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Sri Lanka Journal of Psychiatry
Reading: Koro, psychological retraction of the genitalia: A case series
Brief Reports
Koro, psychological retraction of the genitalia: A case series
A. Ellepola
Teaching Hospital, Anuradhapura, LK
Koro has been found to exist mainly in Asia and Africa. Some authors describe the syndrome as a universal syndrome, rather than a condition found only in certain cultures. It is not uncommon to find koro presenting with comorbid conditions and psychopathology relevant to these conditions. The cases in this article illustrate three different presentations of Koro in Sri Lanka, where the affected males believed that their worth was associated with fertility and sexual potency. One patient had comorbid schizophrenia, and other two had anxious personality traits.
Keywords: Koro, Culture-bound syndromes, Anxiety, Genitalia
How to Cite: Ellepola, A., 2021. Koro, psychological retraction of the genitalia: A case series. Sri Lanka Journal of Psychiatry, 12(2), pp.41–43. DOI: http://doi.org/10.4038/sljpsyc.v12i2.8304
Ellepola, A., 2021. Koro, psychological retraction of the genitalia: A case series. Sri Lanka Journal of Psychiatry, 12(2), pp.41–43. DOI: http://doi.org/10.4038/sljpsyc.v12i2.8304
Ellepola A. Koro, psychological retraction of the genitalia: A case series. Sri Lanka Journal of Psychiatry. 2021;12(2):41–3. DOI: http://doi.org/10.4038/sljpsyc.v12i2.8304
Ellepola, A. (2021). Koro, psychological retraction of the genitalia: A case series. Sri Lanka Journal of Psychiatry, 12(2), 41–43. DOI: http://doi.org/10.4038/sljpsyc.v12i2.8304
1. Ellepola A. Koro, psychological retraction of the genitalia: A case series. Sri Lanka Journal of Psychiatry. 2021;12(2):41-43. DOI: http://doi.org/10.4038/sljpsyc.v12i2.8304
Ellepola A, 'Koro, Psychological Retraction of the Genitalia: A Case Series' (2021) 12 Sri Lanka Journal of Psychiatry 41 DOI: http://doi.org/10.4038/sljpsyc.v12i2.8304
Ellepola, A.. 2021. "Koro, Psychological Retraction of the Genitalia: A Case Series". Sri Lanka Journal of Psychiatry 12 (2): 41–43. DOI: http://doi.org/10.4038/sljpsyc.v12i2.8304
Ellepola, A.. "Koro, Psychological Retraction of the Genitalia: A Case Series". Sri Lanka Journal of Psychiatry 12, no. 2 (2021): 41–43. DOI: http://doi.org/10.4038/sljpsyc.v12i2.8304
Ellepola, A. "Koro, psychological retraction of the genitalia: A case series". Sri Lanka Journal of Psychiatry, vol. 12, no. 2, 2021, pp. 41–43. DOI: http://doi.org/10.4038/sljpsyc.v12i2.8304
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Published by Sri Lanka College of Psychiatrists


How do dung beetles like their dung? Stinky, and from omnivores like us, a new study says.
Scientists already know a lot about the excrement-eating insects' preference for dung types and conditions. But a University of Nebraska team got curious about what dung beetles in North America's Great Plains would do with waste from more exotic animals, such as zebra, waterbuck, or moose. These species live on game farms in the region.
Dung and carrion beetles feed on a rotten rat. Photograph courtesy W. Wyatt Hoback.
For their experiment, Sean D. Whipple and co-author W. Wyatt Hoback, of the University of Nebraska at Kearney, baited pitfall traps with various types of poop, including native and exotic carnivores, herbivores, and omnivores. Some of the traps also contained carrion. Over two years, the traps captured 9,089 dung beetles from 15 different species, allowing the scientists to record which dung was most attractive.
A species of dung roller in the genus Melanocanthon. Photograph courtesy Marlin E. Rice.
To the scientists' surprise, Nebraskan dung beetles preferred exotic-animal dung over dung from native species. Also intriguing was that closely related species of beetle seemed to have differing preferences for carrion and dung, Whipple said. It may be that the beetles are "segregating out their behavior"—in other words, avoiding competition with each other.
During their field research, Whipple and Hoback would also sometimes encounter other species that had been drawn to the dung. "It's always interesting when you flip open the lid [of the trap] and a rattlesnake is there," Whipple said.
Overall, dung beetles are often misunderstood, added Whipple, whose study appeared recently in the journal Environmental Entomology.
"People think of dung beetles as the ones rolling the balls in Africa," he said. "One of the common questions I get is, We have those here? Nebraska has about 50 species," he said.
Two male rainbow scarabs (Phanaeus vindex). Photograph courtesy W. Wyatt Hoback.
What's more, dung beetles aren't just dung rollers—they're also dung tunnelers and dwellers. Rollers shape pieces of dung into balls and roll them away from the pile. They bury their ball to either munch on later or to use as a place to lay their eggs. Tunnelers bury their dung treasure by tunneling underneath the pile. And dwellers actually live inside dung piles, according to National Geographic Kids.
Dung beetles range from about a millimeter in length "all the way up to the big guys," Whipple said, which can be more than two inches (five centimeters) long. The insects live in a variety of habitats, from deserts to forests, and can sport horns or a brilliant iridescence, as seen above.
Whipple also noted that the insects perform a crucial "ecosystem service"—science speak for helping the environment stay healthy. That's because dung beetles process a lot of poop that would otherwise pile up. In parts of Texas, for instance, the insects bury about 80 percent of cattle dung, according to Arizona State University.
As they say, it's a dirty job—but someone's got to do it.


The Bob Woodruff Foundation (BWF) and New York Comedy Festival (NYCF) just added Bill Cosby and Jim Gaffigan to the star-studded line-up for the annual Stand Up for Heroes, an evening of comedy and music benefiting injured service members, veterans and their families on Wednesday, November 6th at 8pm.
The event will also feature performances by Jerry Seinfeld, Bruce Springsteen, Jon Stewart and Roger Waters. Roger Waters will take the stage and perform with a band comprised of nearly two-dozen wounded veterans and active servicemen (and women) many of whom are current patients at Walter Reed Hospital and participants in the BWF funded MusiCorps.
Stand Up for Heroes now at The Theater at Madison Square Garden, brings together leaders from military, business, entertainment, government and philanthropy to raise funds to help post-9/11 injured service members and their families thrive long after they return to the home front.
Tickets for Stand Up for Heroes can be purchased at bobwoodrufffoundation.org or www.nycomedyfestival.com. Corporate sponsor packages and VIP benefit packages can be purchased by emailing SUFH@bobwoodrufffoundation.org.
Stand Up for Heroes, brought to you by Veterans on Wall Street (VOWS), a consortium of financial firms, and supported by founding sponsor GFI Group, Inc. is one of the centerpiece events of the 10th annual New York Comedy Festival, running Nov. 6-10, 2013, at venues throughout New York City.
This year's sponsors for Stand Up for Heroes include: VOWS, GFI Group, Inc., The Paul E. Singer Foundation, Google, Inc., Havas PR & Havas Health, Jack Daniels, JetBlue, JWT, Purdue Pharma L.P.
The Bob Woodruff Foundation (BWF) is the nonprofit organization dedicated to ensuring injured service members and their families are thriving long after they return home. A national organization with grassroots reach, the Bob Woodruff Foundation complements the work of the federal government —diligently navigating the maze of more than 40,000 nonprofits providing services to veterans—finds, funds and shapes innovative programs, and holds them accountable for results. To date, BWF has invested more than $15 million in public education and solutions, reaching more than a million service members, support personnel, veterans and their families. The Bob Woodruff Foundation was co-founded in 2006 by award-winning anchor Bob Woodruff and his family, whose own experiences inspired them to help make sure the nation's heroes have access to the high level of support and resources they deserve, for as long as they need it.


text:
require "psd"
require "oily_png"

module PSDNative
  def self.included(base)
    base::Image.send(:include, PSDNative::ImageMode::RGB)
    base::Image.send(:include, PSDNative::ImageMode::CMYK)
    base::Image.send(:include, PSDNative::ImageMode::Greyscale)
    base::Image.send(:include, PSDNative::ImageFormat::RLE)
    base::ChannelImage.send(:include, PSDNative::ImageFormat::LayerRAW)
    base::Color.send(:include, PSDNative::Color)
    base::Util.extend PSDNative::Util

    base::Renderer::ClippingMask.class_eval do
      remove_method :apply!
    end
    base::Renderer::ClippingMask.send(:include, PSDNative::Renderer::ClippingMask)

    base::Renderer::Mask.class_eval do
      remove_method :apply!
    end
    base::Renderer::Mask.send(:include, PSDNative::Renderer::Mask)

    base::Renderer::Blender.class_eval do
      remove_method :compose!
    end
    base::Renderer::Blender.send(:include, PSDNative::Renderer::Blender)
  end
end

require "psd_native/version"
require "psd_native/psd_native"
require "psd_native/compose"

PSD.send :include, PSDNative

redpajama_set_name: RedPajamaGithub


text:
\section{Conclusions and future work}\label{sec:conclusions}

This paper presented the Dissimilarity Mixture Autoencoder (DMAE), a model that reinterprets a mixture model and its optimization through expectation-maximization as a neural network. The experimental evaluation showed that DMAE is very flexible and competitive, allowing the generalization of different mixture distributions according to the selected dissimilarity function. As a deep clustering model, DMAE poses an important implication that rises for most of the deep clustering methods, it is a different perspective for machine learning in which instead of fitting a model to mimic humans' annotations or labels, the model has to learn concepts with the minimal human intervention in a completely unsupervised fashion.

Current approaches are evaluated on controlled and well-known datasets, however, the application of deep clustering in real-life scenarios has not been completely explored. This is important considering the simplicity of the concepts that are in these sorts of problems, the performance on more challenging tasks that require higher human abstractions is still unknown. Future work is aimed to determine manners to reduce the dependencies to the deep autoencoder structure and methods based on metric learning to automatically find an appropriate dissimilarity function for each problem without the need for the extensive exploration of the dissimilarity function.

\subsection{Autoencoder for Expectation-Maximization}\label{sec:ae_em}

EM is an algorithm that iteratively updates the model's parameters until convergence, it requires to determine general expressions for the \textit{E-step} and the \textit{M-step} at any $t$ iteration. As shown in Eq. \ref{eq:e_step}, the \textit{E-step} consists on the computation of a proposal distribution $q^{(t)}$. For models with a discrete number of latent variables the proposal distribution is equal to the posterior distribution $P(\mathbf{Z}|\mathbf{X}; \Theta^{(t)}, \Phi^{(t)})$ that was derived in Eq. \ref{eq:posterior}. Where $\mathbf{X}\in {\rm I\!R^{N \times m}}$ is a matrix in which each row is a feature vector $\mathbf{x}_i$ of size $m$ for the $N$ samples, and $\mathbf{Z} \in {\rm I\!R^{N \times K}}$ is a matrix in which each row represents the latent components $\mathbf{z}_i$ for a sample $\mathbf{x}_i$.
\begin{equation}\label{eq:e_step}
    q^{(t)} = P(\mathbf{Z}|\mathbf{X}; \Theta^{(t)}, \Phi^{(t)})
\end{equation}
 In the \textit{M-step} the parameters are updated by maximizing the expected value of the complete log-likelihood under the distribution $q^{(t)}$ as presented in Eq. \ref{eq:m_step}.
\begin{equation}\label{eq:m_step}
    \Theta^{(t + 1)}, \Phi^{(t + 1)} = \underset{\Theta, \Phi}{\text{argmax}} \quad {\rm I\!E}_{q^{(t)}}[\log{P(\mathbf{X}, \mathbf{Z}; \Theta, \Phi )}]
\end{equation}
The complete likelihood $P(\mathbf{X}, \mathbf{Z}; \Theta, \Phi)$ of the DMM for a set of $N$ samples and with $K$ components takes the form of Eq. \ref{eq:complete_likelihood}.
\begin{equation}\label{eq:complete_likelihood}
    P(\mathbf{X}, \mathbf{Z}; \Theta, \Phi) = \prod_{i=1}^N \prod_{k=1}^K \pi_k^{z_{ik}} \left(\text{exp}(-\alpha d(\mathbf{x}_i, \theta_k))b_k\right)^{z_{ik}}
\end{equation}
Therefore, the complete log-likelihood would be:
\begin{equation}\label{eq:complete_log_likelihood}
    \log{(P(\mathbf{X}, \mathbf{Z}; \Theta, \Phi)}) = \sum_{i=1}^N \sum_{k=1}^K z_{ik} (\phi_k -\alpha d(\mathbf{x}_i, \theta_k))
\end{equation}
Considering that the $z_{ik}$ components are binary random variables and the definition of $q^{(t)}$ presented in Eq. \ref{eq:e_step}, the conditional expectation ${\rm I\!E}_{q^{(t)}}[z_{ik}]$  is equal to the posterior distribution $P(z_{ik}=1|\mathbf{x}_i)$. Hence, the expectation $L=\mathbb{E}_{q^{(t)}}[\log{(P(\mathbf{X}, \mathbf{Z}; \Theta, \Phi)})]$ of the complete log-likelihood takes the form of Eq. \ref{eq:complete_log_likelihood2}.
\begin{equation}\label{eq:complete_log_likelihood2}
    L = \sum_{i=1}^N \sum_{k=1}^K P(z_{ik}=1|\mathbf{x}_i) (\phi_k -\alpha d(\mathbf{x}_i, \theta_k))
\end{equation}
Since the dissimilarity function must be convex, we can formulate a Jensen's inequality as shown in Eq. \ref{eq:jensen}.
\begin{equation}\label{eq:jensen}
    \sum_{k=1}^K P(z_{ik}=1|\mathbf{x}_i)d(\mathbf{x}_i, \theta_k) \geq d\left(\mathbf{x}_i, \sum_{k=1}^K P(z_{ik}=1|\mathbf{x}_i)\theta_k\right)
\end{equation}
Using the inequality of Eq. \ref{eq:jensen} and considering that the complete log-likelihood must be maximized, we can determine a lower bound as shown in Eq. \ref{eq:lower_bound}.
\begin{equation}\label{eq:lower_bound}
    L \geq \sum_{i=1}^N \left( \widetilde{\phi}_i - \alpha d(\mathbf{x}_i, \widetilde{\theta}_k) \right)
\end{equation}
Where the parameters $\widetilde{\theta}_i$ and $\widetilde{\phi}_i$ for a sample $\mathbf{x}_i$ are a convex combination of all the cluster's parameters using the softmax outputs as weights.
\begin{equation}\label{eq:assigned_params}
\begin{split}
    \widetilde{\theta}_i = \sum_{k=1}^K P(z_{ik}=1|\mathbf{x}_i) \theta_k\\
    \widetilde{\phi}_i = \sum_{k=1}^K P(z_{ik}=1|\mathbf{x}_i) \phi_k
\end{split}
\end{equation}
The loss function $\mathcal{L}$ to minimize is shown in Eq. \ref{eq:dmm_loss}, it is based on the lower bound of the complete log-likelihood and the soft-assigned parameters, and is similar to the distortion measure that is typically used in other clustering methods.
\begin{equation}\label{eq:dmm_loss}
    \mathcal{L} = \sum_{i=1}^N \alpha d\left(\mathbf{x}_i, \widetilde{\theta}_i\right) - \widetilde{\phi}_i
\end{equation}
The dissimilarity mixture autoencoder (DMAE) is the reinterpretation of EM procedure as an encoder-decoder network with shared weights that correspond to the DMM's parameters. In DMAE, an encoding process is performed to compute the soft-assignments or the posterior probability $P(\mathbf{z}_i|\mathbf{x}_i)$, which is equivalent to the \textit{E-step}; likewise, reconstructions $\widetilde{\theta}_i$ and $\widetilde{\phi}_i$ are computed as a linear decoding of the latent representation, and a reconstruction error or distortion measure $\mathcal{L}$ is minimized to update the model's parameters, which is equivalent to the \textit{M-step}.

One of the most important properties of DMAE, is that it is composed of differentiable operations, therefore, $\mathcal{L}$ can be minimized through gradient-based optimization. Approaches like batch learning and online learning can be used since the DMM assumes i.i.d. samples, allowing its application to large datasets with controlled memory consumption. In addition, DMAE can be enhanced using unsupervised representation learning, by exploiting back-propagation and the external gradients of other deep learning components.

\section{Dissimilarity Mixture Autoencoder}\label{sec:dmae}

The dissimilarity mixture autoencoder (DMAE) is a neural network model for feature-based clustering that incorporates a flexible dissimilarity function and can be integrated into any kind of deep learning architecture. The formulation of DMAE consists of three main components. First, it internally incorporates a \textit{dissimilarity mixture model} (DMM) that extends classical methods like Bregman clustering to any convex and differentiable dissimilarity function; second, an \textit{autoencoder for expectation-maximization} is proposed as the learning procedure for the DMM; and third, \textit{unsupervised representation learning} is used to extend the model for deep clustering.

\input{dmae/dmm.tex}
\input{dmae/ae_for_em}
\input{dmae/unsupervised_representation_learning.tex}

\subsection{Dissimilarity Mixture Model}\label{sec:dmm}

Mixture models are a probabilistic approach for clustering that allows representing the density of the samples through the combination of a set of $K$ distributions. In this work, the likelihood $P(\mathbf{x}_i|z_{ik}=1)$ of a data point $\mathbf{x}_i\in {\rm I\!R^{m}}$ that belongs to a cluster $k$ is modeled as an exponential distribution as shown in Eq. \ref{eq:likelihood}.
\begin{equation}\label{eq:likelihood}
    P(\mathbf{x}_i|z_{ik}=1; \theta_k) = \text{exp}(-\alpha d(\mathbf{x}_i, \theta_k))b_k
\end{equation}
Where $z_{ik}$ is a binary latent variable that indicates if the sample $\mathbf{x}_i$ belongs to a cluster $k$, $b_k$ is a uniquely determined value that normalizes the likelihood to be a valid probability density, $\alpha$ is a constant that controls the exponential behavior of the likelihood, and $d(\cdot)$ is a dissimilarity function that measures the affinity between $\mathbf{x}_i$ and the parameters $\theta_k$ associated to the cluster $k$. There is a direct connection between exponential families and the proposed likelihood when $d(\cdot)$ is a Bregman divergence \cite{banerjee2005clustering}. Nevertheless, the proposed likelihood distribution considers a more general differentiable dissimilarity function that must be convex to $\theta_k$. Some remarkable examples of functions that can be used as dissimilarity functions are presented in Table \ref{tab:dissimilarities}.

\begin{table}[ht!]
\centering
\caption{Example of differentiable and convex functions that can be used as dissimilarities.}
\begin{tabular}{ccc}
\toprule
    Name & Expression & Parameters\\
    \midrule
    Euclidean & $||\mathbf{x}-\mu_k||$ & $\theta_k=\mu_k$\\
    Manhattan & $|\mathbf{x}-\mu_k|$ & $\theta_k=\mu_k$\\
    Dot product & $\mathbf{x} \cdot \mu_k$ & $\theta_k=\mu_k$\\
    Itakura-Saito & $\sum_j \frac{x_j}{y_j} - \log{\frac{x_j}{y_j}} - 1$ & $\theta_k=\mathbf{y}_k$\\
    Kullback-Leibler & $\sum_{j=1}^m x_j \log{\frac{x_j}{q_j}}$ & $\theta_k=\mathbf{q}_k$ \\
    Mahalanobis & $\sqrt{(\mathbf{x} - \mu_k)^T \Sigma_k (\mathbf{x} - \mu_k)}$ & $\theta_k=(\mu_k, \Sigma_k)$\\
    \bottomrule
\end{tabular}
\label{tab:dissimilarities}
\end{table}

Following a typical mixture model formulation, it is necessary to determine an expression for the posterior cluster assignment distribution $P(z_{ik}=1|\mathbf{x}_i)$, to this end, we consider a marginal prior distribution $P(z_{ik}=1)=\pi_k$ such that $\pi_k \in [0, 1]$ and $\sum_{i=1}^K \pi_k = 1$, where $K$ is the number of clusters. This marginal distribution represents the discrete nature of the $z_{ik}$ components and introduces the mixture coefficients $\pi_k$, which are additional model parameters. The posterior distribution can be determined through the Bayes rule as presented in Eq. \ref{eq:posterior}.
\begin{equation}\label{eq:posterior}
    P(z_{ik}=1|\mathbf{x}_i) = \frac{\text{exp}(-\alpha d(\mathbf{x}_i, \theta_k))\pi_k b_k}{\sum_{j=1}^K \text{exp}(-\alpha d(\mathbf{x}_i, \theta_j))\pi_j b_j}
\end{equation}
The factor $b_k$ can be analytically determined for Bregman divergences \cite{banerjee2005clustering}, nonetheless, this is not the case for an arbitrary dissimilarity function. For this reason, we propose a reparameterization $\phi_k=\log{(\pi_k b_k)}$ to include this factor as a parameter of the model, allowing the optimization over the non-normalized probability distributions. Likewise, the expression presented in Eq. \ref{eq:softmax} shows that the posterior distribution $P(z_{ik}=1|\mathbf{x}_i)$ or the \textit{responsibility} that component $k$ takes over $\mathbf{x}_i$ can be reinterpreted as the Gumbel-softmax distribution \cite{jang2016categorical}. This is important since the softmax $\sigma(\cdot)$ is a well-known function that is commonly used in deep learning.
\begin{equation}\label{eq:softmax}
    P(z_{ik}=1|\mathbf{x}_i) = \sigma(-\alpha d(\mathbf{x}_i, \theta_k)+\phi_k))
\end{equation}
The $\alpha$ value can be also reinterpreted as the \textit{softmax inverse temperature}, which is a hyperparameter that controls the sparcity of the softmax function, higher $\alpha$ values approximate the softmax outputs to one-hot assignments. Similarly, there is a direct connection between this expression and feed-forward neural networks when the dissimilarity function is a negative dot product $d(\mathbf{x}_i,\theta_k)= -\mathbf{x}_i\cdot \theta_k$ and $\alpha=1$, the posterior $P(\mathbf{z}_i|\mathbf{x}_i)$ for all the components $\mathbf{z_i}=[z_{i1}, z_{i2}, \dots, z_{iK}]$ would be equivalent to a single-layer network with a softmax activation function $\sigma(\mathbf{W} \cdot \mathbf{x}_i + \mathbf{b})$, where $\mathbf{W} \in {\rm I\!R^{K \times m}}$ is a matrix that contains the cluster's parameters $\theta_k$ and $\mathbf{b} \in {\rm I\!R^{K}}$ is a vector with the reparameterized mixing coefficients $\phi_k$.

Although the model's parameters $\Theta=\{\theta_1, \theta_2, \dots, \theta_K\}$ and $\Phi=\{\phi_1, \phi_2, \dots, \phi_K\}$ can be estimated through maximum likelihood estimation, a direct optimization of the log-likelihood for a mixture model is not a well-posed problem and suffers from different issues like sample or posterior collapse and identifiability \cite{bishop2006pattern}. Similarly, an analytical expectation-maximization (EM) procedure has been proposed for Bregman divergences \cite{banerjee2005clustering}, nevertheless, there is not an exact procedure for every dissimilarity function. For this reason, the learning procedure of DMAE is formulated as a reinterpretation of \textit{E-step} and the \textit{M-step} as the encoding, decoding, and optimization stages of an autoencoder.

\subsection{Unsupervised Representation Learning}\label{sub:unsupervised_representation_learning}

A conceptual diagram of DMAE for deep clustering is depicted in Fig. \ref{fig:dmae}, it uses a deep autoencoder for unsupervised representation learning and incorporates the DMM in the latent space for clustering. The complete architecture can be divided into four main components: \textit{deep encoder}, \textit{dissimilarity mixture encoder}, \textit{dissimilarity mixture decoder} and \textit{deep decoder}.

\begin{figure}[t!]
    \centering
    \includegraphics[width=1.0\columnwidth]{dmae/figures/dmae.pdf}
    \caption{Conceptual diagram of the dissimilarity mixture autoencoder.}
    \label{fig:dmae}
\end{figure}

\subsubsection{Deep Encoder}

The deep encoder component transforms the input data into a simpler representation or latent space. It defines a mapping $\mathcal{X} \rightarrow \mathcal{H}$ from the original input space $\mathcal{X}$ to a space $\mathcal{H}$ of dimension $R$. This is achieved through several non-linear transformations that must be learnt during training. Specifically, it defines a function $f_1(\cdot)$ that transforms an input sample $\mathbf{x}_i \in {\rm I\!R^{m }}$ to a latent representation $\mathbf{h}_i \in {\rm I\!R^{R}}$ ($R$ is the size of the latent dimension) using a set of weights $W_e$.
\begin{equation}\label{eq:deep_encoder}
    \mathbf{h}_i=f_1(\mathbf{x}_i, W_e)
\end{equation}
\subsubsection{Dissimilarity Mixture Encoder (DM-Encoder)}

This component soft-assigns a latent representation $\mathbf{h}_i$ into the $K$ clusters. It defines a mapping $\mathcal{H} \rightarrow \mathcal{S}$ from the space $\mathcal{H}$ to a latent space $\mathcal{S}$ of dimension $K$. This representation is equivalent to the assignments $P(z_k=1|\mathbf{x}_i)$ of the DMM that are shown in Eq. \ref{eq:posterior} and varies between a uniformly distributed ($\alpha \rightarrow 0$) and a sparse space ($\alpha \rightarrow \infty$). It defines the clusters parameters $\Theta, \Phi$ and requires a pairwise dissimilarity $d_p(\cdot)$ to compute a vector of dissimilarities $\mathbf{d}_i \in {\rm I\!R^{K }}$, such that each value in $\mathbf{d}_i$ represents the dissimilarity $d(\cdot)$ between $\mathbf{h}_i$ and all the cluster parameters $\theta_k \in \Theta$ as presented in Eq. \ref{eq:dissimilarity}.
\begin{equation}\label{eq:dissimilarity}
   \mathbf{d}_i = d_p(\mathbf{h}_i, \Theta)
\end{equation}
The soft-assignments $\mathbf{s}_i\in {\rm I\!R^{K }}$ are hence determined through the softmax activation function $\sigma(\cdot)$, it uses the \textit{softmax inverse temperature} $\alpha$ to control the sparsity and a vector of biases (reparameterized mixing coefficients) $\Phi \in {\rm I\!R^{K }}$ as shown in Eq. \ref{eq:soft_assignments}.
\begin{equation}\label{eq:soft_assignments}
\centering
    \mathbf{s}_i=\sigma(-\alpha \mathbf{d}_i+\Phi)
\end{equation}

\subsubsection{Dissimilarity Mixture Decoder (DM-Decoder)}

The purpose of this component is to compute the assigned cluster's parameters $\widetilde{\theta}_i$ and $\widetilde{\phi}_i$. It defines a mapping $\mathcal{S} \rightarrow \widetilde{\Theta}$ from the sparse space $\mathcal{S}$ into a space $\widetilde{\Theta}$ of the reconstructed parameters. This can be achieved through a convex combination of all the cluster's parameters $\Theta, \Phi$ as it was shown in Eq. \ref{eq:assigned_params}. Likewise, if the parameters $\Theta$ can be structured as a matrix $\Theta \in {\rm I\!R^{K \times m}}$, then the reconstruction would be equivalent to the matrix multiplications that are presented in Eq. \ref{eq:reconstruction}, i.e., each cluster is represented by a single vector $\theta_k$ and a mixing coefficient $\phi_k$.
\begin{equation}\label{eq:reconstruction}
\centering
\begin{split}
    \widetilde{\phi}_i =  \Phi^T \cdot \mathbf{s}_i\\
    \widetilde{\theta}_i = \Theta^T \cdot \mathbf{s}_i
\end{split}
\end{equation}

\subsubsection{Deep Decoder}

This component defines a mapping $\widetilde{\Theta} \rightarrow \widetilde{\mathcal{X}}$ from the reconstructed parameter space $\widetilde{\Theta}$ to a global reconstructed space $\widetilde{\mathcal{X}}$. It defines a function $f_2(\cdot)$ that uses a set of weights $W_d$ to transform $\widetilde{\theta}_i$ into a global reconstruction $\widetilde{\mathbf{x}}_i$ in the original input space.
\begin{equation}\label{eq:deep_decoder}
\centering
    \widetilde{\mathbf{x}}_i = f_2(\widetilde{\mathbf{h}}_i, W_d)
\end{equation}
The complete model can be optimized using a composed loss function as presented in Eq. \ref{eq:deep_loss}. It is a linear combination of two terms: (1) the reconstruction loss $\mathcal{L}_r$, which is a standard loss for autoencoders and consists of the mean squared error between the input sample $\mathbf{x}_i$ and its reconstruction $\widetilde{\mathbf{x}_i}$; and (2) the clustering regularization $\mathcal{L}_c$, which is the DMM loss adapted to the latent space $\mathbf{h}_i$ as it was derived in the section \ref{sec:ae_em}.
\begin{equation}\label{eq:deep_loss}
\begin{split}
    \mathcal{L} &= \lambda_r \mathcal{L}_r + \lambda_c\mathcal{L}_c\\
    \mathcal{L}_r &= \left(\sum_{i=1}^N || \mathbf{x}_i-\widetilde{\mathbf{x}}_i ||^2\right)\\
    \mathcal{L}_c &= \left (\sum_{i=1}^N d(\mathbf{h}_i, \widetilde{\theta}_i) - \widetilde{\phi}_i \right)
\end{split}
\end{equation}

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

\input{experiments/synthetic.tex}
\input{experiments/real.tex}
\subsection{Real data}

Unsupervised representation learning can be used to extend DMAE for deep clustering, especially, a deep representation may be more suitable for real-world problems that can not be easily modeled with a dissimilarity function.   This approach allows DMAE to cluster over deep representations that may contain more meaningful, compact, and simpler patterns. To assess this, experiments are proposed to validate the relationship between the learned clusters and human-assigned labels on image and text tasks, i.e., instead of fitting a model to predict humans' labels, the problem is addressed from an unsupervised perspective and the neural network has to learn the concepts or patterns with the minimal human intervention.

\subsubsection{Datasets}

\begin{itemize}
    \item \textbf{MNIST}: it is comprised 28x28 images of hand-written digits in gray-scale with intensities between $[0, 255]$. It contains a total of 70.000 samples that are equally divided into 10 different categories that correspond to all the possible numbers in the positional numeral system.
    \item \textbf{REUTERS}: it contains around 810.000 English news with labels according to four categories: market, economics, corporate/industrial, and government/social. As in similar studies, the 2.000 most common terms from a sub-sample of 10.000 examples are randomly selected, and each document is represented through TF-IDF.
    \item \textbf{USPS}: this is a dataset of digits that were scanned from envelopes by the U.S. Postal Service. It contains a total of 9.298 gray-scale images with a resolution of 16x16; It has 10 different categories corresponding to the numbers 0-9, the images are centered and contain several font styles.
\end{itemize}

\subsubsection{Experimental settings}

Similar to related studies in deep clustering \cite{aljalbout2018clustering}, We take a fine-tuning approach that consists of three steps: (1) the deep autoencoder is trained to learn a suitable feature map, (2) an embedded representation is computed using the deep encoder and a shallow clustering strategy is used on the latent space to find a set of initial cluster parameters for DMAE, (3) the end-to-end deep clustering model is trained. We consider an architecture that has been used in \cite{xie2016unsupervised, guo2017deep, guo2018deep} as the deep autoencoder, which is a stacked autoencoder of fully-connected layers (ReLu activation function for all layers except for the latent and the output spaces, which are linear) that defines an encoder of dimensions $D$-500-500-2000-$L$ and a decoder of dimensions $L$-2000-500-500-$D$, where $D$ is the size of the input space (784 for MNIST, 2000 for REUTERS and 256 for USPS) and the dimension of the latent space of the deep autoencoder $L$ is explored between $[5, 30]$.

An ablation study is performed to assess the training steps of DMAE, its main purpose is to evaluate different versions of DMAE without deep representation learning and unsupervised fine-tuning. First, a shallow version of DMAE that implements the dissimilarity mixture model and does not contain any transformation layers (i.e., clustering is performed directly on the original input space with different dissimilarity functions), this model is referred to as DMM. Second, a vanilla autoencoder learns offline, a deep embedded space and then the DMM is trained over static representations that are extracted from the deep autoencoder, it is referred to as AE+DMM. Third, the complete end-to-end DMAE model that simultaneously updates the representation and clustering layer.

For the MNIST and USPS datasets, data augmentation (random rotations using random degrees in the range $[-10, 10]$ and random horizontal and vertical shifts of 10\% of the total images' height and width) is used to train the deep autoencoder. For DMAE, the $\alpha$ hyperparameter is explored in the range $[1, 10000]$ and different dissimilarity functions are explored including Kullback-Leibler divergence, Mahalanobis distance, Euclidean distance, and Manhattan distance. We use K-means to initialize the model with the Euclidean dissimilarity and a shallow version of DMAE is trained on latent representations for the remaining dissimilarities. The number of epochs for pretraining and fine-tuning is explored in the range $[50, 500]$, the optimization is performed using the ADAM optimization method with a learning rate that is explored in the interval $[10^{-5}, 1]$, and the weights for the loss function $\lambda_r$ and $\lambda_c$ are explored in $[0, 1]$.

An appropriate combination of hyperparameters is determined via random search due to the large number of possible combinations. The performance is evaluated in terms of the \textit{unsupervised classification accuracy} and the \textit{normalized mutual information} (view Eq. \ref{eq:uacc}-\ref{eq:nmi}), these metrics are reported as the average over 5 different trials with different random parameter initialization for the deep autoencoder and DMAE. Relevant state-of-the-art deep clustering methods that had been already validated in the same datasets using similar experimental settings are used for comparison, including: deep embedded clustering (DEC) \cite{xie2016unsupervised, guo2018deep}, semi-supervised deep embedded clustering (SDEC) \cite{ren2019semi}, variational deep embedding (VaDE) \cite{jiang2016variational}, joint unsupervised learning (JULE) \cite{yang2016joint}, deep clustering network (DCN) \cite{yang2017towards}, SpectralNet \cite{shaham2018spectralnet}, deep embedded regularized clustering (DEPICT) \cite{ghasedi2017deep}, deep density-based image clustering (DDC) \cite{ren2020deep}, convolutional deep embedded clustering \cite{guo2018deep}, deep clustering via a Gaussian mixture variational autoencoder with graph embedding (DGG) \cite{yang2019deep}, ClusterGAN \cite{mukherjee2019clustergan} and deep embedding clustering framework based on contractive autoencoder (DECCA) \cite{diallo2021}.

\subsubsection{Analysis and results}

Table \ref{tab:real} presents the results on the real data. DMAE achieves competitive performances with absolute differences in the ACC lower than 2\% regarding the best method on each case. The best performance is achieved when using the Mahalanobis dissimilarity on MNIST and USPS and the Euclidean dissimilarity on REUTERS. In general, there is not a single method with the best performance on all the datasets, some models have the highest performance in a specific dataset but are not competitive in the others. Likewise, other models like DEPICT, DDC, and ConvDEC are intended for image data only and are not applied to the REUTERS dataset. Although DMAE has not the best performance on every dataset, the results are consistent and both the NMI and the ACC are close to the ones of the best performing methods.

Regarding the ablation study, the DMM achieves performances that are equivalent to other shallow methods like KMeans. Although the DMM is more flexible thanks to the dissimilarity function, the formulation of a function that describes the complex patterns in images and texts is not straightforward. Deep models are more suitable in these cases considering their high representation capabilities, especially, deep embeddings ease the training process through the induction of clustering-friendly spaces. The results achieved with AE+DMM demonstrate this, the performance is highly improved when the DMM is trained with feature representations that are extracted from a deep autoencoder. Lastly, the end-to-end model outperforms both the shallow DMM and AE+DMM, this demonstrates the compatibility that DMAE has with other deep learning components, allowing to simultaneously improve the representations and the overall clustering results.

\begin{table}[t!]\label{tab:real}
\centering
\caption{Average over 5 trials of the unsupervised classification accuracy and the normalized mutual information for the real datasets. Cases with - corrospond to methods that were proposed for images only.}
\begin{tabular}{@{}ccccccc@{}}
\toprule
\multirow{2}{*}{Method} & \multicolumn{2}{c}{MNIST} & \multicolumn{2}{c}{Reuters} & \multicolumn{2}{c}{USPS}\\
& ACC & NMI & ACC & NMI & ACC & NMI \\ \midrule
KMeans & 0.535 & 0.500 & 0.550 & 0.359 &  0.668 & 0.627 \\
DEC \cite{xie2016unsupervised, guo2018deep} & 0.849 & 0.816 & 0.777 & 0.571 & 0.758 & 0.769 \\
SDEC \cite{ren2019semi} & 0.861 & 0.829 & 0.679 & 0.509 & 0.764 & 0.777\\
VaDE \cite{jiang2016variational} & 0.945 & 0.876 & 0.792 & 0.722 & 0.566 & 0.512\\
JULE \cite{yang2016joint} & 0.906 & 0.872 & 0.751 & 0.774 & 0.914 & 0.881\\
DCN \cite{yang2017towards} & 0.830 & 0.813 & 0.792 & 0.814 & 0.778 & 0.853\\
SpectralNet \cite{shaham2018spectralnet}& 0.971 & 0.924 & 0.803 & 0.532 & 0.825 & 0.804\\
DEPICT \cite{ghasedi2017deep} & 0.965 & 0.917 & - & - & 0.964 & 0.927\\
DDC \cite{ren2020deep} & 0.969 & 0.941 & - & - & \textbf{0.977} & 0.939\\
ConvDEC \cite{guo2018deep} & \textbf{0.985} & \textbf{0.960} & - & - & 0.970 & \textbf{0.953}\\
DGG \cite{yang2019deep} & 0.976 & 0.885 & 0.823 & 0.820 & 0.904 & 0.820\\
ClusterGAN \cite{mukherjee2019clustergan} & 0.964 & 0.921 & 0.816 & \textbf{0.824} & 0.970 & 0.930\\
DECCA \cite{diallo2021} & 0.964 & 0.907 & \textbf{0.839} & 0.6343 & 0.773 & 0.805\\
\midrule
DMM & 0.540 & 0.501 & 0.550 & 0.402 & 0.659 & 0.611 \\
AE+DMM & 0.960 & 0.906 & 0.705 & 0.400 & 0.937 & 0.863 \\
\textbf{DMAE} & \textbf{0.984} & \textbf{0.945} & \textbf{0.827} & \textbf{0.580} & \textbf{0.965} & \textbf{0.952}\\
\bottomrule
\end{tabular}
\end{table}

The experimental evaluation showed that data augmentation has an important role when clustering image datasets. This phenomenon was evaluated for DEC \cite{guo2018deep} and also occurs with DMAE. Similarly, the hyperparameter that controls the assignments' sparsity $\alpha$ does not have any meaningful impact in the evaluated metrics, this is due to the assignment strategy presented in Eq. \ref{eq:prediction} which corresponds to a hard assignment of the most likely cluster, nonetheless, this parameter can be useful for uncertainty modeling because it allows variations between uniform and one-hot assignments.

\subsection{Synthetic data}

One of the main advantages of DMAE is that it is a flexible and interpretable model that can be used in several applications and does not depend on additional neural network components. More precisely, different dissimilarity functions are usually enough for a large number of problems, and deep representations can be used to extend the model if the problem complexity requires it. To assess this behavior, some experiments are performed over synthetic benchmark datasets with different geometrical properties, these datasets ease the visualization of clustering regions and allow a visual comparison with conventional clustering benchmark methods.

\subsubsection{Datasets}

\begin{itemize}
    \item \textbf{Painwheel}: it's a dataset that was originally proposed by Johnson et al. \cite{johnson2016composing} and was used to validate the Gaussian mixture variational autoencoder \cite{dilokthanakul2016deep}. It can be generated from the arcs of 5 circles such that each point is obtained from distorted normal distributions with: angular standard deviation of $0.05$, a radial standard deviation of $0.3$, and a distortion rate of $0.25$.
    \item \textbf{Toroidal}: this dataset corresponds to a set of 4 Gaussian blobs with constant variances ($\sigma=0.05$) on the surface of a three-dimensional torous. The points are generated in ${\rm I\!R^{2}}$ and acceptance-rejection sampling is used to transform the points that lie outside the circular boundaries.
    \item \textbf{Moons}: it contains samples from two interleaving half circles. The samples have Gaussian noise with a standard deviation of $0.1$.
    \item \textbf{Circles}: this dataset contains points that are randomly generated around two centered circles with different sizes. The points have a standard deviation of $0.1$, and there is a factor of $0.1$ between the radius of the inner and outer circles.
\end{itemize}

\subsubsection{Experimental settings}

A total of $1000$ samples are generated for each dataset. DMAE is trained to find a number $K$ of clusters that corresponds to the number of groups defined by each generative process. Different dissimilarity functions and autoencoder architectures are used for each problem, especially: the Mahalanobis dissimilarity is used for the \textit{five arcs} data; a pairwise Euclidean distance that considers boundary constrains is used for the \textit{toroidal}; and a stacked autoencoder of dimensions $2-256-256-100-256-256-2$ is trained to transform the \textit{circles} and \textit{moons} data before the training of an end-to-end model (view Figure \ref{fig:dmae}) with DMAE in the layer of $100$ dimensions. The $\alpha$ parameter is explored between $\alpha \in [0.5, 1000]$, the learning rate is explored in a logarithm scale $lr \in [10^{-5}, 10^{-3}]$, the batch size is $32$ in all the cases and the number of epochs is explored between $[40, 250]$ for both pretraining (if applies) and training. A point is assigned to a cluster according to the rule presented in Eq. \ref{eq:prediction}, which considers the highest posterior probability or responsibility as presented in Eq. \ref{eq:posterior}.
\begin{equation}\label{eq:prediction}
    \hat{y}_i = \underset{k}{\text{argmax}}(P(z_{ik}| \mathbf{x}_i))
\end{equation}
The proposed model is compared against conventional feature based-methods like KMeans; Gaussian mixture model (GMM); and clustering with Bregman divergences (BC) using the Euclidean, Mahalanobis, squared Manhattan, and Kullback-Leibler divergences. Also, a similarity-based clustering method like spectral clustering (SC) is used due to its capability to solve problems with different geometrical properties, in this regard, the affinity matrix is computed using the nearest neighbors approach that uses the dissimilarity function of DMAE as the affinity score between samples. For the experiments, each model is trained 10 times with different initial parameters and the performance is mainly evaluated using the mean and standard deviation of two metrics. First, the \textit{unsupervised classification accuracy} (ACC) that is shown in Eq. \ref{eq:uacc}, which is a metric that has support in $[0, 1]$, such that higher values represent a better classification. This metric is defined as the number of cluster assignments or predictions $\hat{\mathbf{y}}$ that better describe the ground truth $\mathbf{y}$, and is calculated using a linear mapping $g(\cdot)$ that determines the best match between the unsupervised predictions and the original labels, and an indicator function ${\rm I\!I}(\cdot)$ that counts the number of matches over the $N$ samples.
\begin{equation}\label{eq:uacc}
    ACC = \frac{1}{N} \sum_{i=1}^N {\rm I\!I}(y_i = g(\hat{y}_i))
\end{equation}
Second, the \textit{normalized mutual information} (NMI) is a metric that ranges between $[0, 1]$, where higher values represent a higher correlation between two categorical variables. It is a measure of the mutual information $I(\cdot)$ between the assignments $\hat{\mathbf{y}}$ and the ground truth $\mathbf{y}$ and is normalized using the entropy $H(\cdot)$ of each variable as in Eq. \ref{eq:nmi}.
\begin{equation}\label{eq:nmi}
    NMI = \frac{2 I(\mathbf{y}, \hat{\mathbf{y}})}{H(\mathbf{y}) + H(\hat{\mathbf{y}})}
\end{equation}

\subsubsection{Analysis and results}

The results are presented in Table \ref{tab:synthetic} and a visual comparison between DMAE and the baseline methods is presented in Fig. \ref{fig:synthetic_results}. These results show that DMAE can successfully capture the geometrical properties in all the cases, whereas the other feature-based benchmark methods can describe specific patterns that may not be suitable for all the datasets. Especially, a linear method like KMeans has an acceptable behavior on the \textit{painwheel} dataset, although a linear decision boundary is not the best for this case. DMAE presents a good fit using the Mahalanobis distance, while equivalent models like the GMM and BC with a Mahalanobis divergence do not present a similar result. This is a phenomenon that was detailed by Johnson et al. \cite{johnson2016composing} and is a problem that occurs with conventional density-based methods. Likewise, feature-based benchmarks are not appropriate for the \textit{toroidal blobs} dataset since these models assume distances or divergences that are unsuitable for circular data. DMAE can solve this problem because it uses a general dissimilarity function that is only constrained to be convex, therefore, it can easily consider circular boundaries without requiring a rigorous divergence formulation.

\begin{table}[ht!]
\centering
\caption{Average over 10 trials of the unsupervised classification accuracy and the normalized mutual information for the synthetic datasets.}
\begin{tabular}{@{}ccccccccc@{}}
\toprule
\multirow{2}{*}{Method} & \multicolumn{2}{c}{Painwheel} & \multicolumn{2}{c}{Toroidal} & \multicolumn{2}{c}{Moons} & \multicolumn{2}{c}{Circles}\\
 & ACC & NMI & ACC & NMI & ACC & NMI & ACC & NMI\\ \midrule
 KMeans & 0.982 & 0.943 & 0.600 & 0.495 & 0.751 & 0.190 & 0.703 & 0.245\\
 GMM & 0.908 & 0.834 & 0.585 & 0.534 & 0.850 & 0.409 & 0.618 & 0.169\\
 BC & 0.908 & 0.825 & 0.565 & 0.506 & 0.860 & 0.415 & 0.625 & 0.201\\
 SC & \textbf{0.985} & 0.951 & \textbf{1.000} & \textbf{1.000} & 0.878 & 0.560 & \textbf{1.000} & \textbf{1.000}\\
 \midrule
 \textbf{DMAE} & \textbf{0.985} & \textbf{0.964} & \textbf{1.000} & \textbf{1.000} & \textbf{0.949} & \textbf{0.921} & \textbf{1.000} & \textbf{1.000}\\
\bottomrule
\end{tabular}
\label{tab:synthetic}
\end{table}

Feature-based benchmark methods do not behave well in the \textit{moons} and \textit{circles} datasets, this occurs because these cases are non-linearly separable and shallow latent variable models with typical divergences are insufficient. In contrast, a similarity-based method like SC achieves higher overall performances in comparison with feature-based methods in exchange for higher computational and memory complexities, this poses a problem since it does not scale to larger datasets. The results also show that SC is sensitive to outliers, especially if noisy points connect different clusters as it occurs with the \textit{moons} case. DMAE can cluster both cases using deep representation learning, it can automatically learn an appropriate feature map for clustering and does not require the expensive computation of an affinity matrix since it only requires feature representations as input. DMAE presents a trade-off between interpretability and performance, therefore, it can deal with non-linear cases but the learned cluster's parameters lie in a deep embedded space that may not have a human-readable meaning.

\begin{figure}[t!]
    \centering
    \includegraphics[width=1.0\columnwidth]{experiments/figures/synthetic_results.pdf}
    \caption{Clustering regions for DMAE and benchmark methods over synthetic datasets.}
    \label{fig:synthetic_results}
\end{figure}

\section{Introduction}

Unsupervised learning (UL) aims to automatically extract meaningful patterns from unlabeled data, it covers different tasks like clustering, density estimation, dimensionality reduction, anomaly detection, data generation, among others. In recent years, deep learning has been used as an important approach for UL, specifically, neural networks are able to automatically learn high-level abstractions of the data through unsupervised representation learning. Some remarkable examples include: autoencoders (AE), which are among the most studied neural networks and have demonstrated to outperform conventional shallow approaches in the task of dimensionality reduction \cite{hinton2006reducing}; generative models like the Generative Adversarial Networks (GANs) \cite{goodfellow2014generative} or Variational Autoencoders (VAE) \cite{kingma2013auto}, that show impressive results in the generation of image data; and deep clustering methods, which show that a neural network with the appropriate layers and certain regularizers can outperform conventional clustering methods \cite{xie2016unsupervised}.

This paper addresses the task of clustering, its main purpose is to divide data points into groups or clusters, such that, points in the same group are similar and points in different groups are dissimilar. Clustering methods can be divided into two main categories \cite{jiang2016variational}: \textit{similarity-based clustering}, in which a similarity matrix between points must be computed, different similarity criteria allow a better approximation of the cluster's shapes or densities and includes methods like hierarchical clustering, K-Medoids, spectral clustering \cite{ng2002spectral} and affinity propagation \cite{frey2007clustering}. On the other hand, \textit{feature-based clustering} which allows estimating the clusters using a feature representation of each sample while assuming independent and identically distributed (\textit{iid}) samples, it includes methods like K-means, Gaussian mixture models (GMM), clustering with Bregman divergences \cite{banerjee2005clustering} and the recent deep clustering methods \cite{xie2016unsupervised}.

This paper presents the dissimilarity mixture autoencoder (DMAE), a deep neural network model for feature-based clustering that incorporates a dissimilarity function that preserves the flexibility of similarity-based clustering. To this end, DMAE internally represents a mixture of exponential distributions as an encoder-decoder architecture that allows parameter estimation using gradient-based optimization. DMAE reinterprets classical probabilistic notions as neural network components, allowing easy incorporation into deep learning architectures. Our main contributions can be summarized as:

\begin{itemize}
    \item DMAE: a deep clustering model based on a differentiable and convex dissimilarity function between samples and mixture components that can be applied to deeply embedded spaces. It extends clustering methods like K-means, Gaussian mixture models, or clustering with Bregman divergences \cite{banerjee2005clustering} using a general dissimilarity function and deep representation learning.
    \item The proposed model is an encoder-decoder network that uses probabilistic neural network representations and can be used alongside other deep learning components. This allows scalable parameter estimation through gradient-based optimization, online learning, and modern strategies like transfer learning.
    \item Due to its formulation based on dissimilarity functions, DMAE can fit data that comes from different probability distributions while preserving parameter's interpretability, some examples include circular, angular, probability spaces, among others. Likewise, if the problem demands a higher complexity, the proposed model can be extended with deep learning components, performing a trade-off between interpretability and performance.
\end{itemize}

The remainder of this document is organized as follows: Section \ref{sec:related_work} presents related work in clustering and deep clustering; Section \ref{sec:dmae} introduces DMAE and presents its probabilistic motivation; Section \ref{sec:experiments} describes the experimental evaluation and presents the results; Section \ref{sec:conclusions} shows the final remarks and the future works.

\subsection{Feature-based Clustering}

Feature-based methods assume \textit{i.i.d} samples and perform clustering over a feature representation of each sample, therefore, inter-sample relationships are not considered. Most feature-based strategies can be represented as latent variable models, in which the problem lies in the estimation of hidden or unknown cluster parameters. The simplest case is K-Means, which aims to determine a set of $K$ latent centroids by iteratively minimizing the mean squared error between samples and the latent centroids, it is one of the most popular clustering methods due to its simplicity and its scalability, allowing distributed and parallel computation to ease the training on large datasets. The main disadvantages of K-Means are the linear behavior and the hard-assignment approach, which in most cases do not allow the capture of relevant data patterns or represent uncertainty. For this reason, more general methods have been proposed, for instance, fuzzy c-means \cite{bezdek1984fcm} extends K-Means for soft clustering, it assigns each sample to all the clusters using a weight that is computed from normalized Euclidean distances. Other methods have a probabilistic formulation, the most popular is the Gaussian Mixture Model (GMM) \cite{reynolds2009gaussian}, which soft-assigns each point to each cluster according to a posterior distribution that is computed from normal cluster likelihoods and multinomial priors. The feature-based method that is more closely related to our proposed model is Bregman Soft Clustering \cite{banerjee2005clustering}, it is a model that soft-assigns each sample through a mixture of exponential distributions and generalizes other methods when choosing different divergences, e.g., K-Means is equivalent to a Euclidean distance, GMM is equivalent to the Mahalanobis distance (GMM), among others. It is a flexible model that estimates its parameters through a procedural algorithm based on expectation-maximization and can use different similarity functions whereas they can be expressed as a Bregman divergence. Like similarity-based methods, the importance of Bregman clustering lies in the flexibility of the selection of different Bregman divergences, without requiring the computation of a sample-wise affinity matrix.

In recent years, deep clustering methods have gained a special interest in feature-based clustering, these methods use a deep neural network (DNN) to transform the data into simpler and cluster-friendly representations \cite{aljalbout2018clustering}, it aims to induct a latent space through several non-linear transformations where clustering is performed. This is useful if the clustering strategy allows differentiation since a DNN can be fine-tuned through back-propagation to improve the clustering results. A pioneer work in deep clustering is the Deep Embedded Clustering (DEC) \cite{xie2016unsupervised}, it uses an autoencoder to transform the data into a low-dimensional space and an assignment distribution layer to refine the latent space for clustering. The original method consisted of a stacked autoencoder of fully-connected layers for the representation and a t-student distribution for the cluster assignments, however, recent studies have presented improved versions that include convolutional autoencoders \cite{guo2017deep}, boosting \cite{li2018discriminatively} and data augmentation \cite{guo2018deep}. DEC has demonstrated that deep clustering can outperform conventional shallow approaches in different tasks, this has generated a new line of research in both deep learning and feature-based clustering.

The core of current deep clustering methods is the assignment strategy, i.e., determining a specific operation to compute which cluster is more suitable for each data point while allowing backpropagation. For instance, the t-student distribution has been widely used, especially, it can be seen as a normalized Euclidean similarity that is able to represent the K-Means behavior. An alternative is a multinomial logistic regression or SoftMax function, it is typically used in deep learning and allows to approximate the one-hot assignments or the argmax function under certain constraints \cite{agustsson2017soft}. This last approach was explored in the Deep Embedded Regularized Clustering (DEPICT) \cite{ghasedi2017deep}, which incorporates additional regularization terms to allow a uniform distribution of the assignments. Similar studies in vector quantization have explored this idea in detail, for instance, the soft-to-hard vector quantization method \cite{agustsson2017soft} uses a softmax function over euclidean distances as a relaxation to the nearest neighbor assignments that are typically performed in vector quantization. Generative models have been also proposed for deep clustering. For instance, the Variational Deep Embedding (VaDE) \cite{jiang2016variational} and the Gaussian Mixture Variational Autoencoder (GMVAE) \cite{dilokthanakul2016deep} use a process that generates random samples in the deep embedded space from multivariate normal distributions. In a similar manner, adversarial-based approaches like the InfoGAN \cite{chen2016infogan} and the ClusterGAN \cite{mukherjee2019clustergan} have been successfully applied.

Although most of the deep clustering methods are related to feature-based clustering, some works have extended similarity-based methods for deep clustering. For example, the Joint Unsupervised Learning (JULE) \cite{yang2016joint} uses an agglomerative loss that requires an affinity matrix to represent the similarities between each point in the dataset. The SpectralNet \cite{shaham2018spectralnet} is a neural network model that can learn the spectral map that is computed in spectral clustering, also, it uses a siamese network to learn a similarity function between points. Finally, deep clustering via a Gaussian mixture variational autoencoder with graph embedding (DGG) \cite{yang2019deep} is a generative model that extends VaDE, it uses a graph embedded affinity matrix that is also constructed using a siamese network. These methods combine the flexibility of similarity-based clustering and the representation learning capabilities of neural networks, nevertheless, they still require to compute or approximate an affinity or similarity matrix.

\section{Related Work}\label{sec:related_work}

The performance of clustering models is highly dependent on the topology and the properties of the input data, for instance, clusters may form different geometrical shapes (e.g., circular, elliptical, angular, among others) or may be composed of different types of variables (e.g., continuous, discrete, among others). Different clustering strategies are more suitable for different problems, nevertheless, they can be formulated under two main paradigms as it will be described in the following sections.

\input{related_work/similarity_based.tex}
\input{related_work/feature_based.tex}

\subsection{Similarity-based Clustering}

These methods rely on the selection of a similarity function that is used to compute an affinity matrix that represents how similar are different samples. One of the main advantages of similarity-based methods is their flexibility, different similarity functions can be used according to the complexity of the data. For instance, the K-Medoids \cite{park2009simple} method considers a sample-wise similarity matrix to determine a set of $K$ samples as clusters descriptors, likewise, affinity propagation \cite{frey2007clustering} performs a similar task but automatically determines the optimal number of clusters. There are other methods like spectral clustering \cite{ng2002spectral} or hierarchical clustering \cite{johnson1967hierarchical} which also require an affinity matrix to perform spectral decomposition of Laplacian matrices or to determine association rules.

Similarity-based strategies are usually the best option for small datasets since they have the flexibility to describe individual relations between samples. Nevertheless, the main problem of these methods is the computation of the affinity matrix, which has high time and memory complexities and does not scale well to larger datasets. One of the most interesting similarity-based clustering methods is kernel K-Means \cite{dhillon2004kernel}, which is a method that follows a feature-based formulation and results in a similarity-based method due to the kernel trick. Kernel K-Means has demonstrated to generalize other clustering methods like K-Means, spectral clustering, among others \cite{dhillon2004kernel,ye2008discriminative}, however, it has the same scalability issue since it requires the computation of a Gram matrix. Some alternatives have been proposed to address this issue, including the Nystr\"om approximation \cite{wang2019scalable}, random Fourier features \cite{chitta2012efficient}, randomized matrix factorization, among others. However, this is still an active research problem and has not been fully explored.

redpajama_set_name: RedPajamaArXiv


text:
Q: How to fix: unable to get index of element in a set If I have a set of strings:
std::set<string> my_set;

And I want to find the index of an element in this,
I did:
auto it = std::find(my_set.begin(), my_set.end(), string_to_find);
auto index = std::distance(my_set.begin(), it);
for ( auto s : store){
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where store is a vector of class<Store> which contains the string name as a field.
What am I doing wrong, how else does one get the index of an element of a set?
I currently get the error:
error: no match for 'operator[]' (operand types are 'std::set<std::__cxx11::basic_string<char> >'

A: As the error message suggests, std::set doesn't have operator[].
You have already got an iterator, so you can use that to access an element.
auto it = std::find(my_set.begin(), my_set.end(), string_to_find);
if (it != my_set.end()){
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A: std::set does not implement the operator[] because it does not store elements contiguously and there is no way to access it directly through index.
Change
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to
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should work.

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Online learning that enables flexible blended delivery for BTEC Higher National Qualification. All unit content are delivered both online and within the classroom. Students can study all critical course content via a self-paced online course. The course can be studied alongside full time employment, family commitments or any other commitments which doesn't allow you to sacrifice two full days to study.


We have a 5 step film transfer process at Rocky Mountain Audio Video Productions, Inc. to transfer your film to DVD, Blu-Ray or digital files.
Once we receive your film, our process starts with careful cleaning of your film to remove any dust or debris, and exam your film for any splices that need repair. This step also helps lubricate your film, so your film will be returned to you in better shape then when we received them! This is a very important step, which if skipped, can result in a poor image transfer. We perform 8 mm film transfers, Super 8mm, and 16 mm film to DVD, Blu-Ray and digital files.
For 8mm and Super 8mm film, we will combine all your shorter reels onto a larger 400' reel. All your original reel and boxes will be returned to you. Please let us know at the time of your order if you don't want us to combine your reels. With 16mm film we will NOT consolidate your smaller reels onto a larger reel.
We sill scan your 16mm, 8mm, and Super 8mm film at a native resolution of your choice - 2K (2048 x 1536) or HD (1920 x 1080). All of our film is scanned FRAME by FRAME! What this means is that we are scanning every frame of your film, and creating a high resolution digital image of every frame of your film. We'll end up with thousands of these images, that will result in a stunning transfer that will capture the smallest details with amazing clarity and color. *Some other film transfer companies use systems that project onto a mirror or screen that will produce blurry images with multiple frames overlapping one another. To guarantee the best possible transfer, we ensure each frame of your film corresponds to an individual digital file with our Frame by Frame transfers!
Handling of your film: Your film is old and precious! Our telecine film path handles the film ONLY by the edges for maximum film safety. No sprockets, no advance claws and no pinch rollers to possibly damage your film! Utilizing an enlarged film gate, we capture the entire frame of film - nothing lost and no over cropping. Most of all, the light source in our systems is a "no heat" diffused ultra-cool LED light source, so your film will never be burned or stressed by a hot lamp.
As film ages and deteriorates, it can oxidize and create a blue or pink hue to the images. We offer color correction for an additional $.10/ft. We recommend this service during your film transfers, since old films just start to chemically deteriorate due to age.
Before we output your transfer to disc or hard drive, our technicians will thoroughly quality check all aspects of your job. Many people don't realize the process of outputting your film transfer to DVD or Blu-Ray is just as important as the transfer process! We go the extra mile and Author your DVD or Blu-Ray (complete with a custom intro), and don't use an inexpensive dvd/blu-ray recorders that can over compress and degenerate your high quality film transfer. Our authoring process is a time consuming effort that includes using broadcast software to control all aspects of encoding your digitally scanned film to DVD or Blu-Ray, so it will look it's absolute best when viewed.
This will be a 2K or HD Quick Time Movie (motion JPEG-A) for video, if your film has a sound track WAV files will be created for audio.
Note – Film Transfers with Audio: Capturing the audio from film is a separate process from scanning the film to video. The audio will be captured as a separate WAV file. When requesting "native telecine digital files", you will receive 2 files – a QT file for the film transfer and a separate WAV file for the audio. If you want a single file with the audio embedded, we are happy to output a file of your choice for $25.
Most commonly used by archivist is JPEG, but we can also do PNG, BMP, or TIF add $25/project: This option is ideal for archiving or if you are going to do additional editing of the film. There will be exactly one digital file sequentially numbered for each frame of film. If the film was shot at 18 fps, that's the speed the image sequence will play back at. This image sequence may then be brought into an edit program and speed adjusted using your edit software.
* Need a specific file format? Just let us know, we can also output to many other digital formats like ProRes, MP4, etc.


text:
UPC Urges Govt to Expedite Revival of Cooperatives
By Nixon Segawa | 196 Views | Updated On October 20th, 2018 at 16:47
UPC Spokesperson, Michael Osinde addressing the press on Wednesday
Uganda Peoples' Congress (UPC) Spokesperson, Michael Osinde Orachi has applauded government for the steps being taken to revive Cooperative Unions in the country.
Speaking to SoftPower News, Osinde said that the willingness that has been exhibited by government through the Ministry of Trade and Cooperative is a positive move and a recognition of the UPC efforts for decades.
Government recently ordered Bank of Uganda to share the liquidation and audit reports which led to the closure of Cooperative Bank and Uganda Commercial Bank (UCB).
The Ministry of Trade, Industry and Cooperatives has written to the authorities at Bank of Uganda to explain why they closed Cooperative Bank.
Government wants to know the reasons for the closure of the bank to assist the Trade Ministry and the shareholders understand what could have gone wrong which will help the process of reviving Cooperative Bank.
"This is in recognition of the efforts UPC who are the brains behind the cooperatives. We have been pushing for this for decades," Osinde said.
Osinde has however urged government to reconsider the structure of the cooperative education model. In Uganda, the highest level of qualification is a Diploma yet in neighboring countries, this has been already revised, he said.
"In attempting to scale up skilling the youth, we would advise that scholarships are given in line with Cooperatives Education Movement as opposed to just giving them handouts," Osinde noted.
The UPC spokesperson said that Cooperative Movements were of a great advantage as opposed to the current SACCOs that have only issued handouts to people without skilling the people prior.
The cooperatives were a biggest foreign revenue earner in the days of President Milton Obote as opposed to the current regime.
"We like to urge government that the cooperative movement was a very big booster to the economy and once revived there would be a lot of dividends."
Cooperative Bankfarmer cooperativesMichael OsindeNewsSoft Power NewsUgandaUPC
Bobi Wine's Bodyguard Charged with Treason, Remanded to Gulu Prison
Pat Robert Larubi 10 months ago

redpajama_set_name: RedPajamaCommonCrawl


The person in this photo could possibly be a Rector or Scott from Cumberland County, NS.
If you think you know who this person is, please let us know by emailing archives@ccgsns.com stating "unknown photo #42″ in the subject line. If they are identified, we'll post here to let you know.


text:
China Bizwatch
Breaking News, National and World News
Enterpreneur
ECB: Time For "SME Bailout"
10 months ago China Bizwatch
Authored by Christopher Dembik, head of Macro Analysis at Saxo Bank,
Contrary to market expectations, the ECB refrained from cutting further its deposit rate and introduced a package aimed at keeping liquidity and credit flowing with a special focus on the private sector. Most of the measures were smart and flexible, but market reaction was initially very negative, notably due to the poor performance of ECB's Lagarde during the press conference.
What are the main measures?
More favorable terms for the already planned TLTRO III with a rate up to 25bps below the -0,5% deposit rate (in other words, up to -0.75%). The favorable conditions will be applied during the period from June 2020 to June 2021. Lending benchmarks will also be increased from 30% to 50% of the eligible loan stock.
Temporary QE envelope of €120bn until the end of 2020, with a special focus on private sector bonds, such as corporate bonds. It represents a boost of €20bn for six months on the top of the pre-existing monthly purchase of €20bn.
Collateral easing measures are also coming in order to make sure that counterparties will make full use of the announced stimulus measures.
Postponement of the ECB strategic review by 6 months in order to focus solely on the COVID-19 crisis.
In an coordinated effort with the ECB, the EBA released immediately after the ECB statement that the next round of banking stress test will be postponed to 2021 and that banks will be able to run lower capital ratios during the coronavirus crisis.
What is the market reaction?
The initial market reaction was very negative. Ahead of the meeting, the market priced a 12bp cut and was certainly disappointed the ECB did not cut rate. The poor performance of ECB's Lagarde increased market turmoil. The following sentence backfired: "We are not here to close spreads, there are other tools and other actors to deal with these issues".
In her press conference, Lagarde seemed to try to distance herself from Draghi's "Whatever it takes", which has certainly accentuated market tensions.
Most European indexes lost between 8% to 10%. The EuroStoxx Banks was down 12%, which is surprising considering the announcement of loosen banking regulation. BTP Futures were also down sharply, which is, in our view, a clear sign of illiquidity in the market.
What are the remaining questions?
It is obvious that monetary policy is not the ideal tool to fight coronavirus outbreak.
Rightly, ECB's Lagarde mentioned the need for EU member states to implement a decisive fiscal stimulus to help SMEs handling the negative economic consequences of COVID-19 and quarantines.
The ECB has done the job by making sure to provide enough liquidity to the markets in order to avoid a tightening in financial conditions.
Monetary policy can certainly offer a very temporary relief, but it won't be enough to fight the recession into which the euro area seems to have fallen this month.
What we have recently learnt from Asia is the quickness in policy response is essential in order to avoid the contagion and contain as fast as possible market panic.
So far, the Eurozone fiscal response has clearly been insufficient, which means that the crisis may last longer in the euro area than in other parts of the world and recovery will probably be very gradual.
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Tennessee is a key timeshare state with 31 resorts and over 6,000 units. Timeshare owners are comparable to owners of second homes and have traditionally been taxed that way in TN. Recently the property tax assessors in the two largest timeshare areas of Sevier and Davidson Counties have taken the position that timeshare should be classified as a commercial use for ad valorem taxes.
The effect of this change would change the formula for determining the taxes due from 25% of assessed value to 40% of assessed value. The ability for an owner to sell or market their interest when they no longer wish to own it is greatly affected by the fees and costs to own. Increases in property taxes will result in higher maintenance fees which average approximate $750/year per interval.
ROC is running a Bill (HB 85) this legislative session that would clarify that timeshare intervals should be assessed at the Residential rate of 25% of assessed value. The bill was recently introduced in the House and we are actively advocating for its passage.


text:
import Route from '@ember/routing/route'
import Paginate from '../mixins/table-pager/route'

export default Route.extend(Paginate, {
  modelName: 'item',
  // the name of the current controller since I don't know how to auto detect
  controllerName: 'bs-remote',
  queryParams: {
    quickSearchField: {
      refreshModel: true
    },
    q: {
      refreshModel: true
    }
  },
  actions: {
    save () {
      // Do whatever
      this._super(...arguments)
    }
  }
})


In your quest to develop your business plan you will want to obtain information regarding industry analysis, legal and regulatory statutes, manufacturing, training and counseling, and maybe corporate financials and international information and data. This is just a few of the areas you can research through our government's online resources. Though they are free and free is often synonymous with undervalued they are indeed not free. Your tax dollars pay for them and the information is rich and vast. These government resources are put there to help you succeed so there is no better place to start your research than with the resources you pay for. The U.S. Census Bureau, Department of Commerce, Small Business Administration, Internal Revenue Service, The State Department, FedStats, Export.gov, and the U.S. Securities and Exchange Commission all have information that you will find helpful. I will talk a little about each one, what they do, and how you can use them.
The first place you may want to start with your investigation on industry analysis is through The Department of Commerce. The DoC is comprised of twelve separate agencies responsible for many things regarding business from weather forecasts to patent protection. Their mission statement states exactly what they do and there is no better way to sum it up, "The DoC touches the daily lives of the American people in many ways, with a wide range of responsibilities in the areas of trade, economic development, technology, entrepreneurship and business development, environmental stewardship, and statistical research and analysis." There is much to the DoC and they also are a portal to several other government agencies and partners that you may find useful.
The U.S. Census Bureau, a derivative of the DoC, is a great resource for industry analysis and they are arguably the most important branch of the DoC for demographic information ranging from population breakdown, income, education levels, and housing to name just a few which you can find in the Peoples and Households section of their website. The Census Bureau also collects massive amounts of data on economic activity. You may need to search the industry code for the particular type of business you're in or looking for, by searching its NAICS code. You can break information down from a national level and/or by zip code for information on total number of businesses as well as types and average sales in their business and industry section. They also have information on foreign trade and so much more. The fact of the matter is that there is so much data that the U.S. Census Bureau has accumulated it may seem daunting. It is in fact a huge database with so many useful links that I have to write three paragraphs about all the different links worth noting. You should spend some time navigating around, taking notes, and leaving yourself a trail of breadcrumbs one way or another so that you can remember just how you got from place to place.
The U.S. Census Bureau's website is vast, as I previously stated, and there are a number of portals you should know a little about and even bookmark. Censtats is a database that includes county and zip business pattern information, international trade data, demographic and economic data for states and counties, Census Tract Street Locator, and some other databases on top of those. American FactFinder has information regarding demographic information about people that can be broken down in many ways to suit your needs. QuickFacts provides information on people, business, and geography which can be targeted on a national, state, and/or county level. If you need information on industry and manufacturing you can skim through the Current Industrial Report section. There contains vast amounts of information which may satisfy your needs for market analysis, forecasting, and decision-making.
The next noteworthy online resource at your disposal is the SBA or Small Business Administration. If you need information on federal and/or state legal and regulatory information the SBA has important and detailed information on these two important issues. The SBA also funds and supports business's and you can find and apply for government grants, contracts, as well as sign up to receive training and counseling from SCORE. Score are a group of retired executives that volunteer their time to help business owners and aspiring entrepreneurs with development of their business plans and give advice to current businesses management dilemmas and questions and other assistance. If you have never spoken with a SCORE representative you should. They have a plethora of experience and expertise on a variety of subjects and industries. The SBA's website also offers you a place to register your business, find business licenses, and permits. Ultimately the SBA is another great government resource that is a must-add to your toolbox.
If you're interested or need to get your hands on international data and statistics our government provides information through U.S. Census Bureau, The State Department, and Export.gov. The Census Bureau's contains an international database (IDB) that provides rankings by population size as well as global trends and demographic data. The State Department's website has background information that includes information on each country's economy and a great deal more all in alphabetical order by country. You can find out information on that country's history, people, government, relations, travel, and more. Export.gov's slogan is helping U.S. companies succeed globally by combining and providing the resources from across the U.S. government to assist American businesses in planning their international sale's strategy. They have in-depth data about markets and industries on a global scale.
If you need information on corporations The U.S. Securities and Exchange Commission's Edgar Database provides information regarding corporation's financial information as well as their operations. There is a lot more to the Edgar Database. They also have a user guide that will provide you a more detailed analysis of who they are and what all they can do for you.
The last government resource that is worth mentioning is Fedstats. FedStats is a government sponsored website that has vast quantities of data collected by over 100 different agencies. You can search by topic compiled in a nice, user-friendly A-Z format or you can search the agencies in which they receive their data from, also in an alphabetical format. If you're interested in state-by-state statistical data you might try clicking through FedStat's MapStats section which profiles states, counties, cities, congressional districts, and even federal judicial districts. Besides the particular places I have pointed out there is also information on international comparisons, statistics from the individual agencies, press releases, and more.
I have written a similar article on my blog that has the full list of links to many of the areas that I have spoken of in this article if you would like to check it out. I hope that I have provided you valuable resources that will aid you in your pursuit to complete your business plan. Good luck and best wishes for your success!


Sutar, Dayanand and Mithra, Madan and Menon, Reghu and Subramanyam, SV (2001) Conductivity and magnetoresistance in polypyrrole-$PF_6$ at various doping levels. In: Synthetic Metals, 119 (1-3). pp. 455-456.
The temperature dependence of dc conductivity (from 1.5 to 300 K), and magnetoresistance (up to 7T) have been studied on polypyrrole samples doped with $PF^-_6(PPy - PF_6)$ at various doping levels. In this work, the metal-insulator transition (MIT) is tuned by varying the carrier density, and not by the extent of disorder. The conductivity data is analysed using different models. The positive magnetoresistance observed for all the samples shows that electron-electron interaction is dominant.


text: THE FANTASY AMATEUR PRESS ASSOCIATION by James Kepner The Fantasy Amateur Press Association (abbreviated as F.A.P.A, and usually called "fa-pah") is an organization of sixty-five amateur journalists who share an interest in science fiction and fantasy. Members include eight of the Lost Angeles fans, several from New York, Battle Creek, Boston, San Francisco, and other scattered parts of the country, as well as three English fans, one Canadian, and several american servicemen abroad, who us our claim to being an international organization. The F.A.P.A was organized in 1937 by two fans in New York, and it has now become the oldest, and by far the most stable, of non-local fan groups. It is similar to some of the other more widely known amateur journalist groups, such as the National, United, and American Amateur groups. It was organized as a central mailing bureau for the publishers of amateur magazines devoted to science fiction and fantasy. The organization has a simple constitution and a minimum of red tape. Officers are elected annually. The dues are a nominal fee. Members publish their magazines at their own discretion, whenever convenient to them, they at their own expense, and send at least sixty-five copies to a designated officer, who sends out the "mailings" quarterly to each member. A mailing consists of an envelope containing one copy of each magazine published within that quarterly period. There have recently been from thirty to sixty separate items per mailing. On the average, this would include two or three poetry pamphlets, a large percentage of book reviews, literary criticism, articles of well of little known fantasy authors, a sheet or two proposing or discussing further constitutional amendments, a bit of artwork, a few fantasy stories, and occasional satires, as well as a large amount of chain discussion. This last item fills the major part of them mailing. It has become a custom for publishing members to devote the bulk of their magazines to comments on the items in the last mailing. These have developed into chain discussions on any subject imaginable which runs on for long periods of time. Topics include sociology, the physical sciences, politics, religions, and, occasionally, even fantasy. A large amount of space is also devoted to semi-humorous accounts of fan doings, such as gatherings, and the trivial incidents connected with book and magazine collecting. Recently, for instance, such discussions have come up as the plausibility of a fan-colony located in one of the major cities, the defining of a fan's code of ethics, the racial question in the United states, and the desirability of improvement in our educational system. Magazines are generally mimeographed, (although hectography and printing have been used occasionally) and may run from one to thirty of more pages. A few contain art work reproduced by lithography, air brush or the silk screen process. However, most of the artowork is mimeographed. Members range from college graduates to high school students. The membership, formerly limited to fifty persons, has recently been raised to sixty-five, and there are now over a dozen applicants on the waiting list, biding their time until other members are dropped for inactivity or non-payment of dues. On the whole, the Fantasy Amateur Press Association is the most active and the most interesting of all fan organizations, and, I should say, the most likely to continue in that line.


CylancePROTECT is an advanced threat protection program that utilizes machine learning and artificial intelligence to categorize files and running processes. Files deemed 'unsafe' or 'abnormal' are quarantined, while memory exploit attempts are blocked. CylancePROTECT, unlike most antivirus solutions, is not reliant on daily virus definition updates or weekly system scans; so it is less CPU, memory, and hard drive intensive which should provide end user's a better user experience.
When CylancePROTECT detects an issue with connectivity to the cloud-based management console, a yellow indicator will appear over the Cylance icon. When Cylance detects an unsafe or abnormal file that has yet to be remediated a red indicator will appear over the Cylance icon.
It is covered for all faculty and staff University-owned computers through our campus license agreement. For home and personal use, CylancePROTECT: Home Edition is available. Please see the Home Use section below for more information.
Faculty and staff office computers should have CylancePROTECT installed as part of the standard load. Once installed the following icon should be displayed in your system tray or menu bar.
Click each of the tabs in the window to see if any Threats, Exploits, Events, or Scripts appear.
If you do not see anything listed, then it's not likely a CylancePROTECT issue.
If you do see one or more legitimate work-related files listed in one of the tabs, contact the Help Desk at 214-768-HELP. Please be sure to provide your computer name and the file(s) detected.
To help you safeguard your family's personal devices from malware, ransomware, and other cyberattacks, SMU OIT has partnered with Cylance to offer employees exclusive access to the same caliber of anti-malware protection that SMU computers receive for up to 10 of your family's devices at a promotional price. Visit Cylance's website to get started.
*Windows 8/10 systems come pre-packaged with Windows Defender for antivirus.


What it is: A palette filled with 14 all-new paradise-inspired matte and metallic eyeshadows, in vibrant jewel tones to bright pops of color.
What it does: The Riviera Palette is an essential springtime color collection, featuring classic neutrals and seasonal tones that are perfect for creating your ultimate vacation looks. The full-pigment powder formulas deliver high-color payoff and intensity, while the wide shade range allows you to create everything from cool- and warm-toned neutral looks to daring brights, shimmering glams and bold, smoky eyes. This mirrored palette features a dual-ended brush.
How to use: A little goes a long way. Only minimal product is needed to create high-impact looks. The included double-ended brush is compact and perfect for on-the-go application. Use the flat end to deposit color, highlight or pack shadow onto the lid. Use the fluffy end to diffuse and blend out shadow. Use Bahamas as a bold blush color for the face or body.


text:
Securitisation X IAS Regulation X Trade Repositories X Short Selling X Sustainable finance X
17/09/2012 2012/580 Exemption for market making activities and primary market operations under Regulation (EU) 236/2012 of the European Parliament and the Council on short selling and certain aspects of Credit Default Swaps Short Selling Consultation Paper PDF
17/09/2012 2012/582 ESMA consults on market maker and primary dealer exemption for short selling Short Selling, Press Releases Press Release PDF
2012-580.pdf
19/10/2012 2012/680 Links to national websites for the purpose of the notification of net short positions Short Selling Reference PDF
01/11/2012 2012/715 ESMA Opinion on emergency measure by Spanish CNMV under Section 1 of Chapter V of Regulation No. 236/2012 on short selling and certain aspects of credit default swaps Short Selling Opinion PDF
79.2 KB ESMA Opinion ESMA is adopting the following opinion on the notified measure, on the basis of Article 27(2) of Regulation 236/2012 on Short selling and certain aspects of credit default swaps: On the adverse events or developmentsESMA considers that there are adverse developments which constitute a serious threat to financial stability and to market confidence in Spain. On the appropriateness and proportionality of the measure ESMA considers that the measure is appropriate and proportionate to address the above-mentioned threats that persist in Spain. On the duration of the measureESMA considers that the duration of the measure is justified and appreciates the CNMV's statement in its notification of intent whereby the measure may be lifted during the period of enforcement of the measure, if considered necessary.
01/11/2012 2012/717 ESMA Opinion on emergency measure by the Greek HCMC under Section 1 of Chapter V of Regulation No. 236/2012 on short selling and certain aspects of credit default swaps Short Selling Opinion PDF
95.45 KB ESMA Opinion ESMA is adopting the following opinion on the notified measure, on the basis of Article 27(2) of Regulation 236/2012 on Short selling and certain aspects of credit default swaps: On the adverse events or developmentsESMA considers that there are adverse developments which constitute a serious threat to financial stability and to market confidence in Greece. On the appropriateness and proportionality of the measure ESMA considers that the measure is appropriate and proportionate to address the above-mentioned threats that persist in Greece. On the duration of the measureESMA considers that the duration of the measure is justified and appreciates the HCMC's statement in its notification of intent whereby the measure may be lifted during the period of enforcement of the measure, if considered necessary.
10/12/2012 ESMA SSR Review mandate Formal request to ESMA for technical advice on the evalutation of the short selling regulation Short Selling Reference PDF
03/01/2013 2012/875 Report to the European Parliament, the Council and the Commission on the budgetary impli-cations of Regulation (EU) No 236/2012 on short selling and certain aspects of credit default swaps Short Selling Final Report PDF
29/01/2013 2013/149 Emergency measure by the Greek HCMC under Section 1 of Chapter V of Regulation No 236/2012 on short selling and certain aspects of credit default swaps Short Selling Opinion PDF
30/01/2013 2013/159 Q&A on Implementation of the Regulation on short selling and certain aspects of credit default swaps (2nd update) Short Selling Q&A PDF
01/02/2013 2013/158 Guidelines on the exemption for market making activities and primary market operations under Regulation (EU) 236/2012 of the European Parliament and the Council on short selling and certain aspects of Credit Default Swaps Short Selling Final Report PDF
01/02/2013 2013/167 ESMA issues guidelines on market-making and primary dealer exemptions Short Selling, Press Releases Press Release PDF