text:
\section{Introduction}

The study of continuous time temporal networks have attracted growing interest in the past decades, as time-stamped relational data emerges from various sources, including e-mail communication, e-commerce, or even neuron firing data \cite{Holme2012,Goldenberg2009}. Example of use-case includes the study opinion dynamics, epidemic spreading, and recommender systems.
Previous attempt to model such networks have traditionally relied on a discretization of time, allowing the representation of the temporal graph as a discrete sequence of static graphs \cite{Krivitsky2014,Kim2018,Durante2016}. As described in \cite{Latapy2017}, these attempts to read this problem through the well documented lens of graph theory fails to capture mechanisms coming from the causal nature of temporal networks.
Moreover, temporal relational data is often available with a high level of time granularity, allowing us to consider the time-stamps as continuous \cite{Rozenshtein2019}. The interaction patterns are often heavily localized in time, exhibiting a property commonly known as burstiness \cite{NaokiMasuda}. Thus, one of the challenges in temporal network modelling is to find assumptions that allow explaining the observed burstiness. For instance, a good model for temporal social networks should be able to give an idea of the duration that will separate two interactions between users, given a certain historical context.
To this date, few tools are available for continuous time temporal network analysis, notably methods coming from computational social sciences \cite{Butts2008}, or from point process modelling \cite{Bacry2018}. On the other hand, Survival Analysis has proven successful in the study of time-to-event data \cite{Aalen}, and nowadays, a broad range of tools is available for it \cite{Bender2021}.
In order to make these tools applicable to temporal networks, we propose to apply Survival Analysis to the waiting times between events for each dyad.
This method allows to elegantly and flexibly include inductive bias, in order to assess the statistical properties of an observed temporal graph, and predict the future interactions.
A visual summary of our method can be seen on Figure \ref{fig:graph_survival}.
The inter-event times are modelled using survival analysis conditioned on the time-varying features, in order to incorporate a first order dependency of the hazard rate on the history.

\begin{figure}[ht]
	\centering
	\includegraphics[width=0.5\textwidth]{figures/diagram}

	\caption{Summary of the Graph-Survival framework}

	\label{fig:graph_survival}
\end{figure}

Our main contributions are as follows:
\begin{enumerate}
	\item We introduce Graph-Survival: a framework that allows to translate any survival analysis model on a generative temporal network model. This framework allows including modelling assumptions on the inter-arrival times between event for each edge, in order to measure the salience of temporal network properties in observed data, and to predict the evolution of the network.
	\item We propose an algorithm for simulating networks based on this framework. This algorithm allows reproducing synthetically properties observed in real-world network, such as burstiness.
	\item We show how to perform Maximum Likelihood Estimation of model parameters on an observed dataset, and evaluate the quality of the fitting on a downstream link prediction task.
	\item For reproducibility purposes, we provide an open-source implementation of our method (<github url here>)
\end{enumerate}

\paragraph{Outline}
In section \ref{background} we give a brief introduction to the theoretical notions used in the Graph-Survival framework. In \ref{graph_survival} we introduce the framework, including the form of the likelihood and the simulation algorithm. In \ref{benchmark} we give some examples of models within this framework. In \ref{evaluation} we assess the performances of our method through numerical experiments.

\section{Background on Temporal Networks and Survival Analysis}
\label{background}
In this section we provide an overview of temporal networks, survival analysis and point processes.
We then show how to design a first order Markov chain model for temporal network, given a survival analysis model for the edge inter-arrival times.

\subsection{Temporal networks}
\label{tempnet}
Let $\mathcal{U}$ and $\mathcal{V}$ denote a set of source and destination nodes respectively, $\mathcal{E} \subset \mathcal{U} \times \mathcal{V}$ the set of possible edges.
Temporal network data takes the form of a sequence of time-stamped network events $\mathcal{H} = \{w_m=(e_m, t_m)|m=1,...,M\}$, where $ M$ is the number of events, $0<t_1<...<t_M$ is an ordered sequence of pairwise distinct, positive time stamps, $e_m=(u_m,v_m)\in \mathcal{E}$ is the edge involved in the $m$-th event, and $u_m\in \mathcal{U}$ and  $v_m\in \mathcal{V}$ are the source and destination nodes respectively. For instance, each event $((u,v),t)$ can represent a message sent by user $u$ to user $v$ at time $t$.
For any time $t$ we will denote $\mathcal{H}_t=\{w_m \in \mathcal{H}|t_m<t\}$ the history of the network up to time $t$. Moreover, for any edge $e =(u,v)\in \mathcal{E}$, we denote $\mathcal{H}^{(e)}=\{w_m \in \mathcal{H}|e_m=e\}$ the history of events involving the edge $e$.
Finally, we denote by $\mathcal{H}_t^{(e)}$ the history of events involving the edge $e$, up to time $t$.

\subsection{Survival Analysis}
Survival Analysis is the study of time-to-event data. It has been traditionally used in the medical field to model the expected survival time of patients under different setups, for instance the treatments provided or the pre-conditions of the patient \cite{Aalen}.
One of the goals of Survival Analysis is to quantify the effect of input covariates on the risk for a given event to happen. Such studies are often conducted in the presence of \emph{censored} data, meaning that the time-to-event is not directly known, but we have access to a lower/upper bound instead.
A mathematical object that is central in survival analysis is the \emph{hazard function}, denoted $h$. This function is such that for any positive real value time $t\in[0, +\infty[$, the probability for the event of interest (e.g. death of the patient) to happen in an infinitesimal interval $[t,t+dt[$, conditional on this event not occurring before $t$, is equal to $h(t)dt$. It can be shown that the probability for the event to happen only after $t$ (also framed \emph{survival function}), is equal to $S(t)=\exp(-\int_{0}^t h(s)ds)$. Unlike the probability density function or the survival function, the hazard function only has the constraint that it should be strictly positive. As a consequence it appears as a convenient tool to design statistical models on the survival time, conditioned on individual-specific covariates.

\subsection{Temporal Point Processes}
Temporal point processes are random variables whose realizations are sets of positive, real-valued time-stamps.
A fundamental quantity in the study of temporal point processes is the \emph{conditional intensity function}, denoted $t\mapsto\lambda(t|\mathcal{H}_t)$, such that for any $t\in[0, +\infty[$, $\lambda(t|\mathcal{H}_t)dt$ measures the average number of events occurring in the interval $[t,t+dt[$, given the event history up to time $t$ \cite{Daley2010}.
In the case of Poisson processes, the conditional intensity function is independent of the history, meaning that it is a \emph{deterministic} function: $\lambda(t|\mathcal{H}_t)=\lambda(t)$.
In contrast, \emph{self-exciting} processes are governed by a \emph{stochastic} conditional intensity function, whose value at time $t$ depends on the samples previous to $t$.
While Poisson Processes can be accurate when we have a clear a priori idea of the form of the intensity function, they are unable to adapt to new events. On the contrary self-exciting processes allow the conditional intensity function to take into account past events.

\subsection{From survival analysis model to first order Markov chain}
\label{markov}

By supposing that the successive inter-arrival times are governed by a survival analysis model, we can construct a first order Markov chain.

To do so, we suppose that in absence of any event, the point process is generated by a deterministic base intensity $\lambda(t|\mathcal{H}_t=\emptyset)=\mu(t)$.
We also suppose the existence of a vector-valued stochastic function $t\mapsto \bm{x}(t)$, defined for any time $t\in [0,+\infty[$, called \emph{time-varying features}. Then, given a non-empty history of previous events $\{t_1,...,t_n\}$, we suppose that $\bm{x}(t_n ) $is a sufficient statistic of the history up to time $t_n$, for the prediction of the next event:
$\lambda(t|\mathcal{H}_t=\{t_1,...,t_n\})=\lambda(t|\bm{x}(t_n))$.
Next we posit a survival model on the $n$-th inter-arrival time, $\tau_n=t_{n+1}-t_n$, conditioned on the feature vector $\bm{x}(t_n)$:
\begin{definition}
	We define the \emph{transition} hazard rate as the hazard rate in the model of the next event given the features at the time of the previous event. We denote it $\tau \mapsto h(\tau|\bm{x}(t_n))$. Here the notation $\tau$ is used to specify that we are considering the time since the last event.
\end{definition}
This means that the conditional intensity function, conditional on a non-empty history, is equal to $$\lambda(t|\mathcal{H}_t=\{t_1,...,t_n\}\}) = h(t-t_n|\bm{x}(t_n))$$
Alternatively, the occurrence of each new event can be seen as incrementing the conditional intensity function with a time-varying value:
\begin{definition}
	We define on $[0, +\infty[$ the \emph{intensity increment function}:
	$$t \mapsto\gamma(t|\mathcal{H}_t) = h(t-t_n|\bm{x}(t_n)) - \mu(t)$$
\end{definition}
While $h$ is a deterministic function of the time elapsed since the last event, $\gamma$ is a stochastic function of the absolute time of the process. Since $\gamma$ depends on the history $\mathcal{H}_t$ only through the time-varying feature vector $\bm{x}(t_n)$, we will use the notation $\gamma(t|\mathcal{H}_t)=\gamma(t|\bm{x}(t_n))$.

Conditioned on the feature function $\bm{x}$, the procedure defined here defines a self-exciting process, where the intensity function gets set to the value of the hazard function at every new event.

\begin{proposition}
	The conditional intensity function of the point process defined above is given for any $t\in [0, +\infty[$ by
	$$\lambda(t|\mathcal{H}_t) = \mu(t) + \sum\limits_{t_n<t} \mathbbm{1}_{t\in ]t_{n+1}, t_n]} \gamma(t|\mathcal{H}_t)$$
	where $\mathbbm{1}_{t\in ]t_{n+1}, t_n]}$ is the indicator function asserting that the current time is before the next event.
\end{proposition}
This formulation is reminiscent of Hawkes processes \cite{Hawkes1971}, with a difference being that here we make use of time-varying features, and make use only of the last event to predict the next one, while Hawkes processes add a time-varying increment for each previous event in the history.

To summarize, here we show how to start from a survival analysis model, represented by a modelled hazard function, and derive a model for the conditional intensity function of a given point process.

\section{The Graph-Survival framework}
\label{graph_survival}
In this section we show how to apply the previous procedure to derive a temporal graph generative model, given a survival analysis model.

\subsection{General Description}
\subsubsection{Form of the edge intensity function}
\label{edge_survival}
In our framework, given a temporal network such as the one described in \ref{tempnet}, we propose to consider for each edge $e =(u,v) \in \mathcal{E}$, the point process generating the edge-specific history $\mathcal{H}_e$.
\begin{definition}
	For each edge $e\in \mathcal{E}$ we define the edge-wise conditional intensity function $t\mapsto \edge{\lambda}(t|\mathcal{H}_t)$, associated to the point process generating the edge history $\mathcal{H}^{(e)}$
\end{definition}

Note that here we allow the edge-wise conditional intensity function to be conditioned on the history of the whole network, and not only on the edge-wise history $\mathcal{H}_t^{(e)}$.

We propose to posit a model such as the one described in \ref{markov} on each of the edge-wise intensity functions.
To do this, we define a time-varying feature function $\bm{x}:t \mapsto \bm{x}(t|\mathcal{H}_t)\in \mathbb{R}^{|\mathcal{E}| \times D}$, that allows, for any edge $e$ and time $t$, to access an array of features $\edge{\bm{x}}(t|\mathcal{H}_t) \in \mathbb{R}^{D}$, where D is the number of features. We provide examples of such edge features in section \ref{features}

Then, applying the procedure in \ref{markov}, we define the edge conditional intensity function as
$$\edge{\lambda}(t|\mathcal{H}_t) = \edge{\mu}(t) + \sum\limits_{t_n<t} \mathbbm{1}_{t\in ]t_{n+1}, t_n]} \edge{\gamma}(t|\edge{\bm{x}}(t|\mathcal{H}_t)).$$
Based on this formulation, we can parameterize the base rate and the intensity increment function, yielding a fully parametric model of the temporal network history. Note that a non-parametric form of the conditional intensity function could also be considered.

\subsubsection{Loss function}
Supposing a parameterization of the intensity function above, we propose to find the maximum likelihood estimates of the parameters by optimizing the negative log-likelihood.

Given a point process model with conditional intensity $\lambda(t|\mathcal{H}_t)$, the general form of the likelihood of an observed event history $\nset{t}$ on an interval $[0,T]$ is derived in \cite{Daley2010} and given by
\begin{align*}
	p(\nset{t})=
	\left(\prod\limits_{i=1}^n \lambda(t_i|\mathcal{H}_t)\right)
	\exp\left(-\int\limits_{0}^T \lambda(s|\mathcal{H}_s)ds\right).
\end{align*}

We use the independence property of the edges conditional on the time varying features to define the the negative log-likelihood of the model:
\begin{definition}
	The negative log-likelihood of our network point process model is given by
	\begin{align}
		\label{nll}
		L(\mathcal{H})=
		\sum\limits_{e\in \mathcal{E}} \int\limits_{0}^T \edge\lambda(s|\mathcal{H}_s)ds - \sum\limits_{i=1}^{|\mathcal{H}^{(e)}|}
		\log\left(\edge{\lambda}(t|\mathcal{H}_t)\right).
	\end{align}
\end{definition}

\newcommand{L_{(pos)}}{L_{(pos)}}
\newcommand{L_{(neg)}}{L_{(neg)}}
Alternatively, this loss function can be reindexed as a sum indexed by the events, since we have $L(\mathcal{H})= L_{(pos)}(\mathcal{H}) + L_{(neg)}(\mathcal{H})$ with:
\begin{itemize}
	\item $L_{(neg)}(\mathcal{H})= \sum\limits_{w_m\in \mathcal{H}} \left(\sum\limits_{e\in \Ecal} \int\limits_{t_{m-1}}^{tm} \edge\lambda(s|\mathcal{H}_s)ds\right)$

	\item $L_{(pos)}(\mathcal{H})=\sum\limits_{w_m\in \mathcal{H}} -\log(\lambda^{(e_m)}(t|\mathcal{H}_t))$
\end{itemize}

To get to this expression we split the integral on the left hand side of \ref{nll} over the inter-arrival time interval $\{[t_{m-1}, t_m[, m=1,...,M\}$, and we re-index the terms by event.

\newcommand{\tilde{\mathcal{E}}_m}{\tilde{\mathcal{E}}_m}
\newcommand{\tilde{L}_{(neg)}}{\tilde{L}_{(neg)}}
Although the edge sum necessary to compute $L_{(neg)}(\mathcal{H})$ leads to a computational complexity proportional to the square of the number of nodes, the loss function can be approximated by sampling a set of contrastive edge samples $\tilde{\mathcal{E}}_m$ for each event $m$, such that $|\tilde{\mathcal{E}}_m| \ll |\mathcal{E}|$.
We can then substitute the full sum with the renormalized mean of their associated survival terms to approximate $L(\mathcal{H}) \approx \tilde{L}_{(neg)}(\mathcal{H}) + L_{(pos)}(\mathcal{H})$ with $$
	\tilde{L}_{(neg)}= \sum\limits_{w_m\in \mathcal{H}}
	\frac{|\mathcal{E}|}{|\tilde{\mathcal{E}}_m|}
	\sum\limits_{e \in \tilde{\mathcal{E}}_m} \int\limits_{t_{m-1}}^{tm} \edge\lambda(s|\mathcal{H}_s)ds).
$$

This approximated loss function has a number of terms proportional to the number of events in the network history.
It can be further approximated computing it on batches, formed by taking slices of the history.

\subsection{Features}
\label{features}
As mentionned in the previous secion, out method can be implemented by carefully designing time-varying features on the network.
Features that are traditionally used for heuristic-based link prediction include for instance the degrees of the involved nodes, the number of common neighbors.
We derive a time-decaying version of these features, in order to down-weight the effet of events that occured a long time ago, in the feature values.

\subsubsection{Examples of features}
\label{feature_examples}
As is common in network analysis, the choice of features is highly dependent on the nature of the network considered, and the type of analysis being conducted.
In a variety of previous work, some time-varying edge features have been introduced \cite{Butts2008} \cite{Vu2011}  \cite{DuBois2013}.
We provide three examples of time-decaying features.
Here we denote by $\mathcal{H}_t^{u}$ the history of events involving node $u$, and $\gamma$ a decay hyper parameter controlling the rate at which the feature contributions from previous events ecome obsolete.

\begin{itemize}

	\item The exponentially down weighted degree, defined at time $t$ for any node $u$ as
	      $$
		      deg(u,t;\gamma)
		      =\sum_{(e_m, t_m) \in \mathcal{H}_t^{u}}exp(-\gamma_1(t-t_m)).
	      $$

	      This feature quantifies the volume of interactions involving the node $u$, weighted by an exponentially decaying factor favoring recent interactions over old ones.

	\item The exponentially down weighted volume of interaction
	      $$
		      volume(e,t;\gamma_3)=\sum_{
			      \substack{(e_m, t_m) \in H_t}
		      }\exp(-\gamma(t-t')).
	      $$
	      This feature measures the volume of recent interactions between the nodes involved in the edge $e$.

	\item The exponentially down-weighted number of common neighbors is calculated as

	      $$
		      cn(e,t)=\sum\limits_{v \in CN(e,t)} \exp(-\gamma(t-t_{last}(e,v))).
	      $$
	      Where $CN(e,t)$ is the set of common neighbors of the edge $e$ at time $t$ and $t_{last}(e,v)$ is the time of the last interaction between the nodes involved in $e$ and the common neighbor $v$.

\end{itemize}

\subsubsection{Feature computation}

As can be seen in \ref{feature_examples}, in many cases the computation of the feature vector only requires storing a number $O(|U|+|V|)$ of variables, in order to make the feature vector accessible for any edge-time pair
Since the feature function takes value in a space of dimension $|\mathcal{U}|\times |\mathcal{V}|\times D$, its explicit computation for all the events and non-events is unfeasible for reasonable-sized networks. Instead, for the features that we consider in this paper, we can store an array of smaller-dimensional time-varying statistics $\nset{g}$ of the network, such that for any edge, its associated feature vector can be computed at any time: $\edge{\bm{x}}(t) = F_e({g_1(t),...,g_S(t)})$ where $F_e$ is a simple transformation.

For instance, the computation of the number of common previous neighbors between two nodes $u$ and $v$ at a time $t$ can be done simply by keeping in memory the set of previous neighbors for each node, and comparing the two sets at the given time.

\subsection{Simulation}
Here we show how to use our model to generate synthetic networks. This simulation method is based on Ogata's modified thinning algorithm \cite{Ogata1981}. Here we provide a brief explanation of this method.
For every edge $e$, we remind that $t \mapsto\lambda^{(e)}(t|\mathcal{H}_t)$ denotes the conditional intensity of the point process of edge $e$. Similarly, we define $\lambda$, the total conditional intensity function, that generates the sequence of event times in the network:
$$t \mapsto \lambda(t|\mathcal{H}_t) = \sum\limits_{e \in \mathcal{E}} \lambda^{(e)}(t|\mathcal{H}_t).$$
We start from an initial time $t_0$.
At each iteration, we compute a local upper-bound of the total intensity on a right neighborhood of the current time.
This upper-bound allows us to generate the next event time across the network, in a rejection sampling manner. To do so, we generate a sample from an exponential distribution with rate parameter equal to the upper-bound, and then accept this sample with a probability proportional to the total intensity value at the newly generated time.

Once this new event time is generated, we attribute it to an edge randomly using a multinomial distribution over the set of possible edges. The probability for each edge to be selected is proportional to their intensity value at this new sample.
We summarize this method in Algorithm \ref{alg:simulation}

\newcommand{\lambda^{(\star)}}{\lambda^{(\star)}}
\newcommand{\mathcal{H}_{sample}}{\mathcal{H}_{sample}}

\begin{algorithm}[ht]
	\caption{Simulation using the Ogata's modified thinning method}
	\label{alg:simulation}
	\begin{algorithmic}
		\State {\bfseries Input:} initial time $t_0$, upper bound $T$, maximum number of iterations $N$
		\State Initialize $s = t_0$, $\mathcal{H}_{sample}$.

		\While{$s>T$ or $n> N$}{
			\State $\lambda^{(\star)} = \sup_{s>t} \lambda(s|\mathcal{H}_s)$
			\State Sample $w \sim Exponential(\lambda^{(\star)})$
			\State Set s = s+w
			\State Sample $U \sim Uniform([0,1])$

			\If{$U \leq \frac{\lambda(s)}{\lambda^{(\star)}}$}{

				\State Define for each edge $e$ the probability $$
					p^{(e)} = \frac{\lambda^{(e)}(s|\mathcal{H}_s)}{\sum_{e'\in \mathcal{E}}\lambda^{(e')}(s|\mathcal{H}_s)}
				$$
				\State Sample $\tilde{e}\sim Multinomial(1, \{p_e|e\in \mathcal{E}\})$
				\State Add the event $(\tilde{e}, s)$ in $\mathcal{H}_{sample}$

				\State Set $n=n+1$
			}

		}

	\end{algorithmic}
\end{algorithm}

\section{Examples of models}
\label{benchmark}
In order to illustrate our framework, we propose two examples of simple models that follow the construction presented above.
\subsection{Edge Poisson Process}
\label{edge_poisson}
As a first baseline, we consider that each edge history is independently generated by a Poisson Process, meaning that the rates are independent of the network history.
Whithout including prior knowledge on the temporality of the edge base rate, a simple way of doing it is to define the constant Poisson Rate of the edge $e=(u,v)$ as:
\begin{equation}
	\label{poisson_rate}
	\mu_{poisson}^{e} \overset{\Delta}{=} h(2 c+\alpha_u + \alpha_v- d(z_u,z_v))
\end{equation}

Where for each node $u \in \mathcal{U}\cup\mathcal{V}$, $\alpha_u \in \mathbb{R}$ is a sociality coefficient, $z_u \in \mathbb{R}^d$ is an embedding of dimension $d$, $c$ is an offset parameter, $h$ is an activation function, and $d$ is a distance function. In our example we use the softplus function $h(x)= log(1+exp(x))$.
This model for the rate is inspired by the literature about latent space models for graphs \cite{Hoff2002}.
The underlying intuition is that each node $u \in \mathcal{U}$ in the network is equipped with a position $z_u$ in a latent social space, and a disk $B_u$ centered around $z_u$ and with radius $\alpha_u + c$, such that two nodes $u$ and $v$ have a positive log-odd of interaction whenever $B_u\cap B_v$ is non-empty.
In equation \ref{poisson_rate}, the smaller $d(z_u,z_v)$ is, the higher the Poisson rate between $u$ and $v$ will be.

\subsection{First-order Markov Chain}
Whereas the Poisson model yields an intensity function that is independent of the past network history, we propose to include a simple interaction between the past of the network and the next event by constructing a Markov Chain following the procedure in \ref{edge_survival}.
Here we temporarily omit the edge subscript, for notation simplicity.
As a base rate, we choose to use the Poisson Rate defined above.
For the transition survival model, we rely on a recent work \cite{Bender2021} that defines the hazard function as a piece-wise constant one:

$$\forall \tau > 0, h(\tau|x(t_n))= \sum_{j=1}^J \mathbbm{1}_{I_{j}}(\tau) \exp(\theta_j^T x_{t_n}).$$

Thus, the conditional intensity function is given by

\begin{align*}
	\lambda(t) =\mu_{poisson} + \mathbbm{1}_{t>t_n}\gamma(t|x(t_n)).
\end{align*}

Where $t_n$ is the time of the previous event for the considered edge and $\gamma(t|x(t_n))\overset{\Delta}{=} h(t-t_n|x(t_n))$.

To summarize, in the case of the Poisson rate we drop the time-dependence of the edge events, and specify their rate as a constant that depends only on inner properties of the edges, such as their sociality coefficient or their embeddings.
In the first order Markov model, the event time of the next event for a considered dyad, is modelled by conditional on the edge feature values at the last event time for this dyad.

\section{Evaluation}
\label{evaluation}
We evaluate our method in two ways. First we assess its ability to generate realistic networks. Second, we assess its predictive capacity through a downstream Link Prediction task.
\subsection{Datasets}
We work with two real-world datasets, the Irvine College Message Dataset and the Enron Dataset.
The Irvine College Message Dataset (framed \textbf{CollegeMsg} here) is a publicly available dataset (\url{https://snap.stanford.edu/data/CollegeMsg.html}) composed of 59835 directed interactions between students in an online social media platform in the University of California, Irvine, on a total time-span of 193 days. While this dataset has been studied in previous work \cite{Panzarasa2009} to measure the evolution of some topological properties of the network, here we focus on the temporal aspect of edge-specific point process realization.
The Enron Dataset (\textbf{Enron}), publicly available on \url{https://www.cis.jhu.edu/~parky/Enron/} contains 125409 directed message sent between employees of the company Enron between 1999 and 2003.

In our case we preprocess both datasets, by removing simultaneous events. This includes for instance broadcasted e-mails that are present in large amounts in the Enron dataset, or duplicate events in general.
For the Enron Dataset we obtain a final dataset of 4529 links, while we filter the CollegeMsg Dataset to keep only the 20000 first interaction.

\begin{table}[ht]
	\vskip 0.15in
	\begin{center}
		\begin{small}
			\begin{sc}
				\begin{tabular}{lll}
					\toprule
					Dataset    & Number of temporal events & Directed \\
					\midrule
					Enron      & 4529                      & Yes      \\
					CollegeMsg & 20000                     & Yes      \\
					\bottomrule
				\end{tabular}
			\end{sc}
		\end{small}
	\end{center}
	\caption{Properties of the datasets}
	\label{dataset}
	\vskip -0.1in
\end{table}

\subsection{Implementation}
We implement our method using Pytorch, in order to benefit from automatic differentiation.
Our implementation is composed of two separate modules. One module is the intensity function, that takes as input tuples composed of edges, interaction times, time histories and feature values, and provide as an output a positive real-valued score.
The NLLLoss takes as input the intensity values and the interaction times and computes the value of the negative log likelihood define in \ref{nll}.
This implementation allows us to benefit from automatic differentiation. We use the AdamW optimization method \cite{loshchilov2018decoupled}. This allows us to ensure fast convergence to an optimum of both embeddings and linear parameter, while controlling over-fitting of the embeddings by adjusting the weight decay parameter.
In our experiments we set the learning rate to 0.8 and the weight decay to 0.9.
For the Poisson base rate we use embeddings of dimension 20.
For the piece-wise constant hazard of the First-order Markov model, we select cut-off points for  based on the  inter-arrival time distribution of the dataset at the network level.

\subsection{Simulation}

\begin{figure*}[t]
	\centering
	\includegraphics[width=\textwidth]{figures/simul_viz.png}
	\caption{First 50 events on our simulated network}
	\label{fig:simul_viz}
\end{figure*}

We evaluate the ability of our framework to generate realistic network histories.
Real-world temporal networks, along are characterized by a fundamental property called \emph{burstiness}.

The burstiness measure defined in \cite{NaokiMasuda} is defined as follows, for a given time history $t_1,...,t_n$.
First, one measures the coefficient of variation of the inter-arrival times
$\tau_i=t_{i+1}-t_i, i=1,...,n-1$ :

$$cv =\sqrt{\frac{\sum{(\tau_i - \bar{\tau})^2}}{(n-1)*\bar{\tau}^2})}$$

Where $\bar{\tau}$ is the mean inter-arrival time for this sequence.

This value is equal to 1 for a Poisson Process, 0 for if the history is periodic (i.e. the inter-event times are all the same).
Long tail inter-arrival distribution tipycally yield large values for this coefficient of variation.

In order to obtain a score in the interval $[-1,1]$, previous autors have proposed to normalize the coefficient of variation, defining the \emph{burstiness} score:

$$B = \frac{cv-1}{cv+1}.$$

This one has values close to 0 for Poisson processes, -1 for periodic sequences, and close to 1 for very bursty sequences.
In our experiment, we simulated a network history using the time decaying feature defined above and a Markov model.
Then we compute for each edge the burstiness of the associated event sequence.
Finally we plot the histogram of the obtained burstinesses, expecting a number of bursty event sequences.

We simulate a network using the first order Markov model and the exponentially downweighted features defined above, and plot the histogram of the burstinesses associated with the various edges.

We visualize the obtained simulation on Figure\ref{fig:simul_viz}.

Figure \ref{burstiness_sim} shows the resulting histogram of burstiness. We see that the range $[0,1[$ is fairly well populated, meaning that our simulation allows to generate partially the burstiness observed in real-world data.
For comparison, we plot on Figures \ref{burstiness_enron} and \ref{burstiness_college_msg} the burstinesses of the Enron dataset(a corporate e-mail dataset), and on the College Msg dataset respectively.
In their case, the bursty behavior of the edges in the network can be noted since a good amount of edges have a burstiness coefficient between $0$ and $1$.

\begin{figure}[t]
	\centering
	\includegraphics[width=\linewidth]{figures/burstiness_simul.png}
	\caption{Burstiness histogram on our simulated network}
	\label{burstiness_sim}
\end{figure}

\begin{figure}[t]
	\centering
	\includegraphics[width=\linewidth]{figures/burstiness_enron.png}
	\caption{Burstiness histogram on the Enron dataset}
	\label{burstiness_enron}
\end{figure}

\begin{figure}[t]
	\centering
	\includegraphics[width=\linewidth]{figures/burstiness_college_msg.png}
	\caption{Burstiness histogram on the Irvine College Message Dataset}
	\label{burstiness_college_msg}
\end{figure}

\subsection{Future Link Prediction}
\subsubsection{Task Description}
We assess the predictive ability of our model by performing a Future Link Prediction task.
More specifically, we split the history in two parts, and train the models on the first slice of history. For events in the test part of the history, we try predict the destination node based on the observed source node.

\subsubsection{Train-Test splitting and preprocessing}
In order to split the network history we pick three cut-off times $0<t_{train}<t_{val}<t_{test}$ and split the history into three sub-sets:
the train history
$\mathcal{H}_{train} = \{w_m \in \mathcal{H}, t_m \in [0, t_{train}]\}$,
the validation history
$\mathcal{H}_{val} = \{w_m \in \mathcal{H}, t_m \in [t_{train}, t_{val}]\}$,
and the test history
$\mathcal{H}_{test} = \{w_m \in \mathcal{H}, t_m \in [t_{val}, T]\}$.

For each train event in $w=((u,v),t) \in \mathcal{H}_{train}$, we sample $n=1$ a negative neighbor $\bar{v}\in \mathcal{V}\backslash \{u, v\}$ and define a negative event $\bar{w}=((u,\bar{v}),t)$.
We introduce target variables $y=1$ and $\bar{y}=0$ for the positive and negative event respectively.
Using this procedure we obtain a labelled dataset
$$\mathcal{D}={(w_1,y_1),...,(w_n, y_n)}.$$
We treat the hazard models defined above as score functions :$f : w=(e,t)\mapsto f(w)\in \mathbb{R}_+$.
Finally we use the AUC metric to measure the ability of a given score function to separate positive and negative samples.
\subsubsection{Baselines}

We compare 4 different prediction methods:
\begin{itemize}
	\item The Poisson model (\ref{edge_poisson}), assuming a constant intensity in time.
	\item The Markov survival model endowed with a piece-wise hazard, thus including a first order dependency in the history.
	\item The preferential attachment: this one uses the product of the degrees of the nodes at a given time as a predictor for their probability of forming an edge. This heuristic has been shown in \cite{Mara2020} to yield good prediction results compared to state-of-the art baselines.
	\item A random intensity value for any given node pair.
\end{itemize}

The ROC curves for both of the datasets are shown in Figures \ref{roc_enron} and \ref{roc_college_msg}.

We summarize the results in table \ref{auc_results}
\input{results}

\begin{figure}[th]
	\centering
	\includegraphics[width=\linewidth]{figures/roc_enron.png}
	\caption{ROC Curve for the Enron dataset}
	\label{roc_enron}
\end{figure}

\begin{figure}[th]
	\centering
	\includegraphics[width=\linewidth]{figures/roc_college_msg.png}
	\caption{ROC Curve for the Irvine College Message dataset}
	\label{roc_college_msg}
\end{figure}

On both datasets, our method (the blue curve on Figure \ref{roc_enron} and \ref{roc_college_msg}) performs better prediction AUC that the other methods. The gap in prediction is especially large for small intensity threshold values.
It can be seen that methods that the first order Markov and the Preferential Attachment methods perform better than the constant rate method and the Random Feature. This is due to their ability to incorporate time-varying effects in the predictions.

\section{Related Work}
To our knowledge, the previous work that comes closest to the present method is the one presented in \cite{Passino2021}. In this one, the authors propose to posit a mutually exciting model between the different edges, such that the interactions occurring for a given edge influence the ones coming occurring on neighboring edges.
However, they rely mostly on time-varying node embeddings to define the conditional intensity functions, while in our case we rely on static embedding and rely on time-varying features to model the time-evolution of the intensity.

Another closely related line of research uses deep learning to infer embedding trajectories in a latent space\cite{Kumar2019,Rossi2020,Xu2020,Dai2016}. While this line of research already yields promising results on future link prediction or temporal node classification on very large scale networks, smaller size networks don't necessarily contain the information necessary to train time-varying embeddings.

Finally, former work coming from computational social sciences also propose to model temporal networks through their intensity functions, in order to assess the effect of the temporal network topology on the rates of link formation \cite{Butts2008, DuBois2013,Perry2013e}. Specifically, relational event modelling also rely on a piece-wise constant intensity hypothesis to define the likelihood of the observed network. In our case, we provide more flexibility by allowing to posit a custom hazard function on the edge-inter-event times, and making it dependent or not of the evolution of other edges in the network, depending on the specific needs.
\section{Conclusion}
We have introduced Graph-Survival, a framework that allows to translate survival analysis models into generative models for temporal networks.
Using a survival analysis model on the inter-arrival times is a simple and elegant way of capturing the influence of the temporal network topology on the intensity of the dyadic point processes.
Our proposed framework suggests that by encoding the past history in an appropriate vector of sufficient statistics, one can very simply include inductive bias on the time-evolution of the network, and use the resulting model for prediction or simulation.
We empirically demonstrated that a simple example of model in our framework is able to generate bursty network histories, and perform accurate future link prediction.

In further work we plan to further integrate representation learning as a substitute for the hand-crafted features, while investigating the effects of higher-order history dependency on the quality of the model.

\section*{Acknowledgements}

\newpage
\bibliographystyle{acm}

redpajama_set_name: RedPajamaArXiv


Wadi Al Quilt's environmental system represents the desert and depression. It is considered a wetland region filled with towering mountains and springs that cover the land. This region is considered to be one of the most beautiful regions in Palestine. Water flows from the rocks and forms small or large waterfalls that feed different life parts of Jerusalem's wildlife.
Visitors who would like to walk to Wadi Al Quilt can reach it by starting at Anata Village- south north of Jerusalem- and continuing south till passing Al Fawaar springs near Al Kaan Al Ahmar, Al Qilt spring and arriving at Saint Jarojues monastery which is in Jericho city. The visitor also can reach to this Wadi Al Quilt by going on the connecting path between Jerusalem and Jericho city and this path leads to Al Qelt spring, which is full of agrologic sites.
This area is considered a unique area for the Palestinians because it is full of rewinds. Visiting tourists find comfort here because it is far from the noise of the city and is environmentally unique.
The animals that can be founded in this region are the Rocky Hyrax, Gazella and Wolves.
Plants in this region are trees and the seasonal little wild grasses found during spring.
The building of efforts to manage and protect the region.
The development of environmental tourism concepts in the region.
The declination of the region as a place of great rewinds.
An environmental survey to identify the biodiversity of this region and its characteristics.


OAKLAND –As he clutched a box holding his NBA championship ring, Nick Young ensured that his moment lasted longer.
Draymond Green had just presented Young with his ring for being part of the Warriors' 2018 championship team that swept the Cleveland Cavaliers in the Finals. But just like he did during the locker room champagne party and the team's championship parade, Young kept the party going.
So, Young went toward the Warriors' bench. He slapped high fives with Warriors assistant coach Mike Brown and head coach Steve Kerr. He then dapped with Green and Kevin Durant. Young lingered around and chatted some more. Finally, official Ken Mauer had enough. After all, the Warriors and Cavaliers needed to start their game. So Mauer issued a delay-of-game penalty.
No wonder Kerr predicted beforehand, "I don't think anything will surprise us" about Young's ring ceremony. Young spent his 12-year NBA career making teammates and coaches either laugh or roll their eyes with his playful sense of humor and attention seeking.
Young offered plenty of entertainment about 30 minutes before the Warriors presented him with his NBA championship ring.
Beyond filming for his alleged reality TV show, Young auditioned openly for a cameo in Space Jam 2. Los Angeles Lakers star LeBron James is already confirmed to headline the sequel to the 1996 movie starring Michael Jordan along with Looney Tunes characters. But apparently the sequel's success needs more than just the King.
Young has not just tried performing for the cameras. He maintains he has also performed in the recording studio. Young claimed last year that he would release a diss track on Green at the beginning of 2018. It did not happen. Young then said it would come out following the Warriors' championship run. That did not happen, either.
Is this song ever going to be released?
Contrary to Young's initial fears, Green did not mess up Young's ring ceremony. First, the Warriors played an elaborate tribute video filled with plenty of shots that Young made last season in a Warriors uniform. When he stood with his son and Green at center court, Young delayed accepting the ring right away. Why? Young wanted to bask in the applause from both Warriors fans and former teammates. They obliged enthusiastically.
Finally, Young accepted the ring box from Green and shook hands with him. Then, Green playfully picked up Young's son. Sure, Green has teased Young. Green has questioned Young's rap skills. Green often playfully pushed Young around. But Green was also among the Warriors' stars, including Curry and Kevin Durant, to vouch for the front office to sign him in the 2017 offseason. Therefore, this ceremony also meant plenty to Green.
The Warriors had signed Young a year after he salvaged his career with the Los Angeles Lakers. He anticipated his NBA career ending after posting inefficiency numbers in 2014-15 (13.4 points on 36.9 percent shootin) and in 2015-16 (7.3 points per game on 33.3 percent shooting) amid clashes with former Lakers coach Byron Scott over his playing time, inconsistency and playful personality. Things became more dicey when former Lakers teammate D'Angelo Russell released footage on social media that exposed Young admitted to being unfaithful to his fiancé and hip-hop artist, Iggy Azalea, which then to a breakup. But Young felt rejuvenated under Lakers coach Luke Walton for his positive reinforcement and more modern offense. Young averaged 13.2 points on 43 percent shooting in 60 starts, while also coexisting with Russell.
Sometimes that worked to his detriment. Young elicited both laughs and eyerolls when he appeared giddy in the locker room following the Warriors' Game 7 win over the Houston. The Warriors playfully reminded him they do not make a big deal about winning the Western Conference Finals.
After all, Young only made one other playoff appearance amid stops with the Washington Wizards (2007-12), Los Angeles Clippers (2012), Philadelphia 76ers (2012-13) and Los Angeles Lakers (2013-2017). So when Young joined the Warriors, he loved that he could "share the court with some of the best players in the game." He reunited with former teammate JaVale McGee. He gained further insight on the team's preparation and work habits.
Young did not consistently mimic those lessons based on his consistency, dieting and conditioning. Therefore, the Warriors had no plans to retain Young for this season. The Warriors, though, basked more on the joy that Young brought than lamenting the occasional headaches he caused.
Hence, Young does not sound too upset that his NBA career might be over.
It does not sound like Young wants to leave, either.
For one night, though, Young returned to being Swaggy. Unsurprisingly, the Warriors became amused and entertained over it all even if his presence caused a delay-of-game.


Kam has been making websites since 1996, starting out with a 14.4k modem on a party line in the Hawaiian rainforest. He's been developing on Sitecore since the days of Sitecore 5.3, and led many large Sitecore implementations. Kam is the author of Unicorn, Synthesis, Dianoga, and several other open-source Sitecore libraries.
Contact Facets allow you to persist information about visitors into the Sitecore xDB. We're not going to get into the theory behind them in this post; for that go read Pete Navarra's great blog post that summarizes current practices and how to add facets.
I found out the hard way that the constants defined, the value must equal the actual name of the class property for the same attribute.
Well in C# 6 (VS 2015), there's a syntax for that. The nameof statement allows you to get the string name of a variable or property. This essentially hands off the management and maintenance of the const value to the compiler, instead of the developer.


text:
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Rwanda: TVET Schools to Receive a Facelift to Improve Learning
On 19th February 2022, His Excellency Mr. Valentin Zellweger, Ambassador of Switzerland to the Republic of Rwanda alongside Hon. Dr. Valentine Uwamariya, the Rwanda Minister of Education, officially presided over the inauguration of the construction of dormitories and refectories for four out of six TVET schools, previously built by the PROMOST project in the Western Province. The four schools will benefit from the facelift intended to increase student enrolment and improve learning. The official launch was held at Rubengera TVET School in Karongi District.
Construction Background
In the previous implementation phases, PROMOST built six schools in six of the seven districts of the Western Province of Rwanda, to promote the learning of labour-market-oriented skills among the population. The school structures were simple and included modernized equipment to improve the quality of vocational training. Students had classes for both theory and practical sessions.
"During the previous implementation phases, Swisscontact recognized accessibility of these training schools needed to be improved to increase student retention. Many students travelled from far to attend class. The to and fro transit was not easy for them and some dropped out because of this. This affected the female population more. To mitigate this, Swisscontact through the project PROMOST decided to construct dormitories giving the students an option to board in school. In addition, PROMOST also built refectories for them to share their meals," commented Mr. Charles Pouliot, the Swisscontact Director for the Great Lakes region.
His Excellency Mr. Valentin Zellweger, Ambassador of Switzerland to the Republic of Rwanda alongside Hon. Dr. Valentine Uwamariya, the Rwanda Minister of Education inaugurating one of the constructed TVET Schools.
Dormitories and Refectories
The construction took place in four of the six TVET schools previously built by PROMOST. These are Muganza TVET School in Rusizi District, Shangi TVET School in Nyamasheke District, Rubengera TVET School in Karongi District and Muhororo TVET School in Ngororero District.
"The inaugurated infrastructures have been constructed in the framework of Switzerland continuous support to the Rwanda TVET system through the PROMOST program. Switzerland is committed to support Rwanda's vision of becoming a competitive, knowledge-based economy by 2024 and TVET has been identified of being one of the key drivers to achieve that goal. Equipped with skills, the young people of Rwanda will be enabled to become entrepreneurs, employable and informed citizens thereby contributing to economic development of the nation" HE Mr. Valentin Zellweger, Ambassador of Switzerland to Rwanda.
At the start of 2020, PROMOST Rwanda launched the Dual Training System, a pilot program supported by the Ministry of Education in Rwanda and funded by the Swiss Agency for Development and Cooperation (SDC). This was a shift in approach from the basic technical and vocational education and training offered and intended to give the students opportunities to gain more hands-on skills from private companies
"Skills development and employment promotion are central to Rwanda's transformative vision 2050 to secure high standards of living for all Rwandans. To achieve that, Rwanda must build a dynamic and capable workforce for responding to the rising demand for high-skilled jobs. TVET has a key role to play to achieve that." Hon. Dr. Valentine Uwamariya, the Rwanda Minister of Education.
The Gender Aspect
Though retention of students in TVET schools was one of the goals behind the providing boarding facilities, but the approach was also designed to pay more attention to gender equality and women's economic empowerment as explained by the director of Swisscontact in the Great Lakes Region.
"The choice to construct the dormitories is intended to bridge the gender gap in the dual training program we are currently facilitating. During admission, the program ensures to achieve a 50:50 ratio of male and female students. The constructed boarding facilities will be able to accommodate 400 students which translates to 200 female students. These boarding facilities is a big step closer to improving access to quality and relevant TVET training," Mr. Pouliot
Burundi, Rwanda, DR Congo
Initial vocational education and training
Promoting Market Oriented Skills Training and Employment Creation in the Great Lakes Region
The project supports the Governments of Rwanda, Burundi and the Democratic Republic of Congo (DRC) to improve access, quality and relevance of their respective Technical and Vocational Education and Training (TVET) systems. This addresses the key development challenge of unemployment and underemployment brought about by the low quality of skills...
You can deduct donations made to Swisscontact from your taxes.
Charity Number: CHE-107.828.868
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redpajama_set_name: RedPajamaCommonCrawl


These fifa world cup facts, records, statistics , The inaugural edition of the fifa world cup was held in 1930, and was hosted by montevideo, the capital of uruguay. the cup was won by the host nation, who were also.
Ten facts fifa world cup, Ten facts about the fifa world cup . the world cup is an international football competition that takes place every four years. pre-qualification takes place from.
World cup facts – 20 facts world cup | facts.net, World cup facts highlight the interesting history of this popular tournament. it also tells us about brazil and germany's dominance and other fun tidbits..
Russia 2018 – fan guide | 10 facts world cup, 10 facts 2018 fifa world cup. 2 december, 2010 russia, time history, granted host fifa world cup..
Fifa world cup – wikipedia, The fifa world cup, simply called world cup, international association football competition contested senior men' national teams .


text:
Memorial Day Weekend May 27, 2013...Special Events in Washington, DC!
Monday, May 27th is Memorial Day...Special Events in Washington, DC!
Special Events for Memorial Day Weekend
By Rachel Cooper, About.com Guide
Memorial Day is a day of remembrance for those who have died serving our country. This is a great time to honor our veterans and famous Americans by visiting the monuments in Washington, DC. The Memorial Day weekend also marks the beginning of the busy summer season and the Washington, DC region celebrates with family friendly events and special services.
Here is the 2013 Memorial Day event schedule:
National Memorial Day Concert
Sunday, May 26, 2013, 8 p.m. Gates open at 5 p.m.
PBS sponsors a FREE concert on the West Lawn of the U.S. Capitol. The concert features actors Joe Mantegna and Gary Sinise, and other guest artists along with the National Symphony Orchestra.See Photos of the Performers for the 2013 Concert.
National Memorial Day Parade
Monday, May 27, 2013, beginning at 2 p.m.
The parade of Marching Bands and Veterans units from all 50 states steps off at the corner of Constitution Avenue and 7th Streets, NW and proceeds along Constitution Avenue, past the White House, ending at 17th Street. The National Memorial Day Parade is sponsored by the World War II Veterans Committee and includes patriotic floats and helium-filled balloons. See Photos of the National Memorial Day Parade in Washington, DC.
Rolling Thunder Motorcycle Rally
Sunday, May 26, 2013. Departure from the Pentagon at 12 p.m. Speaker Program and Musical Tribute 1:30 p.m. at the Reflecting Pool across from The Lincoln Memorial. Thousands of motorcycles ride through Washington in an annual demonstration seeking to improve veteran benefits and resolve POW/MIA issues. See Photos of Rolling Thunder
Sunday, May 26, 2013, 10 a.m. The Buffalo Thunder Memorial Motorcycle Ride will begin in the Sears parking lot at 2101 Brightseat Rd in Hyattsville, MD and end at the African American Civil War Memorial and Museum at 1925 Vermont Ave NW in Washington, DC.
Flags-In. Soldiers from the 3rd U.S. Infantry Regiment (The Old Guard) will be joined by service members from the U.S. Marine Corps Ceremonial and Guard Company, U.S. Navy Ceremonial Guard, U.S. Air Force Honor Guard and the U.S. Coast Guard Ceremonial Honor Guard in placing more than 250,000 grave decorating flags at Arlington National Cemetery. The flags will remain through Memorial Day.
Monday, May 27, 2013, 11 a.m. A wreath-laying ceremony and concert will be held at the Tomb of the Unknown Soldier in honor of Memorial Day. See Photos of Arlington National Cemetery.
Monday, May 27, 2013, 4 p.m. The Women In Military Service For America Memorial, located at the gateway to Arlington National Cemetery, will hold a ceremony that includes formal military honors, a keynote address, wreath-layings and the Memorial's signature event, the scattering of rose petals in tribute to departed comrades. Members of the public are invited to join in the personal tribute segment of the program.
Navy Memorial
Saturday, May 25, 2013, 11 a.m. Rolling Thunder visits the Navy Memorial and holds a wreath-laying ceremony featuring special guests and live performances.
Monday, May 27, 2013, 10 a.m. Fleet Reserve Association Wreath laying, 12 p.m. U.S. Navy Band Concert, 1 p.m. Naval District Washington Wreath laying.
Click on Link for: More Special Events for Memorial Day Weekend in Washington, DC
0 comments • Rick Reed • May 20 2013 11:34AM
Memorial Day Weekend May 27, 2013… Special Events in Washington, DC!
Monday, May 27th is Memorial Day… Special Events in Washington, DC! Memorial Day 2013 in Washington, DC Special Events for Memorial Day Weekend By Rachel Cooper About. com Guide Memorial Day… more
It's Military PCS (permanent change of station) Move Season!
Two informative real estate related articles from Military. com regarding PCS Moves for Military Service members and their families. As a Military Veteran, I understand the challenges of buying and selling real estate while… more
Mother's Day is this Sunday, May 12, 2013
FUN ACTIVITIES FOR MOTHER'S DAY Mom might tell you that being loved by her family is all she could ask for on Mother's Day. Good to know Mom's consistent! But the fact remains, this is the one day of the year to celebrate… more
A Bottle of Red, a Bottle of White… Maryland Wineries & Events!
April 2013 Wine Events in Maryland Capitol Area April 06 - April 27 Wine & Daffodils Slack Winery We are giving away Daffodil bulbs to help celebrate Spring… more
Baby Boomers are Moving to Urban Areas
We are seeing the trend of Urban Living Areas or "Town Centers" here in the suburbs of Montgomery County, MD (Washington DC suburbs). Below is an example of a new community in Gaithersburg, MD called "Crown" … more
April begins Historic Tours throughout Montgomery County, MD!
April Starts the Season for many Historic Tours throughout Montgomery County, MD! Montgomery County is home to numerous historic sites of significance to African American heritage. Oakley Cabin African… more
Family Fun on the Farm in Germantown, MD
Family Fun on the Farm 18th Annual Bunnyland! Welcome Spring at our Bunnyland! Join us for a hayride down the Bunny Trail, the Rubber Ducky Derby, an Egg Hunt, a ride on our Giant Slides and more. 2013… more
National Cherry Blossom Festival Washington DC March 20-April 14, 2013
National Cherry Blossom Festival Washington DC March 20 – April 14, 2013 Please click on this link for the main page for all information: National Cherry Blossom Festival Washington DC 2013 … more
White ICE Appliances… a New Trend?
Kitchen Throw Down: White Ice Vs. Stainless Steel July 31, 2012 by WYSK If you watch almost any home buying, flipping or design show on HGTV, it's no secret that having "stainless steel appliances (and granite… more
Professional Realtor Needed for The Real Estate Market In 2013
Great article! More importantly while discussing the key points of the current real estate market, this article references the benefits of a Professional Realtor. Look For Improvements In The Real Estate Market In 2013… more
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text:
William Lyons biography
There's a Lyon Behind Every Jaguar
The Story of Sir William Lyons
- by Craig Burlingame JDRC/NWA
(Jaguar Drivers & Restorers Club of Northwest America)
Part 1, Part 2, Part 3, Part 4, Part 5, Bibliography
Sir William Lyons was the man who, for 50 years was Jaguar and is most responsible for the cars we all admire today. New Jaguar buyers today are probably not aware of the influence this one man had in shaping the sexy feline. After all, you won't see the name Lyons on the boot lid like you might see a Porsche or a Ferrari. That wasn't his style. His style was understated elegance with pace, space and grace to match. Lyons somehow always managed to offer an outstanding product for the money. And still his legacy remains, his mark indelibly etched in every Jaguar ever produced, even now, 23 years after his retirement and ten years after his death, his influence is still seen. Even the newest XJ6 carries modifications suggested by Sir William. For without William Lyons, there most certainly would not have been a Jaguar automobile as we know it and the automotive world would have been deprived of one of the greatest motorcars ever conceived and produced.
It all began 90 years ago when on September 4, 1901 William Lyons was born in Blackpool, England. He was the son of a music store shopkeeper. In his early days, he developed a keen interest in motorcycles and owned a "oil-bath" Sunbeam when he was eighteen. But he dreamed of Harley Davidsons and Brough Superiors (the "Rolls-Royce" of British motorcycles).
It was almost by coincidence that the young Bill Lyons should happen to reside across the street from his future business partner, William Walmsley. Bill Lyons was fascinated with Walmsley's very stylish "octagonal" shaped sidecar called the Swallow and so bought one for himself to match up to the Norton motorcycle he was now riding. Convinced that others would like this stylish version of the otherwise "ordinary" sidecar, he decided to go into business with Walmsley, who was 10 years his senior. It was at this point that the young Mr. Lyons displayed the first of his three greatest talents. For Bill Lyons exhibited a phenomenal business sense, or certainly so it seemed for a lad of 20 without formal training. He convinced Walmsley that he could increase production and profits with a few of his ideas. One of which was to secure a larger workspace, as Walmsley's garage was far too small for the big dreams of production Bill Lyons envisioned.
In 1922, they officially formed a partnership and with some financial support from their parents, became the Swallow Sidecar Company. The first of a long list of Lyons' successes, the Swallow Sidecar became very popular and looked especially classy when hooked up with the Brough Superiors Motorcycle.
In the mid-20's the business flourished and they expanded to include coachbuilding. Based on the Austin engine and chasiss, the boys of Blackpool designed and built a stylish little two-seater. At a time when most auto builders were offering basic, no frills black cars, the sight of a stylish 2 color-toned Swallow must have been a very refreshing sight indeed and so it became very popular with the public and a saloon was added as well. So popular were the cars becoming, that they decided to move operations to the Midlands area of England in 1928. They took up residence in the Foleshill Factory in Coventry with its nearby skilled labour supply and industrial resources. Lyons was forever devising new methods of improving production and during this time production went from 12 cars a week to 50.
They also re-bodied the Standard Nine and anything else they could get their hands on. In comparison photos it becomes evident that the sense of styling expressed by Lyons was clearly ahead of its time. The Swallow bodied versions looking so much sharper with the lower profile that Lyons designed. Several of his design themes, characteristics that distinguish all Jaguars up to the present, are already being clearly established at this stage in his career.
But William Lyons' car building career really started with the introduction of his and Walmsley's first true cars, the S.S. 1 and II in 1931. Working with other manufacturer's engine and chassis had placed severe styling restrictions on the free-thinking Lyons. So the next logical step was to design their own chassis and adapt a proven powerplant. With a keen business sense, Lyons did a wise thing by obtaining chassis built to his specification by Rubery Owen works and matching it to the reliable Standard 16 engine. And so it was, without the huge financial burden of engine and chassis manufacturing, they were able to concentrate on coachwork, which was still their specialty.
A funny story often recited about this early S.S. 1 design has Lyons specifying a roofline so low that it probably would never have been driven by drivers of normal height. Just before the car went into production, Lyons went to the hospital and when he got out discovered that the roofline had been raised a few inches. A change probably made by Walmsley, since he was the only one who would dare make such a decision without Lyons' OK. Lyons' comments upon seeing the production vehicle was that the passenger compartment "looked rather like a conning tower".
Introduced in a year when the rest of the motoring world was lacking inspiration and originality, the motoring press of the time was very taken with the car and design and offered these comments; " . . . the S.S. 1 is a new type of automobile in the sense that it is a car built for the connoisseur but is relatively low priced. All the attributes of sports models are incorporated in a refined manner, and this, coupled with a striking appearance, is bound to attract enthusiastic motorists of modest means"- The Motor. This assessment of the first true Lyons car in 1932 was clearly the direction all future S.S.'s and Jaguars would take and would later be coined in promotion materials as the best of all worlds, offering grace, space and pace at a reasonable price.
There has been much discussion over the origin of name of the cars. It's no secret that Lyons liked the S.S. designation used by the Brough Superior motorcycles he so admired. William Lyons himself laid speculation to rest when he said, "There was much speculation as to whether S.S. stood for Standard Swallow or Swallow Special - it was never resolved".
Enjoying the success of the S.S. 1 and 2's, it would have been very easy to sit back and watch the money roll in, which is precisely what Walmsley was content to do. Lyons on the other hand, was far more ambitious and visionary. He wanted his cars to have even better performance than the Standard 16 was able to deliver. So he set about to develop an engine that would be worthy of the new line of cars he was about to introduce, the S.S. Jaguars.
The mid-thirties were a crucial turning point for the fledgling company. Walmsley, always more interested in "tinkering" and model railroading, left the company and S.S. Cars went public. More importantly, Lyons, now Chairman (and emperor) displayed his second greatest talent. As a good businessman, he knew the importance of hiring the best people possible to do jobs which he knew he could not do. A businessman he was, a stylist he was, but an engineer, he was not. So he did the next best thing, hire the best engineers he could find. And for this he had a good knack. Reading like the "Who's Who" of Jaguar, Lyons surrounded himself with very talented and motivated experts.
He had already hired the brilliant coachmaker, Cyril Holland in the early days to oversee the coachbuilding operation, but now he brought in William Heynes to head his now legitimate engineering department. Lyons was impressed with what Harry Weslake was saying could be done to improve engine performance if you put the valves in the overhead position rather than the side. So he hired him to design and build the first true Jaguar engine so that his new line of cars could have performance to match their good looks. This proved to be a stroke of genius as horsepower using the new engine rose from a modest 70 bhp with the side valves to 103.3 bhp with the new overhead cylinder head! Lyons even managed to have the heads built by Standard to his specifications, thereby saving his company the enormous capital outlay necessary for engine production.
It was here that Lyons exhibited yet another of his talents as he had a flair for showmanship and promotion. He astonished a dealer's convention with the unveiling of his new "Jaguar" line. As impressed as they were with the car's fine looks, appointments and performance, they were quite surprised to learn that the cars would sell for about half of what they collectively estimated they would cost.
Having achieved success with their first 4-door saloon, Lyons now directed his attention to developing a true sports car. The result was the very stylish SS 100. Philip Porter put it this way, "The SS 100 was the company's first genuine sports car and to many people it remains the epitome of the stylish pre-war sports car, Lyons at his flamboyant best. The beautiful, flowing feline shape suggested speed and when the new 3.5 litre engine was added to the range, a car of vivid performance was the result". And once again, the new sports car proved an excellent value for the money.
Lyons had been correct in anticipating the kind of car the public wanted and so demand was high. It became necessary to change from wooden frame construction to pressed steel in the saloons in order to meet this demand. Finding a good, reliable supplier of pressed steel was difficult and ruined many a good auto maker trying to make this transition during the depression. Jaguar was fortunate and able to survive this period of the late '30's.
During the war years, 1939-45, Lyons and company helped out with the war production effort. Key aircraft components were manufactured at the Coventry factory and became airborne parts on Lancasters, Stirlings, Mosquitos and Spitfires. They also manufactured everything from sidecars to a lightweight "jeep" type vehicle. All during the war, Lyons made sure his best engineers were always on "fire watch" duty together so Jaguars of the future could start to take shape. Production techniques were refined and advanced engineering ideas were tried including independent front suspension and talk of a new, more powerful engine.
Immediately following the war, Lyons resumed production of the saloon & drophead coupes. Officially listed as the 1 1/2, 2 1/2 and 3 1/2 litre saloons and drophead coupes, they would later be referred to as "Mark IV's". Unfortunately, SS100 production did not resume and it would be another four years before the introduction of the next sports car. The war necessitated another change. From now on, the name of the company would be Jaguar Cars, Ltd. since the SS initials now had a very unpleasant connotation.
During this time, British manufacturers were encouraged to export goods overseas and as a result, Jaguar discovered a very willing market in North America and began establishing a network of dealerships. Although the American market was to play a significant role for Jaguar in the years ahead, it was known to give Lyons fits at times. Several years later, Lyons gave the following instructions to the new Export Manager, John Morgan "I'm not going to accept crazy marketing plans for America because I don't believe in it. It's a volatile market".
One of Lyons' all consuming passions had been to produce a luxury saloon capable of 100 mph. The Mark IV with its 3.5 litre pushrod engine fell short of fulfilling that dream and so plans were underway to develop an engine capable of attaining his goal. The new Mark V, introduced in 1948 used the same engine as the "IV", but came with a restyled body on a new chassis with independent front suspension. But still the 100 mph barrier was just beyond reach.
Having achieved success with the new Mark V chassis, Bill Heynes now turned his attention to developing a new engine capable of 160 bhp. The team Bill Heynes assembled to develop this engine included Harry Weslake and Claude Baily. Not coincidentally, this was the same group that spent many a fire watch together during the war.
Incorporating a hemispherical head and twin overhead camshafts, the XK engine first appeared in 1948, but not in the new Mark V as many anticipated. Instead Lyons decided to display the new engine in a car specially built for the 1948 Motor Show. The XK 120 was hurriedly conceived as a show case for the new engine and it was only planned to produce 200 of these special cars. The idea was to test the new engine in the XK120s to prove the engine's durability in preparation for the Mark VII.
The new two-seater sports car with the XK engine was capable of 120 mph (hence the 120 designation). It proved such an overwhelming success that plans were made to expand the production beyond the original 200 that were envisioned. This car of course went on to become one of the all-time true classics and heralded the return of Jaguar to sports car production.
A "Mark VI" never came about because Bentley was already using that designation and so Jaguar jumped right to the Mark VII which was introduced in 1950. This was the first saloon to receive the new XK engine and finally Lyons dream of a 100 mph saloon was realized.
The early '50's are significant because it saw Jaguar officially enter racing. Previous to this, individuals had campaigned various Jaguars on their own but in 1950 Lyons was convinced that his new cars wouldn't in his words, "embarrass themselves", and so agreed to campaigning his new sports car on the racing circuit with company support. He was very aware of the potential prestige enhancement (and increased sales) that could result from successful achievements on the race course, especially LeMans.
Back then racing was viewed as cheap advertising and Lyons felt confident he had the products to compete. Although the new XK 120 was a very capable sports car, Lyons realized it would require modification to compete on the world circuit.
In very short time Malcolm Sayer, with a background in aircraft design, redesigns the XK 120 body shell to make it more aerodynamic. Heynes redesigned a lighter racing suspension and the new racer was given the code name "C-type" for competition. The design was not only more efficient, but it also looked very impressive as well. Such was the design talent of Malcolm Sayer, another Lyons hire. Like very few others, Sayer was uniquely able to artistically incorporate beautiful styling in a car along with the elements of aerodynamic design. His greatest achievements were yet to come in the form of the "D and E- types". Malcolm Sayer would soon be known as the "other" Jaguar stylist, second only to Lyons himself in creating the Jaguar look.


text:
Avrom Sutzkever was the greatest Jewish poet of his time. He spent his childhood in Siberia and emerged as a writer in the youthful literary flowering of Jewish Vilna. As poet and Jew in the Vilna Ghetto, he was transformed into a living remnant of a people's near death, writing immortal works and helping to conceal Jewish cultural treasures for later rescue. After the war, he became a prophetic symbol and a cultural-historical institution, founding Yiddish literature's greatest journal in Israel. A committed Zionist, he earned his country's highest literary honor even as its powerful never abandoned their suspicion of Yiddish literary creativity.
Avrom Sutzkever's work
From "Ode to The Dove"


text:
new gradle project

add in protobugs

  public  static final class void extends
      com.google.protobuf.GeneratedMessageV3 implements
      // @@protoc_insertion_point(message_implements:void)
      voidOrBuilder {

https://www.grpc.io/docs/tutorials/basic/java/
https://github.com/uw-labs/bloomrpc
https://mali.js.org/guide/metadata.html#receiving

curl
nvm
git

sudo apt-get install software-properties-common
sudo apt-get install gnupg mvn

build netty first....

Lot's of talk about it, and how great... not a lot of best practice; mostly a few years old.
Documentation is ok, but does leave out quite a few key concepts and ideas.

 - that there are standard types and these need to be copied from 'somewhere' in the github repo
 - the quality of the standard libraries and protoc tool; not well documented or clear
 - fai

### Protobuf v3

Main reference is https://developers.google.com/protocol-buffers/docs/proto3
The options from proto2 are supported with proto3 we're using those to help refine the various types that are required.

Email to register numbers protobuf-global-extension-registry@google.com

#### Language tooling
Typescript - to produce the [TS and JS files](https://github.com/improbable-eng/ts-protoc-gen)
Concerned that some of the options aren't available via that. Needs more investigation.

For more information on protobuf options and their implementation -> http://giorgio.azzinna.ro/2017/07/extending-protobuf-custom-options/

OpenAPI / JSONSchema

- Format additional property of Strings
https://json-schema.org/understanding-json-schema/reference/string.html#built-in-formats

- OpenAPI additional [formats for numeric values](https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.3.md#dataTypeFormat)

Type numbers...
 https://github.com/protocolbuffers/protobuf/blob/master/src/google/protobuf/type.proto

redpajama_set_name: RedPajamaGithub


THE QUEENSLAND city of Townsville has turned to the US for sperm donations as Australia faces a critical shortage, the Townsville Bulletin reports.
In the past, clinics used to rely on university students who were short on cash to donate sperm but these days not enough north Queensland men, or even Australian men, are prepared to donate.
Infertility specialist Dr Ron Chang attributed this decline to recent changes to the law that mean sperm donors have to be contactable once the child they helped to conceive turns 18.
"All the donors stopped coming forward because they didn't want a knock on the door in 18 years time," he said.
"I think children should have the right to know their biological parents, but it has a knock-on effect."
The shortage has also prompted IVF clinics nationwide to get creative about attracting potential donors.
New South Wales' largest clinic, IVF Australia, launched an online advertising campaign with tag lines such as: "You've got millions to spare, we only need one."


The New Zealand government has announced fully paid scholarship for students of India. New Zealand government has started the commonwealth scholarship for the students from Commonwealth countries who wish to pursue a Master's or PhD program in New Zealand. Under the Commonwealth Scholarship, the selected students will be paid the full fee of their Master's or PhD program fee at their University in New Zealand.
Interested students have to make an online application and submit to the Ministry of Human Resource Development (MHRD) through its Saksham Portal. MHRD will nominate the deserving student and forward their names to the New Zealand government. After that, the New Zealand government will do the final selection for the Commonwealth scholarship program. The online application can be made until 30 March 2017.
What will the scholarship cover: The selected candidates will be provided full tuition fee, medical & travel insurance, a fortnightly living allowance, travel cost from the starting of the scholarship program to end.
Clause to come back to India: The candidates have to come back to India for two years after completion of the course and help in the development of their country.
The Commonwealth scholarship is valid for a range of subjects around the themes of Renewable Energy and Agriculture development and the scholarship also intend to help India's social, economic and political needs through the study of the subjects. The students selected for the Commonwealth scholarship will have a chance to experience New Zealand's education system which focuses on making the student's analytical thinker and independent so they can compete in any marketplace in the world.


There are so much evedence that what we eat matter. The food that we consume every day can kill us or heal us. Dr. Young S. Kim and her team of researchers of The National Cancer Institute of America in July/August 2012 concluded that a poor diet (too much glucose, bad fats, dairy, too much salt, too many refined and junk foods) could cause a cancer to regrow from the Cancer Stem Cells left behind after conventional treatment. Dr. Kim also concluded that certain natural compounds in foods could stop this regrowth. High level of beneficial natural compounds like vitamins and minerals makes it SUPERFOOD. There are few superfoods that can reverse or prevent cancers.
Green vegetables long with avocado, beans, carrots, apricots, pumpkins, and egg yolk will give you folic acid if your gut bacteria are strong.
This will help your DNA to replicate properly and protect it during radiotherapy.
400 micrograms is a recommended amount. Folate, biotin, choline and inositol, niacin and vitamin B12 are all B vitamins that help in the cancer fight. Niacin has been shown to kill cancer cells. Green vegetables and sprouting seeds are a source of sulforaphanes which have strong epigenetic (gene expression) benefits and have been shown to aid survival from colorectal cancer. A diet rich in greens will help alkalize your body. A slightly alkaline body is important as it improves the performance of your immune system and research shows it stops new metastases.
Like other green cruciferous vegetables (cabbage, kale, Brussels sprouts), broccoli contains fibre which helps eliminate toxins.
Moreover, the fibre binds to damaging agents in the intestine, and is one of the favorite foods of good, helpful gut bacteria (as are carrots, apples, chicory and onions).
Broccoli also contains indoles, and especially indole-3-carbinol which modifies and diminishes aggressive estrogen action, can modify cellular estrogen receptor sites, and aids in fighting estrogen-driven cancers like some breast, prostate, brain and colorectal cancers.
Fresh, raw ginger has a number of very important benefits in cancer. It is a terrific anti-inflammatory agent. It also lowers blood sugar levels, and gingerols have been shown to have effects against prostate, breast, leukemia and other cancer cells. Grate 5 gm or more of ginger each day into your juices. It is also full of helpful vitamins and minerals and is anti-parasitic.
It is a truly wonderful food. Active ingredients like allicin seem to act to stop the spread of cancer in a number of ways, for example by stopping blood supply formation for tumors.
Garlic also kills microbes and yeasts and anti-inflammatory in the body. It contains selenium, tryptophan and sulphur-based active agents that attack cancer cells.
Two or three raw cloves of garlic per day will chase away cancer as well as vampires.
Beets as well as any purple colored fruits and vegetables like cherries, plums, red grapes contain anthocyanins (and sometimes also resveratrol). Anthocyanins have been shown to kill cancer cells; Resveratrrol has research supporting its role in fighting certain cancers like blood and brain cancers.
Fish oil will provide long chain omega-3, a powerful anti-inflammatory in the body. Omega-3 has been shown to re-lengthen telomeres, which shorten when you have cancer, putting the DNA structure at risk and reducing longevity. Fish oils also contain vitamin A, an important vitamin in the fight against cancer (herring, mackerel and salmon are top of the list). Fish oils have been linked to reduced levels of prostate, breast and colon cancer. Research shows they help prevent cachexia (weakness and wasting of the body) when having chemotherapy. You´ll also get a little vitamin D from them, another proven cancer-fighter. Omega-3 from fish is an important ingredient in your cancer diet. Please note that the omega-3 from flaxseed is short-chain, equally important but has different benefits (for example, it helps oxygenate the tissues and provides essential fiber).
: High in zinc and natural vitamin E. Zinc helps vitamin C do its work and accelerates healing time. It is important to a healthy prostate. You need 15 to 25 mg per day. Five tablespoons of sunflower seeds give you 10 mg. Sunflower seeds will also provide a little selenium.
Pumpkin Seeds: Can be mixed with the sunflower seeds and added into cereal, oatmeal or snacks. 5 tablespoons will each provide 20 mg of vitamin E, the ultimate cancer buster, which inhibits cancer cell growth and protects immune cells from free radicals. Vitamin E boosts your immune system´s fighting abilities.
Sesame Seeds: The unique lignans in sesame seeds can reduce blood pressure and lipid levels. Research shows they can fight inflammation and also cancer! Gamma tocopherol vitamin E reduces inflammation around the body. Both sesame and flax lignans are converted to compounds that can arrest estrogenic cancers.
Try to incorporate all of these superfoods into your diet to heal your body and chase away not only cancer but also many other degenerative diseases.


text:
Defending Against Attack
How Paramount Studios defends against a vehicle attack
By Greg Hamm
When Paramount Studios, the longest operating and only remaining studio in Hollywood, wanted a preventative solution that would protect tourists and talent from car attacks, they contacted Perimeter Security Group, an integrator that has become one of the leading providers of perimeter security systems in the country. The 100-year-old facility features 30 stages throughout their 65-acre complex.
"Paramount Studios asked us to create an aesthetically pleasing solution that would stop unauthorized vehicle entry at five places but would still let production vehicles through when needed," senior physical security professional at Perimeter Security Group Troy Blood said. "With all the news of terrorists using vehicles as weapons against people, Paramount Studios recognized that they, too, could be a high profile target."
While not all vehicle attacks are linked to terrorism, groups such as ISIS and al Qaeda have called on followers to use trucks as weapons. In fact, an al Qaeda magazine published an article in 2010 titled "The Ultimate Mowing Machine." The article calls for using a pickup as a "mowing machine, not to mow grass but mow down the enemies of Allah."
It said a four-wheel-drive pickup is needed—"the stronger the better." The article continues, "To achieve maximum carnage, you need to pick up as much speed as you can while still retaining good control of your vehicle in order to maximize your inertia and be able to strike as many people as possible in your first run."
To add an explanation point, John Cohen, ABC News contributor and former counter-terrorism coordinator at the Department of Homeland Security said, "They are urging the followers to go to soft targets, places that are open to the public, typically crowded and because of what they are—they are very difficult to secure."
With all this in mind, Paramount Studios selected Perimeter Security Group, which has provided turnkey installations of custom iron fabrication, security fencing, perimeter protection devices, access controls, video surveillance and automated entry systems for projects ranging from private residences to the most secure military facilities and correctional institutions in the world. As part of this work, the integrator has implemented many systems using Delta Scientific's vehicle access equipment to keep visitors and staff free from harm of terrorists and errant drivers.
"We reviewed Paramount Studios' facilities and vulnerabilities and recommended a combination of Delta's TT212 beam barricade and DSC800 high security bollards. Paramount wanted the job done quickly and at night so we wouldn't disturb tourists or shoots.
"The high-strength wire rope beam barricade will stop a large or fast-moving vehicle weighing 6,000 pounds at 40 mph," Blood said.
The beam is raised to allow passage of authorized vehicles through and, then, lowered to protect cars or trucks from entering. Clear opening ranges from 10.5 to 24 feet are available to allow passage of almost every type of vehicle.
"Beam barricades are very popular because this type of system covers a wider lane at a lower cost than out-of-the-ground barricades or barriers," Blood said. "A low maintenance system, beam barricades are used worldwide at locations where wide roadways need to be secured from attacking vehicles."
Beam barricades are seen at many nations' embassies throughout the world. They are also selected for government facilities, restricted or reserved parking areas, impound yards, freight terminals, shipping and receiving docks, storage and warehouse entrances, arms depots and other places where standard traffic controls or gates are not capable of resisting such high crash forces or vandalism.
"Paramount Studios also selected Delta's manually operated crash-rated bollards for those areas in which vehicle passages are infrequent," Blood said. "The DSC800 will stop a 15,000 pound vehicle at 30 mph. Paramount Studios specified a model with classy cast aluminum decorative sleeves, which slip right over the crash tube. If ever damaged, Paramount will simply slip off the old and slip on the new sleeve.
"Versus hydraulic or pneumatic operation, manually-operated bollards were especially cost effective for this application because they were used in locations were they would seldom be lowered, as a result, this expedited installation because there were no motors or power to contend with and also eliminated the need to work with any additional trades, such as electrical. All we needed to do was to make the barriers plumb. As a result, we were able to install a location within a week."
A Warning on Perimeter Security Systems
According to Blood, no area is more critical to the vehicle barrier selection process than testing. Without adequate testing, there is no assurance that the barrier will resist the threat. Testing is normally by an independent testing company or government agency, such as the State Department and military. Full-scale crash tests, using heavily loaded trucks, are conducted. Comprehensive reports of test results are issued and are available from the testing agency or manufacturer.
This article originally appeared in the January/February 2019 issue of Security Today.
TSA Breaks Record Nationally and in Washington for Firearm Discoveries in 2022
Transportation Security Administration (TSA) officers in Washington detected 164 firearms in travelers' carry-on luggage in 2022, with the majority of the firearms discovered at Seattle-Tacoma International Airport's (SEA) security checkpoints. Read Now
Is Your Access Control System Cyber Secure?
September is National Insider Threat Awareness Month
Research: Cybersecurity Remains Top Concern for Physical Security Professionals
Predicting the Cyber Future
ABLOY IP54-rated Integrated Dust Cover
One of the things that keep security managers on high alert is the real possibility the security locks used to safeguard their properties may unexpectedly fail due to environmental conditions. 3
Connect ONE®
Connect ONE's powerful cloud-hosted management platform provides the means to tailor lockdowns and emergency mass notifications throughout a facility – while simultaneously alerting occupants to hazards or next steps, like evacuation. 3
Gallagher Cloud-based Security Solution
Designed to simplify security operations, Gallagher security for SMB provides business owners with the freedom to manage their security from anywhere, with an easy-to-use mobile app. 3


Amazon Global offers Free Shipping to India and Singapore for Amazon.com purchases of at least $125 of eligible AmazonGlobal Saver products, excluding the Import Fees Deposit, duties, and taxes in your Shopping Cart for check out, delivery to a single address and selection of AmazonGlobal Saver as your shipping speed to group items into as few shipments as possible.
Most of the goods sold by Amazon.com online retails popular items like Kindle, tablets, graphics cards, SSDs, electronic goods, Books and music are eligible.
Amazon.com orders will be delivered within 11-14 business days after all of your purchased items are available for free shipping to India and Singapore (including pre-order items) except for purchase of products like software downloads, music downloads, gift card, Items 20 lb. or more, certain oversize items, gift-wrap, e-documents and excludes all third-party sellers products and services.
Amazon.com free shipping to Singapore and India with qualifying AmazonGlobal purchases will see shoppers bypassing USA to Singapore shipping service companies like V-Post| Comgateway|Borderlinx, especially during festive seasons like Thanksgiving and Christmas SALE.


I am so thrilled to teach and impact the lives of children. This year I celebrate my fourteenth year of teaching third grade at Libby Elementary. I have been married to my husband Dennis for thirty-three years, and together we have three children and three energetic grandsons.
I have high expectations for your child, and I will do all I can to help your child achieve them. It is always my goal to keep the lines of communication open with parents. Please do not hesitate to schedule an appointment or call me at school. My conference period is from 1:00–2:00 daily. My email address is rrougely@carthageisd.org and the phone number for Libby is 903-693-8862.
Thank you so much for allowing me the privilege of teaching your precious child!


text:
Vanessa Colella
Head of Citi Ventures and Chief Innovation Officer
Vanessa Colella is the Head of Citi Ventures and Citi's Chief Innovation Officer, focused on driving growth and innovation across the company. Deeply integrated with Citi's businesses, Citi Ventures champions bold exploration through venture investing, innovation and lab networks, strategic partnerships, and lean startup-based initiatives.
Before assuming the role of CIO, Vanessa led venture investing and strategic growth initiatives for Citi Ventures, and previously ran marketing for Citi's North American Consumer Bank. She joined Citibank in 2010 from U.S. Venture Partners (USVP), where she was an entrepreneur-in-residence. Prior to USVP, Vanessa was Head of NA Marketing and then SVP of Insights at Yahoo, where she was responsible for developing and executing the company's consumer data strategy. She was previously a Partner at McKinsey & Company.
She was recognized as a Global Corporate Venturing Rising Star in 2016 (#3) and was named to Institutional Investor's Fintech Finance 35 list in both 2015 and 2016. Her work conceiving and launching CitiBike led Citi to win Advertising Age's 2013 Creativity Award and the Gold Pencil in 2014.
Vanessa received her masters' degrees from Columbia University and M.I.T. as well as a Ph.D. from M.I.T.'s Media Lab. A charter member of Teach for America, she is a published author and lecturer, taught courses at the Santa Fe Institute, and previously served as a research fellow at Rockefeller University. She holds an S.B. degree in molecular biology from M.I.T. and currently serves as a member of the Corporation's Visiting Committee for the Media Lab. She resides in San Francisco with her husband and son.


text:
Q: How to do a code like 'TransactionScope' works? I was reading about the transaction scope in C# and it works like this:
using (connectionDb)
{
    connectionDb.Open();
    using (var ts = new System.Transactions.TransactionScope())
    { <--
        try
        {
            connectionDb.ExecuteNonQuery();
            ts.Complete();
        }
        catch (Exception)
        {
            throw;
        }
        finally
        { }
    } <--
}

Every clauses in the using brackets works at the same transaction, but I don't understand how the code identifies that the databasecommand are running on the scope without pass the transaction scope param neither the opening of the connection, and neither at the execution of the query.
For example, if i have the following code:
var myObject = new MyObject();
var childObject = new ChildObject();
childObject.Foo(myObject);
childObject.Bar(myObject);

Can I create a scope for the variable myObject and use into the childObject methods without pass her by parameter? like this:
using(var myObject = new MyObject())
{
     childObject.Foo(); -- Here the method use the variable myObject
     childObject.Bar(); -- Here the method use the variable myObject
}

A: When you create a TransactionScope it is stored in the static variable Transaction.Current which is kept in thread local storage. The methods that interact with the transaction look at this variable to determine the transaction they are in. This is referred to as an ambient transaction.

redpajama_set_name: RedPajamaStackExchange


The Louisiana Public Service Commission has approved a one-time refund for customers of Entergy Louisiana and Entergy Gulf States Louisiana.
The commission Wednesday authorized the refunds which will show on customers' bills starting at the end of October. The LPSC estimates the credit to the average residential customer will be about $16.75 for an Entergy Gulf States Louisiana customer and $7.50 for an Entergy Louisiana customer.
The refund comes in response to a lawsuit brought by the commission against Entergy over how the company spreads the cost of electric generation over its subsidiaries in Louisiana, Mississippi, Arkansas and Texas.
The LPSC asked FERC to correct flawed refund calculations made by Entergy for 2007, 2008 and 2009.
Entergy Louisiana and Entergy Gulf States Louisiana has about 900,000 customers combined.