text:
Birthday talk for Beds
PR 49631st October 2005
The Friends of The Withey Beds will celebrate their first birthday later this year. To mark this special occasion, the Friends are hosting an informative talk about the history of the National Willow Collection and the biomass industry as well as the work of Rothamsted Research.
Rothamsted is almost certainly the oldest agricultural research station in the world. It has an international reputation as a centre of excellence for science in support of sustainable land management and its environmental impact. The talk will be presented by Dr Ian Shields, who works at Rothamsted Research, and will be held at Three Rivers District Council Offices on Monday 7 November at 7pm.
As The Withey Beds was once used to grow 'withies' (willow used to make such products as fencing and baskets) the talk will be especially interesting. Everyone is welcome to come along. For more information residents can contact Kay FitzGerald at kay.fitzgerald@threerivers.gov.uk or on 01923 727105.

redpajama_set_name: RedPajamaCommonCrawl


Items where Author is "Benkő, József Miklós"
Benkő, József Miklós (2017) RR Lyrae változócsillagok vizsgálata fotometriai űrtávcsövekkel. Doctor of the Hung. Acad. of Sci. thesis, MTA CSFK.
This list was generated on Fri Apr 26 08:15:09 2019 CEST.


text:
"Sustainable Cannabis" startup Flow Kana raises a huge $22 million Series A financing
image credit : Flow Kana
San Francisco based Marijuana Brand Flow Kana which is notable for its offering of sun-grown cannabis coming from up to 50 farms which the company operates in partnership with several small farmers has now raised $22 million in Series A funding from in a round led by New York based Gotham Green Partners in combination with other firms including Salveo Capital, Poseidon Asset Management and two under investors.
Gotham Green Partners which led the round brought in 68% of the $22 million capital which represents the purchase of a minority interest in the 4 year old marijuana company.
This investment now brings the total raised by Flow Kana to a huge $50 million for the company which is making use of this new investment to expand its services with its network of independent farmers which will in turn increase production capacity at Flow Kana's 87,000 sq. ft. Flow Cannabis Institute which produces and sells premium cannabis oil to various companies who make use of it in the manufacturing of other products.
Commenting on this investment is Salveo Capital's Partner Michael Steinmetz who represented the company in this investment saying "Flow Kana has quickly scaled its business in a relatively short time and is a perfect example of how the cannabis industry is evolving," He continued "Flow Kana's dedication and partnership with local farmers in Mendocino and Humboldt counties in California is critical to their success and a testament to their values. We are very eager to begin working alongside the entire Flow Kana management team as we continue to support the company's strong momentum."
With Marijuana now legal in 10 states including the Washington D.C. capital in America, Several startups have evolved in the growing Marijuana industry to provide controlled and safe sale of Cannabis to eligible adults in a market which is predicted to increase hugely in future time.
Flow Kana which handles the marijuana plants at its centres but does not grow the plant itself will now create more experiences surrounding the product through its Flow Cannabis Institute for a market which has counted several startups come up in the industry including a $1 Billion marijuana startup MedMen which has raised over $130 million in equity funding for its cannabis chain store and retail service.


text:
Sculpting the Air
Martha Sherman, dancelog.nyc
Chris Schlichting's thoughtful and intense "Period" wrapped its dancers around the air, and around the audience, as its three performers created a dense medium out of the ether. The Chocolate Factory Theater's white walls and rough edges framed the work, which was co-presented with Lumberyard, the sponsor of Schlichting's current creative residency.
The movement phrase that echoed throughout the work was the one that began it. Maggie Zepp stood for a long moment of silence, then gently swayed her hands through the air. Very slowly, she added her neck and shoulders, continuing to stir. She stood like a human column, in a cerise t-shirt and black shorts. Joining two bright red-painted water pipes, those three verticals warmed the white performing space.
Zepp's deliberate solo was interrupted by the arrival of Laura Selle Virtucio. She wore a lighter shade of pink, and stood behind Zepp, almost, but not quite, touching her. At first, they stood back-to-back, and Virtucio, too, softly shifted the air around her. The simple upper body movements deepened, and the two women sank into lunges, scooping the air around each other. Gathering momentum, they inhabited more of the stage. Still in a legato pace, they made long strokes of movement, as arms glided out and back like a Swedish massage. They turned, sank low, shifted toward and away from each other, mesmerizing in the weighty silence.
Schlichting was credited with sound, which was almost entirely silence, couched in the dancers' breath. As the audience entered the theater, a harsh hiss greeted us into the brightly lit theater, like a group-wide tinnitus. Everyone settled and a shockingly loud sound – traffic? machinery? guns? – pounded for a few painful moments like a clarion call to get ready. Then Zepp entered, in the silence that accompanied the remainder of the performance. That brief, shocking noise was the first marker in an hour that was littered with markers: a single motion, shift or lighting change signaled new territory; the signals were more subtle than that first one, but still gave the work a fluid shape.
When Tristan Koepke sauntered onstage to join the duet, he, too, changed the shape of the dance. Tall and princely, (what were those tattoos covering his limbs? Beautiful and distracting, one of them looked like a Frank Lloyd Wright window carved into his right calf) Koepke softly split the two women apart, yet towered over them, creating the first of many human triangles. The trio's moves were more diverse, but much still hearkened back to Zepp's opening floating hands. The dancers' interconnections gently framed each other, sometimes in parallel, often exploring alone, while co-inhabiting the space. In a duet for Virtucio and Koepke, their hands were placed and replaced in right angles in front of their faces and chests, like a sophisticated version of a children's game, half-hiding and half-revealing themselves to each other. When the trio wove, the three didn't touch, but were always connected. Schlichting described "Period" as a dance about letting go; but the trio's beckoning movements, connected them in space that was like a viscous version of air, thick and moldable.
The lighting, by Heidi Eckwall, shifted the feel of The Chocolate Factory in pockets of brightness and darkness. Sometimes the stage was flushed with pale pink that caught the pink costumes, then darkened into shadow, framing the dancers' silhouettes against a brightly lit background where the windows were sometimes lit up like frames at the back of the performing space. When Virtucio danced a brief solo upstage, Zepp and Koepke were frozen in shadow downstage.
When the women strode off stage to leave Koepke for his solo, he stood alone and spotlit, staring out at us through hands that captured his face, then he crumpled in place with barely a sound. In a quiet coda to the poetry of "Period," Zepp returned for a brief dark duet with Koepke, silhouetted in a narrow band upstage. He exited with a simple sweep of arms, while Zepp, just as she had begun the evening, offered the final image in a brief moment of light before the rhythms and exertions all dissolved into black. It felt quick and liquid; not quite finished, but lovely nonetheless.
Martha Sherman for dancelog.nyc, July 25, 2018.
Martha Sherman, dancelog.nyc2018-07-252018-07-25/wp-content/uploads/2017/08/cft-logo-light.pngChocolate Factory Theaterhttps://chocolatefactorytheater.org/wp-content/uploads/2018/07/1000_chris_schlichting_2.jpg200px200px


text:
\section{Introduction}

Modern transportation networks have had profound effects on geographical spread of infectious diseases~\cite{cliff2004,cohen2000} giving rise to complicated epidemic evolutions~\cite{colizza2006role}. These evolutions can be modeled as dynamic processes on transportation networks. The epidemic spread on networks can take other forms, such as outbreaks of foodborne diseases~\cite{slutsker1998foodborne}, intercontinental cascade of failures among financial institutions~\cite{elliott2014financial,acemoglu2015systemic}, computer malware propagation on the interent and mobile networks~\cite{kondakci2008epidemic,fleizach2007can}
spread of targeted  fake news~\cite{shao2017spread,shao2018spread} and rumors~\cite{friggeri2014rumor} on social media,
 especially during presidential elections~\cite{shin2017political,jin2017detection,allcott2017social}.
In response to an adverse diffusion on a network, it is critical to trace back sources  to enable appropriate prevention and containment of the spread~\cite{world2008foodborne}.
 Inferential methods have been developed to locate the source of foodborne diseases~\cite{manitz2014,horn2019}

 and influenza pandemics~\cite{neumann2009,shen2016}.

   In the context of online social networks,
   the spread of misinformation can be limited by the identification of influential users~\cite{pei2014,kitsak2010}. Source recovery can also be used to assess the power of diffusions in generating anonymity in network protocols~\cite{bojja2017}.

The epidemic source identification problem has received considerable attention in the past decade. Given a snapshot of the infected nodes in a network, the task is to discover who has originated the epidemics.
Since the seminal work of Shah and Zaman~\cite{Shah2011},
numerous attempts have been made
to address the question and its extensions~\cite{Fioriti2012,Lokhov2014,Zhu2016,Luo2012,Nguyen2016,Prakash2012}.
By now, there are multiple methods that show satisfactory results in limited experimental setups or have proven guarantees in restricted network topologies~\cite{Jiang2017}. However, identifying the source under general conditions still remains a difficult task. Even under fairly simple models such as the Independent Cascade (IC) dynamics, the problem of optimal recovery appears to be NP-hard in infection size~\cite{Lappas2010}. The theoretical guarantees for optimal
and consistent recovery are restricted to regular infinite trees \cite{Shah2011, Zhu2016}, and as we show in this paper, the popular and well-cited methods are quite unreliable in a wide range of real networks.

Source identification has remained largely unsolved and poorly understood for real complex networks. As we will show  through experiments in Section~\ref{sec:sims}, in real networks, even the optimal Bayes estimator applied to small infected sets has difficulty narrowing down to the true source.
It is thus important to recover as much information from the likelihood of the model as possible.
We develop techniques for computing the full likelihood of the infection, as opposed to identifying the most likely sample-path~\cite{Zhu2016}.
Moreover, we fully exploit the information from the boundary of the infection set, in addition to the structure inside the infected subgraph. This idea has been pointed out before~\cite{Chang2015}, but has been mostly neglected by subsequent work; cf.~\cite{Prakash2012, Fioriti2012}. We develop all these ideas without restricting the structure of the network to trees. Our framework also easily extends to the case where there are multiple infecting sources (Appendix~\ref{sec:multi:source}).

In this paper, we develop statistical algorithms that outperform the state-of-the-art
in a wide range of network topologies. Our contributions are distinct in several ways:
\vspace{-1ex}
\begin{enumerate}[leftmargin=*,itemsep=0pt
	\item Our methods are parameter-free, meaning that they do not require knowing the duration of the epidemic or how fast it grows.
	\item We show that the exact maximum likelihood estimator (MLE) of the source---or equivalently the Bayes optimal solution under uniform prior---can be written as a dynamic programming (DP), with easily computable coefficients based on the adjacency matrix of the network.
	\item We develop two schemes to approximate the DP: an efficient greedy elimination (GE), and a novel mean-field approximation (MFA) of the likelihood,  computed by solving a linear system.
	\item Our approximations are more disciplined than existing approaches.
	They do not impose restrictions on the topology of the network. Nor do they appeal to the partial likelihood of the candidate infecting sets. This is in contrast to the use of
	spanning trees to deal with general topologies~\cite{Shah2011} or the \emph{path-based approaches} that rely on the likelihood of individual paths from potential sources to the infected set~\cite{Zhu2016}.
\end{enumerate}
\vspace{-1ex}

We will show that when applied to real networks, both approximation schemes (MFA and GE)
outperform various geometric and spectral approaches, most of which perform no better than random guessing.  We also show that even for basic models of real networks, e.g., models with community structure, most existing methods dramatically fail.
The improvement in performance is most significant for the networks with many cycles, including social networks
that are known to have high transitivity.
In terms of computational efficiency, both the greedy and mean-field approximations are superior to the state-of-the-art likelihood-based and spectral approaches and comparable to centrality-based methods.
In addition, the mean-field algorithm is easily parallelizable through standard linear algebraic routines and can be used to tackle very large-scale epidemics on real networks.

\paragraph{Related work.}
Most of the existing literature on the source identification problem are based on a SIR dynamic where the infection spreads with an exponential rate proportional to the number of infected neighbors. All nodes are \emph{susceptible} to the infection and once \emph{infected} may \emph{recover} with a fixed exponential rate~\cite{kiss2017}. Moreover, the spread of infection through edges are mutually independent. Different variations of SIR may assume that no recovery is possible (SI) or the recovered is not immune to iterated infections (SIS).

Shah and Zaman~\cite{Shah2011} considered the SI dynamics and proposed the Rumor Centrality (RC), which counts the \emph{permitted permutations}, a.k.a. infection paths, inside the infected subgraph.
Their linear time algorithm is an optimal estimator in regular trees and enjoys strong theoretical properties in such idealized settings~\cite{khim2017}.
Zhou and Ying \cite{Zhu2016} consider SIR dynamics on a tree and show that the most likely infection path is rooted at a Jordan center (JC) of the infected set $O$, that is, a node with minimum eccentricity (i.e., maximum distance to others). It has been shown~\cite{Zhu2016,khim2017} that in regular trees, eccentricity ranking generates, with high probability, a confidence set containing the true source, whose size does not grow with the infection size.

The Dynamic Message Passing (DMP) was proposed in~\cite{Lokhov2014} as an approximation of the maximum likelihood estimator in discrete SIR epidemics, by assuming that  the marginal probabilities of infection for each node are independent.
Despite compelling performance, DMP
is computationally intensive and impractical for large networks with moderately dense structures, even for small infection sets.
A spectral algorithm, called Dynamical Age (DA) was introduced in~\cite{Fioriti2012}, based on measuring the sensitivity of the maximum eigenvalue of the Laplacian matrix to the elimination of each node in the infection set. The algorithm was mainly developed to discover the initial node in a growing preferential attachment model. Another spectral method for the discrete SI model is proposed in~\cite{Prakash2012}.

\section{Source detection in SI epidemics}
We consider a continuous-time susceptible-infected (SI) epidemic~\cite{kiss2017}
with rate of infection~$\beta$, on a static undirected network $G(V,E)$ with known edge set $E$ and $V = [n]$. At time zero, all nodes but the source  are in the  susceptible state. Infection is a terminal state and
susceptible nodes
are exposed to the infection at an exponential rate proportional to the number of their infected neighbors. More precisely, given that nodes $I$ are infected at some time $t$, we run exponential clocks $T_j \sim \Expd( \beta \vol(I,j))$ for all $j \in I^c$ and the first to expire determines the next infected node: If $\bm j^* = \argmin_{j} T_j$, then the dynamics move to the infected set $I \cup \{\bm j^*\}$ at time $t + T_{\bm j*}$.
It is clear that the contagion will eventually spread through the entire graph.

The infection source or patient zero, denoted as $\bm {i}_*$, is unknown. What we observe is a snapshot of the contagion at some time $t$, meaning the entire set of infected nodes at that time, which we denote by $O$.
The objective is to find $\bm {i}_* \in O$ or form a confidence set for $\bm {i}_*$ with desired false exclusion probability. Our focus here will be on the single source setting, but the analysis
is extensible to the multi-source setting
(cf. Section~\ref{sec:multi:source}).

\textbf{Notation.}
We write $A \in \{0,1\}^{n \times n}$ for the adjacency matrix of the network
and  $\vol(I,J) := \sum_{i \in I, j \in J} A_{ij}$ for the volume of a cut in the network between subsets $I, J \subset [n]$ of nodes. For singleton subsets,
we often drop the braces,
e.g., $\vol(I, j) := \vol(I, \{j\})$ and $O \setminus j = O \setminus \{j\}$.

\subsection{Time and rate invariant analysis}
We start by examining the  probability of observing a particular set of infected nodes given a starting source.
Let us introduce a parameter-free formulation of the problem (i.e. not dependent on rate $\beta$ and time $t$) that will be the foundation for our analysis of the continuous SI dynamics. The idea has been introduced in~\cite{Chang2015}. We generalize it to multiple sources and find
forward and backward dynamic programming
formulations that allow us to derive efficient approximations.

Suppose that, at some point in time, the infection reaches $I\subset [n]$. Let $O \subset [n]$ be some superset of $I$. We are interested in computing $\rho_{I \to O}$, the chance that all the nodes in $O$ are infected before any node outside. More precisely, let
\begin{align}\label{eq:def:rho}
\rho_{I \to O} := \pr\big( \text{$O$ is infected before $O^c$} \mid \text{$I$ is infected}\big).
\end{align}
We refer to $\rho_{I \to O}$ as the \emph{transition probabilities}.
Note that these transition probabilities are independent of the infection source.
Given that in a snapshot of the contagion, nodes $I$ are infected,
$\rho_{I \to O}$ determines how likely it is that in some future snapshot, $O$ is the set of infected nodes. The Markov property of (continuous-time) SI dynamics allows us to define $\rho_{I \to O}$ without reference to the source, or the time of the first snapshot. We will also show that these probabilities do not depend on the infection rate or the time of the second snapshot.

\subsection{Statistical Inference}\label{sec:bayes:infer}

Given the observed (random) infected set $O$,  the function $I \mapsto \rho_{I \to O}$ is \emph{the likelihood} of the model. Writing $ L_{O}(I) := \rho_{I \to O}$ for this likelihood, we observe that $L_O(I) = 0$ for all $I$ not contained in $O$. So, we can restrict $L(\cdot)$ to all subsets of $O$.
When dealing with the single-source setup, we restrict the parameter space to $I = \{i\}$ and with some abuse of notation write $\rho_{i\to O}$ for $\rho_{\{i\} \to O}$, and $ L_O(i) = \rho_{i \to O}, i \in [n]$ for the likelihood.

We can further consider a Bayesian setup by putting a uniform prior on the source (i.e., uniform over $[n]$). The Bayesian setup allows us to consider various notions of optimality by changing the loss function.
Letting $\bm i_*$ be the random initial source, we have a joint distribution on $(\bm i_*, O)$. Then the posterior probability that the source is $i$, given that we observed infected nodes $O$ is
\begin{align*}
p_i := \pr (\bm i_*  = i\mid O) = \frac{\rho_{i \to O}}{\sum_{j\in O} \rho_{j \to O}} 1\{ i \in O\}.
\end{align*}
Therefore, the maximum a posteriori (MAP) estimate of the source is
$\bm i^*_\text{MAP} = \argmax_{i}\, \rho_{i \to O}$
which minimizes the probability of error.
That is, $i^*_{\text{MAP}}$ minimizes $\pr(\bm {\hat  i} \neq \bm i_*)$ for any estimator $\bm {\hat  i} =  \bm {\hat  i}(O)$.
In some applications, the graph geodesic distance ($d_G$) to the source determines the error of estimation. In that case, the Bayes optimal estimator is
$\bm i^*_\text{dist} = \argmin_{i} \sum_{j\in O} \text{dist}_G(i, j) \, \rho_{j \to O}.$
It is not hard to see that $\bm i^*_\text{dist}$ minimizes $\ex [d_G(\bm {\hat  i}, \bm i_*)]$ among all possible estimators $\bm {\hat  i}$.

A third choice is to output a ranking instead of a single source. In this case an estimator is formally a permutation $\bm {\hat  \sigma} = \bm {\hat  \sigma}_O$ on $[n]$, suppressing the dependence on $O$ for simplicity. We can then consider the \emph{rank loss}  $\ell(\bm {\hat  \sigma}, \bm i_*) = \bm {\hat  \sigma}(\bm {i}_*)$, and we call the associated risk the \emph{expected (source) rank} $= \ex  \bm {\hat  \sigma}(\bm {i}_*)$.
The corresponding optimal Bayes estimator is obtained by minimizing the  posterior risk:
\begin{align*}
\bm {\hat  \sigma}^* := \argmin_{\sigma: [n] \to [n]}\; \ex [ \sigma(\bm {i}_*)\mid O].
\end{align*}
Noting that $\ex [ \sigma(\bm {i}_*)\mid O] = \sum_i \sigma(i) \,p_i$, the optimal estimator in this case is the ranking that sorts $p_i$ into descending order, i.e., $\bm {\hat  \sigma}^*(j_i) = i$ where $p_{j_1} \ge p_{j_2} \ge \cdots \ge p_{j_n}$.

\begin{rem}
	The distance loss might be suitable in some applications, but in general it is a poor measure if the goal is to reveal the actual source. This is especially true in small world networks,  including most social networks, where the expected distance between any pair of nodes is small. On the other extreme, in terms of the precision in recovering the source, is the zero-one loss which is too stringent. The rank loss can be considered a more robust version of the
	zero-one loss, and we will be our main evaluation measure.

\end{rem}

\section{Exact likelihood computation}
The Bayesian estimators introduced in Section~\ref{sec:bayes:infer} require us to evaluate the posterior probabilities $(p_i)$, or equivalently the likelihood values $\rho_{j \to O}$ for all $j\in O$. The main difficulty of the source identification problem is that computing the likelihood is itself challenging. We now develop exact equations that allow us to recursively compute the likelihood values $L_O(I)$ for all subsets $I \subset O$.

\paragraph{Dynamic programming.}
To begin, note that $\rho_{O \to O}=1$ for any $O \subset [n]$. In addition,  $\rho_{I \to O}=1$ whenever $O$ corresponds to a connected component of $G$. We develop two dynamic programming expressions for $\rho_{I \to O}$ for general $I\subset O$:
\begin{prop}\label{prop:bayes}
	For $I \subset O \subset [n]$, the probabilities $\rho_{I \to J}$ defined in~\eqref{eq:def:rho} satisfy the forward program
	\begin{align}\label{eq:for:rec}
	\rho_{I \to O} = \sum_{j\in O\setminus I} \frac{\vol(I, j)}{\vol(I, I^c)}\, \rho_{I\cup j \to O}
	\end{align}
	and the backward program
	\begin{align}\label{eq:back:rec}
	\rho_{I \to O} = \sum_{j\in O\setminus I} \rho_{I \to O\setminus j} \,\frac{\vol(O\setminus j, j)}{\vol(O \setminus j, (O \setminus j)^c)}.
	\end{align}

\end{prop}

In the forward programming~\eqref{eq:for:rec}, $j$ effectively iterates over the boundary of $I$ in $O$, as $\vol(I,j)=0$ if $j$ is outside that boundary. Therefore, the running time of the forward programming benefits from the sparsity of the network. Unlike the forward programming, the iteration over $j$ in~\eqref{eq:back:rec} cannot be restricted to a smaller set.
A corollary of Proposition~\ref{prop:bayes} is that the transition probabilities $\rho_{I \to J}$ are not affected by the rate and the duration of the infection.

Let us now observe some connection with the \emph{path-based analysis}. A permitted permutation or an infection path starting at a node $\bm {i}_*$, refers to a permutation $\sigma$ of nodes with $\sigma_1= \bm {i}_*$, and such that $\sigma_{k+1}$ is connected to at least one node in $\{\sigma_1,\ldots,\sigma_k\}$, for all $k\in [|\sigma|-1]$.

 Notice that the probability of observing a given infection path is
\begin{align}\label{eq:infect:path:prob}
\pr \big( \text{path $\sigma$ observed} \mid \sigma_1=\bm {i}_* \big) = \prod_{k=1}^{|\sigma|-1} \frac{\vol(\sigma_{[k]}^{}, \sigma_{k+1})}{\vol(\sigma_{[k]}^{}, \sigma_{[k]}^c)}
\end{align}
where $\sigma_I := (\sigma_i \mid i \in I)$.
As noted in~\cite{Chang2015}, one can obtain the transition probability $\rho_{\{\bm {i}_*\} \to O}$
by summing~\eqref{eq:infect:path:prob} over all infection paths $\sigma$ such that $\sigma_1=\bm {i}_*$ and $\{\sigma_1,\ldots,\sigma_k\}=O$.
 Our recursive representation is novel, avoids these explicit summations, and will be key  in deriving  approximation schemes for $\rho_{I \to O}$ in Section~\ref{sec:approx}.

Path-based approaches such as Jordan center~\cite{Zhu2016} forgo computing the  complete likelihood (i.e., avoid summing the odds of all infection paths) and instead
find the most probable path, that is, one that maximizes~\eqref{eq:infect:path:prob}. In contrast, equations~\eqref{eq:for:rec} and~\eqref{eq:back:rec} compute the complete likelihood of the infection set, which has the following advantages over the path-based likelihood:
 It fully exploits the structure of the graph inside the infection set, not just a spanning tree or a permitted permutation of nodes in the infected subgraph. Moreover, it takes into account the boundary of the infected subgraph via $\vol(I, I^c)$.

\textbf{NP-hardness.}
The recursions we derived are a dynamic programming (DP) solution that computes the likelihood more efficiently, by avoiding the direct summation over all possible paths.
More precisely, one can obtain the optimal likelihood values by solving recursion~\eqref{eq:for:rec} backwards: Starting from $\rho_{O\to O} = 1$, we can determine $\rho_{I\to O}$ for all proper subsets $I \subset O$ of maximal size (i.e., $|I| = |O|-1$) and then all the proper subsets of those at the previous stage and so on.
However, this DP procedure  may still take $O(2^{|O|})$ time in the worst case.
It is an interesting open question whether polynomial-time solutions for computing the likelihood or its maximum exist.

Although it has been shown in~\cite{Shah2011} that for infinite regular trees, computing the likelihood reduces to enumerating all the infection paths from $i$ to $O$, and therefore a polynomial-time algorithm exists in that case, the behavior of the exact likelihood under more general network topologies is much more difficult to investigate. Researchers have implicitly assumed the problem to be hard beyond trees and have resorted to approximations that are often based on heuristics. In the next section, we propose disciplined approximation schemes that yield much more accurate results under realistic classes of networks.

\section{Approximations}\label{sec:approx}
We now provide two approximations to the likelihood function $L_O(I)$ based on the exact dynamic programming developed in Proposition~\ref{prop:bayes}.

\paragraph{Greedy Elimination (GE).}
We can obtain a singleton source set $I=\{i\}$ that maximizes $\rho_{I \to O}$ with greedy elimination of elements in $O$. The algorithm we propose is based on the backward recursion~\eqref{eq:back:rec} and is detailed in Algorithm~\ref{alg:greedy}. We start with $O_0:=O$ and consider all maximal proper subsets of $O_0$ that induce a connected subgraph of $G$. Among those, we choose the one that maximizes the transition probability to $O_0$, i.e. $\rho_{O_0\setminus j \to O_0}=\vol(O_0\setminus j, j) / \vol(O_0\setminus j, (O_0\setminus j)^c)$. Suppose that $O_1:=O_0\setminus j^*$ is the maximizer. Next, we iterate the same procedure for $O_1$ and so forth, until we reach a singleton set $I:=O_{|O|-1}$.
The procedure has an $O(k^2 m)$ runtime where $k = |O|$and $m$ is the number of edges in the infected subgraph, $G_O$.

GE has a Bayesian justification. Let $\widetilde{O}_k$ be  the random infected set after $k$ steps. Suppose that we want to find the MAP for $\widetilde{O}_{k-1}$ given $\widetilde{O}_k$. The Bayesian posterior probability is
\begin{align*}
\pr(\widetilde{O}_{k-1}=O\backslash j \,|\, \widetilde{O}_k=O) \propto \rho_{O\backslash j \to O} \cdot P(\widetilde{O}_{k-1}=O\backslash j).
\end{align*}
Whenever $G_{O\backslash j}$ is connected, the prior is positive. GE finds a proxy for MAP through maximizing the evidence and ensuring the prior is positive.

Algorithm~\ref{alg:greedy} has similarities with finding the most likely path from a source to the observed snapshot. Chang et. al.~\cite{Chang2015} propose a similar path-based search called GSBA. They start from each node in $O$ and approximate the most likely path and use it as a proxy to the most likely source. Algorithm~\ref{alg:greedy}, however, does this greedy search in a backward fashion.

\begin{figure}[t]
	\begin{minipage}[c]{0.53\textwidth}
		\vspace{0pt}
		\begin{algorithm}[H]
			\begin{algorithmic}[1]
				\Require {Graph $G([n],E)$ and  $O\subset [n]$.}
				\Ensure {$\bm i_\text{GE}^* \in O$}.
				\State $O_0 := O$
				\For {$i := 0$ to $|O|-2$}
				\State $O'_i := \{j\in O_i : G_{O_i\backslash j}\text{ remains connected} \}$
				\State $j^* := \argmax_{j\in O'_i}  \dfrac{\vol(O_i\setminus j, j)}{\vol(O_i\setminus j, (O_i\setminus j)^c)}$.
				\State $O_{i+1} := O_i \setminus j^*$.
				\EndFor
				\State {$\bm i_\text{GE}^* :=$ the single element in $O_{|O|-1}$}.
			\end{algorithmic}
			\caption{Greedy Elimination}
			\label{alg:greedy}
		\end{algorithm}
	\end{minipage}
	\hfill
	\begin{minipage}[c]{0.45\textwidth}
		\vspace{0pt}
		\begin{algorithm}[H]
			\caption{Mean-Field Approximation}
			\begin{algorithmic}[1]
				\Require {Graph $G([n],E)$ and  $O\subset [n]$.}
				\Ensure {$\bm i^*_\text{MFA} \in O$}.
				\State Compute $S, \bm z$ as defined in~\eqref{eq:Sz:def}.
				\State $\bbh := S^{-1}\bm z$.
				\State $\bm i^*_\text{MFA} := \argmax_{j\in O} \bbh_j$.
			\end{algorithmic}
			\label{alg:mean:field}
		\end{algorithm}
	\end{minipage}
\end{figure}

\paragraph{Mean-field Approximation (MFA).
We now approximate $\rho_{I \to O}$ by the mean-field technique. The idea is to treat the set function $I \mapsto \rho_{I \to O}$ as if it was a distribution (or measure) on $O$ and approximate it by the product of its marginals.
Fix a subset $O\subset [n]$. For any $I\subset O$, let $\xb^I = (x^I_j)_{j\in O}$ be the binary representation of $I$, i.e. $x^I_j = 1\{j \in I\}$ for any $j\in O$. We find
$\alpha_0$ and $(b_j)_{j\in O}$ such that
\begin{align}\label{eq:rho:hat:mean:field}
\widehat{\rho}_{I \to O} = \alpha_0 \prod_{j \in O} b_j^{x^I_j-1}
\end{align}
is a good approximation to $\rho_{I \to O}$ for all $I \subset O$, in the sense of minimizing the quadratic deviation from the solution of the recursion~\eqref{eq:for:rec}.
First note that $\alpha_0=1$ since $\rho_{O \to O} = 1$.
Next, we plug-in $\widehat{\rho}_{I \to O}$ into the forward recursion, to get
\begin{align*}
\vol(I, I^c)\,\widehat{\rho}_{I \to O} - \sum_{j \in O\setminus I} \vol(I,j)\, \widehat{\rho}_{I\cup \{j\} \to O} = 0.
\end{align*}
Dividing both sides by $\prod_{j\in O\setminus I} b_j$ gives
$\vol(I, I^c) - \sum_{j \in O\setminus I} \vol(I,j)\, b_j = 0.$
These equations in general cannot be satisfied exactly for all $I \subset O$. Instead, letting $\bb = (b_j)_{j \in O}$, we solve the following least-squares problem:
\begin{align}\label{eq:optim:mean:field}
\bbh \;\in\; \argmin_{\bb}
 \sum_{I:\,I\subset O} \Big(\vol(I, I^c) - \sum_{j \in O\setminus I} \vol(I,j) \,b_j\Big)^2 = \argmin_{\bb} \norm{Q \bb - \bm r}_2^2
\end{align}
where $Q \in \reals^{(2^{|O|}-1) \times |O|}$ and  $\bm r \in \reals^{(2^{|O|}-1) \times 1}$  are defined as follows:
\begin{align*}
Q_{I, j} &= 1\{j\not\in I\}\, \vol(I,j),\; \forall\, I\subset O,\; j\in O, \qquad
\bm r_I = \vol(I, I^c),\; \forall\, I\subset O.
\end{align*}
The solution of~\eqref{eq:optim:mean:field}  satisfies the normal equations $Q^T Q \bbh = Q^T \bm r$.
The following proposition shows that $Q^T Q$ and $Q^T \bm r$ can be computed efficiently. Let $A$ be the adjacency matrix of the network.

\begin{prop}\label{prop:mean:field:lin:system}
	The solution $\bbh$ of~\eqref{eq:optim:mean:field} satisfies

	the linear system
	$S \bbh = \bm z$
	with $S$  and $\bm z$ given by
	\begin{align}
	\begin{split}
	S &= \Xi\big(A_{OO} + A_{OO}^2 -
	A_{OO} \odot (\ub \mathbf{1}^T + \mathbf{1} \ub^T ) + \ub \ub^T \big) \in \reals^{|O| \times |O|}, \\
	\bm z &= ( \mathbf{1}^T \ub + 2\mathbf{1}^T \bm v) \ub  -
	2 \bm v \odot \ub + 2 (A_{OO} \,\bm v + \ub) \label{eq:Sz:def}
	\end{split}
	\end{align}

	where $\ub := A_{OO}\mathbf{1}$ and $\bm v = A_{OO^c}\mathbf{1}$. Here $\odot$ is the element-wise matrix product,  $\Xi(\cdot)$ is a matrix operator that returns the same matrix with double the diagonal entries, and $\mathbf{1}$ is the vector of all ones.
\end{prop}
See Appendix~\ref{app:proof:mean:field} for the proof.
Proposition~\ref{prop:mean:field:lin:system} shows that the mean-field approach reduces to solving a linear system of equations in $|O|$ variables, a task with much better computational complexity than solving the original recursion.
Both $S$ and $\bm z$ can be computed in at most $O(|O|^2)$ time. In the cases where $A$ is sparse (which often the case for real networks), $S$ will be a rank-one perturbation of a sparse matrix (both $A_{OO}$ and $A_{OO}^2$ will be sparse), hence solving the resulting system is often much faster than the worst-case, i.e., faster than $O(|O|^3$).

\section{Simulations}\label{sec:sims}
The methods proposed in this paper, the Greedy Elimination (GE) and the  Mean Field Approximation (MFA), have reasonable runtimes compared to popular source identification procedures and show superior performance in source identification. In this section, we make a comparison based on these two measures
on real and synthetic networks.
As discussed in Section~\ref{sec:bayes:infer}, we consider ranking estimators (i.e., those outputting a permutation of the nodes according to their likelihood of being the source) and focus on the rank loss.
If the method does not return a ranking, we tweak it to do so. We evaluate the methods based on expected rank $R$, the expectation of the rank of the actual source among the list of candidates (cf. Section~\ref{sec:bayes:infer}). We normalize to get a number in [0,1], with zero corresponding to perfect recovery, i.e., we use $(R-1)/n$.

\begin{figure}[t]
\centering
\begin{subfigure}[b]{0.45\textwidth}
\includegraphics[trim={1.1cm .6cm 1.1cm 1.2cm}, clip, width=\textwidth]{img/regular_tree}
\caption{Regular tree}
\label{fig:regular:tree}
\end{subfigure}
\begin{subfigure}[b]{0.45\textwidth}
\includegraphics[trim={0.85cm .6cm 1.1cm 1.2cm}, clip, width=\textwidth]{img/random_tree}
\caption{Random tree}
\label{fig:random:tree}
\end{subfigure}
\begin{subfigure}[b]{0.45\textwidth}
\includegraphics[trim={1.1cm .6cm 1.1cm 1.2cm}, clip, width=\textwidth]{img/internet}
\caption{Internet AS}
\label{fig:internet}
\end{subfigure}
\begin{subfigure}[b]{0.45\textwidth}
\includegraphics[trim={0.85cm .6cm 1.1cm 1.2cm}, clip, width=\textwidth]{img/power}
\caption{US West Power Grid}
\label{fig:power}
\end{subfigure}
\caption{Plots of the expected relative rank versus the infection size for low-transitivity networks.
\label{fig:rank:1}
\end{figure}

\begin{wraptable}{r}{.46\textwidth}
	\caption{Network statistics}

	\label{tab:stats}
	\centering
	\renewcommand\arraystretch{1.2}

	\begin{tabular}{lcccc}
		\multirow{2}{*}{Network} &  \multirow{2}{*}{$n$} & mean & max & clust. \\
		&      & deg. & deg. &  coeff. \\
		\hline
		Internet & 10670 & 4.0 & 2312 & 0.01 \\
		Power & 4941 & 3.0 & 19 & 0.1 \\
		Wiki vote & 7066 & 29.0 & 1065 & 0.13 \\
		UCSC68 & 8979 & 50.0 & 454 & 0.17 \\
		UC64 & 6810 & 46.0 & 660 & 0.19 \\
		DC-SBM & 1962 & 66.0 & 897 & 0.3
	\end{tabular}
\end{wraptable}
We consider a variety of real and simulated networks.
Our selection includes an Internet Autonomous System~\cite{internetas,leskovec2005graphs}, US west-coast power grid~\cite{watts1998collective}, two Facebook-100 networks~\cite{traud2011comparing,traud2012social}, called UC64 and UCSC68, and a Wikipedia voting network~\cite{leskovec2010signed}. In addition, we present our results on a number of synthetic networks that are well studied in the literature, including regular trees, random trees, and degree-correlated stochastic block models (DC-SBM)~\cite{karrer2011stochastic}.

Table~\ref{tab:stats} summarizes the statistics on the largest connected component of these networks. The regular tree is of degree 3 and depth 10. The random tree has 500 nodes. For the DC-SBM network, we generate from a $3$-community planted partition version, i.e., $\ex[ A_{ij}] = \theta_i \theta_j P_{ij}$ where $P_{ij} = 0.5$ if nodes $i$ and $j$ are in the same community and $P_{ij} = 0.02$ if they are in different communities. The degree parameters $\theta_i$ are generated from a rescaled Pareto distribution with $\alpha =2$ and threshold $=1$.

\begin{figure}[t]
\centering
\begin{subfigure}[b]{0.45\textwidth}
\includegraphics[trim={0.85cm .6cm 1.1cm 1.2cm}, clip, width=\textwidth]{img/ucsc68}
\caption{UCSC68}
\label{fig:ucsc68}
\end{subfigure}
\begin{subfigure}[b]{0.45\textwidth}
	\includegraphics[trim={1.1cm .6cm 1.1cm 1.2cm}, clip, width=\textwidth]{img/uc64}
	\caption{UC64}
	\label{fig:uc64}
\end{subfigure}
\begin{subfigure}[b]{0.45\textwidth}
	\includegraphics[trim={1.1cm .6cm 1.1cm 1.2cm}, clip, width=\textwidth]{img/wiki_vote}
	\caption{Wiki vote}
	\label{fig:wiki:vote}
\end{subfigure}
\begin{subfigure}[b]{0.45\textwidth}
\includegraphics[trim={1.1cm .6cm 1.1cm 1.2cm}, clip, width=\textwidth]{img/dc-sbm}
\caption{DC-SBM}
\label{fig:dc:sbm}
\end{subfigure}
\caption{ Plots of the expected relative rank versus the infection size for  high-transitivity networks.}
\label{fig:rank:2}
\end{figure}

The results
are illustrated in Figures~\ref{fig:rank:1}, \ref{fig:rank:2}. The methods we consider besides the optimal Bayes solution (BO), the MFA, and the GE are the Rumor Centrality (RC), the Degree Centrality (DC) and the Jordan Center (JC).
We have also run the Dynamical Age (DA), but due to its overall poor performance and its computational complexity, we have omitted it from the plots.
Our selection of the methods loosely follows the methods surveyed in~\cite{Jiang2017}. Each curve shows the performance of one method for different values of the infection size, $2 \leq |O| \leq 300$.  Each point is an average over 500 infection paths rooted at random sources. To avoid an unreasonable computation time, we skip
the optimal Bayes for the infected sets of size greater than 10.
The BO curve serves as the benchmark for the best achievable performance. Note that even the optimal solution needs to output a large set to catch the source, signifying the inherent difficulty of the problem.

Rumor and Jordan centralities perform optimally on regular trees in Figure~\ref{fig:regular:tree},  as expected by the theory~\cite{Shah2011,Zhu2016}, although the network is not exactly an \emph{infinite} tree. Notice that RC, JC, and BO overlap for infection sizes not exceeding the depth of the tree. Degree centrality also appears as a close competitor in this figure. Moving to other networks, however, these popular methods do not perform better than random guessing. For all three, the expected relative rank is close to 0.5, even in a random tree. The plots in this section show that, despite their popularity, the RC and JC are quite unreliable for source recovery.

Among our proposed methods, MFA outperforms RC, JC, and DC in Figures~\ref{fig:ucsc68}, \ref{fig:uc64}, \ref{fig:wiki:vote}, \ref{fig:dc:sbm}. MFA finds the true source, on average, in its top 30\% guesses.
 The networks with suprior MFA performance have highest transitivity (aka clustering coefficient) in Table~\ref{tab:stats}, that is, many triangles among triples of nodes. Transitivity has been studied extensively and it distinguishes human social networks from random trees and less cyclical networks, such as water distribution systems and traffic network. In this sense, MFA is suitable for rumor source detection in social networks.

GE is the global winner, except in regular trees (Figure~\ref{fig:regular:tree}). We were surprised that a greedy algorithm had such a widespread success.
GE not only performs well in highly transitive networks, but it also outperforms RC, JC, and DC on random trees (Figure~\ref{fig:random:tree}) and less transitive networks (Figures~\ref{fig:internet}, \ref{fig:power}).

\begin{wrapfigure}{r}{0.5\textwidth}
    \centering
    \includegraphics[trim={.9cm .2cm 1.1cm 1.2cm}, clip, width=.5\textwidth]{img/uc64_lapse}
    \caption{Runtime in seconds at infection size 10.}
    \label{fig:uc64:lapse}
\end{wrapfigure}
Figure~\ref{fig:uc64:lapse} illustrates the runtimes (on the log scale) for a single run on the UC64 network, when the infection size is 10 (the maximum size for which the Bayesian results are available).
Degree centrality is the fastest, followed by RC, JC, MFA and GE, all four having comparable speed, with RC and JC having a slight edge.
BO is about 10 times slower and its runtime grows exponentially with infection size.

Based on these results, we advocate for the use of GE as the main tool for identifying sources of epidemics, regardless of the network topology or the nature of  the epidemics (rumor propagation, disease contagion, etc.). MFA should be applied with caution. It is superior in social (transitive) networks, and attractive for its simplicity and the potential for parallelism.

\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


One of the best hip-hop albums..
Wondering if there was ever gonna be another cannibal ox album, I started searching through hypem.com. I found this track which pointed me to this… Read More »One of the best hip-hop albums..
Thursday June 3rd @ Afterlife MC battles, live painting, live tattooing. 4282 N. Drinkwater blvd. Scottsdale, AZ.
Synapse Presents MURS Friday June 4th @ Club M- all ages Saturday, June 5th @ The Anchor 21+ only Tickets on sale this week @… Read More »Murs in Alaska!!!
Nocando's Hurry Up & Wait is the first single of the the LA rapper's first album Jimmy the Lock. The remix keeps it in the… Read More »Remix Session!
@ Club Mardi Gras, 8040 E. Mcdowell Rd. Scottsdale, AZ. Ministry of EDM and Monsoon Presents Movin Monday: An Industry Dance Party! No Cover 21+… Read More »Movin' Mondays!!!
Feeding The Frenzy is a music culture blog based in Seattle, WA founded by OK Dave and Flat Black in 2008. They feature a variety… Read More »feeding the frenzy!!
Here are some parts of a skate video I helped edit and other random Youtube videos.


text:
Ex-mayor goes back home to Labour
WHEN former Ipswich mayor Gillian Auton left the Labour Party and joined the Liberal Democrats 10 years ago, her defection stunned her colleagues and caused shockwaves in the town's political circles.
Now, however, Mrs Auton has buried the hatchet and is once again campaigning for Labour in the seat - even though her membership of the Lib Dems hasn't expired yet.
Mrs Auton left Labour several years ago after accusing the local party of arrogance and not listening to people.
She joined the LibDems, and even fought her old Rushmere seat as a LibDem candidate in several elections without coming near getting elected.
But today she admitted she had always voted Labour in general elections, even while active in the Lib Dems: "In Ipswich if you don't vote Labour, you let the Tories in. And I wasn't having that," she said.
"There have been a number of factors that have led me back towards Labour.
I don't like the way the Lib Dems in Ipswich have gone in with the Conservatives at Civic Centre.
"And I know at least three of the Liberal councillors are very uncomfortable with the coalition - I don't know how long it will last," she said.
And as a former mayor, she had been very impressed by the way the current mayor, Labour councillor Roger Fern, had conducted himself in difficult circumstances.
"It can't be easy to be mayor when your party is in opposition," she said.
Her membership of the Lib Dems expires at the end of June and she will not be renewing it.
She has been telephone canvassing for Labour, and her husband Jim has rejoined the Labour Party.
Mrs Auton's departure from the Liberal Democrats left the party's candidate in Ipswich, Richard Atkins, unmoved.
He said: "We have been finding many, many people coming across to us from Labour - only yesterday we had an ex-Labour MP joining the Lib Dems.
"This is one person going in the opposite direction, it is not significant."
But Labour candidate Chris Mole, who is hoping to return to Westminster after the election, welcomed Mr and Mrs Auton back to the party.
He said: "Gillian and Jim were friends when they were in the Labour Party, and I am delighted they have come back.
"We are finding people understand that the only way to beat the Tories in places like Ipswich is to vote Labour."


HYDAC, for more than 50 years, has been the world's leading producer of hydraulic components, subsystems and systems for both mobile and stationary applications. HYDAC produces its own manufacturing components for the offshore, dredging, aviation, mobile, civil, oil and industry branche. HYDAC offers an extensive supply program with products for hydraulic engineering, such as filters, coolers, accumulators, valves, cylinders, sensors and manifolds. You can also contact HYDAC for electro-technical control technology, magnet technology, condition monitoring and fluid maintenance products.
reduces wear throughout the entire system & extends the area of your application.
HYDAC has developed a new element technology for power plant applications. The filter elements in the Optimicron® Power series exhibit impressively high levels of robustness, safety and a particularly low pressure drop. In addition, Optimicron® filter elements are compact in design and ensure homogeneous incident flow of the fluid.
When the German federal government set up Industrie 4.0 as a research project, it undoubtedly wasn't fully aware of the effects it would have on the industrial landscape in Germany. Industrie 4.0 is a revolution that has been designated as such while it is still in progress. For the three previous industrial revolutions, this wasn't the case.


DesiredMia . LovemakersNL. JohnnyNKeira. ImCharlotte.
BommBommsvenIssis06katerinnaa .FoxylilstarMsMiaSitsOnYousarj25KarrenRoss .GracefulLindsayhanazvImCharlottePORNOMUM .CaribeanXgirlIssis06KateTheCatXXSugarLady27 .BORN2beDOMINANTHOTANDREADY89xkiss94playfulcreation6 .RosieQueenSubKarissa2USLUTprettyDOLLAliceKyteller .shyshyshyKYLExxsvenLovemakersNLroberta4love .Mimi118LovemakersNLKerryMagicPsychedeliCat .ExquisiteEllacutegetcumsKateTheCatXXDarcyhot .CurlyCandyyGracefulLindsayFoxylilstarPORNOMUM .LilyKellerBORN2beDOMINANTBIGTITSDIRTYXXXBIGTITSDIRTYXXX .
KarrenRossxkiss942DIRTY4analShWExoticJem .AliceKytellerImCharlotteEvaSiberiaCaribeanXgirl .KylieSummerValentinaGreyRyanAndAliceTinaKittie .MaiaraWillssCurlyCandyykandywendy2NatashaOlingka .kirochka555MilaLeeMsMiaSitsOnYouEvaSiberia .AliceKytellerKarrenRossExxoticBrandyTinaKittie .BobbyAllanMimi118kandywendy2Hologramapple .DarcyhotKateTheCatXX000Summer000Ilsi .ExoticJemSiennaJoyHologramappleBianaKiss .hottie1097MsMiaSitsOnYouJennieCuteMilaLee .ComplaisantOlayLovemakersNLTeenLewisTry2CatchMe .


Thank you for participating in the new brand for San Diego Restaurant Week!
Below are four options that have been narrowed down from dozens of options. The goal is to create a more clear and organized logo mark to go with new colors and brand messaging.
An important note: There is intentionally no color in this round of the design in order to allow you to focus on the mark itself without being persuaded by color.


text:
Ohio Arts Council grant allows renowned artists to complete residency
BY DOMINIKA CHRUSZCZ
Pondering upon Dresden's Basket House, a large basket-shaped building near Newark, German artists Anne and Christian Manss reflected upon how size can translate to different medias in art history and in their personal work.
Anne Manss, a theatrical designer, has provided set and costume designs for productions around the globe. Christian Manss, a painter, has had his work in several collections around famous locations such as the Weisman Museum in Los Angeles.
In the Mellon Arts Across the Curriculum Proposal, Anne and Christian Manss will complete a seven-week residency that began on on September 14 and runs until November 4 at the Denison Art Space in Newark.
According to the proposal, "Christian and Anne Manss developed this residency project as an opportunity to forge an artistic bridge to Newark and its people, as well as Denison students and faculty."
Christian Manss describes his intentions for the gallery, "We want to work on an interdisciplinary art show with painting, installation and theater—with young adults, interested people from the community and students from Denison."
The Manss installation will be hosted in the Denison Art Space in Newark (DASiN). This installment will consist of an indoor replica of the Matterhorn, a mountain in the Alps, which will alter DASiN's space. The gallery will be constructed with local materials from Licking County or other materials brought by workshop members, as mentioned in the proposition.
Anne and Christian Manss will look to address the theme of global community by combining representations of Dresden, Germany along with Newark & Licking County.
"We would like to build the mountain out of local material—for example: out of baskets [or] other material in the neighborhood," Anne Manss said.
Additionally, "the artists will present weekly activities for Studio art, German, Theatre, and Dance faculty/students of Denison, and—the subject of this grant—workshops (in collage, textiles, and local seasonal themes) and talks for Licking County residents and school children that engage their participation in the installation's formation," in line with the proposal.
The Studio Art team remains ecstatic about the artists' exhibit.
Ron Abram, chair of the Student Art department says, "For us in [the] Studio Art Program, receiving this grant allows us to host the creation of this dynamic residency project in our space in Newark. Majors and non-art majors in our program will be able to meet with Anne and Christian over an extended period of time—seeing and learning about their creative practice first hand."
He went on to say, "Our students will be involved and learning in multiple capacities with them and working in the gallery. Symbolic of the Fine Arts Division at Denison, the project is interdisciplinary in nature merging visual and performing arts. Receiving the grant from the Ohio Arts Council also acknowledges the significance of our gallery and programming in downtown Newark as a space for the local community, and a place that bridges & creates dialogue amongst all of us."
The DASiN is located on 23 West Church Street. Anne and Christian Manss will hold free weekend art workshops on Sept. 29, Oct. 6, Oct. 13, and Oct. 20.
anne and christian manss, art grant, denison art
Swasey fills for Jon Huntsman
Weinberg: Students must leave campus through April 3
$10 million grant brings new career opportunities for Denison Students


text:
Q: arm neon inline assembly output operand casting I am trying the following code:
glm::vec4 a;
asm volatile(
   "mov r1,#64 \t\n"
   "vld1.32 q4,[%[input]],r1 \n\t"
   :[input] "+r" (a.x))

But it generates error "EXC_BAD_ACCESS (code=1, address =0x0)".
If I change the operand type to float, the error disappears:
float a[4];
asm volatile(
   "mov r1,#64 \t\n"
   "vld1.32 q4,[%[input]],r1 \n\t"
   :[input] "+r" (a[0]))

Why does the type matter ? Is it possible to cast glm::vec4 to float here ? I tried a lot of different ways but neither work.

redpajama_set_name: RedPajamaStackExchange


text:
sarahemilybond February 8, 2017 February 13, 2017 Art and Graphics, Forbes, open access, text, The State of the Field, Uncategorized
As I wrote about in this week's Forbes blog post, the Metropolitan Museum of Art in New York yesterday announced the release of 375,000 images of artwork into the Public Domain. Those of you who follow this blog know that I talk endlessly (some might say freely!) about the merits of open content initiatives. Ergo, I am glad to see that the Met has taken this stance towards image rights. What I hoped to emphasize with this post is that the Met is neither the first nor the last to join the open access movement. We should also look to museums like the Walters Art Museum and the Getty as important pioneers in the movement toward open access museum collections.
Below are a few image collections from museums that have similarly adopted the belief that art should be free and open to the public.
Resources for Open Access Image Collections in U.S. Museums:
Metropolitan Museum of Art Image And Data Resources
Getty Open Content Program
Walters Art Museum Image Database
Penn Museum Image Database
National Gallery of Art Image Database
I will leave you with my favorite image from the Met's collection: Gennadios' medallion. I write a lot about gold glass and this third century CE example originally from Alexandria is a splendid example of the craft, with an inscription on the prize that reads: "Gennadios, most skilled in music."
Previous The Itinerarium Egeriae: Mapping Egeria's Pilgrimage On Candlemas
Next The widespread and persistent myth that it is easier to multiply and divide with Hindu-Arabic numerals than with Roman ones.
Published by sarahemilybond
I am an Associate Professor in the History Department at the University of Iowa. I am interested in Roman, late antique, and early medieval history, archaeology, topography and GIS, Digital Humanities, and the role of Classics in pop culture. I obtained a BA in Classics and History with a minor in Classical Archaeology from the University of Virginia (2005). My PhD is in Ancient History from the University of North Carolina at Chapel Hill (2011). My book, Trade and Taboo: Disreputable Professionals in the Roman Mediterranean, is out now from University of Michigan Press (Fall, 2016) and looks at the lives of marginalized tradesmen like gravediggers and tanners. Follow me on Twitter @SarahEBond, read my Blog, or email me at sarah-bond@uiowa.edu.	View all posts by sarahemilybond
d55711128m says:
You never know where the internet will take you. I was just looking for theater times for John Wick 2 and saw a link to your "5 reasons…". Wow, such an amazing piece you wrote. I was especially intrigued by the Latin back tattoo. Thank you for sharing.
Pingback: Museum Details: Drama at the Met – The Museum Mermaid


text:
<!---
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Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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A few things to note:

* The AMP Project follows the [fork & pull](https://help.github.com/articles/using-pull-requests/#fork--pull) model for accepting contributions.
* Familiarize yourself with our [Design Principles](contributing/DESIGN_PRINCIPLES.md).
* We follow [Google's JavaScript Style Guide](https://google.github.io/styleguide/jsguide.html).  More generally make sure to follow the same comment and coding style as the rest of the project.
* Include tests when contributing code.  There are plenty of tests that you can use as examples.
* A key feature of AMP is performance.  All changes will be analyzed for any performance impact; we particularly appreciate changes that make things even faster.  Please include any measured performance impact with substantial pull requests.

## Contributing features

Follow this process for contributing new features:
* Familiarize yourself with the [AMP Design Principles](contributing/DESIGN_PRINCIPLES.md)
* [Create a new GitHub issue](https://github.com/ampproject/amphtml/issues/new) to start discussion of the new feature.
* Before starting on the code get approval for your feature from an [OWNER](https://github.com/ampproject/amphtml/search?utf8=%E2%9C%93&q=filename%3AOWNERS.yaml&type=Code) of your feature's area and a [core committer](https://github.com/ampproject/amphtml/blob/master/GOVERNANCE.md#core-committers).  In most cases the people who can give this approval and are most familiar with your feature's area will get involved proactively or someone else in the community will add them.  If you are having trouble finding the right people add a comment on the issue or reach out on one of the channels in [How to get help](contributing/getting-started-e2e.md#how-to-get-help).
* Consider bringing the eng design for your feature to our [weekly design review](#weekly-design-review).
* Follow the guidelines for [Contributing code](#contributing-code) described above.

## Contributing extended components

A key feature of the AMP HTML project is its extensibility - it is meant to support "Extended Components" that provide first-class support for additional rich features.

Because Extended Components may have significant impact on AMP HTML performance, security, and usage, Extended Component contributions will be very carefully analyzed and scrutinized.

In particular we strive to design the overall component set, so that a large number of use cases can be composed from them. Instead of creating a new component it may thus be a better solution to combine existing components to a similar effect.

For further detail on integrating third party services, fonts, embeds, etc. see our [3p contribution guidelines](https://github.com/ampproject/amphtml/tree/master/3p).

## Contributor License Agreement

The AMP Project hosted at GitHub requires all contributors to sign a Contributor License Agreement ([individual](https://developers.google.com/open-source/cla/individual) or [corporation](https://developers.google.com/open-source/cla/corporate)) in order to protect contributors, users and Google in issues of intellectual property.

When you create a Pull Request a check will be run to ensure that you have signed the CLA.  Make sure that you sign the CLA with the same email address you associate with your commits (likely via the `user.email` Git config as described on GitHub's [Set up Git](https://help.github.com/articles/set-up-git/) page).

## Ongoing participation

We actively encourage ongoing participation by community members.

### Discussion channels

Technical issues, designs, etc. are discussed on [GitHub issues](https://github.com/ampproject/amphtml/issues) and [pull requests](https://github.com/ampproject/amphtml/pulls),
 the [amphtml-discuss Google Group](https://groups.google.com/forum/#!forum/amphtml-discuss) and the [amphtml Slack](https://docs.google.com/forms/d/1wAE8w3K5preZnBkRk-MD1QkX8FmlRDxd_vs4bFSeJlQ/viewform?fbzx=4406980310789882877).

### Weekly status updates

GitHub issues labeled [Type: Weekly Status](https://github.com/ampproject/amphtml/issues?q=label%3A%22Type%3A+Weekly+Status%22) are used to track weekly updates from members of the community.  We encourage everyone who is actively contributing to AMP to add a comment to the relevant Weekly Status issue.

### Weekly design reviews

The community holds weekly design reviews as video conferences via Google Hangouts on Wednesdays at [1pm Pacific](https://www.google.com/?#q=1pm+pacific+in+local+time).

We use GitHub issues labeled [Type: Design Review](https://github.com/ampproject/amphtml/issues?q=label%3A%22Type%3A+Design+Review%22) to track design reviews.  The Design Review issue for a given week will have a link to the design docs being discussed that week as well as a link to the Hangout.  **When attending a design review please read through the design docs _before_ the review starts.**

If the design for an issue/feature you are working on has a scope larger than you can cover in a discussion in the GitHub issue or one of the other discussion channels, consider bringing it to a design review:

* Create a software design document in a shared Google Document open to public comments.
  * A short design doc is fine as long as it covers your design in sufficient detail to allow for a review by other members of the community.
  * Take a look at [Design docs - A design doc](https://medium.com/@cramforce/design-docs-a-design-doc-a152f4484c6b) for tips on putting together a good design doc.  [Phone call tracking in AMP](https://docs.google.com/document/d/1UDMYv0f2R9CvMUSBQhxjtkSnC4984t9dJeqwm_8WiAM/edit) and [New AMP Boilerplate](https://docs.google.com/document/d/1gZFaKvcDffceJNaI3bYfuYPtYU5u2y6UhE5wBPTsJ9w/edit) are examples of past AMP Project design docs.
  * Add this license text to the top of your design doc before sharing it with anyone else in the community (updating the year if necessary):
      ```
      Copyright 2017 The AMP HTML Authors. All Rights Reserved.

      Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

      http://www.apache.org/licenses/LICENSE-2.0

      Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS-IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

      See the License for the specific language governing permissions and limitations under the License.
      ```

* Perform a design pre-review with at least one [core committer](https://github.com/ampproject/amphtml/blob/master/GOVERNANCE.md); you can request a pre-review in the [#design-review Slack channel](https://amphtml.slack.com/messages/design-review/).  It is fine to request a pre-review before your design doc is complete.

* When your design is ready to be discussed at a design review add a comment on the appropriate Design Review GitHub issue.  Post a link to the design doc and a brief summary by **1pm Pacific Monday** on the week of your design review.

* Update your design based on the feedback in the design review and any followup conversations in other channels.  Once your design is finalized, please provide a brief update at the start of a future design review (if you are able to attend) and submit a PDF version of your design doc in the [ampproject design-doc](https://github.com/ampproject/design-docs) repository.

### See Also

* [Code of conduct](CODE_OF_CONDUCT.md)
* [3p contribution guidelines](https://github.com/ampproject/amphtml/tree/master/3p)
* The [GOVERNANCE](GOVERNANCE.md) model

redpajama_set_name: RedPajamaGithub


Tucked behind the legendary Himalayan peaks of Dhaulagiri and Annapurna, the forbidden kingdom of Lo was isolated for centuries. This region of Nepal-now called Mustang-is the off-the-beaten-track getaway of your dreams. Our 10-day trek journeys through this remote land, where vast monasteries stand guard over pocket-sized villages and every ridge offers another arresting vista. With two days in the walled city of Lo Manthang, nearly untouched since the 14th century, you'll be one of the few to discover this magnificent, well-preserved gem. Please note: Due to ongoing construction in Upper Mustang, one day of the trek may require a short Jeep ride.
Welcome to Kathmandu! Meet your MT Sobek representative and transfer to your hotel. In the afternoon, your trip guide will hold a short briefing and orientation, including information on trekking and other important information. In the evening we will gather for a welcome dinner.
Enjoy a morning tour of the Pashupatinath Hindu temple complex on the Bagmati River and Bodhnath's enormous Buddhist stupa - the largest in Asia and the center of Tibetan Buddhist culture in the UNESCO-listed Kathmandu Valley. Later in the afternoon, transfer to the airport and fly to Pokhara, a lively town set beside Phewa Lake with fantastic views of the Annapurna Range.
Weather permitting, board an early scenic flight to the small airstrip in Jomsom, the administrative center of the once-isolated Mustang district of Nepal. Transfer to a Jeep for the drive to Chele, passing medieval-looking villages and the rocky Kali Gandaki River (one of the holiest rivers in Nepal) before entering the forbidden kingdom of Mustang. Enjoy views of the uniquely colored chortens and landscapes of the Upper Mustang, then arrive in Chele, from where your hike begins. Steep climbs reward with stellar village and valley views; gradually descend to camp at oasis-like Samar.
After breakfast, hike to an ancient cave that houses Rhangchung Chorten, hidden away in a deep canyon. Cross two deep ravines and enjoy snowcapped mountain views before arriving at the sacred cave monastery, decorated with khatas (ceremonial scarves), numerous chortens, and other ritual items. From here the trail passes through a wooded area in the narrowing gorge, broadens into a valley, and then climbs to Syangmochen, from where we will drive to Ghemi.
From Ghemi, follow the trail across the valley, across a suspension bridge over the Dhakmar Chu River, then past the most impressive and longest stretch of mani wall (boulders inscribed with carved Buddhist prayers) in Upper Mustang. Descend to the secluded village of Dhakmar, situated under magnificent red sandstone cliffs dotted with cave openings. Climb up a rocky gully to Muila pass and your first view of famous Ghar Gompa, an eighth-century monastery.
Depart Ghar Gompa and, after crossing several valleys, reach the Chago La pass - at 14,038', it's the highest pass on the trek through Mustang. After a long, gradual descent, you finally get your first view of the walled city of Lo Manthang in the distance, set amongst fields and poplar trees. From here, a steady downhill leads you to camp.
Rest your legs and tour Lo Manthang, starting with its two 15th-century monasteries, Thubchen Gompa and Champa Gompa. Atmospheric with burning incense, flickering yak-butter lamps, Buddhist murals, intricate mandalas, and chanting monks, entering these buildings - so remote they've been untouched by modernity for centuries - is a sensorial and spiritual experience. A team of conservation architects has gently restored both monasteries with funding provided by the American Himalayan Foundation. Also here, visit the monastic school at Choede Gompa, the only monastery in Lo Manthang that houses monks.
Leaving Lo Manthang, the trail leads south towards Charang, climbs a pass overlooking Lo Manthang, and then heads down toward the Kali Gandaki Gorge. The final section of the dry, dusty trail drops sharply to the village of Dhi, marked by a patchwork of fertile fields and trees. After a well-deserved break at Dhi, hike up to the village of Yara. Just before reaching Yara, behold an incredible series of 300-foot-high, wind-scoured cliffs resembling giant organ pipes. The cliffs are dotted with numerous cave openings - one of Upper Mustang's most spectacular sights!
Take an excursion from Yara to Luri Gompa, one of the world's few remaining cave monasteries. Luri is also the most important in Upper Mustang, as it is one of the only monasteries in the region belonging to the Kagyu sect of Tibetan Buddhism. The monastery comprises a series of interconnected chapels covering various levels, all carved out of the cliff. The inner room contains a large chorten painted with deities. Kashmiri-style paintings of exquisite flowers, deities, and various figures of Indian origin adorn the dome-shaped roof and the west wall.
From Yara, the trail leads south to Dhechyang Khola, winding steeply uphill before opening to magnificent views of landscape below. Continue downhill into the village of Tangge, a small hamlet whose homes form a fort nestled below cliffs eroded into massive columns. The view of the settlement, with its groups of chortens and mani walls of various shapes and sizes, is simply stunning! Tonight's camp is constructed in a walled compound to protect guests from the dry Mustang winds.
On your way out of Tangge, enjoy an atmospheric walk through town to the trail leading down to the Tangge Khola, where you'll cross a wooden bridge. Hike above the village, then descend to the wide, rocky Yak Khola plain. After another river crossing, ascend steeply as the horizon widens between Charang, Lo Manthang, and Nepal's northern border. From the prayer flag-adorned summit of Pha, revel in your first awe-inspiring view of Dhaulagiri. Tonight, camp near the only water source on this part of the trek.
Today the trail winds uphill, opening to exceptional views of the entire Mustang Valley. Admire them, but then refocus on the trail, which is narrow and, on one side, drops 3,000 feet! Soon the trail veers away from the rim and reveals the extraordinary Kali Gandaki Valley, with myriad peaks surrounding the famous Thorang La pass (17,764'). Begin your descent of a steep, rough trail that leads to Chhuksang where our jeep will be waiting for us. From Chhuksang transfer back to Jomsom enjoying wonderful views of Mt. Nilgiris (23,166'). After many nights of camping, enjoy a night at a cozy guesthouse.
Bid farewell to the Upper Mustang as you fly from Jomsom to Kathmandu via Pokhara. If weather affects your flight (always possible in Nepal), you'll spend another day in Jomsom. Flights in and out of Jomsom are commonly delayed or cancelled due to unpredictable weather. If delayed, all meals at Jomsom are included while you wait. An extra day at the end of the trek ensures connections to your homeward-bound flight. Without delays, spend the day exploring in Kathmandu!
Begin today in Kathmandu Valley with a morning tour of Bhaktapur, an enchanting medieval Nepalese town. A highlight is Durbar Square, with its temples, ancient palace ruins, and exquisite Sun Dhoka - a gilded gate crafted in 1753. This afternoon you're free to roam, relax, or do some last-minute shopping or sightseeing in Kathmandu on your own. In the evening, join the group for a farewell dinner at a local restaurant.
After breakfast, transfer from your hotel to the airport and depart Kathmandu on your homeward-bound flight. Please note that most flights require an overnight en route to your destination.
MT Sobek is a legendary outfitter in Nepal, pioneering unique and exploratory adventures for nearly 50 years.
Our expert MT Sobek Mustang guide Sanjeev Chhetri has been leading treks in the Upper Mustang since 1992.
Trust MT Sobek to seamlessly blend a journey from Kathmandu to Mustang's isolated valleys, paying incredible attention to detail the whole way.
Strenuous hiking on mountain and valley paths, covering 4.5-10 miles per day on rugged terrain at a maximum elevation of 14,038'.
Nine nights camping, one night at a comfortable guesthouse and four nights at hotels in Kathmandu and Pokhara.
Daytime temperatures: 50°F - 85°F. Night time temperatures 20°F - 35°F. High winds and dust are common. Rain or snow possible.
Yes, and you can get a 15-day visa on arrival for $25.
Yes, you will need to bring a medium-weight, down or fiberfill sleeping bag rated from -5°F to 10°F.
It's best not to count on it as coverage is spotty.
Are there any luggage weight restrictions on the flight from Pokhara to Jomsom?
Yes. Luggage is restricted to 15 kg/33 lb per person-including checked and hand-carry luggage. It's not possible to pay for overweight luggage.
What is the Upper Mustang Permit Fee?
The Nepalese government charges $530 per person to trek in the Upper Mustang restricted area. This must be paid upon arrival in Kathmandu in US dollars. This cost also includes the $30 fee for the Annapurna Conservation Area, which is used in conservation efforts.
"I just completed the amazing Mystical Mustang trek with the best guides imaginable. Sanjeev Chhetri and Sirish Bhatt provided extraordinary insight into the area."
"This was the trip of a lifetime! A spectacular, active, and challenging trek combined with great scenery, amazing monasteries, and insight into the changing Buddhist culture of Upper Mustang. I would absolutely recommend MT Sobek!"


text:
package com.itude.mobile.mobbl.core.configuration.mvc;

import com.itude.mobile.android.util.StringUtil;
import com.itude.mobile.mobbl.core.configuration.MBConditionalDefinition;
import com.itude.mobile.mobbl.core.configuration.MBDefinition;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

/**
 * {@link MBDefinition} Class for a for each
 */
public class MBForEachDefinition extends MBConditionalDefinition {
    private String _value;
    private List<MBDefinition> _children;
    private Map<String, MBVariableDefinition> _variables;
    private boolean _suppressRowComponent;

    public MBForEachDefinition() {
        _children = new ArrayList<MBDefinition>();
        _variables = new HashMap<String, MBVariableDefinition>();
    }

    @Override
    public StringBuffer asXmlWithLevel(StringBuffer appendToMe, int level) {
        StringUtil.appendIndentString(appendToMe, level).//
                append("<MBForEach ").//
                append(getAttributeAsXml("value", _value)).//
                append(getAttributeAsXml("suppressRowComponent", _suppressRowComponent)).//
                append(">\n");

        for (MBVariableDefinition var : _variables.values())
            var.asXmlWithLevel(appendToMe, level + 2);

        List<MBDefinition> children = getChildren();

        for (int i = 0; i < children.size(); i++) {
            MBDefinition def = children.get(i);
            def.asXmlWithLevel(appendToMe, level + 2);
        }

        return StringUtil.appendIndentString(appendToMe, level).append("</MBForEach>\n");
    }

    @Override
    public void addChildElement(MBDefinition definition) {
        addChild(definition);
    }

    public void addChild(MBDefinition child) {
        _children.add(child);
    }

    public void addVariable(MBVariableDefinition variable) {
        _variables.put(variable.getName(), variable);
    }

    public MBVariableDefinition getVariable(String name) {
        return _variables.get(name);
    }

    public String getValue() {
        return _value;
    }

    public void setValue(String value) {
        _value = value;
    }

    public <T extends MBDefinition> List<MBDefinition> getChildren() {
        return _children;
    }

    public void setChildren(List<MBDefinition> children) {
        _children = children;
    }

    public Map<String, MBVariableDefinition> getVariables() {
        return _variables;
    }

    public void setVariables(Map<String, MBVariableDefinition> variables) {
        _variables = variables;
    }

    public boolean getSuppressRowComponent() {
        return _suppressRowComponent;
    }

    public void setSuppressRowComponent(boolean suppressRowComponent) {
        _suppressRowComponent = suppressRowComponent;
    }

}


The Global Business with German concentration is an interdisciplinary hybrid combining the International Business major of Hamline School of Business (HSB) and a business-oriented German minor of the College of Liberal Arts (CLA). This concentration is designed to address a need in the marketplace for an understanding degree that combines the traditional values of a Liberal Arts education with the needs of the international business student. Hamline University's international business curriculum covers the fundamentals of the business administration, with an emphasis on international business. Foreign language courses, a study abroad experience in Germany and an internship stress the use of language. Global Business with German is designed for business students to achieve near fluency while getting some concrete experience in German business.
Learn more about career paths, transferable skills, and relevant organizations that relate to a concentration in Global Business with German.
Professors in the School of Business Jim Hagen and David Berg are currently in Dubai to present at the Academy of International Business (AIB) 2017 Annual Meeting.
Hamline University student Mr. Zachary Carson is awarded the prestigious Congress-Bundestag Fellowship to study and work for one year in Germany.


text:
M&E industry appreciate film, Digital India-focussed interim budget 2019
Many are optimistic about the aim to digitally connect 1 lakh villages.
02 Feb, 2019 - 06:57 AM IST | By indiantelevision.com Team
MUMBAI: The government today announced the interim budget for 2019 in the parliament and has been gaining a lot of praise from the people for its pro-poor and pro-middle class approach. However, the media and entertainment section received little attention this time. Apart from a slight reference to anti-piracy laws and single-window clearance for ease of shooting films, the budget speech refrained from mentioning the industry.
However, there is still positivity in the media sector regarding the budget, especially from the entertainment fraternity. Producers Guild of India president Siddharth Roy Kapur said, "We are delighted that the immense contribution of Indian cinema towards employment generation in the country has been acknowledged and applauded in Parliament during the presentation of the Union Budget. The announcement of a single-window clearance mechanism for Indian filmmakers filming within India is a significant step and has the potential to play a huge role in boosting tourism in the country. The amendments in the anti-camcording provisions will support the industry's growth by curtailing illegal recordings of films in cinema halls and will go a long way towards reducing piracy."
Film producer Anand Pandit lauded the interim budget saying, "As a member of the film industry and a producer, I believe that the single window clearance that is being given by the government in this budget to filmmakers is an extremely welcome move. It will make the ease of getting shooting permissions very accessible and a lot less time will be spent in getting the same. I believe this budget looks at filmmakers and the industry as a major force in the country and I congratulate Shri Modiji for his vision in understanding our problems and finding a solution for us. By streamlining the process, producers like me will be able to make films far easier and in shorter amount of time."
Harkness Screens senior vice president sales and marketing – Asia Preetham Daniel reflected the same sentiments saying, "In relation to entertainment industry, single window clearance for filmmakers is a good move as it will remove the hurdles in content generation. Anti-piracy initiatives by the government is a big step forward. Moving from smart cities, digital villages will pave way for better penetration of content in all formats. India has an average screen density of 1 screen per population of 155000. Such positive measures in fiscal budget for entertainment industry coming along with GST rates rationalisation for film exhibition sector will certainly add steam and bring impetus to the growth pace of Indian Entertainment Industry. We are now well positioned to see the industry flourishing."
BookMyShow appreciated the budget's recognition of India's startup ecosystem's contribution to the economy and the creation of a Digital India in Vision 2030. It also said, "It is probably for the first time in several years that the entertainment industry has been hailed as a force of employment generation. We whole-heartedly support the government's move to curb piracy through the introduction of the anti-camcording provision in the Cinematography Act. With these measures, we expect the regulatory framework of the entertainment industry to significantly change for the better."
The statement further added, "While the Finance Minister has offered incentives to the film industry, it is also worth recognising the huge scope that live entertainment offers, for employment and growth of the Indian economy. We hope that the GST Council along with the government can find solutions to streamline the existing tax structures for this sector as well and bring it below the current rate of 28 per cent, to enable a well-rounded ecosystem."
MX Player CEO Karan Bedi said, "The Union Budget's provision for a single clearance window for Indian filmmakers would come as a welcome and positive step to film producers and movie studios across the country. The amendments in the Anti-Camcording provisions would also complement the industry's efforts to snub illegal movie recordings in theatres and encourage the consumption of original content."
Viacom18 group CEO and MD Sudhanshu Vats said, "The Hon'ble FM Shri Piyush Goyal has provided a tremendous fillip to the Indian entertainment industry with the provision of single window clearance for films, in the Union Budget 2019. Such policy provisions that seek to enhance ease of doing business will help the ~Rs 156 billion industry grow at a faster clip. I also welcome the move to include anti-camcording provisions as part of the Cinematography Act. This will ensure that in-theatre pirated recordings now become a penal offence and will act as a strong deterrent to piracy."
Apart from that, digital agencies are also very positive about the interim budget 2019. The emphasis on Digital India, including conversion of one lakh villages into digital over next five years, by the government in its budget speech has got the hopes high for them.
Ethinos Digital Marketing MD Siddharth Hegde said, "This is a positive budget from digital perspective and clearly the government is determined not only on delivering on its current promises but also delivering on its promise of a complete digital revolution in India. The 10 point agenda for 2030 and the set up of digital villages are going to drive digital inclusion. The low data costs are going to drive more for the rural population online and we are likely to see an explosion in vernacular content. This is also likely to give marketers an option to have a more measurable pipe to the rural masses. This budget is a great start and we welcome the move by the government."
Vertoz founder and chairman Hiren Shah mentioned, "The interim budget has laid out a clear vision for the country's development in the coming decade, and Digital India plays an important role with the government stressing on the development of digital infrastructure and digital economy. Coupled with the government's vision of converting one lakh villages into digital villages, we will see a deeper penetration of the internet not only in tier II and III markets but also in rural areas. Currently, we have only scratched the surface and the support from the government can help the industry reach its full potential with respect to market penetration."
He added, "This will broaden the horizon for digital advertising, and consequently for programmatic advertising, in these markets, thus providing an opportunity to brands and marketers to cater to the customer needs with bespoke products and services."
Speaking on the artificial intelligence program that the government has introduced with the budget, Hiren shared, "We are also happy to see artificial intelligence (AI) as a focal part of the budget for Digital India. This support will ensure that AI, supported by big data, will enable the industry to successfully analyse consumer patterns with minimized human intervention. This also brings in the benefits of automation such as more efficiency and faster processing of data for advertising, thus allowing brands to get sales faster and within an effective timeline."
White Rivers Media co-founder and CEO Shrenik Gandhi contended, "The interim budget, as expected was short yet powerful. A lakh digital villages will not only strengthen the bottom of the pyramid but also empower them with the digital universe. Rebate in tax slabs is a great move for not only the middle class but also young self-made professionals. Not many budgets empower this section of voters and the same is an applaud-worthy move."
TRA CEO N Chandramouli shared an interesting insight into how easing of loans for MSME sector, in turn, will benefit the advertising market. He said, "The 2 per cent interest subvention for MSME is definitely a great thought along with the easing of loans through PSB loans under 59 minutes scheme. Together they will give a boost to the industry which contributes to 69 per cent employment in the country. This will also boost the adjunct services industry that caters to MSME sectors like advertising, market research, training and public relations which in turn will also have a positive cascade on the services sector."
Interim Budget 2019Interim BudgetBudget 2019Entertainment Industry in Budget 2019Impact of Budget on Mediaanti-piracy lawssingle-window clearance for shootingproducers guild of indiaSiddharth Roy Kapurproducer Anand PanditPreetham DanielHarkness ScreensBookmyshowDigital IndiaEthinos DigitalSiddharth HegdeVertozHiren ShahViacom18Sudhanshu VatsWhite Rivers MediaShrenik GandhiTraN. ChandramouliMSME loans
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Today, one in three children are overweight or obese. Research suggests that - if untreated - 85% of these children will become obese adults.This statistic alone is reason enough to address weight issues in the early years.
Every year Reception and Year 6 pupils are measured and weighed as part of the National Child Measurement Programme. The information collected is used to deliver services to prevent and manage overweight and obesity in children nationally.
Using the results, a 3 year average is reported to the school so that we can look at ways to improve pupils health and wellbeing and help our children manage and maintain a healthy weight.
2017 summary of results for Camrose is concerning.
19% of Reception pupils are classified as overweight; with 10% being obese - this is double the average proportion!
35% of Year 6 pupils are classified as overweight; with 17% being obese - this is more than 3 times the expected proportion!
We are asking parents to help us, help their children to maintain a healthy weight.


text:
Болтнево — деревня в Духовщинском районе Смоленской области России. Входит в состав Бабинского сельского поселения. Население — 5 жителей (2007 год).

Расположена в северной части области в 4 км к юго-западу от Духовщины, в 1 км западнее автодороги  Смоленск — Нелидово. В 19 км юго-восточнее деревни расположена железнодорожная станция Присельская на линии Москва — Минск.на меня там девочка рыгала!

История
В годы Великой Отечественной войны деревня была оккупирована гитлеровскими войсками в июле 1941 года, освобождена в сентябре 1943 года.

Примечания

Населённые пункты Духовщинского района

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