text:
\section{Introduction}
\label{sec:intro}

Even though collimated sprays of particles (jets) produced from high energy quarks and gluons are ubiquitous at the Large Hadron Collider (LHC), analyzing their substructure has proven to be a powerful tool in the search for physics beyond the Standard Model (SM)~\cite{Larkoski:2017jix,Asquith:2018igt}.  Many theories of physics beyond the SM predict new particles with cascade decays that can result in large multiplicity final states.  When many quarks and gluons are produced in these cascades, multiple large-radius jets with non-trivial substructure can be created~\cite{Cohen:2012yc,Hedri:2013pvl,Hook:2012fd}.  As a result, one powerful method for searching for new particles in the all-hadronic channel is to look for events with a large $\sum_{j\in J} m_j$, where $m_j$ is the jet mass and $J$ is a set of jets in an event.  The key challenge for such an analysis is to estimate the SM background, as high multiplicity multi-jet final states are difficult to accurately predict with current simulation tools.

Based on the approximate factorization of quantum chromodynamic (QCD) jet production at the LHC~\cite{Collins:1989gx}, the authors of Ref.~\cite{Cohen:2014epa} proposed an innovative background estimation technique.  The idea of the procedure is to estimate the conditional probability $p(m_j|\text{jet kinematics})$ with an event selection suppressed in signal and then to convolve it with the jet kinematic spectrum in the signal region (from data).  A comparison between the predicted $m_j$ and observed $m_j$ is then sensitive to the presence of new particles.  The ATLAS collaboration has successfully applied this method in both Run 1 and Run 2 to set strong limits on potential gluino and squark production~\cite{Aad:2015lea,Aaboud:2018lpl}.

The background estimation procedure described above has two major limitations\footnote{The extensive smoothing studies in Ref.~\cite{Cohen:2014epa} help to mitigate binning effects, but do not have an impact on the feature conditioning challenge.}.  First, $p(m_j|\text{jet kinematics})$ is represented as a histogram and each bin is uncorrelated so many events are needed for a precise determination.  Second, there are physics and detector effects which change the distribution of the jet mass between the region it is constructed (`trained') and the region where it is applied (`tested').  For example, the quark/gluon composition of the background may be different between the two regions.  One way to mitigate this source of method bias is to condition on more features of the jet when constructing the conditional probability.  To reduce the impact of changes in quark/gluon composition, one could add the number of charged-particle tracks inside the jet.  Gluon jets tend to have more particles than quark jets due to their larger color factor.  Since the templates $p(m_j|\text{jet kinematics})$ are binned, it is not simple to condition on more features as one needs many more bins and thus larger samples of events for training.

This paper proposes a solution to both of these limitations using modern machine learning.  Deep neural networks are becoming popular tools for classification and regression tasks in high energy physics (HEP) data analysis, but there is a growing machine learning literature on neural network-based generative models as well.  Training a generative model can be viewed as a regression task that maps noise to structure, mimicking the Jacobian from a pre-defined probability distribution to a target probability distribution.  One of the most well-studied paradigms for such models is the Generative Adversarial Network (GAN)\cite{Goodfellow:2014upx} (details in Sec.~\ref{sec:architectures}).  GANs have also been studied in HEP and show great promise for accelerating simulations~\cite{Paganini:2017hrr,Paganini:2017dwg,deOliveira:2017rwa,Chekalina:2018hxi,Carminati:2018khv,Vallecorsa:2018zco,Erdmann:2018jxd,Musella:2018rdi,Erdmann:2018kuh,ATL-SOFT-PUB-2018-001,deOliveira:2017pjk} and may also be useful for other tasks such as sampling from the space of effective field theory models~\cite{Erbin:2018csv}.  In the context of QCD factorization studied in this paper, the GAN will learn the probability distribution of the jet mass given the jet kinematics and any other useful information.

This paper is organized as follows.   Section~\ref{sec:architectures} introduces GANs and how they can be used to exploit QCD factorization.   The application of GANs to the phase space relevant to the Supersymmetry (SUSY) search from Ref.~\cite{Cohen:2014epa,Aad:2015lea,Aaboud:2018lpl} is presented in Sec.~\ref{sec:templates}.  The paper ends with conclusions and outlook in Sec.~\ref{sec:conclusions}.

\section{Machine Learning Architectures}
\label{sec:architectures}

\subsection{Overview of GAN setup}
\label{sec:vanillagan}

The goal of this section is to introduce an approach to learn the conditional distribution of the jet mass given various kinematic features.  Neural networks are chosen due to their flexibility and the resulting algorithms are naturally unbinned.  There are multiple neural network-based approaches to generative modeling such as Variational Autoencoders (VAE)~\cite{DBLP:journals/corr/KingmaW13,Rezende:2014:SBA:3044805.3045035}, Mixture Density Networks (MDN)~\cite{mdn}, and Generative Adversarial Networks (GAN)~\cite{Goodfellow:2014upx}.  GANs are selected because they can readily model asymmetric distributions and accommodate conditional features.

Generative Adversarial Network training uses a pair of neural networks: one to map noise into structure (generator) and one to classify (discriminator) physics-based examples from the generator examples.  The generator is structured as a densely connected feed-forward neural network that takes as input both jet kinematic features and noise and outputs a jet mass.  The generated masses are then input to the discriminator network where they are compared against masses from a physics-based generator that match the kinematic quantities.  The two networks `compete' until the discriminator network is as bad as possible, which means that the generator is proficient at modeling the conditional jet mass distribution.  This minimax structure uses the loss function described in Ref.~\cite{Goodfellow:2014upx}, constructed to minimize the Jensen-Shannon divergence between the distribution of the real data (in this case, a physics-based simulator) and the distribution of the generated data.  A schematic of this GAN is shown in \cref{fig:GAN_overview}.

The GAN networks for the jet mass are relatively small compared to others in the literature, which are mostly used for modeling image data.  For the vanilla GAN implementation, both the Generator and Discriminator networks are composed of three hidden layers between input (kinetic variables and noise/mass for generator/discriminator respectively) and output (generated mass/likelihood for generator/discriminator respectively).  The first layer has 160 neurons, the second has 80 neurons, and the last hidden layer has 40 neurons.  Additionally, the generator network is made more robust by adding 50\% dropout~\cite{JMLR:v15:srivastava14a}.  Network weights were chosen using the Adam optimizer~\cite{DBLP:journals/corr/KingmaB14} with early stopping.  These settings were chosen after a modest hyper-parameter scan.

All of the neural networks are built using \textsc{Tensorflow}~\cite{tensorflow} on Nvidia GeForce 1080 Ti GPUs.  Since the jet mass distribution is skewed toward the high mass tail, the input noise distribution was varied according to a skew-normal distribution~\cite{skewnormal}: $f(x|\alpha)=2\phi(x)\Phi(\alpha x)$, where $\alpha$ is a hyper-parameter and $\phi$ and $\Phi$ are the probability density and cumulative distribution of the standard normal distribution, respectively.  The best value of the skew parameter was identified to be $\alpha=5$ (labeled \textit{skew} in the results plots).  This will be compared to the standard no-skew case ($\alpha=0$; labeled \textit{noskew}).

\begin{figure}
\begin{center}
\includegraphics[width=\textwidth]{figures/GAN_overview.pdf}
\end{center}
\caption{Flowchart describing how GANs are used to learn templates (shown here mass templates for the RPV-SUSY search) given kinematic variables. The generator network is a feed-forward network that takes as input particles with their mass replaced by noise, and generates mass according to a learnt distribution. These fake particles are bundled with real particles and passed to the discriminator, which learns to discriminate between the real and fake distributions. }
\label{fig:GAN_overview}
\end{figure}

\subsection{Modified WGAN}

As an alternative to the vanilla GAN described in Sec.~\ref{sec:vanillagan}, a popular variant called the Wasserstein-GAN (WGAN)~\cite{1701.07875} was also studied (for another HEP application, see Ref.~\cite{Erdmann:2018jxd}). The WGAN differs from the vanilla GAN in that it minimizes the Earth-Mover distance (also known as the Wasserstein Distance):

\begin{align}
W(\mathbb{P}_\text{real},\mathbb{P}_\text{generated}) = \inf_{\gamma \in \Pi (\mathbb{P}_\text{real},\mathbb{P}_\text{generated})} \mathbb{E}_{(x,y) \sim \gamma} [ \parallel x - y \parallel ],
\end{align}

\noindent where $\gamma\in \Pi (\mathbb{P}_\text{real},\mathbb{P}_\text{generated})$ is a joint distribution with marginal distributions $\mathbb{P}_\text{real},\mathbb{P}_\text{generated}$ respectively, and $(x,y) \sim \gamma$ means that the random variable $(x,y)$ is drawn from the distribution $\gamma$. This 'softer' metric was introduced as a way to combat the vanishing gradients that often occured when training regular GANs \cite{1701.07875,Arjovsky2017TowardsPM}. In the algorithm suggested by the WGAN paper to minimise the Earth-Mover distance, the discriminator is a function $f$ that is taken from a space of trial functions that are all $K$-Lipshitz for some $K$. To enforce such conditions, the functions $f$ are constructed as feed-forward neural networks with weights $w$ that are clipped (after every update) to a compact space $[-\alpha,\alpha]$ for some fixed $\alpha$ which enforces $K$-lipshitz for some $K$.

An exact implementation of the WGAN approach resulted in weights that would often aggregate around the specific limit values $\alpha$ chosen, leading to vanishing gradients and generated mass distributions that did not match the physical mass distributions. The challenges surrounding the clipping operation to enforce the Lipshitz condition are discussed in the original WGAN paper, and explored in variations of WGAN where the clipping is replaced by gradient penalty~\cite{DBLP:journals/corr/GulrajaniAADC17} or asymmetric clipping~\cite{2017arXiv170507164G}.

In this paper, a modification to weight clipping is introduced that changes how the weights act on the outputs of neurons to enforce the Lipshitz condition. A schematic for this modification to the WGAN is shown in \cref{fig:SGAN}. The original weight-clipping operation enforces a Lipshitz function because the composition of Lipshitz functions is still a Lipshitz function; in particular, there is the weak assumption that all the activation functions used in the neural networks are themselves Lipshitz (true for the most popular activation functions including sigmoid, tanh, and ReLU). Then, the fact that there is a single constant $K$ for which all the functions $f$ that we are considering are $K$-Lipshitz is due to the weight-clipping -- this restricts the function space to be compact.

Another natural way to enforce the Lipshitz constraint that eliminates boundary effects is to draw weights from a compact space with no boundaries. One way to achieve this is to draw the weights and biases from the unit circle\footnote{In principle, one could generalize this idea to modify the WGAN where all weights and biases are drawn from a generic compact Lie Group.  The outputs of the neurons exist in a vector space with the Lie Group acting by a chosen representation. In this framework, a normal linear neural network uses the Lie Group $\mathbb{R}$ with canonical action on itself, and the modified WGAN shown in Eq.~\ref{eq:modifiedwgan} uses $U(1) \approx S^1$. The point of this construction is that the compactness of the weight-space assures the convergence of the WGAN and is well-formed because Lie Group actions can be differentiated. Such applications of Lie Groups to Machine Learning have been explored elsewhere in the literature, for example in applications to 3D-classification problems~\cite{DBLP:journals/corr/HuangWPG16}.}:

\begin{align}
\label{eq:modifiedwgan}
x^{(k+1)}_n = \psi \bigg(\bigg|\sum_m x^{(k)}_m e^{i \theta^{(k)}_{nm}} + e^{i \phi^{(k+1)}_n}\bigg|\bigg),
\end{align}

\noindent where $x^{(k)}_m$ represents the output of the $m$-th neuron in the $k$-th layer of the neural network and $\psi$ is the activation function.  The bias is replaced by $b_i=e^{i\phi_n}$ and the weights are replaced by $w_{nm}=e^{i\theta_{nm}^{(k)}}$.

\begin{figure}
\begin{center}
\includegraphics[scale=2.25]{figures/modified_GAN.pdf}
\end{center}
\caption{A schematic diagram to illstrate the modified WGAN, where all the weights and biases are angles.  The compactness of the unit circle then guarantees that the trained functions are all $K$-lipschitz for sufficiently large $K$. The inputs to the layer are $x_1,...,x_n$ and there is a hidden layer with two nodes $y_1$ and $y_2$ (in general, there can be many more than two) followed by a single output layer $z_1$.  The equation shows the form of the activation that maps $x_1,...,x_n$ to $y_1$.  }
\label{fig:SGAN}
\end{figure}

\section{Templates for RPV-SUSY}
\label{sec:templates}

\subsection{Overview of RPV-SUSY and the Template Method}
\label{subsec:RPV-SUSY}

Supersymmetry (SUSY)~\cite{Golfand:1971iw,Volkov:1973ix,Wess:1974tw,Wess:1974jb,Ferrara:1974pu,Salam:1974ig} is a well-studied extension of the Standard Model in which there is a new symmetry relating fermions and bosons.  In models of SUSY that conserve $R$-parity~\cite{FARRAR1978575,DIMOPOULOS1981150,PhysRevD.26.287,SAKAI1982533,DIMOPOULOS1982133} -- an additional symmetry that requires SUSY particles to be produced in pairs -- collider signatures often feature large missing transverse momentum (MET) carried by the lightest supersymmetric particle (LSP) which must be neutral under the electromagnetic and strong forces.  However, $R$-parity is not present in all SUSY models and collider-based limits relying on MET are typically insensitive to such models.  One $R$-parity violating (RPV) coupling (often denoted $\lambda''$) results in gluino/neutralino decay into three quarks, as illustrated in Fig.~\ref{fig:feynman}.  When this coupling is present, traditional searches for SUSY are largely ineffective because there can be little MET and no charged leptons.  Despite lacking standard handles for separating potential SUSY events from SM background processes, RPV signatures like those in Fig.~\ref{fig:feynman}, all-hadronic SUSY searches have been able to set strong limits on gluino and squark production in models with a large $\lambda''$ (see references within Ref~\cite{Aad:2015lea,Aaboud:2018lpl} for other constraints on such models).

One approach to search for all-hadronic SUSY decays like those in Fig.~\ref{fig:feynman} is to exploit the high-multiplicity of hard, well-separated partons in the final state.  In the recent ATLAS analysis~\cite{Aaboud:2018lpl}, the main kinematic observable used in the search for $\lambda''$ RPV decays is the total jet mass $M_\Sigma^J$ defined as the sum of the masses of the four leading large $R=1.0$ jets.  Multiple jets with a large mass are generated from well-separated high-energy partons that happen to be clustered within a single jet.  The SM multijet background is estimated by a data-driven method~\cite{Cohen:2014epa}, whereby mass templates are constructed from control regions containing a low purity of potential signal.  These templates are histograms that model the dependence of QCD mass on jet kinematic properties ($p_T$ and $\eta$).  If the jets in the signal region are only due to QCD, then the mass distribution can be estimated by convolving the jet kinematics with the mass templates.  This estimate is compared with the actual mass distribution and deviations would be an indication of BSM physics.

\begin{figure}[h!]

\begin{center}
\begin{tikzpicture}[line width=1.5 pt, scale=1.3]

	\draw (-1,1+0.1)--(0,0+0.1);
	\draw (-1,-1+0.1)--(0,0+0.1);

	\draw (-1,1)--(0,0);
	\draw (-1,-1)--(0,0);

	\draw (-1,1-0.1)--(0,0-0.1);
	\draw (-1,-1-0.1)--(0,0-0.1);

	\draw[gluon,color=red] (0,0)--(1,1);
	\draw[gluon,color=red] (0,0)--(1,-1);
	\draw[color=red] (0,0)--(1,1);
	\draw[color=red] (0,0)--(1,-1);

	\draw[dashed,color=red] (1,1)--(2,1.5);
	\draw[dashed,color=red] (1,-1)--(2,-1.5);

	\draw[] (1,1)--(2,0.5);
	\draw[] (1,-1)--(2,-0.5);

	\draw[color=red] (2,1.5)--(3,2);
	\draw[color=red] (2,-1.5)--(3,-2);
	\draw[vector,color=red] (2,1.5)--(3,2);
	\draw[vector,color=red] (2,-1.5)--(3,-2);
	\draw[] (2,1.5)--(3,1);
	\draw[] (2,-1.5)--(3,-1);

	\draw[] (3,-2) -- (4,-2.5);
	\draw[] (3,-2) -- (4,-2);
	\draw[] (3,-2) -- (4,-1.5);

	\draw[] (3,2) -- (4,2.5);
	\draw[] (3,2) -- (4,2);
	\draw[] (3,2) -- (4,1.5);

	\draw[fill=black!30!white] (0,0) circle (.25);

	\node at (1.5, 1.6) {\color{red} $\tilde{q}$};
	\node at (1.5, -1.6) {\color{red} $\tilde{q}$};
	\node at (2.5, 2.1) {\color{red} $\tilde{\chi}^0$};
	\node at (2.5, -2.1) {\color{red} $\tilde{\chi}^0$};
	\node at (0.4, 0.8) {\color{red} $\tilde{g}$};
	\node at (0.4, -0.8) {\color{red} $\tilde{g}$};
	\node at (2.2, 0.4) {$q$};
	\node at (2.2, -0.4) {$q$};
	\node at (3.4, 0.9) {$q$};
	\node at (3.4, -0.9) {$q$};

	\node at (4.4, -2.5) {$q$};
	\node at (4.4, -2.) {$q$};
	\node at (4.4, -1.5) {$q$};

	\node at (4.4, 2.5) {$q$};
	\node at (4.4, 2.) {$q$};
	\node at (4.4, 1.5) {$q$};

 \end{tikzpicture}
\end{center}
\caption{Schematic Feynman-like diagram for RPV SUSY.}
\label{fig:feynman}
\end{figure}

The goal of this study is to demonstrate that GANs may be a useful alternative method to simple histogram templates for learning the dependence of the jet mass on jet properties.

\subsection{Simulation Setup}
\label{subsec:setup}

To demonstrate the potential of GAN-based templates, $pp\rightarrow\text{jets}$ at $\sqrt{s} = 13 \text{ TeV}$ are generated with \pythia{8.223}~\cite{Sjostrand:2006za} using the fast detector simulation \delphes{3.4.0}~\cite{deFavereau:2013fsa} with the detector card {\tt delphes\_card\_ATLAS.tcl}. Following the ATLAS analysis strategy~\cite{Aaboud:2018lpl}, events are clustered~\cite{Cacciari:2011ma} into jets using the anti-$k_t$ algorithm~\cite{Cacciari:2008gp} with radius parameter $R = 1$.  These jets are groomed according to the trimming procedure~\cite{Krohn:2009th} where subjets with radius $R=0.2$ are created from the large-radius jet constituents and removed if their transverse momentum is below 5\% of the parent jet's $p_T$.  The remaining large-radius jets are only considered if $p_T>200$ GeV and $|\eta|<2.0$ and are divided into control, validation and signal regions according to \cref{tab:regions}.   The control region is used to construct the templates (train the GAN) and the signal region is where they are applied\footnote{We note that the original template proposal from Ref.~\cite{Cohen:2014epa} called for a smoothing procedure that was used in the earlier ATLAS result~\cite{Aad:2015lea} but not the later one~\cite{Aaboud:2018lpl}.  Our analysis here more closely resembles the later approach.  The smoothing should in principle help mitigate binning effects, though would not change the qualitative conclusions about feature dependence.}.  The validation region is expected to be sufficiently devoid of potential signals that it can be used to study the fidelity of the templates.  Even though the \pythia is only leading order in the strong coupling constant, Ref.~\cite{Aaboud:2018lpl} used the same setup and found a good agreement with data so this setup is sensible for testing new methods.

As a baseline, mass templates (histograms) are constructed following the ATLAS study: jets in the control region divided into $4$ $|\eta|$ bins defined uniformly between $0$ and $2$, and $15$ $p_T$ bins defined uniformly in $\log_{10}(p_T)$. This results in $60$ mass histograms in total.  No $b$-tagging is applied, though future extensions to include flavor tagging information are possible.  Using the mass histograms, jets in the validation and signal regions can be dressed with random masses given their $p_T$ and $\eta$.

\begin{table}
\begin{center}
\begin{tabular}{| c | c | c | c | c |}
\hline
Region & $N_\text{jet} (p_T > 200 \ \text{GeV})$ & $p_{T,1}$ & $|\Delta \eta_{1,2}|$ & $M_J^\Sigma$ \\
\hline
Control & $=3$ & - & - & - \\
\hline
Validation & $=4$ & $> 400 \ \text{GeV}$ & $> 1.4$ & - \\
 & $\geq 5$ & - & $>1.4$  & - \\
 \hline
 Signal & $=4$ & $> 400 \ \text{GeV}$ & $< 1.4$ & $> 1.0 \ \text{TeV}$ \\
  & $\geq 5$ & - & $<1.4$ & $> 0.8 \ \text{TeV}$ \\
 \hline
\end{tabular}
\caption{Phasespace requirements for the different regions considered.. }
\label{tab:regions}
\end{center}
\end{table}

\subsection{Machine Learning Results}
\label{subsec:ML results}

After the selections described in the previous section, there were 1.1 million jets in the control region and 30k in the validation region.  The validation region is used to test the efficacy of the neural network training, filling the role of the `test set' and by construction is independent from the training set.  The jets in the control region are split 50\%-50\% for the purpose of training the neural network and `validating' the network to enforce the early stopping condition.

The accuracy of the generated mass templates was quantified\footnote{There is no unique way to monitor the GAN performance during training.  For a typical GAN trained with images, this is qualitatively different than classifier training because the entire multi-dimensional probability is being modeled, not just the likelihood ratio.  The 1D case here is not as extreme, but still different than classification or regression monitoring.  One can use the full GAN loss, the discriminator loss, or any divergence that gives a `distance' between probability distributions.  This particular divergence is popular in HEP and is therefore used as a diagnostic here.} using the separation power metric~\cite{Harrison:1998yr,Hocker:2007ht}:

\begin{align}
\label{eq:separation}
S(\mathbb{P}_1,\mathbb{P}_2) = \frac{1}{2} \int_X \frac{(\mathbb{P}_1(x) - \mathbb{P}_2(x))^2}{\mathbb{P}_1(x) + \mathbb{P}_2(x)} \text{d}x,
\end{align}

\noindent where $\mathbb{P}_1,\mathbb{P}_2$ are probability distributions over a space $X$ and Eq.~\ref{eq:separation} is normalized to be between $0$ and $1$.  For the RPV SUSY case, $X \simeq \mathbb{R}_{p_T} \times \mathbb{R}_\eta \times \mathbb{R}_N \times \mathbb{R}_m$, one real line for each of the jet properties $p_T, \eta$, constituent track multiplicity\footnote{Due to their robustness to pileup and excellent angular resolution, charged-particle tracks are associated to jets and used as proxy for the number of particles inside the jet.} ($N$), and $m$.  The $\mathbb{P}_i$ are the real and generated distributions:

\begin{align}
\label{eq:preal}
\mathbb{P}_\text{real}&(p_T, \eta, N, m)\\
\label{eq:pgen}
 \mathbb{P}_\text{generated}&(p_T,\eta, N,m) = \mathbb{P}_\text{real}(p_T,\eta,N) G (m|p_T,\eta, N),
\end{align}

\noindent where $G (m|p_T,\eta, N)$ is the learned mass distribution by the GAN for a given $p_T,\eta,N$ value, and $\mathbb{P}_\text{real}(p_T,\eta,N) =\int \mathbb{P}_\text{real}(p_T, \eta, N, m)\text{d}m$.  Equation~\ref{eq:pgen} explicitly encodes the QCD factorization of the jet kinematics and the mass given those kinematic properties.  Neither the GAN or physics-based simulator provide $\mathbb{P}$ directly; instead, only examples are drawn from the distributions.  Empirical distributions are constructed from samples and the separation power is approximated by first binning the jets into eight regions of equal statistics in the kinematic variables $p_T, \eta, N$, by splitting the events into two collections based on the jet $p_T$, then splitting each of these collections evenly in $\eta$, and then in $N$.  In each of these eight bins, the jet mass distribution is used to calculate the (binned) separation power for each dataset and then all eight sets\footnote{The estimated separation power, for our dataset size, is not sensitive to increasing bin number beyond $8$.} are combined.

Figure~\ref{fig:err} shows the separation power for various generative models as a function of the number of epochs used to train.  GAN models that were initialized with a high separation error (above $0.6$) training much slower due to vanishing gradients, so only those GANs with an initialized value below 0.6 are considered for Fig.~\ref{fig:err}.  Furthermore, to reduce the impact of fluctuations in the initialization, the average value over ten random initializations are used. By construction, the default template method does not involve neural networks and thus is constant.  As desired, all of the GAN approaches converge to a separation power that is smaller than the template method, as they have access to more and unbinned information.  For both the vanilla GAN and the WGAN, using a skew-normal distribution for the noise accelerates the training time.  The Vanilla GAN also converges faster than the WGAN, though all GAN approaches have a similar final separation power.

\begin{figure}
\begin{center}
\includegraphics[scale=0.6]{figures/GAN_err.pdf}
\end{center}
\caption{Estimated separation power between the generated jet-kinematic distribution and the real jet-kinematic distribution for various GAN architectures.  The template error corresponds to the baseline approach with no neural networks and is thus independent of the number of training epochs.}
\label{fig:err}
\end{figure}

\begin{figure}
\vspace{-1.8cm}
\begin{center}
\hbox{\hspace{0cm}\includegraphics[scale=0.55]{figures/hist_ratio_lines.pdf}}
\end{center}
\vspace{-0.5cm}
\caption{The physics-based (`real') mass distributions compared with distributions from the template method and the vanilla GAN in bins of jet $p_T$ (top row), $\eta$ (middle row), and $N$ (bottom row). The uncertainty in the ratio was calculated as the $1$-sigma error assuming poisson distributions of events in each bin. The error shown in the plots is the calculated statistical error.  The corresponding plot in the control region is qualitatively similar, but converges quicker.}
\label{fig:ratio}
\end{figure}

\begin{figure}
\begin{center}
\includegraphics[scale=0.31]{figures/no_cut.pdf}
\includegraphics[scale=0.31]{figures/max_40.pdf}
\end{center}
\begin{center}
\includegraphics[scale=0.31]{figures/max_26.pdf}
\end{center}
\caption{Computing the ratio error of generated mass distributions ($\sum m_J$ of the masses of the top $4$ jets) to the real distribution, symmetrized by reflecting across the ideal ratio $ = 1$, with statistical uncertainties included. Inclusive error measurements are shown on the top left, for the other plots a cut is placed on $N_\text{track}$ : where for $N_\text{track} < X$ all four jets of the events considered are required to have $N_\text{track} < X$. As we reduce the efficiency of this cut, we see that the GAN's relative performance to the template distributions becomes better - expected because the GAN learns how mass changes in $N_\text{track}$.  For the top right plot, the efficiency of the cut is 50\% overall while for the bottom plot, the efficiency is about 50\% per jet (about 6\% overall).}
\label{fig:sym_ratio}
\end{figure}

The mass distributions in the validation region are presented in Fig.~\ref{fig:ratio}, in bins of jet $p_T$, $\eta$, and $N$.  The average jet mass scales approximately as $\alpha_s\times p_T\times R$ and the width of the distribution also grows with $p_T$.  Given the jet $p_T$, the jet mass should be approximately independent of $\eta$, aside from detector effects.  Gluon jets have a large jet mass than quark jets and also a higher constituent multiplicity so there is a positive correlation between $N$ and mass.

Overall, the level of agreement between the GAN and the real mass distributions is better than for the template method and the real distributions.  This is particularly true for $N$, where the real mass is shifted to lower values for low $N$ (more quark-like) and to higher values for high $N$ (more gluon-like).  The GAN is typically well within 50\% of the real distribution, while the template method can be much more than a factor of 2 off of the real distribution for low and high $N$.  This is particularly important if the quark/gluon composition is different between the control, validation, and signal regions, either by chance or because some quark-tagging is applied to suppress the QCD background in such high multiplicity final states~\cite{Sakaki:2018opq}.

The modeling of the jet mass distribution in the validation region is used to determine systematic uncertainties on the templates.  Figure~\ref{fig:sym_ratio} explicitly constructs the systematic uncertainty as the sum in quadrature of the non-closure from the validation region and the control region statistical uncertainty.  These uncertainties are computed for the $\sum_{j \in J} m_j$ distribution (sum of the masses of the top four jets in the event), which is the main observable used in the RPV SUSY search~\cite{Aad:2015lea,Aaboud:2018lpl}.  In blue we have the deviation from the exact ratio $1$ for the template mass distributions and the GAN mass distributions. The GAN outperforms the template method in both the low mass and high mass limits; also note that when placing a cut on $N_\text{track}$, the performance of the GAN becomes even more pronounced. This is particularly encouraging because we expect such RPV-SUSY signals to be quark jet dominated, with a lower multiplicity on average than a gluon jet dominated background.  For a 50\% quark jet efficiency requirement on all four jets ($N_\text{track} < 26$), the uncertainty for the GAN approach is about 20\% in the high $\sum_{i\in J} m_j$ tail while it is well over 100\% for the template appraoch.

An important part of any background estimation technique is the associated systematic uncertainty.  One of the main sources of uncertainty here is the limited size of the training set in the control region.  The authors of Ref.~\cite{Cohen:2014epa} suggested a bootstrapping technique to estimate the uncertainty by rerunning the template procedure on bootstrapped datasets.  In principle, one could do the same procedure for the GAN training, with one GAN per bootstrap dataset.  More sophisticated methods include modeling GAN weights and biases as nuisance parameters to be profiled by the data with prior distributions.

A challenge for assessing previous GAN applications in HEP is that they have been designed to model high-dimensional feature spaces that are difficult to visualize and study~\cite{Paganini:2017hrr,Paganini:2017dwg,deOliveira:2017rwa,Chekalina:2018hxi,Carminati:2018khv,Vallecorsa:2018zco,Erdmann:2018jxd,Musella:2018rdi,Erdmann:2018kuh,ATL-SOFT-PUB-2018-001,deOliveira:2017pjk}.  The mass distribution example presented here provides a concrete testing ground to study GAN approaches where quantitative agreement can be studied and achieved using existing techniques.  While this study used only leading-order simulations of jet production, the methods are applicable more generally and can be applied to collision data from the LHC experiments.

\section{Conclusions}
\label{sec:conclusions}

Generative Adversarial Networks have been proposed as an alternative to histogram-based mass templates for the background estimation in LHC searches for RPV SUSY. These methods rely on the approximate QCD factorization whereby a jets type and kinematic properties are sufficient for determining the distribution of the jet mass. The neural network approaches are naturally unbinned and can be readily conditioned on multiple jet properties. In addition to using vanilla GANs for this purpose, a modification to the traditional WGAN approach has been investigated where weight clipping is replaced with drawing weights from a naturally compact set (in this case, the circle). Both the vanilla and modified WGAN approaches were able to outperform the histogram method, especially when modeling the dependence on features not used in the histogram construction. When training such generative models for physical applications, the usual limitations of the method apply such as the potential for overfitting, sensitivity to hyperparameters, and vanishing gradients slowing training down - though methods of circumnavigating these limitations have been studied in the last few years, such as using 'softer metrics' such as the Wasserstein metric. These results can be useful for enhancing the sensitivity of LHC searches to high-multiplicity final states involving many quarks and gluons and serve as a useful benchmark where GANs may have immediate benefit to the HEP community.

\acknowledgments

We would like to thank Tim Cohen, Luke de Oliveira, Mustafa Mustafa, Michela Paganini, Max Swiatloski, and Jesse Thaler for their helpful feedback on the manuscript.  This work was supported by the U.S.~Department of Energy, Office of Science under contract DE-AC02-05CH11231.

\bibliographystyle{jhep}

redpajama_set_name: RedPajamaArXiv


IBM Cloud Kubernetes Service is a managed Kubernetes offering to deliver powerful management tools, an intuitive user experience, and built-in security and isolation to enable rapid delivery of applications—all while leveraging IBM Cloud Services, including cognitive capabilities from Watson. IBM Cloud Kubernetes Service provides native Kubernetes capabilities like intelligent scheduling, self-healing, horizontal scaling, service discovery and load balancing, automated rollouts and rollbacks, and secret and configuration management. Additionally, IBM is adding capabilities to the Kubernetes Service, including simplified cluster management, container security and isolation choices, the ability to design your own cluster and leverage other IBM Cloud services such as Watson for your cognitive applications, completely native Kubernetes CLI and API, and integrated operational tools or support to bring your own tools to ensure operational consistency with other deployments.
I'm very excited to partner with Joe Gardiner (@grdnrio) from Portworx (@portwx) on this blog post. Learn more about our partnership here and check out the documentation. The rest of this guide is written by Joe.
IBM Db2 is a widely used enterprise database and is part of a larger Db2 family with big data, analytics, and data-streaming use cases. IBM has released and supports a number of DB2 container images on the Docker Store, making it easier than ever to deploy Db2 on your container platform.
How can I failover my Db2 database in the case of a container host failing?
How can I ensure data availability through a snapshot and restore process?
How I can I upgrade my database without corrupting my data or disrupting service?
How can I migrate my containerized Db2 into another cluster or environment?
The aim of this blog post is to answer the above questions by demonstrating a Db2 deployment architecture on Kubernetes using Portworx persistent volumes. Following this post, you'll understand how to run Db2 in production on Kubernetes.
Chose a multicloud container orchestration platform like Kubernetes.
Install a multicloud container storage solution like Portworx.
For a simple and efficient high availability (HA) setup, run a single instance of Db2 and set container volume replication to repl:"3".
Set io_priority:"high" to schedule Db2 instance on fast storage medium for better IO performance. Use a journal device with your volumes to speed up performance.
Use the Portworx custom block size parameter to support the Db2 512b requirement.
Read on for more details about running an HA Db2 on Kubernetes.
This blog post makes use of the developer edition of Db2 from the Docker Store. This is a verified and supported image and is perfect for testing, with enterprise images available for production.
Db2 can run in a single-node configuration and in a clustered configuration as described here.
The preferred replication technique with Db2 is the use of an Active-Passive replication set with a primary instance replicating to secondary and tertiary nodes. It makes use of etcd as a source of truth to avoid split brain should a node fail. You can read more about the architecture here.
With Portworx, each Db2 Master and Secondary can have its PVC synchronously replicated. This makes recovering database instances a near-zero cost operation, which results in shorter recovery windows and higher total uptime. With Portworx and Kubernetes, database instance recovery can take less than 180 seconds, which is very rapid considering Db2 can take two minutes to start up.
For deployments where you require replication for data protection but where a single database instance is capable of handling the read requests, a single Db2 pod with Portworx replicated volumes offers a simpler and more cost-effective solution to running HA Db2 on Kubernetes.
This is far less complex to manage and configure and requires a third of the Db2 Pods and therefore s third of the CPU and memory because Portworx is already running on your Kubernetes cluster and synchronously replicates data for all of your applications with great efficiency and scale.
To start a Db2 deployment, we first need to create a Storage Class. Here we can define how we want to handle the storage for DB2.
Repl 3: This specifies how many replicated volumes we want across worker nodes in our Kubernetes cluster.
Block_size 512b: This meets the 512 KiB requirement for a relational database in Db2.
Io_profile: db: This implements a write-back flush coalescing algorithm. This algorithm attempts to coalesce multiple syncs that occur within a 50ms window into a single sync.
allowVolumeExpansion: true: This parameter allows us to resize the volume live without any disruption or pod restarts.
Along with the above parameters, we can add Kubernetes snapshot schedules and Kubernetes data encryption policies directly in this storage class definition. This declarative style of configuration is exactly what modern cloud-native infrastructure is all about. No more snowflakes; you can recreate whole environments from source code, including the automation of your common data management tasks.
Next, we need to create the Persistent Volume Claim.
In the above, we are referencing the StorageClass name we created earlier and our storage requirements. Remember, we can resize this later as necessary.
Firstly, note the image being used. This is available from the Docker Store, but it requires Docker credentials to access it. You can see we're using an imagePullSecrets parameter with a regcred secret. Follow the Kubernetes docs to set this up.
HADR and ETCD values above are used for an HA pod deployment scenario, whereas the approach in this blog is to run a single pod and reply on Portworx and Kubernetes rescheduling to provide resilience.
In the example, TO_CREATE_SAMPLEDB is set to true. This is to give us some sample data to work with when proving replication and snapshotting. In a real-world scenario, you will probably want to disable this.
You may also notice that we're setting a fsgroup. The group ID for the db2inst1 user is 1000, so we're ensuring ownership is set correctly for the volume /database mountpoint. This will allow new databases to be created at this location using the db2 cli with kubectl exec.
Let's now walk through a failover scenario. We can simulate a failover by cordoning the node on which Db2 is running and then deleting the Pod. This will force Kubernetes to reschedule the pod.
Once Kubernetes identifies that the pod needs to be rescheduled, it will work with Portworx's Kubernetes scheduler extender, STORK, to identify which node is best suited to host the restarted pod. In our small environment, any of the two remaining nodes will do because we have a copy of the data on all three nodes. In reality, you will likely have much larger clusters, and that's when STORK will benefit you by making sure the pod starts on a node where a copy of the data is locally stored. In the unlikely event that your pod cannot be started on one of those nodes, it will be able to start on any of the cluster nodes and access its data seamlessly through the Portworx storage fabric.
So it seems that just for Reliability and High Availability alone, it would be worth running Db2 on Kubernetes and Portworx. But, there is a lot more that you can do. So many of the data management operations that are error-prone and time consuming are now going to be fully automated the same way in any cloud environment. First, we'll show how volumes can be dynamically expanded without reconfiguring or restarting Db2 and then we will show how Snapshots can be easily restored.
Data management tasks like these need to be predictable and automatable when it makes sense to do so. Since Portworx volumes are virtual and carved out of your aggregate storage pool on your cluster, we thinly provision the volumes so that the expansion doesn't immediately require you to add more storage capacity to your cluster.
Setting the allowVolumeResize parameter in the StorageClass means we can do this through the Kubernetes CLI by editing the yaml. You can either do this directly with a kubectl edit pvc/db2-pvc or you can edit the spec directly and then reapply it. Either way, you will be able to check the volume and see that it is now 10GB without any disruption being caused to the running application.
To learn more about Db2 snapshot schedules, please refer to our docs page.
Stork allows a snapshot to be used as a base from a which a clone PVC can be created. This allows a fresh Db2 deployment containing the snapshot data. You can read more about restoring from a snapshot using Stork in our docs.
As we've just seen, you can easily run an HA Db2 container on Kubernetes using Portworx for replication, snapshots, backups, volume resizing, and even encryption. You can see a demo of this in action!


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An Essential Guide for Leadership Development in Higher Education
Newly revised, "A Guide for Leaders in Higher Education: Concepts, Competencies, and Tools, Second Edition" addresses more broadly issues pertaining to diversity, inclusion, equity, and belonging, and the impact of the COVID-19 pandemic.
During the last 18 months, American institutions of higher education have grappled with an unprecedented combination of global and national crises, including the COVID-19 pandemic, a period of racial reckoning, an economy in recession, and other events of critical importance.
During these turbulent times, the need for strong leadership in higher education "has never been more imperative," argue SC&I Distinguished Professor of Communication Brent D. Ruben, Richard De Lisi, and SC&I Part-Time Lecturer Ralph A. Gigliotti, who have just published "A Guide for Leaders in Higher Education: Concepts, Competencies, and Tools, Second Edition."
The overarching message of the book, the authors said, is to "highlight the need to think broadly about the purposes of higher education and the dynamics of organizational excellence, and to apply these insights effectively in goal setting, planning and change leadership, outcomes assessment, addressing crises, and continuous improvement at both the level of the individual and organization."
In addition, the new edition includes tools to help leaders learn to better connect and engage with colleagues and students and communicate and collaborate more effectively with external constituencies in order to shape decisions and policies.
"Leadership in higher education today is complex and challenging, but also rich with opportunities for those committed to advancing the quality and effectiveness of their institutions," said Ruben, who is advisor for strategy and planning in the Office of the Executive Vice President for Academic Affairs, and senior university fellow at the Rutgers Center for Organizational Leadership, both at Rutgers University-New Brunswick. "Beyond the expertise in their own disciplines or specialty area, effective leadership requires a broad range of organizational competencies. These competencies do not come naturally, nor are they necessarily acquired through formal education. It has become well recognized in all sectors that becoming an effective leader requires a special commitment to learning core leadership concepts, the landscape in which leaders operate, and reflective practice and continuing improvement. We wrote 'Leaders in Higher Education' to address these needs."
Much of the book has been revised to focus "on the current higher education landscape. As a part of this updating, we devoted much greater attention to issues related to diversity, inclusion, equity, and belonging, along with addressing the impact of recent crises such as the COVID-19 pandemic," said De Lisi, emeritus university professor, emeritus dean of the Rutgers Graduate School of Education, and senior fellow at the Rutgers Center for Organizational Leadership.
These revisions are particularly timely, said Gigliotti, assistant vice president at the Rutgers Office of University Strategy and Director of the Rutgers Center for Organizational Leadership, because "during a time of unprecedented challenges facing higher education, the need for effective leadership—for formal and informal leaders across the organization—has perhaps never been more important. Since the first edition of 'A Guide for Leaders in Higher Education' was published in 2017, there have been a variety of significant changes in the higher education landscape. We wanted to capture these changes and describe evolving practices for addressing these challenges effectively."
Discover more about the Communication Department at the Rutgers School of Communication and Information on the website.

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text:
History of Artificial Intelligence
Posted on January 3, 2018 January 3, 2018 by burgerfried
Hello French Fries!
So, my office Secret Santa gave me a bolo tie a couple weeks ago, and I've been wearing it the past couple weeks because I think it looks cool and unique. So, two days ago, I decided that since I have been wearing a bolo tie to my office (instead of a normal tie), I should maybe watch a western or two since I really don't watch westerns. However, I can't seem to find any that are decent quality, modern, and I haven't seen before. I watched the Magnificent 7, Django Unchained, 3:10 to Yuma and the Good the Bad and the Ugly in the past year, so I would like to ask you to suggest in the comments some interesting or good westerns, I'd greatly appreciate it!
For my rant of the day: There has been a lot of focus in the media and in the realm of science fiction on the concept of artificial intelligence. Artists and social commentators have popularly portrayed Artificial Intelligence (AI for short) as manmade self-aware entities that pose a threat to humanity due to our ignorance or arrogance. From Hal 9000 to Ultron, popular culture has portrayed AI as nefarious plots and machinations by artificially produced human-brain-equivalents in metal or plastic bodies with the ability to think abstractly but lacking moral constraints. Consequently, there is a lot of misconception on what AI actually entails, and the purpose of our scientific community's investment in the study and development of AI.
Artificial Intelligence is the ability of non-biological machines to achieve goals through computation. To break this definition down, there are two parts: non-biological machines means manufactured devices or computers (the Artificial aspect of AI). There is nothing out of the norm there, but the next part is where things get tricky. The ability to achieve goals through computation (Intelligence) is a very basic definition of intelligence. There are many theories and ideas of what actually defines the parameters of computation and goals, so the scientific community has yet to come to a consensus on what qualifies for intelligence when it comes to AI. Consequently, as advancements in machine learning and task achievement become more common in occurrence amongst machines, what we as the casual public define as an 'Intelligent Machine' keeps becoming more advanced and difficult to attain as previous achievements begin to be viewed as mundane. While the general public might think that Artificial Intelligence is the equivalent of 'human intelligence', this could not be farther from the truth.
Despite what you may think, the concept of AI is not a modern innovation. The ancient Greeks believed that their god of fire, Hephaestus, had the ability to construct self-aware robots in the form of humanoid assistants and animal-like pets constructed out of gold and silver. This was likely inspired by, and served as inspiration to several ancient and medieval figures from a wide range of civilizations, such as the Seljuq academic Ismail al-Jazari, the Florentine polymath Leonardo Da Vinci, and the Chinese philosopher Yan Shi amongst others. These studious scholars built self-operating humanoid machines called Automatons. Automatons were the earliest forms of robotics; their designs often used technology similar to clocks in order to automate a process or task through movement triggered by certain cues. Famous examples include Leonardo DaVinci's Knight, which could ride a horse and swing a sword; King Philip II of Spain's Mechanical Monk, which could walk around, kiss rosaries and recite prayers; and many examples by Al-Jazari, including an automated soap and towel dispenser (900 years ago, well before the automated ones we have today), a mechanized wine servant who could sense when your glass was empty and refill it, and a full floating orchestra. The Greek myth of Talos has been theorized to have been based on a real giant Automaton that was designed to distinguish foreign vessels from local ones and throw rocks at hostile ships to scare them away. For those of you who put weight on faith over science, there is even biblical reference to Automatons. King Solomon's throne was said to have 6 steps leading up to it, each with a different golden statue of an animal of Solomon's own design that would bow whenever Solomon walked past but not anyone else. His throne itself was designed to move on it's own. While primitive, these machines met the basic definition of AI, they could make an if-then logic argument which triggered them to achieve a task based on their judgment without management or regular input from an overseeing human.
Artificial Intelligence is based on the ability of machines to conduct reasoning, and are in essence attempts to mechanize human logic and reasoning. Thus, while Automatons are able to discern logic, there wasn't much progress past the automatons until the advent of the Computer Science field by Charles Babbage and Ada Lovelace, which was then advanced from theoretical to practical application thanks to the works of Alan Turing . I went into details about Babbage and Lovelace in a previous post, so I won't go into much detail here, but when Babbage and Lovelace invented the first computer and the first algorithm, they sowed the seeds that the Second World War harvested. The war necessitated the invention of the first programmable computer by Alan Turing, which opened a new door for the study of AI. The funny thing about World War 2 and all of it's horrors and desperation is it's total war nature led to large scale advancements in the computer science and technological fields. AI as we know it today was borne by the fires of World War 2, and after the war, many of the scientists and theorists who were assigned to work on developing AI during the war effort decided to continue full time after the war concluded. They were most likely inspired by Alan Turing. Alan Turing, who even today remains one of the most influential academics in the study of AI, wanted to go beyond simple logic-based intelligence and design machines that could actually think like humans. To this end, he repurposed a Victorian era guessing game, called the Imitation Game, into a test, now known as the Turing Test. In the Turing Test, a normal, average person is put in a room and will blindly communicate through a chat interface with either a person or a computer, and they need to determine if they are talking to a person or a computer. If a computer can convince the tester that it's a person, it passes the Turing Test. This test, which Alan Turing proposed in 1950, would form the basis by which AI research and development would be focused. For the last 70 years, AI research and development was built around developing computers and algorithms with the aim of being able to overcome the Turing test to prove it's intelligence.
This brings me back to my original definition of Artificial Intelligence. While the technology has developed, and we have long ago achieved AI as it was originally conceived centuries ago, the parameters of what we define as intelligence. and thus the goals of computer scientists with relation to AI has evolved as technology evolved over time. Since 1950, AI development and computer learning has been almost exclusively focused on beating the Turing test, an achievement that was not accomplished until a scant 3 years ago. This has left the field today somewhat lacking in purpose and direction. This uncertainty, and the fatalistic creativity of the collective human mind, has left the future somewhat cloudy yet full of possibility of what will happen. There are industry titans such as Elon Musk who fear that AI enhancements will lead to the destruction of humanity. I have made no attempt to hide my admiration for Elon Musk, but I do not agree with him in this instance. Artificial Intelligence is limited by human parameters and design, and even when there is an abundance of intelligence as designed by nature (the perfect programmer), it isn't as intelligent as we believe. I personally believe that before we build a Hal 9000 or an Ultron, we would ultimately be able to minimize the harm to our collective selves because Artificial Intelligence is still limited by how we as humans perceive intelligence. While our definition of intelligence has changed over the centuries, our compassion and fear of self harm has not, and our natural, human parameters will limit the scope of artificial intelligence to those same parameters. After all, the goal of Artificial Intelligence in todays understanding is to mimic Human intelligence, and as Albert Einstein once said. "Only two things are infinite, the Universe and human stupidity, and I'm not so sure about the former". So while I respect Mr. Musk, I must disagree with his assessment since the parameters we currently have set for AI mimic our own intelligence, and we are not as intelligent a species as we like to think, so we shouldn't fear Judgement Day anytime soon.
Now for my tip of the day: If you are like me and spend a significant amount of time surfing the web, then you know the annoyance of auto play videos. These are videos or advertisements on different websites that start to automatically play when you first open the webpage. It annoys the crap out of me because when I'm on my computer, I normally got my Spotify playlists playing in the background and the multiple sounds racket gives me a headache. There is good news though, most browsers have a way of disabling autoplay. Intrigued by this proposition? Then read on.
1.) Chrome
When you open chrome, go to the three dots icon in the top right hand corner of the browser, open that and select settings. In the chrome://settings page, on the top left there will be 3 lines next to the word Settings. Select the three lines, expand the Advanced menu underneath, and select 'Privacy and security'. In the chrome://settings/privacy page, underneath the Privacy and security menu, select Content Settings. Go to Flash in the subsequent menu and check 'Ask first'.
2.) Edge (and IE)
Go to the menu icon, and select settings. In settings, select View Advanced Settings. From there look for the 'Use Adobe Flash Player' option and turn it off.
3.) Safari
Select the safari menu from the top and open Preferences. In Preferences, click the websites tab, and scroll down the left side menu until you see Plug-ins. Uncheck the Adobe Flash Player plugin.
4.) Firefox
Click on the main menu (3 horizontal lines). In the menu, navigate to the Add-ons and click on that. Select the Plug Ins option on the left panel. Select the dropdown menu for Shockwave Flash and select 'never activate'.
Just as a disclaimer, while these do work for flash player videos, which are the ones that traditionally start once you open a website, there's been a sneaky and nefarious trend over the past year by website developers of going away from flash plugins in favor of directly embedding the video into the website's HTML5 code. If the website you are opening has the video embedded directly in the HTML instead of through a flash plugin, I apologize, but you are SOL and will have to deal with the nuisance since there isn't a built in way of preventing those. Sometimes an ad blocker might help, but not always.
Well folks, that concludes todays post. Until next time…
…the ketchup is in the sauce.
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text:
Q: Adding class alongside id in Ajax sucess So on success I have content that goes into a div that has both an id and class in PHP and because the class has css attached it needs to be also added on Ajax success..
<div id="notif_actual_text-<? echo $notificationchant['notification_id'] ?>" class="notif_actual_text">

So my question is. How do I add class="notif_actual_text"to
 $("#notif_actual_text-"+notification_id)

A: If you want the element with that id AND with that class:
 $("#notif_actual_text-"+notification_id+".notif_actual_text");

If you want to add that class to that id:
 $("#notif_actual_text-"+notification_id).addClass('notif_actual_text');

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text:
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Publish Date : 2016-12-29
Insights into the immunopathogenesis during Japanese encephalitis virus infection
Shailendra K Saxena
Swatantra Kumar1, Sai V. Chitti1,2	, Ravi Kant1,
1Department of Stem Cell / Cell Culture, Centre for Advance Research (CFAR), King George's Medical University (KGMU), Lucknow 226003 (UP), India
2CSIR-Centre for Cellular and Molecular Biology, Uppal Road, Hyderabad 500007 (TS), India.
Prof. Shailendra K. Saxena, Deptartment of Stem Cell/Cell Culture, Centre for Advance Research (CFAR), King George's Medical University (KGMU), Chowk, Lucknow-226003, India; Phone:+91 522 2257450 Fax: +91 522 2257450; E-mail: shailen@kgmcindia.edu
Saxena S.K., et al. Insights into the immunopathogenesis during Japanese encephalitis virus infection (2017) Cell Immunol Serum Biol 3(1): 83- 86.
© 2017 Saxena S.K. This is an Open access article distributed under the terms of Creative Commons Attribution 4.0 International License.
JEV; Encephalitis; CNS infection; Immunopathogenesis; Cytokines; Chemokines
The magnitude of Japanese encephalitis occurrence is escalating from last few years. Despite the availability of vaccine, new cases are arising; therefore it is important to look for better prevention strategies to control JEV. In order to achieve, one has to explore the various ways of immune evasion strategies employed by JEV. Persistence of JEV in the infected target cells by accumulation of autophagosome is the novel mechanism to escape host immune anti-viral response. JEV nterferes with the IFN signaling pathway by inhibiting nuclear translocation of STAT2 which is mediated by NS5 protein. JEV infection results in the up-regulation of MHC-I on the surface of infected target cells. On the other hand it also activates the cellular autophagy to diminish the innate immune response to promote the cell survival. JEV infects the central nervous system via trans-migrating neutrophils, which can breach the blood brain barrier. Detection of viral RNA via RIG-I has suggested the activation of NF-κB pathway in the infected neurons. As a result of the activation, elevated level of pro-inflammatory cytokines and chemokines contributes to the inflammation of the brain. The primary focus of this review is to discuss the immunopathogenesis during JEV infection including the future perspectives that might be crucial in understanding the involvement of various cytokines and chemokines in the development of neuroinflammation and encephalitis.
Encephalitis is an acute inflammation of brain caused by either host immune response or viral infection. According to the World Health Organization (WHO), viral encephalitis is predominantly caused by Japanese encephalitis virus (JEV) which belongs to genus Flavivirus and family Flaviviridae. JEV is the main cause of viral encephalitis in many countries of Asia with an estimated 68,000 clinical cases every year[1,2]. JEV is a positive sense single strand (+ss) RNA virus of about 11 kb. RNA encodes a poly-protein precursor of approximately 3400 amino acid. Host cell signalases and viral encodes proteases would accomplish the processing of poly-protein and give rise to three structural proteins that includes core (C), preMembrane (prM) and envelop (E) and seven non-structural proteins NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5 which are responsible for the replication, viral transcription and also involves in alteration of host immune responses[3,4]. Interestingly, a larger NS1-related protein NS1' (NS1 prime) is produced as a result of -1 ribosomal frame shift event, generated due to a conserved slippery heptanucleotide motif located near the upstream of NS2A gene and is induced by a downstream RNA pseudo knot structure[5].
The principle vector is Culex mosquito, most important being Culex tritaenorhynchus, present in greatest density in rainy season (June to November). Humans are accidental deadend- hosts as they do not develop a level of viraemia sufficient to infect mosquitoes. The natural cycle of JEV consists of pig-mosquito- pig or bird-mosquito-bird, and pigs serve as a biological amplifiers and reservoirs. Predominantly human JEV infection is asymptomatic or results in mild symptoms. However, a small proportion of infected individual may develop inflammation of the brain which is known as encephalitis. The manifestation of encephalitis depends on which part of the nervous system that has been affected. The early symptoms including non-specific febrile illness, diarrhea and rigor followed by symptoms such as headache, high fever, reduced level of consciousness, seizures, tremors and convulsion[6]. Many survivors of JE may acquire neuropsychiatric sequelae with cognitive and language impairment[7].
Host immune responses during JEV infection
Innate immune response against viral infection depends on rapid recognition of viral antigens by pattern recognition receptors (PRRs) which recognize pathogen associated molecular patterns (PAMPs) which are a conserved structures present on viral pathogens. Toll like receptors (TLRs) are the most well studied PRRs, which plays an important role in recognition of viral components such as nucleic acid. TLRs signaling result in the production of inflammatory cytokines, interferon (IFN) that leads to the maturation of dendritic cells and establishment of antiviral immunity. Various cell types have developed different mechanisms to sense Flavivirus infection and stimulate innate immune response. Double stranded viral RNA (dsRNA) generated during Flavivirus infection as a replication intermediate, serve as PAMPs that can be detected by PRRs. Retinoic acid-inducible gene I (RIG-I), cytoplasmic detector for dsRNA and ssRNA with 5′triphosphate. In neurons the interaction of viral RNA with RIG-I leads to the activation of various intracellular signaling pathways leading to activation of host cell cytokine gene expression and enhancement of other cellular functions such as the activation of nuclear factor κB (NF-κB)[8]. Viruses have evolved various mechanisms to disrupt the host immune system[9] such as bypass the presentation of viral antigenic peptide through MHC-I as the immune escape strategy; subsequently escape the CD8+ T lymphocyte mediated cytotoxic killing of virus infected target cell. Flaviviruses have shown to be up regulate the expression of MHC-I molecule[10] but upon JEV infection the expression of MHC-I molecule on the surface of macrophages were not significantly changed[11]. Persistence of virus, in the infected host cell, by arresting autophagy, is the novel mechanism to escape host innate immune anti-viral response. JEV activates the cellular autophagy to diminish the innate antiviral immune response and promote cell survival. However, the mechanism behind, the accumulation of autophagosome occurs during JEV replication is yet to be studied[12]. Viruses have developed mechanism to commence viral replication in the host target cells by disrupting the action of IFN and evading IFN stimulated anti-viral immune responses[13,14]. Monocytes, dendritic cells and macrophages are the most targeted cells by Flaviviruses[15]. JEV interfere with the IFN-α/β signal transduction pathway through inhibition of tyrosine phosphorylation of receptor associated kinase TYK2 and STAT2[16]. Among all non-structural proteins, NS5 is mainly responsible for the inhibition of IFN signaling pathway by interfering nuclear translocation of STAT protein (Figure 1). NS5 is the largest non-structural protein and contain most conserved, N-terminal and C-terminal domains with methyl transferase (MTase) and RNA dependent RNA polymerase (RdRp) activities respectively. IFN antagonist activity is independent of MTase and RdRp, identification of domain responsible for inhibition of IFN α/β signaling is important for determining virulence factor of JE and may be used to design potent anti-viral drugs and effective vaccine candidate[17]. Other Non-structural proteins such as NS4A and NS4B triggers membrane rearrangement during viral replication. Whereas NS2B and NS3 works together as protease which involves in deactivation of ASK1-p38MAPK signaling that would results in mitochondrial mediated apoptosis (Figure 2). Nitric oxide synthase has been shown to play an important role in protective immune response in case of viral infections. Nitric oxide (NO) prevents JEV infection by interfering with synthesis of viral RNA, accumulation of viral proteins and release of virus from infected cells. Elevated levels of inducible form of nitric oxide (iNOS) have been reported in case of JEV infection[18].
Figure 1: JEV NS5 protein mediated inhibition of inhibition of IFN-α/β signal transduction pathway.
JEV interfere with the anti-viral immune response by inhibition of IFN-α/β signal transduction pathway. NS5 protein of JEV inhibits the nuclear translocation of STAT protein from cytoplasm to nucleus and blocks interferon sensitive response elements (ISRE). JEV activates the cellular autophagy to diminish the innate antiviral immune response and promote cell survival by accumulating autophagosome.
Figure 2: Schematic representation of Immunopathogenesis during JEV infection.
JEV encodes for 3400 amino acid long polyprotein which further processed and generate three structural proteins C, prM and E and seven non-structural proteins which includes NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5. NS1 protein involve in viral replication, NS2A block eIF2α phosphorylation mediated by PKR, NS2B and NS3 work as protease which involve in deactivation of ASK1 p38MAPK signaling that results in mitochondrial mediated apoptosis, NS4A and NS4B triggers membrane rearrangement during viral replication; and NS5 which involves in the IFN antagonist activity by interfering with the nuclear translocation of STAT and contribute to Immunopathogenesis.
CNS infection
Flaviviruses can cross the blood brain barrier (BBB) through passive transport mechanism across the endothelium by 'Trojan Horse Mechanism' in which virus is transported through the infected host inflammatory cells[19]. Central nervous system (CNS) infection is followed by leukocyte migration towards the brain matrix which often has catastrophic consequences. Chemokines have been shown to play an important role in regulating leukocytes trafficking to the sites of inflammation and tissue damage[20]. The mechanism of leukocyte infiltration is poorly understood but, it has been speculated that the pro-inflammatory cytokines and chemokines may be responsible for the cerebral recruitment and infiltration of leukocytes. Trans-migrating neutrophils can result in the production of inflammatory cytokines, reactive oxygen species (ROS), matrix metalloproteinase (MMPs) which can further degrade the basal lamina of cerebral blood vessel result in damage of BBB. Chemokines acts as a chemo-attractant for various cell types of the CNS. RANTES (encoded by CCL5 gene), is a chemokine released by CD8+ cytotoxic T lymphocytes; act as a strong chemo-attractant for T lymphocytes, monocytes, eosinophils and basophils. Viral infection has been shown to induce RANTES expression in a broad range of cells[21]. The RANTES gene promoter is divided into 5 regions (A-E) which plays distinct important role in activation of gene. The A, B and E regions of RANTES gene promoter contains binding site for transcription factors, NF-κB and NF-IL-6 respectively. Elevated levels of cytokines such as TNF-α, IL-8 and macrophage derived chemotactic factor in the serum as well as in cerebrospinal fluid has been shown upon JEV infection[22]. These elevated levels of cytokines in serum and CSF in the patients infected with JEV may be the reason behind the increased rate of mortality[23].
The activation of cell surface receptor such as C-type lectin domain family 5 member A (CLEC5A) and DAP-12 upon JEV infection result in elevated level of pro-inflammatory cytokines that causes the transmigration of leukocytes across the BBB which might be responsible for disease severity. Virus interaction with monocyte and macrophage result in strong stimulation of CLEC5A and DAP-12 mRNA expression in brain that will result in the CLEC5A/DAP-12 activation and secretion of pro-inflammatory cytokines like TNF-α and IL1-α[24]. Adhesion molecules have also been found to be over expressed during inflammation in the brain[25]. Intracellular cell adhesion molecules (ICAM) such as ICAM-1 are ligand for a receptor LFA-1 present on the leukocytes. Reports also suggest that pro-inflammatory cytokines can induce the expression of ICAM-1, leukocytes binds to the endothelial cells via ICAM-1-LFA-1 followed by the transmigration into the brain matrix[26].
Emergence of Japanese encephalitis is of significant public health research concern and global awareness. The failure to overcome the spread of JE cases in Asia and Pacific region, in spite of availability of vaccine indicating us that we need to focus on effective prevention strategy. The burden of JEV infection is expected to continue to increase in the future due to climate change, globalization, socioeconomics, viral evolution and other factors. Growing cases of JEV infection in South-East Asia and other parts of the globe suggest that, there is a critical need for intense research regarding its biology and mechanism that establishes inflammation in brain and cause encephalitis. There should be expansion and strengthening of the vaccination program in the epidemic zone and areas with the highest risk of JEV transmission. JE epidemics generally occur in rural and remote settings, where diagnostic facilities are rudimentary. Currently there is no other prevention strategy other than Chemical control of vector population using insecticides shows insignificant role in JE control. There should be obligation of the environmental management for vector control for instance intermittent irrigation should be implicated, which can substantially reduce vector breeding result in reduced transmission rate. It is pertinent to execute monitoring and supervision of epidemiological areas at the field level for the successful implementation of JE. Furthermore, it is important to understand the JEV infection in CNS which may provides new insights regarding the ways to prevent neuro-inflammation and encephalitis. Since the mortality rates are mainly due to the CNS infection, if the complete entry and immunopathogenesis can be explored, one could hypothesize the major target for ideal therapeutics.
Acknowledge:
The authors are grateful to the Vice Chancellor, King George's Medical University (KGMU), Lucknow and Director, Centre for Cellular and Molecular Biology, Council of Scientific and Industrial Research (CSIRCCMB), India for the encouragement and support for this work. SK Saxena is also supported by CCRH, Government of India, and US NIH grants: R37DA025576 and R01MH085259. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
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For a (very) brief history lesson, the company was founded by Italian Camillo Olivetti in 1908 and became one of the top manufacturers of typewriters in its time. It's still a pretty big company now, but with fewer typewriters.
In terms of its designers, the more well-known, Giovanni Pintori, was head of the art department for Olivetti, receiving numerous awards. For their posters, he specialised in reimagining Olivetti's typewriters into geometric shapes, including birds and eggs for their symbolism of all things new, just because he could. He brought other designers into the mix, including Swiss Walter Ballmer, another key designer and part creator of the Olivetti logo (which remains unchanged since).
While making these beautifully designed typewriters, it's the posters that everyone raved (and still does) about. You could say it's pretty rare for a poster to stand the test of time better than the product it's promoting. And that's all down to design. Each one is pretty unique; it's only Pintori's designs which follow the similar style of a white background with multicoloured lines.
The detail that Olivetti and its designers went into for their advertising in a bid to stand out is clear in its Lettera 22 (which in itself is still very popular) promotional campaign. With so many completely different posters for one specific product, I lost count after 24. Each poster's one-of-a-kind graphic demonstrates what Olivetti was all about - a bold, striking design, with a touch of effortless cool.
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Just goes to show how important a good advertising design is - if people are still talking about your posters decades later, you know you've cracked it.
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