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2016 Election Visualizer
Realtime visualization of people tweeting about Donald Trump and Hillary Clinton.
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Chapter II (Intro To Nock)
Prev: Setup Next: Nock Is Easy
"What one fool can do, another can" (Ancient Simian proverb)
Now that we've installed Arvo, let's learn Nock.
Think of Nock as a kind of functional assembly language. It's not like assembly language in that it's directly executed by the hardware. It is like assembly language in that (a) everything in Urbit executes as Nock; (b) you wouldn't want to program directly in Nock; and (c) learning to program directly in Nock is a great way to start understanding Urbit from the ground up.
Just as Unix runs C programs by compiling them to assembler, Urbit runs Hoon programs by compiling them to Nock. You could try to learn Hoon without learning Nock. But just as C is a thin wrapper over the physical CPU, Hoon is a thin wrapper over the Nock virtual machine. It's a tall stack made of thin layers, which is much easier to learn a layer at a time.
And unlike most fundamental theories of computing, there's really nothing smart or interesting about Nock. Of course, in a strictly formal sense, all of computing is math. But that doesn't mean it needs to feel like math. Nock is a simple mechanical device and it's meant to feel that way.
Specification
Let's start with the Nock spec. It may look slightly intimidating, but at least it isn't long.
No, you can't just look at this and tell what it's doing. But at least there are only 39 lines of it.
1 :: A noun is an atom or a cell.
2 :: An atom is a natural number.
3 :: A cell is an ordered pair of nouns.
4 ::
5 :: nock(a) *a
6 :: [a b c] [a [b c]]
7 ::
8 :: ?[a b] 0
9 :: ?a 1
10 :: +[a b] +[a b]
11 :: +a 1 + a
12 :: =[a a] 0
13 :: =[a b] 1
14 :: =a =a
15 ::
16 :: /[1 a] a
17 :: /[2 a b] a
18 :: /[3 a b] b
19 :: /[(a + a) b] /[2 /[a b]]
20 :: /[(a + a + 1) b] /[3 /[a b]]
21 :: /a /a
22 ::
23 :: *[a [b c] d] [*[a b c] *[a d]]
24 ::
25 :: *[a 0 b] /[b a]
26 :: *[a 1 b] b
27 :: *[a 2 b c] *[*[a b] *[a c]]
28 :: *[a 3 b] ?*[a b]
29 :: *[a 4 b] +*[a b]
30 :: *[a 5 b] =*[a b]
31 ::
32 :: *[a 6 b c d] *[a 2 [0 1] 2 [1 c d] [1 0] 2 [1 2 3] [1 0] 4 4 b]
33 :: *[a 7 b c] *[a 2 b 1 c]
34 :: *[a 8 b c] *[a 7 [[7 [0 1] b] 0 1] c]
35 :: *[a 9 b c] *[a 7 c 2 [0 1] 0 b]
36 :: *[a 10 [b c] d] *[a 8 c 7 [0 3] d]
37 :: *[a 10 b c] *[a c]
38 ::
39 :: *a *a
Bear in mind: this is pseudocode. It is neither Nock nor Hoon. Strictly speaking, it's really just English. All formal systems resolve to informal language at the very bottom. What's important is just that no two reasonable people can read the spec to mean two different things.
Sounds
In characteristic Urbit style, we got tired of three or four-syllable pronunciations of ASCII punctuation characters and assigned them all standard one-syllable names. We'll meet the rest later, but the ones we use in Nock:
`$` buc
`/` fas
`+` lus
`(` pel
`)` per
`[` sel
`]` ser
`*` tar
`=` tis
`?` wut
Nouns
Let's look at the data model first - lines 1-3 above. Ideally, you can look at this and just tell what it's doing. But let's explain it a little anyway.
An atom is a natural number - ie, an unsigned integer. Nock does not limit the size of atoms, or know what an atom means.
For instance, the atom 97 might mean the number 97, or it might mean the letter 'a' (ASCII 97). A very large atom might be the number of grains of sand on the beach - or it might be a GIF of your children playing on the beach. Typically when we represent strings or files as atoms, the first byte is the low byte. But even this is just a convention. An atom is an atom.
A cell is an ordered pair of any two nouns - cell or atom. We group cells with square brackets:
[1 1]
[34 45]
[[3 42] 12]
[[1 0] [0 [1 99]]]
To keep our keyboards from wearing out, line 6 tells us that brackets group to the right:
6 :: [a b c] [a [b c]]
So instead of writing
[2 3]
[2 [6 7]]
[2 [6 [14 15]]]
[2 [6 [[28 29] [30 31]]]]
[2 [6 [[28 29] [30 [62 63]]]]]
we can write
[2 3]
[2 6 7]
[2 6 14 15]
[2 6 [28 29] 30 31]
[2 6 [28 29] 30 62 63]
While this notational convenience is hardly rocket science, it's surprising how confusing it can be, especially if you have a Lisp background. Lisp's "S-expressions" are very similar to nouns, except that Lisp has multiple types of atom, and Lisp's syntax automatically adds list terminators to groups. So in Lisp
'(2 6 7)
is a shorthand for
'(2 6 7 . nil)
and the equivalent noun is
[2 6 7 0]
or, of course,
[2 [6 [7 0]]]
Rules
A Nock program is given meaning by a process of reduction. To compute
Nock(x)
, where x
is any noun, we step through the rules from
the top down, find the first left-hand side that matches x
, and
reduce it to the right-hand side.
Right away we see line 5:
5 :: Nock(a) *a
When we use variable names, like a
, in the pseudocode spec, we
simply mean that the rule fits for any noun a
.
So Nock(x)
is *x
, for any noun x
. And how do we reduce
*x
? Looking up, we see that lines 23 through 39 reduce *x
-
for different patterns of x
.
For example, suppose our x
is [5 1 6]
. Stepping downward
through the rules, the first one that matches is line 26:
26 :: *[a 1 b] b
Line 26 tells us that when reducing any noun of the form [a 1
b]
, the result is just b
. So *[5 1 6]
is 6
.
For a more complicated example, try
*[[19 42] [0 3] 0 2]
The first rule it matches is line 23:
23 :: *[a [b c] d] [*[a b c] *[a d]]
since a
is [19 42]
, b
is 0
, c
is 3
, and d
is [0 2]
.
So this reduces to a new computation
[*[[19 42] 0 3] *[[19 42] 0 2]]
Each side of this matches rule 25:
25 :: *[a 0 b] /[b a]
So we have
[/[3 [19 42]] /[2 [19 42]]]
When we explain /
, we'll see that this is [42 19]
.
Finally, suppose our x
is just 42
. The first rule that
matches is the last:
39 :: *a *a
So *42
is *42
, which is *42
. Logically, Nock goes into
an infinite reduction loop and never terminates.
In practice, this is just a clever CS way to specify the simple
reality that *42
is an error and makes no sense. An actual
interpreter will not spin forever - it will throw an exception
outside the computation.
Functions
We've already seen the *
function (pronounced "tar"), which
just means Nock
. This is the main show and we'll work through
it soon, but first let's explain the functions it uses - =
, ?
,
+
and /
.
Equals: =
=
(pronounced "tis", or sometimes "is") tests a cell for
equality. 0
means yes, 1
means no:
12 :: =[a a] 0
13 :: =[a b] 1
14 :: =a =a
Again, testing an atom for equality makes no sense and logically fails to terminate.
Depth: ?
?
(pronounced "wut") tests whether is a noun is a cell. Again,
0
means yes, 1
means no:
8 :: ?[a b] 0
9 :: ?a 1
(This convention is the opposite of old-fashioned booleans, so we try hard to say "yes" and "no" instead of "true" and "false.")
Increment: +
+
(pronounced "lus", or sometimes "plus") adds 1 to an atom:
10 :: +[a b] +[a b]
11 :: +a 1 + a
Because +
works only for atoms, whereas =
works only for
cells, the error rule matches first for +
and last for =
.
Address: /
/
(pronounced "fas") is a tree address function:
16 :: /[1 a] a
17 :: /[2 a b] a
18 :: /[3 a b] b
19 :: /[(a + a) b] /[2 /[a b]]
20 :: /[(a + a + 1) b] /[3 /[a b]]
21 :: /a /a
This looks way more complicated than it is. Essentially, we define a
noun as a binary tree - where each node branches to a left and right
child - and assign an address, or axis, to every element in the
tree. The root of the tree is /1
. The left child of
every node at /a
is /2a
; the right child is /2a+1
. (Writing (a
+ a)
is just a clever way to write 2*a
, while minimizing the set of
pseudocode forms.)
For a complete tree of depth 3, the axis address space looks like this:
1
2 3
4 5 6 7
8 9 10 11 12 13 14 15
Let's use the example [[97 2] [1 42 0]]
. So
/[1 [97 2] [1 42 0]] [[97 2] [1 42 0]]
because /1
is the root of the tree, ie, the whole noun. Then
its left child is /2
(ie, (1 + 1)
):
/[2 [97 2] [1 42 0]] [97 2]
And its right child is /3
(ie, (1 + 1 + 1)
):
/[3 [97 2] [1 42 0]] [1 42 0]
And delving into /3
, we see /(3 + 3)
and (3 + 3 + 1)
:
/[6 [97 2] [1 42 0]] 1
/[7 [97 2] [1 42 0]] [42 0]
If this seems like rocket science, the problem may be that you're too smart to understand Nock. Forget everything you learned in school and start over from line 1.
It's also fun to build nouns in which every atom is its own axis:
1
[2 3]
[2 6 7]
[[4 5] 6 7]
[[4 5] 6 14 15]
[[4 5] [12 13] 14 15]
[[4 [10 11]] [12 13] 14 15]
[[[8 9] [10 11]] [12 13] 14 30 31]
Once you've spent enough time programming in Urbit, you'll know these axes in your dreams. No - really.


RANSOM A film review by Christopher Heyn Copyright 1996 Christopher Heyn
Rating (out of *****) ***
Ransom has already received rave reviews, and is certain to be one of the highest-grossing films of the fall season. It may even be nominated for a number of Academy awards. Nevertheless, all the praise heaped upon Ron Howard's latest effort is due not to Ransom's brilliance, but to the noticeable lack of quality thrillers that have been released in the last few years. Despite the film's solid acting and high production values, Ransom suffers from weak scripting that sabotages what is otherwise an involving scenario. In a word, Ransom is overrated.
Ransom contains the same problems that plague all of Ron Howard's pictures (yes, even Apollo 13), in that the film certainly isn't bad, but it's nothing great, either. However, Ransom does feature some of the best directing of Howard's career, and Howard has overcome his tendency to make a "nice" film.  Ransom also features some great acting (most notably Mel Gibson and Gary Sinise), and the film wastes no time jumping into the story. However, despite all these positive elements, Ransom has some serious plot and character weaknesses that leave one asking a lot of questions after the film is over.
The film's biggest flaw is the weak motivation of its antagonist, Jerry Shaker (Gary Sinise). A police detective who uses his experience to mastermind what seems to be an airtight scheme to squeeze $2 million out of the high-powered airline entrepreneur Tom Mullen (Mel Gibson), Shaker's character motivation is never fully explained.
At first, it seems Shaker is only after the money. If so, why? Is it because he is deeply in debt to mob figures? Because he and his girlfriend/co-conspirator (played by Lily Taylor) plan to escape to a desert island for the rest of their lives? Who knows? That issue is never even touched upon. Then when Mullen turns the tables on Shaker, offering $4 million as a reward for his capture, dead or alive, Shaker's determination to hold onto Mullen's boy indicates a personal vendetta. Mullen says so himself later in the film. The audience is then left waiting for Shaker's explanation as to why he would risk an otherwise successful police career over such a high-profile stunt. This explanation never comes.
If Shaker's motivation *is* personal, then why doesn't he go ahead and kill Mullen's son once it is clear that Shaker will never get the money he seeks? Best to realize it's a botched affair, kill the boy, and call it a day. When it looks like Shaker and his three co-conspirators are going to be discovered, Shaker hides behind his badge, killing all three of them in a shootout, claiming later to have acted in self-defense. Questions linger, but Shaker is nevertheless a hero, and soon after goes to Mullen's home to collect the $4 million. However, when he does, he's armed with not one, but two handguns. If all Shaker wanted was the money to begin with, why doesn't he just play the hero role, take the check and deal with any fallout later... even leaving the country if the investigation of the case comes crashing down on him? Instead, Shaker risks killing a celebrity executive just when media attention on the case is at its highest. This would certainly indicate Shaker's motivation is a personal one. But again, we never learn what it is.
What's most disappointing about Ransom is that with just one scene or a few well-placed lines, this script flaw could have been easily resolved.
Another flaw in the movie is a result of director Howard lapsing back into "nice" mode at the most inopportune time. When Shaker is on the phone with Mullen, threatening to kill his son, Mullen calls his bluff. Immediately, Shaker kills Mullen's son. Quickly, Howard reveals to the audience that no, Shaker just shot the gun in the son's direction, but did not kill him. Meanwhile, Gibson and his wife (Rene Russo) are weeping out on the balcony over the "death" of their son. As the audience, we know their grief is misplaced, so the scene has no impact whatsoever. Up to this point in the film, we wonder whether Mullen plays things too fast and loose, especially when lives are involved. If we also believed that Mullen's son was dead, it would give credence to those of us that disagreed with Mullen's risky decision. Instead, Howard fumbles a potentially cathartic moment in the film.
One could continue on, giving examples of other logical plot flaws, but that would belabor the point. Ransom has its heart in the right place, and the high emotional pitch of the film is what audiences will respond to. However, those of us who expect a film to not only emotionally move us, but intellectually engage us, will come away from Ransom disappointed. It's unfortunate that the state of the modern thriller is so poor that Ransom is considered brilliant by comparison. One longs for the return of Alfred Hitchcock.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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review and has no editorial control. Unless stated otherwise, the copyright
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I’m trying to get Ubuntu 10.10 running in a VM on my iMac. It mostly works with either VirtualBox or VMWare Fusion, but I can’t get 3D acceleration working. Under VirtualBox, I can get compositing happening, but it’s very slow. Under VMWare Fusion, Ubuntu won’t even acknowledge that I have a 3D card. Help.
Edit: If you can point me to a working guide for dual booting Ubuntu on the iMac 27”, then that would also be great.
Edit: If you can point me to a working guide for dual booting Ubuntu on the iMac 27”, then that would also be great.
I guess I'll have a poke around some other sites.
Note that when dual-booting, it actually doesn't matter if you install GRUB to the MBR or the partition – Mac EFI does the right thing (tm) in either case, and you can't break your OS X boot anyway.
As for dual booting, it should Just Work with 10.04.1 or later - the only non-standard wrinkles are:
* Install GRUB to the partition not the MBR
* Install rEFIt on the Mac so you can select which OS to boot.
I'd ignore the Boot Camp tools as they are rather too focussed on Windows.


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See:
Description
| Interface Summary | |
|---|---|
| JobResult | :: DeveloperApi :: A result of a job in the DAGScheduler. |
| OutputCommitCoordinationMessage | |
| SparkListener | :: DeveloperApi :: Interface for listening to events from the Spark scheduler. |
| SparkListenerEvent |
| Class Summary | |
|---|---|
| AccumulableInfo | :: DeveloperApi ::
Information about an Accumulable modified during a task or stage. |
| AskPermissionToCommitOutput | |
| InputFormatInfo | :: DeveloperApi :: Parses and holds information about inputFormat (and files) specified as a parameter. |
| JobLogger | :: DeveloperApi :: A logger class to record runtime information for jobs in Spark. |
| JobSucceeded | |
| RuntimePercentage | |
| SchedulingMode | "FAIR" and "FIFO" determines which policy is used to order tasks amongst a Schedulable's sub-queues "NONE" is used when the a Schedulable has no sub-queues. |
| SparkListenerApplicationEnd | |
| SparkListenerApplicationStart | |
| SparkListenerBlockManagerAdded | |
| SparkListenerBlockManagerRemoved | |
| SparkListenerEnvironmentUpdate | |
| SparkListenerExecutorAdded | |
| SparkListenerExecutorMetricsUpdate | Periodic updates from executors. |
| SparkListenerExecutorRemoved | |
| SparkListenerJobEnd | |
| SparkListenerJobStart | |
| SparkListenerStageCompleted | |
| SparkListenerStageSubmitted | |
| SparkListenerTaskEnd | |
| SparkListenerTaskGettingResult | |
| SparkListenerTaskStart | |
| SparkListenerUnpersistRDD | |
| SplitInfo | |
| StageInfo | :: DeveloperApi :: Stores information about a stage to pass from the scheduler to SparkListeners. |
| StatsReportListener | :: DeveloperApi :: Simple SparkListener that logs a few summary statistics when each stage completes |
| StopCoordinator | |
| TaskInfo | :: DeveloperApi :: Information about a running task attempt inside a TaskSet. |
| TaskLocality |
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A Walk On The Moon ***
rated R starring Diane Lane, Viggo Mortensen, Liev Schreiber, Anna Paquin, Tovah Feldshuh written by Pamela Gray directed by Tony Goldwyn
1969. The year has been explored numerous times, most notably(to me) in "The Wonder Years", which is the only T.V. show that I actually enjoy watching. In 1969, man landed on the moon, and Woodstock hit the world like no other musical event had before. Actor Tony(GHOST, NIXON) Goldwyn's directorial debut, A WALK ON THE MOON, tells the story of a family whose lives are turned upside down during the summer of '69 in a Catskills bungalow colony.
Pearl Kantrowitz(Diane Lane) is a Jewish housewife from the city who seems to be bored with her marriage to straightforward Marty(Liev Schreiber). She got pregnant at 17 and forfeited her young life to take care of her two children, Alison(Anna Paquin) and Daniel(Bobby Boriello). She seems to want to experiment more, to be young again, and there is no better time than the summer of '69. It is then that the blouse salesman(Viggo Mortensen) arrives at the bungalow colony. His name is Walker Jerome, and he is quite the hippie. He rides around in his bus full of blouses, and soon, the married Pearl is joining him. Pearl's psychic mother-in-law(Tovah Feldshuh) knows about her little outings with the blouse man and urges her to stop it for her family's sake. Meanwhile, the teenage Alison is coping with her first boyfriend, and her first period. She is also going through an experimental hippie phase, and it is not long before she catches her mom at Woodstock with the blouse man. Pearl's mother-in-law finally calls up her son and forces him to get up to the Catskills immediately to try to fix up the whole mess.
There is nothing especially groundbreaking about the story or the technique, but the story is told in such down-to-earth fashion that you can't help feeling intrigued by the coming-of-age of Pearl and Alison. Mortensen's Walker is never more than a caricature though, and the story is by-the-numbers, but the film does manage to capture the whirlwind of turmoil in the Kantrowitz family. Diane Lane is very good, and Academy Award winner Anna Paquin gives her character as much life as possible. All in all, Tony(grandson of Samuel) Goldwyn's A WALK ON THE MOON doesn't offer much insight into the lively 60's, but it does present us with a nice little slice of life that is sure to charm most viewers. No matter how much Miramax has sold out, the company still knows how to put out good movies year-round.
review by Akiva Gottlieb, the Teenage Movie Critic<EMAIL_ADDRESS>http://www.angelfire.com/mo/film watch me on TBS' "Dinner And A Movie" May 21, 8:05pm EST
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Automatically decrypt on page load: Will attempt to use a known password to decrypt the vault as soon as the page loads, instead of having to click the Decrypt button.
© Gareth Watts 2015


God, we’re disappointed in the way others have hurt us and those we love. Some of those disappointments are small and insignificant. Others are so big they feel like a heavy weight on our shoulders. Help us to be thankful for this chance to show what we’re made of. Teach us to respond as You do, with grace and mercy. And when we are the ones doing the disappointing, help us to be quick to ask for forgiveness and quick to forgive ourselves. Amen.
Act on It
List below the names of some people who have disappointed or hurt you in the past. Start with personal relationships, but expand to companies, institutions, people groups, or organizations that have disappointed you in some way. Circle one or two that you’d like to work on forgiving. Brainstorm ways you can respond to each of these with joy, showing what you’re made of by displaying the nature of Christ.


Package algorithms.matrix
Class LinearEquations
- java.lang.Object
-
- algorithms.matrix.LinearEquations
-
public class LinearEquations extends java.lang.Object
a class of utility methods for linear algebra.note, for solving linear least squares problems, a good resource aside from Strang, Golub and van Loan, and Cormen, Leiserson, Rivest, and Stein is: “Algorithms for Linear Least Squares Problems”, Bj̈orck 1991, published in Computer Algorithms for Solving Linear Algebraic Equations; The State of the Art., Vol. 77 of NATO-ASI Series F: Computer and Systems Sciences, pages 57–92. LUPSolve, __, and __ follow pseudocode from Cormen, Leiserson, Rivest, and Stein, "Introduction to Computer Algorithms".
Note that any rectangular matrix can be made into a square matrix by adding zero rows or columns, without changing the nonzero singular values. Bjorck 1991, "Algorithms for Linear Least Squares Problems"
-
-
Nested Class Summary
Nested Classes Modifier and Type Class Description static class
LinearEquations.LDL
static class
LinearEquations.LDM
lower triangular portion of A = L * D * M^Tstatic class
LinearEquations.LU
static class
LinearEquations.LUP
-
Constructor Summary
Constructors Constructor Description LinearEquations()
-
Method Summary
All Methods Static Methods Concrete Methods Modifier and Type Method Description static double[]
_leastSquaresPolynomial(double[][] xy, int polyOrder, boolean solveFullRank)
given data points xy and the assumption that measurement errors are small, fit a polynomial of order polyOrder to the data points, minimizing the error, i.e.static double[][]
choleskyDecompositionViaLDL(double[][] a, double eps)
compute the cholesky decomposition for symmetric positive definite matrix a.static double[][]
choleskyDecompositionViaMTJ(double[][] a)
compute the cholesky decomposition for symmetric positive definite matrix a using the MTJ library.static double[][]
gaussianEliminationViaLU(double[][] a, double tol)
create the reduced row echelon form of matrix a as the Upper right matrix from the LU decomposition, further normalized and reduced.static LinearEquations.LDL
LDLDecomposition(double[][] a)
for a nonsingular symmetric matrix A (with real numbers in matrix of size nXn), perform and L-D-L decomposition which is a variation of L-U decomposition.static LinearEquations.LDL
LDLDecomposition(double[][] a, double eps)
for a nonsingular symmetric matrix A (with real numbers in matrix of size nXn), perform and L-D-L decomposition which is a variation of L-U decomposition.static LinearEquations.LDM
LDMDecomposition(double[][] a)
for a symmetric matrix A (with real numbers in matrix of size nXn), perform and L-D-M decomposition which is a variation of L-U decomposition.static double[]
leastSquaresPolynomial(double[][] xy, int polyOrder)
given data points xy and the assumption that measurement errors are small, fit a polynomial of order polyOrder to the data points, minimizing the error, i.e.static double[]
leastSquaresPolynomial(double[][] xy, int polyOrder, boolean solveForFullRank)
given data points xy and the assumption that measurement errors are small, fit a polynomial of order polyOrder to the data points, minimizing the error, i.e.static LinearEquations.LU
LUDecomposition(double[][] a)
an efficient LUP decomposition for a being a square non-singular matrix and P is the Identity matrix.static LinearEquations.LUP
LUPDecomposition(double[][] a)
an LUP decomposition for a being a square non-singular matrix that tries to reduce errors due to division by small numbers.static double[]
LUPSolve(double[][] ell, double[][] u, int[] p, double[] b)
solving for 'x' in equations: a_0_0*x_0 + a_0_1*x_1 + ...(package private) static void
makePivots1(double[][] u, double tol)
static double[]
solveXFromLUDecomposition(double[][] a, double[] b)
solve for x in A*x=b by LU decomposition
-
-
-
Method Detail
-
LUPSolve
public static double[] LUPSolve(double[][] ell, double[][] u, int[] p, double[] b)
solving for 'x' in equations: a_0_0*x_0 + a_0_1*x_1 + ... a_0_n*x_n = b_0 a_1_0*x_0 + a_1_1*x_1 + ... a_1_n*x_n = b_1 ... a_n-1_0*x_0 + a_n-1_1*x_1 + ... a_{n-1}_n*x_{n-1} = b_{n-1} where a is a square matrix, that is, there are n equations and n unknowns. LUP decomposition is more numerically stable than x = A^-1*b. can find L, U, and P such that P*A=L*U where L is a lower triangular matrix, U is an upper triangular matrix, and P is a permutation matrix. (a permutation matrix is all 0's excepting a single 1 in each column, uniquely. multiplying a vector by a permutation rearranges the members of the vector.) Uses forward substitution then back substitution. L*U*x=P*b. let y=U*x. L*y=P*b (uses forward substitution with these results). U*x=y (uses back substitution with these results). A*x=b (then solves x_i=(y_i - summation_j=i_to_{n-1}(u_i_j*x_j))/u_i_i). runtime complexity is O(n^2) for backward and forward substitutions. The method follows Cormet et al. Chap 28.3.- Parameters:
ell
- is an nxn lower triangular matrix using row major format.u
- is an nxn upper triangular matrix using row major format.p
- is an array of length n holding permutation vector columns. e.g. p_0=0 states that column 0 contains a 1 for row=0. e.g. p_1=2 states that column 2 contains a 1 for row=1. full_permutation_matrix = zeros everywhere except the row, col pairs in pb
- is an array of length n.- Returns:
- array of x
-
solveXFromLUDecomposition
public static double[] solveXFromLUDecomposition(double[][] a, double[] b)
solve for x in A*x=b by LU decomposition- Parameters:
a
- matrixb
- vector- Returns:
- the solution x to a * x = b
-
LUDecomposition
public static LinearEquations.LU LUDecomposition(double[][] a)
an efficient LUP decomposition for a being a square non-singular matrix and P is the Identity matrix. uses Gaussian elimination and the Schur complement while making recursive subdivision subdivisions. The runtime is O(n^3).- Parameters:
a
- square two dimensional array in row major format. a is a non-singular matrix(i.e. has exactly one solution). the rank of a is n (it's dimensions are m x n).- Returns:
- LU a wrapper holding the 2 two-dimensional row major output arrays. L and U. they are both size nXn where n=a.length. L is a unit lower triangular matrix (has 1's on the diagonal) and U is an upper triangular matrix whose diagonals are the matrix pivots. Note that the determinant of A is the product of the U diagonals, that is det(A) = u[0][0] * u[1][1]...*u[n-1][n-1].
-
gaussianEliminationViaLU
public static double[][] gaussianEliminationViaLU(double[][] a, double tol) throws no.uib.cipr.matrix.NotConvergedException
create the reduced row echelon form of matrix a as the Upper right matrix from the LU decomposition, further normalized and reduced.- Parameters:
a
- a rectangular matrixtol
- tolerance for comparing to zero. e.g. the machine precision.- Returns:
- the reduced echelon matrix of a
- Throws:
no.uib.cipr.matrix.NotConvergedException
-
makePivots1
static void makePivots1(double[][] u, double tol)
-
LDMDecomposition
public static LinearEquations.LDM LDMDecomposition(double[][] a)
for a symmetric matrix A (with real numbers in matrix of size nXn), perform and L-D-M decomposition which is a variation of L-U decomposition. If all leading principal sub-matrices of A are non-singular then there exist lower unit triangular matrices L and M and a diagonal matrix D = diag(d1, d2, ...d_n) where d_i = u_i_i. A = L*D*M^T. Note that D is non-singular and that M^T = D^-1*U is unit upper triangular. (A=L*U = L*D*(D^-1*U = L*D*M^T).Once A = L*D*M^T is decomposed, one can solve A*x = b: L*y = b (in (n^2)/2 flops D*z= y (in n flops) M^T*x = z (in (n^2)/2 flops) References: Golub and va Loan, "Matrix Computations", Section 5.1
2n^3/3 flops.- Parameters:
a
- two dimensional array in row major format. a is a symmetric matrix with dimensions n x n.- Returns:
- LDM a wrapper holding the 2 two-dimensional row major output arrays. L and M and the diagonal matrix D as a an array of the diagonal.
-
choleskyDecompositionViaMTJ
public static double[][] choleskyDecompositionViaMTJ(double[][] a)
compute the cholesky decomposition for symmetric positive definite matrix a using the MTJ library.- Parameters:
a
- symmetric positive definite matrix- Returns:
- lower triangular matrix G which G is a lower triangular matrix with positive diagonal entries. a = G*G^T.
-
choleskyDecompositionViaLDL
public static double[][] choleskyDecompositionViaLDL(double[][] a, double eps)
compute the cholesky decomposition for symmetric positive definite matrix a. reference: golub and van loan "matrix computations, theorem 5.2-3. This method uses LDL decomposition to compute G in a = G*G^T where G is a lower triangular matrix with positive diagonal entries.- Parameters:
a
- symmetric positive definite matrixeps
- value for an error tolerance around zero used in the LDL decomposition.- Returns:
- lower triangular matrix G which G is a lower triangular matrix with positive diagonal entries. a = G*G^T.
-
LDLDecomposition
public static LinearEquations.LDL LDLDecomposition(double[][] a)
for a nonsingular symmetric matrix A (with real numbers in matrix of size nXn), perform and L-D-L decomposition which is a variation of L-U decomposition. Computes a unit lower triangular matrix L and a diagonal matrix D = diag(d1, d2, ...d_n) such that A = L*D*L^T.References: Golub and van Loan, "Matrix Computations", Algorithm 5.1.2
n^3/6 flops.- Parameters:
a
- two dimensional array in row major format. a is a symmetric matrix with dimensions n x n.- Returns:
- LDM a wrapper holding the 2 two-dimensional row major output arrays. L and M and the diagonal matrix D as a an array of the diagonal.
-
LDLDecomposition
public static LinearEquations.LDL LDLDecomposition(double[][] a, double eps)
for a nonsingular symmetric matrix A (with real numbers in matrix of size nXn), perform and L-D-L decomposition which is a variation of L-U decomposition. Computes a unit lower triangular matrix L and a diagonal matrix D = diag(d1, d2, ...d_n) such that A = L*D*L^T.References: Golub and van Loan, "Matrix Computations", Algorithm 5.1.2
n^3/6 flops.- Parameters:
a
- two dimensional array in row major format. a is a symmetric matrix with dimensions n x n.eps
- value for a tolerance of an error around 0.- Returns:
- LDM a wrapper holding the 2 two-dimensional row major output arrays. L and M and the diagonal matrix D as a an array of the diagonal. Note that the method will return null when an intermediary calculation is smaller than eps.
-
LUPDecomposition
public static LinearEquations.LUP LUPDecomposition(double[][] a)
an LUP decomposition for a being a square non-singular matrix that tries to reduce errors due to division by small numbers. creates a permutation matrix to pivot rows so that the row reduction divisions are by the largest numbers. uses Gaussian elimination and the Schur complement while making recursive subdivision subdivisions. The runtime is O(n^3).- Parameters:
a
- square two dimensional array in row major format. a is a non-singular matrix (i.e. has exactly one solution). the rank of a is n (it's dimensions are m x n).- Returns:
- LUP a wrapper holding the 2 two-dimensional row major output arrays. L and U and the condensed permutation array p, where P*A=L*U.
-
leastSquaresPolynomial
public static double[] leastSquaresPolynomial(double[][] xy, int polyOrder, boolean solveForFullRank) throws no.uib.cipr.matrix.NotConvergedException
given data points xy and the assumption that measurement errors are small, fit a polynomial of order polyOrder to the data points, minimizing the error, i.e. solve for coefficients c in y_i = summation(c_i*x^i) + error. calculated by c = pseudo-inverse of A * y where A is the components of x as polynomial factors. NOTE that a regularized linear least squares algorithm called Elastic-Net is implemented as thirdparty.scipy.optimization.ElastticNet. This method follows pseudocode in chapter 28 of Cormen, Leiserson, Rivest, and Stein Introduction To Algorithms.- Parameters:
xy
- two dimensional array of format row0=[x0,y0], row1=[x1,y1], etc.polyOrder
- the order of a polynomial to fit. should be .lte. the number of rows.solveForFullRank
- when 'True' AX=b has no solution (e.g. xy.length is larger than xy[0].length) and the algorithm uses (inverse(A^T*A) * A^T) for the pseudo-inverse (see Chap 4.3 from the book "Introduction to Linear Algebra" by W Gilbert Strang and Chap 28 from the book "Introductionvto Algorithms" by Cormen, Leiserson, Rivest, and Stein.) when solveForFullRank is set to 'False' this method uses the SVD to create a pseudoinverse (see Chap 7 from the book "Introduction to Linear Algebra" by W Gilbert Strang.)- Returns:
- coefficients c where y_i = summation(c_i*x^i) + error
- Throws:
no.uib.cipr.matrix.NotConvergedException
- thrown if MTJ SVD operation did not converge
-
leastSquaresPolynomial
public static double[] leastSquaresPolynomial(double[][] xy, int polyOrder) throws no.uib.cipr.matrix.NotConvergedException
given data points xy and the assumption that measurement errors are small, fit a polynomial of order polyOrder to the data points, minimizing the error, i.e. solve for coefficients c in y_i = summation(c_i*x^i) + error. calculated by c = pseudo-inverse of A * y where A is the components of x as polynomial factors. NOTE that a regularized linear least squares algorithm called Elastic-Net is implemented as thirdparty.scipy.optimization.ElastticNet. This method follows pseudocode in chapter 28 of Cormen, Leiserson, Rivest, and Stein Introduction To Algorithms.- Parameters:
xy
- two dimensional array of format row0=[x0,y0], row1=[x1,y1], etc.polyOrder
- the order of a polynomial to fit. should be .lte. the number of rows.- Returns:
- coefficients c where y_i = summation(c_i*x^i) + error
- Throws:
no.uib.cipr.matrix.NotConvergedException
-
_leastSquaresPolynomial
public static double[] _leastSquaresPolynomial(double[][] xy, int polyOrder, boolean solveFullRank) throws no.uib.cipr.matrix.NotConvergedException
given data points xy and the assumption that measurement errors are small, fit a polynomial of order polyOrder to the data points, minimizing the error, i.e. solve for coefficients c in y_i = summation(c_i*x^i) + error. calculated by c = pseudo-inverse of A * y where A is the components of x as polynomial factors. NOTE that a regularized linear least squares algorithm called Elastic-Net is implemented as thirdparty.scipy.optimization.ElastticNet. This method follows pseudocode in chapter 28 of Cormen, Leiserson, Rivest, and Stein Introduction To Algorithms.- Parameters:
xy
- two dimensional array of format row0=[x0,y0], row1=[x1,y1], etc.polyOrder
- the order of a polynomial to fit. should be .lte. the number of rows.solveFullRank
- when 'True' AX=b has no solution (e.g. xy.length is larger than xy[0].length) and the algorithm uses (inverse(A^T*A) * A^T) for the pseudo-inverse (see Chap 4.3 from the book "Introduction to Linear Algebra" by W Gilbert Strang and Chap 28 from the book "Introductionvto Algorithms" by Cormen, Leiserson, Rivest, and Stein.) when solveForFullRank is set to 'False' this method uses the SVD to create a pseudoinverse (see Chap 7 from the book "Introduction to Linear Algebra" by W Gilbert Strang.)- Returns:
- coefficients c where y_i = summation(c_i*x^i) + error
- Throws:
no.uib.cipr.matrix.NotConvergedException
-
-


Provides interfaces and classes for accessing data taken Semcor-formatted concordances, useful for benchmarking detectors.


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A Little About Me. Well i have been a creator forever.
Music, anima, and now i want to make a living making
websites. I have always looked at coding. I always wanted to
know how it works. I started of learning python and it was
really hard. That is what made me love it so much. The fact
that i can do a little. I really want to find out where I
end up and how far I go.
I have a son with autism. Its alot but I know god would
never but too much on me i can not handle. Like This
project. I have been on a long road to finding myself. I had
to let alot of people go because they did not want anything,
but i wanted everything. I have never gave up on anything no
matter how bad it get, i still seem to want to do more. Its
so easy to give up, and i never want anything easy. This
project was hard but i still put my all into it... i stay up
long nights trying to do it on my own. I have made alot of
mistake. But i am still pushing forwards.


class: center, middle # An Introduction to High Performance Computing ??? Notes for the _first_ slide! --- # What is High Performance Computing? -- * Using more than your laptop or desktop (or high powered workstation) -- * Designed with various hardware architectures that fit different problems -- * Allows you to run analyses not possible with a typical computer -- * High memory, high core count, bigdata --- # Before we launch in to ISU-HPC... -- * Are you tired of typing: ``` $ ssh
@hpc-class.its.iastate.edu ``` ???? -- * `cd` into your root directory and try: `ls -a` -- * You should see a hidden folder named `.ssh` -- * `cd` into `.ssh` and create a file named "config": ``` $ touch config ``` --- # Before we launch in to ISU-HPC... -- * Now open the file `config`, add the following contents, and save the file: ``` Host hpc-class HostName hpc-class.its.iastate.edu User
``` -- * Now try it out! ``` $ ssh hpc-class ``` -- * And once you're in hpc-class, a few new tricks: ``` $ hostname $ whoami ``` --- # Performance Monitoring * htop (or top) * iostat * iftop
There are other tools, but these will get you the basic info you need and are installed on most systems by default --- # Terminology * HPC terminology can be confusing, sometimes the same words have different contextual meaning (e.g. threads) ## Terms * Nodes: compute node, head node -- * Processors (a cpu chip) -- * Cores (physical cpus embedded on a single chip) -- * Threads: hyper-threading (software 'trick' to let two processes share a core) -- * Scheduler: Allocates access to resources to users (ISU uses Slurm) --- #Cluster Diagram
--- # Compute Resources at ISU ## Clusters * [hpc-class](http://www.hpc.iastate.edu/guides/classroom-hpc-cluster) * For classes, not research * 48 nodes * 2.0GHz, 16 cores, 64GB RAM -- * [condo2017](http://www.hpc.iastate.edu/guides/condo-2017) * Primarily for sponsored research * 192 Nodes * 2.6GHz, 16 cores, 128GB RAM * 3 High-memory nodes (1 and 2TB RAM) * Free-tier - 88 nodes * 2.0GHz, 16 cores, 128GB RAM * 1 High-memory node (1TB RAM) --- # Compute Resources at ISU ## Clusters * [nova](http://www.hpc.iastate.edu/guides/nova) * Primarily for sponsored research * 100 Nodes * Fast Skylake processors, 36 cores, 192-384GB RAM * 1 High-memory node (3TB RAM) -- Also check out these [UNIX tutorials](https://www.hpc.iastate.edu/guides/unix-introduction) through ISU-HPC! --- # Compute Resources at ISU ## [Custom hardware](http://rit.las.iastate.edu/hardware) * BioCrunch (for multithreaded shared memory programs) * 2.4GHz, 80 threads, 768GB of RAM -- * BigRAM (for large memory needs like de-novo assembly) * 2.6GHz, 48 threads, 1.5TB of RAM -- * Speedy (for single threaded programs, like R) * 3.4GHz, 24 threads, 256GB of RAM -- * Speedy2 (for single threaded programs, like R) * 3.2GHz, 32 threads, 256GB of RAM -- * LASWIN01 (for Windows only software) * 2.6GHz, 24 threads, 64GB of RAM -- * Legion (for massively parallel applications) * 4 nodes * 1.3GHz, 272 threads, 386GB of RAM (each) --- #Compute Resources ## [Xsede](https://www.xsede.org/) * ISU researchers have access to the national supercomputer centers (e.g. TACC, PSC) via Xsede * For problems that need to scale larger than our on-campus resources * Contact campus champions: Jim Coyle or Andrew Severin ## Cloud (AWS, Azure, etc.) * Tempting introductory rates * Be cautious of putting in a credit card (charges can accumulate quickly) * Consider data transfer times and speed * Consult with IT before purchasing - they have special negotiated rates --- # Software ## Modules: * Modules are used to allow multiple people to use the same software with consistent, reproducible results -- * Modules keep your environment clean and free of conflicts (e.g. python2/3 or Java7/8) -- * Think of modules as a software library. You need to check out the software before you can use it. -- * Modules can be searched: -- ``` $ module avail -------------------- /opt/rit/modules --------------------------------- abyss/1.5.2 freesurfer/5.3.0 lib/htslib/1.2.1 abyss/1.9.0 gapcloser/1.12-r6 lib/htslib/1.3 afni/17.0.10 gapfiller/1-10 lib/htslib/1.3.2 albert/20170105 gatk/3.4-46 lib/ICE/1.0.9 ... ``` --- # Software ## Modules * To use a module: -- ``` $ module load
``` -- * To delete a module: -- ``` $ module unload
``` -- * Default behavior is to load the latest version if not specified -- * Use 'module purge' to clear your environment before loading something different --- # Storage -- ## Code * Git (github, bitbucket, gitlab, etc.) -- ## Datasets -- * Storage on the clusters and servers should be treated as temporary -- * Become familiar with the [disk architecture on each machine](https://researchit.las.iastate.edu/getting-best-io-performance-your-computational-jobs) -- * Keep backups --- # Job Scheduler -- * Scheduler assigns jobs from the queue to the compute nodes -- * ISU uses [SLURM](https://researchit.las.iastate.edu/slurm-basics) -- * Think about the scheduler like a hotel reservation - you're charged for the room whether you use it or not, and if you ask for an especially long or large reservation, the room may not be available when you want it. -- * Basic info required: how many nodes, how long -- * [Script writer](https://www.hpc.iastate.edu/guides/condo-2017/slurm-job-script-generator-for-condo) can get you started --- # Slurm Script Cheatsheat: --
--- ## Sample SLURM script (mysbatch.sh) -- ``` #!/bin/bash #SBATCH --time=1:00:00 # walltime #SBATCH --nodes=2 # number of nodes in this job #SBATCH --ntasks-per-node=16 # total number of processor cores in this job #SBATCH --output=myout_%J.log #SBATCH --error=myerr_%J.err module load R Rscript MyThing.R ``` -- Then submit: ``` $ sbatch mysbatch.sh ``` --- #Slurm Job Management Cheatsheat: --
--- # Common stumbling blocks -- * Over or under using resources -- * Not taking advantage of the right machine for the problem -- * Moving data through slow links -- * Trying to scale up programs that weren't designed for large datasets --- # Support * [Research IT](http://rit.las.iastate.edu) * Build software, run custom hardware, consult on performance improvements, etc. * Collaboration between LAS, CALS, IT, etc. http://rit.las.iastate.edu/people *<EMAIL_ADDRESS>* IRC (chat): #bitcom on freenode * [ISU IT HPC Team](http://hpc.iastate.edu) * Cluster Support team * hpc-help@iastate.edu


@import connectors.ExchangeObjects.OnlineTest
@(usr: CachedData, onlineTest: Option[OnlineTest] = None,
applicationStatus: models.ApplicationData.ApplicationStatus.ApplicationStatus,
allocationDetails: Option[connectors.AllocationExchangeObjects.AllocationDetails])(implicit request: Request[_])
@import models.ApplicationData.ApplicationStatus._
@applicationStatus match {
case REGISTERED | CREATED | IN_PROGRESS | WITHDRAWN => {
Once you've submitted your application and completed the diversity questions, you'll be invited to take the online tests. Once you've been invited, you'll have 7 days to complete the tests.
You have @{
onlineTest.map(v => v.getDuration).getOrElse("")
} to complete the tests.
The tests will expire at @{
onlineTest.map(v => v.getExpireDateTime).getOrElse("")
}.
The tests will consist of
Competency questionnaire (untimed)
Verbal reasoning exercise (6 minutes) *
Numerical reasoning exercise (6 minutes) *
Situational judgement questionnaire (untimed)
* Note: if you've applied under the guaranteed interview scheme (GIS), you don't need to do the verbal or numerical reasoning exercises.
The online tests
will be extremely difficult to use on a small device.
You have @{
onlineTest.map(v => v.getDuration).getOrElse("")
} to complete the tests.
The tests will expire at @{
onlineTest.map(v => v.getExpireDateTime).getOrElse("")
}.
The online tests
will be extremely difficult to use on a small device.
}
case ONLINE_TEST_FAILED | ONLINE_TEST_FAILED_NOTIFIED if onlineTest.exists(_.pdfReportAvailable) => {
@* Only display failed section if a PDF report for the failure is available *@
We've assessed your test results and unfortunately you've been
unsuccessful this time.
You'll be able to reapply for Fast Track next year, alternatively
use the find an apprenticeship service to search for other apprenticeships in England.
@onlineTest.map { test =>
@if(test.pdfReportAvailable) {


Just open the downloaded alloy4.dmg file (available at alloy.mit.edu), and double click on the Alloy4 icon.
To keep the application, drag the icon out of the dmg window and place it somewhere in your home directory or on your desktop.
Just download the alloy4.jar file (available at alloy.mit.edu) then double-click on the jar file, or type:
java -jar alloy4.jar
in the console.
Please note:
Platform-dependent libraries are currently available only for Mac OS X, Windows (x86), Linux (x86), and FreeBSD (x86).
On other platforms, you can still run the Alloy Analyzer, but you will need to select the pure Java solver "SAT4J" (through the Options menu).


#!/usr/bin/perl5 -s
print "Content-type: text/html\n\n";
print qq~
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~;
exit;


According to Business Insider, Instagram believes it will soon eclipse Twitter:
Management at Instagram believes its user base will eventually grow to 1 billion monthly active users — making the photo sharing app far, far bigger than Twitter.
If this ends up being true, I think it highlights so many important lessons:
The Medium. Imagery is more powerful than words, even in the 21st Century. Although Twitter allows photos to exist with tweets, it is not a fundamentally visual platform like Instagram is.
Skill Building. Even if illusory, Instagram makes Average Joe believe he is a better photographer; no one is under the presumption that Twitter makes him a better writer.1 That type of persuasiveness carries power.
Outside Assets. Twitter truly shot itself in the foot when it shunned its biggest asset: its developer community.2 Instagram doesn’t have nearly the same third-party power; one has to wonder if Twitter destroyed a huge advantage.
Staleness. An interesting idea to consider is that Instagram and Twitter’s primary rival – Facebook – is wobbling. Today’s culture wants specific, targeted interaction, and Facebook doesn’t offer that.
- Not entirely true, perhaps, but I support that Twitter merely enhances already-present writing skills (good or bad): well-written tweets by WSJ contrast sharply to those written by other, less literate sources. ↩
- http://thenextweb.com/twitter/2012/08/21/twitter-secured/ ↩


Browse Mode
Use browse mode to view your project hierarchy. Folder icons represent projects and subprojects. You can double click any project icon to navigate downward in the project hierarchy, or click the Up button to navigate upwards toward your main directory (see Browse and Scan Shortcuts).
When you make a selection of a project directory and switch to Scan mode, only the source files associated with that project are available for scanning and querying. You may want to select a specific low-level directory to limit query results, or your highest level project directory for the most comprehensive querying capabilities.


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I was fortunate enough to be selected to work on one of my favorite games of all time, Starcraft II. I created unit tests, reported/fixed bugs, and added example use cases to Blizzard's and Google Deepmind's AI API project, which is publicly available on GitHub.
I also worked on a super cool feature that will be in the game in the near future. As much as I would love to tell you guys about it, I'm afraid that the feature has yet to be publicly announced. Check back again soon!
I worked at Blizzard's main campus out in sunny Irvine, CA. Blizzard is famously known for having cool offices an amazing work culture. The endless amount of food and gaming events was enough to keep me happy, but what really makes Blizzard a fun company to work for is its people. Working alongside fellow nerds made programming twice as fun!
Pictured on the left is just a small subset of the incredibly talented Starcraft II team (thats me in the middle). Their passion for games and technology made my time at Blizzard Entertainment a blast!
The premise for the club was based on an early computer hobbyist group in Silicon Valley called the Homebrew Computer Club. The club was essentially a group of electronic enthusiasts who collaborated on projects together. The were driven by their passion for technology, and the club's ultimate goal was to learn and have fun. I was greatly inspired by the enthusiasm held by the original club, so I decided to make my own version on campus, as club for students who love to work on projects together and have fun while doing it!
Each semester, the club will take on a new software project, typically in an unfamiliar domain. At the end of the semester we host an educational talk about the trials and tribulations of our project and share what we learned with our fellow students.
This semester we are working on building an arcade machine, running our own, homemade arcade games. We plan to donate the machine to the school upon its completion.
Initally, when the club was first getting started, I held a more "hands-on" role in the club; I spent a majority of my time coding and collaborating with fellow club members.
Since the club has grown in size significantly, I currently spend most of my time delegating work among the club members. I assess club member's skills, assign them to appropriate teams based on their competency and interests, set deadlines and manager the overall project schedule, and offer feedback and support to less experienced coders in the club.
Turner Studios is located in the urban city of Atlanta, GA. The company is widely known as a powerhouse in the television media industry. Some notable networks they own are: CNN, TBS, Cartoon Network, Adult Swim, and TNT. Working so close to the film industry was an exciting experience. Between seeing celebrities walking through campus and all the tv premier events, there was always something fun going on at Turner Studios.
I spent my time at Turner reverse engineering an old WinForms application. My task was to take document of all services that were being called by the current front-end, and to create RESTful API endpoints to serve the same data to a new React front-end, while maintaining the integrity of the existing WebForms services.
I am a current Software Engineering student at Kennesaw State University(KSU). I enjoy working with and creating artificial intelligence programs, low-level C++ projects, and playing video games.
I am the founder and current president of the KSU Homebrew Computing Club. I am interning for Turner Studios as a Software Developer and currently looking for full-time positions for late 2018. My estimated graduation date is Fall 2018.
I began learning C++ when I was about 13 years old; it was my first programming language ever! Since then, I've had a strong inclination towards the language. As a result, a majority of my professional, personal, and university projects have dealt with C++. I know the language better than most other languages, and I'm always looking expand my knowledge.
I have professional experience working at Blizzard Entertainment building tools and working on an unannounced feature for Starcraft II. I also have a large amount of professional experience working with C# applications and have recently began working on a WPF personal project, which you can read more about below.
Occasionally I'll find myself having linker or include issues when I'm working with large C++ projects. So naturally, I decided it was time to write my own compiler. I chose to write a compiler for the Gameboy Color, with the ultimate goal of the project being to gain a better understanding and appreciation for the compiler process as a whole. (and to have fun of course) Currently writing the Lexer/Preprocessor/Parser. The project is hosted on my github, using CMake configured for CLion.
Inspired by the incredible engineers who came before me, I wanted to work on a project involving assembly. Most of the time working on this project was spent flipping through the Intel 8080 datasheet and carefully debugging to find any errors in my opcode implementation. Initially I was using OpenGL for graphics, but later I switched to SDL 2. SDL2 has controller and audio handling, plus it seemed more fitting for rendering from a 2D bitmap.
There are plenty of open source libraries available that greatly simplify the process of creating ANNs. However, for this project I decided to design and create my own ANN object from scratch in C++. I created a feed-forward ANN with back propagation. In implementation, I tested my ANN's ability to recognize handwritten numerical digits between 0 and 9. The overall accuracy varied based on the amount of layers/neurons I added in my ANN and the amount of training data I presented it with.
Similarly to the ANN project, there are plenty of libraries that handle the hard work of reinforcement learning for you. However, in order to fully appricate and understand how reinforcement learning works, I opted to implement the logic behind the sarsa(lambda) algorithm from scratch in a javascript application. With the object set to explore 20% of the time and exploit 80% of the time and the decay rate set to .5, the object learned to navigate the grid in about 6 days. The process would have been much quicker if I had not have used a GUI. Moving forward, I plan to reimplement this project in a C++ project and compare the results.
The Connect-R project was originally developed in C++ from scratch; it's essentially a dynamic version of Connect Four, meaning you can have any combination of rows, columns, and win conditions. I applied the minimax algorithm to dynamically construct my AI's decision tree. I also added alpha-beta pruning to prevent the function from creating unnessasary branches and incease the AI's response time. The AI's heuristic was designed such a way that it will attempt to set up multiple paths to winning. I am currently in the process of recreating this project using a javascript based game engine called phaser.io.
Bachelor of Science in Software Engineering
Fall 2018
Kennesaw State University
Kennesaw, GA
Neural Networks for Machine Learning
Fall 2016
Coursera
React and Redux in ES6
Spring 2018
Pluralsight
Software Engineer Intern
January 2018 — current
Turner Studios
Atlanta, GA
Software Engineer Intern
May 2017 — August 2017
Blizzard Entertainment
Irvine, CA
Data Structures Teaching Assistant
August 2016 — current
Kennesaw State University
Marietta, GA
Software Engineer Intern
May 2016 — August 2016
Scientific Games
Alpharetta, GA
Student Assistant
August 2015 — August 2016
Kennesaw State University
Kennesaw, GA
Software Tester Intern
November 2014 — June 2015
E-Ring Inc.
Alpharetta, GA


Spring 2017 CS189 at UC Berkeley
Midterm 1
Monday, February 13, 8-10 p.m.
Logistics
- Date and Time: Monday, February 13, 8 - 10 p.m.
- Locations: This is based on your OFFICIAL section enrollment. Please see the pinned Piazza post "Midterm 1 Room Assignments".
- 1 sheet (front and back) of hand written notes is allowed.
- Bring your Cal ID.
- DSP will receive an email from Professor Rao. Contact (admin at eecs189.org) if you do not receive an email by Friday, February 10 at 10 p.m.
Topics
The exam will cover Notes 1 through 6 and bijections from Note 7.
- Propositional Logic
- Proofs
- Induction
- Stable Marriage
- Graph Theory
- Modular Arithmetic
- Bijections and RSA (bijections only)
Preparation
Please see the pinned Piazza post, "Midterm 1 Resources".


For those who have seen the Earth from space, and for the hundreds and perhaps thousands more who will, the experience most certainly changes your perspective. The things that we share in our world are far more valuable than those which divide us.
The path of a cosmonaut is not an easy, triumphant march to glory. You have to get to know the meaning not just of joy but also of grief, before being allowed in the spacecraft cabin.
Never in all their history have men been able truly to conceive of the world as one: a single sphere, a globe, having the qualities of a globe, a round earth in which all the directions eventually meet, in which there is no center because every point, or none, is center — an equal earth which all men occupy as equals. The airman's earth, if free men make it, will be truly round: a globe in practice, not in theory.
Failure is not an option.
Across the sea of space, the stars are other suns.
Never in all their history have men been able truly to conceive of the world as one: a single sphere, a globe, having the qualities of a globe, a round earth in which all the directions eventually meet, in which there is no center because every point, or none, is center — an equal earth which all men occupy as equals. The airman's earth, if free men make it, will be truly round: a globe in practice, not in theory.


This page will show my interests.And I want to find the one who have the same interests.
四月是你的谎言
三月的狮子