text:
Jermayn
Introducing Coral Martinez - Fantomen Inker
In the last two and a half years, Coral Martinez has been receiving credit for her work inking Rafael Ruiz' Phantom stories.
She has worked with Rafael on five Team Fantomen stories at the time of this interview. Three have been published by Frew in English, the story details are below:
"The 22nd Phantom, Part 3: The Man in the Shadows"
Frew #1794 (2017) Fantomen 2-3/2017
"The Scavengers"
Frew #1845 (2019)
Fantomen 1/2018
"The Pit of Doom"
"Eldkraft" (Swedish Title)
"Den gyllene burens fånge" (Swedish Title)
Fantomen 16/2019
Chronicle Chamber has been lucky enough to interview Coral and learn more about Coral, an inker making a name for herself in a male dominated Phantoms world.
Chronicle Chamber: Can you please tell us a bit about yourself? What country are you from?
Coral Martinez: I am from Galicia, a northern region in Spain, where I can enjoy the frozen waters of the Atlantic Ocean.
CC: And where did you Study art?
CM: I studied Art at Universidade de Vigo, and one year at Universidad de Barcelona. During those five years learning "art" (mainly conceptual art) not one of the teachers encouraged me to keep drawing cartoons or comics, because as you would know... this is not real ART.
In my opinion, it is more important to work on your own path, focusing in the area you are interested about (inking was mine), and work very hard on that area.
Fortunately I found some very good comic professionals who helped me become a better inker, by sharing their experiences, knowledge and advice with me.
CC: That's wonderful. Is there anyone in particular who has inspired you and influenced your work?
CM: Well, I don't know if these artists have influenced my work, but they really inspire me. They are Bruce Timm, Mike Mignola, Taiyou Matsumoto, Sandra Hope, Jonathan Glapion, Jordi Tarragona, Kevin Nowlan, Matteo Scalera and Dino Battaglia.
CC: What made you wanting to be an artist? and an Inker?
CM: Wow... This is very difficult to know!
This is not going to be the perfect answer as we are used to reading that since a very young age I knew my future was to be an artist".
When I was a child I instead dreamed of becoming a great ballet dancer (despite not taking ballet lessons) or maybe an archaeologist. I was very good at drawing, and working on creative things.
I decided go to University and study Art, but my teachers did not value my drawings and accurate strokes. Teachers and students prefered flowing paintings... more chaotically techniques and fresh drawings, directly from the soul to the paper, less academical.
So I thought "there may be a field where I can apply all that accurate strokes made with brush", and a good friend of mine talked to me about the inkers of the comic industry.
What a discovery!
CC: We're pleased you made that discovery. Are you able to work full-time as an artist now? What other jobs have you had in the past to supplement your career as an artist?
CM: Today I work as a comic teacher, illustrator and inker.
It is a personal decision, because I really enjoy sharing my knowledge in the comic field with people. I still enjoy day to day work as an inker and illustrator. I published my first book in October 2018. In the past I have worked in fashion and sports stores while I was building my career. CC: What other comic work have you done (prior to The Phantom) that we should become familiar with?
CM: I started working as an inker at 26 years old with some pages to DC Comics on titles like Smallville, Battlestar Galactica and Dynamite Enternainment on titles like HeMan and the Masters of the Universe.
CC: Your work with the Phantom has been as an inker for Rafael Ruiz. Can you tell us about how that happened?
CM: After four years working on these titles, I decided to move to Barcelona, where I started working as an inker in a comics studio called "Comic Up". Mainly we inked Disney stories, like Donald Duck magazines.
During the first few months at the studio they started working with The Phantom comics for Egmont, with pencils done by Rafael Ruiz.
They tested all the inkers on staff to decide who was better suited to ink The Phantom and the rest is history.
CC: Your published work proves they made a good choice. Do you like working on the Phantom? Did you know of the Phantom before you started work on those stories? Did you ever read the character?
CM: Yes, I like working on the Phantom, he is a good character to ink, and is a very dynamic drawing as well.
I knew the Phantom, of course! But just in a physical appearance, the costume, the colors etc. I never had read the character (shame!), because I thought he was a classic character, with lots of BAM! CRASH! and that kind of stuff.
Sorry about that, fans! I'm not proud about that.
CC: We're sure the phans can learn to forgive you! What do you enjoy most about inking?
CM: I love inking, most people think it is a technical process without artistic value, in my opinion and experience, I disagree.
I have the responsibility of the final lines, the final lines that are going to be printed and published. I have to understand the drawings, the personal style of the artist, in this case, Rafael Ruiz, absolve it, respect it and, if it is possible, bring new nuances to his lines and movements.
It's very important to keep the drawings as clean as possible, to help the readers differentiate what's happening in every single scene with the foreground and background.
I personally prefer inking main characters and nature backgrounds (trees, plants, organic stuff) than city landscapes or big buildings. Ships, cars and bicycles are my worst enemies, hahaha!
Of course the first 4 hours inking are better than the rest, I will not lie.
CC: You will love the Phantom then, especially if you can work on more of his jungle adventures! How do you find the challenge of inking someone else's work - are you able to bring your own flair or style to the drawings? Are you able to make your own inking decisions to his pencils?
CM: Well, it is a big responsibility! You can bring your own style, but not you cannot change the main character including his face - that has to stay in the pencillers style! Hahahaha!!
The areas where you can make decisions and leave your personal print are mainly the backgrounds, different surface textures and the way you apply the shadowing lines (soft, fast, aggressive, thick, splatter etc).
Pencils always come very clear and with no doubt on how to ink, but sometimes characters and backgrounds can get a little bit confusing. in these cases, my work is try to add some order in the scene.
CC: Professional comic book creation is a notoriously male-dominated industry and The Phantom is not immune, with precious few female creators in the past. What has your experience been like in this area as you have established yourself in the industry and the Phantom world?
CM: This is a personal point of view based on a personal experience.
When I started working as The Phantom inker I was in a comic studio based in Barcelona, where there were 8 inkers and 5 of them females. That was a very nice place to work, without any kind of sexism at all. Unfortunately, it is not always the case.
For example at a Manga Convention, where fandom and artists are on average 50 / 50 between men and women (if not even more women) there is greater respect. Compare that experience to a generic Comic Superhero Convention where while you still find plenty of women, in my experiences, you can feel a little bit awkward, with people staring at you, and adding stupid comments or sexist behavior.
A sad example was from my first comic agent who told me on a phone call: "Hello! My name is XXX, I saw you in the comic con last week. The first thing I noticed, was your legs... So, you are an inker right?"
Few years ago that kind of conversation would have left me completely shocked, without words trying to answer or make sense of what just happened. Nowadays society is changing and thankfully behaviors are also changing in a positive way.
I hope we will reach the point of call us "artists" and not male artist or female artist. We are people, human beings, that's all. let our work tell the tale.
CC: Wow, that is horrible! I am glad the experiences are getting better. As we finish up, what is the best way for someone to contact or look at your work?
CM: The best place would be my Instagram account.
CC: Thank you, we thank you for your time and we look forward to more of your artwork in our Phantom comics and seeing some Phantom commissions from phans around the world.
CM: Thanks for that interview! I really enjoy share my experience with fans, readers and everybody who wants listen to me talking about my work and my personal world.
Coral Martinez
#CoralMartinez #RafaelRuiz #TeamFantomen #Egmont #Frew
Tony DePaul Correcting Lee Falk's Mistakes
Introducing Eduardo Ferigato - Dynamite's "The Last Phantom" Artist


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text:
\section*{Acknowledgment}
This material is based upon work supported by PETROBRAS, CENPES.
Also, this material is based upon work supported by Air Force Office Scientific Research under award number FA9550-19-1-0020.

\bibliographystyle{unsrt}

\section{Introduction} \label{sec:1-introduction}

    Seismic imaging methods are used to produce images of the Earth's subsurface properties from seismic data.
    The seismic data,
        also called seismogram,
        is recorded on the surface by geophone devices
        that capture elastic waves emitted by artificial sources
        and reflected in the subsoil.

    Further,
        seismograms are processed to form images representing the boundaries of the different rock types in the Earth's subsurface \cite{SamuelHGray2016}.

    Nowadays,
        seismic images are applied to a broad range of seismic applications:
        from near-surface environmental studies
        to oil and gas exploration
        and even to long-period earthquake seismology.
    Among different applications in the industry,
        seismic imaging analysis plays a paramount role
        in the exploration
        and identification of hydrocarbon fuel reservoirs.
    The task of
        salt deposit localization and delineation
        is crucial in such application since overlying rock-salt formations can trap hydrocarbons reservoirs due to their exceeding impermeability \cite{Babakhin2019SemiSupervisedSO}.
    Unfortunately,
        the exact identification of large salt bodies is notoriously tricky \cite{Jones2014SeismicII}
        and often requires manual interpretation of seismic images by domain experts.

    Several automation tools addressing the manual interpretation requirements have been proposed
        \cite{Bedi2018SFAGTMSF, Di2018MultiattributeKC, Hegazy2014TextureAF, Pitas1992ATA,Wrona2018SEISMICFA, Wu2016MethodsTC, Zhao2015ACO}.
    However,
        these methods do not generalize well for complex cases since they rely on handcrafted features.
    Consequently,
        many works emerged proposing Deep Neural Networks (DNNs) for the semantic segmentation of salt bodies since they are a natural way to overcome the need for hand-engineering features.
    Although those DNNs present superior performance on the task compared to traditional methods \cite{Dramsch2018DeeplearningSF, Waldeland2018ConvolutionalNN, Wang2018AutomaticSD, Zeng2019AutomaticSS},
        their requirement of huge amounts of labeled data and the associated costs of expert manual annotation prevents their wider applicability.

    From the machine learning perspective,
        the semantic segmentation of salt bodies
        consists of predicting binary salt masks that depict salt regions
        from the inputted seismic image patches.

    This work aims to mitigate the requirement of a huge amount of manually labeled data
        when training DNNs for the semantic segmentation of salt bodies.
    With such a goal,
        we propose a Data Augmentation (DA) method to increase the number of samples in a dataset for the semantic segmentation of salt bodies.
    Our method consists of training two distinct deep learning models
        to generate pairs of seismic image patches
        and salt masks for using them as DA.

    The first model is a Variational Autoencoder \cite{Kingma2013AutoEncodingVB}
        whose is responsible for generating salt body masks.
    The second is a Conditional Normalizing Flow model \cite{Dinh2014NICENI, Dinh2016DensityEU, Kingma2018GlowGF, Ardizzone2019GuidedIG},
        which receives the generated masks as inputs
        and outputs the associated seismic image patches.
    In each generated pair,
        the mask is responsible for depicting (and bounding) the salt body,
        and the generated seismic image patch is a seismic representation of the respective salt body.
    We conduct experiments on a dataset from two synthetic seismic images and their velocity models,
        from where the masks are extracted by thresholding and clipping.
    The images are taken from distinct and publicly available synthetic seismic models \cite{Stoughton20012DEM, Aminzadeh19953DMP} designed to emulate salt prospects in deep water,
        such as those found in the Gulf of Mexico.
    We compare state-of-the-art semantic segmentation models on this dataset
        when augmenting and not augmenting
        the data with the generated samples.
    The comparison is performed for a plain U-net model \cite{Ronneberger2015UNetCN}
        and nine DeeplabV3+ \cite{deeplabv3plus2018} variants.

    Our main contribution is developing a DA method for the semantic segmentation of salt bodies that effectively improves the quality of machine learning models regardless of their architectures and sizes.
    We also show that the proposed method outperforms several DA methods when applied standalone.

    At last,
        we show that the proposed DA method is easily adapted to a different dataset setup,
        such as a larger context size,
        without losing efficiency.

    \input{sections/1-Introduction/paper-organization.tex}

\section{Related Work} \label{sec:2-related-work}
    \input{sections/2-Related-Work/salt-segmentation.tex}

\section{Background} \label{sec:3-Background}

    This section discusses recent efforts in the Variational Autoencoders (VAEs) and Conditional Invertible Normalizing Flows (CNF).

    \input{sections/3-Background/vae}
    \input{sections/3-Background/normalizing_flows}

\subsection{Conditional Normalizing Flow Models} \label{sec:conditional-flow}

    The Conditional Normalizing Flow (CNF) is an NF extension to the conditional case.
    Thus,
        CNF extends the NF by conditioning the entire generative process on an input $y$ \cite{Ardizzone2019GuidedIG, Winkler2019LearningLW}.

    The goal is to estimate conditional likelihoods using conditional normalizing flows for complicated target distributions in multivariate prediction tasks.

    Given an input $y$ and a target $x$,
        we learn the distribution $p(x|y)$
        using the conditional base distribution $\pi(z_{k}|y)$
        and a sequence of $k$ mappings
        $$

            z_{k} = f_{\omega}(x) = f^{k}(...(f^{1}(x)))
        $$

        which are bijective in $x$
        and $z_{k}$.
    Thus,
        the change of variables formula becomes:
            \begin{equation}
                \label{eq:conditional-change-of-variable-formula}

                p(x|y) = \pi(f_{\omega}(x)|y)\left | det \left (\frac{\partial f_{\omega}(x)}{\partial x} \right ) \right |
            \end{equation}

    The generative process is done by
        first sampling $z_{k}$ from a simple base density,
        which has its parameters determined by an arbitrary function that receives $y$ as input.
    Then,
        $z_{k}$ is transformed by the sequence of bijective mappings $f^{-1}_{\omega}(z_{k})$.

    Therefore,
        the generative process is defined as:
            \begin{equation}
                \label{eq:z-follows-p}

                z_{k} \sim \pi(f_{\omega}(x)|y)
            \end{equation}
            \begin{equation}
                \label{eq:x-fz}

                x = f_{\omega}^{-1}(z_{k})
            \end{equation}

    During the training procedure,

        $x$ is given as inputs to the model

        and passed through the invertible mapping $f_{\omega}(x)$
        to yield $z_{k}$.
    Then the base distribution parameters and the log-likelihood are evaluated.
    As usual,
        in neural networks,
        the flow and base distribution parameters are optimized using stochastic gradient descent with mini-batches.

    In this work,
        the conditional data are just binary masks,
        and since the conditioning is introduced only in the base distribution,
        unlike in other applications,
        in which the conditional information is also used as input
        to (I) the affine coupling layer internal operators \cite{Ardizzone2019GuidedIG}
        and (II) to an affine projection layer \cite{Lugmayr2020SRFlowLT}.

    Therefore,
        the base distribution parameters are set by arbitrary functions such that:

        \begin{equation}
            \label{eq:prior-net}
            \pi(z_{k}|y) = \mathcal{N}(z; \mu(y), \sigma(y)),
        \end{equation}
        where $\mu(y)$ and $\sigma(y)$ are the outputs of a simple convolutional neural network that uses the conditioning term $y$ as input.

\subsubsection{ActNorm}
    Also introduced by the Glow model \cite{Kingma2018GlowGF},
        the ActNorm layer is proposed,
        as an alternative to the standard batch normalization \cite{Ioffe2015BatchNA},
        for models trained with small mini-batches.
    Similar to the batch normalization,
        the ActNorm layer performs a per-channel normalization in the network activations.
    Thus,
        the ActNorm layer performs an affine transformation to shift
        and scale the network activations,
        using its scale
        and bias trainable parameters.

    Additionally,
        the scale and bias parameters are initialized such that
        the activations after the Actnorm layer have approximately zero mean
        and unit variance.

    Finally,
        the log-determinant is computed as follows:
        \begin{equation}
            \label{eq:acnorm-logdet}
            log \left | det\left ( \frac{ \partial s \odot x + b}{\partial x} \right ) \right | = w \cdot sum(log|s|)
        \end{equation}
    In equation \ref{eq:acnorm-logdet},
        $x$ is the layer input,
        $s$ is the scale parameter,
        $b$ is the bias parameter,
        $\odot$ is an element-wise product operation,
        and $w$ stands for the input width dimensions.

\subsubsection{Affine Coupling Layers}

    The affine coupling layer,
        first introduced in Nice and RealNVP \cite{Dinh2014NICENI, Dinh2016DensityEU},
        is a simple bijective layer that is simple to invert
        and has a tractable Jacobian determinant.
    In this layer,
        the input vector is partitioned into two halves,
        and then one partition is updated using complex dependencies computed from the other partition.
    In the end,
        the layer concatenates the partitions into a vector containing the same dimensions as its input.
    Thus,
        given $D$-dimensional input $x$ and a scalar $d < D$,
        the affine coupling layer output $h$ follows:
            \begin{equation}
                \label{eq:affine-coupling-layer}
                h =
                \left\{
                \begin{matrix}
                    h_{1:d} = x_{1:d}; \\
                    h_{d+1:D} = x_{d+1:D} \odot  exp(s(x_1:d)) + t(x_{1:d})
                \end{matrix}
                \right.
            \end{equation}
        where $s$ and $t$
        are functions from $\mathcal{R}^{d} \rightarrow \mathcal{R}^{D - d}$,
        and $\odot $ is the element-wise product.

    The Jacobian of the transformation expressed in equation \ref{eq:affine-coupling-layer},
        is as follows:
            \begin{equation}
                \label{eq:affine-coupling-jacobian}
                J =
                \begin{bmatrix}
                    \mathbb{I}_{d} & 0_{d\times(D-d)} \\
                    \frac{\partial h_{d+1:D}}{\partial x_{1:d}} & diag(exp(s(x_{1:d})))
                \end{bmatrix}
            \end{equation}
    In equation \ref{eq:affine-coupling-jacobian},
        $diag(exp(s(x_{1:d})))$
        is the diagonal matrix whose diagonal elements correspond to the vector
        $s(x_{1:d}))$.
    Since the Jacobian is upper triangular,
        its determinant is efficiently computed as
        $exp \left[ \sum_{j} s( x_{1:d})_{j} \right ]$.

    Since computing the Jacobian determinant of the affine coupling layer does not involve calculating the $s$ and $t$ Jacobians,
        those operators are calculated by a standard neural network block that serves as the layer's backbone.

    Finally,
        computing the inverse of the coupling layer is no more complex than the forward propagation,
        and therefore,
        sampling is as efficient as inference.
    Equation \ref{eq:affine-coupling-layer-inverse} defines the inverse transformation of the affine coupling layer.
        \begin{equation}
            \label{eq:affine-coupling-layer-inverse}
            x =
            \left\{
            \begin{matrix}
                x_{1:d} = h_{1:d}; \\
                x_{d+1:D} = (h_{d+1:D} - t(h_{1:d})) \odot  exp(-s(h_{1:d}))
            \end{matrix}
            \right.
        \end{equation}

\subsubsection{Invertible 1 x 1 convolutions}

    The Glow model architecture \cite{Kingma2018GlowGF} proposes to replace the most commonly used fixed permutations \cite{Dinh2014NICENI, Dinh2016DensityEU} operator with a $1 \times 1$ invertible convolutional layer,
        which they claim to be a generalization of the permutation operators.

    In this layer,
        the convolution weight matrix is initialized as a random rotation matrix, with the same number of filters as the number of input channels $c$.

    Thus,
        the invertible $1 \times 1$ convolution receives a tensor
        $x \in \mathbb{R}^{w \times c}$
        as input and applies the convolutional operator using the weight matrix
        $W \in \mathbb{R}^{c \times c}$ as its convolutional kernel.
    Additionally,
        the weight matrix $W$ is initialized such that its initial log-determinant is $0$.

    Finally,
        the log-determinant of this layer is computed as follows:
            \begin{equation}
                \label{eq:inv-conv-logdet}
                log \left | det\left ( \frac{\partial conv2D(h; W)}{\partial h} \right ) \right | = width \cdot log|det(W)|
            \end{equation}

\subsection{Conditional Normalizing Flow Models} \label{sec:normflow}

    Normalizing Flow (NF) models
        \cite{Dinh2014NICENI, Dinh2016DensityEU, Kingma2018GlowGF}
        learn flexible distributions by transforming a simple base distribution
        with a sequence of invertible transformations,
        known as normalizing flows.
    The Conditional Normalizing Flow (CNF)
        \cite{Ardizzone2019GuidedIG, Winkler2019LearningLW, Lugmayr2020SRFlowLT}
        extends the NF by conditioning the entire generative process on the input data,
        commonly referred to as conditioning data.

    This work aims to estimate conditional likelihoods
        of the seismic image patches $x$,
        given their respective salt masks $y$,
        which is the conditioning data.
    Thus,
        given three vector-spaces $x \in R^{d}$,
        $x \in R^{d}$,
        and $z \in R^{d}$,
        an invertible mapping $f_{\omega}: x \rightarrow z$
        with $\omega$ parameters,
        and a base distribution $\pi(z_{k}|y)$
        then CNF compute the data log-likelihood $log\ p(x|y)$
        using the change of variables formula:

            \begin{equation}
                \label{eq:change-of-variable-formula}
                log\ p(x|y) = log\ \pi(f_{\omega}(x)|y) +
                    log \left |
                        det
                        \left (
                                \frac
                                    { \partial f_{\omega}(x) }
                                    {\partial x}
                            \right )
                        \right |
            \end{equation}

    In equation \ref{eq:change-of-variable-formula},
        the term $| det( \partial f_{\omega}(x)/ \partial x ) |$
        is the Jacobian Determinant absolute value,
        or for simplicity,
        the Jacobian of $f_{\omega}$.
    It measures the change in density
        when going from $x$ to $z$
        under the transformation $f_{\omega}$.
    In general,
        the cost of computing the Jacobian will be $O(D^{3})$.
    However,
        it is possible to design transformations in which the Jacobians are efficiently calculated \cite{Kingma2018GlowGF}.

    Thus,
        given an input $y$
        and a target $x$,
        CNF estimates the distribution $p(x|y)$ using the conditional base distribution $\pi(z_{k}|y)$
        and a sequence of $k$ mappings
        $
            z_{k} = f_{\omega}(x) = f^{k}(...(f^{1}(x)))
        $
        which are bijective in $x$.

    The main challenge in invertible normalizing flows
        is to design mappings to compose the transformation $f_{\omega}$
        since they must have a set of restricting properties
        \cite{Winkler2019LearningLW}.
    All mappings $f^{i} \in f_{\omega}$ must have
        a known and tractable inverse mapping
        and an efficiently computable Jacobian
        while being powerful enough to model complex transformations.
    Moreover,
        fast sampling is desired, so the inverse mappings $(f^{i})^{-1}$ should be calculated efficiently.

    Fortunately,
        the current literature on normalizing flows provides a set of well-established invertible layers with all those properties.

    The CNF model proposed in this work is built upon step-flow blocks \cite{Kingma2018GlowGF},
        which is a stacking of three invertible layers
        \footnote{Due to space constraints we refer to \cite{Kingma2018GlowGF} for more details on those invertible layers.}:
            (I) the Actnorm layer \cite{Kingma2018GlowGF};
            (II) the Invertible 1 × 1 Convolution layer \cite{Kingma2018GlowGF};
            (III) the Affine Coupling layer \cite{Dinh2014NICENI, Dinh2016DensityEU}.

    Additionally,
        the step-flow blocks are combined with the multi-scale architecture \cite{Dinh2016DensityEU}
        that allows the model to learn intermediate representation at different scale levels that are fine-grained local features
        \cite{Rezende2014StochasticBA, Salakhutdinov2009DeepBM, Dinh2016DensityEU}.
    At regular intervals,
        step-flow blocks are surrounded by
        squeezing and factor-out operations,
        which reduces the spatial resolution
        and doubles the channel dimension,
        producing multiple scale levels.

    This approach reduces the computation and memory used by the model
        and distributes the loss function throughout the network.
    It is similar to guiding intermediate layers using intermediate classifiers \cite{Lee2014DeeplySupervisedN}.

    In such a model,
        a major component is the Affine Coupling layer which is expressed as:
            \begin{equation}
                \label{eq:affine-coupling-layer}
                h =
                \left \{
                \begin{matrix}
                    h_{1:d} = x_{1:d}; \\
                    h_{d+1:D} = x_{d+1:D} \odot  e^{s(x_{1:d})} + t(x_{1:d})
                \end{matrix}
                \right.
            \end{equation}
    In equation \ref{eq:affine-coupling-layer},
        $\odot $ is the element-wise product and,
        $s(x_{1:d})$ and $t(x_{1:d})$ are the outputs of the layer's backbone,
        a standard neural network block.
    The input is split into two halves $x = [x_{1:d}; x_{d+1:D}]$,
        and the split $x_{d+1:D}$ is updated using the $s$ and  $t$ operators computed by the layer's backbone from the $x_{1:d}$ split.
    The layer's output is the concatenation of the haves
        $h = [h_{1:d}; h_{d+1:D}]$.

    In summary,
        an observation $x$ is given as inputs to the model
        during the training procedure
        and passed through the invertible mapping $f_{\omega}(x)$
        producing $z_{k}$.
    Then,
        the base distribution parameters and the likelihood are evaluated using the conditioning data $y$ such that:
            \begin{equation}
                \label{eq:prior-net}
                \pi(z_{k}|y) = \mathcal{N}(z; \mu(y), \sigma(y)),
            \end{equation}
        where $\mu(y)$ and $\sigma(y)$
        are the outputs of a simple convolutional neural network that uses the conditioning term $y$ as input.
    In the remainder,
        this convolutional network is referred to as Prior Network.

    Finally,
        the generative process is done by sampling $z_{k}$
        from the base density $\pi(z_{k}|y)$
        and then transformed by the sequence of inverse mappings
        $x = f_{\omega}^{-1}(z_{k})$.

\subsection{Variational Autoencoder} \label{sec:vae}

    Consider a probabilistic model $p_{\theta}(x, z)$ with observations $x$,
        continuous latent variables $z$,
        and model parameters $\theta$.
    In generative modeling,
        we are often interested in learning the model parameters $\theta$ by estimating the marginal likelihood $p_{\theta}(x)$.
    Unfortunately,
        the marginalization over the unobserved latent variables $z$ is generally intractable \cite{Kingma2013AutoEncodingVB}.

    Instead,
        variational inference \cite{Jordan1999AnIT} constructs a lower bound on the marginal likelihood logarithm $log\ p_{\theta}(x)$.
    The lower bound is built by introducing a variational approximation to the posterior.

        \begin{equation}
            \label{eq:elbo}
            log\ p_{\theta}(x) \geq \mathbb{E}_{q} \left [\ log\ p_{\theta}(x|z)\ \right ] - \mathbb{KL}(q_{\phi}(z|x) || \pi(z))
        \end{equation}

    Equation \ref{eq:elbo} is called as
        the Evidence Lower Bound (ELBO),
        where $q_{\phi}(z|x)$ is the posterior approximation,
        $p_{\theta}(x|z)$ is a likelihood function,
        $\pi(z)$ is the prior distribution,
        and $\phi$ and $\theta$ are the model parameters.
    Thus,
        VAEs \cite{Kingma2013AutoEncodingVB} are generative models
        that use neural networks
        to predict the variational distribution parameters.

    From the machine learning perspective,
        VAEs are defined by:
        an encoder model which maps $x$ to $z$;
        a decoder model which maps $z$ back to $x$;
        and a loss function that is the negative of equation \ref{eq:elbo}.

    The loss first term is a likelihood function
        that is minimized during the model training.
    The second term is the Kullback-Leibler Divergence between
        the inferred posterior distribution $q_{\phi}(z|x)$
        and the prior distribution $\pi(z)$.
    It can be interpreted as a regularization term
        that enforces the distribution of the latent variables.
    Furthermore,
        the $\pi(z)$ distribution is a standard and diagonal Gaussian.
    It is worth mentioning that the better the posterior approximation,
        the tighter the ELBO is,
        and so smaller is the gap between the true distribution $p(x)$
        and the lower bound.
    The whole model is trained through stochastic maximization of ELBO through the reparameterization trick \cite{Kingma2013AutoEncodingVB, Rezende2014StochasticBA}.

    In this work, we propose using a VAE to estimate the distribution of salt masks presented in our dataset and thus learning a stochastic generation process of salt masks.

\section{Methodology} \label{sec:4-model}

    This section describes the proposed method for generating the data augmentation samples
        and the related models.

    Our approach is learning to generate sample pairs of seismic image patches
        and their respective salt masks
        that lie in the boundary of a salt body.
    The primary motivation for this approach is that
        if we can correctly segment all salt body boundaries,
        it is straightforward to recover all entire bodies --
        since everything inside a boundary is salt.
    Also,
        since the most common strategy to train models
        for the salt semantic segmentation is
        to split seismic images into patches
        and then using those as training examples,
        patches lying in the boundary of a salt body usually have low frequency in the datasets.
    As a consequence,
        learning to segment such examples becomes a challenge for the models.
    Therefore,
        our methodology consists of training two generative models to generate pairs of seismic image patches
        and their respective salt masks.

    The first model is a standard VAE model used for sampling salt body masks from the dataset's inferred distribution.
    Only the dataset masks $y$ are given as input to the model during the training,
        while a lower bound (the ELBO),
        defined by equation \ref{eq:elbo},
        on the mask distributions is optimized.
    Such as discussed in section \ref{sec:3-Background},
        at inference time,
        we sample the latent variables $z$ from the prior distribution
        $\pi(z) \sim \mathbb{N}(0,\mathbb{I})$
        and then decode $z$ with the VAE's decoder to generate a salt mask $\hat{y}$ belonging to the inferred distribution.

    The second model is a Conditional Normalizing Flow model,
        which receives the sampled salt body masks $\hat{y}$ as inputs to generate the associated seismic image patches $\hat{x}$.
    During the training,
        the CNF model receives the seismic image patches $x$
        and their associated salt masks $y$ as inputs.
    Then the log-likelihood of the patches $log\ p(x|y)$ is maximized using the change of variable formula,
        equation \ref{eq:change-of-variable-formula}.
    The generative process is done by sampling $z_{k}$
        from the base distribution
        $\pi(z_{k}) = \mathbb{N}(\mu(\hat{y}), \sigma(\hat{y}))$,
        which has its parameters determined by the Prior Network.

    At last,
        $z_{k}$ is transformed by the CNF's a sequence of bijective mappings, resulting in the sampled image patch
        $\hat{x} = (f_{\omega}^{-1}(z_{k})$.

    It is worth emphasizing that both models,
        the VAE and CNF,
        are trained separately
        and independently of each other
        and are only used together to generate the samples for the data augmentation.
    Figure 1 illustrates the generation procedure in a flow chart.
        \begin{figure}[H]
            \centering
            \includegraphics[width=0.6\linewidth]
            {sections/4-Methodology/figures/DataAugmentationGenerationProcedure.png}
            \caption{Data augmentation generation procedure.}
            \label{fig:generation-procedure}
        \end{figure}
    The procedure is simple.
    First,
        we use the trained VAE model to sample salt masks $\hat{y}$,
        and then the image patches are sampled using $\hat{y}$ as conditional data.

    In the following,
        we describe the setup,
        hyper-parameter settings,
        and learning procedure for the VAE and CNF models trained in this work.

    \input{sections/4-Methodology/4.1-VAE}
    \input{sections/4-Methodology/4.1-CNF}
\subsection*{Conditional Normalizing Flows}
    We train a CNF model,
        which,
        during the data augmentation generation procedure,
        receives the sampled masks $\hat{y}$ as inputs
        to generate the associated seismic image patches $\hat{x}$.

    The CNF uses multi-scale architecture,
        with four scale levels and $15$ step-flow blocks per level.
    The Invertible $1 \times 1$ Convolution outputs are given as inputs
        to the Affine Coupling Layer at each block, which has its backbone network illustrated in figure \ref{fig:s-and-t-block}.

    \begin{figure}[H]
        \centering
        \includegraphics[width=0.7\linewidth]
        {sections/4-Methodology/figures/CouplingLayer-Backbone.png}
        \caption{Coupling Layer's backbone}
        \label{fig:s-and-t-block}
    \end{figure}

    All backbone weights are initialized by the Xavier Normal initialization method \cite{Glorot2010UnderstandingTD},
        except for the weights of the last convolutional layer,
        which is initialized with zeros.

    In it turns,
        the Prior Network is composed of 4 stacked $3 \times 3$ convolutions followed by a last zero-initialized $1 \times 1$ convolutional layer.

    Figure \ref{fig:prior-net} illustrates the prior network.
        \begin{figure}[ht!]
            \centering

            \includegraphics[width=0.5\linewidth]
            {sections/4-Methodology/figures/PriorNetwork-Composition.png}
            \caption{Prior Network.}
            \label{fig:prior-net}
        \end{figure}
    Additionally,
        padding and strides are set such that each $3 \times 3$ convolutional layer halves the inputs' spatial dimension and

        the last $1 \times 1$ convolutional layer doubles the $z$ channel dimensions
        with $384$ filters.

    The model is trained to minimize the input data negative log-likelihood,
        calculated through equation \ref{eq:change-of-variable-formula}.
    Moreover,
        during the training procedure,
        we regularize the model with the Binary Cross-Entropy loss to ensure spatial correlation between the learned latent space variables $z$
        and the conditional data $y$.
    Such a regularization is analogous to the regularization proposed in the Glow paper \cite{Kingma2018GlowGF},
        which penalizes the miss-classifications of a linear layer when learning a class-conditioned model.

    To compute the Binary Cross-Entropy loss,
        we use an auxiliary network jointly trained with the whole CNF model.
    Such as in the Conditional Glow,
        this auxiliary network receives the latent variable $z$ as input
        and outputs the predicted respective mask.
    In summary,
        the auxiliary network comprises four stacked $3 \times 3$ transposed convolutions \cite{Zeiler2010DeconvolutionalN}, followed by a $3 \times 3$ convolutional layer
        where each transposed convolution doubles the input's spatial resolution.

    Figure \ref{fig:aux-network} illustrates this auxiliary network.

        \begin{figure}[ht!]
            \centering

            \includegraphics[width=0.6\linewidth]
            {sections/4-Methodology/figures/AuxiliarNetwork-Composition.png}
            \caption{Auxiliary network, used to compute the regularization term during the training procedure.}
            \label{fig:aux-network}
        \end{figure}

    Finally,
        the CNF model is trained on the training and validation sets,
        using the Adam Optimizer \cite{Kingma2014AdamAM},
        with its default hyper-parameters, during $396.8K$ iterations
        and mini-batch size $50$.
    Polynomial decay is applied to the learning rate,
    which goes from $10^-4$ to $0$.

    The training procedure takes about four days on a Google Cloud TPU v2-8,
    and the training loss converges around $1.67$.
\subsection*{Variational AutoEncoder}
    We train a simple VAE \cite{Kingma2013AutoEncodingVB} model used to sample salt masks
        given to the CNF model as conditional data during the data augmentation generation procedure.
    The VAE encoder and decoder
        are just simple feed-forward networks.
    The encoder contains four stacked dense layers.

    In its turn,
        the decoder contains five stacked dense layers.

    Figure \ref{fig:vae-diagram} illustrates the VAE architecture.
    \begin{figure}[H]
        \centering
        \includegraphics[width=0.5\linewidth]
        {sections/4-Methodology/figures/VAE-Network-Composition.png}
        \caption{VAE layers composition.}
        \label{fig:vae-diagram}
    \end{figure}
    Moreover,
        the model inputs are flattened,
        and the outputs are reshaped to form images.

    Finally,
        the VAE model is trained via gradient descent with mini-batches of size $300$ to minimize the loss described by equation \ref{eq:elbo}.
    Thus,
        the model is trained for $65.6K$ iterations using the Adam optimizer \cite{Kingma2014AdamAM} with its default parameters,
        and the initial learning rate is $10^{-3}$.
    Additionally,
        the learning rate is scheduled to decay in a polynomial form, with a warm-up phase of $6.5K$ steps.
    The training procedure takes about $8$ hours and $13$ minutes on an NVidia Tesla P100 GPU,
        and the training loss converged around $31.54$.

\section{Dataset} \label{sec:5-dataset}
    We conduct extensive experiments on a dataset
        from two publicly available synthetic salt prospects models,
        and our results are presented on that dataset.
    The Pluto1.5 dataset \cite{Stoughton20012DEM},
        and the SEG/EAGE Salt model \cite{Aminzadeh19953DMP},
        are designed to emulate salt prospects in deep water,
        such as those found in the Gulf of Mexico.

    We extract a 2D migrated image
        from each seismic model,
        and the binary salt mask is extracted from its velocity model by thresholding
        and clipping.
    Additionally,
        each migrated image is normalized by its mean and standard deviation.

    We split the images into training,
        validation,
        and test sets.
    Figure \ref{fig:migrated-img-and-masks} illustrates the migrated images,
        their respective binary masks,
        and our data splits.

    \begin{figure}[ht!]
        \centering
        \includegraphics[keepaspectratio=true,width=0.95\columnwidth]
        {sections/5-Dataset/figures/DataSplits.png}
        \caption{
            Seismic migrated images and their binary masks.
            The first row in the figure presents two entire seismic images,
            and the second row shows their corresponding salt masks.
            The black regions in the salt masks represent non-salt areas in their respective seismic images.
            The vertical bars in the image delimitate the locations of each image
            that are designated for the dataset's train, validation, and test sets.
        }
        \label{fig:migrated-img-and-masks}
    \end{figure}

    In our experiments,
        the images
        and masks,
        illustrated in figure \ref{fig:migrated-img-and-masks},
        are processed into patches for training and evaluating the generative
        and semantic segmentation models.
    Moreover,
        we use distinct strategies
        for processing the seismic images and salt masks into patches
        due to the different goals of the generative and semantic segmentation models.
    In the following,
        we describe both procedures.

    \subsubsection*{Patch Processing for the Generative Models}

        The goal of training
            the VAE and the CNF models
            is to generate pairs of seismic image patches
            and their respective salt masks
            that lie in the boundary of a salt body.
        With this proposal,
            the VAE and CNF models
            are trained using only patches

            which contain at least $10\%$
            and a maximum of $90\%$ of salt.

        Therefore,
            the seismic images train and validation,
            partitions are processed to create an overlapping grid
            of $64 \times 64$ images and masks
            such that adjacent patches in the seismic image have $90\%$ overlap area.
        After it,
            patches that contain at least $10\%$
            and a maximum of $90\%$
            of salt are filtered and used as training examples.
        At last,
            the resulting data is augmented by horizontal flipping.

       The resulting dataset comprises $24.872$ pairs
        of patches and binary masks
        containing different segments of a salt body boundary.
        Figure \ref{fig:image-size-dist} shows examples
            of migrated image patches and their respective salt masks.

        \begin{figure}[ht]
            \centering
            \includegraphics[width=5cm]
            {sections/5-Dataset/figures/SaltAndMasks.png}
            \caption{
            Examples of the migrated image patches and their respective salt masks.
            In the image,
                the second and fourth columns represent salt masks in which black regions represent non-salt areas.
            The first and third columns represent the respective seismic representation patches.}
            \label{fig:image-size-dist}
        \end{figure}

    \subsubsection*{Patch Processing for the Semantic Segmentation Models}

        The semantic segmentation of salt bodies task consists of predicting binary salt masks that depict salt regions from the inputted seismic image patches.
        With the purpose
            of training and evaluating models for this task,

            the dataset training,
            validation,
            and test sets
            are processed to create an overlapping grid of $64 \times 64$ patches.
        The overlapping grid is created
            such that adjacent patches in the image partition have $10\%$ overlap,
            and there are no overlapping partitions.
        At last,
            the data is augmented by horizontal flipping each resulting sample pair.

        The resulting dataset comprises $6,000$ sample pairs,
            distributed as follows:
            $2.450$ samples for the training set;
            $1.434$ samples for the validation set;
            and $2.042$ samples for the test set.

        Finally,
            it is important to note that all image patches are used to create the dataset,
            unlike the previous procedure
            where only the patches on a salt body border are taken.

\section{Experiments} \label{sec:6-experiments}

    In this section,
        we present our experiments and the achieved results.
    The IoU (Intersection Over Union) metric at a $0.5$ threshold evaluates the model's performance,
        such as proposed in the PASCAL VOC challenge \cite{Everingham2009ThePV}.

\input{sections/6-Experiments/6.1-augmentation_effects.tex}
\input{sections/6-Experiments/6.2-performance_comparison}
\input{sections/6-Experiments/6.3-augmentation_methods_comparison}
\input{sections/6-Experiments/6.4-experiment_with_larger_context_size}
\subsection{Augmentations Effects} \label{subsec:6.1-augmentations-effects}

    This experiment aims to determine how many generated pairs
        of salt masks and image patches
        should be used to augment the dataset
        while training models for the semantic segmentation of salt bodies.
    Thus,
        we train several U-net models \cite{Ronneberger2015UNetCN} exploring different values for the augmentation size hyper-parameter,
        which controls how many generated sample pairs are used as data augmentation.
    The data augmentation samples are generated by
        the method and models described in \ref{sec:4-model}.

    In summary,
        the experiment consists of evaluating the IoU metric on the dataset's
        training
        and validation sets
        for models trained with different values for the augmentation size,
        from $0$ to $700$.
    We train ten U-net models at each iteration,
        with varying augmentation samples
        -- i.e. pairs of seismic image patches and salt masks.
    Subsequently,
        the IoU metric is evaluated in training set for each model.
    Then the model with the second-best training metric is selected,
        and the other nine models are discarded.
    The iteration finishes by evaluating the selected model on the validation set.

    We observe that it is required to test some distinct generated augmentation sets
        for achieving good results due to the high level of noise and bias added by our DA method.
    We found that five or fewer trials are usually sufficient
        for finding such an augmentation set,
        but we maintained ten shots to ensure consistency.
    It is important to note that we select the second-best training metric
        to avoid selecting highly over-fitted models.

    All models are trained with the U-net implementation\footnote{https://github.com/jakeret/unet}
        provided by \cite{akeret2017radio} for $60$ epochs,
        using the Adam optimizer \cite{Kingma2014AdamAM} with default hyperparameters,
        and the learning rate equals $10^{-3}$.
    Additionally,
        the models have four encoder and decoder blocks,
        and the first block has $65$ filters for its convolutional layers.
    The number of filters is doubled at each block,
        and the whole model has $7.69M$ trainable parameters.
    The training procedure takes
        an average time of 10 minutes per model
        on a Tesla P100 graphic processing unit (GPU).

    In total,
        we train $80$ U-Net models,
        but we only report the metrics for the eight selected models.
    Figure \ref{fig:aug_performance_curve} presents
        the training and validation performance curve
        for the selected models at different sizes of augmentations,
        ranging from $0$ to $700$.

    \begin{figure}[ht]
        \centering

        \includegraphics[keepaspectratio=true,width=0.8\columnwidth]
        {sections/6-Experiments/figures/iouPerAugmentations.png}
        \caption{IoU per number of augmentations.}
        \label{fig:aug_performance_curve}
    \end{figure}

    Among the different augmentation sizes,
        the model trained without augmentations is the one that has the highest IoU score
        on the training set.
    Then,
        up to $200$ augmentations,
        the training score decays from
        $.96$ to $.92$,
        which is the lowest.
    From this point,
        the training metric rises practically back to the initial level.

    On the other hand,
        we observe an increasing validation score until we achieve $300$ augmentations,
        which presents the highest validation score.
    From $400$ onwards,
        the validation IoU quickly degenerates while we observe a rising training score,
        indicating that the model starts to overfit the augmentations from this point.

    Moreover,
        observing the decay in the training metric
        while observing the growth in the validation metric may indicate that
        the data augmentations regularize the model during training,
        thus avoiding overfit
        and improving its generalization.
    Also,
        the highest validation metric is achieved by the model with the second-lowest training score.
    It is observed in an inflection point,
        from where the validation metrics start to degenerate while the training score still increases.
    It may indicate that at $300$ augmentations,
        the model achieves a good balance between regularization and overfitting.

\subsection{Performance Comparison}
\label{subsec:6.2-performance-comparison}

    This section compares the performance of distinct models
        when trained using
        and not using our generated augmentation samples,
        aiming to show that
        our proposed method results in gains independently on the models' architecture
        and the number of parameters.
    For this purpose,
        we compare the performance of the U-Net against seven variants
        of the DeeplabV3+ model \cite{deeplabv3plus2018}.
    The DeeplabV3+ was chosen because its official implementation provides,
        out-of-the-shelf,
        several changeable backbone architectures with different depths
        and number of parameters.
    Three widely used architectures are explored as backbone:
        Xception \cite{Chollet2016XceptionDL};
        MobileNet \cite{mobilenetv32019};
        Resnet \cite{He2016DeepRL};

    The U-net model is trained with the same configuration as explained in section \ref{subsec:6.1-augmentations-effects},
        and all DeeplabV3+ models are trained using the official implementation
        \footnotemark[\value{footnote}].
    Models with the Xception and ResNet backbones
        have the Atrous rate,
        output stride,
        and decoder output stride hyper-parameters set respectively to
        $[12,24,36]$,
        $8$,
        and $4$.
    Moreover,
        all models with the MobileNet (V2 and V3) backbones,
        the Atrous rates, and output stride hyper-parameters are left empty.
    In models with the MobileNetV3 backbone,
        the decoder output stride hyper-parameter is set to 8.
    We provide more detailed hyper-parameter settings for each model on the supplementary material accompanying this paper.

    Finally,
        for comparison purposes,
        all models are trained using the Adam Optimizer for $40K$ training iterations
        and a mini-batch of $20$ examples per iteration
        on an NVidia Tesla P100 graphic processing unit (GPU).
    \footnotetext{https://github.com/tensorflow/models/tree/master/research/deeplab}

    Table \ref{tab:1-Performance-Comparison} present the number of trainable parameters
        and the obtained results in
        the validation and test sets
        for each model
        when trained with and without the proposed DA method.
    The augmented versions are trained with the addition of $300$ generated pairs
        of salt masks
        and image patches.

    \input{sections/6-Experiments/tables/1-performance_comparison.tex}

    The proposed data augmentation method yields consistent improvements for all models in the IoU metric.
    On average,
        we find a gain of $8.57\%$ in the test metric.
    The best improvement is $25.10\%$,
        found with the DeeplabV3+ using the xception\_71 backbone.
    Also,
        the best result is achieved with the augmented dataset by the resnet\_v1\_101 backbone.
    This model reaches $95.17\%$ in the test score,
        which corresponds to an improvement of $2.14\%$.
    Interestingly,
        the models with the xception\_65 and resnet\_v1\_50 backbones
        and the U-net model present a slight reduction in their validation metric,
        indicating that somehow the augmentation regularized those models,
        helping to improve their generalization on unseen data.
\subsection{Comparison against other Data Augmentation methods}
\label{subsec:6.3-methods-comparison}
    We compare the performance of the proposed DA method
        against seven distinct methods
        from the Albumentation library \cite{info11020125}.
    Thus,
        the performance of the DeeplabV3+ with the mobilenet\_v3\_large backbone is evaluated for models trained using the following methods:
        Elastic Transform;
        Grid Distortion;
        Optical Distortion;
        CLAHE;
        Random Brightness Contrast;
        Random Gamma.

    Those methods were selected because they have shown noticeable results
        for medical and image applications
        \cite{SimardSteinkrausPlatt2003ET, Castro2018ElasticDF, HUANG2019372, agustin2020implementation}.
    Additionally,
        we also evaluate the models' performance when composing our proposed DAs together with the transformations enumerated above.
    It is important to note that
        only the original samples are passed through the transformations.
    Therefore,
        our DA samples are only presented to the models without being passed through any transformation.

    All models are trained using the same settings described in section \ref{subsec:6.2-performance-comparison}
        and took an average time of $1$ hour and $10$ minutes on an Nvidia Tesla P100 GPU.

    Table \ref{tab:2-Augmentation-Comparison} compares the obtained results,
        in the validation and test sets,
        of each Albumentation method
        against our proposed data augmentation
        and the composition of both methods.

    \input{sections/6-Experiments/tables/2-augmentation-comparison}

    Comparing the obtained results for the models trained with a single DA method
        (the center column),
        we observe that our method achieves the best result.
    Moreover,
        except for
        the Random Brightness Contrast
        and Optical Distortion methods,
        all methods improve both the validation and test sets.
    The highest test score,
        $90.39\%$,
        is achieved when applying the Elastic Transform
        and our DAs together.
    Also,
        composing our DAs with the Optical Distortion,
        Random Brightness Contrast,
        and Grid Distortion methods result in better generalization than
        when applying those methods alone.
    Such results may indicate that our approach could complement other DA methods,
        improving the models' performance even more.
    On the other hand,
        it is not all methods that improve results when applied with our DAs together.
    The CLAHE and Random Gamma methods present better results when used alone.
    Unfortunately,
        we could not identify an apparent reason for these results.

\subsection{Performance comparison on a larger context size}
\label{subsec:6.4-experiment-with-larger-context-size}

    In this experiment,
        we compare the performance of the DeeplabV3+ using the mobilenet\_v3\_large backbone
        when trained using
        and not using our generated DAs for $128 \times 128$ patches
        aiming to demonstrate that our proposal also works with larger context sizes,
        i.e., larger patch sizes.

    For this purpose,
        we train the generative models,
        with the same setup described in section \ref{sec:4-model},
        for $128 \times 128$ context size.
    The context patches are processed to create an overlapping grid of $128 \times 128$
        with $90\%$ overlap between adjacent patches.
    Additionally,
        only the patch samples containing between $10\%$ and $90\%$ of salt are used.
    The data is also augmented by applying horizontal flips,
        resulting in $11.056$ samples.

    The CNF model achieves a loss value of $1.81$ after six days
        and $259,2K$ training iterations
        on a Google Cloud TPU v2-8.
    In its turn,
        the VAE loss converged around $61.17$ after $14$ hours and $28.8K$ training iterations
        on an NVidia Tesla P100 GPU.

    For the semantic segmentation model,
        $128\times128$ context patches,
        with $10\%$ overlap between adjacent patches,
        are created,
        and the data is also augmented by applying horizontal flips.

    The resulting dataset contains $1,476$ samples,
        with $608$ in the training set,
        $356$ in the validation set,
        and $512$ in the test set.
    The DeeplabV3+ is trained during 40k iterations three times for
        augmented and not augmented versions,
        taking an average time of $6$ hours and $20$ minutes per model.

    Table \ref{tab:3-128-context-performance-comparison} compares the performance of the model
        with the best validation score of each version.

    \input{sections/6-Experiments/tables/3-larger-context-comparison}

    The augmented version improved the metrics on the validation and test sets
        from $74.09\%$ to $86.19\%$
        and from $84.85\%$ to $90.55\%$,
        respectively.
    When compared against the results for the same model architecture,
        from experiment 6.2 (in $64 \times 64$ contexts),
        the augmented and not augmented models outperformed their respective versions.
    Moreover,
        comparing the improvement percentage in the test set of the models trained for $64 \times 64$ contexts against the models trained for $128 \times 128$ contexts,
        we observe that although the percentage reduced from $9.09\%$,
        to $6.71\%$,
        we still have a significant gain when training the model with our proposed DA.
    We believe that the obtained results show the potential of the proposed DA method on larger context sizes.
    Finally,
        it is important to note that further investigation into context sizes has become prohibitive in this work due to the many executed experiments and the high computational cost required for this.

\section{Conclusion} \label{sec:7-conclusion}

    This work proposes a Data Augmentation (DA) method
        for the semantic segmentation of salt bodies in a seismic image dataset
        built from two public available synthetic salt models.

    The performance of distinct state-of-the-art semantic segmentation models is compared
    when using and not using the proposed DAs during the training procedure.
    The proposed method yields consistent improvements independently from
        the model architecture
        or their number of trainable parameters.
    Moreover,
        our DA method achieves an average improvement of $8.57\%$ in the IoU metric.

    The best improvement,
        of $25.10\%$,
        is achieved by the DeeplabV3+ using the the xception\_71 backbone.
    In it turns,
        the best result is achieved when augmenting data for the DeeplabV3+ with the resnet\_v1\_101 backbone.
    This model reaches $95.17\%$ in the test score,
        which corresponds to an improvement of $2.14\%$.

    We also show that our proposal outperforms seven selected DA methods when they are applied stand-alone.
    Furthermore,
        by applying our data augmentations in composition with the ElastTransform augmentation method,
        DeeplabV3+ using the large version of the MobileNetV3 backbone achieves a $90.39\%$ score,
        which is the highest for this architecture.
    At last,
        the same methodology for $128 \times 128$ context patches is applied,
        achieving results comparable to the obtained for smaller contexts,
        showing that our proposal is adaptable for larger contexts.

    Future work could include
        testing the proposed method in other datasets,
        such as the one used in the TGS Salt Identification Challenge;
        application in other seismic tasks like the seismic facies segmentation \cite{Zhao2015ACO, Wrona2018SEISMICFA, Dramsch2018DeeplearningSF};
        adapting the methodology for natural images;
        and exploring the use of other generative models such as the Conditional GAN \cite{Mirza2014ConditionalGA}.

    Finally,
        our experimental results show that the proposed DA method yields consistent improvements for different models and context sizes,
        helping develop models for the semantic segmentation of salt bodies.

redpajama_set_name: RedPajamaArXiv


An alluring pendant for those with exquisite taste. A cushion cut diamond is delicately perched at the center of two 14K white gold curled leaves. The leaves have a modern satin finish, with a high polish gleaming at the edges. The white diamond = 1/5ct, with I/I1 color/clarity grades. The pendant is strung on a fine 14K white gold cable chain, adjustable from 15.5-16.5 inches, with a lobster claw clasp.


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500 Most Romantic Songs
2017 could have been a dull 12 months for Bollywood releases but it surely undoubtedly wasn't the same for the Hindi music trade. Regardless of opening to blended opinions on the time, 'Kabhi Khushi Kabhie Gham' went on to change into one of many highest grossing Bollywood films internationally. The title observe is an instance of how a tune can weave its manner through a film, Lata Mangeshkar's distinctive and emotive voice whisks you away on a journey into the complicated world of the family. By her vocals she imitates the film's title (which translates to 'in instances of happiness, in instances of sadness'), harm and celebration coalescing in a single stunning performance.
We hope you will have preferred our Hindi romantic songs assortment talked about above. Although there are many Bollywood romantic songs new which are not talked about here, but nonetheless you may contemplate this listing as top Bollywood romantic songs of all time. If you realize about some more finest Hindi romantic songs 90s, then do tell us about them through feedback below. We will try to add your list of romance movie songs on this high 100 Hindi romantic songs list.
Bollywood film music has gained extra reputation as of late as it is stuffed with variety when it comes to music genres. Being a Bollywood music lover, everyone knows that songs of Bollywood movies are extra usually pre-recorded by skilled playback singers at recording studios. Throughout the shooting of the song, actors not solely dance on the beats of the track but in addition they lip sync the lyrics of the music. Bollywood Dance Songs are an awesome blend of music, dance, and heart-stealer voices that can make you free your toes on dance flooring.
India is the country with numerous leisure, kinds of music, tradition, ethics and so on. Indian Bollywood trade has a taste of different songs within the movie in order to entertain more no. of individuals. Many people in India are keen on listening to Hindi songs. if somebody's girlfriend left away he 'll listen to songs of arjit Singh to beat his sad mood(lol). if somebody had adopted the way in direction of motivation he'll listen to some other songs like Zinda from bhaag milkha baag, or aashaaye from Iqbal there are there are lots of songs in Bollywood industry and different folks have different taste of songs.
One of the most melodious rain songs, Rimjhim Rimjhim expresses the prim and gentle romance between Manisha Koirala and Anil Kapoor. Music from none aside from RD Burman and lyrics by Javed Akhtar. 2017 might have been a uninteresting year for Bollywood releases but it surely undoubtedly wasn't the identical for the Hindi music business. From foot-tapping dance numbers to soulful romantic tracks, B-city's singers gave us a dose of every little thing. Among the many high singers for the year had been the regulars like AR Rahman, Atif Aslam, Arijit Singh however even new voices like Akhil Sachdeva and Parineeti Chopra gained recognition.
At the danger of sounding like a kind of perennially discontent oldies frowning upon all issues new, I'll admit that the Hindi film music scene is just not what it used to be. This song has lovely music and lyrics which speak about what all emotions is likely to be going by means of bride's thoughts before her wedding ceremony day. Looking for some of the biggest romantic Hindi songs to hearken to? I've put collectively an enormous list of outdated and new love songs from Bollywood cinema. Most of them the '90s and past, as I'm a '90s child, in any case. This is an ever-expanding checklist and currently I've added songs from all of the years.
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redpajama_set_name: RedPajamaCommonCrawl


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Bright outlook for base metals
Base metal prices were expected to continue to firm in 2021 says Australian financial services firm Fat Prophets.
Prices fell across the board with the advent of COVID19 said resources analyst David Lennox.
Government stimulatory programs continued to work on a number of levels to buoy the sector said Mr Lennox.
"The initial fears when COVID hit was that, yes, there would be a disruption to production and we did see commodity prices initially starting to fall," he said.
"What happened, however, was that very quickly governments realized that was damaging their economies and out came the stimulatory programs.
"… because it went to the workforce to support the unemployed. That very quickly flowed through to the money supply."
The second wave investment emphasis on infrastructure development was also helping said Mr Lennox.
"Firstly, those monies went to the workers, to support many that had been laid off," he said.
"That swelled the money supply, and with that swelling of money supply, we did see a commensurate rise in the value of commodities and the currency de-valued.
"Following on from that, we're now seeing a second wave where we're seeing monies from these programs not going directly to the workforce, but towards infrastructure programs.
"Infrastructure is a major consumer of commodities, and with trillions of dollars now also going down that avenue, we've seen commodity prices reacting accordingly."
The lower US dollar would also have a positive effect on commodities prices and China's early recovery from the pandemic had reinstated significant demand, he said.
"On the supply side for commodities, we've seen very little in the way of mining universally around the world, actually suffering from any major impacts from the shutdown.
"We've seen the odd country here and there, Brazil, Argentina, and perhaps the odd operations here and there in other countries but certainly here in Australia, there was very few disruptions.
"Generally, mining's been seen as a critical industry, and much support was given to ensure that operations were kept open."
'Spectacular' high-grade gold results from NQ project
Superior Resources has announced 'spectacular' results from recent drilling at the Steam Engine gold project in North Queensland, with grades as high as 184g/tonne gold. The Stage 2 drilling results will be incorporated into the planned pre-feasibility study, set to begin immediately after expected finalisation of the project's scoping study in early February 2021. They po...
Biddle to lead march on Alpha torbanite project
Greenvale Mining has appointed experienced Australian mining executive Neil Biddle as managing director, tasked with overseeing plans to fast-track development of Alpha project in Central Queensland. The company describes Alpha as the only remaining deposit of torbanite – a mineral that contains up to 650 litres of hydrocarbons per tonne – in Australia. The project lies 50km south o...
Mining towns COVID-19 safe and jobs ready
COVID-19 has shown up the strength of having mining communities. There were dozens of mining towns in Queensland in the first decades of last century. Much of the recent demand for workers has been taken up by FIFO services. Remaining mining towns can be named on one hand including Mount Isa, Cloncurry and Weipa. Mount Isa Mayor Cr Danielle Slade won office last year on the eve of the pa...
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SweetHoney Clothing | toddler fashion - It's Gravy, Baby!
When Brynlee was a few weeks old, I discovered SweetHoney clothing. At the time I had no idea that a single Facebook group for buying and selling clothing would completely change how I dress my daughters. Oh, but it did. Not long after joining that group, several moms joined together to send Brynlee a package filled with SweetHoney dresses, leggings, shirts, and matching shoes and bows. We were in the midst of just figuring out how to keep going with this sick baby and a 3 & 5 year old. That package of clothes and the accompanying SweetHoney gift card meant so much to me. These moms who I had never met reached out to do something kind for our daughter and I and I will never forget it. It was a glimpse of sunshine in the middle of our storm.
Brynlee in the SweetHoney Summer Bubble 2014 & 2016.
I had never purchased boutique clothing or really given it any thought. Then Brynlee was born and for 3 weeks, I did not get to dress my newborn. When she came home, she was still so sick that she spent most of her time sleeping or puking her feeds. That was when boutique clothing worked it's way into my heart. The only time I spent with my girl that was "normal" was when I was getting her dressed. Everything else was so far from normal that I came to embrace those moments of sliding tiny dresses over her body (making sure not to snag her MIC-KEY button) as the best part of our day together. At this time, she was losing so many feeds that we couldn't pick her up often because we needed her to keep every single ML of milk down possible. Changing her diapers and getting her dressed were the few hands-on moments I had with my sick little girl. Otherwise I was suctioning her trach and hooking up her tube-feeds and just praying she would keep at least some of a feed down.
So now, here we are two years later and still buying boutique clothing. It's our one of our sweet connections that just between Brynlee and I. Choosing her outfits and getting her dressed is still one of my favorite parts of each day. I think back to where we were and where we are now and all that's happened in the time between and I'm just filled with gratitude. Brynlee still loves her dresses and accessories and I'll probably cry when she grows out of it. For now I just soak in the little miracle that I get to dress every day. Sometimes I carry her over to her closet and she'll choose her dress. She loves all kinds of dresses and bubbles and rompers. Most days she asks for a bow, but never keeps it in long. She adores necklaces and went through a stage where she wanted to wear 2 or 3 at a time.
She is no longer dressed in boutique clothing constantly like when she was an infant. She now has drawers filled with play clothes because as much as she loves her dresses, she also loves being outside. In the mud- if at all possible. I've always said that if I become the mom who is yelling at her kid not to get dirty, I will quit buying boutique clothing. We've got many stained dresses and it still doesn't bother me. I think after almost losing your child, your perspective changes in big- and small- ways. A dirty faced Brynlee for me means a healthy Brynlee. A Brynlee who jumps in the mud up to her ears means that I don't have to worry about her inhaling it through her trach. So while she does have plenty of play clothes, if she happens to be in one of her sweet outfits and the opportunity to get dirty arises- this mom isn't going to stop her. I may stand back and wonder if my dawn/peroxide/baking soda mixture will work on those stains, but I'll do so with a smile.
She looks adorable in her boutique clothing. Like you, I didn't mind when my kids got dirty. I'd rather that they have fun and enjoy life, even when dressed nicely.
Cute brynlee's cloth.What an adorable. ! At childtocherish's gift store you will get fabulous kid's cloth that can give a lovely smile to your little one. Good sharing.


There are two slot covers on Samsung Galaxy S4 Zoom. One that covered up the battery and micro SIM card slot, and the second that covered up the microSD external memory card slot. At this section, we just wanna discuss the battery cover / SIM slot cover. For Memory slot's cover, we will talk about on inserting and removing Memory tutorial tab section.
Take a look at the picture above. That's how you can remove the battery cover of Galaxy S4 Zoom. With the camera lens facing upwards, on the left side of the phone (as pictured), you can insert your fingernail into the tiny gap on the side cover and pull it out until the cover detaches. Then you can fully opened the cover like the way you open the door. Don't worry there is a hinge that would make the cover always "cling" to the body of Galaxy S4 Zoom.
After you opened the cover, you'll see the battery compartment with "slide-lock" and also the micro SIM card slot.
To Attach the cover back to its position, all you need to do just closing the door / cover and then press the cover until it attach to the body of the Galaxy S4 Zoom. You might hear that the cover clicks which indicates it properly closed.
To insert the micro SIM card is easy. You just need to remove the battery/slot cover on Samsung Galaxy S4 Zoom SM-C101, then align the micro SIM with cut-edge on inside and gold-colored contact facing upward (16MP lens camera facing upwards), and then jush push the micro SIM using your fingertip (forefinger or middle finger or thumb) and make sure it locks. If the microSIM still pop out, that's the microSIM ins't lock yet. Push it until your hear click and the micro SIM locks.
To Remove the microSIM from its slot on Samsung Galaxy S4 Zoom, you just need to unlock the micro SIM by push it against the slot and release it. If the micro SIM ejects (pop out), in which you can grap and draw it out freely, that means the micro SIM unlocks and you've done a proprely way on removing the microSIM.
Before you able to insert / assemble the battery to its compartment on Samsung Galaxy S4, you must unlock the battery lock by sliding it to right (see the image below).
Then just make sure the Samsung Logo facing upwards and the metal-connector of the Lithium ion battery (2330mAh) on the left side and all you need to do just slide the battery and push it inside its compartment. When it reach the end of the compartment, you can unlock the battery by sliding the battery lock from right to the left.
To remove the battery just do the same way on the way you try to inserting the battery by unlocking the battery lock from left to right, then just draw the battery out and pull it out completely. Close the battery "door" aka cover of Samsung Galaxy S4 Zoom SM-C101 back after you've done.
The memory slot on the Samsung Galaxy S4 Zoom locates on the bottom side of the device. You can easily find if you take a look on the figure below. The microSD card slot covered with small cover. You can easily remove the cover by using your fingernail. Just insert to the gap between cover and the body of Galaxy S4 Zoom, and pull it out.
With gold-colored contacts of the microSIM Memory card facing upwards, then push the card into memory slot until you feel that the microSD card reaches the end of the slot. When you release the pushing card, the card should be locked on the slot and isn't pop out.
To remove the microSD card from the slot of SM-C101, you need to push it first to make the card unlocks and pop out, then you can gripped with your finger and pull the card out. Remember that you must always Unmount it first via Settings ~> More ~> Storage ~> Unmount SD card.
Pressing the power button on Galaxy S4 Zoom SM-C101 for about 8 seconds or 9 seconds would make you able to force restart the device. This is needed whenever the touschreen freezes or stop responding or the Android OS hangs.
You can also make the Galaxy S4 Zoom back to original factory default when the condition still like a brand new phone (the OS and applications), by resetting the device or known as Master/Hard reset. Go to Settings ~> Accounts ~> Backup and reset ~> Factory data reset ~> Reset device ~> Erase everything.
When you need hardware combination on resetting the Galaxy S4 Zoom because you aren't able to access the settings (you forget password/PIN/etc), go to Samsung Service Center and ask for their help.


The Ontario Teachers' Pension Plan must have sufficient assets to meet its long-term pension obligations to members. Ontario Teachers' Federation and the Ontario government set contribution and pension benefit levels based on the plan's funding status.
Find out what three factors determine your annual inflation adjustment.
Find out why pension costs continue to grow.


If you aren't already following Paul Elledge's iPhone: Once A Day series, here's your chance. This breathtaking visual diary is composed of images shot with Paul's iPhone. He devotedly shoots a new, beautiful image....everyday. Click here to view the collection.


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Mary is a 28 year old single mother who has a 3 year old with cerebral palsy and autism. She is due to graduate with bachelor's degree in business management this month. She presently has no insurance for herself, but has Medicaid for her 3 year old son. She is afraid she will lose Medicaid benefits for her son if she seeks full-time employment, but could gain insurance for herself. Consider the following questions: 1. What would you do if you were in Mary's situation? 2. What information/resources could you give to Mary? 3. If you could fix the current health care system, what would you do?


Carver Darden attorney Travis M. Morock was recently selected to join the Board of Directors for the Young Lawyers Division of the Escambia-Santa Rosa Bar Association.
The Escambia~Santa Rosa Bar Association is a nonprofit voluntary organization for attorneys and judges in Escambia and Santa Rosa Counties in Florida. The Association has approximately 700 members and its purpose is multi-fold. The mission is to "engender fellowship among members of the Association; to maintain standards of the Bar; to foster and uphold respect for the laws of the State of Florida and of the United States; and to disseminate among members information from time to time as to current events in legal circles."
The Young Lawyers Division is available to attorneys that are 35 or younger, or have less than 5 years of legal practice. A member attorney must be a member of the Escambia~Santa Rosa Bar Association. In addition to fostering and assisting young lawyers' development in the legal profession, the Young Lawyers Division is active in the community through its members participation in various local charity events throughout the year including: Breakfast with Santa, Backpack Buddies Program, and the High School Mock Trial Competition.


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A Tale of 5 Cities: Plastic Barriers to Zero Waste
For decades, U.S. cities have collected mixed plastic in recycling programs in an unsuccessful attempt to solve the plastic waste crisis. Analyzing the municipal solid waste (MSW) streams of five U.S. cities, this new report sheds light on the different ways legitimate recycling efforts are undermined, and how the simplest and most ethical solution to our plastic problem is to remove all non-recyclable plastic from the system. A key finding of the study is that 64% of plastic in these cities' MSW streams cannot be recycled. It's time for policy and regulations to prioritize reduction and reuse over recycling, and to rethink public programs and budgets for healthier outcomes.
In five major U.S. cities, 64% of plastic collected is NOT recyclable.
Lack of data transparency obstructs solutions. Good data leads to good policy. Data on municipal waste flows is absent, old and difficult to find. This allows the plastic industry to exploit loopholes and push self-serving narratives, and creates challenges for cities and communities that want to shift to true zero waste systems.
Most plastic is designed to be dumped or burned, harming communities. Cities can reduce pollution by banning non-recyclable plastic. Only 8.8% of all plastic in the waste stream in the five cities is actually recycled. The remainder is incinerated, landfilled or could supply plastic-for-fuel or chemical recycling facilities, all of which are harmful to our health and environment.
Recycling rates are low because most plastic produced is not recyclable. Companies, not cities, should pay. 64.3% of all plastic in the waste stream in the five cities is not recyclable through municipal recycling or state redemption programs, and yet communities are paying for it with their health and their pocketbooks.
People (understandably) don't know what's actually recyclable. Cities should prioritize collecting only plastic that can be recycled. In the five cities, only 24% of potentially recyclable plastic (#1, #2, #5) gets recycled; 76% gets incinerated or landfilled. Conversely, 12%-55% of all plastic that ended up in single-stream recycling programs was not recyclable.
While plastic recycling must be improved, it has its limits. Plastic reduction and zero waste systems must be prioritized. Zero waste infrastructure like reuse, refill, and repair provides up to 200x as many jobs as disposal, furthers environmental justice, and improves sustainability.
Across the United States, waste incinerators have plagued communities for decades with harmful air emissions, accidents, and other health and safety-related concerns. As their contracts with these aging incinerators expire in the next few years, cities have a choice to make: they can choose to bind themselves to a new generation of incinerators that will cost millions and continue to pollute our most vulnerable communities, or they can make a just transition to a sustainable system that improves public health and saves money. Communities most impacted by these facilities are taking the lead to create livable communities that manage waste effectively for the health of generations to come.
Learn More About the Failing Incinerators Project
The Campaigns
"Recycling is part of a zero waste future, but it isn't the solution to the plastic crisis we find ourselves in."
Whitney Amaya, East Yard Communities for Environmental Justice
"Because of a lack of data collection, communities are not fully aware of how serious this problem is."
Natalia Figueredo, Ironbound Community Corporation
"We have been sold the myth that recycling is the solution to plastics and the waste that it produces."
Akira Yano, Minnesota Environmental & Climate Justice Table
"The life cycle of plastic is harming communities all along the way; from extraction, to refinery, to single-use litter, to incineration."
KT Andresky, Breathe Free Detroit
Over 65% of the plastic collected through the single-stream program in Baltimore is trash, and likely ends up in the incinerator, threatening the health of the surrounding community. South Baltimore Community Land Trust (SBCLT) is working to change neighborhoods from dumping grounds surrounded by polluting industries to healthy zero waste communities. As a result of SBCLT's work, in collaboration with other local organizations and institutions, the Baltimore City Council unanimously adopted the Fair Development Plan for Zero Waste.
About SBCLT
Image by South Baltimore Community Land Trust
After decades of community activism, the Detroit Renewable Power Incinerator announced the facility's immediate closure. Now that an end has been put to municipal waste incineration in Detroit, Breathe Free Detroit in collaboration with grassroots groups is working to build new zero waste systems for the city. One major hurdle is that only 1.3% of plastic collected in single-stream is recyclable, and residents have to foot the bill for the resulting waste.
About Breathe Free Detroit
Image by Breathe Free Detroit
East Los Angeles, Southeast Los Angeles, and Long Beach have been plagued with two of the three incinerators in California, as well as several oil and plastic production facilities. East Yard Communities for Environmental Justice (EYCEJ) is a community-based environmental health and justice organization that has succeeded in shutting down one incinerator, and is advocating for a zero waste plan that will eliminate single-use plastic and build a network of reuse, refill, and repair shops across the city and a transition away from fossil fuel extraction, refining, and distribution.
About EYCEJ
Image by East Yard Communities for Environmental Justice
Of all of the cities in the study, Minneapolis has the most effective municipal recycling program, due to strong citizen advocates and the work of mission-based recycler Eureka Recycling to highlight the importance of recycling with the goal of waste reduction. Nevertheless, a lot of non-recyclable plastic is still sent to the incinerator, located near where the majority of Minneapolis' Black population lives, which has the highest asthma rate in the state. The Minnesota Environmental & Climate Justice Table is working to show the county that Minneapolis doesn't have to choose between burning and dumping its trash because zero waste is possible, feasible, and affordable.
About Minnesota ECJT
Image by Grounding Minnesota
In Newark, the Essex County Resource Recovery incinerator burns about 2.8 tons of waste per day. It emits more lead into the air than any other U.S. incinerator, in addition to dozens of other toxic chemicals. At least 89.2% of plastic collected is incinerated. The Ironbound Community Corporation (ICC) has been fighting incineration and all other major sources of pollution for more than forty years. Last year, ICC succeeded in passing an environmental justice bill for New Jersey designed to prevent the siting of new industrial facilities or the expansion of current facilities in communities like Newark that are already overburdened with pollution.
Image by Ironbound Community Corporation


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Home / Moving Companies / Texas / El Paso
Moving Companies in El Paso, Texas
Save time and money when you get multiple instant quotes from El Paso moving companies. Our painless process takes less than 5 minutes from start until quotes and mover contact details are displayed. We don't share your information with any moving companies. You choose the best deal.
Get to know El Paso, TX
The city of El Paso sits on the far western corner of Texas on the Rio Grande, across the border from Ciudad Juarez. The area was originally home to maize farmers. As the Spanish and Mexican immigrants began to inhabit the area, they were incorporated into the Mestizo culture. The region felt little impact with the Texas Revolution in 1836. The village was self-governing, with both Mexican and Texan government representatives negotiating until Texas took firm control of the area in 1846. The Treaty of Guadalupe Hidalgo of 1848 made El Paso an official American settlement, and the Compromise of 1850 put the city in the Texas boundary. The town grew after the Civil War and was incorporated in 1873. The arrival of the railroads in 1881 brought lawless residents to the town, leading the nickname "Six Shooter Capital" before the area was pressured by authorities to fix its vice problem. The city grew with Prohibition-era bootlegging, but the subsequent agricultural economic depression before the Great Depression hit El Paso hard. With the onset of World War II, military expansion in the area as well as the discovery of oil in the Permian Basin help pull the economy up. While the post-war expansion slowed by the 1960s, the city experienced continued growth as more land was annexed and because of the significant economic relationship with Mexico. In 2010 El Paso was awarded the title of an All-American City and has been considered one of the safest cities in the country for decades. The city has a large military presence, with Fort Bliss, William Beaumont Army medical Center, and Biggs Army Airfield all located in El Paso. It is also the medical hub of West Texas and Southern New Mexico. Popular attractions include Franklin Mountains State Park, the largest urban park in the country, Ysleta Mission, the oldest continuously operated parish in Texas. In addition to its many arts and music festivals, the city has been the backdrop for a number of movies, including Django Unchained (2012) and No Country for Old Men (2007). The city also is host to the second oldest consecutive college football contest, the Sun Bowl.
El Paso Statistics
It looks like everyone is moving to El Paso, with 442 people moving to the city in 2015. This amounts to a 0.06% population increase.


"Britain 1901–1945." History, British history, Q-files Encyclopedia, 3 Oct. 2013.
History, British history, s.v. "Britain 1901–1945," accessed April 21, 2019.
Britain entered the 20th century as the most powerful and richest country in the world. It had a huge worldwide empire defended by a large, powerful navy. Over the next 45 years, Britain faced major changes in the way it was governed and the way its people were represented. There was also to be a catastrophic downturn in the economy and total involvement in two world wars, which changed the world—and life in Britain—for good.
At the start of the 20th century, women could not vote in parliamentary elections or stand as an MP. Campaigners for women's suffrage—the right to vote—had set up groups in most major towns that combined to form the National Union of Women's Suffrage Societies. Many women, however, thought that its peaceful methods were failing. In 1903 a new militant organization, the Women's Social and Political Union, was set up in Manchester and led by Emmeline Pankhurst and her daughter Christabel. Members of this new group were called suffragettes and would use any means necessary, including violence, to get the vote.
In 1906, the British government passed the Education (Provision of Meals) Act, giving schools the right to provide free lunches to children from poor families for the first time.


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HomeRetail NewsColin's Opens First Store in Serbia
Colin's 1st Store in Serbia at Ada Mall in Belgrade
Colin's, one of the most popular fashion brands from Turkey, has finally arrived in Serbia with the opening of its first store in Ada Mall.
The flagship store, located on the first floor of Ada Mall in Belgrade, consists of 300 sqm. Its assortment includes a wide range of high-quality jeans and casual fashion for the whole family.
Colin's Serbia Flagship Store Celebration
Colin's Store Opening at Ada Mall in Belgrade
The opening was celebrated with many famous guests including the Ambassador of Turkey, Hami Aksoy, who wished Ada Mall and Colin's a successful collaboration. Other guests included local fashion influencers such as Anastasija Djuric, Milan Maglov, and Nela Buncic Vukomanovic.
Colin's Store at Ada Mall in Belgrade
Colin's is a leading brand among younger consumers, that is constantly expanding its global reach. Today the brand has more than 620 stores worldwide, and now Serbia is on its map.
On this topic, Yavuz Eroglu, Colin's Chief Executive Officer, stated at the opening:
"We serve our customers across 620 points of sales worldwide. The fact that we are a brand especially admired by younger consumers encourages us to continue to expand to new countries. We are now turning our route further west and are excited to have opened our first store in Serbia. We hope that our fashion collections will be appreciated here and that we will become more accessible countrywide as we continue to rapidly expand our store network".
Alex Linchev, Manager of Ada Mall, also expressed his appreciation for Colin's choosing Ada Mall as the perfect location for their first store in Serbia.
"By listening to the needs of the market and analyzing our target group, which are mainly young and stylish millennials, we believe Colin's is a perfect match for us. Our mall offers a receptive mix of style, leisure, fun and good energy, and I think that is exactly what attracted Colin's to become part of our center".
About Ada Mall
Ada Mall in Belgrade, Serbia
Ada Mall, developed by GTC Group, is a next-generation shopping center built on Radnička Street in Belgrade, opposite the popular recreational zone Ada Ciganlija.
The mall opened in 2019 and was a 105 million EUR investment. With a gross leasable area of 34,000 sqm on five levels, it offers a unique shopping experience. The complex is well-connected to public transport and features three floors of underground parking with approximately 1,000 parking spaces.
More about Ada Mall: www.adamall.com
Ada Mall
Ada Mall Breaks Record with 2022 Performance