THE TRUTH ABOUT CATS & DOGS A film review by Steve Rhodes Copyright 1996 Steve Rhodes
RATING (0 TO ****): ***
THE TRUTH ABOUT CATS & DOGS is a light hearted and delightful retelling of the CYRANO DE BERGERAC story. It is a fun movie with not a lot of substance, but with plenty of style and wit. Although not quite as good Steve Martin's version called ROXANNE, it nevertheless has a script by Audrey Wells that is superb - full of intelligent humor. Although it is certainly no laugh riot, there are many jokes, small and large, to keep your attention and fix a smile on your face. Not only does the script possess creative humor, it fashions compelling characters for whom the audience really cares. A lightweight show certainly, but a sweet one.
Dr. Abby Barnes (Janeane Garofolo) is a radio talk show veterinarian whose show is called "The Truth About Cats and Dogs." One day a photographer called Brian (Ben Chaplin) calls in with a problem he is having with a wonderful big dog named Hank (Hank the Dog). Hank is upset because he has to be on roller skates for the picture. Brian falls for Abby's voice, but Abby lacks self-confidence with her looks so she lies about whom she is. Actually she is a short but a little chunky brunette with a lovely and attractive smile. Nevertheless, she tells Brain that she is her tall, thin, blonde, and gorgeous neighbor Noelle Slusarsky (Uma Thurman).
The next day on the phone, Abby asks Brian, "How'd it go?" with Hank. He says, "he snored, but we stood it." She inquires, "How'd you do it?", and he explains, "He slept on my face so I couldn't hear him."
Noelle doesn't understand Abby's lack of sexual activity, asking, "You gotta have a boyfriend, don't you? Otherwise it's just you, a cat, and 40 candles on your birthday cake." Abby envies Noelle's beauty, telling her, "You burp and guys think its adorable.  Noelle on the other hand, does not like her life as a model, explaining, "I don't eat anything so I can look good on the outside, but on the inside, there's nothing."
During most of the movie we have almost five foot Abby hanging out with almost six foot Noelle and creating some hilarious visuals. The only major dirty word in the show is a single use of the F-word by Noelle in a great line that almost had me double over in laughter. Both of the actresses are good, but this is Garofolo's show. She is terrific - strong, witty, vulnerable, and extremely humorous. Her funny line at the end of the movie is the best part of the show.
Thurman does a good job of making Noelle into an airhead without resorting to caricature. After Brian gives her a book, Noelle is shocked saying, "nobody has ever given me a book before that doesn't have pictures in it."
Chaplin has a limited role as the sweet and boyishly handsome boyfriend. He does this well, but the stars are the two female leads, and he is there strictly to complement them. Once Abby puts him to the test by wanting to know, "If you are trapped in Biosphere 3, whom would you want to bring: Time's Woman of the Year or Playboy's Bunny of the Year?" Okay guys, what would you have answered?
I do not know exactly what constitutes a "chick flick," but the large audience on opening night was over three quarters women with more all female couples than mixed sex couples so perhaps this is a chick flick. As a male however, I was just as impressed by the show as was my wife.
I like the way, the art of long and intelligent telephone conversation is a central part of the film. The film really teaches you that beauty is more than skin deep. Actually, I thought there should have been more of a contrast between Abby's looks and Noelle's. Abby's clothes are suitably clunky, but Noelle's frocks are too plain and her make-up too washed out. In contrast, Abby had great make-up. I would pick Abby every time as would I suspect will most intelligent men watching the show.
THE TRUTH ABOUT CATS & DOGS runs a fast 1:37 thanks to good editing and pacing by editor Stephen Semel and director Michael Lehmann. It is correctly rated PG-13. There are only a few bad words, no violence, no nudity, and the sex is limited to the telephone. I suspect the film would be fine for most kids over 10. I recommend the show to you and give it ***.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEW WRITTEN ON: April 26, 1996
Opinions expressed are mine and not meant to reflect my employer's.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


(Official Number of Confirmed Cases)
(Chinese PHD)
(First Hand Videos)
(Virus linked to China's Biowarfare Program)
(Reddit First Hand Accounts etc)
(Casual Rioting)
(One of the Plague Ships)
(Forced Quarantine via weld)
(Chinese Death Squad)


This exercise will test your understanding of normal-order β-reduction techniques. First, from left-to-right, identify the argument that will be substituted in during reduction. Then, from left-to-right, identify all instances of the appropriate variable in the function body that this argument will replace.
What is the argument being passed into the lambda function?
What variable will it be substituted in for?
How does β-reduction change the lambda function?


Demo Files for LoadFile4DOM
Last Update: 20.10.2019
This file contains all demo files for LoadFile4DOM as learning resources for AppLSAC in Wikiversity.
Click on the link to display the demo files.
Demo files scanned with scan_demos4index.sh on 20.10.2019
The file ./docs/index.html is used as index.html for https://niehausbert.gitlab.io/loadfile4dom/
GitLab-Respository LoadFile4DOM


--- layout: post status: publish published: true title: The most sickening loss of the season, thus far. author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 949 wordpress_url: http://ivyenvy.com/?p=949 date: '2009-06-02 20:49:38 +0000' date_gmt: '2009-06-03 02:49:38 +0000' categories: - Relief Pitching tags: - Carlos Marmol - Kevin Gregg - Randy Wells - Atlanta comments: - id: 249 author: J. Baxter Graf author_email<EMAIL_ADDRESS>author_url: http://www.ivyenvy.com date: '2009-06-02 23:04:27 +0000' date_gmt: '2009-06-03 05:04:27 +0000' content: I blame Escobar. - id: 250 author: Kurt author_email<EMAIL_ADDRESS>author_url: http:// date: '2009-06-03 07:44:18 +0000' date_gmt: '2009-06-03 13:44:18 +0000' content: "Randy Wells\r\n5 starts\r\n32 IP\r\n1.69 ERA\r\n7 BB to 27 K ratio\r\nHis record: 0-2" - id: 251 author: Kurt author_email<EMAIL_ADDRESS>author_url: http:// date: '2009-06-03 07:48:25 +0000' date_gmt: '2009-06-03 13:48:25 +0000' content: And who pitches to Chipper Jones in that situation! ---
Randy Wells must have entered tonight's game thinking "I'm going to throw a no-hitter to ensure this awful bullpen doesn't ruin another start for me." Well, he nearly accomplished that. He took a no-hitter into the 7th and handed the game over to Marmol in the 8th with the Cubs leading 5-1. After an inning of Marmol and an inning of Gregg, the Braves tied the game at 5 and Wells had another win tossed aside by the bullpen.
I'm not really one for drastic moves but Gregg needs to be released now. He's terrible and I believe I mentioned this in one of the podcasts (or maybe someone else did), I can't stomach the idea of watching Kevin Gregg trotting in to pitch the 9th in a playoff situation. Spend some money, slice and dice the bullpen, make a trade...do whatever it takes to get a real closer. This should have been an easy win and rather than scrapping in extras, those guys should have been in the showers making plans for a night out in Atlanta.
These are the types of games that deflate a team. Losing like this on the road leads to lengthy losing streaks.


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Conditions (5 conditions = passed out)
Injured (-2 Body)
Upset (-2 Heart)
Dazed (-2 Mind)
Exhausted (-2 Soul)
Jinxed (-2 Magic)
Positive Traits
Negative Traits
Relationships
Experience
Looks
Money
School House
Spell Bank
(+2 to casting this spell)
Regular Spell Bank (+1 to casting the below spells)
Wand
Family Heritage
Pet
Pet Notes
Items
Notes
Biography
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Positive Traits
Negative Traits
Looks
House
Motivations
Notes
Styling (Currently Disabled)
Sheet Type
Sorting Hat
| {{char}} {{^spell}}rolls{{/spell}}{{#spell}}casts {{spell}}{{/spell}} | ||
| {{attr}}{{#rollGreater() forwardr 0}}+Forward{{/rollGreater() forwardr 0}}{{#relation}}+Relationship{{/relation}}{{#spell}}+Spell Power{{/spell}} | ||
| {{/rollGreater() result 9}} {{#rollBetween() result 7 9}} | {{/rollBetween() result 7 9}} {{#rollLess() result 7}} | {{/rollLess() result 7}} {{result}} |
| {{#rollGreater() result 9}} Complete Success {{/rollGreater() result 9}} {{#rollBetween() result 7 9}} Partial Success {{/rollBetween() result 7 9}} {{#rollLess() result 7}} Failure {{/rollLess() result 7}} |


| HBASE-4144 |
RS does not abort if the initialization of RS fails |
Minor |
regionserver |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-4160 |
HBase shell move and online may be unusable if region name or server includes binary-encoded data |
Minor |
. |
Jonathan Hsieh |
Jonathan Hsieh |
| HBASE-4168 |
A client continues to try and connect to a powered down regionserver |
Critical |
. |
Anirudh Todi |
Anirudh Todi |
| HBASE-4186 |
No region is added to regionsInTransitionInRS |
Major |
. |
Ted Yu |
Ted Yu |
| HBASE-4196 |
TableRecordReader may skip first row of region |
Major |
mapreduce |
Jan Lukavsky |
Ming Ma |
| HBASE-4148 |
HFileOutputFormat doesn't fill in TIMERANGE_KEY metadata |
Major |
mapreduce |
Todd Lipcon |
Jonathan Hsieh |
| HBASE-4161 |
Incorrect use of listStatus() in HBase region initialization. |
Major |
regionserver |
Pritam Damania |
|
| HBASE-4095 |
Hlog may not be rolled in a long time if checkLowReplication's request of LogRoll is blocked |
Major |
regionserver |
Jieshan Bean |
Jieshan Bean |
| HBASE-4234 |
Let regionserver abort if rollback fail after point-of-no-return on branch |
Major |
regionserver |
Jieshan Bean |
Jieshan Bean |
| HBASE-4253 |
Intermittent test failure because of missing config parameter in new HTable(tablename) |
Major |
. |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-4252 |
TestLogRolling's low-probability failure |
Major |
regionserver |
Jieshan Bean |
Jieshan Bean |
| HBASE-4124 |
ZK restarted while a region is being assigned, new active HM re-assigns it but the RS warns 'already online on this server'. |
Major |
master |
fulin wang |
gaojinchao |
| HBASE-4294 |
HLogSplitter sleeps with 1-second granularity |
Trivial |
master |
Todd Lipcon |
Todd Lipcon |
| HBASE-4341 |
HRS#closeAllRegions should take care of HRS#onlineRegions's weak consistency |
Major |
regionserver |
Jieshan Bean |
Jieshan Bean |
| HBASE-4297 |
TableMapReduceUtil overwrites user supplied options |
Major |
mapreduce |
Jan Lukavsky |
|
| HBASE-4340 |
Hbase can't balance if ServerShutdownHandler encountered exception |
Major |
. |
gaojinchao |
gaojinchao |
| HBASE-4180 |
HBase should check the isSecurityEnabled flag |
Major |
security |
Bochun Bai |
Gary Helmling |
| HBASE-4325 |
Improve error message when using STARTROW for meta scans. |
Major |
shell |
Jonathan Hsieh |
Jonathan Hsieh |
| HBASE-4238 |
CatalogJanitor can clear a daughter that split before processing its parent |
Critical |
. |
Jean-Daniel Cryans |
stack |
| HBASE-4351 |
If from Admin we try to unassign a region forcefully, though a valid region name is given the master is not able to identify the region to unassign. |
Major |
. |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-4363 |
[replication] ReplicationSource won't close if failing to contact the sink |
Major |
. |
Jean-Daniel Cryans |
Jean-Daniel Cryans |
| HBASE-4390 |
[replication] ReplicationSource's UncaughtExceptionHandler shouldn't join |
Major |
. |
Jean-Daniel Cryans |
Jean-Daniel Cryans |
| HBASE-4395 |
EnableTableHandler races with itself |
Blocker |
. |
Jean-Daniel Cryans |
Jean-Daniel Cryans |
| HBASE-4423 |
HBASE-4238 broke TestCatalogJanitor#testCleanParent test |
Major |
test |
stack |
ramkrishna.s.vasudevan |
| HBASE-4417 |
HBaseAdmin.checkHBaseAvailable() doesn't close ZooKeeper connections |
Minor |
Client |
Stefan Seelmann |
Stefan Seelmann |
| HBASE-4400 |
.META. getting stuck if RS hosting it is dead and znode state is in RS_ZK_REGION_OPENED |
Major |
. |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-4445 |
Not passing --config when checking if distributed mode or not |
Major |
scripts |
stack |
stack |
| HBASE-3421 |
Very wide rows -- 30M plus -- cause us OOME |
Major |
. |
stack |
Nate Putnam |
| HBASE-4453 |
TestReplication failing up on builds.a.o because already running zk with new format root servername |
Major |
. |
stack |
stack |
| HBASE-4452 |
Possibility of RS opening a region though tickleOpening fails due to znode version mismatch |
Critical |
. |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-4387 |
Error while syncing: DFSOutputStream is closed |
Critical |
wal |
Todd Lipcon |
Lars Hofhansl |
| HBASE-4295 |
rowcounter does not return the correct number of rows in certain circumstances |
Critical |
mapreduce |
Wing Yew Poon |
Dave Revell |
| HBASE-4512 |
JVMClusterUtil throwing wrong exception when master thread cannot be created |
Trivial |
. |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-4515 |
User.getCurrent() can fail to initialize the current user |
Major |
. |
Jonathan Hsieh |
Gary Helmling |
| HBASE-4473 |
NPE when executors are down but events are still coming in |
Minor |
. |
Jean-Daniel Cryans |
Jean-Daniel Cryans |
| HBASE-4212 |
TestMasterFailover fails occasionally |
Major |
master |
gaojinchao |
gaojinchao |
| HBASE-4537 |
Bad imports in TestUser from HBASE-4515 change |
Major |
. |
Gary Helmling |
Gary Helmling |
| HBASE-4501 |
[replication] Shutting down a stream leaves recovered sources running |
Major |
. |
Jean-Daniel Cryans |
Jean-Daniel Cryans |
| HBASE-4449 |
LoadIncrementalHFiles should be able to handle CFs with blooms |
Major |
. |
Dave Revell |
Dave Revell |
| HBASE-4282 |
RegionServer should abort when WAL close encounters an error with unflushed edits |
Blocker |
. |
Gary Helmling |
Gary Helmling |
| HBASE-4540 |
OpenedRegionHandler is not enforcing atomicity of the operation it is performing |
Major |
. |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-4563 |
When error occurs in this.parent.close(false) of split, the split region cannot write or read |
Blocker |
regionserver |
bluedavy |
bluedavy |
| HBASE-4570 |
Scan ACID problem with concurrent puts. |
Major |
Client, regionserver |
Jonathan Hsieh |
Jonathan Hsieh |
| HBASE-4562 |
When split doing offlineParentInMeta encounters error, it'll cause data loss |
Blocker |
regionserver |
bluedavy |
bluedavy |
| HBASE-4378 |
[hbck] Does not complain about regions with startkey==endkey. |
Major |
hbck |
Jonathan Hsieh |
Jonathan Hsieh |
| HBASE-4508 |
Backport HBASE-3777 to 0.90 branch |
Major |
. |
Ted Yu |
Bright Fulton |
| HBASE-4695 |
WAL logs get deleted before region server can fully flush |
Blocker |
wal |
jack levin |
gaojinchao |
| HBASE-4277 |
HRS.closeRegion should be able to close regions with only the encoded name |
Critical |
. |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-4684 |
REST server is leaking ZK connections in 0.90 |
Critical |
. |
Jean-Daniel Cryans |
Andrew Purtell |
| HBASE-4800 |
Result.compareResults is incorrect |
Major |
Client |
Lars Hofhansl |
Lars Hofhansl |
| HBASE-4806 |
Fix logging message in HbaseObjectWritable |
Trivial |
. |
Jonathan Hsieh |
Jonathan Hsieh |
| HBASE-4799 |
Catalog Janitor logic bug causes region leackage |
Critical |
master |
Max Lapan |
Max Lapan |
| HBASE-4718 |
Backport HBASE-4552 to 0.90 branch |
Major |
. |
Jonathan Hsieh |
Jonathan Hsieh |
| HBASE-4862 |
Splitting hlog and opening region concurrently may cause data loss |
Critical |
. |
chunhui shen |
chunhui shen |
| HBASE-4900 |
Fix javadoc warnings out on 0.90 branch |
Major |
. |
stack |
stack |
| HBASE-4904 |
Fix overcommit to 0.90 (hbase-4352+hbase-4900) |
Major |
. |
stack |
stack |
| HBASE-4982 |
graceful_stop.sh does not pass on the --config its passed to its internal invocations of other hbase scripts |
Major |
scripts |
stack |
stack |
| HBASE-4848 |
TestScanner failing because hostname can't be null |
Major |
. |
stack |
stack |


H.B. Woodrose Seeds
Two of my friends and I received 13 seeds each off of an internet site, in a package that promised an interesting, dreamy trip. I hadn't had much experience with other hallucenogens (though I had taken mushrooms for my fourth time two days prior), so I had no idea what to expect.
We each ground 6 seeds up, then put them in our respective cups, spitting on them to pull the LSA from them (we'd read saliva will remove it from the seed particles). Then we poured a small amount of boiling water into each cup and allowed it to sit for approximately 10 minutes. At this time, the water immediately took on a yellow color, and smelled like an herbal tea.
T-20min - We each took 2 Dramamine pills in order to combat the sickness so many people describe in taking Hawaiian Baby Woodrose.
T+0 - We began drinking the mixture. It had a pleasant taste to it, except for the seed particles themselves, which were bitter and gummy. By this time, I was feeling the dramamine pretty well. I felt as though I had smoked marijuana, but was still able to think somewhat clearly.
T+20min - I finished the mixture, and took another Dramamine, because I wasn't interested in puking (I had puked only twice in my life that I can remember and wasn't going for a third).
T+45min - We all were starting to feel drowsy and incapable of moving without difficulty. One of my friends, C., hadn't taken an extra dramamine, and the other, B., had. C. said that he didn't feel sleepy, but both B. and myself seemed to. Leaning against walls and lying on the floor sound good to everyone, and we all end up sitting in the hallway.
T+55min - C. vomits once. He says it helped him feel better and gave him more energy afterward.
 We decide to go for a walk and he comes with us. Immediately as he goes outside he pukes up everything he has in him. Meanwhile, I'm feeling more drowsy and finding it difficult to think, though I don't feel at all nauseous.
T+2hrs - We return from the walk. I'm still not feeling any noticeable effects except for the extreme lethargy that supposedly accompanies HBW. Both C. and I take Vitamin C in hopes of increasing the chance of seeing visuals (even though it may be too late). By this time, we were thinking we may not end up tripping. My pupils are still massively dialated, however, with almost no color left in them. My pupils are large to begin with, so this is how they usually look when I trip.
T+2:10 - I decide to smoke some marijuana with a roommate in hopes of making the trip kick in. It doesn't seem to have an effect, but I feel somewhat comfortable with my state afterwards, as being stoned is much more familiar. Both B. and C. abstain from smoking with us.
T+3hrs - B. reports that he is able to see in double and occasionally move the location of objects by will. After a suggestion from C., I look in the mirror while in near darkness, and notice that the light seems to brighten and darken randomly, while my face distorts itself somewhat and I see for passing moments it might become that of a beast. Nothing extraordinary happens, however.
T+3:20 - My arms and legs start to feel cold and somewhat numb. By this time, B. is passed out on the couch. C. decides that he will return home, perhaps returning later if he feels any effects. After reading more literature on HBW, I decide to take a short nap to see if I awaken to intense visuals.
T+4 - I wake up and see a few brief patterns under the covers. Other than that, I can only feel the intense lassitude and drowsiness as I step out of bed. I go to the bathroom for what seems like the tenth time that night, and return to bed, this time for good.
T+7 - I break from my sleep, but still feel nothing. I return to bed.
In the morning, after sleeping 13 hours, I was still tired. Overall I was disappointed by the experience, and after talking with B. and C. in the morning, got the impression that they were, too, as they hadn't experienced anything after going to sleep. We may take them again in a few weeks, with the following modifications:
1. Splitting up one person's dosage and having two people take 10 or 11 seeds each.
2. Soaking the seeds for 2-3 hours instead of making a tea.
3. Taking only one or two dramamine instead of 3 to reduce the tired feeling.
4. Taking vitamin C immediately after we're done ingesting the seeds.
I think our biggest problem was too small a dosage and too little time allowed to extract the LSA from the seeds. We didn't hold the drink in our mouths too long, either. Most likely what happened is we took all of the poison in our bodies, and very little of the LSA. For those who are thinking of taking HBW for the first time, I would suggest reading up on preparation methods extensively, as they can really make a difference.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Before reading this post, I highly encourage you to check out my summary as I create content in YouTube apart from my full-time job and I come from a middle class family.
You’re probably here because you listen to my music covers on YouTube. During my YouTube music journey, I regularly get comments from viewers regarding using my music covers for their video.
Music that you hear in my channel are not 100% copyright-claim free as I do not own any of the original composition rights. As I upload my music covers to YouTube, they are registered to YouTube’s Content ID to protect my work.
You might get a copyright claim (NOT a copyright strike) if you use my music in your video.
Good to know
A cover video that I create involve lots of time + energy + effort + money spent on hardware equipment [Mixer, MIDI controller, Laptop, Studio monitors etc.,] + money spent on getting original software [Digital Audio Workstation (DAW) and Virtual Studio Technology (VST) plugins]. You might have already understood creating these high-quality video + audio isn’t cheap it you come from a middle class indian family.
I created this YouTube channel because I love making music (especially music production and music arrangements). As I do this as a hobby only during my free-time i.e. in addition to my full-time job, these covers are a result of my passion towards music that I have from my childhood.
Hence, with your kindness and support (as mentioned in Allowed Permissions) section, you help this channel to reach more people in the planet who love music as you and me 😇
Allowed Permissions
You’re allowed to use my music covers in your video provided you do both the following.
- You must give credit to my YouTube channel https://youtube.com/gogulilangomusic in your video’s description.
- You must give credit to my YouTube channel name Gogul Ilango + channel URL https://youtube.com/gogulilangomusic at the credits section of your video.
Please note - You’re allowed to use my music covers in your video as background music only.
Strictly Prohibited
You should NOT do any of the following, else you will be in the risk of your video being REMOVED or possible LEGAL ACTION being made by music publishing companies, music labels and/or owners of the original composition.
- Reuploading my original cover videos on YouTube and other social media platforms (Instagram, SoundCloud, Twitter, Tik Tok etc.,)
- Uploading my original cover video’s audio to music streaming platforms (Spotify, Amazon Music etc.,)
- Using my original cover video/audio commercially on Television, Radio, Official Trailers and Films.
- Removing my name in my original cover video and reuploading on YouTube and other social media platforms (Instagram, SoundCloud, Twitter, Tik Tok etc.,)
Audio Streaming
I use Soundrop as my digital distributor for my cover songs digital distribution to audio streaming platforms such as Spotify, Amazon Music, Apple Music etc., They have excellent documentation about cover song licensing, digital distribution process, artist tips, music production etc., in their blog which I highly recommend for reading.
In case if you found something useful to add to this article or you found a bug in the code or would like to improve some points mentioned, feel free to write it down in the comments. Hope you found something useful here.


dynamic CSS
Example 1
Jawad
Example 2
Jawad
conditionals
Exapmle for v-if
There is an Error
You made it Success
Exapmle for v-Show
There is an Error
You made it suCCess
Loops
Example 1 for an Arry
- {{ Myvalue}}
Example 2 for an Arry with object
- {{ myDetails.Name }} {{ myDetails.Password }} {{ myDetails.Email }}


The So So Social plug-in uses jQuery and YQL (Yahoo Query Language) to gather RSS feeds from your various social networks and sorts them into an "activity feed". Basically it creates a news feed out of your social networking activity.
I felt this was a sensible set of service to include with the plug-in. If there is a service not represented like posterous or virb or youtube...the code is open...extend it.


A young man made of paper. An anonymous, anarchic city. One conflicted desire: to both fit in and stand out.
Escaping a stagnant, sheltered life, an unusual young man named Michael runs away to an equally unusual city by the sea, one that holds the promise of art, adventure, and acceptance. Trailed by the mysterious circumstances surrounding his transformation from “normal” boy to paper man, it is acceptance that Michael wants more than anything else. Soon, however, he discovers that the city is tearing at the seams. With rumors abounding of an invasion by the militarized Northern power, Michael has more to worry about than the unpredictable seaside weather turning him to pulp.
Everything changes when Michael plunges into a decadent underground art world that adopts him as its own creation. Who is Michael? What kind of person does Michael want to be? As the paper man struggles with an increasingly fragile grasp of his own identity, he is drawn to the city’s most notorious artist, David Doppelmann. With Doppelmann, Michael begins another dangerous transformation, one that will either lead to uncovering his true self, or destroy him and everyone he cares out.
“Beautifully written and finely wrought, this novel is a surreal coming-of-age story that considers the boundaries between art and life….[A] strange and brilliant debut novel.”
“Part Frankenstein, part Pinocchio, attracting comparisons to Kafka’s Metamorphosis… [The Paper Man] is a compelling story that sticks with the reader, examining art and its creation from a dizzying variety of angles and raising fascinating questions about ownership, celebrity, autonomy, the limits of self, and the essence of our humanity.”
“Gallagher Lawson’s The Paper Man is a rare narrative, announcing the arrival of an outstanding new voice that comfortably inhabits the strange place where literary fiction, surrealism, and gritty fantasy converge.”
“Gallagher Lawson belongs to the new wave of writers using fantastical elements to mine deeper into realist territory. A strong debut from a promising writer of agile talents.”
“Only in the purity of paper, in its essence of nothingness, is it possible to create a truly literary character.”
“This bizarre, compelling debut combines the darkness and political intrigue of Kafka with the eerie hyperrealism of a Magritte painting. Michael, the Paper Man, is a highly unconventional and persuasive hero, and David Doppelman among the strangest—arch nemeses? father figures? manifestations of the self?—I’ve ever seen in a novel. Gallagher Lawson is a weird and wonderful writer.”
“THE PAPER MAN is one of the most inspiring and original books I’ve read in years. Gallagher Lawson has written a lustrous fable comprised of loneliness, blood, and love. The Wizard of Oz on laudanum, a Bjork musical in letters, this devastatingly powerful debut challenges us to imagine what it would be like to be made of nothing more than paper, to live in a world where mermaids are murdered and art is the only path to actualization, a world, in many ways, not unlike our fragile own.”
“In the book’s subtle allusiveness you can catch glimpses of the Wizard of Oz, Star Wars, David Lynch films, Miyazaki’s anime fairytales,The Velveteen Rabbit, Eyes Wide Shut, and Jose Saramago’s fables…Days after reading it, certain deeply weird scenes, erotic scenes and destructive scenes featuring paper, are still playing in my mind, still worming through my subconscious, like a waking dream…the book is a haunting debut, a children’s fable for adults that palpably captures what it might mean to hide behind a paper mask, and a social persona as well.”
Read the interview with Laura van den Berg at the LA Times.
Read the review from Publishers Weekly.
Read the review from Ploughshares.
Read the interview with Heather Scott Partington at Electric Literature.
Read the feature on The Paper Man and Unnamed Press at Brazos Bookstore in Houston.
Read the interview with The Last Bookstore.
Check out the playlist for the book at Large Hearted Boy's Book Notes series.
Read the review by Dwarf + Giant.
Read the review from The Rumpus.
Read the review from ZYZZYVA.
Read the review from Electric Literature.
Read Gallagher's self interview at The Nervous Breakdown.
Read the review at Literary Equations.
Check out the book’s Tumblr page for themes, inspiration, atmosphere, and imagery.


FIR Management System (C++)
Police Station Management System is based on the concept of managing criminal records. Before stepping into the main menu a user has to pass through a login system to get access, then the user can manage criminal records by adding, updating, removing, viewing and searching for details. This mini project contains limited features, but the essential ones.
Talking about the features of the Police Station management system, the user can add various information of the criminals. For that, the user has to provide convict’s id, name, age, gender, height, weight, crime, face details, conviction etc. Apart from this, the user can view/check a list of the records, search, modify and remove records too. The user just has to provide convict id in order to search for a criminal’s record.
Features:
- Login System
- CRUD criminal records
- Search records
The system creates an external file to store the user’s data permanently. This program mainly focuses on CRUD with searching function. Police Station Management system is developed using C++ Programming Language and different variables, strings have been used for the development of it.


Welcome
This is abdulelah
in love with programming
Twitter | abdulelah__k
Here is my twitter where I contact with people.
GitHub | abdulelahxd
This is where I share code and work on projects.
in | abdulelahkassara
Here you can see my
professional protfolio.
Projects
Skills
HTML5
80%
CSS3
60%
JavaScript
50%
GitHub
60%
Bootstrap
0%
jquery
0%
Education
High School
Tawjihi Scientific Certificate
LTUC
studied information technology at Luminus technical university college
ASAC
studying web development at Abdul Aziz Al Ghurair School of Advanced Computing


WITNESS LIST
Transportation
February 21, 2018 9:00 AM
Highway Naming Charge
ON:
Chacon, Michael Traffic Operations Division Director (TxDOT), Austin, TX
Human Smuggling Charge
ON:
Esparza, John President (Texas Trucking Association), Austin, TX
Freeborn, Emily Staff Attorney (Children at Risk), Houston, TX
Goodwin, Floyd Major (Texas DPS), Austin, TX
Leeburg Melton, Kirsta Deputy Criminal Chief, Human Trafficking &
Transnational/Organized Crime (Office of the Attorney General), Austin, TX
Ruocco, Thomas G. Division Director (Texas Department of Public Safety), Austin, TX
Ports Funding Charge
ON:
Fisher, David C. "Chris" Port Director & CEO (Port of Beaumont), Beaumont, TX
Gonzales, Patty Director of Communications (Port of Brownsville), Brownsville, TX
Guenther, Roger Executive Director (Port of Houston Authority), Houston, TX
Harmon, Dan Maritime Division Director (TxDOT), Austin, TX
Rees, Rodger Port Director/CEO (Port of Galveston), Galveston, TX
Saathoff, Phyllis CEO (Port Freeport), Freeport, TX
Strawbridge, Sean C. CEO (Port of Corpus Christi), Corpus Christi, TX


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Tim
Head
About
Science
Sports
09 September 2016
AlgebraNet - Learning Algebra from Scratch
01 July 2016
An Improbable Afternoon
09 June 2016
Genome Assembly for Hackers
01 April 2016
Stop growing your ensembles early
29 February 2016
From One Man Band to Orchestra
24 February 2016
Unbiased performance estimates for your classifiers
07 January 2016
Really Interactive Blog Posts
05 January 2016
Interactive Blog Posts
16 December 2015
A Physicist at NIPS2015
25 November 2015
Jupyterhub backed by Carina!
13 August 2015
Project everware: Reusable science
07 August 2015
Bayesian optimisation for smart hyperparameter search
21 July 2015
Basic Gaussian Processes with George
17 June 2015
Bumping or Escaping Local Minima
02 May 2015
Learning Expensive Functions
04 April 2015
Cross-Validation Gone Wrong
11 March 2015
Advanced scikit-learn for TMVA Users
04 February 2015
scikit-learn for TMVA Users
27 January 2015
Getting Ahead on Interesting Papers
27 December 2014
The Artificial Artist
16 December 2014
Analysing the arXiv
02 December 2014
Read any Nature Paper for Free
03 June 2014
Monte Carlo Integration with Quasi Random Numbers
28 May 2014
Early stopping
14 May 2014
Matching Machine Learning
29 April 2014
Knowing when to switch
26 March 2014
Obligatory first post


About me
- eZ & Symfony Community member since 2007
- Part of engineering team since 2011
- Lead engineer eZ Symfony
- Webmaster of http://www.metalfrance.net, powered by eZ Publish (soon to be migrated to v5)
PM Scrum story
«As an eZ Publish user, I don’t want to be
pissed off by a new #@!$% version!»
The challenges
- 100% data compatible (same database schema)
- Include legacy templates into new ones
- Routing fallback
- Load legacy content templates with old override rules
- Settings
- Access Symfony services from legacy PHP code (e.g. modules)
Challenge accepted !
Dual Core architecture
Concrete example: Using eZ Find
$searchPhrase = 'My search phrase';
$sort = array(
'score' => 'desc',
'published' => 'desc'
);
$contentTypeIdentifiers = array( 'folder', 'article' );
$mySearchResults = $this->getLegacyKernel()->runCallback(
function () use ( $searchPhrase, $sort, $contentTypeIdentifiers )
{
// eZFunctionHandler::execute is the equivalent for a legacy template fetch function
// The following is the same than fetch( 'ezfind', 'search', hash(...) )
return \eZFunctionHandler::execute(
'ezfind',
'search',
array(
'query' => $searchPhrase,
'sort_by' => $sort,
'class_id' => $contentTypeIdentifiers
)
);
},
false
);
Call Symfony services from legacy code
// From anywhere in legacy code
/** @var \Symfony\Component\DependencyInjection\ContainerInterface $serviceContainer */
$serviceContainer = ezpKernel::instance()->getServiceContainer();
$repository = $serviceContainer->get( 'ezpublish.api.repository' );
$rootLocation = $repository->getLocationService()->loadLocation( 2 );
- You can call and use every declared public service
-
As controllers can be defined as services, you can use controllers for your modules!
(particularly useful for module migration)
If I migrate to eZ 5, will my site work?
- Short answer: YES!!!
- Define new Twig templates, they will used directly
- If no Twig template yet, all your old content templates
will be taken into account as fallback
- All your modules will continue to work (routing fallback)


init.css
A css stylesheet that needs less HTML structure, for better result .
Description
With init.css, you only focus on inline tags. No need to add divs, or any bloc elements. If you want a kind of gihub pages site for a project and you only want to write the text.
Demo
View it online : Here
View it on codepen : Here


Steve Pikelny
< Home
In the Media
6 NFT Artists And Experimental Studios To Watch In 2023
Art Blocks Spectrum: In Conversation with Steve Pikelny on Dopamine Machines
Outland: Scam Artist by Brian Droitcour
Proof of Concept (Organised by Co-Museum)
Ensemble Interview: Uncirculated Fake Internet Money
Steve Pikelny Interview With laCollection
Minters & Makers (Steve Pikelny, Mitchell Chan, Dadabots)
JPG Twitter Spaces (ft Rhea Myers, Simon Denny, Steve Pikelny)
Episode 50: Art Blocks After Dinner Mints w/ Steve Pikelny
Digitally Rare: free jesus pamphlet fast cash money cash plus dot biz crypto god king condiments
Zora Zine: Feedback Loops and Topographies of Ketchup
Guest on Ipse Dixit w/ Brian L Frye
CryptoGodKing #100 sold at a fake auction at the Goethe Institute in Bejing [at 15:20]


Ant & Dec Shocked To Discover They’re Related
by Lucy ConnollyAnt McPartlin and Declan Donnelly are more like brothers than best friends; they bicker, hold each other to account, and best of all embarrass each other for the entire world to see on television.
Advertisements
And now it seems that while they might not be brothers exactly, they are distantly related… as proven on the latest episode of Ant & Dec’s DNA Journey.
The two Geordie pals were seen gasping in shock as they received the news in Monday night’s episode, the last of a two-part special, with the news making for a surprise ending nobody could have expected.
Advertisements
At the end of their two-year journey to trace their family trees using DNA analysis and genealogy, Ant, 43, and Dec, 44, discovered they both carried the same DNA marker S660. That might not seem like that big of a deal, until you realise the only way you can share that is if you’re related.
The presenters and lifelong friends also found out they share the same Viking warrior descendant through their fathers’ bloodlines – with viewers branding the news ‘the best thing ever’.
The hosts were initially nervous to receive the results of their DNA tests, with Dec reading over the letter in an attempt to understand the results.
He read:
Advertisements
Ant, we’ve discovered your DNA into the deep past and you carry a unique DNA marker called S660. Dec, we’ve traced your DNA into the deep past, and have discovered you also carry the DNA marker S660.
The presenter seemed shocked by the news, exclaiming: ‘Oh my God! That’s really surprising,’ leading Ant to jokingly ask him if he was disappointed.
In an attempt to provide viewers with more of an insight into how this came to light, a genealogist explained: ‘There’s only one way to share a DNA marker – and that’s because you’re related.’
They continued:
Advertisements
Recently, a Viking age warrior’s skeleton was dug up in Dublin. He was in his late 20s, and died over 1,000 years ago. What’s amazing to me is that when his DNA was analysed, it was shown he carried the S660 marker – the same marker that Ant and Dec carry.
This warrior is directly related to them, in the father line. He’s the first warrior we’ve ever found in the world with the S660.
Who’d have thought it? Not only are our favourite presenters related, but an ancient Viking warrior is responsible for their DNA being the same.
To be honest, I’m just waiting for them to incorporate the Viking into this year’s I’m A Celebrity trials in some way…
A Broadcast Journalism Masters graduate who went on to achieve an NCTJ level 3 Diploma in Journalism, Lucy has done stints at ITV, BBC Inside Out and Key 103. While working as a journalist for UNILAD, Lucy has reported on breaking news stories while also writing features about mental health, cervical screening awareness, and Little Mix (who she is unapologetically obsessed with).


--- layout: page title: "The Tree Guitar" ---
The tree guitar was made from a hollow section of silver birch, that was found during a forest school morning in nearby woods a couple of years ago. The tree had recently been blown down in a storm and the trunk still contained a lot of sap, so the first job was to char it out:
The first version of the instrument was an acoustic design, but it quickly became clear that the sides of the trunk were too thick to resonate and project volume. As a result, it was modified into an electric guitar using an under-saddle pickup which could be played using a blues slide, in the style of a pedal-steel guitar. However nylon rather than steel strings were chosen due to their lower tension, and the lower stresses this would create across the instrument.
Fitting the under saddle pickup and wiring in the input jack:
Stringing the guitar, using trusty La Bella 820Bs:
And finally, ready for action - the main riff of Gruffalo Blues which you can hear from the start of the song and underlying the whole track was played on the tree guitar. It was tuned in an open-D tuning again to reduce tension across the instrument, which is why the track is in D!


enter LACMA
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syft.core.hooks package¶
Subpackages¶
Submodules¶
syft.core.hooks.keras module¶
-
class
syft.core.hooks.keras.
KerasHook
¶ Bases:
syft.core.hooks.base.BaseHook
TODO: Hook Keras
syft.core.hooks.tensorflow module¶
-
class
syft.core.hooks.tensorflow.
TensorflowHook
¶ Bases:
syft.core.hooks.base.BaseHook
TODO: Hook Tensorflow
Module contents¶
Hooks which override deep learning interfaces with remote execution functionality.
-
class
syft.core.hooks.
BaseHook
¶ Bases:
object
-
class
syft.core.hooks.
TorchHook
(local_worker=None, is_client=True, verbose=True, queue_size=0)¶ Bases:
syft.core.hooks.base.BaseHook
A Hook which Overrides Methods on PyTorch Variables & Tensors - Currently compatible with PyTorch 0.3.1
The purpose of this class is to:
- extend torch methods to allow for the moving of tensors and variables from one worker to another
- override torch methods to execute commands on one worker that are called on tensors controlled by the local worker.
This class is typically the first thing you will initialize when using PySyft with PyTorch because it is responsible for augmenting PyTorch with PySyft's added functionality (such as remote execution).
Parameters: - local_worker (
workers.BaseWorker
, optional) you can optionally provide a local worker as a parameter which TorchHook will assume to be the worker owned by the local machine. If you leave it empty, TorchClient will automatically initialize aworkers.VirtualWorker
under the assumption you're looking to do local experimentation/development. - is_client (bool, optional) whether or not the TorchHook is being initialized as an end-user client. This can impact whether or not variables are deleted when they fall out of scope. If you set this incorrectly on a end user client, Tensors and Variables will never be deleted. If you set this incorrectly on a remote machine (not a client), tensors will not get saved. It's really only important if you're not initializing the local worker yourself. (Default: True)
- verbose (bool, optional) whether or not to print operations as they occur. (Defalt: True)
Example: >>> from syft.core.hooks import TorchHook >>> from syft.core.hooks import torch >>> hook = TorchHook() Hooking into Torch... Overloading Complete. >>> x = torch.FloatTensor([1,2,3,4,5]) >>> x 1 2 3 4 5 [torch.FloatTensor of size 5]
-
set_hooks
(verbose)¶ Overload functions in torch with our own versions to enable routing
-
class
syft.core.hooks.
KerasHook
¶ Bases:
syft.core.hooks.base.BaseHook
TODO: Hook Keras
-
class
syft.core.hooks.
TensorflowHook
¶ Bases:
syft.core.hooks.base.BaseHook
TODO: Hook Tensorflow


LotsToDo List
A to do app that focuses on simplicity. Only the features you need to get things done.
Design and Development
Responsive Layout
JavaScript
React
HTML
CSS
Node.js
Express
REST API
MySQL


من نحن؟
صحة سكاي هي أول منصة في العالم العربي توفر خدمات
3
My first website with Bulma!
SehaSky is coming to you very soon, planet!
4
My first website with Bulma!
SehaSky is coming to you very soon, planet!
5
My first website with Bulma!
SehaSky is coming to you very soon, planet!


Notebooks
Story: |xue, his father and changes
|xue, his father and changes
Title
|xue, his father and changes
Collection
Summary
|xue is water, a lizard, grass, a |nu-erre bird, a dead elephant, a buffalo, a tsan, an Omuherero and a snake (a ||ge). |xue makes reeds at the water and grabs one of the woodpigeons. |xue becomes |xue and rises up and grabs the bitten woodpigeon and plucks out its feathers, putting them on his head. |xue puts the woodpigeon's body on hot embers and eats it. He hears an Ovaherero and hides. |xue flies away to his mother's country where he sees his father. |xue's mother and father become afraid of him and run away. |xue chases his father, all the while loudly speaking the Berg Damara language. |xue fears the large black reeds where his father hides. |xue sits and drinks water from a little hole and cries for his father. His father sees him, but leaves because |xue is an Omuherero. |xue searches for his father and cries when he does not find him.
Comments
1) p.9403v: !nanni heard this story from his paternal grandfather (Karu); for an illustration of this story see !nanni's picture of 10 April 1880 picturing a smoking |xue and some ≠ne≠nebbi or woodpigeons (NLLB_D_!n024), 2) p.9404v: (16 June 1880) !nanni describes an errand he ran on the previous day to Dr. Stewart, 3) p.9410v: (!nanni) 'When the rain comes, the wind is great, and when the rain goes away, the wind is small'; 'It is not the earth's water, but is the sky's water', 4) p.9416v: see |uma and Da: the names of their parents, 5) p.9428v: Tamme thinks |uma is the 'child of |xue'; a certain fungus (gabaka) which women eat and men fear, 6) This story is found in Book XI & XII-6
Contributors
Date
11 April (started) – 24 April (finished on or about) 1880
Categories
Transformation, Relations with others
Keywords
|xue (his father and changes) , |xue (becomes a ||ge or snake) , |xue (his father steals silently after him) , |xue (becomes an Omuherero) , |xue (fears the reeds) , |xue (becomes another kind of Bushman) , |xue (speaks the Berg Damara's language) , |xue (becomes a tsan and bites his father) , |xue (becomes |xue, a man) , |xue (becomes a buffalo) , |xue (searches for his father) , |xue (cries) , |xue (his mother) , |xue (his country) , |xue (becomes a dead elephant and many birds eat him) , |xue (his father's country) , |xue (the name of his father's country) , |xue (his father fears him because he is dead) , |xue (is not really dead and rises up) , |xue (calls to his father) , |xue (dies) , |xue (becomes |xue and dies) , |xue (carries a little bag) , |xue (makes fire) , |xue (he works things) , |xue (he becomes many things) , |xue (his doings, or works) , |xue (he changes) , |xue (is water) , |xue (is a goru, or lizard) , |xue (makes reeds) , |xue (he 'worked large grass') , |xue (grabs a woodpigeon as grass) , |xue (becomes small grass and bites the woodpigeon) , |xue (becomes |xue, a Bushman) , |xue (puts the woodpigeon's feathers on his head) , |xue (eats the woodpigeon) , |xue (becomes a little bird, or |nu-erre) , |xue (and the Ovaherero) , |xue (his tears fall on the ground) , |xue (and the Omuherero) , |xue (becomes many |nu-erre birds) , |xue (hides) , |xue (is searched for) , |xue (his transformations) , transformation (|xue, his father and changes) , transformation (and the death of |xue) , transformation (|xue becomes many things) , transformation (|xue makes, or works, many things) , transformation (|xue becomes an Omuherero, another kind of Bushman, a tsan, |xue- a man, a buffalo, a dead elephant, water, a goru- lizard, a little bird- |nu-erre, a ||ge or snake, many |nu-erre birds) , resurrection (|xue, his father and changes) , resurrection (of |xue) , resurrection (|xue rises up and changes form) , death (|xue, his father and changes) , death (of |xue) , death (|xue dies and changes form) , death (and |xue's resurrection) , death (and |xue's transformation) , plants (the !khu thorn-bush and its edible fruit) , plants (!kun names of) , plants (that |xue becomes) , plants (|xue, his father and changes) , Omuherero (|xue becomes) , Omuherero (|xue, his father and changes) , woodpigeon (|xue wears its feathers) , woodpigeon (|xue, his father and changes) , Damara (|xue speaks Berg Damara's language) , Damara (|xue, his father and changes) , country (|xue's) , country (|xue, his father and changes) , country (|xue's father's) , country (|xue's mother's) , father (|xue's) , father (|xue, his father and changes) , Ovaherero (and |xue) , Ovaherero (|xue, his father and changes) , language (|xue speaks Berg Damara) , language (|xue, his father and changes) , bag (|xue's) , bag (|xue, his father and changes) , names (of plants and animals that |xue becomes) , names (|xue, his father and changes)
Story Pages
9404-9430


| Class | Description |
|---|---|
| EstimationUtils | |
| EstimatorBasicAvg |
Basic average case estimator for matrix sparsity:
sp = 1 - Math.pow(1-sp1*sp2, k)
|
| EstimatorBasicWorst |
Basic average case estimator for matrix sparsity:
sp = Math.min(1, sp1 * k) * Math.min(1, sp2 * k).
|
| EstimatorBitsetMM |
This estimator implements a naive but rather common approach of boolean matrix
multiplies which allows to infer the exact non-zero structure and thus is
also useful for sparse result preallocation.
|
| EstimatorBitsetMM.BitsetMatrix | |
| EstimatorBitsetMM.BitsetMatrix1 |
This class represents a boolean matrix and provides key operations.
|
| EstimatorBitsetMM.BitsetMatrix2 | |
| EstimatorDensityMap |
This estimator implements an approach called density maps, as introduced in
David Kernert, Frank Köhler, Wolfgang Lehner: SpMacho - Optimizing Sparse
Linear Algebra Expressions with Probabilistic Density Estimation.
|
| EstimatorDensityMap.DensityMap | |
| EstimatorLayeredGraph |
This estimator implements an approach based on a so-called layered graph,
introduced in
Edith Cohen.
|
| EstimatorLayeredGraph.LayeredGraph | |
| EstimatorMatrixHistogram |
This estimator implements a remarkably simple yet effective
approach for incorporating structural properties into sparsity
estimation.
|
| EstimatorMatrixHistogram.MatrixHistogram | |
| EstimatorSample |
This estimator implements an approach based on row/column sampling
Yongyang Yu, MingJie Tang, Walid G.
|
| EstimatorSampleRa |
This estimator implements an approach based on row/column sampling
Rasmus Resen Amossen, Andrea Campagna, Rasmus Pagh:
Better Size Estimation for Sparse Matrix Products.
|
| MMNode |
Helper class to represent matrix multiply operators in a DAG
along with references to its abstract data handles.
|
| SparsityEstimator |
| Enum | Description |
|---|---|
| SparsityEstimator.OpCode |
Copyright © 2021 The Apache Software Foundation. All rights reserved.


Create JavaScript Project Set up a JavaScript project by running one command.
Get startedFocus on Code
You don't have to worry about build tools and configurations. All new projects come with build tools configured, instant reloading for development and build optimizations for production out of the box, so you can focus on your code.
Configurable
You can tweak your project's configuration anytime you want. Everything is set up just like you would set it up yourself, so you can change the configuration of the build tools, remove some of them or add new ones whenever you need.
Latest Tools
Your projects are powered by the latest versions of popular tools. When you start a new project, the latest versions of the appropriate dependencies are installed and configured and you can update them as your project keeps growing.
Get started It's as easy as 1, 2, 3!
Before you start, you need to globally install create-js-project
via npm or yarn:
npm i -g create-js-project
yarn global add create-js-project
Then, follow these 3 simple steps:
-
Create a project, optionally providing a
[project-name]
for your project:create-js-project [project-name]
npm init js-project [project-name]
-
Answer the questions, such as your project's name and desired template.
-
Wait until the installation is complete and start coding!
Templates
We provide you with a handful of templates out of the box:
- $included_templates
We are planning to allow users to import their own templates from a local folder. If you want to help speed up the development of this feature, join the discussion here.


57th Georgia Infantry Regiment
Time Line
- 20 Dec 1860, SC secedes
- 19 Jan 1861, GA secedes
- 12 Apr 1861, Shots at Ft Sumter
- Early 1862, GA calls up volunteer regiments and 57th GA Volunteer Infantry is formed
- 8 Oct 1862, Battle of Perryville, KY (57th GA is in reserve)
- 16 May 1863, Baker's Creek (Champion Hill)
- 18 May 1863, Siege of Vicksburg
- 4 Jul 1863, Surrendered at Vicksburg
- 12 Sep 1863, Paroled
- Remnants of the 57th existed through the rest of the war without major engagements.


Adam J Sullivan
Assistant Professor of Biostatistics
Brown University
Thus far, in our tutorials we have been using the validation or hold-out approach to estimate the prediction error of our predictive models. This involves randomly dividing the available set of observations into two parts, a training set and a testing set (aka validation set). Our statistical model is fit on the training set, and the fitted model is used to predict the responses for the observations in the validation set. The resulting validation set error rate (typically assessed using MSE in the case of a quantitative response) provides an estimate of the test error rate.
The validation set approach is conceptually simple and is easy to implement. But it has two potential drawbacks:
First, the estimate of the test error rate can be highly variable, depending on precisely which observations are included in the training set and which observations are included in the validation set. I will illustrate on our auto
data set. Here we see that there is a relationship between mpg and horsepower and it doesn't seem linear but we're not sure which polynomial degree creates the best fit.
ggplot(auto, aes(horsepower, mpg)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = FALSE, linetype = 2) +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, linetype = 3) +
geom_smooth(method = "lm", formula = y ~ poly(x, 4), se = FALSE, linetype = 4)
Let's go ahead and do the traditional validation set approach to split our data into a training and testing set. Then we'll fit 10 different models ranging from a linear model to a 10th degree polynomial model. The results show us there is a steep decline in our test error (MSE) rate when we go from a linear model to a quadratic model; however, the MSE flatlines beyond that point suggesting that adding more polynomial degrees likely does not improve the model performance.
set.seed(1)
sample <- sample(c(TRUE, FALSE), nrow(auto), replace = T, prob = c(0.6,0.4))
train <- auto[sample, ]
test <- auto[!sample, ]
# loop for first ten polynomial
mse.df <- tibble(degree = 1:10, mse = NA)
for(i in 1:10) {
lm.fit <- lm(mpg ~ poly(horsepower, i), data = train)
mse.df[i, 2] <- mean((test$mpg - predict(lm.fit, test))^2)
}
ggplot(mse.df, aes(degree, mse)) +
geom_line() +
geom_point() +
ylim(c(10, 30))
However, our MSE is dependent on our training and test samples. If we repeat the process of randomly splitting the sample set into two parts, we will get a somewhat different estimate for the test MSE each time. I illustrate below, which displays ten different validation set MSE curves from the auto data set, produced using ten different random splits of the observations into training and validation sets. All ten curves indicate that the model with a quadratic term has a dramatically smaller validation set MSE than the model with only a linear term. Furthermore, all ten curves indicate that there is not much benefit in including cubic or higher-order polynomial terms in the model. But it is worth noting that each of the ten curves results in a different test MSE estimate for each of the ten regression models considered. And there is no consensus among the curves as to which model results in the smallest validation set MSE.
mse.df.2 <- tibble(sample = vector("integer", 100),
degree = vector("integer", 100),
mse = vector("double", 100))
counter <- 1
for(i in 1:10) {
# random sample
set.seed(i)
sample <- sample(c(TRUE, FALSE), nrow(auto), replace = T, prob = c(0.6,0.4))
train <- auto[sample, ]
test <- auto[!sample, ]
# modeling
for(j in 1:10) {
lm.fit <- lm(mpg ~ poly(horsepower, j), data = train)
# add degree & mse values
mse.df.2[counter, 2] <- j
mse.df.2[counter, 3] <- mean((test$mpg - predict(lm.fit, test))^2)
# add sample identifier
mse.df.2[counter, 1] <- i
counter <- counter + 1
}
next
}
ggplot(mse.df.2, aes(degree, mse, color = factor(sample))) +
geom_line(show.legend = FALSE) +
geom_point(show.legend = FALSE) +
ylim(c(10, 30))
A second concern with the validation approach, only a subset of the observations, those that are included in the training set rather than in the validation set, are used to fit the model. Since statistical methods tend to perform worse when trained on fewer observations, this suggests that the validation set error rate may tend to overestimate the test error rate for the model fit on the entire data set.
We can address these concerns using cross-validation methods.
Leave-one-out cross-validation (LOOCV) is closely related to the validation set approach as it involves splitting the set of observations into two parts. However, instead of creating two subsets of comparable size (i.e. 60% training, 40% validation), a single observation (\[x_1, y_1\]) is used for the validation set, and the remaining \[n-1\] observations {\[(x_2, y_2), \dots, (x_n, y_n)\]} make up the training set. The statistical learning method is fit on the \[n − 1\] training observations, and a prediction \[\hat y_1\] is made for the excluded observation. Since the validation observation (\[x_1, y_1\]) was not used in the fitting process, the estimate error \[MSE_1 = (y_1 − \hat y_1)^2\] provides an approximately unbiased estimate for the test error. But even though \[MSE_1\] is unbiased for the test error, it is a poor estimate because it is highly variable, since it is based upon a single observation (\[x_1, y_1\]).
However, we can repeat the procedure by selecting a different row (\[x_2, y_2\]) for the validation data, training the statistical learning procedure on the other \[n-1\] observations and computing \[MSE_2 =(y_2− \hat y_2)^2\]. We can repeate this approach n times, where each time we holdout a different, single observation to validate on. This produces a total of n squared errors, \[MSE_1,\dots, MSE_n\]. The LOOCV estimate for the test MSE is the average of these n test error estimates:
\[ CV_{(n)} = \frac{1}{n}\sum^n_{i=1}MSE_i \tag{1}\]
To perform this procedure in R we first need to understand an important nuance. In the logistic regression tutorial, we used the glm
function to perform logistic regression by passing in the family = "binomial"
argument. But if we use glm
to fit a model without passing in the family argument, then it performs linear regression, just like the lm
function. So, for instance:
glm.fit <- glm(mpg ~ horsepower, data = auto)
coef(glm.fit)
## (Intercept) horsepower
## 39.9358610 -0.1578447
is the same as
lm.fit <- lm(mpg ~ horsepower, data = auto)
coef(lm.fit)
## (Intercept) horsepower
## 39.9358610 -0.1578447
Why is this important? Because we can perform LOOCV for any generalized linear model using glm
and the cv.glm
function from the boot
package. boot
provides extensive facilities for bootstrapping and related resampling methods. You can bootstrap a single statistic (e.g. a median), a vector (e.g., regression weights), or as you'll see in this tutorial perform cross-validation. To perform LOOCV for a given generalized linear model we simply:
glm
cv.glm
# step 1: fit model
glm.fit <- glm(mpg ~ horsepower, data = auto)
# setp 2: perform LOOCV across entire data set
loocv.err <- cv.glm(auto, glm.fit)
str(loocv.err)
## List of 4
## $ call : language cv.glm(data = auto, glmfit = glm.fit)
## $ K : num 392
## $ delta: num [1:2] 24.2 24.2
## $ seed : int [1:626] 403<PHONE_NUMBER>781<PHONE_NUMBER> 434562476 -1277611857 -1105401243<PHONE_NUMBER> 526650482 -1538305299 ...
cv.glm
provides a list with 4 outputs:
The result we primarily care about is the cross-validation estimate of test error (Eq. 1). Our cross-validation estimate for the test error is approximately 24.23. This estimate is a far less biased estimate of the test error compared to our single test MSE produced by a training - testing validation approach.
loocv.err$delta[1]
## [1] 24.23151
We can repeat this procedure to estimate an ubiased MSE across multiple model fits. For example, to assess multiple polynomial fits (as we did above) to identify the one that represents the best fit we can integrate this procedure into a function. Here we develop a function that computes the LOOCV MSE based on specified polynomial degree. We then feed this function (via map_dbl
) values 1-5 to compute the first through fifth polynomials.
# create function that computes LOOCV MSE based on specified polynomial degree
loocv_error <- function(x) {
glm.fit <- glm(mpg ~ poly(horsepower, x), data = auto)
cv.glm(auto, glm.fit)$delta[1]
}
# compute LOOCV MSE for polynomial degrees 1-5
library(purrr)
1:5 %>% map_dbl(loocv_error)
## [1] 24.23151 19.24821 19.33498 19.42443 19.03321
Our results illustrate a sharp drop in the estimated test MSE between the linear and quadratic fits, but then no clear improvement from using higher-order polynomials. Thus, our unbiased MSEs suggest that using a 2nd polynomial (quadratic fit) is likely the optimal model balancing interpretation and low test errors.
This LOOCV approach can be used with any kind of predictive modeling. For example we could use it with logistic regression or linear discriminant analysis. Unfortunately, this can be very time consuming approach if n is large, you're trying to loop through many models (i.e. 1-10 polynomials), and if each individual model is slow to fit. For example, if we wanted to perform this approach on the ggplot2::diamonds
data set for a linear regression model, which contains 53,940 observations, the computation time is nearly 30 minutes!
# DO NOT RUN THIS CODE - YOU WILL BE WAITING A LONG TIME!!
system.time({
diamonds.fit <- glm(price ~ carat + cut + color + clarity, data = diamonds)
cv.glm(diamonds, diamonds.fit)
})
# user system elapsed
# 1739.041<PHONE_NUMBER>.062
An alternative to LOOCV is the k-fold cross validation approach. This resampling method involves randomly dividing the data into k groups (aka folds) of approximately equal size. The first fold is treated as a validation set, and the statistical method is fit on the remaining data. The mean squared error, \[MSE_1\], is then computed on the observations in the held-out fold. This procedure is repeated k times; each time, a different group of observations is treated as the validation set. This process results in k estimates of the test error, \[MSE_1, MSE_2, \dots , MSE_k\]. Thus, the k-fold CV estimate is computed by averaging these values,
\[ CV_{(k)} = \frac{1}{k}\sum^k_{i=1}MSE_i \tag{2} \]
It is not hard to see that LOOCV is a special case of k-fold approach in which k is set to equal n. However, using the k-fold approach, one typically uses k = 5 or k = 10. This can substantially reduce the computational burden of LOOCV. Furthermore, there has been sufficient empirical evidence that demonstrates using 5-10 folds yield surprisingly accurate test error rate estimates (see chapter 5 of ISLR for more details[islr]).
We can implement the k-fold approach just as we did with the LOOCV approach. The only difference is incorporating the K = 10
argument that we include in the cv.glm
function. Below illustrates our k-fold MSE values for the different polynomial models on our auto data. When compared to the LOOCV outputs we see that the results are nearly identical.
# create function that computes k-fold MSE based on specified polynomial degree
kfcv_error <- function(x) {
glm.fit <- glm(mpg ~ poly(horsepower, x), data = auto)
cv.glm(auto, glm.fit, K = 10)$delta[1]
}
# compute k-fold MSE for polynomial degrees 1-5
1:5 %>% map_dbl(kfcv_error)
## [1] 24.56850 19.22648 19.29535 19.46601 19.24090
# compare to LOOCV MSE values
1:5 %>% map_dbl(loocv_error)
## [1] 24.23151 19.24821 19.33498 19.42443 19.03321
We can also illustrate the computational advantage of the k-fold approach. As we saw, using LOOCV on the diamonds
data set took nearly 30 minutes whereas using the k-fold approach only takes about 4 seconds.
system.time({
diamonds.fit <- glm(price ~ carat + cut + color + clarity, data = diamonds)
cv.glm(diamonds, diamonds.fit, K = 10)
})
## user system elapsed
## 3.760 0.564 4.347
We can apply this same approach to classification problems as well. For example, in the previous tutorial we compared the performance of a logistic regression, linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) on some stock market data using the traditional training vs. testing (60%/40%) data splitting approach. We could've performed the same assessment using cross validation. In the classification setting, the LOOCV error rate takes the form
\[ CV_{(n)} = \frac{1}{n}\sum^n_{i=1}Err_i \tag{3} \]
where \[Err_i = I(y_i \ne \hat y_i)\]. The k-fold CV error rate and validation set error rates are defined analogously.
Consequently, for the logistic regression we use cv.glm
to perform a k-fold cross validation. The end result is an estimated CV error rate of .5. (Note: since the response variable is binary we incorporate a new cost function to compute the estimated error rate in Eq. 3)
stock <- ISLR::Smarket
# fit logistic regression model
glm.fit <- glm(Direction ~ Lag1 + Lag2, family = binomial, data = stock)
# The cost function here correlates to that in Eq. 3
cost <- function(r, pi = 0) mean(abs(r - pi) > 0.5)
# compute the k-fold estimated error with our cost function
cv.glm(stock, glm.fit, cost, K = 10)$delta[1]
## [1] 0.4984
To performm cross validation with our LDA and QDA models we use a slightly different approach. Both the lda
and qda
functions have built-in cross validation arguments. Thus, setting CV = TRUE
within these functions will result in a LOOCV execution and the class and posterior probabilities are a product of this cross validation.
library(MASS)
# fit discriminant analysis models with CV = TRUE for LOOCV
lda.fit <- lda(Direction ~ Lag1 + Lag2, CV = TRUE, data = stock)
qda.fit <- qda(Direction ~ Lag1 + Lag2, CV = TRUE, data = stock)
# compute estimated test error based on cross validation
mean(lda.fit$class != stock$Direction)
## [1] 0.4816
mean(qda.fit$class != stock$Direction)
## [1] 0.4872
Thus, the results are similar to what we saw in the previous tutorial, none of these models do an exceptional (or even decent!) job. However, we see that the LOOCV estimated error for the QDA model (.487) is fairly higher than what we saw in the train-test validation approach (.40). This suggests that our previous QDA model with the train-test validation approach may have been a bit optimistically biased!
Bootstrapping is a widely applicable and extremely powerful statistical tool that can be used to quantify the uncertainty associated with a given estimator or statistical learning method. As a simple example, bootstraping can be used to estimate the standard errors of the coefficients from a linear regression fit. In the case of linear regression, this is not particularly useful, since we saw in the linear regression tutorial that R provides such standard errors automatically. However, the power of the bootstrap lies in the fact that it can be easily applied to a wide range of statistical learning methods, including some for which a measure of variability is otherwise difficult to obtain and is not automatically output by statistical software.
In essence bootstrapping repeatedly draws independent samples from our data set to create bootstrap data sets. This sample is performed with replacement, which means that the same observation can be sampled more than once. The figure below from the ISLR[islr] book depicts the bootsrap approach on a small data set (n = 3).
Each bootstrap data set (\[Z^{*1}, Z^{*2}, \dots, Z^{*B}\]) contains n observations, sampled with replacement from the original data set. Each bootstrap is used to compute the estimated statistic we are interested in (\[\hat\alpha^*\]). We can then use all the bootstrapped data sets to compute the standard error of \[\hat\alpha^{*1}, \hat\alpha^{*2}, \dots, \hat\alpha^{*B}\] desired statistic as
\[ SE_B(\hat\alpha) = \sqrt{\frac{1}{B-1}\sum^B_{r=1}\bigg(\hat\alpha^{*r}-\frac{1}{B}\sum^B_{r'=1}\hat\alpha^{*r'}\bigg)^2} \tag{4} \]
Thus, \[SE_B(\hat\alpha)\] serves as an estimate of the standard error of \[\hat\alpha\] estimated from the original data set. Let's look at how we can implement this in R on a couple of simple examples:
Performing a bootstrap analysis in R entails two steps:
boot
function from the boot
package to perform the boostrappingIn this example we'll use the ISLR::Portfolio
data set. This data set contains the returns for two investment assets (X and Y). Here, our goal is going to be minimizing the risk of investing a fixed sum of money in each asset. Mathematically, we can achieve this by minimizing the variance of our investment using the statistic
\[ \hat\alpha = \frac{\hat\sigma^2_Y - \hat\sigma_{XY}}{\hat\sigma^2_X +\hat\sigma^2_Y-2\hat\sigma_{XY}} \tag{5} \]
Thus, we need to create a function that will compute this test statistic:
statistic <- function(data, index) {
x <- data$X[index]
y <- data$Y[index]
(var(y) - cov(x, y)) / (var(x) + var(y) - 2* cov(x, y))
}
Now we can compute \[\hat\alpha\] for a specified subset of our portfolio data:
portfolio <- ISLR::Portfolio
# compute our statistic for all 100 observations
statistic(portfolio, 1:100)
## [1] 0.5758321
Next, we can use sample
to randomly select 100 observations from the range 1 to 100, with replacement. This is equivalent to constructing a new bootstrap data set and recomputing \[\hat\alpha\] based on the new data set.
statistic(portfolio, sample(100, 100, replace = TRUE))
## [1] 0.7661566
If you re-ran this function several times you'll see that you are getting a different output each time. What we want to do is run this many times, record our output each time, and then compute a valid standard error of all the outputs. To do this we can use boot
and supply it our original data, the function that computes the test statistic, and the number of bootstrap replicates (R
).
set.seed(123)
boot(portfolio, statistic, R = 1000)
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot(data = portfolio, statistic = statistic, R = 1000)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 0.5758321 0.002396754 0.08752118
The final output shows that using the original data, \[\hat\alpha = 0.5758\], and it also provides the bootstrap estimate of our standard error \[SE(\hat\alpha) = 0.0875\].
Once we generate the bootstrap estimates we can also view the confidence intervals with boot.ci
and plot our results:
set.seed(123)
result <- boot(portfolio, statistic, R = 1000)
boot.ci(result, type = "basic")
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = result, type = "basic")
##
## Intervals :
## Level Basic
## 95% ( 0.3958, 0.7376 )
## Calculations and Intervals on Original Scale
plot(result)
We can use this same concept to assess the variability of the coefficient estimates and predictions from a statistical learning method such as linear regression. For instance, here we'll assess the variability of the estimates for \[\beta_0\] and \[\beta_1\], the intercept and slope terms for the linear regression model that uses horsepower
to predict mpg
in our auto
data set.
First, we create the function to compute the statistic of interest. We can apply this to our entire data set to get the baseline coefficients.
statistic <- function(data, index) {
lm.fit <- lm(mpg ~ horsepower, data = data, subset = index)
coef(lm.fit)
}
statistic(auto, 1:392)
## (Intercept) horsepower
## 39.9358610 -0.1578447
Now we can inject this into the boot
function to compute the bootstrapped standard error estimate:
set.seed(123)
boot(auto, statistic, 1000)
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot(data = auto, statistic = statistic, R = 1000)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 39.9358610 0.0295956008 0.863541674
## t2* -0.1578447 -0.0002940364 0.007598619
This indicates that the bootstrap estimate for \[SE(\beta_0)\] is 0.86, and that the bootstrap estimate for \[SE(\beta_1)\] is 0.0076. If we compare these to the standard errors provided by the summary
function we see a difference.
summary(lm(mpg ~ horsepower, data = auto))
##
## Call:
## lm(formula = mpg ~ horsepower, data = auto)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.5710 -3.2592 -0.3435 2.7630 16.9240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.935861 0.717499 55.66 <2e-16 ***
## horsepower -0.157845 0.006446 -24.49 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.906 on 390 degrees of freedom
## Multiple R-squared: 0.6059, Adjusted R-squared: 0.6049
## F-statistic: 599.7 on 1 and 390 DF, p-value: < 2.2e-16
This difference suggests the standard errors provided by summary
may be biased. That is, certain assumptions may be violated which is causing the standard errors in the non-bootstrap approach to be different than those in the bootstrap approach.
If you remember from earlier in the tutorial we found that a quadratic fit appeared to be the most approapriate for the relationship between mpg
and horsepower
. Lets adjust our code to capture this fit and see if we notice a difference with our outputs.
quad.statistic <- function(data, index) {
lm.fit <- lm(mpg ~ poly(horsepower, 2), data = data, subset = index)
coef(lm.fit)
}
set.seed(1)
boot(auto, quad.statistic, 1000)
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot(data = auto, statistic = quad.statistic, R = 1000)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 23.44592 0.003943212 0.2255528
## t2* -120.13774 0.117312678 3.7008952
## t3* 44.08953 0.047449584 4.3294215
Now if we compare the standard errors between the bootstrap approach and the non-bootstrap approach we see the standard errors align more closely. This better correspondence between the bootstrap estimates and the standard estimates suggest a better model fit. Thus, bootstrapping provides an additional method for assessing the adequacy of our model's fit.
summary(lm(mpg ~ poly(horsepower, 2), data = auto))
##
## Call:
## lm(formula = mpg ~ poly(horsepower, 2), data = auto)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.7135 -2.5943 -0.0859 2.2868 15.8961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.4459 0.2209 106.13 <2e-16 ***
## poly(horsepower, 2)1 -120.1377 4.3739 -27.47 <2e-16 ***
## poly(horsepower, 2)2 44.0895 4.3739 10.08 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.374 on 389 degrees of freedom
## Multiple R-squared: 0.6876, Adjusted R-squared: 0.686
## F-statistic: 428 on 2 and 389 DF, p-value: < 2.2e-16
This will get you started with resampling methods; however, understand that there are many approaches for resampling and even more options within R to implement these approaches. The following resources will help you learn more:
[islr]: This tutorial was built as a supplement to chapter 5 of An Introduction to Statistical Learning


neon_read.Rd
read in neon tabular data
neon_read( table = NA, product = NA, site = NA, start_date = NA, end_date = NA, ext = NA, timestamp = NA, release = NA, dir = neon_dir(), files = NULL, sensor_metadata = TRUE, keep_filename = FALSE, altrep = FALSE, ... )
| table | the name of a downloaded NEON table in the store, see neon_index |
|---|---|
| product | A NEON |
| site | 4-letter site code(s) to filter on. Leave as |
| start_date | Download only files as recent as ( |
| end_date | Download only files up to end_date ( |
| ext | only match files with this file extension(s) |
| timestamp | only match timestamps prior this. See details in |
| release | Select only data files associated with a particular release tag, see https://www.neonscience.org/data-samples/data-management/data-revisions-releases, e.g. "RELEASE-2021". Releases are associated with a specific DOI and the promise that files associated with a particular release will not change. |
| dir | Location where files should be downloaded. By default will
use the appropriate applications directory for your system
(see |
| files | optionally, specify a vector of file paths directly (e.g. as
provided from neon_index) and specify |
| sensor_metadata | logical, default TRUE. Should we add metadata fields from file names of sensor data into the table? Adds DomainID, SiteID, horizontalPosition, verticalPosition, and publicationDate. Results in slower parsing. |
| keep_filename | Should we include a column indicating the original
file name for each row? Can be a useful source of additional metadata that
NEON may omit from the raw files (i.e. |
| altrep | enable or disable altrep. Logical, default |
| ... | additional arguments to vroom::vroom, can usually be omitted. |
NEON's tabular data files are separated out into separate .csv
files for each site for each month of sampling. In principle,
each file has identical columns. vroom::vroom can read in a
data table that has been sharded into many files like this much
much faster than other parsers can read in each table iteratively,
(and thus can greatly out-perform the 'stacking" methods in neonUtilities
).
When reading in very large numbers of files, it may be helpful to set
altrep = FALSE
to opt out of vroom
's fast altrep mechanism, which
can cause neon_read()
to fail when stacking thousands of files.
Unfortunately, not all datasets are entirely consistent in their use
of columns. neon_read
works around this by parsing such tables in
groups of matching schema, which is still reasonably fast.
NEON sensor data products currently do not include important metadata columns
containing DomainID, SiteID, horizontalPosition, verticalPosition, and
publicationDate in the data files themselves, but only encode this in the
in the raw file names. All though these values are shared across a raw
data file, this information is lost when stacking the tables unless explicit
columns are added to the data. This requires us to parse the files
one-by-one, which is much slower. By default this information is added to
the table, altering the stacked table schema from that of the raw table.
Disable this behavior by setting sensor_metadata = FALSE
. Future
NEON sensor data products may start including this information in
the raw data files, as is already the case for observational data.
neon_read("brd_countdata-expanded")#> Warning: number of columns of result is not a multiple of vector length (arg 1)#>#> #>#> # A tibble: 4,654 x 24 #> uid namedLocation domainID siteID plotID plotType pointID #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 bb46d0cc-0102-449… YELL_009.birdGri… D12 YELL YELL_… distribu… C3 #> 2 f252dd4d-6684-4e3… YELL_009.birdGri… D12 YELL YELL_… distribu… C3 #> 3 b2d4cb65-38ec-42a… YELL_009.birdGri… D12 YELL YELL_… distribu… C3 #> 4 2882f646-59ce-47f… YELL_009.birdGri… D12 YELL YELL_… distribu… C3 #> 5 2aa0d21f-fdc4-496… YELL_009.birdGri… D12 YELL YELL_… distribu… C3 #> 6 61d465df-d551-48a… YELL_009.birdGri… D12 YELL YELL_… distribu… C3 #> 7 ea1ffb98-fceb-486… YELL_009.birdGri… D12 YELL YELL_… distribu… C3 #> 8 4d3f6161-26bc-401… YELL_009.birdGri… D12 YELL YELL_… distribu… C3 #> 9 a93bf746-0df8-462… YELL_009.birdGri… D12 YELL YELL_… distribu… C3 #> 10 643c9113-2906-4d1… YELL_009.birdGri… D12 YELL YELL_… distribu… C3 #> # … with 4,644 more rows, and 17 more variables: startDate <dttm>, #> # eventID <chr>, pointCountMinute <dbl>, targetTaxaPresent <chr>, #> # taxonID <chr>, scientificName <chr>, taxonRank <chr>, vernacularName <chr>, #> # family <chr>, nativeStatusCode <chr>, observerDistance <dbl>, #> # detectionMethod <chr>, visualConfirmation <chr>, sexOrAge <chr>, #> # clusterSize <dbl>, clusterCode <chr>, identifiedBy <chr>## Sensor inputs will add metadata columns by default neon_read("waq_instantaneous", site = c("CRAM","SUGG"))#> Warning: number of columns of result is not a multiple of vector length (arg 1)#> # A tibble: 65,666 x 34 #> startDateTime endDateTime sensorDepth sensorDepthExpUncert #> <dttm> <dttm> <dbl> <chr> #> 1 2019-04-28 00:00:00 2019-04-28 00:00:00 NA NA #> 2 2019-04-28 00:05:00 2019-04-28 00:05:00 NA NA #> 3 2019-04-28 00:10:00 2019-04-28 00:10:00 NA NA #> 4 2019-04-28 00:15:00 2019-04-28 00:15:00 NA NA #> 5 2019-04-28 00:20:00 2019-04-28 00:20:00 NA NA #> 6 2019-04-28 00:25:00 2019-04-28 00:25:00 NA NA #> 7 2019-04-28 00:30:00 2019-04-28 00:30:00 NA NA #> 8 2019-04-28 00:35:00 2019-04-28 00:35:00 NA NA #> 9 2019-04-28 00:40:00 2019-04-28 00:40:00 NA NA #> 10 2019-04-28 00:45:00 2019-04-28 00:45:00 NA NA #> # … with 65,656 more rows, and 30 more variables: sensorDepthFinalQF <chr>, #> # specificConductance <dbl>, specificConductanceExpUncert <dbl>, #> # specificCondFinalQF <dbl>, dissolvedOxygen <dbl>, #> # dissolvedOxygenExpUncert <dbl>, dissolvedOxygenFinalQF <dbl>, #> # dissolvedOxygenSaturation <dbl>, dissolvedOxygenSatExpUncert <dbl>, #> # dissolvedOxygenSatFinalQF <dbl>, pH <dbl>, pHExpUncert <dbl>, #> # pHFinalQF <dbl>, chlorophyll <dbl>, chlorophyllExpUncert <dbl>, #> # chlorophyllFinalQF <dbl>, turbidity <dbl>, turbidityExpUncert <dbl>, #> # turbidityFinalQF <dbl>, fDOM <chr>, rawCalibratedfDOM <dbl>, #> # fDOMExpUncert <chr>, fDOMFinalQF <dbl>, buoyNAFlag <dbl>, file <chr>, #> # domainID <chr>, siteID <chr>, horizontalPosition <chr>, #> # verticalPosition <chr>, publicationDate <dttm>


Scaling Up Entrepreneurship
AKA Ventures is a Washington, DC venture capital firm that supports the most promising emerging technology-enabled companies, helping them become global brands beloved by their customers.
Our team is like our entrepreneurs - We move fast, think big, and are committed to building the next major franchises in technology.
Founded by Aaron Drew and Michael Kapoor, the firm provides entrepreneurs with access to expertise and insights in innovation, executive and technical talent, market intelligence, policy and regulatory affairs, business development and marketing & brand-building.
The question is not putting together $100M, but how to return 10x and write a check back for $1B with confidence


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For the last podcast of the year, we talk about our favorite TechMill and community highlights of the year and what we’re looking forward to in 2017.
Kyle’s highlight list
* TechMill workshop series
* Open Denton / Open Data in Denton.
* https://www.opendenton.com/
* http://data.cityofdenton.com/
* Growth of startup/tech community in Denton.
* Bootstrap Denton 2016 was amazing.
* VR game demo by From the Future.
* New TechMill board members
David’s highlight list
* TechMill workshop series (teaching as well)
* Increase in use of technology in the city, increase in civic engagement
* Joining the TechMill board
Things we’re looking forward to in 2017
* Continuing TechMill workshop series with new topics
* TechMill community
Periscope stream of recording session: https://www.periscope.tv/w/1DXGyVQZvOdJM
Show Notes
Are you or people you know doing cool tech things in Denton that y'all would like to share? Or are there things you'd like to hear us talk about in addition to the interviews? Let us know!
Email<EMAIL_ADDRESS>Twitter: @TechMillDenton
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Finding and Testing Parameters of the Vasicek Model.
The Vasicek Model is perhaps the simplest stochastic differential equation which is generally used to model short term interest rates or FX forward rates, however in theory it can be applied to any mean reverting asset such as commodities or FX spot.
\[dS_t = \lambda(\mu-S_t)dt+\sigma dW_t\]where \(S_t\) represents the asset price at time t and \(W_t\) is standard Brownian Motion.
The SDE takes three parameters:
\(\mu\) describes the mean level, which all trajectory paths should evolve around in long term behaviour.
\(\lambda\) is the speed of mean reversion
\(\sigma\) is the standard deviation, modelling volatility.
In this post, I look at EUR/USD mid-price data from 08/11/2015 to 01/12/2015 (excluding weekends) and force a solution via maximum liklehood estimation for the three parameters.
Continuous Solution
The continuous case solution to the Vasicek differential equation is obtained by observing:
\[d(e^{\lambda t} S_t) = e^{\lambda t}dS_t + \lambda e^{\lambda t}S_t dt\] \[= e^{\lambda t}[\lambda(\mu - S_t)]dt + \sigma e^{\lambda t} dW_t + \lambda e^{\lambda t}S_t dt\] \[\lambda e^{\lambda t} dt + \sigma e^{\lambda t} dW_t\]In integral form:
\[\Rightarrow e^{\lambda t} S_t = S_0 + \lambda\int^{t}_{0}e^{\lambda u}du + \sigma\int^{t}_{0}e^{\lambda u}dW_u\]Evaluating the deterministic integral yields:
\[e^{\lambda t} S_t = S_0 + (e^{\lambda t} -1) + \sigma\int^{t}_{0}e^{\lambda u}dW_u\]Dividing both sides by \(e^{\lambda t}\) and rewriting \(\lambda (s-t)\) as \(-\lambda (t-s)\) we get:
\[S_t = S_0 e^{-\lambda t} + (1-e^{-\lambda t}) + \sigma\int^{t}_{0}e^{-\lambda(t-s)}dW_s\]Discrete Time Analog
In this post, I am using 30 minute intervals over approximately 1 month, so we can simplify greatly. In discrete time, \(S_{t+1} = \alpha S_t + \beta + \Upsilon(\mu_1 , \sigma_1^2)\)
ie: a Linear dependence with an normally distributed random term.
To find the parameters, we observe the conditional probability distribution:
\[f(S_{t+1} | S_{t})\]This is distributed normally as: \(N(S_{t}e^{-\lambda}-\mu(1-e^{-\lambda}),\sigma^2 \frac{1-e^{-2\lambda}}{2\lambda})\)
Let \(\sigma_1^2 = \sigma^2 (\frac{1-e^{-2\lambda}}{2\lambda})\) for brevity.
This can be simulated in code and we can generate sample paths as shown:
Red: \(\mu = 1, \lambda = 1.5, \sigma = 0.25\)
Blue: \(\mu = 1, \lambda = 1, \sigma = 1\)
The higher volatility of \(S_{red}\) is clearly visible with 50 sampling points.
Liklehood Estimation to find Parameters
The Liklehood Estimator is given by:
\[L(\mu,\lambda,\sigma_1) = \prod_{t=1}^{n} f(S_t|S_{t-1}) = \left( \frac{1}{\sqrt{2\pi\sigma^2}} \right)^n e^{-\sum^{n}_{t=1}(S_t - S_{t-1}\frac{e^{-\lambda} - \mu(1-e^-\lambda))^2}{2\sigma_1^2})}\]It suffices to maximise the Log-likehood estimator as the logarithm function is monotonically increasing and continuous.
\[log(L(\mu,\lambda,\sigma_1)) = \frac{-n}{2}log(2\pi\sigma^2) -\sum^{n}_{t=1} \frac{(S_t - S_{t-1}e^{-\lambda} - \mu(1-e^-\lambda))^2}{2\sigma_1^2} - n log(\sigma)\]Now, we partially differentiate \(log(L)\) with respect to \(\lambda\), \(\sigma_1\) and \(\mu\), setting the derivatives to zero gives us solutions for the estimator values.
For example, in the case of \(\mu\) we set:
\[\frac{dlog(L(\mu,\lambda,\sigma_1))}{d\mu} := 0\] \[\Rightarrow \mu_{est} = \frac{\sum^{n}_{t=1}(S_t-S_{t-1})e^{-\lambda}}{n(1-e^{-\lambda})}\]The case is similar for \(\lambda\) and \(\sigma_1\)
Plotting
Data was taken from Bloomberg Terminals at King’s College London
Plotting the Backtest Data (30 minute granularity, no weekend data)
Simulating Trajectories with Estimated Parameters
With the initial starting mid-price, 50 simulations are ran with the MLE parameters, along with the real data in red.
Note: the jumps are due to weekends.
Next Steps
The above results are not particularly surprising. Indeed we expect the final points of simulated trajectory paths to be normally distributed around the real data result. The next steps are to forward test our parameters with new unseen intra-day data. My next post will cover this in more detail once a month has elapsed.


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- The game has 2 players, playing in rounds
- In each turn, a player rolls a dice as many times as he whishes.
Each result get added to his ROUND score
- BUT, if the player rolls a 1, all his ROUND score gets lost.
After that, it's the next player's turn
- The player can choose to 'Hold', which means that his ROUND score gets added to his GLBAL score.
After that, it's the next player's turn
- The first player to reach 100 points on GLOBAL score wins the game
PS: You can put another final score for the game. Just put it in the right field at the bottom ;)


This is some text that we will import.
Note we can only import into a page chunk from a dir chunk. This prevents any sort of recursion and we simply make sure that all dir level chunks are always processed before page level chunks are.
The title is This is test post 6 and this is an example blog post number 6.
The title is This is test post 5 and this is an example blog post number 5.


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From our blog: 7 Months of Silence
7 months, that’s quite a long time between posts! We’ve had a busy 7 months at Cape Medical Supply.
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| 029:041 Khan | : |
The likeness of those who take Auliya' (protectors and helpers) other than Allah is as the likeness of a spider, who builds (for itself) a house, but verily, the frailest (weakest) of houses is the spider's house; if they but knew. |
| 029:041 Maulana | : |
The parable of those who take guardians besides Allah is as the parable of the spider that makes for itself a house; and surely the frailest of the houses is the spider's house -- if they but knew! |
| 029:041 Pickthal | : |
The likeness of those who choose other patrons than Allah is as the likeness of the spider when she taketh unto herself a house, and lo! the frailest of all houses is the spider's house, if they but knew. |
| 029:041 Rashad | : |
The allegory of those who accept other masters beside GOD is that of the spider and her home; the flimsiest of all homes is the home of the spider, if they only knew. |
| 029:041 Sarwar | : |
The belief of considering other things as one's guardians besides God is as feeble as a spider's web. The spider's web is the frailest of all dwellings, if only they knew it. |
| 029:041 Shakir | : |
The parable of those who take guardians besides Allah is as the parable of the spider that makes for itself a house; and most surely the frailest of the houses is the spider's house did they but know. |
| 029:041 Sherali | : |
The case of those who take helpers beside ALLAH is like unto the case of the spider that takes to itself a house; and, surely, the frailest of all houses is the house of the spider, if they but knew. |
| 029:041 Yusufali | : |
The parable of those who take protectors other than Allah is that of the spider, who builds (to itself) a house; but truly the flimsiest of houses is the spider's house;- if they but knew. |
Concepts/Themes Covered: أركان الإسلام > التوحيد > الشرك والمشركون > تنزيه الله جلّ جلاله عن الشريك Pillars of Islam> Islamic> Polytheism and the Polytheists> Allah's Freedom from Partnership أركان الإسلام > التوحيد > الكافرون > صفات الكفار Pillars of Islam> Islamic> The Disbelievers> The Characteristics of the Disbelievers العلوم والفنون > الحقائق العلمية و الإشاة إلى وقائع أيدتهاالإكتشافات العلمية > الحيوانات والحشرات Science and Art> The Scientific Facts and the Indication to Facts which have been supported by the Scientific Discoveries>Animals and Insects
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Marketing is one of the most important things a business can do. Not only does marketing build brand awareness but it can also increase sales, grow businesses and engage customers. Without marketing, your potential customers may never be aware of your business offerings and your business may not be given the opportunity to progress and succeed.
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Provide administration and clerical support
Provide leadership to other marketing disciplines


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Mass shooting that killed 20 in El Paso investigated as 'domestic terrorism': Officials A mass shooting leaves at least 20 dead, dozens injured in an El Paso, Texas, Walmart.
More on Backdooring (or Not) WhatsApp - Schneier on Security Yesterday, I blogged about a Facebook plan to backdoor WhatsApp by adding client-side scanning and filtering. It seems that I was wrong, and there are no such plans.
Check to see if your account has been compromised Hacking is happening almost daily at this point so it is important to check if your account has been compromised by going to www.HAVEIBEENPWNED.com.
Windows Terminal Preview v0.3 Release | Windows Command Line Windows Terminal Preview v0.3 has been published to the Microsoft Store! If you have previously installed the Terminal from the Store, you will receive this update automatically. If you’ve not yet installed Terminal from the Microsoft Store, now would be ...
Everything Andrew Yang Said at the Second Democratic Debate in Detroit #yanggang Everything Andrew Yang Said at the Second Democratic Debate in Detroit #yanggang Get his book: https://amzn.to/31aAa08 YangGang Shirt: https://amzn.to/2YjetOF Donate: https://yang2020.com Andrew Yang just DOMINATED that debate and is the most honest an...
The probability that an EV SSL certificate is associated with a bad domain is 0.013% - Help Net Security New research confirms that a website with an EV SSL certificate decreases the chance of internet users falling victim to fraud and malware.
github/gov-takedowns Text of government takedown notices as received. GitHub does not endorse or adopt any assertion contained in the following notices. - github/gov-takedowns
Swift Client for the Asp.NET Core version of SignalR – Part 2: Beyond the Basics In the previous post we looked at some basic usage of the Swift SignalR Client. This was enough to get started but far from enough for any real-world application. In this post we will look at featu…