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|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.gusdb.wdk.model.BooleanQuestionNode
Represents a Question in boolean context. A boolean Question is defined as such by having a BooleanQuery as its Query. Can take one of two forms: 1. Representing a boolean Question and its two boolean operand Questions. The operands can also be boolean Questions so using this class one can create a large tree of recursive boolean Questions. 2. Representing a normal Question that is not itself boolean but is the operand for its parent boolean Question. This is a leaf Question in a boolean Question tree and its pointers to boolean operands are null values. Also recursively sets operand Answers as parameter values for boolean Questions. Created: Fri 22 October 12:00:00 2004 EST
| Constructor Summary | |
BooleanQuestionNode(Question q,
BooleanQuestionNode parent)
Constructor for a BooleanQuestionNode representing a leaf in a boolean Query tree containing a Question that is not boolean. |
|
BooleanQuestionNode(Question q,
BooleanQuestionNode firstChild,
BooleanQuestionNode secondChild,
BooleanQuestionNode parent)
Constructor for a BooleanQuestionNode representing a boolean Question. |
| Method Summary | |
BooleanQuestionNode |
find(java.lang.String nodeId)
Recursive method to find a node in the tree. |
BooleanQuestionNode |
getFirstChild()
|
BooleanQuestionNode |
getParent()
|
Question |
getQuestion()
|
BooleanQuestionNode |
getSecondChild()
|
java.util.Hashtable |
getValues()
|
BooleanQuestionNode |
grow(BooleanQuestionNode newSecondChild,
java.lang.String operation,
WdkModel model)
This method can only be performed on a leaf node and assumes that none of the nodes in the tree has had any parameter values set yet. |
boolean |
isFirstChild()
|
boolean |
isLeaf()
|
static Answer |
setAllValues(BooleanQuestionNode bqn)
Recursive method that traverses bqn and sets its values,
which may be either normal query values if the node is a leaf or the
Answers of its operands if the node is a boolean Question. |
void |
setValues(java.util.Hashtable values)
|
java.lang.String |
toString()
|
| Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
public BooleanQuestionNode(Question q, BooleanQuestionNode firstChild, BooleanQuestionNode secondChild, BooleanQuestionNode parent)
public BooleanQuestionNode(Question q, BooleanQuestionNode parent)
parent
- If the supplied parent is null; that implies that this node represents
a single-node tree.| Method Detail |
public Question getQuestion()
public BooleanQuestionNode getFirstChild()
public BooleanQuestionNode getSecondChild()
public BooleanQuestionNode getParent()
public boolean isFirstChild()
public BooleanQuestionNode grow(BooleanQuestionNode newSecondChild, java.lang.String operation, WdkModel model) throws WdkModelException
WdkModelException
public BooleanQuestionNode find(java.lang.String nodeId) throws WdkModelException
nodeId
- Binary number representing path to take to find node.
The number is read left to right. A 1 in the number will traverse to the left
child and a 0 in the number will traverse to the right. When the end of the number
is reached, the current node is returned.
WdkModelException
public static Answer setAllValues(BooleanQuestionNode bqn) throws WdkUserException, WdkModelException
bqn
and sets its values,
which may be either normal query values if the node is a leaf or the
Answers of its operands if the node is a boolean Question. The method is
recursively called on each of the operands if the node is a boolean
Question.
bqn
- The root of the tree on which to set answers and values; might be
the root of a subtree of a tree from a previous recursive call.
bqn
. The answer should not be used as the
answer returned by the top (recursive initializer) node; that should be
retrieved by calling makeAnswer() on that node's Question after running
this method.
WdkUserException
WdkModelException
public boolean isLeaf()
public void setValues(java.util.Hashtable values)
public java.util.Hashtable getValues()
public java.lang.String toString()
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


On Data Banks and Privacy Homomorphisms
Ronald L Rivest
Len Adleman
Michael L. Dertouzos
And Dertouzos!
End-to-end Encryption
e.g. LastPass, Apple HealthKit
... an information system working with encrypted data can at most store or retrieve the data for the user
Secure Voting
Homomorphic
"same shape"
f is the encryption function
* is some useful computation
RSA
Encryption:
$$c\equiv m^e \pmod n$$
Decryption:
$$c^d\equiv (m^e)^d\equiv m\pmod n$$
RSA Homomorphism:
$$(m_1 * m_2)^e = m_1^e * m_2^e$$
Security Considerations
Binary Search with ≤
"most severe restriction"
Given:
$$Enc(x) = (-2)x$$
$$Enc(pt) = -8$$
$$Enc(0) ≤' Enc(pt) = 0 ≤' -8 = false$$
$$Enc(10) ≤' Enc(pt) = -20 ≤' -8 = true$$
$$Enc(5) ≤' Enc(pt) = -10 ≤' -8 = true$$
$$Enc(3) ≤' Enc(pt) = -6 ≤' -8 = false$$
∎
Choosen Plaintext Attack
Choosen Plaintext Attack
Semantic Security
Semantically Secure Fully Homomorphic Encryption scheme
"Efficiently" compute any function on ciphertext
Gentry (2009)
See Also:


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--- layout: page title: About Me description: Largely dedicated to discussing faith. background: '/img/bg-about.jpg' ---
Eliehoenai was one of the family heads who returned with Ezra from Babylon (Ezra 8:4). The name means, roughly, "Towards Yahweh are my eyes".


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LiveChat is a sytem built upon express4/socket.io/babel which offers chatting functionality over HTTP.
The supported CLI commands are:
When no command is passed but a message is given, it's executed locally on the server
default commands prefix is '.', start all commands with this except the server-eval
There are several in-chat commands too:
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This Tool converts Stat Blocks from the SRD or PDFs to JSON Files compatible with FoundryVTT
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vignettes/hagis.Rmd
hagis.Rmd
First you’ll want to load in your data set, for right now let’s use a practice data set made for the hagis package and name it Pathotype.Data
:
Pathotype.Data <- system.file("extdata", "practice_data_set.csv", package = "hagis")
Pathotype.Data <- read.csv(Pathotype.Data, stringsAsFactors = FALSE)
head(Pathotype.Data)
#> Isolate Line Rps Total HR..1. Lesion..2.
#> 1 1 Williams susceptible 10 0 0
#> 2 1 Harlon Rps 1a 10 4 0
#> 3 1 Harosoy 13xx Rps 1b 8 0 0
#> 4 1 L75-3735 Rps 1c 10 10 0
#> 5 1 PI 103091 Rps 1d 9 2 0
#> 6 1 Williams 82 Rps 1k 10 0 0
#> Lesion.to.cotyledon..3. Dead..4. total.susc total.resis perc.susc
#> 1 0 10 10 0 100
#> 2 0 6 6 4 60
#> 3 0 8 8 0 100
#> 4 0 0 0 10 0
#> 5 1 6 7 2 78
#> 6 0 10 10 0 100
#> perc.resis
#> 1 0
#> 2 40
#> 3 0
#> 4 100
#> 5 22
#> 6 0
We see in the Rps
column that each gene is prepended with “Rps”. We can remove this to make the graphs cleaner and report the genes in tables as we would in a manuscript. Note that this will work for any string you enter as the first value, pattern
. The second string, replacement
, is the replacement value, the third, x
, is where to look and make the changes.
Pathotype.Data$Rps <- gsub(pattern = "Rps ", replacement = "", x = Pathotype.Data$Rps)
head(Pathotype.Data)
#> Isolate Line Rps Total HR..1. Lesion..2.
#> 1 1 Williams susceptible 10 0 0
#> 2 1 Harlon 1a 10 4 0
#> 3 1 Harosoy 13xx 1b 8 0 0
#> 4 1 L75-3735 1c 10 10 0
#> 5 1 PI 103091 1d 9 2 0
#> 6 1 Williams 82 1k 10 0 0
#> Lesion.to.cotyledon..3. Dead..4. total.susc total.resis perc.susc
#> 1 0 10 10 0 100
#> 2 0 6 6 4 60
#> 3 0 8 8 0 100
#> 4 0 0 0 10 0
#> 5 1 6 7 2 78
#> 6 0 10 10 0 100
#> perc.resis
#> 1 0
#> 2 40
#> 3 0
#> 4 100
#> 5 22
#> 6 0
This practice data set contains 21 isolates’, Isolate
, virulence data on a set of 14 differential soybean cultivars, Line
. This package uses the percentage of susceptible, inoculated, plants to determine effective resistance genes, pathotype diversity and frequency, as well as individual isolates pathotypes.
To help ensure that the proper data are used in calculations, the user is asked to provide some information that instruct hagis about what data to use.
We have strived to make hagis as intuitive to use as possible. Part of that means that we have used the same arguments for the three main functions, summarize_rps()
, calculate_complexities()
and calculate_diversities()
. Each of these functions take the same arguments:
x
this is your data set name, e.g. Pathotype.Data
from the example above, allows for the function to identify where it will be pulling these columns (and their associated row values) from to use (i.e. your data collection Excel spreadsheet)
cutoff
this value sets the cutoff for susceptible reactions. For example, cutoff = 60
means that all genes with 60% or more of the plants rated susceptible will be treated as susceptible. You can change this to whatever percentage you require for your study.
control
specifies the value used in the Rps
column to denote a susceptible control used in the study
sample
specifies the column header for the column which identifies the isolates tested
Rps
specifies the column header for the column which identifies the genes tested
perc_susc
specifies the column header for the column which identifies the percent susceptible plants for each gene
Ordinarily you would use functions in hagis or other R packages like this:
Rps.summary <- summarize_rps(x = Pathotype.Data,
cutoff = 60,
control = "susceptible",
sample = "Isolate",
Rps = "Rps",
perc_susc = "perc.susc")
However, because the functions share arguments we can create a list()
of arguments and share them with some of the functions from hagis. First, we make a list of the arguments that summarize_rps()
, calculate_diversities()
and calculate_complexities()
use that specify our inputs based on our example data:
hagis_args <- list(x = Pathotype.Data,
cutoff = 60,
control = "susceptible",
sample = "Isolate",
Rps = "Rps",
perc_susc = "perc.susc")
Now that we have a list of arguments, we can now save time entering the same data for each function and also avoid typos or entering different cutoff values, etc. between the functions.
Below is an example of tables and graphics that can be produced using the summarize_rps()
function to identify effective resistance genes tested against the sampled Phytophthora sojae population.
The summarize_rps()
function allows you to produce a detailed table showing the number of isolates each gene is susceptible to (N_susc
), as well as offering a percentage of the isolates tested which are pathogenic on each gene (percent_pathogenic
).
Rps.summary <- do.call(summarize_rps, hagis_args)
Rps.summary
#> Rps N_susc percent_pathogenic
#> 1: susceptible 21 100.00
#> 2: 1a 21 100.00
#> 3: 1b 15 71.43
#> 4: 1c 20 95.24
#> 5: 1d 16 76.19
#> 6: 1k 18 85.71
#> 7: 2 14 66.67
#> 8: 3a 5 23.81
#> 9: 3b 20 95.24
#> 10: 3c 4 19.05
#> 11: 4 5 23.81
#> 12: 5 13 61.90
#> 13: 6 11 52.38
#> 14: 7 21 100.00
Using the pander library we can make the table much more attractive in Markdown.
| Rps | N_susc | percent_pathogenic |
|---|---|---|
| susceptible | 21 | 100 |
| 1a | 21 | 100 |
| 1b | 15 | 71.43 |
| 1c | 20 | 95.24 |
| 1d | 16 | 76.19 |
| 1k | 18 | 85.71 |
| 2 | 14 | 66.67 |
| 3a | 5 | 23.81 |
| 3b | 20 | 95.24 |
| 3c | 4 | 19.05 |
| 4 | 5 | 23.81 |
| 5 | 13 | 61.9 |
| 6 | 11 | 52.38 |
| 7 | 21 | 100 |
hagis also provides functions to quickly graph your data using ggplot2
.
Two functions are provided to plot the summary depending on your needs. If you need the frequency, use autoplot(Rps.summary, type = "percentage")
, or if you desire the distribution autoplot(Rps.summary, type = "count")
. Both return the same graph, only the y-axis change; percent for frequency and n for distribution.
Pathotype frequency, distribution as well as statistics such as mean pathotype complexity can be calculated using the calculate_complexities()
function. This function will return a list()
of two data.table()
objects, grouped_complexities
and individual_complexities
.
complexities <- do.call(calculate_complexities, hagis_args)
complexities
#>
#> Grouped Complexities
#> complexity frequency distribution
#> 1: 1 0.00 0
#> 2: 2 0.00 0
#> 3: 3 0.00 0
#> 4: 4 0.00 0
#> 5: 5 4.76 1
#> 6: 6 9.52 2
#> 7: 7 9.52 2
#> 8: 8 33.33 7
#> 9: 9 0.00 0
#> 10: 10 23.81 5
#> 11: 11 14.29 3
#> 12: 12 0.00 0
#> 13: 13 4.76 1
#>
#>
#> Individual Complexities
#> sample N_samp
#> 1: 1 10
#> 2: 2 10
#> 3: 3 10
#> 4: 4 8
#> 5: 5 8
#> 6: 6 8
#> 7: 7 8
#> 8: 8 8
#> 9: 9 6
#> 10: 10 5
#> 11: 11 6
#> 12: 12 8
#> 13: 13 7
#> 14: 14 8
#> 15: 15 11
#> 16: 16 7
#> 17: 17 10
#> 18: 18 10
#> 19: 19 11
#> 20: 20 11
#> 21: 21 13
#> sample N_samp
Once again, using pander we can make these tables much more attractive in Markdown. Since complexities
is a list()
object, we can refer to each object directly by name and print them as follows.
| complexity | frequency | distribution |
|---|---|---|
| 1 | 0 | 0 |
| 2 | 0 | 0 |
| 3 | 0 | 0 |
| 4 | 0 | 0 |
| 5 | 4.76 | 1 |
| 6 | 9.52 | 2 |
| 7 | 9.52 | 2 |
| 8 | 33.33 | 7 |
| 9 | 0 | 0 |
| 10 | 23.81 | 5 |
| 11 | 14.29 | 3 |
| 12 | 0 | 0 |
| 13 | 4.76 | 1 |
| sample | N_samp |
|---|---|
| 1 | 10 |
| 2 | 10 |
| 3 | 10 |
| 4 | 8 |
| 5 | 8 |
| 6 | 8 |
| 7 | 8 |
| 8 | 8 |
| 9 | 6 |
| 10 | 5 |
| 11 | 6 |
| 12 | 8 |
| 13 | 7 |
| 14 | 8 |
| 15 | 11 |
| 16 | 7 |
| 17 | 10 |
| 18 | 10 |
| 19 | 11 |
| 20 | 11 |
| 21 | 13 |
Using summary()
will return the mean, standard error (se) and standard deviation (sd) for pathotypes of a complexities object.
| Mean | SD | SE |
|---|---|---|
| 8.714 | 2.004 | 0.4372 |
Two functions are provided to plot the complexities depending on your needs. If you need the frequency, use autoplot(complexties, type = "percentage")
, or if you desire the distribution autoplot(complexities, type = "count")
. Both return the same graph, only the y-axis change; percent for frequency and n for distribution.
Diversity indices are extremely useful when trying to identify differences between two populations. Here, pathotype diversities are calculated for the sample population using the calculate_diversities()
function. Likewise, individual isolates’ pathotypes, number of isolates used in the study, number of pathotypes within the study are calculated.
diversity <- do.call(calculate_diversities, hagis_args)
diversity
#>
#> hagis Diversities
#>
#> Number of Samples 21
#> Number of Pathotypes 19
#>
#> Indices
#> Simple 0.9047619
#> Gleason 5.912257
#> Shannon 2.912494
#> Simpson 0.9433107
#> Evenness 0.9891509
Or using pander
for reporting, a nice table is generated.
| Simple | Gleason | Shannon | Simpson | Evenness |
|---|---|---|---|---|
| 0.9048 | 5.912 | 2.912 | 0.9433 | 0.9892 |
To generate a table of diversities, use diversities_table()
. hagis will automatically create a pander
object for you. This is because it is much easier to read the resulting table in the console than the raw data.frame
and insert into reports.
| Frequency | Pathotype |
|---|---|
| 1 | 1a, 1b, 1c, 1d, 1k, 2, 3a, 3b, 3c, 4, 5, 6, 7 |
| 1 | 1a, 1b, 1c, 1d, 1k, 2, 3a, 3b, 3c, 5, 7 |
| 1 | 1a, 1b, 1c, 1d, 1k, 2, 3a, 3b, 3c, 6, 7 |
| 1 | 1a, 1b, 1c, 1d, 1k, 2, 3b, 4, 5, 6, 7 |
| 1 | 1a, 1b, 1c, 1d, 1k, 2, 3b, 4, 5, 7 |
| 1 | 1a, 1b, 1c, 1d, 1k, 2, 3b, 4, 6, 7 |
| 2 | 1a, 1b, 1c, 1d, 1k, 2, 3b, 7 |
| 1 | 1a, 1b, 1c, 1d, 1k, 2, 6, 7 |
| 1 | 1a, 1b, 1c, 1d, 1k, 3a, 3b, 5, 6, 7 |
| 1 | 1a, 1b, 1c, 1d, 1k, 3b, 7 |
| 1 | 1a, 1b, 1c, 1k, 2, 3b, 3c, 4, 6, 7 |
| 1 | 1a, 1b, 1c, 1k, 3b, 5, 6, 7 |
| 1 | 1a, 1b, 1c, 1k, 3b, 5, 7 |
| 1 | 1a, 1b, 1d, 1k, 2, 3a, 3b, 5, 6, 7 |
| 2 | 1a, 1c, 1d, 1k, 2, 3b, 5, 7 |
| 1 | 1a, 1c, 1d, 1k, 2, 3b, 6, 7 |
| 1 | 1a, 1c, 1d, 3b, 5, 7 |
| 1 | 1a, 1c, 3b, 5, 6, 7 |
| 1 | 1a, 1c, 3b, 5, 7 |
To generate a table of individual pathotypes, use individual_pathotypes()
. Here again, hagis provides a pander
object for ease of use.
| Sample | Pathotype |
|---|---|
| 1 | 1a, 1b, 1d, 1k, 2, 3a, 3b, 5, 6, 7 |
| 10 | 1a, 1c, 3b, 5, 7 |
| 11 | 1a, 1c, 3b, 5, 6, 7 |
| 12 | 1a, 1b, 1c, 1d, 1k, 2, 6, 7 |
| 13 | 1a, 1b, 1c, 1d, 1k, 3b, 7 |
| 14 | 1a, 1b, 1c, 1k, 3b, 5, 6, 7 |
| 15 | 1a, 1b, 1c, 1d, 1k, 2, 3b, 4, 5, 6, 7 |
| 16 | 1a, 1b, 1c, 1k, 3b, 5, 7 |
| 17 | 1a, 1b, 1c, 1d, 1k, 2, 3b, 4, 5, 7 |
| 18 | 1a, 1b, 1c, 1d, 1k, 3a, 3b, 5, 6, 7 |
| 19 | 1a, 1b, 1c, 1d, 1k, 2, 3a, 3b, 3c, 6, 7 |
| 2 | 1a, 1b, 1c, 1k, 2, 3b, 3c, 4, 6, 7 |
| 20 | 1a, 1b, 1c, 1d, 1k, 2, 3a, 3b, 3c, 5, 7 |
| 21 | 1a, 1b, 1c, 1d, 1k, 2, 3a, 3b, 3c, 4, 5, 6, 7 |
| 3 | 1a, 1b, 1c, 1d, 1k, 2, 3b, 4, 6, 7 |
| 4 | 1a, 1c, 1d, 1k, 2, 3b, 5, 7 |
| 5 | 1a, 1c, 1d, 1k, 2, 3b, 6, 7 |
| 6 | 1a, 1c, 1d, 1k, 2, 3b, 5, 7 |
| 7 | 1a, 1b, 1c, 1d, 1k, 2, 3b, 7 |
| 8 | 1a, 1b, 1c, 1d, 1k, 2, 3b, 7 |
| 9 | 1a, 1c, 1d, 3b, 5, 7 |
Since hagis uses ggplot2
to generate its plots, you can easily theme the outputs using common ggplot2
themes and other options.
Use ggplot2’s theme_minimal()
theme.
If your Rps gene names are too long, flipping the axis can make the graph more legible without rotating the x-axis labels.
You can use named, e.g. “red”, “yellow”, “blue” colors in R or you can use custom hexadecimal color codes. Illustrated below is using MSU Green, hex code #18453b, using theme_bw()
with a serif font.


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Question
In an array, all numbers in the array repeat twice except two numbers, which repeat only once.
Assume all the numbers are placed randomly. Find the 2 numbers.
Analysis
The main idea of the solution is how to remove repeated elements. It’s definitely using XOR.
We need to try solve this problem like we did in ‘Single number 1’ but how? We can divide the array into 2 part, each part containing 1 non-repeating number.
The difficulty and the trick, is how do we divide?
Solution
xor = arr[0]^arr[1]^arr[2]…..arr[n-1]
We can extract the rightmost set bit of any number n by taking ( n & ~(n-1))
We take any set bit of xor and divide the elements of the array in two sets – one set of elements with same bit set and other set with same bit not set.
By doing so, we will get x in one set and y in another set.
Code
C++ Code from GFG
/* This finction sets the values of *x and *y to nonr-epeating
elements in an array arr[] of size n*/
void get2NonRepeatingNos(int arr[], int n, int *x, int *y)
{
int xor = arr[0]; /* Will hold xor of all elements */
int set_bit_no; /* Will have only single set bit of xor */
int i;
*x = 0;
*y = 0;
/* Get the xor of all elements */
for(i = 1; i < n; i++)
xor ^= arr[i];
/* Get the rightmost set bit in set_bit_no */
set_bit_no = xor & ~(xor-1);
/* Now divide elements in two sets by comparing rightmost set
bit of xor with bit at same position in each element. */
for(i = 0; i < n; i++)
{
if(arr[i] & set_bit_no)
*x = *x ^ arr[i]; /*XOR of first set */
else
*y = *y ^ arr[i]; /*XOR of second set*/
}
}
/* Driver program to test above function */
int main()
{
int arr[] = {2, 3, 7, 9, 11, 2, 3, 11};
int *x = (int *)malloc(sizeof(int));
int *y = (int *)malloc(sizeof(int));
get2NonRepeatingNos(arr, 8, x, y);
printf("The non-repeating elements are %d and %d", *x, *y);
getchar();
}


Elderly mother finds siblings dead at Brisbane home
by AAPPolice are investigating a possible murder-suicide after the bodies of two siblings aged in their 50s were found by their frail, elderly mother in separate bedrooms at their Brisbane home.
Police say the 54-year-old man and a 58-year-old woman might have been dead for some time before their bodies were discovered by their 89-year-old mother, who lived at the same address.
Detectives found the siblings’ bodies in separate bedrooms at a home in Macbeth Place at Sunnybank Hills just after 2.30pm on Tuesday afternoon.
The elderly mother managed to alert neighbours, who called the police.
Police said there were no obvious signs of death, and that autopsies and further forensic examinations would be conducted.
They said it was too early to determine whether it was a murder-suicide, but that mental health issues would form part of the investigation.
The siblings’ mother has been taken to hospital for assessment.
Lifeline 131 114
beyondblue 1300 224 636


Satellites are the most important nodes in the global communication infrastructure on and off-earth. They process zillions of data every year. Despite it’s popular applications, it’s rarely available in rural areas. This can be solved by the use of Satellite Constellations which forms a grid of networks in space. To facilitate high-bandwidth data transfer between satellites a novel Laser Communication Link needs to be designed that automatically acquires and tracks the signal to the next satellite. A laser communication link combined with satellite constellation grid will truly provide global high-speed internet access with future applications in LunerNet, a Lunar Internet framework initiative by NASA which will provide internet on the Moon.
View Project View DocsWith increasing popularity in CubeSat technologies, it has gotten ever so important to have low-cost systems that complement the economical and self-reliant nature of today’s cubesats providers. One of the most important parts of an end to end small satellite business is ground-based tracking. Satellite tracking provides valuable information on the whereabouts. Satellite tracking industry is booming with the use of large antennas and high power transmitters at cost-prohibitive nature but at the cost of expense and lead time.
View Project View DocsThis took over a year for the development and testing phases. Building a model rocket from scratch requires, building up the infrastructure first. This consists of Thrust Bench, Launchpad services and Rocket Motor Dev. The on board flight computer is built with fault tolerant design philosophy. It performs data logging of 10 channel stream at 30 times per second. More features: Killswitch, Apogee Detection, Safety Net.
View Project GithubHumans have been intrigued by the question of whether life exists out there since our inception. That fundamental curiosity inspired great people to go where no man has gone before. Venus is one such planet which is worth the visit. Venus is one such planet. A technology demonstration on Earth would be ideal to prototype and test the systems before arriving on Venus.
View Project View PaperIn the recent days, wide range of research has been carried out on visual enhancement of under images in submarine and military operations to discover submerged structural designing and sea floor exploration. But, diving in the deep ocean for a long time has increased the difficulties for analysis of underwater images. Further, other factors such as scattering resulting from presence of particles inside the water and blurring effects reduce the quality of images being captured by underwater optic camera. A novel algorithm based on bidirectional Empirical Mode Decomposition (BEMD) to enhance the visual quality of the underwater images.
View ProjectComputers have fueled our growth for the last century rapidly. They are can solve complex problems in a matter of seconds. Yet, it’s very difficult to understand human speech. Things that are inherently easy for humans, is unfortunately really hard for computers. Fortunately for us, for the first time ever, due to rapid advancements in machine learning and processing power, it is possible for computers to understand human speech, analyse images and talk back. Google Assistant has utilised a decade of text-to-speech research from Google and has open sourced it. This will enhance human ability for decades to come.
View ProjectAm a big fan of space flight and the journey it has still to cover. Nasa's Curiosity Rover or ISRO's Martian Orbital Mission truely inspired me,of how a group of like minded people can set out to do something impossible on the face of the planet.I love reading books, especially sci fi books, one of favourite is The Martian. Andy Weir the author of The Martian, used a physics simulator to simulate the motion of his fictional ship (Ares V) with respect to earth and mars, so that he could write in accurate dates in his book. That was the starting point of this project. Back then, it didnt make any sense to start learning programming, but now since I had a goal to achieve, everything was different.
View ProjectTo build a system with localized sensors along these roads that sense the sudden crossing of animals and alert the driver in the vehicles through caution lights that are set upon the roads so that he can slow down or stop the vehicle and prevent the roadkill.
View Project


genericStateDecorator Class
Generic State decorator
Item Index
Properties
Methods
applyState
-
event
-
states
Add Classes representing the current states to $element and remove invalid states
Parameters:
-
event
ObjectThe jQuery Event object
-
states
Arrayan array of states to set
Returns:
clear
()
Object
public
Removes all states from the instance
Returns:
getAppliedStates
-
$element
Returns a list of newly-added states (???)
Parameters:
-
$element
ObjectA jQuery element
Returns:
getState
()
Array
public
Returns the state(s) of the instance
Returns:
hasState
-
The
Checks to see if the state has been applied
Parameters:
-
The
Stringstate to check
Returns:
normalizeState
-
states
Converts a string of states into an array
Parameters:
-
states
Arrayan array of states
Returns:
removeState
-
value
Removes the state(s) from the instance
Parameters:
-
value
Array | StringThis can be an Array of strings or comma delimited string representing multiple states. It can also be a string representing a single state
Returns:
reset
()
Object
public
Sets states to those originally defined on the node
Returns:
state
-
event
-
states
Updates states on a state event
Parameters:
-
event
ObjectThe jQuery Event object
-
states
Arrayan array of states to set
Returns:
Properties
states
Array
private
An array of string representing the current state(s)


page url : $pageUrl
message : $message
screenshot
source
sourceRendered
source is here : $sourcelink
$source
WARNING : this is a the preview of the html only.The html uses some dynamic includes Those files might have changed since the test was ran. Refer to the screenshot to have what the page really looked like.


Active Opportunities
Disabled Opportunity
Pending Request
Accepted Request
Decline Request
Reschedule Request
No Pending opportunities
Accepted By Business
Accepted By User
No Accpeted opportunities
Declined by business
Declined by user
No Declined opportunities
User Reschedule request
Business Reschedule request


{( "common/dialog.html" )} {( "common/navbar.html" )} {( page .. ".html" )} {( "common/footer.html" )} {( "common/scripts.html" )}


Slides can be nested inside of other slides, try pressing down.
Press up to go back.
Press ESC to enter the slide overview. Hold down alt and click on any element to zoom in on it using zoom.js. Alt + click anywhere to zoom back out.
Set data-state="something"
on a slide and "something"
will be added as a class to the document element when the slide is open.
This slide has the data-state="soothe"
.
These guys come in two forms, inline:
The nice thing about standards is that there are so many to choose from
and block:
For years there has been a theory that millions of monkeys typing at random on millions of typewriters would reproduce the entire works of Shakespeare. The Internet has proven this theory to be untrue.
function linkify( selector ) {
if( supports3DTransforms ) {
var nodes = document.querySelectorAll( selector );
for( var i = 0, len = nodes.length; i < len; i++ ) {
var node = nodes[i];
if( !node.className ) ) {
node.className += ' roll';
}
};
}
}
Courtesy of highlight.js.
There's a few styles of fragments, like:
grow
shrink
roll-in
fade-out
highlight-red
highlight-green
highlight-blue
Press b or period on your keyboard to enter the 'paused' mode. This mode is helpful when you want to take disctracting slides off the screen during a presentation.


| Home | Libraries | People | FAQ | More |
The following tables illustrate how Boost.AFIO relates to some proposed closure execution engines. These proposals are related to the internal implementations of such systems at Google and Microsoft. N3378 was Google's initial proposal for such an engine, and its successor, N3562 was a joint effort between Microsoft and Google. Anyone wishing to compare Boost.AFIO to an engine related to one of these proposals should find the following tables useful.
Table 1.5.


We closed the room of official ℄ friends and untested players, and moved all players to the big main room of clan.
Direct link: ℄? ＬＥＧＥＮＤ Candidates/Friends
Admin Hakem teaches popsplits and tricksplits on private servers.
We closed the room of official ℄ friends and untested players, and moved all players to the big main room of clan.
Direct link: ℄? ＬＥＧＥＮＤ Candidates/Friends
Admin Hakem teaches popsplits and tricksplits on private servers.


Downloads
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Project source:
https://github.com/K1DV5/dman


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WITNESS LIST
Natural Resources Committee
April 19, 2011 - 8:00 AM
Registering, but not testifying:
For:
COYLE, JEFF (Southwest Texas Water ReSources)
DeRUShA, Cody (Self)
DeRusha, Kimberly (Self)
GillELAND, Bruce (Self)
GILLELAND, JUDIE (Self)
Gilleland, Rader (Self)
Gilleland, Weldon (Self)
GULLEY, ANNALEE (Southwest Texas Water Resources)
LADENSOHN, DAVID (Self)
REAGAN, CAROLYN (Self)
REAGAN, ROBERT (Self)
REAGAN, W.C. (Self)
SANDS, JASOn (Self)
SANDS, RODNEY (Self)
Wilde, Larry (Self)
Against:
Albrecht, Roxanna (Self)
Arce Jr, Antonio S. (Uvalde Area Chamber of Commerce)
BROWN, KIRBY (TEXAS WILDLIFE ASSOCIATION)
CARDENAS, JOE (Self)
FLORES, RAUL (CouNty of Uvalde)
GARCIA, JACQUELYN (Self)
Garrett, Kelly (Uvalde Area Chamber of Commerce)
Garver, Jennifer (City of Uvalde, TX)
JoNes-Lewey, SKy (Hill CouNtry AlliAnce; Nueces River Authority)
KRAMER, KEN (LONE STAR CHAPTER, SIERRA CLUB)
McAdams, HARRY E MCADAMS (City of Uvalde, Texas)
MUSE, CHRISTY (HILL COUNTRY ALLIANCE)
Williams, Patrick (Self)
WoodArd, ShAyne (GUAdAlUPe BASin COAlition)
For:
BOEhNE, Thomas (Self)
Danielson, Velma (Southwest Texas Water-Resources LP)
DAWSON, GENE (PAPEr DAWSON ENGINEERS)
Gilleland, Linda (Self)
SMITH, Rodney (Southwest TexAs WAter Resources LP)
1
WITNESS LIST
For:
WALKER, TOMMY (Self)
Against:
Albrecht, Dereck (Self)
CARNES, J (Self; CITY OF UVALDE)
HARRELL, JohN (City of UVALdE)
Hilderbran, VIC (Self)
MIMS, CON (Nueces River AuThoriTy ANd Self)
NEUTZE, GUS (Self)
PARKER JR, JOE (Self)
PiNedA, RAFAeL (Self)
SPurgeoN, CARY (Self)
Yeackle IV, Henery John (Self; Myself KOWA Board member)
On:
Dreher, Karl (Edwards Aquifer Authority)
GREEN, RONALD (Keep Our Water Association)
KirKhAM, GAry (Self)
For:
Cox, Catherine (Guardians of the Navasota River)
DODLEY, MARK (Guardians of the NavAsota River)
ROTHER, DENNIS (HArvey WIldlife MANAgement AssociAtioN)
Registering, but not testifying:
For:
KRAMER, KEN (LONE STAR CHAPTER, SIERRA CLUB)
For:
GARZA, DAVID (City of PhARR, TX)
Against:
MORRISON, JAMES (Texas Rural Water Assoc.)
Sanchez, SteveN (North Alamo Water Supply Corp.)
On:
Holcomb, Doug (Texas Commission on Environmental Quality)
Registering, but not testifying:
For:
HAWK, CURTIS (City of Burleson)
MILLSAP, MIKE (CiTY OF HARLiNGEN)
Munoz, Ned (Texas Association of Builders)
WYNN, MONTY (TEXAS MUNICIPAL LeaguE)
2
WITNESS LIST
Against:
ADAMS, SCOTT (Fort DAVIS WAtER SUPPLY CORP)
Dahlberg, Sherilyn (Sharyland Water Supply Corporation)
FRANKS, TERRY (AQUA TEXAS)
MACMANUS, BRIAN (East Rio Hondo Water Supply Corporation)
Against:
Jennings, BART (City of AUSTIN, AUSTIN Water Utility)
Registering, but not testifying:
For:
BOYLE, JIM (East AldiNe MaNagemeNt District)
Munoz, Ned (TX. Assn. of Builders)
Against:
AdAMS, SCOTT (Fort DAVIS WAtEr Supply CORP)
FRANKS, TERRY (AQUA TEXAS)
MACMANUS, BRIAN (East Rio Hondo Water Supply Corporation)
HB 2962 - Committee Substitute
For:
Allen, Joe B (Allen Boone Humphries Robinson LLP)
Against:
MORRISON, JAMES (Texas Rural Water Assoc.)
Registering, but not testifying:
For:
CASEY, DANIEL (FAST GROWTH SCHOOL COALITION)
SHIELDS, JULIE (TX ASSOC. OF SCHOOL BOARDS)
WINDSOR, JEFFREY (SPRING ISD)
Against:
Allen, Joe B (Association of Water Board DiRectoRs)
CALDERON, Bill (Harris County MUD 439)
Haag, Phil (TX. Association of Water Board Directors)
WYNN, MONTY (TEXAS MUNICipAL League)
HB 3555 - Committee Substitute
For:
PORTER, CHRISTINE (Spring ISD)
SPRAGUE, ROY (CYPRESS-FAIRBANKS ISO)
Against:
Cohen, HOWARD (Self; SchwARtz, PAgE & HARDing, L.L.P.)
LaRy, TRey (Association of Water Board Directors)
3
WITNESS LIST
HB 3620 - Committee Substitute
For:
Taylor, Mark (Hays Caldwell Public Utility Agency)
For:
Jones, Russell (City of Sugar Land)
MORRISON, RICHARD (FORT BEND COUNTY)
REID, TOM (CITY of PEARLAND TX)
Against:
ClARK, KENNETH (GAlVESTON COUNTY)
Gartman, DONald (GalvestoN County Economic Alliance)
HANEY, DEE (City of Texas City)
HENRY, MARK (GALVESTON COUNTY COMM COURT)
Holbrook, Ray (Gulf Coast Water Auth.)
Istre, Robert (Gulf Coast Water Authority)
KNEUPPER, DOUG (GCWA Municipal Advisory Committee)
LATIMER, WILLIAM (GULf COAST WATER AUTHORITY)
RICHARDSON, DARRELL (GULF COAST WATER Authority -INDUSTRIAL
ADVISORY COMMITTEE-STERLING CHEMICALS, INC, BP, MARAthON, VALERO)
Registering, but not testifying:
For:
Allen, Joe B (Ft. Bend County Water ContRol ImpRovement DistRict #2)
ShorT, Jim (ForT Bend County)
Against:
Barnett, Paula (BP America)
BENSON, DAVID (City of League City, TX)
BRUNDRETT, BOB (Texas City Independent School District)
FREEMAN, RONALD (GULF COAST WATER AUTHORITY)
HAYLEY, JIMMY (TEXAS CITY/LA MARQUE CHAMBER OF COMMERCE)
HOLZHEAUSER, STEVE (PORT OF TEXAS CITY)
KLUMPYAN, JULIE (Valero Energy)
MARR, MICHAEL (BP Texas City and Texas City- La Marque Chamber of Commerce)
MATA, CARLOS (Self)
Paulissen, David A (Galveston County, WCID #1 (GM); City of Dickinson)
Senter, Jenny (TexAs City LAMARQUE Chamber of Commerce)
For:
Allen, Joe B (Allen Boone HumphRies Robinson LLP)
HAWK, CURTIS (City of Burleson)
Kelton, Kerry (MSEC EnTerPRoses InC.)
MORRISON, JAMES (Texas Rural Water Assoc.)
4
WITNESS LIST
Against:
Dahlberg, Sherilyn (Sharyland Water Supply Corporation)
Sanchez, Steven (North Alamo Water Supply Corp.)
On:
Jennings, BART (City of AUSTIN, AuSTIN Water Utility)
Registering, but not testifying:
For:
AdAms, SCOTT (Fort DAVIS WAtER Supply Corp.)
Against:
FRANKS, TERRY (AQUA TEXAS)
For:
ALLISON, Jim (TexANA GRoundwAter Conservation District)
For:
CALDERON, Bill (DHK Development)
KONOPKA, DOUG (Self)
For:
CALDERON, Bill (Self)
Registering, but not testifying:
For:
CALVILLO, GILBERT (IMH Special Asset NT 192)
CARLTON, JOHN (IMH Special Asset 192)
Registering, but not testifying:
For:
Cohen, HOWARD (BRidgeland Development, L.P.)
Houghton, PeteR (BRidgeland Development, L.P.)
Registering, but not testifying:
For:
CASEY, S TIM (Self)
CHATELAIN, RAYMOND (CLL MUD BOARD MEMBER)
COLTHARP, WILLIAM (CLL MUD #1)
FREEMAN, RONDALD (CLL MUD 1)
5
WITNESS LIST
For:
GrAUEll, Bill (SonTERRA M.U.D.)
HAMALA, RICHARD (CLL MUD 1)
Hulme, Dewey (City of Jarrell)
Schwertner, James (Self)
For:
Wilson, Kirk (Cambridge Companies)
For:
Jones, Russell (City of Sugar Land)
MORRISON, RICHARD (Fort Bend County)
REID, TOM (City of Pearland, TX)
Against:
ClARK, KENNETH (GAlVESTON COUNTY)
Gartman, DoNald (GalvestoN County Economic Alliance)
HANEY, DEE (City of Texas City)
HENRY, MARK (GALVESTON COUNTY COMMISSIONERS COURT)
HOLBROOK, RAY (Self)
Istre, Robert (Gulf Coast Water Authority)
KNEUPPER, DOUG (GCWA Municipal Advisory Committee)
LATIMER, WILLIAM (Gulf Coast Water Authority)
RICHARDSON, DARRELL (INDUSTRIA ADVISORY COMMIttEE; BP, MARATHON,
STERLING CHEMICALS, VALERO)
Registering, but not testifying:
For:
Allen, Joe B (Ft. Bend County WateR ContRol & ImpRovement DistRict No. 2)
Ingram, Maya (Fort BEnd County)
Against:
BENSON, DAVID (City of League City)
FREEMAN, RONALD (Gulf Coast Water Authority)
MATA, CARLOS (STERLING CHEMICALS INC)
MorgAN, Keith (GULF COAST Water)
Paulissen, David (Galveston County WCID #1; City of Dickerson)
6


About BookRRaven
BookRRaven started as a collaboration between three aspiring developers as a way to showcase their coding talent. Unfinished it was taken up by Jonathan Ben-Ammi to complete and become one of the cornerstones of his coding portfolio. BookRRaven has potential for connecting unsigned artists with venues who want raw tallent. For more information please visit JonathanBen-Ammi.com


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keigeki
1.0.0
傾撃
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#include <assert.h>
Go to the source code of this file.
Classes | |
| struct | kmVec3 |
Macros | |
| #define | kmScalar float |
Typedefs | |
| typedef struct kmVec3 | kmVec3 |
| #define kmScalar float |
Returns the cosine of the angle between 2 vectors
Adds 2 vectors and returns the result. The resulting vector is stored in pOut.
Scales a vector to length s
Returns KM_TRUE if the 2 vectors are approximately equal
Assigns pIn to pOut. Returns pOut. If pIn and pOut are the same then nothing happens but pOut is still returned
Returns the vector passed in set to unit length
Returns a vector perpendicular to 2 other vectors. The result is stored in pOut.
Returns a vector perpendicular to 2 other vectors
Returns the cosine of the angle between 2 vectors
Fill a kmVec3 structure using 3 floating point values The result is store in pOut, returns pOut
| kmVec3* kmVec3InverseTransformNormal | ( | kmVec3 * | pOut, |
| const kmVec3 * | pVect, | ||
| const struct kmMat4 * | pM | ||
| ) |
Returns the length of the vector
Returns the square of the length of the vector
Returns the square of the length of the vector
Returns the vector passed in set to unit length the result is stored in pOut.
Transforms a 3D vector by a given matrix, projecting the result back into w = 1.
Scales a vector to length s. Does not normalize first, you should do that!
Adds 2 vectors and returns the result
Subtracts 2 vectors and returns the result. The result is stored in pOut.
Subtracts 2 vectors and returns the result
Transforms a 3D normal by a given matrix
Transforms a vector (assuming w=1) by a given matrix


#include <stdlib.h> size_t wcstombs(char *dest, const wchar_t *src, size_t n);
1. A wide character has been encountered that can not be represented as a multibyte sequence (according to the current locale). In this case, (size_t) -1 is returned.
2. The length limit forces a stop. In this case, the number of bytes written to dest is returned, but the shift state at this point is lost.
3. The wide-character string has been completely converted, including the terminating null wide character (L'\0'). In this case, the conversion ends in the initial state. The number of bytes written to dest, excluding the terminating null byte ('\0'), is returned.
The programmer must ensure that there is room for at least n bytes at dest.
If dest is NULL, n is ignored, and the conversion proceeds as above, except that the converted bytes are not written out to memory, and no length limit exists.
In order to avoid the case 2 above, the programmer should make sure n is greater than or equal to wcstombs(NULL,src,0)+1.
The function wcsrtombs(3) provides a thread safe interface to the same functionality.


The multi-valued suggest box is a text-input field with interactive autocompletion that supports multiple values. It uses REST to retrieve suggestions from the server. The full process is documented in Creating a Multi-Valued Auto-Complete Field Using GWT SuggestBox and REST. You can extend it to have suggestions display any information you want, as we have done here.
Try typing blue, mac, or * to search for crayon colors.


Last updated: 2017-07-25
Code version: 1b369e6
The \(\varepsilon-\)complexity coefficients are computed by sucessively downsampling and approximating or interpolating the original function. The initial implementation of the \(\varepsilon-\)complexity computation used piecewise polynomials as the approximation method. For our R
implementation we used three approximation methods, B-splines, cubic splines and interpolating subdivision. The latter method coded in R
while existing libraries were used for the two spline methods.
knitr::read_chunk("../code/sim-example.R")
library(ggplot2)
library(dplyr)
library(ggthemes)
library(tssims)
library(ecomplex)
library(tsfeats)
library(fArma)
# Generate simulations
devtools::load_all()
prefix = "sim"
seed = 1
set.seed(seed)
len <- 200
gs <- tssims::sim_suite()
gs <- lapply(gs, function(x) lapply(x, tssims::jitter_params))
ys1 <- lapply(gs$group1, function(mod) tssims::gen(mod)(len))
ys2 <- lapply(gs$group2, function(mod) tssims::gen(mod)(len))
df <- data.frame(do.call(cbind, c(ys1, ys2)) %>% apply(., 2, normalize))
names(df) <- tssims::sim_names()
names(df) <- c("ARMA 1", "Logistic 1", "Weierstrass 1", "Cauchy 1", "FARIMA 1", "FBM 1",
"ARMA 2", "Logistic 2", "Weierstrass 2", "Cauchy 2", "FARIMA 2", "FBM 2")
palette(eegpalette()[c(1,3,4)])
nsims <- length(names(df))
id <- factor(rep(rep(c(1,2), each = 200), 6))
dfm <- df %>% tidyr::gather(variable, value)
dfm$variable <- factor(dfm$variable)
plot_fit <- function(res, fname){
xlab = "log( S )"; ylab = "log( epsilons )"
plot(
log(res$S),
log(res$epsilons),
yaxt = "n",
pch = 2,
ylab = ylab,
xlab = xlab,
main = fname
)
abline(res$fit, lwd = 1.6, col = eegpalette()[3])
}
In theory developed by Piryatinska and Darkhovsky1, the \(log-log\) regression of approximation errors taken at each downsampling level \(S\) should be roughly linear. The implementation of the three approximation methods were checked to determine that the relationship held. Below is the plot for the interpolating subdivision method.
# Plot example fit for each function
par(mfrow = c(2,3))
fits <- apply(df[1:6], 2, ecomplex::ecomplex, method = "lift")
names(fits) <- c("ARMA", "Logistic", "Weierstrass", "Cauchy", "FARIMA", "FBM")
out <- Map(plot_fit, fits, names(fits))
knitr::read_chunk("../code/ecomplex-plot-feature-space.R")
devtools::load_all()
library(ggplot2)
library(dplyr)
prefix = "ecomplex"
sim_df1 <- readRDS(cache_file("approx_types1", prefix))
# sim_df <- readRDS(cache_file("approx_types", prefix))
sim_df1 <- sim_df1 %>% dplyr::filter(variable != "ecomp_all_B")
sim_df1$id2 <- as.factor(rep(c(1,2,1,3,1,4,1,5,1,6,1,7), each = 30))
# pal <- c("gray10", viridis::viridis(60)[c(5,10,15,20,25,30)])
pal <- eegpalette(0.7)[c(1,3)]
gp <- ggplot(data = sim_df1, aes(x = value, y = yvalue))
gp + facet_grid(~ variable, scales = "free_y") +
geom_point(aes(colour = id, shape = fnames), size = 2) +
scale_color_manual(values = pal) +
theme_minimal() +
labs(x = "A", y = "B")
# save_plot("approx-feature-space", prefix)
The purpose of adding addtional approximation methods was to improve the performance of the complexity coefficients in classification and segmentation tests. We hypothesized that lowering approximation error would improve the performance of the complexity coefficients in classification and segmentation tasks.
For the following test the two groups consisted of 30 simulations generated by randomly perturbing the parameters of each process of function within a small window about its initial parameters. A plot of the simulations in the feature space of the two complexity coefficients shows that the complexity coefficient generated by each method lie in roughly the same area in the coefficient space.
The B-spline produced the lowest approximation error for all methods but did not out perform the other methods in the classification task.
devtools::load_all()
prefix = "ecomplex"
err_means <- readRDS(cache_file("all_errs", prefix))
rf_errs <- readRDS(cache_file("rf_errs", prefix))
err_means <- data.frame(err_means)
names(err_means) <- names(rf_errs)
row.names(err_means) <- row.names(rf_errs)
knitr::kable(err_means, caption = 'Approximation error for each method.')
| Lift | Cspline | Bpsline | Combined | |
|---|---|---|---|---|
| ARMA | 0.100 | 0.104 | 0.066 | 0.066 |
| Logistic | 0.308 | 0.326 | 0.198 | 0.198 |
| Weierstrass | 0.108 | 0.116 | 0.070 | 0.070 |
| Cauchy | 0.118 | 0.124 | 0.076 | 0.076 |
| FARIMA | 0.134 | 0.142 | 0.086 | 0.086 |
| fBm | 0.036 | 0.042 | 0.024 | 0.024 |
knitr::kable(rf_errs, caption = 'Classification error for each method.')
| Lift | Cspline | Bpsline | Combined | |
|---|---|---|---|---|
| ARMA | 0.033 | 0.000 | 0.067 | 0.067 |
| Logistic | 0.000 | 0.000 | 0.017 | 0.000 |
| Weierstrass | 0.524 | 0.427 | 0.411 | 0.427 |
| Cauchy | 0.492 | 0.556 | 0.476 | 0.476 |
| FARIMA | 0.330 | 0.297 | 0.313 | 0.330 |
| fBm | 0.199 | 0.215 | 0.215 | 0.182 |
The complexity coefficients were used as features in a random forest classifier, the same classifier we used in later applications. No approximation method was clearly dominant but the methods with higher approximation errors performed as well or better than the B-spline method. While all three approximation methods are similar – essentially, they all use lower order polynomial approximation – the results do indicate improved approximation doesn’t lead to improved performance in classification.
For later applications, the cubic splines method was used because of the computational efficiency of the implementation.
sessionInfo()
R version 3.4.1 (2017-06-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.5 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.0
LAPACK: /usr/lib/lapack/liblapack.so.3.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] eegcomplex_<IP_ADDRESS> bindrcpp_0.2 fArma_3010.79
[4] fBasics_3011.87 timeSeries_3022.101.2 timeDate_3012.100
[7] tsfeats_<IP_ADDRESS>00 ecomplex_0.0.1 tssims_<IP_ADDRESS>00
[10] ggthemes_3.4.0 dplyr_0.7.1 ggplot2_2.2.1
loaded via a namespace (and not attached):
[1] Rcpp_0.12.11 sapa_2.0-2
[3] lattice_0.20-35 tidyr_0.6.3
[5] splus2R_1.2-1 assertthat_0.2.0
[7] rprojroot_1.2 digest_0.6.12
[9] R6_2.2.2 plyr_1.8.4
[11] RandomFields_3.1.50 backports_1.0.5
[13] evaluate_0.10.1 ForeCA_0.2.4
[15] pracma_2.0.7 highr_0.6
[17] rlang_0.1.1 lazyeval_0.2.0
[19] gmwm_2.0.0 fracdiff_1.4-2
[21] rmarkdown_1.6 labeling_0.3
[23] devtools_1.13.2 splines_3.4.1
[25] stringr_1.2.0 munsell_0.4.3
[27] compiler_3.4.1 pkgconfig_2.0.1
[29] htmltools_0.3.6 tibble_1.3.3
[31] gridExtra_2.2.1 roxygen2_6.0.1
[33] quadprog_1.5-5 randomForest_4.6-12
[35] withr_1.0.2 MASS_7.3-45
[37] commonmark_1.2 grid_3.4.1
[39] gtable_0.2.0 git2r_0.18.0
[41] magrittr_1.5 pdc_1.0.3
[43] scales_0.4.1 fractaldim_0.8-4
[45] stringi_1.1.5 reshape2_1.4.1
[47] viridis_0.3.4 sp_1.2-5
[49] xml2_1.1.1 tools_3.4.1
[51] glue_1.1.1 abind_1.4-3
[53] yaml_2.1.14 colorspace_1.2-6
[55] memoise_1.1.0 knitr_1.16
[57] bindr_0.1 RandomFieldsUtils_0.3.25
[59] ifultools_2.0-4


G20 Summit: Donald Trump pitches for free and fair trade... would seek a level playing field for all and a free and fair trade at the G20 Summit... would seek a level playing field for all and a free and fair trade at the G20 Summit in Germany, his top trade official has said ... policy is to expand trade in a way that are free and fair. Insisting on fair trade is the best way to ensure the long-term strength of ...
Learn more
here
To view the table and graph associated with this release, please click on the following link: http://media3.marketwire.com/docs/1098744.pdf


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Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
Serves 8-10
Stuffing makes for a glorious Christmas side dish, this recipe can be made in advance to reduce stress on the big day.
Want to make this recipe gluten-free or vegetarian? Check the tips section.
Melt the butter in a large saucepan over a low heat until foaming. Add the onions and fry gently for 8-10 minutes, or until softened. Remove from the heat.
Stir in the remaining ingredients and season generously with salt and pepper. Mix until well combined.
If you are making stuffing balls, lightly grease a baking tray and, using wet hands, shape the stuffing into balls about 2½cm/1in in diameter and place on the prepared tray. If preferred transfer the stuffing mixture to a 20cm/8in square tin and press down to smooth the surface. Cover with cling film and transfer to the fridge for at least 30 minutes, but up to two days (assuming the meat is fresh). (The stuffing can be frozen at this stage, but will need to be thoroughly defrosted before cooking.)
When ready to cook, preheat the oven to 200C/180C Fan/Gas 6.
Roast the stuffing for 30-40 minutes until cooked through (balls will take about 30 minutes, and a tray 40 minutes).


-
Em Dash: Hello
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Alee Azam: Hello Em!
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Em Dash: Wanna Work On Loco?
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Alee Azam: Nah, Studying.
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Alee Azam: You Should Too.


Simple Modal Link
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Help Two
When
e.g., 1962; Oct 14,1066; 1880-1886
(hint)
Time Ranges
Examples of valid time ranges are:
1992
430 BC
2000-2006
Feb 1884 - Jan 1886
June 22, 1992 12am - June 30, 1992
October 1992
Oct 12, 1992, 12:23:05 am


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---
title: Overview | Rasmussen College Style Guide 2016
fabricator: true
---
Overview
This UI toolkit is a highly-modular design system for the rapid development of the Rasmussen College rebranded website. It contains different materials that can be assembled into more complex page layouts.
This guide contains real working examples, code snippets, documentation, and style guidelines.
Development Usage
Start by adding the following files to your page:
<link rel="stylesheet" href="css/styles.css">
<script src="scripts/display.js"></script>
Taxonomies
-
Atoms:
- Basic HTML tags that can't be broken down any further without ceasing to be functional
- Includes abstract elements like color palettes, font stacks, invisible things like animation
- Eg. form labels, inputs, buttons, links
-
Molecules:
- Relatively simple groups of UI elements functioning together as a unit
- Atoms assembled/combined in specific combination/formation to serve a purpose
- Eg. form label, search input, and button combined to create a search form
-
Organisms:
- Relatively complex UI components composed of groups of molecules and/or atoms, and/or other organisms
- eg. search form (from molecule example) placed within context of a global header
- Consist of similar or different molecule types
- Similar types: categorical page of products, repeated molecules (clothing or programs)
- Different types: header with logo image, primary navigation, search form
-
Templates:
- Page-level objects that place components into a layout and articulate the design's underlying content structure
- eg. header with search form on homepage
- Provides context for relatively abstract molecules and organisms
- Demonstrate how components look/function together in context of layout to prove the parts add up to a well-functioning whole
- Focus on the page's underlying content structure {rather than the page's final content}
- Articulate important properties like image sizes and character lengths for headings/text passages
-
Pages:
- Specific instances of templates that replace placeholder content with real representative content
- Essential for testing the effectiveness of the underlying design system
- Able to take a look at how patterns hold up when real content is applied to the design system
- Provide a place to articulate variations in templates