Tokamak is a Spring Boot OAuth 2.0 JWT Authorization Server with a React front end to simplify the management your apps and accounts.
Tokamak is a Spring Boot OAuth 2.0 JWT Authorization Server with a React front end to simplify the management your apps and accounts.


|
Mouse Input
Once the plugin is loaded, several things can be done with a simple click on the pager window:
LeftClick + Ctrl - anywhere in the Pager this lets you drag the Pager around
LeftClick + Shift - same as MiddleClick - when there's no 3-button mouse
RightClick - inside the current Desktop & outside all Desktops, access the plugin Menu
RightClick + Ctrl - access the plugin Menu anywhere in the Pager
RightClick + Shift - toggle in & out of the slit
RightMouseButton + LeftClick - Show the BroamsMenu
MouseScroll - change Desktops, when the pager has the focus
FocusButton - focus on target Window - default LeftClick
FocusButton + Shift - bring target Window to current Desktop (not xoblite)
MoveButton - focus on target Window & drag it around - default MiddleClick
MoveButton + Shift - drag non-target Window in the Pager
ChangeButton - change Desktops - default RightClick
The Menu
Most items should be self-explanatory - except perhaps the "Settings" submenu.
Making any change via the plugin menus saves those settings to the RC file.
"Reload Settings" allows manual changes to the RC file to take place directly.
bbPager Styling is read in the following order:
style settings in bbpager.bb - Settings|Edit Style
bbPager settings in the style file
default style elements (windows are not always visible)
Keyboard Input
Make bbPager the focused window (by clicking on empty space in the active desktop) & it can accept keyboard input. The keys you can press to access a few
functions quickly are below. Note: to re-position you need NumLock on.
Numpad Keys 1-9, except 5 Set bbPager position (7 is TopLeft, 3 BottomRight, etc.)
Numpad Key 5 or Enter or Back Toggle autoHide
Numpad Add Toggle alwaysOnTop
Numpad Subtract Toggle vertical|horizontal orientation
Escape This cancels window movement if a window has been grabbed in the pager
Miscellaneous Notes
Moving bbPager: When in autohide mode, dragging of the pager window is not possible - this is where the
positioning menu comes in handy. You cannot drag the pager window off the virtual/viewable screen area manually either.
To put the pager on a different monitor, Ctrl+LeftClick and drag the plugin into the prospective monitor's screen area
- using the placement menu or placement keyboard shortcuts will now position the pager window in this monitor's screen
area accordingly.
Autohide: Enabling autohide when the pager window is not on a screen edge will cause the pager to snap to the nearest screen edge and
enter the hidden state.
Moving windows: When an active window is moved, the workspace is changed to the one it is moved to.
Holding down the Shift key can prevent the changing of workspaces - as long as the target workspace is not empty.
When unfocused windows are moved the workspace does not follow the window.
Maximised windows cannot be moved within the same workspace.
When they are moved to another workspace they will stay in their maximised state.
Maximised windows use the cursor position to decide which desktop it is dragged to, while unmaximised windows use upper left corner of the
window to decide this.
Default settings are:
bbPager.x: 895 X position of the pager
bbPager.y: 10 Y position of the pager
bbPager.sizeRatio: 20 Number of times screen is scaled down for bbPager
bbPager.alignment: horizontal Chooses whether rows or columns are used
bbPager.columns: 1 Number of columns for desktops in vertical mode
bbPager.rows: 1 Number of rows for desktops in horizontal mode
bbPager.desktopChangeButton: 2 Mouse button for changing desktops
bbPager.windowMoveButton: 3 Mouse button for moving windows
bbPager.windowFocusButton: 1 Mouse button for focusing windows
bbPager.onTop: true Always on top?
bbPager.autoHide: false Toggle autohide mode for the pager
bbPager.snap: 20 Distance before window locks to screen edge
bbPager.desktopNumbers: true Show numbers of desktops?
bbPager.fullScreen: false Position according to multi-monitors?
bbPager.useSlit: false Hold the pager inside the Slit?
bbPager.alphaValue: Value of transparency (0-8) -> default is Menu.alpha
Mouse buttons: For setting mouse buttons to certain actions, 1 is the Left Mouse Button, 2 is the Right Mouse Button, and 3 is the Middle Mouse Button.
@BBShowPlugins Standard BB bro@m to show all plugins
@BBHidePlugins Standard BB bro@m to hide all plugins
@bbPager.Reload Make bbPager reload its settings
@bbPager.OpenRC Open the bbpager.rc file with your preferred editor
@bbPager.OpenStyle Open the bbpager.bb file with your preferred editor
@bbPager.LoadDocs Load the documentation file in your browser
@bbPager.Vertical Change the alignment to vertical mode
@bbPager.Horizontal Change the alignment to horizontal mode
@bbPager.About Display message box about the plugin
Value & True|False bro@ms can also be set for most of the bbPager entries:
@bbPager.snap
@bbPager.onTop
@bbPager.autoHide
@bbPager.alphaValue
@bbPager.sizeRatio
@bbPager.desktopNumbers
@bbPager.togglePlugins
@bbPager.useSlit
@bbPager.rows
@bbPager.columns
POSITIONING: These set the position of the pager window to that specified, according to bbPager.fullScreen (false = current monitor only; true = full screen)
@bbPager.TopLeft
@bbPager.TopCenter
@bbPager.TopRight
@bbPager.CenterLeft
@bbPager.CenterRight
@bbPager.BottomLeft
@bbPager.BottomCenter
@bbPager.BottomRight
|


Find out more about this tribute here.
Long time ago me and my brother Kyle here,
We was hitchhikin' down a long and lonesome road.
All of a sudden, there shined a shiny demon.
In the middle of the road. And he said:
"Play the best song in the world, or I'll eat your soul." (soul)
Well me and Kyle, we looked at each other,
And we each said. "Okay."
And we played the first thing that came to our heads,
Just so happened to be,
The Best Song in the World, it was The Best Song in the World.
Look into my eyes and it's easy to see
One and one make two, two and one make three,
It was destiny.
Once every hundred-thousand years or so,
When the sun doth shine and the moon doth glow
And the grass doth grow
Needless to say, the beast was stunned.
Whip-crack went his whoopy tail,
And the beast was done.
He asked us: "(snort) Be you angels?"
And we said, "Nay. We are but men." Rock!
Ahhh, ahhh, ahhh-ah-ah,
Ohhh, whoah, ah-whoah-oh!
This is not The Greatest Song in the World, no.
This is just a tribute.
Couldn't remember The Greatest Song in the World, no, no.
This is a tribute, oh, to The Greatest Song in the World,
All right! It was The Greatest Song in the World,
All right! It was the best muthafuckin' song the greatest song in the world.
And the peculiar thing is this my friends:
The song we sang on that fateful night it didn't actually sound Anything like this song.
This is just a tribute! You gotta believe it!
And I wish you were there! Just a matter of opinion.
Ah, fuck! Good God, God lovin',
So surprised to find you can't stop it.
All right! All right!


Provides tools to identify and visualize branches in a phylogenetic tree that are significantly responsive to some intervention, taking as primary inputs a phylogenetic tree (of class phylo) and a data frame (or matrix) of corresponding tip (OTU) labels and p-values.
| Version: | 1.10.6 |
| Imports: | ape, phylobase, phyext2, RColorBrewer, phyloseq, graphics, utils, stats, MASS, vegan |
| Published: | 2017-10-02 |
| Author: | John R. Stevens and Todd R. Jones |
| Maintainer: | John R. Stevens <john.r.stevens at usu.edu> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Citation: | SigTree citation info |
| Materials: | NEWS |
| In views: | Phylogenetics |
| CRAN checks: | SigTree results |
| Reference manual: | SigTree.pdf |
| Vignettes: |
SigTree Tutorial Vignette |
| Package source: | SigTree_1.10.6.tar.gz |
| Windows binaries: | r-devel: SigTree_1.10.6.zip, r-release: SigTree_1.10.6.zip, r-oldrel: SigTree_1.10.6.zip |
| OS X binaries: | r-release: SigTree_1.10.6.tgz, r-oldrel: SigTree_1.10.6.tgz |
| Old sources: | SigTree archive |
Please use the canonical form https://CRAN.R-project.org/package=SigTree to link to this page.


There is no "I" in DjangoCon US. Thank you to all our volunteers who make DjangoCon US happen!
{% for organizer in site.organizers %}
{% unless organizer.hidden %}
{% endunless %}
{% endfor %}


test_heart_rate_server module
-
test_heart_rate_server.
test_add_attendant_to_db
(info, db, expected)
-
test_heart_rate_server.
test_add_heart_rate_to_patient_db
(hr_info, timestamp, db, expected)
-
test_heart_rate_server.
test_add_patient_to_attendant_db
(info, db, expected)
-
test_heart_rate_server.
test_check_bad_input
(data, expected)
-
test_heart_rate_server.
test_check_heart_rate
(hr_info, timestamp, pat_db, att_db, expected)
-
test_heart_rate_server.
test_current_time
()
-
test_heart_rate_server.
test_find_first_time
(time, times, expected)
-
test_heart_rate_server.
test_find_patient
(patient_id, db, expected)
-
test_heart_rate_server.
test_find_physician_email
(patient_id, db, expected)
-
test_heart_rate_server.
test_get_patient_average_heart_rate
(patient_id, db, expected)
-
test_heart_rate_server.
test_get_patient_heart_rates
(patient_id, db, expected)
-
test_heart_rate_server.
test_get_patient_id_list
(attending_username, db, expected)
-
test_heart_rate_server.
test_get_patient_status
(patient_id, db, expected)
-
test_heart_rate_server.
test_is_tachycardic
(age, hr, expected)
-
test_heart_rate_server.
test_patients_for_attending_username
(patient_id_list, pat_db, expected)
-
test_heart_rate_server.
test_read_attending
(data, expected)
-
test_heart_rate_server.
test_read_heart_rate_info
(data, expected)
-
test_heart_rate_server.
test_read_patient
(data, expected)
-
test_heart_rate_server.
test_verify_attendant_exists
(attending_username, db, expected)
-
test_heart_rate_server.
test_verify_heart_rate_post
(data, expected)
-
test_heart_rate_server.
test_verify_internal_average
(in_dict, expected)
-
test_heart_rate_server.
test_verify_new_attending
(in_dict, expected)
-
test_heart_rate_server.
test_verify_new_patient_info
(data, expected)


Grid based design is where a grid consisting of rows and columns is used as a template on which the position of elements can be determined helping in their placement on the page. The norm is to work from top to bottom and left to right which the eye naturally follows (if your from a western culture).
They are a response to todays world where there are a large variety of devices with different screen sizes that can access the internet. A responsive webpage will adjust itself automatically according to the size of the screen or more accurately the window the page is in, displaying the content in an appropriate way. This is done by changing the attributes such as size of elements, their position or if they are shown at all.
Semantic structure is where the HTML code is written in such a way that it conveys meaning when read. For example a semantic element would be nav meaning navigation,other examples are footer and article.
I found the following points interesting.


User preference options...
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It looks like the connection is lost, and the page you're trying to get in not in your cache.
While waiting for connexion, you can see which pages are still available while offline.


parser
public enum parser : Error
Parser related errors
-
Thrown when GPX version is < 1.1
Declaration
Swift
case unsupportedVersion
-
When an issue occurred at line, but without further comment.
Declaration
Swift
case issueAt(line: Int)
-
Thrown when issue occurred at line. (Mostly wraps XML parser errors)
Declaration
Swift
case issueAt(line: Int, error: Error)
-
Thrown when file is XML, but not GPX.
Declaration
Swift
case fileIsNotGPX
-
Thrown when file is not XML, let alone GPX.
Declaration
Swift
case fileIsNotXMLBased
-
Thrown when file does not conform schema. (unused)
Declaration
Swift
case fileDoesNotConformSchema
-
Thrown when file is presumed to be empty.
Declaration
Swift
case fileIsEmpty
-
When multiple errors occurred, to give an array of errors.
Declaration
Swift
case multipleErrorsOccurred(_: [Error])


Close
Submit
Edit
Done
What type of place is this?
Flag a Problem
Revert
Delete
Reposition
Done
Label
Content
Yes
No
Format OK
Open to the public?
Yes
No
Open to the public?
Yes
No
General


This method disables the instance of AsyncLocalStorage
. All subsequent calls
to asyncLocalStorage.getStore()
will return undefined
until
asyncLocalStorage.run()
or asyncLocalStorage.runSyncAndReturn()
is called again.
When calling asyncLocalStorage.disable()
, all current contexts linked to the
instance will be exited.
Calling asyncLocalStorage.disable()
is required before the
asyncLocalStorage
can be garbage collected. This does not apply to stores
provided by the asyncLocalStorage
, as those objects are garbage collected
along with the corresponding async resources.
This method is to be used when the asyncLocalStorage
is not in use anymore
in the current process.
Calling asyncLocalStorage.enterWith(store)
will transition into the context
for the remainder of the current synchronous execution and will persist
through any following asynchronous calls.
Calling asyncLocalStorage.exit(callback)
will create a new asynchronous
context.
Within the callback function and the asynchronous operations from the callback,
asyncLocalStorage.getStore()
will return undefined
.
The callback will be ran asynchronously. Optionally, arguments can be passed to the function. They will be passed to the callback function.
If an error is thrown by the callback function, it will not be caught by
a try/catch
block as the callback is ran in a new asynchronous resource.
Also, the stacktrace will be impacted by the asynchronous call.
This methods runs a function synchronously outside of a context and return its return value. The store is not accessible within the callback function or the asynchronous operations created within the callback.
Optionally, arguments can be passed to the function. They will be passed to the callback function.
If the callback function throws an error, it will be thrown by
exitSyncAndReturn
too. The stacktrace will not be impacted by this call and
the context will be re-entered.
This method returns the current store.
If this method is called outside of an asynchronous context initialized by
calling asyncLocalStorage.run
or asyncLocalStorage.runAndReturn
, it will
return undefined
.
Calling asyncLocalStorage.run(callback)
will create a new asynchronous
context.
Within the callback function and the asynchronous operations from the callback,
asyncLocalStorage.getStore()
will return an instance of Map
known as
"the store". This store will be persistent through the following
asynchronous calls.
The callback will be ran asynchronously. Optionally, arguments can be passed to the function. They will be passed to the callback function.
If an error is thrown by the callback function, it will not be caught by
a try/catch
block as the callback is ran in a new asynchronous resource.
Also, the stacktrace will be impacted by the asynchronous call.
This methods runs a function synchronously within a context and return its return value. The store is not accessible outside of the callback function or the asynchronous operations created within the callback.
Optionally, arguments can be passed to the function. They will be passed to the callback function.
If the callback function throws an error, it will be thrown by
runSyncAndReturn
too. The stacktrace will not be impacted by this call and
the context will be exited.
Generated using TypeDoc
When having multiple instances of
AsyncLocalStorage
, they are independent from each other. It is safe to instantiate this class multiple times.


QBittorrent Chrome
QBittorrent Server
Address
Refresh Rate
Seconds
User Name
Password
Notifications
Display Notifications
Categories
Name
Download Directory


Please join us this spring on The Mornington Peninsula to celebrate our marriage
We met in 2003, at a music festival in Brussels. We hit it off immediately, sharing our love of cooking and classical music. We had a magical time together in Spain during Lucas' first year at med school, and we both knew it was time. We were engaged in June 2015, and now it's finally time to tie the knot and begin the next chapter of our lives as husband and wife!


FROM: MRS MARIAM ABACHA NO:28b ABACHA'S CRESCENT GIDADO BY CLOSE KANO - NIGERIA. DEAR PATINER, COMPLIMENT OF THE SEASON TO YOU I am mrs.mariam Abacha, the wife to the late Head of State, of the federal Republic of Nigeria from 1993 -1998 - General Sani Abacha. My late husband made a lot of money as the Head of state of Nigeria for 5years. He has different accounts in many banks of the world. He has not left any stone unturned in accruing riches for his family. The present democratic government of Nigeria led by President (Gen.) (Rtd)Olusegun Obasanjo has not find favour with my family since their inception. This may be as a result of his hatred for my late husband who kept him in jail for over two years for a coup attempt, before the death of my husband. He was released immediately my husband died and he was later made the present President. He has confiscated and frozen all my family account in Nigeria and some other American, Europe and Asian continents, It has been in both the local and international news. Now, my purpose of all these introduction and proposal is just seeking for your candid assistance in saving this sum 0f $50.000,000.00 USD FIFTY- MILLION.DOLLARS]which my late husband had hidden from the Nigerian govt.during his regime. and which is presently somewhere in a financial and security company outside the entire Nigeria and West African region. This is a huge sum of money, I cannot trust much on most saboteur friends of my family in Nigeria who could not be trusted. I got your contact through a search made on net for a trustworthy person . I deemed it necessary to contact you for this trustworthy transaction. All whom I needed is a sincere, honest, trustworthy and God-fearing individuals whom my mind will absolve to help me in this deal.If you have feelings about my situation, don't hesitate to stand for me. If my proposal is sudden to you, all I need now is for you to standas the Beneficiary ofthis money to claim it and save for me. There is no difficulty, I will send your name as the recipient as well as the beneficiary of the money.On your identification and confirmation from me, the fund will be handle to you. All i need is your confirmention of willingness and i will give you the full details. You will be compensated with 25% of the total fund for all your efforts and 5% will be for whatever expenses that will be incurred, while 70% is to be used in buying share in any company provided this fund is save for me for your account in your country. Please this is a very serious matter, it is a save my soul request from you and I will be delighted too much to receive a positive response from you in order to move into action. I will give you details on request from you. N:B PLEASE YOU CAN CONTACT ME ON MY PRIVENT<EMAIL_ADDRESS>THANKS YOURS FAITHFULLY MRS M ABACHA.


| PERLPRAGMA(1) | Perl Programmers Reference Guide | PERLPRAGMA(1) |
use MyMaths; my $l = MyMaths->new(1.2); my $r = MyMaths->new(3.4); print "A: ", $l + $r, "\n"; use myint; print "B: ", $l + $r, "\n"; { no myint; print "C: ", $l + $r, "\n"; } print "D: ", $l + $r, "\n"; no myint; print "E: ", $l + $r, "\n";
to give the output
A: 4.6 B: 4 C: 4.6 D: 4 E: 4.6
i.e., where "use myint;" is in effect, addition operations are forced to integer, whereas by default they are not, with the default behaviour being restored via "no myint;"
The minimal implementation of the package "MyMaths" would be something like this:
package MyMaths; use warnings; use strict; use myint(); use overload '+' => sub { my ($l, $r) = @_; # Pass 1 to check up one call level from here if (myint::in_effect(1)) { int($$l) + int($$r); } else { $$l + $$r; } }; sub new { my ($class, $value) = @_; bless \$value, $class; } 1;
Note how we load the user pragma "myint" with an empty list "()" to prevent its "import" being called.
The interaction with the Perl compilation happens inside package "myint":
package myint; use strict; use warnings; sub import { $^H{"myint/in_effect"} = 1; } sub unimport { $^H{"myint/in_effect"} = 0; } sub in_effect { my $level = shift // 0; my $hinthash = (caller($level))[10]; return $hinthash->{"myint/in_effect"}; } 1;
As pragmata are implemented as modules, like any other module, "use myint;" becomes
BEGIN { require myint; myint->import(); }
and "no myint;" is
BEGIN { require myint; myint->unimport(); }
Hence the "import" and "unimport" routines are called at compile time for the user's code.
User pragmata store their state by writing to the magical hash "%^H", hence these two routines manipulate it. The state information in "%^H" is stored in the optree, and can be retrieved read-only at runtime with "caller()", at index 10 of the list of returned results. In the example pragma, retrieval is encapsulated into the routine "in_effect()", which takes as parameter the number of call frames to go up to find the value of the pragma in the user's script. This uses "caller()" to determine the value of $^H{"myint/in_effect"} when each line of the user's script was called, and therefore provide the correct semantics in the subroutine implementing the overloaded addition.
The Perl core uses a handful of keys in "%^H" which do not follow this convention, because they predate it. Keys that follow the convention won't conflict with the core's historical keys.
Don't attempt to store references to data structures as integers which are retrieved via "caller" and converted back, as this will not be threadsafe. Accesses would be to the structure without locking (which is not safe for Perl's scalars), and either the structure has to leak, or it has to be freed when its creating thread terminates, which may be before the optree referencing it is deleted, if other threads outlive it.
| 2019-10-21 | perl v5.30.3 |


About
The international food conference bring the best chefs around the world to tell us about their experience, knowledge and techniques. This space is for all the people in the gastronomic industry, chefs, food and beverage managers, restaurant owners and students. This year we have prepared a special program for every people, we have lectures, exhibitions, forum, workshop and the event special feature, networking, swim deep into an endless line of people looking for someone like you with your talent, talk about job offers, techniques and more.
Please contact us per Email for any further questions about International Food Conference 2021!
<EMAIL_ADDRESS>The logo of International Food Conference 2021 was decided throught the logo competition from 10. May to 15. June
Take a look at the last two IF Conference Summits which took place in Bogota and Madrid


don't worry, i know that there were bad words on the original index.html on /testdir/people. but i removed them to not call out


Search for Ofsted inspection reports for:
Start now on the Ofsted website
Last updated: 20 March 2017


Made with ♥ by
Oskar Radon
Raincoat.io
About
Raincoat.io
Heading out? Let us help you decide whether or not to bring a raincoat.
Checking the weather...
Right now
Later


Verification technique automatically captures faces and verifies, no more stopping for capturing required
Identifies images and real faces for anti-spoof function
Utilizes deep learning to effectively identify living faces
Pioneering Techniques, Quicker and Farer
Verifies in 0.5 second, outpacing conventional infrared techniques
Up to 3m verification distance, no more crowded queuing
Touchless Makes Convenience and Hygiene
Visible light technique for better convenience and hygiene, Touchless verification for prevention of spread of disease
Applies deep learning techniques
to widen face verification angle
While most algorithms only support 15° face verification,
ZKBioFace visible light technique supports 30° face verification
One-stop Service
Able to connect to ZKBioSecurity and ZKTime Web 2.0 for better convenience and management system
Users may upload personal information themselves
New users may upload personal information through mobile app.,administrators may just enroll in system and perform registration for better convenience and efficiency.


CourtWatch supports community-based organizations looking to monitor their local judges to provide accountability and feedback to the court in order to improve court processes, particularly for underrepresented and vulnerable groups. Individuals can enter and save data and see basic analyses and graphs of their watches broken down by judge.


{% extends "base.html" %} {% block h1_title %}Sorry, that external account is already associated with another {{ TEXT_BRANDING.account }}{% endblock %} {% block title %}Already associated{% endblock %} {% block content %}
You've attempted to link an external account with your {{ TEXT_BRANDING.account }}, but unfortunately the external account is already associated with another {{ TEXT_BRANDING.account }}.
You can try again after logging out to see which account it's linked to.
If you've inadvertently created multiple {{ TEXT_BRANDING.accounts }}s and would like them merged, please email {{ SUPPORT_EMAIL_NAME }}.
{% endblock %}


Before Change
"distributed_replicated", "independent"],
"""The method for managing variables: parameter_server,
replicated, distributed_replicated, independent""")
flags.DEFINE_enum("tf_local_parameter_device", "gpu", ["cpu", "gpu"],
"""Device to use as parameter server: cpu or gpu. For
distributed training, it can affect where caching of
variables happens.""")
After Change
"""Device to use as parameter server: cpu or gpu. For
distributed training, it can affect where caching of
variables happens.""")
flags.DEFINE_enum("tf_device", GPU, [CPU, GPU],
"Device to use for computation: cpu or gpu")
flags.DEFINE_enum("tf_data_format", "NCHW", ["NCHW", "NHWC"], """Data layout to
use: NHWC (TF native) or NCHW (cuDNN native).""")
flags.DEFINE_boolean("tf_use_nccl", True,
"Whether to use nccl all-reduce primitives where possible")


Welcome, I'm Tim Malesky and on this page you'll see a small sample of my work.
Here is a sampling of some art...

And for your viewing pleasure, Some very old student pieces...


Tensorizing Recurrent Neural Nets
This post is the summarization of the paper “Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning” by Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu Hangen He, and David Barber. Published as a conference paper at NIPS 2017, Long Beach, CA, USA.
Paper, supplementary materials
Main ideas and contributions
- Starting from a skewed stacked RNN architecture, the authors proposed a novel RNN where each hidden unit is parameterized by a high rank tensor (≥2).
The image above compares a three hidden layers (depth L = 3) skewed recurrent neural network with the proposed model. The tRNN model here has only one hidden layer, but its hidden units are parameterized by a P-by-M matrix rather than a single vector. Furthermore, the interaction between hidden units are captured by the convolution operation (defined later) to take advantages of the tensor representation. According to the authors, the first dimension of the hidden units is “locally connected” in order to share parameters, while the second dimension is “fully connected” for global interaction. This idea will be clearer when we discuss the convolution. In general, the construction of a hidden unit output is computed as following (\(circledast\) is the convolution operator):
\[H^\text{cat}_{t-1, p} = \left\{ \begin{array}{l} x_t W^x + b^x, \text{ if } p = 1 \\ h_{t-1, p-1}, \text{ else } %_ \end{array} \right. \ \ \ \ \begin{array}{l} A_t = H^\text{cat}_{t-1} \circledast \{W^h, b^h\} \\ H_t = \phi(A_t) \\ y_t = \varphi(h_{t+L-1, p} W^y + b^y) \end{array}\]Note that the actual output at \(y_t\) is computed using the last tensor (in this case it is a vector) of the hidden tensor \(H_{t+L-1}\) As in the figure above, \(y_t\) is computed from \(h^3_{t+2}\) - the last layer of the hidden unit \(H_{t+2}\) Also, the implicit depth P = L = 3 leads us to the same computation.
-
The paper introduces cross-layer convolution and memory cell convolution (for the LSTM extension). The explanation to the convolution is presented in the second section. Note that in this post, I moved the time notation to the top of each symbol when it’s convenient so that the subscript contains only indexing variables (e.g. \(A_{t, p, m^o}\) becomes \(A^t_{p, m^o}\)
-
The 2D-tRNN model is further extended to LSTM and higher order tensors (3D).
This extension to LSTM is pretty straight-forward as each gate is computed using the convolution operator \(\circledast\) in lieu of the standard matrix multiplication. For the higher order tensors extension, the concatenated tensor is constructed by appending the projection (multiplied with weights, added with bias) of \(x_t\) to one corner of the hidden unit tensor. This can be understood as going down the tensor dimension-wise, when you reach a 2D matrix with row size of M, append \(x_t W^x + b^x\) to it. In the same way, \(y_t\) is generated with the opposite corner.
Dissecting convolution
It took me sometime to fully grasp the author’s idea . Despite that “sometime”, my understanding can be captured in a single image (and now I feel silly). In the case of 2D hidden units, the operation and the dimensionality of each tensor is:
\[A_t = H^\text{cat}_{t-1} \circledast \{W^h, b^h\}, \text{ where: } \ \ \ \begin{array}{l} A_t \in R^{P \times M^o} \\ H^\text{cat}_{t-1} \in R^{(P+1) \times M} \\ W^h \in R^{K \times M^i \times M^o} \\ b^h \in R^{M^o} \end{array}\]In here, \(A_t\) is the activation tensor. \(H^\text{cat}_{t-1}%_\) represents the concatenation of a hidden unit’s rank-2 tensor output (matrix) and the input vector from the layer above it (in this case, it is the input vector \(x_t\) From the skewed sRNN figure above, the concatenation is pretty clear: \(h_t\) is pointed to by \(x_t\) and \(h_{t-1}\) hence the tensor used in computing \(A_t\) and \(h_t\) will be the concatenation between \(x_t\) and \(h_{t-1}\) The same principle applies for the tRNN. The convolution kernel consists of weight \(W^h\) and bias \(b^h\) \(W^h\) is a rank-3 tensor of K filters, each filters has \(M^i\) input channels and \(M^o\) output channels. In this paper, the authors noted that they let \(M = M^i = M^o\) for simplicity. The detail of the convolution is given in the supplementary document:
\[A^t_{p, m^o} = \sum^K_{k=1} \left( \sum^{M^i}_{m^i=1} H^{cat; t-1}_{p - \frac{K-1}{2}+k, m^i} \cdot W^k_{k,m^i,m^o} \right) + b^h_{m^o}\]It might sound obvious, but it is easier for me to think of the indices as “selector”. For example, \(A^t_{p, m^o}\) is the element \(square\) of tensor \(A^t\) that is selected by \(p, m^o)\) Usually, the “selector” is lowercase and the total number of element in a dimension is uppercase. In the figure below, I circled in red the “selectors”.
It might helps with a kind of story. The matrix \(A^t \in R^{P \times M^o}\) is what we need to compute for the next time step. We set out to compute each element of this matrix. \(A^t_{p, m^o}\) is given by the convolution operator defined in the formula above. In the figure, the gray small box represents \(A^t_{p, m^o}\) The two selector \(p\) and \(m^o\) control the center of the convolution in \(H^{\text{cat}; t-1}\) and which output channel to pick in \(W^h\) respectively. For example, if \(K=5\) and where want to computer \(A^t\) at \(p=3, m^o=3)\) then for each slice of \(W^h\) the 5 columns associated with \(m^o=3\) will be picked out. Next, on \(H^{\text{cat}; t-1}\) there are also 5 rows selected. Since \(p=3\) the index of these row vectors are \(2,3,4,5,6\}\) (centered at \(p+1=4\) These 5 pairs of vectors are indexed by \(k = \{1,2,3,4,5\}\) Sum of the dot products of the pairs is \(A^t_{p, m^o}\) Note that the authors use zero-padding to keep the shape of output same as the input. In the case of memory cell convolution, the values used in padding are the border values to avoid interference with the memory values.
Evaluations
The authors uses three main tasks to demonstrate the effectiveness of their proposed model:
-
Algorithmic tasks consist of learning to sum two 15-digit numbers and learning to repeat the input sequence.
Sum two 15-digit integers || Repeat the input Input : --2545-9542------- || Input : -hiworld---------- Output: -----------12087-- || Output: --------hiworld---
The advantage of the proposed model (3D-tLSTM with Channel Normalization) is that it requires much less training samples to reach more than 99 percent accuracy compared with Stacked LSTM and Grid LSTM. However, there are no mention to the training time or other modification of tLSTM. Furthermore, the best performing models have 7 and 10 layers depth. There are not much explanation for these hyper-parameters. The 7-layer requires less training samples than the 10-layer in addition task, but the 10-layer in turn requires much less samples in memorization. I expected the 7-layer model requires less samples in both tasks.
-
MNIST classification tasks consist of normal MNIST and randomized pixel sequence called pMNIST. In these tasks, the pixels of an hand-written digit image are fed into the neural nets as a sequence. In this task, I do not see any advantage of using tLSMT compared with state-of-the-art methods such as DilatedGRU.
-
Wikipedia language modeling task deal with next-character prediction on the Hutter Prize Wikipedia dataset. Similar to the aforementioned MNIST tasks, there are no clear win for tLSTM compared with Large FS-LSTM-4 method (in term of both BPC and number of parameters).
Why tensors?
It is true that we can design a “vector” recurrent neural net that works in the same way as the proposed tRNN here. After all, the variables is stored in arrays on our computer. However, tensors represent a concrete way to think about a “block of numbers”, and more importantly, we can define and argue about convolution easily with it. Let’s take image data as an example. The image below shows the popular 2D convolution operation. Tensor representation gives us a concrete way to think about color channels and its corresponding channels in each convolution kernel. Furthermore, notice that we “scan” each convolution channel in the intensity dimension (on the matrices) but not the color dimension. As such, the weights in the intensity dimension are shared. Similarly, in recurrent neural networks, the tensor hidden unit in some sense enables a more abstract representation and allow weight sharing.
Conclusion
This paper proposes a nice way to increase the structural depth of a recurrent neural network model. Instead of parameterizing hidden units with vectors, tRNN modeled hidden units as tensors for more efficient weight sharing and implicit depth. Such approach greatly reduces the number of parameters in a recurrent neural network while maintaining the depth. tRNN model is also extended to use LSTM module and higher order (3D) tensor for better performance and data abstraction. The effectiveness of tLSTM is demonstrated on three different tasks in which tLSTM achieve state-of-the-art performance with some improvement on number of parameters (minor) and required number of training samples. On the other hand, while the proposed model might improve running time and number of parameters, there was no discussion on the training time and training complexity of tLSTM. It would be interesting to implement the 2D and 3D models to understand the benefit of tLSTM better.
In this post, I wrote about my understanding and commented on the paper “Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning” by Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, and David Barber. I left out some details such as Channel Normalization or the constraints on \(L, P, K\}\) These minor optimization tricks can be found on the paper. After this post, I plan to implement this technique to see if it is suitable for my current work. In particular, I would like to see if the training cost can potentially be reduced for a stacked LSTM approach to model product names.


We are Pixer
Heavy gunfire and an explosion sent people running for their lives from the complex in an affluent neighborhood in the Kenyan capital. Footage from the scene showed armed officers escorting office workers and several injured.
People to safety as the sound of shots rang out. Cars in the compound could be seen aflame as a helicopter flew overhead.


Kalimba Tabs Notation Changer v1.2
How to use »
Numbered/Lettered Tabs
Auto Convert »
Converted Tabs
More Tabs & Tutorials
Stereo Hearts - Gym Class Heroes | Kalimba Cover Tutorial | Easy Tabs With Lyrics
How To Connect Kalimba Pickup To Phone | Kalimba Audio Tutorial


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Enabling pop-up blockers
Some security software (e.g. pop-up blockers) may prevent the programme running because they open in a separate browser window. Pop-up blockers (like those used in Win XP SP2) should be disabled before running the course. To do this:
- Click ‘Tools’ on your browser toolbar
- Click ‘Internet Options…’
- Click the ‘Security’ tab
- Ensure the ‘Internet’ icon is selected
- Click ‘Custom Level…’
- Scroll down to ‘Miscellaneous’
- Under ‘Allow script-initiated windows without size or position constraints’ select the ‘Enable’ radio button
Click here to close this page.


The exit of the actors and musicians happens in silence. The shite is the first one to exit, followed by the waki. If a tsukurimono was used in the play, the stage assistants would remove it next. Once the actor or stage assistant has passed the Second Pine, the curtain rises and then falls once he has entered the anteroom. From this point, the musicians exit in the same order they entered, the nokhan player first, followed by the kotsuzumi, ōtsuzumi,and taiko players. Upon reaching the Second Pine, the curtain is pulled back on one side to allow for the musicians' exit. The jiutai members exit through the back left side door, starting with the back row members. As they are exiting the stage, actors, assistants and musicians exhibit modesty and decorum. The audience holds its applause until the last musician is about to exit.


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Experience
Data Scientist
The Climate Corporation | St. Louis, MO | Jul 2019 - Jan 2021
Working on analyzing customer’s agronomic data to create value to our customers and the company.
- Successfully built data processing pipeline using PySpark that processes ~5TB of customer’s agronomic data in spark cluster at user specified cadence.
- Performed EDA on the customer data and created insights to support various business decisions.
- Worked on customer segmentation to provide tailored solutions to customers.
- Design statistical model to test hypothesis.
- Worked on packaging and shipping python package to internal package artifactory to share and enable cross-team access.
- Document and share work among other teammates for collaboration and visibility of work.
- Use git for version control, and reviewed other people’s code for bug fixes and to ensure the code meets the PEP8 coding standards
AI Developer C2P Group | Brooklyn, NY | July 2018 - Dec 2018
Work on building deep learning based image recognition system for products in retail industry to enable automatic retail checkout.
- Evaluated business requirements and prepared detailed specifications that follow project guidelines required to develop written programs.
- Implemented Data Exploration to analyze images and to select Deep Learning and CNN architectures using Python SciPy, NumPy and Keras.
- Built Object Detection and Object Classification model using TensorFlow’s Object Detection API.
- Used state-of-the-art Deep Learning Architecture (faster R-CNN inception v2) and to train the Object Detection model with Back Propagation, Batch Gradient Descent and Adam Optimizer algorithm. Developed coding tools based on Python, Bash Script to automate Image Acquisition and Augmentation. Launching Amazon EC2 Cloud Instances using Amazon Images (Linux/ Ubuntu) and Configuring launched instances with respect to specific applications.
- Trained the Object Detection model on a Google Cloud GPU (NVIDIA Tesla V100) and local GPU (NVIDIA GeForce GTX 1080 Ti) Server.
- Evaluated the performance of the trained model with ROC and AUC.
- Perform Hyperparameter Tuning to fine tune the model for boosting the Accuracy.
- Built a Flask API to serve the trained model in Heroku to explain and communicate the insights, model scores and performance of the model to both technical and business teams.
- Used Agile methodology and SCRUM process for project developing.
Graduate Research Assistant
Youngstown State University | Youngstown, OH | Aug 2016 - May 2018
- Utilized an ensemble learning algorithm (Gentle AdaBoost) to detect nanoparticles in Transmission Electron Microscope (TEM) image.
- Developed a series of coding tools based on C++ and Python for image processing and augmentation of dataset.
- Performed 160 experiments using Amazon EC2 to compare the performance of two special image features: Haar, Local Binary Pattern.
- Significantly improved the performance of object detection model from 87% to 98%.
- This is an interdisciplinary project in collaboration with department of chemistry.
Project(s)
Predicting breast cancer using mini MIAS database of mammograms:
This project uses transfer learning approach to classify mammograms into malignant or benign. A pre-trained VGG16 is used as a base neural network architecture and trained on the mini MIAS database of mammograms.
Project Link: GitLab
Education
MS. in Computing & Information Systems
Youngstown State University, OH | 2016 - 2018
Bachelor in Electronics & Communication Engineering
Pulchowk Campus - Institute of Engineering, Tribhuvan University | 2010 - 2014
Hobbies
- Singing & listening music
- Hangout with friends


R wrapper of the 'libmf' library <http://www.csie.ntu.edu.tw/~cjlin/libmf/> for recommender system using matrix factorization. It is typically used to approximate an incomplete matrix using the product of two matrices in a latent space. Other common names for this task include "collaborative filtering", "matrix completion", "matrix recovery", etc. High performance multi-core parallel computing is supported in this package.
| Version: | 0.4.2 |
| Depends: | R (≥ 3.3.0), methods |
| Imports: | Rcpp (≥ 0.11.0) |
| LinkingTo: | Rcpp, RcppProgress |
| Suggests: | knitr, prettydoc |
| Published: | 2017-09-02 |
| Author: | Yixuan Qiu, Chih-Jen Lin, Yu-Chin Juan, Wei-Sheng Chin,
Yong Zhuang, Bo-Wen Yuan, Meng-Yuan Yang, and other
contributors. See file AUTHORS for details. recosystem author details |
| Maintainer: | Yixuan Qiu <yixuan.qiu at cos.name> |
| BugReports: | https://github.com/yixuan/recosystem/issues |
| License: | BSD_3_clause + file LICENSE |
| Copyright: | see file COPYRIGHTS |
| URL: | https://github.com/yixuan/recosystem |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Materials: | README NEWS |
| CRAN checks: | recosystem results |
| Reference manual: | recosystem.pdf |
| Vignettes: |
recosystem: Recommender System Using Parallel Matrix Factorization |
| Package source: | recosystem_0.4.2.tar.gz |
| Windows binaries: | r-devel: recosystem_0.4.2.zip, r-release: recosystem_0.4.2.zip, r-oldrel: recosystem_0.4.2.zip |
| OS X binaries: | r-release: recosystem_0.4.2.tgz, r-oldrel: recosystem_0.4.2.tgz |
| Old sources: | recosystem archive |
Please use the canonical form https://CRAN.R-project.org/package=recosystem to link to this page.


---
layout: default
title: React Native guide
description:
---
This is a collection of guides documenting common activities
while developing react-native applications.
You can use our Yeoman generator for bootstrapping a new react-native application.
Follow the instructions in the README.
Unfortunately, there are still a lot of manual steps in configuring the continuous delivery pipeline for a mobile application
written in react-native. We actively search for better ways of doing this, but in the meantime, you'll need to do the following
(please be aware that this guide assumes that you bootstrapped the application as described above):
In order to be able to actually delivery the application via BuddyBuild and iTunes Connect, you will need to modify a few
native resources.
In Xcode, inside the project's Overview
Bootstrapping
Continuous delivery
iOS project files
1. Product name
Build Settings
, choose a Product Bundle Identifiers
, and a
Product Name
.
Set up Code Signing Identity
to iOS Developer
for automated code signing in
BuddyBuild.
Depending on the target platform, modify default application icons in Images.xcassets
.
React-native comes with a standard launch screen but you might want to modify it. In Xcode, change LaunchScreen.xib
to fit your needs.
In order to be able to actually delivery the application via BuddyBuild and Google Play, you will need to modify a few native resources.
If you want to override the default icons, you will need to copy app icons for all resolutions to the directories in android/app/src/main/res
. Be careful
only to override the image ressources. If you are looking for a fast and easy way to create all necessary icons, you can use
an online app icon creator.
Before BuddyBuild can deploy your application to Google Play, you will need to upload an APK manually once. You can create this
initial APK by following the instructions given in the react-native documentation.
After you have created the APK, be sure to revert the changes you made to your Android project files. BuddyBuild does all the code signing
for you.
Next, we'll set up the app in our Apple Developer account.
In your Apple Developer account, navigate to Certificates, IDs & Profiles -> Identifiers -> App IDs
and create a new App identifier.
Be sure to use the Bundle ID
you set up for the app when configuring the iOS project files.
We also need to set up the application for delivery via iTunes Connect.
In iTunes Connect, navigate to My Apps
and click the +
button. Follow the instruction to create a new application.
In iTunes Connect, navigate to Users & Roles
.
Assign the newly created app to the codecentric labs zero user by adding it to the list of applications on the Roles
tab.
For the Android version, we will need to set up a new application in Google Play.
In the Google Play Developer Console, create a new app by clicking
the button.
Configuring a new application on Google Play is a little bit more involved than the iOS equivalent. Fortunately, you
can check what's missing by using the why can't I publish anything
link on the upper right corner. For your
convenience, we included default media for this step in the project's default-assets
folder.
Once you have finished all other configuration steps, you can upload your APK to the tracks you want BuddyBuild to publish in.
CircleCI will be used to automatically merge into master
and tag a release whenever changes are pushed
to develop
and all tests are green. We will later set up BuddyBuild to publish the application from
master
to enable continuous delivery.
In the CircleCI dashboard, add the app's GitHub repository as a new project.
A circle.yml
and a merge script are created during bootstrap, so no additional build configuration is required.
By default, only read access is granted to CircleCI. To enable write access, you will need to create an additional SSH and add
it to CircleCI and GitHub. Follow these instructions.
The final step is configuring BuddyBuild to build, sign and deploy new version of the application whenever a commit on the
master
branch is detected.
BuddyBuild can automatically detect and configure react-native builds. All you need to do is add the GitHub repository of the application and
choose which application should be added. Make sure that BuddyBuild is working on the master
branch when doing this.
First, disable all branches but master
by unchecking everything in the branch settings dialogue. Configure master
to only
build on pushes. Also, make sure that only the Release
version is being built.
Go to each project's settings and follow the instructions given on the application signing pages. For iOS, this will include exporting and
uploading your certificates. For Android, you will need to create and upload a keystore.
Go to each project's deployment tab and set up delivery via BuddyBuild for each build of the master
branch, adding members to the test groups as needed. Configure
deployments via Testflight and Google Play Alpha or Beta channels to happen once a day from the master
branch. Please note that you will need
After the latest changes to Jest, we recently switched back to using the default React Native test setup. You can have a look at the test directory in our template to see the details.
We use the following libraries for writing and running unit tests:
You can run the tests from the command line by executing npm test
. For WebStorm, there is no dedicated Jest test runner
yet, but you can create a test run configuration by following these instructions.
As CircleCI is automatically running npm test
, no additional setup is needed for continuous integration.
We use appium for functional testing. To run our appium tests as part of our continuous delivery pipeline, we use Bitbar Testing (Testdroid) and a custom build step on BuddyBuild. The following sections describe our approach.
Follow the instructions to install appium for your platform.
We write our actual tests with WD.js, which you will need to install via rpm install --save-dev wd
.
In order to execute the tests locally and on BuddyBuild, we use a npm script:
"functional-tests": "jest --config=jest-functional-tests.json"
Have a look at a sample configuration file for
the details.
We will also need to exclude our specs from the normal test runs by adding
"testPathIgnorePatterns": [
"<rootDir>/specs/",
"<rootDir>/node_modules/"
]
to the Jest configuration block.
In your Bitbar project overview, you need to create a new project. Make sure to choose the right type
(either "Appium Android" or "Appium iOS") from the drop down. You will need to use the exact project name when setting up your WD test later on.
Also, find the device you want to use and note it's name.
In order to have BuddyBuild run the appium tests on Testdroid, we need to set up some environment variables. TESTDROID
needs to be set
to 1
. Also, you will need to set TESTDROID_API
to the API key found on your Testdroid
account page.
At this point, we will also add the custom build script
to our project. Please note that the script needs to be inside the native project folder.
The example given here is for an Android project. For iOS projects, you can use $BUDDYBUILD_IPA_PATH
directly. The build script will upload the latest
version of the app to Testdroid, export its file name as environment variable and start the functional tests.
Have a look at an example to see how to set up WD.js to run both locally and on BuddyBuild. For more information on how to configure the required properties, have a look at the Testdroid documentation.
Start the appium server with appium
, then run the tests with npm run functional-test
.
We found the following libraries helpful.
We use the Flux architecture when building React Native applications. For implementing it, we use the following libraries:
We have had good experiences with the following libraries for accessing hardware:
After struggling with react-native-redux-router's abstractions, we decided to implement our own navigation and routing stack with the NavigationExperimental primitives when we need to. We found react-native-tabs to work fine for us when implementing tabbed navigation.
We use react-native-vector-icons a lot.
To work around rendering differences on Android and iOS, we usually use react-native-platform-stylesheet
We use the following libraries when having the need for backend communication:
We are using Apteligent for monitoring of mobile applications.
Steps for integrating apteligent into mobile application for android are:
compile 'com.crittercism:crittercism-android-agent:+'
<uses-permission android:name="android.permission.INTERNET"/>
<uses-permission android:name="android.permission.ACCESS_NETWORK_STATE"/>
import com.crittercism.app.Crittercism;
import android.os.Bundle;
@Override
protected void onCreate(Bundle bundleInstance) {
super.onCreate(bundleInstance);
Crittercism.initialize(getApplicationContext(), "CRITTERCISM_APP_ID");
}
CRITTERCISM_APP_ID can be found in App settings when you access the application


Tim McGilchrist
Principal Software Engineer at Tarides
Twitter: @lambda_foo Github: tmcgilchrist Email<EMAIL_ADDRESS>LinkedIn: Tim McGilchrist
Feed: RSS / Atom
© Tim McGilchrist 2007-2021
Powered by Hakyll


MusicVAE demo
Drums OneHot
Interpolation (3x3 steps)
Time:
Inputs:
Results:
Sample
Time:
Samples:
Drums NADE
Interpolation(3x3 steps)
Time:
Inputs:
Results:
Sampling
Time:
Samples:
Melody OneHot
Interpolation (5 steps)
Time:
Inputs:
Results:
Sampling
Time:
Samples:
Melody 16-bar Conductor OneHot
Interpolation (5 steps)
Time:
Inputs:
Results:
Sampling
Time:
Samples:


Tools to interface with Cytobank's API via R, organized by various endpoints that represent various areas of Cytobank functionality. Learn more about Cytobank at <https://www.cytobank.org>.
| Version: | 1.3.0 |
| Depends: | curl (≥ 2.7), httr (≥ 1.2.1) |
| Imports: | jsonlite, methods, stats |
| Suggests: | knitr, rmarkdown, testthat |
| Published: | 2018-11-23 |
| Author: | Preston Ng [aut, cre], Chris Ciccolella [aut], Katherine Drake [aut] |
| Maintainer: | Preston Ng <preston at cytobank.org> |
| License: | Artistic-2.0 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | CytobankAPI results |
| Reference manual: | CytobankAPI.pdf |
| Vignettes: |
CytobankAPI advanced analysis guide CytobankAPI quickstart guide |
| Package source: | CytobankAPI_1.3.0.tar.gz |
| Windows binaries: | r-devel: CytobankAPI_1.3.0.zip, r-release: CytobankAPI_1.3.0.zip, r-oldrel: CytobankAPI_1.3.0.zip |
| OS X binaries: | r-release: CytobankAPI_1.3.0.tgz, r-oldrel: CytobankAPI_1.3.0.tgz |
| Old sources: | CytobankAPI archive |
| Reverse depends: | CytobankBridgeR |
| Reverse imports: | CytobankAPIstats |
Please use the canonical form https://CRAN.R-project.org/package=CytobankAPI to link to this page.


{extend name="public:base" /} {block name="jquery"}{/block} {block name="head"} {/block} {block name="header"} {include file="public/search" /} {/block} {block name="content"}
{/block} {block name="footer"} {include file="public/footer-nav" /} {/block} {block name="script"} {/block}


McLaren celebrate first top-three result since 2014
SAO PAULO: Former champions McLaren marked a breakthrough on their long road back up the Formula One pecking order with a first top- three race result since 2014 at the Brazilian Grand Prix on Sunday.
Even if Spaniard Carlos Sainz did not actually stand on the podium, inheriting third place after six times champion Lewis Hamilton was demoted, there was still plenty of cause for celebration.
"It’s a weird first podium not being there straight after the race, but I’m extremely happy with our race today," said Sainz.
The 25-year-old Spaniard had started last on the grid after an engine problem in qualifying but made the most of a one-stop strategy.
The last time a McLaren driver stood on the podium was at the season-opening Australian Grand Prix of 2014 when Kevin Magnussen ended up second and Jenson Button third due to then Red Bull driver Daniel Ricciardo being disqualified from second.
The once-dominant team, that provided winning cars to a cast of champions including Ayrton Senna, Niki Lauda, Alain Prost and Lewis Hamilton, have not won a race since Button in Brazil in 2012.
Where double champion Fernando Alonso tried in vain, during his troubled second four-year stint at Woking with McLaren and Honda struggling together, his compatriot succeeded in his first season at what is now a Renault-powered team.
Sunday's result also secured fourth place in the constructors' standings, McLaren's highest finish since they were third overall in 2012.
McLaren are now 49 points clear of Renault's factory team, with one race remaining and a maximum 44 available.
"The achievement this year together with the podium today is the perfect motivation for all of us to keep working hard in order to score podiums again in the future for McLaren based on our own outright performance," said team boss Andreas Seidl.
(Writing by Alan Baldwin in London, editing by Ken Ferris)


|
Are you ready kids? "Aye Aye Captain" I can't hear you! "AYE AYE CAPTAIN" Oohh... Who lives in a pineapple under the sea? "Spongebob Squarepants" Absorbant and yellow and porous is he! "Spongebob Squarepants" If nautical nonsense be something you wish "Spongebob Squarepants" Then drop on the deck and flop like a fish! "Spongebob Squarepants" READY? Spongebob Squarepants Spongebob Squarepants Spongebob Squarepants |


About Me
My name is Sharon Kim and I was born and raised in Georgia. I have graduated from Georgia State University with a bachelor's degree in computer science. Although it has been almost 2 years since I have graduated, during that time I have been able to find my joys and distinguish which area would be fit for me. I have decided to dedicate my time to Georgia Tech's Full-Stack Coding Bootcamp.I have heard great things about this course and that it was really helpful and challenging. am really excited to take this course and cannot wait to see what it holds for me!
Skills that I am gaining: