[User] Please transcribe this text

[Assistant] That means you don't even consider the edge feature and how strong the spawns are, or yeaah, the whole structure is being left out in this case.


[User] Please transcribe this text

[Assistant] So what graphformers do, like how they improve in these two points is that it is being built directly upon the standard transformer encoder and also they add


[User] Please transcribe this text

[Assistant] Some structural encodings into the model to even improve the models performance.


[User] Please transcribe this text

[Assistant] So first of all, that's, well explain what is the structural encodings. So the first one they use is called centrality encodings, centrality encoding. So what is centrality? Centrality is a measurement of how important a node is in a graph.


[User] Please transcribe this text

[Assistant] So here I'll give you some examples. It could be degree, it could be eigenvector, it could be betweenness. So I imagine you have a social network.


[User] Please transcribe this text

[Assistant] Other users on social media and each node represents a person.


[User] Please transcribe this text

[Assistant] So the degree you can interpret it as how, um, how


[User] Please transcribe this text

[Assistant] This note are socially active.


[User] Please transcribe this text

[Assistant] Because that means how frequent they interact with other people.


[User] Please transcribe this text

[Assistant] What is it different from the degree centrality? So eigenvector is actually, if this node is connected to other more popular nodes.


[User] Please transcribe this text

[Assistant] That means that the eigenvector will be probably higher because those nodes it connected to are also popular or are also maybe with high eigenvector or say high, uh, higher degree. But if you, uh,


[User] Please transcribe this text

[Assistant] Uh, connected like to the nodes that is less popular. Probably the degree of that node will be also high, but the eigenvector will be not as high to the ones that connected to also high, uh, to also more popular nodes.


[User] Please transcribe this text

[Assistant] Yeah, it's the shortest path. Like, if one note has to walk from itself to another note, how many times they have to pass you? So the visionless can somehow be interpreted as on how diverse and uniqueness your social activity is. Like, if you know some,


[User] Please transcribe this text

[Assistant] Some people that is not with much friends, that means the shortest path from every node to that, uh, that node will have to like pass you.


[User] Please transcribe this text

[Assistant] So that means that you have a more unique social activity compared to others. So, uh, the interpretation part, uh, of the, of the centrality, uh, of the centrality attributes is actually very from


[User] Please transcribe this text

[Assistant] A graph to another because it depends on what is the semantic meaning of each of the node and edges.


[User] Please transcribe this text

[Assistant] So anyways, for this paper, they chose to use degree centrality. And how they do that is to add a learnable vector.


[User] Please transcribe this text

[Assistant] To the node featuring itself in the input layer.


[User] Please transcribe this text

[Assistant] So here we just, uh, quickly review what the degree of a node, uh, is. So it could be like for directly, uh, for directed graphs, it could be in degree and out degree. So for example, if you see node,


[User] Please transcribe this text

[Assistant] Note six, the in degree will be one, two, three, four, five, six, and the out degree will be one and two and three, right?


[User] Please transcribe this text

[Assistant] Yeah, that's probably it. So that's, uh, what directed, uh, grass are counting the decrease. But what if it is like undirected? For example, if all these are without arrow,


[User] Please transcribe this text

[Assistant] For also, uh, note 6, it will be 1, 2, 3, 4, 5, 6, 7, 8. So total degree will be 8.


[User] Please transcribe this text

[Assistant] Okay, and, uh, this is just equa- the equation of how we actually do that. And this z minus and z plus is just, uh, the


[User] Please transcribe this text

[Assistant] So I actually forgot what, which one this, but anyways, one is in, one is out. I'll go double check later.


[User] Please transcribe this text

[Assistant] So anyway, so one is in and one is out and, uh, but they are just the index, uh, they are just the indexes of that, uh, of that learnable vector and as you recall,


[User] Please transcribe this text

[Assistant] Summation, ehm, the summation of these three will be the actual input of the, uh, of the ith node.


[User] Please transcribe this text

[Assistant] And, uh, yeah, because I was kind of confused at first of that index thing. So, uh, here I give you a, like a small example what the index thing actually means. So for example, if you have, uh, if you're like z minus, it's like this array.


[User] Please transcribe this text

[Assistant] And the index is like 1 to 10, which will representing the indegree of the, uh, uh, uh, well, indegree of the, of a node.


[User] Please transcribe this text

[Assistant] So if your learned value is from 10 to 100, that means that, uh,


[User] Please transcribe this text

[Assistant] The network is learned to pay less attention if you have less neighbor and pay more attention if you have more neighbor.


[User] Please transcribe this text

[Assistant] And the second structural encoding is spatial encoding. Here they want to answer that, uhm, how to exactly add position, positional embeddings like transformer does.


[User] Please transcribe this text

[Assistant] We know that transformers actually add positional embeddings, right?


[User] Please transcribe this text

[Assistant] Uh, graph data, how do we actually do that? So we need a connectivity between the nodes in the graph. So it may not be very intuitive, but you can see that if you only have a data set that's with a bunch of nodes and bunch of edges,


[User] Please transcribe this text

[Assistant] So how exactly are you going to present it in 3D world or even say like 2D world?


[User] Please transcribe this text

[Assistant] How do you draw a graph like this? So you may probably need something to indicate the distance. For example, why is it placed like this instead of this node to be here?


[User] Please transcribe this text

[Assistant] So uhm, part of the thing that is to add a learnable scalar which will serve as a bias term in the self-attention module. Here the self-attention module is the attention, is the attention module inside the transformer encoded.


[User] Please transcribe this text

[Assistant] And what exactly the SPD value will be for a pair of nodes. So if the two nodes are connected, SPD equals to the hop distance in between. So for example, from this node to this one, so probably the shortest path is one and two, right? So it's two.


[User] Please transcribe this text

[Assistant] Ever walk from this node to this one. So these two are not connected and so the SPD will be assigned to minus one.


[User] Please transcribe this text

[Assistant] And you can see that this is the attention value, right? So this part is basically just, uh, just how transformer calculated.


[User] Please transcribe this text

[Assistant] So last encoding is edge encoding.


[User] Please transcribe this text

[Assistant] So here they want to answer the question that how to better incorporate the edge feature information by exploiting the global information transformer architecture provided.


[User] Please transcribe this text

[Assistant] And, uh, yeah, so here, uh, because there are many attempts to, uh, to incorporate the edge feature. So here they just wanted to like further improve the performance of incorporating the edge feature to make it more expressive.


[User] Please transcribe this text

[Assistant] Yeah, so they tried to learn a...


[User] Please transcribe this text

[Assistant] Many edge combination, right? So from edge 1 to edge n is the shortest path from node i to j.


[User] Please transcribe this text

[Assistant] And, uh, for each edge, and, uh, each edge has its corresponding edge feature. So that's XEN. And XEN will, uh, will be multiplied with, uh, this.


[User] Please transcribe this text

[Assistant] Every edge plus the edging, uh, every edge here, the edge, every edge feature, uh, multiply with the, uh,


[User] Please transcribe this text

[Assistant] Uh, structural encoding and what the contributions are.


[User] Please transcribe this text

[Assistant] So for centrality encoding, other than just have the semantic correlation between nodes, uh, they can, uh, with this centrality encoding, the model can also incorporate the measure


[User] Please transcribe this text

[Assistant] And with spatial encoding, it allows each node to attend to the information too.


[User] Please transcribe this text

[Assistant] Any position at a learned level, uh, by saying at a learned level is that, uhm, just like, uhm, because, because it...


[User] Please transcribe this text

[Assistant] It is the index that is the special, uh, the shortest path hop distance. So, uh, how, how much focus, uh, on what hop is being learned by the, by the bias term, by the bias term.


[User] Please transcribe this text

[Assistant] And also the same with this one, the edge encoding.


[User] Please transcribe this text

[Assistant] And, uh, yeah, it allows to, uh, it allows the model to leverage information from all possible related edges of the whole graph, uh, compared to, uh, previous attempts, uh, previous attempts are all


[User] Please transcribe this text

[Assistant] Uh, all incorporate only the edges that is directly associated to the node.


[User] Please transcribe this text

[Assistant] So how exactly we're using all these encodings inside the transformer encoder. So this one is just describing the attention module in the transformer encoder.


[User] Please transcribe this text

[Assistant] So, uh, if you, if you remember, uh, the note feature will be.


[User] Please transcribe this text

[Assistant] Added with the centrality encoding, which is degree centrality.


[User] Please transcribe this text

[Assistant] This is just what inside the transformer. And here after, before softmax, the attention value will actually be added with spatial encoding, which is the hop distance of the shortest path.


[User] Please transcribe this text

[Assistant] And, uh, this one is the edge encoding, which they, uh, they, they are, they are taking a thought product of a learnable.


[User] Please transcribe this text

[Assistant] Edge embedding vector with the edge features.


[User] Please transcribe this text

[Assistant] And, uh, they are, like, uh, doing a layer normalization before the, uh, multi-head retention and the feed-forward neural network, uh, feed-forward network. And, uh, this is proof in one of the work that it helps, um, stabilize the optimization.


[User] Please transcribe this text

[Assistant] The methodology inside this work is that they added a special node.


[User] Please transcribe this text

[Assistant] As you recall, the readout function is to aggregate all the learned node attributes.


[User] Please transcribe this text

[Assistant] And to combine it into a graph representation. So instead of doing that, graphformer use a special node called vnode.


[User] Please transcribe this text

[Assistant] And, uh, then they will make connection between VNode and each or the other node individually. And VNode just like a normal node while updating, while training, but, uh, it could


[User] Please transcribe this text

[Assistant] Because VNode is actually an imaginary node. Instead, we don't want other nodes to put too much focus on it because it is, uh, we wanted it to aggregate all the information, but we don't want


[User] Please transcribe this text

[Assistant] Wrong attention on it. So non-physical edges, which means like from VNode to VJ or from VI to VNode.


[User] Please transcribe this text

[Assistant] This work resets all the spatial encodings, and this is what I guess, the edge embedding as well, for corresponding non-physical indexes to a distinct learnable scalar, like to make a difference.


[User] Please transcribe this text

[Assistant] What I guess is that they set it to zero because they don't want it to, like, it to put more focus on it.


[User] Please transcribe this text

[Assistant] So uh, after we will start to analyze, uh,


[User] Please transcribe this text

[Assistant] Why graph former is like better than traditional GNN and why is that?


[User] Please transcribe this text

[Assistant] Why is graph warmer, uh, like how powerful is graph warmer? So fact one is that, uh, shortest, uh, the distance, uh, the shortest path can be used to improve why filer lemon test.


[User] Please transcribe this text

[Assistant] So, uh, what does this WL test do? It's like to test if two graphs are isomorphic. So what does it mean, like two graphs are isomorphic? So there are like three, uh, three,


[User] Please transcribe this text

[Assistant] Uh, for these two graph, uh, the degree sequence is, uh, because this one has degree two, right? And this one has degree two, this one has degree


[User] Please transcribe this text

[Assistant] 3, this one has degree 3, so like from left to right is like all red and blue. So this graph's degree sequence is 2, 2, 2, 2, 3, 3. So this three is like coming from the blue node.


[User] Please transcribe this text

[Assistant] Uh, which we call it like, it is failed to distinguish these two.


[User] Please transcribe this text

[Assistant] Uh, these two graphs, even though they're actually different.


[User] Please transcribe this text

[Assistant] But for a WL test, they cannot distinguish these two graphs. But for, if we use SPD sequence for, uh, the, for this graph and, uh, the red node and, well,


[User] Please transcribe this text

[Assistant] Let's say that for SPD, they are calculating like from this node.


[User] Please transcribe this text

[Assistant] To this one is the shortest path is 1. To this one, the shortest path is 1. To itself is 0. To this one, the shortest path is 2. And to this one is 2. And to this one is 3.


[User] Please transcribe this text

[Assistant] So, the same case goes to all the red notes and for the, uh, blue notes.


[User] Please transcribe this text

[Assistant] The shortest path to yourself is zero, to this one is one, to this one is one, to this one is one as well, to this one is two, to this one is two.


[User] Please transcribe this text

[Assistant] Based on the, uh, what I just described, we can have these SPD sequences. So for the left graph, the, and for red node, the sequence looks like this. For right graph, the red node and the sequence like this, you can already see the difference, right?


[User] Please transcribe this text

[Assistant] And for blue notes, the left one looks like this, and the right one looks like this. Even though for the blue note, it cannot distinguish that, but for the red note, it can be distinguished.


[User] Please transcribe this text

[Assistant] So, and inside, uh, this paper, uh, graph isomorphism network.


[User] Please transcribe this text

[Assistant] And they prove that message passing is equivalent to the, uh, WL algorithm. So here we can say, uh, say that Graphformer has a more expressive, uh, message passing way than


[User] Please transcribe this text

[Assistant] The graph warmer layer can represent, uh, aggregate and combine steps.


[User] Please transcribe this text

[Assistant] So the first one is that they wanted to use mean aggregation and also the same setting for combined function.


[User] Please transcribe this text

[Assistant] Yeah, if the, uh, if the distance is one, because they are only considering the, uh, the first hop neighbor instead of like two hops more.


[User] Please transcribe this text

[Assistant] Uh, infinity so that after the softmax, they can approximate the hard softmax.


[User] Please transcribe this text

[Assistant] And also because we're not doing attention anymore, so the query and the key will be set to zero and the value will be set to identity matrix. For some aggregation.


[User] Please transcribe this text

[Assistant] Uh, it is, it is under the premises that you have the mean aggregation implementation already, so it will be mean.


[User] Please transcribe this text

[Assistant] So where is the degree coming from? Is that if you remember the centrality encoding we're using is


[User] Please transcribe this text

[Assistant] So this information is already known inside the model. So if you have mean aggregation and you want it to like approximate the sum


[User] Please transcribe this text

[Assistant] What if you wanted to approximate the max


[User] Please transcribe this text

[Assistant] Max, instead of softmax. Yeaah, we're still doing softmax, but because it is being set to negative infinity though.


[User] Please transcribe this text

[Assistant] And this, uh, uh, valuing key will be set to the standard by, uh, basis of, uh,


[User] Please transcribe this text

[Assistant] And the, uh, query will be set to zero, and the bias term will be set to, uh, T1. So T is just the temperature.


[User] Please transcribe this text

[Assistant] So the temperature must be set, uh, large enough to also push softbacks approximating hardbacks. It's just like the settings above.


[User] Please transcribe this text

[Assistant] So fact three is that by choosing proper weight,


[User] Please transcribe this text

[Assistant] If we set like, because we are not doing the attention right now, so the prayer and key are set to be zero, and the bias will be set to temperature one.


[User] Please transcribe this text

[Assistant] And also the, uh, the value will be set to identity matrix. So also you still need to choose a large enough T to, uh, to, uh, to


[User] Please transcribe this text

[Assistant] Like, so after these two wires turn up, uh, all temperature times with, uhm,


[User] Please transcribe this text

[Assistant] All one vector, right? So after these two times together, it will be like t1s, uh, uh,


[User] Please transcribe this text

[Assistant] And if you, uh, you have to choose large enough t for this term to dominate the spatial encoding term. So.


[User] Please transcribe this text

[Assistant] That's how they, uh, prove, uh, the power, how powerful the graphformer is. Okay, so well, any questions here?


[User] Please transcribe this text

[Assistant] If not, I will just go on to the experiment part.


[User] Please transcribe this text

[Assistant] So first of all, for this, for this experiment,


[User] Please transcribe this text

[Assistant] They use two kinds of graphformer settings. So one is with 12 layers and the feature, the hidden layer, is 768 dimensional features. And they have a smaller model, which is


[User] Please transcribe this text

[Assistant] Uh, which has six layers and five one two dimensional hidden layers. So dimension, uh, five one two, uh, dimensional features. Yeah.


[User] Please transcribe this text

[Assistant] And all the models are trained on 8 Nvidia V100 GPUs, so I'm not sure what kind they're using, but each of the V100 can be 16GB.


[User] Please transcribe this text

[Assistant] And yeah, uh, this is the result on this PCQ Enforium LSC. So LSC is from that large scale challenge data set.


[User] Please transcribe this text

[Assistant] Okay, so, uh, they are using MAE, which is mean absolute error, uh, as the, uh, as the, uh, main trick here.


[User] Please transcribe this text

[Assistant] Yeah, this is the, their result. They're comparing with, uh, GCN, GIN and, uh, the very, uh, and the version with, of them with


[User] Please transcribe this text

[Assistant] V-notes, so the same setting as what graphformer have. So probably, yeah, so this is just variants of the traditional GNNs and also the GT and GT-wide is the former attempt of graph, uh, graph transformers. So GT-wide is like,


[User] Please transcribe this text

[Assistant] Well, directly wanted to make it a fair comparison because the hidden, uh, hidden size is much


[User] Please transcribe this text

[Assistant] Uh, smaller than graph former, so they increase their, uh, hidden, hidden, um, the feature size of the graph transformer to also 768.


[User] Please transcribe this text

[Assistant] But, uh, as we can see, the parameter increase, uh,


[User] Please transcribe this text

[Assistant] But the, uhm, the result is not, like, actually reflecting the parameter increase. But for graphformer, from small to large,


[User] Please transcribe this text

[Assistant] And this is how they address the conclusion that graph former architecture is not, uh,


[User] Please transcribe this text

[Assistant] Except the uhm, the over-smoothing.


[User] Please transcribe this text

[Assistant] Because the performance is actually still getting better.


[User] Please transcribe this text

[Assistant] While the layer increases and the parameter increases.


[User] Please transcribe this text

[Assistant] And for the other experiment, uh, they're measuring the transferable capability of a graph former model, pre-trained on that data set we just, we just saw.


[User] Please transcribe this text

[Assistant] And because this graph former is a very large model,


[User] Please transcribe this text

[Assistant] And so, uh, these kind, uh, these data, uh, the data set they're using is relatively small for them.


[User] Please transcribe this text

[Assistant] Uh, for grass, which is from the paper called...


[User] Please transcribe this text

[Assistant] Flock. So Flock is just, uh, using a adversarial, uhm, attack, uhm, attack.


[User] Please transcribe this text

[Assistant] And the result on OGBG. So OGBG means open graph benchmark on graph level prediction data sets. As we, as you recall, they are using these two on.


[User] Please transcribe this text

[Assistant] Datasets. So this one used APSDR.


[User] Please transcribe this text

[Assistant] As the metric and for this one, they use AUC. AUC is just the area under this, uh, area under this curve.


[User] Please transcribe this text

[Assistant] Average position, ehm, position and it's true positive.


[User] Please transcribe this text

[Assistant] Uh, divided by true positive plus false positive. So they also like compare with the traditional GNN and also the traditional GNN fine-tuned with, uh, uh,


[User] Please transcribe this text

[Assistant] The graphformer is using the extra data using this data augmentation method.


[User] Please transcribe this text

[Assistant] So for, uh, the last experiment they're using, uh, uh, because for this one, they don't actually, uh, um, well.


[User] Please transcribe this text

[Assistant] So here they train a new model called graph warmer slim.


[User] Please transcribe this text

[Assistant] On the benchmarking GNN datasets, which is Think, yeaah. So it's as follow. And GT and SYN is actually the former attempt of Graph Transformer. But as you can see, they don't perform better than the traditional GNNs. So.


[User] Please transcribe this text

[Assistant] And, uh, yeah, so that is why, yeah, I guess that is why they said, uh, their title is Do Transformers Really Perform Bad on Graph Data?


[User] Please transcribe this text

[Assistant] So that is because formal attempts did not exceed, uh,


[User] Please transcribe this text

[Assistant] Uh, do not like being comparable to the traditional GNNs.


[User] Please transcribe this text

[Assistant] And they are trained on this large scale dataset also with 12 layers and are trained for 100k iterations. So for the first test, they are trained with a million steps.


[User] Please transcribe this text

[Assistant] Uh, the truth of this, uh, positional encoding is because, uh, in one of the literature, it shows that it outperforms other precision, other kind of a, of positional encoding used in GNN literature.


[User] Please transcribe this text

[Assistant] So as you can see with this one and this one, uh, the, uh, the,


[User] Please transcribe this text

[Assistant] Error is actually decreased, so which means the spatial encoding in GraphFormer is actually outperforms, but, uh, wait, outperform this one.


[User] Please transcribe this text

[Assistant] Yeah, and they just keep adding their structural encodings inside. So for centrality, it also has a error hop.


[User] Please transcribe this text

[Assistant] And, uh, and also for edge encoding, they tried three different kinds. One is via node, one is via aggregation, one is, uh, yeah.


[User] Please transcribe this text

[Assistant] Attention bias. So this one is what I proposed, but they also use like two different kind, uh, two different places to add the edge encoding to see if, if, if like, if this one is actually the best.


[User] Please transcribe this text

[Assistant] So as you can see, var node and var aggregation is actually not much difference, but


[User] Please transcribe this text

[Assistant] If it is added, uh, as the bias of the attention, it actually has a, also a arrow hop.


[User] Please transcribe this text

[Assistant] Okay, so is there any questions by now?


[User] Please transcribe this text

[Assistant] Uh, transformer, uh, for previous attempt, they, uh, there are like some of the variations. So one is that they use one additional GNN to produce just the QKV vectors.


[User] Please transcribe this text

[Assistant] In some other words, use two-branch FFN, uh, before network to produce node representation and edge representation separately. Unlike graphformer is like, uh, use edge


[User] Please transcribe this text

[Assistant] Uhm, use a learned edge embedding to like add it into the attention module and


[User] Please transcribe this text

[Assistant] And then, and then generate a new node representation.


[User] Please transcribe this text

[Assistant] And the third point is that the, uh, some of the, some of the previous attempt adding the adjacency matrix and the inner


[User] Please transcribe this text

[Assistant] And the fourth one is they only aggregate the information from.


[User] Please transcribe this text

[Assistant] Neighborhood, so which means that just one hop, not the one that you can collect along the shortest path.


[User] Please transcribe this text

[Assistant] And the last one is they use leblation eigenvector, which is the same one as leblation positional encoding.


[User] Please transcribe this text

[Assistant] Oh yeaah, I'm already here. So yeaah, but this one in the ablation test, we actually, they actually proved that the spatial encoding this work outperformed.


[User] Please transcribe this text

[Assistant] Some of them use, uh, some of them use to concatenate all the features to calculate.


[User] Please transcribe this text

[Assistant] The attentional probabilities. So that's just for the aggregation. So different kind of aggregation. So it's just inside the general GNN workflow.


[User] Please transcribe this text

[Assistant] And for path-based attention model, uh, they try to model the inference between the center node and its higher order.


[User] Please transcribe this text

[Assistant] So uhm, it's still neighbors, but they are only like focusing on the higher order neighbors.


[User] Please transcribe this text

[Assistant] And for distance weighted aggregation, they adopt a distance encoding as extra node attribute. So, yeaah.


[User] Please transcribe this text

[Assistant] This is just all different kind of aggregations and what they're trying to do to, like, improve their, uh, performances.


[User] Please transcribe this text

[Assistant] And for different kind of positional encoding, there, there are absolute, uh, Weisweiler-Lehman positional encoding to represent different nodes labeled by WL algorithm and also the relation positional encoding. So use the relation eigenvector as the positional encoding.


[User] Please transcribe this text

[Assistant] So which is under premises that nearby node have similar positional features and farther nodes have dissimilar positional features.


[User] Please transcribe this text

[Assistant] And for edge features and for attention-based GNN, the edge is weighted by the similarity of the features of each of its two nodes, like how the node representation is similar to the other.


[User] Please transcribe this text

[Assistant] Connected by this edge, so it will be weighted by that similarity value.


[User] Please transcribe this text

[Assistant] And the other attempt is to project edge features to an embedding vector and multiply it by attention coefficient and send the result to an additional quick forward sub.


[User] Please transcribe this text

[Assistant] Layer to produce edge representations.


[User] Please transcribe this text

[Assistant] Okay, so unlike graphformer, this just projects the edge features to an embedding vector where the graphformer is trying to learn a more proper and suitable edge embedding by using it in the bias term.


[User] Please transcribe this text

[Assistant] Okay, if not, I will just make the conclusion of this paper. So first of all, the contribution is that this is a direct application of Transformer to graph representation learning.


[User] Please transcribe this text

[Assistant] And, uh, they, uh, add three different novel graph structural encodings and the performance of


[User] Please transcribe this text

[Assistant] This model achieves better performances than popular GNNs on benchmark datasets. But there are still some limitations because Transformer is such a big model, so the quadratic time complexity of the self-attention module


[User] Please transcribe this text

[Assistant] And, uh, yeah, so, uh, and as you can see, they are all performed in, uh, on the, those molecular data set, which per, per graph is not that big.


[User] Please transcribe this text

[Assistant] So future works include more efficient runtime improvement and also add domain knowledge encodings.


[User] Please transcribe this text

[Assistant] Or different choices of, sorry, typo, of centrality encodings, because actually the centrality, they are using the degree centrality right now. But for some of the case, probably other centrality, like I mentioned, eigenvector or betweenness or some others like page rank or cause will probably be more suitable.


[User] Please transcribe this text

[Assistant] So adding domain knowledge is probably one of the direction as well. And also the node representation extraction with an applicable graph sampling strategy.


[User] Please transcribe this text

[Assistant] And, uh, yeah, because this work can, uh, it's also based on the node features. So node representation instruction is definitely one of its, uh,


[User] Please transcribe this text

[Assistant] Implied application, so with a applicable graph sampling strategy, it could be used on these tasks.


[User] Please transcribe this text

[Assistant] Okay, so that's my presentation and, uh, the reference used, uh, this is the reference that the material I used in this, in all of the slides income, uh, in total. Yeah, so, uh,


[User] Please transcribe this text

[Assistant] Yeah, that is all I have for today. Thank you very much. So, uh, if there's any questions.


[User] Please transcribe this text

[Assistant] Yeaah, okay, so uhm, the question is, do you think graph warmers performance is partly because


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[Assistant] Uh, of the huge number of parameters compared to other GNNs, I think it's definitely part of the reason, but as you can see, they also compare with other previous attempts of


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[Assistant] Uh, the, um, the traditional GNNs. So probably that's, that won't be the only case. The structural encodings probably help a lot as well.


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[Assistant] I think that definitely could be a case because, uh, for chemistry data sets, it actually, uh, it kind of probably more, uh,


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[Assistant] More rely on the, probably the sy-symmetric feature of the, uh, of the molecule itself.


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[Assistant] So probably adding the, uh, these, all these, uh, all these structural encodings helped in the, uh, in this chemistry dataset, but because the inherent problem of the transformer model is it's too big and there's no other


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[Assistant] So I think that definitely could be a case that it only works for chemistry data sets.


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[Assistant] We'll play the same way like we did last, uh, two quizzes and let's hope for the best. That is what I have to say. Wish you well.


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[Assistant] You can't expect me to be a cook, housekeeper, and chirold into one during the day, and a loving wife in the evening just because you feel like it. Oh dear me no.


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[Assistant] Ninety-six, eleven, and ninety-seven, twenty-four. Not tuck in during the breakfast master. So that's one hour each. Forty-five, F, thirty-nine. Long fingernails. One hour.


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[Assistant] He took aim at London's mayor. At least seven dead and 48 wounded in terror attack. And mayor of London says there is no reason to be alarmed.


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[Assistant] You expect it's going to work and you can kind of have a giggle sometimes if it looks good.


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[Assistant] And then you have that horrible phase where you have to make, you know, make it.


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[Assistant] My favorite thing is kind of just freestyling your laver material. You attach that to that and that might work. You give it a go and sometimes it works.


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[Assistant] I kind of left home knowing that you didn't need that much to live on and that I didn't, didn't need that much money. You just need sort of some shelter, some food.


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[Assistant] What's new with Betsy and Jay? Weekdays at 5 on 3. Recognize that feeling? It is the feeling of being together.


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[Assistant] Enjoying the company of a familiar face and sharing moments that matter to you, to me, to us. What matters to some could impact all of us, especially in a place like Northeast Ohio. Here, stories are shared from East


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[Assistant] To west, to south. Stories of perseverance, of family, of love. If a story brings us together, it's what matters most.


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[Assistant] Germany's super rich. No other European country has as many billionaires.


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[Assistant] The press frequently reports on the country's high income inequality and low social mobility. But little is known about the super rich.


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[Assistant] Who are Germany's super wealthy? How do they live? And how do they see the country they live in?


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[Assistant] We wanted to get closer to the discreet world of Germany's ultra-rich, of company owners and heirs worth millions. We wanted to find out what makes those on top of the world tick.


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[Assistant] Every year an exclusive event takes place in the Schlosshotel Kronberg near Frankfurt, to which the public is not invited.


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[Assistant] Collectively, the guests at this elegant evening are worth billions of euros.


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[Assistant] This is the face of wealth in Germany, mainly male. And although we're close to the super rich here, their world remains somehow out of reach.


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[Assistant] Hamburg, home to Manager Magazine's parent company, The Spiegel Group.


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[Assistant] Hardly any publication keeps closer tabs on Germany's ultra-rich. Every year, the magazine's team gathers information on wealthy Germans and, using the Forbes model, makes a special edition with a list of Germany's 1001 richest people.


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[Assistant] Editing Chief Stefan Klossmann has been around Germany's ultra-wealthy for years.


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[Assistant] Creating a list of the wealthiest people is especially difficult in Germany, though not because there aren't enough of them.


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[Assistant] For months, our interview requests were rejected. Agreed film shoots were cancelled last minute. None of the rich wanted to talk to us about money.


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[Assistant] Finally, we got lucky in the financial hub Frankfurt. Here, in a prime location, tucked behind the bank towers, sits the asset management company Forkom. Forkom manages German business family's fortunes.


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[Assistant] Its chairman, Christian von Bechtholzheim, provided some insight as to why rich Germans are so shy.


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[Assistant] After a lot of back and forth with his press team, one ultra-rich German did agree to meet us. Michael Otto is the chairman of the supervisory board of the Otto Group, and one of the ten richest Germans.


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[Assistant] We asked him why Germans are reluctant to show off their wealth.


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[Assistant] His mother ran a small drugstore in post-war Hanover. Her son had bigger plans.


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[Assistant] In 1972, the idea of opening the first self-service drugstore in Germany came to him. Today, he's a multi-billionaire. Why was he happy to step into the spotlight?


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[Assistant] Within 40 years, Roßmann became the most profitable drugstore chain in Europe, with stores in six countries.


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[Assistant] Rossmann is active in other business areas as well, and he speculates on the stock market.


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[Assistant] It's a German peculiarity, and it's not the only one.


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[Assistant] It seems the average ultra-wealthy German is rarely conspicuous. Unlike in the US, athletes, actors and TV personalities rarely make it onto the German list.


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[Assistant] But while rich Germans' fortunes have exploded since the financial crisis due to the increase in value of real estate, stocks and assets, those with average incomes have had to swallow losses.


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[Assistant] Even liberal economics institutes are concerned about social inequality in Germany. How do the super rich see this disparity?


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[Assistant] Michael Otto is one of the rich whose wealth has increased. He successfully transitioned his mail-order business into a digital enterprise. Over 20 companies now belong to the Otto Group.


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[Assistant] We wanted to know how Otto sees the debate about rich and poor. Do the rich understand the worries of the poor?


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[Assistant] Ensuring that the wealth of rich families can increase despite current zero interest rates is the mission of Christian von Bechtolz Heinz Company.


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[Assistant] Is it fair that rich Germans are able to increase their wealth, while the rest of the population gets left behind?


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[Assistant] Von Bechtel Times' company Fulcrum is a so-called multi-family office. Family offices take care of the needs of very wealthy families, managing and increasing their assets. It only makes sense to use family offices if your wealth is upward of 30 million euros. Who can afford such a thing?


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[Assistant] Rainer Schaller seems to have plenty of this entrepreneurial energy. His company is MacFit, Europe's largest fitness studio chain.


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[Assistant] Its headquarters are in an old baking factory in Berlin. Rainer Schaller started off small, but today his wealth is valued at 250 million euros.


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[Assistant] Shala went from secondary school to complete a salesman apprenticeship and became the manager of three supermarkets. Then he decided to start something new.


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[Assistant] It was 1997 when Schaller opened his first fitness studio in Würzburg, close to his home village. He made use of some unusual methods.


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[Assistant] Ten years later, Rainer Schaller reached his goal. He's number one in Europe and still expanding.


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[Assistant] McFit now owns 10 fitness companies as well as its own model agency.


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[Assistant] Huge fortunes and thousands of companies have been passed from one generation to the next.


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[Assistant] There are heirs who don't want to and others who shouldn't take over their parents' businesses.


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[Assistant] Michael Otto inherited the mail order company from his father and successfully managed it.


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[Assistant] Raul Rossmann, the younger of his two sons, will take over as manager of the drugstore chain.


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[Assistant] Raoul's father had to show him the appeal of being in charge.


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[Assistant] Despite their competitiveness, the Rossmann family reached a harmonious agreement with regard to succession.


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[Assistant] Selecting heirs and successors is usually not quite as amicable as at the Rossmans.


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[Assistant] Christian von Bechtolsheim has seen many inheritance disputes in rich families.


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[Assistant] It's his job to preserve the family's assets and protect it from all sorts of dangers.


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[Assistant] Von Bechtel Zeim speaks from personal experience. His own family's history dates back 900 years.


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[Assistant] He takes us to the hunting lodge of the Bechtholzheim family in Thüringen.


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[Assistant] It looked like the Lodz had been lost forever during the division of Germany.


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[Assistant] The family's hunting lodge survived expropriation and socialism without much damage.


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[Assistant] Today von Bechtorsheim also owns hundreds of hectares of forest nearby and regularly invites business acquaintances for hunts.


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[Assistant] A recurring topic in the special issues of the manager magazine are the super wealthy's networks.


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[Assistant] In Berlin, Rainer Schaller is opening a new club.


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[Assistant] A new branch of his fitness empire. Shalo also has become a member in the network of important people from the sports, business and entertainment industries.


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[Assistant] Networks are important for business. But do the rich also have political clout? Can political influence be bought in Germany?


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[Assistant] The heart of it is the ranking of the 1001 richest Germans.


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[Assistant] Schaller thinks he's found the perfect location to realize his vision in Oberhausen.


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[Assistant] At the moment the space is still being used to make steel parts.


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[Assistant] Schaller experienced his most traumatic experience to date in the area. In 2010, 21 people died and 54 were injured in a stampede at the Love Parade in Duisburg. Schaller had been the parade's organizer.


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[Assistant] How does an entrepreneur in the fast lane deal with that kind of tragedy?


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[Assistant] Dick Rossman has also seen setbacks and crises.


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[Assistant] Hasman emerged from that crisis stronger than ever. He started speculating again, but, so he assures us, only with his private wealth.


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[Assistant] Can a large fortune also be a burden?


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[Assistant] The conditions in Germany are currently more favorable than ever for the rich.


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[Assistant] They pay significantly less tax than they did 25 years ago.


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[Assistant] Only a minority still feels compelled to immigrate abroad.


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[Assistant] Article 14 of the German Constitution states, property entails obligations. Its use shall also serve the public good. Do the rich in Germany live up to this responsibility?


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[Assistant] Michael Otto is one of Germany's biggest donors. His money helps fund the environmental, cultural and social sectors.


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[Assistant] Like most rich people, he prefers to decide himself what he spends his money on, rather than leave that to the state.


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[Assistant] In Germany, wealthy people like to donate, and this makes important contributions to society and public life.


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[Assistant] But generally, they're against the proposal of redistributing wealth via higher taxes for the rich.


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[Assistant] Christian von Bechtorzheim sees higher taxes on the rich as dangerous.


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[Assistant] The special issue is ready. Things have basically stayed the same. The rich have a few billion more.


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[Assistant] The richest 1% of Germans now has personal wealth worth a quarter of the country's assets, while a quarter of adults have no wealth or are in debt.


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[Assistant] For this report we never really got access to the world of the wealthiest Germans. They prefer to fly silently under the radar, away from the public eye, until the next award ceremony.


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[Assistant] Everything alright with you? I don't know. Oh really?


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[Assistant] It is, it is, and it feels like it's not over either. It just feels like it keeps going on and on and on.


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[Assistant] I would imagine for you it's kind of hard, you're in the middle of promoting this movie that you worked for a really long time on, and then this week, you know, this terrible thing happens and not that movies really matter or anything, but it puts you in an uncomfortable position, doesn't it?


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[Assistant] Did you spend a lot of time in Las Vegas? Have you been there a lot? I spent a little time there, yea. I know you're interested in that Las Vegas mythology. But you grew up there. I did, yea.


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[Assistant] Disneyland is there and, but you're still, it's a town, it's a community. Right. And uhm, and I think, you know, of course Las Vegas, you know, it's always in the movies and there are all these, you know, crazy things and the advertising campaigns, what happens in Vegas stays in Vegas.


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[Assistant] You know, all that is true for the people who are visiting there, but for the people that live there, it's kind of a regular place to live, except for instead of nine to five, your mom or dad might work midnight to eight a.m. You know?


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[Assistant] Yes, Clito and the Clitos and I grew up right across the street from each other in Las Vegas. And we've been best friends since I was nine, Clito was ten years old. How'd you meet?


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[Assistant] Hi, I'm Jimmy Kimmel. If you like that, click the subscribe button below and good things will happen forever.


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[Assistant] She tried to jump that gulch in the upper pasture. I found her lying down at the bottom.


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[Assistant] Maybe you're right. If I go home now, he'll think he's won his point that I'm just a child.


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[Assistant] Well, someone's got to win. And if it isn't your grandpa, I hope it's you.


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[Assistant] Oh, you mean Mr. Willis has. Well, there's plenty to do yet. You can see that, can't you, Beaver?


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[Assistant] Her free behavior, winning looks, will make a lawyer burn his books. I touched her not, alas, not I. And yes, I love her till I die.


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[Assistant] Should I remain confined there, so long as foe was in his sphere? At your request, she to deny. Yet, what I love her till I die.


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[Assistant] Cupid is winged and dulled range. Her country saw my lucked out change. But change her earth, or change the sky. Yet, I will love her till I die.


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[Assistant] But this fierce pleasure subsided in me as fast as did the accelerated throb of my pulses.


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[Assistant] Without experiencing afterwards the pang of remorse and the chill of reaction.


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[Assistant] A ridge of lighted heath, alive, glancing, devouring, would have been a great emblem of my mind when I accused and menaced Mrs. Reed.


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[Assistant] The same ridge, black and blasted after the flames are dead, would have represented as meatly my subsequent condition.


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[Assistant] When half an hour's silence and reflection had shown me the madness of my conduct, and the dreariness of my hated and hating position. Something of vengeance I had tasted for the first time.


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[Assistant] It's after flavour, metallic and corroding, gave me a sensation as if I had been poisoned. Willingly would I now have gone and asked Mrs. Reed's pardon, but I knew partly from experience and partly from instinct.


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[Assistant] That was the way to make her repulse me with double scorn, thereby re-exciting every turbulent impulse of my nature.


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[Assistant] I would feign exercise some better faculty than that of fierce speaking, feign fine nourishment for some less fiendish feeling than that of sombre indignation. I took a book, some Arabian tales, I sat down and endeavoured to read. I could make no sense of the subject.


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[Assistant] My own thoughts swam always between me and the page I had usually found fascinating. I opened the glass door in the breakfast-room. The shrubbery was quite still.


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[Assistant] The black frost rained, unbroken by sun or breeze, through the grounds. I covered my head and arms with the skirt of my frock, and went out to walk in a part of the plantation which was quite sequestered.


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[Assistant] But I found no pleasure in the silent trees, the falling furcones, the congealed relics of autumn, russet leaves swept by past winds in heaps, and now stiffened together.


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[Assistant] I leaned against a gate, and looked into an empty field where no sheep were feeding, where the short grass was nipped and blanched.


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[Assistant] Thence flakes fell at intervals, which settled on the hard path and on the hoary lee without melting.


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[Assistant] I stood, a wretched child enough, whispering to myself over and over again,


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[Assistant] All at once I had a clear voice call, Miss Jane, where are you? Come to lunch. It was Bessie. I knew well enough, but I did not stir. A light step came tripping down the path. You naughty little thing, she said. Why don't you come when you're cold? Bessie's presence, compared with the thoughts over which I had been brooding, seemed cheerful, even though as usual she was somewhat cross.


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[Assistant] The fact is, after my conflict with and victory over Mrs. Reed, I was not disposed to care much for the nursemaid's transitory anger, and I was disposed to bask in her youthful lightness of heart. I just put my two arms round her, and said, Come, Bessie, don't scold.


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[Assistant] The action was more frank and fearless than any I was habituated to indulge in. Somehow it pleased her. "'You are a strange child, Miss Jane,' she said, as she looked down at me. "'A little roving solitary thing.' "'And you're going to school, I suppose?' I nodded. "'Won't you be sorry to leave poor Bessie?' "'What does Bessie care for me? She's always scolding me.'


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[Assistant] Because you're such a queer, frightened, shy little thing. You should be bolder. What? To get more knocks? Nonsense! But you are rather put upon that certain. My mother said when she came to see me last week that she would not like a little one of her own to be in your place. Now come in and have some good news for you. I don't think you have, Bessie. Child! What d'you mean? What sorrowful eyes you fix on me!


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[Assistant] Well, but Mrs and the young ladies and Master John are going out to tea this afternoon, and you shall have tea with me.


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[Assistant] I'll ask Cook to make you a little cake, and then you shall help me to look over your drawers, for I'm soon to pack your trunks.


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[Assistant] Mrs. intends you to leave Gateshead in a day or two, and you shall choose what toys you like to take with you. Bessie, you must promise not to scold me any more till I go.


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[Assistant] Don't start when our chance to speak rather sharply is so provoking. I don't think I shall ever be afraid of you again, Bessie, because I've got used to you, and I shall soon have another set of people to dread. If you dread them, they'll dislike you. As you do, Bessie. I don't dislike you, Miss. I believe I'm fonder of you than of all the others.


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[Assistant] You don't show it. You little sharp thing. You've got quite a new way of talking. What makes you so venturesome and hardy? Why, I shall soon be away from you, and besides


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[Assistant] I was going to say something about what had passed between me and Mrs. Reed.


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[Assistant] But on second thoughts I considered it better to remain silent on that head. And so you're glad to leave me? Not at all, Bessie. Indeed, just now I am rather sorry. Just now? And rather? How coolly my little lady says it.


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[Assistant] I dare say now, if I were to ask you for a kiss, you wouldn't give it me. You'd say you'd rather not. I'll kiss you, and welcome. Bend your head down. Bessie stooped. We mutually embraced, and I followed her into the house quite comforted. That afternoon lapsed in peace and harmony.


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[Assistant] And in the evening, Bessie told me some of her most enchanting stories and sang me some of her sweetest songs. Even for me, life had its gleams of sunshine.


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[Assistant] It was a sad night for the artigan and the other musketeers. Eventually each of them went to their room to try and sleep. Before going to bed,


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[Assistant] Athos gave orders to the four servants to leave at dawn and travel to Armentieres, each by a different road.


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[Assistant] D'Artagnan came to see Athos and asked what was to be done.


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[Assistant] Told the musketeers that the funeral of Constance would happen at noon. At noon the four friends went to the convent.


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[Assistant] The funeral bells were ringing, the church was open, and Constance's body lay there, wearing a white dress.


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[Assistant] At the church door de Artignan's courage failed him and he turned around to look for Athos, but Athos had disappeared. Athos had walked to the garden. He was following my lady's foot tracks. He discovered that she had left through a back door that led into the garden and had gotten in her carriage there. Athos went back to the inn and found Planche there waiting for him.


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[Assistant] Planche had followed the tracks of the carriage and seen the inn where the horses had stopped. He knew that my lady had gone to Armentier.


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[Assistant] There was only one inn in the town. It was called The Post.


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[Assistant] Planche went to the inn and learned from the inn owner that a young woman had arrived the night before in a carriage.


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[Assistant] Athos thanked Planche and called for his horse. He rode quickly to the city of Lille, approximately 15 miles away.


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[Assistant] He returned to Bethune that evening with a tall man.


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[Assistant] He left him waiting at an inn outside of the town and then rode to meet his friends at their inn. By now it was late night. Athos bought a fresh horse from the inn owner, then called his friends together and told them that the time had come for their journey.


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[Assistant] Within a few minutes, all three were ready and waiting outside the inn.


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[Assistant] Athos came down last and saw that Deartignon was already on his horse.


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[Assistant] The three men looked at each other surprised, but Athos did not say anything.


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[Assistant] Planche brought Athos his horse, and he jumped lightly up on its back.


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[Assistant] He returned ten minutes later, riding next to a tall man, wearing a large red cloak.


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[Assistant] Again, the musketeers looked at each other surprised.


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[Assistant] Athos still said nothing, however, and the musketeers, Planchet and the man in the red cloak, left on the road to Armentiere.


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[Assistant] As they came close to Armentier, the sky became dark and it began to rain heavily.


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[Assistant] They covered their heads with their cloaks and rode on in the rain.


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[Assistant] As they entered the town, a man stepped out into the middle of the road. He raised an arm, telling the men to stop. Athos recognized his servant, Grimaud.


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[Assistant] Grimaud led the group into the forest. Many minutes passed. Grimaud again raised his arm, and the men saw a small and lonely house sitting near the river.


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[Assistant] Close to the house, on the river bank, sat a small boat. One window of the house had a light in it. Then a man climbed down from a tree, who had been hiding in the branches. It was Portho's servant, Musquiton.


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[Assistant] He jumped off his horse and gave it to Grimaud. He then walked quietly towards the window. He climbed the wall and stared in the window. He could see a woman sitting in a chair near a fire. From the window he could not see her face.


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[Assistant] But he knew it was certainly the woman he wanted to find.


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[Assistant] At this moment, one of the musketeer's horses made a sound.


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[Assistant] My lady turned her head and saw Athos' face in the window. She screamed and jumped up. Athos kicked the window open with his knee.


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[Assistant] The glass broke and Athos, full of thoughts of revenge, jumped into the room. My lady ran to the door and threw it open.


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[Assistant] To Artignan, stood outside the door, wet from the rain and pale in his face.


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[Assistant] He held a pistol in his hand and looked like the spirit of death. My lady fell back with a scream of fear.


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[Assistant] My lady looked up at the men, white and afraid. What do you want? She suddenly screamed. Athos stared down at the woman on the floor.


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[Assistant] You know, it's, you know, there are analogs to that in almost everything.


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[Assistant] They're birds, they got like, they are birds, right?


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[Assistant] Uh, this has really, really just been a nice pleasant little feather.


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[Assistant] This case had no DNA, no fingerprints, no gunshot residue. I became outraged at how thin the evidence was. Twenty years passed after I was convicted.


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[Assistant] They withheld evidence on purpose to get a conviction, period.


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[Assistant] I think the best thing that a lawyer can do with their license is get an innocent guy out of prison.


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[Assistant] Uhm, nothing- I mean, hey, shit happens, for sure, but-


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[Assistant] So I think it's just, uhm, growing up and like, you know, learning about different stuff and learning about some shit, for one. Yeah. And uhm.


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[Assistant] All different types of feelings and like vibes.


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[Assistant] Yes, dude. I was at the orthodontist getting my...


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[Assistant] Attachments for my Invisalign shaved off my teeth. Okay.


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[Assistant] And I was like, ooh, this sounds kinda jiggy. So I took out my phone. I pulled up voice memos. My ass pressed record.


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[Assistant] No, we're like, yeah, we've always gotten along really well, and like just always kind of been as close as possible. And we both think we're both really funny. So it's a lot of like, when we do fight, you know, one of us says something hilarious and then


[User] Please transcribe this text

[Assistant] Uhm, it's cool. I mean, it's always been like that on tour. I've never done a tour without my family with me, so.


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[Assistant] I don't know what that's like, uhm, so I can't really like say if it's like better or worse. Yeah, of course, yeah.


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[Assistant] That's always nice to have family around. I mean, I love him. And I love my mom. My mom's right there. My dad is on the crew.


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[Assistant] I have shin splints, and like,


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[Assistant] He was like, super like, overworked, and so basically the muscle that's being built here was like, transferring over here, and then like, that's what's happening basically, so it's like, all coming in here, and it's like- Oh my god.


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[Assistant] Well, all I can say is that it's all connected. But the rest is up to you.


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[Assistant] I know what it means. Yeaah. You don't have to give it away. But I don't like to give it away. No, of course. Like, I don't even want to... Yeaah.


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[Assistant] I'm holding my flashlight like it's a dick. Okay, here we go. If you want, I can hold it. I'm gonna try not to- No, it's okay. Ooh, up and-


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[Assistant] Now you have my logo on your face. Okay, great. That looks sick, but- Stamp of approval.


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[Assistant] That's for me to know, nigga, and for you to find out." Porky replied sharply.


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[Assistant] Big Ed drove the car up the driveway of the motel until he found their unit then parked in front of the door. They all piled out.


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[Assistant] Dave removed a small overnight bag from the glove compartment and carried it in


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[Assistant] The motel room was similar to motel rooms all over the country. Small, neat, with a dresser and tiny kitchenette.


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[Assistant] Porkey peeped out the window as someone knocked softly. Satisfied that it was the right person, Porkey opened the door.


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[Assistant] Sid walked in with a small, weasel-faced man following him.


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[Assistant] Sid was short and fat, with a jet black complexion.


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[Assistant] Who the fuck you got in there, Porky?


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[Assistant] He asked sharply, his hand slipping under his overcoat. The question caught Porky by surprise.


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[Assistant] His bodyguard leaned against the door, his hand conspicuously bulging from his overcoat. Dave, Dave, get the fuck out here, Porky yelled, wiping the sweat from his brow with the back of his hand. The door opened quickly and Dave walked out. His face twitched nervously. He could feel the muscles contract, but couldn't control them. Guns and violence were something he had an innate fear of.


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[Assistant] The room was full of hostility. Neither person trusted the other.


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[Assistant] his city was beginning to get a reputation as a city full of informers


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[Assistant] He stuck a cigar in his mouth, his stomach swelling with knots of fear. The knock came almost unexpectedly. Big Ed moved over to the door and glanced out. His hand clutched a 38 automatic. It better be a goddamn woman, he growled. He relaxed and opened the door, holding the gun down so she wouldn't see it. A small, light brown-skinned woman entered. She was dressed expensively. At the sight of Porky,


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[Assistant] She hesitated for a brief second, shocked by his hugeness.


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[Assistant] Her 300 pound Sid was dwarfed by comparison.


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[Assistant] Poki looked at her like an expensively dressed gorilla smoking a cigar. Give me that stuff, woman. Sid ordered. I got ten pieces, Poki, that you can put a six on.


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[Assistant] Porkey took the stuff from her outstretched hand and held it out to Big Ed, who pocketed his gun and carried the dope to the dresser.


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[Assistant] He sifted the dope and milk through his sifter over eight times until they were thoroughly mixed together.


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[Assistant] Then he measured out a $10 spoon and dropped it in the cooker.


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[Assistant] Dave started cooking the dope up for him as he tied up his arm


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[Assistant] Big head closed his eyes and wet his lips as though he were tasting the dope. It ain't nothing to write home about Porky, but it will sail. It's just about as good as that stuff we got now.


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[Assistant] Everybody in the room seemed to have been holding his breath, waiting for the decision. Sid laughed, relieving some of the tension in the air. Is that good enough for you, Porky? He asked, as his fingers closed with the money. Staring at all the loose dope on the dresser, Dave got excited and started to fart loudly. Porky laughed.


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[Assistant] That's one way I can always tell dope, Sid. He said in an amiable tone.


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[Assistant] Every time that nigga gets around some dope, he starts to fart.


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[Assistant] Both men laughed agreeably as they watched Dave rush to the empty cooker and push a small amount of the dope into it and drop water on top of it. Sid picked up the money and started toward the door. Anytime you want to get down, Porky, just give me a ring. It's nice doing business with you. His bodyguard peeped out the door first, while Sid gave the envelope full of money to his woman to carry. It was just another precaution he was taking, one Porky was quite familiar with. If anyone got busted,


[User] Please transcribe this text

[Assistant] As soon as the door closed behind them, Porky wrapped up the ten pieces of pure he had bought. Get them works clean, we leaving, he snapped. That's yours and Dave's, Ed, he said, pointing towards the small amount of dope they had cut up to test. He waited until Ed had picked up his dope. Let's go, Porky yelled, nervous now that the business had been completed. Dave snatched the works out of his arm, wishing he could have jacked off the works a couple more times.


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[Assistant] He rinsed out the works quickly. Porky and Big Ed were already at the door.


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[Assistant] Bring your goddamn ass home now, Dave, or we gonna leave you in this fucking ass hotel.


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[Assistant] They can keep their deposit and shove it up their ass for all our care.


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[Assistant] They hadn't been in Chicago an hour, and now they were on their way back home with a car full of dope.


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[Assistant] The wind rattled her windows, and she could feel a draft coming through the broken pane, where papers covered the jagged hole.


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[Assistant] The noise from her single radiator had kept her awake most of the night.


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[Assistant] The steam hissed constantly and the pipes rattled loudly. When she became fully awake, her first thoughts were fear-ridden.


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[Assistant] So uhm, uh,


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[Assistant] She fumbled around searching for old used pieces of cotton.


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[Assistant] After she found five of them she put them in a cooker and poured water on them. She lit two matches and held them under the cooker until the water got hot.


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[Assistant] So uhm, uh, uh,


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[Assistant] She could hear Minnie hollering on the other side. Wake up, Terry! Somebody's taking your car away!


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[Assistant] Terry ran to the door, then turned around and ran back and grabbed her coat off the only chair in the room.


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[Assistant] She put it on and ran out of the room, following Minnie, running barefooted.


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[Assistant] They got outside just in time to see a tow truck pulling away from the curb.


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[Assistant] She stopped as the icy snow began to freeze around her bare feet.


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[Assistant] For the past two months, she'd come to rely upon the transportation.


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[Assistant] Her stomach had become so large from her pregnancy that it was almost impossible for her to catch a trick.


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[Assistant] Her only chance of making money had been when Terry took her around to the shopping centers to shoplift. Both women stared after the car dumbfounded.


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[Assistant] He watched as her head jerked up and fire leaped from her eyes. Some of the old arrogance was still in her, but he knew it wouldn't last long.


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[Assistant] Just wait until the junkie sickness hit her. Then he'd see just how proud she would be. What we gonna do, Minnie?


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[Assistant] He was happy to see us, but rather angry because another of his shoes had disappeared. This time it was an old shoe instead of a new one.


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[Assistant] I could see that Holmes thought this was very interesting.


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[Assistant] He thought about it for a few moments, but he said nothing.


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[Assistant] Except that perhaps there was a great shoe thief in London.


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[Assistant] At lunch, Sir Henry told Holmes that he had decided to go to Baskerville Hall.


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[Assistant] Then Holmes told them about the strange man in the taxi.


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[Assistant] He asked Doctor Mortimer if anyone with a large black beard lived in or near Baskerville Hall. Yes, said Doctor Mortimer.


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[Assistant] Barrymore. Sir Charles's butler has a black beard.


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[Assistant] Well then, we must find out whether Barrymore is in London or at Baskerville Hall, said Holmes.


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[Assistant] I shall send a message to Barrymore at the hall.


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[Assistant] It will say, Sir Henry has arrived in London. Is everything at Baskerville Hall ready for him?


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[Assistant] Then I will send another message to the local post office.


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[Assistant] This one will say Please give this message to Mr. Barrymore.


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[Assistant] Return the message to Sir Henry Baskerville if Barrymore is away.


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[Assistant] I'll give the post office your address at this hotel.


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[Assistant] Before this evening, we will know where Barrymore is.


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[Assistant] I know that Barrymore and his wife have a very fine home.


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[Assistant] Sir Charles told everyone what he had written in his will so the Barrymores knew they would get the money. That's very interesting, said Holmes.


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[Assistant] I hope you don't suspect everyone who got something from Sir Charles Will, said Dr. Mortimer. I received eleven thousand pounds. Indeed, said Holmes. And who else received money?


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[Assistant] Many people got a little bit of money. He gave some money to some hospitals in the area.


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[Assistant] But the rest all went to Sir Henry, who received 740,000 pounds.


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[Assistant] I had no idea the Baskervilles were so wealthy, said Holmes in surprise.


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[Assistant] Their lands are worth more than one million pounds, Dr. Mortimer said. Holmes looked more interested than ever.


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[Assistant] There are many people, gentlemen, who would do anything for that much money. Even commit murder. I have one more question.


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[Assistant] If something happened to Sir Henry, who would get Baskerville Hall and all its lands?


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[Assistant] Well, as you know, Sir Charles had two younger brothers.


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[Assistant] Sir Henry is the only son of the middle brother.


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[Assistant] The youngest brother of the three, Roger, was a criminal. The police wanted him, so he had to leave England.


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[Assistant] They say he looked exactly like old Sir Hugo, who first saw the hound. He was the same kind of man, too. Drunken, cruel, and careless.


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[Assistant] He went to South America and died of a sickness there.


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[Assistant] If Sir Henry died, Baskerville Hall would go to James Desmond, who is a cousin of the Baskervilles. James Desmond is an old man who lives in the north of England.


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[Assistant] I think that you should go to Baskerville Hall as quickly as possible, but you must not go alone.


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[Assistant] Unfortunately, I cannot leave London at this time because I am working on another case. I hope my friend Watson will go with you in my place. If there is any danger, you could not have a better man by your side. Both Sir Henry and I agreed to this idea.


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[Assistant] We decided to leave for Baskerville on the next Saturday.


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[Assistant] Just as we were leaving Sir Henry's room, he gave a cry and got down on his knees by the table.


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[Assistant] Well, here's my brown shoe that was lost, he said, reaching under the table.


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[Assistant] That's strange, said Dr. Mortimer. We looked around the room before lunch, and the shoe wasn't there.


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[Assistant] None of the people in the hotel could explain how the shoe had got back into the room.


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[Assistant] On the way back to Baker Street in the taxi, Ohm sat thinking deeply.


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[Assistant] All through the afternoon and the evening he was very quiet.


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[Assistant] You went on thinking silently and smoking pipe after pipe. Just before dinner, a message arrived. It was from Sir Henry and said,


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[Assistant] I have just heard that Barrymore is at the hall.


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[Assistant] So Barry Moore was not following Sir Henry to-day, said Holmes.


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[Assistant] Perhaps we will soon find out who the man really is. At that moment, the doorbell rang. It was the driver of the bearded man's taxi.


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[Assistant] I got a message that someone wanted to see me at this address, said the driver. Is there something wrong? No, no, my good man, said Holmes.


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[Assistant] If you can answer my questions clearly, I'll give you some money.


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[Assistant] I would like you to tell me about the man in your taxi this morning.


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[Assistant] He had a black beard, and he was watching this house at ten o'clock.


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[Assistant] Then he told you to follow the two gentlemen who came out of it.


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[Assistant] The taxi driver was surprised at how much Holmes knew.


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[Assistant] This is a very serious business, said Holmes.


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[Assistant] I am also a detective, so do not be afraid to tell me everything you know. Well, the man told me his name, said the driver.


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[Assistant] Homes look like a cat that has just caught a mouse.


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[Assistant] That was very stupid of him, he said. What was it? His name, said the taxi driver, was Sherlock Holmes.


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[Assistant] For a moment, Holm stared at the man in surprise. Then he burst out laughing. Excellent, my good man!


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[Assistant] Now, tell me when he got into the taxi, what he looked like, and anything else you know. Some of what the man told us we already knew. However,


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[Assistant] We learned that after the taxi had disappeared, it had gone to the Waterloo station. The man had to catch a train there.


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[Assistant] The driver said that the man had on good clothes and had a long black beard.


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[Assistant] That long beard looks strange on such a young man," said the driver.


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[Assistant] The man looked about forty years old, and he was not very tall.


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[Assistant] The driver did not know the color of the man's eyes.


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[Assistant] Holmes gave the man some money and sent him away. Then he said, Well, Watson, we have a clever enemy. At this moment he is winning the game.


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[Assistant] We do not know who he really is or what he wants with Sir Henry.


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[Assistant] We have no answers to anything that has happened.


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[Assistant] I hope your time at Baskerville Hall will be more successful.


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[Assistant] However, you must be very careful whom you trust.


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[Assistant] There is too much danger in this case, and a lot of money.


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[Assistant] Wee Willy Winkie runs through the town, upstairs and downstairs in his nightgown.


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[Assistant] Wrapping at the window, crying through the lock. Are the children on bed? Now it's 8 o'clock.


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[Assistant] Your friend worked downtime with you, surely? He asked, as Snake pulled away from the curb. Yeah, baby, and she's the only cool fae down there, too.


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[Assistant] So uhm, uuuh.


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[Assistant] You two whores ain't nothing but sisters under the skin, Shirley. What you got planned? You choose me and carry Anza with Teddy? Both women laughed. Now that the fear of not copping was over, both women started to relax. What's wrong with that snake? Shirley asked. Nothing.


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[Assistant] Except I know that you're a bad chasing bitch, and it looks like your partner might be following in your footsteps. Snake answered coldly.


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[Assistant] Ever since they started dealing, women had been no problem.


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[Assistant] So uhm, uh, uh,


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[Assistant] The question went unanswered because Snake was already on his way across the street.


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[Assistant] So uhm, uh,


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[Assistant] Terry stared at her small amount. She realized that it would not be enough to take her sickness off if she snorted it. Minnie read her mind. You better get you a set of works, honey, if you want to feel that dope. For a moment, a feeling of being utterly lost washed over her. In the back of her mind, she knew she was getting ready to go over the last barrier. There was no way around it. She had to get rid of her junk sickness and the dope she had,


[User] Please transcribe this text

[Assistant] When Snake passed the couch, he stopped for a moment and stared as an addict hit Terry in the arm. As he pressed the dropper, her eyes closed. Snake walked on past. It was just another step down the line for her, he thought. Not surprising really.


[User] Please transcribe this text

[Assistant] What's happening Porky? What's the real deal? He yelled across the bedroom. Porky was busy trying to hit his woman in the groin.


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[Assistant] What's wrong with you, nigga? Don't you believe in waiting until someone lets you in a door? He snarled. The day's events are just about worn Porky out. He had never seen the likes of this before.


[User] Please transcribe this text

[Assistant] His two dogs stood up and growled as Snake walked across the floor. You still got that piece for me, ancient porky? He asked nervously.


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[Assistant] Poki nodded towards Big Ed, who was standing in front of a mirror, trying to hit in the neck. He got it.


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[Assistant] If I had known it was gonna be like this, Snake, I'd never have sold that piece to you.


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[Assistant] Before Porky could change his mind, Snake pulled out his money and started counting out the $400. He walked over to the dresser and laid the money in front of Big Ed. You think you'll have enough to sell me another piece tomorrow, Porky? Hell no, Porky exclaimed. Fuck no, you're getting a goddamn break on this fourth now. You think I'll go for the same fucking thing again tomorrow? Pulling the needle out of Smokey's groin, Porky cursed.


[User] Please transcribe this text

[Assistant] This goddamn bitch ain't got no veins nowhere.


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[Assistant] You might be able to hit that same vein as the last time you hit me. I ain't been back in this since, cuz I don't just let anybody hit me under the arm. She pulled her dress down and stood up. For a brief moment, Snake wanted to say no. But after looking in her face, he knew he couldn't turn her down. He waited until she removed her blouse and raised her arm. As he probed for the vein with his finger, he thought cruelly to himself,


[User] Please transcribe this text

[Assistant] At least the bitch changed bras since the last time. After about ten minutes, he finally hit a vein.


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[Assistant] The dark blood that filled up the dropper told him he had a hit.


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[Assistant] Before the works had time to clog up, he pushed down slightly on the bulb, and the blood and dope disappeared in the hidden vein, under a mass of dark hair.


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[Assistant] Before Snake reached the car, Porky had received a long distance call from Chicago.


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[Assistant] He was now beginning to get some action on all the calls he had made in the past few days.


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[Assistant] He talked quietly into the receiver for a few moments, shook his head in agreement, then hung up. He walked slowly towards the door and opened it. Everybody out, he said. As Big Ed passed him, he whispered, get ready, Ed. We'll be leaving for Chicago in the next few minutes. As soon as the room emptied, he smiled gleefully at his woman. Give me $6,000 together, Smoke. I'm getting ready to ride now, and when I get back,


[User] Please transcribe this text

[Assistant] Smokey walked over to the corner nearest to the bed, where the dog slept.


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[Assistant] She pulled back a floor rug that the dogs lay on most of the day.


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[Assistant] Uh, uh, uh, uh,


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[Assistant] You still got that three thousand I brought back earlier pork in your pocket. So all you need is three grand more. Better give me four girl. You never can tell what might happen when you're in Chicago. He continued to dress as he talked over his shoulder to her. Count up how much we got left in the box too. If it's too much, you better get ready to make a trip to the bank.


[User] Please transcribe this text

[Assistant] You better hold out a piece of stuff for yourself, in case I should be longer than what I planned.


[User] Please transcribe this text

[Assistant] He slipped on the pants to his suit and put on a gold silk shirt.


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[Assistant] The suit he wore was very expensive, a black silk mohair. After dressing, he stuck a large cigar in his mouth.


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[Assistant] Okay, uh, uh,


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[Assistant] I should be back before daylight in the morning, but if I ain't, don't worry. Oh yea, if anybody wants to buy a piece of stuff, sell it for 500.


[User] Please transcribe this text

[Assistant] You can call Snick and Teddy up and let them know they can cop another piece. Only will cost them an extra hundred.


[User] Please transcribe this text

[Assistant] So uhm, uuuh.


[User] Please transcribe this text

[Assistant] So uhm, uh,


[User] Please transcribe this text

[Assistant] So uhm, uh,


[User] Please transcribe this text

[Assistant] So uhm, uh,


[User] Please transcribe this text

[Assistant] He broke the stingy brim down and set the hat ace-deuce across his head while he was adjusting it in the mirror.


[User] Please transcribe this text

[Assistant] Smokey stuck her hand under the pillow and came out with a small .38 derringer pistol. She silently held the gun out to him.


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[Assistant] He stuck it down in his huge waistline, his enormous stomach, completely covering the small pistol.


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[Assistant] Big Ed and Dave led the way down the stairs, with Porky following them closely, like some monstrous prehistoric beast.


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[Assistant] OUTSIDE.


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[Assistant] Porky climbed in the back, taking up most of the seat, while his gunman got in the front.


[User] Please transcribe this text

[Assistant] He waved at Smokey, who was watching him silently from the window, and the large car leaped away from the curb. Five hours later they roared in off the highway.


[User] Please transcribe this text

[Assistant] Chicago's lights seemed to wink at them as Big Ed steered the car off the freeway and into the heart of a black ghetto.


[User] Please transcribe this text

[Assistant] Porkey gave him directions from the backseat until finally they pulled up in front of a motel.


[User] Please transcribe this text

[Assistant] So uhm, uh,


[User] Please transcribe this text

[Assistant] People had very limited access to move-about polling stations or where they'd vote would be extreme distances. They had to find a way of making election day some, uh, a day that people could accommodate their schedules.


[User] Please transcribe this text

[Assistant] The Jimmy Valentine of song, the crown prince of crooners, my pal Jimmy Hale.


[User] Please transcribe this text

[Assistant] Every day, every hour on the hour, I'll tell you how lovely you are, how charming, how gorgeous. That can become monotonous.


[User] Please transcribe this text

[Assistant] When I get through, you'll be so decked out and furs and jewels. Everybody will say, there goes Barry Clayton's beautiful wife.


[User] Please transcribe this text

[Assistant] Thank you, thank you very much. And now may I present my good friend, the popular songster of the airwaves, Jimmy Hale!


[User] Please transcribe this text

[Assistant] Oliver was now officially an Undertaker's Assistant.


[User] Please transcribe this text

[Assistant] It was a good sickly time of year and coffins were selling well. Oliver gained a lot of experience in a short time.


[User] Please transcribe this text

[Assistant] He saw that the people who had cried the loudest in church usually recovered the fastest afterwards.


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[Assistant] He noticed how in other wealthy families the wife or the husband often seemed quite cheerful and calm despite the recent death, just as if nothing had happened.


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[Assistant] Oliver was very surprised to see all this, and greatly admired them for controlling their sadness so well.


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[Assistant] Noah was jealous because Oliver went out to burials while he was left back in the shop, so he treated him even worse than before. Charlotte treated him badly because Noah did.


[User] Please transcribe this text

[Assistant] And Mrs. Sowerbury was his enemy because Mr. Sowerbury was supposed to be his friend.


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[Assistant] Uhm, and provide an equivalent experience to people with visual impairments.


[User] Please transcribe this text

[Assistant] Here we go. Computer-enabled abilities lab, uhm, or SEAL for short, uhm, and we, uh,


[User] Please transcribe this text

[Assistant] Uhm, food, all of this stuff. People funnel their attention through computing devices in order to unlock these experiences in real life.


[User] Please transcribe this text

[Assistant] Uh, that's when you, that's the secret sauce. So let me just be a little more, uhm, specific here.


[User] Please transcribe this text

[Assistant] So in terms of racing game, our actual approach to solving this problem and making it blind accessible, making it really feel like racing, is to race a whole bunch of laps around the track ourselves.


[User] Please transcribe this text

[Assistant] Number two is the nature of upcoming terms. And so with the RAD, we designed two audio cues, uh, to, uh, convey those two separate things.


[User] Please transcribe this text

[Assistant] Uhm, but that will be complemented by the turn indicator system, that other piece of information, which is the nature of upcoming turns.


[User] Please transcribe this text

[Assistant] The, uhm, the pitch of the beeps, uh, indicates the sharpness of the turn. And we, uh, you know, we have three different turn sharpnesses. Uhm, the reason there are four beeps is that they establish a rhythm so that, uh, the turn itself will always start on the fourth beat.


[User] Please transcribe this text

[Assistant] Uh, wherever they're going, right? That's the analogy. And so, uhm,


[User] Please transcribe this text

[Assistant] Our observation is that, again, when we, you know, thinking about equivalent experiences, people who are sighted, uhm, you know,


[User] Please transcribe this text

[Assistant] And in the context of a game where people predominantly preferred NavStick, they preferred this, even though they also agreed that NavStick took a lot of extra effort. For the game, actually looking around and doing things themselves was the whole point of the game to them. They really enjoyed that a lot.


[User] Please transcribe this text

[Assistant] So I'm going to, uhm, show one more result about mental maps.


[User] Please transcribe this text

[Assistant] Uhm, and they, they agreed, very small confidence interval. And then we quizzed them. We put them in random grocery store aisles and say, you know, and said, okay, so where's the peanut butter? Uhm, and we found that people performed much better, uh, when they were able to explore first with NavStick than when they used the menu.


[User] Please transcribe this text

[Assistant] And then my other PhD student, Gaurav Jain, uhm, is leading a project which we're calling Wearable Vision. He has a computer vision background, working with several, uh, you know, really talented undergrads and masters students, uhm, in which


[User] Please transcribe this text

[Assistant] So this is still pretty early work, but essentially we're, uhm, uh, using SLAM and the idea is like how do we give


[User] Please transcribe this text

[Assistant] Several different, you know, national organizations for, you know, serving blind users are headquartered here in New York, and so I have relationships with them. We have, you know, they sit alongside us, so we co-design with these users.


[User] Please transcribe this text

[Assistant] Uhm, actually making the haptics work. I think for general purpose navigation, real world environments, they'll work just fine.


[User] Please transcribe this text

[Assistant] Some don't like haptics. And so I think that we should be aiming to design solutions in both sound and haptics.


[User] Please transcribe this text

[Assistant] Uhm, and, and so that, I would say that's getting closer to capital H hard. Uhm, first person shooters, uhm, I think very doable. Uh, platforming games, uhm, uh, time pressure is so immense, uhm, but I think the,


[User] Please transcribe this text

[Assistant] Yeah, that's, that's exactly what's going on. So not only does the visual system, you know, operate in parallel in many different ways, but it also just has a much higher throughput in general. Uhm, and so you're kind of losing both ways with audio compared to visual channel.


[User] Please transcribe this text

[Assistant] My free time, I also wrote a product management book called Principles of Product Management. Yes.


[User] Please transcribe this text

[Assistant] And teenagers, they probably are old enough that can, they can just use like a regular communication app. They don't need to use like a special app for kids. So let's focus on the preschool age, like three or five years old. And let's also think about the parent needs, right?


[User] Please transcribe this text

[Assistant] Cool. So let's think about, so given that kind of like, uh, benefit statement, let's think about what a solution space could look like. Uh, so again, we have two customer segments. We have the parents and we have the kids. So from a parent perspective, I think, um,


[User] Please transcribe this text

[Assistant] If they open this app, they probably want to see a list of, uh, the contacts they have. Uh, probably like, you know, hit, hit, hit, um,


[User] Please transcribe this text

[Assistant] So they can interact with each other by kind of just like sending a bunch of text messages, stickers or like pictures and so on. Or they can just make a bunch of other like, you know, phone calls and video calls.


[User] Please transcribe this text

[Assistant] But eventually it can become a platform for other developers and other people to build games on this.


[User] Please transcribe this text

[Assistant] And then you have a wide selection of like games and interactions that kids can play with beyond just like simple text and you know, sending pictures to each other.


[User] Please transcribe this text

[Assistant] 20 questions, right? Some sort of simple game that kind of like let us interact with each other. Uh, and then we can...


[User] Please transcribe this text

[Assistant] So we talked about the parent user journey around like adding a contact, monitoring activity, and blocking contacts. Uh, so I think adding a contact, you know, they can coordinate with other parents, they can get the kids.