@NotExtensible public interface InvocationOnMock extends Serializable
A placeholder for mock, the method that was called and the arguments that were passed.
| Modifier and Type | Method and Description |
|---|---|
Object |
callRealMethod()
calls real method
|
<T> T |
getArgument(int index)
Returns casted argument at the given index.
|
<T> T |
getArgument(int index,
Class<T> clazz)
Returns casted argument at the given index.
|
Object[] |
getArguments()
Returns arguments passed to the method.
|
Method |
getMethod()
returns the method
|
Object |
getMock()
returns the mock object
|
Object getMock()
Method getMethod()
Object[] getArguments()
<T> T getArgument(int index)
getArguments()
.
This method is preferred over getArgument(int, Class)
for readability. Please read
the documentation of getArgument(int, Class)
for an overview of situations when
that method is preferred over this one.index
- argument index<T> T getArgument(int index, Class<T> clazz)
getArgument(int)
, but is necessary to circumvent issues when dealing with generics.
In general, getArgument(int)
is the appropriate function to use. This particular
function is only necessary if you are doing one of the following things:
getArgument(int)
.getArgument(int)
instead, you can circumvent the compilation
issues by storing the intermediate result into a local variable with the correct type.index
- argument indexclazz
- class to cast the argument to


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Splits a tensor into sub tensors.
tf.compat.v1.split
tf.compat.v2.split
tf.split( value, num_or_size_splits, axis=0, num=None, name='split' )
If num_or_size_splits
is an integer, then value
is split along dimension
axis
into num_split
smaller tensors. This requires that num_split
evenly
divides value.shape[axis]
.
If num_or_size_splits
is a 1-D Tensor (or list), we call it size_splits
and value
is split into len(size_splits)
elements. The shape of the i
-th
element has the same size as the value
except along dimension axis
where
the size is size_splits[i]
.
# 'value' is a tensor with shape [5, 30] # Split 'value' into 3 tensors with sizes [4, 15, 11] along dimension 1 split0, split1, split2 = tf.split(value, [4, 15, 11], 1) tf.shape(split0) # [5, 4] tf.shape(split1) # [5, 15] tf.shape(split2) # [5, 11] # Split 'value' into 3 tensors along dimension 1 split0, split1, split2 = tf.split(value, num_or_size_splits=3, axis=1) tf.shape(split0) # [5, 10]
value
: The Tensor
to split.num_or_size_splits
: Either an integer indicating the number of splits along
split_dim or a 1-D integer Tensor
or Python list containing the sizes of
each output tensor along split_dim. If a scalar then it must evenly divide
value.shape[axis]
; otherwise the sum of sizes along the split dimension
must match that of the value
.axis
: An integer or scalar int32
Tensor
. The dimension along which to
split. Must be in the range [-rank(value), rank(value))
. Defaults to 0.num
: Optional, used to specify the number of outputs when it cannot be
inferred from the shape of size_splits
.name
: A name for the operation (optional).if num_or_size_splits
is a scalar returns num_or_size_splits
Tensor
objects; if num_or_size_splits
is a 1-D Tensor returns
num_or_size_splits.get_shape[0]
Tensor
objects resulting from splitting
value
.
ValueError
: If num
is unspecified and cannot be inferred.


Implementing a DOM library can be as simple as a method invocation. However, Utils requires care to implement properly.
In order to derive optimal success from Utils, defensive tactics must be used. They are listed below.
The most important step in implementing Utils is asserting its existence before running application code. The following example:
if (typeof Utils === "object" && Utils) { (function () { // application code }()); }
illustrates how one assertion can enable a script to gracefully degrade when its core dependency does not exist; whereas a similar script without the assertion can err.
Wrappers are an important step in implementing Utils. Their use enables safe degradation of the “dynamic interface”, meaning properties whose value is determined at run time. This example:
function createText( doc, text ) { var result = null; if (Utils.create.text) { result = Utils.create.text( doc, text ); } return result; }
is one instance wherein a method of the “dynamic interface” can safely be called, and degrade if unavailable.
However, results of API invocations should also be asserted before use. In conjunction with wrappers, this strategy should be a large factor in an implementation of Utils. The snippet below:
function createText( doc, text ) { var result = null; if (Utils.create.text) { result = Utils.create.text( doc, text ); } return result; } function appendNode( par, node ) { return Utils.node.append( par, node ); } function appendText( text, par ) { var node = createText( global.document, text ); if (node) { appendNode( par, node ); } }
is an example of wrappers used in conjuction with result assertions. A text node-like object is appended to a node-like object if the specified creational method exists with a successful result.
Whilst Graceful Degradation requires a great amount of effort to achieve, use of Utils simplifies degradation of scripts. The result is highly flexible code, which is unencumbered by respectable environments.
To demonstrate an implementation of Utils, a full example is provided below:
var global = global || this; if (typeof Utils === "object" && Utils) { (function () { function createText( doc, text ) { var result = null; if (Utils.create.text) { result = Utils.create.text( doc, text ); } return result; } function appendNode( par, node ) { return Utils.node.append( par, node ); } function appendText( par, text ) { var node = createText( global.document, text ); if (node) { appendNode( par, node ); } } function selectBody( doc ) { var result = null; if (Utils.select.body) { result = Utils.select.body( doc ); } return result; } function addBodyText( text ) { var body = selectBody(global.document); if (body) { appendText( body, text ); } } addBodyText("foo\r\n"); addBodyText("bar\r\n"); addBodyText("baz\r\n"); }()); }
which can be found in a test page[0].


(HTML (HEAD (TITLE "foo )TITLE "\n (SCRIPT Alanguage javascript " )SCRIPT "\n )HEAD (BODY )BODY )HTML


Xperience Capsule
Reveal your story
“The only source of knowledge is experience.” - Albert Einstein
КулТИП 2014
How can this be be accomplished?
- Associate personal story to Point of Experience
- Pictures, audio guides, video recordings, 3d models, text...
- Point and content are Experience Capsule
- Types of capsules
- Detect when the user is near a capsule
Who can use the application?
over 50% of the world population use a smartphone
Who wins?
- Users
- Cultural institutions
- Our brand, Xperience Capsule
Business
- Revenue
- Marketing
- Competition?
- Social media
- Tourist guides
- Geocaching networks
- Augmented reality
Our idea:
combine all to share experience!


GTD - Intro
Getting Things Done
As to the methods, there may be a million and ten some, but principles are few. The man who grasps principles can successfully select his own methods.
The man who tries methods, ignoring the principles, is is sure to have trouble!
Art of Stress-free productivity
- Work
- Family
- Finances
- Sports
- ...
23 percent of employees reported feeling burned out at work very often or always, while an additional 44 percent reported feeling burned out sometimes
- Some get tired ...
- Some try different methods ...
- Some ask for help
1. The mind is NOT made for storage
- Creating Idea's
- Not storing them
2. Mind like water
3. Carve out mental space
1. Collect
- Store incoming information
- Not in the brain (Mind Sweep)
- Trusted source (Must be with you always)
- Discipline / Key ?
2. Process
- Actionable
- What is the immediate next step?
- To get forward
- 2 min rule !
3. Organize
- By Project
- By Context
- ...
4. Weekly Review
- Important
- Key to get the system to work
- Plan them
Personal Kanban?
- Reduced backlog size versus a clear head
- Kanban allows for Work In Progress (WIP) limiting
- Visual control
- Process definition
- Prioritisation
- Time critical actions


Are you envisioning to transform your digital business and user experience to the next level?
read moreA small story that emphasises the importance of test-driven development.
Last updated 3 mins ago
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Forkphorus & Android
This is a guide to packaging Scratch projects as Android apps using Forkphorus.
There's two ways to do this:
- Embed a link to the packager on the homepage
- Embed a file generated by the HTML packager
We will discuss each of these separately.
Note that the Google Play Store has a registration fee of $25, and the iOS App Store has a fee of $99/year.
First Method - Embedding a link
This is the easiest method. Forkphorus bug fixes and performance improvements (as well as new bugs) will automatically be applied and changes to your project will also apply automatically. The main issue is that this requires an internet connection to redownload your project, but it will probably work good enough for most people.
If iOS support is important, you must use the PhoneGap method instead.
- Load your project in the Forkphorus homepage
- Scroll down to Package this project
- If you want to enable Turbo Mode, check the Turbo Mode checkbox
- Setting the resolution to "Full screen" will allow the project to use as much screen space as possible. If this is not what you want, choose another option.
- Click the Package button and save the URL of the window opened somewhere -- we will need it in the next steps.
At this point you just need to find a way to embed that URL into an app. I am currently aware of two ways to do this:
Using AppInventor
Perhaps using AppInventor just to embed a webpage is a bit overkill, but it works more than good enough. What we do in these steps won't require any programming knowledge or anything -- just follow what the steps say. AppInventor gives you a bit more flexibility as to how your app functions if you so desire.
- Go to the AppInventor home page and hit Create Apps!. Sign in with a Google account.
- Hit Start new project, name the project (this doesn't matter), and hit OK
- From the Palette on the left, drag a WebViewer into the Viewer in the middle of the screen.
- In the components menu, select WebViewer1 and configure it like so:
Width Fill parent... Height Fill parent... HomeURL The URL generated by Forkphorus (https://forkphorus.github.io/app.html?...) - In the components menu, select Screen1 and consider the following optional settings:
These are all optional. There are some other options you can set but many of them will have no effect.AppName The name of your app (eg. Paper Minecraft) TitleVisible false (unchecked) to remove an ugly titlebar from your app ShowStatusBar false (unchecked) to hide the native Android status bar while your app is running ScreenOrientation Landscape or Portrait if you want to force a specific screen orientation, otherwise leave this to its default value. Icon A nice icon for your project. - To create an APK:
- Select Build > App (provide QR code for .APK) and scan the QR code on your phone
- or Select Build > App (save .apk to my computer)
- For help on publishing to the Google Play Store, see Publishing Apps to Google Play (App Inventor 2)
Using AppsGeyser
- Open the AppsGeyser page Create Smart URL App
- Insert the link Forkphorus generated into the Website URL box.
- Choose a color theme, and hit NEXT
- Fill out details such as your App's Name, Description, and Icon, and then hit Create
- From here, navigate the treacherous interface to Publish or Download your APK.
Second Method - Using the HTML packager and PhoneGap
An alternative method involves creating an independent HTML file for your project and embedding that. The main advantage is that this will work offline, independent of an internet connection and will always produce the same result.
PhoneGap seems to support iOS, but I have not tested this. You will need a signing key.
Note that we will not need to install PhoneGap. We will be using their online build service.
- Generate a ZIP archive using the HTML packager
- Click "PhoneGap Options" to open the special settings for PhoneGap
- Check "Enable PhoneGap", this will give us a ZIP instead of HTML and enable some additional options
- Change "PhoneGap config.xml" as needed. The default configuration will work just fine for testing. However, if you want to publish the app, you should make some changes:
- Put your App's name between
<name>…<name>
- Put your App's description between
<description>…<description>
- Put your name between
<author href="your website" email="your email" >…<author>
and set href and email to your website and email respectively. - Change the ID in
id="io.github.forkphorus.example"
to something unique to your app (you can remove the io.github.forkphorus part, it just has to be unique)
- Put your App's name between
- Optionally choose a custom icon.
- Go to the PhoneGap build service and sign in with or create an Adobe account
- Create a new Private app and upload the generated ZIP to the service.
- Wait for the builds to complete.
- Click on the Android icon to download the APK. You can also scan the QR code.
- To update the APK, run the packager again, go to the PhoneGap build service, and use the Update code button:
- If uploading to the Google Play Store, you should read Android Signing (PhoneGap Documentation) or Building a release version for build.phonegap.com
If you use the PhoneGap CLI/Desktop app you will find that Forkphorus is broken in the preview. This is a PhoneGap bug -- it will work in the final build.


The LyricsGenerator class¶
-
class
tflyrics.
LyricsGenerator
(artists: list = [], per_artist: int = 5, vocabulary: list = None, text_provider: tflyrics.text_provider.TextProvider = None)¶ An adapter between the Genius API and a TF Dataset.
A LyricsGenerator object queries the Genius API and provides a TensorFlow Dataset that can be fed to a model for training. Each sample of the dataset is a sequence of unicode characters taken from song lyrics. More precisely, each example in a LyricsGenerator is a character sequence that maps to a sequence with same length but shifted content: for example, there could be a LyricsGenerator where “Hello wo” maps to “ello wor”, and “ello wor” maps to “llo worl”.
-
as_dataset
(batch_size: int = None, seq_length: int = 100) → tensorflow.python.data.ops.dataset_ops.DatasetV2¶ Get a TensorFlow dataset equivalent to this object.
Get a TensorFlow dataset whose samples are substrings of song lyrics provided by a Genius object. More specifically, each sample in the database is a pair of substrings that have the same size but are shifted with respect to each other: e.g. [(“Hello”, “ello “), (“ello “, “llo W”), (“llo W”, “lo Wo”), (“lo Wo”, “o Wor”), (“o Wor”, ” Worl”), …].
- Parameters
batch_size – batch size of the dataset
seq_length – length of each substring that forms a sample
- Returns
a TensorFlow Dataset of substrings
-
preprocess
¶ Split the lyrics of a song into multiple substrings.
Preprocess a string containing lyrics, extracting substrings that have a fixed, specified size from it. Return the substrings as sequences of integers rather than characters.
- Parameters
text – a string containing lyrics
seq_length – fixed size of each output sequence
- Returns
a list of substrings extracted from the song, as lists of ints
-


#include <StcpSocket.hpp>
|
| | StcpSocket () |
| |
| | ~StcpSocket () |
| |
| fc::tcp_socket & | get_socket () |
| |
| void | accept () |
| |
| void | connect_to (const fc::ip::endpoint &remote_endpoint) |
| |
| void | bind (const fc::ip::endpoint &local_endpoint) |
| |
| virtual size_t | readsome (char *buffer, size_t max) |
| |
| virtual size_t | readsome (const std::shared_ptr< char > &buf, size_t len, size_t offset) |
| |
| virtual bool | eof () const |
| |
| virtual size_t | writesome (const char *buffer, size_t len) |
| |
| virtual size_t | writesome (const std::shared_ptr< const char > &buf, size_t len, size_t offset) |
| |
| virtual void | flush () |
| |
| virtual void | close () |
| |
| void | get (char &c) |
| |
| fc::sha512 | get_shared_secret () const |
| |
Uses ECDH to negotiate a aes key for communicating with other nodes on the network.
◆ StcpSocket()
| hsrcore::net::StcpSocket::StcpSocket |
( |
| ) |
|
◆ ~StcpSocket()
| hsrcore::net::StcpSocket::~StcpSocket |
( |
| ) |
|
◆ accept()
| void hsrcore::net::StcpSocket::accept |
( |
| ) |
|
◆ bind()
| void hsrcore::net::StcpSocket::bind |
( |
const fc::ip::endpoint & |
local_endpoint | ) |
|
◆ close()
| void hsrcore::net::StcpSocket::close |
( |
| ) |
|
|
virtual |
◆ connect_to()
| void hsrcore::net::StcpSocket::connect_to |
( |
const fc::ip::endpoint & |
remote_endpoint | ) |
|
◆ eof()
| bool hsrcore::net::StcpSocket::eof |
( |
| ) |
const |
|
virtual |
◆ flush()
| void hsrcore::net::StcpSocket::flush |
( |
| ) |
|
|
virtual |
◆ get()
| void hsrcore::net::StcpSocket::get |
( |
char & |
c | ) |
|
|
inline |
◆ get_shared_secret()
| fc::sha512 hsrcore::net::StcpSocket::get_shared_secret |
( |
| ) |
const |
|
inline |
◆ get_socket()
| fc::tcp_socket& hsrcore::net::StcpSocket::get_socket |
( |
| ) |
|
|
inline |
◆ readsome() [1/2]
| size_t hsrcore::net::StcpSocket::readsome |
( |
char * |
buffer, |
|
|
size_t |
len |
|
) |
| |
|
virtual |
This method must read at least 16 bytes at a time from the underlying TCP socket so that it can decrypt them. It will buffer any left-over.
◆ readsome() [2/2]
| size_t hsrcore::net::StcpSocket::readsome |
( |
const std::shared_ptr< char > & |
buf, |
|
|
size_t |
len, |
|
|
size_t |
offset |
|
) |
| |
|
virtual |
◆ writesome() [1/2]
| size_t hsrcore::net::StcpSocket::writesome |
( |
const char * |
buffer, |
|
|
size_t |
len |
|
) |
| |
|
virtual |
every sizeof(crypt_buf) bytes the aes channel has an error and doesn't decrypt properly... disable for now because we are going to upgrade to something better.
◆ writesome() [2/2]
| size_t hsrcore::net::StcpSocket::writesome |
( |
const std::shared_ptr< const char > & |
buf, |
|
|
size_t |
len, |
|
|
size_t |
offset |
|
) |
| |
|
virtual |
The documentation for this class was generated from the following files:


Standard Combobox
The standard version of the component that most people will likely use regularly.
Custom Filter
The input supports custom filters through the filterFn
input function. Simply pass a function
which accepts an array and a search term, and then returns the desired filtered array.
Custom Search Function
Custom search function can be provided by [searchFunction]
to combobox. It will be called on mouse
click on combobox's primary button, or on enter key press, while focused on combobox input.
Display Object Property
Passing object arrays to the Combobox is supported through the displayFn
input function. To
access the this
keyword inside the passed function, use an arrow function.
Observable Async Example
The dropdownValues
input can accept an Observable with the async pipe,
which could be useful for autocomplete.
This example also demonstrates how to create a callback function from data returned by the observable.
Custom Item Template
The way the items are rendered in the popover list can be customized through providing an itemTemplate
to the component.
Combobox In A Reactive Form
This examples shows how to put combobox
in a Reactive Form. Also how to sets it's state to disabled
Custom Height Example
There is also the possibility to define a fixed height for the dropdown list by specifying the desired height using
the [maxHeight]
attribute.
Reactive Form
The Combobox component may also be used within Angular Reactive Forms.
Also in combobox can be used [communicateByObject]
. Enabling this option changes the way the component
communicates with the outside. When combobox communicates by objects, writing in text input doesn't change binded
value, until it matches one of displayed dropdown options. Also on this mode, combobox on communication uses model
with same type as it's passed to [dropdownValues]


The PRIEmitter application can publish its data on a TCP/IP socket or on a multicast UDP socket. The former guarantees delivery of all the data, with some limits on throughput due to increased overhead, while the latter offers potentially greater throughput at the risk of missing or dropped messages.


distRcpp
This package uses Rcpp to quickly compute population/distance-weighted measures. Geodesic distances can be computed using either Haversine or Vincenty formulas. The package also has functions to return raw distance measures. If you are able to install Rcpp on your machine, you should be able to install this package and use these functions.
Install the latest development version from Github with
devtools::install_github('btskinner/distRcpp')
NB This package is still in early beta stages. It does not have much
in the way of error handling. Data must be pre-processed so that no
missing (NA
) values are given to the functions.
Available functions
Weighted measures
dist_weighted_mean()
Interpolate values for a vector of locations (X) that are the inverse-distance-weighted average of measures taken at surrounding locations (Y). For each point, x, nearby values of the measure taken at Y are weighted more heavily than those from locations that are farther away.
popdist_weighted_mean()
Interpolate values for a vector of locations (X) that are the population/inverse-distance-weighted average of measures taken at surrounding locations (Y). For each point, x, nearby values of the measure taken at Y are weighted more heavily than those from locations that are farther away. Measures taken in more heavily populated y are given more weight than those with lower populations. This weighting scheme is a compromise between distance and population and is useful for interpolating measures that need to take both into account.
Distances
dist_1to1()
Compute and return the geodesic distance between two spatial points. Returns distance in meters.
dist_1tom()
Compute and return the geodesic distance between one location and a vector of other locations. Returns vector of distances in meters.
dist_mtom()
Compute and return the geodesic distance between each coordinate pair in two vectors. Returns n x k matrix of distances in meters, where n = # of locations in first vector and k = # of locations in second vector.
dist_df()
Compute distance between corresponding coordinate pairs and return
vector of distances in meters. For use when creating a new data.frame
or dplyr tbl_df()
column.
dist_min()
Compute minimum distance between each starting point, x, and possible end points, Y. Returns vector of minimum distances in meters that equals # of starting points (size of X).
dist_max()
Compute maximum distance between each starting point, x, and possible end points, Y. Returns vector of maximum distances in meters that equals # of starting points (size of X).
Benchmark
Compare speed with base R function when measuring the distance between every United States population-weighted county centroid as measured in 2010 (N = 3,143 with complete measurements).
Load data
## libraries
libs <- c('dplyr','microbenchmark','geosphere','distRcpp')
lapply(libs, require, character.only = TRUE)
## read data
df <- get(data(county_centers))
df
## # A tibble: 3,147 × 9
## fips clon00 clat00 clon10 clat10 pclon00 pclat00 pclon10 pclat10
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01001 -86.57718 32.52328 -86.64449 32.53638 -86.50183 32.50032 -86.49416 32.50039
## 2 01003 -87.74826 30.59278 -87.74607 30.65922 -87.76054 30.56538 -87.76238 30.54892
## 3 01005 -85.33131 31.85652 -85.40546 31.87067 -85.30675 31.84787 -85.31004 31.84404
## 4 01007 -87.12324 33.04005 -87.12715 33.01589 -87.12702 33.02595 -87.12766 33.03092
## 5 01009 -86.55477 33.97846 -86.56725 33.97745 -86.58262 33.96260 -86.59149 33.95524
## 6 01011 -85.70491 32.09828 -85.71726 32.10176 -85.70278 32.11414 -85.70119 32.11633
## 7 01013 -86.66223 31.73588 -86.68197 31.75167 -86.65606 31.77192 -86.65355 31.77354
## 8 01015 -85.81754 33.74199 -85.82251 33.77171 -85.82205 33.72213 -85.81944 33.72546
## 9 01017 -85.28875 32.89123 -85.39181 32.91794 -85.26586 32.86135 -85.26647 32.86044
## 10 01019 -85.62193 34.18416 -85.65424 34.06952 -85.62710 34.17993 -85.62919 34.17933
## # ... with 3,137 more rows
## subset to 2010 population-weighted centroids (pclon10, pclat10)
p <- df %>% select(pclon10, pclat10) %>% na.omit %>% data.frame()
Check for equality
dist_R <- distm(p)
dist_Rcpp <- dist_mtom(p[,1],p[,2],p[,1],p[,2])
dist_R[1:5,1:5]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.0 248335.5 133369.0 83691.8 162207.0
## [2,] 248335.5 0.0 274424.4 282744.5 394877.3
## [3,] 133369.0 274424.4 0.0 215905.4 263771.5
## [4,] 83691.8 282744.5 215905.4 0.0 114301.5
## [5,] 162207.0 394877.3 263771.5 114301.5 0.0
dist_Rcpp[1:5,1:5]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.0 248335.5 133369.0 83691.8 162207.0
## [2,] 248335.5 0.0 274424.4 282744.5 394877.3
## [3,] 133369.0 274424.4 0.0 215905.4 263771.5
## [4,] 83691.8 282744.5 215905.4 0.0 114301.5
## [5,] 162207.0 394877.3 263771.5 114301.5 0.0
all.equal(dist_R, dist_Rcpp)
## [1] TRUE
Benchmark
Mid-2012 MacBook Air, 2 GHz Intel Core i7, 8 GB 1600 MHz DDR3 SDRAM
microbenchmark(
dist_R = distm(p),
dist_Rcpp = dist_mtom(p[,1],p[,2],p[,1],p[,2]),
times = 100
)
## Unit: milliseconds
## expr min lq mean median uq max neval cld
## dist_R 2579.012 2748.543 2873.0631 2870.9757 2954.7282 3918.1701 100 b
## dist_Rcpp 834.670 848.832 873.2247 865.0157 888.6319 995.3689 100 a


Candidate Profile
| Name | Ben Khosa (Harbrinder Singh Khosa) |
| Party | Liberal Democrat |
| phone | 0208 892 3191 |
|<EMAIL_ADDRESS>|
Background
Tell us about your education?
GCSE O level standard went on to Technical College. Qualified as an Electronics and TV Technician.
What are three jobs you have held?
- Senior Technician with a national electrical firm.
- Manager/ Proprietor of small businesses.
What are you links to the North Twickenham/St Margarets community?
I moved to North Twickenham in 1985 when I bought and ran my first business. I have lived here ever since, running other small businesses whilst playing an active role in the community and the Save the Children Charity Fundraising. Both my children were educated by local schools and Richmond Upon Thames College, and have gone on to further education.
Why did/do you want to be a councilor?
I believe I am willing and able to make a beneficial impact on the community. I also hope to make as many friends as possible.
Issues
What do you think are the top three issues for North Twickenham/St Margarets and what do you should be done about them?
- Parking Problems in and around Moor Meads need resolving, no easy answer but need to make resources immediately available.
- Plastics and Cardboard recycling needs to be introduced ASAP
- Wards should be subdivided and key local decisions should be taken in consultation with the representatives of these subdivisions perhaps Chairs of Residents Associations
What areas are you passionate about and what committees would you like to sit on?
Old and vulnerable people care.
Are there any big things you are looking to achieve or change?
I want to ensure that the residents concerns are effectively represented at the Council. I also wish to create a feeling of community harmony.
If elected, how will you keep in-touch with your constituents?
Vince Cable-like surgeries, email, website?
All of the above. I think I would like to “copy” Vince in all that he does. Of course I would continue to keep in touch with the Lib Dem “comments”
Fun
Going to eat out, spending time with friends and family, travelling donating and adding to the lives of people less fortunate.
What is your favourite musical group/band/cd?
In the old days ABBA was my favourite but generally I watch Indian music channels
What is your favourite book?
Generally I like self-improvement books or short stories.
What is the last book you read?
Wutthering Heights by Emily Bronte
What are three places you have lived?
- India
- UK
- Canada
What is your favourite meal?

Canada


Click on the button to copy the text from the text field. Try to paste the text (e.g. ctrl+v) afterwards in a different window, to see the effect.
The copy is not supported in IE8 and earlier.


Typescript-Webpack-WebWorker-Example
Open console to see how main thread and worker exchange messages.


public interface NoteStoringPreProcessorManager
| Modifier and Type | Method and Description |
|---|---|
void |
addProcessor(NoteStoringEditableContentPreProcessor notePreProcessor)
Add the given preprocessor to the list of processors.
|
void |
addProcessor(NoteStoringImmutableContentPreProcessor notePreProcessor)
Add the given preprocessor to the list of processors.
|
void |
process(NoteStoringTO noteStoringTO)
Run the registered preprocessors before creating a new note.
|
void |
processEdit(Note noteToEdit,
NoteStoringTO noteStoringTO)
Run the registered preprocessors before updating an existing note.
|
void |
removeProcessor(NoteStoringEditableContentPreProcessor notePreProcessor)
Remove the given preprocessor from the list of processors.
|
void |
removeProcessor(NoteStoringImmutableContentPreProcessor notePreProcessor)
Remove the given preprocessor from the list of processors.
|
void addProcessor(NoteStoringEditableContentPreProcessor notePreProcessor)
notePreProcessor
- the preprocessor to addvoid addProcessor(NoteStoringImmutableContentPreProcessor notePreProcessor)
notePreProcessor
- the preprocessor to addvoid process(NoteStoringTO noteStoringTO) throws NoteStoringPreProcessorException, NoteManagementAuthorizationException
NoteStoringEditableContentPreProcessor
s and afterwards the
NoteStoringImmutableContentPreProcessor
s.noteStoringTO
- the TO to pass to the preprocessorsNoteStoringPreProcessorException
- in case any of the preprocessors failedNoteManagementAuthorizationException
- thrown to indicate that the note cannot be created because of access restrictionsvoid processEdit(Note noteToEdit, NoteStoringTO noteStoringTO) throws NoteStoringPreProcessorException, NoteManagementAuthorizationException
NoteStoringEditableContentPreProcessor
s and afterwards the
NoteStoringImmutableContentPreProcessor
s.noteToEdit
- the note to editnoteStoringTO
- the TO to pass to the preprocessorsNoteStoringPreProcessorException
- in case any of the preprocessors failedNoteManagementAuthorizationException
- thrown to indicate that the note cannot be created because of access restrictionsvoid removeProcessor(NoteStoringEditableContentPreProcessor notePreProcessor)
notePreProcessor
- The processor.void removeProcessor(NoteStoringImmutableContentPreProcessor notePreProcessor)
notePreProcessor
- The processor.Copyright © 2019 Communote team. All rights reserved.


|
|
Boost Sorting Algorithm LibraryOverview |
The Sorting Algorithm Library provides a generic implementation of high-speed sorting algorithms that outperform those in the C++ standard in both average and worst case performance. These algorithms only work on random access iterators. They are hybrids using both radix and comparison-based sorting, specialized to sorting common data types, such as integers, floats, and strings. These algorithms are encoded in a generic fashion and accept functors, enabling them to sort any object that can be processed like these basic data types. Sample usages are available in the samples directory.
Unlike many radix-based algorithms, the underlying Spreadsort algorithm is designed around worst-case performance. It performs better on chunky data (where it is not widely distributed), so that on real data it can perform substantially better than on random data. Conceptually, Spreadsort can sort any data for which an absolute ordering can be determined. Situations where Spreadsort is fastest relative to std::sort:
1. Large number of elements to sort (N >> 1000)
2. Slow comparison function (such as floating point numbers on x86 processors or strings)
3. Large data elements (such as key + data sorted on a key)
Situations where Spreadsort is slowest relative to std::sort:
1. Integers and mostly-sorted data on x86 processors
These functions are defined in namespace boost
.
Each of integer_sort, float_sort, and string_sort have 3 main versions:
The base version, which takes a first iterator and a last iterator, just like std::sort: integer_sort(array.begin(), array.end()); The version with an overridden shift functor, providing flexibility in case the >> already does something other than a bitshift. The rightshift functor takes two args, first the data type, and second a natural number of bits to shift right. For string_sort this variant is slightly different; it needs a bracket functor equivalent to [], taking a number corresponding to the character offset, along with a second getlength functor to get the length of the string in characters. In all cases, this operator must return an integer type that compares with the "<" operator to provide the intended order (integers can be negated to reverse their order). In other words: rightshift(A, n) < rightshift(B, n) -> A < B integer_sort(array.begin(), array.end(), rightshift()); And the version with a comparison functor for maximum flexibility. This functor must provide the same sorting order as the integers returned by the rightshift: rightshift(A, n) < rightshift(B, n) -> compare(A, B) integer_sort(array.begin(), array.end(), negrightshift(), std::greater<Data_type>());The Spreadsort algorithm is a hybrid algorithm; when the number of elements being sorted is below a certain number, comparison-based sorting is used. Above it, radix sorting is used. The radix-based algorithm will thus cut up the problem into small pieces, and either completely sort the data based upon its radix if the data is clustered, or finish sorting the cut-down pieces with comparison-based sorting.
The Spreadsort algorithm dynamically chooses either comparison-based or radix-based sorting when recursing, whichever provides better worst-case performance. This way worst-case performance is guaranteed to be the better of comparisons and operations where N is the number of elements being sorted, K is the length in bits of the key, and S is a constant. This results in substantially improved performance; integer_sort tends to be 50% to 2X faster than std::sort, while float_sort and string_sort are roughly 2X faster than std::sort.
Performance graphs are provided for integer_sort, float_sort, and string_sort in their description. Runtime Performance comparisons and graphs were made on a Core 2 Duo laptop running Windows Vista 64 with MSVC 8.0, and an old G4 laptop running Mac OSX with gcc. bjam was used to control compilation.
Histogramming with a fixed maximum number of splits is used because it reduces the number of cache misses, thus improving performance relative to the approach described in detail in the Original Spreadsort Algorithm. The importance of cache-friendly histogramming is described in the paper on Adaptive Left Radix, though without the worst-case handling described below.
The time taken per radix iteration is:
O(N) iterations over the data
O(N) integer-type comparisons (even for float_sort and string_sort)
O(N) swaps
bin operations
To obtain O(N) worst-case performance per iteration, the restriction is applied, and becomes O(N). For each such iteration, the number of unsorted bits log(range) (referred to as K) per element is reduced by S. As S decreases depending upon the amount of elements being sorted, it can drop from a maximum of to the minimum of .
Assumption: std::sort is assumed to be . As introsort exists and is commonly used, if you have a quibble with this please take it up with the implementor of your std::sort; you're welcome to replace the recursive calls to std::sort to calls to introsort if your std::sort library call is poorly implemented. Introsort is not included with this algorithm for simplicity and because the implementor of the std::sort call is assumed to know what they're doing.
To maintain a minimum value for S (), comparison-based sorting has to be used to sort when , where log(meanbinsize) (lbs) is a small constant, usually between 0 and 4, used to minimize bin overhead per element. There is a small corner-case where if and , then the data can be sorted in a single radix-based iteration with an S = K (this bucketsorting special case is by default only applied to float_sort). So for the final recursion, worst-case performance is: 1 radix-based iteration if , or comparison-based iterations if but . So for the final iteration, worst-case runtime is . if and , then more than 1 radix recursion will be required.
For the second to last iteration, can be handled, (if the data is divided into pieces) or if , then it is faster to fallback to std::sort. In the case of a radix-based sort plus recursion, it will take worst-case time, as , and . Alternatively, comparison-based sorting is used if , which will take time. So either way is the worst-case time for the second to last iteration, which occurs if or .
This continues as long as , so that for where m is the maximum number of iterations after this one has finished, or where , then the worst-case runtime is .
at works out to:
as the sum from 0 to m is
substituting in for m
since this involves iterations, this works out to dividing K into an average pieces per iteration. To finish the problem from this point takes for m iterations, plus the worst-case of for the last iteration, for a total of time.
When , the problem is divided into pieces per iteration, or std::sort is called if . For this range:
Providing runtime of if recursive, or if comparison-based,
which simplifies to , which substitutes to ,
which given that (otherwise a lesser number of radix-based iterations would be used)
also comes out to .
Asymptotically, for large N and large K, this simplifies to: , simplifying out the constants related to the range. Providing an additional runtime on top of the performance of LSD Radix Sort, but without the O(N) memory overhead. For simplicity, because lbs is a small constant (0 can be used, and performs reasonably), it is ignored when summarizing the performance in further discussions. By checking whether comparison-based sorting is better, Spreadsort is also , whichever is better, and unlike LSD Radix Sort, can perform much better than the worst-case if the data is either evenly distributed or highly clustered.
This analysis was for integer_sort and float_sort. string_sort differs in that S_min = S_max = sizeof(Char_type) * 8, lbs is 0, and that std::sort's comparison is not a constant-time operation, so strictly speaking string_sort runtime is . Worst-case, this ends up being O(N * K) (where K is the mean string length in bytes), as described for American Flag Sort, which is better than the O(N * K * log(N)) worst-case for comparison-based sorting.
integer_sort and float_sort have tuning constants that control how the radix-sorting portion of those algorithms work. The ideal constant values for integer_sort and float_sort vary depending on the platform, compiler, and data being sorted. By far the most important constant is MAX_SPLITS, which defines how many pieces the radix-sorting portion splits the data into per iteration.
The ideal value of MAX_SPLITS depends upon the size of the L1 processor cache, and is between 10 and 13 on many systems. A default value of 11 is used. For mostly-sorted data, a much larger value is better, as swaps (and thus cache misses) are rare, but this hurts runtime severely for unsorted data, so is not recommended.
On some x86 systems when the total number of elements being sorted is small ( less than 1 million or so), the ideal MAX_SPLITS can be substantially larger, such as 17. This is suspected to be because all the data fits into the L2 cache, and misses from L1 cache to L2 cache do not impact performance as severely as misses to main memory. Modifying tuning constants other than MAX_SPLITS is not recommended, as the performance improvement for changing other constants is usually minor.
If you can afford to let it run for a day, and have at least 1GB of free memory, the perl command: "./tune.pl -large -tune" (UNIX) or "perl tune.pl -large -tune" (Windows) can be used to automatically tune these constants. This should be run from the libs/algorithm/sorting directory inside the boost home directory. This will work to identify the ideal constants.hpp settings for your system, testing on various distributions in a 20 million element (80MB) file, and additionally verifies that all sorting routines sort correctly across various data distributions. Alternatively, you can test with the file size you're most concerned with "./tune.pl number -tune" (UNIX) or "perl tune.pl number -tune" (Windows). Substitute the number of elements you want to test with for "number". Otherwise, just use the options it comes with, they're decent. With default settings "./tune.pl -tune" (UNIX) "perl tune.pl -tune" (Windows), the script will take hours to run (less than a day), but may not pick the correct MAX_SPLITS if it is over 10. Alternatively, you can add the -small option to make it take just a few minutes, tuning for smaller vector sizes (one hundred thousand elements), but the resulting constants may not be good for large files (see above note about MAX_SPLITS on Windows).
The tuning script can also be used just to verify that sorting works correctly on your system, and see how much of a speedup it gets, by omiting the "-tune" option. This runs at the end of tuning runs. Default args will take about an hour to run and give accurate results on decent-sized test vectors. "./tune.pl -small" (UNIX) "perl tune.pl -small" (Windows) is a faster option, that tests on smaller vectors and isn't as accurate.
If any differences are encountered during tuning, please call tune.pl with -debug > log_file_name. If the resulting log file contains compilation or permissions issues, it is likely an issue with your setup. If some other type of error is encountered (or result differences), please send them to the library author at<EMAIL_ADDRESS>Including the zipped input.txt that was being used is also helpful.
Revised 04 December, 2006
Copyright © 2009 Steven Ross
Distributed under the Boost Software License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)


In order to keep your application(s) more secure, this plugin only uses temporary credentials via STS, scoped to each application. To set this up:
- Log into the AWS Management Console, and navigate to the Identity and Access Management console.
- Click on Roles, then click Create New Role.
- Give an appropriate name for this role (for example, "JenkinsCodePipelineProject").
- In the "Select Role Type" screen, click "Role for Cross-Account Access" then select Allows IAM users from a 3rd party AWS account to access this account.
- The account and external IDs for this Jenkins project are listed below
- In the policy screen, select Custom Policy and copy-paste the following policy:
{"Version": "2012-10-17", "Statement": [{"Effect": "Allow", "Action": ["codepipeline:PollForJobs",
"codepipeline:AcknowledgeJob", "codepipeline:PutJobSuccessResult", "codepipeline:PutJobFailureResult", "codepipeline:GetJobDetails"], "Resource": "*"}]}
- Click Create Role, then copy-paste the Role ARN into the below field.
- Click the Test Connection button to ensure that the permissions are set up properly.


--- title: "Multi Dimentional Data Analytics of Road Accidents in India" excerpt: "A Statistical Data Analysis project on Road Accidents of India." collection: projects ---
A statistical data analysis project on Road Accidents of India over a period of 5-7 years using various Python Libraries.
The project is performed using Jupyter Notebook and Tableau.
The various datasets regarding Road Accidents of India were gathered from Open Government Data Platform (Open GDP)


With roads in developed countries like the US changing up to 15% annually, Mapillary addresses a growing demand for keeping maps updated by combining images from any camera into a 3D visualization of the world. Mapillary’s independent and collaborative approach enables anyone to collect, share, and use street-level images for improving maps, developing cities, and advancing the automotive industry.
Today, people and organizations all over the world have contributed more than 600 million images toward Mapillary’s mission of helping people understand the world’s places through images and making this data available, with clients and partners including the World Bank, HERE, and Toyota Research Institute.
Mapillary’s computer vision technology brings intelligence to maps in an unprecedented way, increasing our overall understanding of the world. Mapillary runs state-of-the-art semantic image analysis and image-based 3d modeling at scale and on all its images. In this post we discuss two recent works from Mapillary Research and their implementations in PyTorch - Seamless Scene Segmentation [1] and In-Place Activated BatchNorm [2] - generating Panoptic segmentation results and saving up to 50% of GPU memory during training, respectively.
Seamless Scene Segmentation
Github project page: https://github.com/mapillary/seamseg/
The objective of Seamless Scene Segmentation is to predict a “panoptic” segmentation [3] from an image, that is a complete labeling where each pixel is assigned with a class id and, where possible, an instance id. Like many modern CNNs dealing with instance detection and segmentation, we adopt the Mask R-CNN framework [4], using ResNet50 + FPN [5] as a backbone. This architecture works in two stages: first, the “Proposal Head” selects a set of candidate bounding boxes on the image (i.e. the proposals) that could contain an object; then, the “Mask Head” focuses on each proposal, predicting its class and segmentation mask. The output of this process is a “sparse” instance segmentation, covering only the parts of the image that contain countable objects (e.g. cars and pedestrians).
To complete our panoptic approach coined Seamless Scene Segmentation, we add a third stage to Mask R-CNN. Stemming from the same backbone, the “Semantic Head” predicts a dense semantic segmentation over the whole image, also accounting for the uncountable or amorphous classes (e.g. road and sky). The outputs of the Mask and Semantic heads are finally fused using a simple non-maximum suppression algorithm to generate the final panoptic prediction. All details about the actual network architecture, used losses and underlying math can be found at the project website for our CVPR 2019 paper [1].
While several versions of Mask R-CNN are publicly available, including an official implementation written in Caffe2, at Mapillary we decided to build Seamless Scene Segmentation from scratch using PyTorch, in order to have full control and understanding of the whole pipeline. While doing so we encountered a couple of main stumbling blocks, and had to come up with some creative workarounds we are going to describe next.
Dealing with variable-sized tensors
Something that sets aside panoptic segmentation networks from traditional CNNs is the prevalence of variable-sized data. In fact, many of the quantities we are dealing with cannot be easily represented with fixed sized tensors: each image contains a different number of objects, the Proposal head can produce a different number of proposals for each image, and the images themselves can have different sizes. While this is not a problem per-se – one could just process images one at a time – we would still like to exploit batch-level parallelism as much as possible. Furthermore, when performing distributed training with multiple GPUs, DistributedDataParallel
expects its inputs to be batched, uniformly-sized tensors.
Our solution to these issues is to wrap each batch of variable-sized tensors in a PackedSequence
. PackedSequence
is little more than a glorified list class for tensors, tagging its contents as “related”, ensuring that they all share the same type, and providing useful methods like moving all the tensors to a particular device, etc. When performing light-weight operations that wouldn’t be much faster with batch-level parallelism, we simply iterate over the contents of the PackedSequence
in a for loop. When performance is crucial, e.g. in the body of the network, we simply concatenate the contents of the PackedSequence, adding zero padding as required (like in RNNs with variable-length inputs), and keeping track of the original dimensions of each tensor.
PackedSequence
s also help us deal with the second problem highlighted above. We slightly modify DistributedDataParallel
to recognize PackedSequence
inputs, splitting them in equally sized chunks and distributing their contents across the GPUs.
Asymmetric computational graphs with Distributed Data Parallel
Another, perhaps more subtle, peculiarity of our network is that it can generate asymmetric computational graphs across GPUs. In fact, some of the modules that compose the network are “optional”, in the sense that they are not always computed for all images. As an example, when the Proposal head doesn’t output any proposal, the Mask head is not traversed at all. If we are training on multiple GPUs with DistributedDataParallel
, this results in one of the replicas not computing gradients for the Mask head parameters.
Prior to PyTorch 1.1, this resulted in a crash, so we had to develop a workaround. Our simple but effective solution was to compute a “fake forward pass” when no actual forward is required, i.e. something like this:
def fake_forward():
fake_input = get_correctly_shaped_fake_input()
fake_output = mask_head(fake_input)
fake_loss = fake_output.sum() * 0
return fake_loss
Here, we generate a batch of bogus data, pass it through the Mask head, and return a loss that always back-progates zeros to all parameters.
Starting from PyTorch 1.1 this workaround is no longer required: by setting find_unused_parameters=True
in the constructor, DistributedDataParallel
is told to identify parameters whose gradients have not been computed by all replicas and correctly handle them. This leads to some substantial simplifications in our code base!
In-place Activated BatchNorm
Github project page: https://github.com/mapillary/inplace_abn/
Most researchers would probably agree that there are always constraints in terms of available GPU resources, regardless if their research lab has access to only a few or multiple thousands of GPUs. In a time where at Mapillary we still worked at rather few and mostly 12GB Titan X - style prosumer GPUs, we were searching for a solution that virtually enhances the usable memory during training, so we would be able to obtain and push state-of-the-art results on dense labeling tasks like semantic segmentation. In-place activated BatchNorm is enabling us to use up to 50% more memory (at little computational overhead) and is therefore deeply integrated in all our current projects (including Seamless Scene Segmentation described above).
When processing a BN-Activation-Convolution sequence in the forward pass, most deep learning frameworks (including PyTorch) need to store two big buffers, i.e. the input x of BN and the input z of Conv. This is necessary because the standard implementations of the backward passes of BN and Conv depend on their inputs to calculate the gradients. Using InPlace-ABN to replace the BN-Activation sequence, we can safely discard x, thus saving up to 50% GPU memory at training time. To achieve this, we rewrite the backward pass of BN in terms of its output y, which is in turn reconstructed from z by inverting the activation function.
The only limitation of InPlace-ABN is that it requires using an invertible activation function, such as leaky relu or elu. Except for this, it can be used as a direct, drop-in replacement for BN+activation modules in any network. Our native CUDA implementation offers minimal computational overhead compared to PyTorch’s standard BN, and is available for anyone to use from here: https://github.com/mapillary/inplace_abn/.
Synchronized BN with asymmetric graphs and unbalanced batches
When training networks with synchronized SGD over multiple GPUs and/or multiple nodes, it’s common practice to compute BatchNorm statistics separately on each device. However, in our experience working with semantic and panoptic segmentation networks, we found that accumulating mean and variance across all workers can bring a substantial boost in accuracy. This is particularly true when dealing with small batches, like in Seamless Scene Segmentation where we train with a single, super-high resolution image per GPU.
InPlace-ABN supports synchronized operation over multiple GPUs and multiple nodes, and, since version 1.1, this can also be achieved in the standard PyTorch library using SyncBatchNorm. Compared to SyncBatchNorm, however, we support some additional functionality which is particularly important for Seamless Scene Segmentation: unbalanced batches and asymmetric graphs.
As mentioned before, Mask R-CNN-like networks naturally give rise to variable-sized tensors. Thus, in InPlace-ABN we calculate synchronized statistics using a variant of the parallel algorithm described here, which properly takes into account the fact that each GPU can hold a different number of samples. PyTorch’s SyncBatchNorm is currently being revised to support this, and the improved functionality will be available in a future release.
Asymmetric graphs (in the sense mentioned above) are another complicating factor one has to deal with when creating a synchronized BatchNorm implementation. Luckily, PyTorch’s distributed group functionality allows us to restrict distributed communication to a subset of workers, easily excluding those that are currently inactive. The only missing piece is that, in order to create a distributed group, each process needs to know the ids of all processes that will participate in the group, and even processes that are not part of the group need to call the new_group()
function. In InPlace-ABN we handle it with a function like this:
import torch
import torch.distributed as distributed
def active_group(active):
"""Initialize a distributed group where each process can independently decide whether to participate or not"""
world_size = distributed.get_world_size()
rank = distributed.get_rank()
# Gather active status from all workers
active = torch.tensor(rank if active else -1, dtype=torch.long, device=torch.cuda.current_device())
active_workers = torch.empty(world_size, dtype=torch.long, device=torch.cuda.current_device())
distributed.all_gather(list(active_workers.unbind(0)), active)
# Create group
active_workers = [int(i) for i in active_workers.tolist() if i != -1]
group = distributed.new_group(active_workers)
return group
First each process, including inactive ones, communicates its status to all others through an all_gather
call, then it creates the distributed group with the shared information. In the actual implementation we also include a caching mechanism for groups, since new_group()
is usually too expensive to call at each batch.
References
[1] Seamless Scene Segmentation; Lorenzo Porzi, Samuel Rota Bulò, Aleksander Colovic, Peter Kontschieder; Computer Vision and Pattern Recognition (CVPR), 2019
[2] In-place Activated BatchNorm for Memory-Optimized Training of DNNs; Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder; Computer Vision and Pattern Recognition (CVPR), 2018
[3] Panoptic Segmentation; Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, Piotr Dollar; Computer Vision and Pattern Recognition (CVPR), 2019
[4] Mask R-CNN; Kaiming He, Georgia Gkioxari, Piotr Dollar, Ross Girshick; International Conference on Computer Vision (ICCV), 2017
[5] Feature Pyramid Networks for Object Detection; Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie; Computer Vision and Pattern Recognition (CVPR), 2017


In week 2 and 3 we saw that particles that move with speed v have energy and momentum due to their movement. (We are only interested in the energy of movement, the kinetic energy.)
Momentum
We have also seen that the momentum and the energy are conserved.
Now that we have learned about the special nature of spacetime, how should we define v in these formulas? Normally the speed is
v = distance over time
but which time should we be using? What's more, with the speed of light being always constant, conservation of momentum and energy does not seem to work anymore.
Einstein was facing these tough questions and he found a solution. He found that there was natural definition for momentum and energy such that they would be conserved like before and such that they work perfectly fine with a speed limit. He proposed that the correct formula for momentum (no need to remember that one) is:
where
For the energy, Einstein found the following relation, which should remind you of the Pythagorean theorem:
(this


User Guide¶
- Quick Start
- Introduction
- Installation
- Before you begin
- Install from a tag
- Install a specific release
- Install the development branch
- Remove an old version
- Install on Windows
- Install on Linux
- Install on Mac
- Requesting an Installation
- Build a Container
- Build Environment
- Container Recipes
- Singularity Flow
- Bind Paths and Mounts
- Persistent Overlays
- Running Services
- Container Checks
- Environment and Metadata
- Reproducible SCI-F Apps
- Singularity and Docker
- Troubleshooting
- Appendix


Uses of Package
org.drip.function.definition
| Package | Description |
|---|---|
| org.drip.analytics.support |
Assorted Support and Helper Utilities
|
| org.drip.dynamics.hjm |
HJM Based Latent State Evolution
|
| org.drip.dynamics.hullwhite |
Hull White Latent State Evolution
|
| org.drip.dynamics.ito |
Ito Stochastic Process Dynamics Foundation
|
| org.drip.dynamics.kolmogorov |
Fokker Planck Kolmogorov Forward/Backward
|
| org.drip.dynamics.lmm |
LMM Based Latent State Evolution
|
| org.drip.dynamics.physical |
Implementation of Physical Process Dynamics
|
| org.drip.dynamics.process |
Ito-Dynamics Based Stochastic Process
|
| org.drip.execution.athl |
Almgren-Thum-Hauptmann-Li Calibration
|
| org.drip.execution.dynamics |
Arithmetic Price Evolution Execution Parameters
|
| org.drip.execution.impact |
Market Impact Transaction Function Implementation
|
| org.drip.execution.optimum |
Almgren-Chriss Efficient Trading Trajectories
|
| org.drip.execution.parameters |
Empirical Market Impact Coefficients Calibration
|
| org.drip.execution.principal |
Information Ratio Based Principal Trades
|
| org.drip.execution.strategy |
Discrete/Continuous Trading Trajectory Schedule
|
| org.drip.execution.tradingtime |
Coordinated Variation Trading Time Models
|
| org.drip.exposure.regression |
Regression Based Path Exposure Generation
|
| org.drip.function.definition |
Function Implementation Ancillary Support Objects
|
| org.drip.function.e2erf |
E2 erf and erf-1 Implementations
|
| org.drip.function.e2erfc |
E2 erfc Estimation Function Implementation
|
| org.drip.function.enerf |
En erf Series and Generators
|
| org.drip.function.matrix |
Support for Functions of Matrices
|
| org.drip.function.r1tor1 |
Built-in R1 To R1 Functions
|
| org.drip.function.r1tor1solver |
Built-in R1 To R1 Solvers
|
| org.drip.function.rdtor1 |
Built-in Rd To R1 Functions
|
| org.drip.function.rdtor1descent |
Rd To R1 Gradient Descent Techniques
|
| org.drip.function.rdtor1solver |
Built-in R^d To R^1 Solvers
|
| org.drip.graph.asymptote |
Big O Algorithm Asymptotic Analysis
|
| org.drip.learning.bound |
Covering Numbers, Concentration, Lipschitz Bounds
|
| org.drip.learning.kernel |
Statistical Learning Banach Mercer Kernels
|
| org.drip.learning.regularization |
Statistical Learning Empirical Loss Regularizer
|
| org.drip.learning.rxtor1 |
Statistical Learning Empirical Loss Penalizer
|
| org.drip.learning.svm |
Kernel SVM Decision Function Operator
|
| org.drip.measure.chisquare |
Chi-Square Distribution Implementation/Properties
|
| org.drip.measure.continuous |
R1 Rd Continuous Random Measure
|
| org.drip.measure.gamma |
R1 Gamma Distribution Implementation/Properties
|
| org.drip.numerical.estimation |
Function Numerical Estimates/Corrections/Bounds
|
| org.drip.numerical.fourier |
Fourier - Rotation Counter, Phase Adjuster
|
| org.drip.numerical.integration |
R1 Rd Numerical Integration Schemes
|
| org.drip.numerical.laplacian |
Laplace Transform - Quadrature Based Evaluation
|
| org.drip.numerical.quadrature |
R1 Gaussian Integration Quadrature Schemes
|
| org.drip.optimization.canonical |
Linear Programming Framework Canonical Elements
|
| org.drip.optimization.constrained |
KKT Fritz-John Constrained Optimizer
|
| org.drip.optimization.cuttingplane |
Polyhedral Cutting Plane Generation Schemes
|
| org.drip.param.market |
Curves Surfaces Quotes Fixings Container
|
| org.drip.portfolioconstruction.allocator |
MVO Based Portfolio Allocation Construction
|
| org.drip.portfolioconstruction.bayesian |
Black Litterman Bayesian Portfolio Construction
|
| org.drip.portfolioconstruction.constraint |
Portfolio Construction Constraint Term Suite
|
| org.drip.portfolioconstruction.objective |
Portfolio Construction Objective Term Suite
|
| org.drip.portfolioconstruction.optimizer |
Core Portfolio Construction Optimizer Suite
|
| org.drip.pricer.option |
Deterministic/Stochastic Volatility Settings/Greeks
|
| org.drip.product.option |
Options on Fixed Income Components
|
| org.drip.sequence.custom |
Glivenko Cantelli Supremum Deviation Bounds
|
| org.drip.sequence.functional |
Efron Stein Functional Supremum Bounds
|
| org.drip.sequence.metrics |
Sequence Bounds Agnostic Metrics Estimators
|
| org.drip.spaces.cover |
Vector Spaces Covering Number Estimator
|
| org.drip.spaces.functionclass |
Normed Finite Spaces Function Class
|
| org.drip.spaces.metric |
Hilbert/Banach Normed Metric Spaces
|
| org.drip.spaces.rxtor1 |
Rx To R1 Normed Function Spaces
|
| org.drip.spaces.rxtord |
Rx To Rd Normed Function Spaces
|
| org.drip.specialfunction.bessel |
Ordered Bessel Function Variant Estimators
|
| org.drip.specialfunction.beta |
Estimation Techniques for Beta Function
|
| org.drip.specialfunction.definition |
Definition of Special Function Estimators
|
| org.drip.specialfunction.derived |
Special Functions Derived using Others
|
| org.drip.specialfunction.digamma |
Estimation Techniques for Digamma Function
|
| org.drip.specialfunction.gamma |
Analytic/Series/Integral Gamma Estimators
|
| org.drip.specialfunction.generator |
Special Function Series Term Generators
|
| org.drip.specialfunction.group |
Special Function Singularity Solution Group
|
| org.drip.specialfunction.hankel |
Ordered Hankel Function Variant Estimators
|
| org.drip.specialfunction.hypergeometric |
Hyper-geometric Function Estimation Schemes
|
| org.drip.specialfunction.incompletegamma |
Upper/Lower Incomplete Gamma Functions
|
| org.drip.specialfunction.lanczos |
Lanczos Scheme for Gamma Estimate
|
| org.drip.specialfunction.loggamma |
Analytic/Series/Integral Log Gamma Estimators
|
| org.drip.specialfunction.ode |
Special Function Ordinary Differential Equations
|
| org.drip.specialfunction.property |
Special Function Property Lemma Verifiers
|
| org.drip.specialfunction.scaledexponential |
Scaled Exponential Function Implementation Distribution
|
| org.drip.spline.basis |
Basis Spline Construction/Customization Parameters
|
| org.drip.spline.bspline |
de Boor Rational/Exponential/Tension B-Splines
|
| org.drip.spline.params |
Spline Segment Construction Control Parameters
|
| org.drip.spline.pchip |
Monotone Convex Themed PCHIP Splines
|
| org.drip.spline.stretch |
Multi-Segment Sequence Spline Stretch
|
| org.drip.spline.tension |
Koch Lyche Kvasov Tension Splines
|
| org.drip.state.curve |
Basis Spline Based Latent States
|
| org.drip.state.estimator |
Multi-Pass Customized Stretch Curve
|
| org.drip.xva.derivative |
Burgard Kjaer Dynamic Portfolio Replication
|
| org.drip.xva.proto |
Collateral, Counter Party, Netting Groups
|
-
Classes in org.drip.function.definition used by org.drip.analytics.support Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.dynamics.hjm Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.dynamics.hullwhite Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.dynamics.ito Class Description R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair. -
Classes in org.drip.function.definition used by org.drip.dynamics.kolmogorov Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.dynamics.lmm Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.dynamics.physical Class Description R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair. -
Classes in org.drip.function.definition used by org.drip.dynamics.process Class Description R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair. -
Classes in org.drip.function.definition used by org.drip.execution.athl Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.execution.dynamics Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.execution.impact Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.execution.optimum Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.execution.parameters Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.execution.principal Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.execution.strategy Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.execution.tradingtime Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.exposure.regression Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.function.definition Class Description CartesianComplexNumber CartesianComplexNumber implements the functionality for dealing with the Cartesian Form of Complex Numbers.PoleResidue PoleResidue holds the Residue for given variate, if it is a Pole.R1PropertyVerification R1PropertyVerification evaluates the Specified Pair of Rx To R1 Functions, and holds the Verification Status.R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair.R3ToR1 R3ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair.RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates.RxToR1Property RxToR1Property evaluates the Specified Pair of Rx To R1 Functions, and verifies the Properties.SizedVector SizedVector holds the Rd Unit Direction Vector along with its Magnitude.UnitVector UnitVector implements the Normalized Rd Unit Vector.VariateOutputPair VariateOutputPair records the Multidimensional Variate and its corresponding Objective Function Value. -
Classes in org.drip.function.definition used by org.drip.function.e2erf Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.function.e2erfc Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.function.enerf Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.function.matrix Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.function.r1tor1 Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.function.r1tor1solver Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.function.rdtor1 Class Description RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.function.rdtor1descent Class Description RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates.UnitVector UnitVector implements the Normalized Rd Unit Vector. -
Classes in org.drip.function.definition used by org.drip.function.rdtor1solver Class Description RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates.SizedVector SizedVector holds the Rd Unit Direction Vector along with its Magnitude. -
Classes in org.drip.function.definition used by org.drip.graph.asymptote Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.learning.bound Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.learning.kernel Class Description RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.learning.regularization Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.learning.rxtor1 Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.learning.svm Class Description RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.measure.chisquare Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair. -
Classes in org.drip.function.definition used by org.drip.measure.continuous Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.measure.gamma Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair. -
Classes in org.drip.function.definition used by org.drip.numerical.estimation Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair. -
Classes in org.drip.function.definition used by org.drip.numerical.fourier Class Description CartesianComplexNumber CartesianComplexNumber implements the functionality for dealing with the Cartesian Form of Complex Numbers. -
Classes in org.drip.function.definition used by org.drip.numerical.integration Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.numerical.laplacian Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.numerical.quadrature Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.optimization.canonical Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.optimization.constrained Class Description RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.optimization.cuttingplane Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.param.market Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.portfolioconstruction.allocator Class Description RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.portfolioconstruction.bayesian Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.portfolioconstruction.constraint Class Description RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.portfolioconstruction.objective Class Description RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.portfolioconstruction.optimizer Class Description RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.pricer.option Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.product.option Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.sequence.custom Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.sequence.functional Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.sequence.metrics Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.spaces.cover Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.spaces.functionclass Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.spaces.metric Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.spaces.rxtor1 Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.RdToR1 RdToR1 provides the evaluation of the Rd To R1 objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.spaces.rxtord Class Description R1ToRd R1ToRd provides the evaluation of the R1 To Rd Objective Function and its derivatives for a specified variate.RdToRd RdToRd provides the evaluation of the Rd To Rd objective function and its derivatives for a specified set of Rd variates. -
Classes in org.drip.function.definition used by org.drip.specialfunction.bessel Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair. -
Classes in org.drip.function.definition used by org.drip.specialfunction.beta Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair.R3ToR1 R3ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair. -
Classes in org.drip.function.definition used by org.drip.specialfunction.definition Class Description CartesianComplexNumber CartesianComplexNumber implements the functionality for dealing with the Cartesian Form of Complex Numbers.R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair.R2ToZ1 R2ToZ1 provides the Evaluation of the Complex Objective Function and its Derivatives for a specified Variate Pair. -
Classes in org.drip.function.definition used by org.drip.specialfunction.derived Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair. -
Classes in org.drip.function.definition used by org.drip.specialfunction.digamma Class Description CartesianComplexNumber CartesianComplexNumber implements the functionality for dealing with the Cartesian Form of Complex Numbers.R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.specialfunction.gamma Class Description PoleResidue PoleResidue holds the Residue for given variate, if it is a Pole.R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.specialfunction.generator Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair. -
Classes in org.drip.function.definition used by org.drip.specialfunction.group Class Description CartesianComplexNumber CartesianComplexNumber implements the functionality for dealing with the Cartesian Form of Complex Numbers.R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.specialfunction.hankel Class Description CartesianComplexNumber CartesianComplexNumber implements the functionality for dealing with the Cartesian Form of Complex Numbers.R2ToZ1 R2ToZ1 provides the Evaluation of the Complex Objective Function and its Derivatives for a specified Variate Pair. -
Classes in org.drip.function.definition used by org.drip.specialfunction.hypergeometric Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair. -
Classes in org.drip.function.definition used by org.drip.specialfunction.incompletegamma Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.specialfunction.lanczos Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.specialfunction.loggamma Class Description PoleResidue PoleResidue holds the Residue for given variate, if it is a Pole.R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.specialfunction.ode Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.R2ToR1 R2ToR1 provides the Evaluation of the Objective Function and its derivatives for a specified variate Pair. -
Classes in org.drip.function.definition used by org.drip.specialfunction.property Class Description R1PropertyVerification R1PropertyVerification evaluates the Specified Pair of Rx To R1 Functions, and holds the Verification Status.R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.R1ToR1Property R1ToR1Property evaluates the Specified Pair of R1 To R1 Functions, and verifies the Properties.R2ToR1Property R2ToR1Property evaluates the Specified Pair of R2 To R1 Functions, and verifies the Properties.R3ToR1Property R3ToR1Property evaluates the Specified Pair of R3 To R1 Functions, and verifies the Properties. -
Classes in org.drip.function.definition used by org.drip.specialfunction.scaledexponential Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.spline.basis Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.spline.bspline Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.spline.params Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.spline.pchip Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.spline.stretch Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.spline.tension Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.state.curve Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.state.estimator Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.xva.derivative Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate. -
Classes in org.drip.function.definition used by org.drip.xva.proto Class Description R1ToR1 R1ToR1 provides the evaluation of the objective function and its derivatives for a specified variate.


--- layout: page title: Education permalink: /education/ ---
My younger days were spent attending school at B/ Hindu College, Colombo, Sri Lanka. Like all the kids, most of my time was spent in mischieves, rather than studying. I ended up keeping the scores up and finally finished Ordinary Level (Middle School), and Advanced Level (High School) at the same school.
After a second attempt at getting into college, I got admission to the University of Colombo . My first two years were spent in studying Botany, Zoology, Bio Chemistry and Computer Science. Computer Science, and Molecular Biology enticed me equally, and I decided to apply for a Specialization (i.e. Major) in bioinformatics, which seemed to be the natural choice for me.
After completion of my undergrad Degree, I was offered to teach as Demonstrator (Teaching Assistant) for the lab courses in my home department: Plant Sciences. While working there for an year, I sat for GRE and applied for various colleges for Graduate Studies. After many applications and rejections, I was offered a position to pursue a Master’s Degree in Botany Department at Miami University in Oxford, Ohio. I have worked in Liang lab for a period of two years, and have worked on several projects along with my thesis.
After Successfully obtaining my Master’s Degree, I am currently reading for my PhD degree in Bioinformatics at Iowa State University. As a second year PhD student I’m currently working for Carolyn Lawrence, and Erik Vollbrecht in a more wet-lab setting.


| # | Sta | Rlx | Name | Row Text |
| 0 | | 0 | autoRow0 | a "tree" is a kind of "plant" (0d3a-8739-9f42-5e1c) |
| 1 | ST | 0 | autoRow1 | photosynthesis means producers ; green plants convert from carbon dioxide and water and solar energy into carbohydrates and food and oxygen for themselves (0962-4a47-4e17-ce97) |
| 2 | ST | 0 | autoRow2 | photosynthesis is a source of ; makes food ; energy for the plant by converting carbon dioxide , water , and sunlight into carbohydrates (a9fc-ec58-9496-d635) |
| 3 | ST | 0 | autoRow3 | early photosynthetic life increases the oxygen level in Earth 's atmosphere (3c17-20cb-5bf2-680d) |
| 4 | ST | 0 | autoRow4 | a plant absorbs carbon dioxide from the air to perform photosynthesis (7b20-0992-8ec1-c73c) |
| 5 | ST | 0 | autoRow5 | absorbing something from the air decreases the amount of that something in the air (4989-d132-39f1-7c78) |
| 6 | ST | 0 | autoRow6 | as the population of plants decreases , carbon in the atmosphere will increase (163a-1ae9-b8d6-c3ab) |
| 7 | ST | 0 | autoRow7 | carbon dioxide can be found in the air (3cff-4d77-9e27-7dc5) |
| 8 | ST | 0 | autoRow8 | plants are a source of oxygen through photosynthesis (a908-944e-04aa-71bc) |
| 9 | ST | 0 | autoRow9 | a source of something increases the amount of that something (da68-3173-2681-d90e) |
| 10 | ST | 0 | autoRow10 | the photosynthetic life performs photosynthesis (4509-bfe2-61ad-8a15) |
| 11 | ST | 0 | autoRow11 | photosynthesis is a source of oxygen (b63a-5449-2c11-e70d) |
| 12 | ST | 0 | autoRow12 | sunlight is a kind of solar energy (59d6-9954-ef5e-12b2) |
| 13 | ST | 0 | autoRow13 | oxygen can be found in air (fd2c-50ef-261b-d5c2) |
| 14 | ST | 0 | autoRow14 | a source of something emits ; produces ; generates ; provides that something (b877-a58b-f75a-3998) |
| 15 | ST | 0 | autoRow15 | absorbing something from the air means removing that something from Earth 's atmosphere (d8c7-2c45-02f6-ba6c) |


Hi, I am Natalia
-
Software engineer
- Graduated from Rutgers University with BS in Computer Science in 2021
-
Passionate about making user-friendly applications that are easy to test and update
-
Love math, riddles and mind games
PROJECTS
CHESS
Android
TGIF
Java, MySQL
MineSweeper
Python


What is Plan My Day?
Have you ever found yourself sitting at home on your day off from your responsibilites and wanted to
go out and do something but you forgot to make plans?
Or maybe you're in a new city and you have
a few hours to discover something new?
Plan My Day is here to help! Just type in the name of the
city you wish to search in and you'll be given a list of events or places to visit that you
otherwise may have not thought to look up.


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Dammam
Jeddah
Makkah
Medina
Riyadh
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hotel
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GIFs
Facade Turn-based RPG
This is a gif of the turn-based RPG system I am working on, currently it's in a very early state!
99th Street Platformer (I guess?)
Demonstrating the wallriding and grinding mechanics
"GIFs
This is a gif of the turn-based RPG system I am working on, currently it's in a very early state!
Demonstrating the wallriding and grinding mechanics
"


amqp-client
An AMQP 0-9-1 client for Crystal.
Installation
- Add the dependency to your
shard.yml
:
dependencies:
amqp-client:
github: cloudamqp/amqp-client.cr
- Run
shards install
Usage
require "amqp-client"
AMQP::Client.start("amqp://guest:guest@localhost") do |c|
c.channel do |ch|
q = ch.queue("my-queue")
q.subscribe(no_ack: false) do |msg|
puts "Received: #{msg.body_io.to_s}"
ch.basic_ack(msg.delivery_tag)
end
# publish directly to a queue without confirm
q.publish "msg"
# publish directly to a queue, blocking while waiting for confirm
q.publish_confirm "msg"
# publish to any exchange/routing-key
ch.basic_publish "msg", exchange: "amq.topic", routing_key: "a"
# publish to any exchange/routing-key and wait for confirm
ch.basic_publish_confirm "msg", exchange: "amq.topic", routing_key: "a"
end
end
Contributing
- Fork it
- Create your feature branch (
git checkout -b my-new-feature
) - Commit your changes (
git commit -am 'Add some feature'
) - Push to the branch (
git push origin my-new-feature
) - Create a new Pull Request
Contributors
- Carl Hörberg - creator and maintainer
- Anders Bälter


{% extends 'assessment-rooted.html' %} {% load add_class %} {% load crispy_forms_tags %} {% block content %} {% crispy form %}
Copying this approach from another assessment will delete all {{assessment.get_rob_name_display|lower}} information currently saved in this assessment. This includes all domains and metrics which currently exist, as well as any completed evaluations for studies already assessed.
It does not change the assessment which the approach is copied from.


2019 CLC is under registration. Please register your team before Aug 13, 2019
2019 CLC is under registration. Please register your team before Aug 13, 2019
2019 Swan Warriors U3700 Team Competition is open for registration. Check out U3700 for detail.
Swan Summer Camp is open for registration. For details please check out Summer Camp .
Swan Spring Camp is open for registration. For details please check out Spring Camp .
We will have a Spring Festival celebration party on 2/2 Saturday from 6pm - 10pm. The whole club is reserved and there will be no open play during this time.
$1,000 - $1,500 cash prize every Sunday, 12:30pm - 7pm. 6 levels: U2400, U2200, U1900, U1700, U1500, U800. For details please check outSwan Warriors League.
TT Dream Open is a non-sanctioned charity tournament. All proceeds from this event will be donated directly to Africa's poor kids via TZU-CHI Foundation. Asian Dream Foundation will donate 5 times the amount of your donation to TZU-CHI Foundation.
$4,500 Prize Money.
2018 CLC has concluded. Congratulations to winners Google Pie and Super Micro B#5.
2018 Swan U1800 has started registration.
4-Star Tournament - $8,000 Prize – WOW! Register online at Omnipong. Check out Tournament Flyer and Registration Form for details.
2018 Swan Warriors Summer Camp is jointly organized by Swan Warriors School and PPC Swan. Early bird discount is available before 4/1/2018. Click here for detail.
Swan player Lawrence Pang had suddenly passed away on May 14. He is an old friend of many players here. Please visit his Memorial page to pay tribute to him and see how to help his family.
Swan Spring Camp is open for registration. For details please check out Camp .
2018 U3700 Charitable Team Competition has started registration.
48 players. $1000 Total prize and 9 Trophies. Registration fee $20 will be donated directly to Africa's poor kids via TZU-CHI Foundation.
Organizer Asian Dream Foundation will also match 5 times of your donation to TZU-CHI Foundation. Register to play and help now!
Congrats to winners TTA Pong and Super Micro! Check out 2017 CLC for photos, videos and results.
108 players will compete for $5,000 prize in Tao Swan Pre-National Open.
PPC Anniversary Party. Club will close for open play and coaching from 5pm for setting up the party
2017 Swan U1800 Competition has started.
Swan Club will offer two new weekly Kid group classes and one Adult Group class starting from Mar 15. For details please check out Group .
Swan Spring Camp is open for registration. You can register now to get early bird discounts. For details please check out Camp .
2017 U3700 Charitable Team Competition has started.
Free Kid Group classes resume on Jan 3 after the Holiday break. The class is offered daily at 3:30-4:30pm Mon - Thur. Register early to to get your a spot.
Swan Winter Camp is open for registration. For details please check out Camp .
Swan club will open free to public since Dec 6 2016. For details please check out here.
New Free Kid Group classes are open for registration. For details please check out Kid Group Class .
Swan club held the 2016 Company/Club League Championship . Congratulations to PTTC for winning the championship and SVTTC Pips Mafia as the runner up.


{% extends base_template %} {%- import "layout/macros.html" as macros %} {%- block subtitle %}Contestants{%- endblock %} {%- block social_meta %} {%- endblock %} {%- macro summary(item_class) %}
Hey, Boulder! We are the Startup Capital of the US. Show them why we are #1!
Vote with your dollars for the best Boulder startup. The Esprit Venture Challenge is the Boulder Chamber’s friendly crowdfunding contest. Promote innovation in Boulder. Let’s keep our community #1! [more]


--- title: Mirador --- {%- include head.html -%}
{% assign nav=site.navigation %} {%- include header.html navigation=nav -%}Mirador is a configurable, extensible, and easy-to-integrate image viewer, which enables image annotation and comparison of images from repositories dispersed around the world. Mirador has been optimized to display resources from repositories that support the International Image Interoperability Framework (IIIF) APIs. It provides a tiling windowed environment for comparing multiple image-based resources, synchronized structural and visual navigation of content using OpenSeadragon, Open Annotation compliant annotation creation and viewing on deep-zoomable canvases, metadata display, book reading, bookmarking and more. Additional information available at the official Mirador site.
Manuscripts can be viewed as separate pages, or combined to emulate the format of a printed book.
Images can be viewed individually, or within a carousel slideshow format.
Mirador renders extremely large objects with ease using deep zoom functionality provided by OpenSeadragon. In addition, the viewer supports dynamic grid-based layouts for multiple views of image content.


AWSFileStorage
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