text:
\section{Introduction}\label{sec:Intro}
Anomaly detection involves detecting statistically significant
deviations of test data from nominal distribution. In typical
applications the nominal distribution is unknown and generally
cannot be reliably estimated from nominal training data due to a
combination of factors such as limited data size and high
dimensionality.

We propose an adaptive non-parametric method for anomaly detection
based on score functions that maps data samples to the interval
$[0,\,1]$. Our score function is derived from a K-nearest neighbor
graph (K-NNG) on $n$-point nominal data. Anomaly is declared whenever the score of a test
sample falls below $\alpha$ (the desired false alarm error). The efficacy of our method rests upon its close
connection to multivariate p-values. In statistical hypothesis
testing, p-value is any transformation of the feature space to the interval $[0,1]$ that induces a uniform distribution on the nominal data.
When test samples
with p-values smaller than $\alpha$ are declared as anomalies, false alarm error is less than $\alpha$.

We develop a novel notion of p-values based on measures of level
sets of likelihood ratio functions. Our notion provides a characterization of the optimal
anomaly detector, in that, it is uniformly most powerful for a
specified false alarm level for the case when the anomaly density is
a mixture of the nominal and a known density. We show that our score function is
asymptotically consistent, namely, it converges to our multivariate p-value as data length approaches infinity.

Anomaly detection has been extensively studied. It is also referred
to as novelty detection \cite{Campbell, Markou}, outlier detection
\cite{Ramaswamy}, one-class classification \cite{Vert, Tax1} and
single-class classification \cite{Yaniv} in the literature.
Approaches to anomaly detection can be grouped into several
categories. In parametric approaches~\cite{basseville} the nominal
densities are assumed to come from a parameterized family and
generalized likelihood ratio tests are used for detecting deviations
from nominal. It is difficult to use parametric approaches when the distribution is unknown and data is limited.
A K-nearest neighbor (K-NN) anomaly detection approach is presented in \cite{Ramaswamy,Zhang2}. There
an anomaly is declared whenever the distance to the K-th nearest
neighbor of the test sample falls outside a threshold. In comparison
our anomaly detector utilizes the global information available from
the entire K-NN graph to detect deviations from the nominal. In addition it has provable optimality properties.
Learning theoretic approaches attempt to find
decision regions, based on nominal data, that separate nominal
instances from their outliers. These include one-class SVM of
Sch$\mathrm{\ddot{o}}$lkopf et. al. \cite{Scholkopf} where the basic
idea is to map the training data into the kernel space and to
separate them from the origin with maximum margin. Other algorithms
along this line of research include support vector data description
\cite{Tax},  linear programming approach \cite{Campbell}, and single
class minimax probability machine \cite{Lanckriet}.
While these approaches provide impressive computationally efficient solutions on real data, it is generally difficult to precisely relate tuning parameter choices to desired false alarm probability.

Scott and Nowak \cite{Scott1} derive decision regions based on
minimum volume (MV) sets, which does provide Type I and Type II error control.
They approximate (in appropriate function classes) level sets of the
unknown nominal multivariate density from training samples. Related
work by Hero \cite{Hero1} based on geometric entropic minimization
(GEM) detects outliers by comparing test samples to the most
concentrated subset of points in the training sample. This most
concentrated set is the $K$-point minimum spanning tree(MST) for
$n$-point nominal data and converges asymptotically to the minimum
entropy set (which is also the MV set). Nevertheless, computing
$K$-MST for $n$-point data is generally intractable. To overcome
these computational limitations \cite{Hero1} proposes heuristic
greedy algorithms based on leave-one out K-NN graph, which while
inspired by $K$-MST algorithm is no longer provably optimal. Our
approach is related to these latter techniques, namely, MV sets of
\cite{Scott1} and GEM approach of \cite{Hero1}. We develop score
functions on K-NNG which turn out to be the empirical estimates of
the volume of the MV sets containing the test point. The volume,
which is a real number, is a sufficient statistic for ensuring
optimal guarantees. In this way we avoid explicit
high-dimensional level set computation. Yet our algorithms lead to statistically optimal solutions with the ability to control false alarm and miss error probabilities.

The main features of our anomaly detector are summarized. (1) Like
\cite{Hero1} our algorithm scales linearly with dimension and
quadratic with data size and can be applied to high dimensional
feature spaces. (2) Like \cite{Scott1} our algorithm is provably
optimal in that it is uniformly most powerful for the specified
false alarm level, $\alpha$, for the case that the anomaly density
is a mixture of the nominal and any other density (not necessarily
uniform). (3) We do not require assumptions of linearity,
smoothness, continuity of the densities or the convexity of the
level sets. Furthermore, our algorithm adapts to the inherent
manifold structure or local dimensionality of the nominal density.
(4) Like \cite{Hero1} and unlike other learning theoretic approaches
such as \cite{Scholkopf,Scott1} we do not require choosing complex
tuning parameters or function approximation classes.

\section{Anomaly Detection Algorithm: Score functions based on K-NNG}\label{sec:alg}
In this section we present our basic algorithm devoid of any statistical context. Statistical analysis appears in Section~\ref{sec:analysis}.
Let $S=\{x_1,x_2,\cdots,x_n\}$ be
the nominal training set of size $n$ belonging to the unit cube $[0,1]^d$. For notational convenience we use $\eta$ and $x_{n+1}$ interchangeably to denote a test
point. Our task is to declare whether the test point is consistent with nominal data or deviates from the nominal data. If the test point is an anomaly it is assumed to come from a mixture of nominal distribution underlying the training data and another known density (see Section~\ref{sec:analysis}).

Let $d(x,y)$ be a distance function denoting the distance between
any two points $x,\,y \in [0,1]^d$. For simplicity we denote the
distances by $d_{ij}= d(x_i,x_j)$. In the simplest case we assume
the distance function to be Euclidean. However, we also consider
geodesic distances to exploit the underlying manifold structure. The
geodesic distance is defined as the shortest distance on the
manifold. The {\em Geodesic Learning} algorithm, a subroutine in
Isomap~\cite{Tenenbaum,Tenenbaum2} can be used to efficiently and
consistently estimate the geodesic distances. In addition by means
of selective weighting of different coordinates note that the
distance function could also account for pronounced changes in local
dimensionality. This can be accomplished for instance through
Mahalanobis distances or as a by product of local linear
embedding~\cite{Roweis}. However, we skip these details here and
assume that a suitable distance metric is chosen.

Once a distance function is defined our next step is to form a $K$ nearest neighbor graph
(K-NNG) or alternatively an $\epsilon$ neighbor graph ($\epsilon$-NG).
K-NNG is formed by connecting each $x_i$ to the $K$ closest points
$\{x_{i_1},\cdots,x_{i_K}\}$ in $S-\{x_i\}$. We then sort the $K$ nearest distances for each $x_i$ in increasing order $d_{i,i_1}\leq
\cdots\leq d_{i,i_K}$ and denote $R_S(x_i)=d_{i,i_K}$, that is,
the distance from $x_i$ to its $K$-th nearest neighbor. We construct $\epsilon$-NG where $x_i$ and $x_j$
are connected if and only if $d_{ij}\leq \epsilon$. In this case we define
$N_S(x_i)$ as the degree of point $x_i$ in the $\epsilon$-NG.

For the simple case when the anomalous density is an arbitrary
mixture of nominal and uniform density\footnote{\tiny When the
mixing density is not uniform but, say $f_1$, the score functions
must be modified to $\hat p_K(\eta) = \frac{1}{n}\sum_{i=1}^n
\mathbb{I}{\scriptstyle\left\{\frac{1}{R_S(\eta)f_1(\eta)}\leq\frac{
1}{R_S(x_i)f_1(x_i)}\right\}}$ and $\hat p_{\epsilon} (\eta) =
\frac{1}{n}\sum_{i=1}^n
\mathbb{I}{\scriptstyle\left\{\frac{N_S(\eta)}{f_1(\eta)}\geq\frac{
N_S(x_i)}{f_1(x_i)}\right\}}$ for the two graphs K-NNG and
$\epsilon$-NNG respectively.} we consider the following two score
functions associated with the two graphs K-NNG and $\epsilon$-NNG
respectively.  The score functions map the test data $\eta$ to the
interval $[0,\,1]$.
\begin{eqnarray}
\text{K-LPE:\quad}\hat{p}_K(\eta) = \frac{1}{n}\sum_{i=1}^n
\mathbb{I}_{\{R_S(\eta)\leq R_S(x_i)\}}\label{eq:LPE1}\\
\text{$\epsilon$-LPE:\quad}\hat{p}_\epsilon(\eta) =
\frac{1}{n}\sum_{i=1}^n \mathbb{I}_{\{N_S(\eta)\geq
N_S(x_i)\}}\label{eq:LPE2}
\end{eqnarray}
where $\mathbb{I}_{\{\cdot\}}$ is the indicator function.

Finally, given a pre-defined significance level $\alpha$ (e.g.,
$0.05$), we declare $\eta$ to be anomalous if
$\hat{p_{K}}(\eta),\,\hat{p_{\epsilon}}(\eta)\leq \alpha$. We call this algorithm {\em Localized
p-value Estimation} (LPE) algorithm. This choice is motivated by its close connection to multivariate p-values(see Section \ref{sec:analysis}).

The score function K-LPE (or $\epsilon$-LPE) measures the relative
concentration of point $\eta$ compared to the training set. Section
\ref{sec:analysis} establishes that the scores for nominally
generated data is asymptotically uniformly distributed in $[0,\,1]$.
Scores for  anomalous data are clustered around $0$. Hence when scores below level
$\alpha$ are declared as anomalous the false alarm error is smaller than $\alpha$ asymptotically (since the
integral of a uniform distribution from $0$ to $\alpha$ is
$\alpha$).

\setlength{\unitlength}{1\textwidth}

\begin{figure}[!htb]
\centering
\begin{picture}(1,.35)
\put(-.04,0){\includegraphics[scale = 0.49]{ill1.eps}}
\put(.31,0){\includegraphics[scale = 0.49]{ill2.eps}}
\put(.66,0){\includegraphics[scale = 0.49]{ill3.eps}}
\end{picture}
\caption{\small \sl {\bf Left}: Level sets of the nominal bivariate
Gaussian mixture distribution used to illustrate the K-LPE
algorithm. {\bf Middle}: Results of K-LPE with $K=6$ and Euclidean distance metric for $m=150$ test points drawn from a equal mixture of 2D uniform and the (nominal) bivariate distributions. Scores for the test points are based on $200$ nominal training samples. Scores falling below a threshold level $0.05$ are declared as anomalies. The dotted contour corresponds
to the exact bivariate Gaussian density level set at level $\alpha=0.05$. {\bf Right}: The
empirical distribution of the test point scores associated with the bivariate Gaussian appear to be uniform while scores for the test points drawn from 2D uniform distribution cluster around zero.}\label{fig:illustration}
\end{figure}

Figure \ref{fig:illustration} illustrates the use of K-LPE algorithm
for anomaly detection when the nominal data is a 2D Gaussian
mixture. The middle panel of figure \ref{fig:illustration} shows the
detection results based on K-LPE are consistent with the theoretical
contour for significance level $\alpha = 0.05$. The right panel of
figure \ref{fig:illustration} shows the empirical distribution
(derived from the kernel density estimation) of the score function
K-LPE for the nominal (solid blue) and the anomaly (dashed red)
data. We can see that the curve for the nominal data is
approximately uniform in the interval $[0,\,1]$ and the curve for
the anomaly data has a peak at $0$. Therefore choosing the threshold
$\alpha=0.05$ will approximately control the Type I error within
$0.05$ and minimize the Type II error. We also take note of the inherent robustness of our algorithm. As seen from the figure (right) small changes in $\alpha$ lead to small changes in actual false alarm and miss levels.

To summarize the above discussion, our LPE algorithm has three
steps:

\noindent
{\bf (1) Inputs:} Significance level $\alpha$, distance metric (Euclidean, geodesic, weighted etc.).\\
{\bf (2) Score computation: } Construct K-NNG (or $\epsilon$-NG) based on
  $d_{ij}$ and compute the score function K-LPE
  from Equation \ref{eq:LPE1} (or $\epsilon$-LPE from Equation \ref{eq:LPE2}).\\
{\bf (3) Make Decision: } Declare $\eta$ to be anomalous if
  and only if $\hat{p}_{K}(\eta)\leq \alpha$ (or $\hat{p}_{\epsilon}(\eta)\leq
  \alpha$).

\noindent{\bf Computational Complexity:} To compute each pairwise
distance requires O(d) operations; and O($n^2d$) operations for all
the nodes in the training set. In the worst-case computing the K-NN
graph (for small $K$) and the functions $R_S(\cdot),\,N_S(\cdot)$
requires O($n^2$) operations over all the nodes in the training
data. Finally, computing the score for each test data requires
O(nd+n) operations(given that $R_S(\cdot),\,N_S(\cdot)$ have already
been computed).

\noindent {\bf Remark:} LPE is fundamentally different from
non-parametric density estimation or level set estimation schemes
(e.g., MV-set). These approaches involve explicit estimation of
high dimensional quantities and thus hard to apply in high
dimensional problems. By computing scores for each test sample we avoid high-dimensional computation. Furthermore, as we will see in the following section the scores are estimates of multivariate p-values. These turn out to be sufficient statistics for optimal anomaly detection.

\section{Theory: Consistency of LPE}\label{sec:analysis}

A statistical framework for the anomaly detection problem is
presented in this section. We establish that anomaly detection is
equivalent to thresholding p-values for multivariate data. We will
then show that the score functions developed in the previous section
is an asymptotically consistent estimator of the p-values.
Consequently, it will follow that the strategy of declaring an
anomaly when a test sample has a low score is asymptotically
optimal.

Assume that the data belongs to the d-dimensional unit cube
$[0,\,1]^d$ and the nominal data is sampled from a multivariate
density $f_0(x)$ supported on the d-dimensional unit cube
$[0,\,1]^d$. Anomaly detection can be formulated as a composite
hypothesis testing problem. Suppose test data, $\eta$ comes from a
mixture distribution, namely, $f(\eta) = (1-\pi) f_0(\eta) + \pi
f_1(\eta)$ where $f_1(\eta)$ is a mixing density supported on
$[0,1]^d$. Anomaly detection involves testing the nominal hypotheses
$H_0: \pi = 0$ versus the alternative (anomaly) $H_1 : \pi > 0$. The
goal is to maximize the detection power subject to false alarm level
$\alpha$, namely, $\mathcal{P}(\mbox{declare } H_1 \mid H_0) \leq
\alpha$.

\begin{defn}
Let $\mathcal{P}_0$ be the nominal probability
measure and $f_1(\cdot)$ be $\mathcal{P}_0$ measurable.
Suppose the likelihood ratio $f_1(x)/f_0(x)$ does not have \emph{non-zero} flat spots on any open ball in $[0,\,1]^d$.
Define the p-value of a data point $\eta$ as
$$p(\eta)=\mathcal{P}_0\left(x:\frac{f_1(x)}{f_0(x)}\geq\frac{f_1(\eta)}{f_0(\eta)}\right)$$
\end{defn}
Note that the definition naturally accounts for singularities which may arise if the support of $f_0(\cdot)$ is a lower dimensional manifold. In this case we encounter $f_1(\eta)>0,\,f_0(\eta)=0$ and the p-value $p(\eta)=0$. Here anomaly is
always declared(low score).

The above formula can be thought of as a mapping of $\eta
\rightarrow [0,\,1]$. Furthermore, the distribution of $p(\eta)$
under $H_0$ is uniform on $[0,\,1]$. However, as noted in the
introduction there are other such transformations. To build
intuition about the above transformation and its utility consider
the following example.  When the mixing density is uniform, namely,
$f_1(\eta) = U(\eta)$ where $U(\eta)$ is uniform over $[0,1]^d$,
note that $\Omega_{\alpha}=\{\eta \mid p(\eta)\geq \alpha\}$ is a
density level set at level $\alpha$. It is well known (see
\cite{Scott1}) that such a density level set is equivalent to a
minimum volume set of level $\alpha$. The minimum volume set at
level $\alpha$ is known to be the uniformly most powerful decision
region for testing $H_0:\pi=0$ versus the alternative $H_1: \pi>0$
(see \cite{Hero1,Scott1}). The generalization to arbitrary $f_1$
is described next.
\begin{thm}
The uniformly most powerful test for testing $H_0: \pi = 0$ versus
the alternative (anomaly) $H_1 : \pi > 0$ at a prescribed level
$\alpha$ of significance $\mathcal{P}(\mbox{declare } H_1 \mid H_0)
\leq \alpha$ is:
$$
\phi(\eta) = \left \{ \begin{array}{cc} H_1, \,\, p(\eta) \leq
\alpha \\ H_0, \,\, \mbox{otherwise} \end{array} \right .
$$
\end{thm}
\begin{proof}
We provide the main idea for the proof. First, measure theoretic arguments are used to establish $p(X)$ as a random variable over $[0,\,1]$ under both nominal and anomalous distributions.
Next when $X \stackrel{d}{\sim} f_0$, i.e., distributed with nominal
density it follows that the random variable $p(X)\stackrel{d}{\sim} U[0,1]$. When $X \stackrel{d}{\sim} f = (1-\pi)f_0 + \pi f_1$ with $\pi>0$ the random variable, $p(X)\stackrel{d}{\sim} g$ where
$g(\cdot)$ is a monotonically decreasing PDF supported on $[0,\,1]$. Consequently, the uniformly most powerful test for a significance level $\alpha$ is to declare p-values smaller than $\alpha$ as anomalies.
\end{proof}
Next we derive the relationship between the p-values and our score
function.
By definition, $R_S(\eta)$ and
$R_S(x_i)$ are correlated because the neighborhood of $\eta$ and
$x_i$ might overlap. We modify our algorithm to simplify our analysis.
We assume $n$ is odd (say) and can be written as $n = 2m+1$. We divide training
set $S$ into two parts:
$$S = S_1\cap S_2=\{x_0,
x_1,\cdots,x_{m}\}\cap\{x_{m+1},\cdots,x_{2m}\}$$

We modify $\epsilon$-LPE to $\hat
p_\epsilon(\eta)=\frac{1}{m}\sum_{x_i\in S_1}
\mathbb{I}_{\{N_{S_2}(\eta)\geq
 N_{S_1}(x_i)\}}$ (or $K$-LPE
to $\hat p_K(\eta)=\frac{1}{m}\sum_{x_i\in S_1}
\mathbb{I}_{\{R_{S_2}(\eta)\leq
 R_{S_1}(x_i)\}}$). Now $R_{S_2}(\eta)$ and $ R_{S_1}(x_i)$ are independent.

Furthermore, we assume $f_0(\cdot)$ satisfies the following two
smoothness conditions:
\begin{enumerate}
  \item the Hessian matrix $H(x)$ of $f_0(x)$ is always dominated by
  a  matrix with largest eigenvalue $\lambda_M$, i.e.,
      $\exists M ~\text{s.t.}~ H(x)\preceq M~\forall x\text{ and }\lambda_{\max}(M)\leq \lambda_M$
  \item In the support of $f_0(\cdot)$, its value is always lower bounded
  by some $\beta>0$.
\end{enumerate}

We have the following theorem.
\begin{thm}\label{thm2}
Consider the setup above with the training data $\{x_i\}_{i=1}^n$ generated i.i.d. from $f_0(x)$. Let $\eta \in [0,1]^d$ be an arbitrary test sample. It follows that for a suitable choice $K$ and under the
above smoothness conditions,
$$
|\hat p_K(\eta) - p(\eta)| \stackrel{n \rightarrow
\infty}{\longrightarrow} 0 \,\,\,\mbox{almost surely, }\,\forall
\eta\in[0,1]^d
$$
\end{thm}

For simplicity, we limit ourselves to the case when $f_1$ is uniform. The proof of Theorem~\ref{thm2} consists of two steps:
\begin{itemize}
  \item We show that the expectation
$\mathbb{E}_{S_1}\left[\hat p_\epsilon(\eta)\right]\stackrel{n
\rightarrow \infty}{\longrightarrow}p(\eta)$ (Lemma \ref{lem:mean}).
This result is then extended to K-LPE (i.e.
$\mathbb{E}_{S_1}\left[\hat p_K(\eta)\right]\stackrel{n \rightarrow
\infty}{\longrightarrow}p(\eta)$) in
Lemma \ref{lem:meanK}.
  \item Next we show that $\hat p_K(\eta)\stackrel{n
\rightarrow \infty}{\longrightarrow}\mathbb{E}_{S_1}\left[\hat
p_K(\eta)\right]$ via concentration inequality (Lemma
\ref{lem:cond}).
\end{itemize}

\begin{lem}[$\epsilon$-LPE]\label{lem:mean}
By picking $\epsilon= m^{-\frac{3}{5d}}\sqrt{\frac{d}{2\pi e}}$,
with probability at least $1-e^{-\beta m^{1/15}/2}$,
\begin{eqnarray}
l_m(\eta)\leq\mathbb{E}_{S_1}\left[\hat p_\epsilon(\eta)\right]\leq
u_m(\eta)
\end{eqnarray}
where
\begin{eqnarray*}
l_m(\eta)=\mathcal{P}_0{\left\{x: \left(f_0(\eta)-\Delta_1\right)\left(1-\Delta_2\right)\geq
\left(f_0(x)+\Delta_1\right)\left(1+\Delta_2\right)\right\}}-e^{-\beta
m^{1/15}/2}\\
u_m(\eta)=\mathcal{P}_0{\left\{x:\left(f_0(\eta)+\Delta_1\right)\left(1+\Delta_2\right)\geq
\left(f_0(x)-\Delta_1\right)\left(1-\Delta_2\right)\right\}}+e^{-\beta
m^{1/15}/2}
\end{eqnarray*}
$\Delta_1 = \lambda_M m^{-6/5d}/(2\pi e (d+2))$ and $\Delta_2 =
2m^{-1/6}$.

\end{lem}
\begin{proof} We only prove the lower bound since the upper bound follows along similar lines.
By interchanging the expectation with the summation,
\begin{eqnarray*}
\mathbb{E}_{S_1}\left[\hat
p_\epsilon(\eta)\right]&=&\mathbb{E}_{S_1}\left[\frac{1}{m}\sum_{x_i\in
S_1} \mathbb{I}_{\{N_{S_2}(\eta)\geq
 N_{S_1}(x_i)\}}\right]\\
&=&\frac{1}{m}\sum_{x_i\in
S_1}\mathbb{E}_{x_i}\mathbb{E}_{S_1\setminus x_i}\left[
\mathbb{I}_{\{N_{S_2}(\eta)\geq
 N_{S_1}(x_i)\}}\right]\\
 &=&
 \mathbb{E}_{x_1}[\mathcal{P}_{S_1\setminus x_1}(N_{S_2}(\eta)\geq
 N_{S_1}(x_1))]
\end{eqnarray*}
where the last inequality follows from the symmetric structure of
$\{x_0,x_1,\cdots,x_m\}$.

Clearly the objective of the proof is to show
$\mathcal{P}_{S_1\setminus x_1}(N_{S_2}(\eta)\geq
 N_{S_1}(x_1))\stackrel{n
\rightarrow \infty}{\longrightarrow}
\mathbb{I}_{\left\{f_0(\eta)\geq
 f_0(x_1)\right\}}$. Skipping technical details, this can be
 accomplished in two steps. (1) Note that $N_S(x_1)$ is a
 binomial random variable with success probability
 $q(x_1):=\int_{B_\epsilon}f_0(x_1+t)\text{d}t$. This relates $\mathcal{P}_{S_1\setminus x_1}(N_{S_2}(\eta)\geq
 N_{S_1}(x_1))$ to $\mathbb{I}_{\left\{q(\eta)\geq
 q(x_1)\right\}}$. (2) We relate $\mathbb{I}_{\left\{q(\eta)\geq
 q(x_1)\right\}}$ to $\mathbb{I}_{\left\{f_0(\eta)\geq
 f_0(x_1)\right\}}$ based on the function smoothness condition. The
 details of these two steps are shown in the below.

Note that $N_{S_1}(x_1)\sim \text{Binom}({m},q(x_1))$. By Chernoff
bound of binomial distribution, we have
\begin{eqnarray*}
\mathcal{P}_{S_1\setminus x_1}(N_{S_1}(x_1)-m q(x_1)\geq \delta)\leq
e^{-\frac{\delta^2}{2m q(x_1)}}
\end{eqnarray*}
that is, $N_{S_1}(x_1)$ is concentrated around $mq(x_1)$.  This
implies,
\begin{eqnarray}\label{eq:Pr_In} \mathcal{P}_{S_1\setminus x_1}(N_{S_2}(\eta)\geq
 N_{S_1}(x_1))\geq \mathbb{I}_{\left\{N_{S_2}(\eta)\geq m q(x_1)+\delta_{x_1}\right\}}
 -e^{-\frac{\delta_{x_1}^2}{2m
 q(x_1)}}\end{eqnarray}

We choose $\delta_{x_1}=q(x_1)m^\gamma(\gamma \text{ will be
specified later})$ and reformulate equation~(\ref{eq:Pr_In}) as
\begin{eqnarray}\label{eq:Pr_In3}
\mathcal{P}_{S_1\setminus x_1}(N_{S_2}(\eta)\geq N_{S_1}(x_1))\geq
\mathbb{I}_{\left\{\frac{N_{S_2}(\eta)}{m\text{Vol}(B_\epsilon)}\geq
\frac{q(x_1)}{\text{Vol}(B_\epsilon)}\left(1+\frac{2}{m^{1-\gamma}}\right)\right\}}
 -e^{-\frac{q(x_1) m^{2\gamma-1}}{2}}\end{eqnarray}

Next, we relate $q(x_1)(\text{or }
\int_{B_\epsilon}f_0(x_1+t)\text{d}t)$ to $f_0(x_1)$ via the
Taylor's expansion and the smoothness condition of $f_0$,
\begin{eqnarray} \label{eq:step2} \left|\frac{\int_{B_\epsilon}f_0(x_1+t)\text{d}t}{\text{Vol}(B_\epsilon)}-
f_0(x_1)\right| \leq
\frac{\lambda_M}{2}\cdot\frac{1}{\text{Vol}(B_\epsilon)}\int_{B_\epsilon}\|t\|^2\text{d}t
=\frac{\lambda_M \epsilon^2}{2d(d+2)}
\end{eqnarray}
and then equation~(\ref{eq:Pr_In3}) becomes
\[\mathcal{P}_{S_1\setminus x_1}(N_{S_2}(\eta)\geq N_{S_1}(x_1))\geq
\mathbb{I}_{\left\{\frac{N_{S_2}(\eta)}{m\text{Vol}(B_\epsilon)}\geq
\left(f_0(x_1)+\frac{\lambda_M
\epsilon^2}{2d(d+2)}\right)\left(1+\frac{2}{m^{1-\gamma}}\right)\right\}}
 -e^{-\frac{q(x_1) m^{2\alpha-1}}{2}}\]

By applying the same steps to $N_{S_2}(\eta)$ as equation
\ref{eq:Pr_In} (Chernoff bound) and equation \ref{eq:step2}
(Taylor's explansion), we have with probability at least
$1-e^{-\frac{q(\eta) m^{2\alpha-1}}{2}}$,
{\small
$$
\mathbb{E}_{x_1}[\mathcal{P}_{S_1\setminus x_1}(N_{S_2}(\eta)\geq N_{S_1}(x_1))]\geq
\mathcal{P}_{x_1}{\scriptstyle{\left\{\left(f_0(\eta)-\frac{\lambda_M
\epsilon^2}{2d(d+2)}\right)\left(1-\frac{2}{m^{1-\gamma}}\right)\geq
\left(f_0(x_1)+\frac{\lambda_M
\epsilon^2}{2d(d+2)}\right)\left(1+\frac{2}{m^{1-\gamma}}\right)\right\}}}
-e^{-\frac{q(x_1) m^{2\alpha-1}}{2}}
$$}

Finally, by choosing $\epsilon^2 = m^{-\frac{6}{5d}}\cdot
\frac{d}{2\pi e}$ and $\gamma = 5/6$, we prove the lemma.
\end{proof}

\begin{lem}[$K$-LPE]\label{lem:meanK}
By picking $K =\left(1-2m^{-1/6}
\right)m^{2/5}\left(f_0(\eta)-\Delta_1\right)$, with probability at
least $1-e^{-\beta m^{1/15}/2}$,
\begin{eqnarray}
l_m(\eta)\leq\mathbb{E}_{S_1}\left[\hat p_K(\eta)\right]\leq
u_m(\eta)
\end{eqnarray}
\end{lem}
\begin{proof}

The proof is very similar to the proof to Lemma \ref{lem:mean} and
we only give a brief outline here. Now the objective is to show
$\mathcal{P}_{S_1\setminus x_1}(R_{S_2}(\eta)\leq
 R_{S_1}(x_1))\stackrel{n
\rightarrow \infty}{\longrightarrow}
\mathbb{I}_{\left\{f_0(\eta)\geq
 f_0(x_1)\right\}}$.The basic idea is to use the
result of Lemma \ref{lem:mean}. To accomplish this, we note that
$\{R_{S_2}(\eta)\leq
 R_{S_1}(x_1)\}$ contains the events $\{N_{S_2}(\eta)\geq K\}\cap\{N_{S_1}(x_1)\leq
 K\}$, or equivalently
 \begin{eqnarray}
\{N_{S_2}(\eta)-q(\eta) m\geq K-q(\eta)
m\}\cap\{N_{S_1}(x_1)-q(x_1)m\leq
 K-q(x_1)m\}
 \end{eqnarray}

By the tail probability of Binomial distribution, the probability of
the above two events converges to 1 exponentially fast if $K-q(\eta)
m<0$ and $K-q(x_1)m> 0$. By using the same two-step bounding
techniques developed in the proof to Lemma \ref{lem:mean}, these two
inequalities are implied by
\begin{eqnarray*}
K-m^{2/5}\left(f_0(\eta)-\Delta_1\right)<
0\,~\text{and}\,~K-m^{2/5}\left(f_0(x_1)+\Delta_1\right)> 0
\end{eqnarray*}
Therefore if we choose $K=\left(1-2m^{-1/6}
\right)m^{2/5}\left(f_0(\eta)- \Delta_1\right)$, we have with
probability at least $1-e^{-\beta m^{-1/15}/2}$,
\begin{eqnarray*}\mathcal{P}_{S_1\setminus x_1}(R_{S_2}(\eta)\leq
R_{S_1}(x_1))\geq
\mathbb{I}_{{\left\{\left(f_0(\eta)-\Delta_1\right)\left(1-\Delta_2\right)\geq
\left(f_0(x_1)+\Delta_1\right)\left(1+\Delta_2\right)\right\}}}
-e^{-\beta m^{-1/15}/2}\end{eqnarray*}

\end{proof}

\noindent{\bf Remark:} Lemma \ref{lem:mean} and Lemma \ref{lem:meanK} were proved with specific choices for $\epsilon$ and $K$.
However, $\epsilon$
and $K$ can be chosen in a range of values, but will lead to different lower and upper bounds. We will show in Section \ref{sec:ex} through simulations that our LPE algorithm is generally robust to choice of parameter $K$.

\begin{lem}\label{lem:cond}
Suppose $K=cm^{2/5}$ and denote
$\hat{p}_K(\eta)=\frac{1}{m}\sum_{x_i\in S_1}
\mathbb{I}_{\{R_{S_2}(\eta)\leq
 R_{S_1}(x_i)\}}$. We have
\[\mathcal{P}_0\left(|\mathbb{E}_{S_1}\left[\hat{p}_K(\eta)\right]-\hat{p}_K(\eta)|>\delta\right)\leq 2e^{-\frac{2\delta^2m^{1/5}}{c^2\gamma_d^2}}\]
where $\gamma_d$ is a constant and is defined as the minimal number
of cones centered at the origin of angle $\pi/6$ that cover
$\mathbb{R}^d$.
\end{lem}
\begin{proof}
We can not apply Law of Large Number in this case because
$\mathbb{I}_{\{R_{S_2}(\eta)\leq
 R_{S_1}(x_i)\}}$ are correlated. Instead, we need to use the more generalized concentration-of-measure inequality such as MacDiarmid's inequality\cite{Mac}. Denote
$F(x_0,\cdots,x_m)=\frac{1}{m}\sum_{x_i\in
S_1}\mathbb{I}_{\{R_{S_2}(\eta)\leq R_{S_1}(x_i)\}}$. From Corollary
11.1 in \cite{Lugosi},
\begin{eqnarray}\label{eq:mac}
\sup_{x_0,\cdots,x_m,x_i'}|F(x_0,\cdots,x_i,\cdots,x_m)-F(x_0,\cdots,x_i',\cdots,x_n)|\leq
K\gamma_d/m\end{eqnarray}

Then the lemma directly follows from applying McDiarmid's
inequality.
\end{proof}

Theorem \ref{thm2} directly follows from the combination of Lemma
\ref{lem:meanK} and Lemma \ref{lem:cond} and a standard application of the first Borel-Cantelli lemma. We have used Euclidean distance
in Theorem \ref{thm2}. When the support of $f_0$ lies on a lower dimensional manifold (say $d'< d$) adopting the geodesic metric leads to faster convergence. It turns out that $d'$ replaces $d$ in the expression for
$\Delta_1$ in Lemma 3.

\section{Experiments}\label{sec:ex}
We apply our method on both artificial and real-world data. Our
method enables plotting the entire ROC curve by varying the thresholds on our scores.

To test the sensitivity of K-LPE to parameter changes, we first run
$K$-LPE on the benchmark artificial data-set {\tt Banana}
\cite{banana} with $K$ varying from $2$ to $12$. {\tt Banana}
dataset contains points with their labels($+1$ or $-1$). We randomly
pick $109$ points with $+1$ label and regard them as the nominal
training data. The test data comprises of 108 $+1$ data and 183 $-1$
data (ground truth) and the algorithm is supposed to predict $+1$
data as ``nominal'' and $-1$ data as ``anomaly''. See Figure
\ref{fig:robust}(a) for the configuration of the training points and
test points. Scores computed for test set using Equation
\ref{eq:LPE1} is oblivious to true $f_1$ density ($-1$ labels).
Euclidean distance metric is adopted for this example.

False alarm (also called false positive) is defined as the
percentage of nominal points that are predicted as anomaly by the
algorithm. To control false alarm at level $\alpha$, point with
score $< \alpha$ is predicted as anomaly. Empirical false alarm and
true positives (percentage of anomalies declared as anomaly) can be
computed from ground truth. We vary $\alpha$ to obtain the empirical
ROC curve. We follow this procedure for all the other experiments in
this section. We are relatively insensitive to $K$ as shown in
Figure \ref{fig:robust}(b).

For comparison we plot the empirical ROC curve of the one-class SVM
of \cite{Scholkopf}. There are two tuning parameters in OC-SVM ---
bandwidth $c$ (we use RBF kernel) and $\nu\in(0,1)$ (which is
supposed to control FA). Note that training data {\em does not}
contain $-1$ labels and this implies we can never make use of $-1$
labels to cross-validate, or, to optimize over the choice of pair
$(c,\nu)$. In our OC-SVM implementation, by following the same
procedure, we can obtain the empirical ROC curve by varying $\nu$
but {\em fixing} a certain bandwidth $c$. Finally we iterated over
different $c$ to obtain the best (in terms of AUC) ROC curve and it
turns out to be $c=1.5$. Fixing $c$ for entire ROC is equivalent to
fixing $K$ in our score function. Note that in real practice what
can be done is even worse than this implementation because there is
also no natural way to optimize over $c$ without being revealed the
$-1$ labels.

In Figure~\ref{fig:robust}(b), we can see that our algorithm is
consistently better than one-class SVM on the {\tt Banana} dataset.
Furthermore, we found that choosing suitable tuning parameters to
control false alarms is generally difficult in the one-class SVM
approach. In our approach if we set $\alpha = 0.05$ we get empirical
$FA = 0.06$ and for $\alpha = 0.08$, empirical $FA = 0.09$. For
OC-SVM we can not see any natural way of picking $c$ and $\nu$ to
control FA rate based only on training data.

\begin{figure}[t]
\begin{centering}
\begin{minipage}[t]{.48\textwidth}
\includegraphics[width = 1\textwidth]{banana_config.eps}\\
\makebox[7 cm]{(a) Configuration of {\tt banana} data}
\end{minipage}
\begin{minipage}[t]{.48\textwidth}
\includegraphics[width = 1\textwidth]{banana_robust1.eps}\\
\makebox[7 cm]{(b) SVM vs. K-LPE for Banana Data}
\end{minipage}
\caption{\small \sl Performance Robustness of LPE;(a) The
configuration of the nominal training points (red `+') and unlabeled
test points (black ` $\bullet$') for the {\tt banana} dataset
\cite{banana}; (b) Empirical ROC curve of $K$-LPE on the {\tt
banana} dataset
 with $K=2,4,6,8,10,12$ (with $n=400$) vs the empirical
ROC curve of one class SVM developed in \cite{Scholkopf}.}
\label{fig:robust}
\end{centering}
\end{figure}

\begin{figure}[t]
\begin{centering}
\begin{minipage}[t]{.48\textwidth}
\includegraphics[width = 1\textwidth]{clair_config.eps}\\
\makebox[7 cm]{(a) Configuration of data}
\end{minipage}
\begin{minipage}[t]{.48\textwidth}
\includegraphics[width = 1\textwidth]{clairvoyant1.eps}\\
\makebox[7 cm]{(b) Clairvoyant vs. K-LPE}
\end{minipage}
\caption{\small \sl Clairvoyant ROC curve vs. K-LPE; (a)
Configuration of the nominal training points and unlabeled test
points for the data given by Equation \ref{eq:f0f1}; (b) Averaged
(over $15$ trials) empirical ROC curves of $K$-LPE algorithm vs
clairvoyant ROC curve (when $f_0$ is given by Equation
\ref{eq:f0f1}) for $K=6$ and for different values of $n$
($n=40,160$).} \label{fig:clair}
\end{centering}
\end{figure}

In Figure \ref{fig:clair}, we apply our $K$-LPE to another 2D
artificial example where the nominal distribution $f_0$ is a mixture
Gaussian and the anomalous distribution is very close to uniform
(see Figure \ref{fig:clair}(a) for their configuration):
\begin{eqnarray}\label{eq:f0f1}
f_0\sim\frac{1}{2}\mathcal{N}\left(\begin{bmatrix}8\\0\end{bmatrix},\begin{bmatrix}1&
0\\0&
9\end{bmatrix}\right)+\frac{1}{2}\mathcal{N}\left(\begin{bmatrix}-8\\0\end{bmatrix},\begin{bmatrix}1&
0\\0& 9\end{bmatrix}\right), \quad
f_1\sim\mathcal{N}\left(0,\begin{bmatrix}49& 0\\0&
49\end{bmatrix}\right)\end{eqnarray} In this example, we can exactly
compute the optimal ROC curve. We call this curve the {\em
Clairvoyant ROC} (the red dashed curve in Figure
\ref{fig:clair}(b)). The other two curves are averaged (over $15$
trials) empirical ROC curves with respect to different sizes of
training sample ($n=40,160$) for $K=6$. Larger $n$ results in better
ROC curve. We see that for a relatively small training set of size
$160$ the average empirical ROC curve is very close to the
clairvoyant ROC curve.

Next, we ran LPE on three real-world datasets: {\tt Wine}, {\tt
Ionosphere}\cite{Asuncion+Newman:2007} and MNIST US Postal Service
({\tt USPS}) database of handwritten digits. The procedure and setup
of the experiments is almost the same as the that of the {\tt
Banana} data set. However, there are two differences. (1) If the
number of different labels is greater than two, we always treat
points with one particular label as nominal($+1$) and regard the
points with other labels as anomalous($-1$). For example, for the
{\tt USPS} dataset, we regard instances of digit $0$ as nominal
training samples and instances of digits $1,\cdots,9$ as anomaly.
(2) For high dimensional data set, the data points are normalized to
be within $[0,1]^d$ and we use geodesic distance
\cite{Tenenbaum}(instead of Euclidean distance) as the input to LPE.

\setlength{\unitlength}{1\textwidth}

\begin{figure*}[!htb]
\centering
\begin{picture}(1,.30)
\put(-0.03,0){\includegraphics[scale = 0.33]{wine3.eps}}
\put(0.31,0){\includegraphics[scale = 0.33]{ionosphere3.eps}}
\put(0.65,0){\includegraphics[scale = 0.33]{USPS3.eps}}
\put(-0.03,-.01){\makebox[0.33\textwidth]{(a) {\tt Wine}}}
\put(0.31,-0.01){\makebox[0.33\textwidth]{(b) {\tt Ionosphere}}}
\put(0.65,-0.01){\makebox[0.33\textwidth]{(c) {\tt USPS}}}
\end{picture}
\caption{\small \sl ROC curves on real datasets via {\sc LPE}; (a) {\tt
Wine} dataset with $D=13, n=39, \epsilon=0.9$; (b) {\tt Ionosphere}
dataset with $D=34,n=175,K=9$; (c) {\tt USPS} dataset with
$D=256,n=400,K=9$.}\label{fig:real}
\end{figure*}

The ROC curves of these three datasets are shown in
Figure~\ref{fig:real}. In {\tt Wine} dataset, the dimension of the
feature space is $13$. The training set is composed of $39$ data
points and we apply the $\epsilon$-LPE algorithm with $\epsilon =
0.9$. The test set is a mixture of 20 nominal points and $158$
anomaly points (ground truth). In {\tt Ionosphere} dataset, the
dimension of the feature space is $34$. The training set is composed
of $175$ data points and we apply the $K$-LPE algorithm with $K=9$.
The test set is a mixture of $50$ nominal points and $126$ anomaly
points (ground truth). In {\tt USPS} dataset, the dimension of the
feature space is $16\times 16=256$. The training set is composed of
$400$ data points and we apply the $K$-LPE algorithm with $K=9$. The
test set is a mixture of $367$ nominal points and $33$ anomaly
points (ground truth).

For comparison purposes we note that for the {\tt USPS} data set by
setting $\alpha = 0.5$ we get empirical false-positive $6.1\%$ and
empirical false alarm rate $5.7\%$ (In contrast $FP=7\%$ and
$FA=9\%$ with $\nu=5\%$ for OC-SVM as reported in \cite{Scholkopf}).
Practically we find that $K$-LPE is more preferable to
$\epsilon$-LPE due to easiness of choosing the parameter $K$. We
find that the value of $K$ is relatively independent of dimension
$d$. As a rule of thumb we
found that setting $K$ around $n^{2/5}$ was generally effective.

\section{Conclusion}\label{sec:dis}
In this paper, we proposed a novel non-parametric adaptive anomaly detection algorithm which leads to a computationally efficient solution with provable optimality guarantees. Our algorithm takes a K-nearest neighbor graph as an input and produces a score for each test point. Scores turn out to be empirical estimates of the volume of minimum volume level sets containing the test point. While minimum volume level sets provide an optimal characterization for anomaly detection, they are high dimensional quantities and generally difficult to reliably compute in high dimensional feature spaces. Nevertheless, a sufficient statistic for optimal tradeoff between false alarms and misses is the volume of the MV set itself, which is a real number. By computing score functions we avoid computing high dimensional quantities and still ensure optimal control of false alarms and misses.
The computational cost of our algorithm scales linearly in dimension and quadratically in data size.

\footnotesize
\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


The warm and welcoming foyer of Tuscan Table is lined with rakes, tines-up, and crowned by one of the many gorgeous wooden chandeliers, in an easy interplay of the decor's rustic and elegant elements.
These memories, and even my limited Italian returned to me the moment I walked into the welcoming foyer of Tuscan Table. At South Portland's poshest eatery, the latest from restaurateur/entrepreneur Ed Manganello (of Tuscan Bistro in Freeport and Royal River Grill House in Yarmouth), they wear their pig proudly. It's right there on the sign and painted on the side of the building.
Nicola Manganello Harnden of Nicola's Home (and Ed's daughter) is the designer responsible for giving the 184-seat restaurant the rustic, grounded glamour she's known for. With a background in art (the Yarmouth native studied sculpture at MECA) and 20 years of designing home and commercial interiors, Harnden was more than equipped to transform the once humdrum space into something truly spectacular.
A mezzanine-view overlooks the restaurant's signature pendant lamps and expansive oval bar where the high, curving shelves hold gem-like bottles of locally distilled spirits and classic aperitifs.
One of the two staircases accessing the mezzanine is flanked by a mural of over-sized sunflowers done in unexpectedly moody jewel tones.
It's an environment designed to transport people up and away to someplace urbane and cosmopolitan. Harnden calls it "something truly special and completely different from the norm out there of chains—which Tuscan Table is not." It also offers plentiful, free parking, which is more than you can say for the Old Port.
The showpiece of the restaurant is the dramatic 37-foot-diameter oval marble-topped bar, which encircles the center of the space, seats 40, and occupies the lion's share of the dining room. Its elongated round-table effect makes for a convivial cocktail hour as you can't help but chat with your neighbor and admire what they've ordered. Beautiful oversized pendant lamps of woven wood descend from the 20-foot ceiling, casting a warm glow and bringing to mind full fishermen's nets. A mezzanine level overlooks the action and contains 18 more tables, while the restaurant's two-story wine vault, visible from both floors, provides the eatery with its extensive wine menu.
Textural details, from the stone-and-terra-cotta tile surrounding the bar to the rich, variegated fabrics of the pillows on the banquettes, give the space a sumptuous feeling. Harnden also added touches of whimsy here and there. The wallpaper in the women's bathroom is bedecked with what she calls "dancing elephants," while the straw starburst mirrors alone made me want to go back and freshen up my lipstick again, just for fun.
As with all of her projects, Harnden aims to "keep the eye intrigued," she says, so that each visit reveals a new layer to the dining experience—it is a measure she accomplishes beautifully with this latest commercial venture.
Executive Chef Brandon Jennings provides a rotating menu of Italian and Mediterranean fare, offering handmade pasta and locally sourced meat and seafood. The open kitchen boasts two Le Panyol wood-fired ovens for turning out delicate, char-crusted pizzas and grilled small plates. There are plenty of classic options, from chicken parmigiana to spaghetti carbonara, and on the lighter side, fresh-caught oysters, scallops, salmon, and haddock. Pork is on the menu, I am happy to report, primo to entrée—from the charcuterie selection (a Berkel slicer at the bar shaves salumi paper thin) to the pork Milanese to the meatballs, the last of which I devoured happily, savoring every bite.
It's nice to know I won't have to jump on a plane to enjoy the kind of memorable Italian dishes and ambiance I experienced as a newlywed. In fact, it's just a short jaunt down I-295. Diners in search of the same should not be leery of the location. Whether you go for a quick lunch, a buzzy happy hour, or a lingering dinner, Nicola's Home has built nothing less than a portal to the Old Country and for this particular diner, something of a time machine, taking me back to my very own luna di miele. Although just yards away from Macy's and Books-A-Million, for a few hours we were back on the banks of the Arno.
Tuscan Table in South Portland is open for lunch and dinner seven days a week. Hours are 11 a.m. to 9 p.m. Sunday through Thursday, 11 a.m. to 10 p.m. Friday and Saturday.


The credit card or debit card charge UOL*MYSITE.COM UT was first spotted on October 22, 2014. This charge has been reported as trusted by 15 users, 50 users marked the credit card charge as suspicious.
Do you know what the source of this charge is? Have you seen UOL*MYSITE.COM UT on your credit card, debit card, or prepaid charge card? If so, please comment below!
We have another web hosting since a long time but I have been supporting deductions of $6.95 on my Visa credit card for more than two years and now they are charging 8.95$ each month.


I don't know how to separate in my mind what I fear and what I expectand what I hope! But when I think back, there have been other times whenanti-trade movements in the US and elsewhere have been popular. For example, in the 1970s and 1980s there was a strong belief in the US that trade with Japanwas unfair and destructive. In the late 1990s and early 2000s, there were anti-globalization protests in cities all over the world. What often seems to happen is that when it seems like a trade war might be starting, and certain smaller actions are taken to limit trade, it then becomes very clear thatlimiting trade has specific and large costs. Limiting trade is a little like touching an electrified fence: after doing it, most people feel the shock and back away. I hope that any trade war is short and small. Trade is about finding ways to create win-win scenarios for both countries; trade wars usually end upas lose-lose scenarios for everyone.
China's economic growth in the last 40 years is one of the most amazing events in world economic history. Economists and historians will still betalking and writing about it in 50 years, and in 100 years. China will certainly become the largest economy in the world very soon—by some measures,it already is. But the most powerful country is a different question from the biggest economy. On a per person basis, per capita GDP is going to be muchlower in China than in high-income countries like the US and western Europe fordecades to come. Power doesn't have a simple formula. It involves diplomacy,technology, and even the extent to which a country is trusted or admired byothers.
There's a basic formula for economic growth. It has four ingredients:investment in physical capital, workers with more human capital (that is, skilland education), new technology, and mixing these first three ingredients together in a situation where there are incentives for innovation,entrepreneurship, and hard work. The early stages of China's economic development focused mostly on high levels of investment in physical capital andchanging the incentives for entrepreneurship and work. But in the last coupleof decades, there is an ongoing shift toward more growth coming from humancapital and new innovation and technology.
I think most Americans have both feelings. It's common to hear Americanstalk admiringly of how quickly China has built its cities or its train system.
But there is some worry, too, which seems natural to me. Imagine that you are living in a city in China, and although your city is doing just fine, anearby city has extremely rapid growth in population and economic output. You would wonder what is happening in that other city, and be concerned that the growth of that other city might in some way create changes and costs for yourcity.
The advantages of backwardness, of course, is a famous term from an economics research paper written back in the early 1960s. It pointed out that when a country is poor it can benefit by selling to countries with higher incomes, and also by using technology already developed in other places. But the disadvantage of backwardness is that a country has lower income, which isoften combined with lower levels of education, health, environmental protection, ability to travel and relax, and other nice things. The disadvantages of backwardness are more important.
As I understand the arguments about a "latecomer curse," a common topicwas that although China's economy had grown with large use of energy and raw materials, it was hard to keep having ever-more energy and ever-more rawmaterials—and there were environmental costs as well. The "curse" argument iscorrect in saying that China could not keep growing in exactly the same way.The sources of China's growth in the future need to be different from the pastsources of growth. But this is not unusual. For every country, economic growth involves an ongoing process of transformation and change.
In which areas should China improve if the nation wants to pursuelong-term, steady development?
I'd point to three areas. First, China needs to "rebalance" its economy(as the economists say) so that its growth that is based on higher levels ofdomestic consumption, rather than higher levels of exports or physical capitalinvestment. Second, China has successfully created an educational system thatgets everyone through nine years of school, but it now needs to aim at getting almost everyone through 12 years of school—and more students attending college, too. Third, China's financial system still needs to develop. A sturdy financial system both encourages and places limits on risk-taking. But some of China's bank lending and corporate bond borrowing seem to offer reasons for concern.
If you take the size of the economy (GDP) and divide by population, China is now at about 25% of the US level. China's economy has strong momentum,and as long as the country keeps embracing the needed changes, I expect it will continue growing at a strong pace. In 20 or 30 years, maybe the middle-income trap will emerge as a problem, but I don't see it as a problem for China's economy in the short-term or the medium-term.
China might want to look at some US institutions and industries that have been fairly successful over time and see if there are lessons to be learned. For example, many of the world's top colleges and universities are inthe US. The US has some of the world's largest financial markets, used bypeople all over the world. The US has a number of the world's leading companiesin logistics, the business of delivering goods and services. The US economy hasa long tradition that many small companies get started, and a few grow intonational and even global giants.
The idea of starting companies based on inventions has deep roots in US history. When Europeans were settling in North America, a common situation wasthat land and natural resources were plentiful, but there was comparatively little labor. Thus, finding innovations and machines that reduced the need forlabor was quite valuable. When the Constitution of the United States was written about 240 years ago, it actually included a requirement that the brand-new US government would establish a national patent office. The US has shown a willingness over itshistory to allow new companies to grow, even when it means hardship or evenbankruptcy for the previous generation of companies.
There are obvious choices of highly prominent Chinese companies like Alibaba, Baidu, and Tencent. I've always been impressed by Haier; in fact, I have a small Haier refrigerator in my office to keep my supply of Diet Cokecold. Haier seems to combine high-quality manufacturing with an ongoing push toimprove its technology.
But what I admire most are those people running smaller companies and trying something really different. Only a few of them will succeed in a bigway, but the jobs they create, and the knowledge and experience they build, is so important in keeping the economy moving forward. As one example, a few months ago I met the founder of Emotech, a company which is working on the human-robot interface, trying to create robotic personal assistants which show emotion in a physical way, with motion and lights. On future visits to China, I hope to meet some people running smaller and medium-sized businesses, not just the well-known giant firms.
Many countries have had companies that are state-owned. As one example,about 10% of the economy of the United Kingdom was state-owned companies in the 1970s. The problem with any state-ownedcompany is how to provide incentives for efficiency and innovation if there isno competition. For example, the government to compare the efficiency of thecompany to firms from the same industry in other countries, or require that thecompany expand service and reduce prices over time, or allow private firms tocompete with the state-owned firm. Of course, if government subsidizes state-owned firms whenever they make losses, the incentives for these firms to improve are reduced. In China, many state-owned firms, especially the smaller ones, have already shifted to private ownership. I expect that shift will gradually continue over time.
US companies complain about their relations with government all the time, too! There's an old saying among economists that "government should steer, not row." It means that although government can offer overalldirections, including regulations and taxes, businesses need to be the ones that run the specific details of their own operations. If government is trying to get involved in details, sometimes all a business can do is try to limit itsinvolvement and draw back from a certain project or product.
Most American entrepreneurs are perfectly willing to follow governmentrules, but they complain that the rules often do not seem clear or simple. As a result, entrepreneurs complain that it takes a lot of time and energy just to figure out what is needed to follow the rules—and even then, they may sometimes feel uncertain as to whether they have actually succeeded in following the rule. Other complaints are that broader government systems are not always performing well: for example, that schools are not providing workers with the right education and skills, or that roads,railways, and transportation systems should be improved.
China is still a middle-income country in the world economy. The steps that are needed to move from middle-income to upper-income are just as large asthe steps that China has already been taking during the last four decades. I'm not sure what the next round of policy reforms will look like, but to continue China's fast pace of growth, the ongoing policy changes will need to be large, too.
One big emphasis of China's government in the last few years has been the need for reform of the financial sector, and this seems important to me, too. Many of the problems that the US and the world economy experienced during the Great Recession of 2008-2009 emerged from the financial sector. In China, there are changes underway in the regulation of banks, insurance companies, and securities firms. The People's Bank of China has been evolvingin how it carries out monetary and credit policies. One important lesson from global experienceis to pay close attention when the amount of borrowing and credit risesdramatically in any given area of an economy, because sometimes it is a danger sign.
Back in the 19th century, a great English economist and philosopher named John Stuart Mill noticed that many English people of his time viewed the people ofIreland as lazy and drunk. But Mill noticed that when those same Irish people emigrated to the United States, they suddenly seemed to become hard-working and productive. Mill observed that the way in which people act is often determinedby the situation in which they find themselves. I have very much enjoyed theChinese people I have met. But everywhere I go, I enjoyed meeting people whowant to share my interests and want to talk with me. And everywhere I go, I tryto avoid people who are mean and unkind.


I continue to be impressed with the quality of .NET Developers in Israel.. we had a full house last night for a 2+ hour presentation on building business applications with Silverlight and RIA Services.
The audience was very engaged and had lots of good, relevant questions which created a really good conversation.
Check out the slides and demo.
I started off by demoing the Right-to-Left text support for Hebrew that is baked in as part of Silverlight 4.
Then I started from scratch, with File\New Project and built out a simple data-oriented application.. basically I walked though most of this series.
You can find the completed demo, including the files and code snippets you would need to repeat the demo yourself, which i would be high flattered if you did at your own user's group or for your development team. You an find the demo steps in the series.
Thanks to Jackie Goldstein and Guy Burstein for organizing the event and for Jackie and Kim for the unique tour of a Kibbutz – it was great fun!


At long last I managed to get the radio controls for the Mountain Wheelchair to work!
It's taken so much and time and effort to get this to work. Not wanting to get too technical, I basically needed a way to convert an RC Receiver 1-2milisecond pulse to an analogue 0-5volts. I tried all sorts! In the end I found a very elegant, simple and cost effective solution from an American company called Astro Flight. There were some issues with the shipping but once the item arrived it was a very simple plug-and-play and at last, the mountain wheelchair has a working radio control system.
As I've said before, it was extremely important to get this working because it will allow me to drive the wheelchair into the mountains without needing to be concerned for the safety of the driver.


text:
package com.frameworkset.platform.sysmgrcore.purviewmanager.menu;

import java.io.Serializable;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
import java.util.Map;

import javax.servlet.jsp.PageContext;

import com.frameworkset.platform.security.AccessControl;
import com.frameworkset.platform.sysmgrcore.entity.Organization;
import com.frameworkset.platform.sysmgrcore.manager.db.OrgCacheManager;
import com.frameworkset.common.tag.tree.COMTree;
import com.frameworkset.common.tag.tree.itf.ITreeNode;

/**
 * 机构 可访问税务机关 授权树
 *
 * @author Administrator gao.tang
 */
// 角色管理资源操作授予--机构资源授权树
public class SetReadorgnameTree extends COMTree implements Serializable {

	String resTypeId = "";

	String roleId = "";

	String roleTypeId = "";

	String currOrgId = "";
	private  Map checks;
	private Map permissionsSource;
	private final static  Object obj = new Object();
	public void setPageContext(PageContext pageContext)
	{
		super.setPageContext(pageContext);
//		permissions = (Map)session.getAttribute("com.frameworkset.platform.sysmgrcore.purviewmanager.menu.SetReadorgnameTree.permissions");

		checks = new HashMap();
		permissionsSource = new HashMap();

		 resTypeId = request.getParameter("resTypeId");

         roleId = request.getParameter("currRoleId");

		 roleTypeId = request.getParameter("role_type");

		 currOrgId = request.getParameter("currOrgId");
		//是否批量权限授予
		String isBatch = request.getParameter("isBatch");
		if("false".equals(isBatch) && "user".equals(roleTypeId))
		{
			this.checks = ((AccessControl)accessControl).getRoleSelfResource(roleId, resTypeId, roleTypeId,"readorgname");
			permissionsSource = ((AccessControl)accessControl).getSourceUserRes_Role(currOrgId, roleId, resTypeId, "readorgname");

		}
//		if(!super.accessControl.isAdmin())
//		{
//			if(permissions == null)
//			{
//
//				Map temp_permissions = new HashMap();
//
//				String sql = "select ORG_ID  from V_TB_RES_ORG_USER b where b.OP_ID='readorgname' and b.user_id = ?";
//
//				PreparedDBUtil db = new PreparedDBUtil();
//
//				try {
//					db.preparedSelect(sql);
//					db.setString(1, super.accessControl.getUserID());
//					db.executePrepared();
//					for(int i = 0; i < db.size(); i ++)
//					{
//						temp_permissions.put(db.getString(i, "ORG_ID"), obj);
//					}
//					permissions = temp_permissions;
//					session.setAttribute("com.frameworkset.platform.sysmgrcore.purviewmanager.menu.SetReadorgnameTree.permissions", permissions);
//
//				} catch (SQLException e) {
//					e.printStackTrace();
//				}
//			}
//		}

	}
	public boolean hasSon(ITreeNode father) {
		String treeID = father.getId();
		if (father.isRoot())
			return true;

		try {
			if (treeID.equals("lisan"))
				return false;
			if (treeID.equals("00"))
				return false;
			return OrgCacheManager.getInstance().hasSubOrg(treeID);

		} catch (Exception e) {
			e.printStackTrace();
		}

		return false;
	}

	public boolean setSon(ITreeNode father, int curLevel) {
		String treeID = father.getId();
		//对应表td_sm_roleresop中的RESTYPE_ID字段
		String resTypeId = request.getParameter("resTypeId");
		//对应表td_sm_roleresop中的ROLE_ID字段
		String roleId = request.getParameter("currRoleId");
		//对应表td_sm_roleresop中的TYPES字段
		String roleTypeId = request.getParameter("role_type");
		//所选用户对应机构ID
		String currOrgId = request.getParameter("currOrgId");
		//是否批量权限授予
		String isBatch = request.getParameter("isBatch");

		try {

			List orglist = OrgCacheManager.getInstance().getSubOrganizations(
					treeID);

			if (orglist != null) {
				Iterator iterator = orglist.iterator();
				boolean isadmin = this.accessControl.isAdmin() ;
				while (iterator.hasNext()) {
					Organization sonorg = (Organization) iterator.next();
					//System.out.println("sonorg.getRemark5() = " + sonorg.getRemark5());
					String treeName = "";
					String orgId = sonorg.getOrgId();
					String remark5 = sonorg.getRemark5();
					if (remark5 == null
							|| remark5.trim().equals(""))
						treeName = sonorg.getOrgName();
					else
						treeName = remark5;
					Map map = new HashMap();
					map.put("orgId", orgId);
					map.put("resId", orgId);
					map.put("resName", treeName);
					// res_id:restype_id:res_name
					String checkValue = orgId + "#" + resTypeId + "#"
							+ treeName;
					String nodeType = "org";
					// if(AccessControl.hasGrantedRole(roleId,roleTypeId,orgId,resTypeId)){
					// nodeType = "org_true";
					// }else{
					// nodeType = "org";
					// }
					// 已经授权的复选框显示选中状态
					//给机构授权,给用户授权
					if("false".equals(isBatch) && "user".equals(roleTypeId)){
						String ms = (String)this.permissionsSource.get(orgId);
						Object self = (Object)checks.get(orgId);
						boolean isDesabled = false;
						if(ms != null && self == null)
							isDesabled = true;
						if(self != null)
						{
							ms = ms == null?"用户自身拥有资源":"用户自身拥有资源;" + ms;
						}
						if(ms == null)
						{
							ms = "";
						}

//						ms = accessControl.getSourceUserRes_jobRoleandRoleandSelf(currOrgId,
//										roleId, treeName, resTypeId,
//										orgId, "readorgname");
//						isDesabled = accessControl.getUserRes_jobRoleandRoleandSelf(currOrgId,
//										roleId, treeName, resTypeId,
//										orgId, "readorgname");

						// System.out.println("ms = " + ms);
						boolean hasper = !"".equals(ms) && ms != null;
						if (hasper) {
							ms = "-->资源来源:" + ms;
						}

//						if(!isadmin)
//							hasper = accessControl.checkPermission(orgId, "readorgname", "orgunit");

						if ( isadmin || hasper
								|| accessControl.isOrganizationManager(orgId)
								|| super.accessControl.checkPermission(orgId,
			                            "readorgname", AccessControl.ORGUNIT_RESOURCE)) {
							if(hasper  )
							{
								if(!isDesabled )
								{
									map.put("node_checkboxchecked", new Boolean(true));
								}
								else {
									map.put("node_checkboxchecked",new Boolean(true));
			     					map.put("node_checkboxdisabled",new Boolean(true));
			     					map.put("node_checkboxname", "isDesabledCheckbox");
								}
							}
							addNode(father, orgId, (treeName + ms).trim(), nodeType, true, curLevel,
									(String) null, (String) orgId,
									checkValue, map);
						} else {
							if (accessControl.isSubOrgManager(orgId)) {
								addNode(father, orgId, treeName, nodeType, false,
										curLevel, (String) null, (String) orgId,
										(String) null, map);
							}
						}
					}
					else //给角色授权,给用户批量授权,给角色批量授权
					{
						/*
						 * 1.管理员本身拥有这个机构访问权限,可以授给其管理的用户
						 * 2.超级管理员可以将所有机构访问权限授给其他用户
						 * 3.管理员可以将自己可管理的机构的可访问权限授给他可管理的用户
						*/
						boolean hasper = accessControl.checkPermission(orgId, "readorgname", "orgunit");
						boolean isCheck = AccessControl.hasGrantedRole(roleId,roleTypeId,orgId,"orgunit");
						//System.out.println("isCheck = " + isCheck);
						if(isCheck){
							map.put("node_checkboxchecked", new Boolean(true));
						}
						if ( isadmin || hasper || accessControl.isOrganizationManager(orgId) ) {

							addNode(father, orgId, (treeName).trim(), nodeType, true, curLevel,
									(String) null, (String) orgId,
									checkValue, map);
						} else {

							if (accessControl.isSubOrgManager(orgId)) {
								addNode(father, orgId, treeName, nodeType, false,
										curLevel, (String) null, (String) orgId,
										(String) null, map);
							}
						}
					}
				}

				// if(father.isRoot())
				// {
				// Map rootparam = new HashMap();
				// // rootparam.put("nodeLink",request.getContextPath() +
				// "/sysmanager/accessmanager/role/operList_org_ajax.jsp?resTypeId=orgunit");
				// String treeName ="顶级机构";
				// String resId ="00";
				// rootparam.put("resName",treeName);
				// rootparam.put("resId",resId);
				// String nodeType = "lisan";
				// if(AccessControl.hasGrantedRole(roleId,roleTypeId,"00",resTypeId)){
				// nodeType = nodeType + "_true";
				// }else{
				// // nodeType = "org";
				// }
				// // if (accessControl.checkPermission("00",
				// // "addsuborg", AccessControl.ORGUNIT_RESOURCE)) {
				// // addNode(father, resId, treeName,
				// // nodeType, true, curLevel, (String) null,
				// // "00", "00", rootparam);
				// // }
				//
				// Map map = new HashMap();
				// map.put("nodeLink",request.getContextPath() +
				// "/sysmanager/accessmanager/role/operList_org_ajax.jsp?resTypeId=orgunit");
				// treeName ="离散用户管理";
				// resId ="lisan";
				// map.put("resName",treeName);
				// map.put("resId",resId);
				// String lisannodeType = "lisan";
				// if(AccessControl.hasGrantedRole(roleId,roleTypeId,resId,resTypeId)){
				// lisannodeType = "lisan_true";
				// }else{
				//
				// }
				//
				// if (super.accessControl.checkPermission("lisan",
				// "lisanusermanager", AccessControl.ORGUNIT_RESOURCE) ||
				// super.accessControl.checkPermission("lisan",
				// "lisanusertoorg", AccessControl.ORGUNIT_RESOURCE )) {
				// addNode(father, resId, treeName,
				// lisannodeType, true, curLevel, (String) null,
				// (String) resId, (String) resId, map);
				// }
				//
				//
				// }
			}
		} catch (Exception e) {
			e.printStackTrace();
		}

		return true;
	}

}

redpajama_set_name: RedPajamaGithub


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The Late Breaking Abstract Submission was open on April 19,2017 and closed April 25, 2017 end of day.
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text:
The first Shatabdi experience
I saw the first Shatabdi express in my life, way back in 1998. That was the Coimbatore - Chennai Shatabdi Express, and I still remember the distinct livery and the soft music inside the coach. Shatabdi expresses trace their roots back to 1988. These trains were introduced on the Centenary year of Pandit Jawaharlal Nehru (the first Prime Minister of India). (Shatabdi, in Sanskrit, means Centenary). Shatabdi Expresses, like their long distance counterparts - Rajdhani - have all-inclusive fares, and meals are served on board the train.
The First Shatabdi Express in India ran between New Delhi and Jhansi, which was later extended to Bhopal. The Bhopal Shatabdi (numbered 2001/2002) is the fastest in the country and is also the longest (in terms of distance covered). Shatabdi expresses have only sitting accomodation, and all coaches are airconditioned. There are two classes, viz, Second Chair Car and Executive Chair Car. While the former has a 2x3 seating arrangement, the latter has 2x2 seating. As on date, the railways operate about 12 pairs of such services. Of the 12, 8 operate out of New Delhi (the single largest operator of Shatabdi Expresses).
My first journey on a Shatabdi express was scheduled to happen on July 16, 2009. The excitement on traveling on a Shatabdi had left me sleepless the previous night. I woke up at around 0500 in the morning. An autorickshaw was arranged to drop up (myself and my dad) at the Railway Station in time for us to get into the 2006 Kalka-New Delhi Shatabdi Express. The Auto was to pick us from our place of stay (in Sector 31D of Chandigarh) at 0545 in the morning - but the blue-coloured smoke-spitting contraption managed to reach our place only around 0600. The diesel-powered engine (which emitted more smell of Kerosene than Diesel) had just two gears, and it couldn't manage more than 40kmph. While I am not fan of speed, this journey was agonisingly slow and irritating. Quite a few radio taxis overtook us on the way. We soon passed the CTU bus depots and turned towards the railway station.
The railway station was crowded at the time, and it should be noted that the only departure at that hour was that of my train. Traffic cops standing outside had a tough time making vehicles move away from the "drop-off" area of the station. We paid the rickshaw driver and walked into the railway station. There were no trains on any of the platforms, while Gonda's WDM2 #16726 was seen roaming around the station for some odd reason. After roaming around the platform for a while, I decided to take some snaps of the station building. After a couple of snaps, I was back on the platform.
The coach position indicators were busy displaying "NR", and there was no sign of any coach position being displayed. The PA system came on often with the characteristic Windows start-up chime, and a lady voice announced, "Train Number Two Zero Zero Six, Kalka Shatabdi from Kalka to New Delhi via Ambala would arrive on Platform Number one at its scheduled arrival time.. six.. hours and... forty eight... minutes". The same announcement kept repeating itself every three minutes. Even as the digital clocks on the platform went past 0640, there were no sign of the coach positions being displayed. I checked up with a guy manning a refreshment stall on the platform, and he provided some hints about the coach position.
The platform was getting filled by the moment, and this Shatabdi surely had most of its patrons joining from Chandigarh. The clientele varied from weary looking passengers (intending to connect to other trains from New Delhi) to well dressed business executives. A short toot was heard a while before 0644, and a while loco appeared from the horizon at around 0645. I was high on hopes about getting an LHB rake (The newer generation high-speed coaches, manufactured on a Transfer of Technology agreement signed by the Indian Railways with the German coach maker Linke Hoffman Busch). However, the loco slowly moved across the points to reveal a tired looking ICF Generator Car. That conveyed to me that my first LHB ride is still away.
My 2006 Shatabdi snaking across points, into Chandigarh Railway Station...
The train slowly crawled past the points, and painfully progressed towards the starter. On judging the speed and the arrangement of the first coach, I guessed that my coach would stop pretty far from where I was standing. I signalled my dad to start moving ahead, and what ensued was a mad dash on the platform, with almost all passengers running towards the loco. The train came to a dead halt at 0649, about a minute delayed on its scheduled arrival time. My coach - C11 - was almost entirely booked from Chandigarh, and so was most of the coaches at the front end of the train. To make matters comfortable to the boarding passengers, the coach attendants decided to keep only one of the doors open. There are another stampede resembling rush at the door, and it took agonisingly long for the 68 passengers in that particular coach to get on board.
WAP7 #30222 of Ghaziabad, that worked my train, at New Delhi
The train showed no signs of movement even as the clock ticked past its scheduled departure time. The loco gave off a short toot, and inched forward sharp at 0656, late by 3 minutes now. The loco incharge of my train was WAP7 #30222 of Ghaziabad, and my coach was NR 15854 (appeared to be on the early 90s). My coach was the 5th in the 16 coach rake. A while after the train moved back on to the main line, a feeble sound from the PA system announced "Indian Railways welcomes all passengers joined from Chandigarh on board the Shatabdi Express". The train sped up to about 110kmph by now. The waiter onboard came around, shoving off a tray on my table. The tray had a packet of Marie biscuits, two eclairs toffees, one stainless steel spoon and a paper packet, aptly named tea kit, containing a packet of dairy whitener, two packets of sugar and two tea bags (of some local unheard of brand).
The waiter then came around distributing thermos-flasks containing hot water. Then began the tea-making exercise. The real challenge came as the cup filled to 80% its capacity, and the train sped to 110kmph. The horizontal oscillations were too high for those speeds and I managed to finish the circus called tea-making, not before spilling a few drops of tea on the tray. I managed to gulp down the tea, and settled down to read the newspaper that the waiter had distributed. An RPF officer came around asking each passenger to identify their baggage (kept on the overhead rack), and a small red sticker was pasted on each bag, conveying a message that the bags have been 'checked'. By now, the train slowed down and we were soon approaching Ambala Cantonment railway station. I headed straight to the door for a couple of photos. The Train Superintendent objected to me standing at the door, and I decided to move to the next coach. Ambala welcomed us in with a WDP1 working an unknown passenger, and the celebrated WDP4B working another passenger train.
We stopped at Ambala, at 0733, perfectly on time. Very little passengers joined us at this station. The train started from here at 0739, late by a minute. The PA system came alive yet again to welcome passengers, this time for those boarding from Ambala. Soon after the train cleared Ambala station limits, the waiter came around distributing Welcome drinks (Yeah! We were welcomed into the train a long 43 minutes after I was on board). A small tetra-pack of Jumpin Mango Punch was our welcome drink. A while after our trays were cleared, the waiter came around throwing another tray at each seat. This tray had our breakfast.
The Breakfast comprised of two slices of bread, one small butter chiplet, a small packet of Jam, a packet of tomato sauce, two miniature pieces of Veg Cutlet, some boiled green peas, two rectangular pieces of potato (which was affectionately called by the caterers as French Fries) and some odd pieces of boiled vegetable. The entire serving would equal what is consumed as a normal adult with evening tea. The overall serving was disappointing, and no normal person would have alighted the train without being hungry. Another tea was distributed, helping a lot of us wash down the disappointment of the abysmally small serving of breakfast. Another Tea@110kmph exercise followed, and I got the better of this one by preparing tea without spilling even a drop outside!
Interiors of my coach, after all passengers had alighted at New Delhi
We had a steady stream of crossings, and a very good number of trains crossed us on the Ambala-New Delhi Run. The list of crossings also included two LHB Shatabdis. Needless to say, each LHB rake did add salt to my injury. My coach, although chilling cold, appeared to be pretty badly kept. The seats had old rexine upholstery, and the seating was very uncomfortable. I headed to the door as the train crossed Naya Azadpur. The speed to a crawl as we crossed Subzi Mandi. The signals lined up showing up amber and double ambers. A couple of EMUs overtook us on the other line(s) as we crawled past the coaching yard. The loco trip sheds were filled to the brim as usual, and my train made a very slow progress towards New Delhi railway station.
My Shatabdi crawling amidst EMU trains near New Delhi...
The train slowly crawled on to Platform 1. Another mad rush ensued at the doorways now. Me and my dad remained seated at our places till most of the crowd made its way to the platform. After most passengers alighted, we unloaded our baggage, and my dad slowly walked outside. I took some snaps of my coach before I alighted. Platform 1 was now crowded to the brim. The exit gate was packed. Since we were connecting to a different train from New Delhi, I had to find information about the platform from where my train would leave. I went out to the common area to find a huge board displaying details of trains departing later in the day. I found that my train would depart from Platform 3, and we lugged our baggage to the platform. Later, WDS4 #19651 of Shakurbasti came around, and pushed the rake to the yard... ending a long awaited journey on the Shatabdi Express.
While Shatabdi expresses are a good alternative to airlines on short routes, the service on board these trains leave a lot to be desired. The basic problem is the attitude of the staff on board, and more importantly the quality and quantity of food served on board. While the train scores well in terms of speed, passenger comfort is bad in old coaches. I look forward for a ride on a Shatabdi running with the newer LHB coaches.
Slogan Murugan said…
My only Shatabdi ride was between Chennai and Bangalore and I can remember being fed uninterrupted from start to finish! Burrrp.
24 July 2009 at 18:00:00 GMT+5:30
My one n only Shatabdi journey was on august 1st 2009, when I travelled along with my parents from Chennai to Mysore on the 2007 MAS/MYS Shatabdi Express. The rakes were greyish in colour (I suppose these are the new ones you are referring to) as compared to the violet yellow colour scheme I had seen earlier. The interiors including the seats and upholstery were extremely attractive. Even the windscreen design has been re-designed on the lines of keralaSRTC busses !!! The music inside the coach was pleasant n soothing.
The journey began at sharp 6 am n we reached mysore by 1305 hrs (a delay of 5 min) I for one was thrilled to reach mysore from bangalore in just 1hr 55min !!! (conventional journey takes approx. 3 hrs) The catering staff were clean,courteous all along the journey n conversant in english n hindi (I believe that it was outsourced)
Coming to the in-coach service, I honestly feel short of praises for the quality of service offered. All throughout the journey we were being served with food n beverages at regular intervals. The service started with the staff giving us the bottle of mineral water & the morning English newspaper. The good thing was that they were providing The HINDU,NEW INDIAN EXPRESS & DECCAN CHRONICLE on board n we (at least some of us) could get to choose one between them. Then we were served with Tea/Coffee (based on our choice), toffees n marie biscuits. Sometime later a 2nd round of Tea/Coffee was served. Breakfast was served at around 8 am, by the time we reached katpadi jn (near vellore) The veg. breakfast was sumptuous n consisted of pongal vada with chutney n sambar; 2 slices of bread with amul butter n jam sachets. The catering staff asked each n every passenger about their preference (veg or non-veg) before serving the breakfast. Soon after the breakfast I dozed off for about an hour. When I woke up, I found a pack of Frooti kept on my table by the staff !!!
We reached bangalore at sharp 10:40 am, where the engine is changed to the Diesel version for the b'lore/mys run (i dont know the engine specs, but i know that its the most powerful diesel one around that is used to pull other superfast trains also) Although many people got down here, I was pleasantly surprised to see around 30-40% people still bound towards mysore... (Mysore DOES deserve a shatabdi service !!!)
Normally, in all long distance trains bound for mysore; the service n security is almost non-existent on the b'lore-mys leg (as if the train is run to mysore just for shunting purposes) But I was proved wrong again... We were served with very good quality tomato soup with pepper sachets n some crisp biscuits. But the icing moment was when we were served lunch by noon (I didn't expect lunch on the MAS->MYS run)the veg. lunch consisted of veg.biriyani n curd rice along with a raitha which was truly top-class... I truly felt proud of being a Indian then in the presence of some foreign tourists !!! Needless to say that we were again asked about our meal preference...
Overall, I found the service extremely good in terms of both quality n quantity... In fact it was better than many low cost airlines that I had flown earlier... My parents remarked that this food tasted far better than most of the so-called good restaurants in chennai !!! (I couldn't help but agreeing)
Overall, I feel that this was a wonderful n memorable journey. I look forward to takin a trip again... I strongly believe that more no. of shatabdi's should be run b/w b'lore n chennai,coimbatore,kochi,hyderabad... I wish that after the mys-b'lore line doublin is over; they extend the 2027/2028 shatabdi exp to mysore...
10 August 2009 at 21:09:00 GMT+5:30

redpajama_set_name: RedPajamaCommonCrawl


text:
James Eells (October 25, 1926 – February 14, 2007) was an American mathematician, who specialized in mathematical analysis.

Biography
Eells studied mathematics at Bowdoin College in Maine and earned his undergraduate degree in 1947. After graduation he spent one year teaching mathematics at Robert College in Istanbul and starting in 1948 was for two years an instructor at Amherst College in Amherst, Massachusetts. Next he undertook graduate study at Harvard University, where in 1954 he received his Ph.D under Hassler Whitney with thesis Geometric Aspects of Integration Theory.

In the academic year 1955–1956 he was at the Institute for Advanced Study (and subsequently in 1962–1963, 1972–1973, 1977, and 1982). He taught at Columbia University for several years. In 1964 he became a full professor at Cornell University. In 1963 and in 1966–1967 he was at the University of Cambridge, and after a visit to the new mathematics department developed by Erik Christopher Zeeman at the University of Warwick Eells became a professor of mathematical analysis there in 1969. Eells organized many of the University of Warwick Symposia in mathematics.

In 1986 he became the first director of the mathematics section of the Abdus Salam International Centre for Theoretical Physics in Trieste; for six years he served as director in addition to his appointment at the University of Warwick. In 1992 he retired and lived in Cambridge.

Eells did research on global analysis, especially, harmonic maps on Riemannian manifolds, which are important in the theory of minimal surfaces and theoretical physics.  His doctoral students included John C. Wood.

In 1970 he was an invited speaker at the International Mathematical Congress in Nice (On Fredholm manifolds with K. D. Elworthy).

He was co-editor of the collected works of Hassler Whitney. Eells's doctoral students include luc LEMAIRE Peter Štefan (1941–1978), Giorgio Valli (1960–1999) and . Eells was married since 1950 and had a son and three daughters.

Publications

with J. H. Sampson:

Singularities of smooth maps, London, Nelson 1967
with Luc Lemaire: ; re-published with a follow-up report in the books Harmonic Maps, 1992, and Two Reports on Harmonic Maps, 1994, by publisher World Scientific
with Luc Lemaire: Selected topics in harmonic maps, AMS 1983
with Andrea Ratto: Harmonic maps and minimal immersions with symmetries – methods of ordinary differential equations applied to elliptic variational problems, Princeton University Press 1993
with B. Fuglede: Harmonic maps between Riemannian polyhedra, Cambridge University Press 2001

See also
Eells–Kuiper manifold

References

External links

 Toledo, Domingo. James Eells 1926–2007. Notices Amer. Math. Soc. 55 (2008), no. 6, 704–706.
 Chiang, Yuan-Jen; Ratto, Andrea. Paying tribute to James Eells and Joseph H, Sampson: in commemoration of the fiftieth anniversary of their pioneering work on harmonic maps. Notices Amer. Math. Soc. 62 (2015), no. 4, 388–393.

20th-century American mathematicians
1926 births
2007 deaths
Bowdoin College alumni
Harvard University alumni
Amherst College faculty
Columbia University faculty
Cornell University faculty
Academics of the University of Warwick
Scientists from Cleveland
Mathematicians from Ohio
Geometers
Mathematical analysts

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text:
Harrisonburg Salvation Army partners with first responders to hand out 250 Thanksgiving meals
By John Hood
Published: Nov. 24, 2020 at 2:54 PM EST|Updated: Nov. 24, 2020 at 7:37 PM EST
HARRISONBURG, Va. (WHSV) — Tuesday morning, cars lined the outside of the Salvation Army Harrisonburg Corps as volunteers passed out 250 Thanksgiving meals for families in need in the community.
According to Capt. Harold Gitau, all of the food was donated by community members and area businesses, like George's INC.
This year, the Salvation Army had help once again from the Harrisonburg Fire Department and the Harrisonburg Police Department, who packed all 250 bags and helped control traffic as families drove by.
"The Harrisonburg Police Department and the Harrisonburg Fire Department always do this as their tradition to come in and help manage and control the traffic and help with giving out the bags," Capt. Gitau said. "So today has been a blessing, and we've been here just blessing people."
Each bag contained either a turkey or chicken, along with sides such as mashed potatoes or stuffing. Capt. Gitau said because of the community's help, more families will be able to enjoy a traditional Thanksgiving meal.
"This is pretty much the mission of the Salvation Army which is to preach the gospel of Jesus Christ and to meet human needs in his name without discrimination," Capt. Gitau said. "We are here we are so blessed that the Salvation Army is supported by the community to be able to do the most good."
If you are looking to volunteer with the Salvation Army and give back this holiday season, the organization is looking for bell ringers during its Red Kettle Campaign.
Starting this Friday, you will see more kettles around the Friendly City as volunteers stand outside stores like Kroger and Wal-Mart. Bell Ringers will be at different locations three times a week up until Christmas.
This year, you can also donate to the campaign virtually by clicking here.
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