You can turn on line numbers in the grid inspector, under the Layout tab.
The grid lines are also numbered in the reverse direction, using negative numbers. So we can refer to the last grid lines as -1, the second last as -2 and so on. You can see both the positive and negative numbers in the grid inspector.
This means that we can position an element in the last two rows and last two columns of a grid, no matter how many cells there are!
.big-grid {
display: grid;
grid-template-columns: repeat(10, 1fr);
grid-template-rows: repeat(10, 1fr);
}
.big-grid .element:nth-child(1) {
grid-row: 2 / 4;
grid-column: 2 / 4;
}
.big-grid .element:nth-child(2) {
grid-row: 2 / 5;
grid-column: -2 / 6;
}
.big-grid .element:nth-child(3) {
grid-row: 6;
grid-column: 1 / -1;
}
.big-grid .element:nth-child(4) {
grid-row: -1 / -4;
grid-column: -1 / -4;
}
Notice that it doesn't matter which order you put the start and end values in. Your element will appear between the two named lines - it's not going to try and wrap around the outside or anything wild like that.
We also have the option to specify how big we want our element to be, without necessarily knowing what the
start and end position is going to be, using the span
keyword.
.element {
grid-column: 2 / span 2; // element will begin at line 2, and span 2 columns
}
.element {
grid-row: span 2 / -1; // element will span 2 rows, ending at the last row
}
.element {
grid-column: span 3; // element will be placed normally, but span 3 columns
}
You'll notice that the grid just kind of slots the auto-placed elements into the next available cell after
each element that has a fixed position. You can change it so that elements are placed as close to the start
of the grid as possible, using grid-auto-flow: dense
.
For a more indeth discussion of how this works, check out the MDN article on Auto-placement in CSS Grid Layout
The final way that we can cause an element to cover several cells is by using grid-template-areas
with
grid-area
. grid-template-areas
allows us to attach labels to grid cells. We can then
use grid-area
to declare which cell(s) an element will occupy.
It looks something like this:

A

B

C

.container {
display: grid;
grid-template-columns: 1fr 1fr;
grid-template-rows: 1fr 1fr;
grid-template-areas: 'a a'
'b c';
}
.a {
grid-area: 'a';
}
.b {
grid-aread: 'b';
}
.c {
grid-area: 'c';
}
In the grid-template-areas
property, each string represents a row of the grid.
The string consists of space-separated tokens. Each token represents a column. The tokens
can be whatever name you like. You can use single characters (which can make it a bit easier
to represent the grid in the CSS), or you can use meaningful names.
The items labelled by grid-area
will then be placed in the corresponding position on the grid.
If a token is repeated, then the item will span multiple cells, like A
above. The repeated tokens need to be adjacent though,
and resulting area has to be rectangular in shape. You can also turn on the grid area labels in the grid inspector -
the option is in the Layout tab.
grid-areas
can be a handy way to make your CSS a little more semantic.
grid-template-areas: 'heading heading heading'
'nav content content'
'. content content'
'footer footer footer'
A '.' denotes an empty cell.
While all these properties provide a very powerful way of rearranging your grid, you should bear in mind the same caveats
as when reordering flexboxes. The tab order will follow the document order, not the visible order, so extensively
rearranged elements can result in an inaccessible experience. For this reason, I'd suggest preferring a single
span
value over specifying start and end values, and ensuring that your grid-template-areas
follow your source order fairly closely.
Everything we've looked at so far has assumed that we know how many elements are going to be in our grid. Often though, that isn't the case. Luckily, grid has us covered there too!
If we add more elements than we have defined in our grid, the browser will just add another row to the grid
and continue adding elements there. (If you're using grid-auto-flow: column
, then it will add a
new column instead.) Additional rows or columns that are added like this are called the implicit grid.
(As opposed to the explicit grid, which is rows/columns that you set up... well... explicitly.)
The grid inspector depicts implicit grid cells with a dotted, rather than dashed line.
Let's start with the most extreme example. We've defined a grid container, but not specified any rows or columns.
.container {
display: grid;
width: 500px;
height: 500px;
}
We get a grid with one column per row. The single column stretches for the entire width of the row. Each row has a height
of auto
. auto
is a special value that means the element will have a min-height
of
min-content
, and a max-height
of 1fr
. Basically, the elements are going to fill
the available space, without overflowing.
We can specify the height of our automatically generated rows using grid-auto-rows
.
.container {
display: grid;
width: 500px;
height: 500px;
grid-auto-rows: 100px;
}
Now our rows don't fill up all the available space! We can control the spacing of these auto-generated rows using
the same align-content
property we used in flexbox.
.container {
display: grid;
width: 500px;
height: 500px;
grid-auto-rows: 100px;
align-content: flex-end;
}
If we're automatically adding columns, (because we've got grid-auto-flow: column
), then we can
use justify-content
to control the spacing of the columns.
We can also combine explicit grid with implicit grid.
.container {
display: grid;
grid-template-columns: 200px 1fr;
grid-template-rows: 50px 150px 60px;
grid-auto-rows: 100px;
}
One thing to remember about using the implicit grid is that you can't use negative numbers to place elements at the end of rows or columns. The browser has no way of knowing in advance how many rows and columns it's going to need, so it has no way to calculate where the last row or column is.
Grid also has some other nice tricks for building dynamic and responsive layouts.
For example, sometimes, we don't actually know how many columns we want. We just want
as many columns as will fit, given all the columns are a certain width. We can do this
using the repeat()
function, with the auto-fit
or auto-fill
keywords.
The repeat()
function can be used with grid-template-columns
and grid-template-rows
to simplify writing repetitive templates. You pass it a number of repetitions and a size. So
grid-template-columns: repeat(4, 100px)
is the same as grid-template-columns: 100px 100px 100px 100px
.
The magic begins, though, when we combine repeat()
with the auto-fit
and auto-fill
keywords. repeat(auto-fit, 100px)
means "fit as many items as you can in columns 100px wide".
You can see that in a large enough container, the browser will create a 100px wide column for each element, and they'll all appear in a single row. If you shrink the container, so it's too narrow to fit six 100px wide columns, then one of the columns will disappear, and the element in that column will wrap to the next row.
What about auto-fill
? repeat(auto-fill, 100px)
means "create as many 100px-wide columns as you can".
The difference is very subtle, and the result is going to look identical, when using fixed-width columns.
While the two may look identical, there is a slight difference. If you check out the auto-fill
example using grid
inspector, you will see that the browser has created a bunch of implicit columns. It has, in effect, filled the row with
columns. This difference becomes more interesting when we use dynamically-sized columns.
You're already familiar with one type of dynamic sizing - the fr
unit. However, you can't use frs with auto-fit
and auto-fill
! If you did, you'd essentially be telling the browser to place your items in an unknown number of columns of
an unknown width. Browsers are clever, but they're not that clever.
However! We do have another alternative. The minmax()
function allows us to provide a range of sizes for our cells.
grid-template-columns: repeat(4, minmax(100px, 200px)
will create four columns, which will shrink down to 100px wide, or
grow as large as 200px wide, depending on the size of the container.
A slightly more interesting use of minmax()
is to combine it with fr
to create columns that have a minimum
size, but stretch to fill all the available space. For example, grid-template-columns: repeat(4, minmax(100px, 1fr))
.
This isn't super great though, as the grid is going to overflow or be cut off when the container is smaller than 400px wide, and we're going to end up with massively wide columns in larger containers, which could look a bit weird, depending on our design.
What we really want is the best of both worlds. We want as many 100px wide columns as will fit in the container, and we want them
to stretch to fill the available space. It probably won't surprise you to learn that we can achieve that by combining
auto-fit/auto-fill
, minmax
, and fr
.
grid-template-columns: repeat(auto-fit, minmax(100px, 1fr));
You can see here, as the container width increases, the cells grow to fill it, right up until the point where the container is wide enough to fit two 100px columns. At that point, it switches to a two-column layout. Then, the elements keep growing, until there's enough space to fit three columns. And so on, and so forth. As I'm sure you can imagine, this is a nifty way of creating responsive layouts, which respond to the size of the container, rather than the viewport (and which don't rely on media queries).
The difference between auto-fit
and auto-fill
becomes apparent once the container is wide enough
to fit all the elements + 1 in a single row.
grid-template-columns: repeat(auto-fill, minmax(100px, 1fr));
In the first example - the auto-fit
one - the elements kept stretching to fill all the available space.
In the second example - the auto-fill
one - they won't stretch much beyond their minimum size, leaving a
a large gap at the end of the row.
The reason for this becomes apparent if we look at the auto-fill
example
with the grid inspector.
Remember, auto-fill
keeps creating columns, even if there's nothing to go in them. Once the container is
wide enough to fit all our items + 1, it creates an empty implicit column 100px - leaving an apparent gap at the end of
the row. As the container continues to grow, the columns will stretch to fill the available space again, but only until
there is enough room to add another column. Then they will all snap back to their minimum size, and the browser will add
another implicit column.
So that just leaves one question burning everybody's lips - can I use it? Well, the answer is yes, mostly.
Globally, at least 91% of users are using a browser which supports the current grid spec. As long as you don't have a requirement to support IE, you're fine. (QQ browser accounts for only 0.2% of global usage, so it's not really worth worrying about, unless you've got a large user base in China. As always, you should check the usage figures for your specific domain.)
But what if you do need to support IE? Well, you've got a couple of options. IE does support a version of the grid spec - in fact it was the first browser to support grid at all. It lacks some of the auto-placement stuff, and requires a vendor prefix. Depending on your needs, it might be possible to just build your grid as you would normally, and use autoprefixer to add the necessary prefixes.
For more information on using autoprefixer, have a look at the series on using grid in IE on CSS tricks .
The other option is to use fallbacks. While IE's support of grid is a little lacking, it does support flexbox quite well. While you're not going to be able to achieve quite the same schmick two-dimensional layouts with flexbox as you are with grid, you can often make a workable approximation. So, rather than depriving all your users of the fancy grid layout, you give the users on modern browers the grid layout, and you give the IE users the flexbox layout. This is called progressive enhancement. It's just like the idea that we give users on mobile phone a small, simplified layout, and add more features as the screen gets larger and can accomodate more stuff.
How does it work? The simplest version is to rely on the fact that browsers ignore CSS that they don't understand, and always use the last value of a property. So if we do something like...
.container {
/* for IE */
display: flex;
flex-wrap: wrap;
/* for everyone else */
display: grid;
grid-template-columns: repeat(auto-fit, minmax(100px, 1fr));
}
IE is going to use the flex values, and ignore the grid values, because it doesn't understand them. Other browsers will understand the flex values, but they'll immediately be overriden by the grid values, which appear later in the stylesheet.
This works in many cases, but can cause issues when you need to set a property that is valid in both cases, but means something different. For example, if we added
.item {
width: 100%
}
to the above example, the item would be 100% of the width of the container in IE, but only 100% of the width of the cell in
other browsers. In these cases, we can use the @supports
query. We pass the @supports
query a CSS
rule, and it will only execute the rules inside the query if it understands the given rule. So we could do
width: auto;
@supports(display: grid) {
width: 100%;
}
Browsers which don't support display: grid
(ie IE) will use a width of auto
, while other browsers will use
a width of 100%
. (In the case of IE, it doesn't actually matter what you pass into the @supports
query,
because IE doesn't event understand @supports
, so it will ignore everything inside it, no matter what. But it can be useful for
providing fallbacks for other CSS properties, like display: subgrid
, which is an exciting new grid feature currently
only available in Firefox.)
Yes, this is a little more work than just creating a single layout for all browsers, but it does mean that all users are getting the best possible experience available in the browser that they are using, and the >90% of users using modern browsers aren't missing out due to the few users who aren't.
columns
float
Hopefully, all of this has given you some things to think about! The important thing to remember though is that, in software, there isn't usually one right answer. Instead, it's about trade-offs. You need to think about how things work in your particular situation, and decide on the best course of action yourself.
If you'd like some more information about any of the topics we've covered, there are a heap of resources available both online and offline. Here's some that I've found helpful.
So yeah, apparently nobody has written a book on clean coding in the last twenty years. I'm not sure what to make of that.
And finally, the one resource that every web developer needs: MDN (Mozilla Developer Networks). This site contains a reference for every HTML element, CSS property and JavaScript API in existence. It also contains browser support tables for just about everything, as well as tutorials on things like accessibility, and grid.
And, if all that's not enough, my Twitter DMs are open at @ErinJZimmer. Feel free to drop me a line!


- p - Variable in class jp.go.enri.prml.vb.ONDEParameterDistribution
-
the parameters of Dirichlet prior/posterior distribution of the offset mixing coefficients \omega
- param - Variable in class jp.go.enri.prml.bf.NDEBF.Result
-
the parameters of distribution model
- param - Variable in class jp.go.enri.prml.bf.ONDEBF.Result
-
the parameters of distribution model
- param - Variable in class jp.go.enri.prml.em.NDEEM.Result
-
the parameters of distribution model
- param - Variable in class jp.go.enri.prml.em.ONDEEM.Result
-
the parameters of distribution model
- param - Variable in class jp.go.enri.prml.vb.NDEVB.Result
-
the parameter for variational Bayes.
- param - Variable in class jp.go.enri.prml.vb.ONDEVB.Result
-
the parameter for variational Bayes.
- pi - Variable in class jp.go.enri.prml.dist.NDE
-
the mixing coefficients
- pi - Variable in class jp.go.enri.prml.dist.ONDE
-
the mixing coefficients
- pointer - Variable in class jp.go.enri.prml.bf.NDEBF.Increment
-
Pointer
- pointer - Variable in class jp.go.enri.prml.bf.ONDEBF.Increment
-
Pointer
- print() - Method in class jp.go.enri.prml.bf.NDEBF.Increment
-
print
- print() - Method in class jp.go.enri.prml.bf.ONDEBF.Increment
-
- PRMLFactory - Class in jp.go.enri.prml.factory
-
Read the cofiguration and generate instances.
- PRMLFactory() - Constructor for class jp.go.enri.prml.factory.PRMLFactory
-
- Psi(double) - Static method in class jp.go.enri.util.crm.integral.LateralOverlapProbability
-
Return the Psi(x) defined in SASP-WG/WHL/11-WP/05.
- ptEstimation() - Method in class jp.go.enri.prml.vb.NDEParameterDistribution
-
Average estimation of parameter values.
- ptEstimation() - Method in class jp.go.enri.prml.vb.ONDEParameterDistribution
-
Average estimation of parameter values.
- PySyDE(double, double, double, double) - Static method in class jp.go.enri.util.crm.integral.LateralOverlapProbability
-
Calculate Py(S) in the case where the TSE distributions follow different DE distributions.
- PySyDE(double, double, double) - Static method in class jp.go.enri.util.crm.integral.LateralOverlapProbability
-
Calculate Py(S) in the case where the TSE distribution follows a single DE distribution.
- PySyDEDE(double, double, double, double, double) - Static method in class jp.go.enri.util.crm.integral.LateralOverlapProbability
-
Calculate Py(S) in the case where the TSE distribution follows DDE distribution.
- PySyN(double, double, double, double) - Static method in class jp.go.enri.util.crm.integral.LateralOverlapProbability
-
Calculate Py(S) in the case where the TSE distributions follow different Gaussian distributions.
- PySyN(double, double, double) - Static method in class jp.go.enri.util.crm.integral.LateralOverlapProbability
-
Calculate Py(S) in the case where the TSE distribution follows a single Gaussian distribution.
- PySyNDE(double, double, double, double, double) - Static method in class jp.go.enri.util.crm.integral.LateralOverlapProbability
-
Calculate Py(S) in the case where the TSE distribution follows N-DE distribution.
- PySyNDEApprox(double, double, double, double, double) - Static method in class jp.go.enri.util.crm.integral.LateralOverlapProbability
-
Calculate Py(S) in the case where the TSE distribution follows N-DE distribution.
- PySyNN(double, double, double, double, double) - Static method in class jp.go.enri.util.crm.integral.LateralOverlapProbability
-
Calculate Py(S) in the case where the TSE distribution follows NN distribution.
- PySyNVSDE(double, double, double, double) - Static method in class jp.go.enri.util.crm.integral.LateralOverlapProbability
-
Calculate Py(S) in the case where the TSE distribution follows N distribution and DE distribution.
- PySyNVSDEAppro(double, double, double, double) - Static method in class jp.go.enri.util.crm.integral.LateralOverlapProbability
-
Calculate Py(S) in the case where the TSE distribution follows N distribution and DE distribution.


API Reference¶
Deck¶
-
class
card_deck.
Deck
¶ A card deck
-
draw
(count: int = 1, from_bottom: bool = False)¶ Draw a card from the deck
-
insert
(card: card_deck.cards.Card, position: int = 0)¶ Insert a card at the given position, zero being the top
-
insert_many
(cards: list, position: int = 0)¶ Insert a list of cards at the given position, zero being the top
-
peek
(count: int = 1, from_bottom: bool = False)¶ Peek at the top card(s)
-
shuffle
()¶ Shuffle the deck
-
Cards¶
-
class
card_deck.
Card
(name: str)¶ A playing card with no special attributes
- Parameters
name (
str
) -- The name of the card
-
class
card_deck.
StandardCard
(type, suit)¶
You can get a set of standard cards with card_deck.standard_cards
-
class
card_deck.
UnoCard
(color: card_deck.cards.Color, type: card_deck.cards.UnoCardType)¶
You can get a set of uno cards with card_deck.uno_cards


Spring, also known as springtime, is one of the four temperate seasons, succeeding winter and preceding summer. There are various technical definitions of spring, but local usage of the term varies according to local climate, cultures and customs. When it is spring in the Northern Hemisphere, it is autumn in the Southern Hemisphere and vice versa. At the spring (or vernal) equinox, days and nights are approximately twelve hours long, with daytime length increasing and nighttime length decreasing as the season progresses.


1. The value of reflection, both self-reflection in terms of self-awareness and knowing your own strengths and limitations. But also technical reflection and how they both help to retain and absorb new ideas.
2. How disappointing it can be when you can’t wrap your head around a concept or solve a problem and knowing the effect it can have on my attitude.
3. Sometimes having to really force myself to have a growth mindset when I’m struggling with a concept or problem.
I think self-awareness plays an important role in your ability to reflect and be introspective as well as how clearly you see yourself in relation to others. Empathy is often tied very closely with your capacity to learn and apply critical thinking skills, along with the importance and respect you place on learning from others shared expertise, experiences and values.
What has surprised you the most about the core learning?I think one of the things that has surprised me the most is the value of reflection, both in terms of self-reflection and reflection on the technical side of learning. I often found it very helpful in alleviating some of my unease or uncertainty about a problem or topic I was unsure about. This along with the ideas of neuroplasticity and having a growth mindset have really helped when I have been overwhelmed by all of the new concepts.
What were the most challenging aspects of the core learning?One of the more challenging aspects of the core learning was coming to terms with and understanding what some of my own strengths and limitations are. Then using the methods covered under the core leaning to help work on those weaknesses through things like scheduling, goal setting, breaking down problems, mindfulness and understanding your mindset.
Why do you think we, a programming school, are spending so much time focusing on core learning in a web development bootcamp course?I think they are important skills, especially when working in groups and with others in the workplace. Your ability to communicate, value and empathies with those around is often a good measure of someone’s emotional intelligence and often indicative of how well someone can inspire, lead and motivate others.
Does the time you spent studying core learning here feel like a waste of time? Should you have just used that time to practicing programming instead? Justify your answer.No, I think a lot of the skills are not only valuable in day to day life and in the workplace but are useful in helping us understand why and how you feel and act a certain way. I think the ability to understand your values, yourself and those around you is instrumental in being a positive and effective member your community.
Preparing for BootcampI’m attending Dev Academy because I enjoy creative problem solving and I’m looking forward to learning some new skills that mesh well with my current design skill set to hopefully expand future career opportunities. I don’t have any long term goals set in stone, but something that mixes both design and web development would be very appealing.
What do you think your biggest strengths and limitations will be in Bootcamp?
Attention to detail:
While I have a good eye for visual details, it’s been hard transferring this skill to something less visually focused and more structurally focused.
Thoughtfulness and respect:
I think this will be extremely important during bootcamp, to be considerate of those around me and give everyone their due respect.
Listening and receiving feedback:
During bootcamp I think it will be crucial to be open to new ideas and perspectives, as well as receiving and incorporating advice or feedback from students and teachers alike.
Calmness:
I have no doubt I will probably be stressed and frustrated at many points during the bootcamp, so I think it will important to be mindful and self-aware enough to know when to take a break.
Introvert:
I think it will be important to speak up, question and ask for help or support when needed, even though I initial might be a little resistant.
Procrastination:
I think time management and planning will be key to ensuring everything remains on schedule and to ensure I am making the most of my time at EDA.
Biggest critic:
During bootcamp I think it will be important to not be too critical of my work and maintain a growth mindset throughout, don’t get too hung up on perfectionism.
Ensuring I don’t get burnt out and look after and maintain my own mental health. It will be important to plan and manage my time and ensure that if I do get stuck I follow the suggested problem solving and support guidelines.
What non-technical skills - human skills - would you like to see developed in yourself while at EDA?Good communication skills, especially with a bunch of strangers or people I have never worked with before. As a bit of an introvert it can often be a bit of a hurdle to build a fast rapport with a new person or team member. So being able to develop those skills would be one of my priorities.
What are your expectations of yourself on BootcampTo be present and willing to learn and grow. To put in the extra time and work to get the best result. To be open and clear communicating with peers and teachers. To work in a respectful manner that is hopefully reflected by the students and facilitators within the program.


perl5143delta - what is new for perl v5.14.3
This document describes differences between the 5.14.2 release and the 5.14.3
release.
If you are upgrading from an earlier release such as 5.12.0, first
read perl5140delta, which describes differences between 5.12.0 and
5.14.0.
The "Digest->new()" function did not
properly sanitize input before using it in an eval() call, which could
lead to the injection of arbitrary Perl code.
In order to exploit this flaw, the attacker would need to be able
to set the algorithm name used, or be able to execute arbitrary Perl code
already.
This problem has been fixed.
Poorly written perl code that allows an attacker to specify the count to perl's
'x' string repeat operator can already cause a memory exhaustion
denial-of-service attack. A flaw in versions of perl before 5.15.5 can
escalate that into a heap buffer overrun; coupled with versions of glibc
before 2.16, it possibly allows the execution of arbitrary code.
This problem has been fixed.
There are no changes intentionally incompatible with 5.14.0. If any exist, they
are bugs and reports are welcome.
There have been no deprecations since 5.14.0.
- PerlIO::scalar was updated to fix a bug in which opening a filehandle to a
glob copy caused assertion failures (under debugging) or hangs or other
erratic behaviour without debugging.
- ODBM_File and NDBM_File were updated to allow building on GNU/Hurd.
- IPC::Open3 has been updated to fix a regression introduced in perl 5.12,
which broke "IPC::Open3::open3($in, $out, $err,
'-')". [perl #95748]
- Digest has been upgraded from version 1.16 to 1.16_01.
See "Security".
- Module::CoreList has been updated to version 2.49_04 to add data for this
release.
perlcheat
- •
- perlcheat was updated to 5.14.
- h2ph was updated to search correctly gcc include directories on platforms
such as Debian with multi-architecture support.
- In Configure, the test for procselfexe was refactored into a loop.
- FreeBSD
- The FreeBSD hints file was corrected to be compatible with FreeBSD
10.0.
- Solaris and NetBSD
- Configure was updated for "procselfexe" support on Solaris and
NetBSD.
- HP-UX
- README.hpux was updated to note the existence of a broken header in HP-UX
11.00.
- Linux
- libutil is no longer used when compiling on Linux platforms, which avoids
warnings being emitted.
The system gcc (rather than any other gcc which might be in
the compiling user's path) is now used when searching for libraries such
as "-lm".
- Mac OS X
- The locale tests were updated to reflect the behaviour of locales in
Mountain Lion.
- GNU/Hurd
- Various build and test fixes were included for GNU/Hurd.
LFS support was enabled in GNU/Hurd.
- NetBSD
- The NetBSD hints file was corrected to be compatible with NetBSD 6.*
- A regression has been fixed that was introduced in 5.14, in
"/i" regular expression matching, in
which a match improperly fails if the pattern is in UTF-8, the target
string is not, and a Latin-1 character precedes a character in the string
that should match the pattern. [perl #101710]
- In case-insensitive regular expression pattern matching, no longer on
UTF-8 encoded strings does the scan for the start of match only look at
the first possible position. This caused matches such as
""f\x{FB00}" =~ /ff/i" to
fail.
- The sitecustomize support was made relocatableinc aware, so that
-Dusesitecustomize and -Duserelocatableinc may be used together.
- The smartmatch operator ("~~") was
changed so that the right-hand side takes precedence during
"Any ~~ Object" operations.
- A bug has been fixed in the tainting support, in which an
"index()" operation on a tainted
constant would cause all other constants to become tainted. [perl
#64804]
- A regression has been fixed that was introduced in perl 5.12, whereby
tainting errors were not correctly propagated through
"die()". [perl #111654]
- A regression has been fixed that was introduced in perl 5.14, in which
"/[[:lower:]]/i" and
"/[[:upper:]]/i" no longer matched the
opposite case. [perl #101970]
Perl 5.14.3 represents approximately 12 months of development since Perl 5.14.2
and contains approximately 2,300 lines of changes across 64 files from 22
authors.
Perl continues to flourish into its third decade thanks to a
vibrant community of users and developers. The following people are known to
have contributed the improvements that became Perl 5.14.3:
Abigail, Andy Dougherty, Carl Hayter, Chris 'BinGOs' Williams,
Dave Rolsky, David Mitchell, Dominic Hargreaves, Father Chrysostomos,
Florian Ragwitz, H.Merijn Brand, Jilles Tjoelker, Karl Williamson, Leon
Timmermans, Michael G Schwern, Nicholas Clark, Niko Tyni, Pino Toscano,
Ricardo Signes, Salvador Fandin~o, Samuel Thibault, Steve Hay, Tony
Cook.
The list above is almost certainly incomplete as it is
automatically generated from version control history. In particular, it does
not include the names of the (very much appreciated) contributors who
reported issues to the Perl bug tracker.
Many of the changes included in this version originated in the
CPAN modules included in Perl's core. We're grateful to the entire CPAN
community for helping Perl to flourish.
For a more complete list of all of Perl's historical contributors,
please see the AUTHORS file in the Perl source distribution.
If you find what you think is a bug, you might check the articles recently
posted to the comp.lang.perl.misc newsgroup and the perl bug database at
http://rt.perl.org/perlbug/ . There may also be information at
http://www.perl.org/ , the Perl Home Page.
If you believe you have an unreported bug, please run the perlbug
program included with your release. Be sure to trim your bug down to a tiny
but sufficient test case. Your bug report, along with the output of
"perl -V", will be sent off to
<EMAIL_ADDRESS>to be analysed by the Perl porting team.
If the bug you are reporting has security implications, which make
it inappropriate to send to a publicly archived mailing list, then please
send it to<EMAIL_ADDRESS>This points to a closed
subscription unarchived mailing list, which includes all the core
committers, who be able to help assess the impact of issues, figure out a
resolution, and help co-ordinate the release of patches to mitigate or fix
the problem across all platforms on which Perl is supported. Please only use
this address for security issues in the Perl core, not for modules
independently distributed on CPAN.
The Changes file for an explanation of how to view exhaustive details on
what changed.
The INSTALL file for how to build Perl.
The README file for general stuff.
The Artistic and Copying files for copyright
information.


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import play.twirl.api.HtmlFormat @import uk.gov.hmrc.play.views.html.layouts._ @import uk.gov.hmrc.play.views.html.helpers._ @import views.html.layouts.GovUkTemplate @import uk.gov.hmrc.play.views.html.layouts.HeadWithTrackingConsent @import uk.gov.hmrc.play.views.html.layouts.TrackingConsentSnippet @this( uiHead: HeadWithTrackingConsent, trackingConsentSnippet: TrackingConsentSnippet, uiHeaderNav: HeaderNav, uiFooter: Footer, uiServiceInfo: ServiceInfo, uiReportAProblemLink: ReportAProblemLink, uiArticle: Article, uiFooterLinks: FooterLinks, uiEuExitLinks: EuExitLinks, uiMainContentHeader: MainContentHeader, hmrcGovUkTemplate: GovUkTemplate ) @(appConfig: config.FrontendAppConfig, title: String, mainClass: Option[String] = None, mainDataAttributes: Option[Html] = None, bodyClasses: Option[String] = None, sidebar: Html = HtmlFormat.empty, contentHeader: Option[Html] = None, mainContent: Html = HtmlFormat.empty, serviceInfoContent: Html = HtmlFormat.empty, scriptElem: Option[Html] = None, articleClass: Option[String] = None)(implicit request: Request[_], messages: Messages) @head = { @uiHead( linkElem = None, headScripts = None) } @headerNavLinks = {


Back to home
Choose input Excel file and output folder and generate output.
Choose excel input file
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Embed related schema
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AirBadminton takes the game outdoors
Singapore Badminton Association president Lawrence Leow (in black) with former national shuttler Wong Shoon Keat (in grey) and People's Association chief executive director Desmond Tan (left) having a game of AirBadminton at Heartbeat@Bedok on Saturday.
Launched by the Badminton World Federation this year, AirBadminton involves a wind-resistant shuttlecock and can be played outdoors.


The BIBFRAME Work is related to the FRBR concept of a Work but unlike FRBR, a BIBFRAME Work is actually a sub-class of a more general or abstract class derived from a RDFS Resource, making BIBFRAME a linked-data vocabulary.
A BIBFRAME Work has the following properties:
The BIBFRAME Work is also the parent class for a number of different types of Works listed below:
Resource intended to be perceived visually and understood through the use of language in written or spoken form.
<https://lccn.loc.gov/12032828> a bf:Text ; bf:title [ a bf:WorkTitle ; bf:mainTitle "The pioneer Quakers" ] ; relator:aut <http://id.loc.gov/authorities/names/nr89010376> . <http://id.loc.gov/authorities/names/nr89010376> rdfs:label "Richard Hallowell" .
Resources expressed in an audible form, including music or other sounds.
<https://lccn.loc.gov/85751262> a bf:Audio ; bf:musicMedium [ a bf:MusicMedium ; rdfs:label "Libretto" ], [ a bf:MusicMedium ; rdfs:label "Oratorio"@en ] ; bf:title [ a bf:WorkTitle ; bf:mainTitle "Messiah"@en ] ; bf:voice [ a bf:MusicVoice ; rdfs:label "mixed"@en ] ; relator:cmp <http://id.loc.gov/authorities/names/n79105494> . <http://id.loc.gov/authorities/names/n79105494> rdfs:label "George Frideric Handel" .
Data encoded in a defined structure. Includes numeric data, environmental data,etc., used by applications software to calculate averages, correlations, etc., or to produce models, etc., but not normally displayed in its raw form.
<http://bibcat.org/Work/84330c68-2d7d-11e6-8b16-ac87a3129ce6> a bf:Dataset ; bf:content <http://data.un.org/Data.aspx?q=religion&d=POP&f=tableCode%3a28%3brefYear%3a2010&c=2,3,6,8,10,12,14,15,16&s=_countryEnglishNameOrderBy:asc,refYear:desc,areaCode:asc&v=1> ; bf:title [ a bf:WorkTitle ; rdfs:label "Population by religion, sex and urban/rural residence for 2011" ] ; relator:cre <http://data.un.org/> .
Images intended to be perceived as moving, including motion pictures (using liveaction and/or animation), video recordings of performances, events,etc.
<http://tiger.coloradocollege.edu/record=b2348805> a bf:MovingImage ; bf:title [ a bf:WorkTitle ; rdfs:label "The ten commandments" ] ; relator:act <https://en.wikipedia.org/wiki/Anne_Baxter>, <https://en.wikipedia.org/wiki/Charlton_Heston>, <https://en.wikipedia.org/wiki/Yul_Brynner> ; relator:drt <http://id.loc.gov/authorities/names/n80073529> ; schema:url <https://en.wikipedia.org/wiki/The_Ten_Commandments_(1956_film)> . <http://id.loc.gov/authorities/names/n80073529> a bf:Person ; rdfs:label "Cecil B. DeMille" .


| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| src | 95.83% | 69/72 | 73.33% | 11/15 | 60% | 3/5 | 95.83% | 69/72 | |
| src/config | 17.81% | 13/73 | 0% | 0/36 | 0% | 0/15 | 19.4% | 13/67 | |
| src/controllers | 22.12% | 50/226 | 4.65% | 4/86 | 13.73% | 7/51 | 24.15% | 50/207 | |
| src/models | 34.48% | 10/29 | 0% | 0/10 | 0% | 0/6 | 38.46% | 10/26 | |
| src/util | 75% | 18/24 | 50% | 6/12 | 100% | 0/0 | 75% | 18/24 |


{% extends "politics/Page.html" %} {% load staticfiles otree %} {% block title %} Breaking News (1/2) {% endblock %} {% block content %} {% include Constants.vignette %}
The investigation process is currently underway. We don't know yet whether the president would be impeached or not. You would be informed on the outcome of the investigation later on.
If the president is impeached, then {% if subsession.winning_cand == player.preferences %}you will lose all {{payoff_win}} points {% else %} you lose nothing{% endif %} from what you obtained in the previous election. Other players whose supports {% if subsession.winning_cand == 'A' %} President A {% else %} President B {% endif %} will lose their {{Constants.payoff_win}}. But, in turn, a temporary government will take over and implement a middle point policy. This will make everyone obtains {{Constants.payoff_impeach}} equally, including you.
{% formfield player.opinion label="Question (1/2): How likely do you think the president is guilty? We would like to know your opinion" %}You may press the next button whenever you are settled with your answer.
{% next_button %} {% endblock %}


“Our loaves are hand-crafted daily, in variety of flavors, shapes, and sizes by our skilled bread bakers.”
Owoyemi - CEO BakeryWELCOME TO THE STORE
Tart bear claw cake tiramisu chocolate bar gummies dragée lemon drops brownie. Jujubes chocolate cake sesame snaps
Tart bear claw cake tiramisu chocolate bar gummies dragée lemon drops brownie. Jujubes chocolate cake sesame snaps
Tart bear claw cake tiramisu chocolate bar gummies dragée lemon drops brownie. Jujubes chocolate cake sesame snaps
“A combination of artistry and craftmanship, Owoyemi's breads are made without shortcuts, so they will be the most flavorful, most healthy, and most beautiful loaves possible.”
Owoyemi - CEO Bakerybreads every day
Live with passion
Leverage agile frameworks to provide a robust synopsis for high level overviews. Iterative approaches to corporate strategy foster collaborative thinking to further…
Read moreLeverage agile frameworks to provide a robust synopsis for high level overviews. Iterative approaches to corporate strategy foster collaborative thinking to further…
Read moreLeverage agile frameworks to provide a robust synopsis for high level overviews. Iterative approaches to corporate strategy foster collaborative thinking to further…
Read more+234<PHONE_NUMBER>
+234<PHONE_NUMBER>


| {{field.prompt}} |
|---|
| {{service[field.name] | date:'EEEE, MMMM d, y':'UTC'}} {{service[field.name] | date:'EEEE, MMMM d, y'}} {{service[field.name] | currency}} {{service[field.name] || '-'}} |
| APP.INVOICE.OVERVIEW.LABELS.TOTAL_PRICE {{invoice.totalPrice | currency}} |


Material Tabs Revisited
19 May 2015This isn’t a detailed tutorial on how to implement material tabs but just a quick revisit on’em. I assume you’ve created your project and everything is well set. Head over Materialpallte for awesome color choices and download an xml type of colors.
</p>
With Toolbar aboard in replacement of ActionBar, lets start by adding it to our new layout. roughly that would translate to something like this.
Then came material tabs
For my lazy fellas we gotta head over to our browsers for two major classes, Mr. SlidingTabLayout.java
and Mrs.SlidingTabStrip.java
.
I like to get mine from Google IO app Here but you can also get them elsewhere. There is no rule that you should get them from somewhere but my laziness tells me so.
Their names are self explanatory so lets see what happens under the hood.
With no error nearby, head over to xml
and add some lines
first, add SlidingTabLayout just below Toolbar
. snippet for SlidingTablayout goes like this
<android.aaron.com.material.tabs.SlidingTabLayout
android:id="@+id/tabs"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:background="@color/primary">
</android.aaron.com.material.tabs.SlidingTabLayout>
android.aaron.com.material.tabs.SlidingTabLayout
. Make sure you replace it with your package.
Now add Viewpager
below them. Something like
<android.support.v4.view.ViewPager
android:id="@+id/pager"
android:layout_width="match_parent"
android:layout_height="match_parent"
android:layout_alignParentTop="true">
</android.support.v4.view.ViewPager>
With xml ready to rock and roll, head back to java
Start by doing the findViewById()
thing or use Butterknife if you are farmiliar with it (You should actually use the later...). So far so good? Thats what I thought.
Them Fragments
I was thinking of using one fragment but then why not create seperate ones for diffrent purpose. Lets say we wanna create something like this...
Create three fragments. A simple fragment class looks like this
public class PageTwo extends Fragment {
public PageTwo() {
// Required empty public constructor
}
@Override
public View onCreateView(LayoutInflater inflater, ViewGroup container,
Bundle savedInstanceState) {
// Inflate the layout for this fragment
View view = inflater.inflate(R.layout.fragment_page_two, container, false);
return view;
}
}
}
Mr Adapter, where you at?
Create a class, name it something like MaterialTabsAdapter or something not weird. With simplicity kept constant, extend FragmentPagerAdapter
in the getItem(int position)
method, you can get fragments by,
@Override
public Fragment getItem(int position) {
Fragment fragment;
switch (position) {
case 0:
fragment = new PageOne();
return fragment;
case 1:
fragment = new PageThree();
return fragment;
case 2:
fragment = new PageTwo();
return fragment;
default:
fragment = new PageOne();
return fragment;
}
}
This means that when we are in a particular page we load a particular fragment
@Override
public int getCount() {
//The number of fragments we got
return 3;
}
@Override
public CharSequence getPageTitle(int position) {
//Sample titles for our tabs
if (position == 0) {
return "Movies";
} else if (position == 1) {
return "Series";
} else if (position == 2) {
return "Films";
}
return "";
}
So far we've done nearly everything. The last lap is to switch the circuit on and watch
Let there be magic...
Lets make this baby to walk on its own.
Pull over from adapter and head to MainActivity.java
, assuming that's the correct class and add the following
MaterialTabsAdapter myFragmentAdapter = new MaterialTabsAdapter(getSupportFragmentManager());
viewPager.setAdapter(myFragmentAdapter);
slidingTabLayout.setDistributeEvenly(true);
slidingTabLayout.setViewPager(viewPager);
Easy right, Keep it simple Stupid (KISS). This was my first post and it's probably terrible. Too bad there ain't comment section, but if you feel like sharing something with me<EMAIL_ADDRESS>is my mail. Cheers!


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Ingredients:
- 2 cups mixed salad greens1 cup shelled edamame
- thawed 1/2 medium raw beet, peeled and shredded (about 1/2 cup)
- 1 tablespoon plus
- 1½ teaspoons red-wine vinegar
- 1 tablespoon chopped fresh cilantro
- 2 teaspoons extra-virgin olive oil
- Freshly ground pepper to taste
Directions:
- Arrange greens, edamame and beet on a large plate.
- Whisk vinegar, cilantro, oil, salt and pepper in a small bowl. Drizzle over the salad and enjoy.
Nutrition information:
Per serving: 325 calories; 16 g fat(1 g sat); 12 g fiber; 25 g carbohydrates; 18 g protein; 172 mcg folate; 0 cholesterol; 6 g sugars; 0 g added sugars; 3,973 IU vitamin A; 31 mg vitamin C; 170 mg calcium; 5 mg iron; 682 mg sodium; 499 mg potassium
|
Ingredients:
- 2 teaspoons ground coriander
- 2 teaspoons ground cumin
- 1½ teaspoons ground cinnamon
- 1½ teaspoons ground turmeric
- 1¼ teaspoons salt
- ¾ teaspoon ground pepper
- ⅛ teaspoon cayenne pepper
- 1 small head cauliflower, cut into small florets (about 6 cups)
- 2 tablespoons extra-virgin olive oil, divided
- 1 large onion, chopped
- 1 cup diced carrot
- 3 large cloves garlic, minced
- 1½ teaspoons grated fresh ginger
- 1 fresh red chile pepper, such as serrano or jalapeño, minced, plus more for garnish
- 1 (14 ounce) can no-salt-added tomato sauce
- 4 cups low-sodium vegetable broth
- 3 cups diced peeled russet potatoes ( ½-inch)
- 3 cups diced peeled sweet potatoes ( ½-inch)
- 2 teaspoons lime zest
- 2 tablespoons lime juice
- 1 (14 ounce) can coconut milk
- Chopped fresh cilantro for garnish
Directions:
- Preheat oven to 450°F.
- Combine coriander, cumin, cinnamon, turmeric, salt, pepper and cayenne in a small bowl. Toss cauliflower with 1 tablespoon oil in a large bowl, sprinkle with 1 tablespoon of the spice mixture and toss again. Spread in a single layer on a rimmed baking sheet. Roast the cauliflower until the edges are browned, 15 to 20 minutes. Set aside.
- Meanwhile, heat the remaining 1 tablespoon oil in a large pot over medium-high heat. Add onion and carrot and cook, stirring often, until starting to brown, 3 to 4 minutes. Reduce heat to medium and continue cooking, stirring often, until the onion is soft, 3 to 4 minutes. Add garlic, ginger, chile and the remaining spice mixture. Cook, stirring, for 1 minute more.
- Stir in tomato sauce, scraping up any browned bits, and simmer for 1 minute. Add broth, potatoes, sweet potatoes, lime zest and juice. Cover and bring to a boil over high heat. Reduce heat to maintain a gentle simmer and cook, partially covered and stirring occasionally, until the vegetables are tender, 35 to 40 minutes.
- Stir in coconut milk and the roasted cauliflower. Return to a simmer to heat through. Serve garnished with cilantro and chiles, if desired.
Nutrition information:
Per serving: 272 calories; 15 g fat(10 g sat); 7 g fiber; 33 g carbohydrates; 5 g protein; 74 mcg folate; 0 mg cholesterol; 8 g sugars; 0 g added sugars; 9,200 IU vitamin A; 52 mg vitamin C; 86 mg calcium; 4 mg iron; 509 mg sodium; 911 mg potassium
|


Contains the functions for adapter trimming. More...
#include <seqan/align.h>
#include <seqan/find.h>
Go to the source code of this file.
Classes | |
| struct | seqan::AdapterScoringMatrix |
| Struct used to define a new custom scoring matrix. More... | |
| struct | seqan::ScoringMatrixData_< int, Dna5, AdapterScoringMatrix > |
| Struct containing data for the seqan::AdapterScoringMatrix custom scoring matrix. Matches score 1, mismatches -1 and matches against N with 0. More... | |
| struct | Mode |
| A struct encapsulating information about the match algorithm. More... | |
| struct | Auto |
| Tagging struct for the automatic match algorithm. More... | |
| struct | User |
| Tagging struct representing the the match algorithm working with values supplied by the user. Saves those values as members. More... | |
| struct | AdapterTrimmingStats |
| Struct to hold information about certain adapter trimming statistics. More... | |
| struct | STRING_REVERSE_COMPLEMENT< TValue > |
| A metafunction which constructs a ModifiedString type for an Alphabet. More... | |
Typedefs | |
|
typedef Score< int, ScoreMatrix< Dna5, AdapterScoringMatrix > > | TScore |
Functions | |
| template<typename TSeq1 , typename TSeq2 , bool TTop, bool TLeft, bool TRight, bool TBottom> | |
| seqan::Pair< unsigned, seqan::Align< TSeq1 > > | alignPair (TSeq1 &seq1, TSeq2 &seq2, const seqan::AlignConfig< TTop, TLeft, TRight, TBottom > &config, bool band=false) |
| Align two sequences, returning the score and align object of the alignment. More... | |
| template<typename TRow > | |
| unsigned | countTotalGaps (TRow &row) |
| Returns the total number of gaps in a row object. More... | |
| template<typename TAlign > | |
| unsigned | getOverlap (TAlign &align) |
| Determines the overlap between two (free-shift) aligned gapless sequences. More... | |
| template<typename TAlign > | |
| unsigned | getInsertSize (TAlign &align) |
| Given an alignment with two overlapping (forward and reverse) sequences, this function determines the actual size of the insert they cover. More... | |
| template<typename TSeq > | |
| unsigned | stripPair (TSeq &seq1, TSeq &seq2) |
| Removes adapter contamination from paired-end reads. More... | |
| template<typename TSeq > | |
| unsigned | stripPair (TSeq &seq1, TSeq &adapter1, TSeq &seq2, TSeq &adapter2) |
| Removes adapter contamination from paired-end reads with adapter information. More... | |
| template<typename TSeq > | |
| unsigned | stripPairBatch (seqan::StringSet< TSeq > &set1, seqan::StringSet< TSeq > &set2, AdapterTrimmingStats &stats) |
| Removes adapter contamination from paired-end reads. More... | |
| template<typename TSeq , typename TAdapter > | |
| seqan::Pair< unsigned, seqan::Align< TSeq > > | alignAdapter (TSeq &seq, TAdapter &adapter) |
| Aligns a sequence to an adapter. More... | |
| bool | isMatch (int overlap, int mismatches, const Auto &) |
| Checks if a overlap of an alignment is accepted, based on mismatches and the length of the overlap. More... | |
| bool | isMatch (int overlap, int mismatches, const User &userOptions) |
| Checks if a overlap of an alignment is accepted, based on mismatches and the length of the overlap. More... | |
| template<typename TSeq , typename TAdapter , typename TSpec > | |
| unsigned | stripAdapter (TSeq &seq, TAdapter &adapter, TSpec &spec) |
| Remove adapter sequence from a sequence. More... | |
| template<typename TSeq , typename TAdapter , typename TSpec > | |
| unsigned | stripAdapterBatch (seqan::StringSet< TSeq > &set, TAdapter &adapter, TSpec const &spec, AdapterTrimmingStats &stats, bool reverse=false) |
| Remove adapter sequence from a set of sequences. More... | |
| template<typename TSeq , typename TSpec > | |
| unsigned | stripReverseAdapterBatch (seqan::StringSet< TSeq > &set, TSeq &adapter, TSpec const &spec, AdapterTrimmingStats &stats) |
| Simple interface to align the reverse complement of an adapter to a batch of sequences. to a set of reads and remove significant matches. More... | |
Contains the functions for adapter trimming.
| seqan::Pair<unsigned, seqan::Align<TSeq> > alignAdapter | ( | TSeq & | seq, |
| TAdapter & | adapter | ||
| ) |
Aligns a sequence to an adapter.
| seq | The sequence which adapter contamination shall be removed. |
| adapter | The adapter that might contaminate the sequence. |
| seqan::Pair<unsigned, seqan::Align<TSeq1> > alignPair | ( | TSeq1 & | seq1, |
| TSeq2 & | seq2, | ||
| const seqan::AlignConfig< TTop, TLeft, TRight, TBottom > & | config, | ||
| bool | band = false |
||
| ) |
Align two sequences, returning the score and align object of the alignment.
| seq1 | The first sequence of the alignment. |
| seq2 | The second sequence of the alignment. |
| config | The alignment configuration used by the Seqan's globalAlignment method. |
| band | Whether the alignment's lower diagonal should be banded at -2. (Two diagonals below the main diagonal.) Useful to speed up computation for 5'-3' overlap alignments. |
| unsigned countTotalGaps | ( | TRow & | row | ) |
Returns the total number of gaps in a row object.
| row | The gapped row object used by seqan alignments. |
| unsigned getInsertSize | ( | TAlign & | align | ) |
Given an alignment with two overlapping (forward and reverse) sequences, this function determines the actual size of the insert they cover.
| align | An alignment object with two aligned overlapping sequences. |
| unsigned getOverlap | ( | TAlign & | align | ) |
Determines the overlap between two (free-shift) aligned gapless sequences.
| align | An alignment object with two aligned overlapping sequences. |
| bool isMatch | ( | int | overlap, |
| int | mismatches, | ||
| const Auto & | |||
| ) |
Checks if a overlap of an alignment is accepted, based on mismatches and the length of the overlap.
| overlap | The number of overlapping positions in the overlap alignment. |
| mismatches | The number of allowed mismatches in the overlapping region. |
| bool isMatch | ( | int | overlap, |
| int | mismatches, | ||
| const User & | userOptions | ||
| ) |
Checks if a overlap of an alignment is accepted, based on mismatches and the length of the overlap.
| overlap | The number of overlapping positions in the overlap alignment. |
| mismatches | The number of allowed mismatches in the overlapping region. |
| userOptions | Parameters specifying requirements for the overlap. |
| unsigned stripAdapter | ( | TSeq & | seq, |
| TAdapter & | adapter, | ||
| TSpec & | spec | ||
| ) |
Remove adapter sequence from a sequence.
| seq | The sequence whose adapter contamination should be removed. |
| adapter | The adapter sequence that might contaminate the sequence. |
| spec | The match algorithm that decides whether a match was significant. |
| unsigned stripAdapterBatch | ( | seqan::StringSet< TSeq > & | set, |
| TAdapter & | adapter, | ||
| TSpec const & | spec, | ||
| AdapterTrimmingStats & | stats, | ||
| bool | reverse = false |
||
| ) |
Remove adapter sequence from a set of sequences.
| set | A StringSet of sequences whose adapter contaminations should be removed. |
| adapter | The adapter sequence that might contaminate the sequences. |
| spec | The match algorithm that decides whether a match was significant. |
| reverse | Indicates whether we align forward or reverse sequences against an adapter. Exists mainly to enable correct writing of the appropriate fields when gathering statistics. |
| unsigned stripPair | ( | TSeq & | seq1, |
| TSeq & | seq2 | ||
| ) |
Removes adapter contamination from paired-end reads.
| seq1 | The forward read. |
| seq2 | The backward read. |
| unsigned stripPair | ( | TSeq & | seq1, |
| TSeq & | adapter1, | ||
| TSeq & | seq2, | ||
| TSeq & | adapter2 | ||
| ) |
Removes adapter contamination from paired-end reads with adapter information.
| seq1 | The forward read. |
| adapter1 | The adapter that contaminates the forward read. |
| seq2 | The backward read. |
| adapter2 | The adapter whose reverse complement contaminates the backward read. |
| unsigned stripPairBatch | ( | seqan::StringSet< TSeq > & | set1, |
| seqan::StringSet< TSeq > & | set2, | ||
| AdapterTrimmingStats & | stats | ||
| ) |
Removes adapter contamination from paired-end reads.
| seq1 | The set of forward reads. |
| seq2 | The set of backward reads. |
| unsigned stripReverseAdapterBatch | ( | seqan::StringSet< TSeq > & | set, |
| TSeq & | adapter, | ||
| TSpec const & | spec, | ||
| AdapterTrimmingStats & | stats | ||
| ) |
Simple interface to align the reverse complement of an adapter to a batch of sequences. to a set of reads and remove significant matches.
| set | A StringSet of sequences whose adapter contaminations should be removed. |
| adapter | The adapter sequence whose reverse complement might contaminate the sequences. |
| spec | The match algorithm that decides whether a match was significant. |


org.amino.ds.lockfree
Interfaces
IStack
Classes
EBDeque
EBStack
LockFreeDeque
LockFreeDictionary
LockFreeList
LockFreeList.Entry
LockFreeList.ListStateHolder
LockFreeOrderedList
LockFreePriorityQueue
LockFreeQueue
LockFreeSet
LockFreeVector


In this tutorial, you will learn to use TFLearn and TensorFlow to estimate Titanic passengers' likelihood of survival based on their personal information (such as gender, age, etc.). To tackle this classic machine learning task, we are going to build a deep neural network classifier.
Make sure you have TensorFlow and TFLearn (the bleeding edge version) installed. If you don't, please follow these instructions.
On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this tutorial, we carry out an analysis to discover who these people are.
Let's take a look at the dataset (TFLearn will automatically download it for you). For each passenger, the following information is provided:
VARIABLE DESCRIPTIONS:
survived Survived
(0 = No; 1 = Yes)
pclass Passenger Class
(1 = 1st; 2 = 2nd; 3 = 3rd)
name Name
sex Sex
age Age
sibsp Number of Siblings/Spouses Aboard
parch Number of Parents/Children Aboard
ticket Ticket Number
fare Passenger Fare
Here are some samples extracted from the dataset:
| survived | pclass | name | sex | age | sibsp | parch | ticket | fare |
| -------- | ------ | ---- | --- | --- | ----- | ----- | ------ | ---- |
|1|1|Aubart, Mme. Leontine Pauline|female|24|0|0|PC 17477|69.3000|
|0|2|Bowenur, Mr. Solomon|male|42|0|0|211535|13.0000|
|1|3|Baclini, Miss. Marie Catherine|female|5|2|1|2666|19.2583|
|0|3|Youseff, Mr. Gerious|male|45.5|0|0|2628|7.2250|
There are 2 classes in our task 'not survived' (class 0) and 'survived' (class 1), and the passengers data have 8 features.
The dataset is stored in a CSV file, so we can use the TFLearn load_csv()
function to load the data from the CSV file into a python list
. We specify the 'target_column' argument to indicate that our labels (survived or not) are located in the first column (id: 0). The function will return a tuple: (data, labels).
import numpy as np
import tflearn
# Download the Titanic dataset
from tflearn.datasets import titanic
titanic.download_dataset('titanic_dataset.csv')
# Load CSV file, indicate that the first column represents labels
from tflearn.data_utils import load_csv
data, labels = load_csv('titanic_dataset.csv', target_column=0,
categorical_labels=True, n_classes=2)
```
Data are given 'as is' and need some preprocessing to be ready for use in our deep neural network classifier.
First, we will discard the fields that are not likely to help in our analysis. For example, we make the assumption that the 'name' field will not be very useful in our task, since a passenger's name and his or her chance of surviving are probably not correlated. With such thinking, we can go ahead and discard the 'name' and 'ticket' fields.
Then, we need to convert all our data to numerical values, because a neural network model can only perform operations over numbers. However, our dataset contains some non-numerical values, such as 'name' and 'sex'. Because 'name' is discarded, we just need to handle the 'sex' field. In this simple case, we will just assign '0' to males and '1' to females.
Here is the preprocessing function:
# Preprocessing function
def preprocess(passengers, columns_to_delete):
# Sort by descending id and delete columns
for column_to_delete in sorted(columns_to_delete, reverse=True):
[passenger.pop(column_to_delete) for passenger in passengers]
for i in range(len(passengers)):
# Converting 'sex' field to float (id is 1 after removing labels column)
passengers[i][1] = 1. if passengers[i][1] == 'female' else 0.
return np.array(passengers, dtype=np.float32)
# Ignore 'name' and 'ticket' columns (id 1 & 6 of data array)
to_ignore=[1, 6]
# Preprocess data
data = preprocess(data, to_ignore)
```
We are building a 3-layer neural network using TFLearn. First, we need to specify the shape of our input data. In our case, each sample has a total of 6 features, and we will process samples per batch to save memory. So our data input shape is [None, 6] ('None' stands for an unknown dimension, so we can change the total number of samples that are processed in a batch).
# Build neural network
net = tflearn.input_data(shape=[None, 6])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net)
```
TFLearn provides a model wrapper ('DNN') that automatically performs neural network classifier tasks, such as training, prediction, save/restore, and more. We will run it for 10 epochs (i.e., the network will see all data 10 times) with a batch size of 16.
```
# Define model
model = tflearn.DNN(net)
# Start training (apply gradient descent algorithm)
model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
```
Output:
---------------------------------
Run id: MG9PV8
Log directory: /tmp/tflearn_logs/
---------------------------------
Training samples: 1309
Validation samples: 0
--
Training Step: 82 | total loss: 0.64003
| Adam | epoch: 001 | loss: 0.64003 - acc: 0.6620 -- iter: 1309/1309
--
Training Step: 164 | total loss: 0.61915
| Adam | epoch: 002 | loss: 0.61915 - acc: 0.6614 -- iter: 1309/1309
--
Training Step: 246 | total loss: 0.56067
| Adam | epoch: 003 | loss: 0.56067 - acc: 0.7171 -- iter: 1309/1309
--
Training Step: 328 | total loss: 0.51807
| Adam | epoch: 004 | loss: 0.51807 - acc: 0.7799 -- iter: 1309/1309
--
Training Step: 410 | total loss: 0.47475
| Adam | epoch: 005 | loss: 0.47475 - acc: 0.7962 -- iter: 1309/1309
--
Training Step: 492 | total loss: 0.51677
| Adam | epoch: 006 | loss: 0.51677 - acc: 0.7701 -- iter: 1309/1309
--
Training Step: 574 | total loss: 0.48988
| Adam | epoch: 007 | loss: 0.48988 - acc: 0.7891 -- iter: 1309/1309
--
Training Step: 656 | total loss: 0.55073
| Adam | epoch: 008 | loss: 0.55073 - acc: 0.7427 -- iter: 1309/1309
--
Training Step: 738 | total loss: 0.50242
| Adam | epoch: 009 | loss: 0.50242 - acc: 0.7854 -- iter: 1309/1309
--
Training Step: 820 | total loss: 0.41557
| Adam | epoch: 010 | loss: 0.41557 - acc: 0.8110 -- iter: 1309/1309
--
```
Our model finished training with an overall accuracy around 81%, which means that it can predict the correct outcome (survived or not) for 81% of the total passengers.
It's time to try out our model. For fun, let's take Titanic movie protagonists (DiCaprio and Winslet) and calculate their chance of surviving (class 1).
```
# Let's create some data for DiCaprio and Winslet
dicaprio = [3, 'Jack Dawson', 'male', 19, 0, 0, 'N/A', 5.0000]
winslet = [1, 'Rose DeWitt Bukater', 'female', 17, 1, 2, 'N/A', 100.0000]
# Preprocess data
dicaprio, winslet = preprocess([dicaprio, winslet], to_ignore)
# Predict surviving chances (class 1 results)
pred = model.predict([dicaprio, winslet])
print("DiCaprio Surviving Rate:", pred[0][1])
print("Winslet Surviving Rate:", pred[1][1])
```
Output:
DiCaprio Surviving Rate: 0.13849584758281708
Winslet Surviving Rate: 0.92201167345047
Impressive! Our model accurately predicted the outcome of the movie. The odds were against DiCaprio, but Winslet had a high chance of surviving.
More generally, it can be seen through this study that women and children passengers from first class have the highest chance of surviving, while third class male passengers have the lowest.
```python
from __future__ import print_function
import numpy as np
import tflearn
# Download the Titanic dataset
from tflearn.datasets import titanic
titanic.download_dataset('titanic_dataset.csv')
# Load CSV file, indicate that the first column represents labels
from tflearn.data_utils import load_csv
data, labels = load_csv('titanic_dataset.csv', target_column=0,
categorical_labels=True, n_classes=2)
# Preprocessing function
def preprocess(passengers, columns_to_delete):
# Sort by descending id and delete columns
for column_to_delete in sorted(columns_to_delete, reverse=True):
[passenger.pop(column_to_delete) for passenger in passengers]
for i in range(len(passengers)):
# Converting 'sex' field to float (id is 1 after removing labels column)
passengers[i][1] = 1. if data[i][1] == 'female' else 0.
return np.array(passengers, dtype=np.float32)
# Ignore 'name' and 'ticket' columns (id 1 & 6 of data array)
to_ignore=[1, 6]
# Preprocess data
data = preprocess(data, to_ignore)
# Build neural network
net = tflearn.input_data(shape=[None, 6])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net)
# Define model
model = tflearn.DNN(net)
# Start training (apply gradient descent algorithm)
model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
# Let's create some data for DiCaprio and Winslet
dicaprio = [3, 'Jack Dawson', 'male', 19, 0, 0, 'N/A', 5.0000]
winslet = [1, 'Rose DeWitt Bukater', 'female', 17, 1, 2, 'N/A', 100.0000]
# Preprocess data
dicaprio, winslet = preprocess([dicaprio, winslet], to_ignore)
# Predict surviving chances (class 1 results)
pred = model.predict([dicaprio, winslet])
print("DiCaprio Surviving Rate:", pred[0][1])
print("Winslet Surviving Rate:", pred[1][1])
```


Kitura Compression
Step 1: Add Kitura Compression to your project
TODO: Link to SPM instructions
import Kitura Compression to your project
import KituraCompression
Step 2: Initilise Kitura Compression
Create an instance of Kitura Compression:
let compression = Compression()
You can also configure Compression with various arguments:
public init (threshold: Int = 1024, chunkSize: Int = 16384, compressionLevel: CompressionLevel = CompressionLevel.defaultCompression, compressionStrategy: CompressionStrategy = CompressionStrategy.defaultStrategy, memoryLevel: Int32 = 8)
Where:
- threshold is the byte threshold for the response body size before compression is considered for the response, defaults to 1024.
- chunkSize is the size of internal output slab buffer in bytes, defaults to 16384.
- compressionLevel is the level of zlib compression to apply. The supported values are: .noCompression, .bestSpeed, .bestCompression, .defaultCompression
- compressionStrategy is used to tune the compression algorithm. The possible values are: .defaultStrategy, .filtered, .huffmanOnly, .rle, .fixed
- memoryLevel specifies how much memory should be allocated for the internal compression state. The default value is 8.
TODO: Usage example?
Next steps
Add logging: Get useful feedback from your server about startup and errors
Add routing: Add REST APIs, such as HTTP GET, to your server


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Do you know that Human beings spend approximately 6 months of their lives waiting in line for things, it means like 3 days a year of queueing up.It is a great loss of our precious life-time if we look at the bigger picture. Thus, this very idea provided us with the motivation to make this product- "AFSC". Through this, We aim to provide a self sufficient, safe and hassle free experience to the consumer by saving their precious time they waste standing in queues to fill the mundane forms and speed up the process exponentially with the help of our device. That's just a blip on the radar !
- AFSC provides "On-the-Go" ability for its users with the sustainance of Q-R Codes.
- Now thinking from the perspective of bank staff, AFSC provides an extremely reliable and catalogued way of all the forms from the people.
- Making our New system much more managable and organised than previous loosely arranged heaps of forms that no one can get hold of without getting a backache.So, we got you covered.
- So, Scan it, Fill it and Submit it, all in the same place with minimum contact. Isn't it amazing?
It is just a teaser of the levels of implementation and applications AFSC has in various scenarios sectors of banking and how automating and changing our tradition ways can save the people and banks tremendous aomunts of time, money, resources, work-force and what not.
Problems our idea solves
Benefits of the bank
Banking services have always been the source of major inconvenience to the customers mainly due to inefficient working, lousy form handling and deposition for money withdrawal, deposit or transfer.
- A large number of forms are misplaced or lost which causes a lot of problems keeping up with the hard copies.
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- Lastly, it provides a sense of relief for the employees since all the forms are saved in secure firebase storage which provides high level protection and provides the employee, a much needed sense of relief.
All Forms at one location
Who doesn't like a catalogued of all the things we need at the same place and time at the scan of a Q-R !
- Whether you are looking to open a bank account or deposit a cheque, we got you covered.
- You have a choice, to look for that one form you want to use or scan, sit and maybe have a samosa while your form is being conveniently preinted out waiting for you to fill it. What would you do?
Its easy, good and fast and makes our lives better. What else can we ask for?
Contactless and Queue less Platform
Contactless and completely digital system that ensures a smooth banking experience coupled with security.
- As we all are aware of how global the world is now-a-days, which can be dangerous in times like right now where COVID-19 pandemics spread its wrath. We may have won battle this time but we should be prepared for the war since we were clearly not ready this time.
- Making this system contactless was a major emphasis because we know the process of banking in overwhelmingly long and a single piece of paper ends up on hands of so many people, hence higher chances of spreading unwanted pathogen.
Team
Our Team


NEIN-CASTLE
Felix Magath not in running for Hearts job as foreigner is in frame to replace Craig Levein
by Derek McGregorGERMAN Felix Magath has been given a firm ‘Nein’ for the Hearts job.
It’s believed a foreign candidate is in the frame to replace axed Craig Levein.
But it’s not former Bayern Munich boss Magath.
The 66-year-old made his interest known but was told he’s not for the Jambos.
Alan Irvine, Neil McCann, Stuart McCall and Steve Cotterill have all met Ann Budge, but there has been no approach for Motherwell boss Stephen Robinson.


Hush has been building the first ever open source private messenger service based on Zcash technology. Hush & Komodo not only share core developers, but coinciding views on the importance of independent right to privacy.
Since our last update we have made further progress towards securing and preserving Cryptopia crypto-asset holdings for the benefit of those entitled to them. Below is an outline of the progress we have made since our last update on 20 August 2019.
GatewayBOT07-Dec-19 10:15 AM
[dscrd-hush] <phm87> an update on cryptopia liquidation should occur in December
GatewayBOT07-Dec-19 12:52 PM
[dscrd-hush] <passcombo.com > who want some hush tip? leave z addr here
[dscrd-hush] <passcombo.com > or dm me
[dscrd-hush] <passcombo.com > not gonna lie - small amounts for testing prupose
GatewayBOT07-Dec-19 01:51 PM
[dscrd-hush] <dukeleto> don't post zaddrs publicly
[dscrd-hush] <dukeleto> @Captain MtGox told me they would give me funds back too, and had people do KYC. Nobody ever got their funds back. I would not give your personal details to random 3rd parties and also don't hold your breath
GatewayBOT07-Dec-19 02:00 PM
[dscrd-hush] <passcombo.com > yes better dm
GatewayBOT07-Dec-19 02:14 PM
[dscrd-hush] <AkiraX> Posting your zaddr in silent dragon is as public as it gets. :D. Jk
[dscrd-hush] <AkiraX> :troll:
GatewayBOT07-Dec-19 02:26 PM
[dscrd-hush] <passcombo.com > I hope mtgox give my back my btc when it will be 100k $ :troll:
[dscrd-hush] <passcombo.com > might be best ever holding strategy
GatewayBOT07-Dec-19 03:11 PM
[dscrd-hush] <passcombo.com > @Weepy Package <- ban this guy
[dscrd-hush] <passcombo.com > spammer scammer
[dscrd-hush] <Captain> yes that is right, however I will try when the time comes and if it workes I will let everyone know!


GitHub
University of Manitoba
2023
2013
Java, C, C++, HTML, CSS, JavaScript, Kotlin, R.
GitHub, Node, Android, ArcGIS
Fisheries & Oceans Canada
2011 - 2013, 2014 - 2017
Marine Conservation Cambodia
July 2013 - Aug 2014


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This program will generate a list of numbers from 1-100.
When you 'Fizz' the list, it will transform all numbers divisible by 3 to 'Fizz.'
When you 'Buzz' the list, it will transform all numbers divisible by 5 to 'Buzz.'
When you 'FizzBuzz' the list, it will do both together.


The Anonymize Options ... button near the top of the main
window opens the window shown below:
Attributes are only added to this list when they are encountered by
loading a file. (more info), so if this window
is viewed before loading files, it will be empty.
If an attribute is encountered multiple times within the same file, it will
be anonymized with the same value. The anonymization value for UIDs (eg: SOPInstanceUID)
is ignored, and instead a new UID is generated.
The purpose of this screen is to give the user more control over what
is anonymized and the values used. In particular, if the user notes
that some fields unexpectedly contain title='PHI : Protected Health Information'>PHI
that should be anonymized, then that field may be marked for anonymization.
Changes made on this screen are only valid for the current program session.
The default list of DICOM attributes to be anonymized was derived from
the DICOM 2011 standard. In general, information that identifies the
patient, physicians, and treatment location are anonymized. The list
is defined in the configuration file.


Working Since 2014 to Make The World happy again
Our mission is to inspire and empower people to change their world.From leaders who are already making an impact, to people who are interested in becoming more involved, but don’t know where to start, our goal is to make our programs accessible to anyone, anywhere.
We have decided to take some important initiative steps for our society, for our people. And we'll be able to do this with the help of our NGO and of course you people. Our NGO's main focus is to bring change and development in surrounding communities. Our members will be working upon various drawbacks of the society and will try to overcome them. Our team has decided to work on the education, health, welfare of the society and various other. We are going to work in such a way that we will try to reach the people who are in need and will help them in every aspect. Our focus will be on how we can make the life of the needy people easier.
As an independent non-governmental social development organization we works for:
-
Education is a gift for life. We help children reach school and stay there.We help children unlock their potential and realize their dreams. We believe that every child is extraordinary and can scale great heights if provided with the right learning opportunities. With regular and generous contributions from our supporters, we are able to march steadily towards achieving these goals.
We are committed to ensure that all children, irrespective of their origin, are able to go to school, play, interact and learn with other children of their age. We help children with limited means hone their creativity and skills under the guidance of trained teachers and make persistent efforts to enable them to secure a life of dignity for themselves, their families and the community at large. -
MarkHumans, time to time do awareness programme related to health, with help of volunteer Doctors, who devote their time for the betterment of the society.
- To increase public awareness that disease are significant public health problem.
- To increase public awareness of symptoms and signs of disease.
- To improve the knowledge and attitudes of patients about detection, treatment and control of disease.
- To promote the family and community educational material essential for positive lifestyle habits.
- To create public awareness about the ill=effects of alcohol, smoking and drugs, etc.
- This health awareness programme will also focus to develop a panel of local people who can handle any critical situation. These programmes help local community.
- To increase knowledge, attitude and skills of all health professionals regarding sign, symptoms and management strategies for health hazards to improve disease control.
- To encourage health professionals to treat patients carefully.
- To develop resource and material for use of health professionals.
- To promote research all over the world to curb health hazards.
- To encourage continuing educational programmes on accurate information on diagnosis and treatment of diseases.
-
India is a densely populated country where a large number of people live without access to the basic needs and primary facilities.So we distribute blanket and other things time to time.
Child Education
Awareness Programs
Health related awareness programmes have the following objectives:-


"""
From original at https://github.com/facebookresearch/detectron2/blob/master/detectron2/checkpoint/detection_checkpoint.py
Original copyright of Facebook code below, modifications by Yehao Li, Copyright 2021.
"""
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import os
import pickle
import torch
from typing import Any
from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer
from torch.nn.parallel import DistributedDataParallel
import xmodaler.utils.comm as comm
from xmodaler.utils.env import TORCH_VERSION
from xmodaler.utils.file_io import PathManager
from .c2_model_loading import align_and_update_state_dicts
[docs]class PeriodicEpochCheckpointer(PeriodicCheckpointer):
[docs] def step(self, iteration: int, epoch: int, **kwargs: Any) -> None:
"""
Perform the appropriate action at the given iteration.
Args:
iteration (int): the current iteration, ranged in [0, max_iter-1].
kwargs (Any): extra data to save, same as in
:meth:`Checkpointer.save`.
"""
iteration = int(iteration)
epoch = int(epoch)
additional_state = {"iteration": iteration}
additional_state.update(kwargs)
if (iteration + 1) % self.period == 0:
self.checkpointer.save(
"{}_Epoch_{:05d}_Iter_{:07d}".format(self.file_prefix, epoch, iteration), **additional_state
)
if self.max_to_keep is not None:
self.recent_checkpoints.append(self.checkpointer.get_checkpoint_file())
# pyre-fixme[58]: `>` is not supported for operand types `int` and
# `Optional[int]`.
if len(self.recent_checkpoints) > self.max_to_keep:
file_to_delete = self.recent_checkpoints.pop(0)
if self.path_manager.exists(
file_to_delete
) and not file_to_delete.endswith(f"{self.file_prefix}_final.pth"):
self.path_manager.rm(file_to_delete)
if self.max_iter is not None:
# pyre-fixme[58]
if iteration >= self.max_iter - 1:
self.checkpointer.save(f"{self.file_prefix}_final", **additional_state)
[docs]class XmodalerCheckpointer(Checkpointer):
"""
Same as :class:`Checkpointer`, but is able to handle models in xmodaler
model zoo, and apply conversions for legacy models.
"""
[docs] def __init__(self, model, save_dir="", *, save_to_disk=None, **checkpointables):
is_main_process = comm.is_main_process()
super().__init__(
model,
save_dir,
save_to_disk=is_main_process if save_to_disk is None else save_to_disk,
**checkpointables,
)
self.path_manager = PathManager
[docs] def _load_file(self, filename):
if filename.endswith(".pkl"):
with PathManager.open(filename, "rb") as f:
data = pickle.load(f, encoding="latin1")
if "model" in data and "__author__" in data:
# file is in Detectron2 model zoo format
self.logger.info("Reading a file from '{}'".format(data["__author__"]))
return data
else:
# assume file is from Caffe2 / Detectron1 model zoo
if "blobs" in data:
# Detection models have "blobs", but ImageNet models don't
data = data["blobs"]
data = {k: v for k, v in data.items() if not k.endswith("_momentum")}
return {"model": data, "__author__": "Caffe2", "matching_heuristics": True}
loaded = super()._load_file(filename) # load native pth checkpoint
if "model" not in loaded:
loaded = {"model": loaded}
return loaded
[docs] def _load_model(self, checkpoint):
if checkpoint.get("matching_heuristics", False):
self._convert_ndarray_to_tensor(checkpoint["model"])
# convert weights by name-matching heuristics
model_state_dict = self.model.state_dict()
align_and_update_state_dicts(
model_state_dict,
checkpoint["model"],
c2_conversion=checkpoint.get("__author__", None) == "Caffe2",
)
checkpoint["model"] = model_state_dict
# for non-caffe2 models, use standard ways to load it
incompatible = super()._load_model(checkpoint)
if incompatible is None: # support older versions of fvcore
return None
model_buffers = dict(self.model.named_buffers(recurse=False))
for k in ["pixel_mean", "pixel_std"]:
# Ignore missing key message about pixel_mean/std.
# Though they may be missing in old checkpoints, they will be correctly
# initialized from config anyway.
if k in model_buffers:
try:
incompatible.missing_keys.remove(k)
except ValueError:
pass
return incompatible


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MY TOP 10 LAPTOPS WITH A SHORT DESCRIPTION AND AN ADVICE WHAT IT CAN BE USEFUL FOR
1st
ASUS ROG The concept of the gaming laptop that's thin and light enough to travel with is extremely compelling. ... Beyond its raw components, though, the Asus ROG Scar III delivers on all fronts with a colorful screen, various inputs – and flashy aesthetics, making it one of the best Asus laptops, gaming or otherwise, but the problem is that it costs a lot!
ACER Predator helios-300 The Acer Predator Helios 300 is, by all accounts, a budget gaming laptop. ... It can handle the most demanding games at medium to high settings. It has a refresh rate of 144Hz, which you'll rarely see in gaming laptops of its kind. And it has a crisp, 1080p display with a true-to-life color palette and it's much cheaper than ASUS ROG!
ACER NITRO 5 (2018) is a sub-$1,000 gaming laptop with a solid midtier graphics card, speedy overall performance and a bevy of ports. Despite one major shortcoming -- a disappointing display -- the Nvidia GeForce GTX 1050 Ti-equipped machine is a solid value for casual gamers
ASUS Tuf Gaming is the all-new force for those who demand superior durability, dependable stability and great value in PC gaming. ... Every TUF Gaming product is rigorously tested for stability and durability, loaded with gamer-pleasing features, and exhibits unique and distinctive style
ASUS ZenBook These are the qualities that define the elegant new ZenBook 14. It's one of the world's smallest 14-inch laptops, and features the breathtaking frameless NanoEdge display and the revolutionary ScreenPad ™ 2.0 to give you the freedom to discover your creative power
Dell XPS 15 It packs an Intel Core i3 CPU, 4GB of RAM, a 128GB SSD, an Intel UHD 620 Graphics GPU and a 1920 x 1080 non-touch display.It's design is brilliant but it's too expensive(as for me)
Acer Extensa 2540
With the Extensa 2540, Acer delivers a simple, yet very well-equipped 15.6-inch office notebook. Our review model is geared towards users who need or desire to have a device with Windows 10 Pro. The laptop is powered by a Kaby Lake CPU.
Lenovo IdeaPad Y520-15 is a Windows 10 Home laptop with a 15.60-inch display that has a resolution of 1366x768 pixels. It is powered by a Celeron Dual Core CPU and it comes with 4GB of RAM. The Lenovo IdeaPad 110 packs 1TB of HDD storage. Graphics are powered by Intel HD Graphics
MSI GF63-8RCS Looking much like MSI's more premium GS65 Stealth Thin, the GF63 flaunts nifty, narrow bezels, key backlighting, and a brushed-aluminum finish. Inside, its components are tamer than the look suggests: You'll get a Intel Core i5 or i7 CPU, along with Nvidia GeForce GTX 1050 or GTX 1050 Ti graphics
Xiaomi Mi Gaming Laptop the first gaming laptop from Xiaomi whith Intel Core i5-i9, Windows 10 Home, permited FullHD (1920x1080px) and 128 - 256gb memory. It was designed in 2018 but also there's a 2019 model but that's no need to buy it because it costs a lot more. It's brilliant for games because of it's video card: nVidia GeForce GTX 1060.


Custom Http Library
GET
POST
PUT
DELETE
Add Person Details
ID
First Name
Last Name
Gender
Age
Email
Company ID
Add Person
Update User Details
Enter person ID to UPDATE
ID
Get Person
Enter person ID to DELETE
ID
Delete Person


Music List (explanation) |
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| Description | This example demonstrates how you can instruct a Beatnik Player embedded in an HTML page to play music files from a list. The advantage of this is that the user does not need to leave the preview page, but can keep selecting new files from the list, or read further information on the page as they listen to a file they have chosen. The code is also much easier to author than for a technique that might make use of a frameset. |
| Methods Used | magicEmbed, play |
| Skill Level | Beginner |
| Start... |


Video Intro |
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IntroductionPredictEd, is a free and open source predictive text editor application, for Microsoft windows. It suggests words for auto completion as you start typing.  Press the tab key to select a predicted word. It presents five such words and three phrases below your cursor. Pressing the tab key again selects the next word or phrase in the list. PredictEd learns, as you type in real time, and creates a knowledge base unique to your style of writing, and your subject matter. You get the most relevant suggestions as it builds up its knowledge from the words and phrases you type. Gradually it becomes your personal typing assistant. You can also feed it your own writings in the form of text files, and instantly get the same result. PredictEd keeps learning anyway, and builds up the knowledge, as you work. It also has some more smart features like formatting as you type. So that you have a basic formatted document as soon as you finish typing. It also capitalizes the words, inserts spaces after periods and inserts paragraph breaks after new lines. It saves the file after each paragraph automatically. It can save the text as a rich text file with formatting, or as a plain text file. And it can open those formats. PredictEd has the usual text editing features such as cut, copy, paste, setting fonts and text sizes, undo and redo, and find and replace functions. You can also set a default font, text size and text colour, and background colour. It supports Unicode text, which means it will work for most of the languages. It learns in a similar way independently of the language and script being used. PredictEd is supported on Microsoft Windows 7 to 10. The installation comes with a built in word list (knowledge map), which was generated using some default text files. So it will immediately show suggestions, but it is highly recommended to train it using your own writings, so that you get most relevant suggestions. Anyhow it will learn as you type. This tool helps to speed up typing and ease the stress on fingers, especially for those with repetitive stress injury, or carpel tunnel. Initially it will appear as if you have slowed down because now you need to check the suggestions or predictions and decide whether to continue typing or press TAB. But gradually it becomes your habit and the typing will speed up. Takes a bit of practice and getting used to predictive typing. The source code of this application is available for anyone to improve, fork, contribute or for use in your own application, without any restrictions. |
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InstallationDownload the Windows Installer from: GitHub Unzip the downloaded file and run setup.exe. Follow the instructions. Run the program from start menu or by double clicking PredictEd desktop icon. Please read the License before installing and using this software.
System RequirementsMinimum requirements: Microsoft Windows 7 to 10 Dual core CPU 2 GB RAM 20 MB of free space on hard disk A monitor with at least 800 x 600 resolution.
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UsageAutocompletion and Word Prediction. Start typing. As you type the suggestions appear below your cursor. Press TAB key to select a suggestion. Pressing the TAB key repeatedly selects others suggestions from the list of suggestions. When you complete a word and press Space Bar, the list of suggestions changes into a list of predictions. Press TAB to select a predicted word. Pressing the TAB repeatedly selects more words. You can either use the suggestions or predictions or continue typing as usual. Phrases Three of the suggestions are multiple words or phrases. They are on a darker grey background. Press TAB key repeatedly to select the phrases. Saving Click the Save or Save As buttons on the left or commands from the File menu. The text can be saved in the RTF (Rich Text Format) or Plain text format. Note: Plain text is always saved as Unicode UTF-8
Auto Save Every time you press enter key, the text is automatically saved. So in case of loss of data you can recover your work. The backup file is located at My Documents/Oormi Creations/PredictEd/PredictEd_AutoBackup.rtf Open File Click the File Open button or command from the File menu to open a file. RTF (Rich Text Format) or Plain text format files can be opened. Note: The maximum size of text that can be loaded into the Rich Edit Control (Text Window) is 2 MB by default. You can change it in Settings windows. See below.
Editing PredictEd supports the usual editing functions like cut (Control X) copy (Control C), paste (Control V), undo (Control Z) and redo (Control Y). You can also use the Edit menu commands or the Copy and Paste buttons on the left.
Note: When no text is selected and Copy button is clicked, whole of the text is copied into the clipboard. You can find words or replace words from the menu command Edit > Find and Replace. A window is shown in which you can enter the text to find or replace. Formatting Select the text and click the Format button on the left. A formatting window is shown where you can specify the font, size, colour etc. Format as you type Surround the text with formatting pair characters to format the text while you type. Type: **Bold text** , Result: Bold text Type: //Italic text// , Result: Italic text Type: __Underlined text__ , Result: Underlined text Clear Formatting Select the text and click the Clear Format button. The text is reset to the default style, font and size. Typing Duration A clock is shown at the bottom of the text editor box which shows the duration of your session. This is a useful reminder for those with painful joints or RSI to stop typing after some time and take a break, stretch etc. This will reset to 0 when you start a new file or open a file. Training Click the Train button or the Train command from Options menu. The Training window appears. Click the Add Files button to add some text files. You can use Control-Click or Shift-Click to select multiple files. You can click the Add button again to add more text files from other folders. You can add up to 20 text files for one training session. Click the Start button to start the training. Depending upon the file size it can take some time. Close the window when "Finished training" message appears.
Note: Ensure that the files are plain text files, either ASCII or Unicode UTF-8
Note: Press the Close button to abort the training anytime. No new data will be saved. Only Long term memory is updated during training. The file name is PredictEd_LTM.txt. The Short term memory is meant for real time learning. The location of this file is at: My Documents/Oormi Creations/PredictEd
Note: You may see a message saying "List is full. Only relations and weights will be updated". This means that the list has reached a limit of 5000 entries. No new keywords are being added, but predictions and their weights are getting updated. Currently this limit is hard-coded. You can set it to any other value if you compile from the source. Usually a longer list will consume more memory and will be slower. It is highly recommended that you feed your own writing as training data. PredictEd will learn your style of writing, the words you often use and sentences you often write. This will help you to type even faster. You will get suggestions in the context of your writing. It is language independent. Text files written in any language will work as long as they are in Unicode UTF-8 format. You can feed it text files of different languages at once. The original data will be backed up as soon as you start training. The location of the knowledge data is: My Documents/Oormi Creations/PredictEd In case something goes wrong or you accidentally lose knowledge data, you can restore it by simply renaming the backup files. Note: The knowledge data is in plain text CSV format. You can edit or delete the entries in it. Just ensure that you keep the structure intact, else PredictEd may crash or may suggest incorrect words. You can have different knowledge data for different writing projects. Simply move the old knowledge files or rename them when you train PredictEd with new training data. This will enable you to have different contexts. Erase Knowledge data You can erase the Long term memory by clicking the menu command Options > Erase LTM This is recommended if you wish to train it from blank. Next training will store fresh data in LTM. Your old LTM data will be backed up as usual. Save Knowledge data Usually the knowledge data is saved when you exit PredictEd. But you can also do it manually by click on the menu command Options > Save Knowledge This saves both LTM and STM. Merge LTM and STM Usually when STM (The memory that stores real time learned data) is full (5000 entries), it is merged into LTM and is cleared. You can do this manually from the menu command Options > Merge STM into LTM This enables learning of new words as you type. Note: The number of entries in LTM and STM are shown at the bottom of the editor box. These counters appear when you start typing. Settings Click menu command Options > Settings to open the settings window. You can set parameters like maximum memory for the text editor, margins, background colour, and default font, text size and font colour etc. Checking for updated versions Click menu Help > Check for updates Or you can visit the GitHub Page for PredictEd to get the latest version. PredictEd checks for a new version every time you start it. Help Click menu Help > Online Help to view this help document. Known Issues The number of entries in LTM and STM are fixed at 5000 for now. You can set any number by editing the source code and building it. The format as you type can behave strangely if you have formatting pair characters in your text somewhere. The word counter will show 0 after a limit of 100000 characters. |


execline
Software
skarnet.org
The emptyenv program
emptyenv empties the current environment, or cleans it up; then
executes a program.
Interface
emptyenv [ -p ] prog...
emptyenv -c prog...
emptyenv [ -o ] [ -P ] prog...
By default, emptyenv unsets all environment variables, then
execs into prog with its arguments. Options control which
environment variables are unset.
Options
- -p : keep the PATH environment variable.
- -c : clean up. Do not empty the environment. Instead,
remove every variable used internally by the execline programs, to avoid
any interference with or information leakage to external programs.
- -o : pop environment
variables starting with ELGETOPT_. You might want to do this
before executing a final program from a script that uses
elgetopt.
- -P : pop environment
variables starting with #, 0 to 9, and
EXECLINE_. You might want to do this before executing a final program
from a script launched by execlineb.