text:
\section{INTRODUCTION}

Although interest in topological data analysis has grown sharply in recent years, most of the  important developments have appeared outwith the statistical literature.  We suggest that a consequence is that the potential benefits of adapting some established statistical methods to topological data may not have been fully exploited.  In this paper we argue that event history methods are highly suitable for the analysis of data on certain topological features, that the use of such methods can increase the power and effectiveness of topological data analyses, and in turn that exploitation of topological features can bring added value to statistical analyses.

We use Fig. \ref{fig:intro1} and  Fig. \ref{fig:intro2} to illustrate, at the cost of preempting Section 3, where  they will be explained more fully. Figure \ref{fig:intro1} shows four simulated stationary and isotropic random fields, each on a $60\times 60$ discrete lattice. They are simulated to have correlation functions that are indistinguishable for practical purposes. All four have standard N(0,1) marginal distributions.
 Panel (a) is a simulation from a Gaussian random field, panels (b) and (c) are independent realisations of one particular non-Gaussian random field, and panel (d) is a realisation of a second non-Gaussian random field.  Panel (d) is  visually quite different from the others, but we see no obvious differences between (b) or (c) and the Gaussian random field (a).  Figure \ref{fig:intro2} however shows simple  Nelson-Aalen plots based on topological features of these data, to be described in Section 3. Now the differences are stark.    Given that all four have the same marginal and correlation properties, we know of no other method that can draw out higher order differences so readily.  Further, panel (a) is  seen to be consistent with a Gaussian random field while the other panels are not.

\begin{figure}
\centerline{\includegraphics[height=5in,width=5in]{fig1.pdf}}
\caption{Four simulated random fields, each on a 60$\times$60 lattice.}
\label{fig:intro1}
\end{figure}

\begin{figure}
\centerline{\includegraphics[height=3in,width=3in]{fig2.pdf}}
\caption{Nelson-Aalen plots for connected components for the random fields of Fig. \ref{fig:intro1}.  Short dashed lines correspond to panel (a), dotted  lines to panels (b) and (c), and long dashed lines to panel (d).  The shaded regions indicate $\pm$ one standard deviation.  The solid line shows the expected value for a Gaussian random field with the same correlation structure as the data in Fig. \ref{fig:intro1}.}
\label{fig:intro2}
\end{figure}

Topological data analysis is in part founded on persistent homology. Important early contributions include   \cite{frosini1992measuring},   \cite{robins1999towards} and, particularly, \cite{Edelsbrunner2002}, who introduced the idea of persistence of features along filtrations.  However, the earliest appearance of the expression ``topological data analysis'' seems not to have been until work by \cite{de2004topological} and \cite{bremer2004topological}. Later, \cite{carlsson2009topology} was key in the popularisation of topological data analysis, demonstrating how ideas from topology could solve many of the issues faced when applying geometric methods to complex data.  Other  developments are described by \cite{perea2019}.

In Section 2 we give an introduction to  the key  constructs and concepts underpinning persistent homology and topological data analysis, aimed at a statistical reader.  In Section 3 we develop the idea of Nelson-Aalen plots for  topological features of random fields, as already seen in our introductory example.  We apply the methods in two application areas, one being properties of climate model residuals on a sphere, and the other the distribution of neutral atomic hydrogen \textup{H}\,{\sc i} in the interstellar medium. In Section 4 we consider topological methods for embedded metric trees and propose Cox regression methods as a simple way to deal with potentially censored tree data.  This method is applied to vascular structures in fundus images of the human eye, where it has potential to be an effective screening tool for the early detection of diseases.

\section{TOPOLOGICAL DATA ANALYSIS}

Topology is the study of features in a  space that are invariant under continuous deformation.  We will concentrate on topological data analysis methods that are based on persistent homology, which is the most popular variant of topological data analysis.  Detailed descriptions  are provided by, inter alia,  \cite{Edelsbrunner2002}, \cite{edelsbrunner2008persistent}, \cite{carlsson2009topology} and \cite{Edelsbrunner2012PersistentHT}, with an excellent computational description given by \cite{Otter2017}.  Other variants of topological data analysis are summarised by \cite{wass18}.

We assume we have a metric space and inherited distance function. Dealing with arbitrary features in a space is complicated, and so a starting point is to give a representation in a common form.  One such is a simplicial complex, which is a collection of vertices, edges, triangles, tetrahedra and higher dimensional equivalents.  Thus the letters T, D, and A when written on a page could be represented by four vertices connected by three edges (T), a triangle (D) and a triangle with two extra external edges to two extra vertices (A).
Simplicial complexes are not unique but they facilitate the use of linear algebra for the computation of topological invariants, which are features that do not change when the underyling space is bent, stretched or otherwise deformed, but without  gluing or tearing.

The most important topological invariants are the homology groups, which are formally algebraic constructs, one per natural number, that collect together invariant features at each dimension. The ranks of these groups are called Betti numbers. The zero-dimension homology group $H_0(\mathcal{X})$ of a simplicial complex $\mathcal{X}$ consists of the disjoint elements, often referred to as connected components.   For example, if the letters T, D, and A are written on a page then there are three connected components and the zero-order Betti number is $\beta_0=3$. In three dimensions, if we take for example letter balloons of T, D and A,  there are still three connected components.

The next homology group $H_1(\mathcal{X})$ consists of holes constructed from one-dimensional edges. These are loops or cycles that cannot be shrunk to a single point. For T, D, and A on a page there are two, so $\beta_1=2$.  For  T, D, and A as letter  balloons  we have $\beta_1=4$.  This is because the D and the A are each homologous to a torus.  The hole through the centre of the torus is obvious: the other hole arises because a loop around the circle of revolution that forms the torus cannot be shrunk to a point without leaving the surface.

The second-dimension homology group $H_2(\mathcal{X})$ consists of two-dimensional cycles which enclose an empty three-dimensional space, often called a void. For T, D, and A in balloon form there are three, with one per connected component.  For T, D, and A on a page there are none.

We now turn to persistent homology, which begins with a filtration of a space or simplicial complex $\mathcal{X}$. This is a nested sequence of subsets
\begin{equation}
	\emptyset = \mathcal{X}_0 \subseteq \mathcal{X}_1 \subseteq \cdots \subseteq \mathcal{X}_d \cdots \subseteq \mathcal{X}_n  = \mathcal{X},
\label{eqn:filtration}
\end{equation}
indexed by a parameter $d$ that can be discrete or continuous.  Each subset
is itself a valid simplicial complex, with associated homology groups. Panel (a) of Fig. \ref{fig:trees} illustrates.  It shows a rooted metric tree embedded in two dimensions, and is a simplified version of the metric trees we will consider  in Section 4. There are no cycles and so no holes. In this example the feature of interest  is the connectivity of the tree as we move towards the root, filtering inward by radial distance. So $\mathcal{X}_d$ consists of the subset of $\mathcal{X}$ that is outside a circle of radius $r=1/d$ from the root.  The plot shows two examples.  The complex outside the outer circle consists of just one connected component.  The complex outside the inner circle consists of nine connected components.

\begin{figure}
\centerline{\includegraphics[height=5in,width=5in]{treefig.pdf}}
\caption{Illustrations of persistent homology: (a) embedded metric tree filtered by radius from root; (b) corresponding persistence diagram for connected components; (c) point cloud filtered by ball diameter; (d) corresponding persistence diagram for holes.
}
\label{fig:trees}
\end{figure}

In persistent homology the appearance and disappearance of features is tracked as we move through the filtration.  A feature is said to be born when it appears and to die when it disappears or merges with another feature.  Thus if we start with $r=\infty$ in Fig. \ref{fig:trees} there are no features.  This continues as $r$ is decreased  until it reaches the point on the tree that is furthest from the root, at radius $r_{\rm max}$ say.  The first component is born at this level.  As $r$ is decreased further, additional components are born at vertices that have only inward facing edges.  Existing components merge at vertices with two outward facing edges. In  these cases, by convention the component that was born first is assumed to continue, and the component that was born second is said to die at this radius. The process continues until the root is reached and the tree consists of a single connected component.

The birth and death levels of components are often represented by a persistence diagram, as in panel (b)  of Fig. \ref{fig:trees}.  This is a plot of birth times and death times of features as we move through the filtration.  In the example we have plotted against  $r_{\rm max}-r$ so that the first component to be born is on the left of the horizontal axis. Points near the diagonal are short-lived, points further from the diagonal are longer-lived.  In the example we have two subtrees with common root, which merge only at $r=0$ and so there are two points with the same maximum death time.

A common way to represent a topological space is through a point cloud, as in
 panel (c)  of Fig. \ref{fig:trees}.  In these cases a filtration is usually defined by growing balls of increasing diameter, centred on the points. There are different ways of forming simplicial complexes in these cases, the most common of which is the Vietoris-Rips complex, which defines a subset of points to be a  simplex in $\mathcal{X}_d$ when all pairwise distances between points are less than $2d$.  The persistence diagram in panel (d) relates to the birth and death of holes for this example.  The grey broken lines indicate the ball radius shown in (c).  We see that a medium sized hole is about to be born towards the upper right and this will have modest persistence.  A large hole in the centre will be born when the ball radius is increased a little more.  This will be the last hole to die and is represented by the highest point in panel (d).

Topological data analysis is often used when there is a real-valued function $z_x$ associated with each point $x$ in a space $\mathcal{X}$.  A lower level set $\mathcal{X}_t$ is defined as
\begin{equation}
 \mathcal{X}_t= \{x: z_x\leq t, x \in \mathcal{X} \},
\label{eqn:levelset}
\end{equation}
and upper level sets can defined similarly.  A filtration is defined by increasing or decreasing the threshold $t$ and persistence of topological features in the level sets is tracked.  Figure \ref{fig:levsets} illustrates.  In panel (a) we show a two-dimensional image on a discrete $20 \times 20$   grid.  We assume that pixels that share an edge are connected but those that share only a vertex are not. Panels (b) and (c) show two level sets: pixels with image values less than  $t=-1$ and  less than $t=0$ respectively, marked as dark squares.  In panel (b) there are $\beta_0=6$ connected components.  There are also two regions in that panel that are not in the level set and are completely separated by connected components.  Hence $\beta_1=2$. The level set has expanded by $t=0$ in  (c) and the six previous components have merged, along with others, to form one large component.  One new component has appeared to the lower left, and a small single-pixel component is close by, so $\beta_0=3$.  Five holes are evident.

\begin{figure}
\centerline{\includegraphics[height=5in,width=5in]{revlevfig.pdf}}
\caption{A simulated Gaussian random field  together with level sets  $\mathcal{X}_{-1}$ and $\mathcal{X}_{0}$, and barcode for connected components.}
\label{fig:levsets}
\end{figure}

The final panel of Fig. \ref{fig:levsets} shows a so-called barcode for connected components for the image in panel (a).  This is an alternative to a persistence diagram for the presentation of lifespans of topological features as we filter through the level sets.  There is one line per feature, starting at the birth level and ending at the death level.  In the example the lowest field value is in the dark region to the top right.  This corresponds to the first pixel to appear in the level set, which by convention is the last to die, giving the long line at the bottom of the barcode  in (d).  The second component to be born is at a local minimum toward the top-left of the image.  This component also has the second-longest lifetime until it merges with the first at around $t=-0.25$.   The third-longest lifetime corresponds to the cluster at the lower left of the figure, which is relatively late to be born but quite isolated from other components and hence persists for some time.

A variety of approaches for the analysis of persistence diagrams and barcodes have been proposed.  Wasserstein or bottleneck distances can be used to measure the difference between two persistence diagrams, but these measures are not calibrated to any interpretable scale. There remains no useful equivalents of, say, means or variances for persistence diagrams, which would be needed for inference.  \cite{mileyko11}, \cite{turner14} and  \cite{munch15} showed how to obtain a {F}r\'{e}chet  mean for a group of persistence diagrams, but unfortunately this is not unique. An alternative is to base inference on summaries such as the landscape functions of
\cite{bubenik2015}, which essentially consider  nested profiles of the point patterns,  or on counts of points over subregions, possibly with smoothing to produce the persistent images of, for example,  \cite{adams2015}. Other proposals include a form of parametric bootstrap suggested by
\cite{adler17} and the accumulated persistence suggestion of \cite{bisc19}.
\cite{wass18} provides a recent review.  Our approach is quite different.

\section{NELSON-AALEN FOR RANDOM FIELDS}

\subsection{Method}

Let $\mathcal{Z}$ be a zero-mean real-valued random field defined on a finite discrete space $\mathcal{X}$.  We assume a discrete space because in the applications we have in mind the field will often be an image, and invariably pixellated.   Let
 $z_x$ be the field value at location $x \in \mathcal{X}$. We assume $z_x$ is drawn from a continuous distribution with no jump discontinuities and that there are no ties.  We will require cor$(z_x,z_{x^\prime})<1$ for all $x \neq x^\prime$ in $\mathcal{X}$.

Our filtration corresponding to \eqref{eqn:filtration} will be the nested sequence
of sub-level sets (\ref{eqn:levelset}).   But instead of tracking the persistence of features, which underpins most published applications of topological data analysis, we will take a counting process approach based on the birth levels of new features in the filtration. As well as being computationally much more simple, the approach leads naturally to the use of methods that are familiar in event history analysis, with level playing the role of time  and with negative values allowed.  As features of interest we will concentrate here on connected components within $\mathcal{X}_t$, namely the  $0$-dimensional features.

A component is born at location $x$ at level $t$ if  $z_x=t$ and $x$ corresponds to a local minimum of $\mathcal{Z}$.  To define a local minimum in the discrete space $\mathcal{X}$ we need to define the neighbours $(x)$ of each location $x$.  We are free to do this as we wish, though shared edges or nearest neighbours seem sensible for pixellated or lattice-based fields.

Let $N_x(t)$ count the number of components that are born at location $x$ up to and including level $t$, and let $Y_x(t)$ be the associated predictable at-risk indicator. This is an indicator of whether, given the evolution of level sets to just before level $t$, it is possible for location $x$ to hold a local minimum at $t$.  So
\begin{equation*}
N_x(t) = \left\{ \begin{array}{ll}
1\;\;\;\; & z_x\leq t,\;\; z_{(x)}> z_x 1_x,\\
\\
0& {\rm otherwise}, \end{array} \right.
\;\;\;\;\;\;\
Y_x(t) = \left\{ \begin{array}{ll}
1\;\;\;\; & z_x \geq t,\;\; z_{(x)}> t 1_x,\\
\\
0 & {\rm otherwise}, \end{array} \right.
\label{eqn:counts}
\end{equation*}
where $1_x$ is a unit vector of the same length as $z_{(x)}$. Define $N(t)=\sum_xN_x(t)$ and $Y(t)=\sum_xY_x(t)$. Now let
\begin{equation}
\hat{A}(t) = \int_{-\infty}^t \frac{dN(u)}{Y(u)}.
\label{eqn:nelaal}
\end{equation}
This is an analogue of the Nelson-Aalen estimator of the cumulative hazard for a survival time variable \citep{aal08}. If preferred, we can redefine the level scale $t$ to $t^\ast=\exp(t)$ so as to be a more familiar non-negative argument. Like the Nelson-Aalen estimator,  $\hat{A}(t)$ is defined
for all $t$ and has a finite number of discontinuities at the observed birth times. It is non-negative and uniformly bounded for fixed $\mathcal{X}$.    It is a cadlag process, meaning continuous to the right with left limits. However,   the
standard Nelson-Aalen variance estimator
\begin{equation}
 {\hat{\rm{var}}\{\hat{A}(t)}\} = \int_{-\infty}^t \frac{dN(u)}{Y^2(u)}
\label{eqn:var}
\end{equation}
does not in general apply, for a variety of reasons.  One is possible non-independence between counting processes at different locations given non-independence of the underlying field values.  Another, related, reason is informative censoring:  knowledge that location $x$ falls out of the risk set at level $u$ provides information about other locations.  And a third reason is that we have not constructed a martingale with respect to the observed data filtration.

We propose such Nelson-Aalen estimators for topological features as tools for comparing  properties of fields beyond marginal and correlation structures, for comparing observed field data or model residuals with an assumed parametric model, and as a diagnostic for assumed or fitted correlation structures.  We allow either a single field $\mathcal{Z}$ or   independent and identically distributed replicates $\mathcal{Z}_1, \mathcal{Z}_2, \ldots,\mathcal{Z}_N$ defined on a common space $\mathcal{X}$.

\subsection{Asymptotics}

If $\mathcal{X}$ is fixed and there are $N$ independent replicates $\hat{A}_1(t), \ldots, \hat{A}_N(t)$, then as each $\hat{A}_i(t)$ is non-negative and bounded above, for each level $t$ the variance of $\hat{A}_i(t)$ is finite  and the classic central limit theorem applies.  Hence pointwise inference for large $N$ is straightforward, based on the sample mean $\bar{A}(t)$ and associated sample standard deviation.

For simultaneous confidence bounds we need to be a little careful because of the discontinuities in $\hat{A}_i(t)$.  \cite{hahn77} for instance points out that there can be uniformly bounded cadlag processes for which no central limit theorem applies, depending upon properties of their jumps.  \cite{bloz94} provide sufficient conditions under which $\surd N\bar{A}(t)$ converges weakly to a Gaussian process with finite variance and smooth sample paths in $(0,\tau)$, for any arbitrarily large $\tau$ for which pr$\{Y(\tau)>0\}>0$. These conditions are verified in Supplementary Material.  Hence a functional central limit theorem applies.

Further, for fixed $\mathcal{X}$  we can show that $\bar{A}(t)$ is consistent in $N$ for
\begin{equation}
  A(t) = \displaystyle\int_{-\infty}^t J(u)\frac{ \sum_x {\rm pr}(z_{(x)}> u 1_x  \mid z_x=u) f_x(u)}{\sum_x {\rm pr}(z_x>u, \;z_{(x)}> u 1_x)} du,
\label{eqn:limit}
\end{equation}
where $f_x(.)$ is the marginal density of $z_x$ and $J(u)=1$ if $E\{Y(u)\}>0$, zero otherwise. If $N=1$ and there is just a single realisation, the estimator $\hat{A}(t)$ is also consistent for (\ref{eqn:limit}), but this time as the  cardinality of $\mathcal{X}$ increases, provided  cor$(z_x, z_{x^\prime})$ approaches zero as the distance between $x$ and $x^\prime$ increases.

\subsection{Inference}

For inference, we need to distinguish between the single and multiple replication situations.  We will begin by assuming multiple replications: $\hat{A}_1(t), \ldots, \hat{A}_N(t)$.

We can consider the replicates $\hat{A}_i(t)$ as functional data.  It is helpful to discretise the time/level scale of the estimator (\ref{eqn:nelaal})  to a  grid of $M$ distinct levels $\tau_1=t_1,\ldots,t_M=\tau_2$.  This is a common assumption in functional data analysis and deals effectively with the discontinuities in our estimators, which with probability one will not occur at measurement points.
There are then a huge array of analysis techniques immediately available: see for example the excellent review by  \cite{wang16} for general approaches, and
\cite{chiou09} for a specific example of treating discretised hazards as functional data.  With our applications in mind, we will mention only the construction of simultaneous confidence bands.  This problem has had attention for functional data that are either sparse  \citep{yao05, ma12} or dense \citep{degras11, cao12}.  Given that our discretisation is by design, a dense functional data approach is appropriate.

Let $\hat{A}_{ij}$ be the Nelson-Aalen estimator for field $\mathcal{Z}_i$ at level $t_j$.  Let $\bar{A}_j$ and $\hat{\sigma}_j^2$ be the sample mean and sample variance of the estimators at $t_j$, and let $\hat{\rho}_{jk}$  be the estimated correlation between $\hat{A}_{.j}$ and $\hat{A}_{.k}$.   \cite{degras11}
establishes that for fixed $\tau_1$ and $\tau_2$, as both $N$ and $M$ increase
\begin{equation*}
\bar{A}_j\pm c_\gamma\hat{\sigma}_j/\surd N
\label{eqn:ci}
\end{equation*}
has asymptotic coverage $1-\alpha$, where $c_\alpha$ is the upper $\alpha$-quantile
of the maximum absolute value over $(\tau_2-\tau_1)/\tau_2$ of a Gaussian process with standard margins and with correlation function equal to an appropriately scaled limit of $\hat{\rho}_{jk}$.  The threshold $c_\gamma$ needs to be obtained numerically:  software is available  in the R package {\tt SCBmeanfd} \citep{scb16}.

Turning to the case of a single replication only, we assume a parametric model $F_\theta$ is available for the random field $\mathcal{Z}$ and we  propose a parametric bootstrap approach.  Parametric and more general bootstrap methods for functional data have been studied empirically and theoretically by, for example, \cite{cuevas06}. We require
a consistent estimator $\hat{\theta}$ of the parameter vector $\theta$. We then generate $B$ simulated realisations of $\mathcal{Z}$ on $\mathcal{X}$ from $F_{\hat{\theta}}$, and  obtain the bootstrap Nelson-Aalen estimators $\hat{A}_b(t)$ for $b=1,\ldots,B$.  Pointwise intervals that have  correct coverage asymptotically in $B$ and $\mid \mathcal{X} \mid$ can be obtained from the bootstrap quantiles or a Normal approximation justified by the central limit theorem.

For simultaneous confidence bands we  suggest a Monte Carlo method used by \cite{crain11} that avoids explicit estimation of the correlation function.  We again discretise the original and bootstrap estimators $\hat{A}_b(t)$ to a dense grid of $M$ points over a fixed range of levels $(\tau_1, \tau_2)$. Let $\hat{A}_j$ be the Nelson-Aalen  estimate (\ref{eqn:nelaal}) at level $t_j$ obtained from the original data, and let $\hat{A}_{bj}$ be corresponding value from bootstrap replicate $b$. Let $\bar{A}_j$ and $\hat{\sigma}_j$ be the bootstrap mean and estimated standard deviation at  $t_j$. Define
\[ G_b= \max_j\{ \mid \hat{A}_{bj}- \bar{A}_j \mid/\hat{\sigma}_j \},\]
and take $\hat{d}_\alpha$ to be the upper $\alpha$-quantile of the empirical distribution of $G_1, \ldots, G_B$.  Then for large $M$ an approximate simultaneous confidence band for $A(t)$ over  $(\tau_1, \tau_2)$ is
\[ \hat{A}_j\pm  \hat{d}_\alpha \hat{\sigma}_j \hspace*{1cm} j=1,\ldots, M.\]

\subsection{Simulation studies}

We consider random fields on $d\times d$ lattices and investigate three models. The first,
M1,  is a stationary and isotropic Gaussian random field with standard N(0,1) marginals and Mat\'{e}rn correlation function.  If locations $x$ and $x'$ are separated by Euclidean distance $u$ then
\[ \mbox{cor}\left(z_x,z_{x'}\right)= \frac{2^{1-\nu}}{\Gamma(\nu)}\left(\sqrt{2\nu}u/\eta\right)^\nu K_\nu\left(\sqrt{2\nu} u/\eta\right), \]
where $K_\nu(.)$ is a modified Bessel function of the third kind. Let $\theta_1=(\eta, \nu)$.

The second model, M2, is a marginally transformed $\chi^2_1$ field.   We simulate  from the Gaussian random field model M1, with parameters $\theta_2$.  Values are then squared to give $\chi^2_1$ margins. Let $y_x$ be the value at location $x$.  We next marginally transform to N(0,1) using  $z_x=\Phi^{-1}\{F_1(y_x)\}$, where $\Phi(.)$ and $F_1(.)$ are the N(0,1) and $\chi^2_1$ cumulative distribution functions, respectively.

The third model, M3, is a marginally transformed $F_{3,3}$ field.  We begin by constructing two independent $\chi^2_3$ fields, each from the sum of three squared independent
Gaussian random fields  M1 with  Mat\'{e}rn  parameters $\theta_3$.  The ratio of these $\chi^2_3$ fields has $F_{3,3}$ distribution and we again marginally transform back to N(0,1).

All simulated fields are mean-corrected to help with the comparisons.  The purpose of the marginal transformations is to generate fields that are not Gaussian random fields but which have Gaussian N(0,1) marginal distributions.  This is so that we can assess the added value of topological methods over and above simple comparisons of marginal distributions.  Similarly, in our simulations, for each choice of correlation $\theta_1$ for
model M1 we choose  $\theta_2$ and  $\theta_3$  so as to match as closely as we can the correlation of the final M2 and M3 fields to Mat\'{e}rn with parameters $\theta_1$. Details of this are presented in Supplementary Material.  Again, the point is to show that differences between fields are not explained by differences in  correlation functions.  We have no particular interest in M2 and M3 other than as easily generated non-Gaussian random fields to be used as comparators.

The previously-presented Fig. \ref{fig:intro1} gives in panel (a) an example simulation from M1 on a $60 \times 60$ lattice with $\theta=(5,1).$  Panels (b) and (c) provide two examples of M3, and panel (d) an example of M2, with matched correlations.  The topological Nelson-Aalen plots for these fields are in Fig. \ref{fig:intro2}.  The solid line is the limiting value \eqref{eqn:limit}, which we obtained using the multivariate Gaussian distribution function routine in the R package {\tt mvtnorm}.

Table \ref{tab:cov1}   shows coverage of nominal 95\% confidence intervals and  bands  based on Nelson-Aalen plots for connected components from Gaussian random fields M1 simulated on a 60$\times$60 lattice.  Pointwise coverage is evaluated  at selected percentiles of the at-risk distributions, ie  levels $t$ satisfying $E\{Y_x(t)\}=p$ for five different values of $p$. The simultaneous confidence bands cover the central 90\% of the at-risk distributions, discretised to a grid of 200 equally spaced points. For each parameter combination we give results for three methods.  The first, for reference, is a naive approach that assumes  standard methods based on the variance estimator (\ref{eqn:var}) can be applied and using \cite{borgan87} for simultaneous confidence bands.   The second method assumes we have
a single replication and uses a parametric bootstrap with  $B=200$ simulated Gaussian random fields, generated  with  Mat\'{e}rn correlation parameters taken as the maximum likelihood estimates from the sole replicate.
The third method assumes we have replicates.   The standard and parametric bootstrap results are based on 1000 single simulations, the results with replications are from 1000 batches of $N=40$ replicates, which matches an application in the next subsection.
There is severe undercoverage at times
for the standard confidence intervals, but the other methods both produce good results.

Simulation results for M2 and M3 are provided in Supplementary Material,  along with further simulations to assess size and power when using our method to test for  a Gaussian random field.

\begin{table}
\begin{center}
\caption{Coverage of nominal 95\% pointwise confidence intervals and 95\% simultaneous confidence band (SCB) around Nelson-Aalen component plots for Gaussian random fields. Results from  1000 simulations  on $60 \times 60$ lattices,  with Mat\'{e}rn correlation function with parameters $\eta$ and $\nu$ }
\label{tab:cov1}
\begin{tabular}{clrrrrrr}
 & &  \multicolumn{5}{c}{Percentile}\\
$(\eta, \nu)$  & Method   & 0.9 & 0.7 & 0.5 & 0.3 & 0.1 & SCB \\
\\
(5,1) & Standard        & 84.0& 92.2& 93.5& 88.9& 82.2  & 73.9\\
&Par. bootstrap& 95.3 &94.4 & 95.8& 95.3& 96.2& 94.1\\
&Replications    &  94.2 &94.7 &95.7 &93.9 &94.7 &94.4\\

\\
(10,1) & Standard   & 67.8 & 82.7 & 83.3 & 73.7 & 65.5 & 39.1\\
&Par. bootstrap&  95.3 &95.1 & 95.6 & 95.7 & 94.7 & 93.6 \\
&Replications    & 95.1 &94.0& 94.9 & 95.9 & 94.0 & 96.4\\
 \\

\\
(5,2) &  Standard  &    93.4 & 94.6 & 95.0 & 93.2 & 90.8 & 82.9\\
&Par. bootstrap  & 95.4 &95.2& 95.7& 95.5 &95.8& 95.9\\
&Replications     & 94.0 & 94.6 &95.0& 94.3& 93.7 & 93.4\\
\end{tabular}
\end{center}
\end{table}

In Table \ref{tab:class} we provide a brief comparison between using the proposed Nelson-Aalen estimator and  existing topological data analysis techniques when discriminating between random fields, all on 60$\times$60 lattices.  The statistical properties of topological summaries such as persistence diagrams and landscape functions are not understood, which means we cannot use these methods to assess the adequacy of an assumed model such as a Gaussian random field, or for single-sample problems.  However, if we have two or more groups of replicated data  we can compare classification performance.

We took  training data to be batches of 40 random fields simulated from models M1, M2 and M3, for each of the three parameter choices.   We then used a support vector machine to develop pairwise probabilistic classifiers, using the default parameter choices for the {\tt svm} routine in the R package {\tt e1071}.   We built classifiers based on the proposed Nelson-Aalen  estimator  $\hat{A}(t)$ defined at (\ref{eqn:nelaal})  and on the first 10 landscape functions \citep{bubenik2015} for each of components and holes, choosing the best of these to compare with $\hat{A}(t)$.   Landscape functions are popular summaries of persistence diagrams and are available through the R package {\tt TDA}.  We replicated the training data 20 times and assessed classifier performance on separate groups of 200 simulated random fields for each model.

We assume test data would be allocated to the group with the highest class probability. In Table \ref{tab:class} we provide the accuracy of the classifier, which is the proportion of test data that are allocated to the correct group, and as a calibration measure we give the mean class probabilities for the correct groups.
 For example, a classifier that estimates the probability of the correct group to be 0.51  gives accurate allocation but is poorly calibrated, whereas one that estimates the correct class probability to be 0.99 would be both accurate and well calibrated.  The table  shows that classification based on the Nelson-Aalen estimator consistently outperforms classification based on the landscape functions in both accuracy and calibration.  Additional simulation results provided in Supplementary Material support this conclusion.

\begin{table}
\begin{center}
\caption{Classification of random fields.    Accuracy (Acc.) measures the proportion of random fields that were correctly allocated and calibration (Cal.) gives the mean estimated class probabilities for the correct classes. Values are multiplied by 100 for presentation}
\label{tab:class}
\begin{tabular}{clrrrrrr}
Mat\'{e}rn &Method &  \multicolumn{2}{c}{M1 v M2}&  \multicolumn{2}{c}{M1 v M3}&  \multicolumn{2}{c}{M2 v M3}\\
 $(\eta, \nu)$  &   &  Acc.  & Cal.  &  Acc.   & Cal.  &  Acc.  & Cal.  \\
\\
(5,1) & Nelson-Aalen & 100.0 &97.7 & 99.4 & 96.6 & 100.0 & 97.5\\
 & Landscape      &96.5& 92.5& 91.7& 85.5&  95.4& 91.6\\
\\
(10,1) & Nelson-Aalen &99.7 &97.2 &95.2 &91.0 &100.0 &97.3\\
 & Landscape &99.7& 97.0& 80.7& 72.0&  95.8& 90.1\\
\\
(5,2) & Nelson-Aalen &100.0 &97.6 &99.7 &97.1 &100.0& 97.4\\
 & Landscape &  92.3& 87.7& 79.4& 71.0&  87.5 &80.1\\
\end{tabular}
\end{center}
\end{table}

\subsection{Applications: climate modelling, interstellar medium}

The Community Earth System Model Large Ensemble project \citep{kay2015community} is a publicly available ensemble of 40 high resolution climate model simulations. Although the models used to generate the data are deterministic, spatial and spatio-temporal statistical modelling is often applied as a means to both explore and concisely summarise the output \citep{cast16assess, edwards19}. Such models often assume that the modelled residuals form a  Gaussian random field. We can use the topological Nelson-Aalen approach to  assess this assumption, at least in part, and as a  quick and easy method to compare ensemble members and to examine the suitability of assumed correlation structures.

To illustrate our method we considered annual wind intensities at $10$m for the year 2020.  Each ensemble member has data on a 192$\times$288 grid over the surface of a sphere and at each data point we standardised by the mean and standard deviation across members.  To keep the correlation matrices manageable for this illustration we used
every third lattice point in both the latitudinal and longitudinal directions.  Following
\cite{castruccio2014beyond} and \cite{castruccio2016compressing} we also trimmed the regions near the poles, leaving data on a $51 \times 96$ lattice for each ensemble member.

Topological Nelson-Aalen plots for connected components are shown as functional boxplots \citep{sun11} in the left panel of  Fig. \ref{fig:naexamples} together with a simultaneous confidence band for the mean.  There is reassurance that the standard assumption of a Gaussian random field for these climate residuals is reasonable: the band almost uniformly includes the expected value for a Gaussian random field
 with correlation function taken to be the empirical correlation found in the data.  To illustrate the potential of our method as a diagnostic for fitted correlation models, the figure also includes expected values under three  modelled correlation structures. These are the three dotted lines in the plot. In order from the top,  the first line corresponds to a stationary
Mat\'{e}rn model. It is clearly poor for these data, and  the same is true for the second dotted line, which corresponds to  a regional block model with separate Mat\'ern correlation functions fitted to land, ocean and coastal regions.  In contrast, as seen by the lowest dotted line, a semiparametric non-stationary model proposed by
\cite{konzen2019} fits these data well.

\begin{figure}
\centerline{\includegraphics{naexamplesfig.pdf}}
\caption{Nelson-Aalen plots for connected components. In (a) the grey region and lines represent functional boxplots of 40 separate realisations of climate data residuals.  The long dashed lines represent the mean and associated 95\% simultaneous confidence band, and the other lines are limiting values for Gaussian random fields with different correlation matrices. The solid line uses the empirical correlation matrix, the others are explained in the text.  Panel (b) considers the neutral hydrogen data: the short dashed lines are Region 1, the dotted lines Region 2 and the long dashed lines Region 3.  The shaded regions indicate $\pm$ two standard deviations.  The solid lines
show the limiting values for Gaussian random fields with matched correlations.  The upper lines, which are almost indistinguishable, are for Regions 1 and 2, the lower is for Region 3. }
\label{fig:naexamples}
\end{figure}

Our second application is into the distribution in the Milky Way of neutral atomic hydrogen, \textup{H}\,{\sc i}, using data obtained  by the Galactic All-Sky Survey  using the Parkes 64 m radio telescope
\citep{mclure09, kalberla10}. Neutral atomic hydrogen makes up around 90\% of the atoms in the interstellar medium, which embeds stars and galaxies.  Turbulence and shocks lead to a highly heterogeneous random pattern to the local density of \textup{H}\,{\sc i}, and a common assumption in astrophysics is  that after the removal of trends and large-scale features, the distribution of the log-density of  \textup{H}\,{\sc i} is that of a Gaussian random field \citep{elmeg04, monin07}.  This assumption was investigated by \cite{hend2020}, who considered topological features of three distinct regions of the southern sky.  Based on counts of components and holes, but not event history, the authors concluded that two regions were consistent, after monotonic transformation, with Gaussian random fields but the third was not.

The right panel of Fig. \ref{fig:naexamples} shows Nelson-Aalen plots for connected components for these data.  Each region consists of density measurements over a 256$\times$256 lattice and following   \cite{hend2020} we considered the residuals after trend removal using a thin plate smoothing spline. The data were again marginally transformed to N(0,1) to preempt questions as to whether simple monotonic transformations could suffice to convert the data to Gaussian random fields.  Region 3 is clearly distinct from the others and not consistent with the fitted Gaussian random field. Regions 1 and 2  have very similar Nelson-Aalen plots and almost indistinguishable limiting values for Gaussian random fields with Mat\'ern correlations fitted to the data.  There is evidence however that at the higher levels these regions are not consistent with the fitted Gaussian random fields.

\section{COX PROPORTIONAL HAZARDS FOR EMBEDDED METRIC TREES}
\subsection{Topology and event history for tree data}

We now turn to trees $\mathcal{T}=\{\mathcal{N},\mathcal{E}\}$ made up of nodes $\mathcal{N}$  and associated edges $\mathcal{E}$ embedded  in $\mathbb{R}^2$ or $\mathbb{R}^3$, and with a single common ancestor.  A simple example was shown in  panel (a) of Fig. \ref{fig:trees}.

A number of approaches for the comparison of tree structures have been inspired by persistent homology. \cite{kanari2018} considered persistence of one-dimensional features on contours equidistant from the root, whether path length or radial distance, as previously illustrated in Panel (a) of Fig. \ref{fig:trees}. \cite{LI2017}  independently proposed a similar approach in the comparison of neuronal tree structures. \cite{BENDICH2016}
applied persistent homology methods to brain artery trees in  $\mathbb{R}^3$,  \cite{MAOLI2017} used a geodesic distance function to derive persistence diagrams for the branching architectures of plants, and
 \cite{BRODZKI2018}  used persistent homology to classify bronchial trees.

None of the methods of which we are aware can deal with the possibility of censored or incomplete trees.  For example,  an image may capture only part of a tree, in which case edges that cross the image boundary would  not be fully observed.  Edges that reach the boundary are not true first order nodes or leaves, and  should
not be considered as birth or death locations when applying any of the persistent homological techniques.  Equally they do contain information and these edges should not be ignored or discarded.

This type of problem is of course fundamental to event history and survival analysis methodology, where methods for censored or truncated data are very well established.    Hence our proposal is straightforward:  construct a filtration as above  but consider as an event the occurrence of either a leaf or a branching point.  More specifically we propose the following.

\begin{enumerate}
\item Filter outwards by radial distance $r$.
\item Consider a leaf event to occur at $r$ if a known  leaf has that radial distance from the root.  Consider a branch event similarly. If desired, we might distinguish bifurcations from trifurcations and so on.  Censored points cannot be events.
\item Construct a risk set consisting of all edges in $\mathcal{E}$ that intersect a circle of radius $r$ centered at the root, as in panel (a) of Fig. \ref{fig:trees}.
\item Use standard event history methods to contrast events with risk sets.
\end{enumerate}

Let $\mathcal{C}_r$ be  a disc of radius $r$ centred on the root and define $\mathcal{T}_r=\mathcal{T} \cap \mathcal{C}_r$. Covariates can be included provided they are exogenous or dynamically defined on
$\mathcal{C}_r$.  Thus spatial location, branch order, edge weights, proximity to other branches in $\mathcal{C}_r$ and so on can be handled easily using a Cox or other event time regression model. Further, we can construct risk sets from multiple trees and use fixed or random effects (frailties) to include tree-to-tree comparisons.

For this method to work we need three common event history assumptions.  First, that  events occur independently given the risk sets and covariates. Second, that censoring is independent.  Third, that whichever hazard or intensity model is assumed is appropriate.
Under these assumptions standard results apply, including asymptotics for Cox or other model estimates.

\subsection{Application: vascular patterns in the eye}

The left panel of Fig. \ref{fig:eyefig} shows the vascular pattern of a human eye, taken
from a high dimensional image provided by \cite{budai13}.  The right panel of the figure shows a tree representation, with vertices joined by linear edges, a simplification of the optic nerve area to provide a root, and with the omission of  vessels that appear to originate outwith the plot region.  Vessels that reach the boundary of the plot region are marked as censored points.  This is a two-dimensional projection of a three-dimensional tree and branches can cross.  There are no cycles.

\begin{figure}
\centerline{\includegraphics{eyefig.pdf}}
\caption{Left panel:  vascular patterns in a healthy human eye, with data taken from
\cite{budai13}. Right panel: tree representation.  Censored edges are marked by points. }
\label{fig:eyefig}
\end{figure}

The data in Fig. \ref{fig:eyefig}  are from  one of the 15 fundus images of eyes of healthy patients provided by  \cite{budai13}, who also give fundus images of 15 patients with diabetic retinopathy.  We use our methods to compare diabetic retinopathy and healthy patients, and also to investigate the effect of a number of covariates.  These are the width of the vessel, the Euclidean distance between nodes, the ratio of path length to Euclidean distance between nodes as a measure of curvature, the number of other nodes within radius 200 units of the current node (about 10\% of the image size), the generation order, and the azimuth to the root measured from the vertical. We assumed log-linear effects for all covariates except azimuth, for which we assumed a  smooth effect via a $P$-spline with four degrees of freedom.    We also included patient-level terms as either fixed effects or frailties. In total over the 30 patients there were 12590 edges.  At this large sample size the fixed effect and frailty model fits were almost identical.

Table \ref{tab:cox} summarises some of the results.  In the upper part we consider an event to be the termination of a vessel at a leaf, and in the lower part we consider branching points.  Given the large sample, statistical significance is almost irrelevant and so to compare the magnitudes of effects we take the hazard ratio (hr) between
the 20\% and 80\% points of the  covariate distributions.

As might be expected, large vessels are highly unlikely to terminate in a leaf but are more likely to branch.  The hazard functions for both types of event fall as Euclidean distance from an earlier node increases.  There is no real effect of either curvature of an edge or proximity to other nodes, but there is some evidence that branching becomes less likely as the generation of an edge increases.  There is a modest azimuth effect on branching, with a plot of the smooth fit (not shown here) indicating a decrease in branching hazard as aziumth moves away from the vertical, fairly symmetrically in both directions.

Of most interest is the difference between patients.  The estimated frailty terms for both leaf and branch events are shown in Fig. \ref{fig:frailfig}.  There are relatively small differences between patients in vessel branching frequency, but large differences in the hazard for terminating in a leaf, given covariate effects. Interestingly, we have complete separation between healthy and diabetic  retinopathy patients in these frailties.

\begin{table}[h]
\begin{center}
\caption{Cox proportional hazards results for eye trees}
\label{tab:cox}
\begin{tabular}{lrrrrr}
 & \multicolumn{5}{c}{Event = leaf}\\
  & Coefficient & SE & $\chi^2$ & df & hr\\
Width & -0.3849 & 0.0079 & 2369.51 & 1 & 0.02\\
Euclid & -0.0051 & 0.0002 & 797.16 & 1 & 0.41\\
Path length ratio & 1.0142 & 0.1260 & 64.76 & 1 & 1.05\\
Nodes within 200 & 0.0037 & 0.0037 & 1.02 & 1 & 1.02\\
Order & -0.0147 & 0.0049 & 8.90 & 1 & 0.86\\
Azimuth &  &  & 34.66 & 4 & 1.18\\
Patient effect &  &  & 823.98 & 29 & 2.29\\
\\
 & \multicolumn{5}{c}{Event = branch}\\
 & Coefficient & SE & $\chi^2$ & df & hr\\
Width & 0.0591 & 0.0016 & 1420.28 & 1 & 1.88\\
Euclid & -0.0058 & 0.0001 & 1874.37 & 1 & 0.37\\
Path length ratio & -0.9058 & 0.2406 & 14.18 & 1 & 0.95\\
Nodes within 200 & 0.0375 & 0.0033 & 128.54 & 1 & 1.21\\
Order & -0.0622 & 0.0051 & 149.35 & 1 & 0.54\\
Azimuth &  &  & 119.30 & 4 & 1.41\\
Patient effect &  &  & 103.60 & 29 & 1.37\\
\end{tabular}
\end{center}
\end{table}

\begin{figure}
\centerline{\includegraphics[height=3in,width=3in]{treefrail.pdf}}
\caption{Subject-level frailties for 15 diabetic retinopathy patients (triangles) and 15 healthy patients (circles).}
\label{fig:frailfig}
\end{figure}

\section{DISCUSSION}

There are a number of areas for further work.  In Section 3 we suggested empirical variances for the Nelson-Aalen estimator when there are repeated samples of random fields, as in the climate example, and parametric bootstrap estimators when there was interest in a particular model, such as a Gaussian random field. In Supplementary Material,  an adjusted parametric bootstrap assuming local misspecification as in \cite{Copas2005} is suggested and
provides a conservative approach for the case of a single sample and no model. However, it  would be useful formally to develop a non-parametric variance estimator.

In both Section 3 and Section 4 we concentrated on the birth times of connected components. We might also consider death times, though the identification of risk sets is less straightforward in this case, which in turn means that limiting properties will be harder to derive.  For complexes in two or higher dimensions, including the random fields of Section 3, we could consider higher order features.  In two-dimensions it will be particularly simple to use our methods to investigate death times of holes, which as a result of Alexander duality \citep{edel12} can be analysed in the same manner as births of components simply by reversing the direction of the filtration.

\section{ACKNOWLEDGEMENT}

We thank Evandro Konzen and Jian Shi for providing software to fit their non-stationary correlation model to the climate data.

\clearpage

\bibliographystyle{biometrika}

redpajama_set_name: RedPajamaArXiv


text:
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redpajama_set_name: RedPajamaCommonCrawl


text:
#ifndef __SYSDEP_I386_PTRACE_USER_H__
#define __SYSDEP_I386_PTRACE_USER_H__

#include <sys/ptrace.h>
#include <linux/ptrace.h>
#include <asm/ptrace.h>

#define PT_OFFSET(r) ((r) * sizeof(long))

#define PT_SYSCALL_NR(regs) ((regs)[ORIG_EAX])
#define PT_SYSCALL_NR_OFFSET PT_OFFSET(ORIG_EAX)

#define PT_SYSCALL_ARG1_OFFSET PT_OFFSET(EBX)
#define PT_SYSCALL_ARG2_OFFSET PT_OFFSET(ECX)
#define PT_SYSCALL_ARG3_OFFSET PT_OFFSET(EDX)
#define PT_SYSCALL_ARG4_OFFSET PT_OFFSET(ESI)
#define PT_SYSCALL_ARG5_OFFSET PT_OFFSET(EDI)
#define PT_SYSCALL_ARG6_OFFSET PT_OFFSET(EBP)

#define PT_SYSCALL_RET_OFFSET PT_OFFSET(EAX)

#define REGS_SYSCALL_NR EAX /* This is used before a system call */
#define REGS_SYSCALL_ARG1 EBX
#define REGS_SYSCALL_ARG2 ECX
#define REGS_SYSCALL_ARG3 EDX
#define REGS_SYSCALL_ARG4 ESI
#define REGS_SYSCALL_ARG5 EDI
#define REGS_SYSCALL_ARG6 EBP

#define REGS_IP_INDEX EIP
#define REGS_SP_INDEX UESP

#define PT_IP_OFFSET PT_OFFSET(EIP)
#define PT_IP(regs) ((regs)[EIP])
#define PT_SP_OFFSET PT_OFFSET(UESP)
#define PT_SP(regs) ((regs)[UESP])

#ifndef FRAME_SIZE
#define FRAME_SIZE (17)
#endif
#define FRAME_SIZE_OFFSET (FRAME_SIZE * sizeof(unsigned long))

#define FP_FRAME_SIZE (27)
#define FPX_FRAME_SIZE (128)

#ifdef PTRACE_GETREGS
#define UM_HAVE_GETREGS
#endif

#ifdef PTRACE_SETREGS
#define UM_HAVE_SETREGS
#endif

#ifdef PTRACE_GETFPREGS
#define UM_HAVE_GETFPREGS
#endif

#ifdef PTRACE_SETFPREGS
#define UM_HAVE_SETFPREGS
#endif

#ifdef PTRACE_GETFPXREGS
#define UM_HAVE_GETFPXREGS
#endif

#ifdef PTRACE_SETFPXREGS
#define UM_HAVE_SETFPXREGS
#endif

#endif

redpajama_set_name: RedPajamaGithub


In the field of corporate social responsibility (CSR) we are monitoring and analyzing business and policies and practices in environmental, social and corporate governance area. In the scope of our work we support policies and practices that strengthen relationships between different stakeholders, contribute to their social responsibility, as well as to a higher level of implementation of human rights of all people through socially responsible actions. With professional knowledge and through connecting businesses and experts, we aim to help organizations in holistic and comprehensive management of CSR and in identifying risks and opportunities that traditional business practices may overlook.
WHAT IS CORPORATE SOCIAL RESPONSIBILITY AND SUSTAINABLE DEVELOPMENT?
According to the EU Commission (2002), CSR Corporate social responsibility is a concept whereby companies integrate social and environmental concerns in their business operations and in their interaction with their stakeholders on a voluntary basis.
We can introduce businesses to tools for measuring and reporting CSR, as well as professionally assist in their integration into business strategy. The motivation for socially responsible behavior is to measure the impact the company creates, as measuring the results is encouragement for the company to improve year by year.
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text:
Q: Data pivot transformation I am aware of some packages in R for text mining like tm but I am unable to use it for my task.
I have a text file that has data something like this:
 452924301037
    5May2014
       John
    7May2014
       Mark
       Sam
 452924302789
    6May2014
       Bill

I want the above data in a data frame as something like this:
UserID, Date, Names
452924301037,5May2014,John
452924301037,7May2014,Mark Sam
452924302789,6May2014,Bill

How can I do this in R?
Example 2:
Input textfile:
452924301037
    5May2014
       John
           Cricket
           Football
    7May2014
       Mark
           Hockey
452924302789
     6May2014
       Bill
           Billiards

And I want to setup a data frame as below:
Game, Player, Date, UserID
Cricket, John, 5May2014, 452924301037
Football, John, 5May2014, 452924301037
Hockey, Mark, 7May2014, 452924301037
Billiards, Bill, 6May2014, 452924302789

A: A possible solution using data.table and zoo:
# read the textfile
txt <- readLines('textlines.txt')

# load the needed packages
library(zoo)
library(data.table)

# convert the text to a data.table (an enhanced form of a dataframe)
DT <- data.table(txt = txt)

# extract the info into new columns
DT[grepl('\\d+{8,}', txt), User_id := grep('\\d+{8,}', txt, value = TRUE)
   ][grepl('\\D+{3}\\d+{4}', txt), Date := txt
     ][, (c('User_id','Date')) := lapply(.SD, na.locf, na.rm = FALSE), .SDcols = 2:3
       ][txt!=User_id & txt != Date, .(Names = paste0(txt, collapse = ' ')), by = .(User_id, Date)]

which gives:
        user_id     date    Names
1: 452924301037 5May2014     John
2: 452924301037 7May2014 Mark Sam
3: 452924302789 6May2014     Bill

To see what each step does, run the following code:
# extract the user_id's
DT[grepl('\\d+{8,}', txt), User_id := grep('\\d+{8,}', txt, value = TRUE)][]
# extract the dates
DT[grepl('\\D+{3}\\d+{4}', txt), Date := txt][]
# fill the NA-values of 'User_id' and 'Date' with na.locf from the zoo package
DT[, (c('User_id','Date')) := lapply(.SD, na.locf, na.rm = FALSE), .SDcols = 2:3][]
# filter out the rows where the 'txt'-column has either a 'User_id' or a 'Date'
# collapse the names into one string by 'User_id' and 'Date'
DT[txt != User_id & txt != Date, .(Names = paste0(txt, collapse = ' ')), by = .(User_id, Date)][]

For the added 2nd example, you could do:
DT <- data.table(txt = trimws(txt))

DT[grepl('\\d+{8,}', txt), User_id := grep('\\d+{8,}', txt, value = TRUE)
   ][grepl('\\D+{3}\\d+{4}', txt), Date := txt
     ][, (c('User_id','Date')) := lapply(.SD, na.locf, na.rm = FALSE), .SDcols = 2:3
       ][txt!=User_id & txt != Date
         ][, Name := txt[1], by = .(User_id, Date)
           ][Name != txt]

which gives:
         txt      User_id     Date Name
1:   Cricket 452924301037 5May2014 John
2:  Football 452924301037 5May2014 John
3:    Hockey 452924301037 7May2014 Mark
4: Billiards 452924302789 6May2014 Bill

redpajama_set_name: RedPajamaStackExchange


text:
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Facebook appears to be gearing up its challenge to the music streaming industry after poaching one of YouTube's key music directors in Tamara Hrivnak.
Hrivnak leaves her position as director of music partnerships at YouTube and Google Play to lead Facebook's global music strategy and business development.
She confirmed the news in a post on the social media platform saying she was looking forward to "deepening Facebook's relationship with the music industry".
"Music is important and it matters – it connects us and binds us to times, places, feelings and friends," said Hrivnak. "My career has been dedicated to growing opportunities for music in the digital landscape.
Hrivnak's experience as a respected music lawyer with a history of working on licensing deals suggests that Facebook is considering licensing music.
Prior to joining Google in 2011, Hrivnak served as vice president of digital strategy and business affairs for Warner/Chappell Music.
Music videos have become more popular on Facebook in recent years, however this has emerged as something of a problem because many of them are not licenced.
In an effort to curtail the issue, Facebook announced in December 2016 that it was developing a content ID system to stem music infringement and it is likely that Hrivnak role will oversee this.


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text:
Vrasselt ist der Name folgender Orte:

 Vrasselt (Emmerich am Rhein), Ortsteil der Stadt Emmerich am Rhein, Kreis Kleve, Nordrhein-Westfalen
 Vrasselt (Issum), Ortsteil der Gemeinde Issum, Kreis Kleve, Nordrhein-Westfalen

Siehe auch:
 Frasselt

redpajama_set_name: RedPajamaWikipedia


1. I, LOUISE SQUIRES am collecting your name, home address, and email and telephone contact number for the purposes of identifying you, and enabling me to make contact with you both routinely and non-routinely, for example, in urgent or emergency situations, by telephone, text or email, or in less likely circumstances, by post.
2. I will NOT share your data with any other individuals, agencies, or organisations unless I have a legitimate concern for either your own or someone else's safety and wellbeing. In the rare circumstances that disclosure might become necessary, or be legally required – and this could include the criminal justice agencies, such as the police – I will always endeavour to inform you of my intention to do so and wherever practicable, involve you in the decision to disclose. In certain circumstances you may instruct me to share some of all of your clinical records with another agency. Should this be the case I will approach you to sign a Consent to Third Party Disclosure authorisation form before doing so and involve you in the review of any records held about you, before making the disclosure.
3. In the event that I am taken seriously ill or have a serious accident and am therefore incapacitated and therefore prevented from making contact with you, or if I die during the course of your therapy episode, a nominated colleague and supervisor will be instructed to access my client register (name, email address and telephone number only). My colleague/supervisor is then permitted by me to contact you to explain my personal situation and to discuss any ongoing support needed. You are at liberty to decline any support offered by them.
4.The contact details collected at initial assessment, and any other client notes I hold are stored safely and confidentially under lock and key. No other person has routine access rights to my file or individual clinical records with the exception of the circumstances detailed in 3, above.
5. Unless specific erasure of your records are formally requested by you, all records pertaining to you that are held by Louise Squires will be retained securely for a maximum of 7 years following the ending of the therapy relationship. This is a requirement of my insuring body.
6. Telephone numbers and any text or app-based conversations between me as the 'data controller' and you as the 'data subject' will be maintained only for the period that we are working together therapeutically, and for 6 months thereafter, following which I will delete any numbers and messages between us. My mobile devices are password protected.
7. Emails generated between us, including the initial contact form, will be maintained only for the period that we are working together therapeutically, and for 6 months thereafter, following which I will delete the correspondence.
8. This Privacy Statement and Agreement is available for you to read on my website. The Therapy Contract and Informed Consent Form contains a summary of this Privacy Statement and will be provided back to you once you denote your understanding and agreement by signing the form. By signing the Informed Consent form you are also denoting your Agreement to the terms and conditions contained within Louise Squires Therapy Contract and terms and conditions.
11. The GDPR will affect how counsellors, psychotherapists and counselling and psychotherapy services (the 'data controllers') store and use sensitive, personal client data (the 'data subjects') and the client's rights to access and request erasure of their data.
Personal data relating to criminal convictions and offences are not included, but similar, additional safeguards apply to their processing.
Where the personal data are no longer necessary for the purpose for which they were originally collected/processed.
When you withdraw your consent to records being made and held.
When you object to the processing and there is no overriding legitimate interest for Louise Squires to continue the processing of your data.
If you believe that your personal data is or has been unlawfully processed by Louise Squires –in breach of GDPR.
Where erasure is required as a legal obligation.
In the exercise or defence of legal claims.
There are additional requirements when the request for erasure relates to children's personal data. This is because a child may not have been fully aware of the risks involved in the processing at the time of consent being secured.
14. Your right to data portability. Under the GDPR you can request and reuse your personal data for your own purposes across other services. If Louise receives a request from you to transfer your personal data, she will provide it in a structured, commonly used and machine-readable form, free of any charge to you.
15. Your right of access. You will have the right to request to see the information that is being held about you, whether these are electronic or manually stored records. Your request should be made to Louise Squires in writing and you should expect to be given access within a calendar month of your request being acknowledged, free of any charge to you. If you believe any record held about you to be incorrect or inaccurate in any way then you can ask for it to be corrected by Louise Squires. If there is a disagreement between us about the accuracy of any records that Louise holds about you then an additional note will be added to the record to acknowledge your objections to what it recorded about you.


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Home | News & Media | News | Disappointing lack of progress on continuity trade agreements
Disappointing lack of progress on continuity trade agreements Feb 22 2019 | British Chambers of Commerce
Commenting on the latest update to existing trade agreements if the UK leaves the EU without a deal, Dr Adam Marshall, director general at the British Chambers of Commerce (BCC), said: "The lack of progress revealed today is an incredibly disappointing, though not unexpected admission. This is unfortunately yet another example where politicians have overpromised and underdelivered – and it is businesses and consumers both at home and abroad that will pay the price.
"The British Chambers of Commerce has warned government for over two years that the replication of EU trade agreements would not be easy. We have also warned politicians that some trading businesses don't even know they are benefiting from the provisions of these agreements, and will be surprised to see their costs going up or their products treated differently overseas in the unwanted event of 'no deal' on March 29.
"There are firms now who have shipped their products to important overseas markets like South Korea whose customers may now face higher costs and new administrative burdens and purchases languishing at the dock after March 29. UK firms and their partners have spent time building strong business relationships and will not appreciate sudden and expensive changes in trading arrangements.
"It's time for government to work far more closely with business communities to ensure that more existing agreements are rolled over and their benefits to businesses and consumers preserved. The best way to do this in the first instance is to avoid a messy and disorderly exit from the EU on March 29 – which would see the terms of trade for some businesses worsen overnight."


The Theodore Gordon Flyfishers, Inc. and the Environmental Consortium of Hudson Valley Colleges & Universities are pleased to announce the 2012 Theodore Gordon Flyfishers, Inc. Founders Fund Scholarship.
This scholarship is available to students enrolled, and who will continue to be enrolled during the award year (2012-2013 academic year), in a full-time four-year undergraduate degree (junior standing or higher as of fall 2012) or graduate degree program at a member institution of the Environmental Consortium of Hudson Valley Colleges & Universities. The scholarship is also available to community college students in their second year who have been accepted into a four-year accredited member institution of the Environmental Consortium of Hudson Valley Colleges & Universities and will be enrolled as a junior or senior in the award year (as of fall 2102). The eligible applicant's major shall be in an area of environmental studies such as but not limited to ecology, hydrology, conservation biology, natural resource management, zoology or environmental law and policy. Based on a 4.0 scale, the applicant shall demonstrate merit with a current 3.5 GPA in the environmental concentration courses and an overall minimum 3.0 GPA. The scholarship is open to U.S. citizens only.


Hotel in Italien - Hotel Praiano, Hotel Holiday.
Holiday Hotel's strength is in its location. The panorama at the heart of Amalfi's coast is incomparably beautiful. Praiano was an ancient fisher town and Dogi's summer resort. It was once located between the ancient Republica Marinara di Amalfi (Amalfi's Sea Republic) and the rich town of Positano. At "Holiday Hotel" in Praiano (3 stars) you can see the clear blue sea from every room, each different from the others and provided with a balcony where you can peacefully enjoy your view.
Each detail has been given particular attention: the floor is made up of hand-made majolica tiles from Vietri, modern style furniture, and bedcover colours in marine style integrate standard amenities such as phones, TV-sets, refrigerators with fully stocked mini-bars, air conditioning, heating, private bathrooms, and hairdryers.
A bar, a restaurant, whose famous cuisine is based on local speciality and season products, a solarium, a lift, and a parking lot are at our guests' disposal.
Last but not least the seaside: you can reach it on foot (10 minutes), by car or by bus (available in summer time, only).


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Tobacco Buyout Up for House Vote
Republican leaders are close to sending a tobacco-buyout bill to the floor of the U.S. House of Representatives as part of a measure that would repeal a tax break for U.S. exporters, Fox News reported June 2.
Under the measure, farmers in the tobacco states of Georgia, Kentucky, North Carolina, and Tennessee would be paid to leave the government program that sets price and production control on U.S.-grown tobacco.
"I think this is the best opportunity politically we've had," said Rep. Jack Kingston (R-Ga.), an advocate for the buyout.
However, senior Republicans said moderates could object to combining the buyout with the tax bill.
Anti-smoking groups — which want the buyout tied to a measure authorizing the Food and Drug Administration to regulate tobacco — are against the plan. Matthew Myers, president of the Campaign for Tobacco-free Kids called the House proposal a "back-room deal to protect the tobacco industry while deliberately avoiding any action that would reduce the death toll from tobacco use."
The U.S. Senate passed the tax bill last month without the tobacco buyout.
Our daughter has a strong, sober network of friends and is happier than we have ever seen her. Thanks to Wellness, we have been reunited as a family.


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Women in aircraft maintenance
How low is the involvement of women in aviation today?
How can we encourage the growth of women's presence in the field?
What are the benefits and disadvantages of a career in aircraft maintenance?
When people talk about aircraft, there is usually one association to it – men in coveralls. Although, through history it is statistically more common to see males in this field and there is a stereotype that this is a "man's job", it is proven many times that women are just capable of performing these jobs as men are. Lillian Todd, as the first female aircraft designer who invented the first successful aircraft, and Amelia Earhart, a pilot and an advisor to aeronautical engineering, have proved this themselves. Also, one of the most known women in the world was also an aircraft mechanic in WW2 - Queen Elizabeth II herself. Despite these successful women, there is a small amount of females in aircraft maintenance nowadays. Why is that?
As the USA is the first country to notice this problem, they were the first to start solving it. In 2020, due to FAA demographic report, there were 215,905 non-pilot female certificate holders and only 7,573 were aircraft maintenance technicians. That means that only around 3.5% of them are aircraft mechanics. But if we take a look at the number of 301,087 aircraft maintenance technicians in the USA, that tells us that only 2.5% of all aircraft mechanics are actually female. But in the end, if we take a look that only 5 years ago there were 8,419 women working as aircraft maintenance technicians, it means that situation is only getting worst.
We should also consider that the USA is the country with the highest involvement of women in this field and that the situation in the rest of the world is even worst. In many countries, these numbers are going even lower than 1%.
Since the industry has got large growth in the past decade we are wondering why are these numbers so low.
Women in aviation - Facts
How do we encourage the growth of women's presence in the filed?
At this moment we have to be aware that women should be a part of the maintenance staff as well and that it is our job to encourage them to join us. We have a huge interest of women population in aviation, but on positions like pilots and stewardesses. Therefore, there is a lack of support in other aviation areas, mainly in maintenance. This is a consequence of not knowing what an aircraft mechanic and his job actually is. Taking this into account, we wanted to do some research. We've explained to numerous women briefly what is it about and 56% of them said they would consider it as a career option. That tells us that if they were informed properly we could have a lot higher interest in women joining our industry. Likely, there is this wonderful community Association of Women in Aviation Maintenance (AWAM) who is supporting all of the female presence in the aircraft maintenance industry in all possible ways. They have set all the primary goals for solving this issue. Educating the young people about this industry, helping students with their scholarships, helping newcomers find jobs and increasing awareness of companies, and most importantly, support all of the women that are already working in the aircraft maintenance industry.
And that is the right way to encourage women for choosing an aircraft mechanic career. It's our job to show them support and appreciation.
What are the advantages and disadvantages (pros and cons) of a career in aircraft maintenance?
Not so difficult job - Some would say that a job of an aircraft mechanic is a heavy job, but they would be wrong. Nowadays, technology has improved so much in the field of aviation that made tools much easier to operate with, so there are almost no high-strength efforts needed.
High salary – In the USA, average aircraft mechanic salary is around 75.000$ a year, which is almost double the overall average salary in the USA.
High career possibilities - There are so many different paths in aircraft maintenance and so many ways to improve and get a better position and even higher salary
Nature of the job - Working in aviation is a special thing and aircraft mechanics are in the elite places of this industry. Everyone loves to meet the aircraft mechanics and they are always overwhelmed about that. The job itself is great. It is highly dynamic, and you will be learning new things all the time. Most experienced mechanics will tell you that the more you learn about aircraft more those become interesting to you. Some surveys have shown that 77% of aircraft mechanics really love their job and that they are happy with their career.
High growth trend - The unemployment level is around 4% and is getting lower every year.
Flight benefits – Many aviation employers offer discounts or even free filghts to various locations
Working with your hands – This is a hands-on career in which will enjoy the most those who love physical work.
Taking pride in the safety of others – Or to say it poetically – aircraft mechanic keeps the skies safe. Without them inspecting, repairing and maintaining aircraft, passenger travel wouldn't exist.
24 hours shift – Aviation is never sleeping and aircraft mechanics have to cover all 24 hours of the day so they work both, day and night shifts.
Dirty" job – This can be both, an advantage and a disadvantage, depending on people's taste, but our research found out that women don't have problem with this.
Fast-paced, high-pressure – As said previously, it can be an advantage to take pride in the safety of others, but it can also be a disadvantage to work under pressure because the safety depends on aircraft mechanics
We contacted some women representatives in this industry and asked them about their experience. They were very nice and supportive so check out their testimonials.
Joséphine Van De Voorde
Workplace: iGO Solutions
Expirience: 8 years
How did you decide to start your career in aircraft maintenance?
I always knew I couldn't work in an office spending 8 hours in front of a computer, I needed to move, to do something with my hands, so mechanic appears like a good job, the pleasure of fixing problems, the pleasure of going from something not working to something which works because of you! And why in aviation? Well, I don't do anything like the others so, I wanted a badass job, and what's more badass than some big heavy "stack of metal" capable of flying through the Sky?
How do you see it now?
Now I'm really happy to do this work, there are always things to learn, you constantly have to update yourself, and each day is different from the other. Also, there's a lot of different type of aircraft, so you really have the choice to decide.
What are your goals in future as aircraft mechanic?
My goals for the future are to work outside of France, discover new countries thanks to my job. Also this year I'm gonna practice to become B1.3 to learn about helicopters. Then I'll have the choice to work almost wherever I want. If you are recruiting in Finland I would be really interested (ahahahaha).
Message to women who are thinking of taking this path?
My message to women who are thinking about taking this path is: Follow your dreams! Yes it's a "men's" world, but all you need is self-confidence, and they will understand that you're just like them.
Ana García
Workplace: Las Americas international Airport - MCL Services & Aircraft Maintenance
Expirience: 3.5 years
When i was in military highschool the last two years it was required to choose one of the four school technic courses ( nursing, accounting, computer science and Aircraft Maintenance Technician ) I choosed AMT - Basic Theories. It was not what I spected and I said: — !this is not what I want! . A few months later after graduation, my first job changed my mind. Working on an International Airport as an Auxiliar of Maintenance Technician. It's totally diferent than be on an hangar with small, old and abandoned planes. The experience is AMAZING I'm in love with the area Im at right now. It has those big airplanes which I really love.When I was in military high school the last two years I was required to choose one of the four school technic courses ( nursing, accounting, computer science, and Aircraft Maintenance Technician ) I chose AMT - Basic Theories. It was not what I expected and I said: —! This is not what I want! A few months later after graduation, my first job changed my mind. Working on an International Airport as an Auxiliar of Maintenance Technician. It's totally different than being in a hangar with small, old, and abandoned planes. The experience is AMAZING I'm in love with the area I'm in right now. It has those big airplanes which I really love.
The exact phrase which defines how I feel seeing this every day is "I am nothing Without this". I can't imagine being a Nurse, a Lawyer, or a Police. This became part of my life.
Use my aircraft tools as much as I can. Being a good engineer and learn as much as I can. Develop the ability to create, repair and correct and always make it perfect every day.
Try and if you like it ... make it part of you. It is a WONDERFUL experience, find out for yourself and you won't regret it. Get as much knowledge as you can.
Published in MRO jobs
More from MRO jobsMore posts in MRO jobs »
What will Boeing 737MAX recertification mean for the MRO market?
Lack of manpower in aircraft maintenance
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Pros and cons of working as an aircraft mechanic
How to become an aircraft licensed engineer


When the Soviet Union collapsed, many observers hoped that decollectivization would improve the infamously inefficient Soviet agricultural sector and raise collective farm workers out of poverty. The initial results of market reform in Russian agriculture were a severe disappointment in both respects. Under Putin, Russia finally allowed agricultural land to be bought and sold. The effects of this latest reform neither met the expectations of its supporters nor realized the fears of its opponents. Russia's experience with land reform suggests that while private ownership of farmland may offer significant advantages, successful land reform requires much more than the creation of legal rights. This Article explores the role of property law in post-Soviet Russian agriculture and charts the development and effect of land markets in rural Russia, revealing broad implications for the effects of land privatization on agriculture, the barriers to creating well functioning land markets, and the significance of property law for economic development.


See more objects with the tag interior, decoration, container, domestic, dining, asymmetry, octagonal, decorative, brightly colored, bowls, geometric, eating, glass, concave.
See more objects with the color darkred maroon darkslategrey navy dimgrey or see all the colors for this object.
This was an Object of the Day.
This is a Bowl. It was designed by Klaus Moje and made by Klaus Moje. It is dated 1981 and we acquired it in 1983. Its medium is kiln-formed fused glass. It is a part of the Product Design and Decorative Arts department.
Twisted, semi-opaque red-orange and deep translucent blue glass canes were heat-laminated together to create this bowl, with its pattern of audaciously contrasting blue parallelograms and red ground.
This object was featured in our Object of the Day series in a post titled Something Brilliant to Behold.
This object was purchased from Gallerie Edith Gottschalk and donated by Louis Comfort Tiffany Foundation and catalogued by David McFadden. It is credited This acquisition made possible through Louis Comfort Tiffany Foundation.
maple and lacquered mdf, cut with cnc tools.
Courtesy of Lanzavecchia + Wai Design Studio.
Gift of Dr. J. William Fielding.
Museum purchase from General Acquisition Endowment.
On underside: "Klaus Moje (in script) 6/1981" scratched on blue pattern and signed in ink on red.
Signed twice on underside (in script): "Klaus Moje 6/1981"; once scratched on blue pattern, and once signed in ink on red.
This object was previously on display as a part of the exhibition	Making Design.


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71st Golden Globe Awards Nominations; 2013 SAG Nominations; Sitcom Stars on Talk Shows (Week of December 16, 2013)
The 71st Annual Golden Globe Awards nominees have been announced! HBO leads with 9 (down from its 15 from last year) nominations. Starz, Netflix and Showtime followed with 6 (Showtime had 8 last year). NBC and CBS lead the broadcast nominees with 5 each this year followed by ABC's 4 and Fox with 3. House of Cards was the most nominated television series with 4 this year, while Breaking Bad, The Good Wife had 3 each. The telecast airs LIVE on Sunday, Jan. 12, 2014 from 8-11pm ET/5-8pm PT on NBC hosted again by Tina Fey and Amy Poehler. Here are the major nominees:
BEST TELEVISION SERIES – COMEDY OR MUSICAL
The Big Bang Theory (CBS)
Brooklyn Nine-Nine (Fox)
Girls (HBO)
Modern Family (ABC)
Parks & Recreation (NBC)
COMMENTS: A rookie makes the cut: Brooklyn Nine-Nine, but surprised no New Girl again this year. Modern Family should win it.
BEST PERFORMANCE BY AN ACTRESS IN A TELEVISION SERIES – COMEDY OR MUSICAL
Zooey Deschanel, New Girl (Fox)
Lena Dunham, Girls (HBO)
Edie Falco, Nurse Jackie (HBO)
Julia Louis-Dreyfus, The Veep (HBO)
Amy Poehler, Parks and Recreation (NBC)
COMMENTS: No real surprises again here. Dreyfus will take it.
BEST PERFORMANCE BY AN ACTOR IN A TELEVISION SERIES – COMEDY OR MUSICAL
Jason Bateman, Arrested Development (Netflix)
Don Cheadle, House of Lies (Showtime)
Jim Parsons, The Big Bang Theory (CBS)
Michael J. Fox, Michael J. Fox Show (NBC)
Andy Samberg, Brooklyn Nine-Nine (Fox)
COMMENTS: A legend returns! Michael J. Fox!
BEST TELEVISION SERIES – DRAMA
The Good Wife (CBS)
Breaking Bad (AMC)
Downton Abbey (PBS)
House of Cards (Netflix)
Masters of Sex (Showtime)
COMMENTS: No Homeland is a surprise. I'd have to say Downton is the fave.
BEST PERFORMANCE BY AN ACTRESS IN A TELEVISION SERIES – DRAMA
Kerry Washington, Scandal (ABC)
Tatiana Maslany, Orphan Black (BBC America)
Julianna Margulies, The Good Wife (CBS)
Taylor Schilling, Orange is the New Black (Netflix)
Robin Wright, House of Cards (Netflix)
COMMENTS: No Claire Danes? Go Kerry Washington. She's got it handled.
BEST PERFORMANCE BY AN ACTOR IN A TELEVISION SERIES – DRAMA
James Spader, The Blacklist (NBC)
Bryan Cranston, Breaking Bad (AMC)
Kevin Spacey, House of Cards (Netflix)
Michael Sheen, Masters of Sex (Showtime)
Liev Schreiber, Ray Donovan (Showtime)
COMMENTS: No Jeff Daniels or Damian Lewis, so maybe Cranston takes it.
Jacqueline Bisset, Dancing on the Edge (mini-series) (Starz)
Janet McTeer, White Queen (mini-series) (Starz)
Hayden Panettiere, Nashville (ABC)
Sofia Vergara, Modern Family (ABC
Monica Potter, Parenthood (NBC)
COMMENTS: Go Sofia! Hayden Panettiere gets nominated a second straight year, as no Maggie Smith this year.
Josh Charles, The Good Wife (CBS)
Rob Lowe, Behind the Candelabra (mini-series) (HBO)
Aaron Paul, Breaking Bad (AMC)
Corey Stoll, House of Cards (Netflix)
Jon Voight, Ray Donovan (Showtime)
COMMENTS: No Eric Stonestreet this year. Ty Burrell needs to be on this. We have no comedy blood in this.
View the FULL list of television nominees and to discuss this.
Awards season is heating up with the Golden Globes (above), now we have another. The 20th Annual Screen Actor Guild Awards (SAG) nominees were announced. Leading the pack were Breaking Bad and Modern Family. Among the broadcast networks, ABC led with 4 nominations. HBO led overall with 13 nominations. The awards will air live Saturday, Jan. 18, 2014 on TNT and TBS at 8 PM ET/5 PM PT.
Here are the main TV nominees:
Outstanding Performance by a Male Actor in a Drama Series
Steve Buscemi (Boardwalk Empire, HBO)
Bryan Cranston (Breaking Bad, AMC)
Jeff Daniels (The Newsroom, HBO)
Peter Dinklage (Game of Thrones, HBO)
Kevin Spacey (House of Cards, Netflix)
Outstanding Performance by a Female Actor in a Drama Series
Claire Danes (Homeland, Showtime)
Anna Gunn (Breaking Bad, AMC)
Jessica Lange (American Horror Story: Asylum, FX)
Kerry Washington (Scandal, ABC)
Maggie Smith (Downton Abbey, PBS)
Outstanding Performance by a Male Actor in a Comedy Series
Alec Baldwin (30 Rock, NBC)
Ty Burrell (Modern Family, ABC)
Jason Bateman (Arrested Development, Netflix)
Jim Parsons (The Big Bang Theory, CBS)
Don Cheadle (House of Lies, Showtime)
Outstanding Performance by a Female Actor in a Comedy Series
Edie Falco (Nurse Jackie, Showtime)
Tina Fey (30 Rock, NBC)
Julia Louis-Dreyfus (Veep, HBO)
Mayim Bialik (The Big Bang Theory, CBS)
Julie Bowen (Modern Family, ABC)
Outstanding Performance by an Ensemble in a Drama Series
Boardwalk Empire (HBO)
Homeland (Showtime)
Outstanding Performance by an Ensemble in a Comedy Series
30 Rock (NBC)
Arrested Development (Netflix)
Veep (HBO)
View the complete nominees!
It's Friday, so it is time for "Sitcom Stars on Talk Shows" for the coming week. This is where we list what sitcom stars are appearing on talk shows for the coming week, the stars are both current and past sitcom stars, so this is for all sitcom fans of all eras!
For the week of Dec. 16-20, 2013, we have many current sitcom stars and past sitcom stars on TV talk shows! All times listed are Eastern. So get your DVRs (or whatever you use) ready! Ready? Here is all what you need to know for the coming week!
Jesse Tyler Ferguson (Modern Family/The Class) - Watch Jesse on Conan at 11pm on TBS.
Tom Papa (Come to Papa) - Tom visits Conan at 11pm on TBS.
J.B. Smoove (The Millers/Curb Your Enthusiasm) - J.B. is a guest on The Tonight Show with Jay Leno at 11:35pm on NBC.
Bill Cosby (The Cosby Show/Cosby/The Bill Cosby Show) - Bill appears on a repeat of Late Night with Jimmy Fallon at 12:37am on NBC.
Christina Applegate (Up All Night/Samantha Who?/Married with Children) - Christina stops by LIVE! with Kelly and Michael, so check your local listings.
Meagan Good (Cousin Skeeter/Raising Dad - Meagan talks about her new movie Anchorman 2: The Legend Continues on The Wendy Williams Show at 12am on BET or on your local stations.
Billy Gardell (Mike & Molly) - Billy guests on The Queen Latifah Show, so check your local listings.
Marlo Thomas (That Girl) - Marlo chats with the ladies of The View on ABC at 11am ET/10am CT-PT.
Molly Ringwald (The Facts of Life/Townies) - Molly talks about her new career as a jazz singer and author on Fox News Sunday with Chris Wallace on Sunday, so check your local listings for time and station.
Alyssa Milano (Who's the Boss?/Romantically Challenged) - Catch Alyssa on The Late Late Show with Craig Ferguson at 12:37am on CBS.
Cristin Milioti (How I Met Your Mother) - Cristin stops by The Late Show with David Letterman at 11:35pm on CBS.
Miley Cyrus (Hannah Montana) - Miley is a guest on a repeat of Late Night with Jimmy Fallon at 12:37am on NBC.
Eric Andre (Don't Trust the B---- in Apartment 23) - Eric appears on a repeat of Last Call with Carson Daly at 1:35am on NBC.
Tom Hanks (Bosom Buddies) - Tom drops by Conan at 11pm on TBS.
Kathy Griffin (Suddenly Susan) - Kathy will be on The Queen Latifah Show, so check your local listings.
Rob Reiner (All in the Family) - Rob visits The Talk on CBS at 2pm ET/1pm CT-PT.
Steve Carell (The Office) - Steve and other cast members of Anchorman 2: The Legend Continues will be guests on Watch What Happens: Live at 11pm on Bravo.
David Koechner (The Office) - David will be on Good Day New York on WNYW-Fox5 in New York between 7-9am.
Megan Hilty (Sean Saves the World) - Megan talks about Sean Saves the World on NBC's Today in the 10am hour.
Danica McKellar (The Wonder Years) - Danica is a guest on Big Morning Buzz Live with Carrie Keegan on VH1 at 10am.
Christina Applegate (Up All Night/Samantha Who?/Married with Children) - Watch Christina on The Tonight Show with Jay Leno at 11:35pm on NBC.
Kevin Nealon (Weeds/Hiller and Diller/Champs) - Kevin visits The Tonight Show with Jay Leno at 11:35pm on NBC.
Billy Crystal (Soap) - Billy appears on a repeat of Late Night with Jimmy Fallon at 12:37am on NBC.
Steve Carell (The Office) - Steve and the cast of Anchorman 2: The Legend Continues are guests on Comedy Central's The Daily Show with Jon Stewart at 11pm. The cast will also be on Larry King Now on Wednesday and Thursday at 2/5pm PT/ET on Ora.tv and Hulu.
Jennifer Lawrence (The Bill Engvall Show) - Jennifer stops by Conan at 11pm on TBS.
William Shatner ($#*! My Dad Says) - William is a guest on The Arsenio Hall Show, so check your local listings.
Julia Louis-Dreyfus (Veep/The New Adventures of Old Christine/Seinfeld) - Julia appears on The Ellen DeGeneres Show, so check your local listings.
Danica McKellar (The Wonder Years) - Danica talks about growing up as Winnie and making math fun on Katie, so check your local listings. She will also be on Fox and Friends, PIX Morning Show on WPIX in New York between 7-9am, and New York Live on WNBC in New York at 12:30pm ET.
Megan Hilty (Sean Saves the World) - Megan chats on New York Live on WNBC in New York at 12:30pm ET.
Niecy Nash (The Soul Man/Reno 911!) - Niecy partners with Clinton Kelly to create a tasty, wintery dish on ABC's The Chew at 1pm ET/12pm PT/CT.
Soleil Moon Frye (Punky Brewster/Sabrina, the Teenage Witch) - Tune in to see Soleil on The Queen Latifah Show, so check your local listings.
Sarah Jessica Parker (Sex and the City/Square Pegs) - Sarah appears on Watch What Happens: Live at 11pm on Bravo.
NeNe Leakes (The New Normal) - NeNe will be on ABC's Good Morning America between 7-9am.
Steve Carell (The Office) - Catch Steve on The Late Show with David Letterman at 11:35pm on CBS. He will also be on ABC's Good Morning America between 7-9am.
Adam Scott (Parks and Recreation) - Adam visits Jimmy Kimmel Live! at 11:35pm on ABC.
Christina Applegate (Up All Night/Samantha Who?/Married with Children) - Christina continues to make the rounds on Conan at 11pm on TBS.
Skylar Astin (Ground Floor) - Skylar is a guest on Conan at 11pm on TBS.
Joseph Gordon Levitt (3rd Rock from the Sun) - Joseph appears on a repeat of Late Night with Jimmy Fallon at 12:37am on NBC.
Stephen Merchant (Hello Ladies) - Stephen is a guest on a repeat of Late Night with Jimmy Fallon at 12:37am on NBC.
Meagan Good (Cousin Skeeter/Raising Dad) - Meagan appears on The Arsenio Hall Show, so check your local listings.
Tatyana Ali (Love That Girl/The Fresh Prince of Bel-Air) - Tatyana chats on Bethenny, so check your local listings.
Niecy Nash (The Soul Man/Reno 911!) - Niecy will be on The Better Show, so check your local listings.
Jay Thomas (Cheers/Love & War/Murphy Brown) - Watch Jay on The Late Show with David Letterman at 11:35pm on CBS.
Michael Strahan (Brothers) - Michael visits The Tonight Show with Jay Leno at 11:35pm on NBC.
Meagan Good (Cousin Skeeter/Raising Dad) - Meagan is a guest on a repeat of The Wendy Williams Show at 12am on BET or on your local stations.
Stacy Keach (Titus) - Stacy appears on Tavis Smiley on PBS, so check your local listings for time.
Kim Richards (Hello, Larry) - Kim and her sister Kyle are guests on The Aresenio Hall Show, so check your local listings.
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Posted by Pavan -- SitcomsOnline.com at 12:00 PM
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The boards have been received by both Larry and Kelly. I have notes out to them to get an update on the installation. Larry was having some issues with loading Octoprint on his Raspberry Pi. I know that Kelly had gotten a hold of another ATX power supply to use. I am hoping to have a real update over the next several days.
9/25/2017 7:00 am Update. I talked to Larry and he got his PI Zero working and was playing working on configuring Octoprint. We was have some issues with the network setup. I have to agree with him, the network setup for the Jessie image that Octoprint uses is terrible when it comes to static IP address setup on the wireless adapter. There is just no way to do it via the standard config files. After you get it started and whatnot, then you can go in and do some setup. It is still complicated.
Steve Smith and I have been working on a PI Hat of this board that has this functionality as well as a bunch of other features. They include; External RGB LED (4 pin XH) management, Full 12 and 5v support, external switching, fully integrated with Octoprint, EPO – Emergency Power Off, and a cooling fan for the PI. There are some other features that I will keep until we have worked out the prototypes and the software.
There is more to come regarding the hat and the current beta.
Next Post: Conversation with Kelly on Facebook Messenger 10/15/2017 – Fried printer!