hey everybody ivan from weights and biases here in this video i'd like to show you how you can train yellow version five on google co-op using a small data set and then track and visualize your experiments using weights and biases dashboard so since we're gonna be using google call up for training that means that you can follow along by just clicking the link in the description and opening the call of notebook first let's clone and set up your lv5 repo by clicking on the first cell this will get cloned the yellow v5 repository from github into colab as well as install all the required modules along the way once the setup is complete you should be able to see all the yellow folders and files in the cola file view now to make sure that we actually have you know the working and functioning neural network here let's run the inference on one of the example images from the repo looking good so far now the example data set that we're gonna be using is coco 128 which is essentially a really small data set with just 128 first images from the original coco data set this will allow us to see how the training visualizations work without having to wait for too long you know for the model to train let's click this cell to download the coco 128 data set we can now see it appear in the column files view um let's check out a few images from there now let's move on to the exciting part which is training uh versions weights and biases are a part of yellow version five all we have to do is to just pip install wnb by clicking the cell to be able to then track our experiments so the next cell will start the training here we can specify the input resolution of our images number of epochs the batch size as well as the data set that we're using and last but not least we also specify the pre-trained yellow version 5 weights to take advantage of transfer learning after seeing the logs that we have the successfully loaded model we can sign into our weights and biases accounts to visualize the results if you don't have an account you can take a few moments to create one and since i you know already have an account i'll just you know authenticate into the weights and biases dashboard now the training has started if it's your first yellow version 5 run weights and biases will automatically create a private project in your account where you can find your dashboard click on the view project link to open it and see the data about how your model is doing that's being automatically pulled in real time when you open the project right at the top you'll see one of my favorite features around here which is the bounding box debugger let's enlarge the window and take a closer look at it the bounding box debugger plots random images from the valve data set with the bounding boxes and the confidence scores giving you the intuition for what's going on in the model during training beyond just you know the raw numbers and the training logs you can see how the model performed in the images at each step of the training choose the confidence threshold of the planet bounding boxes or to display or not display certain classes from your data set if you scroll a bit lower you'll see a lot of useful metrics including the average precision gpu usage training and validation losses those are being updated in real time as your model trains now let's do something interesting we're going to train a model for a second time but with different hyper parameters just for example i'll change the input resolution from 640 to 416 batch size from 30 to 64 and epochs from 10 to 35 and let's start our training now taking a look at the dashboard again you'll see in your run appear in the runs panel on the left and now look at this we can actually compare the performance of different runs with different configurations side to side with several runs selected the bounding box debugger now let's see how each of the models does on each of the images selected there we can specify which images we wanted to look at at a time as well as at what training step and what what are going to be the confidence thresholds and the classes that we wanted to display this is a great way to get some intuitive understanding for how different configurations of your models actually work the training charts now let us see the numbers for each model nicely plotted next to each other and the gpu usage metrics can give you some invaluable insights as to how each of the models utilizes the gpu so you can pick the right hardware for the training but there is more if we click on the run and then click on the magical overview button for the run we'll find all the hyper parameters used for training our model this applies to all of the runs that will have and this way you can keep track of the different configurations of different models while doing your experiments and seeing how they perform you can also see the charts for each of the runs all the different system metrics the logs during training and like like yeah like it also got the logs you know as well as a few other useful files stored there so using weights and biases to track your yellow version 5 runs and train models gives you some really awesome insights and intuitions for how the training is going while letting you effectively keep track of all of your experiments you can try it out yourself on google co-op you can find a link to the notebook in the description uh feel free to train some models and track some awesome experiments we hope you enjoy it

Hey everybody, Ivan Weights & Biases here. In I'd Google Colab Weights & Biases dashboard. So, training, description, cell. This git clone GitHub Colab, modules. Once, YOLO Colab view. Now, here, repo. Looking far! Now, train. Let's colab there. Now, part: training. First, since Weights & Biases wandb this experiments. So, training. Here, images, epochs, size, least, learning. After model, Weights & Biases results. If account, I'll Weights Biases Now, started. If Weights & Biases automatically. dashboard. Click "view project" project, debugger. Let's it. The scores, logs. You on training, plotted boxes, lower, metrics, Average Precision, GPU usage, losses. Those trains. Now, interesting: example, I'll 416, 64, a new left. And this: selected, there. We want time, step, thresholds, want display. This work. The other. And GPU GPU, a run, model. This perform! You metrics, experiments. You the - experiments. We it!


in this video we're going to introduce two new things so one of the technique a very awesome general technique called data augmentation and we're also going to introduce a new data set called CFR which is kind of like the next data set that everyone uses after M NIST it's slightly bigger images and it's color images and it's images of kind of everyday objects but these images are still small and it's still a relatively easy classification task in fact these days the best classifiers get up to 99 percent accuracy which is really amazing it's definitely better accuracy than I could get personally data augmentation generally is the practice of taking your data and modifying it so this has a lot of different advantages one advantage is that it helps the model generalize the data better right so in cases where you don't have enough training data you can sort of get more training data by taking the things you haven't changing a little bit right so you know one things you do might be like rotate your images like maybe like five degrees randomly and you know you know that like a frog rotated five degrees is still a frog or a frog you know flipped across like a horizontal axis I mean a vertical axis is probably still a frog right so we do all these transformations before we send the data through to get classified we probably wouldn't do this on our testing data but on the training data it's totally for a game and the particular transformations you do are really based upon your knowledge of your data set like how much can you really distort an image before it becomes potentially something different it's something to to think about an applied domain knowledge to also different types of images might have different appropriate transformations right so you know a face turned upside down might be really different it might be really you know maybe not a kind of face you want to classify but a satellite image rotate 180 degrees it's probably still a satellite image of the same thing that you might really see so aerial photography you can do kind of more rotations and more vertical flipping whereas images from an upright camera you probably wouldn't want to do too much rotation around the vertical axis because in the real 3d world you know the vertical axis is something that we care about so as usual we start off with a whole bunch of imports especially the Charis library that we use extensively here and if you have any questions about what I'm doing up until the data generation I really recommend going back and reviewing previous videos on convolutional neural networks and maybe even the video on perceptrons and multi-layer perceptrons so first we import carrots and some other useful tools so then we set some configuration parameters and we download the Seafire library there's get two versions of see far there's see far ten which has ten classes and see for a hundred which is a little harder because it has a hundred classes in this case I've actually spelled out the class names for you it's airplane automobile bird cat deer dog frog horse ship and truck very similar to M NIST we set those variables in X train why train X test and Y test so you know X train is the actual pixels of each image that we're dealing with in our training set and y train is the labels and so on as usual I think it's smart to take a look at a few of the images and get a feel for the data that we're working with before we start to try to classify it so here I've plotted X train 1 which is probably the second image in our X train set and you can see that it's probably a truck but even it's kind of hard to tell it's at such low resolution that this task is fairly hard even for a human we can look at say X 3 and 10 and see that I think that's a deer but it's it's quite dim some of these images actually are our tricky next we one-hot encode our output labels and we normalize our images so instead of they come by default with their RGB values between 0 and 255 and we want to change that to be between 0 & 1 so we do that and then we set up a kind of standard small convolutional network in Karis so it's a sequential model and we use a 2d convolution with a 3x3 convolution and we use the padding to kind of keep the size the same so we Pat out a little bit to make sure the convolution doesn't change the dimension and then we use the relu activation function we do the standard max pooling flatten things out and then have to dense layers and we've inserted two dropout leaders to help with overfitting and this is exactly networks that we looked at in the earlier convolution section on they we did an EM NIST the only difference here actually is that our input data is three dimensional instead of two dimensional because our input images are actually colored images so color here is the third dimension whereas M missed their black and white images so it's a two dimensional input data next we compile the model with the usual categorical cross entropy because we're doing a multi-class classification and the atom optimizer and then before we start getting crazy with the data augmentation let's just fit this model on the basic data and we can run this and you'll see that it gets reasonably good accuracy you can see think it's reasonably good accuracy the validation accuracy here is actually higher generally than the accuracy because we have dropout but it looks like the validation accuracy tops out at around 60 to 63 percent which is reasonably respectable but we only have 50,000 training examples so it does seem like this could benefit potentially from augmenting the data so let's do that and caris actually makes it quite easy to do data augmentation and we do this by setting up an image data generator now there's two reasons to use data generators in general so what this does is instead of setting a variable with all the data it has a function that will return batches of training data examples so one reason to use it and the reason we're using it here is because it lets us do modifications to the data as we pass it through so this data generator is actually going to take the images and it's going to stretch them and squeeze them and rotate them and that all these things it can do because it's doing a generator each time it'll be a little bit different the other thing that a generator can do is actually just prevent you from having to put all your images in memory and I'll tell you when you work on real-world data sets that becomes a big issue so in most production systems you probably be using a data generator versus just passing in gigantic variables into your fit function generators good to learn anyway but great for this example and here I've set with shift range 2.1 which means that it's going to shift the width of the image by up to 10% so it's gonna expand and squeeze the image on by up to 10% but it's cool to actually look at the carrots acquisition and see if there's any other things that we might want to try like for example we might want to do a little bit of rotation on the image or another thing that we could do is horizontal flip does that mean that half the time we actually classify the mirror image i'm the image of the image so why don't we why don't we also add that and now we call data and dot fit on X train and actually what this does is it attaches the data generator to the X train variable so at each step it's going to pull a batch size worth of images from X train but it's going to modify those images with the functions that we added so you know once you set up the data gen then we call fit generator this is a lot like model that fit but instead of passing in complete arrays you actually pass in a function that returns these arrays in batches right so our data gen here is passing through X train and y train in chunks of size batch size so we actually need to set batch size equals config that batch size in here and then I like to set the the steps per epoch because otherwise you won't actually really have epochs right because you know an epoch is typically when you've stepped through all the data but when you have a generator you're essentially generating infinite amounts of data right so there aren't really epochs but it's nice to have sort of natural breakpoints where you check the validation data and so I set steps for epoch typically equal to B the size of the data and that's extra InTouch shape 0 divided by config dot batch size so this means that we're basically and have the same number of steps for epoch as we would have had if we had just called model dot fit and so we can set epochs and validation data and callbacks in exactly the same way that we would have otherwise and so it's interesting to think before this runs what this is going to do to accuracy and validation accuracy tip Clee what we'll see is it'll make the accuracy lower because we've actually made the classification tasks harder right we've sort of added some random noise into that task but hopefully in order to complete that task it's gonna actually make the model generalize better and maybe if we're lucky it'll make the validation accuracy higher than it would have otherwise if you compare the the validation accuracy of our CNN with augmentation it's actually better than our vanilla CNN and it's steadily improving over time so we can add all kinds of things and this is a rich vein of things to try to make your model better but we can go back in and add all kinds of transformations to keep improving the generalization of our model in this data set so you're just backing up in general this kind of augmentation is different in every domain in text one thing people do is will actually translate their text into a different language using Google Translate and then translate it back into the original language which kind of adds like a little bit of noise but often keeps the same semantics and an audio people add you know kind of background sound effects or distort the audio just a little bit to you know kind of get variations on the audio so this is a really general technique it works really effectively and I hope you enjoyed this video

is a CIFAR personally. rotated Keras Keras CIFAR Karas Pad two layers with MNIST they're adam Keras width Keras documentation dot x_train dot in


all right so you know you've got your model training and you want to make it a little bit better so this video is kind of like a sequel to the video on convolutional neural networks and I want to talk about learning rate and batch size and a couple ways that you can often make your models better now unlike a lot of other courses on deep learning I don't really start with learning rate and batch size and things like that because I do think beginners tend to spend way too much time kind of optimizing and tweaking these parameters where they don't really matter that much but they can matter and they can be really effective for making your models train faster or kind of getting the last piece of performance on your model so just as a quick refresher the learning rate is kind of like the size of the step that the model makes as its looking for the best possible weights so you know really low learning rate will mean that your model might take a really long time to find the best set of parameters and a really high learning rate might mean it's kind of jumping over the best possible place or even sort of jumping into regions where you get numerical instability batch size you might remember is the number of examples that a model looks at when it decides which direction to send all the weights so you know a really small batch size means that each step your models optimizing only say over one example or two examples it could add a lot of noise to your model now a really big batch size sometimes it doesn't have enough noise and it's hard for the model to actually find the best place so on one hand smaller batch sizes actually can ironically help your model train better by adding a little bit of noise into that search but maybe a bigger effect or another important effect is that a bigger batch size can help your model train faster especially on GPUs because on a GPU you can actually compute the derivative on all the batches all at once so sometimes as long as you can fit this batch into memory and an associated model into memory a bigger bigger batch size it might not actually slow down your training by much at all in fact that computation might be sort of atomic and the same speed regardless of your batch size now if you say your batch size is too big it just won't fit into memory your thing will crash so at that point you'll know that you need to reduce your batch size but one thing I see with a lot of people starting out is I think they tend to set their bachelor set is too small and waste more time in their training than they really need to so let's jump into the code and run some experiments so I'm going to do these experiments on the CFR data set that we've used in a previous class on data augmentation that you should probably take a look at if you haven't already so we're going to do the standard imports and then we're going to pull in the CFR ten data set you know these things tend to be different on different data sets but I wanted to use this data set that's small enough that we can run a lot of experience quickly but maybe not M NIST where that's actually so small that you might get unusual results so as usual I normalize the data and and convert the labels into one hot encoded versions of themselves and I'm going to start with a very very small i'm convolutional net were probably small as possible convolutional net ruth for just one convolution and one pooling layer so certainly you could make this network bigger and get better performance but I kind of want to see what happens as we modify the learning rate and the batch size first so the first thing I want to show you is what happens if you set the learning rate to something very small so you know by default Adam sets the learning rate to point zero zero one right so let's see what happens if we set it to point zero zero zero one okay so here the blue line is the accuracy of the CNN with the lower learning rate and the orange line is the accuracy of CNN with the default learning rate and so you can see lower learning rate nothing really bad happens but it learns slower and that can be really annoying when you're training lots of models right so when I see this this blue line it's it's consistently below the orange line although I think that'll probably catch up over time okay so suppose you look at this and you say you know what learning rate of point zero zero zero one that doesn't work as well as the default point zero zero one let's you know let's raise it up by a few factors let's make it say point one this would be considered a pretty high learning rate but you know who knows maybe the model will learn faster so here we call model def it and look at that we see an accuracy of about 10% and it's not even getting better another real telltale sign is there are losses 14 now remember our losses kind of in log space really it's like log loss so if you see a loss that's you know above 4 or 5 you should think of that as a massively huge loss right I think like what's actually happening here is that the models returning like Nan's and infinities and and just crazy things right and so this model is never gonna get better it just sits at a loss of 14 and accuracy of 10 percent and you remember we have 10 classes so an accuracy of 10 percent is really the models just guessing right so this is what happens when you said learning rate too high you get in a really really bad place so lower learning rates tend to be safer than higher learning rates what about back size so you know our default batchat is 32 what happens if we set it to say 128 right so there's a 4x increase in batch size if we call model that fit the first thing you'll notice is that it takes a little bit longer to start because it's loading more into memory but then actually it runs a lot faster and so that's actually pretty nice but you know if it runs faster and we sacrifice accuracy that's not very interesting and remember it's running faster because each step that it's taking is over bigger chunk so it's looking at the same number of training examples but it's doing it in larger chunks you all right cool so so here I mean this is a reason that people often use don't use larger batch sizes the CNN with the 128 batch size is the each step is faster but it's actually performing worse than the CNN with the smaller batch size but there's actually a subtle point there that it's important to know which is that as you increase the batch size you should also include increase the learning rate so if you multiply the batch size by four you should also generally speaking and multiply the learning rate by four so with bigger batches because you're averaging over larger number samples you can get away with higher learning rates than you otherwise could so let's try this 128 batch size again but with the 4x learning rate versus the baseline thirty-two batch size you okay so actually what you see here is that when we Forex the learning rate with the larger batch size now we get the best of both worlds so we have a CNN that's training faster and it's training at about the same level of accuracy and validation accuracy as our baseline CNN so one more fancy thing you can do and especially this is really effective when you train over a large number of epochs is you can reduce the learning rate on plateau so the idea here is that once your model kind of gets stuck and can't find a better optimum it lowers the learning rate so it can kind of fine-tune in the area that it's in right so there's actually this is such a common technique that there's actually a Carus callback that we can just use it will do it for us it has lots of options but you know the defaults as usual are pretty sensible so we just add to our callbacks a callback reduce LR on plateau and now in order to get this effect we actually have to have our learning actually plateaus so let's set the epochs to say 300 and time-lapses a little bit to not make a really long video okay and so this model has been training for a while and you can see what happened is at about the 35th epic it stopped improving and so our reduce learning rate system automatically reduced the learning rate and you can see that the accuracy and the validation accuracy actually popped up quickly and then petered out and with a lot of models you actually see that effect happen a few times each probably like a little bit smaller than the time before but it can really add some accuracy on the end of your model training so you know again I don't think learning rate is always the first thing to mess with unless your model is really not training you're having numerical issues generally the Charis defaults are pretty good and you know most people go through a phase I think where they spend too much time kind of changing the optimizer and you know changing you know momentum and things like that but it definitely does make sense to understand what learning rate and batch size are and spend a little time tweaking them and I think doing this kind of reduced learning rate on plateau system is generally best practice for long running long training models Thanks

CIFAR CIFAR 10 first. batch size? dot 4x Keras epoch Keras


Because Baltimore 's electorate is overwhelmingly Democratic , whoever won the Democratic primary election was seen as the favorite over the Republican nominee in the general election . Baltimore 's large African American population initially made it seem likely that the next mayor would also be African American . Kweisi Mfume was the preferred candidate of local politicians , but he opted not to run . Though Carl Stokes and Lawrence Bell , members of the City Council , declared for the race , local leaders were underwhelmed with the quality of declared candidates .


The rear portion had to be sunk by Restigouche .


[ Abramowitz ] gave a defense ( in my opinion a weak one ) ( ... ) I have a high tolerance for insensitive guy stuff , but this got to me .


First the base , or invert , was poured .


It includes the Grangetown Viaduct .


They had reached the rendezvous and waited until 10 : 00 for the destroyers to appear .


This is best witnessed by the large , magnificent mastaba tombs that Djedkare 's viziers built .


Brain
Age makes Edu-tainment fun...for the first time ever.

by Cole Smith

April
13, 2006 - For
those of you that believe that you may have suffered
irreversible brain damage from playing mindless shooters
and watching too many episodes of Nick and Jessica,
help is on the way.

There's no doubt that you've suffered
brain damage, but thanks to Brain Age, it is irreversible.

The
slogan for Brain Age is: Train Your Brain in Minutes
a Day.

I know it sounds like work, but this is way
more fun than flossing.

It's like playing a bunch
of puzzle-based mini-games.

As long as you still have
the capacity to learn, you will get better at these
challenges each time that you play them and thus you
will train your brain.

You
begin with a test to determine your brain age.

You
will receive a score that you can think of as a relative
IQ level.

The older your brain age, the worse off
you are.

You don't want to have the mind of the average
70-year old, but it can be a real shocker when you
find that out.

However, your brain age will become
younger over the days and weeks as you play this game.

Now
here's the bad news, there is math involved.

Yes,
you will have to perform some calculations.

None of
them are exceptionally hard and it's nothing that
you haven't done in school.

It's just that you might
not remember your multiplication table if you've been
out of school for a while and do everything with a
computer or calculator.

I didn't do very good on the
math at first but after a few days it comes back to
you.

You really do have all the resources to do well
in this game, it's just a matter or retrieving those
old files that might be buried in a sludge of gray
matter.

Now here's the good news, you are limited
to playing this game for only five minutes a day.

The
gameplay is based on the theories of Japanese neuroscientist,
Dr. Ryuta Kawashima who believes that these exercises
will stimulate somewhat dormant areas of the brain.

Nourishing oxygen is then introduced to these regions
which are stimulated by these challenges, allowing
them to become more functional in your day-to-day
activity.

As these newly stimulated regions become
linked, your brain will become a much more powerful
tool.

The
DS is held sideways so that it opens like a book.

You will make use of all the controls as well as the
touch screen and the stylus which is used for drawing
and writing.

When writing the correct answers or numbers,
you might find that the CPU doesn't recognize some
of your handwriting.

This could be due to poor spelling
and poor handwriting.

In any case if you continue
to play the game in the nature that it was intended,
your spelling and handwriting will show signs of improvements.

I found the other controls functioned perfectly.

You'll be shown
several simple equations that you have to solve.

There
is a speed element to it so you'll eventually train
yourself to be faster and more accurate.

You'll notice
that you improve a lot quicker if you have an audience,
or better yet, some competition.

Up to 16 players
can take part in the various tests.

Brain Age also
comes with a version of the popular puzzle game Sudoku.

It's a popular number-based game similar to a crossword
puzzle.

You have a series of grids, nine rows by nine
columns.

Each puzzle begins with some numbers appearing
in various grids.

You have to fill in the empty grids
with numbers from one to nine without repeating any
in either the corresponding row or column.

There is
only one number that will fit into each of the 81
grids.

In yet another
math game you will have to keep track of the number
of people at a party, as they come and go.

There's
more to Brain Age than just math games.

In another
test you will have to memorize as many four-letter
words as you can in two minutes.

You will also have
to memorize sequences of numbers.

There are dots to
connect, syllables to count, differences to detect
and even a segment which will have you read out loud.

Apparently reading out loud is very brain stimulating.

Here, try reading this out loud: "Why am I so
#~*&+%$ dumb?"

What word did you use in place of all those symbols?

Go ahead, scream
it

At
first some of these challenges might seem almost impossible.

Numbers race past, sequences and questions are popping
up all over the place, but it's something you'll get
used to in a few days.

Kawashima is the game's genial,
genius host.

He guides you through the series of tests
and helps explain why it's important to stimulate
your Prefrontal Cortex.

Maybe if I play the game long
enough I'll know what that is.

Like
the writing recognition software, the voice recognition
system is pretty darn good.

It's not perfect and it
can be particularly frustrating when you do have the
right answer, or read the text perfectly, and the
system fails to register it.

You will usually find
that you have to speak slowly and enunciate more clearly.

It's not a big deal to get used to in the long run.

The
multi-player mode is fun for competition and while
it can be inspiring, it's not all that interesting
and you'll tire of it quite quickly.

The replay value
is in the single-player mode.

The game will keep track
of your stats and you'll be able to see how much you've
improved.

I've only been at it for a few days and
I'm already seeing some improvement.

I did twice as
good as last time - I got two answers right instead
of one

Features:

Brain
Age presents players with a series of mental brain-training
challenges that incorporate word memorization, counting
and reading.

Brain
Age: Train Your Brain in Minutes a Day for Nintendo
DS is a fun, rewarding form of entertainment everyone
can enjoy, as it helps players flex their mental
muscles.

Brain
Age is inspired by the research of Professor Ryuta
Kawashima, a prominent Japanese neuroscientist.

His studies evaluated the impact of performing certain
reading and mathematic exercises to help stimulate
the brain.

Features
voice recognition and handwriting recognition.

Come
with the popular newspaper puzzle game, Sudoku.

By
Cole SmithCCC
Senior Writer

Rating
out of 5

Brain
Age: Train Your Brain In Minutes A Day
(DS)

2.5

GraphicsNothing
special in the graphics department, not
that it needs it.

4.0

ControlEither
the voice and handwriting recognition system
have the occasional glitch or I can't talk
and write properly.

3.3

Music
/ Sound FX / Voice ActingNot
much of a show but the entire game is presented
in a professional manner that is not clinical.

4.5

Play
ValueOnce
you get hooked, it's a one-way ticket to
Nerdsville for you smarty.

4.1

Overall
Rating -
Great
Not an average.

See Rating legend above
for a final score breakdown.

March
2, 2006 - Video
games have always been given a bad rap.

If you're
not shooting at someone, you're running over someone.

If you're not running over someone, you're stabbing
someone...and so on and so forth.

Video games are usually violent and they don't learn
you much, as is evident by my lack of proper grammar
in this sentence.

If
you've been noticing lately that ye olde cerebral
cortex has been diminishing in all aspects of functionality,
fear not, my fellow braindead vegetables!

Nintendo
is using their DS technology and the research inspired
by Professor Ryuta Kawashima to make you smart again,
but be forewarned - If you're stupid now, then use
the Brain Age to get super smart, then stop playing
it, you'll become dumb again - just like the book
Flowers For Algernon (Charly) and let me tell you,
that was one sad book.

But you should read it anyway
because reading is good for you and video games are
bad for you.

Well, all video games with the exception
of Brain Age and Panty Raider.

Oh wait....I've just
been handed something....it appears I was mistaken
and I owe you an apology.

I'll
give Nintendo credit - after years of reducing our
brains to mush with fantasies of pointy eared elf-like
heroes who seem to be as common as Elvis impersonators
in their universe, plumbers who save the world without
exposing their buttcracks when they bend over and
giant tie-wearing apes that throw barrels instead
of their own feces, it's about time Nintendo gave
us something back.

Brain
Age: Train Your Brain In Minutes A Day!

is scheduled
for an April release and if history has shown us anything,
none of you idiots will buy it.

Because
you're idiots and aren't interested in getting smarter.

Please note that the awesome
mathematical puzzler Sudoku is also included.

Brain
Age presents quick mental activities that help keep
your DS brain in shape.

Activities include quickly
solving simple math problems, counting people going
in and out of a house simultaneously, drawing pictures
on the Touch Screen, reading classic literature out
loud, and more.

You can also play sudoku, the number
puzzle game which has become an extremely popular
feature in U.S. newspapers.

On
your first day of exercise, you will take a series
of tests and get a score that determines how old your
brain is.

This number is called your "DS Brain
Age".

By performing daily exercises just minutes
a day over weeks and months, the better you'll get
at the exercises and the lower your DS Brain Age will
become.

We
all know as we grow older our bodies change and it
becomes important to regularly exercise to maintain
health and fitness.

New research
indicates mental acuity may be strengthened, like
muscles, with brain exercises.

The
Brain Age exercises are designed to stimulate your
brain.

Solving simple math and logic problems quickly,
and reading aloud, have been proven to be effective
methods of achieving this goal.

The
distinctive Nintendo DS Touch Screen lets users write
their answers with a Stylus pen, just as though they
were writing on paper or using a Personal Digital
Assistant or "PDA".

Furthermore, the Nintendo
DS's voice input identifies particular words you'll
speak during the Stroop Test.

Brain
Age tracks your progression through each exercise
with easy-to-read line charts.

Consistently using
Brain Age each day will open new exercises to test
your ability.

Baby
Boomers and test-prepping school kids alike want to
challenge themselves and find ways to stay sharp.

Brain Age acts like a treadmill for the mind.

With
the simplicity of the Nintendo DS, and Brain Age's
challenging and rewarding exercises, everyone can
stimulate their DS brain, improving speed and accuracy
of their game play.


Q:

Where did people watch trailers before internet was invented?

Now, with the Internet, we can simply watch trailers of a film on YouTube or other sites.

But how did people watch trailers before all this was created?

Were trailers shown in the cinemas only?

A:

Where did people watch movie trailers?

Before the internet, there were movie theaters, broadcast TV, and cable TV.

Before cable TV there were movie theaters and broadcast TV.

Before broadcast TV there were movie theaters.

Before movie theaters there was no place to watch trailers that were not made to advertise movies that were not made.


just seeing if you were back from your trip.

 maybe we can go for lunch
sometime this week.


/*============================================================================

The Medical Imaging Interaction Toolkit (MITK)

Copyright (c) German Cancer Research Center (DKFZ)
All rights reserved.

Use of this source code is governed by a 3-clause BSD license that can be
found in the LICENSE file.

============================================================================*/

#include "berryIWorkbenchPartReference.h"

namespace berry {

IWorkbenchPartReference::~IWorkbenchPartReference()
{
}

}


The content of the Library of Parliament has moved.

For more information, contact the Library or return to the Parliament of Canada homepage.


Wells comes down the track in an attempt to loft it away.

He is deceived by the lack of pace and is well into the stroke by the time the ball comes to him.

There is only one place that the ball can go after he missed it- middle stump.

It hits the top of middle and Wells is disappointed 37/15.6 the slower ball does the trick.

It hits the top of middle and Wells is disappointed 37/1

17.4 the change of pace does the trick.

Birt is looking to paddle the ball away and makes his intentions clear as Starc is running in.

He sees the movement and pitches it short.

Birt looks to adjust and ends up gloving it to the keeper.

Good, entertaining innings by Birt 144/2

17.4 to Birt, the change of pace does the trick.

Good, entertaining innings by Birt 144/219.3 to Jaques, he is gone now as he plays a tired drive straight into the hands of Henriques at long-off.

A very good innings by Jaques 162/3

5.6 to Wells, the slower ball does the trick.

It hits the top of middle and Wells is disappointed 37/15.6 to Wells, the slower ball does the trick.

It hits the top of middle and Wells is disappointed 37/1

1.6 and that's the celbration for the Test recall.

He bowls one on the off stump and it straightens a touch.

Nevill doesn't help himself by coming down the track.

He misses the ball and it hits the top of off 7/1

2.1 to Lumb, that's two in two.

Lumb makes room and looks to drive the ball.

He misses and the ball hits the middle stumps.

Sixers in serious trouble now 7/211.5 to Maddinson, full-toss on the middle stump.

He is struck on the pads and has to go 73/4

1.6 to Nevill, and that's the celbration for the Test recall.

He misses the ball and it hits the top of off 7/1

10.3 to Smith, does anybody remember the catch by McGrath off Vaughan?

Laughlin has pulled off a similar catch.

Smith goes for the slog sweep and Laughlin puts in a forward dive to pull off a stunner 64/3

14.5 to Thornely, fuller ball on the off stump.

Thornely looks to slog it and gets an inside edge.

The ball crashes onto the off stump and Sixers slip further 93/616.4 to Henriques, short ball outside the off stump.

HEnriques is early into the pull shot as he gets a top edge that is taken easily by Jaques at short fine-leg 110/716.5 to Lee, fuller ball on the middle stump.

He is bowled 110/818.2 to Hazlewood, on the off stump and a slower ball.

Hazlewood goes for the drive early and is beaten.

The ball hits the outside of the off stump 114/9


In many hydrocarbon well applications, well logging is used to collect data on formations which may contain reservoirs of hydrocarbon fluids.

Various types of well logging instruments utilize a pulsed neutron generator having one or more sealed neutron generator tubes.

A traditional method involves storing and introducing tritium gas to the neutron generator tubes via use of a uranium reservoir.

A sealed stainless steel cylinder is employed to contain a uranium hydride storage bed.

Heat is applied externally to an outer wall of the uranium-based tritium reservoir until sufficiently high temperatures cause tritium to be released from the uranium.

At sufficiently low temperatures, the tritium re-adsorbs to the uranium.

However, uranium can be difficult to use and it has certain inherent disadvantages because of the pyrophoric characteristics of uranium.

Additionally, the structure of existing neutron generator systems can create difficulties in servicing or replacement of system components.


Context: Provided is a universal Ku band LNB (9.75/10.600 GHz) which is fitted at the end of the dish and pointed at the correct satellite constellation; most digital receivers will receive the free to air channels. Some broadcasts are free-to-air and unencrypted, some are encrypted but do not require a monthly subscription (known as free-to-view), some are encrypted and require a monthly subscription, and some are pay-per-view services. To view the encrypted content a VideoGuard UK equipped receiver (all of which are dedicated to the Sky service, and cannot be used to decrypt other services) needs to be used. Unofficial CAMs are now available to view the service, although use of them breaks the user's contract with Sky and invalidates the user's rights to use the card.

Question: What is the universal band that digital recievers will receive free to air channels on?

9.75/10.600 GHz
universal Ku band
Ku band


Context: At the beginning of the 20th century, important advancement in geological science was facilitated by the ability to obtain accurate absolute dates to geologic events using radioactive isotopes and other methods. This changed the understanding of geologic time. Previously, geologists could only use fossils and stratigraphic correlation to date sections of rock relative to one another. With isotopic dates it became possible to assign absolute ages to rock units, and these absolute dates could be applied to fossil sequences in which there was datable material, converting the old relative ages into new absolute ages.

Question: What did using fossils help change for scientists?


Context: Packet mode communication may be implemented with or without intermediate forwarding nodes (packet switches or routers). Packets are normally forwarded by intermediate network nodes asynchronously using first-in, first-out buffering, but may be forwarded according to some scheduling discipline for fair queuing, traffic shaping, or for differentiated or guaranteed quality of service, such as weighted fair queuing or leaky bucket. In case of a shared physical medium (such as radio or 10BASE5), the packets may be delivered according to a multiple access scheme.

Question: If there is no radio or 10BASE5, how are the packets delivered?


Context: Many complexity classes are defined using the concept of a reduction. A reduction is a transformation of one problem into another problem. It captures the informal notion of a problem being at least as difficult as another problem. For instance, if a problem X can be solved using an algorithm for Y, X is no more difficult than Y, and we say that X reduces to Y. There are many different types of reductions, based on the method of reduction, such as Cook reductions, Karp reductions and Levin reductions, and the bound on the complexity of reductions, such as polynomial-time reductions or log-space reductions.

Question: What are two examples of different types of reduction?

Karp reductions and Levin reductions
Cook reductions, Karp reductions


Context: In 1096, Crusaders passing by the siege of Amalfi were joined by Bohemond of Taranto and his nephew Tancred with an army of Italo-Normans. Bohemond was the de facto leader of the Crusade during its passage through Asia Minor. After the successful Siege of Antioch in 1097, Bohemond began carving out an independent principality around that city. Tancred was instrumental in the conquest of Jerusalem and he worked for the expansion of the Crusader kingdom in Transjordan and the region of Galilee.[citation needed]

Question: When did Tancred lay siege to Antioch?


Context: In October 2010, the open-access scientific journal PLoS Pathogens published a paper by a multinational team who undertook a new investigation into the role of Yersinia pestis in the Black Death following the disputed identification by Drancourt and Raoult in 1998. They assessed the presence of DNA/RNA with Polymerase Chain Reaction (PCR) techniques for Y. pestis from the tooth sockets in human skeletons from mass graves in northern, central and southern Europe that were associated archaeologically with the Black Death and subsequent resurgences. The authors concluded that this new research, together with prior analyses from the south of France and Germany, ". . . ends the debate about the etiology of the Black Death, and unambiguously demonstrates that Y. pestis was the causative agent of the epidemic plague that devastated Europe during the Middle Ages".

Question: What scientific journal in France published a prior analysis of the Black Death?


A Porthcawl RNLI crew with a medic and the coastguard search and rescue helicopter were sent to Sker Beach, near Kenfig Nature Reserve, at 12:50 GMT on Sunday.
Crewman Chris Page said the rider had head injuries and was very cold from lying unconscious on wet sand.
She was treated before being flown to Cardiff's University Hospital of Wales.

A woman has been airlifted to hospital after falling from a horse on a Bridgend county beach.


Verheydt, who had been with Maastricht since 2015, has signed a three-year contract with Harry Kewell's side.
The 25-year-old scored 13 goals in 43 appearances in all competitions last season.
Meanwhile, Watford winger Dennon Lewis, 20, has joined the Reds on a loan deal until 31 December.
Lewis, who made 29 National League appearances on loan at Woking last season, played under Kewell when the Australian was coach of Watford's under-23 team.
Find all the latest football transfers on our dedicated page.

League Two side Crawley Town have signed striker Thomas Verheydt from Dutch second-tier side MVV Maastricht for an undisclosed fee.


The 32-year-old had been playing in the Isthmian Premier Division with Leatherhead following his release by Newport at the end of last season.
Pidgeley has made 260 appearances in spells with nine clubs, including Chelsea, Watford and Millwall.
Forest Green are currently second in the National League table, one point behind leaders Cheltenham Town.
Pidgeley could make his Rovers debut when they host Aldershot on Friday.

National League side Forest Green Rovers have signed goalkeeper Lenny Pidgeley until the end of the season.


The suspected green mamba was found on a ship that had docked in Aberdeen from west Africa last month.
The snake died after it was placed in a freezer by the animal charity after attempts to rehome it with specialist reptile keepers failed.
The snake was later identified as a harmless green tree snake.
Police Scotland said a complaint was under consideration.
Scottish SPCA Ch Supt Mike Flynn said: "We were called out after a green snake arrived in Aberdeen on a boat from Africa.
"The snake was thought to be a green mamba, one of the deadliest snakes in the world. The snake was taken by police escort to our Aberdeenshire animal rescue and rehoming centre.
"Sadly the snake, which staff genuinely believed to be a green mamba, had to be put to sleep after our attempts to rehome it to specialist reptile keepers were unsuccessful."
He added: "We could not keep the snake in our centre due to severe health and safety concerns, as the closest anti-venom is held in Bedford. Green mambas also require a Dangerous Wild Animal Licence which the society does not have.
"The safety of our staff and the public is paramount and as such the snake was placed in a freezer where it passed away.
"The Scottish SPCA is proud of its policy not put healthy animals to sleep. Animals are only put to sleep on veterinary advice if they are too ill or too aggressive to be rehomed, or where we are legally required to do so.
"The decision to euthanise the snake was not taken lightly. Unfortunately, the snake has since been identified as a harmless green tree snake. This has been an honest mistake on the society's part as we genuinely believed this was an extremely deadly snake."
The western green mamba feeds on small animals and rodents and is mainly found in the coastal tropical rainforests of western Africa.
Experts say its bite can be fatal in as little as 30 minutes.

A complaint about the Scottish SPCA putting what was thought to be one of the world's most deadly snakes to sleep is being investigated by police.


The all-rounder has been included in the T20 squad to face Sri Lanka on 5 July at the Ageas Bowl, having been part of the World T20 squad in March.
"It's nice to be involved again," the 26-year-old slow left-armer and right-handed batsman told BBC Radio Solent.
"I wasn't expecting to be as I thought Moeen Ali would, but him being rested hands me another opportunity."
Dawson failed to make an appearance during England's run to the World T20 final in India this year, but his limited-overs performances for Hampshire this season have kept him in contention.
"I'm not sure what team they'll go with," he said.
"I've had some good performances for Hampshire in one-day and T20 cricket and if I keep going with that, hopefully I might get a chance."

Hampshire's Liam Dawson says it would be "amazing" to make his England Twenty20 debut on his home ground.


The Egypt-backed plan had envisaged a regional forum which analysts say might have forced Israel to reveal whether or not it has nuclear weapons.
The proposal was blocked by the US, the UK and Canada. The next review is set for  2020.
Israel neither confirms nor denies it has a stockpile of nuclear weapons.
Speaking after four weeks of negotiations, US Under-Secretary of State Rose Gottemoeller accused Egypt and other Arab countries of "not willing to let go of these unrealistic and unworkable conditions" for future talks.
She also said some participants tried to "cynically manipulate" the whole process.
But Egypt warned that the failure to reach a deal "will have consequences in front of the Arab world and public opinion", the Associated Press news agency reports.
Last month, Egypt had proposed to stage a regional conference - with or without Israel's participation and without an agreed agenda.
Some analysts suggested that this move might have forced Israel - which is not a party to the Treaty on the Non-Proliferation of Nuclear Weapons (NPT) - to publicly clarify its position on nuclear weapons.
Decisions at NPT review conferences - held every five years - are made by consensus.
The failure of the current talks means the next gathering could only be held in 2020 at the earliest.

A UN conference aimed at preventing the proliferation of nuclear weapons has ended in failure after a row over a nuclear-free Middle East proposal.


Mothers-to-be are more likely to get malaria as their immunity is lowered, says the Royal College of Obstetricians and Gynaecologists (RCOG).
Malaria carries serious risks for mother and baby including miscarriage, stillbirth and premature labour.
The tropical disease is transmitted by mosquitoes and causes a fever, flu-like symptoms, vomiting and diarrhoea.
There have been no malaria-related deaths in pregnant or recently pregnant women in the UK in the past decade but the RCOG says it has been receiving inquires from worried women.
In the UK, about 1,500 cases of malaria are reported each year and about 10 people will die, said the RCOG.
It says all non-essential trips to areas with a high risk of malaria should be avoided.
Risk areas include large areas of Africa, Asia including China and India, Central and South America, parts of the Middle East and some Pacific Islands.
If the trip is unavoidable, the college advises women to seek advice from a centre with expertise in malaria which will provide information on ways to reduce the risk of infection.
Women should make sure they are aware of the risk, take out measures such as mosquito nets for bite prevention and take anti-malarial medication.
Philippa Marsden, who chairs the RCOG's patient information committee, said although the risks were still relatively small it was important that women were well-informed.
Cath Broderick of the RCOG women's network said: "If women are worried about symptoms after returning from a high-risk country and think they may have malaria, they should see a doctor immediately and inform them of their recent travels."
Symptoms can take a week or more to develop after being bitten.

Pregnant women should visit countries with a risk of malaria only if their trip is essential, experts are warning.


Researchers at the University of Sheffield said the tumours were effectively "fertilising" the bone to help themselves grow.
The study, in the journal Nature, said it may be possible to protect bone from a tumour's nefarious influence and consequently stop the cancer's spread.
Cancer charities said this opened up "a whole new avenue for research".
Around 85% of breast cancers that spread around the body end up in bone, at which point the cancer is difficult to treat and more deadly.
The scientists, in Sheffield and the University of Copenhagen, discovered patients with secondary cancers had higher levels of an enzyme called LOX being produced by their tumours and released into the blood.
Bone is constantly being broken down and rebuilt. But in a series of experiments on mice, the research team showed LOX was disrupting the process and leaving lesions and holes in the bone.
Using drugs to block LOX prevented the cancer from spreading.
Dr Alison Gartland, a reader in bone and cancer biology at the university, told the BBC News website: "We think it's a significant breakthrough in trying to prevent metastases (secondary tumours) in breast cancer.
"The cancer cells in the primary tumour are actually fertilising the soil for the future growth of itself, LOX is changing the environment in bone to make it better to grow."
The animal tests also showed that a set of osteoporosis drugs called bisphosphonates could prevent the spread of cancer.
Bisphosphonates also interfere with the way bone is recycled in order to strengthen it.
They are already given to some cancer patients, but the Sheffield team believe they could have a much larger role.
The effect was discovered only in oestrogen-negative breast cancers. They account for around a third of cases, but are far more deadly.
Katherine Woods, from Breast Cancer Campaign and Breakthrough Breast Cancer, said: "By unveiling the role that the protein LOX is playing, these results open up a whole new avenue for research and treatments that could stop breast cancer spreading to the bone.
"The research also adds weight to the growing body of evidence supporting the role of bisphosphonates in stopping secondary breast cancer in its tracks.
"The reality of living with secondary breast cancer in the bone is a stark one, which leaves many women with bone pain and fractures that need extensive surgery just when they need to be making the most of the time they have left with friends and family."
The findings may also apply in colon cancer.

Breast cancers can manipulate the structure of bone to make it easier to spread there, a study has found.


If she dont love me
What can I do?
Just put on my best pair of shoes
Because, oh, Im me
Because she said, Hes the one
That drill the charms
Honey, thats so wrong
I just want to know whats wrong with me
Being in love with you?
Yeah
Ninth grade had the jingles of the swinging rainbow jacket
Bar slang low, Bronxs only Django
Snap, hed gone full stopper, thats where my props go
Thats where my pops went
See, my percentage is a pennant to the planet
Knock it out the ball park, Frankie
I should not tire this tire
To a better love, let the wings spread
Itll always come back, baby
Come back, shellac black, baby
A comeback, flat black paint on a Chevy
If she dont love me, what can I do?
Just put on my best pair of shoes
Because, oh, Im me
Because, yeah, Im me
Because, oh, Im me
Because Im me, Im me
Never mind my tone when I told Pretty Tony
Listen to my tone, you aint catchin you a Tony
Award-winning walk when you running with a shottie
Why you running from us? Why you messing with us?
We aint got no guns, we just let the bears witness
The Grizzly, maybe Polar
You aint ready, you aint ready ready rolled up
Pulling a machete, cut the bamboo paper
And lets roll out, baby, like Frankie
I just want to know
Whats wrong with me?
Watch the shit, its good


Is Dexter ill? Is Dexter ill? Is Dexter ill? Is Dexter ill today?
No, Mr. Kirk, Dexters in school.
Im afraid hes not, Miss Fishpaw. Dexters truancy problem is way out of hand. The Baltimore County School Board have decided to expel Dexter from the entire public school system.
Oh, Mr. Kirk, Im as upset as you to learn of Dexters truancy, but surely expulsion is not the answer!
Im afraid expulsion is the only answer! Its the opinion of the entire staff that Dexter is criminally insane, sane, sane, sane
That boy needs therapy
Psychosomatic
That boy needs therapy
Purely psychosomatic
That boy needs therapy
Lie down on the couch, what does that mean?
Youre a nut! Youre crazy in the coconut!
What does that mean? That boy needs therapy
Im gonna kill you, that boy needs therapy
Rannygazoo, lets have a tune
Now when I count three
That, that, that, that, that boy, boy needs therapy
He was-he was white as a sheet
And he also made false teeth
Avalanche is above, business continues below
Did I ever tell you the story about
Cowboys, and m-midgets, and the Indians and fron-frontier psychiatrist?
I-I felt strangely hypnotised
I was in another world, a world of 20,000 girls
And milk and rectangles
To an optometrist!
The man with the golden eyeball
And tighten your buttocks, pour juice on your chin
I promise my girlfriend Id the violin, violin, violin
Frontier psychiatrist
Frontier, frontier, frontier, frontier psychiatrist
Frontier, frontier, frontier, frontier psychiatrist
Frontier, frontier, frontier, frontier psychiatrist
Frontier, frontier, frontier, frontier psychiatrist
That boy needs therapy
Psychosomatic
That boy needs therapy
Purely psychosomatic
That boy needs therapy
Lie down on the couch, what does that mean?
Youre a nut! Youre crazy in the coconut!
What does that mean? That boy needs therapy
Im gonna kill you, that boy needs therapy
Rannygazoo, lets have a tune
Now when I count three
That, that, that, that, that boy, boy needs therapy
He was-he was white as a sheet
And he also made false teeth
Frontier psychiatrist
Can you think of anything else that talks oth-other than a person?
A-a-a bird? Yeah
Sometimes a parrot talks
Hello, hello, hello, hello
Hello, hello, hello, hello
Yes, some birds are funny when they talk
Can you think of anything else?
Um, a record, record, record, record?


Watch the steps
Get a drink, have a good time now
Welcome to paradise, paradise, paradise...
Since I left you
I found the world so new
Since I left you
I found the world so new
I found the world so new...
Since I left you
I found the world so new
E-Everyday
Since I left you
I found the world so new
E-Everyday
Since I left you
I found the world so new
E-Everyday
Since I left you
I found the world so new...
Since I left you
I found the world so new
E-Everyday
Since I left you
I found the world so new
E-Everyday
I found the world so
I found the world so new
Since I left you
I found the world so
I found the world so new
Since I left you


Hey, alright
Kip Kasper, KLON Radio
How we feelin out there?
Hows your drive-time commute?
I remember when she took me to a world in my mind
Who wants time when theres nothing? she said to the sky
And Id like to see her every day
I know I cant be gone every weekend
Lets wake up side by side
Lets stay here til we die
Dont make me close my eyes
Why wont they leave?
Why wont they leave?
Lets wake up side by side
Lets stay here til we die
Dont make me close my eyes
Why wont they leave?
(Why wont they leave?
Why wont they leave?)
Isnt he great?
Far out
Come on, I know a spot where we can go


Munch! Munch!
Once upon a time, I was a noisy eater
Im hungry, I want something to eat
Something with a crunch and very sweet
Just woke up so you know the scenario
Im craving cereal like Cheerios
Maybe frosted flakes, I might munch
But what popped in my mind is Captain Crunch
So I went to the kitchen to freak my choice
In my stomach I heard a voice
It said: Please feed me, my tank is on empty
Im seeing things, so please dont tempt me
So I said okay, and got my bowl
Got my milk and cereal, made sure it was cold
Sat down at the table and said my grace
Because I knew what was about to take place
Yum yum, eat em up, my belly said
The crunches so loud errbody jumped out they bed!
Here come old flat-top, he come grooving up slowly
He got ju-ju eyeballs, hes one holy roller
Hes got...
Crunch!
Did you ever know a noisy eater?
Aauuuuuuuuuuuu!
Now its lunchtime, and Im hungry again
I dont know where I should begin
Maybe Ill get some super salmon, mmm thats good
I dont want no chicken, it tastes like wood
But I have to have some chips, ones that crunch
So you can hear me go munch, munch, munch
Some old lady tapped me on the shoulder out of a crowd
And said Quiet down Biz, you munching too loud!
Here come old flat-top, he come grooving up slowly
He got ju-ju eyeballs. hes one holy roller
Hes got...
Well excuse me, um, um, well excuse me, um
Well excuse me, um, um, well excuse me
Here come old flat-top, he come grooving up slowly
He got ju-ju eyeballs. Hes one holy roller
Hes got...
Quiet down Biz, you munching too loud!
Yum, yum, yum, yum yum
Mmm, thats good!
They all thought I had stopped
And wheres the gravy?
No matter!


Context: The Standard Industrial Classification and the newer North American Industry Classification System have a classification system for companies that perform or otherwise engage in construction. To recognize the differences of companies in this sector, it is divided into three subsectors: building construction, heavy and civil engineering construction, and specialty trade contractors. There are also categories for construction service firms (e.g., engineering, architecture) and construction managers (firms engaged in managing construction projects without assuming direct financial responsibility for completion of the construction project).

Question: What systems classify trade contractors?


Context: The physicians of the Yuan court came from diverse cultures. Healers were divided into non-Mongol physicians called otachi and traditional Mongol shamans. The Mongols characterized otachi doctors by their use of herbal remedies, which was distinguished from the spiritual cures of Mongol shamanism. Physicians received official support from the Yuan government and were given special legal privileges. Kublai created the Imperial Academy of Medicine to manage medical treatises and the education of new doctors. Confucian scholars were attracted to the medical profession because it ensured a high income and medical ethics were compatible with Confucian virtues.

Question: Who were potachi?


Context: There is evidence that there have been significant changes in Amazon rainforest vegetation over the last 21,000 years through the Last Glacial Maximum (LGM) and subsequent deglaciation. Analyses of sediment deposits from Amazon basin paleolakes and from the Amazon Fan indicate that rainfall in the basin during the LGM was lower than for the present, and this was almost certainly associated with reduced moist tropical vegetation cover in the basin. There is debate, however, over how extensive this reduction was. Some scientists argue that the rainforest was reduced to small, isolated refugia separated by open forest and grassland; other scientists argue that the rainforest remained largely intact but extended less far to the north, south, and east than is seen today. This debate has proved difficult to resolve because the practical limitations of working in the rainforest mean that data sampling is biased away from the center of the Amazon basin, and both explanations are reasonably well supported by the available data.

Question: There have been insignificant changes in the Amazon rain forest vegetation through the last what


Context: This shift has already commenced in some countries; for instance, pharmacists in Australia receive remuneration from the Australian Government for conducting comprehensive Home Medicines Reviews. In Canada, pharmacists in certain provinces have limited prescribing rights (as in Alberta and British Columbia) or are remunerated by their provincial government for expanded services such as medications reviews (Medschecks in Ontario). In the United Kingdom, pharmacists who undertake additional training are obtaining prescribing rights and this is because of pharmacy education. They are also being paid for by the government for medicine use reviews. In Scotland the pharmacist can write prescriptions for Scottish registered patients of their regular medications, for the majority of drugs, except for controlled drugs, when the patient is unable to see their doctor, as could happen if they are away from home or the doctor is unavailable. In the United States, pharmaceutical care or clinical pharmacy has had an evolving influence on the practice of pharmacy. Moreover, the Doctor of Pharmacy (Pharm. D.) degree is now required before entering practice and some pharmacists now complete one or two years of residency or fellowship training following graduation. In addition, consultant pharmacists, who traditionally operated primarily in nursing homes are now expanding into direct consultation with patients, under the banner of "senior care pharmacy."

Question: Who can't pay Australian pharmacists for doing Home Medicines Reviews?


Context: In the final years of the apartheid era, parents at white government schools were given the option to convert to a "semi-private" form called Model C, and many of these schools changed their admissions policies to accept children of other races. Following the transition to democracy, the legal form of "Model C" was abolished, however, the term continues to be used to describe government schools formerly reserved for white children.. These schools tend to produce better academic results than government schools formerly reserved for other race groups . Former "Model C" schools are not private schools, as they are state-controlled. All schools in South Africa (including both independent schools and public schools) have the right to set compulsory school fees, and formerly model C schools tend to set much higher school fees than other public schools.

Question: What was abolished during the apartheid era?


Context: There are hints in the surviving records of the ancient Egyptians that they had some knowledge of prime numbers: the Egyptian fraction expansions in the Rhind papyrus, for instance, have quite different forms for primes and for composites. However, the earliest surviving records of the explicit study of prime numbers come from the Ancient Greeks. Euclid's Elements (circa 300 BC) contain important theorems about primes, including the infinitude of primes and the fundamental theorem of arithmetic. Euclid also showed how to construct a perfect number from a Mersenne prime. The Sieve of Eratosthenes, attributed to Eratosthenes, is a simple method to compute primes, although the large primes found today with computers are not generated this way.

Question: What does the Sieve of Eratosthenes do?

compute primes
compute primes
compute primes
compute primes
compute primes


I liked the app notifications when they worked gotta pay 2.99 a month Crazy....mbe a year Not a month for a calendar app. Most widget features wont work unless u pay never got to see if they even were worth 299 a month....i like the popup reminders hopefully someome will make a free one.


it could have an option for those who work through their cellphones


This is a GREAT ap. It is easy and intuitive to use. Only 4 stars because you cant trust it with repetitive tasks. It nearly always skips the next occurrence when you mark one occurrence complete. Doesnt matter what the frequency is. I have submitted feedback twice Nov  Dec 2019 with no response.


Now I have to upgrade my account to use the pomodoro feature and many other stuff


The new UI as of 2022 aug. is a bad change. The team who urged the change simply failed. Total mess of sticky notes and note books and filters which forget the settings. Please send us a link with the old version of the app its lightyears far better.


Gets the job done easier


Almost perfect I would really like to keep the app for permanent use but there is one big hindrance. New tasks are not added to my synced calendars. Which means I cant see them on other devices PC etc. This is critical to me.


Works great until it doesnt. Works great on a 10.1 in. tablet with pen. Its great for the most part but very rarely erases tiny parts of text the straight line from 1 missing etc. so I need to double check everything to make sure nothings magically disappeared before sending in my assignment.


Not hood as samsung note. No policy and poor in colored text to notice important words


The bee landed on the flower because it had pollen.


The bee landed on the flower because it wanted pollen.


When Debbie splashed Tina, she got in trouble.


When Debbie splashed Tina, she got wet.


The bus driver yelled at a kid after she drove her vehicle.


The bus driver yelled at a kid after she broke a window.


Jimbo was running from Bobbert because he smelled awful.


Jimbo was running from Bobbert because he wanted to get to the car first.


Jimbo attacked Bobbert because he stole an elephant from the zoo.


Jimbo attacked Bobbert because he could not control his rage.


Life is a mystery
Everyone must stand alone
I hear you call my name
And it feels like home
When you call my name
Its like a little prayer
Im down on my knees
I want to take you there
In the midnight hour
I can feel your power
Just like a prayer
You know Ill take you there
I hear your voice
Its like an angel sighing
I have no choice
I hear your voice
Feels like flying
I close my eyes
Oh God, I think Im falling
Out of the sky
I close my eyes
Heaven, help me
When you call my name
Its like a little prayer
Im down on my knees
I want to take you there
In the midnight hour
I can feel your power
Just like a prayer
You know Ill take you there
Like a child
You whisper softly to me
Youre in control
Just like a child
Now Im dancing
Its like a dream
No end and no beginning
Youre here with me
Its like a dream
Let the choir sing
When you call my name
Its like a little prayer
Im down on my knees
I wanna take you there
In the midnight hour
I can feel your power
Just like a prayer
You know Ill take you there
When you call my name
Its like a little prayer
Im down on my knees
I wanna take you there
In the midnight hour
I can feel your power
Just like a prayer
You know Ill take you there
Life is a mystery
Everyone must stand alone
I hear you call my name
And it feels like home
Just like a prayer
Your voice can take me there
Just like a muse to me
You are a mystery
Just like a dream
You are not what you seem
Just like a prayer, no choice
Your voice can take me there
Just like a prayer, Ill take you there
Its like a dream to me
Just like a prayer, Ill take you there
Its like a dream to me
Just like a prayer, Ill take you there
Its like a dream to me
Just like a prayer, Ill take you there
Its like a dream to me
Just like a prayer
Your voice can take me there
Just like a muse to me
You are a mystery
Just like a dream
You are not what you seem
Just like a prayer, no choice
Your voice can take me there
Just like a prayer
Your voice can take me there
Just like a muse to me
You are a mystery
Just like a dream
You are not what you seem
Just like a prayer, no choice
Your voice can take me there
Your voice can take me there
Like a prayer
Its like a prayer, your voice can take me there
Its like a prayer
Its like a prayer, your voice can take me there
Its like a prayer
Its like a prayer, your voice can take me there
Its like a prayer
Its like a prayer, your voice can take me there
Its like a prayer


Strike a pose
Strike a pose
Vogue
Vogue
Look around, everywhere you turn is heartache
Its everywhere that you go
You try everything you can to escape
The pain of life that you know
When all else fails and you long to be
Something better than you are today
I know a place where you can get away
Its called a dance floor
And heres what its for, so
Come on, vogue
Let your body move to the music
Hey, hey, hey
Come on, vogue
Let your body go with the flow
You know you can do it
All you need is your own imagination
So use it thats what its for
Go inside, for your finest inspiration
Your dreams will open the door
It makes no difference if youre black or white
If youre a boy or a girl
If the musics pumping it will give you new life
Youre a superstar
Yes, thats what you are, you know it
Come on vogue
Let your body groove to the music
Hey, hey, hey
Come on, vogue
Let your body go with the flow
You know you can do it
Beautys where you find it
Not just where you bump and grind it
Soul is in the musical
Thats where I feel so beautiful
Magical, lifes a ball, so
Get up on the dance floor
Vogue
Let your body move to the music
Hey, hey, hey
Come on, vogue
Let your body go with the flow
You know you can do it
Vogue
Beautys where you find it
Vogue
Beautys where you find it
Greta Garbo, and Monroe
Dietrich and DiMaggio
Marlon Brando, Jimmy Dean
On the cover of a magazine
Grace Kelly, Harlow, Jean
Picture of a beauty queen
Gene Kelly, Fred Astaire
Ginger Rogers dance on air
They had style, they had grace
Rita Hayworth gave good face
Lauren, Katharine, Lana too
Bette Davis, we love you
Ladies with an attitude
Fellas that were in the mood
Dont just stand there, lets get to it
Strike a pose, theres nothing to it
Vogue
Vogue
Vogue
Vogue
Oooh, youve got to
Let your body move to the music
Oooh, youve got to just
Let your body go with the flow
Oooh, youve got to just
Vogue


I made it through the wilderness
Somehow I made it through
Didnt know how lost I was until I found you
I was beat, incomplete
Id been had, I was sad and blue, but you made me feel
Yeah, you made me feel shiny and new
Like a virgin
Touched for the very first time
Like a virgin
When your heart beats next to mine
Gonna give you all my love, boy
My fear is fading fast
Been saving it all for you, cause only love can last
Youre so fine and youre mine
Make me strong, yeah you make me bold, oh your love thawed out
Yeah, your love thawed out what was scared and cold
Like a virgin
Touched for the very first time
Like a virgin
With your heartbeat next to mine
Whoa whoa whoa
Whoa whoa whoa, ah
Whoa whoa whoa
Youre so fine and youre mine
Ill be yours til the end of time cause you made me feel
Yeah, you made me feel Ive nothing to hide
Like a virgin
Touched for the very first time
Like a virgin
With your heartbeat next to mine
Like a virgin
Ooh-ooh, like a virgin
Feels so good inside
When you hold me, and your heart beats, and you love me
Oh-ooh oh-oh-oh, ooh-ooh, ooh
Ooh baby, yeah
Cant you hear my heart beat for the very first time?


Some boys kiss me, some boys hug me
I think theyre okay
If they dont give me proper credit, I just walk away
They can beg and they can plead
But they cant see the light
Cause the boy with the cold hard cash is always Mister Right
Cause we are living in a material world
And I am a material girl
You know that we are living in a material world
And I am a material girl
Some boys romance, some boys slow dance
Thats all right with me
If they cant raise my interest, then I have to let them be
Some boys try, and some boys lie
But I dont let them play, no way
Only boys that save their pennies make my rainy day
Cause we are living in a material world
And I am a material girl
You know that we are living in a material world
And I am a material girl
Living in a material world
And I am a material girl
You know that we are living in a material world
And I am a material girl
Living in a material world
Living in a material world
Living in a material world
Living in a material world
Boys may come, and boys may go
And thats all right, you see
Experience has made me rich, and now theyre after me
Cause everybodys living in a material world
And I am a material girl
You know that we are living in a material world
And I am a material girl
Living in a material world
And I am a material girl
You know that we are living in a material world
And I am a material girl
A material, a material, a material, a material world
Living in a material world
Living in a material world
Living in a material world
Living in a material world
Living in a material world
Living in a material world
Living in a material world
Living in a material world


Uhh, this feel right right here, Quik
Its like its supposed to happen this one right here
Young! God damn
Let me justify my thug on this one right here
It goes one oclock, two oclock, three oclock, Roc
Five six seven oclock, eight oclock, Roc
11 oclock, 12 oclock, the party bout to pop
Then, Roc-A-Fella yall, its your boy S. Dot
And I aint never been to jail; I aint never pay a nigga
To do no dirt for me I was scared to do myself
I will never tell, even if it means sittin in a cell
I aint never ran, never will
I aint never been smacked; a nigga better keep his hands
To himself or get clapped for whats under that mans belt
I never asked for nothin I dont demand of myself
Honesty, loyalty, friends and then wealth
Death before dishonor and I tell you what else
I tighten my belt fore I beg for help
Foolish pride is what held me together through the years
I wasnt felt which is why I aint never played myself
I just play the hand Im dealt, I cant say Ive never knelt
Before God and asked for better cards at times to no avail
But I never sat back feelin sorry for myself
If you dont give me heaven Ill raise hell
Til its heaven
Justify my thug for you
My thug
My thug for you
To justify my thug
My thug
My thug for you
For you
Now if you shoot my dog, Ima kill yo cat
Just the unwritten laws in rap, know that
For every action theres a reaction, dont have me relapsin
Relaxins what Im about, but about mine
Dont be actin like you cant see street action
Take me back to Reasonable Doubt time
You see my minds on the finish line, facin the wreck
I put my muhfuckin faith in the TEC, tell Satan not yet
You understand I am chasin my breath
I am narrowly escapin my death, oh yes
I am the Michael Schumacher of the Roc roster
Travellin Mach 5, barrel in my apparel can stop God
God forgive me but I cant let them deliver me to you
Until, I won this race, then eventually
My engine gon burn out, I get whatever is meant for me
However it turns out fine, red line!
Justify my thug for you
My thug
My thug for you
To justify my thug
My thug
My thug for you
For you
They say an eye for an eye, we both lose our sight
And two wrongs dont make a right
But when you been wronged and you know all along that its just one life
At what point does one fight?
Fore you knock the war, try to put your dogs in his
Ten-and-a-halfs, for a minute-and-a-half
Bet that stops all the grinnin and the laughs
When you play the game of life and the win aint in the bag
When your options is none and the pen is all you have
Or the block, niggas standin tight as lemons on the ave
Tryin to cop a shop - call theyself cleansin in the cash
But cant put they name on paper cause, then you on blast
Mr. President, theres drugs in our residence
Tell me what you want me to do, come break bread with us
Mr. Governor, I swear theres a cover up
Every other corner theres a liquor store, fuck is up?
Justify my thug for you
My thug
My thug for you
To justify my thug
My thug
My thug for you
For you


Youre gonna love this
You cant touch this
Cause Im a bad bitch
We hit the elevator right up to the rooftop
The bass is pumping, make me wanna screw the top off
Yeah, well be drinking and nobodys gonna stop us
And well be kissing anybody thats around us
I just wanna have fun tonight
Put me under the flashing light
Oh-oh-oh-oh
Let me blow up this house tonight
We go hard or we go home
We gon do this all night long
We get freaky if you want
Na-na-na-na-na
We go hard or we go home
We gon do this all night long
We get freaky if you want
Bitch, Im Madonna
Bitch, bitch Im Madonna
Bitch, bitch Im Madonna
Were jumping in the pool and swimming with our clothes on
I poured a beer into my shoe and got my freak on
The neighbors pissed and says hes gonna call the Five-O
If they show up then we are gonna give a good show
I just wanna go up tonight
Pull me under the flashing light
Oh-oh-oh-oh
Let me blow up this house tonight
Uhh
Beep-beep, bitch move
We go hard or we go home
We gon do this all night long
We get freaky if you want
Na-na-na-na-na
We go hard or we go home
We gon do this all night long
We get freaky if you want
Bitch, Im Madonna
Bitch, bitch, Im Madonna
Bitch, Im Madonna
Bitch, Im Madonna
Bitch, Im Madonna
Bitch, Im Madonna
Bitch, Im Madonna
Bitch, Im Madonna
Bitch, Im Madonna
Who do you think you are?
You cant mess with this lucky star
Oh-oh-oh-oh
Who do you think you are?
Uh, poured up with my nose up and that rose up in that thinga
Im froze up, with my stove up, cause he eating like its his dinner
I run shit, I dont fall back, cause Im on track, Im a sprinter
Im bossed up, I got em awestruck, its not a toss up, Im the winner
Beep-beep, bitch move, fore I bang-bang with that Uzi
Thats Miu Miu on my shoes, aint got a thing left for me to prove
Its that bottle service all night, its that pop and urban just right
Its that go hard or go home zone, bitch
Im Madonna, these hoes know
We go hard or we go home
We gon do this all night long
We get freaky if you want
Na-na-na-na-na
We go hard or we go home
We gon do this all night long
We get freaky if you want
Bitch, Im Madonna
We do it like this
Youre gonna love this
You cant touch this
Cause Im a bad bitch
Who do you think you are?
Who do you think you are?
Who do you think you are?
Who do you think you are?
Go hard or go home zone, bitch
Im Madonna, these hoes know


Time goes by so slowly
Time goes by so slowly
Time goes by so slowly
Time goes by so slowly
Time goes by so slowly
Time goes by so slowly
Every little thing that you say or do
Im hung up, Im hung up on you
Waiting for your call, baby, night and day
Im fed up, Im tired of waiting on you
Time goes by so slowly for those who wait
No time to hesitate
Those who run seem to have all the fun
Im caught up, I dont know what to do
Time goes by so slowly
Time goes by so slowly
Time goes by so slowly
I dont know what to do
Every little thing that you say or do
Im hung up, Im hung up on you
Waiting for your call, baby, night and day
Im fed up, Im tired of waiting on you
Every little thing that you say or do
Im hung up, Im hung up on you
Waiting for your call, baby, night and day
Im fed up, Im tired of waiting on you
Ring, ring, ring goes the telephone
The lights are on, but theres no one home
Tick tick tock, its a quarter to two
And Im done, Im hanging up on you
I cant keep on waiting for you
I know that youre still hesitating
Dont cry for me, cause Ill find my way
Youll wake up one day, but itll be too late
Every little thing that you say or do
Im hung up, Im hung up on you
Waiting for your call, baby, night and day
Im fed up, Im tired of waiting on you
Every little thing that you say or do
Im hung up, Im hung up on you
Waiting for your call, baby, night and day
Im fed up, Im tired of waiting on you
Every little thing, every little thing
Im hung up, Im hung up on you
Waiting for your call, waiting for your call
Im fed up, Im tired waiting on you
Time goes by so slowly
Time goes by so slowly
Time goes by so slowly
Time goes by so slowly
So slowly, so slowly, so slowly
So slowly, so slowly, so slowly
So slowly, so slowly, so slowly
So slowly, so slowly, so slowly, so slowly, so
I dont know what to do
Every little thing that you say or do
Im hung up, Im hung up on you
Waiting for your call, baby, night and day
Im fed up, Im tired of waiting on you
Every little thing that you say or do
Im hung up, Im hung up on you
Waiting for your call, baby, night and day
Im fed up, Im tired of waiting on you
Every little thing, every little thing
Im hung up, Im hung up on you
Waiting for your call, waiting for your call
Im fed up, Im tired waiting on you


Papa, I know youre going to be upset
Cause I was always your little girl
But you should know by now, Im not a baby
You always taught me right from wrong
I need your help, daddy, please be strong
I may be young at heart, but I know what Im saying
The one you warned me all about
The one you said I could do without
Were in an awful mess
And I dont mean maybe
Please
Papa dont preach
Im in trouble, deep
Papa dont preach
Ive been losing sleep
But I made up my mind, Im keeping my baby
Ooh, Im gonna keep my baby, mmm
He says that hes going to marry me
We can raise a little family
Maybe well be all right, its a sacrifice
But my friends keep telling me to give it up
Saying Im too young, I ought to live it up
What I need right now is some good advice
Please
Papa dont preach
Im in trouble, deep
Papa dont preach
Ive been losing sleep
But I made up my mind, Im keeping my baby
Ooh, Im gonna keep my baby, ooh, ooh
Daddy, daddy if you could only see
Just how good hes been treating me
Youd give us your blessing right now
Cause we are in love
We are in love , so, please
Papa dont preach
Im in trouble, deep
Papa dont preach
Ive been losing sleep
But I made up my mind, Im keeping my baby
Ooh, Im gonna keep my baby, ooh, ooh
Papa dont preach
Im in trouble, deep
Papa dont preach
Ive been losing sleep
Papa dont preach
Im in trouble, deep
Papa dont preach
Ive been losing sleep
Oh, Im gonna keep my baby, ooh
Dont you stop loving me, daddy
I know, Im keeping my baby


#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?
#Person2#: I found it would be a good idea to get a check-up.
#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.
#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?
#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.
#Person2#: Ok.
#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?
#Person2#: Yes.
#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.
#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.
#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.
#Person2#: Ok, thanks doctor.

Summary: get a check-up


#Person1#: Hello Mrs. Parker, how have you been?
#Person2#: Hello Dr. Peters. Just fine thank you. Ricky and I are here for his vaccines.
#Person1#: Very well. Let's see, according to his vaccination record, Ricky has received his Polio, Tetanus and Hepatitis B shots. He is 14 months old, so he is due for Hepatitis A, Chickenpox and Measles shots.
#Person2#: What about Rubella and Mumps?
#Person1#: Well, I can only give him these for now, and after a couple of weeks I can administer the rest.
#Person2#: OK, great. Doctor, I think I also may need a Tetanus booster. Last time I got it was maybe fifteen years ago!
#Person1#: We will check our records and I'll have the nurse administer and the booster as well. Now, please hold Ricky's arm tight, this may sting a little.

Summary: vaccines


#Person1#: Excuse me, did you see a set of keys?
#Person2#: What kind of keys?
#Person1#: Five keys and a small foot ornament.
#Person2#: What a shame! I didn't see them.
#Person1#: Well, can you help me look for it? That's my first time here.
#Person2#: Sure. It's my pleasure. I'd like to help you look for the missing keys.
#Person1#: It's very kind of you.
#Person2#: It's not a big deal.Hey, I found them.
#Person1#: Oh, thank God! I don't know how to thank you, guys.
#Person2#: You're welcome.

Summary: find keys


#Person1#: Why didn't you tell me you had a girlfriend?
#Person2#: Sorry, I thought you knew.
#Person1#: But you should tell me you were in love with her.
#Person2#: Didn't I?
#Person1#: You know you didn't.
#Person2#: Well, I am telling you now.
#Person1#: Yes, but you might have told me before.
#Person2#: I didn't think you would be interested.
#Person1#: You can't be serious. How dare you not tell me you are going to marry her?
#Person2#: Sorry, I didn't think it mattered.
#Person1#: Oh, you men! You are all the same.

Summary: have a girlfriend


#Person1#: Watsup, ladies! Y'll looking'fine tonight. May I have this dance?
#Person2#: He's cute! He looks like Tiger Woods! But, I can't dance. . .
#Person1#: It's all good. I'll show you all the right moves. My name's Malik.
#Person2#: Nice to meet you. I'm Wen, and this is Nikki.
#Person1#: How you feeling', vista? Mind if I take your friend'round the dance floor?
#Person2#: She doesn't mind if you don't mind getting your feet stepped on.
#Person1#: Right. Cool! Let's go!

Summary: dance


#Person1#: Happy birthday, Aims!
#Person2#: Thank you, Lisa.
#Person1#: Here is a present for you. I hope you like it.
#Person2#: Oh, great! I love it! You know I've been expecting this for a long time.
#Person1#: I'm very glad to hear that.
#Person2#: Come here ; let me introduce some friends to you.

Summary: birthday


#Person1#: Here we come.
#Person2#: Thank you. What's the fare?
#Person1#: $ 10.
#Person2#: How can it be?
#Person1#: Well, the rate is two dollars for the first two kilometers and twenty cents for each additional two hundred meters.
#Person2#: I see. Thanks for your drive.

Summary: pay the fare


#Person1#: Can I help you?
#Person2#: Yes. I sent in my resume at the end of last week. I'm applying for the accounts assistant position.
#Person1#: May I have your name please?
#Person2#: My name is Judy Liao. That's spelled L I A O.
#Person1#: Alright. . . And did you have some specific questions about your application?
#Person2#: Not really. I was in the neighborhood, and I just wanted to stop in to see if you received my resume.
#Person1#: Oh, that's no problem. Just give me a moment, and I can check. Judy Liao. Let's see. . . Yes, here it is. Judy Liao. We have received your resume.
#Person2#: Thank you.
#Person1#: Is there anything else I can help you with?
#Person2#: Yes, maybe. The ad in the newspaper said you wanted the resume, a cover letter, and two letters of recommendation. I included those things in the envelope. Is there anything else I should send?
#Person1#: No, that is all we need. If we have those things included, that is sufficient.
#Person2#: Do you know when they will start setting up interviews for the job?
#Person1#: I'm not really sure about that. But I know we are still receiving resumes. Maybe after a week or two they will start calling applicants.
#Person2#: I see. Well, thank you very much for helping me. You have been very helpful.
#Person1#: If you have any further questions, you can call any time.
#Person2#: Thank you.
#Person1#: Thank you. Goodbye.

Summary: a job application


#Person1#: This is a good basic computer package. It's got a good CPU, 256 megabytes of RAM, and a DVD player.
#Person2#: Does it come with a modem?
#Person1#: Yes, it has a built-in modem. You just plug a phone line into the back of the computer.
#Person2#: How about the monitor?
#Person1#: A 15 - inch monitor is included in the deal. If you want, you can switch it for a 17 - inch monitor, for a little more money.
#Person2#: That's okay. A 15 - inch is good enough. All right, I'll take it.

Summary: a computer package


#Person1#: Excuse me, do you know where the visa office is?
#Person2#: Yes, I do. I'll walk you there.
#Person1#: Thanks.
#Person2#: Are you applying to study or work abroad?
#Person1#: I want to study abroad.
#Person2#: What do you want to study?
#Person1#: I hope to study English Literature.
#Person2#: Have you got your visa yet?
#Person1#: Not yet. I have an interview with a visa official today.
#Person2#: I see. Is it your first interview?
#Person1#: No, I'Ve already been here for 3 interviews.

Summary: a visa


Thanks !! I am so ready for a good homegrown tomato . The squirrels and chipmunks have pulled almost all of ours . And ca n't wait to try your corn ! _____ From : Mills , Gail Sent : Monday , July NUM , NUM 9:46 AM To : Ellis , Regina ; Griffith , Pam ; Weed , Andrea ; Burton , Donna ; Beckett , Amanda Subject : Corn and Tomatoes for you in my office , come get what you want _____ CONFIDENTIAL : ATTORNEY/CLIENT PRIVILEGED ; ATTORNEY WORK PRODUCT The information contained in this email is intended for the individual or entity above . This email is protected by the Electronic Communications Privacy Act , NUM U.S.C. Sections NUM and is legally protected by the attorney/client privilege and/or work product doctrine . If you are not the intended recipient , please do not read , copy , use , forward or disclose this communication to others ; also , please notify the sender by replying to this message , and then delete this message from your system . Thank you . Circular NUM Notice - Regulations adopted by the Internal Revenue Service require us to inform you that any federal tax advice contained in this communication ( including attachments ) ( I ) is not intended or written by Burr & Forman LLP to be used , and can not be used , by any taxpayer for the purpose of avoiding penalties that may be imposed on the taxpayer , and ( II ) is not written to support the promotion or marketing of any transaction ( s ) or matter ( s ) addressed in this communication .


Gail Livingston Mills Attorney at Law Direct : ( 205 ) 458-5300 Cell : ( 205 ) 616-9988 Main : ( 205 ) 251-3000 Fax : ( 205 ) 244-5681 420 North 20th Street , Suite 3400 Birmingham , Alabama 35203 www.burr.com < http://www.burr.com/ >