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These observations further substantiate the ligand-binding promiscuity of PXR and its role as a sensor responding to a wide variety of chemicals. In this respect, one can note that, while binding to different subpockets and therefore stabilizing different regions of the LBP, all compounds act as PXR agonists, differing only by their binding affinity. No specific interaction, such as, for example, with the AF-2 helix, appears required to activate the receptor. For instance, OXA and PRE, which make very little or no interaction with helix H12, serve as potent agonists, whereas E2 that is engaged in many contacts with the activation (check it out) helix appears as a poor PXR activator. Nevertheless, our fluorescence anisotropy study reveals a clear direct effect of ligand binding on the recruitment of coactivators in the context of the RXR–PXR heterodimer, suggesting that ligand-dependent PXR activation involves regulatory regions of the LBD that still remain to be identified.
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S8D; the HIV-1 integrase and S1P1 inhibitors in 1, SI Appendix, Fig. S8 E and F; the P2X4 and HIV reverse-transcriptase inhibitors in 2, SI Appendix, Fig. S8 G and H). Finally, all ligands except estrogens of group 4 established contacts with the aromatic cage of subpocket 1 through π-stacking or C–H/π interactions, and half of them occupy at least subpocket 2 also. Therefore, subsites 3 and 4 are the least occupied.
How can you deal with variable-length input sequences? What about variable-length output sequences?
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Bi, bf, bo, and bg are the bias terms for each of the four layers. Note that TensorFlow initializes bf to a vector full of 1s instead of 0s. This prevents forgetting everything at the beginning of training.
“Dropout Improves Recurrent Neural Networks for Handwriting Recognition”
You immediately start running, anticipating the ball’s trajectory. You track it and adapt your movements, and finally catch it (under a thunder of applause). Predicting the future is what you do all the time, whether you are finishing a friend’s sentence or anticipating the smell of coffee at breakfast. In this chapter, we are going to discuss recurrent neural networks (RNN), a class of nets that can predict the future (well, up to a point, of course). They can analyze time series data such as stock prices, and tell you when to buy or sell. In autonomous driving systems, they can anticipate car trajectories and help avoid accidents. More generally, they can work on sequences of arbitrary lengths, rather than on fixed-sized inputs like all the nets we have discussed so far.
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The English sentences are fed to the encoder, and the decoder outputs the French translations. Note that the French translations are also used as inputs to the decoder, but pushed back by one step. In other words, the decoder is given as input the word that it should have output at the previous step (regardless of what it actually output).
Lastly, you could have a sequence-to-vector network, called an encoder, followed by a vector-to-sequence network, called a decoder (see the bottom-right network). For example, this can be used for translating a sentence from one language to another. You would feed the network a sentence in one language, the encoder would convert this sentence into a single vector representation, and then the decoder would decode this vector into a sentence in another language. This two-step model, called an Encoder–Decoder, works much better than trying to translate on the fly with a single sequence-to-sequence RNN (like the one represented on the top left), since the last words of a sentence can affect the first words of the translation, so you need to wait until you have heard the whole sentence before translating it.
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What if the output sequences have variable lengths as well? If you know in advance what length each sequence will have (for example if you know that it will be the same length as the input sequence), then you can set the sequence_length parameter as described above. Unfortunately, in general this will not be possible: for example, the length of a translated sentence is generally different from the length of the input sentence. In this case, the most common solution is to define a special output called an end-of-sequence token (EOS token). Any output past the EOS should be ignored (we will discuss this later in this chapter).
In general a cell’s state at time step t, denoted h(t) (the “h” stands for “hidden”), is a function of some inputs at that time step and its state at the previous time step: h(t) = f(h(t–1), x(t)). Its output at time step t, denoted y(t), is also a function of the previous state and the current inputs. In the case of the basic cells we have discussed so far, the output is simply equal to the state, but in more complex cells this is not always the case, as shown in Figure 4-3.
Chapter 4. Recurrent Neural Networks
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The rest of the code is the same as earlier. This can provide a significant speed boost since there is just one fully connected layer instead of one per time step.
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To train an RNN, the trick is to unroll it through time (like we just did) and then simply use regular backpropagation (see Figure 4-5). This strategy is called backpropagation through time (BPTT).
In short, an LSTM cell can learn to recognize an important input (that’s the role of the input gate), store it in the long-term state, learn to preserve it for as long as it is needed (that’s the role of the forget gate), and learn to extract it whenever it is needed. This explains why they have been amazingly successful at capturing long-term patterns in time series, long texts, audio recordings, and more.
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Now we are ready to train the RNN. The execution phase is exactly the same as for the MNIST classifier in Chapter 1, except that we reshape each training batch before feeding it to the network.
This could be explained by the larger size of FIP that might be too sterically constrained within its binding site, as suggested by the significant shift of the aromatic cage residue Y306 toward the solvent compared to its position in other PXR structures. A short residence time of FIP within the LBP could also explain why a covalent coupling is not observed by MS (short incubation time of 1 h), whereas it can be seen in the crystal structure, where the ligand and the receptor are coincubated for several days during the crystallization process.
You now have all the tools you need to understand the sequence-to-sequence tutorial’s implementation. Check it out and train your own English-to-French translator!
Conversely, you could feed the network a single input at the first time step (and zeros for all other time steps), and let it output a sequence (see the bottom-left network). This is a vector-to-sequence network. For example, the input could be an image, and the output could be a caption for that image.
Native MS experiments were performed on a hybrid electrospray quadrupole time-of-flight mass spectrometer (Synapt G2 HDMS; Waters) coupled to an automated chip-based nanoelectrospray source (Triversa Nanomate; Advion Biosciences) operating in the positive ion mode. PXR LBD was buffer-exchanged as described above and incubated during 5 min at 20 °C to a final concentration of 5 µM (in 150 mM NH4Ac, pH 8/0) with a 2-molar excess of binary mixtures (EE2/TNC, E2/END, E2/CC, E2/HEP, E2/CLO, or E2/ZEA; 10 µM each). Prior to analyses, mass spectrometer calibration was performed using singly charged ions produced by a 2-mg/mL solution of cesium iodide in 2-propanol/water (1v/1v; Sigma) over the m/z range 1,000 to 10,000. Instrumental parameters were optimized to get an optimal m/z ion transmission without dissociation of weak noncovalent interactions by raising the backing pressure, the cone voltage, and the extraction voltage to 6 mbar, 20 V, and 5 V, respectively (acquisition performed with MassLynx 4/1).
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In general you would have more than just one input feature. For example, if you were trying to predict stock prices, you would likely have many other input features at each time step, such as prices of competing stocks, ratings from analysts, or any other feature that might help the system make its predictions.
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Before we start, we need to choose a word representation. One option could be to represent each word using a one-hot vector. Suppose your vocabulary contains 50,000 words, then the nth word would be represented as a 50,000-dimensional vector, full of 0s except for a 1 at the nth position. However, with such a large vocabulary, this sparse representation would not be efficient at all. Ideally, you want similar words to have similar representations, making it easy for the model to generalize what it learns about a word to all similar words. For example, if the model is told that “I drink milk” is a valid sentence, and if it knows that “milk” is close to “water” but far from “shoes,” then it will know that “I drink water” is probably a valid sentence as well, while “I drink shoes” is probably not. But how can you come up with such a meaningful representation?
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A single gate controller controls both the forget gate and the input gate. If the gate controller outputs a 1, the forget gate is open and the input gate is closed. If it outputs a 0, the opposite happens. In other words, whenever a memory must be stored, the location where it will be stored is erased first. This is actually a frequent variant to the LSTM cell in and of itself.
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Now suppose you want to feed the sentence “I drink milk” to your neural network. You should first preprocess the sentence and break it into a list of known words. For example you may remove unnecessary characters, replace unknown words by a predefined token word such as “[UNK]”, replace numerical values by “[NUM]”, replace URLs by “[URL]”, and so on. Once you have a list of known words, you can look up each word’s integer identifier (from 0 to 49999) in a dictionary, for example [72, 3335, 288].
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Other residues are displayed as lines. Oxygen, nitrogen, sulfur, and chlorine atoms are colored in red, blue, yellow, and green, respectively. Residues and secondary structural elements discussed in the text are labeled.
Moreover, RNNs’ ability to anticipate also makes them capable of surprising creativity. You can ask them to predict which are the most likely next notes in a melody, then randomly pick one of these notes and play it. Then ask the net for the next most likely notes, play it, and repeat the process again and again. Before you know it, your net will compose a melody such as the one produced by Google’s Magenta project. Similarly, RNNs can generate sentences, image captions, and much more. The result is not exactly Shakespeare or Mozart yet, but who knows what they will produce a few years from now?
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You can easily create a layer of recurrent neurons. At each time step t, every neuron receives both the input vector x(t) and the output vector from the previous time step y(t–1), as shown in Figure 4-2. Note that both the inputs and outputs are vectors now (when there was just a single neuron, the output was a scalar).
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If you don’t look at what’s inside the box, the LSTM cell looks exactly like a regular cell, except that its state is split in two vectors: h(t) and c(t) (“c” stands for “cell”). You can think of h(t) as the short-term state and c(t) as the long-term state.
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To train an RNN on long sequences, you will need to run it over many time steps, making the unrolled RNN a very deep network. Just like any deep neural network it may suffer from the vanishing/exploding gradients problem (discussed in Chapter 2) and take forever to train.
The dynamic_rnn() function uses a while_loop() operation to run over the cell the appropriate number of times, and you can set swap_memory=True if you want it to swap the GPU’s memory to the CPU’s memory during backpropagation to avoid OOM errors. Conveniently, it also accepts a single tensor for all inputs at every time step (shape [None, n_steps, n_inputs]) and it outputs a single tensor for all outputs at every time step (shape [None, n_steps, n_neurons]); there is no need to stack, unstack, or transpose. The following code creates the same RNN as earlier using the dynamic_rnn() function.
It will contain 100 recurrent neurons and we will unroll it over 20 time steps since each training instance will be 20 inputs long. Each input will contain only one feature (the value at that time). The targets are also sequences of 20 inputs, each containing a single value.
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At the beginning of training, embeddings are simply chosen randomly, but during training, backpropagation automatically moves the embeddings around in a way that helps the neural network perform its task. Typically this means that similar words will gradually cluster close to one another, and even end up organized in a rather meaningful way.
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Second, when the output vocabulary is large (which is the case here), outputting a probability for each and every possible word would be terribly slow. If the target vocabulary contains, say, 50,000 French words, then the decoder would output 50,000-dimensional vectors, and then computing the softmax function over such a large vector would be very computationally intensive. To avoid this, one solution is to let the decoder output much smaller vectors, such as 1,000-dimensional vectors, then use a sampling technique to estimate the loss without having to compute it over every single word in the target vocabulary.
Embeddings are also useful for representing categorical attributes that can take on a large number of different values, especially when there are complex similarities between values. For example, consider professions, hobbies, dishes, species, brands, and so on.
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Note that at inference time (after training), you will not have the target sentence to feed to the decoder. Instead, simply feed the decoder the word that it output at the previous step, as shown in Figure 4-16 (this will require an embedding lookup that is not shown on the diagram).
This is a time series prediction task: you are given snapshots of polarimetric radar values and asked to predict the hourly rain gauge total. Luis Andre Dutra e Silva’s interview gives some interesting insights into the techniques he used to reach second place in the competition. In particular, he used an RNN composed of two LSTM layers.
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There is no output gate; the full state vector is output at every time step. However, there is a new gate controller that controls which part of the previous state will be shown to the main layer.
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