text:
"Chapter 2" of season six.
"American Horror Story" Recap: 'Chapter 2'
HOLLYWOOD—Well, the season premiere of "American Horror Story" had toned down on the sex and gore compared to last season. This was not the case tonight! Make sure your little kids aren't watching this show, and here we go on episode two of "My Roanoke Nightmare."
We start where we left off with Shelby coming across a group of people wearing older looking clothes and a scalped man falling at her feet. She is able to get away but watches the group's ritual led by Kathy Bates' a so far unnamed character. Bates is nailing a man to a log and placing a pig head on the man's head. She calls him a deserter and the group watches as the man is burned over a fire. Shelby runs and passes out in front of Lee's car.
Matt in his interview recounts that the police found nothing of the human sacrifice and insist that Shelby is tested for hallucinogenic drugs while at the hospital. Shelby is convinced what she saw was an elaborate hoax and is willing to stay and fight for their home now. Lee's daughter Flora comes to visit her mother and Flora right away starts talking to someone invisible named Priscilla. Priscilla has told Flora she is tired of all the blood and when a vase crashes to the floor Lee finds an old child's bonnet.
At night, pig screeches bring Shelby and Matt outside armed with a bat. The sounds soon separate the couple in the woods until they reunite in front of a large burning stick figure with meat hanging from it and a severed pig's head on top. Matt uses the bat to knock the burning wood figure down and the police are called. The police are finally able to see some evidence, but only say they will find some people to question and a cop car will be parked in the driveway.
The same night after Shelby and Matt have gone back to sleep the house phone starts to ring and Matt wakes up to answer it. There is static and a woman saying she is hurt and needs help. Matt notices the phone cord is not plugged in and hangs up the phone, but he still hears the woman. He walks into the other room and finds two nurses over the bed of an old woman. The old lady does not want her medication so the two nurses shoot her in the head and spray paint an "M" on the wall for the first letter in the old lady's first name. Lee wakes up to Matt screaming at the cop parked outside, but when the three of them look around the house nothing is there. Matt rationalizes that it might be neurological damage from his attack prior to moving.
Lee's ex-husband comes by to pick up Flora. Lee and her ex think Flora is playing hide-and-seek, but when they find her, she says Priscilla is going to kill them all and save Flora for last. Lee's ex says Flora is never coming back and he is going to a judge which sends Lee off the wagon. Matt and Shelby find her in the kitchen and the kitchen knives are stuck in the ceiling. Matt puts Lee in bed and leaves her when Shelby calls for him. Lee alone sees the nurses for a split second and when she goes into the hallway she sees moving pig tails nailed to the wall and the pig man in the mirror.
Shelby had called Matt into the kitchen because there was a girl in older clothes standing in the yard. The couple goes outside and finds cellar doors that lead to a cellar that looks like it has been lived in. They find a TV and a video recorder and we get to see the character Dennis O'Hare will be playing this season. O'Hare plays Dr. Elias Cunningham who was living in the cellar in 1997. He says the forces in the house have physically harmed him and are trying to kill him.
Cunningham says he is an author and university professor who was researching the two nurses who had set up an assisted living facility in the house. They used the house to kill the elderly based on their names so they could spell out 'MURDER,' but the word was left at "MURDE" because something stopped them, something evil in the house. 'MURDE' apparently shows through paint and once Matt takes down the wallpaper in the room the nurses were in they see the word. Cunningham's video ends with him in the house and something coming up behind him.
Shelby and Matt want out, and even though the bank had changed some details about the house online, it seems the couple is stuck with the house. As the bank guy leaves, Lee returns with Flora in the back seat. Uh oh, kidnapping is not something an ex-cop wants to do, and Matt and Shelby are able to cool Lee's ex down so he can drive over and pick up Flora. But, Flora saw the little girl outside and is now missing! All that is left is her yellow jacket 100 feet up a tree.
Until next Wednesday horror fanatics!
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Tenuta Cucco produces around 70.000 bottles a year, with a predominant percentage of Barolo from the Vigna Cucco, Cerrati and Vughera vineyards. The age of the vines ranges from 15 to 50 years old, with a lower quantitative yield favouring high quality in the products.
During summertime the grapes are tested several times by our agronomist in order to identify the exact picking date, making specific analysis for each vineyard.
Once ripe, the grapes are handpicked in crates and pressed within few hours, while the grapes are always transferred manually into the stemmer-crusher.
The Chardonnay is delicately pressed and, after a static decantation, induced at a white fermentation at controlled temperature between 16-18°C for twenty days. The red grapes are induced to fermentation, after being stem-crushed, at a controlled temperature of approximately 26°C, with daily pump overs in order to favour the extraction of colours and scents. The Chardonnay does not undergo malolactic fermentation in order to maintain major freshness.
With regards to the Dolcetto and Langhe Nebbiolo, the maceration on the skins only lasts 4-5 days. Following the racking, the alcoholic fermentation completes without the skins (in white), exalting scents and tasting tenderness.
Barbera and Barolo instead macerate also after the alcoholic fermentation, for 8 and 30 days respectively (at submerged cap), donating complexity and longevity to the future wines.
The vineyards of Tenuta Cucco extend over 13 hectares in which besides Nebbiolo, the most extended vine-variety, we also grow Dolcetto d'Alba, Langhe Chardonnay and Barbera d'Alba.


text:
\section{Introduction}
\input{sections/intro}

\section{Related Work}
\input{sections/related}

\section{Method}
\input{sections/methodology}

\section{Experiments}
\input{sections/experiments}

\section{Conclusion}
\input{sections/conclusion}

\bibliographystyle{IEEEtran}

\subsection{Databases}
\subsubsection{RAF-DB}
Real-world Affective Faces Database (RAF-DB) is a large-scale facial expression database. Unlike most other FER datasets, all of its images have been manually annotated by several annotators.
It is annotated with seven different emotions (sadness, angriness, happiness, surprise, disgust, fear, neutral). It contains a train set of 12271 aligned images, and a test set of 3068 images with the same label distribution as the train set.\par

\subsubsection{AffectNet}

AffectNet \cite{affectnet} is the largest FER database with 400k face images collected through webscrapping techniques. These have been manually annotated by 12 annotators for the 7 basic emotions. We only use the images annotated for the 7 basic emotions, which represent 280k training samples and 3.5k test samples.

\subsection{Implementation Details}
For RAF-DB, we train our model using the ADAM optimizer with a batch size 16 and a base learning rate 5e-5 with polynomial decay. Face images are resized to 224x224, and augmented with random rotation [-10$^{\circ}$, 10$^{\circ}$] followed by a random horizontal flip. The best model was chosen after 75 epochs by keeping the weights that maximized the accuracy on a validation set sampled with the same label distribution as the test set. For AffectNet we used the same parameters, we used a base learning rate of 4e-5 and kept the best model after 40k iterations with class-balanced mini-batches.
The ablation study was conduted using a VGG16 backbone pre-trained on VGGFace \cite{vggface}. To improve the results and approach those obtained by the state-of-the-art methods, we employed a ResNet50 backbone pre-trained on VGGFace \cite{vggface}. For RAF-DB, we use an off-the-shelf alignment method \cite{arnaud2019treegated} to fetch facial landmarks and create heatmaps from them. The face alignment points are made available for AffectNet.

\subsection{Ablation Study}
\subsubsection{Impact of the layer to which PAL is applied}
\input{tables/new_ablation}

First, we look at the impact of the choice of the convolutional layer on which we apply PAL.
We observe on Figure \ref{layers} an important performance boost when the method is applied on the last layers.

\begin{figure}[!t]
\centering
\includegraphics[width=3.4in]{images/layersv4.png}
\caption{Performance of the model learnt with PAL applied to different convolutional layers of the VGG with \textit{Grad} attribution method and \textit{Mean of Half} channel strategy. The plot shows the mean accuracy score and 95\% confidence interval.}
\label{layers}
\end{figure}

This could be explained by the fact that the information in the early layers is very low-level, and therefore the model won't be able to focus on the important areas. Selvaraju \textit{et al.} \cite{grad_cam} point out that the last convolutional layers of a CNN are a good "compromise between high-level semantics and detailed spatial information". Another reason is that in the first layers there are very few channels, so even using the "Mean of Half" channel strategy might be too strong a constraint.\par
Finally, we observe a drop in performance for layers 9 and 13, which can be explained by the fact that these layers precede a max-pooling layer, represented in Figure \ref{layers} by vertical lines. This means that their attribution maps are sparse (three pixels out of four have a zero value for a $(2, 2)$ pooling layer) and therefore can't take the appearance of our semantic prior. For layers 2 and 5 that also come before a pooling layer, we observe that the variance of these values is higher.\par

\subsubsection{Attribution Methods}

To explore the impact of using different attribution methods, we train our model with \textit{Grad} and \textit{Grad*Input}. We see on Table \ref{ablation} that \textit{Grad*Input} works better whichever channel strategy we use. This could be explained by the fact that the former outputs sharper attribution maps (\cite{ancona2018better}, \cite{shrikumar2017just}). The gradient accounts for the importance of a certain feature in the output prediction, and the input accounts for how strongly it is expressed in the output prediction.

\subsubsection{Channel Strategy}

We also evaluate the effects of different channel strategies on our method. We can see on Table \ref{ablation} that forcing all channels to resemble our prior information heatmap is too strong a constraint. However, the \textit{Mean} strategy improves the baseline accuracy by \text{0.98\%} which shows that a weaker constraint also improves the predictive power of the model. Finally, the \textit{Mean of Half} channel strategy improves the baseline accuracy score by \text{1.42\%} (using \textit{Grad*Input}). We can therefore assume that the freedom given to half of the channels benefits the model because it can take advantage of the prior spatial information but also look in other places without constraint. Ultimately, we see that applying PAL on the mean of all channels works better than applying it on all channels thanks to its weaker constraint. We can also see that the \textit{Mean of Half} strategy significantly outperforms the baseline thanks to a good compromise between guiding the model towards salient facial areas and letting it explore other features.

\subsection{Comparison with State-of-the-art Methods}

\begin{table}[!t]
    \renewcommand{\arraystretch}{1.1}

	\caption{Comparison on RAF-DB and AffectNet. Accuracy scores in \%}
	\label{tab:rafdb_res}
	\centering
	\input{tables/results_table}
\end{table}

\begin{figure}
\centering
\label{confmat}
\includegraphics[width=88mm]{images/new_conf.png}
\caption{Confusion matrix on the test set of RAF-DB}
\label{fig:confusion}
\end{figure}

\begin{figure*}[!t]
\centering
\includegraphics[width=150mm]{images/half_mapsv2.PNG}
\caption{On the first row are examples are chosen from the RAF-DB test set, with the ground truth expressions above them. On the second and third rows, examples of \textit{Grad*Input} attribution maps for the PAL-trained model with \textit{Mean of Half} channel strategy. The second row shows the mean map for the first half of the channels (constrained) while the third row shows the mean map for the second half of the channels (not constrained). On the fourth row, \textit{Grad*Input} attribution maps without PAL. Under the images are the predictions from the model trained with Pal (top) and without (bottom).}
\label{half_maps_fig}
\end{figure*}

Table \ref{tab:rafdb_res} compares our best results with existing baselines as well as current state-of-the-art methods on the RAF-DB and AffectNet databases. First, adding PAL significantly improves the already competitive baselines on both RAF (+$0.96\%$) and AffectNet (+$3.69\%$) with the exact same number of parameters, memory footprint and inference time. Also, the privileged information (in this case, the localisation of the facial landmarks) is necessary for training with PAL but not at inference time, hence this performance upgrade comes at virtually no cost in inference.

Second, for RAF-DB, we extend the current state-of-the-art by improving the top accuracy up to $89.54\%$. On AffectNet, our method outperforms the previous best result by improving the top accuracy from $65.40\%$ to $65.83\%$. This is presumably thanks to the fact that, unlike its closest frontrunners (DMUE \cite{DMUE} and FDLR \cite{fdlr} on RAF-DB, and Distilled Student \cite{distilled} and PAENet \cite{PAENet} on AffectNet), the proposed method forces the model to focus on the most salient facial areas \textit{a priori} by guiding its attribution, while still letting the model have some freedom in its attribution thanks to the proposed Mean of half strategy. Last but not least, note that the proposed improvement could in theory be used along with these methods, \textit{i.e.} distillation \cite{distilled} or identity-aware training \cite{THIN}.

\subsection{Qualitative Analysis}

\subsubsection{Analysis of different class prediction performance}

Figure \ref{fig:confusion} shows the confusion matrix of a Resnet-50 trained with PAL on RAF-DB. As RAF-DB is an imbalanced dataset, (with e.g. 4772 training samples for the happiness class and 281 for fear): hence unsurprisingly the majority classes such as happiness, neutral, sadness and surprise obtain the best accuracies while fear and disgust have been mislabeled for more than 30\% of the test images. Additionally, fear can easily be mistaken for sadness, because of an open mouth and lowered eyebrows generally indicate that a person is crying. It can also be confused with surprise because of the jaw drop and the raised eyebrows. The class disgust can also be mislabeled as happiness if the corner of the lips are pulled, for anger because of a nose wrinkler, or for neutral in the case of a low intensity expression. Lastly, the anger class, despite having only 705 training samples, is better recognized than e.g. disgust, most likely as it involves more specific and easily recognizable muscular facial activations.

\subsubsection{Attribution methods}

PAL aims to improve the predictions of a model by guiding its attribution maps. Figure \ref{half_maps_fig} shows that, when using PAL, the attribution maps are generally neater and the predictions are usually more accurate. Notice, for example, how, e.g. on column 4, the baseline wrongly predicts the Sad class whereas our model correctly predicts Happy. Likely, this is due to the fact that PAL allows better repartition of the features throughout the relevant face regions (mouth, eyes, and so on), which in turn improves the predictive accuracy, echoing the work of Dapogny \textit{et al.} \cite{dapogny2016confidenceweighted}. This is the case for the first half of the channels for a method trained with the proposed Mean of half strategy. Furthermore, this strategy allows to provide the model freedom to put emphasis on certain face regions when it is needed, e.g. as shown on columns 5-8, also contributing to the accuracy improvement.

\subsection{Attribution Methods}

All Convolutional Neural Networks can be written as a composition of functions \cite{chen:hal-02507512}. Let $f$ denote a CNN, then $f$ can be re-written as follows:
\begin{equation}
f(I) = f^L \circ f^{L-1} \circ ... \circ f^1(I)
\end{equation}
\noindent
where $I$ denotes the input image, $L$ denotes the total number of functions and $f^l$ denotes a function, usually a convolutional layer, an activation function, a pooling function or a batch normalization. $\Theta$ denotes the set of trainable parameters of the whole network $f$. With this in mind, we can extract feature maps after the activation functions which are essentially built like images (height*width*channels). These feature maps can be analysed, and their \textit{contribution} or \textit{attribution} can be computed with several attribution methods. These methods are able to evaluate the relevance of certain pixels of the input image, but they can also be used to evaluate the relevance of the pixels of the intermediate feature maps.
In this work, we choose to use these attribution scores to guide the learning, and not only for model evaluation purposes. We will therefore introduce the different attribution methods used in this paper.\par
The first and more straightforward of these approaches (which will be denoted as \textit{Grad} in what follows) consists in computing the absolute value of the gradient of the sum of all coordinates $f_o$ of the output vector $f(I)$ w.r.t. each pixel of $f^l$. \linebreak
We choose to compute the derivative of $\Sigma f_o$ to guide all of the network's predictions. Let $a_{i,j,c}^l$ denote the attribution of the pixel $(i,j)$ of the $c$-th channel of the feature map given by layer $l$. The \textit{Grad} attribution method can be written as:

\begin{equation}
a_{i,j,c}^l(I) = |\frac{\partial \Sigma f_o}{\partial f_{i,j,c}^l}(I)|
\end{equation}

As shown in \cite{chen:hal-02507512}, any ReLU-based CNN can be decomposed as a piece-wise affine function of each pixel of $f^l$ and the contribution of each pixel is exactly the following:
\begin{equation}
a_{i,j,c}^l(I) = |\frac{\partial \Sigma f_o}{\partial f_{i,j,c}^l}(I)| \cdot f_{i,j,c}^l(I)
\end{equation}
\noindent
This will be denoted as the \textit{Grad*Input} model.\par
One intuition of the difference between the two is that \textit{Grad} corresponds to how a small change in the input will impact the output of the network, whereas \textit{Grad*Input} corresponds to the total contribution of a feature on the output of the network.\par

\subsection{Privileged Attribution Loss}

Let $a^*$ denote a standardized prior map on the attribution model. The loss we build aims to make the model "pay more attention" to certain regions in the input image, those regions being the ones highlighted in $a^*$. To incentivize that behaviour, we maximize the cross-correlation between the attribution maps $a^l$ and the prior maps $a^*$. If we have $\mu (a^l) = \Sigma_{i,j} a^l_{i,j,c}$ and $\sigma^2(a^l) = \sum_{i,j}(a^l_{i,j,c} - \mu (a^l))(a^l_{i,j,c} - \mu (a^l))$, then:

\begin{equation}
\Lambda_{PAL}^l(\Theta) = -\sum_{i,j,c}\frac{a^l_{i,j,c} - \mu(a^l)}{\sigma(a^l)} * a^*_{i,j,c}
\end{equation}

The rationale behind using a cross-correlation term instead of e.g. a L2 norm matching \cite{liu2019incorporating} is to provide an additional degree of freedom to the network, as we want the constrained attribution map to have the same profile as the prior, regardless of its dynamics. The \textit{attention} term of the loss gets added to a cross-entropy term for classification. The total loss is therefore:

\begin{equation}
\Lambda_{tot}(\Theta) = \Lambda_{CE}(\Theta) + \Lambda^l_{PAL}(\Theta)
\end{equation}
\noindent

\subsection{Channel Strategies}

In what precedes we have constrained the attribution $a^l_{i,j,c}$ of each pixel $(i,j)$ of each channel $c$ to correspond to a certain prior $a^*$. However, forcing all channels to be similar to a certain prior $a^*$ (we will call that the \textit{All Channels} strategy) might be too strong a constraint. A weaker formulation consists in forcing the \textit{mean} of the attributions across all channels to resemble a prior $a^*$. We will call that the \textit{Mean} strategy. In this case, with $C$ being the number of channels of $f^l$, the attribution becomes:

\begin{equation}
a_{i,j}^l = \frac{1}{C} \sum_{c=1}^C a_{i,j,c}^l
\end{equation}

This again might be too strong a constraint, and we could only force a fraction of the channels to resemble $a^*$. This would allow certain channels to stay free and learn other patterns that might be important for the predictions. The attribution model would then look like this, with $C_1<C$:

\begin{equation}
a_{i,j}^l = \frac{1}{C_1} \sum_{c=1}^{C_1} a_{i,j,c}^l
\end{equation}
\noindent
In what follows, we set $C_1 = \frac{C}{2}$ and refer to this approach as the \textit{Mean of half} strategy.

\subsection{Prior heatmap}

The method presented in this paper is generic and can be applied to many computer vision tasks. Its strength comes from the fact that it uses prior information of particularly relevant regions at train time to constrain the attribution of feature maps in the network, while no additional information is needed at inference time. The choice of the prior might orientate the final decisions a lot.\par
In this work, we choose to use face alignment points as a prior.
From a 2D face image \textit{I}, the face alignment model \textit{g} locates 68 facial landmarks that form the face shape $\textbf{y} \in \mathbf{R}^{68^*2}$ (the \textit{n}-th lign of this matrix corresponding to the 2D coordinates of the \textit{n}-th facial landmark).\newline
Let $\delta_y$ denote the dirac delta function. From these facial landmark coordinates, we create an image $a^*$ with:
\begin{equation}
    a^*_{i,j}= \sum_{k=1}^{68} \delta_y(i,j)
\end{equation}
\noindent
Which ultimately gives an image with pixel values of 1 at the landmarks and 0 elsewhere.
We then apply a gaussian filter with a standard deviation $\sigma$ of 3 to this image $a^*$. We then have:

\begin{equation}
a^{*filtered}_{i,j} = \sum_{k=1}^{68} \frac{1}{\sqrt{2\pi\sigma^2}}exp(-\frac{(i-y_{k,1})^2+(j-y_{k,2})^2}{2\sigma^2})
\end{equation}

Examples of such prior heatmaps are shown in Figure \ref{heatmaps}. Notice how the network manages to integrate the prior information, and focuses on the highlighted relevant localisations. As we will show in the experiments, this allows to dramatically improve the FER performance, without needing additional information at inference time.
\subsubsection{Constrained Attribution Learning}

Recent work has been done to improve the interpretablility of Deep Neural Networks \cite{ancona2018better}. Attribution methods, also sometimes called "relevance methods", aim at evaluating the impact of each input feature on the predictions. These methods have originally been used to explain network predictions, but have also recently been used to constrain the learning of these models. These models either use occlusion-based or gradient-based attribution methods.\par
Occlusion-based methods \cite{zeilerocclusion} evaluate the attribution of a certain patch of pixels by comparing the predictions given by the input image with the predictions given by the occluded input image.
For instance, Du \textit{et al.} \cite{du2019learning} applied this to a Natural Language Processing (NLP) problem and proposed to sparsify the explanations by reducing the score of non-important features using expert-annotated important clauses in the input sentences. 'Negative examples' are created by removing these clauses from the input sentences. However, computing occlusion-based attributions can have a very high computational cost especially on image data.\par
To reduce the evaluation time of these methods, gradient-based attribution methods were introduced. Simonyan \textit{et al.} \cite{simonyan2014deep} first introduced image-specific class saliency maps. Selvaraju \textit{et al.} \cite{grad_cam}, with \textit{Grad-CAM}, created a class-specific attribution method to produce visual explanations, and also used it for weakly-supervised segmentation \cite{grad_cam}. Grad-CAM computes heatmaps by multiplying each channel of the last convolutional feature map by its "importance" (the mean over that specific channel of the gradient of the chosen output class w.r.t. the last convolutional layer's output). In 2016, Shrikumar \textit{et al.} \cite{shrikumar2017just} proposed a technique to sharpen the attribution maps called \textit{Gradient*Input}, where the partial derivatives of the output are multiplied by the input features. The idea is that the gradient tells us how important a certain feature is, and the input tells us how strongly it is expressed in the final prediction. Other propagation-based methods have been introduced in the second half of the 2010's like \textit{Integrated Gradients} (Sundarajan \textit{et al.} \cite{sundararajan2017axiomatic}), which is more stable than \textit{Gradient*Input} and satisfies the completeness axiom, which generally better captures the explanation of each input feature, at the cost of a larger computational load. \par

For efficiency reasons, some models decide to constrain gradient-based attributions. However, there are different ways to constrain this attribution. Erion \textit{et al.} \cite{erion2020improving} decided to constrain their model so that its attributions would satisfy desirable properties. They therefore introduced an attribution method called 'Expected Gradients', which guarantees that the attributions don't depend on the method's implementation \cite{ancona2018better}. They use 'Expected Gradients' to improve their predictive capacity by constraining it to have more desirable properties like sparsity and smoothness. Nevertheless, they don't make use of extra information which could help them in identifying key features.\par
Attribution methods can also be guided by the use of privileged information. Liu \textit{et al.} \cite{liu2019incorporating} applied this to NLP and added a constraining term to their loss with a L2 norm between their attribution (Integrated Gradients) and their prior information (toxic or non-toxic words). In 2017, Ross \textit{et al.} \cite{ross2017right} introduced the idea of being "right for the right reasons" (RRR) for Computer Vision. They regularize the gradients of the input features w.r.t. the outputs by penalizing the 'unimportant' features with a prior binary mask. This mask indicates whether or not a certain feature should be relevant for a certain prediction. However, in their paper, they only constrain their gradient inputs to improve their explanations, and not to improve the performance of their model.\par
On the contrary, we use the attribution maps to spatially constrain the learning process in order to improve the predictive power of a FER model. Therefore, we will present spatially constrained methods for Facial Expression tasks.

\subsubsection{Constrained Learning for Facial Expression tasks}

Spatially constrained methods have become very popular in various Facial Expression tasks because of the importance of certain areas of the input images, for example eyes, eyebrows or the mouth.\par
First of all, some methods learn to guide their model with specific spatial modules. This way, Zhao \textit{et al.} with EfficientFace \cite{efficient} used an approach in which the model learns to dynamically focus on local and global salient facial features with a local-feature extractor. This method learns the important spatial features without a semantic prior, and therefore doesn't guarantee that it is focusing on the important areas. This can also be a problem for its explainability.\par
Second, some methods use semantic information learnt from intermediary tasks to improve their predictions on their main task. Shao \textit{et al.} \cite{jaanet} proposed JAA-Net, which aims to predict Facial Action Units by learning to predict face alignment points first. They then use the face alignment features to refine attention maps that guide the Facial Action Unit detection. This method leverages a prediction relative to another task to spatially guide its model on the main task. Pu \textit{et al.} \cite{pu2021auexpression} have proposed a FER method that constrains the model by predicting facial action units first. These methods need explicit predictions for an intermediary task for their model to focus on the important areas. \textbf{These methods strongly depend on the efficiency of their model on the intermediary task. This way, the process is more complex and makes the predictions on the main task more error-prone}.\par
Another way of guaranteeing that the model focuses on the most salient areas can be to directly take advantage of additional information. For example, Jacob \textit{et al.} \cite{Jacob_2021_CVPR} used heatmaps, where the areas of the facial action units are highlighted, as prior information for Facial Action Units prediction. In this method, they build attention maps and constrain them to resemble the prior information attention maps. This allows the model to focus on specific areas that are key to predict the presence of an action unit. The said method uses a semantic prior at training time to guide its model towards specific important areas, without needing it to predict at test time. \textbf{However, they only constrain attention maps created by a separate network branch, which doesn't directly guarantee that the model focuses on these salient areas. On the contrary, PAL leverages additional semantic information in a more straightforward way by guiding its attribution maps, and therefore the exact areas that the model concentrates on.} Furthermore, our method offers to improve the model's predictive power by forcing the attributions to concentrate on certain areas while still leaving them a certain degree of liberty.\par
In the next section, we will present our methodology, first focusing on the different possible attribution methods, then our loss and channel strategy. Finally, we will explain our heatmap prior information.\par
\subsubsection{Constrained Learning for Facial Expression tasks}

Spatially-constrained methods have become very popular for FER tasks as the spatial repartition of features is paramount \cite{dapogny2018confidence}. First, some methods rely on guiding their model with explicit spatial modules: Zhao \textit{et al.} \cite{zhao2021robust} propose to dynamically learn to focus on local and global salient facial features with a local-feature extractor. This method learns the important spatial features without any semantic prior, and therefore doesn't guarantee the relevance of the salient areas w.r.t. the task at hand. Second, some methods use semantic information learnt from auxiliary tasks to improve their predictions w.r.t. another, main task: Shao \textit{et al.} proposed JAA-Net \cite{jaanet}, which aims to predict Facial Action Units by first learning to align facial landmarks. They then use the extracted features to refine attention maps and guide the facial action unit detection.
Pu \textit{et al.} \cite{pu2021auexpression} have proposed a FER method that constrains the model by predicting facial action units first. However, there is a risk of propagating errors between the action unit and FER prediction modules.\par
Another way of guaranteeing that the model focuses on the most salient areas can be to directly take advantage of additional information instead of trying to model it with an intermediate network. Jacob \textit{et al.} \cite{Jacob_2021_CVPR} employed heatmaps in which the areas relative to specific facial action units are highlighted, as a prior information for facial action units detection. In this method, they constrain the attention maps of the network to resemble the prior information heatmaps. This allows the model to focus on specific areas that are key to predict the presence of an action unit using an external information, without needing this information at inference time. However, by construction, the constrained attention maps are outputted by a separate network branch, hence doesn't guarantee that the model focuses on these salient areas. On the contrary, PAL leverages additional semantic information in a more straightforward way by guiding its attribution maps, and therefore the exact areas that the model concentrates on. Therefore, in the next section, we will present methods that constrain their attribution maps to guide their learning.\par

\subsubsection{Constrained Attribution Learning}

Attribution methods, also sometimes called \textit{relevance} or \textit{visual explanation} methods, aim at evaluating the impact of each input feature on the predictions. These methods have originally been used to explain network predictions, but have also recently been used to constrain the learning of these models. These models either use occlusion-based or gradient-based attribution methods.
Occlusion-based methods \cite{zeilerocclusion} evaluate the attribution of a certain patch of pixels by comparing the predictions given by the input image with the predictions given by the occluded input image. Du \textit{et al.} \cite{du2019learning} applied this natural language processing by reducing the score of non-important features using expert-annotated important clauses in the input sentences. However, occlusion-based attribution methods are usually iterative and involve prohibitive computational costs especially on images.\par
To reduce the runtime, gradient-based attribution methods were introduced. Simonyan \textit{et al.} \cite{simonyan2014deep} first introduced image-specific class saliency maps. Selvaraju \textit{et al.} \cite{grad_cam} proposed \textit{Grad-CAM}, a method that consists in computing class-specific heatmaps by multiplying each channel of the last convolutional feature map by its "importance" (the mean over that specific channel of the gradient of the chosen output class w.r.t. the last convolutional layer's output). In 2016, Shrikumar \textit{et al.} \cite{shrikumar2017just} proposed a technique to sharpen the attribution maps called \textit{Gradient*Input}, where the partial derivatives of the output are multiplied by the input features. The idea is that the gradient tells us how important a certain feature is, and the input tells us how strongly it is expressed in the final prediction. Other propagation-based methods have been introduced in the second half of the 2010's like \textit{Integrated Gradients} (Sundarajan \textit{et al.} \cite{sundararajan2017axiomatic}), which is more stable than \textit{Gradient*Input} and generally better captures the explanation of each input feature, at the cost of a larger computational load. \par

For efficiency reasons, most approaches nowadays use gradient-based attribution methods instead of occlusion-based methods for attribution constraining. Nevertheless, there are different ways to constrain this attribution. Erion \textit{et al.} \cite{erion2020improving} decided to constrain their model so that its attributions would satisfy desirable properties, \textit{i.e.} sparsity and smoothness. However, they do not use extra information which could help them in identifying key features. By contrast, Liu \textit{et al.} \cite{liu2019incorporating} applied attribution constraining to NLP and added a L2 norm between their attribution (Integrated Gradients) and an external prior information (toxic or non-toxic words). In the same vein, Ross \textit{et al.} \cite{ross2017right} introduced the idea of being "right for the right reasons" (RRR) for Computer Vision. They proposed to regularize the gradients w.r.t. the input features by penalizing the 'unimportant' features with a prior binary mask. This mask indicates whether or not a certain feature should be relevant for a certain prediction. However, in their paper, they only constrain their gradient inputs to improve their explanations, and not to improve the performance.
By contrast, in this work, we use the attribution maps to spatially constrain the learning process in order to improve the predictive power of a FER model. In the next section, we introduce our methodology, first by focusing on the different possible attribution methods, then our loss and channel strategy. Finally, we will explain our heatmap prior information.

\input{images/figures/heatmaps}
\noindent

redpajama_set_name: RedPajamaArXiv


text:
Atlanta's Sports Scene Heats Up December
As many cities prepare for hibernation before the start of the new year, Atlanta warms up its red carpet for visitors with marquee sporting events.
"December's lineup of collegiate and professional games keeps Atlanta in the forefront as a premier sporting destination," said William Pate, president and CEO, Atlanta Convention & Visitors Bureau. "These marquee matchups bring new visitors to the city to enjoy our hotels, restaurants, shopping and attractions."
The first weekend of December brought the Southeastern Conference (SEC) football championship game to Atlanta. The 2018 meeting between University of Alabama and University of Georgia on Saturday, Dec. 1 drew in more than 77,000 fans to the city.
Following the SEC Championship game, Atlanta's latest sports franchise, Atlanta United FC, topped the Portland Timbers 2-0 on Dec. 8 for the team's first appearance and win in the MLS Cup. Atlanta United set records for home attendance with more than 900,000 fans filling Mercedes-Benz Stadium over 17 games. The team's accomplishments also include finalists for the MLS Most Valuable Player, Defender of the Year and Coach of the Year.
Dec. 15 welcomed the fourth-annual Celebration Bowl matchup between the Mid-Eastern Athletic Conference (MEAC) and Southwestern Athletic Conference (SWAC). The bowl, sponsored by Air Force Reserve, celebrates the pageantry and history of Historically Black Colleges and Universities. North Carolina A&T defeated Alcorn State in the school's second-consecutive Celebration Bowl victory.
The year ends with the Chick-fil-A Peach Bowl Dec. 29. The game is one of the New Year's Six bowls and is included in the rotation for the College Football Playoffs. This year's game sold out prior to the team selection, marking the 20th time the bowl has sold out in its 22-year history. Attendance is estimated at 73,000 with $45-50 million in economic impact as University of Florida faces University of Michigan.
Atlanta shifts into high gear as it prepares for Super Bowl LIII Feb. 3, 2019. This marks Atlanta's third Super Bowl and is expected to attract more than 1 million fans to the city for the week-long celebration.

redpajama_set_name: RedPajamaCommonCrawl


This event aims to bring everyone who works with young children together to learn and grow.
Young Child Expo & Conference is for teachers, psychologists, special education teachers, social workers, physical, occupational and speech therapists, pediatricians, nurses, educators, academics and students.
Over 100 speakers, more than 80 presentations, Full Day and/or Half Day Workshops, 40 exhibitors, and 1500 attendees expected.


Welcome to the American Society of Echocardiography's 3D Echocardiography Library. Here you can view videos demonstrating how adding a third dimension is helpful in the diagnosis and treatment of cardiac disease.
2D TEE 0 degrees MV2D mid-esophageal TEE view of the mitral valve at 0 degrees demonstrating flail prosthetic valve leaflet.
2D TEE at 45 degrees, zoomed on the AV2D mid-esophageal TEE view of the aortic valve at 45 degrees demonstrating a single opening.
2D TEE at 45 degrees, zoomed on the AV with color2D mid-esophageal TEE view of the aortic valve at 45 degrees demonstrating valvular regurgitation.
2D TTE apical 4-chamber view2D TTE apical 4-chamber view demonstrating systolic anterior motion of the anterior mitral valve leaflet.
2D TTE apical 4-chamber view2D TTE apical view demonstrating the univentricle with both atrioventricular valve on the same plane.
2D TTE apical 4-chamber view with color of the tricuspid valve2D TTE right ventricular inflow view with color demonstrating severe tricuspid regurgitation.
2D TTE PLAX2D TTE parasternal long axis image of the bioprosthetic mitral valve.
2D TTE PLAX zoom MV2D TTE parasternal long axis view zoomed image of the bioprosthetic mitral valve demonstrating leaflet abnormality.
2D TTE RV inflow view with color2D TTE right ventricular inflow view with color demonstrating severe tricuspid regurgitation.
3D TEE view of the mass from the left ventricular perspective3D TEE view of the mass from the left ventricular perspective, demonstrating its position on the ventricular septum.
3D TEE view of the mass from the LVOT3D TEE zoomed view of the mass from the left ventricular perspective, demonstrating its position on the ventricular septum.
4D image of Mitral regurgitation with multiple jets4D image of mitral regurgitation with multiple jets. Planimetry of proximal jets done to measure orifice areas.
4D image of Mitral regurgitation with single jet4D image of mitral regurgitation with single jet. Planimetry of proximal jets done to measure orifice area.
4D image of normal mitral valve4D model of mitral valve in systole.
Aortic valve perforation 2DTricuspid valve with thickened leaflets and nodular calcification of the free edges.
Aortic valve perforations 3DPerforations involving all three cusps. Color flow Doppler confirmation is necessary to rule out dropout artifacts.
Aortic valve perforations 3D CFDPerforations involving all three cusps with secondary regurgitation.
Flail P2 scallop 3DEnface left atrial view of the mitral valve showing a flail P2 segment with two ruptured chords.
Functional mitral regurgitation 3DEnface left atrial view of the mitral valve showing complete lack of leaflet coaptation.
Mitral bioprosthesis stenosis 3D LV viewEnface LV view of a stenosed mitral bioprosthesis showing the three prosthesis struts in the left ventricle.
Mitral periprosthetic leak 3D CFDEnface left atrial view of a mitral prosthesis with an anterior periprosthetic leak.
Mitral valve endocarditis on Real time 3D-TEEReal time 3D-TEE of the mitral valve showing a vegetation attached to the P2 leaflet.
Model of 4D Left Ventricle4D model of left ventricle measuring ejection fraction, stroke volume and regional wall motion.
Model of 4D Mitral Valve4D model of mitral valve made using Tomtec program.
Multi-planar reconstruction of a 3D color datasetMulti-planar reconstruction of a 3D color dataset demonstrating measurement of the effective regurgitant oriface.


text:
Complete issue (July 2017)
All the articles published in the July 2017 issue.
Publish Date ‐ 3 July 2017
People and Papers Awards 2017
Congratulations to all the winners of the Institution's 2017 People and Papers Awards, which were presented by President Ian Firth at a ceremony held on Thursday 8 June at Prince Phillip House, London.
The Papers Awards recognise outstanding work published in The Structural Engineer and Structures research journal, along with presentations given at HQ and around the regional groups. The People Awards acknowledge talented young engineers and those members who have made an outstanding contribution to Institution life.
Kenneth Severn Award 2017: How should structural engineers balance safety and serviceability require
The Institution of Structural Engineers' Kenneth Severn Award is an annual essay competition for young engineers in which the question is set by the President. In this essay, 2017 winner Amy Cleaves considers how engineers can be educated to achieve elegance in their designs.
Design of the roof for the Grandview Heights Aquatic Centre, Surrey, Canada
Constructed to meet the diverse needs of one of Canada's fastest-growing cities, Grandview Heights Aquatic Centre features an undulating roof structure with hanging timber 'cables' suspended between large concrete buttresses. It is believed to be the world's largest-spanning pure catenary timber roof, highlighting wood's potential as a cost-effective, structurally efficient and aesthetically pleasing building material for aquatic facilities.
Business Practice Note No. 7: Building Information Modelling
In this latest note, Matthew Byatt offers readers an introduction to BIM and a guide to the benefits it can bring to a project.
Eight-stage guide to implementing BIM on a project
Six years since the UK government announced its BIM mandate, large parts of the industry are still getting to grips with what this really means to their business. Duncan Reed guides readers through the key stages, documents, terminology and, above all, processes that are 'Level 2 BIM'.
Statement from Structural-Safety: The consequences of tragic events
In humanitarian, social and engineering terms, the catalyst for profound change is often a catastrophe whose name is remembered for years to come. The Grenfell Tower fire in London will become one such tragedy and the ramifications of the fire will resonate into the future.
Temporary Works Toolkit. Part 13: The importance of understanding construction methodology
A structure in its permanent state cannot be 'wished into place'. Appropriate consideration must be given to construction methodology so that permanent works can be optimised, temporary works minimised and trade interfaces simplified. This article presents a list of general, high-level considerations that relate to construction methodology and optimisation of temporary/permanent works, with a number of examples provided.
Viewpoint: Timber hybrid construction – an approach whose time has come
From straightforward projects to complex design briefs, timber hybrid construction is gaining momentum. Andrew Wylie discusses its benefits.
Viewpoint: Reinforced concrete design: Eurocodes and British Standards compared
The UK government has recently triggered Article 50, starting the official departure process from the EU. This has led to a hotly debated topic within our industry: do we continue to use the Eurocodes or revert to the previous British Standards? Peter Watkins draws his own comparison of the main differences between the two.
Book review: Precast Concrete Structures (2nd ed.)
This extensively revised guidance with worked examples will be of use to both students and engineers looking to use precast concrete for design to the Eurocodes, concludes Jenny Burridge.
Verulam (readers' letters - July 2017)
Stability and the middle-third rule, Brexit and the Eurocodes, climate change, seismic engineering, the Edinburgh Schools Inquiry, and engineers' contribution to bridge aesthetics are all topics that have spurred readers to put pen to paper this month.
Diary dates (July 2017)
Upcoming events at HQ and around the Regional Groups.
Spotlight on Structures (July 2017)
Congratulations to the winners of the annual Structures prizes, which recognise outstanding examples of pure and applied research. The prizes, for papers published in the journal during 2016, were announced at the Institution's People and Papers Awards ceremony in London on 8 June.
Structures is a collaboration between the Institution and Elsevier, publishing internationally-leading research across the full breadth of structural engineering which will benefit from wide readership by academics and practitioners.
Access to Structures is free to Institution members (excluding Student members) as one of their membership benefits, with access provided via the "My account" section of the Institution website. The journal is available online at: www.structuresjournal.org
The Drawing Board (July 2017)
This month's prize is shared between two winners: Kevin Lyons of Lyons O'Neill and Stephen Fernandez of Arup.


text:
Ukraine war: Death toll from missile strikes climbs as Zelenskyy vows to strengthen military
By Euronews with Reuters • Updated: 11/10/2022
A medical worker runs past a burning car after a Russian attack in Kyiv, Ukraine, Oct. 10, 2022. - Copyright Credit: AP
This article summarises Monday's events. Click here for the latest developments on Tuesday.
Ukraine has vowed to strengthen its armed forces after Russia launched its biggest aerial assaults on cities since the beginning of the war, forcing thousands to flee to bomb shelters and prompting Kyiv to halt electricity exports to Europe.
Missiles hit targets across Ukraine early on Monday, killing 19 people and wounding 105, emergency services said, as they tore into intersections, parks and tourist sites.
Explosions were reported in Kyiv, Lviv, Ternopil and Zhytomyr in western Ukraine, Dnipro and Kremenchuk in the centre, Zaporizhzhia in the south and Kharkiv in the east, according to Ukrainian officials.
The barrage of dozens of cruise missiles fired from air, land and sea was the most widespread wave of air strikes to hit away from the front lines, at least since the initial volleys on the war's first day, on 24 February.
Russian President Vladimir Putin said he ordered "massive" long-range strikes after accusing Ukraine of an attack on a bridge linking Russia to annexed Crimea on Saturday, but the United States said the scale of the attacks meant they had likely been planned for longer.
Ukrainian President Volodymyr Zelenskyy spoke to US President Joe Biden on Monday and wrote on Telegram afterwards that air defence was the "number 1 priority in our defence cooperation".
"We will do everything to strengthen our armed forces," he said in a late Monday address. "We will make the battlefield more painful for the enemy."
Ukraine war: Zelenskyy says Ukraine won't be 'intimidated' by Russian missile strikes
Russian bombs strike Kyiv and other western cities
Ukraine war: Missile strikes on Kyiv a response to Crimea bridge bomb, says Russian president Putin
Biden told Zelenskyy the United States would provide advanced air defence systems. The Pentagon said on 27 September that it would start delivering the National Advanced Surface-to-Air Missile System over the next two months or so.
Russia's ambassador to the United States, Anatoly Antonov, said more help to Ukraine raised the risk of a wider war.
"Such assistance, as well as providing Kiev with intelligence, instructors and combat guidelines, leads to further escalation and increased the risks of a clash between Russia and NATO," Antonov told media.
Monday's rush-hour attacks were deliberately timed to kill people and knock out Ukraine's power grid, according to Zelenskyy.
Prime Minister Denys Shmyhal reported 11 major infrastructure targets were hit in eight regions, leaving parts of Ukraine with no electricity, water or heat. He promised to restore utilities as quickly as possible.
As it tried to end blackouts, Ukraine halted electricity exports to the European Union, at a time when the continent already faces surging power prices that have stoked inflation and hampered industrial activity.
FILE - Flame and smoke rise from the Crimean Bridge connecting Russian mainland and the Crimean peninsula over the Kerch Strait, in Kerch, Crimea, Oct. 8, 2022AP
Battlefield setbacks for Russia
Russia's air strikes come three days after a blast damaged the bridge it built after seizing Crimea in 2014. Russia blamed Ukraine and called the deadly explosion "terrorism".
"To leave such acts without a response is simply impossible," said Putin, alleging other, unspecified attacks on Russian energy infrastructure. He threatened more strikes if Ukraine hits Russian territory.
The United States, however, said Russia's attacks on such a scale could not have been put together in just a couple of days.
"It likely was something that they had been planning for quite some time," White House national security spokesperson John Kirby told CNN. "That's not to say that the explosion on the Crimea bridge might have accelerated some of their planning."
Ukraine, which views the bridge as a military target sustaining Russia's war effort, celebrated the blast without claiming responsibility.
After weeks of setbacks on the battlefield, Russian authorities are facing the first sustained domestic criticism of the war, with commentators on state television demanding ever tougher measures.
Belarus: EU warns Lukashenko of sanctions over joint grouping of troops with Russia
Ukraine war: Russia president Putin accuses Kyiv of 'terrorism' in Crimea bridge attack
Russia appoints new military commander after series of Ukraine setbacks
Putin responded to Ukrainian advances by ordering a mobilisation of hundreds of thousands of reservists, proclaiming the annexation of occupied territory and threatening to use nuclear weapons.
On Saturday, Russia made its third senior military appointment in the space of a week with air force General Sergei Surovikin taking over as commander of forces in Ukraine. He previously commanded Russia's brutal air campaign in Syria.
Russia says it is waging a "special military operation" in Ukraine to rid it of nationalists and protect Russian-speaking communities. Ukraine and the West say it is an unprovoked war of aggression.
Ukraine's defence ministry said in a Monday evening update Russia had staged at least 84 missile and air strikes, and Ukraine's air defences had destroyed 43 cruise missiles and 13 drones.
Russia's defence ministry said it had hit all its intended targets. Reuters could not independently verify battlefield accounts.
Diplomatic pressure mounts on Russia
Russia also suffered a setback on the diplomatic front, with the United Nations General Assembly voting to reject its call for the 193-member body to hold a secret ballot this week on whether to condemn Russia's annexations of four partially occupied regions in Ukraine. read more
The General Assembly decided, with 107 votes in favour, that it would hold a public vote.
The president of the United Arab Emirates, a member of the group of oil producers known as OPEC+ that rebuffed the United States last week by announcing steep production cuts, will travel to Russia on Tuesday to meet Putin and push for "military de-escalation", UAE state news agency WAM reported.
Biden and Group of Seven leaders will hold a virtual meeting on Tuesday to discuss their commitment to support Ukraine, the White House said.
With Ukraine set to receive up to 140 tanks, F16 jets move up the agenda
Ukraine war: Moscow creeps towards Bakhmut, Russian 'propaganda institutions', nuclear treaty woes
RUSSIA'S INVASION OF UKRAINE RUSSIA-UKRAINE INVASION UKRAINE WAR MILITARY FORCES
United Kingdom Brexit European Union France film Music Strike
UNITED KINGDOM BREXIT EUROPEAN UNION FRANCE FILM


I would find it quite handy the new "InformationWindow" in the down right corner of Isadora2.1. But i simply can´t read the text :( Anyone else has the same Problem?
@ michel. Thank you for the quick response. I´m on Mavericks still and just before a premiere so i will not upgrade until the end of next week. Is el capitan safe and working with izzy?
I am using Isadora on El Capitan and it works fine. But If the text is your only problem I would not update if you are happy how things are working now, maybe wait until Izzy brings an update and see if that helped.
this clears the font cache, does this change anything on the look of the font?
No it did not.. But for now i just ignore it.. But thanx for the try!
Really strange that. As Michel said we have spotted a few instances of this but it seems very erratic and we can't quite figure it out.
To me it looks like a text spacing issue and not necessarily the font itself.
It´s not a big deal as i still can use the old "alt click" . For the Moment i just hide it. Maybe it changes when i one day update to yosemite or el capitan.
and yes it looks like a spacing issue.
After updating to Yosemite it works fine now! Seems to be an Issue of Mavericks.


Notre tow-haired Marseilsneezlesise teste sneezles pluralité lors d'un stripe prosperity!
Hotwife BRIDE TAKES A harsh rectal FROM THE finest stud ON HER WEDDING DAY!
Tearing up his wifey in a shithole motel.


text:
The two device categories we have been calling the personal computer and the smart phone are evolving and merging into a new category. For convenience in this discussion we'll call this device category
HAND-EYE-EAR DIRECT (HEED)
At this early state of the evolution the purest expression of the HEED category is the Apple iPad.
HEED devices have their own application style. Compared to typical personal computer applications, HEED applications are graphic and interactive without requiring precise user gestures, and are not heavily dependent on text input.
HEED applications are attractive because they are well matched to the way our brains work. However, the devices as presently conceived limit the power of many users to build applications for themselves, because the application-building process departs grossly from the way our brains work.
There is no way to build a large range of high-value quick-turnaround HEED applications that a user who is not a programmer can understand. Until that exists tool-building by users will be limited, the HEED category will not reach its potential to empower its users, and its rate of penetration into business will be limited.
A powerful broad-spectrum development approach that is accessible to nonprogrammers will open up opportunities to build applications in the large middle ground between highly skilled programmers and end users.
•If you are a consultant or system integrator, you see new business opportunities for offerings that you can rapidly prototype, demonstrate, and deliver, for example, walk-around order-entry and inventory systems with databases behind them.
•If you are a business manager who sees an opportunity to capture data at the point it appears, you try out your concept with a department member rather than committing to an IT project.
•If you are a power user, you now have the construction tool you have been looking for.
On offer here are the means for users in this large middle ground to build their own HEED applications:
1.a radically simpler and more intuitive way to think about the internals of these applications, and
2.a graphical WYSIWYG (What You See Is What You Get) way to build them.
This site is at http://heed.melconway.com . It can be read at three levels:
1.Executive summary: Read Value, Offer, Contact.
2.What is the technology? Add Think, Build. Reading and following the links in the smaller italicized paragraphs at the end adds a lot more detail.
3.What makes it so different? Add Rationale.