[User] Please transcribe this text

[Assistant] That's the first time you ever had competition in the room off coming off the core surgery.


[User] Please transcribe this text

[Assistant] And then all of the media stuff are behind them all and it's Peter King with the old Peyton to step in at any time and go and get Lutz and he does exactly what Peter King say like Peter King don't know what time it is. Like Sean Peyton ain't tell Peter King that shit.


[User] Please transcribe this text

[Assistant] Come on man, Peter King is not Nostradamus. Okay, let's get it, let's get it, let's not get it twisted. Peter King is not Nostradamus. Peter King is an insider. We're relationships to all those coaches.


[User] Please transcribe this text

[Assistant] And them guys telling them that sometimes they use these guys to spread the word. That's what they do. So anyway.


[User] Please transcribe this text

[Assistant] Man, listen man, I'm, I'm really kinda, I'm kinda animated about this kicker trade because this is not good value to give up for a kicker.


[User] Please transcribe this text

[Assistant] When it comes down to it, you might not feel that way right now, but wait until you get in the heat of the moment and a game against the Atlanta Falcons. So you need a field goal to win and you marshal fresh off the box, rookie kick out there to try to kick you a horn, kick you a win and he misses.


[User] Please transcribe this text

[Assistant] So like I said, man, if you got aspirations, if you just, the Houston Texans, you got a rookie kicker, okay, cool. But if you're the saints and you supposedly have two boy aspirations, you got two fresh out the box rookies at the punter in the kicker position, might have some issues there, man.


[User] Please transcribe this text

[Assistant] This don't come back to buy. Shout out to 95Live, man. Appreciate y'all being in the stream. I think it does, man.


[User] Please transcribe this text

[Assistant] To a degree, it's going to come back some kind of way, man. It's a rookie kicker and this is a team that has deep playoff runs. That could be an issue.


[User] Please transcribe this text

[Assistant] Alright, anyway, that was one of the surprises of the day. If that wasn't good enough for you, the Saints and Dennis Allen got, uh, move happy in the wrong positions. You didn't have to flip the...


[User] Please transcribe this text

[Assistant] Specialty room. We didn't have to flip the specialist. We didn't.


[User] Please transcribe this text

[Assistant] We didn't have to flip and say, well, we got to get Will Lutz out of here. Like he was toxic. Will Lutz wasn't, uh, wasn't a problem. Surprise move number two. Undrafted punter Lou Hetley claims the starting role. Experience took a back seat.


[User] Please transcribe this text

[Assistant] As the saints opted for a bowl move in the punting department, 30 year old, undrafted rookie free agent punter, Lou Hedley, who was in Australia.


[User] Please transcribe this text

[Assistant] Emerge victorious in the battle for the starting punting position over Blake Gilligan. Now listen.


[User] Please transcribe this text

[Assistant] We watched the battle between Lou Headley the punter and Blake Gillick and they were neck and neck, man.


[User] Please transcribe this text

[Assistant] Once again, experience didn't win. It's like, it's like, okay, both of them, both the rookies were better than the experienced men. No, no, they weren't.


[User] Please transcribe this text

[Assistant] No, they weren't. Blake Gilligan and Lou Headley were neck and neck.


[User] Please transcribe this text

[Assistant] And you're gonna say, well Q is all about the money. No, they have money. But once again, the saints right now, let me share the screen. Look at what the saints are doing now.


[User] Please transcribe this text

[Assistant] You can see that the saints have almost $14 million according to over the cap and, and, and team cap space. So this wasn't a money move to be real with you. It wasn't.


[User] Please transcribe this text

[Assistant] This was a Dennis Allen move and I kept cautioning the family members that Dennis Allen makes stupid moves when he comes down to some of this stuff, he makes boneheaded moves. So I don't, it's one of them things where you,


[User] Please transcribe this text

[Assistant] Two rookies in the spell. We really didn't need to do that. One. Yeah. Okay. One of them with a veteran, but two fresh out the box rookies at the punter and kicker position, man. I don't like that, man.


[User] Please transcribe this text

[Assistant] You know, it wasn't like these guys was blowing away the veterans, man. It really wasn't. Alright, anyway.


[User] Please transcribe this text

[Assistant] Hetley's journey from Australia rules football to Miami's college football scene is culminated in the spot on the Saints roster. An orthodox path showcases the team's willingness to embrace unique talents.


[User] Please transcribe this text

[Assistant] While Gilligan who had previously shown promise was waived.


[User] Please transcribe this text

[Assistant] That is absolutely amazing that Blake Gilligan gets waved.


[User] Please transcribe this text

[Assistant] For that guy. And then of course another surprise. Semi-surprise. Cause I'm not really was surprised about


[User] Please transcribe this text

[Assistant] This. And I talked to Niles about it. He's like, man, what you think about the, the Bradley Roby thing? I was like, man, a Bradley Roby thing to me wasn't really a surprise.


[User] Please transcribe this text

[Assistant] The Saints had one year left on Bradley Roby's contract. It was $3 million.


[User] Please transcribe this text

[Assistant] And he was not going to be the Nickelback. And I kept telling people, some IQ, I think Adibou is going to lose his job to Alante Taylor. I told them people, no, he wasn't. These people have Alante Taylor all lined up and it is what it is.


[User] Please transcribe this text

[Assistant] So at the end of the day, Allante Taylor is now the Nickelback


[User] Please transcribe this text

[Assistant] And the starting cornerback is Paulson Adibos. So in the end what ended up happening is what I said it was.


[User] Please transcribe this text

[Assistant] They just pushed Allante, I mean, pushed Bradley Roby all the way down to the nickel position, but why spend $3 million for


[User] Please transcribe this text

[Assistant] A guy that really ain't that good. He's been, he was a disappointment since we got him down here. No disrespect to Bradley Roby, but he never caught on.


[User] Please transcribe this text

[Assistant] He never caught on with the team. The Saints gave up that stuff to go and get Bradley Roby and it never really mattered.


[User] Please transcribe this text

[Assistant] Iowa's decision to part with Roby hinted at a strategic shift in the team's defensive lineup. This move opens the door for an emerging talent.


[User] Please transcribe this text

[Assistant] Allante Taylor to step in a more prominent role back by a strong rookie season as the dust settles.


[User] Please transcribe this text

[Assistant] On the surprising roster changes, Saints are embracing a new era and infusing.


[User] Please transcribe this text

[Assistant] Uh, fresh faces and re-emerging roles or re-imagining roles fans are left eagerly, uh, awaiting the start of the season to see how these unexpected moves will impact the team's performances on the field.


[User] Please transcribe this text

[Assistant] The NFL's ever-involving landscape proves that cut-day decisions can reshape the course of a franchise and, and usher in new possibilities. Now, the Bradley Roby thing was nothing to me. You know, I ain't gonna say it's nothing.


[User] Please transcribe this text

[Assistant] The Bradley Roby thing is something that needs to be said that he's a guy that was making a lot of money.


[User] Please transcribe this text

[Assistant] He really wasn't that good. You seen guys out playing, guys like Isaac Yidam, guys like Isaac Yidam.


[User] Please transcribe this text

[Assistant] The papers said he had been brutally cut down in his manhood, and girls put his picture next to their hearts.


[User] Please transcribe this text

[Assistant] I didn't see Maud before she disappeared. Good job. Or I'd have probably ended up with Mrs. Suxby. Mrs. Suxby was so game when the judge put on the black cap and sentenced her to death.


[User] Please transcribe this text

[Assistant] She always looked behind me. As if she was expecting someone else to be with me.


[User] Please transcribe this text

[Assistant] I heard that Mr. Lillard died, and so I returned to Briar to see if I could find something to show me where Maud had gone.


[User] Please transcribe this text

[Assistant] Of course I love you, Tess. My feelings are the same.


[User] Please transcribe this text

[Assistant] I won't leave you? He said. I will protect you no matter what.


[User] Please transcribe this text

[Assistant] I don't know. Can you walk a long way?


[User] Please transcribe this text

[Assistant] We can walk away from the sea. When they stop looking for us, we can find a way to leave the country.


[User] Please transcribe this text

[Assistant] It's no use. We've surrounded the place and won't let you go. Please, let her sleep.


[User] Please transcribe this text

[Assistant] So until now, we have seen CNNs in all of the models, in all of the state of the art models that have been used until now for computer vision.


[User] Please transcribe this text

[Assistant] So in this, uh, they put this, the vision transformer through numerous tests. Uh, they perturb the input, they perturb the model and they just, uh, check how it performs with respect to the previous state of the art. That is the resonance.


[User] Please transcribe this text

[Assistant] We'll just perturb the input to the model and see how it, uh, how the model behaves, how the model reacts to the input perturbation. And then we'll, uh, perturb the model itself. That is we'll prune some layers, we'll do some lesion studies, we'll see that


[User] Please transcribe this text

[Assistant] And there are some properties that make it, uh, very suitable for using computer vision. It's properties such as the spatial locality awareness, where it just judges, where it just sees the pixels in vicinity of each other.


[User] Please transcribe this text

[Assistant] So there's translation invariance. No matter where the subject is in the image, it should be able to successfully predict that there's a dog in the image.


[User] Please transcribe this text

[Assistant] As I already said, yeaah, there are some strong inductive biases for computer vision tasks due to these properties. Because for example, if I want to predict an image of a dog and I see some pixels in the vicinity, I


[User] Please transcribe this text

[Assistant] We just presume that there is a strong, uh, uh, inductive bias for the computer vision task.


[User] Please transcribe this text

[Assistant] The field of uh, the effective field of the convolution neural network. So for example, if I have, if I'm doing some cats versus dogs classification. So for example, the first of the convolution neural network, the first of the layers will be effective in just, uh, uh, finding out the


[User] Please transcribe this text

[Assistant] in, in classifying or in finding some other high level features. So, so this kind of a hierarchical structure, the first layers find low level features, the middle of the layers find higher level features and layers towards the end find the highest level of the features. For example, if I'm


[User] Please transcribe this text

[Assistant] So there's sort of a hierarchical structure in this. That's pretty helpful.


[User] Please transcribe this text

[Assistant] The other thing that, uh, the other features that we see in the current day models that really augment the processing of these CNNs is some layers such as max pooling. So these max pooling layers just act as distillation layers. They just distill the information that has already been processed.


[User] Please transcribe this text

[Assistant] The other thing, uh, that is very, very, very use, very useful in today's model is this, is this residual connections or otherwise called the skip connections.


[User] Please transcribe this text

[Assistant] They are very helpful in doing some things such as they allow the easy gradient flow to the earlier layers.


[User] Please transcribe this text

[Assistant] There may be some, uh, the gradient may vanish if, if we are using a very long model. So the earlier layers may not receive a lot of gradient, may not receive a lot of proper gradient. So with these residual connections, with these residual and skip connections, we are able to


[User] Please transcribe this text

[Assistant] So these are the resonants and these are the skip connections that we see. They help in proper.


[User] Please transcribe this text

[Assistant] So now comes the vision transformer. So basically, it's a derived form for transformer. After much success in NLP, the natural language processing tasks, transformers have been tied in a lot of computer vision tasks.


[User] Please transcribe this text

[Assistant] We, we do not presume that spatial locality is necessary because attention caters to each and every pair in the input embedding. So there's no concept of spatial locality in this. So there are no inductive biases. The model has to figure out its own.


[User] Please transcribe this text

[Assistant] So this is the high level of architecture of this vision transformer. It looks much like a standard transformer. So we just use the transformer encoder in this.


[User] Please transcribe this text

[Assistant] So I'll tell you how this works. I'll give you a brief overview of how this works even though this was presented the day before yesterday. So first of all, as we


[User] Please transcribe this text

[Assistant] As we give the model some images, these images are first broken down into non-overlapping patches.


[User] Please transcribe this text

[Assistant] So now when these, when these images are broken down into the patches, they're flattened into 1D vectors.


[User] Please transcribe this text

[Assistant] After that, uh, we also prepend a sort of a embedding like we do in the BERT model. The BERT model also uses this CLS token. We use a similar kind of a token here in VIT also. We prepend this token here.


[User] Please transcribe this text

[Assistant] And we also add position embeddings, just to preserve that.


[User] Please transcribe this text

[Assistant] Positional information in the image because it's broken down into patches and the model should have some way of knowing what the original position of the patch was in the original image.


[User] Please transcribe this text

[Assistant] So that's the input processing. After this, all those vectors are fed into the, are fed into the VIT model.


[User] Please transcribe this text

[Assistant] This VIT model is just a transformer encoder as we see towards the right hand side of the image.


[User] Please transcribe this text

[Assistant] Uh, we just feed all those vectors into the model.


[User] Please transcribe this text

[Assistant] We just, uh, read out the final representation of this CLS token towards the end to classify our things. So for example, we just read, read the representation of the CLS token


[User] Please transcribe this text

[Assistant] Of the highest layer of the encoder and just feed into the MLP head, feed into the multi-layer perceptron layer. And the output of this layer is, is our softmax, is our layer that is, uh, predicting things that helps us classify.


[User] Please transcribe this text

[Assistant] And the other thing that the authors or the other vision transformer try to try to do is they just kept this model as similar to the transformer encoder as possible. So they did this because they wanted to leverage the parallelism in the original, uh, transformer encoders.


[User] Please transcribe this text

[Assistant] So because of, because of which transformers were very, very successful, uh, was the parallel, the high level parallelism in the original model.


[User] Please transcribe this text

[Assistant] So they tried to reserve that in this vision transformer too.


[User] Please transcribe this text

[Assistant] So simply we break the image down into patches. We feed those patches and we first flatten them. We feed those patches into this layer. This layer projects those flattened vectors to some other space or some different dimensions.


[User] Please transcribe this text

[Assistant] Then simply those, uh, embeddings are input to the transformer encoder.


[User] Please transcribe this text

[Assistant] The output is, the output of the encoder, the output of the vision transformer is the representation, is the final representation of the CLS token. This CLS token is just fed into some MLP layer.


[User] Please transcribe this text

[Assistant] So uh, that was the high level review of uh, vision transformers. So now we come to this paper. So what is the work of this paper? What, what this paper tries to do is


[User] Please transcribe this text

[Assistant] It just measures the robustness of VIT models of different sizes that are pre-trained on different datasets and compares the results to the performance of resonance, the previous state of the art.


[User] Please transcribe this text

[Assistant] How do we really test this robustness? There are two ways we can test this. We can either feed the model with perturbed inputs, that is we disturb the inputs somehow and see if our model is resilient to the disturbed inputs or not.


[User] Please transcribe this text

[Assistant] See how the model performs without those layers. This is basically done just to see if there is some redundancy in the model.


[User] Please transcribe this text

[Assistant] So there are, based on the number of trainable parameters, our model can be divided into three, three categories.


[User] Please transcribe this text

[Assistant] The other way this paper has categorized this, uh, VIT is on the basis of the model that, on the basis of the dataset that the model has been pre-trained on.


[User] Please transcribe this text

[Assistant] So there are three types of datasets, there are three datasets that have been used to pre-train, to pre-train this VIT model. The first of them is the simple image net. There's only 1.3 million images.


[User] Please transcribe this text

[Assistant] The second is the ImageNet 21k. It has almost 13 million images and the largest of them is the JFT 300 million, which has 300 million images.


[User] Please transcribe this text

[Assistant] So as we'll see, the model performs the best when it's pre-written on this image, on this dataset, the JFT 300 million. All right, so let's move on. Uh, so let's, let's.


[User] Please transcribe this text

[Assistant] Let's see the first of those categories of perturbations when we perturb the input to the model. We just distort the image somehow.


[User] Please transcribe this text

[Assistant] So the first of them is this natural corruption. So, I mean, there's already...


[User] Please transcribe this text

[Assistant] And a lot of things, contrast, elastic, jpeg, fog, frost, motion blur we can add.


[User] Please transcribe this text

[Assistant] There are a lot of things that we can do artificially too. And then we feed those images into the model and see how, how the model performs. So as we can see, uh, the, the,


[User] Please transcribe this text

[Assistant] That, that are already inherent to the CNNs and that help it perform a lot better than, uh, VIT in the, in the region, in the arena where there's not much data. So we can see in the, in the data set having the least number of, uh, images, it performs, the ResNet performs better than


[User] Please transcribe this text

[Assistant] Inherent to resonant, even the inductive bias that is inherent to resonant does not help it beat the VIT.


[User] Please transcribe this text

[Assistant] Because there are a lot of connections, uh, because there are a lot of...


[User] Please transcribe this text

[Assistant] Attention heads and attention spans in, in the transformer, it performs even better than a CNN. So as you can see in this JFT, uh,


[User] Please transcribe this text

[Assistant] Uh, as we can see on this JFT dataset that has 300 million images, uh, the, the VIT, the vision transformer, huge, and that has, uh, that has patches of 14 by 14 pixels. It performs even better than the high, it performs even better than the best resonant model.


[User] Please transcribe this text

[Assistant] So this image in its seed, uh, simulates these natural corruptions for us. And mind you, these are, the result of this type of perturbation is really very important because


[User] Please transcribe this text

[Assistant] Because if we deploy our model to some critical systems.


[User] Please transcribe this text

[Assistant] We would expect our model to, you know, withstand these, these type of corruptions. So no, no matter if it's snowing or there's frost or there's fog, you may expect your, you may expect your model to withstand all of those things and perform, and perform fairly in all those type of situations.


[User] Please transcribe this text

[Assistant] So, uh, now comes, uh, real world distribution shift.


[User] Please transcribe this text

[Assistant] So these are similar to by this other, uh, I don't know, yeah, ImageNet R. These type of distribution shifts are similar to by this dataset, the ImageNet R, where we see, even though this is a sculpture of a panda, and it's not a real panda, we as humans can easily recognize that this is a panda.


[User] Please transcribe this text

[Assistant] So we expect our computer vision model to also recognize that this is a partner. So, so we just test our vision transformer and the resonance on these type of data and see which one performs better.


[User] Please transcribe this text

[Assistant] It does not look like a real leopard. There are some distribution shits.


[User] Please transcribe this text

[Assistant] The contours are all similar to that of a real leopard and this one here. I mean, the overall picture does resemble, but there's a real world distribution shift in this.


[User] Please transcribe this text

[Assistant] So this is how the two models compare with each other in this. As I already told, in the lower data regime where there's not much data, Resinets perform better than VIT as expected because there's no strong inductive bias in the VIT.


[User] Please transcribe this text

[Assistant] But as the data increases and as we add more and more images to our data sets, we see the VIT performs at least as well as Resonance or in some cases even better than, uh, Resonance as you can see here is a slight improvement.


[User] Please transcribe this text

[Assistant] In the performance, in the performance of the VIT. As we can see data, the data set that we pretend the model on plays a very, very important role as we can see here.


[User] Please transcribe this text

[Assistant] So like on, on, on 1.3 million images, they have 20% accuracy, 40% accuracy on 13 million images and 6 and 70% accuracy on, on, on data set having, uh, if pre-tenant data set having 300 million images.


[User] Please transcribe this text

[Assistant] So now comes the, these natural adversarial examples. So as we can see, these are really difficult, uh, these type of data sets are really very difficult for a model to, uh, for a model to predict. So as we can see here, if we see this dragonfly on this door mat, so 99% of the times


[User] Please transcribe this text

[Assistant] So it's really difficult for the model to predict which is which. And we can see here if this man is, it looks like he's, he's riding a unicycle.


[User] Please transcribe this text

[Assistant] The model somehow predicts the posture of the man and predicts that he's riding a unicycle and predicts that it's a unicycle.


[User] Please transcribe this text

[Assistant] But we as humans can see that there's a racket in the, uh, man's hand, so we...


[User] Please transcribe this text

[Assistant] Very low accuracy in, in the image data set having, uh, in the data set having low number of images and very high accuracy in the regime with this, with this lot of images in the data set.


[User] Please transcribe this text

[Assistant] So yeaah, now, uh, now this, this type of study is a very important study. So now we are seeing the robustness versus model size. So how does, how well does the model play if we just scale it? So we see here.


[User] Please transcribe this text

[Assistant] That, if we pretend the model on the same data set, the JFT 300 million, having 300 million and a million, and if we scale the model, this is how it performs.


[User] Please transcribe this text

[Assistant] But the other thing to note here is that the slope of the model, the slope of VIT, the vision transformer, is much better than the slope of the resonator.


[User] Please transcribe this text

[Assistant] Which means that if we are successfully able to scale the vision transformer, even to bigger sizes, even to much larger number of trainable parameters, we can still improve the error rate.


[User] Please transcribe this text

[Assistant] The thing that really matters here in this image is the slope.


[User] Please transcribe this text

[Assistant] Okay, now let's come to adversarial perturbations.


[User] Please transcribe this text

[Assistant] So these are really, really advanced type of perturbations. These are also called white box attacks because we are, because these types of attacks can already be carried out if we know the architecture of the model.


[User] Please transcribe this text

[Assistant] So these type of attacks, you know, they take into account all the other things that are inside the model, the model weights, the individual layers of the model, how they interconnect, how they behave, how they stack on top of each other. That's why they are called whiteboard, white box attacks. So mind you, if I, if I prepare a white box attack for a VIT,


[User] Please transcribe this text

[Assistant] And try that white box attack on a resonant, it would perform, it would perform well and vice versa also. So if I, if I prepare a white box attack for a, for a resonant,


[User] Please transcribe this text

[Assistant] And try that attack on and try the perturbations that I've generated on a, on a VIT, on a vision transformer. It won't, it won't work well.


[User] Please transcribe this text

[Assistant] So these are the perturbations that have been, uh, generated. There are, uh, that have been generated through these adversarial attacks. So basically there are two types of adversarial attacks, the FGSM and the PGD.


[User] Please transcribe this text

[Assistant] And we can see a boxy type of pattern when it comes to the vision transformers, because as we know, we break the image into the patches before feeding them onto the vision transformer. So we can see those patches, patchy type of pattern in this, in the foreground


[User] Please transcribe this text

[Assistant] The perturbations are more in the foreground.


[User] Please transcribe this text

[Assistant] So now let's come to adversarial spatial perturbations. So what we do in these perturbations is we either shift the image by some pixels towards the right or left and also rotate the image at the same time.


[User] Please transcribe this text

[Assistant] So we just choose one value among this range, minus 16 to 16, and we choose one value in this range and perform the, and perform those translations and rotations at the same time and see how and how our model performs on this.


[User] Please transcribe this text

[Assistant] How the, uh, data is being processed inside the model, as we will soon see.


[User] Please transcribe this text

[Assistant] So this is how the model performs on those adversarial spatial perturbations.


[User] Please transcribe this text

[Assistant] We can see the same story here also.


[User] Please transcribe this text

[Assistant] The other thing to notice here is that the 32 by 32 VIT does not perform well.


[User] Please transcribe this text

[Assistant] The largest of the, the VIT where we break down the image into the largest of the chunks, 32 by 32 chunks, 32 by 32 patches, non-overlapping patches.


[User] Please transcribe this text

[Assistant] Alright, so now, uh, let's focus on text texture bias.


[User] Please transcribe this text

[Assistant] Important type of a bias that we may want to eliminate in, in your computer vision application or computer vision model.


[User] Please transcribe this text

[Assistant] We just see the outline of his figure. So for example, if I show you a cheetah and a leopard.


[User] Please transcribe this text

[Assistant] So for example, there are many types of banana. So some bananas are yellow, some may be green. So if I show, if I show a computer vision model, a yellow banana, it may well predict that it's a banana, but if I show it a green banana,


[User] Please transcribe this text

[Assistant] There's a slight chance that it may not be able to predict it, even though the shape of both the bananas are similar.


[User] Please transcribe this text

[Assistant] So just because the texture was different, the model was not able to predict the other type of banana very successfully. Just because the color of the other banana was


[User] Please transcribe this text

[Assistant] For this testing by a technique called neural style transfer, as we'll see in the next slide. So this is neural style transfer as a lot of you may know.


[User] Please transcribe this text

[Assistant] We just give it an image and a pattern and we just somehow split the texture on the image.


[User] Please transcribe this text

[Assistant] Outline of one image and a texture of the other image. That's what's, that's what is neural transfer.


[User] Please transcribe this text

[Assistant] So how these two models perform on this type of dataset on this neural style transfer dataset is we see that


[User] Please transcribe this text

[Assistant] Larger patch sizes in VIT seem to perform very better. They somehow seem to preserve the shape better than the VIT that uses smaller patches. It's very contrary to what you have seen before.


[User] Please transcribe this text

[Assistant] Before we have seen that a VIT having, that a VIT breaking the image into smaller patches performed well in all of the tasks and all of the input perturbations we had seen earlier. But now what we see is that a VIT that breaks down the image into larger patches performs well. So this is very, very strange. One reason we can give for this is that.


[User] Please transcribe this text

[Assistant] Uh, we also need to see here that Dresden performs much better than CNNs in this regime. When there's a lot of data. When we put them on the JFT.


[User] Please transcribe this text

[Assistant] Alright, now comes model perturbations. Before what we did is we just did some perturbation of the input to the model. Now what we're doing is we are just, we are just playing with the model itself, the architecture of the model itself.


[User] Please transcribe this text

[Assistant] Yes, and see how that performs. What we also can do is we just restrict attention.


[User] Please transcribe this text

[Assistant] Restrict attention on the layers and see how it performs.


[User] Please transcribe this text

[Assistant] The other thing that is really very important here is the layer correlation. So as you will see, there is a lot of redundancy in the vision transformer. So we can really prune out some layers during inference time and the accuracy won't go down too much.


[User] Please transcribe this text

[Assistant] So we just are measuring the correlations between the representations of each layer. So as we know, this transformer encoder is basically a stacked layer of, uh, it's a stacked layer of blocks where each block is a self-attention layer.


[User] Please transcribe this text

[Assistant] So we, what we do here is we just measure the correlation in the output of each of those layers.


[User] Please transcribe this text

[Assistant] So as we can see, there's a lot of correlation in the output of those layers towards the end.


[User] Please transcribe this text

[Assistant] As you can see in this image too. So lots of correlation between the outputs of the layers. Lots of correlation means lots of redundancy.


[User] Please transcribe this text

[Assistant] There's a lot of similarity in the outputs of each of those models towards the end.


[User] Please transcribe this text

[Assistant] So does that mean these layers are not necessary?


[User] Please transcribe this text

[Assistant] So this is the CLS Topping Correlation. Remember CLS Topping, uh,


[User] Please transcribe this text

[Assistant] This is its correlation. So what we see is that its correlation is very high towards the, towards the start of the encoder.


[User] Please transcribe this text

[Assistant] Uh, so what I was saying is we can see from this image is that the CLS token does not change much towards the start of the model.


[User] Please transcribe this text

[Assistant] And it starts to change quite drastically towards the end layers.


[User] Please transcribe this text

[Assistant] It only starts to process the CS token to the end layers.


[User] Please transcribe this text

[Assistant] And it's quite opposite for the CLS token. The CLS token changes very slowly towards the start of the model and it changes quite rapidly towards the end of the model. So what we make out from it is that CLS token is processed towards the end layers.


[User] Please transcribe this text

[Assistant] Is it wise to keep all the layers during the inference time? I mean, do they play any, do they play any, uh, real meaningful tasks during inference? Uh, so what reason studies show is that


[User] Please transcribe this text

[Assistant] We can remove layers from already trained networks during inference time without much degrade in the performance.


[User] Please transcribe this text

[Assistant] The other thing that's quite shocking is that larger models are more robust to layer removal.


[User] Please transcribe this text

[Assistant] So model trained with larger dataset utilizes each of the layers much properly than a model that has not been trained on a dataset as large.


[User] Please transcribe this text

[Assistant] Then if you remove a similar layer, for instance, if you remove the self attention layer only or the MLP layer only.


[User] Please transcribe this text

[Assistant] Prune the model in film's time. The results are quite shaky. Then the results of a date of a model that are not trained on, on such a large data set. As you can see in the image.


[User] Please transcribe this text

[Assistant] So now comes restricted attention. So what happens if we restrict the attention during the inference time?


[User] Please transcribe this text

[Assistant] So how we do this restriction is that we use a certain distance-based mask.


[User] Please transcribe this text

[Assistant] But it is some special mask that they use to restrict attention.


[User] Please transcribe this text

[Assistant] So what they see is that rested attention in the earlier layers hits hard.


[User] Please transcribe this text

[Assistant] We restrict attention to less and less number of distances from the representation vector, we see a strong drop in the performance.


[User] Please transcribe this text

[Assistant] There is one more thing to note, notice here, which I forgot to tell you, is that we, we cannot, uh, restrain the attention of the CLS token itself.


[User] Please transcribe this text

[Assistant] CLS token is allowed to have self attention, self attention calculations with any of the other representation vectors. We restrict only the self attention for the other embedding vectors, not the CLS token itself because it's a CLS token


[User] Please transcribe this text

[Assistant] I mean, it's some, it's some very high level attack that, that they do not talk about in the paper, but they just tell that it's a, it's a white box type of attack where you, where you keep the model type into the consideration.


[User] Please transcribe this text

[Assistant] This conclusion from, uh, from Facebook, I mean...


[User] Please transcribe this text

[Assistant] What I think, they are not trying to reduce the redundancy, they are just


[User] Please transcribe this text

[Assistant] Yes, you're just making use of the fact that since there is a lot of redundancy in the model itself, they can skip some layers and it's not possible in the inference time as they say.


[User] Please transcribe this text

[Assistant] Because inference time, during inference time it has been seen that if we decrease the number of layers, the performance does decrease.


[User] Please transcribe this text

[Assistant] So it's not like the smaller models are as robust as the larger models. There is some tasks that larger models do play quite better than the smaller models. So even though there's a lot of redundancy in the layers itself,


[User] Please transcribe this text

[Assistant] It's just the fact that they can remove a certain layer and that there is not a lot of drastic change in the accuracy and the, in the accuracy of the model as, as, as I already show in this, in this slide. If you just do not once in the layer.


[User] Please transcribe this text

[Assistant] There is a lot of correlation, but we do not know why, we do not know why the output still goes down when we prune out the layers.


[User] Please transcribe this text

[Assistant] There's, there's, there's one more reason I can think here is that since we are waiting for the CLS tokens representation to evolve, as you can see in this figure,


[User] Please transcribe this text

[Assistant] Towards the start, the CRS tokens don't change much.


[User] Please transcribe this text

[Assistant] But it's towards the end that CLS tokens change because as you can see, there's no correlation towards the end for the CLS tokens. So maybe this model is waiting for the representations to develop


[User] Please transcribe this text

[Assistant] And not working for the representations at that time when, when you're working on the CLS representation. Maybe that's the reason. Did I, did I, did I make, make my point through? Do you want to start what I said?


[User] Please transcribe this text

[Assistant] Working on the CLS tokens towards the end and working on the representations towards the start layers.


[User] Please transcribe this text

[Assistant] Yeah, I think they can, there can be an auxiliary loss as you already said, and maybe by introducing that auxiliary dose, who knows the model performs even better.


[User] Please transcribe this text

[Assistant] When we, when we, when we just force the model to learn something new in each representation and not just present the same representation of the other layers, maybe who knows the model performs even better when we discourage such correlations.


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[Assistant] It was love at first sight.


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[Assistant] You know, in my job you worked very closely with the wife and I did and I was with her all the time.


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[Assistant] He says, uh, bad things could happen if I refuse, and sure enough,


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[Assistant] Listen, I'm not gonna allow you to do this roundtable.


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[Assistant] Did not plan it at all. I literally changed shirts like minutes before I set everything up here. Cause I was like, you know what? I'm gonna do the No Mercy shirt. Cause I just want y'all to understand.


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[Assistant] Future NFC South Champions, that's what we here for. This is why I'm here. Don't be out here with the fake news that he got under the post over there. He should have put his site up like Big Q did. Smart man Big Q. Should have put his site up. You know what I'm saying? Big little country, you in the now today. But it's okay.


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[Assistant] It is, it is, it is okay. Listen, I already deal with y'all Falcons fans yesterday in the Twitter space.


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[Assistant] See, see, see, you're already being disrespectful. What, what you mean? My man said pizza pizza.


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[Assistant] I'ma tell you a quick story. I was in Atlanta last weekend and I thought I was in New Orleans cause I kept seeing Mercedes, I kept passing by them Mercedes Benz.


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[Assistant] I'm thinking, I'm thinking, yo, I kept calling Atlanta, New Orleans.


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[Assistant] I kept thinking and it was like, you in Atlanta. I was like, oh yeah, that is right.


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[Assistant] But it's gonna be interesting. We all got a bunch of relatively new guys. And I stand by with what I told you, Big Low. You're starting the wrong quarterback. But for week one, I'm thankful.


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[Assistant] Don't be livin'. Don't be out here tellin' me, listen, I ain't as big money as you, bruh. Like, let's not do this. We know why my big money showed up. Don't be out here doin' all that. Don't be out here doin' all that.


[User] Please transcribe this text

[Assistant] You know, we were having this discussion point yesterday during our show about the fact that we kept five tight ends and then we picked up three guys and we still decided to keep five tight ends. That tells me a lot that we're going to play a lot of two tight end sets.


[User] Please transcribe this text

[Assistant] Uhm, and I also believe that we were talking about Chanel with the Devo, even though you got Mini Devo, Dia Devo, even though we didn't really show that in the preseason, we saw a lot of that in training camp while we were there.


[User] Please transcribe this text

[Assistant] So I think, you know, when we, at first when, when Frank was out here talking vanilla offense, vanilla offense, but then when you sit down and think about what we saw.


[User] Please transcribe this text

[Assistant] He, he might be onto something. Now, granted, there were some things on the offensive line that scared me, but it's obvious that they're not gonna allow.


[User] Please transcribe this text

[Assistant] Bryce to get killed. So they would rather play two tight end sets. Make sure that Bryce does not get killed.


[User] Please transcribe this text

[Assistant] You better, you better hope they don't, cause y'all about to catch a fake, cause we know, I know somebody on your team that don't have regressiveness. Desmond Ritter. Okay. Okay. Okay.


[User] Please transcribe this text

[Assistant] I'm not worried about Ritter, I'm worried about, I'm worried about Bijan. I'll keep it a buck with y'all. I like Bijan. I was high on Bijan coming into the draft.


[User] Please transcribe this text

[Assistant] Now, I question whether or not why you took Bijan, but I was high on Bijan.


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[Assistant] Because we already had defense coordinator. In Atlanta, found some of our good, good coaches.


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[Assistant] You know, I knew that was going to happen from a mile away, but you know.


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[Assistant] I'm just trying to understand for the sake of clarity.


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[Assistant] Cause as soon as I heard Baker got a starting job, took him off. He had to go. Not drafting him.


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[Assistant] Look man, I keep it a buck. It's the Saints. I mean when you talk, like, it revolves around the quarterback. They got the best quarterback in the country. In the division.


[User] Please transcribe this text

[Assistant] But I'm just letting you know that y'all got the best quarterback. That's what's gonna make us sweeter when we beat y'all week two.


[User] Please transcribe this text

[Assistant] So, I mean, that might be a fact. I know you're not a Denizales guy, Q, but, you know.


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[Assistant] I'm gonna tell you the same, I'm gonna tell you the same thing I was telling that Focke's fan in that room yesterday. Let me tell y'all something, man. You guys think that y'all got Bijan Robinson, y'all are...


[User] Please transcribe this text

[Assistant] Y'all are tongue everlasting up in this people. I'ma give you guys a reality check. Y'all are not gonna just run over us. But that guy was in denial talking about, oh we got the best this, the best that.


[User] Please transcribe this text

[Assistant] I gotta let it, I gotta let it out. That's how you, you reduce your mental, reduce your stress. You gotta let it all out. You gotta let it, let it expand to the world.


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[Assistant] And I'm gonna do it because I'm gonna let y'all figure out who the, who Baker likes the most. So I'm gonna keep him on my bench and I'm gonna decide.


[User] Please transcribe this text

[Assistant] If he's the one, then that means Mike Evans is gonna be the guy. Whoever's the one's gonna be trash. But whoever you decide that the one is, that means you want the two.


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[Assistant] He ruined, he damn near, he damn near ruined DJ Moore. Like he ruins the ones. That's what he does. So I'm gonna take both of them and then we gonna hope for the best and one of these guys is gonna be good.


[User] Please transcribe this text

[Assistant] I mean, that's actual, actual lifestyle, but you know, I know you can't relate, you know, y'all be out here trying to rise up and, and get shot down and all the other things, so, you know.


[User] Please transcribe this text

[Assistant] I mean, I honestly, I'm gonna be honest with y'all, I think that we're underrating what Miles Sanders could potentially be for our offense. I don't, I think folks, and I'm even looking at it in fantasy leagues right now about where he's been going.


[User] Please transcribe this text

[Assistant] He got up to an upside. I think they're gonna give him a lot of work. I know there's been this thing where we're gonna split the work with Miles. I ain't buying it one bit. I think Miles is gonna get the bulk of the work. And I know Rashad disagrees with me in regards to that.


[User] Please transcribe this text

[Assistant] But, I just think- What'd you say? About Miles getting all to work.


[User] Please transcribe this text

[Assistant] Yeah, you know what I'm saying? So, from that standpoint, uhm, I feel like, you know, when you look into that,


[User] Please transcribe this text

[Assistant] Like I just think Miles is gonna get a lot of work. Like I just think he's gonna put in a lot of work. And I think our defense, even though we're switching to a 3-4 scheme, I think we got the players that make it work.


[User] Please transcribe this text

[Assistant] Like I'm not, I'm not as panicked as others, cause I, I trust Ero.


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[Assistant] Ejero, excuse me. I trust him as a DC. Uhm, you know, and I know a lot of folks were panicking about the waiver claims we have and I'm like, they obviously trust the guys they got there.


[User] Please transcribe this text

[Assistant] I mean, are we really gonna talk about guinea pigs right now? Wouldn't y'all be the guinea pigs?


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[Assistant] Do I need to send you a bottle now? I want to make sure it gets there before September 10th.


[User] Please transcribe this text

[Assistant] Look man, look man, this is some good stuff that comes out of a bottle though, man. You might need, this is an expensive bottle, I don't know if I can ship this to you, but like, if there's something that you, like if you really need it, you might need it for the 10.


[User] Please transcribe this text

[Assistant] But there, there, there are some, there are some institutions and, and organizations across the college and...


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[Assistant] She can't bring them colors into my house. If she, if she, and the thing is, she can't go to Central unless they give her a full rod. And even then, you can leave your colors outside. I will have an Aggie shirt waiting for you at the door.


[User] Please transcribe this text

[Assistant] For her to throw on. Something to change it. Or she can take it, go to like a, a dressing room somewhere, make your changes, and return. But you can't bring them colors in my house. Those are things that cannot occur.


[User] Please transcribe this text

[Assistant] I'm letting South Carolina State slide just a tad because I'm from the state, but that's the only reason why. That's the only reason. And even then, the jokes are flying as soon as you show up with them colors in my house. It's just what it is.


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[Assistant] We were a CJ Henderson away from winning the division last year.


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[Assistant] I'm just gonna say that out there, we were out here winning, winning, winning divisions with Sam Darnold, almost winning divisions with Sam Darnold, PJ Walker, and even your boy Baker Mayfield down in Bucksland. That's what we were doing. So now you're telling me with the number one overall pick, I can't win this division? That's what you guys are trying to tell me.


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[Assistant] You know what I'm saying? You know, them dirty birds are going to be third and I'm going to pray to God that the bucks at last place don't get killed.


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[Assistant] Like that, that's my four. That's my order. But I do agree that the division's gonna be competitive, and I think we're not gonna be as bad as what folks are saying.


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[Assistant] That's how much hate in my heart I have for Baker Mayfield. That is how much hate in my heart I have for Baker Mayfield. Ruined my mental health for half a year last year.


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[Assistant] I'm telling you, if, if Baker are here looking super mediocre, they're gonna swap the trash so fast, and then once that happens, Baker's gonna ask for his release. Cause that's what he definitely did with Carolina.


[User] Please transcribe this text

[Assistant] Once Baker realized he wasn't a guy, he wanted his ass out of there.


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[Assistant] I'm a Panthers fan bro, I know exactly what that circle life you talking about my brother. I've been, I've been, I've been through.


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[Assistant] Man, let me tell you something. This owner ain't seen the playoffs yet. He been through the lows.


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[Assistant] That's why I know we gotta, that's why we gotta start strong at the beginning of the season. We can't be messing around, yo. We start like 0-2 in the division? I mean...


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[Assistant] I ain't gotta worry about Frank Rice head. He ain't gotta worry about owner.


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[Assistant] You let, you let, you let know at the tap, tap by messing around trying to get another quarterback next year. We that bad?


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[Assistant] Dog, he took that team over in 2018.


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[Assistant] Never forget that Camdude didn't play with no...


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[Assistant] I don't think any offensive players other than him got drafted. Maybe one guy?


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[Assistant] Jimmy, Jimmy, Jimmy C fell, Jimmy C fell victim the same way that Matt Corral did. He was just a guy till it was time to...


[User] Please transcribe this text

[Assistant] Yeah, like when that whole debate was happening, man, with him, Stroud, and Richardson, I could've seen a scenario where they could've took Richardson, but I did not see Stroud. I mean, I always thought it was gonna probably be Bryce. Like, it was interesting to watch it unfold, cause I, and I said this during the draft time, I feel like Richardson has the highest upside of the draft.


[User] Please transcribe this text

[Assistant] From a quarterback position, he had the biggest upside of the draft. But it's a matter of whether or not you think he's ready. And it seems like he's a little bit more ready than anticipated in Minneapolis.


[User] Please transcribe this text

[Assistant] Well, when you got, when you got an owner like, uh, my man, Ursay, bruh, that's sometimes how it goes.


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[Assistant] It actually equals zero. Championship's like us. So. But it's okay.


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[Assistant] Yo, Steve Wilks pretty much said that the owner of the Cardinals


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[Assistant] Told them to keep contacting the GM even though he was suspended on a burner phone.


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[Assistant] Yo, real talk. They gots to go. Gotta lose team.


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[Assistant] Bruh, owners gotta lose the team. They gotta 524. They gotta lose the team, bruh.


[User] Please transcribe this text

[Assistant] They suspended that man and he pretty much said, I don't give a damn about my suspension. Take this burner phone and you reach out to me all the time about everything we do. That's wild guys.


[User] Please transcribe this text

[Assistant] Even that was, and the thing is, that suspension was minor details, right? Like when you out here penalizing GMs and everybody else, you, you, like, those guys should have the highest level of integrity. You know what I'm saying? And you out here still skirting around these rules.


[User] Please transcribe this text

[Assistant] Yeaah, that's my point. So like, when you're talking about a man that was in the Florence Law School, bring him up straight up and say, yo man, these guys out here with these burner phones, telling me I still got work with the GM and they still fired me after one year.


[User] Please transcribe this text

[Assistant] Uhm, you can follow us on YouTube, uh, Panther Nation Podcast, look up, or you can just search PNP Rashad and Dave, you'll see it, we'll be near the top.


[User] Please transcribe this text

[Assistant] Uhm, we, we recently did a, did a cut to it episode with Steve Smith Sr. to go over the draft a few months back.


[User] Please transcribe this text

[Assistant] Uh, so just check us out, man. We cover the Panthers, we also cover things within the NFL, and we do fantasy football. I started the Fancy Hour a few weeks ago. So I go live on Mondays, but the PNP official show goes live every Tuesday at 8pm EST. So come check us out, guys.


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[Assistant] He could not walk in a straight line on his weak legs, even though he was not too old. The man met an old minister on his horse.


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[Assistant] The man stopped to ask the minister a question.


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[Assistant] said the man. The minister said,


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[Assistant] But tourists and artists do not know it yet. It is a quiet piece of countryside.


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[Assistant] The fields there are always green and there is plenty of water in the rivers. In the valley, the air will calm you.


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[Assistant] The sky is deep blue and the fields are small. Everything is healthy and green. To the south of the valley, the land is dry and the fields are big. Marlott is a village in the Vale of Blackmoor.


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[Assistant] It is an ancient custom for women in the village to dance every holiday. For the May Day dance they all wear white dresses.


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[Assistant] A pretty girl named Tess had a red bow around her hair. The women saw a carriage pass by. Derbyfield was in the carriage. They could hear him sing about his good fortune. Tess was ashamed. Father is tired! said Tess. Her face was red. The girls got on with the dance. The men watched and danced too.


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[Assistant] Three young strangers stopped to look. They were brothers. The two elder brothers started walking again, but the youngest was interested in the girls. He danced with several women. On his way out, he saw Tess. He thought her attractive.


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[Assistant] She seemed sad because he had not danced with her. He wanted to dance with her. He wanted to know her name. But it was too late. So he joined his older brothers. Tess saw the young stranger too.


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[Assistant] But she was too worried about her father's strange appearance to pay much attention to him. When she got home, her family's small cottage saddened her. They only had one candle. The furniture was old. Six children lived in the tiny space. When Tess arrived,


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[Assistant] Her mother was doing the laundry and putting the baby to bed. Joan Derbyfield had many children. Taking care of them made her look old, but she was still a little bit pretty.


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[Assistant] Tess was pretty, like her mother had once been. I'll help you, mother, Tess said. I'm glad to see you. I have something to tell you, said her mother.


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[Assistant] They have found out that we're the oldest family in the county. Our real name is Durberville. You should be proud. And that's why your father was in the carriage. Some people thought it was because he had been drinking. Will this help us? asked Tess. Oh yes.


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[Assistant] Our famous family members should come to meet us the minute they hear. Where is father now? asked Tess, worried. Her mother paused.


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[Assistant] You know his heart is too weak from being fat.


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[Assistant] The doctor said today that he might die soon, but no one is sure.


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[Assistant] Tess knew her mother liked going to the bar to find her father.


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[Assistant] Then her mother could drink a beer and forget her family.


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[Assistant] It was one of the only times the hard-working Mrs. Derbyfield went out.


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[Assistant] Tess had to look after the six small Derby Field children.


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[Assistant] Thinking of Renesmee brought her to that center stage place in my strange new and roomy but distractible mind. So many questions. Tell me about her, I insisted as he took my hand. Being linked barely slowed us. She's like nothing else in the world, he told me.


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[Assistant] I felt a sharp pang of jealousy over this stranger. He knew her and I did not. It wasn't fair. How much is she like you? How much like me? Or like I was anyway? It seems a fairly even divide. She was warm blooded, I remembered. Yes, she has a heartbeat, though it runs a little bit faster than a human's. Her temperature is a little bit hotter than usual too.


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[Assistant] She sleeps. Really? Quite well for a newborn. The only parents in the world who don't need sleep, and our child already sleeps through the night. He chuckled. I like the way he said our child. The words made her more real. She has exactly your color eyes, so that didn't get lost after all. He smiled at me. They're so beautiful. And the vampire parts, I asked.


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[Assistant] So uhm, uuuh.


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[Assistant] I knew there was a lot that I needed to see for myself before it would be real. I wasn't sure how much more I was ready for, so I changed the subject. Why is Jacob still here? I asked. How can he stand it? Why should he? My ringing voice trembled a little. Why should he have to suffer more? Jacob isn't suffering, he said in a strange new tone, though I might be willing to change his condition.


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[Assistant] It seemed odd now that I had needed him so much then. That sense of absence without him near had vanished. It must have been a human weakness. You'll see exactly how I can say that, Edward muttered. I promised him that I would let him explain, but I doubt you'll see it much differently than I do. Of course, I'm often wrong about your thoughts, aren't I? He pursed his lips and eyed me. Explain what? Edward shook his head.


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[Assistant] I don't understand.


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[Assistant] He stroked my cheek and then smiled gently when my face smoothed out in response. Desire momentarily overruling annoyance. It's harder than you make it look. I know. I remember. I don't like feeling confused. I know.


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[Assistant] So uhm, uh,


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[Assistant] I won't be angry with you for all those times you got me into trouble with Aunt Polly. You mean you're dying, Tom?


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[Assistant] I'm leaving everything valuable I have to that new girl. Be sure that she gets


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[Assistant] It's my foot, Auntie. I think the doctor will have to cut it off.


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[Assistant] Yeah, yeah, hello," replied Huckleberry. What do you have there in that bag? It's a dead cat. Wow, Huck! Will you let me see him? Where did you get it? A boy gave it to me. What do you plan to do with a dead cat, Huck? I'm going to use it to take off these warts.


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[Assistant] After someone dies and their body is put in the ground, you take your cat to that person's grave just before twelve o'clock midnight. After twelve o'clock.


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[Assistant] You will start to hear a low sound, something like the wind.


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[Assistant] Or you might even hear someone talking very softly.


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[Assistant] These are terrible spirits that have come to carry the dead man away.


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[Assistant] You won't be able to see them, but you can hear them.


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[Assistant] And when they start to leave with the body, you throw the cat at them and say, Spirits follow body.


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[Assistant] Cat follows spirits. Warts follow cat.


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[Assistant] I promise you, that will take away anyone's warts. Have you ever done it before? Not yet.


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[Assistant] But you remember that old Hoss Williams just died, right? Yes, I remember, Hook. But they put him in the ground on Saturday. The spirits have already taken his body. Now, think for a minute.


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[Assistant] If he was buried on Saturday, the spirits couldn't have taken him away at twelve o'clock, because it would be Sunday at that time.


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[Assistant] Bad spirits don't dare do anything on Sunday. That's God's day. So I'm thinking they'll come tonight. You know, you're right. So can I come with you? Are you sure you won't be too frightened? Of course not. Come and get me at my house tonight.


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[Assistant] Make a sound like a cat, and I'll come out to meet you.


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[Assistant] Yes, but you answer this time because the last time I did that you never answered and one of your neighbors started throwing rocks at me. I'm sorry, Huck. My aunt was keeping her eye on me that night. There was nothing I could do. But tonight I promise there won't be a problem.


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[Assistant] Thomas Sawyer, what's your excuse for being late this time?


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[Assistant] I met Huckleberry Finn on my way to school and we had a nice long talk.


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[Assistant] I've told you a hundred times never to speak with that boy. I'll have to make sure you remember it next time. Come here and take off your coat.


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[Assistant] Maybe that will help you to remember. Now, go and sit with the girls.


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[Assistant] Okay, I'll teach you how to draw during lunch. Or are you going back home?


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[Assistant] If I show, do you promise not to tell the teacher?


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[Assistant] I can't believe what we just saw. Do you think the doctor's dead, Huck? If he is, then Indian Joe will be hanged.


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[Assistant] Muff Potter didn't see what happened after the doctor hit him. Tom, we must agree never to tell anyone. That's right, our agreement in blood.


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[Assistant] And if they ever tell, they will die immediately.


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[Assistant] Great Expectations by Charles Dickens. Chapter 10.


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[Assistant] The felicitous idea occurred to me a morning or two later, when I woke.


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[Assistant] In pursuance of this luminous conception, I mentioned to Biddy, when I went to Mr. Wopsle's great-aunts at night, that I had a particular reason for wishing to get on in life, and that I should feel very much obliged to her if she would impart all her learning to me. Biddy, who was the most obliging of girls, immediately said she would, and indeed began to carry out her promise within five minutes.


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[Assistant] Until Mr. Wopsle's great aunt collected her energies and made an indiscriminate totter at them with a birch-rod. After receiving the charge with every mark of derision, the pupils formed in line, and buzzingly passed a ragged book from hand to hand. The book had an alphabet in it, some figures and tables, and little spelling.


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[Assistant] Upon a competitive examination on the subject of boots.


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[Assistant] With the view of ascertaining, who could tread the hardest upon whose toes?


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[Assistant] This mental exercise lasted until Biddy made a rush at them and distributed three defaced Bibles, shaped as if they had been unskillfully cut off the chump end of something.


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[Assistant] More illegibly printed at the best than any curiosities of literature I have since met with. Speckled all over with iron mould, and having various specimens of the insect world smashed between their leaves.


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[Assistant] When the fights were over, Biddy gave out the number of a page, and then we all read aloud what we could, or what we couldn't, in a frightful chorus. Biddy leading with a high, shrill, monotonous voice.


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[Assistant] When this horrible din had lasted a certain time, it mechanically awoke Mr. Wopsle's great aunt, who staggered at a boy fortuitously and pulled his ears.


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[Assistant] This was understood to terminate the course for the evening, and we emerged into the air with shrieks of intellectual victory. It is fair to remark


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[Assistant] Being but faintly illuminated through the agency of one low-spirited dip-candle and no snaffers.


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[Assistant] It appeared to me that it would take time to become uncommon under these circumstances.


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[Assistant] Nevertheless, I resolved to try it, and that very evening Biddy entered on our special agreement by imparting some information from her little catalogue of prices, under the head of moist sugar, and lending me, to copy at home, a large old English D., which she had imitated from the heading of some newspaper.


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[Assistant] And which I supposed until she told me what it was to be a design for a buckle.


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[Assistant] Of course there was a public house in the village, and of course Joe liked sometimes to smoke his pipe there. I had received strict orders from my sister to call for him at the Three Jolly Bargemen that evening, on my way home from school, and bring him home at my peril.


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[Assistant] There were some alarmingly long chalk scores in it on the wall at the side of the door, which seemed to me to be never paid off. They had been there ever since I could remember, and had grown more than I had. But there was a quantity of chalk about our country.


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[Assistant] I found the landlord looking rather grimly at these records. But as my business was with Joe and not with him, I merely wished him good evening.


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[Assistant] And passed into the common room at the end of the passage, where there was a bright large kitchen fire, and where Joe was smoking his pipe, in company with Mr. Wopsle and a stranger. Joe greeted me as usual with, Hello, Pippo chap! And the moment he said that, the stranger turned his head and looked at me.


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[Assistant] He was a secret looking man, whom I had never seen before. His head was all on one side, and one of his eyes was half shut up, as if he were taking aim at something with an invisible gun.


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[Assistant] He had a pipe in his mouth, and he took it out, and after slowly blowing all his smoke away, and looking hard at me all the time, nodded. So I nodded. And then he nodded again.


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[Assistant] But as I was used to sit beside Joe whenever I entered that place, of resort, I said,


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[Assistant] You were saying, said the strange man, turning to Joe, that you was a blacksmith. Yes, I said it, you know.


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[Assistant] What'll you drink, Mr. Gargery, at my expense, to top up with? Well, said Joe, to tell you the truth.


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[Assistant] I ain't much in a habit of drinking at anybody's expense but my own. Habit? No. Return the stranger.


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[Assistant] But once and away, and on a Saturday night too, come, put a name to it, Mr. Gargery. I wouldn't wish to be stiff company," said Joe. RUM. RUM, repeated the stranger. And will the other gentlemen originate a sentiment? RUM, said Mr. Wopsle. THREE rums, cried the stranger, calling to the landlord. GLASSES round.


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[Assistant] This other gentleman," observed Joe, by way of introducing Mr. Wopsle, "'is a gentleman that you would like to hear give it out. Our clerk at church.' "'Aha!' said the stranger quickly, and cocking his eye at me.


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[Assistant] The lonely church right out on the marshes with graves round it. That's it! said Joe.


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[Assistant] The stranger, with a comfortable kind of grunt over his pipe, put his legs up on the settle that he had to himself.


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[Assistant] He wore a flapping broad brimmed traveller's hat, and under it a handkerchief tied over his head in the manner of a cap, so that he showed no hair. As he looked at the fire, I thought I saw a cunning expression, followed by a half laugh, come into his face.


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[Assistant] I'm not acquainted with this country, gentlemen, but it seems a solitary country towards the river. Most marshes is solitary," said Joe. No doubt, no doubt.


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[Assistant] Do you find any gypsies now, or tramps, or vagrants of any sort out there? No?" said Joe. None but a runaway convict now and then.


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[Assistant] And we don't find them easy. Eh, Mr. Wopsle?


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[Assistant] Mr. Wopsle, with a majestic remembrance of all discomfiture, assented, but not warmly. "'Seems you have been out after such,' asked the stranger.


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[Assistant] Returned Joe, not that we wanted to take them, you understand. We went out as lookers on. Me and Mr. Wopsle and Pip. Didn't us, Pip? Yes, Joe. The stranger looked at me again, still cocking his eye.


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[Assistant] It's a kind of family name what he gave himself when an infant and is called by.


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[Assistant] Son of yours? Well," said Joe, meditatively, not, of course, that it could be in any wise necessary to consider about, but because it was the way, at the jolly bargeman, to seem to consider deeply about everything that was discussed over pipes. Well? No. No, he ain't. Nevy? said the strange man.


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[Assistant] Mr. Wopsle struck in upon that, as one who knew all about relationships, having professional occasion to bear in mind what female relations a man might not marry, and expounded the ties between me and Joe.


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[Assistant] And seem to think he had done quite enough to account for it when he added, as the poet says, and here I may remark that when Mr. Wopsle referred to me,


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[Assistant] He considered it a necessary part of such reference to rumple my hair and poke it into my eyes. I cannot conceive why everybody of his standing who visited at our house


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[Assistant] But some large-handed person took some such ophthalmic steps to patronise me.


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[Assistant] He stirred his rum and water pointedly at me, and he tasted his rum and water pointedly at me, and he stirred it and he tasted it, not with a spoon that was brought to him, but with a file. He did this so that nobody but I saw the file, and when he had done it, he wiped the file and put it in a breast pocket. I knew it to be Joe's file, and I knew that he knew my convict the moment I saw the instrument.


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[Assistant] I sat gazing at him spellbound, but he now reclined on his settle, taking very little notice of me, and talking principally about turnips.


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[Assistant] There was a delicious sense of cleaning up and making a quiet pause before going on in life afresh in our village on Saturday nights, which stimulated Joe


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[Assistant] To dare to stay out half an hour longer on Saturdays than at other times. The half-hour and the rum and water running out together, Joe got up to go, and took me by the hand.


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[Assistant] I think I've got a bright new shilling somewhere in my pocket, and if I have, the boy shall have it.' He looked it out from a handful of small chains.


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[Assistant] Her Mr. Wopsle parted from us at the door of the Jolly Bargeman, and Joe went all the way home, with his mouth wide open, to rinse the rum out with as much air as possible.


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[Assistant] I took it out of the paper, and it proved to be a good one. But what's this? said Mrs. Joe, throwing down the shilling and catching up the paper. Two? One pound notes? Nothing less.


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[Assistant] Then two fat sweltering one-pound notes that seemed to have been on terms of the warmest intimacy with all the cattle markets in the county. Joe caught up his hat again, and ran with them to the jolly bargemen to restore them to their owner. While he was gone,


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[Assistant] I sat down on my usual stool and looked vacantly at my sister, feeling pretty sure that the man would not be there.


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[Assistant] Presently Joe came back, saying that the man was gone, but that he, Joe,


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[Assistant] I had sadly broken sleep when I got to bed, through thinking of the strange man taking aim at me with his invisible gun.


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[Assistant] And of the guiltily, coarse, and common thing it was, to be on secret terms of conspiracy with convicts.


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[Assistant] I coax myself to sleep by thinking of Miss Havisham's next Wednesday, and in my sleep I saw the file coming at me out of a door without seeing who held it, and I screamed myself awake.


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[Assistant] Sometimes it occurred to her that this was the happiest time of her life, as a new bride. To fully enjoy her new life and feel entirely content, she must have to be in a land far away.


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[Assistant] With musical names where wedding nights complete a life of perfect happiness.


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[Assistant] Of course, such places and feelings can only be imagined in novels.


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[Assistant] She had neither the opportunity nor the courage. Charles, however, could never guess her true feelings. As their outward familiarity grew, she inwardly separated herself from him. Charles's conversation bored her, lacking emotion, laughter, and dreams. He had no desire to go to the theatre. He couldn't swim nor fire a pistol.


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[Assistant] She thought, however, that a man should know about everything. But this one had nothing to teach, knew nothing, wanted nothing. He thought she was happy, and she hated him for that. Emma knew how to run the house. She requested patients' payments respectfully. When they had one of the neighbors to dinner,


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[Assistant] She knew how to present the dishes in an elegant fashion. Charles's respect for himself grew, having such a wife to care for him. His mother still came to see him, whenever she and her husband had troubles between them. The elder Madame Bovary seemed, however, to rather dislike her daughter-in-law.


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[Assistant] Who wasted their money and spent her days dreaming. In the days before Charles' first wife died, his mother felt that she was still his favorite.


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[Assistant] But now she saw that as Charles fell in love with Emma, he seemed to love his mother less. Charles didn't know what to say.


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[Assistant] He respected his mother and he was completely in love with his wife. Although Emma knew she was not in love, she tried to make herself fall in love.


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[Assistant] In the moonlight she used to read him love poems.


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[Assistant] Or sing the most romantic love song she knew. She still felt nothing.


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[Assistant] And Charles seemed neither more emotional nor more loving. Soon enough, Charles's sweet touches and tender kisses seemed nothing more than a routine. It was just one more habit he enjoyed. A patient of his had given the doctor's wife a young dog, which she often took for walks.


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[Assistant] One afternoon, sitting on the grass, Emma said to herself, Oh God, oh God, why did I get married? She wondered if she would have met some other man. They weren't all like this one. Perhaps he would be handsome and intelligent. But no, life for her was cold and silent. She called Jolly and took the dog between her knees. Come, kiss mistress.


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[Assistant] Towards the end of September, they received an invitation to La Vaux-Blessard, the home of the Marquis de Endervilliers, who had quite a high position in politics.


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[Assistant] A servant was sent to Tostes to pay for an operation.


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[Assistant] And had noticed the excellent cherry trees in the doctor's little garden.


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[Assistant] Lavo byasards, cherry trees, were not doing well.


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[Assistant] And the marquis sent Bovary a request for a tree, and afterwards came to thank him.


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[Assistant] He saw Emma and noticed her womanly figure and gentle manners. He then invited the young couple to a dinner at La Vaublessard. On Wednesday at three o'clock, Monsieur and Madame Bovary left for La Vaublessard.


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[Assistant] Who needs to set an example? You are right, Lawrence. This is a serious matter.


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[Assistant] You'll put me behind grey stone walls.


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[Assistant] Oh, if I ever get a hold of that fella Stevens, I'll give him something to smell that's nastier than peaches, or my name ain't William Dalton.


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[Assistant] When I lost my poor wife, I cried. And wished I were dead. I nearly went crazy. I couldn't eat.


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[Assistant] But we mustn't want to die because others have died. It will pass. Come, and see us sometime. My daughter thinks of you. She says you've forgotten her.


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[Assistant] There's nothing I'd like better, and I expect the little girl will agree.


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[Assistant] I must ask her first. However, I'll say goodbye to you and get back home. If it's yes, I'll open the front door.


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[Assistant] You're right, Tabs. Maybe there are treats inside. Hmm, I wonder. What's inside a haunted house? Let's find out. Ready, Tabs? Tabs! What?


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[Assistant] I better go into the haunted house to find tabs. This should be fun!


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[Assistant] Hmm, well, I guess this is a dead end. I wonder where everyone went.


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[Assistant] Fun, spooky skeletons, cobwebs, a talking pumpkin, spiders, a witch and a floating tabs ghost. Upload answer tabs.


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[Assistant] There was so much hatred aimed in his direction, and the use of horrible racial epithets was in, was in play.


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[Assistant] Uh, and met up with his mother in St. Louis, lived in the worst parts of St. Louis, became a petty thief.


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[Assistant] You know, as a young kid, as a 13, 14 year old kid, and he was gigantic, and he couldn't read, and he had nothing going for him in this life.


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[Assistant] He could no longer use the excuse, especially to Patterson above all,


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[Assistant] That this had a lot to do with the mob. Everything had to do with the mob. So how good an excuse was that after a while?


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[Assistant] In the popular culture and imagination of America, the way it is not now.


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[Assistant] So much so that Floyd Patterson was invited to the White House by Jack Kennedy. Jack Kennedy?


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[Assistant] Urged Patterson to fight Liston and to beat him.


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[Assistant] And he was saying basically not only beat him for athletic reasons, but beat him because it's good in the broadest political terms.


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[Assistant] When he won the heavyweight championship, he at least thought he would come home to a hero's welcome.


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[Assistant] And the plane landed in Philadelphia and he looked out the window and it was raining slightly.


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[Assistant] And he was hoping to see hundreds or maybe even thousands of people in the tarmac, you know, welcome home champ. The usual thing that you see happening with championship teams or fighters coming home. And there was nobody. And as he stood in the door of the airplane.


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[Assistant] You know, this guy who was waiting for his great moment in life saw that nothing had happened. No one had come to greet him. And his shoulders just slumped.


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[Assistant] The feeling of hope, of acceptance into the greater world vanished in an instant.


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[Assistant] Is the embodiment of power, heavyweight champion of the world. And yet he owned nothing, least of all himself.


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[Assistant] Listen, I'm really sorry for behaving badly this morning. Can't we just forget about it and be good friends?


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[Assistant] I've been beaten a hundred times in school.


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[Assistant] It's nothing. And why does she think I'd tell the teacher she broke his book? I'd never do that.


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[Assistant] When the teacher asks the class, he'll know who did it just by looking at her guilty face.


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[Assistant] Miss Thatcher, answer me. Did you touch my book this morning?


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[Assistant] Hello, Russmore 555? This is Gabriel. That you, Vera? Yes, I'm all right. I know, but somebody has to do the dirty work. It's part of the job. All right, I'll be back soon. Now, baby, stop worrying about me.


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[Assistant] Yes, that's the great thing. Enjoy ourselves.


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[Assistant] Try to come to church next Sunday, Daniel. It'll do you a heap more good than those dances at Brothers Pond.


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[Assistant] So, summer finally came and now school was closed for a few months.


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[Assistant] But surprisingly, Tom didn't feel as happy as he usually did.


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[Assistant] Becky Thatcher had left town to stay with her family in another village for a while.


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[Assistant] This made the day seem very long and boring. So Tom had a lot of time to think.


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[Assistant] And what he thought about most was the memory of Indian Joe killing the doctor in the graveyard. No matter how hard he tried, he couldn't get it out of his mind. Soon, Tom fell ill.


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[Assistant] He stayed in bed for two weeks where he could avoid hearing the news from the village. He didn't want to know what was happening.


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[Assistant] And then suddenly he would become ill again. Part of what made Tom feel so bad.


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[Assistant] was that the judge had arrived to hear the people's story about what happened.


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[Assistant] And soon he would make the final decision about what was to happen to Muff Potter. Everyone in the village was talking about it. And every time Tom heard them, his heart would jump a bit.


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[Assistant] One day, he met Huckleberry in the empty building and asked him,


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[Assistant] Later that evening the boys went to visit Potter. They had done this before.


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[Assistant] Always going to his window and giving him some of Huck's tobacco and a little something to eat. Potter always appreciated their visits.


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[Assistant] The boys noticed this, and it made them feel even sadder. Tonight, as they talked, Potter said,


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[Assistant] Tom felt terrible. He couldn't sleep well at all.


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[Assistant] And he spent the following two days sitting outside the courthouse.


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[Assistant] There, the judge was listening to the village's report about how the murder happened.


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[Assistant] As much as Tom wanted to go inside to hear what was happening, he wouldn't let himself. Huck was there too.


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[Assistant] But they tried not to sit with each other or talk at all.


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[Assistant] So no one would suspect them of knowing something.


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[Assistant] Whenever Tom heard the people talk about what was happening, the information was always bad.


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[Assistant] It seemed that Indian Joe had told his story well, and the judge believed it.


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[Assistant] Tom walked around thinking all day and night.


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[Assistant] When he returned home, he found himself lying in bed, thinking more about Muff Potter and the Judge.


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[Assistant] The next morning, the whole village went to the courthouse because this was to be the day that the judge made his decision.


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[Assistant] Potter came in looking sad and without hope.


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[Assistant] Indian Joe sat in the seats with everyone else to watch. He looked very calm. Then the judge came in. Two men were questioned.


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[Assistant] One man had seen Potter the morning after the murder, washing himself in the river.


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[Assistant] He said that when Potter had realized someone had seen him, he ran away.


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[Assistant] The other man talked about finding Potter's knife near the dead doctor's body.


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[Assistant] Muff Potter's lawyer never asked a single question, which surprised everyone in the courtroom.


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[Assistant] And when more men were questioned, Potter's lawyer still just sat there listening, not saying a single word.


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[Assistant] Finally, after all of the questions had been asked and stories had been told, Potter's lawyer stood and said to the judge,


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[Assistant] As Tom stood up and walked to the front of the room, everyone watched him with their eyes wide and their mouths open. During the questioning, Tom looked very frightened. The lawyer began by asking


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[Assistant] Tom could see Indy and Joe looking directly at him, and he quickly lost his ability to speak.


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[Assistant] He sat there silent for a moment and finally managed to say,


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[Assistant] Suddenly everyone in the room started talking to each other. The judge had to ask them to be silent.


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[Assistant] The people in the courtroom started laughing so much when they heard this.


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[Assistant] The judge, once again, had to ask them to be quiet.


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[Assistant] Tom told his story and as he did, the people's eyes got whiter and whiter and Indian Joe looked more and more angry.


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[Assistant] Suddenly from the crowd, a knife came flying at Tom's head.


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[Assistant] Luckily, it missed him and flew into the wall, almost cutting Tom's ear.


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[Assistant] And then, Indian Joe was running to the window, hitting anyone who tried to stop him.


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[Assistant] I arrived at the scene. I was escorted into the bedroom where Sonny Liston was laying on the bed. There was no sign of a struggle. There were no apparent wounds on his body.


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[Assistant] That was hard to determine because of the deterioration of the body.


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[Assistant] But there was absolutely nothing to indicate that Sonny Liston died anything but a natural death.


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[Assistant] Chapter 8 Getting to know Mr Rochester


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[Assistant] Thornfield Hall became quite busy the next day, now that the Master had returned. People kept coming to visit him on business. I enjoyed the new, cheerful atmosphere.


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[Assistant] But I could not make Adele concentrate on her lessons.


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[Assistant] Because she was constantly talking about the presents Mr. Rochester had promised to bring her. That evening, we were invited to have tea with him. I immediately recognized the traveler I had helped, with his dark hair and skin, his square forehead, and his stern look. His leg was supported on a chair, but he made no effort to greet me when I entered. In fact, he neither spoke nor moved.


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[Assistant] Have you brought a present for Miss Aire with you as well? Adele asked him. A present? Who wants a present? He said angrily. Did you expect a present, Miss Aire? Do you like presents? I haven't much experience of them, sir, I answered. Anyway, I have no right to expect a present, as I haven't done anything to deserve one.


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[Assistant] Miss Eyre? continued Mr. Rochester. How old were you when you started at Lowood? About ten.


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[Assistant] And you stayed there eight years, so you're now eighteen. I nodded. I would never have been able to guess your age, he went on. Now, what did you learn there? Can you play the piano? A little? Of course, that's what all young women say. Go and play a tune on the piano in the library. I did as he asked. That's enough, he called after a few minutes.


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[Assistant] Yes, you do indeed play a little, just like any schoolgirl. Better than some, perhaps. Now, bring me your sketches. I fetched them from my room. Having looked carefully at them, he chose three. These are interesting, he said.


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[Assistant] Who taught you to draw wind and space and feeling? But put them away now, Miss Eyre. Do you realize it's nine o'clock? Adele should be in bed by now. Good night to you all. Mr. Rochester's mood had suddenly changed, and he clearly wished to be alone.


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[Assistant] Few pitchers could figure him out, and most finally gave up trying. Asked for advice, Preacher Rose said, I throw him four wide ones, and then try to pick him off first.


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[Assistant] Musial's agent and business partner, Biggie Gariani,


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[Assistant] Here he'd stand the man's ultimatum straight to the top.


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[Assistant] In a career bristling with Hall of Fame achievements, Busey will never perform better than in 1948.


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[Assistant] We also tied Ty Cobb's 20th century record for most five hit games in a season. Four.


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[Assistant] Although he never collected 10 hits in a doubleheader, Musial had a day to remember. St. Louis, 1954.


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[Assistant] If Muzio lacked national exposure, it wasn't the fault of KMOX.


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[Assistant] Broadcast the man's exploits over one of America's strongest radio signals.


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[Assistant] Buzyl's 58-year-old father, already suffering from lung ailments, died of a stroke two months after the environmental disaster known as the denoura smog.


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[Assistant] Musial's father and grandfather were among the thousands of Slavic immigrants who came to the Monongahela Valley in search of work. There was more than enough for every member of the family.


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[Assistant] Meanwhile, one college held out the promise of a basketball scholarship.


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[Assistant] Desperate at the thought that he was through at 20, Musial sought out the man who recommended he be signed four years earlier.


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[Assistant] And the Cardinals defeated the Yankees in five games in the World Series.


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[Assistant] A month after three Cardinals signed to play in the Mexican League, Alphonse Pasqual and Dodger defector Mickey Owen tried to lasso Museo, who was making $13,500 a year with yards of greens.


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[Assistant] The Cardinals won their third World Series in five seasons, beating the Red Sox in seven games.


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[Assistant] Choosing not to integrate until 1954, the Cardinals slipped, perennial pennant contenders to also ramp. Usual meanwhile, played on.


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[Assistant] After winning his seventh and last batting title in 1957, Stan remained the man the following year, when he hit 337 at 37. But in 1959,


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[Assistant] After hitting above 300 in each of his 17 seasons, the wheels began to come off.


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[Assistant] In 1960, he was determined to climb back to the heady world of 300 hitting.


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[Assistant] From the bench, Musial privately determined that he would quit at the All-Star break.


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[Assistant] 22 Septembers ago, he had two hits in each game.


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[Assistant] Uh, no, let me see, I think perhaps we better have you in alphabetical order. Uh, Ainsworth, will you take this desk here? The rest of you, sort yourselves out.


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[Assistant] He had pictures in his pocket and pins, and the first thing he'd walk up to you and say, you wanted an autograph, kid?


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[Assistant] And he was crying on the railroad station dock there. As the train pulled away, he was shaking hands with everybody and saying goodbye.


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[Assistant] Hello Navigator. Still nothing to be seen. Where do we go from here?