Clypeus
Conchas
Electricae
Fonticulus
Fumerolas
Fungoids
Osseus
Recepta
Shrubs
Stratum
Tubus
Tussocks
Combo Maps
Biology Data Maps
Geology
Crystalline Shards
Fumaroles
Gas Vents
Geysers
Lava Spouts
Combo Maps
Geology Data Maps
Tools
System Signals
Codex Route Plotter
Thargoid Link Decoder
Canonn Github
Suggest a Map


| Accession |
Provider |
Description |
Accession Master |
|
| E-TABM-1 |
Blah |
Blah |
Blah |
Show history
|
|
Total 4 result(s)
|
No matching array designs found! Please check your query.
No matching log entries were found
| Datasource |
|
| EFO |
|
| Data Path |
|
| Index Path |
|
| Webapp Path |
|
| Time |
Task Type |
Event |
Accession/URL |
User |
Comment |
| 10:34 |
experiment
RESTART
|
STARTED |
E-TABM-1 |
user |
Blah...
|
No associated events were found in system logs


So you want to know about Taylor White?
My name is Taylor White and I am from Whittier, California. I joined the Army at 18 in 2008 and got out in 2016 and moved to Florida. I have been working as a Network Engineer and now want to be a Software Engineer. My hobbies include playing video games(Kingdom Hearts is the greatest!), making small video games, watching anime, and outdoorsy stuff. My goals are to eventually be a game developer and then to open my own non-profit to teach under-privileged children how to code and make video games, so that they can have a valuable skill.
### Try out a microgame I made Here ###
- Kingdom Hearts
- Metroid
- Overwatch
- Halo
- Mass Effect
- Gears of War
- Pokemon
- Monster Hunter
- Command & Conquer
- Telltale: The walking Dead series
Top Ten video games series
- U.S. Army (2008-2016)
- Presidio(Network Engineer(2016-present)
- High School Diploma
- Some College
- CodeFellows


{% assign sectionClass = "section-even centered" %}
{% assign sectionTitle = "Fast and Scalable" %}
{% assign sectionIcons = "icon-lightning-bolt.png" %}
{% capture sectionContent %}
Reading a small CSV file? No problem!
Reading a huge XLSX file? No extra code needed!
Writing an ODS file with millions of rows? {{ site.spout_html }} can do it in no time!
{% endcapture %}
{% include section.html site_url = include.site_url %}


Sphinx-4 Hello Digits Demo |
This is a simple command line program that shows how to write a simple Sphinx-4 application that recognizes connected digits, using a BNF-style grammar. When you run the demo, the following will show up, just start saying numbers.
Say any digit(s): e.g. "two oh oh four", "three six five". Start speaking. Press Ctrl-C to quit. You said: one two three four Start speaking. Press Ctrl-C to quit. You said: four five six nine Start speaking. Press Ctrl-C to quit.
Check the bin
directory to see if it has the
HelloDigits.jar
file. If not, type the following in a
shell from the Sphinx-4 top level directory:
ant -find demo.xml
First make sure that you have JSAPI setup correctly. Then, to run the demo, go to the top level directory and type:
sphinx4> java -jar bin/HelloDigits.jar
NOTE:
lib/sphinx4.jar
is built. If not, go to the top level
directory and type: ant


--- layout: post title: "Day 16 - Bar Brothers Workout" subtitle: "Finally, this week I reached the weight I wanted to gain from the beginning of the week, I still want to reach 92kg but the odds are very small, at least I managed to implement the first goal." date: 2019-11-06 background: '/img/posts/intro-barbrothers.png' ---
The weight of 94.8kg is pretty much the weight I wanted to reach since the beginning of the week after last week I managed to reach 94.4kg and gained almost 3kg last weekend.
Now that I've managed to reach that weight and it's still midweek today, there may be a chance that I'll reach 93k at the end of the week, but it's probably not possible, but maybe I should eat healthier and lower my carbs.
Next week I'm going to drop the only carbs I eat during the week and it's rice, which means I need to increase more protein, for example chicken, or tuna, or other fish, or eggs.
In the evening I went for a 5km walk, it was really late and I was really tired, so when I came home and wanted to start cardio training he had no powers, so I ate tuna and soup, and then I went to sleep. We'll see what happens with me in the end.
Do not stop following my posts, tomorrow I upload photos.
Good night.
Cheers!


If you are system administrator, you have right to lock and unlock any user of any ranking. i.e. Include other system administrators
To lock an user, select item 'Lock/Unlock user' on main menu (figure 2.1.3), then screen like figure 4.4.1 will be seen, which let you desire to lock
or unlock an user. Select radio button 'Lock User', then click the 'Next' button. Figure 4.4.2 will be shown to let you select active user(s) to lock.
Select the check-box in front of desired user's alias, and then click 'Save' button to lock selected user(s).
Locked user will be forced logout from the system immediately if he/she is still online, and he/she cannot login to SMS again. His/Her messages will be
kept on the system, and also his/her message group profiles. i.e. He/she won't be kicked out from message group(s) he/she belongs to. However, all
his/her messages will show that he/she is an inactived user as his/her alias colour is changed from red to grey.
|
Similarly, if you want to unlock a locked user, select 'Unlock User' radio button on figure 4.4.1, and select desired locked user(s) on figure 4.4.2,
and click 'Save' button to unlock user(s).
Please note that only appropriate users will be listed on figure 4.4.2. Therefore, if it has no any appropriate user available, the user list will
be empty.
|


---
layout: default
---
Assignment: Final Project Video
Assignment: Final Project Video
Please create a video presentation of your final project, after having completed the final finishing touches.
This should be no more than 8 minutes, and should capture the essence of the project.
As you've already written the script for this, the work consists of
- Recording - Mainly screen capture, but a bit of your face would be nice for the opening and closing at least
- Editing - Please do basic editing (if required), such as removing silences and false starts
This video, along with the project source code and deployed program, will be your main achievement from this course, which you can show off in your professional portfolio.
Please upload the video to YouTube and submit the link to the video as the submission for this assignment.
Please also link to the video from your project README (optional, but highly recommended).
Please also put a link from the video to the project.
- For making videos, I'd recommend the following software:
- Screen capture software such as OBS (if you're on Mac, QuickTime works well for this).
- (optional) This has an option for a webcam face overlay in the corner.
- (Optional) Video editing software such as KDEnlive
- for editing out silences and false starts.
- If you find another tool that works for you, please share it with the class (there's so much out there).
- Please include your bl.ocks.org profile page as the first scene of the video.
- Please add an image to your GitHub profile, it makes your bl.ocks.org profile look more professional.


Links:
Example.com
Long URL
Text box:
Frame:
The title can be set with ?title
A notification can be sent with ?notify
Your location if ?location is set:
Microphone ID w/ ?mic:
Click here to request full screen
and here to exit
Click here to request pointer lock
Click here to show flash video


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Selected first name must contain the following:
Only first capital (uppercase) letter
Second and other letters only lowercase
Selected last name must contain the following:
Don`t use special symbol, numbers, only english letters
Password must contain the following:
Minimum one capital (uppercase) letter
Minimum one number
Minimum 8 characters


Welcome
Web Developer and Designer
Pending per review :
Users
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val seeds: List<RecommendationSeed>
An array of recommendation seed objects.
seeds - An array of recommendation seed objects.
seeds


| SchemaSpy Analysis of ClaimsExtracted from gunpla org r${env.SVN_REVISION} on Force.com | Generated by SchemaSpy |
| Generated by SchemaSpy on Thu Aug 24 11:55 EDT 2017 | |
| Database Type: Force.com - 21 |
|
XML Representation Insertion Order Deletion Order (for database loading/purging scripts) |
| Table | Children | Parents | Columns | Comments |
|---|---|---|---|---|
| Camping_Item__c | 12 | Labels: Camping Item | Camping Items | ||
| GW_Volunteers__Job_Recurrence_Schedule__c | 1 | 1 | 16 | Labels: Job Recurrence Schedule | Job Recurrence Schedules |
| GW_Volunteers__Volunteer_Hours__c | 3 | 26 | Labels: Volunteer Hours | Volunteer Hours | |
| GW_Volunteers__Volunteer_Job__c | 4 | 27 | Labels: Volunteer Job | Volunteer Jobs | |
| GW_Volunteers__Volunteer_Recurrence_Schedule__c | 1 | 1 | 18 | Labels: Volunteer Recurrence Schedule | Volunteer Recurrence Schedules |
| GW_Volunteers__Volunteer_Shift__c | 1 | 2 | 22 | Labels: Volunteer Shift | Volunteer Shifts |
| GW_Volunteers__Volunteers_Settings__c | 20 | Labels: Volunteers Settings | Volunteers Settings | ||
| npe01__Contacts_And_Orgs_Settings__c | 39 | Labels: Contacts And Orgs Settings | Contacts And Orgs Settings | ||
| npe01__OppPayment__c | 1 | 19 | Labels: Payment | Payments | |
| npe01__Payment_Field_Mapping_Settings__c | 11 | Labels: Payment Field Mapping Settings | Payment Field Mapping Settings | ||
| npe03__Custom_Field_Mapping__c | 11 | Labels: Custom Field Mapping | Custom Field Mapping | ||
| npe03__Custom_Installment_Settings__c | 11 | Labels: Custom Installment Settings | Custom Installment Settings | ||
| npe03__Recurring_Donation__c | 2 | 26 | Labels: Recurring Donations | Recurring Donations | |
| npe03__Recurring_Donations_Error_Queue__c | 11 | Labels: DEPRECATED-RD Error Queue | Recurring Donations Error Queue | ||
| npe03__Recurring_Donations_Settings__c | 28 | Labels: Recurring Donations Settings | Recurring Donations Settings | ||
| npe4__Relationship__c | 1 | 1 | 16 | Labels: Relationship | Relationships |
| npe4__Relationship_Auto_Create__c | 13 | Labels: Relationship Auto-Create | Relationship Auto-Create | ||
| npe4__Relationship_Error__c | 14 | Labels: DEPRECATED-Relationship Error | Relationship Errors | ||
| npe4__Relationship_Lookup__c | 13 | Labels: Relationship Lookup | Relationship Lookup | ||
| npe4__Relationship_Settings__c | 19 | Labels: Relationship Settings | Relationship Settings | ||
| npe5__Affiliation__c | 16 | Labels: Affiliation | Affiliations | ||
| npe5__Affiliations_Settings__c | 10 | Labels: Affiliations Settings | Affiliations Settings | ||
| npo02__Household__c | 53 | Labels: Household | Households | ||
| npo02__Households_Settings__c | 45 | Labels: Households Settings | Households Settings | ||
| npo02__Opportunity_Rollup_Error__c | 14 | Labels: DEPRECATED-Opportunity Rollup Errors | Opportunity Rollup Errors | ||
| npo02__User_Rollup_Field_Settings__c | 13 | Labels: User Rollup Field Settings | User Rollup Field Settings | ||
| npsp__Address__c | 1 | 37 | Labels: Address | Addresses | |
| npsp__Address_Verification_Settings__c | 20 | Labels: Address Verification Settings-DEPRECATED | Address Verification Settings-DEPRECATED | ||
| npsp__Allocation__c | 2 | 15 | Labels: GAU Allocation | GAU Allocations | |
| npsp__Allocations_Settings__c | 15 | Labels: Allocations Settings | Allocations Settings | ||
| npsp__Batch__c | 13 | Labels: Batch | Batches | ||
| npsp__Batch_Data_Entry_Settings__c | 11 | Labels: Batch Data Entry Settings | Batch Data Entry Settings | ||
| npsp__Data_Import_Settings__c | 18 | Labels: Data Import Settings | Data Import Settings | ||
| npsp__DataImport__c | 3 | 97 | Labels: NPSP Data Import | NPSP Data Imports | |
| npsp__DataImportBatch__c | 1 | 18 | Labels: NPSP Data Import Batch | NPSP Data Import Batches | |
| npsp__Engagement_Plan__c | 2 | 20 | Labels: Engagement Plan | Engagement Plans | |
| npsp__Engagement_Plan_Task__c | 1 | 2 | 19 | Labels: Engagement Plan Task | Engagement Plan Tasks |
| npsp__Engagement_Plan_Template__c | 3 | 17 | Labels: Engagement Plan Template | Engagement Plan Templates | |
| npsp__Error__c | 18 | Labels: Error | Errors | ||
| npsp__Error_Settings__c | 16 | Labels: Error Settings | Error Settings | ||
| npsp__Fund__c | 10 | Labels: DEPRECATED-Fund | DEPRECATED-Funds | ||
| npsp__General_Accounting_Unit__c | 1 | 27 | Labels: General Accounting Unit | General Accounting Units | |
| npsp__Grant_Deadline__c | 14 | Labels: Deliverable | Deliverables | ||
| npsp__Household_Naming_Settings__c | 16 | Labels: Household Naming Settings | Household Naming Settings | ||
| npsp__Level__c | 1 | 18 | Labels: Level | Levels | |
| npsp__Opportunity_Naming_Settings__c | 13 | Labels: Opportunity Naming Settings | Opportunity Naming Settings | ||
| npsp__Partial_Soft_Credit__c | 14 | Labels: Partial Soft Credit | Partial Soft Credits | ||
| npsp__Relationship_Sync_Excluded_Fields__c | 9 | Labels: Fields to Exclude from Sync | Relationship Sync Excluded Fields | ||
| npsp__Schedulable__c | 13 | Labels: DEPRECATED-Schedulable | DEPRECATED-Schedulables | ||
| npsp__Trigger_Handler__c | 17 | Labels: Trigger Handler | Trigger Handlers | ||
| 50 Tables | 1,008 | |||
| 0 Views | 0 |


--- layout: post title: "Bifunctors" description: "Bifunctors are like functors, only they vary in two dimensions instead of one. An article for object-oriented programmers." date: 2018-12-24 14:40 UTC tags: [Software Design] image: "/content/binary/bifunctor.png" image_alt: "Bifunctor diagram." --- {% include JB/setup %}
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This article is a continuation of the article series about functors and about applicative functors. In this article, you'll learn about a generalisation called a bifunctor. The prefix bi typically indicates that there's two of something, and that's also the case here.
As you've already seen in the functor articles, a functor is a mappable container of generic values, like Foo<T>
, where the type of the contained value(s) can be any generic type T
. A bifunctor is just a container with two independent generic types, like Bar<T, U>
. If you can map each of the types independently of the other, you may have a bifunctor.
The two most common bifunctors are tuples and Either.
A normal functor is based on a structure-preserving map of the contents within a container. You can, for example, translate an IEnumerable<int>
to an IEnumerable<string>
, or a Maybe<DateTime>
to a Maybe<bool>
. The axis of variability is the generic type argument T
. You can translate T1
to T2
inside a container, but the type of the container remains the same: you can translate Tree<T1>
to Tree<T2>
, but it remains a Tree<>
.
A bifunctor involves a pair of maps, one for each generic type. You can map a Bar<string, int>
to a Bar<bool, int>
, or to a Bar<string, DateTime>
, or even to a Bar<bool, DateTime>
. Notice that the last example, mapping from Bar<string, int>
to Bar<bool, DateTime>
could be viewed as translating both axes simultaneously.
In Haskell, the two maps are called first
and second
, while the 'simultaneous' map is called bimap
.
The first
translation translates the first, or left-most, value in the container. You can use it to map Bar<string, int>
to a Bar<bool, int>
. In C#, we could decide to call the method SelectFirst
, or SelectLeft
, in order to align with the C# naming convention of calling the functor morphism Select
.
Likewise, the second
map translates the second, or right-most, value in the container. This is where you map Bar<string, int>
to Bar<string, DateTime>
. In C#, we could call the method SelectSecond
, or SelectRight
.
The bimap
function maps both values in the container in one go. This corresponds to a translation from Bar<string, int>
to Bar<bool, DateTime>
. In C#, we could call the method SelectBoth
. There's no established naming conventions for bifunctors in C# that I know of, so these names are just some that I made up.
You'll see examples of how to implement and use such functions in the next articles:
Other bifunctors exist, but those two are the most common.As is the case with functors, laws govern bifunctors. Some of the functor laws carry over, but are simply repeated over both axes, while other laws are generalisations of the functor laws. For example, the first functor law states that if you translate a container with the identity function, the result is the original input. This generalises to bifunctors as well:
bimap id id ≡ id
This just states that if you translate both axes using the endomorphic Identity, it's equivalent to applying the Identity.
Using C# syntax, you could express the law like this:
bf.SelectBoth(id, id) == bf;
Here, bf
is some bifunctor, and id
is the identity function. The point is that if you translate over both axes, but actually don't perform a real translation, nothing happens.
Likewise, if you consider a bifunctor a functor over two dimensions, the first functor law should hold for both:
first id ≡ id second id ≡ id
Both of those equalities only restate the first functor law for each dimension. If you map an axis with the identity function, nothing happens:
In C#, you can express both laws like this:
bf.SelectFirst(id) == bf; bf.SelectSecond(id) == bf;
When calling SelectFirst
, you translate only the first axis while you keep the second axis constant. When calling SelectSecond
it's the other way around: you translate only the second axis while keeping the first axis constant. In both cases, though, if you use the identity function for the translation, you effectively keep the mapped dimension constant as well. Therefore, one would expect the result to be the same as the input.
As you'll see in the articles on tuple and Either bifunctors, you can derive bimap
or SelectBoth
from first
/SelectFirst
and second
/SelectSecond
, or the other way around.  The name Consistency law, however, is entirely my own invention. If it has a more well-known name, I'm not aware of it.
In pseudo-Haskell syntax, you can express the law like this:
bimap f g ≡ first f . second g
This states that mapping (using the functions f
and g
) simultaneously should produce the same result as mapping using an intermediary step:
In C#, you could express it like this:
bf.SelectBoth(f, g) == bf.SelectSecond(g).SelectFirst(f);
You can project the input bifunctor bf
using both f
and g
in a single step, or you can first translate the second dimension with g
and then subsequently map that intermediary result along the first axis with f
.
The above diagram ought to commute:
It shouldn't matter whether the intermediary step is applying f
along the first axis or g
along the second axis. In C#, we can write it like this:
bf.SelectFirst(f).SelectSecond(g) == bf.SelectSecond(g).SelectFirst(f);
On the left-hand side, you first translate the bifunctor bf
along the first axis, using f
, and then translate that intermediary result along the second axis, using g
. On the right-hand side, you first project bf
along the second axis, using g
, and then map that intermediary result over the first dimension, using f
.
Regardless of order of translation, the result should be the same.
Similar to how the first functor law generalises to bifunctors, the second functor law generalises as well. For (mono)functors, the second functor law states that if you have two functions over the same dimension, it shouldn't matter whether you perform a projection in one, composed step, or in two steps with an intermediary result.
For bifunctors, you can generalise that law and state that you can project over both dimensions in one or two steps:
bimap (f . g) (h . i) ≡ bimap f h . bimap g i
If you have two functions, f
and g
, that compose, and two other functions, h
and i
, that also compose, you can translate in either one or two steps; the result should be the same.
In C#, you can express the law like this:
bf.SelectBoth(x => f(g(x)), y => h(i(y))) == bf.SelectBoth(g, i).SelectBoth(f, h);
On the left-hand side, the first dimension is translated in one step. For each x
contained in bf
, the translation first invokes g(x)
, and then immediately calls f
with the output of g(x)
. The second dimension also gets translated in one step. For each y
contained in bf
, the translation first invokes i(y)
, and then immediately calls h
with the output of i(y)
.
On the right-hand side, you first translate bf
along both axes using g
and i
. This produces an intermediary result that you can use as input for a second translation with f
and h
.
The translation on the left-hand side should produce the same output as the right-hand side.
Finally, if you keep one of the dimensions fixed, you essentially have a normal functor, and the second functor law should still hold. For example, if you hold the second dimension fixed, translating over the first dimension is equivalent to a normal functor projection, so the second functor law should hold:
first (f . g) ≡ first f . first g
If you replace first
with fmap
, you have the second functor law.
In C#, you can write it like this:
bf.SelectFirst(x => f(g(x))) == bf.SelectFirst(g).SelectFirst(f);
Likewise, you can keep the first dimension constant and apply the second functor law to projections along the second axis:
second (f . g) ≡ second f . second g
Again, if you replace second
with fmap
, you have the second functor law.
In C#, you express it like this:
bf.SelectSecond(x => f(g(x))) == bf.SelectSecond(g).SelectSecond(f);
The last two of these composition laws are specialisations of the general composition law, but where you fix either one or the other dimension.
A bifunctor is a container that can be translated over two dimensions, instead of a (mono)functor, which is a container that can be translated over a single dimension. In reality, there isn't a multitude of different bifunctors. While others exist, tuples and Either are the two most common bifunctors. They share an abstraction, but are still fundamentally different. A tuple always contains values of both dimensions at the same time, whereas Either only contains one of the values.
Do trifunctors, quadfunctors, and so on, exist? Nothing prevents that, but they aren't particularly useful; in practice, you never run into them.
Next: Tuple bifunctor.


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FROM THE DESK OF MR. STEVE BAKER DEPARTMENT OF PETROLEUM RESOURCES MAITAMA DISTRICT, GARKI-ABUJA I am Mr. Steve Baker, a member of the Contract Award Committee of the above department. Terms of Reference: My term of reference involves the award of contracts to multinational companies. My office is saddled with the responsibility of contract award, screening, categorization and prioritization of projects embarked upon by Department of Petroleum Resources (DPR) as well as feasibility studies for selected projects and supervisig the project consultants involved. The DPR is now compiling beneficiaries to be paid for the first Fiscal Quarter of 2005. The crux of this letter is that the finance/contract department of the DPR deliberately over -invoiced the contract value of the various contracts awarded. In the course of disbursements, this department has been able to accumulate the sum of Thirteen Million, Seven Hundred and Thirty One Thousand U.S Dollars as the over-invoiced sum. This money is currently in a suspense account of the DPR account with the Debt Reconciliation Committee (DRC). We now seek to process the transfer of this fund officially as contract payment to you as a foreign contractor, who will be fronting for us as the beneficiary of the fund. In this way we can facilitate these funds into your nominated account for possible investment abroad. We are not allowed as a matter of government policy to operate any foreign account to transfer this fund into. However, for your involvement in assisting us with this transferinto your nominated account we have evolved a sharing formula as follows: (1) 25% for you as the foreign partner (2) 75% for I and my colleagues We shall be relying on your advice as regard investment of our share in any business in your country. Be informed that this business is genuine and 100% safe considering the high-power government officials involved. Send your private fax/telephone numbers. Upon your response we shall provide you with further information on the procedures. Expecting your prompt positive response as I Look forward to a good business relationship with you. Sincerely, Steve Baker


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#assign architecture="active">
How Ebean compares to JPA.
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What Ebean's PersistenceContext is and it's related scopes.
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Before Change
dependent = [m for m, deps in static_deps.iteritems()
if n in deps]
for m in dependent:
static_deps[m].remove(n)
if len(static_deps[m]) == 0:
// no other dependencies
no_incoming.add(m)
After Change
dep in self._equations and
self._equations[dep].type == STATIC_EQUATION]
try:
sorted_eqs = topsort(static_deps)
except ValueError:
raise ValueError("Cannot resolve dependencies between static "
"equations, dependencies contain a cycle.")
// put the equations objects in the correct order
for order, static_variable in enumerate(sorted_eqs):
self._equations[static_variable].update_order = order
// Sort differential equations and parameters after static equations


It is very well said that-
"Engineers are the world builders"
but knowledge without the practical know-how is as good as a pencil without a lead. SAE India Collegiate Club of MNNIT is
that platform which imparts the real time application of what is
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Being a faculty advisor to such a club where the students work hard towards achieving their goals is a matter of great pride and responsibility.
I wish them the best of luck for their future
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Man who lost 20 stone in 12 months finally has an 'amazing' girlfriend
A man who lost 20 stone in 12 months is enjoying a new lease of life, after getting an ‘amazing’ girlfriend.
Darren ‘Dibsy’ McClintock lost half of his entire body mass after embarking on a weight loss journey last year.
The 28-year-old’s no-nonsense personal trainer banned Dibsy from local takeaways, in a bid to turn his life around after doctors warned he was ‘eating himself to death’.
He has now revealed he’s in a relationship with a trainee nurse called Jade, 28, who got in touch with him after following his progress online.
The pair exchanged messages over Facebook before going on their first date, at a bowling alley, in August and becoming an ‘official’ couple a month later.
Dibsy said: ‘Jade is such an amazing girl. She’s so caring and is really supportive of my weight loss and what I’m doing.
‘The two of us just click and get on so well.
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‘It’s great to have someone like that in my life – 18 months ago I wouldn’t have thought it was possible.’
He added: ‘I’m definitely punching above my weight.’
Dibsy said Jade, who’s also from Middlesbrough, first messaged him over Facebook last October, right at the start of his weight loss journey, to say good luck.
‘It was nice to get the message and I replied saying thank you but I didn’t think much else of it,’ he said.
Jade reached out for a second time in July, when Dibsy conquered Ben Nevis alongside personal trainer Mike Hind for charity.
He added: ‘After Jade messaged me again we started chatting properly and got on really well so exchanged numbers.’
Within a few days the pair arranged to meet face-to-face and they went for a walk at a nearby beauty spot, which coincidentally is where Dibsy spread his dad’s ashes.
‘When she said she wanted to go there I knew it was meant to be,’ he said.
Their first proper date was at a bowling alley in August and just over a month later the pair made their relationship ‘official’.
Dibsy said he was able to get on so well with Jade in the early stages because of a renewed self-confidence which has come about as a result of the weight loss.
‘My mindset now is completely different to how it was 18 months ago,’ he explained.
‘Before I wouldn’t have tried anything new because I was scared about what people would think of me.
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‘But now I feel comfortable in my own skin and like I can just be myself, it’s an incredible turnaround.
‘I’m really happy now.’
Much of the Middlesborough man’s astounding success is down to personal trainer Mike Hind, who has been with Dibsy every step of the way.
He said: ‘When Dibsy and I first started working together I asked him what he wanted to achieve.
‘He told me he wanted to a girl to settle down with, just like all his friends have.
‘To see him achieve that now after putting so much work in is incredible, I’m so happy for him.
‘Dibsy and Jade make a great couple.
‘It just goes to show what eating well and keeping fit can do for you, it’s completely turned his life around.’
Dibsy suffered a setback last month when an irregular heartbeat, which he had dealt with previously, returned and sidelined him from the gym.
He underwent an operation to fix the problem last week and, miraculously, he is already back on the treadmill continuing his weight loss journey.
Dibsy now has his sights set on the new year, when he plans to have three surgeries to remove excess skin which has been left as a result of the weight loss.
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Dibsy added: ‘It’s onward and upward from here.’


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