In this project, deep neural networks and convolutional neural networks are implemented in order to clone driving behavior. Keras is used for fraining, validation and testing. The model will output a steering angle to an autonomous vehicle. Also, the output is visualised in a car simulator to show that the car can drive autonomously around a track.
The goals / steps of this project are the following:
My project includes the following files:
Using the Udacity provided simulator and my drive.py file, the car can be driven autonomously around the track by executing
python drive.py models/<modelname.h5>
The model model_LeNet.h5 generate the best results, i.e., vehicle is able to drive autonomously around the track without leaving the road.
The model.py file contains the code for training and saving the convolution neural network. The file shows the pipeline I used for training and validating the model, and it contains comments to explain how the code works.
This project aims to clone or mimic our behaviour while driving and make a car drive autonmosly. In order to do this, I had to o capture good driving behavior, I recorded (through the Udacity simulator) two laps on track one using center lane driving. First track is a normal complete lap and the second is an inverted lap (from exit to start). Here is an example image of center lane driving:
I then recorded the vehicle recovering from the left side and right sides of the road back to center so that the vehicle would learn to recover when driving off the centre because side images provide more useful information than if we were training only with centre images. There are some potential failure points during driving, so I also captured this specific images for vehicle recovering:
Then I repeated this process on track two in order to get more data points.
To augment the data set, I also flipped images and angles thinking that this would help for training because most of the time the model was learning to steer to the left, by addiing flipped versions of the images we can tackle. Now the vehicle learns to steer either to the right or to the left. For example, here is an image that has then been flipped:
After the collection process, I had the following:
Once data was collected, I then normalised it. Normalisation is useful to eliminates big variations across the data set. I decided to implement a 0-1 normalisation, i.e., data ranges from 0 to 1 and then zero-mean centered.
Last feature was to crop the image 70 pixels from top and 20 pixels from bottom. The reason of this is to avoid the model to "over" learn unnecessary features like the sky, trees and even the car hood.
This work was based on the LeNet5 architecture introduced by LeCun et al. in their 1998. Changes to the architecture was the input size. LeNet5 originally takes inputs of (28x28x1), in this case we feed the model with images of shape (160x320x3). Another change was the input depth, I increased it from 6 to 8, the rest is pretty much the same as LeNet.
LeNet5 architecture:
I randomly shuffled the complete data set and put 20% of the data into a validation set (straight on the model.fit function).
I used this training data for training the model. The validation set helped determine if the model was over or under fitting. The final number of epochs used for training was 5. I used an adam optimizer so that manually training the learning rate wasn't necessary.
The overall strategy for deriving a model architecture was to tests different data sets, at the end I came up with a collection of three sets of images. The first one capturing a complete turn, the second one intended to capture left and right turns, side objects, lane lines, etc., and finally the the last one was to consider the track in the other way.
The final model hyperparameters:
One interesting point is that my model does not contain dropout layers. My first believe was to include it because dropout techniques are very helpfull in order to reduce overfitting. Mean squared erros of both training and validation set were reasonable stable and this derive in nice results. I actually try once with droput layer (a droput rate of .20 and .70), after the last convolution but results were not that accurate.
The model was trained and validated on different data sets to ensure that the model was not overfitting. The model was tested by running it through the simulator and ensuring that the vehicle could stay on the track.
My final model consists of the following architecture:
| Layer | Description |
|---|---|
| Input | 160x320x3 COLOURED image |
| Convolution 3x3 | 1x1 stride, same padding, outputs 28x28x8 |
| RELU | Activation (Nonlinearity) |
| Max pooling | 2x2 stride, outputs 14x14x6 |
| Convolution 3x3 | 1x1 stride, same padding, outputs 10x10x16 |
| RELU | Activation (Nonlinearity) |
| Max pooling | 2x2 stride, outputs 5x5x16 |
| Flatten | Flatten the output shape 3D->1D |
| Fully connected | Array of 120 elements |
| RELU | Activation (Nonlinearity) |
| Max pooling | 2x2 stride, outputs 5x5x16 |
| Fully connected | Array of 84 elements |
| RELU | Activation (Nonlinearity) |
| Fully connected | 1 output (steering angle) |
Training data was chosen to keep the vehicle driving on the road. I used a combination of center lane driving, recovering from the left and right sides of the road in multiple scenarios where the model was failing, and doing turns in the other way.
The final step was to run the simulator to see how well the car was driving around track one. There were few spots where the vehicle fell off the track, these spots were considered during creation of the training set. Doing a lap in the other way around made a huge positive difference when testing the model through the simulator.
At the end of the process, the vehicle is able to drive autonomously around the track without leaving the road!
Here's a link to my video result.
It is very interesting how the deep learning approach to make a car drive autonomously is also very powerful. In this case my model performs well for track 1 but for track 2 still need to be trained.
One further step would be to try other deep learning architectures. I based my work on the LeNet-5 architecture, next step is to try the NVIDIA approach for self drving cars a very powerful deep learning architecture for self-driving cars.
Thank you for reading this report.
Daniel


resources
Table of Contents
1 MONADS
1.1 An interesting blog entry:
1.2 Douglas Crockford
This talk shows us how to apply Monads in Javascript. Douglas Crockford's YouTube talk on Monads in Javascript The source code in GitHub
1.3 More on Javascript monads
- Understanding Monads With JavaScript
- http://stackoverflow.com/questions/8656172/monads-in-javascript
- http://channel9.msdn.com/Events/aspConf/aspConf/Understanding-Monads-with-JavaScript
- https://github.com/five-eleven/monadic
- https://gist.github.com/1618403
- http://channel9.msdn.com/Shows/Going+Deep/Hewitt-Meijer-and-Szyperski-The-Actor-Model-everything-you-wanted-to-know-but-were-afraid-to-ask
- http://www.youtube.com/watch?v=w9hHHvhZHY
- http://www.i-programmer.info/programming/jquery/4837-jquery-promises-deferred-a-webworkers.html
1.4 Promises
- Wrangle Async Tasks With JQuery Promises
- A proof that Promises/A is a Monad (This looks very interesting.)
λ Λ
(defun org-xor (a b) "Exclusive or." (if a (not b) b))


Docs
Graphical and Console Access
Once a virtual machine is started you are able to connect to the consoles it exposes. Usually there are two types of consoles:
- Serial Console
- Graphical Console (VNC)
Note: You need to have
virtctl
installed to gain access to the VirtualMachineInstance.
Accessing the serial console
The serial console of a virtual machine can be accessed by using the
console
command:
$ virtctl console --kubeconfig=$KUBECONFIG testvmi
Accessing the graphical console (VNC)
Accessing the graphical console of a virtual machine is usually done through
VNC, which requires remote-viewer
. Once the tool is installed you can
access the graphical console using:
$ virtctl vnc --kubeconfig=$KUBECONFIG testvmi
Debugging console access
Should the connection fail, you can use the -v
flag to get more output
from both virtctl
and the remote-viewer
tool, to troubleshoot the problem.
$ virtctl vnc --kubeconfig=$KUBECONFIG testvmi -v 4
Note: If you are using virtctl via ssh on a remote machine, you need to forward the X session to your machine (Look up the -X and -Y flags of
ssh
if you are not familiar with that). As an alternative you can proxy the apiserver port with ssh to your machine (either direct or in combination withkubectl proxy
)
RBAC Permissions for Console/VNC Access
Using Default RBAC ClusterRoles
Every KubeVirt installation after version v0.5.1 comes a set of default RBAC cluster roles that can be used to grant users access to VirtualMachineInstances.
The kubevirt.io:admin and kubevirt.io:edit ClusterRoles have console and VNC access permissions built into them. By binding either of these roles to a user, they will have the ability to use virtctl to access console and VNC.
With Custom RBAC ClusterRole
The default KubeVirt ClusterRoles give access to more than just console in VNC. In the event that an Admin would like to craft a custom role that targets only console and VNC, the ClusterRole below demonstrates how that can be done.
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
name: allow-vnc-console-access
rules:
- apiGroups:
- subresources.kubevirt.io
resources:
- virtualmachineinstances/console
- virtualmachineinstances/vnc
verbs:
- get
The ClusterRole above provides access to virtual machines across all namespaces.
In order to reduce the scope to a single namespace, bind this ClusterRole using a RoleBinding that targets a single namespace.


Popular Areas in marketing sheep wool compost
wool mulch (goats forum at permies)
any one used wool as mulch? price does not even cover shipping now so . to dispose of! wont burn it and it takes forever to rot in the compost piles! . have made selling sheep wool a low priority! main market was china and .
http://www.permies.com/t/1486/goats/wool-mulch
BBC NEWS | Business | Using the wool no-one wants
Oct 5, 2006 . The coarse wool from Swaledale sheep doesn't earn much at . As with all sheep farms, the wool from these animals has to be sent to the British Wool Marketing . Adding wool to compost improves nitrogen content and water .
http://news.bbc.co.uk/2/hi/5317358.stm
the patio pack
A patio plant and hanging plant sheep manure compost fertilizer and water . We found that the wool retained and wicked enough water to sustain the plants . We have been on the market for five years and have serviced thousands of plants .
http://thepatiopack.com/
Sheeps Wool Insulation
Sheep's wool insulation also offers a number of technical benefits that . 95% natural fibre content one of the highest available on the market. . Sheep wool fibres are biodegradable and can be composted into the ground to enrich the soil .
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Frederick County Virtual Farmer's Market | Sheep, Goats & Wool ...
Members raise sheep, goats, rabbits, chickens, ducks, bees, alpacas, cattle, horses, vegetables, hops and hay. Plus, a wide variety of wool products: fleeces, .
http://www.discoverfrederickmd.com/farmersmarket/html/sheep_goats_wool.htm
Wool Feasibility Study BiH - UNDP Bosnia and Herzegovina
If one would be asked to describe the wool market in BiH in one word, . Compost production is a new production which can be organized on a sheep farm.
http://www.undp.ba/download.aspx?id=2383


--- title: 产品 date: 2017-02-16 20:54:09 layout: nav --- To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video
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About WITS
Women in Technology Society (WITS) is more than just a club on campus; we’re a community of supportive, innovative women* a mission to open up the doors for female* involvement in technology-related fields. At WITS, we provide a platform for all non cis-men to connect with each other, developing the skills, community, and industry connections necessary to harness the power of technology. Members are able to take advantage of our resources and succeed through experiencing informative events,interactive workshops, inspirational speakers, and most notably, SheHacks, the largest all-women’s hackathon in Southwest Ontario.
Regardless of your background, as long as you have a passion for technology and want to make a difference, we'd LOVE to get to know you at our events throughout the year and as a part of our exec team!
*Any female-identifying, non-binary individual. WITS welcomes all genders and backgrounds to events with the exception of SheHacks, our only non cis-men only event.
Join Us
Applications for WITS 2018-2019 Exec team are now open!
Our team experienced an incredible amount of success and outpouring of support last year, but we’re now aiming to accomplish more. If you want to be a part of a team that works closely with the biggest names in tech (Google, Microsoft, Shopify, and much more), challenges conventions, breaks barriers, and empowers hundreds of young, bright females every year, WITS is at the forefront of the tech community this year.
Stay in Touch
Sign up to become a WITS member
We love staying in touch with our members! Leave us with your email and we promise to send the most useful tips, updates, and information!
Schedule
-
9am
Registration
1 Hour // NCB Atrium
-
9:30am
Breakfast + Team Forming
30 Mins // NCB Atrium
-
10am
Opening Ceremonies
by Scotiabank
ft. StdLib & Orchid
1 Hour // NCB113
-
11am
Hacking Starts
10 Hours // NCB
-
11am
Wokrshop 1: Intro to Agile
by Scotiabank
1 Hour // NCB 114
-
12pm
Wokrshop 2: Intro to APIs
by StdLib
1 Hour // NCB 117
-
12:30pm
Lunch
1 Hour // NCB Atrium
-
1pm
Intro to Python
by Digital Echidna
1 Hour // NCB 117
-
2pm
Intro to Web Dev
by Coinsquare
1 Hour // NCB 117
-
2:30pm
Snacks
1 Hour // NCB Atrium
-
4pm
Therapy Dogs
1 Hour // NCB Atrium
-
6:30pm
Dinner
1 Hour // NCB Atrium
-
9pm
Hacking Ends
-
9:10pm
Demo
30 Mins // NCB Atrium
-
9:40pm
Closing Ceremonies
40 Mins // NCB 113
Our Team
SheHacks II is organized by Women in Technology Society (WITS). As a club, our mission is to encourage, connect, and inspire women to explore the technology industry. Every year, we host events to grow our community of women in tech and promote diversity and inclusion within Western University.
Partners
FAQ
I don't know how to code. Can I still come?
Of course! The event is designed for first-time hackers to try something new. We actively encourage people from all backgrounds, disciplines, and majors to attend! We have workshops that’ll teach you everything you need to know. Just bring yourself, a willingness to learn, and a positive attitude!
Can I attend with no prior hackathon experience?
YES! Over 60% of our attendees last year were first-time hackers!
Do I need to come with a team?
You can come solo or with a team - whichever you like. If you already have a team (up to 5 people), you can register with them. If you don't have a team but would like one, we can match you up with one before the event, or you can form your own the morning of during breakfast and registration.
Which sponsors are attending?
We have representatives from Scotiabank, Coinsquare, TechAlliance, Communitech, DMZ, Propel Entrepreneurship Centre, Ivey Msc, and more!
Is this event women-only?
Yes! As we’re focused on creating a talent pipeline from minorities in tech, this event is specifically created as a place for newcomers to explore the tech industry in a fun, relaxing, and non-competitive way. If you're in high school, university, college, or graduate school and want to break into tech, you're 100% welcome here!
I'm not a girl/woman, can I still come?
SheHacks is designed to be a inclusive space to support women who are interested in technology. If you identify as a woman, you're welcome to join us!
Why can’t men attend?
The tech industry is still dominated by men, which unfortunately can make the environment a toxic and unwelcoming one for women. At the Women in Tech Society, we're simply bringing women up to speed to be equals, as women are still disadvantaged in the social and political spheres.
What is the cost?
Absolutely FREE, due to our generous sponsors! Food and drink will be covered by us, and you'll also receive a swag bag full of amazing goodies.
Which schools are allowed to attend?
We welcome participants from all over Canada, with a particular emphasis on Western University, Waterloo University, Laurier University, York University, and University of Toronto!
Where can I register?
You can sign up here. Apply fast, as spots fill up quickly!
Where/when is the hackathon?
It will take place at North Campus Building (Western University) on Sunday March 25th from 10AM to 10PM! You can find a map here.
What do I need to bring?
Your laptop, chargers, friends, and a desire to learn!
Is there a travel reimbursement?
We will be sending a bus from Toronto, Waterloo, and Hamilton to London and back.
*By women, we welcome all cis, trans, intersex, agender, and non-binary individuals to attend.


To mark up subheadings, consider putting the subheading into a p
element after the h1
- h6
element containing the main
heading, or putting the subheading directly within the h1
- h6
element containing the main
heading, but separated from the main heading by punctuation and/or within, for example, a
span class="subheading"
element with differentiated styling.
Headings and
subheadings, alternative titles, or taglines can be grouped using the header
or div
elements.


The transform methods
Table of Contents
About the transform methods
Transform methods
About the transform methods
The default coordinate system has its origin in the upper-left corner. The positive x-axis extends to the right. The positive y-axis extends downward.
You can change this coordinate system by using one or more of the transform methods described on this page. This image shows the default coordinate
system.
This image shows a drawing without transformations.
The transform methods are defined in the RVG, RVG::Group, and
RVG::Use classes. You can also chain these methods to the shape methods and to the
image method.
Transform methods
matrix
obj.matrix(sx, rx, ry, sy,
tx, ty) -> obj
Description
Replaces the current transformation matrix with a new matrix having the specified values. See the
SVG standard
for more information.
Arguments
- sx, sy
- The scale factor in the x-dimension and y-dimension, measured in user coordinates.
- rx, ry
- The amount of rotation about the x-axis and y-axis, measured in degrees.
- tx, ty
- The translation on the x-axis and the y-axis, measured in user coordinates.
rotate
obj.rotate(angle[, cx, cy]) ->
obj
Description
Rotates the axes about the origin or, if cx and cy are present, about the specified point.
Arguments
- angle
- The amount of rotation. Positive angles rotate clockwise, negative angles rotate counter-clockwise.
- cx, cy
- If present, the point to rotate about.
Example
See scale.
scale
obj.scale(sx[, sy]) -> obj
Description
Scales the axes.
Arguments
- sx
- The amount of scaling on the x-axis.
- sy
- If present, the amount of scaling on the y-axis. Otherwise defaults to sx.
Example
skewX
obj.skewX(angle) ->
obj
Description
Skews the x-axis.
Arguments
- angle
- The skew amount, measured in degrees.
Example
See skewY
skewY
obj.skewY(angle) ->
obj
Description
Skews the y-axis.
Arguments
- angle
- The skew amount, measured in degrees.
Example
translate
obj.translate(tx[, ty]) -> obj
Description
Moves the origin.
Arguments
- tx
- The location of the new origin on the x-axis.
- ty
-
The location of the new origin on the y-axis. If omitted, defaults to
tx.
Example


German military refuses to take two Airbus A400M planes due to be delivered
Germany's air force said on Wednesday it had decided not to take two Airbus A400M planes that were due to be delivered, citing recurring technical problems with the military transporters.
BERLIN: Germany's air force said on Wednesday it had decided not to take two Airbus A400M planes that were due to be delivered, citing recurring technical problems with the military transporters.
The air force said while the A400M formed the backbone of its air transport, there were technical issues - such as with the propellers.
"The overall technical defects and the realization that the two planes due to be delivered also do not possess the characteristics that were guaranteed in the contract, have resulted in the armed forces not taking these aircraft," the Luftwaffe, or air force, said in a statement.
(Reporting by Sabine Siebold; Writing by Michelle Martin; Editing by Paul Carrel)


{{today}}
To Whom It May Concern,
{{firstName}} {{lastName}} has been working as a member of the Prep for Prep Admissions Committee on a volunteer basis from August ___, 2014 and has participated to present.
{{hisher}} duties include proctoring exams, assembling department materials for mailings, and organizing paperwork. From August ___, 2014 to present, {{firstName}} has volunteered {{totalHours}} hours assisting the Admissions department.
{{heshe}} is a mature, diligent, and hard working young person. {{heshe}} has performed all of {{hisher}} duties with great skill. I have found {{himher}} to be cooperative, dependable, courteous, and very willing to assist whenever needed. {{hisher}} help in the admissions process at Prep for Prep has been greatly needed and very much appreciated.
Please do not hesitate to email me or call me at<PHONE_NUMBER> ext. ____ if you require additional information.
Sincerely,


Boost.Range |
Boost v. 1.35 introduced some larger refactorings of the library:
range_size<T>::type
has been completely removed
in favor of range_difference<T>::type
boost_range_begin()
and boost_range_end()
have been renamed range_begin()
and range_begin()
, respectively.range_result_iterator<T>::type
and
range_reverse_result_iterator<T>::type
are have
been renamed
range_iterator<T>::type
and
range_reverse_iterator<T>::type
.
© Copyright Thorsten Ottosen 2008.
Distributed under the Boost Software License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at www.boost.org/LICENSE_1_0.txt)


Insightful, results-driven IT Professional with notable success in designing, managing and administering organization-wide Information Technology systems, Users Support, combined with extensive experience in planning, executing, monitoring, and closing a variety of IT projects. Highly organized and self-motivated, have excellent communication skills, and thrive in high-pressured environments.
analyzing, planning, measuring, and improving all aspects of the availability of IT services. Ensuring that all IT infrastructure, processes, tools, roles etc., are appropriate for the agreed service level targets.
Register and classify received incidents and to undertake an immediate effort in order to restore failed IT Service as quickly as possible
Round the clock support to all country office staff.migrate/deploy window server service component.
What gives me more joy is reading books it helps my mind explore the world beyond ordinary imagination


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Chelsea v Crystal Palace: match preview
by Guardian StaffStamford Bridge has not been a fortress for Chelsea this season. They have won three of their nine home games in all competitions and they will miss the suspended Jorginho against Crystal Palace, who have picked up impressive results at West Ham, Manchester United and Arsenal. Mason Mount is also a doubt with an ankle injury. Jacob Steinberg
Saturday 12.30pm BT Sport 1
Venue Stamford Bridge
Last season Chelsea 3 Crystal Palace 1
Referee Mike Dean
This season G9 Y43 R2 5 cards/game
Odds H 4-11 A 19-2 D 23-5
CHELSEA
Subs from Caballero, Cumming, Christensen, Guehi, Alonso, James, Gilmour, Pedro, Hudson-Odoi, Batshuayi, Giroud
Doubtful Mount (ankle)
Injured Barkley (ankle, 23 Nov), Rüdiger (groin, 30 Nov), Loftus-Cheek (achilles, unknown)
Suspended Jorginho (one match)
Discipline Y20 R0
Form LWWWWW
Leading scorer Abraham 9
CRYSTAL PALACE
Subs from Guaita, Woods, Riedewald, Camarasa, Dann, McCarthy, Meyer, Benteke, Kelly, Dreher, Tavares, Lokilo, Townsend
Doubtful Cahill (head), Kouyaté (head)
Injured Sakho (match fitness, unknown)
Suspended None
Discipline Y22 R0
Form DWWLDL
Leading scorer Ayew 4


This article is long and requires some knowledge of parsing (not too advanced knowledge, I promise) but if you like computer science I feel you would really, really enjoy reading it; so let’s go!
The parser version of the Takmela depends on combining the CS concepts of a graph-structured stack and the continuation.
We can begin to understand how Takmela works by looking at a common problem in parsing from context-free grammars; namely the left-recursion problem. Consider the classic arithmetic expression example:
expr → expr ‘+’ term | term term → NUM
Let’s try to parse it with the input 1+2+3
A naive top-down parser will try to ‘invoke’ expr , attempt to parse the first alternative, and so immediately try to recursively invoke expr again without consuming any input. This will lead to an infinite loop.
A less naive parsing algorithm can do the following: if it encounters left recursion in expr it will freeze parsing at that point and try other alternatives first. If one of the others succeed the parser will go back in time into the first (recursive) alternative, and substitute any ‘successes’ of expr obtained so far into the left recursive call. Awesome right?
Let’s try to work through a hypothetical time-jumping parser, and parse 1+2 using our left recursive grammar.
We’ll use a notation like expr/0 to indicate a ‘call’ to expr, i.e a parsing attempt, starting at input position 0.
We’ll use a notation like expr → expr • ‘+’ term to indicate a CodePos, for example the preceding CodePos says "we are in the middle of running the first alternative of expr, we have successfully parsed the recursive call, and the ‘code’ is now right before trying to match the plus sign"
Let’s run our parser!
Rulesexpr → expr ‘+’ term | term term → NUMInput
1 + 2Trace
If we try to use the same algorithm to parse 1+2+3 a rather interesting thing will happen: we will resume the frozen expr/0 twice! Once will be like the above when 1 was fully parsed as an expr, and the second time when 1+2 is fully parsed (i.e when the full trace above is completed). When 1+2 is parsed it will resume expr/0 at expr → expr • ‘+’ term all ready to match + and 3.
We are using a technique of (1) freezing or setting aside the remaining execution of some parse (2) Invoking it with results from other parts of the program.
This concept of ‘frozen program that we can re-execute’ is called a continuation, and is the core of what we’re trying to do with Takmela.
Notice however that implementing our hypothetical time-jump parser feels rather complex, doesn’t it? We’ll need to implement something resembling a call stack for non-left-recursive parsing and yet deal with representing continuations, resuming them, and so on.
To simplify things we’ll go further, everything will use continuations; there will be no ‘normal calls’, and our parser doesn’t even need to analyze the grammar to detect what calls are left-recursive.
Turns out this "suspend all the things" approach makes the parser rather powerful; all the nasty things like left-recursion, ambiguity, wrong behavior due to nullable nonterminals; all these things become normal processing steps when we don’t use direct calls. The algorithm will just keep eating away at the input, branching as many times as it needs.
We are now in a position to understand the full algorithm. Let’s define the little pieces from which we’ll form our parsing engine…
A Call is a pair (non_terminal, input_pos) ; for example a call (term, 5) or term/5 means we’re trying to parse a term, starting from input position 5.
 For example in the first alternative of expr, we could have a CodePos like this: expr → expr ‘+’ • term which means we have parsed an expr and a plus sign, but are yet to parse the final term
A Continuation is a tuple (Call, CodePos)
The role of a continuation is to tell us what should happen after a callee has succeeded. Example Suppose a rule a → b c d was called at position 5 , calling b moved us to position 7 , and the sub-call to c at position 7 has succeeded; what should happen after c’s success? We should resume the call to a / 5 right before d , thus one of the continuations for the callee c / 7 is (a/5, a → b c • d)
Notice that the call a/5 means "attempting to parse an ‘a’ starting at position 5"; resuming that call will continue from wherever c has finished parsing, not from position 5.
Our program state is stored into these variables:
K is the continuation table, it maps from Call → Set <continuation> , as in the example above. This is our graph-structured stack. (if you’re into writing interpreters, this graph looks like a family of related call stacks in a compact data structure)
S is the success table or memoisation table; it stores all calls that have succeeded so far. It maps from Call → Set<final_input_pos>. For example an item (expr, 5) → 10 means that a parse for expr starting from position 5 has succeed, and the rest of the parsing (resumed via a continuation) should continue at position 10.
If we want to store parse trees, we can have an additional entry in the success table alongside the final position. More about parse trees in our example with ambuiguity
In addition to K and S, we will have newK and newS where we collect any newly created continuations or successes at a given iteration of the algorithm. They have the same structure as the original K & S.
A final part of the program state is the Awakenings set, used to prevent redundant re-parsing of past successful parses. Awakenings is a set of (input_pos, continuation) pairs, so having an awakening like (5, (a/2, a → b c • d)) means "we have already processed the call to c from its parent (a, 2), and this call to c has succeeded at position 5".
In an imagined ML-like language, our program state would be fully expressed like this:
type Call = (RuleName, InputPos) type Continuation = (Call, CodePos) K :: Map<Call, Set<Continuation>> S :: Map<Call, Set<InputPos>> newK :: sametype K newS :: sametype S Awakenings :: Set<(InputPos, Continuation)>
The parser works in iterations, it starts with exactly one call (calling the root non-terminal at input position zero) and each iteration processes pending calls, matching tokens as much as it can until it finds a nonterminal (if so it calls/processes it) or reaches the end of the rule (if so, the rule succeeds). This processing might generate new continuations and successes.
(reminder: continuations are entries to K/newK which are created from calling non-terminals, and successes are entries in S/newS created from process(..) working through a rule till the end).
After processing, new continuations and successes are joined ; so a new success might lead to resuming some existing continuation (going ‘back in time’). Similarly a new call/continuation might join with an existing success, meaning "there are existing results of calling the same rule at the same position (outside of this continuation), let’s resume the new call’s continuations with them".
Any new items (continuations and successes) from the joining will be processed in the next iteration.
(If you’re familiar with Datalog implementation all of this should sound familiar; Takmela is basically a special case of semi-naive).
In pesudocode:K = {}; newK = {} // init continuations S = {}; newS = {} // init successes callRootRule() // invokes 'process' on the rule’s branches, to match tokens etc // might generate some newK and newS records while(true) { kWorklist = diff(newK, K) // we need only the continuations not seen before sWorklist = diff(newS, S) // ditto if(kWorklist.size == 0 && sWorklist.size == 0) { break // stop if we reach a fixed point } newK = {} newS = {} K = merge(kWorklist, K) S = merge(sWorklist, S) joinNewContinuationsWithPastSuccesses(kWorklist, S) // may invoke `process` on some nonterminals // and may generate (schedule) new conts. // and successes joinNewSuccessesWithPastContinuations(sWorklist, K) // same as above }
This is the essential outline of the algorithm, we’ll now explain it again in pseudocode but in detail, and show the role of the awakenings set too.
K = {}; newK = {} // init continuations S = {}; newS = {} // init successes awakenings = {} call(startRule, 0) while(true) { kWorklist = diff(newK, K) sWorklist = diff(newS, S) if(kWorklist.size == 0 && sWorklist.size == 0) { break // stop if we reach a fixed point } newK = {} newS = {} K = merge(kWorklist, K) S = merge(sWorklist, S) awakenings.clear() joinNewContinuationsWithPastSuccesses(kWorklist, S) // may invoke 'process' on some nonterminals // and may generate (schedule) new conts. and // successes joinNewSuccessesWithPastContinuations(sWorklist, K) // same as above } func call(string callee, string caller, int inputPosNow, CodePos codePos) { Cont cont = ((caller, inputPosNow), codePos) bool firstCall = ! ( K.containsKey((callee, inputPosNow)) || newK.containsKey((callee, inputPosNow)) ) newK[callee, inputPosNow].add(cont) // schedule a 'join new continuation with successes' if (firstCall) { process(callee, inputPosNow) } } func process(string ruleName, int inputPos) { Rule r = rules[ruleName] for (RuleBranch b : r.branches) { processBranch(ruleName, b.codeStart(), inputPos) } } func processBranch(string ruleName, CodePos codePos, int inputPosNow) { if (ruleName == "root" && eof(inputPosNow)) { fire event onSuccessfulParse() // ← this is where the algorithm actually yields results // if we return parse trees, we’d pass them here return } bool stop = false int inputPos = inputPosNow while (codePos != endOfRuleBranch) { if (codePos is Terminal) { Terminal t = codePos if (!match(t, inputPos)) { stop = true break } else { inputPos++ } } else if (codePos is Nonterminal) { Nonterminal toCall = codePos call(toCall.name, ruleName, inputPos, next(codePos)); stop = true break } codePos = next(codePos) } // end while if (!stop) { // Reached the end of this rule branch without stopping due to match failure or // nonterminal call // This means the rule has succeeded, let's schedule a 'join new success with continuations' newS[ruleName, inputPosNow].add(inputPos) } } func joinNewContinuationsWithPastSuccesses(kWorklist, S) { for(k of kWorklist.entries()) { (callee, posAtCallee) = k.key Setcs = k.value for(Cont c : cs) { successes = S[callee, posAtCallee] for(int s : successes) { if(!awakenings.containsKey((s, c))) { awakenings.add((s,c)) processBranch(c.caller, c.codePos, s) } } } } } func joinNewSuccessesWithPastContinuations(sWorklist, K) { for (s : sWorklist) { (callee, posAtCallee) = s.key successPositions = s.value for (int p : successPositions) { contSet = K[callee, posAtCallee] for (cont : contSet) { if (!awakenings.contains((p, cont))) { awakenings.add((p, cont)); processBranch(cont.caller, cont.codePos, p) } } } } }
We will now do a trace of parsing 1+2 using Takmela, then again with 1+2+3
Hint: if the trace appears confusing this might help
You can collapse this trace and open it in a separate page, link here
{{trace_one_plus_two}}The next step is to trace 1+2+3 which you'll find here.
For many more examples, you can go here.
If you want actual code you’ll find the Java implementation here. If you want examples more complex than 1+2 you’ll find that I’ve included some tricky cases in the Examples TOC, including ambiguity and nullable rules. If you want to see some real-world usage of Takmela, the demo program itself uses Takmela to parse a subset of GraphViz dot files: This is the grammar and this is the code for parsing it
The parsing algorithm is implemented in about 500 lines of Java code, and using more expressive languages would likely reduce that size even further; not bad for a general parser. Remember that Takmela should work with any context-free grammar, including ones with left recursion (both direct and indirect), ambiguity, nullable non-terminals, and even left-recursion-like behavior that results from nullable non-terminals (e.g a → b a c when b is nullable). And it does that without any analysis or transformation of its grammar; it just runs.
But let’s talk about Datalog…
// Notice that constants always start with small letters while // variables will always start with capital letters parent(a, b). parent(b, c). parent(a, d). parent(d, e). // If there’s a Z such that X is a parent of Z, and Z is a // parent of Y, we can infer the relation "X is a grandparent of Y" grandparent(X, Y):- parent(X, Z), parent(Z, Y). // All parents are ancestors ancestor(X, Y):- parent(X, Y). // An ancestor of an ancestor is also an ancestor // Notice that Z is used in exactly the same way as in ‘grandparent’ ancestor(X, Y):- ancestor(X, Z), ancestor(Z, Y).Datalog is an amazing language, you supply it with some straightforward rules and a database (even one as simple as the four facts above) and it will make you feel as if the computer is thinking. Here are some sample runs of Datalog queries, which run against the above program:
?- parent(a, X) X = [b, d] ?- grandparent(X, e) X = [a] ?- ancestor(a, X) X = [b, c, d, e] ?- ancestor(X, c) X = [b, a]
There are many ways to implement Datalog; which can be roughly classified as top-down and bottom-up.
A top-down algorithm will start with a query like ancestor(X, c) and try to run any subgoals required to solve the parent query; e.g ancestor(X, c) yields calls to parent(X, c) from rule 1, and to ancestor(X, Z) & ancestor(Z, c) from rule 2.
A bottom-up algorithm, like the famous semi-naive, will start from the facts and keep applying the rules in an attempt to infer all possible data; as if your queries were all variables. It will be as if you must always run grandparent(V0, V1) or ancestor(V0, V1).
Top-down Datalog engines obviously do less work when the query has some solution-constraining data, like ancestor(X, c) [note: not talking about constraint-solving]. There are clever algorithms, like the so called Magic Sets, that fool bottom-up algorithms into working like top-down algorithms. They do this by generating a few new rules and a new query, such that running seminaive on the modified program will simulate a top-down query engine, constraining data and all, running the original program. It’s really a sign of humanity’s ingenuity.
Let’s think about how a Datalog query might be executed. It feels as if any query engine (let’s focus on top-down) has to do the following:
This sounds a lot like parsing, and especially like general parsers a la Takmela. To take the idea further I literally did a copy/paste on Takmela’s parser, changed various things, and tested it with some Datalog programs. It worked.
| Parsing | Datalog | |
| Input position | An integer | n/a |
| Call | Rule Name + input pos | Rule Name + any constant arguments, e.g ancestor(a, ?) |
| CodePos | Where are we in a non-terminal rule? | Where are we in a Datalog rule (aka a Horn Clause)? |
| Continuation | Call + CodePos + "parse tree so far" if we want parse trees. | Call + CodePos + any bound intermediate variables e.g {Z = b} |
| Success | Call → Set<FinalInputPos> | Call → Set<ResultTuple> |
| Surface matching | Matching terminals | Querying facts |
| More complex matching | Calling non-terminals | Calling Subgoals |
| When resuming a continuation | Continue from last input position | Make sure variables match |
Datalog as implemented in Takmela doesn’t have the concept of an input position because facts can be matched multiple times while executing a query, perhaps in some variant of the language (linear logic?) a concept of "facts used so far" would correspond to the input position.
A continuation in Datalog-via-Takmela also includes the values of variables that have been already bound; reminding us of the relationship between a continuation and a programming language’s call stack.
Let's see a trace of ancestor(b, X) , which can be found here. You'll find it closely resembles our traces of Takmela's parsing, with the GSS, successes, and so on. The source code for the core Takmelogic engine can be found here, I suggest opening it with Takmela's parser code side by side and comparing them!
A Java implementation of the first iteration (lol) of Takmelogic can be found within the same project as the Takmela parser in the takmelogic package.
More traces can be found in the Trace TOC pageSometimes while executing a Datalog query one needs to wait until a certain subquery has been completely executed. Consider for example a special operation count that works like this:
num_ancestors(X, N):- N:= count ancestor(Y, X).
The predicate num_ancestors will attempt to find all tuples where Y is an ancestor of X and return the number thereof. It is important to schedule the execution such that a call to num_ancestors(d, N) will wait until all possible results of ancestor(Y, d) have already been computed. This is also important for negation; if we want to test that a certain predicate is not true, we need to make sure no future inference of this predicate will invalidate our conclusion
A plain Takmelogic implementation doesn’t have that guarantee but we can add it. There's a technique that's compatible with the GSS approach we're using, but perhaps that's a topic for another article.
Earley’s algorithm is the ancestor of many general parsers. It uses a GSS implicitly.
Earley's parsing technique has been applied to Prolog/Datalog queries, presented in the paper parsing as deduction in 1983 and improved upon by many other researchers, so I'm definitely not the first person to consider a link between general parsing and Datalog!
Takmela is closely related to a parsing technique presented in Mark Johnson's "Squibs and Discussions: Memoization in Top-Down Parsing" (link), with the main difference that Mark Johnson’s technique uses Scheme’s built in continuations language feature, while Takmela represents continuations as an explicit data structure; this opens the door for implementation of the parser in typical programming languages that don’t support continuations, and access to the GSS means the ability to add features like visualizing a running parser, or optimizations that e.g compress or prune the graph-structured stack (If I understood correctly -- and I may have not -- Marpa seems to make great use of such optimizations).
Takmela was directly inspired by GLL and started as an attempt to simplify it in order to understand it (because I needed a parser for my other projects and wasn’t satisfied with existing tools). In terms of generality and worst-case time complexity Takmela can’t beat GLL; which represents the state of the art in parsing right now. If Takmela were to add something it would be simplicity of understanding and implementation, which could make it easier for more people to make use of general parsing or or add improvements.
The bad news: I haven’t written a correctness proof for Takmela nor worked out the algorithm’s time complexity. I’ve tried, but it seems I lack the academic experience needed to do so. I do have some rough ideas on how to start but that's it.
Before you dismiss me outright, please consider that it’s okay to ask for help, right? I want to release this work to the world, but I fully acknowledge my lack of the knowledge needed to finish it. I’m not writing this article to celebrate some accomplishment but for the sake of outreach. If you’re a person who finds this research direction interesting and who actually knows what they’re doing; you’re so welcome to contact me about mentoring, guidance, supervision, or other forms of collaboration. My email is<EMAIL_ADDRESS>What would you gain out of this? I suggest the possibility of discovering something which makes the field a slightly more interesting place; this article is about a ~500 line general parser and a similarly sized Datalog interpreter, both of which can be explained to someone else in about an hour. The only thing missing is the correctness and complexity proofs; and then both parsing and Datalog could become much more ubiquitously used. We also have the potential of the GSS as a new computer science workhorse, through introducing more variants of Takmela’s algorithm for many other uses. Thank you for giving someone the benefit of doubt in the name of pursuing ideas!


I decided to try tripping on gravol. I had heard that dimenhydrinate was half as potent as it should be (50mg = 25mg). Me and two friends popped the pills at a friends house. I had 30, another friend I will call K had 24 and my other friend H had 20.
When I got home I was alone and I went to the bathroom. While sitting in the bathroom I looked up and noticed the bathroom was full of smoke. I was like dude its working. I ran out and tried to call my friends... no answer. Oh well. I looked at the walls and there were red and looked as if they were bleeding and in the corner I saw smoke coming up. I walked to the wall and it flew out at me.
 My dad came home about 30 min after the pills started working and when he got in the house I heard him talking to me but apparently he wasn’t. I would answer questions he wasn’t asking. He asked if I had been drinking or taken any drugs.
At first I denied it. I couldn’t keep topic. While talking to him I would be talking about 3 things at a time. Finally I confessed and said I took some gravol. About 30 min after my dad got home my mom came home and we had some pizza. I couldn’t eat though when I picked up the pizza I just shook and dropped it and anyways I was too busy looking at things and answering questions that my parents asked and questions I thought they asked.
They decided to take me to the hospital not knowing if my dosage of gravol was too much. The drive to the hospital was very strange. When I arrived at the hospital I immediately was put into a room. They kept asking me questions like “do u know where u are?” and “do u know what the date is”. They hooked me up to this heart rate monitor and every time I saw something my heart rate would jump. For almost all the time at the hospital I thought my dad was my friend K and I kept saying “hey K do u see those things coming out of the wall”.    I then pissed in it and they went to test it.
Meanwhile they gave me the choice to either drink charcoal or get my stomache pumped. I chose drinking the char coal. They gave me this big bottle of it and I had to drink it all.  About a minute after I finished the charcoal I threw up all over my clothes. They moved me to a new bed and I took off my shirt and pants.  I was lying in my new bed when a doctor came in and told me my test came back clean. I was relieved cause I had been doing weed and xanax earlier that week. They then decided that I could go so I put on my bunny hug and got out of the bed. Then I seen these huge moths and I was scared so I started to run.
I was running up and down the halls in the hospital screaming and being chased by huge moths. Finally I touched them and they just disappeared. After they caught up with me they took me outside and I got in to the car and we drove home. When I got home I went to bed but I couldn’t get to sleep with my eyes open cause I kept seeing things that would keep me awake.  The next morning I woke up and my parents weren’t that happy with me. Gravol was I weird trip and I know that I will probably never try it again. I went temporarily insane that night
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.


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'We don't have a 5G problem,' says Nokia's head of software
by ReutersHELSINKI: The head of Nokia’s software business Bhaskar Gorti defended the Finnish mobile network maker on Wednesday against market fears that it is lagging behind its peers Ericsson and Huawei in 5G development.
“I don’t think we have a 5G problem,” Gorti told Reuters in an interview, calling its development a marathon where a lot of focus had been put on the first ten minutes of the race.
Last month Nokia cut its outlook for this year and next because of the need to step up its investments in 5G – news that knocked a third off its share price.
It also suspended dividend payments to conserve cash and pledged to hurry up the introduction of new 5G chipsets.
That was in sharp contrast to Sweden’s Ericsson, which had lifted its market forecast for this year and its sales target for 2020 just a week earlier.
Nokia was wrong-footed by waiting for 5G radio standards to enter force late last year, while rivals were quicker to revamp products in anticipation of early rollouts in the United States and Korea, industry sources and analysts told Reuters.
Amit Hirchandani, sector analyst at Citi in London, calls Nokia a “show me” story in which it must demonstrate clear progress on fixing and deploying its 5G products in the coming months to restore investor confidence.
Gorti said the 5G cycle consisted of several different parts, adding he believed Nokia to be ahead its peers in aspects such as software development, internet of things deployments or wireless access.
“I personally think we have an at least 2 to 3 years or more lead on our nearest competitor Ericsson (in software),” he said.
A SHIFT TOWARD SOFTWARE
The operating profit from Nokia’s software business grew 108% to 156 million euros ($172.6 million) year-on-year in the third quarter, topping the profit from Nokia’s long-time main business the networks at 128 million for the quarter.
Gorti said it was important to understand that 80% of the mobile network is actually software and that the virtualisation of networks would only speed up for new 5G networks.
Virtualisation means that mobile network hardware can increasingly be replaced by software operated from a cloud service – a key development for Nokia and its peers to keep up with as virtualization is now entering the radio access network (RAN) technology where most investment has gone so far.
Nokia, unlike Ericsson and Huawei, has opted to participate in the development of open RAN technology, promising to support multi-vendor interoperability so that its operator clients are not tied to one vendor only.
“We think, if anything, the more things will run on general platforms it will drive the economics better for everybody,” Gorti said, adding Nokia was committed to supporting open RAN development in the future as well.


Constructor
new()
Methods
onEvent<T> (type:EventType, ?data:T):Void
Calling this function will fire events to all connected listeners
Parameters
- type
- what type of event to register the handler for
- data
- an Events typedef containing event information
registerHandler (type:EventType, handler:EventHandler):Void
Use this function to start listening to events
Parameters
- type
- what type of event to register the handler for
- data
- an Events typedef containing event information
unregisterHandler (type:EventType, handler:EventHandler):Void
Use this function to stop listening to events
Parameters
- type
- what type of event to register the handler for
- data
- an Events typedef containing event information