Diabate scores 18 to carry Portland past Maine 71-62
by The Associated PressPORTLAND, Ore. (AP) — Tahirou Diabate had 18 points as Portland topped Maine 71-62 on Saturday night.
Diabate hit 9 of 11 shots.
JoJo Walker had 13 points for Portland (3-1). Malcolm Porter added 12 points and six rebounds. Chase Adams had seven assists .
Sergio El Darwich had 21 points for the Black Bears (1-2). Andrew Fleming added 14 points and six assists. Nedeljko Prijovic had 10 points and seven rebounds.
Portland plays Portland State on the road on Wednesday. Maine plays Washington on the road on Tuesday.
___
For more AP college basketball coverage: https://apnews.com/Collegebasketball and http://twitter.com/AP_Top25
___
This was generated by Automated Insights, http://www.automatedinsights.com/ap, using data from STATS LLC, https://www.stats.com
The Associated Press


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alert("Hey! You clicked me.");
}
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label: "Close"
}
]
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it s just basically curl, the point is : the fastest you know what happen and don t rely on allmade zcripts, the fastest you ll ve able to use those scripts and splitting stuff
cause you ll know why where and how it should be done
mrlynch31-Mar-19 04:53 AM
i'm actually learning from the scripts.. there's a lot of stuff i wasn't aware of
Deleted User31-Mar-19 04:54 AM
if you understand... 5 scripts
you got it
1
it can seem really really stupid
but the key, magic, etc is just to look at scripts you have to execute on the guide. read it, deeply then you ll have the "critical line" in view, then you ll start digging without losing time
wish I could fight with you all on testnet :(
blackjok3r31-Mar-19 05:01 AM
Man I will try login to mine I assume its dead. If anyone has any questions about it let me know. Happy to help any new comers. What dwy has said is just about right read the scripts understand what does what. There are many ways to improve. Comes down to responding the fastest to other nodes. That's exactly how to get better counts. The hard part is finding something that makes you respond faster. Less utxo means less to process. You can also customise the notarizer to make it just respond faster...
Deleted User31-Mar-19 05:02 AM
some leads gave good optim result on h decryption
blackjok3r31-Mar-19 05:02 AM
I still have one more thing I'm going to try out this week on testnet, that is replace useing listuspent with a call that simply returns iguana a single eligible utxo so it no longer has to process anything at all(edited)
Deleted User31-Mar-19 05:03 AM
array is back ?
that s not what you wanted to do at first ? ^^
oh, a simple utxo
but wouldn t that rely on external listunspent ? new rpc to avoid loops ?
blackjok3r31-Mar-19 05:04 AM
Yeah like instead of needing to fetch all utxo it just gets a single one and locksnit directlyninside daemon(edited)
Anything we can do to stop iguana and daemon needing to talk to each other via RPC will speed it up
Deleted User31-Mar-19 05:05 AM
interact with wallet.dat :p
blackjok3r31-Mar-19 05:06 AM
Pretty much, cut out 90% of the entire operation
Deleted User31-Mar-19 05:06 AM
he s been deep on this, or maybe it was decker also
blackjok3r31-Mar-19 05:07 AM
Decker helped with lock utxo code yes. Hut mostly been on my own with iguana, hundreds of hours reading code in the early days, learnt c codenin the process haha
Deleted User31-Mar-19 05:07 AM
btw, maybe some sort of real splitting bw wallet and daemon will help
like the ability to interact with wallet without daemon
:p
blackjok3r31-Mar-19 05:08 AM
Hmmm iguana used to do those things, but I don't think its possible any more
Deleted User31-Mar-19 05:10 AM
am not technically able to speak about that but after digging a bit in cpp files to see how you created the cleanwallettx
I mean it s just a reuse of made functions on core, maybe those functions could give us a way to speed it
deepest point in code...
blackjok3r31-Mar-19 05:12 AM
Yes for sure. Cutting out any process any RPC calls and off loading it directly to the database inside komodod is the only thing really left to catch Shossain... I don't know how he does it, but I'll catch him one day
Deleted User31-Mar-19 05:12 AM
but am just a noob and can t pretend to understand this ^^
hmmm
I believe there is a way :D
blackjok3r31-Mar-19 05:12 AM
Damn don't have SSH key for testnet here going yo have to wait until i get home
Deleted User31-Mar-19 05:13 AM
replacing spaces with n betrayed you typing on phone :p
1
blackjok3r31-Mar-19 05:13 AM
Lol yes... Actually managed to login haha
Deleted User31-Mar-19 05:15 AM
btw... after 1 year of bash scripting and seeing how py improved the stability and error handling of lazynode... I couldn t recomment people smthg else than to go directly on py for their experiments :p(edited)
like a dinausor I didn t want it, but youngsters were right
blackjok3r31-Mar-19 05:17 AM
Yeah python is good, will probably use either your stuff or write mine in it before next season.. My node is a horrible mess from all my experiments and bug fixing this season
Deleted User31-Mar-19 05:17 AM
someday I ll go allin on docker and call patchkez and emmanuel and say sorry, you were right :p
1
1
blackjok3r31-Mar-19 05:18 AM
Not so sure on that myself. It depends what using it for hey
blackjok3r31-Mar-19 05:26 AM
lol wow my node wasnt dead at all. It was still notarizing, but I restarted it so it can add everyone... That could be reason for ppls low counts as my node wouldnt recognise their pubeys.
7th spot
Should probably set up a split script and see if I can catch SHossain(edited)
utxo counts on my node is now 25 KMD max, 10 AC max. 2700 will kill your node dead. My main net one had 1400 for the first few months last season and simply didnt work.
SHossain31-Mar-19 08:55 AM
@Alright my connection is 1gbps.
peers:
KMD = 58
BEER =31
PIZZA = 29
blackjok3r31-Mar-19 09:40 AM
You have some magical internet god on your side then
2
TonyL31-Mar-19 09:41 AM
I bet that aliens from other planet helps
SHossain31-Mar-19 09:41 AM
yes
TonyL31-Mar-19 09:41 AM
Maybe some 3000 year tech they shared with SHossain
@Strob what's the uptime on your node if you don't mind me asking
Strob31-Mar-19 01:27 PM
@mrlynch My first notarization was mined on Mar 26, 2019 12:26:30 PM(edited)
Why?(edited)
mrlynch31-Mar-19 01:28 PM
thanks!
i'm trying to catch up
Strob31-Mar-19 01:28 PM
Your first was Mar 30, 2019 2:28:44 AM
Hurry up I'm adding my nvme raid today!
1
1
You went to manual split finally?
mrlynch31-Mar-19 01:32 PM
i've got my vps on a ssd
yes.. manual split in cron
a lot quicker
Strob31-Mar-19 01:33 PM
Sadly I discovered I can't boot on m nvme (pcie softraid)so I will try putting the home and swap on the nvme and the system core on ssd for booting(edited)
then I will try manual split to see the difference
mrlynch31-Mar-19 01:34 PM
if you mount the nvme in /home make sure you backup your home
as in if you mount it as /home
Strob31-Mar-19 01:37 PM
I will do a fresh install on another server then swap the server I think
Then I will try manual splitting
with cron
blackjok3r31-Mar-19 01:38 PM
wouldnt worry about RAID myself... I used RAID1 and regret it a bit, blockchain means you can just resync incase of data lost
Strob31-Mar-19 01:38 PM
I will use raid 0
for speed
blackjok3r31-Mar-19 01:38 PM
what does that give you? Does is actually give you more speed ?
Strob31-Mar-19 01:38 PM
more speed and more space
blackjok3r31-Mar-19 01:39 PM
not sure it does give mroe speed. The random reads are what your after not hella fast sequential reads
I ran my ndoe 100% in a RAM disk fore quite some time, didnt really help over standard SSD
worth testing I guess though. testnet is good
Strob31-Mar-19 01:41 PM
Not sure either, yes it'S faster only in sequencial write.
blackjok3r31-Mar-19 01:41 PM
read is all that matters, random reads.
mrlynch31-Mar-19 01:41 PM
software raid?
or no raid?
and regular backups?
blackjok3r31-Mar-19 01:42 PM
Im using RAID1 hardware. BUt I think just a straigh disk would be better really... Though I cant really test that now
SHossain is using Optanes I think... That seems to give a pretty good advantage... but man, it would take years to pay them off lol


Search LINCS cells by generic names, synonyms, diseases and explore across LINCS datasets, perturbagens and annotations.
Example search for mcf7 or explore more details through the Cell Landing Pages "MCF7". A comprehensive list of generic names and synonyms are also stored and can be used as search terms.
Search can be performed by a wide set of annotations such as organs Breast, Diseases Melanoma, Assays Fluorescence imaging cell growth inhibition assay and Small Molecules Enzastaurin.
Search also facilitates AND , OR , NOT queries. Some examples 1) find cells that are used in P100 and L1000 assays. 2) Find cells that are derived from Lung.


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Bullet Collision Detection & Physics Library
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do not change those serialization structures, it requires an updated sBulletDNAstr/sBulletDNAstr64 More...
#include <btConvexInternalShape.h>
Public Attributes | |
| btCollisionShapeData | m_collisionShapeData |
| btVector3FloatData | m_localScaling |
| btVector3FloatData | m_implicitShapeDimensions |
| float | m_collisionMargin |
| int | m_padding |
do not change those serialization structures, it requires an updated sBulletDNAstr/sBulletDNAstr64
Definition at line 135 of file btConvexInternalShape.h.
| float btConvexInternalShapeData::m_collisionMargin |
Definition at line 143 of file btConvexInternalShape.h.
| btCollisionShapeData btConvexInternalShapeData::m_collisionShapeData |
Definition at line 137 of file btConvexInternalShape.h.
| btVector3FloatData btConvexInternalShapeData::m_implicitShapeDimensions |
Definition at line 141 of file btConvexInternalShape.h.
| btVector3FloatData btConvexInternalShapeData::m_localScaling |
Definition at line 139 of file btConvexInternalShape.h.
| int btConvexInternalShapeData::m_padding |
Definition at line 145 of file btConvexInternalShape.h.


getyourguide
Businessmodel of GetYourGuide
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Thirdly, GetYourGuide allows established travel agencies and companies to generate revenue through provision of affiliate marketing services.
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For its end-users GetYourGuide’s greatest value is that it enables travellers to book a range of activities and services from a single platform, before arriving at a destination. The Company’s various partnerships also enable users to access deals and offers that may otherwise not have been available.
The GetYourGuide platform is attractive to third-party suppliers as it enables them to advertise their products and services to a larger audience with no registration or listing cost. It also offers value to established affiliate partners which can generate revenue through marketing and integration.
Channels
GetYourGuide can be accessed via its desktop and mobile websites at www.getyourguide.com, as well as through its iOS and Google Play apps.
Customer Relationships
For customers GetYourGuide is a self-service platform. Users are able to make bookings with all available suppliers and in all available locations directly through www.getyourguide.com, with no interference from the Company itself.
The Company’s multilingual sales teams can be contacted via email and telephone, with separate phone numbers for customers in the UK, the US, Spain, France, Italy, Switzerland and Germany and an international number for all other users.
GetYourGuide’s relationships with suppliers and affiliates are more personalised. Each of its third-party suppliers receives assistance from a personal account manager. Affiliate partners also receive a bespoke service that requires direct interaction with the GetYourGuide sales and marketing team.
Key Activities
GetYourGuide develops and maintains an online platform through which tourists are able to book tours, activities, classes and events in multiple destinations worldwide.
It also manages partnerships with affiliate sellers and suppliers enabling operators to reach a larger audience with their offers and for travel agencies to generate revenue through commissions.
Key Partners
GetYourGuide maintains an affiliate partner network that comprises numerous travel agents, resellers, and leisure companies, through which GetYourGuide products are marketed. This includes high-profile companies such as Opodo, Lufthansa Holidays, Tui and STA Travel. The Company has a three-tiered partnership system.
Basic general affiliates receive a variety of tools and advertising materials, as well as access to the activity-tracking platform, while advanced partners, with which the Company has an established relationship, receiving greater commissions and access to the GetYourGuide API integrations tools. A third tier is reserved for selected partners that require more tailored solutions. Once a company is admitted to the partner program they are eligible for a 6% base commission rate. Travel agents immediately receive a 10% rate, but are not given access to the integration tools made available to other partners.
GetYourGuide also partners with numerous professional suppliers of local tourist services across a number of countries worldwide. This includes tour operators, local attractions, transport and activities providers, and sports and events organisers, which provide the Company’s catalogue of products and services. These partners are afforded access to translation, analytics and payment services tools provided by GetYourGuide.
Key Resources
GetYourGuide’s key resources are its technology infrastructure, including the platform itself and its servers, and its network of suppliers, without which the Company would have no products to sell.
Its personnel is also a key resource, in particular its sales and marketing teams, which manage the Company’s partnerships, and its engineering teams, which maintain and develop the Company’s proprietary software.
Cost Structure
GetYourGuide incurs significant costs as an employer of a large and multilingual workforce, including several regional sales and marketing teams.
This regional structure also leads to costs in operating several international offices, all of which accrue rental and utility costs.
GetYourGuide is also obliged to pay commissions on sales to its affiliate marketing partners.
Revenue Streams
It is free for suppliers to list their products and services on the GetYourGuide platform, however a commission is charged for every booking that is accepted by these suppliers. The size of this commission is agreed directly and on an individual basis between GetYourGuide and the supplier.


I started this project for a few reasons, but mainly to keep busy coding and learn a little bit more about web development. This blog uses Angular.js to create a single-page application. If you know anything about blogs or angular, feel free to contact me with questions or suggestions. I have no real interest in CSS or graphic design, so yes, this is a free template.
The second reason I started it was because after seeing the 2014 Godzilla film, I told a few people how it was not a good movie, but it was a pretty decent Godzilla movie. If you judge all the films of the franchise against each other, you'll find that there's certain themes, formats, and eccentricities that you might be more open to than a standalone film.
After re-watching "Godzilla Raids Again" on special effects master Eiji Tsubaraya's birthday in 2015, I realized how my perception of the film had changed since watching it in 1998. I thought it wasn't one of the better films, probably because I was a teenaged idiot, but it actually has a lot of fond moments. The blog is a way to catalogue what I enjoyed and disliked about each films after now having seen each one and having a better appreciation for filmmaking. When it is complete, you and I will have a resource to turn to when we want to throw on the perfect G-flick for any occasion!


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BeerTinder is an app that connects the user to their ideal beer. User will receive random images of beer, one by one, with a brief description and tasting notes about that beer. The user will decide to “swipe” right or left if they think the beer is good. If the user swipes right = “good” then the user will be prompt of where the location of the beer is and cost. The swiped right beers will be saved in the users profile. Users will also be able to search to for beer. Sketch of Final Product:
View ProjectwebScraper is a web app that lets users view and leave comments on the latest news. Using Mongoose and Cheerio to scrape news from another site. Whenever a user visits the site, the app scrapes stories from a news outlet from reddit and display it for the user. Each scraped article is saved to the application database.
View ProjectBurger! is a restaurant app that lets users input the names of burgers they'd like to eat. A burger logger with MySQL, Node, Express, Handlebars and a homemade ORM (yum!). Using the MVC design pattern; Node and MySQL to query and route data in the burger app. Handlebars generates the HTML.
View ProjectUsing Django's framework and Python to build a CRUD application that allows the user create, view, update, and delete useful code snippets. Think of it as the MVP for an app you'd use to keep track of idiomatic code samples in different languages. Each code snippet should allows the user to record a title; the language the snippet is in; the snippet itself; and a brief description.
View Project"FriendFinder" application is a full-stack site that will take in results from users' surveys, then compare their answers with those from other users. The app will then display the name and picture of the user with the best overall match. Used Express to handle routing.
View ProjectGifTastic is a web page that populates with gifs of the users choice. The GIPHY API is called and JavaScript and jQuery change the HTML of the site. When the user clicks on a button, the page grabs 10 gif images from the GIPHY API and place them on the page. This data is provided by the GIPHY API.
View ProjectHangman app runs in the browser, and dynamically updates HTML powered by JavaScript code. Game theme is beer brands. Used key events to listen for the letters that the player will type. Correct letters display on the page: Number of Guesses Remaining: (# of guesses remaining for the user). Letters Already Guessed: (Letters the user has guessed, displayed like L Z Y H). After the user wins/loses the game automatically choose another word and make the user play it.
View ProjectThis is a React-based rendition a New York Times Article Search application. This assignment requires React components, working with helper/util functions, and utilizes the React mounting lifecycle to query and display articles based on the user searches. Also using Node, Express and MongoDB so that users can save articles to read later.
View ProjectTrain scheduler application incorporates Firebase to host arrival and departure data. The app will retrieve and manipulate this information with Moment.js. This website was created to provide up-to-date information about various trains, namely their arrival times and how many minutes remain until they arrive at their station.
View ProjectThis Simpsons Trivia game uses JavaScript for the logic and jQuery to manipulate HTML.
View ProjectCrystal Collector is an interactive game for web browsers. The app dynamically updates the HTML pages using the jQuery library.
View Project


import torch
import pandas as pd
import altair as alt
from torchviz import make_dot
Let's go back to our regression example.
p = 5
n = 100
x = torch.randn(n, p) # predictors (n observations, p features)
y = torch.randn(n, 1) # outcomes (n observations)
loss_record = []
β = torch.randn(p+1,1)
β.requires_grad_()
for e in range(500):
ŷ = torch.matmul(x, β[1:]) + β[0]
L = torch.sum((y-ŷ)**2)/n
L.backward() # compute gradients
loss_record.append(L.item())
with torch.no_grad(): # take the gradient descent step
β -= 10e-5 * β.grad
β.grad.zero_()
Remember that the linear model is $\hat y =x\beta_{1:p} + \beta_0$. Let's say we want to add some complexity to this model by figuring out if there are some other predictors we can build (call them $z$) that are a transformation of the predictors $x$ that are better for predicting $y$ in a linear model than $x$ is. To do that, we'll first let $z = g(xW + b)$ with $W$ being a $p \times h$ matrix and $b$ a $1 \times h$ matrix so each row of $z$ ends up with $h$ columns and $g$ is an element-wise ReLU function. The purpose of $W$ and $b$ is to make $h$ predictors out of our original $p$ using a linear transformation. The purpose of $g$ is to add complexity to the model.
Let's dig into this, one step at a time. First we'll investigate the transformation $xW$. As we said, we're trying to build new features from our old features, so let's look at just one of these new features, $z_1$:
Graphically, this looks exactly like logistic regression, but with the $\sigma()$ replaced by $g()$. We also renamed the parameters and output, but that's just cosmetic, not consequential.
Since we'll be using this structure again and again we'll just abbreviate it as follows:
Notice that it's implicit here that $w_1$ is a vector of parameters that is being dot-multiplied by the vector $x$, $b_1$ is being added, and then the result is being transformed by some function $g$ (omitted in the drawing).
Now let's zoom back out to our full matrix equation $z = g(xW+b)$. One way to look at this is that we're doing $h$ "$g$-linear" regressions (assuming we have $W$ and $b$) and getting their predictions. The matrix equation $z = g(xW+b)$ expresses all of these "regressions" simultaneously.
from torch.nn.functional import relu as g
h = 10
W = torch.randn(5, h).requires_grad_()
b = torch.randn(1, h).requires_grad_()
z = g(torch.matmul(x,W) + b)
z.shape
As you see, we're getting 100 observations of 10 "features". Let's look at one of them:
z[0:10,1]
We could also have done them one-at-a-time:
w1 = W[:,1]
b1 = b[:,1]
z1 = g(torch.matmul(x,w1) + b1)
z1[0:10]
Same thing.
The graphical representation of this is:
One way to think about $z$ is as a different "representation" of what's in $x$. If we're predicting whether an image is a cat or a dog based on the pixel values $x$, then $z$ will perhaps learn to encode something like the number of red pixels, whether or not there are triangles in the image, etc. Ultimately the two representations contain the same information since $z$ is just a transformation of $x$, but somehow we're looking at that information in a different way, or highlighting different aspects of it.
Now our final model is a linear model done on the new predictors $z$. That model is $\hat y =z\alpha_{1:h} + \alpha_0$. I'm calling the coefficient vector $\alpha$ because now this needs $h$ coefficients (plus 1 intercept) for each of the $h$ new features, whereas $\beta$ was a $p$-length vector. So, in full, our model is now $\hat y = g(xW+b)\alpha_{1:h} + \alpha_0$.
α = torch.randn(h+1,1).requires_grad_()
ŷ = torch.matmul(z,α[1:]) + α[0]
Note that without the ReLU (or some nonlinear function) between $x$ and $z$, there is always a vector $\beta$ such that $x\beta_{1:p} + \beta_0 = (xW+b)\alpha_{1:h} + \alpha_0$ for every $W$, $b$, and $\alpha$. In effect, adding the transformation $W$ doesn't actually add anything to the model- we're expanding the number of parameters to fit without actually expanding the expressivity of the model because there is always a model with fewer parameters that perfectly captures the predictions of the over-parametrized model. That is why the nonlinearity $g$ is necessary.
EXERCISE:
Let's say we have
$$ W = \left[ \begin{array}{ccc} 1 & 1 & 1 \\ 1 & 2 & 3 \end{array} \right] \quad b = \left[ \begin{array}{ccc} 0 & 0 & 0 \end{array} \right] \\ \alpha_{1:3} = \left[ \begin{array}{c} 1 \\ -1 \\ 0 \end{array} \right] \quad \alpha_0 = 0 \notag $$Find $\beta$ so that $x\beta_{1:2} + \beta_0 = (xW+b)\alpha_{1:3} + \alpha_0$ no matter what $x$ is.
Finally, we can compute our MSE loss:
L = torch.sum((y-ŷ)**2)/n
L
make_dot(L, {'W':W, 'b':b, 'α':α})
Now $L$ depends both on what $W$ and $b$ are and on what $\alpha$ is. In other words, both of these sets of coefficients are parameters of the model that we can tweak and optimize. $W$ and $b$ determine what kinds of "new predictors" we get out $x$, while $\alpha$ puts those new features together into the final prediction. What's amazing is that we can fit all of these parameters simultaneously using gradient descent: just update both sets of parameters using the negative gradient at each step. Note that this model has $h\times (p+1) + h+1$ total parameters (the values of $W_{ij}$, $b_j$, and $\alpha_j$) to be fit, whereas our original linear model only had $p+1$ parameters.
Furthermore, this poses no problem for pytorch:
L.backward()
α.grad
W.grad
b.grad
As before, now that we have gone through this once, we can put it all together into a loop:
p = 5
n = 100
x = torch.randn(n, p) # predictors (n observations, p features)
y = torch.randn(n, 1) # outcomes (n observations)
h = 10
W = torch.randn(5, h).requires_grad_()
b = torch.randn(1, h).requires_grad_()
α = torch.randn(h+1, 1).requires_grad_()
loss_record = []
for e in range(500):
z = g(torch.matmul(x,W)+b)
ŷ = torch.matmul(z,α[1:]) + α[0]
L = torch.sum((y-ŷ)**2)/n
L.backward() # compute gradients (in this case δL/δα, δL/δW)
loss_record.append(L.item())
with torch.no_grad(): # take the gradient descent step
α -= 10e-3 * α.grad
α.grad.zero_()
W -= 10e-3 * W.grad
W.grad.zero_()
b -= 10e-3 * b.grad
b.grad.zero_()
loss_df = pd.DataFrame({'i':range(500), 'loss':loss_record})
alt.Chart(loss_df, height=100).mark_line().encode(x='i', y='loss')
And here are our predictions for a new $x$:
new_x = torch.randn(10, 5) # 10 new observations
z = g(torch.matmul(new_x,W)+b)
torch.matmul(z,α[1:])+α[0]
EXERCISE
Apply this strategy to our logistic regression to make a more complex classification model. In other words, implement the machine learning algorithm with these characteristics:
- Model: $\hat p_i = \sigma(g(x_i W+b))\alpha_{1:h} + \alpha_0)$, with $\sigma$, $g$, $W$, $b$, and $\alpha$ as previously described
- Loss: log-loss
- Search Algorithm: gradient descent
We just developed a machine learning method out of these three components:
- "linear-relu" $\times$ 10 + linear model (model)
- MSE loss (loss)
- gradient descent (search algorithm)
This is a neural network. The 10 "new predictors" are together a hidden layer (hidden because we don't see them at the end of the day, just the output $\hat y$) and each of them individually is called a hidden unit. The "layer" terminology comes from the graphical representation, where the successive hidden representations look like successive layers that the information passes through while being transformed from predictors to prediction:
So, in the language of neural networks or deep learning, what we've built is a network with input of size 5, one linear hidden layer of size 10, ReLU activation function, and a linear output layer of size 1.
While we could define ever-more complex interactions of parameters and data, it turns out that simply stacking linear regressions with nonlinear activation functions on top of each other (e.g. "linear-relu" regressions) gets us enough complexity to model practically any relationship we want. Let's have a look at an example where $y = x^2$ in reality:
p = 1
n = 100
x = torch.randn(n, p) # predictors (n observations, p features)
y = (x[:,0]**2).unsqueeze(dim=1) # outcomes (n observations)
data = pd.DataFrame({'y':y[:,0], 'x1':x[:,0]})
alt.Chart(data).mark_point().encode(x='x1', y='y')
If we fit a linear model, we don't do very well:
loss_record = []
β = torch.randn(p+1,1).requires_grad_()
b = torch.randn(1).requires_grad_()
for e in range(500):
ŷ = torch.matmul(x, β[1:]) + β[0] # ŷ = xβ + b(calculate predictions)
L = torch.sum((y-ŷ)**2)/n # L = Σ(yᵢ-ŷᵢ)² (use predictions to calculate loss)
L.backward() # compute gradients (in this case δL/δβ, δL/δW)
loss_record.append(L.item())
with torch.no_grad(): # take the gradient descent step
β -= 10e-2 * β.grad
β.grad.zero_()
The loss does go down, but overall it's still really high:
loss_df = pd.DataFrame({'i':range(500), 'loss':loss_record})
alt.Chart(loss_df, height=100).mark_line().encode(x='i', y='loss')
And when we plot the model predictions against reality we see how bad it is:
preds = pd.DataFrame({'ŷ':ŷ.detach().numpy()[:,0], 'y':y[:,0], 'x1':x[:,0]})
(alt.Chart(preds).mark_line().encode(x='x1', y='ŷ') +
alt.Chart(preds).mark_point().encode(x='x1', y='y'))
However, we can introduce a hidden layer (of size 10):
h = 10
W = torch.randn(p, h).requires_grad_()
b = torch.randn(1, h).requires_grad_()
α = torch.randn(h+1, 1).requires_grad_()
loss_record = []
ϵ = 10e-3
for e in range(500):
z = g(torch.matmul(x,W) + b)
ŷ = torch.matmul(z,α[1:]) + α[0]
L = torch.sum((y-ŷ)**2)/n # L = Σ(yᵢ-ŷᵢ)² (use predictions to calculate loss)
L.backward() # compute gradients (in this case δL/δα, δL/δW)
loss_record.append(L.item())
with torch.no_grad(): # take the gradient descent step
α -= ϵ * α.grad
α.grad.zero_()
W -= ϵ * W.grad
W.grad.zero_()
b -= ϵ * b.grad
b.grad.zero_()
loss_df = pd.DataFrame({'i':range(500), 'loss':loss_record})
alt.Chart(loss_df, height=100).mark_line().encode(x='i', y='loss')
preds = pd.DataFrame({'ŷ':ŷ.detach().numpy()[:,0], 'y':y[:,0], 'x1':x[:,0]})
(alt.Chart(preds).mark_line().encode(x='x1', y='ŷ') +
alt.Chart(preds).mark_point().encode(x='x1', y='y'))
And now the loss is lower and the fit is clearly much improved.
EXERCISE
Plot $z_1$ vs $x$. Repeat for each $z_j$. What do you see?
Now make plots of each $z_j$ vs. $y$. Is the relationship between each $z_j$ and $y$ closer to linear than the relationship between $x$ and $y$? Does this explain why the linear regression $\hat y = \alpha_{1:h} z + \alpha_0$ produces a better fit than the linear regression $\hat y = \beta_{1:p} x + \beta_0$?
EXERCISE
Change the value of $h$ in the code above so that hidden layer has size 3 instead of 10. How does that change the fit? What if you set $h=100$?
EXERCISE
When fitting linear models, it's a common practice to break continuous variables up into categories. For instance, let's say we're predicting someone's weight based on their age. Instead of modeling $\text{weight} = \beta_0 + \beta_1 \times \text{age}$, we could break age up into 10 categories (say: age < 10 years, 10 < age < 20,... 90 < age) and fit our model using those categories as predictors instead. Basically, we have a set of regions $A = \{(0,10],(10,20],\dots (90,\infty]\}$ and our new variables are $z_j = I_{A_j}(x)$ where $I_{A_j}$ is the indicator function that returns 1 if $x \in A_j$ and 0 otherwise. Now we're modeling $\text{weight} = \alpha_0 + \alpha_1 z_1 + \dots \alpha_{10} z_{10}$ instead of $\text{weight} = \beta_0 + \beta_1 \times \text{age}$.
Considering a population including both adults and children, do you think the relationship between age and weight will be linear? What will that mean if we try to fit the model $\text{weight} = \beta_0 + \beta_1 \times \text{age}$?
How is this problem addressed by transforming $x$ into 10 new predictors $z_j$ using the indicator functions? What is the relationship between any $z_j$ and $y$?
How does this relate to our neural networks?
We saw how we can get a nonlinear fit out of stacking linear models and nonlinear activation functions together.


{% extends 'layouts/main.html' %} {% block body %} {# We need to submit the form with Javascript. We can't login automatically otherwise email scanners visit the page and invalidate the login page. Doing it with Javascript prevents the user from needing to do an extra click #}


[gecode-users] New Constraints and theirs Propagatores
Christian Schulte
cschulte at kth.se
Tue Jun 12 08:56:01 CEST 2018
Check MPG, all described there. Christian
--
Christian Schulte, https://chschulte.github.io/
Professor of Computer Science, KTH, cschulte at kth.se<mailto:cschulte at kth.se>
Expert Researcher, RISE SICS, christian.schulte at ri.se<mailto:christian.schulte at ri.se>
From: users-bounces at gecode.org <users-bounces at gecode.org> On Behalf Of Bouchene Sabrina
Sent: Monday, June 11, 2018 15:11
To: users at gecode.org
Subject: [gecode-users] New Constraints and theirs Propagatores
Using Gecode, how can we make a predifined constraint such as the constraints: rel, distinct, ....and how to integrate the propagation algorithm that we define for constraints on Gecode? can you give me the steps and an illustrative example eg for constraint case: distinct.
Thank you.
Best regards.
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Git and Github
Git is a popular VCS that is used by quite a few developers, and is very useful for open source projects. Git is software we download on our computers that allows us to make “save points” for our project. It also allows copies to be made so that others can modify the project too. Then they can all be merged into a whole. To make this easier it is a good idea to use a someone else to host the project. And that is where GitHub comes in.
That in a very broad way is what I learned in camp this week. Plus the code to make it work, and quite a bit about being a responsible programmer. Thanks for your time.


Rummage is designed to be easy to pick up. Its interface consists of three tabs: Search, Files, and Content. In the Search tab, a user specifies where they want to search, what they want to search for, and optionally what they want to replace it with. Search features can be tweaked with various options. The files that get searched can also be narrowed with patterns and filters.
Rummage uses the default regular expression library (Re) that comes with Python. It also optionally works with the 3rd party Regex library (if installed).
As matches are found, general info about the matches will be displayed in the Files and Content tabs. You can double click files to open them in your favorite editor (see Editor Preferences to configure Rummage for your editor).
Rummage also comes with a simple regular expression tester to test out patterns. It also provides a feature where patterns can be saved for later and/or frequent use. You can even create chains that will apply a series of saved searches.
Once Rummage is installed, you can run it from the command line (assuming your Python scripts/bin folder is in your system path):
rummage
If you have multiple Python versions installed, you can call Rummage for that specific Python version by appending the major and minor Python version to the end:
rummage3.6
In some environments, it may make sense to run Rummage with pythonw
which is mainly for launching GUI scripts
(pythonw
is not available on Linux). In some environments, it may be required (see
Running in Anaconda).
pythonw -m rummage
Search and replaces are configured in the Search tab. The search tab can essentially be broken up into two sections: text search configuration and file search configuration.
The first part of the Search tab contains mostly text search inputs, with the exception of the very first control, which is used to configure where to search. The second text box is used to specify what we are searching for in the content of each file. The last text box specified what we want to replace the found text with. Each text box retains a limited history of recent inputs that can be accessed via the drop down control to the right.
The replace text box is only needed if you are performing a replace. The search input can also be omitted, and if so, Rummage will simply return files that match the provided file patterns (covered in Configuring File Search).
Below the text boxes is a collapsible panel that contains the text search options. The options consist of various checkboxes and controls that enable/disable search and replace features. The available features will vary depending on which regular expression engine you are using. Each feature is documented in Search Options.

The bottom part of the search tab focuses on controlling which files get searched. Various checkboxes and inputs are available that can narrow the actual files that get searched. You can filter out hidden files, symlinks, files of specific sizes, or creation/modification dates.
You can also restrict which files get searched by providing wild card patterns (or regular expression if preferred). By default, the patterns are applied to the base file or folder name. See File Patterns to learn more about accepted wild card pattern syntax and how to configure optional file pattern features.
Hidden Files
Rummage assumes dot files as hidden on all systems. Additionally, on Windows and macOS, it will also look at a file's filesystem attributes to determine if the system is potentially hiding the file as well.
New 4.4.0
Added symlink following via the Follow symlinks toggle.
Once a search or replace is initiated, the results will begin to appear in the Files and Content tabs. You can then double click a file to open it in your editor, or right click them to bring up a context menu with additional options.
Column Options
You can hide/show columns by right clicking the list header to get a special context menu. You can then deselect or select the the column(s) you wish to hide/show respectively. You can also reorder the columns if desired.
Rummage comes with a simple regular expression tester. It has a simple text box to place content to search, and another text box that will show the final results after the find and replace is applied. Below those text boxes, there are two text input boxes for the find and replace patterns. Lastly, all search and replace flag options are found under the pattern input boxes.
To use the tester, simply enter the content to search, set your desired options, and input your find and replace pattern. As you change your pattern or options, matches will be updated and highlighted, and the result box will be updated with any replacements.
When you are satisfied with your result, click the Use
button, and your pattern and settings will be populated in the
main window.
Regular expressions can be very complex, and sometimes you might want to save them for future use.
When you have a pattern configured that you want to save, simply click the Save Search
button and a dialog will pop up
asking you to name the search. When done, click the Save
button on the dialog and your search patterns and options
will be saved.
You'll notice that there are two input boxes. The first requires a unique name (only word characters, underscores, and hyphens are allowed). The second is an optional comment in case you wish to elaborate on what the pattern is for.
Underneath the inputs will be the actual search settings being saved.
To load a pattern that was saved previously, click the Load Search
button. You will be presented with a dialog
showing all your saved searches. Highlight the pattern you want to load and click the Load
button. Your pattern and
options will be populated in the main dialog.
If you wish to edit the name or comment of a search, you can double click the entry or click the "Edit" button.
There are times you may have a task that requires you to do multiple find and replaces that are all related, but are too difficult to represent as a single find and replace. This is where search chains can be helpful.
Search chains are essentially a sequence of multiple saved search and replace patterns.
You can create a search chain by clicking the Search Chains
button which will bring up the search chain manager.
Here you can create or delete search chains.
To use search chains, you must put Rummage in "search chain" mode by selecting the check box named Use search chains
in the main window. When "search chain" mode is enabled, all controls that don't apply to search chains will be
disabled, and the search box will be replaced with a drop down for selecting existing chains you've already created.
When a search is performed, Rummage will iterate over each file with all the saved searches in the chain.
Regular expressions are great, but sometimes regular expressions aren't enough. If you are dealing with a replace task that requires logic that cannot be represented in a simple replace pattern, you can create a "replace plugin".
Replace plugins are written in Python and are loaded by first selecting the Use replace plugin
check box in the main
dialog.
Then the main dialog's Replace with
text box will become the Replace plugin
text box with an associated file picker.
Here you can point to your replace plugin file.
Replace plugins aren't meant to be full, complex modules that import lots of other relative files. They are meant to be a single, compact script, but inside that script, you can import anything that is already installed in your Python environment.
Replace plugins should contain two things:
rummage.lib.rumcore.ReplacePlugin
class.get_replace
that returns your class.The plugin class is fairly straight forward and is shown below.
class ReplacePlugin(object):
"""Rummage replace plugin."""
def __init__(self, file_info, flags):
"""Initialize."""
self.file_info = file_info
self.flags = flags
self.on_init()
def on_init(self):
"""Override this function to add initialization setup."""
def get_flags(self):
"""Get flags."""
return self.flags
def get_file_name(self):
"""Get file name."""
return self.file_info.name
def is_binary(self):
"""Is a binary search."""
return self.file_info.encoding.encode == 'bin'
def is_literal(self):
"""Is a literal search."""
return self.flags & LITERAL
def replace(self, m):
"""Make replacement."""
return m.group(0)
ReplacePlugin
's replace
function will receive the parameter m
which is either a regex
or re
match object
(depending on what regular expression engine is selected). The return value must be either a Unicode string or byte
string (for binary files).
The ReplacePlugin
's file_info
property is a named tuple providing information about the current file such as name,
size, creation date, etc.
class FileInfoRecord(namedtuple('FileInfoRecord', ['id', 'name', 'size', 'modified', 'created', 'encoding'])):
"""A record for tracking file info."""
The ReplacePlugin
's flags
property contains only Rummage search related flags (the flags are abstracted at this
level and are converted to the appropriate regular expression flags later). They can also be accessed from
rummage.lib.rumcore
. The flags are shown below.
# Common regular expression flags (re|regex)
IGNORECASE = 0x1 # (?i)
DOTALL = 0x2 # (?s)
MULTILINE = 0x4 # (?m)
UNICODE = 0x8 # (?u)
# Regex module flags
ASCII = 0x10 # (?a)
FULLCASE = 0x20 # (?f)
WORD = 0x40 # (?w)
BESTMATCH = 0x80 # (?b)
ENHANCEMATCH = 0x100 # (?e)
REVERSE = 0x200 # (?r)
VERSION0 = 0x400 # (?V0)
VERSION1 = 0x800 # (?V1)
FORMATREPLACE = 0x1000 # Use {1} for groups in replace
POSIX = 0x2000 # (?p)
# Rumcore search related flags
LITERAL = 0x10000 # Literal search
Example Plugin
In the example below, we have a replace plugin that replaces the search result with the name of the file. It is assumed this is not a binary replace, so a Unicode string is returned.
from __future__ import unicode_literals
from rummage.lib import rumcore
import os
class TestReplace(rumcore.ReplacePlugin):
"""Replace object."""
def replace(self, m):
"""Replace method."""
name = os.path.basename(self.get_file_name())
return name
def get_replace():
"""Get the replace object."""
return TestReplace
Rummage allows the exporting of the results to either CSV or HTML. Simply select File→Export and pick either CSV or HTML. The HTML output will be styled similar to the GUI interface with the results in tables with sortable columns.
Large Result Sets
Really, really large sets of results will probably be best suited for CSV as a browser may have a hard time loading the entire data set at once.


{% extends "layout.html" %} {% block meta %} {% endblock %} {% block body %} Back to project
This isn't implemented yet.
Once it is you'll be able to change the name of the project as well as the people involved


Learning Objectives
Students completing DMD 100: Digital Multimedia Design Foundations will be able to do the following:
- Objectives related to developing Process and Procedural Skills (Designing)
- Integrate skills, knowledge, and creative practices in digital media in exploratory exercises that draw on content and methods from information sciences and technology, communications, and visual arts and design.
- Use digital technologies in multimedia projects through exploration, expression, and communication that engage a multiplicity of ideas, forms, actions, and settings.
- Gather and analyze information from research sources to identify and apply relevant content that can aid concept development, improve design processes and products.
- Objectives related to developing Critical Skills (Critiquing)
- Adapt an interdisciplinary vocabulary of terms, methods, and processes to review applying information and critique ideas in individual contexts and collaborative settings
- Demonstrate communication skills by creating and presenting ideas, concepts, and designs in written, verbal, and visual forms.
- Implement new ideas and develop a diverse array of options for problem solving in response to critical review and the iterative design process for improving work.
- Objectives related to developing Attitudes and Awareness (Assessing context)
- Recognize how influences, constraints, personal interests, ethics, and resources impact design decisions and creative options.


RT @marick: "The goal isn’t getting from A to B in the simplest way. The goal is to make the code at B look as simple as possible." http ...
5/26/2011, 11:56:10 AM
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5/26/2011, 11:56:10 AM
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If you haven't already, download the QueryList.xlsx file, configure your list of queries, and copy-paste below. See sample screenshot.
DBMS: MySQL MS SQL Server Server: Port:
Database: Username: Password:
Execute


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nice page jeremy! this is a really nice page, e-mail me and we'll talk then okay? bye!


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QUAD WRANGLE
Copyright (c) 2001 Keith Carter
The game is played on a 7x7 board, with the following setup:
- MOVES - A move consists of either:
- A growth, i.e., a drop of a stone on an empty adjacent (orthogonal or diagonal) empty cell;
- An orthogonal or diagonal slide of a stone already on board (like a chess Queen) to an empty cell;
- A drop of a stone on any empty cell.
- Any opponents adjacent stones to the new piece (either through growth or slide) are flipped to the moving player's color. Notice that the third option (simple drop) do not flip the color of adjacent stones.
- GOAL - Wins the player with most stones when the board is filled.
An example Blue's move. The 3 upper right empty cells cannot be occupied by Red, and all surroundings blue stones are safe. Blue grows to cell [1] and wins. Red can only reply into cell [2] and then blue's growth to b2, the game does not have any growth or slide left.
This game is based on Ataxx concepts. Carter also created another variant called Blobs, where the slide is replaced by the Ataxx jump, but the drop option still exists.


FROM THE DESK OF: MR GEORGE KABORE AUDITING AND ACCOUNTING SECTION, AFRICAN DEVLOPMENT BANK ( A.D.B ) OUAGADOUGOU BURKINA-FASO,WEST AFRICA. ATTENTION PLEASE I AM MR GEORGE KABORE THE DIRECTOR IN CHARGE OF AUDITING AND ACCOUNTING SECTION OF AFRICAN DEVELOPMENT BANK (A.D.B) OUAGADOUGOU BURKINA-FASO WEST AFRICA. WITH DUE RESPECT AND REGARD. I HAVE DECIDED TO CONTACT YOU ON A BUSINESS TRANSACTION THAT WILL BE VERY BENEFICIAL TO BOTH OF US AT THE END OF THE TRANSACTION. DURING OUR INVESTIGATION AND AUDITING IN THIS BANK, MY DEPARTMENT CAME ACROSS A VERY HUGE SUM OF MONEY BELONGING TO A DECEASED PERSON WHO DIED IN A PLANE CRASH AND THE FUND HAS BEEN DORMANT IN HIS ACCOUNT WITH THIS BANK WITHOUT ANY CLAIM OF THE FUND IN OUR CUSTOMER EITHER FROM HIS FAMILY OR RELATION BEFORE OUR DISCOVERY TO THIS DEVELOPMENT. THE SAID AMOUNT WAS (U.S 22.85M DOLLARS). AS IT MAY INTEREST YOU TO KNOW, I GOT YOUR IMPRESSIVE INFORMATION THROUGH INTERNET DIRECTORY. MEANWHILE ALL THE WHOLE ARRANGEMENT HAS BEEN IN PLACE FOR THE CLAIM AS.  IN FACT I COULD HAVE DONE THIS DEAL ALONE BUT BECAUSE OF MY POSITION IN THIS COUNTRY AS A CIVIL SERVANT (A BANKER), WE ARE NOT ALLOWED TO OPERATE A FOREIGN ACCOUNT AND WOULD EVENTUALLY RAISE AN EYE BROW ON MY SIDE DURING THE TIME OF TRANSFER BECAUSE I WORK IN THIS BANK. THIS IS THE ACTUAL REASON WHY IT WILL REQUIRE A SECOND PARTY OR FELLOW WHO WILL FORWARD CLAIMS AS THE NEXT OF KIN WITH AFFIDAVIT OF TRUST OF OATH TO THE BANK AND ALSO PRESENT A FOREIGN ACCOUNT WHERE HE WILL NEED THE MONEY TO BE RE-TRANSFERRED INTO ON HIS REQUEST AS IT MAY BE AFTER DUE VERIFICATION AND CLARIFICATION BY THE CORRESPONDENT BRANCH OF THE BANK WHERE THE WHOLE MONEY WILL BE REMITTED FROM TO YOUR OWN DESIGNATION BANK ACCOUNT.     I LOOK FORWARD TO RECEIVE YOUR PHONE AND FAX NUMBERS FOR EASY COMMUNICATION, YOURS FAITHFULLY, MR GEORGE KABORE MOBILE: (00226 76086692 -------------------------------------------------------------------------------------------- Nueva Barra de Herramientas de OZÚ. Navega Más facilmente por Internet. ¡Descargatela gratis! http://barra.ozu.es/ -------------------------------------------------------------------------------------------- Correo enviado desde http://www.ozu.es


PPC - Achieve More Efficient Pay-Per Click (PPC) Marketing
PPC (pay-per click) search marketing software offers a means for accomplishing two core requirements for PPC optimization:
PPC Automation - While it would be impossible (and inadvisable!) to automate every aspect of pay-per-click advertising, automating away redundant, time-consuming tasks affords you a means of increasing productivity. Often, this means being able to produce an amount of work that would otherwise have been unachievable for you and your business.
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Automation And Your PPC Search Campaign
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The Harbor Estuary Program (HEP) brings the benefits of the Clean Water Act to the people who live, work, and recreate on our shared waterways. HEP provides a forum and pathways for its partners to develop shared goals and plans for action. By informing the community about the Estuary’s health, HEP provides transparent progress on water quality, recreational access, habitat restoration, and related ecosystem services. HEP connects people with volunteer opportunities, citizen science, scientific research, meetings and seminars, educational materials, and grant opportunities.
The Harbor Estuary Program (HEP) brings the benefits of the Clean Water Act to the people who live, work, and recreate on our shared waterways. HEP provides a forum and pathways for its partners to develop shared goals and plans for action. By informing the community about the Estuary’s health, HEP provides transparent progress on water quality, recreational access, habitat restoration, and related ecosystem services. HEP connects people with volunteer opportunities, citizen science, scientific research, meetings and seminars, educational materials, and grant opportunities.


California school shooter used 'kit gun': Authorities
wo fellow students were killed and several others were injured before Berhow, who had turned 16 that day, shot himself in the head. He died a day later
The teenager who gunned down two classmates at a California school last week before shooting himself used a "kit gun" that is untraceable, authorities said Thursday.
In a tweet, Los Angeles County Sheriff Alex Villanueva said the so-called "ghost gun" used by Nathaniel Berhow was assembled from parts that do not have serial numbers.
He said kit guns also are known as "80 per cent guns" - are sold at gun shows or can be purchased online, allowing owners to skirt California's gun laws.
Ghost guns "are sold as a kit, and you can legally buy it, assemble the weapon yourself and you have a gun that is not registered and no one knows that you have it, it's very dangerous," Villanueva told ABC7.
The sheriff added in a statement that it was unclear who assembled the .45-caliber pistol used by Berhow last Thursday during his rampage at Saugus High School, a short commute from Los Angeles.
Two fellow students were killed and several others were injured before Berhow, who had turned 16 that day, shot himself in the head. He died a day later.
Authorities are still trying to piece together what prompted the shooting, especially as Berhow had no history of troubling behaviour.
Villanueva said Berhow's father had six registered guns that were lawfully removed from the home and subsequently destroyed. A later search turned up the "kit gun."
The sheriff said investigators are looking at Berhow's electronic communication in the hope it will shed light on his motive.


Razorpay provides an end-to-end online payments solution. We accept and validate Internet payments via Credit Card, Debit Card, Net-Banking, UPI and popular Wallets from the end customers in real-time.


Home > Reference > Functions reference > Repeating functions
Repeating functions Repeating functions perform calculations on repeating fields. Click a function name for details. This function Returns Extend In a calculation involving both repeating and non-repeating fields, allows a value in a non-repeating field to be used with every repetition in a repeating field. GetRepetition The contents of the specified repetition of a repeating field. Last The last valid, non-blank value in the specified field. Related topics About functions About formulas


Keywords: mixin
, execute
, before
, after
, around
A mixin is a (possibly anonymous) action whose execution is coupled to some other named target action (possibly in a different module or object) such that any call to the target action is preceded or followed by a call to the mixin.
Ivy also often refers to a mixin as a "monitor", and as an object that only contains mixins for some other object as a "monitor object".
Each mixin is declared with a mode:
mixin X before Y
executes mixin actionX
before target actionY
mixin X after Y
executes mixin actionX
after target actionY
Mixins are invoked with the same arguments as their target actions. An after
mixin can also see the (named) return value of the target action.
Any precondition or postcondition of a mixin is added to the target action as a precondition or postcondition of the target.
Mixins may be declared with separate visibility qualifiers from implementations. It is typical to declare mixins with pre and post conditions for an action that is otherwise abstract in a specification
block, while placing the body of the action in the implementation
block.
Mixins are ubiquitous enough that they have several abbreviations and synonyms:
- The keyword
execute
is a synonym formixin
, soexecute X before Y
is a mixin declaration identical tomixin X before Y
- Actions that exist solely to be mixins may be declared anonymously and associated with a target in a single declaration:
before Y(<arguments>) { <statements> }
after Y(<arguments>) { <statements> }
- Since the arguments for an mixin must match those of their target action, the arguments list of an anonymous mixin may be omitted -- it will be implicitly identical to that of the target, with the same argument names:
before Y { <statements> }
after Y { <statements> }
As a further abbreviation, a pair of anonymous before/after mixins may be declared using
around X { <before-statements> ... <after-statements> }
where...
is a literal punctuation token consisting of three dots.Examples
Example: adding a precondition
action pre_connect(x:client,y:server) = {
require semaphore(y)
}
execute pre_connect before connect
This example adds a precondition to any call to some action connect(x:client,y:server)
. It is equivalent to declaring:
before connect(x:client,y:server) {
require semaphore(y)
}
Or even more tersely:
before connect {
require semaphore(y)
}
Example: adding a postcondition
action post_incr(inp:t) returns(out:t) = {
ensure inp < out
}
execute post_incr after incr
This example adds a postcondition to any call to some action incr(inp:t) returns (out:t)
. It is equivalent to declaring:
after incr(inp:t) returns(out:t) {
ensure inp < out
}
Or even more tersely:
after incr {
ensure inp < out
}


Internet 1.0.1
- HTML: language in which elements in the internet are written.
- Servers: computers specialized in storing websites and sending these information rather than displaying it to users directly
- Browser: front end that lets user experience websites written in html code
- HTTP: The protocol used to exchange information between different computers
Information is not displayed the way it is organized in the editor. A line break in the editor does not equal a line break on the displayed website. Such matters of "design" are solved by tags like the < br > Tag
Tags:
- Come in the form of < tag > content < /tag > or just plain < tag >, when there is no content to which the tag refers (= void tag)
- Can be inline (e.g. < br > to insert a line break, < b > bold, to make text bold < /b> , < em > to make text italic < /em >) or block (e.g. < p > content < /p > or < form > content < /form >
- Are not displayed on the webpage and are only aimed at the computer interpreting it to display the actual content in the way I as a programmer want it to
- Can have attributes; e.g. the < img >-Tag for pictures has the attribute of a web adress where the respective picture is located:
- Because computers are “stupid”, they only understand correct code and are not able to correct mistakes made by the programmer, e.g. a missing > at the end of a tag


I build this website as part of the challenge. I will design and develop the UI of different elements daily for the next 100 days. And I am going to showcase all my 100 days of work here.


We take pride in our work, and it shows. Every time you order a beverage from us, we guarantee that it will be an experience worth having. Whether it's our Heavy Workout or something as simple as a normal Cardio Excercise, you will be coming back for more.
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Girinagar, Bangalore-560085
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DMRC recruitment 2019: Pay scale at Rs 1 lakh, vacancies for Manager posts
DMRC recruitment 2019: For the posts of Manager, the candidates will get a monthly salary of Rs 90,200, while Rs 70,180 for the posts of Assistant Manager. The candidates can apply through the website- delhimetrorail.com
by Careers DeskDMRC recruitment 2019: The Delhi Metro Rail Corporation (DMRC) has invited applications from eligible candidates for recruitment for the posts of Manager. There are 12 vacancies for the posts of Manager, and Assistant Manager.
The interested candidates can apply through the official website- delhimetrorail.com.
The selected candidates for the posts of Manager will get a remuneration of Rs 90,200 per month, while for the posts of Assistant Manager, the candidates will be in the pay scale of Rs 70,180 per month.
DMRC recruitment 2019: Vacancies
Total vacant posts: 12
Name of the posts: Manager, Assistant Manager
Eligibility criteria:
Education qualifications:
The candidates will hold a degree or diploma in Electrical Engineering with minimum 60 per cent marks from a government recognised university or institute.
Experience: The candidates should have work experience in the field of maintenance, erection and testing.
Age Limit:
The upper age limit of the candidates should not cross 61 years. The candidates belong to reserved category will get an age relaxation of 1 year.
Pay scale:
Assistant Manager: The candidates will get a monthly salary of Rs 70,180
Manager: The candidates for the posts of Manager will get a remuneration of Rs 90,200 per month.
Selection process:
The candidates will be selected on the basis of Personal Interview (PI), physical ability, knowledge, skills.
The candidates can apply through the website- delhimetrorail.com.
For all latest Govt Jobs 2019, Railway Jobs, Bank Jobs, and SSC Jobs Notification log on to IndianExpress.com.  Stay Connected.


Preprocess Data¶
These are the functions that I used to preprocess that data that I had. It is assumed that the data directory specified by path is structured like this:
dataDir
|--class1
| |--data
| |--label
|--class2
|--data
|--label
The primary functions that should be used directly from this file are renameLabels and preprocessData. The rename labels function should be used to make the label and image names match, and then the preprocess function should be used to split the images. Be aware that these functions leave the original data intact and only add train, validate, and test directories so the images will effectively take up twice the size. This was intended to that the preprocessData function could be run again to give images of separate sizes. After running the appropriate functions, the data of interest will be structured like so:
dataDir
|--class1
|--subdir1
| |--data
| |--label
|--subdir2
|--data
|--label
Example¶
Here is an example of how the functions in this file should be used.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # defining variables
path = "../../../data/groundcover2016/"
classNames = ['wheat','maize','maizevariety','mungbean']
ignore = ["CE_NADIR_"]
replace = {".tif" : ".jpg"}
ignoreDirectories = ['train','validate','test']
size = 5000
shape = (256,256)
trainProportion = 0.8
validateProportion = 0.1
# Rename and proprocess labels
renameLabels(path,ignoreDirectories,ignore,replace)
preprocessData(path,classNames,size,shape,trainProportion,validateProportion)
|
-
segmentationPreprocessData.
makeSplitDirs
(path, classNames)[source]¶ Make directories to put data into.
Parameters: - path (String) – Where to start putting the directories
- classNames (List of Strings) – Class directories to put into each category
-
segmentationPreprocessData.
preprocessData
(path, classNames, subdirSize, imageShape, trainProp, valProp)[source]¶ Calls the other functions in the right order and feeds them the appropriate arguments. Assumes that renameLabels has already been called or that it doesn’t need to be called.
Parameters: - path (String) – Location of the base directory where the data is located.
- classNames (List of Strings) – Names of classes in the that need preprocessing
- subdirSize (int) – Number of images and labels to put into each sub-directory
- imageShape (tuple of int) – Size to split images into
- trainProp (float [0->1]) – Proportion of data to put into training directory
- valProp (float [0->1]) – Proportion of data to put into validation directory
-
segmentationPreprocessData.
randomSplit
(l, propTrain, propValidate)[source]¶ Create list of indexes to split the data into training, validation, and testing sets.
Parameters: - l (int) – length of the list to
- propTrain (float [0->1]) – proportion of data that should be training data
- propValidate (float [0->1]) – proportion of data that should be validation data
Returns: - trainInd (List of int) – Indices to use for training data
- valInd (List of int) – Indices to use for validation data
- testInd (List of int) – Indices to use for testing data
-
segmentationPreprocessData.
renameLabels
(dataPath, ignoreDirs, ignore, replace)[source]¶ Goes through the data directories and renames labels so that they match the image names.
Parameters: - dataPath (String) – Location to the base directory of the data
- ignoreDirs (List of Strings) – Directories in the base directory to ignore
- ignore (List of Strings) – List of patterns to ignore
- replace (Dictionary of Strings) – Patterns to replace and what to replace them with
-
segmentationPreprocessData.
splitImageMP
(params)[source]¶ Splits images into smaller pieces.
Splits and renames the image label pairs of data into pieces of the specified size. Image and label pairs will be placed into sub-directories of the specified size so that the directories do not get too large to deal with. Removes the full size image from the directory when it is done splitting it.
Parameters: - path (String) – Location of data
- shape (tuple of ints) – Size that the images should be split into
- whichDir (String) – Which directory in the path directory to use
- whichClass (String) – Which class to use
- subdirSize (int) – Approximately how many images to put into each subdirectory
-
segmentationPreprocessData.
splitImagesIntoDirectories
(path, ignoreDirs, propTrain, propValidate)[source]¶ Split image and label pairs into train, validate, and test sets.
Parameters: - path (String) – Location to the base directory of the data
- ignoreDirs (List of Strings) – Directories in the base directory to ignore
- propTrain (float [0->1]) – proportion of data that should be training data
- propValidate (float [0->1]) – proportion of data that should be validation data


DataFrame and ML Pipeline
Overview
NNFrames is a package in Analytics Zoo aiming to provide DataFrame-based high level API to facilitate Spark users and speed-up development. It supports native integration with Spark ML Pipeline, which allows user to combine the power of Analytics Zoo, BigDL and Apache Spark MLlib. NNFrames provides both Python and Scala interfaces, and is compatible with both Spark 1.6 and Spark 2.x.
Highlights
-
Easy-to-use DataFrame(DataSet)-based API for training, prediction and evaluation with deep learning models.
-
Effortless integration with Spark ML pipeline and compatibility with other feature transformers and algorithms in Spark ML.
-
In a few lines, run large scale inference or transfer learning from pre-trained models of Caffe, Keras, Tensorflow or BigDL.
-
Training of customized model or BigDL built-in neural models (e.g. Inception, ResNet, Wide And Deep).
-
Rich toolset for feature extraction and processing, including image, audio and texts.
Examples:
The examples are included in the Analytics Zoo source code.
- image classification: model inference using pre-trained Inception v1 model. Scala version Python version
- image classification: transfer learning from pre-trained Inception v1 model. Scala version Python version
Primary APIs
NNEstimator and NNModel
Analytics Zoo provides NNEstimator
for model training with Spark DataFrame, which
provides high level API for training a BigDL Model with the Apache Spark
Estimator/
Transfomer
pattern, thus users can conveniently fit Analytics Zoo into a ML pipeline. The fit result of
NNEstimator
is a NNModel, which is a Spark ML Transformer.
please check our NNEstimator API for detailed usage.
NNClassifier and NNClassifierModel
NNClassifier
and NNClassifierModel
extends NNEstimator
and NNModel
and focus on
classification tasks, where both label column and prediction column are of Double type.
NNImageReader NNImageReader loads image into Spark DataFrame.
please check our ImageProcessing for detailed usage.


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{% extends "content_template.html" %} {% block per_page_title %} {{ _('Features') }} {% endblock %} {% block maincolumn_content %}
{{ _('Keep users of your service informed by sending email and text message updates through GC Notify.') }}
{% if not current_user.is_authenticated %}{{ _('Create an account and try it for yourself') }}
{% endif %}{{ _('Write and send emails and text messages to many people from one single template. In GC Notify, you’ll create a message template that you can edit and reuse as often as you need. This way you won’t need to write a new message each time.') }}
{{ _('Tailor your messages with relevant information to each recipient based on a single template using variables. Variables add placeholders, such as a recipient’s name and reference number, that can be filled each time you send a message.') }}
{{ _('Send up to 50,000 messages at once or automate sending when an action is triggered. To send in bulk, upload an Excel spreadsheet with a list of contacts and schedule the date and time up to 72 hours in advance. To automate sending, integrate the API with your web application or back office system.') }}
{{ _('See how many of your messages get delivered. Track the current status of any message to learn when it’s delivered, when it’s not, and why it failed. Check these stats in a real-time dashboard for up to 7 days after sending, and download reports for your records.') }}
{{ _('Set different permission access for different team members to control which members of your team can see, create, edit, and send messages. GC Notify also lets you invite new users, including people without a government email address.') }}
{{ _('Use the default Government of Canada look and feel provided by default, or customise your branding for your department or organisation. You can choose the name that messages come from and the email address that users reply to.') }}


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David
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English version of Introduction
為了呼應國際之努力，本次研討會希望提供各界交流之管道，因此邀請德國漢諾威音樂與戲劇大學音樂生理學暨音樂家醫學中心 (Institute of Music Physiology and Musicians' Medicine (IMMM), University of Music and Drama Hanover) 主任兼教授 Eckart Altenmüller醫生，擔任本研討會專題主講者，同時徵求國內外學者進行論文發表、舉辦專題講座及工作坊，冀望理論與實務的結合，能帶給國內外關心音樂、演奏與健康議題之醫療及照護專業人員、音樂家及音樂教師、音樂系學生及家長等各界人士，吸取新知及互相交流之重要機會。
本屆研討會之討論主題如下：
Three days immersion in the world of UX/UI prototyping. Meet the most important design influencer of the moment, assist to speeches given by worldwide known designers and much, much more.
The unique possibility to enhance your professionality with the smallest effort.
Three days immersion in the world of UX/UI prototyping. Meet the most important design influencer of the moment, assist to speeches given by worldwide known designers and much, much more.
The unique possibility to enhance your professionality with the smallest effort.
Three days immersion in the world of UX/UI prototyping. Meet the most important design influencer of the moment, assist to speeches given by worldwide known designers and much, much more.
The unique possibility to enhance your professionality with the smallest effort.
By Taxi
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By HSR
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Prototype
Design
音樂、科技與健康中心
*Works Selected Registration is for people who will not attend the conference but pieces are selected.
Register now會場現場繳費
Please pay the registration fee in the days of the conference.


Hello there, awesome person!
This is a link to GitHub
This is a link to Khan Academy
This is a link to Word Reference


|
LTAPI
API to access LiveTraffic's aircraft information
|
Represents a dataRef and covers late binding. More...
#include <LTAPI.h>
Public Member Functions | |
| LTDataRef (std::string _sDataRef) | |
| Constructor, set the dataRef's name. More... | |
| bool | needsInit () const |
| bool | isValid () |
| Found the dataRef and it contains formats we can work with? More... | |
| bool | FindDataRef () |
| Finds the dataRef (and would try again and again, no matter what bValid says) More... | |
| XPLMDataTypeID | getDataRefTypes () const |
| Get types supported by the dataRef. More... | |
| bool | hasInt () const |
Is int a supported dataRef type? More... | |
| bool | hasFloat () const |
Is float a supported dataRef type? More... | |
| int | getInt () |
| Get dataRef's integer value. Silently returns 0 if dataRef doesn't exist. More... | |
| float | getFloat () |
| Get dataRef's float value. Silently returns 0.0f if dataRef doesn't exist. More... | |
| bool | getBool () |
| Gets dataRef's integer value and returns if it is not zero. More... | |
| int | getData (void *pOut, int inOffset, int inMaxBytes) |
| Gets dataRef's binary data. More... | |
| void | set (int i) |
| Writes an integer value to the dataRef. More... | |
| void | set (float f) |
| Writes a float vlue to the dataRef. More... | |
Static Public Attributes | |
| static constexpr XPLMDataTypeID | usefulTypes |
Defines which types to work with to become valid More... | |
Protected Attributes | |
| std::string | sDataRef |
| dataRef name, passed in via constructor More... | |
| XPLMDataRef | dataRef = NULL |
| dataRef identifier returned by X-Plane More... | |
| XPLMDataTypeID | dataTypes = xplmType_Unknown |
| supported data types More... | |
| bool | bValid = true |
| does this object have a valid binding to a dataRef already? More... | |
Represents a dataRef and covers late binding.
Late binding is important: We read another plugin's dataRefs. The other plugin (here: LiveTraffic) needs to register the dataRefs first before we can find them. So we would potentially fail if we search for them during startup (like when declaring statically). With this wrapper we still can do static declaration because the actual call to XPLMFindDataRef happens only the first time we actually access it.
| LTDataRef::LTDataRef | ( | std::string | _sDataRef | ) |
Constructor, set the dataRef's name.
| bool LTDataRef::FindDataRef | ( | ) |
Finds the dataRef (and would try again and again, no matter what bValid says)
|
inline |
Gets dataRef's integer value and returns if it is not zero.
| int LTDataRef::getData | ( | void * | pOut, |
| int | inOffset, | ||
| int | inMaxBytes | ||
| ) |
Gets dataRef's binary data.
|
inline |
Get types supported by the dataRef.
| float LTDataRef::getFloat | ( | ) |
Get dataRef's float value. Silently returns 0.0f if dataRef doesn't exist.
| int LTDataRef::getInt | ( | ) |
Get dataRef's integer value. Silently returns 0 if dataRef doesn't exist.
|
inline |
Is float
a supported dataRef type?
|
inline |
Is int
a supported dataRef type?
| bool LTDataRef::isValid | ( | ) |
Found the dataRef and it contains formats we can work with?
|
inline |
| void LTDataRef::set | ( | float | f | ) |
Writes a float vlue to the dataRef.
| void LTDataRef::set | ( | int | i | ) |
Writes an integer value to the dataRef.
|
protected |
does this object have a valid binding to a dataRef already?
|
protected |
dataRef identifier returned by X-Plane
|
protected |
supported data types
|
protected |
dataRef name, passed in via constructor
|
staticconstexpr |
Defines which types to work with to become valid


HW2(Yanfei Yu / yy777)
1. Die Simulator.
2.
3. Non-transitive dice generator.
4. Non-transitive dice generator.
5. All pairs comparison.
P(Green>Blue) = P(Blue>Yellow) = P(Yellow>Orange) = P(Orange>Green) = 2/3.
 Therefore Efron's dice is a set of non-transitive dice.


Toggle
Hide, switch or change the appearence of different contents through a toggle.
Usage
To apply this component, just add the data-uk-toggle="{target: #ID}"
attribute to a <button>
or
element. You can use any selector with the toggle attribute.
The toggle will add or remove a class from the item. By default, it adds the .uk-hidden
class to hide the element.
Example
Example
<button class="uk-button" data-uk-toggle="{target:'#my-id'}">...</button>

...

Multiple items
You can also toggle multiple items at the same time. Just use the appropriate selector.
Example
NOTE In this example we added the .uk-hidden
class to one of the items, so that only the other item will be shown. The toggle will switch visible states between both elements.
Markup
<button class="uk-button" data-uk-toggle="{target:'.my-class'}">...</button>

...

...

Custom class
If you don't want to toggle the .uk-hidden
class, you can also add your own custom class.
Example
NOTE In this example we used the .uk-panel-box-primary
class to switch between different panel styles.
Markup
<button class="uk-button" data-uk-toggle="{target:'#my-id', cls:'uk-panel-box-primary'}">...</button>

...

Animations
The Toggle component allows you to add animations to items when toggling between them. Just add one of the .uk-animation-*
classes from the Animation component to the animation
parameter. The class will be applied to the in as well as the out animation. If you prefer a different out animation, just add another class.
Example
Markup
<button class="uk-button" data-uk-toggle="{target:'#my-id', animation:'uk-animation-slide-left, uk-animation-slide-bottom'}">...</button>

...

JavaScript options
This is an example of how to set options via attribute:
data-uk-toggle="{target:'#my-id'}"
| Option | Possible value | Default | Description |
|---|---|---|---|
target |
CSS selector | false | Elements where classes should be toggled |
cls |
string | 'uk-hidden' | Class to toggle |
animation |
mixed | false | Any uk-animation class name. |


Stuart Little * (out of * * * * )
Directed by Rob Minkoff. Cast: Geena Davis, Hugh Laurie, Jonathan Lipnicki, Jeffrey Jones, Connie Ray, Allyce Beasley, Brian Doyle-Murray, Estelle Getty, Julia Sweeney, Dabney Coleman, Harry Gould, Patrick O'Brien. Voices: Michael J. Fox, Nathan Lane, Chazz Palminteri, Jennifer Tilly, Stan Freberg, Steve Zahn, Jim Doughan, David Alan Grier, Bruno Kirby. 1999 - 85 minutes Rated PG (for the life of me, I have no idea why). Reviewed by Dustin Putman, December 17, 1999.
Based on the charming children's book by E.B. White, "Stuart Little" is one of the most joyless family films in a long time. Not the most annoying, or the most sloppy, or even the most problematic, but simply joyless in every aspect, from its acting to its storytelling to its screenplay. You sit there, watching aimlessly at the screen, and can't help but recall far superior films that were very similar in their approach (1993's "Homeward Bound: The Incredible Journey" comes to mind), but at least had a little substance to their characters and situations. The movie clocks in at a swift 85 minutes--thank goodness for small favors, but I wish the favor had been bigger (not that I'm greedy or anything).
For reasons unknown, unless they are suffering from some sort of mental or psychological disorder, Mr. and Mrs. Little (Hugh Laurie, Geena Davis) visit an orphanage one day and decide out of the blue that they'd rather adopt Stuart, the sweet orphan mouse (voiced by Michael J. Fox), in place of a human child. The Little's young son George (Jonathan Lipnicki) is, at first, disappointed in his new brother, but finally becomes close friends with him until he learns it doesn't matter what species something is, as long as you love them and they love you. Before I go any further, let me take this time to make an aside:
[Since Mr. and Mrs. Little, no doubt, grew up in the '60s and early '70s, it makes perfect sense that they, perhaps, experimented with a few too many mind-altering substances in the past.]
The plot thickens when the house cat Snowbell (voiced by Nathan Lane) becomes jealous of Stuart, a lower life form, being treated as a family member, while he is looked upon as nothing more than a pet. With the help of a group of stray cats in the Manhattan area, Snowbell devises a plan to hire two other mice to pose as Stuart's long-lost parents so Stuart will have to part with the Little's, and he can once again garner all the attention.
If my criticism about Mr. and Mrs. Little seems overly harsh, particularly for a family film, let me also say that there is no excuse for "Stuart Little" to be as hopelessly banal and empty-headed as it is. My nitpicking about the parent characters is (1) due to the obvious idiocy of the screenplay, by M. Night Shyamalan (of "The Sixth Sense" fame!) and Greg Booker; and (2) results from the boredom that the picture provides. The writing, surprisingly, does not offer one witty line in its whole running time, and I believe I lightly, and briefly, laughed once. Don't ask me what it was I laughed at; I only recollect doing it.
Some might argue that certain movies are for children and no one else, but I, nonetheless, firmly stick behind my theory that the only really worthwhile "family films" are those that the entire family will enjoy and be satisfied with. Why else do you think "The Wizard of Oz" and "Willy Wonka and the Chocolate Factory" are classics, and why else will "Stuart Little" be destined to join the endless line of ho-hum garbage that litters the video store shelves? The aforementioned "Homeward Bound," and even 1995's "Babe," which also took the idea of presenting animal characters that could talk, were successful because they had a heart and a mind to go along with the children-targeted stuff. In other words, the makers of those films did not hold their audiences in contempt, spitting out half-baked ideas that couldn't have even looked good on paper. With "Stuart Little," that is what you get.
There's no need to mention any of the actors that were unfortunate enough to find themselves trapped in this dead zone of entertainment, but I will pinpoint the top-billed thesp, and take this time to speak to her directly: Geena, honey, in 1989, when you won that Academy Award for "The Accidental Tourist," could you have possibly predicted that ten years later you would be the host of a disastrous Oscar Pre-Show, and the star of a movie called "Stuart Little?" And in 1991, when you came out with the superb road movie, "Thelma and Louise," did you really have 1995's godawful "Cutthroat Island" on your career plan? For someone who only works once or twice every two years, you sure don't know how to choose projects, do you? Maybe it is not Mrs. Little, but you yourself that took the drugs in the '60s or '70s. LSD has been known to have side effects many years down the road, so it is quite feasible.
Smaller kids may enjoy "Stuart Little," but I can't imagine anyone else coming away from this film happy. While only loosely based on E.B. White's book, perhaps director Rob Minkoff should have chosen to adapt White's more personal and sweet "Charlotte's Web" into a live-action movie. On second thought, maybe not. With the subpar level of filmmaking displayed here, they would have easily found some way to turn "Charlotte's Web" into a figurative train wreck, too.
- Copyright 1999 by Dustin Putman Http://www.young-hollywood.com Http://www.atnzone.com
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{% extends "base.html" %} {% block content %}
The navigation bar that appears at the top of each page can be used to navigate the database and select common functions. Alternatively return to the home page at any time by clicking ‘Artemis’ at the top left of the screen.
The search function can be accessed from the search bar that appears at the top of all pages or from the search button on the home page. To find a record simply enter search terms in the box. The search function works best with unique identiffiers for example part or assembly ID. It is possible however to search based on any facet.
Type ‘c8m’ in the search box to return all parts whose name contains ‘c8m’
Once you have completed a search you may wish to refine your results. In order to filter your search results and exclude unwanted results you can use the ‘filter type’ function.
In the search view, at the top left there is a box called ‘filter type’ - simply select the type of information that you want to filter on. Once selected the relevant terms will appear as dropdown suggestions on the search bar. Just type into the search bar to alter the suggestions list, and click on any suggestions you want to add to your search.
To remove a search filter simply click the ‘x’ symbol next to the filter value in the search bar (where it will show up after being selected).
To filter for items that have a specific batch ID select ‘batch’ in the ‘filter type’ box. You will now see batch IDs listed as suggestions in the search bar dropdown field.
Click on the batch ID number that you are looking for to add it to your search parameters.
Note that filtering is only valuable on particular fields. For example, if you filter on a field that is likely to be unique across all records, such as the record ID, you will get little benefit - as there will be only 1 result for every value. In this case, you would be better searching for the ID directly, or if you do not know it, searching for other bits of information you have about the record you are looking for.
To add a new part select the ‘New part’ option in the navigation bar that appears at the top of each page. Alternatively select 'New Part' button on the homepage.
You will be presented with a blank parts record including basic fields common to most parts. Where there is information available corresponding to a field enter the details. For those fields without information, leave them blank.
Once the fields are updated click ‘save this part’ to ensure changes are stored.
Note that some fields will only allow certain controlled values - in this case, select a suitable value from the dropdown list (the available options are controlled via the admin interface).
Note that the "replaces (if any)" field will only give the option for the new part to replace an old part that has ALREADY been marked as obsolete. So if you do wish to use new part data to replace an obsolete part record, then you should first search for that old part record via the search interface, and once you find it mark it as obsolete and save it. Then return to the "New part" page and you will be able to select the ID of the part to replace in the dropdown list.
To add a new unique field select the ‘add another field’ symbol at the bottom of the table. A dailogue box will appear, and you can enter your field name. Your new field name will appear at the bottom of the fields list, and you can then provide a value for the named field.
If you wish to create multiple new parts at the same time, this can be done via the Batch of new parts option.
This works as per the "Add New Part" option, except that you can specify you many parts to create. Note, of course, that all the new parts being created must have identical information provided for them. Once saved, you will be provided with a list of the IDs for the parts that were created.
This is the same as adding a batch of new parts - if, for example you wish to give a group of IDs to a particular manufacturer, just choose to "Batch of new Parts". Then, on the new part form, specify the number of parts to create but provide no further information. Alternatively, add some information to identify the reason the records were created - e.g. a note such as "issued to manufacturer X" or perhaps a "manufacturer" field.
To add a new assembly select the New assembly option in the navigation bar that appears at the top of each page. Alternatively select the 'New assembly' button on the homepage.
You will be presented with a blank assembly record including basic fields common to most parts. Where there is information available corresponding to a field enter the details. For those fields without information, leave it blank.
Once the fields are updated click ‘save this assembly’ to ensure changes are stored.
Creating a new assembly is very similiar to creating a new part, except there are some additional starting fields that can be filled in, to store the extra assembly information.
Search for a part using the search page. Once found, click on the part ID to open the part page.
Then click the "add to an assembly" button and type in the ID of the assembly to which you wish to add the current part. The page will be refreshed once you submit, and you will see the assembly information on the right hand side.


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The carousel is set to circular. In some uneven scroll/visible/start combinations, we were seeing skipped items, or wrong animations. Test case to fix that issue.