The only drugs I had done till now were alcohol and weed (about 5 times over the past 6 months), and more recently, DXM (very low dosage, single bottle of Vicks 44). Except for my first weed trip last year, all the others gave me only an enjoyable(?) body buzz. On my first weed trip, I freaked out with severe paranoia and guilt.
I had tried nutmeg 2 times before, once taking in excess of 5 table spoons gradually over 5-6 hours, without feeling anything more than a body buzz, and a very heavy head the next day.
On a fine Thursday evening, with nothing much to do and after reading a couple more Nutmeg trip reports on Erowid, I went to a local organic food store and grabbed a small Nutmeg shaker (a West Indian variety).
Since I had read somewhere that taking Nutmeg with milk or milk shake reduces the effects, I decided to swallow 4 teaspoons straight down, with water. The setting was in my apartment which I share with 2 sober guys. I had some psychedelic screen savers and tracks ready on my computer and was eager to try out a new racing game I'd recently acquired. On every trip, my major concern has been hiding the fact that I'm stoned from these people. This time I decided I would retire to my room after an early dinner, to avoid any paranoid arising out of this, as it has earlier.
This is how my trip went. Times are accurate upto 15 mins.
8:30pm (T): Gag down 3 heaped teaspoons of Nutmeg with water. Have a reasonably heavy dinner.
9:30pm (T+1.00): Feel nothing coming on, ingest another teaspoon of nutmeg.
11:30pm (T+3.00): Playing TestDrive 5, chatting on the Internet, listening to music, reading more Nutmeg reports, waiting for the trip to come on. Drinking a lot of water to counter the dry mouth and prevent dehydration.
12:15am (T+3.45): Talked with a few friends on the phone, still nothing, except for a slightly heavy head. Decide to turn in for the night. Disappointed, but happily sleepy. (I'm an insomniac).
Approx 2:30-6:30am (T+6.00-T+10.00): Wake up with a start, have to go to pee. Feel a sudden headrush coming on as I take a leak. Stagger back to my room, trying to stay very very quiet so my roommates don't wake up. I know I'm completely stoned. Very similar to the first weed trip I freaked out on. Mind working on overdrive. Random thoughts which I can't control.
After my first weed trip, which I went on with an experienced friend, he told me that I freaked out that time, because I was trying to control my trip. This time I try to let go and enjoy it. This is what I took Nutmeg for dammit, I let myself into it, now I've gotto enjoy it.
I attach headphones to my PC and extend them with some difficulty to my bed, so I can listen to the music lying down. I also put on some visualization I like. I'm beginning to enjoy the music, and I'm mesmerized by the beautiful beautiful patterns that move in synch with my music. I can almost float with the music when I close my eyes. Getting good CEVs. Similar to those I freaked out on seeing on my weed trip.
By and by I start getting tired of the music. Moreover, I keep getting the feeling that my body is out of water, which is why it cannot metabolize the Nutmeg properly, which is why I'm having a slow, long-stretched trip. I keep drinking more and more water, then I get an uncomfortable feeling that my bladder's full, but I can't notice it because I'm numb. So I get up to take a leak....I must've trudged to the bathroom about 8-10 times.
The worst part is I can't fall asleep. I keep telling myself to turn off the music and try falling asleep. But then I'm afraid I'll stop breathing if I do, and after a few seconds of sleep, I'm fully awake again.
Finally I decide I'm bored of the music, and decide to see a light hearted movie to lift my spirits. I put Shrek on (on my computer) and settle down into a happy half-awake state watching the movie.
About halfway through the movie, a most unfortunate thing happens. The video freezes, but the sound keeps playing. After sometime the video started (my computer's unstable) but was out of synch with the sound. This freaked me out completely, I mean COMPLETELY. On one plane I realized that there was nothing wrong with me, all I needed to do was restart the movie, but feelings of paranoia crept in. I started thinking I had messed up my brain, and I was receiving Sound signals out of synch with my Visual signals. With nobody to talk to, I couldn't confirm my doubts, but I was sure my brain was messed up.I took to making random noises, playing with my clock, squeaking my bed to, etc to make sure I was receiving sound and visual signals correctly.
This is when I was trapped in a terrible thought loop.
Finally I sat awake in bed and realized I had to do something about it. I got up to pee yet again, and then realized I was talking to myself in the bathroom and on the way back (My roommate hinted at this the next day).
6:30am (T+10.00 hours): Finally I sit at my computer and realize this is going way to long. Even my bad trip on Weed hadn't lasted this long. I read up more trip reports on a website and convince myself that Nutmeg trips usually last longer, and I'm fine, this is normal.
7:30am (T+11.00 hours): Bored of reading news, answering mails. Wonder whether to skip school, nothing important to do and safer to stay home. I am getting really tired of the trip now. Want it to end. Though as high as a kite, decide to try and sleep the rest of the trip off, if possible.
This is where the feelounds (read the term somewhere online, and no other word could describe it anyway) kicked in. I was trying to go to sleep, when for some reason I started thinking 3 words over and over again-Sound, Eyes, Echo (perhaps, a remnant of my mind loop). Eventually the sound became more and more rythmatic, until I realized it coincided with my heart beat. From then on, Every with every slight move I made, the words jumped at me. If I decided to sigh, the words would come louder. An arm movement or blink of an eye would be a volley of these words repeated over and over again. It became so unbearable that I tried to stay as still as possible.
I wished I couldn't feel my heartbeat, but then I'd die. This made me breath deeper and from then on I heard the words echo with every breath.
10:30am (T+14.00 hours): I wake up thinking I've finally slept off my trip, but it was not to be. What I'd thought was a heavy head was actually the same heavy body buzz. I go to the bathroom yet again, and notice that my roommates have left for the day. Decide to watch some TV, but find it boring.
Now getting REALLY tense about the trip. Have I damaged my brain completely? When is this thing going to be over. I have a couple of get-togethers lined up for Friday evening, but I'm sure I'll be fine in another 2-3 hours.
Remember my experienced friend telling me that Alcohol helps getting down from a weed trip, and helped him go to sleep.  Gulp down the Vodka raw with water and feel nauseatic for a few minutes.
Realize for the first time during the trip that my Short Term Memory has taken a holiday. I find myself walking back and forth between the kitchen and my room, before I realize it and stop.
Watch TV and try to sleep on the couch. Sleep still evades me. Listen to some more music but can't myself to watch Shrek. Afraid of the mind loop getting hold of me again.
Go out to smoke a cigarette and look at the people below hurrying to work or school. Remember feeling jealous of them.  Can feel a depression coming up. I want this trip to stop. I want to work hard like all these other people, and I'm wasting my life away on Nutmeg. Now I'll be like this (half insane) all life long, and just because I OD'd on Nutmeg?!  My trip should've been over several hours back. What's happening to me. How will I face the people I have to meet in the evening (They're all sober, beyond an occasional drink or two) and I wouldn't for my life want them to know what I did. I feel disgusted with myself.
 I've read about Nutmeg OD causing liver failure and death. Briefly consider dialing 9-1-1: maybe it would be a better idea to go to hospital with a chance to survive than die.
Gradually overcome the panic attack, and promise myself over and over again that if I survive this experience, I'm going sober for good.  Even decide to give up smoking and drinking to make up for the damage I've caused to my body.
2:30pm-6:30pm (T+18.00 - T+22.00 hours): Decide to call my experienced. He's at work but after hearing me blabber on the phone, he promises to drop in on the way home.
Friend drops in and we talk a while. I'm starting to feel positive now. My friend convinces me that a bad trip was part of trying out new substances, and I had to take it in my stride. I convince him to hang out at my place for some more time, while I get control over myself. Watch TV a while, and then walk over to his place, since he wants to smoke a joint.
I've resigned to my fate and decided I'll have to go through the rest of the evening in my present fucked state.
6:30pm-10:30pm (T+22.0 hours - T+26.0 hours)
The rest of the evening went by without a hitch.  Made it a point to drink a couple of beers and some strong cocktails help me sleep through the night.
I was still feeling as bad as I was in the afternoon, but the alcohol and helped me a bit. I was used to the alcohol kick, and it was much easier to control.
Finally managed to go to sleep at about 11:30pm, vowing never to do nutmeg again, and positive I would be fine when I woke up, which I was, except for a slight alcohol hangover.
 Sorry but I just had to get it all out. I've gained a new respect for my body and mind now, and hope to value them more from now on.
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.


Query (Relative to Container)
Ignore Classes
Select Similar
Minimal Xpath
Relative Xpath
Select appropriate field :
---
Car Postcode
Dealer Name
Dealer Telephone
Dealer URL
Dealer Email
Car Model
Car Make
Car Make & Model
Model Variant
Car Price
Car Year
Mileage
Body Type
Fuel Type
Engine size
Fuel Consumption
Acceleration
Gearbox
Drivetrain
CO2 Emissions
Doors
Seats
Insurance Group
Annual Tax
Colour
Private or Trader advertiser
Car description
Car Specification
Image xpath
Next Image xpaths
Next page xpath
Save Xpath
Save Result
Review
Results (
)
Help &
Examples
Username:
Password:
Login
>> Go
Current URL:
Save listing Url.
...
Last saved
:
Logout
Remove selected


The TMChangeDirectory API call sets or reads the current working directory. There is an 'operation' flag that indicates if the call is to set or read the current directory. Here are the values of 'operation':
0 : current directory is set
1 : current directory is read
The value in 'cd_buf' is a DirectoryPath structure that either contains the directory to be set or the current directory read depending on the 'operation' flag. 'cd_buf' must be a DirectoryPath structure.
For a description of the above types, see the TMEX Type Descriptions.
Parameters
session_handle
Specifies the session handle returned from the API function TMExtendedStartSession that specifies the desired 1-Wire network port. This parameter is required by most TMEX API functions.
state_buffer
Specifies a pointer to a memory location that TMEX keeps all of the state information for the 1-Wire networks. This parameter is required by most TMEX API functions.
operation
Specifies whether reading or setting the current directory. (1) indicates reading and (0) indicates setting.
cd_buf
Specifies a pointer to a memory location that contains a DirectoryPath structure. This structure filled before calling this API if the operation is setting(0). If the operation is reading(1) then this buffer will contain the current directory from the root after the API call.
Return Value
1 => operation is successful
<0 => TMEX File Operations Error Return Code
See Also
TMDirectoryMRFile, TMFirstFile


Simple Branch
Show Simple Branch
Simple If-Else
Simple If Else Action
Switch
Switch Action
Below is the link to my homepage and theme site
Homepage Site
Theme site


Thank you for visiting the site of the premier young professionals service organization in the DC Metropolitan area!
For over 23 years, Thursday Network has supported the programs of the Greater Washington Urban League by serving the diverse needs of Washington, D.C., Montgomery County and Prince George’s County, Md. Our mission is to provide a place for young professionals to support the GWUL and focus our energies on community service and economic empowerment,…
In 1910, the National Urban League (NUL) started a movement in the African-American community toward establishing economic self-reliance, parity, power and civil rights. Are you ready to join “The Movement”? If so, look no further than Thursday Network (TN). We are the Washington, DC and Suburban MD chapter of the National Urban League Young Professionals (NULYP).
Founded in 1992, Thursday Network is comprised of more than 130 young professionals committed to serving the Washington Metropolitan area through community programs and partnership that address the Empowerment Goals of the National Urban League. The mission of TN is to provide a forum for young adults, ages 21 – 40, to focus their energies on community service, professional development, and political involvement.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin vitae ante a sem vehicula dapibus et at purus. Fusce hendrerit…
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin vitae ante a sem vehicula dapibus et at purus. Fusce hendrerit…
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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin vitae ante a sem vehicula dapibus et at purus. Fusce hendrerit…
Thursday Network relies on your generosity to continue serving our community. We greatly appreciate your contributions.


On a beautiful Saturday evening, a star was born. Last May 23, RisingStars Philippines wrapped up Season 1 proudly, as it crowned its Ultimate RisingStar.
After going through months of competition, a short presentation chronicled the journey of the Top 4 to where they were now standing. Together with host Ogie Alcasid, co-hosts Mico Aytona and Venus Raj, Rocelle, Krezia, Lee’Anna, and Remy performed a medley of song and dance numbers. Along with other former RisingStars, it was the perfect way to start the championship night.
After going through months of competition, a short presentation chronicled the journey of the Top 4 to where they were now standing. Together with host Ogie Alcasid, co-hosts Mico Aytona and Venus Raj, Rocelle, Krezia, Lee’Anna, and Remy performed a medley of song and dance numbers. Along with other former RisingStars, it was the perfect way to start the championship night.
After going through months of competition, a short presentation chronicled the journey of the Top 4 to where they were now standing. Together with host Ogie Alcasid, co-hosts Mico Aytona and Venus Raj, Rocelle, Krezia, Lee’Anna, and Remy performed a medley of song and dance numbers. Along with other former RisingStars, it was the perfect way to start the championship night.
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On a beautiful Saturday evening, a star was born. Last May 23, RisingStars Philippines wrapped up Season 1 proudly, as it crowned its Ultimate RisingStar.After going through months of competition, a short presentation chronicled the journey of the Top 4 to where they were now standing. Together with host Ogie Alcasid, co-hosts Mico Aytona and Venus Raj, Rocelle, Krezia, Lee’Anna, and Remy performed a medley of song and dance numbers. Along with other former RisingStars, it was the perfect way to start the championship night.


Invented (or discovered!?) in 1970, Needleman–Wunsch finds optimal global alignments between two sequences.
Proposed in 1981, Smith–Waterman finds optimal local alignments. The algorithm voids negative scores in the scoring matrix. When a negative score is found, a "new" alignment begins at that position.


Here we have a custom view that have css and js files declared inside the custom.json file. These makes the files global for the admin as they are included inside the head tag of the document. You can inspect this page and see for yourself.
css
js
custom.json
head


Back Step X of Y
Please write down your tax reference number
Your reference is: TC/2016/123456
You will need to pay to submit your appeal
Your appeal details have now been saved. You will now have to pay your fee of £20.
Once your initial payment has been received, the tribunal will process your appeal so it can continue to the next stage.


 I assume that its the /PC part because I just made it up as I could see no rules for how to choose format.
Oh dear I seem to have necro'ed section of the forum. :) Was wondering when to expect a reply. Perhaps other areas of the forum are more active.


Saket Upadhyay
Talks and Presentations
-
Automating malware process scanning with Python3PyCode2021 Conference
-
Firefly Inspired IDS Optimisation (NICS)CyVIT 2021: International Cybersecurity Conclave
-
Malware Hunting with Machine LearningPenn State World Campus Tech Club, Penn State University, Pennsylvania, USA
-
Multi-Model Malware Detection and Classification.Hybrid Intelligent Systems (HIS 2019) Conference, Bhopal, India[
YouTube] [Web] [Slides] [GitHub]


import uuid from "uuid";
/**
* The base Decoration class. Decorations are elements in the figure that can update but don't map directly
* to nodes and branches.
*/
export class Decoration {
constructor() {
this._created = false;
this._title={
text:"",
xPadding:0,
yPadding:0,
rotation:0
};
this._id= `s${uuid.v4()}`;
this._interactions=[];
}
/**
* Get or set svg layer elements will be added to.
* @param l
* @return {BaubleManager|*}
*/
figure(f = null) {
if (f === null) {
return this._figure;
} else {
this._figure = f;
return this;
}
}
/**
* Get or set the figtree instance this manager works for. This allows the manager to access the figure's scale ect.
* @param f
* @return {BaubleManager|*}
*/
layer(l=null){
if(l===null){
return this._layer
}else{
this._layer=l;
return this
}
}
/**{
* Getter, setter for Decoration title. The options are below and are passed to the d3 text element.
* @param options
* @return {Decoration|{rotation: number, text: string, xPadding: number, yPadding: number}}
*/
title(options = null) {
if (!options) {
return this._title;
} else {
this._title = {...this._title, ...options};
return this;
}
}
/**
* Set a call back to be fired when the event listener is triggered.
* @param eventListener - a string that defines the event listener
* @param value - call back that will be passed d,i,n
* @return {Decoration}
*/
on(string, value) {
this._interactions[string] = value;
return this;
}
/**
* Get or set the transition duration and ease. Defualts to the figtree instance.
* @param t - {Object} [t={tranmsissionDuration: transmissionEase:}]
* @return {Bauble|BaubleManager|*|Bauble}
*/
transitions(t = null) {
if (t === null) {
if (this._transitions) {
return this._transitions;
} else {
return this.figure().transitions();
}
} else {
this._transitions = t;
return this;
}
}
/**
* Get or set the scales used in the element defaults to the figtree instance.
* @param scales
* @return {Bauble.scales|*}
*/
scales(scales = null) {
if (scales) {
this._scales = scales
} else {
if (this._scales) {
return this._scales;
} else {
return this.figure().scales
}
}
}
/**
* Decorations have a create method which enters them onto a selection and an updateCycle method
* that updated the already existing object. These are all handled by the update method which
* @param selection
*/
create(selection) {
throw new Error("Don't call the base class method")
};
/**
* Decorations have a create method which enters them onto a selection and an updateCycle method
* that updated the already existing object. These are all handled by the update method which
* @param selection
*/
update(selection) {
if (!this._created) {
this.create(selection);
this._created = true;
}
this.updateCycle();
}
/**
* Decorations have a create method which enters them onto a selection and an updateCycle method
* that updated the already existing object. These are all handled by the update method which
* @param selection
*/
updateCycle(selection) {
throw new Error("Don't call the base class method")
}
/**
* get or set the x position for the decorations
* @param d
* @return {*|Decoration}
*/
x(d = null) {
if (d !== null) {
this._x = d;
return this;
} else {
return this._x;
}
}
/**
* get or set the y position for the decorations
* @param d
* @return {*|Decoration}
*/
y(d = null) {
if (d !== null) {
this._y = d;
return this;
} else {
return this._y;
}
}
}


Moses
Home
(current)
About
Works
Contact
Web Designer/Developer
Just a Nerd who fell in love with web design
Works
About
Contact


Keira
has played
soccer
karate
squash
gymnastics
volleyball
lives in
brooklyn
favorite food
mango
speaks
english
is learning spanish
favorite musicians
david bowie
kanye
posty
hobbies
volleyball
surfing
painting
this is how to add a link:
how to add a link


Duygu Sezen Islakoğlu
PhD Student at Utrecht University
I am a second-year PhD student in the Data Intensive Systems group at Utrecht University under supervision of Dr. Mel Chekol and Prof. Yannis Velegrakis. I am interested in the intersection of knowledge graphs and language models.
Before joining Utrecht University, I was a Master of Artificial Intelligence student at KU Leuven. I worked with Dr. Sebastijan Dumancic and Prof. Hendrik Blockeel for my master thesis "Understanding Events in Continuous-time Dynamic Graphs". In 2020, I graduated from Koç University with double major in Computer Engineering and Mathematics.
I created "Open Office Hours" project in Turkey, where students can easily reach out academicians and ask their questions. Please contact me if you want to be involved.
- I am also interested in photography and design.
- A place that I really love: Nesin Mathematics Village.
news
| July 10-14, 2023 | I participated in ACM Summer School on Data Science 2023 in Athens. |
| Jun 21, 2023 | I was at Google I/O Connect 2023 Amsterdam. |
| Jun 7, 2023 | I presented my work during the poster session at Werkconferentie Open Overheid. |
| Apr 19-20, 2023 | I attended ICT Open 2023. |
| Feb 16-17, 2023 | I attended a SIKS Course: "Social AI". |
| Oct 31, 2022 | I was a moderator at ICAI Day: AI & Climate Change. |
| Nov 17-19, 2022 | I attended a SIKS Course: "Data Science: From Data Lakes to Knowledge Graphs". |
| Aug 7-10, 2022 | I attended Oxford Machine Learning Summer School. |
| Jun 1, 2022 | Dr. Mel Chekol and I have been received a teaching award for our MSc course Knowledge and Data Engineering. |
| May 15, 2022 | Our paper Symbiotic Child Emotional Support with Social Robots and Temporal Knowledge Graphs is accepted at Human-Centered Design of Symbiotic Hybrid Intelligence Workshop at Hybrid Human-Artificial Intelligence Conference 2022 in Netherlands. |
| Dec 7, 2021 | I gave a short presentation at DBDBD 2021 about Learning Embeddings of Events in Continuous-time Dynamic Graphs. |
| Nov 17-19, 2021 | I attended a SIKS Course: “Research methods and methodology for IKS” |


git - internals
ressources
[4:48]les fichiers objects (de
.git/objects/
) sont compressés avec zlib.
The anatomy of a Git commit : Christoph Burgdorf - 20141118
objects files
written in .git/objects/
list all objects files in .git/objects/
:
$ find .git/objects -type f
.git/objects/56/a6051ca2b02b04ef92d5150c9ef600403cb1de
.git/objects/ff/e298c3ce8bb07326f888907996eaa48d266db4
.git/objects/2e/65efe2a145dda7ee51d1741299f848e5bf752e
.git/objects/7b/35c3cb173e25102b0c26600ed2d45361c7a44e
.git/objects/27/4c0052dd5408f8ae2bc8440029ff67d79bc5c3
.git/objects/0e/ed1217a2947f4930583229987d90fe5e8e0b74
4 types of objects files :
- commit (the commit description)
- tree (directory state)
- blob (content files state)
- annotated tag
warning objects files creation
Theses files are created once the git add
command is executed. It means you could have a lot of orphans objects (not linked with a ref
)
if you have added to the index
a lot of files or directories without putting them into a commit
.
The command git prune -n
(or git fsck --unreachable
) display the list of orphans objects.
The command git prune -v
delete the orphans objects then print the deleted list.
immutability
git objects
files are immutable, once created, git reads them, and that’s all. There is no modification ever on it.
They are only deleted when the user execute the prune
command if they are unreachable from any refs
.
lire les fichiers objects
La commande git cat-file <object-hash> -p
permet d’afficher le contenu d’un fichier object.
Ex commit :
$ git cat-file 87a68b76ea470d7ad0f6ecbc5b46eda45e628e43 -p
tree 3852c9e63207a9ca2ccf00a5ba5233661ee6ff00
author Robert DUCHMOUL<EMAIL_ADDRESS>1524570143 +0200
committer Robert DUCHMOUL<EMAIL_ADDRESS>1524570143 +0200
test
Ex tree :
$ git cat-file 3852c9e63207a9ca2ccf00a5ba5233661ee6ff00 -p
100644 blob 274c0052dd5408f8ae2bc8440029ff67d79bc5c3 number.txt
Ex blob :
$ git cat-file 274c0052dd5408f8ae2bc8440029ff67d79bc5c3 -p
1234
La même commande en remplaçant -p
par -t
pour avoir le type de l’object.
$ git cat-file 87a68b76ea470d7ad0f6ecbc5b46eda45e628e43 -t
commit
$ git cat-file 3852c9e63207a9ca2ccf00a5ba5233661ee6ff00 -t
tree
$ git cat-file 274c0052dd5408f8ae2bc8440029ff67d79bc5c3 -t
blob
lire le fichier index
$ git ls-files --stage
100644 274c0052dd5408f8ae2bc8440029ff67d79bc5c3 0 number.txt
What does the git index contain EXACTLY?
files structure
blob file
A blob store the content of a file. Structure is header + content.
Header is : the file type (blob), a space, the length value in byte, a null char to terminate.
Ex : blob 16\u0000
A blob for a file containing what is up, doc?
would output this object file content blob 16\u0000what is up, doc?
.
Then git compute the sha1 for the object file content bd9dbf5aae1a3862dd1526723246b20206e5fc37
.
Then git compress the content with zlib.
Then git create a new object file in .git/objects/
.
The first two sha1 hash chars are the directory (bd
), the 38 last the object file name (9dbf5aae1a3862dd1526723246b20206e5fc37
).
tree file
Exactly the same as blob, but the content is not a file content but a list of the represented directory :
100644 blob 2e65efe2a145dda7ee51d1741299f848e5bf752e letter.txt
100644 blob 56a6051ca2b02b04ef92d5150c9ef600403cb1de number.txt
commit file
Same but the content is different, it contains the referenced base tree, the author, the committer (could be different),
a blank line, then the commit message (could be on several lines) :
tree 83207f0274383b4a79ff6d6c297e95204ba961bc
author Matthew Brett<EMAIL_ADDRESS>1487004984 +0000
committer Matthew Brett<EMAIL_ADDRESS>1487004984 +0000
An example commit
index file
It is a lot more complicated and support structure versions.
Cf appropriate documentation in the git repo itself.


Meguro Kimono is a specialty Kimono and Gofuku store in Asahikawa, the third biggest city in Northern Japan. Since our establishment in the Meiji era, we have kept close relationships with makers and repairers all over the country, and we provide our clients detailed and personalized services with such a nurtured expertise. Hokkaido is a newer prefecture, in comparison to other areas of Japan, but the passion from those interested in Japanese authenticity is as much as those in any other areas, if not more.
Merchants in Edo-period came up with a concept called “Sanpo-yoshi” in Japan. “Sanpo” means three element; seller, customer and society. And “Yoshi” means “Good”. We too value the concept and will always thrive to pursue a good business for not only the seller benefit, but also the customer and society benefits.


Single line items
Inbox
Starred
Sent mail
Drafts
Icon with text
Inbox
Outbox
Trash
Spam
Avatar with text
Alphonso Engelking
Andrews Boyd
Angela Decker
Lorem Ipsum
Avatar with text and icon
Alphonso
Andrews
Angela
Lorem
Avatar with text and control
Alphonso
Andrews
Angela
Lorem
Control with text and icon
Alphonso
Andrews
Angela
Lorem
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GtkTreeModelFilterGtkTreeModelFilter — A GtkTreeModel which hides parts of an underlying tree model |
| GtkTreeModel * | child-model | Read / Write / Construct Only |
| GtkTreePath * | virtual-root | Read / Write / Construct Only |
A GtkTreeModelFilter is a tree model which wraps another tree model, and can do the following things:
Filter specific rows, based on data from a “visible column”, a column storing booleans indicating whether the row should be filtered or not, or based on the return value of a “visible function”, which gets a model, iter and user_data and returns a boolean indicating whether the row should be filtered or not.
Modify the “appearance” of the model, using a modify function. This is extremely powerful and allows for just changing some values and also for creating a completely different model based on the given child model.
Set a different root node, also known as a “virtual root”. You can pass in a GtkTreePath indicating the root node for the filter at construction time.
The basic API is similar to GtkTreeModelSort. For an example on its usage, see the section on GtkTreeModelSort.
When using GtkTreeModelFilter, it is important to realize that GtkTreeModelFilter maintains an internal cache of all nodes which are visible in its clients. The cache is likely to be a subtree of the tree exposed by the child model. GtkTreeModelFilter will not cache the entire child model when unnecessary to not compromise the caching mechanism that is exposed by the reference counting scheme. If the child model implements reference counting, unnecessary signals may not be emitted because of reference counting rule 3, see the GtkTreeModel documentation. (Note that e.g. GtkTreeStore does not implement reference counting and will always emit all signals, even when the receiving node is not visible).
Because of this, limitations for possible visible functions do apply. In general, visible functions should only use data or properties from the node for which the visibility state must be determined, its siblings or its parents. Usually, having a dependency on the state of any child node is not possible, unless references are taken on these explicitly. When no such reference exists, no signals may be received for these child nodes (see reference couting rule number 3 in the GtkTreeModel section).
Determining the visibility state of a given node based on the state of its child nodes is a frequently occurring use case. Therefore, GtkTreeModelFilter explicitly supports this. For example, when a node does not have any children, you might not want the node to be visible. As soon as the first row is added to the node’s child level (or the last row removed), the node’s visibility should be updated.
This introduces a dependency from the node on its child nodes. In order to accommodate this, GtkTreeModelFilter must make sure the necessary signals are received from the child model. This is achieved by building, for all nodes which are exposed as visible nodes to GtkTreeModelFilter's clients, the child level (if any) and take a reference on the first node in this level. Furthermore, for every row-inserted, row-changed or row-deleted signal (also these which were not handled because the node was not cached), GtkTreeModelFilter will check if the visibility state of any parent node has changed.
Beware, however, that this explicit support is limited to these two cases. For example, if you want a node to be visible only if two nodes in a child’s child level (2 levels deeper) are visible, you are on your own. In this case, either rely on GtkTreeStore to emit all signals because it does not implement reference counting, or for models that do implement reference counting, obtain references on these child levels yourself.
gboolean (*GtkTreeModelFilterVisibleFunc) (GtkTreeModel *model
,GtkTreeIter *iter
,gpointer data
);
A function which decides whether the row indicated by iter
is visible.
model |
the child model of the GtkTreeModelFilter |
|
iter |
a GtkTreeIter pointing to the row in |
|
data |
user data given to |
[closure] |
void (*GtkTreeModelFilterModifyFunc) (GtkTreeModel *model
,GtkTreeIter *iter
,GValue *value
,gint column
,gpointer data
);
A function which calculates display values from raw values in the model.
It must fill value
with the display value for the column column
in the
row indicated by iter
.
Since this function is called for each data access, it’s not a particularly efficient operation.
model |
||
iter |
a GtkTreeIter pointing to the row whose display values are determined |
|
value |
A GValue which is already initialized for
with the correct type for the column |
[out caller-allocates] |
column |
the column whose display value is determined |
|
data |
user data given to |
[closure] |
GtkTreeModel * gtk_tree_model_filter_new (GtkTreeModel *child_model
,GtkTreePath *root
);
Creates a new GtkTreeModel, with child_model
as the child_model
and root
as the virtual root.
Since: 2.4
void gtk_tree_model_filter_set_visible_func (GtkTreeModelFilter *filter
,GtkTreeModelFilterVisibleFunc func
,gpointer data
,GDestroyNotify destroy
);
Sets the visible function used when filtering the filter
to be func
.
The function should return TRUE
if the given row should be visible and
FALSE
otherwise.
If the condition calculated by the function changes over time (e.g.
because it depends on some global parameters), you must call
gtk_tree_model_filter_refilter()
to keep the visibility information
of the model up-to-date.
Note that func
is called whenever a row is inserted, when it may still
be empty. The visible function should therefore take special care of empty
rows, like in the example below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |
static gboolean visible_func (GtkTreeModel *model, GtkTreeIter *iter, gpointer data) { // Visible if row is non-empty and first column is “HI” gchar *str; gboolean visible = FALSE; gtk_tree_model_get (model, iter, 0, &str, -1); if (str && strcmp (str, "HI") == 0) visible = TRUE; g_free (str); return visible; } |
Note that gtk_tree_model_filter_set_visible_func()
or
gtk_tree_model_filter_set_visible_column()
can only be called
once for a given filter model.
filter |
||
func |
A GtkTreeModelFilterVisibleFunc, the visible function |
|
data |
User data to pass to the visible function, or |
[allow-none] |
destroy |
Destroy notifier of |
[allow-none] |
Since: 2.4
void gtk_tree_model_filter_set_modify_func (GtkTreeModelFilter *filter
,gint n_columns
,GType *types
,GtkTreeModelFilterModifyFunc func
,gpointer data
,GDestroyNotify destroy
);
With the n_columns
and types
parameters, you give an array of column
types for this model (which will be exposed to the parent model/view).
The func
, data
and destroy
parameters are for specifying the modify
function. The modify function will get called for each
data access, the goal of the modify function is to return the data which
should be displayed at the location specified using the parameters of the
modify function.
Note that gtk_tree_model_filter_set_modify_func()
can only be called once for a given filter model.
Since: 2.4
void gtk_tree_model_filter_set_visible_column (GtkTreeModelFilter *filter
,gint column
);
Sets column
of the child_model to be the column where filter
should
look for visibility information. columns
should be a column of type
G_TYPE_BOOLEAN
, where TRUE
means that a row is visible, and FALSE
if not.
Note that gtk_tree_model_filter_set_visible_func()
or
gtk_tree_model_filter_set_visible_column()
can only be called
once for a given filter model.
Since: 2.4
GtkTreeModel *
gtk_tree_model_filter_get_model (GtkTreeModelFilter *filter
);
Returns a pointer to the child model of filter
.
Since: 2.4
gboolean gtk_tree_model_filter_convert_child_iter_to_iter (GtkTreeModelFilter *filter
,GtkTreeIter *filter_iter
,GtkTreeIter *child_iter
);
Sets filter_iter
to point to the row in filter
that corresponds to the
row pointed at by child_iter
. If filter_iter
was not set, FALSE
is
returned.
filter |
||
filter_iter |
An uninitialized GtkTreeIter. |
[out] |
child_iter |
A valid GtkTreeIter pointing to a row on the child model. |
TRUE
, if filter_iter
was set, i.e. if child_iter
is a
valid iterator pointing to a visible row in child model.
Since: 2.4
void gtk_tree_model_filter_convert_iter_to_child_iter (GtkTreeModelFilter *filter
,GtkTreeIter *child_iter
,GtkTreeIter *filter_iter
);
Sets child_iter
to point to the row pointed to by filter_iter
.
filter |
||
child_iter |
An uninitialized GtkTreeIter. |
[out] |
filter_iter |
A valid GtkTreeIter pointing to a row on |
Since: 2.4
GtkTreePath * gtk_tree_model_filter_convert_child_path_to_path (GtkTreeModelFilter *filter
,GtkTreePath *child_path
);
Converts child_path
to a path relative to filter
. That is, child_path
points to a path in the child model. The rerturned path will point to the
same row in the filtered model. If child_path
isn’t a valid path on the
child model or points to a row which is not visible in filter
, then NULL
is returned.
Since: 2.4
GtkTreePath * gtk_tree_model_filter_convert_path_to_child_path (GtkTreeModelFilter *filter
,GtkTreePath *filter_path
);
Converts filter_path
to a path on the child model of filter
. That is,
filter_path
points to a location in filter
. The returned path will
point to the same location in the model not being filtered. If filter_path
does not point to a location in the child model, NULL
is returned.
Since: 2.4
void
gtk_tree_model_filter_refilter (GtkTreeModelFilter *filter
);
Emits ::row_changed for each row in the child model, which causes the filter to re-evaluate whether a row is visible or not.
Since: 2.4
void
gtk_tree_model_filter_clear_cache (GtkTreeModelFilter *filter
);
This function should almost never be called. It clears the filter
of any cached iterators that haven’t been reffed with
gtk_tree_model_ref_node()
. This might be useful if the child model
being filtered is static (and doesn’t change often) and there has been
a lot of unreffed access to nodes. As a side effect of this function,
all unreffed iters will be invalid.
Since: 2.4
“child-model”
property“child-model” GtkTreeModel *
The model for the filtermodel to filter.
Flags: Read / Write / Construct Only
“virtual-root”
property“virtual-root” GtkTreePath *
The virtual root (relative to the child model) for this filtermodel.
Flags: Read / Write / Construct Only


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Types
Icon Set
An icon sets contains an arbitrary number of glyphs
Content
Content icons represent nouns
Special Content
Special content are types of content not as frequently used
User Actions
User action icons represent possible actions a user can take
View
View icons represent ways to modify a page view
Media Actions
Media actions specify actions a user can take on media elements
Objects
Object icons represent concepts more literally
Indications
Indications represent the status of an element
Symbols
Symbol icons represent shapes or figures
Social
Social icons represent common social sites.
States
Disabled
An icon can show that it is disabled
Variations
Size
An icon can vary in size
Link
An icon can be formatted as a link
Flipped
An icon can be flipped
Loading
An icon can be spin to be used as a loading indicator
Rotated
An icon can be rotated
Circular
An icon can be formatted to appear circular
Square
An icon can be formatted to appear square
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An icon can be formatted with different colors
Inverted
an icon can have its colors inverted for contrast


Graduate Student in USC
Establish MySQL and Node.js environment in Ubuntu Server
Nov 9, 2017 / Written by Chengyu
This article shared my experience in deploying MySQL and Node.js remotelly with SSH Encryption connection and terminal.
1. Connect to the remote server
You can use embedded Terminal in Mac or PuTTY in Windows to connect to the server.
ssh 'username'@'Ip_address'
After you typing so, it will require you to enter the corresponding password if necessary. If you can see the following textfield. Congratulation! You have logged in successfully.
Terminal output after login
2. Download and install MySQL
Before downloading your MySQL, please check whether you have download it before. By typing "mysql", if the terminal can recognize this instruction, you can skip the following steps to step 3. If not, please type:
sudo apt-get update
sudo apt-get install mysql-server
sudo means you run this instruction as an admin. The download will take a few minutes and whenever you finish this step, typing mysql will help you enter the MySQL from terminal.
You can also verify the version of your mysql by typing: mysql --v
.
Please don't forget to setup your password for the database when download and installing. And after that you can type mysql -u root -p to enter the local database and you also need to enter the password you setup in the downloading process.
After login your MySQL
mysql> update user set update user set authentication_string=password("NEWPASSWORD") where User='root';
Before downloading Node.js in your server, please check wheter you have already had it before by typing "node".
sudo apt-get npm
npm init
Congraduation! You can put your Node.js file into server and run it now!
But, if you want to execute MySQL instrucitons in your Node file, please don't forget to download the necessary plug-in node_modules as well:
npm install express npm install mysqlHere is one example to connect the MySQL in Node file.
var mysql = require('mysql'); var app, express; //establish a server express = require("express"); app = express(); var bodyParser = require('body-parser'); app.use(bodyParser.json()); // support json encoded bodies app.use(bodyParser.urlencoded({ extended: true })); // support encoded bodies var con = mysql.createConnection({ host: "localhost", user: "your_username", password: "your_username's_corresponding_passwd", database: "your database" }); app.listen(8888) //any legal port you want to monitor


Minutes played by Nets, playoffs
This notebook shows the minutes played by starters and bench Nets players in their two playoff series.
The hypothesis for looking at minutes played is that: did the cumulative effect of the number of minutes played by K. Durant lead to his fatigue (which could be measured by something like shooting percentage) in game 7 of the second series?
This analysis only looks on the minutes played side of the relationship, whereas a measure of player performance would also be required to answer the question of fatigue.
code: https://gist.github.com/bhoung/3833c6afa438296c6c6dfe8964d20da7
link to YouTube screencast #5 (2021-06-25) here
##
## brownbr01 claxtni01 duranke01 greenje02 griffbl01 hardeja01 harrijo01 irvinky01
## 12 12 12 12 12 12 12 12
## jamesmi02 johnsty01 shamela01
## 12 12 12
References:


#Physics Informed Neural Networks
PINNs was introduced by Maziar Raissi et. al in their paper Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations which are used for solving supervised learning tasks and also follow an underlying differential equation derived from understanding the Physics. In more simple terms, we try solving a differential equation with a neural network and using the differential equation as the regulariser in the loss function.
\(\quad \frac{df}{dt} = f(u, t),\) where initial condition is \(\ \ u(t=0) = u_0\)
We approximate function \(u(t)\) using a Neural Network as \(NN(t)\) and apply the following loss function -
Initial Loss: \(\quad L_0 = (NN(t=0) - u_0)^2\)
Regulariser Loss:= \(\quad L_r = \frac{dNN(t)}{dt} - f(NN(t),t)\)
And we minimise the \(Total Loss\) using Backpropagation-
Total Loss = Initial Loss + Regulariser Loss
where \(u(t,x)\) denotes the latent (hidden) solution, \(N[⋅]\) is a nonlinear differential operator, and \(Ω\) is a subset of \(\mathbb{R}^D\) , and proceed by approximating \(u(t,x)\) by a deep neural network. We define \(f(t,x)\) to be given by
\[\quad \quad f := u_t + \mathcal{N}[u],\]This assumption results in a physics informed neural network \(f(t,x)\). This network can be derived by the calculus on computational graphs: Backpropagation.
### In case if you are running this notebook in Local Machine and have CUDA installed then uncomment this below line. ###
### Change the cuda version depending on your setup. Here Cuda 11.x is represented as `cuda111`
# !pip install --upgrade pip
# !pip install --upgrade "jax[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
!pip install deepchem[jax]
!pip install pyDOE
Collecting deepchem[jax]
Downloading deepchem-2.5.0-py3-none-any.whl (552 kB)
[K |████████████████████████████████| 552 kB 5.4 MB/s
[33mWARNING: deepchem 2.5.0 does not provide the extra 'jax'[0m
[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from deepchem[jax]) (1.0.1)
Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from deepchem[jax]) (1.1.5)
Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from deepchem[jax]) (1.4.1)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from deepchem[jax]) (1.19.5)
Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from deepchem[jax]) (0.22.2.post1)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->deepchem[jax]) (2.8.2)
Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->deepchem[jax]) (2018.9)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->deepchem[jax]) (1.15.0)
Installing collected packages: deepchem
Successfully installed deepchem-2.5.0
Collecting pyDOE
Downloading pyDOE-0.3.8.zip (22 kB)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pyDOE) (1.19.5)
Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from pyDOE) (1.4.1)
Building wheels for collected packages: pyDOE
Building wheel for pyDOE (setup.py) ... [?25l[?25hdone
Created wheel for pyDOE: filename=pyDOE-0.3.8-py3-none-any.whl size=18184 sha256=4da795f38a3c13631115b175ca51b0cca2ebc9226d122d2f90ddf4975609baaf
Stored in directory: /root/.cache/pip/wheels/83/ce/8a/87b25c685bfeca1872d13b8dc101e087a9c6e3fb5ebb47022a
Successfully built pyDOE
Installing collected packages: pyDOE
Successfully installed pyDOE-0.3.8
import deepchem as dc
import os
PINNs_URL = "https://deepchemdata.s3.us-west-1.amazonaws.com/datasets/raissi_pinns_data.tar.gz"
dc.utils.data_utils.download_url(
url=PINNs_URL, dest_dir=os.getcwd())
targz_file = os.path.join(os.getcwd(), 'raissi_pinns_data.tar.gz')
dc.utils.data_utils.untargz_file(targz_file, os.getcwd())
Deepchem has recently introduced Jax support for building models and JaxModel
superclass is the main API for building
Burgers’ equation is a partial differential equation that was originally proposed as a simplified model of turbulence as exhibited by the full-fledged Navier-Stokes equations. It is a nonlinear equation for which exact solutions are known and is therefore important as a benchmark problem for numerical methods. More Refrence
Here is the differential Equation we are trying to solve
\[\begin{array}{l} \ \ \ u_t + u u_x - (0.01/\pi) u_{xx} = 0,\ \ \ x \in [-1,1],\ \ \ t \in [0,1] \end{array}\]Here are the initial conditions
\[\ \ \ u(x, 0) = -\sin(\pi x),\] \[\ \ \ u(-1, t) = u(1, t) = 0.0\]Now let us define:
\[\ \ \ f := u_t + u u_x - (0.01/\pi) u_{xx},\]and we approximate \(u(x, t)\) using Neural Network as \(NN(\theta, x, t)\) where \(\theta\) are the weights of neural networks
Now here are the three main data points that will be used for training our Neural Network to approximate \(u(x,t)\)
We will train points lying between \(x \in [-1,1]\) and \(t=0\) to follow as part of the L2 Loss
\[\begin{array}{l} min_\theta _(NN(\theta, x, t) + \sin(\pix))^2 \end{array}\]We will train points lying between \(t \in [0,1]\) and \(x= \pm1\) as part of the L2 Loss
\[\min\limits_{\theta} (NN(\theta, x, t) + \sin(\pi x))^2\]$$f(\theta, x, t):= \quad \frac{\partial NN(\theta, x, t)}{\partial t} + NN(\theta, x, t)\frac{\partial NN(\theta, x, t)}{\partial x} - (0.01/\pi)\frac{\partial^2 NN(\theta, x, t)}{\partial^2 x} $$
$$min \quad _\theta \quad f(\theta, x, t)$$
</br>
In this tutorial, we will be combing data conditions 1 and 2 under the same L2Loss
Now lets load the Burger’s Data provided from the author. pre_process_shock_data
is used to load the data in a format suitable for Neural Networks. Understanding this function is not neccesary for working through the tutorials.
## Data Preprocessing
# Create Dataset
import scipy.io
from scipy.interpolate import griddata
from pyDOE import lhs
import numpy as np
import random
random.seed(0)
np.random.seed(0)
def pre_process_shock_data(data):
N_u = 100
N_f = 10000
t = data['t'].flatten()[:,None]
x = data['x'].flatten()[:,None]
Exact = np.real(data['usol']).T
X, T = np.meshgrid(x,t)
X_star = np.hstack((X.flatten()[:,None], T.flatten()[:,None]))
u_star = Exact.flatten()[:,None]
# Doman bounds
lb = X_star.min(0)
ub = X_star.max(0)
xx1 = np.hstack((X[0:1,:].T, T[0:1,:].T))
uu1 = Exact[0:1,:].T
xx2 = np.hstack((X[:,0:1], T[:,0:1]))
uu2 = Exact[:,0:1]
xx3 = np.hstack((X[:,-1:], T[:,-1:]))
uu3 = Exact[:,-1:]
X_u_train = np.vstack([xx1, xx2, xx3])
X_f_train = lb + (ub-lb)*lhs(2, N_f)
X_f_train = np.vstack((X_f_train, X_u_train))
u_train = np.vstack([uu1, uu2, uu3])
idx = np.random.choice(X_u_train.shape[0], N_u, replace=False)
X_u_train = X_u_train[idx, :]
u_train = u_train[idx,:]
return X_u_train, u_train, X_f_train, X_star
mat_data = scipy.io.loadmat(os.path.join(os.getcwd(), 'PINNs/burgers_shock.mat'))
labeled_X, labeled_y, unlabeled_X, full_domain = pre_process_shock_data(mat_data)
We have three Numpy arrays labeled_X
, labeled_y
and unlabeled_X
which will be used for training our neural network,
1) labeled_X
consists of \(x \in [-1,1]\) & \(t=0\) and \(t \in [0,1]\) & \(x= \pm1\). labeled_y
has the value of \(u(x, t)\):
Let us verify that labeled_X
& labeled_y
also consists of data points satisfying the condition of
\(\ \ \ u(x, 0) = -\sin(\pi x), \quad \quad x \in [-1,1]\) & \(t=0\)
import matplotlib.pyplot as plt
ind = labeled_X[:, 1] == 0.0
print(f"Number of Datapoints with with t = 0 is {len(labeled_X[labeled_X[:, 1] == 0.0])}")
plt.scatter(labeled_X[ind][:, 0], labeled_y[ind], color = 'red', marker = "o", alpha = 0.3)
<matplotlib.collections.PathCollection at 0x7f721a00b390>
Let us verify that at labeled_X
& labeled_y
also consists of datapoints satisfying the condition of
\(\ \ \ u(-1, t) = u(1, t) = 0.0, \quad \quad t \in [0,1]\) & \(x= \pm1\)
ind = np.abs(labeled_X[:, 0]) == 1.0
print(f"Number of Datapoints with with |x| = 1 is {len(labeled_X[np.abs(labeled_X[:, 0]) == 1.0])}")
np.max(labeled_y[ind]), np.min(labeled_y[ind]), np.mean(labeled_y[ind])
(2.326502818752141e-16, -1.0854441803149965e-16, 8.236830416577723e-17)
We will be using Deepchem’s PINNModel
class to solve Burger’s Equation which is based out of Jax
library. We will approximate \(u(x, t)\) using a Neural Network represented as \(NN(\theta, x, t)\)
For our purpose, we will be using the Haiku library for building neural networks. Due to the functional nature of Jax, we define neural network with two things
import jax
import jax.numpy as jnp
import haiku as hk
def f(x, t):
x = jnp.hstack([x, t])
net = hk.nets.MLP(output_sizes = [20, 20, 20, 20, 20, 20, 20, 20, 1],
activation = jnp.tanh)
return net(x)
init_params, forward_fn = hk.transform(f)
rng = jax.random.PRNGKey(500)
x_init, t_init = jnp.split(X_u_train, 2, 1)
params = init_params(rng, x_init, t_init)
As per the docstrings of PINNModel, we require two additional functions in the given format -
>>>
>> def gradient_fn(forward_fn, loss_outputs, initial_data):
>> def model_loss(params, target, weights, rng, ...):
>> # write code using the arguments.
>> # ... indicates the variable number of positional arguments.
>> return
>> return model_loss
And to understand more about PINNModel, you can see that the same gradient_fn gets called in the code for computing the gradients.
For our purpose, we have two variables \((x, t)\) and we need to tell the PINN Model how to compute the final gradient. For carrying out this process we will be using these main features from jax library for calculating the loss -
jacrev
simplifies the array shapes and hence is easier.We need to compute two losses for solving our differential equation-
u_pred = forward_fn(params, rng, x_b, t_b)
initial_loss = jnp.mean((u_pred - boundary_target) ** 2)
This is slightly complicated as we need to compute
\[f(\theta, x, t):= \ \ \frac{\partial NN(\theta, x, t)}{\partial t} + NN(\theta, x, t)\frac{\partial NN(\theta, x, t)}{\partial x} - (0.01/\pi)\frac{\partial^2 NN(\theta, x, t)}{\partial^2 x}\]The partial derivative operation in the first and second terms can be calculated using jacrev
function-
u_x, u_t = jacrev(forward_fn, argnums=(2, 3))(params, rng, x, t)
The second partial derivative operation in the third term can be applying jacrev
twice-
u_xx = jacrev(jacrev(forward_fn, argnums=2), argnums=2)(params, rng, x, t)
from jax import jit, vmap, jacrev
def gradient_fn(forward_fn, loss_outputs, initial_data):
"""
This function calls the gradient function, to implement the backpropogation
"""
boundary_data_x = initial_data['labeled_x']
boundary_data_t = initial_data['labeled_t']
boundary_target = initial_data['labeled_u']
@jax.jit
def model_loss(params, target, weights, rng, x_train, t_train):
@functools.partial(jax.vmap, in_axes=(None, 0, 0))
def small_loss(params, x, t):
u = forward_fn(params, rng, x, t)
u_x, u_t = jacrev(forward_fn, argnums=(2, 3))(params, rng, x, t)
u_xx = jacrev(jacrev(forward_fn, argnums=2), argnums=2)(params, rng, x, t)
con = 0.01/np.pi
return u_t + u * u_x - con * u_xx
u_pred = forward_fn(params, rng, boundary_data_x, boundary_data_t)
f_pred = small_loss(params, x_train, t_train)
loss_u = jnp.mean((u_pred - boundary_target) ** 2)
loss_f = jnp.mean((f_pred) ** 2)
return loss_u + loss_f
return model_loss
>>>
>> def create_eval_fn(forward_fn, params):
>> def eval_model(..., rng=None):
>> # write code here using arguments
>>
>> return
>> return eval_model
Like previously we have two arguments for our model \((x, t)\) which get passed in function
# Tells the neural network on how to perform calculation during inference
def create_eval_fn(forward_fn, params):
"""
Calls the function to evaluate the model
"""
@jax.jit
def eval_model(x, t, rng=None):
res = forward_fn(params, rng, x, t)
return jnp.squeeze(res)
return eval_model
We will be using optax library for performing the optimisations. PINNModel executes the codes for training the models.
import optax
scheduler = optax.piecewise_constant_schedule(
init_value=1e-2,
boundaries_and_scales={5000: 0.1, 10000: 0.1, 15000: 0.1})
opt = optax.chain(
optax.clip_by_global_norm(1.00),
optax.scale_by_adam(b1=0.9, b2=0.99),
optax.scale_by_schedule(scheduler),
optax.scale(-1.0))
labeled_x, labeled_t = jnp.split(labeled_X, 2, 1)
boundary_data = {
'labeled_x': labeled_x, 'labeled_t':labeled_t, 'labeled_u': labeled_y
}
boundary_data_x = initial_data['labeled_x']
boundary_data_t = initial_data['labeled_t']
boundary_target = initial_data['labeled_u']
j_m = PINN_Model(
forward_fn = forward_fn,
params = params,
boundary_data = boundary_data,
batch_size = 1000,
optimizer = opt,
grad_fn = gradient_fn,
eval_fn = create_eval_fn,
deterministic = True,
log_frequency = 1000
)
dataset = dc.data.NumpyDataset(unlabeled_X)
val = j_m.fit(dataset, nb_epochs=4000)
test_dataset = dc.data.NumpyDataset(full_domain)
u_pred = j_m.predict(test_dataset)
U_pred = griddata(X_star, u_pred.flatten(), (X, T), method='cubic')
Error = np.abs(Exact - U_pred)
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
from scipy.interpolate import griddata
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.gridspec as gridspec
fig = plt.figure(figsize=(9, 5))
ax = fig.add_subplot(111)
h = ax.imshow(U_pred.T, interpolation='nearest', cmap='rainbow',
extent=[t.min(), t.max(), x.min(), x.max()],
origin='lower', aspect='auto')
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.10)
cbar = fig.colorbar(h, cax=cax)
cbar.ax.tick_params(labelsize=15)
ax.plot(
X_u_train[:,1],
X_u_train[:,0],
'kx', label = 'Data (%d points)' % (u_train.shape[0]),
markersize = 4, # marker size doubled
clip_on = False,
alpha=1.0
)
line = np.linspace(x.min(), x.max(), 2)[:,None]
ax.plot(t[25]*np.ones((2,1)), line, 'w-', linewidth = 1)
ax.plot(t[50]*np.ones((2,1)), line, 'w-', linewidth = 1)
ax.plot(t[75]*np.ones((2,1)), line, 'w-', linewidth = 1)
ax.set_xlabel('$t$', size=20)
ax.set_ylabel('$x$', size=20)
ax.legend(
loc='upper center',
bbox_to_anchor=(0.9, -0.05),
ncol=5,
frameon=False,
prop={'size': 15}
)
ax.set_title('$u(t,x)$', fontsize = 20) # font size doubled
ax.tick_params(labelsize=15)
plt.show()
fig = plt.figure(figsize=(14, 10))
ax = fig.add_subplot(111)
gs1 = gridspec.GridSpec(1, 3)
gs1.update(top=1-1.0/3.0-0.1, bottom=1.0-2.0/3.0, left=0.1, right=0.9, wspace=0.5)
ax = plt.subplot(gs1[0, 0])
ax.plot(x,Exact[25,:], 'b-', linewidth = 2, label = 'Exact')
ax.plot(x,U_pred[25,:], 'r--', linewidth = 2, label = 'Prediction')
ax.set_xlabel('$x$')
ax.set_ylabel('$u(t,x)$')
ax.set_title('$t = 0.25$', fontsize = 15)
ax.axis('square')
ax.set_xlim([-1.1,1.1])
ax.set_ylim([-1.1,1.1])
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(15)
ax = plt.subplot(gs1[0, 1])
ax.plot(x,Exact[50,:], 'b-', linewidth = 2, label = 'Exact')
ax.plot(x,U_pred[50,:], 'r--', linewidth = 2, label = 'Prediction')
ax.set_xlabel('$x$')
ax.set_ylabel('$u(t,x)$')
ax.axis('square')
ax.set_xlim([-1.1,1.1])
ax.set_ylim([-1.1,1.1])
ax.set_title('$t = 0.50$', fontsize = 15)
ax.legend(
loc='upper center',
bbox_to_anchor=(0.5, -0.15),
ncol=5,
frameon=False,
prop={'size': 15}
)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(15)
ax = plt.subplot(gs1[0, 2])
ax.plot(x,Exact[75,:], 'b-', linewidth = 2, label = 'Exact')
ax.plot(x,U_pred[75,:], 'r--', linewidth = 2, label = 'Prediction')
ax.set_xlabel('$x$')
ax.set_ylabel('$u(t,x)$')
ax.axis('square')
ax.set_xlim([-1.1,1.1])
ax.set_ylim([-1.1,1.1])
ax.set_title('$t = 0.75$', fontsize = 15)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(15)
plt.show()


Unity UI: Unity User Interface Canvas Basic Layout Visual Components Interaction Components Animation Integration Auto Layout Rich Text Events MessagingSystem InputModules SupportedEvents Raycasters Reference Rect Transform Canvas Components Canvas Canvas Scaler Canvas Group Canvas Renderer Visual UIInteractionComponents Text Image Raw Image Mask RectMask2D UI Effect Components Shadow Outline Position as UV1 Interaction Components Selectable Base Class Transition Options Navigation Options Button Toggle Toggle Group Slider Scrollbar Dropdown Input Field Scroll Rect Auto Layout Layout Element Content Size Fitter Aspect Ratio Fitter Horizontal Layout Group Vertical Layout Group Grid Layout Group Events script-EventSystem script-GraphicRaycaster script-PhysicsRaycaster script-Physics2DRaycaster script-StandaloneInputModule script-TouchInputModule script-EventTrigger UI How Tos Designing UI for Multiple Resolutions Making UI elements fit the size of their content Creating a World Space UI Creating UI elements from scripting Creating Screen Transitions


{% extends 'layouts/layout-bsa-design-system.html' %} {% set active = "components" %} {% set activesub = "Verification table" %} {% block pageTitle %} Timeline - NHSBSA design system {% endblock %} {% block beforeContent %} {% include "includes/breadcrumbs__components.html" %} {% endblock %} {% block content %}
{% filter escape %}{% include "includes/components/verification-table-mys.html" %}{% endfilter %}
{% filter escape %}{% include "includes/css/verification-table-mys.scss" %}{% endfilter %}
Use where information is ordered by time and unfiltered. If the information isn’t displayed in time order or needs to be filtered, consider another approach.
Times and dates should follow GDS patterns on date and time format
Secondary titles aren't mandatory.


{{ message }}
Addon title is required.
Title is too long (maximum 255 characters!).
Game is required.


exit - End the application
exit ?returnCode?
Terminate the process, returning returnCode to the
system as the exit status.
If returnCode isn't specified then it defaults
to 0.
Since non-zero exit codes are usually interpreted as error cases by
the calling process, the exit command is an important part of
signalling that something fatal has gone wrong. This code fragment is
useful in scripts to act as a general problem trap:
proc main {} {
# ... put the real main code in here ...
}
if {[catch {main} msg]} {
puts stderr "unexpected script error: $msg"
if {[info exist env(DEBUG)]} {
puts stderr "---- BEGIN TRACE ----"
puts stderr $errorInfo
puts stderr "---- END TRACE ----"
}
# Reserve code 1 for "expected" error exits...
exit 2
}
exec, tclvars
exit, process
Copyright © 1993 The Regents of the University of California.
Copyright © 1994-1996 Sun Microsystems, Inc.
Copyright © 1995-1997 Roger E. Critchlow Jr.


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We can also generate high-level information from the raw event data above.
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The JCC in Manhattan Sat 23 Nov 2013 @ 04:00PM We'll be performing an acoustic set for the JCC's Saturday R&R program. Open to all.