Videos
Videos covering my favorite problems
Videos covering my favorite problems
Challenging problems handpicked from the book
My work and solutions for practice AMCs.
My progress & timeline for making IMO
My progress through Volume I of AoPS
Keeping track
11/30/18: It's impossible now. Tommorow is my second official SAT (5th unofficial). Starting from tommorow, Every weekend, I'll study 3 hours in the morning, evening, and night for a total of 9 hours. But that's only Saturday & Sunday. For the other 5 days, I'll do just one hour at night from the Pre-Calc book.
9/26/18: Things are looking bleak. But here's the plan: It's the 26th today. Realistically, I can finish Chapter 28 today. Over the course of Thursday and Friday, I'm gonna form an outline of the 62 problems in Chapter 29. I'll use the outline to finish Chapter 29 Friday night. Satuday & Sunday is solely devoted to getting started on Pre-Calc. Ideally, I'd finish Pre-Calc by Novemeber 26th (in 2 months). Then, I'd spend the intense month of December on Volume II. By January,I'll be done with Volume II. Let's hope the future looks good.
8/30/18: It's August 30th, and I'm on Chapter 20 for Volume I. By September 2nd (9/2/18), I should be done with Volume I. On September 3 & September 4, I will take 2 AMCs each, for a total of 4 Mocks completed. School will start September 5, and things will get really hetic, but I will still take 2 mock AMCs each day until September 7th, 2018. My Pre-Calc AoPS book will arrive September 7th, and I will start it on 9/7/18, and hopefully finish Pre-Calculus by 10/15/18. On 10/16/18, I start Volume II, and I will finish it by 12/16/18. I will take every Practice & Mock AIME from 12/16/18 to 12/31/18. From 1/1/19, I start EGMO, and finish it hopefully sometime around Mid-February (before AMC). I'll take every USAMO/USAJMO & USAMO/USAJMO Mock available, and problems from all other popular olympiads.
7/28/18: The ultimate plan is to make the IMO. But first, the AMC. As of 7/28/18, I've almost completed Intro to Geo. Over the rest of the summer, I will finish Volume I, by the end of August (next month). Over the course of finishing Volume I, I will take every AMC 10A & B from 2010, as well as good mocks from AoPS. As the school year begins in September, I will begin Volume II, finish Volume II by the end of December (due to high workload). By end of December, I will theoretically be ready to score >12 on the AIME, thus giving me a high enough index to qualify for USAMO under any circumstance. Upon qualifying for USAMO, I will take WOOT, every USAMO after 2010. I'll also use Evan Chen's EGMO. Lastly, I will do all other popular Olympiads. And that's that. That's the plan.


I know it isn't much, but I still want to tahnk you for all that you have done over the years for the whole family. No matter what kinds of prblems we have had, whether it be related to schoolwork or just about mental health, you always have words of wisdom for us to live by.
To have people around you that are not afraid of giving feedback without dear of offending this person. As someone who has made and continues to make a mistakes, this feedback is invaluable.
Through your hard work as both a CPA and mother, you have helped guide Ariel and myself to many successes. Without you driving us and giving us the guidance, we would not bet the people who we are today. Hopefully I can treat you to a nice dinner when I get my first paycheck!


The fight against Israel haters both in the real world and online is not an easy one. Too make it a little easier, the Judean People’s Front has made and compiled numerous images and memes that can be used to drive home your points when mere words and facts don’t suffice.
##Images By The JPF
(From Jerusalem Myth #4)
(From Apartheid Law vs. Israeli Law)
(From The Strategy of Divestment)
(From Genocide Libel)
(From Obama’s Palestinian Blindness and Unconscious Anti-Semitism)
(From Blue-and-Whitewashing
(From Rock Throwing Isn’t Just Terrorism, It’s Child Abuse)
(An imagining of how the BBC would report on Israel’s response to Palestinians torching Joseph’s Tomb)
##Images By Others
!Palestinian Names](http://i.imgur.com/jEWXQ53.jpg)


You will be given a random number at the start of the game.
There are four crystals below. By clicking on a crystal you will add a specific amount of points to your total score.
You win the game by matching your total score to the random number, you lose the game if your total score goes above the random number.
The value of each crystal is hidden until clicked.
Each new game, the crystals will have a new value.
Good Luck!


ioBrew is a webapp I created for keeping track of drinks owed between friends in a fun, social way Some of the features are cross user functionality, achievement tracking, and automatic email reminders, just to name a few. For this project I built out both the frontend and the backend, along with creating the web page's design.
The following technologies were used in the creation of this project:
Feel free to check out the link below to the live site hosted on Heroku. If you're a code junkie, check out the link below that one for the github repo containing my source code:
Fitkora is a freelance project designed for a small business owner. It's hosted on the CloudCannon CMS and I made it editable for my client.
The following technologies were used in the creation of this project:
Check out the link below to the live site hosted on BlueHost. If you want to check out the source code click the link for my github repo:
Ironspiration is a site created during a weekend long hackathon. The site is a place where Iron Yard code school students can post past projects they've completed to give future students ideas and share best practices.
The following technologies were used in the creation of this project:
Feel free to check out the link below to the live site hosted on Heroku. If you're a code junkie, check out the link below that one for the github repo containing my source code:


--- layout: blog_entry image: title: Photography On Linux ---
With the recent changes to Adobe software to creative cloud we have been wanting to make the switch to Linux and open source software for our workflow. With software like Darktable and the Gimp we have been able to make the switch easily. Darktable is a Lightroom alternative that produces very good results. The interface does take time to get used to but when you are antiquated with it you can make presets to speed thing along.
The Gimp is a Photoshop alternative the interface is somewhat different it is a bit slower then photoshop. The speed will improve when version 2.10 is released as it will support GPU acceleration via OpenCL.
The Linux distribution we have chosen is Arch Linux the software is up to date all the time as new versions of software are released. Arch gets them within a few hours or within a day. Ubuntu was used for some time but there where to many bugs and stability problems. Linux has good support for color management and hardware calibrators for accurate color while editing photo or video. Linux suits us very well and is fast and open source software is very good and we will be contributing to some of the software we use by offering feedback to the developers. Here is the finale resulting photo after all editing.


Hello, my name is Erin McGeever.
I'm a Full Stack Web Developer
based in Boulder, CO.
About Me
- Bulleted Version
- Story Version
- Current Location: Boulder, CO
- Hometown: Longmont, CO
- Education:
- CU Boulder, 2015
- BA Biolgy
- MS Project Mangement
- UC Denver Coding Bootcamp, 2020
- Certificate of Completion
- Full Stack Web Development
- CU Boulder, 2015
- Hobbies:
- Stand Up Paddle Boarding
- Reading
- Tennis
- Interests:
- Singing Disney songs
- Phone games
- The color blue
When I was in high school, I took AP Computer Science and completely failed it. I was so discouraged from doing anything computer science related, and thus, I studied biology, creative writing, and russian lit in UC Boulder. But I did still have to take one computer science course for a GE, Introduction to Programming: Java. Something must have clicked back in high school because that class was a breeze. From there, I wanted to be a CS minor but I could not fit it into my schedule in four years. I tried self-studying throughout the years as well, but I never had the time or the motivation to sit down and commit.
So when I graduated and moved to home because I had my beautiful daughter during college. I decided to do the DU Coding Bootcamp, and it was the best decision I've ever made in my life.
When I'm not coding (or quarantined), I play with my 7 year old daughter, and sing my heart out to a myriad of Disney songs. And, if you couldn't tell, I love the color blue. Feel free to connect with me on LinkedIn!


One of the infamous counterintuitive ideas of quantum mechanics is that two systems that appear too far apart to influence each other can nevertheless behave in ways that, though individually random, are too strongly correlated to be described by any classical local theory.
To understand this, we have outlined a simple Bell test experiment here. Imagine you have two systems (see blue and red systems below). Within each there are two measurements performed: \(A\), \(A'\), \(B\) and \(B'\), that have outcomes \(1\) (or \(-1\)). Bell showed that if these measurements are chosen correctly for a given entangled state, the statistics can not be explained by any local hidden variable theory, and that there must be correlations that are beyond classical.
and the correlated expectation is given by
with \(0\) giving outcome \(+1\) and \(1\) giving outcome \(-1\). A correlation of 1 means both observables have even parity, and a correlation of -1 means both observables have odd parity.
It is simple to show that this inequality must be true if the theory obeys the following two assumptions, locality and realism:
** Locality**: No information can travel faster than the speed of light. There is a hidden variable \(\lambda\) that defines all the correlations so that
and \(C\) becomes
Realism: All observables have a definite value independent of the measurement (+1 or -1). This implies that either \(|B(\lambda)+B'(\lambda)|=2\) (or \(0\)) while \(|B(\lambda)-B'(\lambda)| = 0\) (or \(2\)) respectively. That is, \(|\mathrm{C}|= 2\), and noise will only make this smaller.
Perfectly reasonable, right? However, as you see, \(|C|>2\). How is this possible? The above assumptions must not be valid, and this is one of those astonishing counterintuitive ideas one must accept in the quantum world. Before you launch the scores below, let’s try to understand what is happening and how each observable is measured and combined to give \(|C|\).
The Bell experiment we have provided uses the entangled state \(\frac{1}{\sqrt{2}}(|00\rangle+|11\rangle)\) and the two measurements for system A are \(Z\) and \(X\), while the two for B are \(W=\frac{1}{\sqrt{2}}(Z+X)\) and \(V=\frac{1}{\sqrt{2}}(Z-X)\).
For an ideal implementation the four correlated expectation values give,
which gives \(|C|=2\sqrt{2}\).
To run this experiment with our hardware we need the following quantum score and 4 measurements.
In the first part of the experiment the qubits are initially prepared in the ground state \(|00\rangle\). The \(H\) takes the first qubit to the equal superposition \(\frac{|00\rangle+|10\rangle}{\sqrt{2}}\) and the CNOT gate flips the second qubit if the first is excited, making the state \(\frac{|00\rangle+|11\rangle}{\sqrt{2}}\). This is the entangled state (commonly called a Bell state) required for this test. In the first experiment the measurements are of the observable \(Z\) and \(W= \frac{1}{\sqrt{2}}(X+Z)\). To rotate the measurement basis to the \(W\) axis, use the sequence of gates \(S\)-\(H\)-\(T\)-\(H\) and then perform a standard measurement. The correlator \(\langle ZW \rangle\) should be close to \(1/\sqrt{2}\) and is found using the above equation.
Finally, in the third and fourth experiment the correlators \(\langle XW\rangle\) and \(\langle XV\rangle ` are measured and should be close to :math:`1/\sqrt{2}\) and \(-1/\sqrt{2}\) respectively. The \(W\) and \(V\) measurement is performed the same way as above and the \(X\) via a Hadamard gate before a standard measurement.
Here you can see the results we got when we ran this experiment on the processor:
Try it out for yourself! Compare what we got with the simulations (with both ideal and realistic parameters).


NEWS.md
create_focal_vs_comp(comp_x_var = ...)
argument.run_cv()
to use purrr::map_dfr()
using the fit_one_fold()
functioncomp_bayes_lm
class.comp_bayes_lm()
modeling function takes in a data frame at a level of observation equivalent to that which the model is actually fit to: each row is a unique focal observation/tree rather than focal-competitor observation pairs/trees. The function outputs a model object with several associated modeling S3 methods.predict.comp_bayes_lm()
takes in a model object as its first argument and input data as its second argument. The output, an unnamed vector, has length equal to the input data.


9to5Mac Daily: November 15, 2019 – HomeKit Secure Video, more
by Chance MillerListen to a recap of the top stories of the day from 9to5Mac. 9to5Mac Daily is available on iTunes and Apple’s Podcasts app, Stitcher, TuneIn, Google Play, or through our dedicated RSS feed for Overcast and other podcast players.
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New episodes of 9to5Mac Daily are recorded every weekday. Subscribe to our podcast in iTunes/Apple Podcast or your favorite podcast player to guarantee new episodes are delivered as soon as they’re available.
Stories discussed in this episode:
- Logitech now rolling out HomeKit Secure Video support to Circle 2 Wired camera
- Germany forces Apple to let other mobile wallet services use iPhone’s NFC chip
- 15-inch vs 16-inch MacBook Pro comparison: Should you upgrade?
- 9to5Mac Happy Hour 251: 16-inch MacBook Pro, service bundle rumors, Apple Music Replay
- Comment: Apple Watch should be on every wrist after $129 Black Friday special
- Amazon clears out Apple’s 15-inch MacBook Pro inventory with up to $450 off
- Don’t wait for Black Friday, Apple Watch Series 4 is up to $350 off right now!
- 9to5Toys on Twitter
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Drop us a line at<EMAIL_ADDRESS>You can also rate us in Apple Podcasts or recommend us in Overcast to help more people discover the show!
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Note that it does not include add-ons which are focused on a specific technology (such as SOAP) which may add scan rules specific to that tech.
Add-ons included:
| Active Scan Rules | The release status Active Scanner rules |
| Active Scan Rules (beta) | The beta status Active Scanner rules |
| Active Scan Rules (alpha) | The alpha status Active Scanner rules |
| DOM XSS Active Scan Rule | DOM XSS Active scanner rule |
| Passive Scan Rules | The release status Passive Scanner rules |
| Passive Scan Rules (beta) | The beta status Passive Scanner rules |
| Passive Scan Rules (alpha) | The alpha status Passive Scanner rules |
| Retire.js | Identify vulnerable or out-dated JavaScript packages |


|
Meanco
|
Vis.js is looking for people who can help maintain and improve the library. We've put a lot of effort in building these visualizations, fixing bugs, and supporting users as much as we can. For some time now, we’ve been lacking the manpower to maintain the library the way we have in recent years. has left the company for a new opportunity, and has moved internally to a daughter company, with severe impact on his time and availability for Vis.js. At the moment is the official maintainer from Almende, but does not have much time to help out.
Although Almende is looking to replace the expertise required for Vis.js, we don't expect to be able to do comprehensive project management any time soon. At the same time we’d like to spare Vis.js from becoming abandonware, especially given the relative healthy user base. For the longer term future we would be happy if vis.js could stand on its own feet, community supported.
If you want to support the project please just start by ./how_to_help.md "helping out".
If you have shown some commitment to the project you can ask to become a member of the community support team. This team has write permissions to the repository and is helping maintaining it. Currently this team consists of:


{% extends "global/Page.html" %} {% load staticfiles otree_tags %} {% block title %} Part 2 {% endblock %} {% block styles %} {% endblock %} {% block content %}
In the second part of the experiment you might be a B-participant.
If this is the case, an A-participant will perform the task for you.
Please select the A-participant that you would want to perform the task for you.
If you are indeed the B-participant then this choice will be implemented.
| A-Participant's Chosen Name | Number of Points in Part 1 | Your Decision |
|---|---|---|
| Scarlett | 0 points | |
| Oliver | 0 points |


You can add some locations to your favorites. It will give you a way to quickly browse files from these locations.


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Copyright (c) 2017 Marian Aldenhövel
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
PackageName: {{session.appVersion.appName}}
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VersionNumber: {{session.appVersion.versionNumber}}
DB-System: {{ db.useWebSQL ? "WebSQL" : "SQLite" }}
cordova: {{session.device.cordova}}
model: {{session.device.model}}
platform: {{session.device.platform}}
uuid: {{session.device.uuid}}
version: {{session.device.version}}
manufacturer: {{session.device.manufacturer}}
isVirtual: {{session.device.isVirtual}}
serial: {{session.device.serial}}
{{ "NA" | translate }}


Weather Dashboard
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After the graphics/imagery work in the last experience, I thought to review the definition of what ‘quality’ meant for pseudo-random number generators, again.
I added code for the xoroshiro128+ RNG, which uses various combinations of xor, shift, and or operators to generate random numbers quickly.
On the Raspbery Pi4 running Raspian:
$ ./xmcpi.x
xmcpi.f90 number of images: 1
approximating pi in 1 x<PHONE_NUMBER>0 points
nsum<PHONE_NUMBER>.0000000 msum<PHONE_NUMBER>0.000000
pi 3.1415926535897931 iterated pi: 3.1415778439661208
pi error 1.4809623672284999E-005
Elapsed wall clock time 278. seconds, using 1 images.
pi@raspberrypi:~/rnums $ mpirun -np 2 -H localhost:4 ./xmcpi.x
xmcpi.f90 number of images: 2
approximating pi in 2 x<PHONE_NUMBER>0 points
nsum<PHONE_NUMBER>.0000000 msum<PHONE_NUMBER>0.000000
nsum<PHONE_NUMBER>.0000000 msum<PHONE_NUMBER>0.000000
pi 3.1415926535897931 iterated pi: 3.1415832579718690
pi error 9.3956179241239113E-006
Elapsed wall clock time 279. seconds, using 2 images.
$ mpirun -np 4 -H localhost:4 ./xmcpi.x
xmcpi.f90 number of images: 4
approximating pi in 4 x<PHONE_NUMBER>0 points
nsum<PHONE_NUMBER>.0000000 msum<PHONE_NUMBER>0.000000
nsum<PHONE_NUMBER>.0000000 msum<PHONE_NUMBER>0.000000
nsum<PHONE_NUMBER>.0000000 msum<PHONE_NUMBER>0.000000
nsum<PHONE_NUMBER>.0000000 msum<PHONE_NUMBER>0.000000
pi 3.1415926535897931 iterated pi: 3.1415965419986285
pi error -3.8884088353619006E-006
Elapsed wall clock time 403. seconds, using 4 images.
The Rpi4 is overheating, and slows down, resulting in 400+ seconds elapsed when running 4 cores at the same time. However, the results are exactly the same numerically as for the Intel AMD64-compatible CPUs.
The xoroshiro128+ code is platform independent, with this very simple empirical test.
So let’s try a different test: the statistical tests frequently mentioned by RNG practitioners (authors/users/critics). This is the TestU01 suite which includes a number of well-known RNGs and a scheme for testing anything we might come up with.
The code is, roughly
#include "unif01.h"
#include "bbattery.h"
#include "xoroshiro128plus_xd_xjr.h"
#include "xoroshiro128plus_xtc.h"
#include "pcg_od_ojr.h"
int main (void)
{
unif01_Gen *gen;
xoroshiro128plus_srandom(134893149852ull,<PHONE_NUMBER>5ull);
gen = unif01_CreateExternGen01 ("xoroshiro128plus_xd", xd_random_r8);
bbattery_SmallCrush (gen);
unif01_DeleteExternGen01 (gen);
...
Which is pretty dense code but basically runs the SmallCrush battery of tests on the double-precision floating-point values generated by xoroshiro128plus_xd.
The test subjects are:
The SmallCrush test is a shorter test suite, and all tests pass.
The Crush test is a longer test suite. Of these, only 3 tests fail with unlikely p-values (outside 0.001 to 0.999):
So, these RNGs are not so bad, accoring to TestU01.
Looking at the binary (or hexadecimal) representation of the floating point results, it’s pretty clear there are issues with the [1,2)-1 and the times-a-constant methods of creating floating-point numbers from integer random numbers.
Some of the least significant bits are either always zero, or mostly zero. For example, the frequency of bits in the “xtc” method gives
bit nbr actual expected
bit 0 2522555 5050050
bit 1 3786768 5050050
bit 2 4416094 5050050
bit 3 4733053 5050050
bit 4 4893247 5050050
bit 5 4969861 5050050
and the “xjr” method gives
bit 0 0 5050050
bit 1 2523599 5050050
bit 2 3785404 5050050
bit 3 4415926 5050050
bit 4 4734824 5050050
bit 5 4892002 5050050
meaning none of the lsbs are ever 1, and the next few bits are not one half the time as they should be. The number 5050050 is the expected number of ones.
The “good” method by Downey looks like
bit 0 5048674 5050050
bit 1 5048621 5050050
bit 2 5050834 5050050
bit 3 5048751 5050050
bit 4 5049737 5050050
bit 5 5049658 5050050
What is an objective, scientific method to measure this? Chi-square, of course. This test is for discrete distributions in large numbers. Here we have a mantissa of 23 bits (single-precision) or 53 bits (double-precision) which should all be independent and uniformly 1 or 0 half the time. We’ll run a Chi-square test of the distribution of the 1-bits in these mantissas.
And for good measure I added a test of half-octet, meaning 4 bits, which should always be averaging 7.5 in value.
The other measure is the frequency of exponents. This should be exponential, matching the concept that numbers ¼ the size have an exponent ¼ as likely. For example
Expon. Freq.
...
-20 98
-19 167
-18 388
-17 753
-16 1480
...
-4 6258023
-3 12508379
-2 25025356
-1 50049539
This is easily tested by taking log2 of the frequency and checking that log2(freq) is linear with the exponent.
(The above list of exponent frequencies is from od_random_r4 for 10G values. The smaller exponents -31 through -21 have been omitted.)
Most of the single-precision methods work fine, since they essentially are taking 64-bits (or 48 for the drand48 builtin) and using only 23 of them. Any issues with lsbs do not appear.
The acceptable methods for single-precision are the PCG and xoroshiro128+ and ++ RNGs with the Downey (d), jr (1-2]-1 method, and times-a-constant method. These also pass the half-octet tests.
Not surprisingly, for double-precision, only the Downey (d) methods pass the bits tests. The xoroshiro+ passes the half-octets tests, while PCG only about a third of the time. As the half-octet tests are biased (some bits have more influence on the average value of a half-octet) it’s not clear to me this is really a useful test.
Here are some same results for 20 tests of PCG and xoroshiro128+ for 100M numbers:
test chisqure-bits chisquare-halfoctets Note
od_...r8 20 pass 9 of 20 pass PCG
xd_...r8 20 pass 20 pass xoroshiro128+
This contrasts with integer64 tests, where fewer of both PCG and xoroshiro128+ tests passed the halfoctet tests. Rerunning those using the same seeds results in:
test chisqure-bits chisquare-halfoctets Note
pcg 20 pass 6 of 20 pass PCG
x...128+ 20 pass 4 pass xoroshiro128+
It is not surprising they differ; the floating point numbers are processed from the random integers by shifts and sometimes replacement.
One variant to be considered is a cryptologic transformation of the random result, before conversion to float, such as with AES128-CTR mode, to see if that changes the result. While slower, it is not better.
Statistical tests composed by statistical practitioners are not as complete as you might hope. Easy-to-check defects in RNGs are missed. And this has been true for years.
The practitioners have the wrong objectives in mind: generating statistics not generating usable random numbers.
Do the least-significant-bits of pseudo-random floating-point numbers matter? Especially the double-precision kind? Why, for some applications, yes they do. When taking differences, when making exact calculations locally and averaging over large spatial areas (such as particle-in-cell methods), when testing sensitivity of numerical models by masking (truncating values), and so on.
April 2020
Asahi Super Dry
Links:
Pierre L'Ecuyer website
xoshiro / xoroshiro generators and the PRNG shootout
OpenBSD Numerics Experience - 1 - RNG
OpenBSD Numerics Experience - 2 - RNG floats
OpenBSD Numerics Experience - 3 - FFTW
OpenBSD Numerics Experience - 4 - CAF
OpenBSD Numerics Experience - 5 - MPI Networking
OpenBSD Numerics Experience - 6 - Memory Models
OpenBSD Numerics Experience - 7 - Python Image Display
OpenBSD Numerics Experience - 8 - RNGs, again
OpenBSD Numerics Experience - 9 - Nim
OpenBSD Numerics Experience - A - Graphical Display
OpenBSD Numerics Experience - B - ParaView
OpenBSD Numerics Experience - C - Numerical Debugging
OpenBSD Numerics


Gerwyn Price defends Grand Slam of Darts title in stunning win over Peter Wright
Gerwyn Price turned in a sensational display in the final of the Grand Slam of Darts, hammering Peter Wright 16-6 to defend his title in spectacular fashion.
The Iceman was in stunning form in Wolverhampton, as he averaged 107.86, hitting 11 180s and checking out at 48%.
The Welshman beat Michael van Gerwen in the semi-finals and followed up that superb win with an even better performance against an incredulous Snakebite.
Price made his intentions clear from the very start of the game when he checked out 100 in a 12-darter against the throw in the opening leg.
This set the tone for a match in which he was never behind and dominated throughout, claiming the £125,000 top prize in some style.
Wright hardly played badly, finishing with an average of 96.66, but he was hanging onto Price’s coattails in the first half of the contest, before being left for dust as the game reached its conclusion.
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The resounding victory for Price also saw him become only the seventh player to retain a major title in PDC history after Phil Taylor, Michael van Gerwen, Raymond van Barneveld, Rod Harrington, Adrian Lewis and Gary Anderson.
It was performance of such quality from the new world number three that he will now be seen as a legitimate contender for the World Championship which gets underway next month.
The 34-year-old has never been past the third round at the Alexandra Palace but will now be expected to go a long way this time round.
‘I knew in the middle of that game that I was playing really well, hitting trebles for fun and putting Peter under pressure,’ said Price.
‘He wasn’t playing his best but I knew that I was at the top of my game, I’m chuffed to bits to get hold of this again and finally get the crowd to cheer.
‘This week they’ve been fantastic and I’m not used to this. They truly have got behind me this weekend and I appreciate it, they pushed me on to win this.’
Price will go to number three in the world with this win, and he thinks he is even better than that: ‘I think i’m number one,’ he said. ‘I’ve had a good year, a good 18 months.’
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Wright was gracious in defeat and could only accept that his opponent was near-unbeatable.
‘I was just chasing him all the way through and when he missed I was on 100-and-something, if I was lucky. He played fantastic all the way through,’ said Snakebite.
‘I had no answer, but I switched to these darts and said to myself “I think I can get to the final” and I did.’


Below you will find my projects, take a look around
This was one of my first ever projects. Built with HTML, CSS and JS
Go to live siteThis is my first team based ever project. Built with HTML, CSS and JS
Go to live siteFirst app made with React.js. Also used HTML, CSS and JS
Go to live siteThis was my final project that had my group and me using everything we learned from the bootcamp and really seeing how far we had come. Built with HTML, CSS, React.js, Node.js, MYSQL and Auth0
Go to live site


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Passkeys
Passkeys are a modern alternative to usernames and passwords, they are secure


November 22nd 2017 - Tejpal Virdi
An important imaging task is super-resolution: the process of enhancing the resolution of an image. Recently, researchers have gone at this problem using Deep Learning, and the methods have been successful. In this post, we look at a common medical procedure and show how it can be drastically improved with the use of GANs.
Introduction
Capturing real-time images of the insides of a patient may be difficult due to the restricted space and intricacies of the human body. In particular, a colonoscopy requires a video camera on a tube to be placed through the gastrointestinal tract, which goes to almost twenty-three feet long. The procedure is often accompanied by major discomfort, including cramping pains and abdominal swelling due to the aggressiveness of the procedure. Luckily, new methods have been introduced that can reduce the complexity of the procedure. With the development of nanotechnology, this examination can be done with a small capsule [1]. Also, recent advancements in transient electronics, have allowed for safe and painless medical diagnosis. However, the size of the device often compromises the quality of the images. A Low-resolution (LR) image cannot be used to its full potential, giving low-accuracies in classification models as well as difficulty for doctors when examining the images by eye.
Recently, Generative Adversarial Networks have been used for super-resolution [2]. Given an LR image, there are multiple possibilities for a corresponding and accurate HR image and the generator is trained to learn one of these mappings. Super-resolution has been commonly used in various domains such as satellite imaging, security, face-recognition, as well as medicine. The important features in a HR medical image are crucial for an accurate interpretation of the data.
This post analyzes the benefits of applying a super-resolution procedure to medical data, in terms of classification accuracy and qualitative results. It also enables the development of cheaper and smaller cameras and although compromising on quality, with the use of super-resolution technology, the small cameras can be successfully implemented.
Medical images are produced in two main forms: scans and photographs. Scans include X-rays, MRIs, CTs, etc. while common medical photography includes fundus photography and photography of internal tracts. Scan super-resolution has been explored by mapping MRI to CT scans as well as enhancing the resolution of low-dose scans. This post looks specifically into photography from colonoscopy procedures.
Data
We train the super-resolution GAN (SRGAN) on 1200 32x32 images from CVC-ColonDB [3].
Methods
The SRGAN follows the typical GAN format but the architectures for the discriminator and generator are different:
The most important feature of the Generator architecture is the B residual blocks. The residual block is the building block of the Residual Network (ResNet), which is one of the top architectures used for Image Recognition. The goal of any network is to find the mapping between an input and output. It does this by learning different functions along the way, one being the identity function. The identity function is very important since the output image is likely to have a similar structure to the input. A model without residual blocks never learns the exact identity function (input = output) but instead gets very close to it. The network would make tiny gradient changes to get closer to the function, resulting in the vanishing gradient problem. The ResNet fixes this by forcing the network to learn the identity mapping through an element-wise addition:
The discriminator is a pretty straightforward CNN. It uses a series of Convolution Layers, Leaky ReLUs, and Batch Normalization layers to learn the difference between high and super-resolution images. It takes in an input image and returns a scalar between 0 and 1, which is the certainty that the input is of super-resolution. A probability of 0.5 means that the discriminator cannot tell if the generated image is high-resolution or super-resolution.
SRGAN uses mean squared error (MSE) as the loss function, but the truth labels are a little different.
The minimization or solution space of a per-pixel loss function usually involves the generation of a smoother/more general image, which is counter-productive for super-resolution. Instead, SRGAN uses a perceptual loss, which has been proven effective and efficient for style transfer and super-resolution. Perceptual loss is based on comparing outputs to high-level features extracted from pretrained networks, specifically the VGG-19. This loss encourages the network to have similar features rather than exact pixel values to the original image. Although this may not perform well quantitatively, the sharper details from the use of a perceptual loss result in a qualitatively superior output.
Examples
Here is an example image processed by SRGAN. As you can see, despite the low resolution of the initial image, the network was able to upscale it succesfully. However, it had a hard time dealing with spots of reflection.
References
- “PillCam Capsule Endoscopy.” PillCam Capsule Endoscopy - Given Imaging, www.givenimaging.com/en-int/Innovative-Solutions/Capsule-Endoscopy/Pages/default.aspx.
- Ledig, Christian, et al. “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.” [1609.04802] Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, 25 May 2017, arxiv.org/abs/1609.04802.
- “CVC Colon DB.” Machine Vision Group, mv.cvc.uab.es/projects/colon-qa/cvccolondb.


Sound Board
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Made by Matt Lerman (Practice Website)


Ref; https://youtu.be/GwomIM_PHZo
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Movie Search
Select a movie title on a page, right-click, and choose:
Movie Search -> Get Movie Details
to get more info on the movie
Movie Search -> Add To List
to add the movie to a list
Preferences
Restore Preference Defaults
Movie Lists
Remove All Movie Lists


+
Code for America helps American cities
use web technology to do a better job
of providing services to citizens.
Ruthie BenDor
2012 Fellow at Code for America
<EMAIL_ADDRESS>twitter: @unruthless
Entrepreneurship and small business development
are vital
to creating the jobs and economic activity
that define dynamic cities.
Opening a new business in any city
requires coordination
between many different departments.
(Everything from submitting building plans and applications to securing health permits!)
This presents a
cost-prohibitive
and confusing
hurdle to fledgling ventures.
I want to focus on growing my business, not on compliance.
Zoning Map: Check a site's zoning and prior use.
SiteMy.Biz: Find available commercial space.
Fee Calculators: TIFs, PDFs, business license taxes, oh my
Launch early August
Online document submission and payment
TRAKiT integration
Generalizing codebase for use with other cities


The base filename is the name of the file optionally including the director/subdirectory path, and in the case of `ftp', `http', and `root' filetypes, the machine identifier. Examples:
myfile.fits !data.fits /data/myfile.fits fits.gsfc.nasa.gov/ftp/sampledata/myfile.fits.gz
When creating a new output file on magnetic disk (of type file://) if the base filename begins with an exclamation point (!) then any existing file with that same basename will be deleted prior to creating the new FITS file. Otherwise if the file to be created already exists, then CFITSIO will return an error and will not overwrite the existing file. Note that the exclamation point, '!', is a special UNIX character, so if it is used on the command line rather than entered at a task prompt, it must be preceded by a backslash to force the UNIX shell to pass it verbatim to the application program.
If the output disk file name ends with the suffix '.gz', then CFITSIO will compress the file using the gzip compression algorithm before writing it to disk. This can reduce the amount of disk space used by the file. Note that this feature requires that the uncompressed file be constructed in memory before it is compressed and written to disk, so it can fail if there is insufficient available memory.
An input FITS file may be compressed with the gzip or Unix compress algorithms, in which case CFITSIO will uncompress the file on the fly into a temporary file (in memory or on disk). Compressed files may only be opened with read-only permission. When specifying the name of a compressed FITS file it is not necessary to append the file suffix (e.g., `.gz' or `.Z'). If CFITSIO cannot find the input file name without the suffix, then it will automatically search for a compressed file with the same root name. In the case of reading ftp and http type files, CFITSIO generally looks for a compressed version of the file first, before trying to open the uncompressed file. By default, CFITSIO copies (and uncompressed if necessary) the ftp or http FITS file into memory on the local machine before opening it. This will fail if the local machine does not have enough memory to hold the whole FITS file, so in this case, the output filename specifier (see the next section) can be used to further control how CFITSIO reads ftp and http files.
If the input file is an IRAF image file (*.imh file) then CFITSIO will automatically convert it on the fly into a virtual FITS image before it is opened by the application program. IRAF images can only be opened with READONLY file access.
Similarly, if the input file is a raw binary data array, then CFITSIO will convert it on the fly into a virtual FITS image with the basic set of required header keywords before it is opened by the application program (with READONLY access). In this case the data type and dimensions of the image must be specified in square brackets following the filename (e.g. rawfile.dat[ib512,512]). The first character (case insensitive) defines the data type of the array:
b 8-bit unsigned byte i 16-bit signed integer u 16-bit unsigned integer j 32-bit signed integer r or f 32-bit floating point d 64-bit floating pointAn optional second character specifies the byte order of the array values: b or B indicates big endian (as in FITS files and the native format of SUN UNIX workstations and Mac PCs) and l or L indicates little endian (native format of DEC OSF workstations and IBM PCs). If this character is omitted then the array is assumed to have the native byte order of the local machine. These data type characters are then followed by a series of one or more integer values separated by commas which define the size of each dimension of the raw array. Arrays with up to 5 dimensions are currently supported. Finally, a byte offset to the position of the first pixel in the data file may be specified by separating it with a ':' from the last dimension value. If omitted, it is assumed that the offset = 0. This parameter may be used to skip over any header information in the file that precedes the binary data. Further examples:
raw.dat[b10000] 1-dimensional 10000 pixel byte array raw.dat[rb400,400,12] 3-dimensional floating point big-endian array img.fits[ib512,512:2880] reads the 512 x 512 short integer array in a FITS file, skipping over the 2880 byte header
One special case of input file is where the filename = `-' (a dash or minus sign) or 'stdin' or 'stdout', which signifies that the input file is to be read from the stdin stream, or written to the stdout stream if a new output file is being created. In the case of reading from stdin, CFITSIO first copies the whole stream into a temporary FITS file (in memory or on disk), and subsequent reading of the FITS file occurs in this copy. When writing to stdout, CFITSIO first constructs the whole file in memory (since random access is required), then flushes it out to the stdout stream when the file is closed. In addition, if the output filename = '-.gz' or 'stdout.gz' then it will be gzip compressed before being written to stdout.
This ability to read and write on the stdin and stdout steams allows FITS files to be piped between tasks in memory rather than having to create temporary intermediate FITS files on disk. For example if task1 creates an output FITS file, and task2 reads an input FITS file, the FITS file may be piped between the 2 tasks by specifying
task1 - | task2 -where the vertical bar is the Unix piping symbol. This assumes that the 2 tasks read the name of the FITS file off of the command line.


Functions for stochastic matrix modifications.
teleport(teleport_prob = 0.15)
vote_equal(stoch)
vote_self(stoch)
| teleport_prob | Probability of 'teleportation'. |
|---|---|
| stoch | Input stochastic matrix. |
teleport()
returns a modifier function.
vote_equal()
and vote_self()
are modifier functions and return modified
version of input stochastic matrix.
Modification logic behind teleport()
assumes that at each step
of Markov chain (described by column-stochastic matrix) the decision is made
whether to change state according to stochastic matrix or to 'teleport' to
any state with equal probability. Probability of 'teleport' is
teleport_prob
. This modification is useful because it ensures
irreducibility of stochastic matrix (with teleport_prob
in (0; 1)).
Note that in order to obtain modifier one should call function
teleport()
with some parameter.
vote_equal()
and vote_self()
modify columns with elements only equal to
0. The former fills them with 1/nrow(stoch)
and the latter changes only the
respective diagonal element to 1. This is equivalent to jump to any state
with equal probability and to stay in the current state respectively.
#> [,1] [,2]
#> [1,] 0.5 0.33
#> [2,] 0.5 0.67
vote_equal(input_stoch)#> [,1] [,2]
#> [1,] 0.5 0.3
#> [2,] 0.5 0.7
vote_self(input_stoch)#> [,1] [,2]
#> [1,] 1 0.3
#> [2,] 0 0.7


--- title: Welcome to fastai v2 keywords: fastai sidebar: home_sidebar summary: "NB: This is still in early development. Use v1 unless you want to contribute to the next version of fastai" description: "NB: This is still in early development. Use v1 unless you want to contribute to the next version of fastai" nb_path: "nbs/index.ipynb" ---
To learn more about the library, read our introduction in the paper presenting it.
Note that the docs are in a submodule, so to clone with docs included, you should use:
git clone --recurse-submodules https://github.com/fastai/fastai2
If you're using a fork of fastai2, you'll need to fork the fastai-docs
repo as well.
You can get all the necessary dependencies by simply installing fastai v1: conda install -c fastai -c pytorch fastai
. Or alternatively you can automatically install the dependencies into a new environment:
conda env create -f environment.yml
source activate fastai2
Then, you can install fastai v2 with pip: pip install fastai2
.
Or you can use an editable install (which is probably the best approach at the moment, since fastai v2 is under heavy development):
git clone --recurse-submodules https://github.com/fastai/fastai2
cd fastai2
pip install -e ".[dev]"
You should also use an editable install of fastcore to go with it.
If you want to browse the notebooks and build the library from them you will need nbdev, which you can install with conda or pip.
To use fastai2.medical.imaging
you'll also need to:
conda install pyarrow
pip install pydicom kornia opencv-python scikit-image
To run the tests in parallel, launch:
nbdev_test_nbs
or
make test
For all the tests to pass, you'll need to install the following optional dependencies:
pip install "sentencepiece<0.1.90" wandb tensorboard albumentations pydicom opencv-python scikit-image pyarrow kornia
After you clone this repository, please run nbdev_install_git_hooks
in your terminal. This sets up git hooks, which clean up the notebooks to remove the extraneous stuff stored in the notebooks (e.g. which cells you ran) which causes unnecessary merge conflicts.
Before submitting a PR, check that the local library and notebooks match. The script nbdev_diff_nbs
can let you know if there is a difference between the local library and the notebooks.
nbdev_build_lib
or make fastai2
.nbdev_update_lib
.For those interested in offical docker containers for this project, they can be found here.


Note
Much of the following is explained with more detail in the nitime documentation, and then in further detail in numerous online tutorials.
The use of git and the following workflow allows people to contribute changes that can easily be incorporated back into the project, while (hopefully) maintaining order and consistency in the code. All changes should be tracked and reversible.
Create a fork of the central psychopy/psychopy repository
Create a local clone of that fork
- For small changes
- make the changes directly in the master branch
- push back to your fork
- submit a pull request to the central repository
- For substantial changes (new features)
- create a branch
- when finished run unit tests
- when the unit tests pass merge changes back into the master branch
- submit a pull request to the central repository
Go to github, create an account and make a fork of the psychopy repository You can change your fork in any way you choose without it affecting the central project. You can also share your fork with others, including the central project.
Install git on your computer. Create and upload an ssh key to your github account - this is necessary for you to push changes back to your fork of the project at github.
Then, in a folder of your choosing fetch your fork:
$ git clone git@github.com:USER/psychopy.git
$ cd psychopy
$ git remote add upstream git://github.com/psychopy/psychopy.git
The last line connects your copy (with read access) to the central server so you can easily fetch any updates to the central repository.
Periodically it’s worth fetching any changes to the central psychopy repository (into your master branch, more on that below):
$ git checkout master
$ git pull upstream master # here 'master' is the desired branch of psychopy to fetch
Now that you’ve fetched the latest version of psychopy using git, you should run this version in order to try out yours/others latest improvements. See this guide on how to permanently run your git version of psychopy instead of the version you previously installed.
Run git version for just one session (Linux and Mac only): If you want to switch between the latest-and-greatest development version from git and the stable version installed on your system, you can choose to only temporarily run the git version. Open a terminal and set a temporary python path to your psychopy git folder:
$ export PYTHONPATH=/path/to/local/git/folder/
To check that worked you should open python in the terminal and try to import psychopy:
$ python
Python 2.7.6 (default, Mar 22 2014, 22:59:56)
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import psychopy
PsychoPy depends on a lot of other packages and you may get a variety of failures to import them until you have them all installed in your custom environment!
You can make minor changes directly in the master branch of your fork. After making a change you need to commit a set of changes to your files with a message. This enables you to group together changes and you will subsequently be able to go back to any previous commit, so your changes are reversible.
I (Jon) usually do this by opening the graphical user interface that comes with git:
$ git gui
From the GUI you can select (or stage in git terminology) the files that you want to include in this particular commit and give it a message. Give a clear summary of the changes for the first line. You can add more details about the changes on lower lines if needed.
If you have internet access then you could also push your changes back up to your fork (which is called your origin by default), either by pressing the push button in the GUI or by closing that and typing:
$ git push
Informative commit messages are really useful when we have to go back through the repository finding the time that a particular change to the code occurred. Precede your message with one or more of the following to help us spot easily if this is a bug fix (which might need pulling into other development branches) or new feature (which we might want to avoid pulling in if it might disrupt existing code).
- BF : bug fix
- FF : ‘feature’ fix. This is for fixes to code that hasn’t been released
- RF : refactoring
- NF : new feature
- ENH : enhancement (improvement to existing code)
- DOC: for all kinds of documentation related commits
- TEST: for adding or changing tests
NB: The difference between BF and FF is that BF indicates a fix that is appropriate for back-porting to earlier versions, whereas FF indicates a fix to code that has not been released, and so cannot be back-ported.
For more substantial work, you should create a new branch in your repository. Often while working on a new feature other aspects of the code will get broken and the master branch should always be in a working state. To create a new branch:
$ git branch feature-somethingNew
You can now switch to your new feature branch with:
$ git checkout feature-somethingNew
And get back to your master branch with:
$ git checkout master
You can push your new branch back to your fork (origin) with:
$ git push origin feature-somethingNew
When you’re done run the unit tests for your feature branch. Set the debug preference setting (in the app section) to True, and restart psychopy. This will enable access to the test-suite. In debug mode, from the Coder (not Builder) you can now do Ctrl-T / Cmd-T (see Tools menu, Unit Testing) to bring up the unit test window. You can select a subset of tests to run, or run them all.
It’s also possible to run just selected tests, such as doctests within a single file. From a terminal window:
cd psychopy/tests/ #eg /Users/jgray/code/psychopy/psychopy/tests
./run.py path/to/file_with_doctests.py
If the tests pass you hopefully haven’t damaged other parts of PsychoPy (!?). If possible add a unit test for your new feature too, so that if other people make changes they don’t break your work!
You can merge your changes back into your master branch with:
$ git checkout master
$ git merge feature-somethingNew
Merge conflicts happen, and need to be resolved. If you configure your git preferences (~/.gitconfig) to include:
[merge]
summary = true
log = true
tool = opendiff
then you’ll be able to use a handy GUI interface (opendiff) for reviewing differences and conflicts, just by typing:
git mergetool
from the command line after hitting a merge conflict (such as during a git pull upstream master).
Once you’ve folded your new code back into your master and pushed it back to your github fork then it’s time to Share your improvement with others.


KIMONO KITSUKE
You are wearing a nagajuban. The white nagajuban worn under your kimono is the first layer
worn over top of your slip and undershirt (juban). The nagajuban is a long kimono-shaped robe
that prevents the delicate Kimono from touching your skin.
KIMONO MATERIAL
Choose the correct type of fabric for the season.


Let's get techy
This pages explains MoSKito from Developers perspective. No marketing bla-bla.
Promise.
Introduction
Since the start of the MoSKito Project we were using following definiton of MoSKito:
MoSKito is a multi-purpose, non-invasive, interval based monitoring system kit for collection, storage and instant analysis of application’s performance and behavior data.
MoSKito concentrates on three aspects of your application:
- Health Monitoring - Is my Application doing OK? Do I have to worry about next night?
- Performance Monitoring - Why is my Application slow? Will my Application become slow eventually?
- Architecture Monitoring - Does my Application work as intended? How does it work actually?
Producers
Intervals
Journeys
Thresholds
Accumulators
Stats
It's all about the stats and stats its all about.


(mongodb >=1.0.0)
MongoDB\BSON\toPHP — Returns the PHP representation of a BSON value
Unserializes a BSON document (i.e. binary string) to its PHP representation.
The typeMap
paramater may be used to control the PHP
types used for converting BSON arrays and documents (both root and embedded).
Fields containing deprecated BSON types (i.e. undefined, symbol, DBPointer) are represented only by bare-bones objects of the classes MongoDB\BSON\Undefined, MongoDB\BSON\Symbol, and MongoDB\BSON\DBPointer, when converting BSON to PHP. These objects are created from BSON data and used for storing these types back into the database, but can not be instantiated as they have a private constructor.
The unserialized PHP value.
| 版本 | 说明 |
|---|---|
| 1.4.0 |
If the input contains an unsupported, deprecated BSON type, the driver will now no longer log a warning to the debug log, but instead will create an object representing this type. |
| 1.3.2 |
MongoDB\Driver\Exception\UnexpectedValueException is no longer thrown if the input contains an unsupported, deprecated BSON type. Such types will be ignored (as they were in versions before 1.3.0), although the driver will now log a warning to the debug log (see: mongodb.debug). |
| 1.3.0 |
MongoDB\Driver\Exception\UnexpectedValueException is thrown if the input contains an unsupported, deprecated BSON type. Previously, such types were ignored. |
Example #1 MongoDB\BSON\toPHP() example
<?php
$bson = hex2bin('0e00000010666f6f000100000000');
$value = MongoDB\BSON\toPHP($bson);
var_dump($value);
?>
以上例程会输出：
object(stdClass)#1 (1) { ["foo"]=> int(1) }


From the newsletter ... |
|
|
I have always had a soft spot for the film Ben Hur. I guess it's partly because of the sheer spectacle (all those galley slaves rowing their socks off, not to mention that chariot race!), and partly because of the haunting musical score by Miklos Rosza. Perhaps there's even a bit of curiosity: they do say that if you look carefully, you will see a red Ferrari intrude into one of the scenes of the ancient Roman Empire. I have looked, and so far, I haven't spotted so much as a Reliant Robin! But I think the main reason I like the film is the way it shows how Jesus Christ can bring forgiveness and healing into our lives.  A local Roman official has been responsible for an act of terrible cruelty which has devastated Ben Hur's family. In response, he can only think of taking revenge. His family has been destroyed, so he will destroy the culprit in return. Then, as the film draws to its climax, Ben Hur finds himself standing at the foot of Jesus' cross. He watches the Son of God suffering and dying; he hears him say "Father, forgive them, for they know not what they do." And, as he later reports, "I felt his voice take the sword out of my hand." Forgiveness and reconciliation came into his life, because of his encounter with the crucified Jesus. One Good Friday afternoon, the BBC screened Ben Hur. As I'm always in church at that time taking services, I set the video recorder to tape it. Later that day, as I sat and watched the recording, I was annoyed to find that at the very climax of the film the BBC had chosen to splash news flash captions across the screen. As I tried to watch Jesus dying on the cross, the picture was hidden by a flash telling us that a historic peace deal was being struck at that very moment in Northern Ireland. And that caption came up not once, but several times during the crucial last few minutes of the film. Suddenly, I stopped being annoyed. It was an amazing piece of timing. The truth is, that if there is to be real reconciliation and forgiveness between the warring parties in Northern Ireland, it will be when all sides repent and submit to Jesus, who died to bring humanity back to God and into peace. Two years on, we still wait for that to happen, and the Good Friday Agreement seems a long way off. But the cross is still the hope for the province. As I write this, another conflict is capturing the headlines. Israel, the very place where Jesus lived, died and rose again, is the scene of violence between Palestinian and Israeli. There too, the hope for real peace lies in Jesus, the Prince of Peace. He will not force the hand of nations and governments, but where men and women turn to him, he will bring his peace and forgiveness. What is true on a national scale is true for individuals. Do you know the peace of Jesus Christ in your heart? Cogges Parish | Other articles | Previous issue | Feedback © 2000; Published in Cogges Parish monthly newsletter, number 256, November 2000 |


AreaChart3DDemo2
An area chart with some negative data values (this requires special handling by the renderer).
Under the covers, this chart used a
CategoryPlot3D
with an
AreaRenderer3D
, and displays data from a
StandardCategoryDataset3D
.


{% extends 'base.html' %} {% block content %}
(partially based on the Coursera honour code and the Pydata code of conduct)
Please read this code of conduct carefully, and consider whether these are the type of rules by which you feel comfortable abiding. After you have accepted them, there will be no negotiating them down, and there have unfortunately historically been cases of expulsion from breaches of the code of conduct. It is a binding agreement, not a set of guidelines.
We believe that for a community centered on education to thrive, there must be a set of common principles, and there must be emotional safety for all those involved. On occasion, those will collide with values such as freedom of speech, or with cultural norms that vary between cultures and communities.
This code of conduct aims to clarify the expectation. It is both binding and enforced, meaning that anyone can be expelled for breaches of the code of conduct. It explains what the expected behavior is, rather than attempting to list all unacceptable behaviors. Students and all Staff are expected to follow the LDSA Code of Conduct, in letter and in spirit.
Enforcement is done by a disciplinary commission, which has the power to issue warnings, or expulsion orders. Multiple warnings may result in expulsion, even if none of the warnings themselves would have been sufficient. In situations where there is ambiguity, this commission has final word. Students who are expelled from any activity of the Academy will be denied access to future activities, and will not (where relevant) be issued any refunds.
Your answers to assignments must be your own work (except for assignments that explicitly permit collaboration).
You may not share your assignment solutions with anyone else unless explicitly permitted by the instructor. This includes anything written by you, as well as any official solutions provided by the course staff. Plagiarism (copying words, ideas, or any other materials from another source without giving credit) is unacceptable.
You may not engage in any other activities that will dishonestly improve your results or dishonestly improve or damage the results of others.
When students are stuck on an assignment, they can (and should!) reach out to other students and mentors for help. The code of conduct is to answer such requests with hints. These can be, for instance, a link to some documentation, or a reference of what the logic should be, and (if nothing else works) pseudo code with the sequence to follow.
Please do not share the code solutions, as it is important that students manage to get the answers themselves so that the logic "sinks in" on their own merits. Having answers posted on slack channels, GitHub issues etc makes the temptation to look up the answer too great.
Posting code answers online will be considered a breach of the code of conduct that will result in a warning. Repeated and intentional postings of answers can result in eventual suspension of the offending student, mentor or instructor. When in doubt, ask a mentor for advice on whether something is a hint or if infringes the code of conduct.
The Lisbon Data Science Academy is dedicated to providing a harassment-free conference experience for everyone, regardless of gender, sexual orientation, gender identity and expression, disability, physical appearance, body size, race, or religion. We do not tolerate harassment by participants or staff in any form.
All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate. Be kind to others. Do not insult or put down other attendees. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate for the Academy.
The academy is a community of professionals and every single member is expected to behave like one. To be professional means that everyone is treated with respect in both letter and spirit. This is especially important during stressful or difficult times in order to prevent disagreements from descending into anything resembling insulting or personal.
To be professional means:
Be objective. Not liking something is not a reason to request an adjustment or any other kind of change. Usually when you don't like something it is because, somewhere at its core, it is unfair. If it is actually unfair, you can usually find a nice and clean way to explain why it is unfair and this is what should be done without exceptions.
Be respectful. Personal insults or emotion-driven accusations do nothing to help resolve conflicts. It's amazing how much of a difference it makes to say something like "I'm not sure if this is being done the best way possible, can we talk about it?"  Sarcasm, comments with the intent of humiliating or personal comments will not be tolerated, and have lead to warnings and subsequent expulsions in the past.
Be constructive. It's incredibly easy to find problems. It's much more difficult to suggest solutions but this is where progress comes from. Saying something like "I think that there's an issue with X
and there's a potential solution in Y
- what does everyone think?" rather than "X
is stupid".
More important than any amount of technical ability is being respectful and professional toward your fellow human beings.
The Academy is, as part of its principles, a zero-plastic and environmentally friendly organization. That is why you will find senior data scientists serving you expressos to avoid using capsules. As a student you will be expected to follow the same high environmental standards, which are tighter than those demanded by law.
That means, among other things, that you are expected not to bring discardable plastics (e.g. PET bottles) into the Academy lectures, and will be expected to (whenever possible) recycle your waste.
The code of conduct is strict, and goes way beyond what many organizations expect. Please understand that by accepting it, you are agreeing that you will be bound by it.
If you are found to have violated this code of conduct, you may receive a warnings or an expulsion order, depending on the severity, and the interpretation of the disciplinary commission is final.
As students of Lisbon Data Science Starter Academy, you are eligible for a full refund within 14 days after the Bootcamp start date.
If the refund deadline has not passed, you can ask for a refund by sending an email to<EMAIL_ADDRESS>.
Within 30 days, you will receive an answer on whether your refund has been accepted or not.
In the email, please make sure that you include the following information:
Full Name
Proof of payment
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FocusChannelPriority
public struct FocusChannelPriority
The priority of the channel used by the FocusManager
to create Channel
objects.
Use a initializer if you want to create priorities directly in application.
The predetermined focus channel includes recognition
(priority = 300), information
(priority = 200) and content
(priority = 100).
-
<#Description#>
Declaration
Swift
public init(requestPriority: Int, maintainPriority: Int)
Parameters
requestPriority
<#requestPriority description#>
maintainPriority
<#maintainPriority description#>
-
A priority of
call
channel.Declaration
Swift
public static let call: FocusChannelPriority
-
A priority of
userRecognition
channel.Declaration
Swift
public static let userRecognition: FocusChannelPriority
-
A priority of
dmRecognition
channel.Declaration
Swift
public static let dmRecognition: FocusChannelPriority
-
A priority of
alerts
channel.Declaration
Swift
public static let alerts: FocusChannelPriority
-
A priority of
information
channel.Declaration
Swift
public static let information: FocusChannelPriority
-
A priority of
media
channel.Declaration
Swift
public static let media: FocusChannelPriority
-
A priority of
beep
channel.Declaration
Swift
public static let beep: FocusChannelPriority
-
A priority of
sound
channel.Declaration
Swift
public static let sound: FocusChannelPriority
-
A priority of
background
channel.Declaration
Swift
public static let background: FocusChannelPriority