to understand if you have not seen a statically typed OOP language before.
a similar language, I would highly recommend this segment.
a superclass wants to require a subclass to override some method.
why you would want to check it statically.
I also want to make a nice connection to higher order functions.
They're complementary, but doing surprisingly similar things.
because it does have pure virtual methods and multiple inheritance.
So that was a lot of jargon and buzzwords.
I will explain it all piece by piece, and I think you'll appreciate it.
that it expects all of its subclasses to override some method.
variables, and things like that, that will be useful to lots of different subclasses.
But there are parts that the superclass doesn't know about.
This happens a lot it GUIs and graphical programs.
around and placing them on the screen, and all this sort of stuff.
of a graphical thing on your screen, there's no sensible default for.
You wouldn't want size ten by ten or zero by zero.
some method that determines the size.
So in Ruby we could just put that in a comment, right?
That the superclass would just say all subclasses better override this method.
Then they can add it.
And you shouldn't be making instances of the superclass.
Normally we define classes so that we can make instances of them.
well, it doesn't make sense to make an instance of some class like A.
then make instances of those subclasses.
So you see an example here on the slide.
A class A defines some method, m1.
And the body of m1 calls m2.
And I'm emphasizing here, this is self.m2.
And class A doesn't define m2.
you're going to get a method missing error.
all the subclasses of A that we do make instances of provide a definition of m2.
So that approach is not going to work in a statically typed OOP language.
These type-checkers want to make sure that method missing errors never occur.
And so they can't allow code like that.
we could get around this easily enough, right?
we do define m2, and we just make it raise an exception, right?
better override m2, because otherwise, you're going to get this exception.
We would like to do better.
if they forget to override something that they're supposed to.
C# calls abstract methods, and C++ calls pure virtual methods.
what its result type is, without providing a definition.
that tries to make an instance of the superclass of class A.
So now two things happen.
First, you can't make instances of class A.
or it implements the method in the way you indicated in the superclass.
So in Java, this is how you would write it.
and you don't provide a body.
this method will be defined.
you better provide a definition of that method.
It catches errors at compile time.
subclasses are really supposed to define.
But it doesn't make your language anymore powerful.
like in this previous version where you raised an exception.
to catch more bugs before we ever run the program.
are anything more than that.
So now let me make a connection between abstract methods and high-order functions.
Which on the surface don't seem to have very much to do with each other.
They are both ways, however, of passing code to other code.
Abstract methods are very OOP, they're very related to dynamic dispatch.
give some code to other code in the superclass.
even though it doesn't know what m2 is.
It's up to the subclass to provide a definition of m2.
And then thanks to dynamic dispatch, m1 will call m2.
takes another function g as an argument.
And the similarity is f does not know what g is.
Now there's now subclass to provide a definition of g.
The caller to f provides some other code, some other function into it.
in functional programming, it's the caller providing code.
the common functionality is in m1 or f.
f need, in order to complete their computation.
is that they are why C++ doesn't bother tab interfaces.
abstract methods doesn't also need interfaces.
instead of implementing an interface, just subclass a class.
you're not getting any code from that superclass.
They're all abstract, what C++ calls pure virtual.
to others that you should be a subtype of that superclass.
Because you are going to implement all of those methods.
And this is exactly what interfaces are for.
only being allowed to have one superclass.
is you declare a class with all abstract, pure virtual methods.
And then you just have other classes subclass that class.
does not have multiple inheritance.
And that's why you don't see it in Ruby and you don't see in C++.


text:
Throne Speech Report Card
Why BankSwitch? Why Now?
posted by Colleen Lynch
Toronto350 supports the BankSwitch initiative that is ongoing now! Read on to see how it works and how to sign up.
HOW BANKSWITCH WORKS in 9 SECONDS
Sign-up so we can track and announce our growth.
Tell your branch manager you're switching to the most sustainable bank in April 2021.
Wait for the banks to announce new fossil fuel policies.
For more, read on....
1. The Production Gap is terrifying.
Visit ProductionGap.org to see the full report.
Most climate policy is focused on consumption. The Paris Accord is based on each country's consumption. But oil companies are planning to produce far more fossil fuels than the world is planning to consume — because they're betting at least some countries will fail to limit consumption.
It's very hard to stop production for the international market. Activists only have a few options: they can block oil production and transport with their bodies (which is awesome, but isn't for everybody), pressure governments to regulate production (which the Liberals are unlikely to do) or block the money pipeline. Bank Switch accounts for the production gap by targeting the loans that allow fossil fuel expansion to happen!
2. Divestment Isn't Enough.
Divestment campaigns have gathered a lot of momentum in recent years and they're a good thing. Divesting from a company lowers its stock price, but it doesn't impact the company's day-to-day balance sheet. Fossil fuel companies can still build new projects even while their stock price is falling if they have access to loans. So we have to stop the loans.
3. CDN Banks are the biggest, baddest fossil fuel funders.
For more data check out the amazing Banking On Climate Change Report.
The Big 5 Canadian Banks are all in the top 25 funders of fossil fuels in the world. Since the Paris Agreement they have poured $481 BILLION into their efforts to guarantee a climate apocalypse! Although a few American banks have doled out more money, they are much bigger banks so it represents a smaller percentage of their total lending. The Banking on Climate Change report also tallies up fossil fuel policies – will a bank fund thermal coal? what about arctic drilling or fracking?. In terms of policies, the CDN banks really are the absolute worst: scoring between 0.5 and 3 points out of a possible 200 points! Even horrifically awful American banks like Chase have some standards and score between 15 and 25 out of 200.
4. The Tar Sands are completely reliant on CDN banks.
Among those especially dangerous fossil fuel projects that US banks are backing away from are the Tar Sands. Tar Sands oil has especially high CO2 intensity per barrel. That's because it takes a lot of energy to get the oil out of the sand, so companies burn natural gas to get it out! Even global oil companies are starting to worry about their carbon footprint – so they're moving their money from high CO2 oil to lower CO2 oil. Foreign money is fleeing from the tar sands as fast as it can — without the CDN banks it would be completely dead.
5. CDN banks are feeling the pressure.
BankSwitch isn't the first campaign to call out CDN banks. Toronto350 and others have been holding them to account for years. RBC hates being known as the dirtiest CDN bank and just last month they made a lot of noise about a minor update to their thermal coal policies. The new policy doesn't have much material impact because of the way it is worded, but it shows that RBC is willing to make changes to improve its image.
5. No Institution is a MONOLITH.
While it's tempting to think of the banks as pure evil, based on their evil behaviour, every institution is internally complex. There are people inside the CDN banks arguing that it's time to get out of the fossil fuel game. We know this, because we know some of them personally. Some bankers want to get out of fossil fuels for moral reasons others for purely economic reasons — fossil fuels look like a riskier and riskier investment with every passing year. BankSwitch is designed to help these people win arguments in internal meetings by providing them with more ammunition.
We ask you to contact branch managers first in order to get the whole bank talking about climate change. And we all know that the more people talk about fossil fuel investments the more indefensible they appear. Branch managers also genuinely care if they lose even a few accounts – so a handful of phone calls are more likely to flip one person in the bank at this level. Finally, by tallying up all the Canadians who want a sustainable banking option, we get the banks' marketing and client acquisition teams thinking about the issue. Could a policy announcement steal thousands of clients from their biggest competitors? That's the type of argument that might just win over even the most cynical executives.
6. BankSwitch makes the banks COMPETE!
Let's be honest: TD Bank is not afraid of Climate Pledge Collective. CIBC probably isn't up at night worrying what Toronto350 will say next. But what if BMO decided to end their fossil fuel lending and launch a big ad campaign about it? What if they set up a special phone line so you could transfer all your accounts in a few minutes? Now that would scare the other banks.
Giving the banks six months to think about their fossil fuel policies is likely to be more effective than all of us moving our money to credit unions one by one, because the banks will have to worry about what the other big banks will decide to do. They know that everyone is having internal discussions about the carbon bubble and climate risk, they know that other banks are rethinking the future of the energy market right now. Setting a date a switch date of April 2021 encourages all the big banks to put their best foot forward as soon as possible.
7. Flipping the banks might change government policy too!
Here at Climate Pledge Collective we have long been baffled by the Liberals' willingness to pour more and more money into keeping the oil sands alive. Strong climate policies are popular with voters in almost every province — and the Liberals didn't win a single seat in Alberta even after buying a pipeline. So what gives? We think they're worried that because the Canadian banks are overexposed to the oil sands, pulling the plug on this dying industry will create a financial crisis. If the CDN banks can start weening themselves off oil right now, it will make it a lot easier for the CDN government to do the same.
8. You can do it from home.
We hate to be the ones to tell you, but there's a huge pandemic raging all around the world. While it's still possible and important to protest in person, Climate Strikes are drawing smaller crowds. If you're one of those people, like me, who can't attend in-person protests right now because of health concerns, this is something meaningful you can do — even while the pandemic rages on. I booked an appointment with a representative at my branch and they called me at home. It only took about twenty minutes all told — including booking the appointment, taking the call and sending a follow-up email with links to the Banking on Climate Change report to pass on to the branch manager.
Ready to get involved?
We've noticed that this campaign spreads best through personal contacts. It's easy to get people to like a tweet or share a meme, but getting them to actually take time out of their day and make a phone call requires a little bit of hand holding.
The number one thing you can do is make a list of ten or twenty people who might be interested and send them an email or make a call. If you're on a group list of parents or alumni or what-have-you, drop them a line about BankSwitch.
Reprinted from climatepledgecollective.org
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Exploratory Data Analysis and Graphics

  Jan 20, 2015

In data science, or any related quantitative field, we strive to understand and leverage our data for our objectives. These data will usually be part of a bigger project that we're working on where the workflow looks something like the following:

Obtain data

Explore data

Research/create data product

Fine tune project and release

Rinse and repeat

Each step is critical and sequential where the assumptions and decisions made early on will affect the subsequent steps, for better or worse. Data collection is the most important step, where the metrics and methods determine what can be done with it. In each sub-field of quantitative analysis, be it academic sciences or industry, the data collection processes can vary drastically. Conversely, the exploratory data analysis phase is universal and can be broken down to several basic components: basic statistics, graphics, hypotheses formulation, and assessment of the best models/approaches to use for objectives.

Before moving on there is one caveat: Never trust data at face value. Always be skeptical and keep an eye out for any suspicious anomalies throughout. Furthermore, ensure that your understanding of the data is correct. For example, you may see data labeled as "Date" with values like "2015-01-04" and later find out that it is in GMT and not your local time zone. You can fit a model to bad or misconceived data, which means that no matter how well your predictions perform, they will be incorrect. Even if the model is right in some cases, it will be right for the wrong reasons.

Anscombe's Quartet: The power of graphics

Some data properties that would be obvious when visualized can be overlooked with basic statistics. A classic example of this is Anscombe's Quartet. This data set comprises of four different groups (I, II, III, and IV) with X and Y features.

Despite the obvious differences between the four groups, summary statistics and even linear fits to the data are identical to the second or third decimal place.

Group I, Group II, Group III, and Group IV

Mean x: 9.0

Mean y: 7.5

Variance x: 11.00

Variance y: 4.13

Correlation between x and y: 0.816

Linear regression coefficients: \( Y = 3.00 + 0.50\,X \)

The graphic1 below highlight the differences clearly. If we had not visualized the data we could have assumed that the groups were the same.

Imagine that each group had one million rows. Checking the data manually at this volume wouldn't be effective. Visualizing the data with simple graphics to show the right context is key in this process. The code used to generate the stats and graphics can be found here.

Anscombe's Quartet would be unlikely to occur in the data we work with, but it could be close. Close enough that one may conclude that the differences or similarities are small and make assumptions that can impact a project negatively.

Show me the data

A simple graphic is analogous to a simple sentence: Concise and to the point. A complex graphic is like a run-on sentence where the message can be lost amongst the audience. Hence, it's important to keep your graphics simple. When you do this, you are better informed and can quickly work through the data exploration.

At Stitch Fix, we interview a lot of candidates and love giving them problems that make them think. For example, we give them a dataset that has generic labels, which is either generated or anonymized. In the data there are nuanced statistics that the candidate can find, or better yet show us something that we didn't see before. The data challenges have no wrong or right answer. We want to see what the candidate can do and how they would approach a problem where there's no obvious solution.

As a sanity check, we'll  do the data challenge ourselves to make sure that what we're asking the candidates to do is feasible. In one test I looked at a tabular dataset we've given out before and came up with the following graphics to get an overview of the data. We refer to the columns in the data as features.

From the graphic above  I can quickly assess which columns in the table are binary, categorical, or continuous. I can also identify distributions, which influences the kind of assumptions we can make for later stages of analysis.

Viewing pairwise correlations between the features can be cumbersome when there's a lot of them. The correlation matrix is a wonderful way to visualize the pairwise correlations into a dense yet readable form. The most strongly correlated features are in blue and the inversely correlated are in red. Values that show no correlation are white. This matrix tells us about the relationships between the features in the data.

To find the subset of features that explain the bulk of the variation in the data, I use principal component analysis (PCA). It's a simple yet powerful tool. Using a scatter plot is the ideal way to visualize a PCA of data projected onto two components. In this case we have a high number of observations, so I place histograms on the top and right side to represent PC1 and PC2 components, respectively, to ensure that I don't miss any obvious densities.

At this point, I can make some educated hypotheses about the data and model it to predict certain behaviors. If I were to take the it to the production stage, I can now assess which models should be used and move forward.

Conclusion

In a nutshell, graphics are powerful for data exploration and should be combined with basic descriptive statistics. Keep the graphics simple for maximum clarity. Both tools will show us different properties that we'll need for the more advanced stages of research and product development.

In the next post we'll discuss how to optimize the exploratory data analysis stage through sampling.

†
This post has been cross-posted on Stitch Fix and Insight.

1
All graphics shown here were made using Seaborn, a statistical graphics package in Python.
←

        Crafted with <3 by John Otander (@4lpine).

        </> available on Github.

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This depression caused by prejudice – which the researchers call deprejudice – can occur at many levels. In the classic case, prejudice causes depression at the societal level (e.g., Nazis' prejudice causing Jews' depression), but this causal chain can also occur at the interpersonal level (e.g., an abuser's prejudice causing an abusee's depression), or even at the intrapersonal level, within a single person (e.g., a man's prejudice against himself causing his depression).
Understanding that many people with depression are not "just" depressed – they may have prejudice against themselves that causes their depression – has powerful theoretical implications for treatment.


So, now come to the point --- how could we say that the rich are great people. No, they are not great people who can't ever sympathize with the suffering of the poor.
I will drop here a bombshell. I know this might get many run away from the airport runway. No wonder, these days with delight we travel by air pretty often. Usually, we load ourselves with expensive personal items. Well, before we check-in, we do hand over all our precious luggage to the Airlines. Do you know what might happen if your bag containing a great many personal items gets lost in the transit? You will be very disappointed to know this. You might probably be paid just puny Rs 2000/- to Rs 4000/- as "compensation" —- almost like the cost for a bagful of "potatoes", please mind, cost of the expensive bag excluded. This amount also comes to you only after putting in much effort. Who will compensate for your personal luxury items in the bag? Practically no one.
It was a nightmare for my friend Mr. Sharma (name changed..) whose bag was lost by GoAir airlines on 11 May 2018. He journey was from Bagdogra to Guwahati. And it is still a nightmare for him since he lost a small laptop which was inside the bag. More than other items, the loss of a laptop with its invaluable data has devastated him.
Don't ever trust any public grievance offices from New Delhi, they don't even pick up the phone calls.
Since that fateful day, he has written more than 10 times to the Civil Aviation Ministry, New Delhi. But all futile exercise. Don't ever trust any public grievance offices from New Delhi, they don't even pick up the phone calls. For Mr. Sharma, the only achievement after so much of struggle is that the Airlines raised the compensation from Rs 2000/- to Rs 5000. Poor Mr. Sharma thought the Airlines will have a sense of compassion to understand his ordeal. But the language of humanity hardly moves them.
Now let's evaluate the case other way round. Suppose, Jehangir Wadia, the CEO/owner of GoAir, wants to visit Shillong and puts up at the hotel of Mr. Sharma himself. Suppose Mr. Sharma's servant lost the baggage of Mr. Wadia? Could Mr. Sharma tell without a sense of guilt to Mr. Wadia that my servant has lost your bags in the first place? What would be the action and reaction if Mr. Sharma would just give Mr. Wadia Rs 2000/- as compensation?
I know Mr. Wadia would go mad and spew fire upon Mr. Sharma. He will also immediately find ways to file a case against Mr. Sharma for his utter NEGLIGENCE. And, I'm dead sure, with the help of competent lawyers, Mr. Wadia would demand the "pound flesh" of Mr. Sharma. So, now come to the point — how could we say that the rich are great people. No, they are not great people who can't ever sympathize with the suffering of the poor. Their "humanity" within has long been stabbed to death by their greed. Therefore, they can be unsympathetic to other poor people and keep making money. Is the richest man in the world — Jeff Bezos, owner of AMAZON, not now being heavily criticized for being unusually unkind to his own employees? He has been blamed for "chaotic conditions" in the workplace, utter lack of occupational safety and low wages. Hope you can figure out the usual character of rich men from here.
Or else poor Mr. Sharma's letters of grievance would have been heard immediately as those business companies promptly respond the calls from prospective customers. Alas, now Mr. Sharma has no option but to face the ordeal of the major loss.
Oh dear regular fliers, so don't be under the illusion that you are in a "democracy". You, in fact, are literally in the regime of "capitalism". Laws are made to save "Shylocks of the modern times". All poor citizens have to slog and beg around for survival.


text:
Parks & Facilities Map
Field & Facility Status
Wilmette Platform Tennis Club
Wilmette Golf Club
Mallinckrodt
Centennial Recreation Complex & Gardens:
Centennial Family Aquatic Center
Centennial Ice Rinks
Wilmette Tennis Club
Centennial Prairie Garden
Community Recreation Center:
Center Fitness Club
Early Childhood Center of Wilmette
General Recreation
Mallinckrodt Adult Programs
League Schedules & Standings
Center Fitness Club/Classes
Ice Skating & Hockey
Park District Info
Mission, Inclusion, History
Parks & Planning Dept.
Donations & Memorials
Park District Gift Cards
Special Holiday Hours at WPD
Community Recreation Center
The CRC is open to the public with a front desk attendant available for assistance.
12/20: 9 a.m.-6 p.m.
12/21; 12/22; 12/23: 9 a.m.-4 p.m.
12/24 & 12/25: CLOSED
12/29 & 30: 9 a.m.-8 p.m.
1/1: CLOSED
1/2: 9 a.m.-6 p.m.
CFC is open to the public with a front desk attendant available for assistance. We will be operating for our regular hours, unless otherwise noted in the Holiday Hours list.
Monday-Friday: 6 a.m.-8 p.m.
Saturday-Sunday: 7 a.m.-6 p.m.
12/24: 7:30 a.m.-2:00 p.m.
12/25: CLOSED
1/1: 9:00 a.m.-3:30 p.m.
Administrative Offices at Village Hall
Village Hall is closed to the public through December, but our main office line is operating and will have staff available for assistance. The office phone line will be operating for our regular hours, unless otherwise noted in the Holiday Hours list.
Monday & Friday: 8:30 a.m.-12:30 p.m.
Tuesday & Wednesday: 1:30-4:30 p.m.
12/24: 8:30 a.m.-12:30 p.m.
Centennial Ice is open to the public with a front desk attendant available for assistance. We will be operating for our regular hours, unless otherwise noted in the Holiday Hours list.
Monday-Sunday: 6:30 a.m.-8 p.m.
12/24: 6:30 a.m.-Noon
Tennis is open to the public with a front desk attendant available for assistance. We will be operating for our regular hours, unless otherwise noted in the Holiday Hours list.
Monday-Sunday: 8 a.m.-8 p.m.
12/24: 8 a.m.-Noon
Lakeview Center
Lakeview will be CLOSED for the holiday season. Starting in January 2021, Lakeview will be open Mondays & Wednesdays.
Monday & Wednesday: 9 a.m.-1 p.m.
12/21-1/4: CLOSED
Mallinckrodt Center
Mallinckrodt is closed to the public. I you need assistance, please contact Lisa Sullivan at lsullivan@wilpark.org.
Late Fall Explore!
Platform Tennis Club
Active Adults
NSSRA
Jr. Trevians Football
Parks & Planning
Wilmette Park District
1200 Wilmette Avenue
© 2021 Wimette Park District. All rights reserved. Site by: PUREi


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There are great opportunities for food and drink manufacturers based abroad that are seeking to enter the UK market.
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text:
\section{Introduction}
 Percolation~\cite{BroadbentHarmmersley57} is a cornerstone of the theory of critical phenomena~\cite{StaufferAharony94},
and a central topic in probability~\cite{Grimmett99,BollobasRiordan09}.
 In two dimensions, Coulomb gas arguments~\cite{Nienhuis87} and
conformal field theory~\cite{Cardy87} predict the exact values of the bulk critical exponents $\beta =5/36$ and $\nu=4/3$,
which have been confirmed rigorously in the specific case of triangular-lattice site percolation~\cite{SmirnovWerner01}.
Exact values of the percolation thresholds $p_c$ on several two-dimensional lattices are also known~\cite{Essam72}.
In particular, it is known rigorously~\cite{Kesten80} that $p_c=1/2$ for bond percolation on the square lattice.
For all $d$ greater than or equal to the upper critical dimension~\cite{Toulouse74} of $d_c=6$, the mean-field values for the exponents $\beta=1$ and
$d \nu=3$ are believed to hold; this has been proved rigorously ~\cite{AizenmanNewman84,HaraSlade90} for $d\ge19$.

For dimensions $2 < d < 6$ by contrast, no exact values for either the critical exponents or the percolation thresholds are known.
Significant effort has therefore been expended on obtaining ever more accurate estimates, especially in three dimensions.

In addition to percolation thresholds and critical exponents, crossing probabilities \cite{LanglandsPichetPouliotSaintAubin92,Cardy92}
also play an important role in studies of percolation.
For lattices drawn on a torus, the analogous quantities are wrapping probabilities~\cite{LanglandsPouliotSaintAubin94}, and in two dimensions their values can be determined exactly~\cite{Pinson94}.
The three-dimensional case~\cite{MartinsPlascak03} has been far less studied however.
Precise estimation of wrapping probabilities on the simple-cubic lattice represents one of the central undertakings of the current work.

In addition to their intrinsic importance, wrapping probabilities have proved to be an effective practical means of estimating percolation thresholds~\cite{NewmanZiff00,FengDengBlote08}.
Using Monte Carlo (MC) simulations and performing a careful finite-size scaling analysis of various wrapping probabilities in the neighborhood of the transition,
we obtain very accurate estimates of $p_c$ for both site and bond percolation.
We observe numerically that the leading finite-size corrections for certain wrapping probabilities appear to be governed by an exponent $\approx-2$,
rather than by the leading irrelevant exponent $y_i\approx-1.2$.

We then estimate the thermal exponent $y_t=1/\nu$ by fixing $p$ to our best estimate of $p_c$, and
studying the divergence with linear size $L$ of the derivative of the wrapping probability, which is proportional to the covariance of its indicator with the number of bonds.
We find this procedure for estimating $y_t$ preferable to methods in which $y_t$ is estimated by studying
how quantities behave in a neighborhood of $p$ values around $p_c$. In particular, we believe the current method produces more reliable error estimates.

The remainder of this paper is organized as follows. The simulation method and the sampled quantities
are discussed in Sec.~\ref{sampled quantities}.
The results for the wrapping probabilities and thresholds are given in Sec.~\ref{estimating pc}.
Critical exponents and the excess cluster number are discussed in Sec.~\ref{results at pc}.
We then finally conclude with a discussion in Sec.~\ref{discussion}.

 \begin{table*}
 \begin{center}
 \caption{Fits of the wrapping probabilities $R^{(x)}$, $R^{(a)}$, and $R^{(3)}$, and the ratios $Q_1$ and $Q_2$ for bond percolation.
         We did not obtain stable fits with $y_i$ free for $R^{(3)}$.}
 \scalebox{1.0}{
 \begin{tabular}[t]{|l|l|l|l|l|l|l|l|l|l|}
 \hline
                               & $L_{\rm min}$ & $\chi^2/$DF& $p_c$     & $y_t$    & $\scrO_c$       & $q_1$      & $b_1$   & $y_i$     & $b_2$\\
 \hline
 {\multirow{6}{*}{$Q_1$}}      & 16            & 53/40  & 0.248\,812\,03(5) & 1.16(1)  & 0.865\,37(1)    & $-0.36(1)$ &$-0.0423(5)$& $-1.2$  & ~~0.341(5)\\
                               & 24            & 33/33  & 0.248\,811\,98(6) & 1.16(2)  & 0.865\,35(2)    & $-0.31(2)$ &$-0.040(2)$ & $-1.2$  & ~~0.31(2) \\
                               & 32            & 28/26  & 0.248\,811\,93(7) & 1.19(3)  & 0.865\,33(2)    & $-0.31(3)$ &$-0.036(3)$ & $-1.2$  & ~~0.25(5) \\
 \cline{2-10}
                               & 16            & 44/39  & 0.248\,811\,84(8)  & 1.16(1)  & 0.865\,39(3)   & $-0.36(1)$ &$-0.10(4) $ & $-1.34(9)$ &~~$0.50(8)$  \\
                               & 24            & 31/32  & 0.248\,811\,88(9)  & 1.19(2)  & 0.865\,29(4)   & $-0.32(3)$ &$-0.10(8) $ & $-1.3(2)$  &~~$0.5(2)$   \\
                               & 32            & 28/25  & 0.248\,811\,96(14) & 1.19(3)  & 0.865\,4(2)    & $-0.31(3)$ &$-0.02(4) $ & $-1.0(5)$  &~~$0.2(3)$     \\
 \hline
 {\multirow{6}{*}{$Q_2$}}      & 32            & 28/25  & 0.248\,811\,20(5) & 1.17(2)  & 0.633\,58(3)    & $-0.80(5)$ &$-0.104(4)$ & $-1.2$  & ~~0.05(7) \\
                               & 48            & 16/18  & 0.248\,811\,95(6) & 1.14(2)  & 0.633\,50(3)    & $-0.89(8)$ &$-0.088(9)$ & $-1.2$  & $-0.3(2)$  \\
                               & 64            & 10/11  & 0.248\,811\,84(11)& 1.12(3)  & 0.633\,4(2)     & $-1.0(2)$  &$-0.05(4)$  & $-1.2$  & $-1(1)$  \\
 \cline{2-10}
                               & 32            & 28/26  & 0.248\,812\,02(6) & 1.17(2)  & 0.633\,58(5)    & $-0.80(5)$ &$-0.097(8)$ & $-1.08(3)$  & ~~~~- \\
                               & 48            & 16/19  & 0.248\,811\,93(7) & 1.14(2)  & 0.633\,46(7)    & $-0.89(8)$ &$-0.15(4) $ & $-1.22(7)$  & ~~~~- \\
                               & 64            & 10/12  & 0.248\,811\,82(11)& 1.12(3)  & 0.633\,3(2)     & $-1.0(2)$  &$-0.5(6) $  & $-1.5(4)$   & ~~~~- \\
 \hline
 {\multirow{6}{*}{$R^{(x)}$}}  & 16            & 41/37  & 0.248\,811\,81(4) & 1.143(7) & 0.257\,77(2)  & $-1.22(3)$ &~~0.005(2) & $-1.2$    & $-0.23(1)$ \\
                               & 24            & 30/31  & 0.248\,811\,83(4) & 1.15(2)  & 0.257\,78(3)  & $-1.22(6)$ &~~0.003(3) & $-1.2$    & $-0.26(4)$ \\
                               & 32            & 25/24  & 0.248\,811\,82(6) & 1.15(2)  & 0.257\,76(5)  & $-1.20(8)$ &~~0.006(7) & $-1.2$    & $-0.20(10)$\\
 \cline{2-10}
                               & 16            & 41/37  & 0.248\,811\,82(4) & 1.144(7) & 0.257\,79(2)  & $-1.22(3)$ &~~0.18(2)  & $-1.83(4)$ & ~~~~-  \\
                               & 24            & 31/31  & 0.248\,811\,84(4) & 1.15(2)  & 0.257\,79(2)  & $-1.22(6)$ &~~0.22(8)  & $-1.9(2)$  & ~~~~-  \\
                               & 32            & 25/24  & 0.248\,811\,82(6) & 1.15(2)  & 0.257\,77(4)  & $-1.20(8)$ &~~0.1(1)   & $-1.7(3)$  & ~~~~-  \\
 \hline
 {\multirow{6}{*}{$R^{(a)}$}}  & 16            & 40/39  & 0.248\,811\,82(4) & 1.149(7) & 0.459\,99(3)  & $-1.65(4)$ &~~0.004(2)& $-1.2$  & ~~$0.73(2)$ \\
                               & 24            & 25/32  & 0.248\,811\,82(5) & 1.14(2)  & 0.459\,97(5)  & $-1.74(9)$ &~~0.003(4)& $-1.2$  & ~~$0.72(6)$\\
                               & 32            & 22/25  & 0.248\,811\,83(6) & 1.14(2)  & 0.459\,98(7)  & $-1.7(2) $ &~~0.005(9)& $-1.2$  & ~~$0.7(2)$   \\
 \cline{2-10}
                               & 16            & 40/39  & 0.248\,811\,82(4) & 1.149(7) & 0.459\,97(2)  & $-1.65(4)$ &~~0.81(6) & $-2.06(3)$ & ~~~~- \\
                               & 24            & 25/32  & 0.248\,811\,82(4) & 1.14(2)  & 0.459\,95(3)  & $-1.74(9)$ &~~0.8(2)  & $-2.05(8)$ & ~~~~- \\
                               & 32            & 22/25  & 0.248\,811\,82(5) & 1.14(2)  & 0.459\,96(5)  & $-1.74(2)$ &~~1.0(9)  & $-2.1(3)$  & ~~~~- \\
 \hline
 {\multirow{3}{*}{$R^{(3)}$}}  & 16            & 44/38  & 0.248\,811\,85(6 )& 1.14(1)  & 0.080\,41(2)  & $-0.66(2)$ &~~0.010(1)& $-1.2$  & $-0.076(8)$  \\
                               & 24            & 35/31  & 0.248\,811\,91(6 )& 1.15(2)  & 0.080\,43(3)  & $-0.63(5)$ &~~0.007(3)& $-1.2$  & $-0.04(3)$   \\
                               & 32            & 23/24  & 0.248\,811\,85(8 )& 1.17(3)  & 0.080\,39(4)  & $-0.59(5)$ &~~0.014(6)& $-1.2$  & $-0.15(9)$   \\
 \hline
 \end{tabular}
}
 \end{center}
 \label{Tab:fit-bond}
 \end{table*}

\section{Sampled quantities}
\label{sampled quantities}
We study bond and site percolation on a periodic $L\times L\times L$ simple-cubic lattice with linear system sizes $L=8$, 12, 16, 24, 32, 48, 64, 128, 256, and 512.
For each system size, we produced at least $2.5\times 10^7$ independent samples.
Each independent bond (site) configuration is generated by independently occupying each bond (site) with probability $p$.
The clusters in each configuration are identified using breadth-first search.
The number of sites in each cluster defines its size.

 We sampled the following observables in our simulations:
 \begin{enumerate}[(a)]
 \item The number of occupied bonds $\scrN_b$ for bond percolation, and the number of occupied sites $\scrN_s$ for site percolation.
 \item The number of clusters $\scrN_c$.
 \item The size $\scrC_1$ of the largest cluster.
 \item The cluster-size moments $\scrS_m  = \sum_{C}|C|^m$ with $m=0,2,4$. The sum runs over all clusters $C$, and $\scrS_0$ is simply the number of clusters.
 \item An observable $\scrS :=\max\limits_{C}\,\max\limits_{y\in C}\,d(x_C,y)$ used to determine the shortest-path exponent.
   Here $d(x,y)$ denotes the graph distance from site $x$ to site $y$, and $x_C$ is the vertex in cluster $C$ with the smallest vertex label, according to some fixed (but arbitrary) vertex labeling.
 \item The indicators $\scrR^{(x)}$, $\scrR^{(y)}$, and $\scrR^{(z)}$, for the event that a cluster wraps around the lattice in the $x$, $y$, or $z$ direction, respectively.
 \end{enumerate}

 From these observables we calculated the following quantities:
 \begin{enumerate}[(i)]
 \item The mean size of the largest cluster $C_1 = \langle \scrC_1 \rangle$, which at $p_c$ scales as $C_1\sim L^{y_h}$ with
$y_h = d_f = d - \beta/\nu$, where $d_f$ is the fractal dimension.
 \item The cluster density $\rho = \langle \scrN_c \rangle/L^d$.
 \item The mean size of the cluster at the origin, $\chi=\langle \scrS_2 \rangle/L^d$, which at $p_c$ scales as $\chi \sim L^{2y_h-d}$.
 \item The dimensionless ratios
   \begin{equation}
     Q_1  = \frac{\langle {\scrC_1}^2\rangle}{\langle \scrC_1\rangle^2}\;,\;\;\; Q_2  = \frac{\langle {\scrS_2}^2\rangle}{\langle 3{\scrS_2}^2 - 2\scrS_4\rangle}\;.
     \label{eq:R}
   \end{equation}
 \item The shortest-path length $S=\langle \scrS \rangle$, which at $p_c$ scales as $S \sim L^{\dm}$ with $\dm$ the shortest-path fractal dimension.
 \item The wrapping probabilities
 \begin{equation}
   \begin{aligned}
     R^{(x)} = & \langle \scrR^{(x)} \rangle = \langle \scrR^{(y)} \rangle = \langle \scrR^{(z)} \rangle \;, \\
     R^{(a)} = & 1 - \langle (1-\scrR^{(x)})(1-\scrR^{(y)})(1-\scrR^{(z)})\rangle \;, \\
     R^{(3)} = & \langle \scrR^{(x)}\scrR^{(y)}\scrR^{(z)}\rangle\;.
   \end{aligned}
 \end{equation}
 Here $R^{(x)}$ gives the probability that a winding exists in the $x$ direction, $R^{(a)}$ gives the probability that a winding exists in at least one of the three possible directions,
 and $R^{(3)}$ gives the probability that windings simultaneously exist in all three possible directions.
 Near $p_c$, we expect each of these wrapping probabilities to behave as $\sim f((p-p_c)L^{y_t})$, where $f$ is a scaling function.
 \item The covariance of $\scrR^{(x)}$ and $\scrN_b$
 \begin{equation}
 \begin{aligned}
   g^{(x)}_{bR} &= \langle \scrR^{(x)} \scrN_b\rangle - \langle \scrR^{(x)}\rangle \langle \scrN_b \rangle \\
              &= p(1-p)\frac{\partial R^{(x)}}{\partial p}\;.
 \end{aligned}
 \label{eq:g}
 \end{equation}
 At $p_c$, we expect $g^{(x)}_{bR}\sim L^{y_t}$.
 An analogous definition of $g^{(x)}_{sR}$, with $\scrN_b$ being replaced with $\scrN_s$, was used for site percolation.
 \end{enumerate}

 To derive \eqref{eq:g}, one can explicitly differentiate $\langle \scrR^{(x)}\rangle$ with respect to $p$,
 and use the fact that $\langle \scrN_b \rangle = p|E|$ where $|E|$ is the total number of edges on the lattice.

 The complete set of data for all observables, for both bond and site percolation,
 is available as Supplemental Material \cite{Supplemental}.

 \begin{figure}
 \RbondFig
 \centering
 \caption{
   Plots of $R^{(x)}(p,L)$ (top) and $R^{(a)}(p,L)$ (bottom) vs $L$ for fixed values of $p$, for bond percolation.
   In both cases, the curves correspond to our preferred fit of the MC data for $R(p,L)$ by the ansatz~(\ref{eq:pc});
   the dashed curve corresponds to setting $p=0.248\,811\,82$.
   The shaded blue strips indicate an interval of $1\sigma$ above and below the estimates $R^{(x)}_c = 0.257\,78(6)$ and $R^{(a)}_c = 0.459\,97(8)$.
 }
 \label{Fig:R-pc}
 \end{figure}

 \begin{figure}
 \centering
 \yiFiga \\
 \yiFigb
 \caption{
  Plots of $Q_1$ and $Q_2$ vs $L^{-1.2}$ (top),
  and $R^{(x)}$ and $R^{(a)}$ vs $L^{-2}$ (bottom), with $p=0.248\,811\,8$, for bond percolation.
   The solid lines are simply to guide the eye.
 }
 \label{Fig:yi}
 \end{figure}

\section{Estimating $p_c$}
\label{estimating pc}
\subsection{Bond percolation}

 We estimate the thresholds of bond and site percolation by studying the finite-size scaling of the
 wrapping probabilities $R^{(x)}$, $R^{(a)}$, and $R^{(3)}$, and the dimensionless ratios $Q_1$ and $Q_2$.
 Around $p_c$, we perform least-squares fits of the MC data for these quantities by the ansatz
 \be
 \scrO(\epsilon,L)  = \scrO_c + \sum_{k=1}^{2}q_k\epsilon^kL^{ky_t} + b_1L^{y_i} + b_2L^{-2}\;\;,
 \label{eq:pc}
 \ee
 where $\epsilon = p_c - p$, $\scrO_c$ is a universal constant, and $y_i$ is the leading correction exponent.
 We perform fits with both $b_1$ and $b_2$ free, as well as fits with $b_2$ being set identically to zero.
 By performing fits with $y_i$ free we estimate that $y_i=-1.2(2)$.
 We also perform fits with $y_i$ fixed to $y_i=-1.2$.

As a precaution against correction-to-scaling terms that we have neglected in our chosen ansatz,
we impose a lower cutoff $L\ge L_{\min}$ on the data points admitted in the fit,
and we systematically study the effect on the $\chi^2$ value of increasing $L_{\min}$.
In general, our preferred fit for any given ansatz corresponds to the smallest $L_{\min}$
for which $\chi^2$ divided by the number of degrees of freedom (DFs) is $O(1)$,
and for which subsequent increases in $L_{\min}$ do not cause $\chi^2$ to drop by much more than one unit per degree of freedom.

 \begin{table*}
 \begin{center}
 \caption{Fits of the wrapping probabilities $R^{(x)}$, $R^{(a)}$, and $R^{(3)}$, and the ratios $Q_1$ and $Q_2$ for site percolation.
 For $R^{(x)}$ we obtain unstable results when $y_i$ is free.}
 \scalebox{1.0}{
 \begin{tabular}[t]{|l|l|l|l|l|l|l|l|l|l|}
 \hline
                               & $L_{\rm min}$ & $\chi^2$/DF & $p_c$  & $y_t$    & $\scrO_c$     & $q_1$      & $b_1$           & $y_i$    & $b_2$\\
 \hline
  {\multirow{6}{*}{$Q_1$}}     & 32            & 19/16  & 0.311\,606\,9(2)  & 1.14(2)  & 0.865\,05(2)    & $-0.22(2)$ &~~$0.062(2) $ & $-1.2$  &~~0(3) \\
                               & 48            & 11/11  & 0.311\,607\,0(2)  & 1.11(3)  & 0.865\,09(3)    & $-0.25(3)$ &~~$0.054(6) $ & $-1.2$  &~~0.2(2) \\
                               & 64            & ~~3/6    & 0.311\,607\,7(3)  & 1.12(6)  & 0.865\,26(7)  & $-0.24(6)$ &~~$0.01(2)  $ & $-1.2$  &~~1.4(5) \\
 \cline{2-10}
                               & 32            & 19/16  & 0.311\,606\,9(2)  & 1.15(2)  & 0.865\,06(3)    & $-0.22(2)$ &~~$0.063(4) $ & $-1.11(2)$ & ~~~~~~-\\
                               & 48            & 10/11  & 0.311\,607\,1(2)  & 1.11(3)  & 0.865\,12(4)    & $-0.25(3)$ &~~$0.09(2)  $ & $-1.22(7)$ & ~~~~~~-\\
                               & 64            & ~~3/6    & 0.311\,607\,7(3)  & 1.12(6)  & 0.865\,27(5)  & $-0.24(6)$ &~~$0.9(10)  $ & $-1.8(3) $ & ~~~~~~-\\
 \hline
  {\multirow{3}{*}{$Q_2$}}     & 64            &~~3/6    & 0.311\,607\,6(2)  & 1.12(4)  & 0.633\,3(1)     & $-0.56(9)$ &~~$0.02(3)  $ & $-1.2$  &~~5.1(7) \\
 \cline{2-10}                  & 48            &13/11   & 0.311\,607\,2(1)  & 1.14(2)  & 0.633\,06(4)    & $-0.52(4)$ &~~$0.9(1)  $ & $-1.52(3)$ &~~~~~~-   \\
                               & 64            &~~2/6     & 0.311\,607\,6(2)  & 1.12(4)  & 0.633\,29(8)    & $-0.56(9)$ &~~$ 4(2)    $ & $-1.9(2) $ &~~~~~~-   \\
 \hline
  {\multirow{3}{*}{$R^{(x)}$}} &16          & 42/39  & 0.311\,607\,85(5) & 1.13(1)  & 0.257\,89(2)  & $-0.76(4)$ &~~$0.004(1) $   & $-1.2$ & $-0.22(1)$  \\
                               &24          & 30/31  & 0.311\,607\,74(6) & 1.14(2)  & 0.257\,84(3)  & $-0.75(5)$ &~~$0.009(2)  $   & $-1.2$ & $-0.29(3)$   \\
                               &32          & 24/24  & 0.311\,607\,66(7) & 1.14(2)  & 0.257\,80(3)  & $-0.73(5)$ &~~$0.015(4)$     & $-1.2$ & $-0.39(6)$   \\
 \hline
  {\multirow{6}{*}{$R^{(a)}$}} &16          & 39/40  & 0.311\,607\,70(5) & 1.12(2)  & 0.460\,02(2)  & $-1.09(6)$ &~~0.023(2)       &$-1.2 $  &~~$0.08(2)$    \\
                               &24          & 25/32  & 0.311\,607\,67(7) & 1.13(2)  & 0.459\,99(4)  & $-1.05(6)$ &~~0.025(3)       &$-1.2 $  &~~$0.05(4)$    \\
                               &32          & 19/24  & 0.311\,607\,65(8) & 1.13(2)  & 0.459\,98(5)  & $-1.06(7)$ &~~0.027(6)       &$-1.2 $  &~~$0.02(9)$    \\
 \cline{2-10}
                               &16          & 36/40  & 0.311\,607\,75(6) & 1.12(2)  & 0.460\,06(3)  & $-1.09(6)$ &~~0.055(5)      &$-1.33(4)$ &~~~~~~-         \\
                               &24          & 25/32  & 0.311\,607\,68(8) & 1.13(2)  & 0.460\,01(5)  & $-1.05(7)$ &~~0.039(9)      &$-1.21(9)$ &~~~~~~-         \\
                               &32          & 19/24  & 0.311\,607\,65(9) & 1.13(2)  & 0.459\,99(7)  & $-1.06(7)$ &~~0.03(2)       &$-1.1(2)$  &~~~~~~-          \\
\hline
  {\multirow{6}{*}{$R^{(3)}$}} &16          & 50/38  & 0.311\,608\,01(8) & 1.14(2)  & 0.080\,55(1)  & $-0.38(3)$ & $-0.010(8)$   &$-1.2$  & $-0.30(1)$ \\
                               &24          & 27/30  & 0.311\,607\,79(9) & 1.14(2)  & 0.080\,49(2)  & $-0.39(4)$ & $-0.004(2)$   &$-1.2$  & $-0.38(3)$  \\
                               &32          & 18/23  & 0.311\,607\,65(11)& 1.15(3)  & 0.080\,45(3)  & $-0.38(4)$ & $-0.002(3)$   &$-1.2$  & $-0.47(5)$  \\
\cline{2-10}
                               &16          & 40/38  & 0.311\,607\,89(7) & 1.15(2)  & 0.080\,510(9) & $-0.38(3)$ & $-0.21(1) $   &$-1.77(2)$ &~~~~~~-     \\
                               &24          & 26/30  & 0.311\,607\,77(8) & 1.14(2)  & 0.080\,48(2)  & $-0.39(4)$ & $-0.30(5) $   &$-1.88(5)$ &~~~~~~-    \\
                               &32          & 18/23  & 0.311\,607\,66(10)& 1.15(3)  & 0.080\,46(2)  & $-0.38(4)$ & $-0.6(2)  $   &$-2.1(2)$  &~~~~~~-     \\
\hline
 \end{tabular}
}
 \label{Tab:fit-site}
 \end{center}
 \end{table*}

 Table~\ref{Tab:fit-bond} summarizes the results of these fits.
 From the fits, we can see that the finite-size corrections of $Q_1$ and $Q_2$ are dominated by the exponent $y_i\approx -1.2$.
 From $Q_1$ and $Q_2$, we estimate $p_c=0.248\,811\,9(3)$, and their universal critical values $Q_{1,c}=0.865\,4(2)$ and $Q_{2,c}=0.633\,5(2)$.

 For $R^{(x)}$ and $R^{(a)}$, fixing $y_i=-1.2$ and including both the $b_1$ and $b_2$ terms we find that $b_1$ is consistent with zero, while $b_2$ is clearly nonzero.
 Furthermore, if we set $b_2=0$ and leave $y_i$ free, we find $y_i\approx -2$.
 This suggests that either the amplitudes of the leading corrections of $R^{(x)}$ and $R^{(a)}$ vanish identically, or at least that they are sufficiently small that they cannot be detected from our data.
 Due to these weak finite-size corrections, the values of $p_c$ fitted from $R^{(x)}$ and $R^{(a)}$
are much more stable than those obtained from $Q_1$ and $Q_2$.
 From $R^{(x)}$ and $R^{(a)}$, we estimate $p_c=0.248\,811\,82(10)$.
 For $R^{(3)}$, we report only the fits with corrections $b_1L^{-1.2} + b_2L^{-2}$.
 If $y_i$ is left free the fits become unstable, regardless of whether the $b_2L^{-2}$ term is included.
 From $R^{(3)}$, we estimate $p_c = 0.248\,811\,85(15)$ which is consistent with the value obtained from $R^{(x)}$ and $R^{(a)}$.
 From these fits, we estimate the universal wrapping probabilities to be $R^{(x)}_{c} = 0.257\,78(6)$,
 $R^{(a)}_{c} = 0.459\,97(8)$ and $R^{(3)}_{c} = 0.080\,41(8)$.

 In Fig.~\ref{Fig:R-pc}, we illustrate our estimate of $p_c$ by plotting $R^{(x)}$ and $R^{(a)}$ vs $L$.
 Precisely at $p=p_c$, as $L\to\infty$ the data should tend to a horizontal line, whereas the data with $p\neq p_c$ will bend upward or downward.
 Figure~\ref{Fig:R-pc} shows that our estimate of $p_c$ lies slightly below the central value $0.248\,812\,6$ reported in~\cite{LorenzZiff98a}.

 In Fig.~\ref{Fig:yi}, we plot the data at $p=0.248\,811\,8$ for $R^{(x)}$ and $R^{(a)}$ vs $L^{-2}$, and for $Q_1$ and $Q_2$ vs $L^{-1.2}$.
 The figure strongly suggests that the correction $L^{-1.2}$ dominates in $Q_1$ and $Q_2$, but vanishes (or is very weak) in $R^{(x)}$ and $R^{(a)}$.

\subsection{Site percolation}
 For site percolation, we again estimate $p_c$ by fitting $Q_1$ and $Q_2$, $R^{(x)}$, $R^{(a)}$, and $R^{(3)}$ with Eq.~\eqref{eq:pc}.
 The fitting procedure is similar to that of bond percolation, and the results are summarized in Table~\ref{Tab:fit-site}.
 From the table, we can see that the fits of $Q_1$ and $Q_2$ are less stable for site percolation than for bond percolation.
 The ratio of $\chi^2$ per $\mathrm{DF}$ remains large until $L_{\rm min}\ge 32$ for $Q_1$ and $L_{\rm min}\ge48$ for $Q_2$,
 and the resulting estimates of $p_c$ range from $0.311\,606\,9(2)$ to $0.311\,607\,7(3)$.

 The fits of the wrapping probabilities are better behaved, as was the case for bond percolation.
 For $R^{(3)}$, fixing $y_i=-1.2$ and including both the $b_1$ and $b_2$ terms, we find that $b_1$ is consistent with zero, while $b_2$ is clearly nonzero.
 Furthermore, if we set $b_2=0$ and leave $y_i$ free, we find $y_i\approx -2$.
 This suggests that the amplitude of the leading correction of $R^{(3)}$ is smaller than the resolution of our fits, and might possibly be zero.
 The fits of the $R^{(a)}$ data, however, quite clearly indicate the presence of the $b_1L^{-1.2}$ term.
 For $R^{(x)}$, we report only the fits with corrections $b_1L^{-1.2} + b_2L^{-2}$;
 if $y_i$ is left free the fits become unstable, regardless of whether the $b_2L^{-2}$ term is included.
 As for $R^{(a)}$, the amplitude $b_1$ appears to take a nonzero value.
 These observations suggest that the leading correction $L^{-1.2}$ does not generically vanish for all wrapping probabilities, but rather that the amplitudes in some cases are smaller than the resolution of our
 simulations.

 Comparing the various fits, we estimate $p_c=0.311\,607\,7(2)$ for site percolation, which is consistent with
 the previous result $0.311\,607\,7(4)$ ~\cite{DengBlote05}.
 In addition, we estimate the universal wrapping probabilities to be $R^{(x)}_{c} = 0.257\,82(6)$, $R^{(a)}_{c} = 0.459\,99(8)$, and $R^{(3)}_{c} = 0.080\,46(6)$,
 which are consistent with those estimated from bond percolation.
 In Fig.~\ref{Fig:site-R}, we show plots of $R^{(x)}$ and $R^{(a)}$ which illustrate our estimate of $p_c$.

 \begin{figure}
 \RsiteFig
 \centering
 \caption{
   Plots of $R^{(x)}(p,L)$ (top) and $R^{(a)}(p,L)$ (bottom) vs $L$ for fixed values of $p$, for site percolation.
   In both cases, the curves correspond to our preferred fit of the MC data for $R(p,L)$ by ansatz~(\ref{eq:pc});
    the dashed curve corresponds to setting $p=0.311\,607\,7$.
   The shaded blue strips indicate an interval of $1\sigma$ above and below the estimates $R^{(x)}_c = 0.257\,82(6)$ and $R^{(a)}_c = 0.459\,99(8)$.
 }
 \label{Fig:site-R}
 \end{figure}

\section{Results at $p_c$}
\label{results at pc}

 In this section, we estimate the critical exponents $y_t$, $y_h$, and $\dm$, as well as the excess cluster number.
 Fixing $p$ at our estimated thresholds for bond and site percolation, we study the covariances $g^{(x)}_{bR}$ and $g^{(x)}_{sR}$,
 the mean size of the largest cluster $C_1$, the mean size of the cluster at the origin, $\chi$, the shortest-path length $S$, and the cluster density $\rho$.
 The MC data for $g^{(x)}_{bR}$, $g^{(x)}_{sR}$, $C_1$, $\chi$ and $S$ are fitted by the ansatz
 \be
 \scrA = L^{y_\scrA} ( a_0 + b_1L^{-1.2} + b_2L^{-2})\;.
 \label{eq:A}
 \ee
 We perform fits using different combinations of the two corrections $b_1L^{-1.2}$ and $b_2L^{-2}$ and compare the results.

\subsection{Estimating $y_t$}

We estimate $y_t$ by studying the covariances $g^{(x)}_{bR}$ and $g^{(x)}_{sR}$, both of which scale as $\sim L^{y_t}$ at the critical point.
We find this procedure for estimating $y_t$ preferable to methods, such as that employed in~\cite{DengBlote05}, in which $y_t$ is estimated by studying
how quantities behave in the neighborhood of $p_c$ as the system deviates from criticality. In particular, we believe the current method produces more reliable error estimates.

 We fit the data for $g^{(x)}_{bR}$ at $p=0.248\,811\,8$ and $g^{(x)}_{sR}$ at $p=0.311\,607\,7$ to Eq.~\eqref{eq:A},
 and the results are shown in Table~\ref{Tab:fit-g}.
 The estimate of $y_t$ from $g^{(x)}_{sR}$ produces a smaller error bar than that from $g^{(x)}_{bR}$.
 From these fits we take our final, somewhat conservative, estimate to be $y_t=1.141\,0(15)$.

 \begin{table}
 \caption{Fits of covariances $g^{(x)}_{bR}$ and $g^{(x)}_{sR}$.}
 \scalebox{0.95}{
 \begin{tabular}[t]{|l|l|l|l|l|l|l|}
 \hline
                                  &$L_{\rm min}$ & $\chi^2$/DF & $y_t$ & $a_0$ & $b_1$ & $b_2$\\
 \hline
 {\multirow{4}{*}{$g^{(x)}_{bR}$}} & 16          & 4/4  & 1.140\,4(9)     & 0.231(1)   & $-0.03(2)$    & ~~0.1(2)    \\
                                   & 24          & 4/3  & 1.140\,6(13)    & 0.231(2)   & $-0.02(5)$    & ~~0.0(4)    \\
 \cline{2-7}
                                   & 16          & 4/5  & 1.140\,9(4)     & 0.230\,7(3)& $-0.012(3)$   &    ~~~~-      \\
                                   & 24          & 4/4  & 1.140\,6(6)     & 0.231\,1(6)& $-0.017(7)$   &    ~~~~-      \\
 \hline
 {\multirow{4}{*}{$g^{(x)}_{sR}$}} &  16  & 5/4  & 1.141\,6(4)     & 0.155\,1(3)& $-0.004(7)$   & $-0.06(5)$ \\
                                   &  24  & 4/3  & 1.141\,1(6)     & 0.155\,4(6)& $-0.02(2) $   & $-0.1(2) $ \\
 \cline{2-7}
                                 &  16  & 7/5  & 1.141\,1(2)     & 0.155\,5(1)& $-0.013(1) $    &    ~~~~-       \\
                                 &  24  & 4/4  & 1.141\,4(3)     & 0.155\,3(2)& $-0.010(2)$     &    ~~~~-       \\
 \hline
 \end{tabular}
  }
 \label{Tab:fit-g}
 \end{table}

 \begin{figure}
 \ytFig
 \centering
 \caption{Plots of ($\ln g^{(x)}_{bR} - y_t\ln {L}$) (top) and ($\ln g^{(x)}_{sR} - y_t\ln {L}$) (bottom)
 vs $\ln {L}$ illustrating our estimate $y_t= 1.141\,0(15)$. The dashed curves are simply to guide the eye. }
 \label{Fig:yt}
 \end{figure}

 In Fig.~\ref{Fig:yt}, we plot ($\ln g^{(x)}_{bR} - y_t\ln {L}$) and ($\ln g^{(x)}_{sR} - y_t\ln {L}$) vs $\ln L$ using three different values of $y_t$:
 our estimate, as well as our estimate plus or minus three standard deviations.
 Using the true value of $y_t$ should produce a horizontal line for large $L$.
 In the figure, the data using $y_t=1.136\,5$ and $y_t=1.145\,5$ respectively bend upward and downward, suggesting that the true value of $y_t$ does indeed lie within
$3\sigma$ of our estimate.
 The data with $y_t = 1.141$ appear to be consistent with an asymptotically horizontal line. We note that while the curve appears to be increasing around the point at $L=512$ for bond percolation,
 it instead slightly decreases for site percolation, suggesting that in fact this movement is dominated (or even entirely caused) by noise.

 \begin{table}
 \caption{Fits of $C_1$ and $\chi$. The superscripts $b$ and $s$ denote bond and site percolation, respectively.}
  \scalebox{0.95}{
 \begin{tabular}[t]{|l|l|l|l|l|l|l|}
 \hline
                                  & $L_{\rm min}$ & $\chi^2$/DF & $y_h$ & $a_0$ & $b_1$ &  $b_2$\\
 \hline
  {\multirow{4}{*}{$C^{b}_1$}}    &   16         & 3/4  & 2.522\,86(5)    & 0.939\,4(3)   &$-0.014(6)$   & ~~$0.22(4)$  \\
                                  &   24         & 3/3  & 2.522\,89(7)    & 0.939\,3(4)   &$-0.009(11)$  & ~~$0.2(1) $  \\
 \cline{2-7}
                                  &   24         & 5/4  & 2.522\,98(3)    & 0.938\,8(2)   &~~$0.009(2)$    & ~~~~-  \\
                                  &   32         & 3/3  & 2.522\,94(4)    & 0.939\,0(2)   &~~$0.005(3)$    & ~~~~-  \\
 \hline
   {\multirow{4}{*}{$\chi^{b}$}}   &   16         & 4/4  & 2.523\,03(4)    & 1.125\,7(5)   &~~$0.14(1)$    & ~~0.18(7)   \\
                                  &   24         & 3/3  & 2.523\,00(5)    & 1.126\,2(7)   &~~$0.12(2)$    & ~~0.3(2)    \\
 \cline{2-7}
                                  &   24         & 6/4  & 2.523\,08(3)    & 1.125\,1(3)   &~~$0.157(4)$   & ~~~~-       \\
                                  &   32         & 4/3  & 2.523\,05(3)    & 1.125\,5(4)   &~~$0.151(6)$   & ~~~~-       \\
 \hline
 {\multirow{2}{*}{$C^{s}_1$}}     &    16        & 5/4  & 2.522\,99(3)      & 0.471\,16(7)  & ~~$0.024(2)$   & $-0.44(2)$  \\
                                  &    24        & 5/3  & 2.523\,00(5)      & 0.471\,1(2)   & ~~$ 0.024(4)$   & $-0.45(4)$  \\
 \hline
 {\multirow{4}{*}{$\chi^{s}$}}     &    32        & 0.9/2  & 2.522\,91(5)      & 0.284\,1(2)   & $-0.001(7)$  & $-1.15(9)$  \\
                                  &    48        & 0.7/1  & 2.522\,94(9)      & 0.284\,0(4)   & $-0.007(18)$ & $-1.3(3)$  \\
\cline{2-7}
                                  &    32        & 0.9/3  & 2.522\,92(1)      & 0.284\,06(3)  & ~~~~~~-        & $-1.16(1)$  \\
                                  &    48        & 0.9/2  & 2.522\,91(2)      & 0.284\,08(7)  & ~~~~~~-        & $-1.17(5)$  \\
\hline
 \end{tabular}
}
 \label{Tab:fit-C1}
 \end{table}

 \begin{figure}
 \yhFig
 \centering
 \caption{Plots of ($\ln C^{b}_1 - y_h \ln {L}$) (top) and ($\ln C^{s}_1 - y_h \ln {L}$) (bottom) vs $\ln {L}$ to show our
          estimate $y_h = 2.522\,95(15)$. The dashed curves are simply to guide the eye.
 }
 \label{Fig:yh}
 \end{figure}

\subsection{Estimating $y_h$}
 We estimate $y_h$ by studying the divergence of $C_1$ and $\chi$ as $L$ increases with $p$ fixed to our best estimates of $p_c$.
 We fit the MC data for $C_1$ and $\chi$ with Eq.~\eqref{eq:A},
 with the exponent $y_{\scrA}$ then corresponding to $y_h$ and $2y_h-d$, respectively.
 The results are reported in Table~\ref{Tab:fit-C1}. We use superscripts $b$ and $s$ to distinguish bond and site percolation.
 For $C^{b}_1$ and $\chi^{s}$, the amplitude $b_1$ is quite small, while $b_1$ in $\chi^{b}$ and $C^{s}_1$ is clearly present.
 In the fits of $\chi^{s}$ with one correction term $b_1L^{-1.2}$, the ratio of $\chi^2$ per DF remains large until $L_{\rm min}\ge 64$.
 We therefore show the fits with the correction $b_2L^{-2}$ instead.
 Comparing these fits, we estimate $y_h = 2.522\,95(15)$.

 In Fig.~\ref{Fig:yh}, we plot ($\ln C^{b}_1 - y_h \ln L$) and ($\ln C^{s}_1 - y_h \ln L$) vs $\ln L$
 using three different values of $y_h$: our estimate, as well as our estimate plus or minus three standard deviations.
 As $L$ increases, the data with $y_h = 2.522\,50$ and $2.523\,40$ respectively slope upward and downward, while
 the data with $y_h = 2.522\,95$ are consistent with an asymptotically horizontal line.

  \begin{table}
 \caption{Fits of $S$. The superscripts $b$ and $s$ denote bond and site percolation, respectively.}
 \begin{tabular}[t]{|l|l|l|l|l|l|l|}
 \hline
                                  &$L_{\rm min}$  & $\chi^2$/DF & $\dm$            &  $a_0$       & $b_1$     & $b_2$   \\
 \hline
 {\multirow{2}{*}{${S}^{b}$}}
                                  &24             &   2/3      & 1.375\,26(5)      & 1.814\,9(5)  & ~~$-0.65(2)$    &   $-3.8(2)$    \\
                                  &32             &   1/2      & 1.375\,33(7)      & 1.814\,2(7)  & ~~$-0.59(5)$    &   $-4.4(4)$    \\
                                  &48             &   0/2      & 1.375\,30(9)      & 1.815(1)     & ~~$-0.63(9)$    &   $-4(1)$    \\
 \hline
 {\multirow{3}{*}{${S}^{s}$}}
                                  &16             &  5/4       & 1.375\,80(2)      & 1.383\,4(2)  & ~~$-3.432(5)$   &   $2.72(3)$    \\
                                  &24             &  4/4       & 1.375\,77(3)      & 1.383\,6(3)  & ~~$-3.45(2)$    &   $2.82(3)$    \\
                                  &32             &  4/2       & 1.375\,76(5)      & 1.383\,7(4)  & ~~$-3.45(3)$    &   $2.9(3)$    \\
 \hline
 \end{tabular}
 \label{Tab:fit-S}
 \end{table}

 \begin{table}
 \caption{Fits of $\rho$. The superscripts $b$ and $s$ denote bond and site percolation, respectively.}
 \scalebox{1.00}{
 \begin{tabular}[t]{|l|l|l|l|l|l|}
 \hline
                                  &$L_{\rm min}$  & $\chi^2$/DF & $\rho_{c}$      &  $b$       & $b_1$\\
 \hline
  {\multirow{4}{*}{${\rho}^{b}$}}   &16             & 3/5    & 0.272\,932\,83(1) & 0.679(3)   & ~~$0.1(6)$  \\
                                    &24             & 1/4    & 0.272\,932\,83(1) & 0.674(6)   & ~~$3(4)  $  \\
 \cline{2-6}
                                    &16             & 2/7    & 0.272\,932\,83(1) & 0.678\,9(6)& ~~~~-  \\
                                    &24             & 2/6    & 0.272\,932\,83(1) & 0.679(2)   & ~~~~-  \\

 \hline
  {\multirow{4}{*}{${\rho}^{s}$}}   &12             & 4/6    & 0.052\,438\,218(3)& 0.674\,5(5) & ~~$ 0.02(8)$ \\
                                    &16             & 4/5    & 0.052\,438\,218(3)& 0.674\,7(8) & $-0.02(21)$ \\
                                    &24             & 4/4    & 0.052\,438\,218(3)& 0.674(2)    & ~~$0.2(10)$ \\
\cline{2-6}
                                    &12             & 4/7    & 0.052\,438\,218(3)& 0.674\,6(2) & ~~~~-  \\
                                    &16             & 4/6    & 0.052\,438\,218(3)& 0.674\,6(3) & ~~~~-  \\
                                    &24             & 4/5    & 0.052\,438\,218(3)& 0.674\,6(5) & ~~~~-  \\
 \hline
 \end{tabular}
 }
 \label{Tab:fit-rhok}
 \end{table}

\begingroup
\squeezetable
\begin{table*}
  \begin{center}
  \caption{Summary of estimated thresholds, critical exponents, universal wrapping probabilities, and excess cluster number of bond
   and site percolation on the simple-cubic lattice.
 We note that the values of $y_t$ and $y_h$ in~\cite{DengBlote05} marked by the superscript $\ast$ contained typographical errors.
 The final error bars reported in~\cite{DengBlote05} were also underestimated, taking insufficient account of systematic errors.}
  \begin{tabular}[t]{llllllllllll}
    \hline
    \hline
    Ref.                         &     $p_c$(bond)        &   $p_c$(site)      &$y_t=1/\nu$&$y_h=d_f$ & $\dm$ &    $y_i$& $R^{(x)}$ &  $R^{(a)}$&$R^{(3)}$  &   $b$      \\
    \hline
    \cite{LorenzZiff98a}        &    $0.248\,812\,6(5)$  &                    &   1.12(2)    & 2.523(4)     &       &         &              &      &&      \\
    \cite{LorenzZiff98b}        &                        & $0.311\,608\,0(4)$ &              &              &       &         &              &      &&      \\
    \cite{BallesterosFernandezMayorSudupeParisi99}  &     & $0.311\,608\,1(13)$& $1.141(2)$   & $2.523\,0(3)$&       &$-1.61(13)$&            &      &&      \\
    \cite{MartinsPlascak03}      &    $ 0.249\,0(2)$      & $0.311\,5(3)$      & $1.15(2)$    &              &       &         &  0.265(6)    &0.471(8)   &0.084(4)  &  \\
    \cite{DengBlote05}           &                        & $0.311\,607\,7(4)$ &   1.145\,0(7)& 2.522\,6(1) &       &          &               &    &&\\
    \cite{ZhouYangZiffDeng12}   &    0.248\,812\,0(5)    &                    &   1.142(3)   & 2.523\,5(8)  &       &         &              &      &&       \\
    \cite{ZhouYangDengZiff12}   &                        &                    &              &              &1.375\,6(6)&       &              &      &&       \\
    \cite{KozlovLagues10}        &                        &                    &  1.142(8)    &              &       &$-1.0(2)$&              &      &&          \\
    This work                    &    0.248\,811\,82(10)  &0.311\,607\,7(2) &1.141\,0(15) & 2.522\,95(15)&1.375\,6(3)&$-1.2(2)$& 0.257\,80(6) &0.459\,98(8)&0.080\,44(8)& $0.675(2)$  \\
    \hline
  \end{tabular}
  \label{Tab:summary}
  \end{center}
\end{table*}
\endgroup
\subsection{Estimating $\dm$}
 We estimate the shortest-path fractal dimension $\dm$ by studying the quantity $S$ at our estimated thresholds.
 The MC data for $S$ are fitted to Eq.~\eqref{eq:A} with the exponent $y_\scrA$ replaced by $\dm$, and the results are reported in Table~\ref{Tab:fit-S}.
 We again use the superscripts $b$ and $s$ to distinguish bond and site percolation.
 In the fits, both $b_1$ and $b_2$ are clearly observable for $S^{b}$ and $S^{s}$.
 And when we set $b_2=0$, the ratio of $\chi^2$ per DF remains relatively large.
 We also did the fits by replacing the correction with $b_2$ by a constant term $c_0$ in Eq.~\eqref{eq:A},
 and obtained $\dm ({\rm bond}) = 1.375 \, 55 (6)$ and $\dm ({\rm site}) = 1.375 \, 59 (6)$.
 Comparing these fits, we estimate $\dm = 1.375\,6(3)$.

 To illustrate this estimate, Fig.~\ref{Fig:dmin} shows a log-log plot of $S$ versus $L$.
 \begin{figure}
 \dminFig
 \centering
 \caption{Log-log plot of $S$ versus $L$ for bond and site percolation. Two straight lines with slope 1.375\,6 are included for comparison.
 }
 \label{Fig:dmin}
 \end{figure}

 \subsection{Excess number of clusters}
 The cluster density tends to a finite limit $\rho_c=\lim_{L\to\infty}\lim_{p\to p_c}\rho$ at criticality.
 While the value of $\rho_{c}$ is non-universal, the {\em excess cluster number}
 $b := \lim_{L\to \infty}\lim_{p\to p_c} L^d(\rho - \rho_{c})$ is universal \cite{ZiffFinchAdamchik97}.
 To estimate $b$, we study $\rho$ with $p$ fixed to our estimated thresholds for bond and site percolation and fit the data to the ansatz
 \be
 \rho = \rho_{c} + L^{-3}(b + b_1L^{-2}) \; .
 \label{eq:rhok}
 \ee

 The resulting fits are summarized in Table~\ref{Tab:fit-rhok}, where we again use superscripts $b$ and $s$ to differentiate the bond and site cases.
 We report fits both with $b_1$ free and with $b_1=0$.
 We find that $\rho$ can be well fitted to~\eqref{eq:rhok} with $b_1=0$ fixed.
 Leaving $b_1$ free, we find that $b_1$ is consistent with zero, suggesting that the leading correction exponent might be even smaller than $-2$.
 We also performed fits in which the leading correction exponent was fixed to $-1.2$ and $-3$, and in both cases the resulting estimates of $\rho_c$ and $b$ were consistent
 with those reported in Table~\ref{Tab:fit-rhok}.
 Leaving the leading correction exponent free produces unstable fits however.
 Comparing these fits, we estimate $b = 0.675(2)$.

 Our estimate of $b$ is determined on th periodic $L\times L\times L$ simple cubic lattice; on the $L\times L$ square lattice $b=0.883\,5(8)$ \cite{ZiffFinchAdamchik97}.
 The excess cluster number was studied in \cite{LorenzZiff98a} on an $L\times L\times L'$ lattice with $L'\gg L$.
 Naively, extrapolating their results to $L'=L$ gives an estimate of $b\approx 0.412$ which is significantly below our estimate.
 We also note that our estimate of the number of clusters $\rho_c=0.272\,932\,83(1)$ differs slightly from the estimate $\rho_c=0.272\,931\,0(5)$ reported in \cite{LorenzZiff98a}.

\section{Discussion}
\label{discussion}
 We study in this paper standard bond and site percolation on the three-dimensional simple-cubic lattice with periodic boundary conditions.
 Using extensive Monte Carlo simulations and finite-size scaling analysis,
 we report the estimates: $p_c = 0.248\,811\,82(10)$ (bond) and $p_c = 0.311\,607\,7(2)$ (site).
 The bulk thermal and magnetic exponents are estimated to be $y_t = 1.141\,0(15)$ and $y_h = 2.522\,95(15)$,
 the shortest-path fractal dimension to be $\dm=1.375\,6(3)$, and the leading irrelevant exponent to be $y_i=-1.2(2)$.
 The universal value of the excess cluster number is estimated to be $b=0.675(2)$.

 We emphasize that the reported estimates of $p_c$ are obtained by studying wrapping probabilities,
 which are found to have weaker corrections to scaling than dimensionless ratios constructed from moments
 of magnetic quantities such as $\scrC_1$ and $\scrS_m$.
 In particular, we find evidence suggesting that the leading correction exponent in certain wrapping
 probabilities ($R^{(x)}$ and $R^{(a)}$ for bond percolation, $R^{(3)}$ for site percolation) may be $\approx -2$ rather than $-1.2$, although the reasons are not clear.
 The universal values of the wrapping probabilities
 we studied are estimated to be: $R^{(x)}_{c} = 0.257\,80(6)$, $R^{(a)}_{c} = 0.459\,98(8)$, and $R^{(3)}_{c} = 0.080\,44(8)$,
 by comparing the results for bond and site percolation.

 From these values we can estimate other wrapping probabilities discussed in the literature, such as
 \begin{equation}
 \begin{aligned}
 \! R^{(1)}  : &= \langle \scrR^{(x)}(1-\scrR^{(y)})(1-\scrR^{(z)}) \rangle \nonumber \\
               &= \frac{1}{3}(2R^{(a)} + R^{(3)} - 3R^{(x)})\;, \nonumber \\
 \! R^{(2)}  : &= \langle \scrR^{(x)}\scrR^{(y)}(1 - \scrR^{(z)})\rangle   \nonumber  \\
               &= \frac{1}{3}(3R^{(x)} - 2R^{(3)} - R^{(a)})\;, \nonumber \\
 \! R^{(x,y)} :&= \langle \scrR^{(x)}\scrR^{(y)} \rangle = \frac{1}{3}(3R^{(x)} +  R^{(3)} - R^{(a)}) \;.
 \end{aligned}
 \end{equation}
 In words, $R^{(1)}$ is the probability that a winding exists in one given direction but not in the other two directions;
 $R^{(2)}$ is the probability that a winding exists in two given directions but not in the third;
 and $R^{(x,y)}$ is the probability that a winding exists in two given directions, regardless of whether a winding exists in the third direction.
 We obtain $R^{(1)}_{c} = 0.075\,67(14)$, $R^{(2)}_{c} = 0.050\,85(14)$, and $R^{(x,y)}_{c} = 0.131\,29(12)$.

 Table~\ref{Tab:summary} summarizes the estimates presented in this work.
 For comparison, we also provide an (incomplete) summary of previous estimates.

\section{Acknowledgments}
 This research was supported in part by NSFC under GrantS No. 91024026 and No. 11275185, and the Chinese Academy of Science.
 It was also supported under the Australian Research Council's (ARC) Discovery Projects
 funding scheme (Project No. DP110101141), and T.G. acknowledges support from the Australian Research Council through a Future Fellowship
(Project No. FT100100494).
 Y.D. is grateful for the hospitality of Monash University at which this work was partly completed.
 The simulations were carried out on the NYU-ITS cluster, which is partly supported by NSF Grant No. PHY-0424082.

redpajama_set_name: RedPajamaArXiv


Grill, Bake, or Pan-fry trout until fork tender, remove and discard skin; break up trout into bite size pieces.
In lightly greased frying pan or flat iron, arrange 1/2 c of each cheese over one tortilla. Top cheese with 1 trout fillet pieces, approximately 3/4 cup. Sprinkle chopped onions and peppers if desired. Add second tortilla to top the first one. Pan fry over medium heat about 3 minutes, turn over and continue cooking until cheese is melted in the middle and desired crispness is achieved.
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text:
Q: How to send callback from a class to activity in android? I have a ScanActivity from which I instantiate a class(Preview) which clicks images and sets a callback on the Camera object. If the operation on callback is successful, I want to return back to ScanActivity. Ex:
class Preview extends SurfaceView implements SurfaceHolder.Callback{
    Camera mCamera;
    Preview(){} //constructor to initialize mCamera
    public void start(){
      mCamera.startPreview();
      mCamera.setPreviewCallback(new Camera.PreviewCallback() {
        public void onPreviewFrame(byte[] data, Camera camera) {
             /*
              if(scanSuccessful){
                  return to activity
                }
            */
        }
    }
}

class ScanActivity extends Activity{

    public void onCreate(){
        Preview p = new Preview();
        preview.start();
    }
}

From the callback I want to return back to the calling activity(ScanActivity) in case of a successful scan. I know broadcast receiver is one way of achieving it, is there any other way? And what are the pros and cons of using them?

A: Few ideas, not necessarily full list - You might want to:

*

*start activity for result

*delegate work to service, if it it's time consuming

*use listener interface - how to create own listener interface in android.

*use Android's dialogs
Regarding question about pros and cons it is very subjective, and AFAIK against SO rules :)
However, I think that point 4 as being quite new addition to Android's SDK  is a way to go. I'd also advice against point 3 - to my best knowledge this would run on UI thread, and in case of heavy duty work - might cause application to become unresponsive.

A: brainovergrow is correct. You need a setResult method which allow you to send a result to parent activity. I think you will be helpful a Parcelable mechanism also to send data between activities.
Code :
public interface PreviewCallback {
    void onPreviewFrame(byte[] data, Camera camera);
}

class Preview extends SurfaceView implements SurfaceHolder.Callback{
    Camera mCamera;
    PreviewCallback listener;
    Preview(){} //constructor to initialize mCamera

    public void setCallback(PreviewCallback callback) {
        listener = callback;
    }

    public void start(){
      mCamera.startPreview();
      mCamera.setPreviewCallback(new Camera.PreviewCallback() {
        public void onPreviewFrame(byte[] data, Camera camera) {
              listener.onPreviewFrame(data,camera);
        }
    }
}

class ScanActivity extends Activity implements PreviewCallback {

    public void onCreate(){
        Preview p = new Preview();
        preview.setCallback(this);
        preview.start();
    }

    @Override
    public void onPreviewFrame(byte[] data, Camera camera) {
        // do your work
    }
}

redpajama_set_name: RedPajamaStackExchange


Hinyana, Vajrayana, Mahayan, which is the greatest vehicle?
or is there something buddha has enunciated?
The answer is here today.
In the Path of Buddha, he educated people concisely the daily art of living practical teachings, which some considered the provisional teachings. But if you look at even today or in future, so called such provisional teachings are equally essential. Also, in his teachings, there is neither imaginative nor salvation by someone other than you, and who there's no one illusory up above in the sky waiting to do salvation. You are you own salvation, and act right now by following the noble path. If you walk noble, you will reach in noble place. So, what is noble path? Is it intellectually attractive?
In this forward moving modernization, independently thinking and contemplative people's mass movement around the world, is displaying the pattern of 'Seeking Mind' rather than 'Following Mind', no matter what they used to follow, or born with so called. Globally, all citizens are naturally abdicating 'what was so called their', and 'what they are born with', after all human intelligence is growing, check your younger generations how smart they are, and how antiquated we are. Everything is changing, the only constant is the Change. The change of human in future is towards in dependency towards choice, as more people are educated, and social dilation. No more will be given, everything is a choice. Those independent seeking mind are discovering the true essence of a religion (education), that produces effect here and now, and there's no someone in the sky, who will come to you when the earth needs. If you look at the history, it has always been dark in the deep heart and sorrows, which can be seen in their eyes. It is only the buddha's short, quick, simply presented truth words has been an attraction for everyone.
But, when you open the door towards the Buddhahood path, today's religious followers show you more doors (vehicle/path) towards different vehicle (Yana or Yaan or yan), and wherever you enter in their sect, and sub-sect, everyone deludes you and completely shut your third eye (wisdom) to your seeking mind. They make you sheep, so be human and see something beyond horizon you have. Precisely, there are Theravada (Hinyana-), Mahayana, Vajrayana (Mantra/Tantra-magical). So, i give you an antidote. If anyone think of the assembly, then, there are only the four assemblies: Monks, Nuns, Laymen, Laywomen. Monks, Nuns have always been the teacher of peace towards many laymen and laywomen in societies. So, what is the true vehicle as said by buddha?
Buddha has said that the noble 8 fold path is only the path, in his own words as "Ek yana Maggo", Meaning- Ek (only One), Yana (Vehicle), Maggo (Path). So, the only vehicle is the path and the path stated is the 8 NOBLE PATH (Astingka Magga). If you walk (practice) on this following path then, it is itself a vehicle (yana). By Practicing, you will experience or immerse truly, which is by definition means is the the vehicle to take you in the destination. Eg. If you practice the Right View, you will experience the positive effect of wisdom brought about by the right view path. If you practice the wrong view path, you will experience the negative effect. Choice is yours and so will sow the seeds and will bear fruit accordingly. There is no luck or chances or will grace you, only one can show you a path with full compassion. But You have to walk on your own. So, always walk on the Path (Yana) of 8 noble path, not the illusory vajrayana, mahayana, or theravada yana which were later created.
I'm just grateful for the Buddha and his words that i have been able to decipher intelligently, and Purely. Tathagatha, i take a refugee completely in an immersive way. I'm fortunate to hear your such profound, immeasurable, pure teachings, and let me put it in your pristine way to best of my understanding. Let me understand the wisdom of Buddha.
Hope everyone understands the true vehicle you stated in this time of diluted teachings.
Arjun, Thanks for the article on sound ….can't say I comprehended it all but makes one think, and that's a good thing……the article on Buddhism shows you are seeking.
Thanks Arjun…..interesting articles….I have found a book on writeing which I will let you know about soon. Let you know of it when I find the title again, which I've misplaced….. Paul o.


text:
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Lexus tries to woo German car buyers (VIDEO)
Entry posted by Nic_low · April 9, 2014
http://dai.ly/x1mqm3t
In its latest advertising effort, Lexus is asking buyers to 'wake up from the hypnotism' and seriously consider before making their purchases. What it is trying to hint at is that people, in general, think that German cars are better. Unsurprisingly, rivals such as Mercedes-Benz, Audi and BMW are roped into the commercial as the 'main culprits'.
http://dai.ly/x1mqm6f
While most of us would think that the pendulums are work of computer graphics, Lexus has also shown us how the interesting ad is made, with a 'behind-the-scenes' footage. Now jump to the video to catch the craftsmen at work.
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7 tips for the perfect 7-day Scottish road trip
I recently headed to Scotland for a seven-day road trip with my sister, and it would be an understatement to say that we didn't do much planning ahead of time. Hell, I only packed my luggage about six hours before my outbound flight from Singapore. We figured that we could make it up along the way (and we pretty much did). If you're thinking of making plans for a road trip holiday to Scotland, here are seven tips that I have gleaned from my time there. 1. Pick the right car Choosing the 'right' car can really make or break a roadtrip. In many ways, it's a balancing act - you could go for something affordable, compact and fuel efficient, but compromise on boot space, engine power and driving fun. Alternatively, you could opt for something more premium and high-performance, but that obviously comes at a greater financial cost, petrol and otherwise. I'd really recommend something that's comfortable and easy to drive (you'll be doing many hours of driving) and has good amounts of space. Having heated seats is also a big bonus during the winter time. The Skoda Superb that I elected on manages to strike a great balance. It's easy to drive, comfortable over long distances and has an enormous boot. At the same time, the 2.0-litre engine provides plenty of punch for the occasional frisky driving, while still being reasonably fuel efficient. 2. Pack warm, very warm It gets cold in Scotland in November. In places like Fort William and Inverness, temperatures can easily drop to sub-zero. So, you want to make sure that you wrap up and keep warm. Bring gloves, a beanie, and a comfortable and thick down jacket. The weather can be quite erratic too. A bright sunny day can be punctuated by the occasional 20-minute torrential rain, or you might get a persistent bout of light drizzle. Be sure to pack an umbrella and a waterproof outer layer, too. 3. Make sure you have data Especially if you're heading out into the countryside (where Scotland is most beautiful), you want to be sure that you have mobile data. Beyond just navigation, it also lets you search for nearby attractions, places to eat, and perhaps the nearest petrol station as well. This is especially important if you don't have a clearly planned out itinerary (like us). You don't want to be reliant on finding a wi-fi hotspot - there'll be long stretches of road/driving where wi-fi is simply not an option. Having data also ensures that you have ready access to Spotify, important during some of the longer stretches of driving as you move from one location to another.
4. Indulge in local delights Yes, most people probably know about haggis, a Scottish dish of sheep puck that's famously divisive (I personally really enjoy it). But, there's plenty of other local fare to be enjoyed, most notably the seafood. Additionally, be sure to also chat with the locals. The Scottish people are an extremely friendly, charming and humorous bunch. It might take a moment or two to acquaint to the heavy Scottish accent, but once you do you'll realise that the locals are a deeply sardonic and yet warm and welcoming bunch of people. 5. Start each day early In the wintertime, the sun sets around 4:30pm every day. And, when it gets dark, it gets really dark. To maximise your daylight hours, you should start each day early (the sky is bright by about 7:30am each morning). Take full advantage of the available daylight hours by going on morning hikes up to some picturesque locations, visit as many whisky distilleries as you can cram into one afternoon, or just soak in the crisp air and blissful serenity. 6. Drive safe The roads are largely quiet this time of the year, and Scottish drivers are generally very courteous, but you still want to be careful. You'll encounter many narrow single track roads, and potholes are fairly abundant. It would also be wise to stock up on snacks and water whenever you can. Snacks are useful for keeping yourself alert and awake, especially on long journey or when driving at night. Also, if you do decide to drive at night (or basically anytime after 5:00pm in November), you want to be extra careful. Between towns, the roads are long, narrow, windy and 100% unlit. 7. Pray for good weather There's not much you can do in terms of 'plannin'' the weather, but pray nonetheless (do a rain dance if you have to). Having good weather (ie. no rain) can make all the difference between a cheerful and a dreary, depressing day. It also means that the hundreds of photos you take will turn out much more vibrant and beautiful.
SGCM_editorial
The reason why people think Singaporeans are cheap
A recent Roads.sg video of two men using super unorthodox techniques to fill up a gas tank has caught the attention of many Singaporeans. At the time of writing this article, the post has since garnered over 100 comments (I'll share some at the end of this article) which are pretty one-sided. Here's the video if you haven't already seen it. If you have, watch it again. It' bloody hilarious! Video from Roads.sg YouTube Channel Confirm Singaporean According to the Roads.sg video description, this incident occurred across the causeway. Screenshot from Roads.sg YouTube Channel The owner of what we believe to be a 2016 Toyota Vios, is definitely a Singaporean. I can say this with assurance because I've pumped petrol in J.B. before and you only ever see Singaporeans do stupid shit like this while Malaysians laugh at us. The back of the car looks similar! Do you save money in the end? If you're not too familiar with what is going on, the logic is simple. Bubbles are formed when you pump petrol in your car and these bubbles take up precious space in your fuel tank that could go to more fuel. Therefore, if you shake your car, the bubbles will dissipate, allowing you to get more petrol and more savings. Alright, you might save a couple of bucks, but the repercussions don't make sense to me. Not worth it You will eventually wreck your rear suspension which is not cheap to repair. You look like an idiot You give Singaporeans a bad name You will kena caught on video like this guy and, everyone will know you for the wrong reasons What the people say The answer is nothing. You saved nothing. Pretty sure he is. Facebook comments taken from Roads.sg Facebook Page That's funny af.
jameskarlchan
An embarrassment to all Mercedes-Benz Drivers, worldwide
A video of an aggressive Mercedes-Benz driver (who does not react well to being honked at) has gone viral. The video was uploaded on SG Road Vigilante's YouTube channel and has since gained over 10,000 views in a single day! Here's the video courtesy of SG Road Vigilante Using the powers of inference The video of the dashcam footage has little to no audio, but it's pretty clear what's happening if you use your inference skills. Image by SG Road Vigilante YouTube Channel The Mercedes-Benz C-Class feels like his lane is too slow for his liking and wants to switch lane loyalties. If you notice carefully, he starts his lane change before signalling. Image by SG Road Vigilante YouTube Channel Dashcam driver CLEARLY speeds up when he realises what's happening and when he does not manage to prevent the C-Class from cutting into his lane, he jams on the brakes and the horn which is super uncalled for and childish. EGO, so much EGO! Does the C-Class driver let this go? Of course not! Men and their ego right? (the writer is a dude too, so it's not sexist) Image by SG Road Vigilante YouTube Channel Mr C-Class then pokes his head out all angry like with accompanying hand gestures. He then puts his car to a complete stop on a two-lane road to get out of his vehicle to show his displeasure and holding up traffic behind dashcam driver. Image by SG Road Vigilante YouTube Channel He approaches the dashcam driver and disappears from the screen completely for a couple of seconds before heading back into his C-Class. Image by SG Road Vigilante YouTube Channel Thought that was all? Nope. He comes back with his mobile phone this time to take a video of the dashcam driver. Towards the end of the video, you can see part of a video of dashcam driver's mobile phone. Bro, what are you doing? The kicker comes when C-Class decides to make some derogatory sign to the dashcam driver. Like, what's the point here? Here's what he did. You decide. Image by SG Road Vigilante YouTube Channel Want to know what this sign is? Here you go courtesy of Urban Dictionary. Does this mean the dashcam driver was a girl? If it was directed to a guy, that's just…. weird AF! Even so, this is so uncalled for and rude and vulgar. When you buy a Mercedes-Benz If you drive a Mercedes, you're telling people some basic information about yourself – You're used to a certain standard of living, you like to stand out and show that you've got class and that you're a confident and independent individual. Mr C-Class' actions show us that instead, he's a childish and petty person who would be better off walking from point A to point B. Dashcam driver, you're no better for antagonising Mr C-Class. Here's what the people have to say Image by SG Road Vigilante Facebook Page Pretty much split. Image by SG Road Vigilante Facebook Page So wise.
It doesn't matter whose lot it was - Don't forget about the most important thing!
What's the most important thing? Stick to the end to find out! It's a good one. Like legit. A video uploaded on SG Road Vigilante has sparked a furious debate online that split netizens evenly on two sides of the coin. The video shows a Mercedes-Benz "stealing" a carpark lot from the cam car driver. Or is it really "stealing"? That is the question. The video, which was uploaded last week has since received over 260,000 views and almost 1000 comments! If you haven't seen the video, here's the link courtesy of SG Road Vigilante's Facebook page Have you been in this situation before? In the video, cam car is patiently waiting for a carpark lot at IMM carpark for about 10 minutes. A White Mercedes-Benz CLS Class pulls up a couple of cars in front of the cam car. When the driver of the CLS (let's call him Mr CLS) spots a family walking towards the area he's waiting at; he decides to reverse to "claim more ground". Needless to say, once cam car driver notices what Mr CLS is doing, cam car inches forward to get the soon-to-be-vacant lot as he was waiting there for a long time already. Mr CLS gives him no chance to do so and reverses into the now-empty parking lot. This extra guy To make matters even worse, someone steps out of the CLS (supposedly the son of the driver) and walks over to cam car to give justification why the lot belongs to the occupants of the CLS instead of the cam car . He even makes a couple of rude gestures to the cam car once that's done. So whose lot is it? Some people say that the CLS had the right of way Calculative sia this guy and very specific too Some people jumped to the defence of the patient cam car driver The most important thing It doesn't matter who the lot belonged to but we can all agree that on the most important thing....... This guy, is a complete a**hole Thanks Johnson! Couldn't have said so better myself.
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text:
Evaluate Hispanic Families through the incorporation of evidence-based practices.
4 Professional Use of Self and Ethical Practice – Instructions
The purposes of this assignment are to:
Identify the practical aspects of making a successful treatment referral (SBIRT).
Considering the scenario below and respond to the questions.
Use ideas from your readings (including the course and SBIRT readings and materials) to explain and support response.
A Mexican American politician and his wife have five children. He is running for the U.S. Senate within his state and the campaign is going well. The politician has told the therapist confidentially that he has been having an affair, but that he is calling it off. The Mexican family has come to therapy because of repeated substance abuse by their 18 year old son. The boy has told the therapist on the side that he knows that his father has had an affair, that he is angry at his father for ignoring him, and that he plans to tell everything he knows to the papers.
Who is the client in the different parts of the situation?
What are the ethical/legal issues that might be involved in this case? How should they be addressed? What would the consequences of addressing them be?
What could a family practitioner do to address the possibility of these ethical dilemmas through informed consent at the beginning of therapy?
What barriers exist to substance use treatment within the Hispanic population? What are some strategies to overcome them?


Should you be using a sportsbook broker?
For years sports betting brokers have been a necessary tool for professional bettors but recently we have seen an increasing number of amateur bettors finding value at these brokerages. Sports betting brokers or agents act as a conduit between gamblers and previously unavailable sports betting markets.
Some sportsbooks do not accept new accounts from players living in countries with unreasonable gambling restrictions. Brokers licensed in nations that regulate sports betting can offer agent accounts to gamblers from these sportsbooks.