text:
\section{Introduction}
Complex network analysis has emerged as one of the most exciting domains of data analysis and mining over the last decade. One of the most prolific sub field is community discovery in complex network, or {\em CD} in short. The concept of a ``community'' in a (web, social, or informational) network is intuitively understood as a set of individuals that are very similar, or close, to each other, more than to anybody else outside the community \cite{myreview}. This has often been translated in network terms into finding sets of nodes densely connected to each other and sparsely connected with the rest of the network. Community discovery can be seen as a network variant of traditional data clustering. To efficiently detect these structures is very useful for a number of applications, ranging from targeted vaccinations and outbreak prevention \cite{bionet}, to viral marketing \cite{viralm} and to many web data analysis tasks such as finding tribes in online information exchanges \cite{gurumine, dashun}, data compressing, clustering \cite{boldi} and sampling \cite{sampling}.

\begin{figure*}
\centering
\subfloat[A global view of the Facebook graph from 15k users.]{\includegraphics[scale=0.12]{global.pdf}}\qquad\qquad\qquad\qquad
\subfloat[The ``ego minus ego'' network of one Facebook user among the 15k.]{\includegraphics[scale=0.24]{ego.pdf}}
\caption{The real world example of the ``local vs global'' structure intuition.}
\label{fig:hairball-vs-ego}
\end{figure*}

The classical problem definition of community discovery finds a very intuitive counterpart for small networks, where the denser areas are easily identifiable by visual inspection, while the problem becomes much harder for medium and large scale networks. At the global level, very little can be said about the modular structure of the network, because on larger scales the organization of the system becomes simply too complex. The friendship graph of Facebook includes more than 845 millions nodes as of February 2012\footnote{http://newsroom.fb.com/content/default.aspx?NewsAreaId=22}, but the difficulty of the CD task can be appreciated even considering a tiny fragment of the Facebook friendship graph, illustrated in Figure \ref{fig:hairball-vs-ego}(a). We depicted the connections among 15,000 nodes, i.e., less than 0.002\% of the total network. Even in this small subset of the network, no evident organization can be identified easily. Big networks are not analyzable with the naked eye. Very often, a visualization of ten thousands nodes results in a \textit{structureless} hairball. In cases like this, also generic community discovery algorithms tend to return not meaningful communities, as they typically try to cluster the whole structure and return some huge communities and a long list of small branches (see \cite{myreview}). Often, superimposing an order with a top-down approach leads to failure.

On the contrary, human eyes are good in finding denser areas in simple networks, i.e., the structure of cohesive groups of nodes that emerge considering a {\em local} fragment of an otherwise big network. But what does {\em local} mean? Commonsense goes that people are good at identifying the reasons why they know the people they know; therefore, each node has presumably an incomplete, yet clear, vision of the social communities it is part of, and that surrounds it. The consequences of exploiting this idea for the CD problem is effectively illustrated by Figure \ref{fig:hairball-vs-ego}(b). Here, we chose one of the 15k nodes from the previous example and extracted what we call its ``ego minus ego'' network, i.e. its ego network in which the ego node has been removed, together with all its attached edges. Suddenly, everything around the ego makes sense and some groups can be easily spotted. These groups correspond to the high school and university friends, mates from different workplaces and the members of an online community (we know all these details because the chosen ego is one of the authors of this paper). The ego is part of all these communities and knows that particular subsets of its neighborhood are part of these communities too. Probably, different egos have different perspectives over the same neighbors and it is the union of all these perspectives that creates an optimal partition of the network. In other words: if node $A$ and node $B$ are considered in the same communities by all the nodes connected to both $A$ and $B$, then they should be grouped in the same community. This is achieved by a {\em democratic} bottom-up mining approach: in turn, each node gives the perspective of the communities surrounding it and then all the different perspectives are merged together in an overlapping structure.

In the vast CD literature, the general approach for the detection of the modular structure of a network is usually to develop a particular (greedy) algorithm, testing a general quality function with a particular heuristic and then return a set of communities extracted from the global structure (we discuss some of these methods in Section \ref{sec:related}). This approach generally fails for large networks due to the difference in structural organization at global and local scale. To cope with this difficulty, we propose a change of mentality. Since our community definition works perfectly in the small scale, then it should be applied only at this small scale. We propose a simple local-first approach to community discovery in complex networks by letting the hidden modular organization of a network emerge from local patterns.

Essentially, we adopt a {\em democratic} approach to the discovery of communities in complex networks. We ask each node to vote for the communities present in its local view of the network. For this reason, we chose to name our algorithm \textbf{D}emocratic \textbf{E}stimate of the \textbf{M}odular \textbf{O}rganization of a \textbf{N}etwork, or \mbox{\it DEMON}\  in short. In practice, we extract the ego network of each node and apply a Label Propagation CD algorithm \cite{lp} on this structure, ignoring the presence of the ego itself, that will be judged by its peers neighbors. We then combine, with equity, the vote of everyone in the network. The result of this combination is a set of (overlapping) modules, the guess of the real communities in the global system, made not by an external observer, but by the actors of the network itself. Our democratic algorithm is {\em incremental}, allowing to recompute the communities only for the newly incoming nodes and edges in an evolving network. Nevertheless, \mbox{\it DEMON}\  has also a low theoretical linear time complexity. The main core of our method has also the interesting property of being easily {\em parallelizable}, since only the ego network information is needed to perform independent computations, and it can be easily combined in a MapReduce framework \cite{mapreduce}; although the post-process Merge procedure is not trivially solvable in a MapReduce framework (and for this reason we leave a discussion about the parallel implementation as future work). The properties of \mbox{\it DEMON}\  support its use in massive real world scenarios.

We provide an extensive empirical validation of \mbox{\it DEMON}. In our experimental setting, we are particularly interested in investigating what useful knowledge we can discover. We test the results obtained with our method against selected state-of-the-art algorithms, both overlapping and not overlapping, since we believe that the possibility to cluster the same nodes in different communities is one of the crucial properties that a community discoverer should allow: online social networks have proved that individuals are part of many different communities and groups of interest. To evaluate this knowledge, we make use of a multilabel predictor fed with the extracted communities as input, with the aim of correctly classifying the metadata attached to the nodes in real life. Our datasets include the international store Amazon, the database of collaborations in movie industry IMDb, and the register of the activities of the US Congress GovTrack.us.

The rest of the paper is organized as follows: in Section \ref{sec:related} we present related works in community discovery literature. Section \ref{sec:problem} is dedicated to the problem representation and definition. Section \ref{sec:algo} describe the \mbox{\it DEMON}\  algorithm structure, with algorithmic details and an account of the formal properties of the method. Our experiments are presented in Section \ref{sec:experiments}, and finally Section \ref{sec:conclusion} concludes the paper.

\section{Related Work}\label{sec:related}
The problem of finding communities in complex networks is very popular among network scientists, as witnessed by an impressive number of valid works in this field. A huge survey by Fortunato \cite{fortunato} explores all the most popular techniques to find communities in complex networks. Traditionally, a community is defined as a dense subgraph, in which the number of edges among the members of the community is significantly higher than the outgoing edges. However, this definition does not cover many real world scenarios, and in the years many different solutions started to explore alternative definitions of communities in complex networks \cite{myreview}.

A variety of CD methods are based on the {\em modularity} concept, a quality function of a partition proposed by Newman \cite{clauset-modularity, modularity}. Modularity scores high values for partitions in which the internal cluster density is higher than the external density. Hundreds of papers have been written about modularity, either using it as a quality function to be optimized, or studying its properties and deficiencies. One of the most advanced examples of modularity maximization CD is \cite{onnela}, where the authors use an extension of the modularity formula to cluster multiplex (evolving and/or multirelational) networks. A fast and efficient greedy algorithm, Modularity Unfolding, has been successfully applied to the analysis of huge web graphs of millions of nodes and billions of edges, representing the structure in a subset of the WWW \cite{mod-unfolding}.

Many algorithms have been proposed that are unrelated to modularity. Among them, a particular important field is the application of information theory techniques, as for example in Infomap \cite{infomap} or Cross Associations \cite{cct}. In particular, Infomap has been proven to be one among the best performing non overlapping algorithms \cite{lanci-comparative}. For this reason we chose Infomap as alternative to modularity approaches as a baseline method. Further, modularity approaches are affected by known issues, namely the resolution problem and the degeneracy of good solutions \cite{resolution-limit}. Similarly to Infomap, Walktrap \cite{walktrap} is based on flow methods and random walks.

A very important property for community discovery is the ability to return overlapping partitions, i.e., the possibility of a node to be part of more than one community. This property reflects the common sense intuition that each of us is part of many different communities, including family, work, and probably many hobby-related communities. Specific algorithms developed over this property are Hierarchical Link Clustering \cite{hlc}, HCDF \cite{hybrid} and k-clique percolation \cite{palla}.

Finally, an important approach is known as Label Propagation \cite{lp}: in this work authors detect communities by spreading labels through the edges of the graph and then labeling nodes according to the majority of the labels attached to their neighbors, iterating until a general consensus is reached. With a reasonable good quality on the partition, this algorithm is extremely fast and known to be one of the very few quasi-linear solutions to the community discovery problem, even if its plain application leads to worse results than Infomap and it does not return an overlapping partition. A related work is also \cite{bagrow}, whose aim is also to discover local communities. However, authors are only interested in those local communities and they do not return any global structure modular organization.

To to extract useful knowledge from the modular structure of networked data is also a prolific track of research. We recall the GuruMine framework, whose aim is to identify leaders in information spread and to detect groups of users that are usually influenced by the same leaders \cite{gurumine}. Many other works investigate the possibility of applying network analysis for studying, for instance, the dynamics of viral marketing \cite{viralm}.

\section{Networks and Communities}\label{sec:problem}
We model networks and their properties in terms of simple graphs.
For the sake of simplicity, a network is represented as an undirected, unlabeled and unweighted simple graph, denoted by $\mathcal{G}=(V,E)$ where $V$ is a set of nodes and $E$ is a set of edges, i.e., pairs $(u,v)$ representing the fact that there is a link in the network connecting nodes $u$ and $v$. It should be noted, however, that our method can handle weighted, directed and labeled multi-graphs.

In general terms, our problem definition is to find communities in complex networks. However, this is an ambiguous goal, as the definition itself of ``community'' in a complex network, similarly to the notion of clustering in statistics and data mining, is not unique \cite{myreview}. Furthermore, in a complex and semantically rich setting as the modern Web, one may want to cluster many different kinds of objects for many different reasons. Therefore, we need to narrow down our problem definition as follows.

We define two basic graph operations. The first one is the Ego Network extraction $EN$. Given a graph $\mathcal{G}$ and a node $v \in V$, $EN(v, \mathcal{G})$ is the subgraph $\mathcal{G}'(V', E')$, where $V'$ is the set containing $v$ and all its neighbors in $E$, and $E'$ is the subset of $E$ containing all the edges $(u, v)$ where $u \in V' \wedge v \in V'$. The second operation is the Graph-Vertex Difference $-g$: $-g(v, \mathcal{G})$ will result in a copy of $\mathcal{G}$ without the vertex $v$ and all edges attached to $v$. The combination of these two functions yields the $EgoMinusEgo$ function: $EgoMinusEgo(v, \mathcal{G}) = -g(v, EN(v, \mathcal{G}))$. Given a graph $\mathcal{G}$ and a node $v \in V$, the set of {\em local communities} $\mathcal{C}(v)$ of node $v$ is a set of (possibly overlapping) sets of nodes in $EgoMinusEgo(v, \mathcal{G})$, where each set $C \in \mathcal{C}(v)$ is a community according to node similarity: each node in $C$ is more similar to any other node in $C$ than to any other node in $C' \in \mathcal{C}(v)$, with $C \neq C'$. Finally, we define the set of {\em global communities}, or simply communities, of a graph $\mathcal{G}$ as:

\begin{equation}\label{eq:1}
\mathcal{C} =  Max ( \bigcup_{v \in V}\mathcal{C}(v) )
\end{equation}

where, given a set of sets $\mathcal{S}$, $Max(\mathcal{S})$ denotes the subset of $\mathcal{S}$ formed by its maximal sets only; namely, every set $S \in \mathcal{S}$ such that there is no other set $S' \in \mathcal{S}$ with $S \subset S'$. In other words, by equation (\ref{eq:1}) we generalize from local to global communities by selecting the maximal local communities that cover the entire collection of local communities, each found in the $EgoMinusEgo$ network of each individual node.

\section{The Algorithm}\label{sec:algo}
In this section we present our solution to the community discovery problem. The pseudo code of \mbox{\it DEMON}\ is specified in Algorithm \ref{alg:demon}.

\subsection{The Core of the Algorithm}\label{sec:algo-core}

\begin{algorithm}[t]
\small
\begin{algorithmic}[1]
\REQUIRE{$\mathcal{G}:(V,E); \ \mathcal{C} = \emptyset; \epsilon  \in \left[0..1\right]$}
\ENSURE{set of overlapping communities $\mathcal{C}$}
\smallskip
\FORALL{$v\ \in\ V$}
\STATE $e\leftarrow EgoMinusEgo(v, \mathcal{G})$
\STATE $\mathcal{C}(v) \leftarrow LabelPropagation(e)$
\FORALL{$C\ \in\ \mathcal{C}(v)$}
\STATE $C  \leftarrow C \cup v$
\STATE $\mathcal{C} \leftarrow Merge(\mathcal{C},C,\epsilon)$
\ENDFOR
\ENDFOR

\STATE {\bf return} $\mathcal{C}$
\end{algorithmic}
\caption{The pseudo-code of \mbox{\it DEMON}\  algorithm.}
\label{alg:demon}
\end{algorithm}

The set of discovered communities $\mathcal{C}$ is initially empty. The external (explicit) loop of \mbox{\it DEMON}\  cycles over each individual node, and it is necessary to generate all the possible points of view of the structure and get a complete coverage of the network itself. For each node $v$, we apply the $EgoMinusEgo(v, \mathcal{G})$ operation defined in Section \ref{sec:problem}, obtaining a graph $e$. We cannot apply simply the ego network extraction $EN(v, \mathcal{G})$ because the ego node $v$ is directly linked to all nodes $\in EN(v, \mathcal{G})$. This would lead to noise in the subsequent steps of \mbox{\it DEMON} , since by our definition of local community the nodes would be put in the same community if they are close to each other. Obviously a single node connecting the entire sub-graph will make all nodes very close, even if they are not in the same community. For this reason, we remove the ego from its own ego network.

Once we have the $e$ graph, the next step is to compute the communities contained in $e$. We chose to perform this step by using a community discovery algorithm borrowed from the literature. Our choice fell on the Label Propagation (LP) algorithm \cite{lp}. This choice has been made for the following reasons:

\begin{enumerate}
\item LP  shares with this work the definition of what is a community.
\item LP  is known as the least complex algorithm in the literature, reaching a quasi-linear time complexity in terms of nodes. However,
\item LP  will return results of a quality comparable to more complex algorithms \cite{myreview}.
\end{enumerate}

Reason \#2 is particularly important, since Step \#3 of our pseudo code needs to be performed once for every node of the network. It is unacceptable to spend a superlinear time for each node at this stage, if we want to scale up to millions of nodes and hundreds of millions edges. Given the linear complexity of Step \#3, we refer to this as the internal (implicit) loop for finding the local communities.

We briefly describe in more detail the LP algorithm, given its importance in the \mbox{\it DEMON}\  algorithm, following the original article \cite{lp}. Suppose that a node $v$ has neighbors $v_{1}, v_{2}, ... , v_{k}$ and that each neighbor carries a label denoting the community that it belongs to. Then $v$ determines its community based on the labels of its neighbors. A three-step example of this principle is shown in Figure \ref{fig:labelprop}. The authors assume that each node in the network chooses to join the community to which the maximum number of its neighbors belong. As the labels propagate, densely connected groups of nodes quickly reach a consensus on a unique label. At the end of the propagation process nodes with the same labels are grouped together as one community. Clearly, a node with an equal maximum number of neighbors in two or more communities can belong to both communities, thus identifying possible overlapping communities. The original algorithm does not handle this situation. For clarity, we report here the procedure of the LP algorithm, that is the expansion of Step \#3 of Algorithm \ref{alg:demon} and represents our inner loop:

\begin{figure}
\centering
\includegraphics[scale=0.185]{labelprop.pdf}
\caption{A simple simulation of the Label Propagation process for community discovery.}\label{fig:labelprop}
\end{figure}

\begin{enumerate}
\item Initialize the labels at all nodes in the network. For any given node $v$, $C_v(0) = v$.
\item Set $t = 1$.
\item Arrange the nodes in the network in a random order and set it to $V$.
\item For each $v_i \in V$, in the specific order, let $C_{v_i}(t) = f(C_{v_{i1}}(t-1),  \ldots , C_{v_{ik}} (t-1))$. $f$ here returns the label occurring with the highest frequency among neighbors and ties are broken uniformly randomly.
\item If every node has a label that the maximum number of their neighbors have, or $t$ hits a maximum number of iterations $t_{max}$ then stop the algorithm. Else, set $t = t + 1$ and go to (3).
\end{enumerate}

\subsection{The Merge Function}\label{sec:algo-merge}
The result of Step \#3 of Algorithm \ref{alg:demon} is a set of local communities, according to the perspective of node $v$: at the end of the LP algorithm we reintroduce, in each local community, the node $v$.
These communities are likely to be incomplete and should be used to enrich what \mbox{\it DEMON}\  already discovered so far. Thus, the next step is to merge each local community of $\mathcal{C}$ in order to obtain the result set. The $Merge$ operation is defined as follows.

\begin{algorithm}[t]
\small
\begin{algorithmic}[1]
\REQUIRE{$\mathcal{C} = Community\ set; C = Community; \epsilon \in \left[0..1\right]$}
\ENSURE{set of overlapping communities $\mathcal{C}$}
\smallskip
\FORALL{$ I\ \in\ \mathcal{C}$}
\IF{$C.size \le I.size and C\subseteq_{\epsilon}I$}
\STATE $u = C\cup I; $
\STATE $\mathcal{C}-C; \mathcal{C}-I;$
\STATE $\mathcal{C} = \mathcal{C} \cup u;$
\ENDIF

\ENDFOR
\STATE {\bf return} $\mathcal{C}$
\end{algorithmic}
\caption{The pseudo-code of Merge  function.}
\label{alg:merge}
\end{algorithm}

Two communities $C$ and $I$ are merged if and only if  at most the $\epsilon\%$ of the smaller one is not included in the bigger one;
in this case, $C$ and $I$ are removed from $\mathcal{C}$  and their union is added to the result set.
The $\epsilon$ factor is introduced to vary the percentage of  common elements provided from each couple of communities: $\epsilon=0$ ensure that two communities
are merged only if one of them is a proper subset of the other, on the other hand with a value of  $\epsilon=1$ even communities that do not share a single node
are merged together.

\subsection{DEMON Properties}\label{sec:algo-properties}
To prove the correctness of the \mbox{\it DEMON}\  algorithm w.r.t.\ the problem definition in Section \ref{sec:problem}, we prove by induction that the following holds:

\begin{property}\label{eq:2} At the $k$-th iteration of the outer loop of \mbox{\it DEMON}, for all $k \geq 0$:
\begin{equation}
\mathcal{C} = Max ( \bigcup_{v = v_1, \ldots, v_k}\mathcal{C}(v) )
\end{equation}
where $v_1, \ldots, v_k$ are the nodes visited after $k$ iterations.
\end{property}
Property (\ref{eq:2}) trivially holds for $k=0$, i.e., at initialization stage. For $k > 0$, assume that the property holds up to $k-1$. Then $\mathcal{C}$ contains the maximal local communities of the subgraph with nodes ${v_1, \ldots, v_{k-1}}$. By merging every local community $C$ of node $v_k$ into $\mathcal{C}$, we guarantee that $C$ is added to the result only if it is not covered by any preexisting community, and, if added, any preexisting community covered by $C$ is removed from $\mathcal{C}$. As a result, after merging all communities in $\mathcal{C}(v_k)$ into $\mathcal{C}$ in Steps \#4-6, the latter is the set of maximal communities covering all local communities discovered in ${v_1, \ldots, v_{k}}$. Therefore, we can conclude that \mbox{\it DEMON}\  is a correct and complete implementation of the CD problem stated by equation (\ref{eq:1}). More generally, denoting by $\mbox{\it DEMON}(\mathcal{G}, \mathcal{C})$ the set of communities $\mathcal{C}'$ obtained by running the \mbox{\it DEMON}\ algorithm on graph $\mathcal{G}$ starting with the (possibly non-empty) set of communities $\mathcal{C}$, the following properties hold.

\begin{property}\label{eq:correcteness} {\bf Correctness} and {\bf Completeness}.\\
If $\mbox{\it DEMON}(\mathcal{G}, \mathcal{C}) = \mathcal{C}'$, where $\mathcal{G} = (V, E)$, then
\begin{equation}
\mathcal{C}' = Max ( \mathcal{C} \cup \bigcup_{v \in V}\mathcal{C}(v) )
\end{equation}
\end{property}
In other words, given a preexisting set of communities $\mathcal{C}$ and a graph $\mathcal{G}$, \mbox{\it DEMON}\ returns all and only the communities obtained extending $\mathcal{C}$ with the communities found in $\mathcal{G}$, coherently with the definition of communities given in equation (\ref{eq:1}).

\begin{property}
{\bf Determinacy} and {\bf Order insensitivity}.\\
There exists a {\em unique}\ $\mathcal{C}' = \mbox{\it DEMON}(\mathcal{G}, \mathcal{C})$ for any given $\mathcal{G}$ and $\mathcal{C}$, disregarding the order of visit of the nodes in $\mathcal{G}$.
\end{property}
This is a direct corollary of property (\ref{eq:correcteness}) and of the uniqueness of the set $Max(\mathcal{S})$ for any set of sets $\mathcal{S}$, under the assumption that the set of local communities $\mathcal{C}(v)$ is also uniquely assigned, for any node $v$. Therefore, the order in which the nodes in $\mathcal{G}$ are visited by \mbox{\it DEMON}\ is irrelevant.

\begin{property}
{\bf Compositionality}. Consider any partition of a graph $\mathcal{G}$ into two subgraphs $\mathcal{G}_1$, $\mathcal{G}_2$ such that, for any node $v$ of $\mathcal{G}$, the entire ego network of $v$ in $\mathcal{G}$ is fully contained either in $\mathcal{G}_1$ or $\mathcal{G}_2$. Then, given an initial set of communities $\mathcal{C}$:
\begin{equation}\label{eq:compositionality}
\mbox{\it DEMON}(\mathcal{G}_1 \cup \mathcal{G}_2, \mathcal{C}) = Max (\mbox{\it DEMON}(\mathcal{G}_1, \mathcal{C}) \cup \mbox{\it DEMON}(\mathcal{G}_2, \mathcal{C}))
\end{equation}
\end{property}
This is a consequence of two facts: $i)$  each local community $\mathcal{C}(v)$ is correctly computed under the assumption that the subgraphs do not split any ego network, and $ii)$ for any two sets of sets $\mathcal{S}_1, \mathcal{S}_2$, $Max(\mathcal{S}_1 \cup \mathcal{S}_2) = Max(Max(\mathcal{S}_1) \cup Max(\mathcal{S}_2))$.

\begin{property}
{\bf Incrementality}. Given a graph $\mathcal{G}$, an initial set of communities $\mathcal{C}$ and an incremental update $\Delta \mathcal{G}$ consisting of new nodes and new edges added to $\mathcal{G}$, where $\Delta \mathcal{G}$ contains the entire ego networks of all new nodes and of all the preexisting nodes reached by new links, then
\begin{equation}\label{eq:incrementality}
\mbox{\it DEMON}(\mathcal{G} \cup \Delta \mathcal{G}, \mathcal{C}) = \mbox{\it DEMON}(\Delta \mathcal{G}, \mbox{\it DEMON}(\mathcal{G}, \mathcal{C}))
\end{equation}
\end{property}
This is a consequence of the fact that only the local communities of nodes in $\mathcal{G}$ affected by new links need to be reexamined, so we can run \mbox{\it DEMON}\ on $\Delta \mathcal{G}$ only, avoiding to run it from scratch on $\mathcal{G} \cup \Delta \mathcal{G}$.

Properties (\ref{eq:compositionality}) and (\ref{eq:incrementality}) have important computational repercussions. The compositionality property entails that the core of \mbox{\it DEMON}\ algorithm as described in subsection \ref{sec:algo-core} is highly parallelizable, because it can run independently on different fragments of the overall network with a relatively small combination work. Each node of the computer cluster needs to obtain a small fragment of the network, as small as the ego network of one or a few nodes. The Map function is simply the LP algorithm. The incrementality property entails that \mbox{\it DEMON}\ can efficiently run in a streamed fashion, considering incremental updates of the graph as they arrive in subsequent batches; essentially, incrementality means that it is not necessary to run \mbox{\it DEMON}\ from scratch as batches of new nodes and new links arrive: the new communities can be found by considering only the ego networks of the nodes affected by the updates (both new nodes and old nodes reached by new links). This does not trivially hold for the Merge function presented in subsection \ref{sec:algo-merge}, therefore the actual parallel implementation of \mbox{\it DEMON}\ is left as future work. However, different and simpler Merge functions can be define to combine the results provided by the core of the algorithm, thus preserving its possibility to scale up in a parallel framework.

\subsection{Complexity}
We now evaluate the time complexity of our approach. \mbox{\it DEMON}\ core (Section \ref{sec:algo-core}) is based on the Label Propagation algorithm, whose complexity is $\mathcal{O}(n + m)$ \cite{lp}, where $n$ is the number of nodes and $m$ is the number of edges. LP is performed once for each node. Let us assume that we are working with a scale free network, whose degree distribution is $p_k = k^{-\alpha}$. This means that there are $\frac{n}{k^\alpha}$ nodes with degree $k$. If $K$ is the maximum degree, the complexity would be $\sum_{k = 1}^{K}(\frac{n}{k^\alpha} \times (k + \frac{k(k -1)}{2}))$ because for each node of degree $k$ we have an ego network of $k$ nodes and at worst $\frac{k(k -1)}{2}$ edges. This number is very small for the vast majority of nodes, being the degree distribution right skewed, thus many nodes have degree 1 or 2. We omit the solution of the sum with the integral and we report that the complexity is then dominated by a single term, ending up to be $\mathcal{O}(nK^{3-\alpha})$. This means that the stronger is the $\alpha$ exponent, the faster is \mbox{\it DEMON} : with $\alpha = 3$ we have few super-hubs for which we basically check the entire network few times and the rest of nodes add nothing to the complexity; with $\alpha = 2$ we have many high degree nodes and we end up with higher complexity, but still subquadratic in term of nodes (as, with $\alpha = 2$, $K << n$).

\section{Experiments}\label{sec:experiments}
We now present our experimental findings. We make use of three networked datasets, representing very different phenomena. We first concentrate on evaluating the quality of a set of communities discovered in these datasets, comparing the results with those of other competing methods in terms of the predictive power of the discovered communities. Since real world data are enriched with annotated information, we measure the ability of each community to predict the semantic information attached with the metadata of the nodes within the community itself.

Next, we assess the community quality using a global measure of community cohesion, based on the intuition that nodes into the same community should possess similar semantic properties in terms of attached metadata.

The selected competitors for our assessment are: Hierarchical Link Clustering (HLC) \cite{hlc}, that has been proven able to outperform all the overlapping algorithms, including the $k$-clique Propagation algorithm by Palla et al \cite{palla}; two random walks based methods, one focusing on minimizing random walk entropy (Infomap \cite{infomap}) and the other relying on a general flow method (Walktrap \cite{walktrap}); a leading eigenvector-based community discovery, namely Modularity maximization in the fast greedy implementation introduced in \cite{clauset-modularity}. Finally, we present some examples of knowledge that we are able to extract from the communities found by the \mbox{\it DEMON}\  algorithm.

Note that we are not able to provide the analytic evaluation for Amazon dataset: for that network HLC algorithm was not able to provide results due to memory consumption problems, while the other community discovery algorithms usually returned some huge communities that was not possible to analyze (see Section \ref{sec:prediction} and particularly Figure \ref{fig:sizedistr} for more information).

The experiments were performed on a Dual Core Intel i7 64 bits @ 2.8 GHz, equipped with 8 GB of RAM and with a kernel Linux 3.0.0-12-generic (Ubuntu 11.10). The code was developed in Java and it is available for download with the network datasets used\footnote{http://www.di.unipi.it/$\sim$coscia/demon/}. For performances purposes, we mainly refer to the biggest dataset, i.e. Amazon: the core of the algorithm (Section \ref{sec:algo-core}) took less than a minute, while the Merge function (Section \ref{sec:algo-merge}) with increasing thresholds can take from one minute to one hour.

\subsection{Networks}
We tested our algorithms on three real world complex networks extracted from available web services of different domains. A general overview about the statistics of these networks can be found in Table \ref{tab:networks}, where: $|V|$ is the number of nodes, $|E|$ is the number of edges and $\bar{k}$ is the average degree of the network. Congress and IMDb networks are similar to the ones used in \cite{hlc}, generally updating the source dataset with a more recent set of data, and we refer to that paper for a deeper description of them. The networks were generated as follows:

\textbf{Congress}. The network of legislative collaborations between US representatives of the House and the Senate during the 111st US congress (2009-2011). We downloaded the data about all the bills discussed during the last Congress from GovTrack\footnote{http://www.govtrack.us/developers/data.xpd}, a web-based service recording the activities of each member of the US Congress. The bills are usually co-sponsored by many politicians. We connect politicians if they have at least 75 co-sponsorships and delete all the connections that are created only by bills with more than 10 co-sponsors. Attached to each bills in the Govtrack data we have also a collection of subjects related to the bill. The set of subjects a politicians frequently worked on is the \textit{qualitative attribute} of this network.

\textbf{IMDb}. We downloaded the entire database of IMDb from their official APIs\footnote{http://www.imdb.com/interfaces} on August 25th 2011. We focus on actors who star in at least two movies during the years from 2001 to 2010, filtering out television shows, video games, and other performances. We connect actors with at least two movies in which they both appear. This network is weighted according to the number of co-appearances. Our \textit{qualitative attributes} are the user assigned keywords, summarizing the movies each actor has been part of.

\textbf{Amazon}. We downloaded Amazon data from the Stanford Large Network Dataset Collection\footnote{http://snap.stanford.edu/data/index.html}. In this dataset, frequent co-purchases of products are recorded for the day of May 5th 2003. We transformed the directed network in an undirected version. We also downloaded the metadata information about the products, available in the same repository. Using this metadata, we can define the \textit{qualitative attributes} for each product as its categories.

\begin{table}
\centering
\begin{tabular}{|l|rrr|}
\hline
Network & $|V|$ & $|E|$ & $\bar{k}$ \\
\hline
Congress & 526 & 14,198 & 53.98 \\
IMDb & 56,542 & 185,347 & 6.55 \\
Amazon & 410,236 & 2,439,437 & 11.89 \\
\hline
\end{tabular}
\caption{Basic statistics of the studied networks.}
\label{tab:networks}
\end{table}

\subsection{Quality Evaluation via Label Prediction}\label{sec:prediction}
We first assess \mbox{\it DEMON}\ performances using a classical prediction task. We attach the community memberships of a node as known attributes, then its qualitative attributes (real world labels) as target to be predicted; we then feed these attributes to a state-of-the-art label predictor and record its performance. Of course, a node may have one or more known attributes, as both \mbox{\it DEMON}\ and HLC are overlapping community discoverers; and it may have also one or more unknown attributes, as it can carry many different labels.

For this reason, we need a multilabel classificator, i.e. a learner able to predict multiple target attributes \cite{brl}. We chose to use the Binary Relevance Learner. The BRL learns $|L|$ binary classifiers $H_l:X \rightarrow \{l, \neg l\}$, one for each different label $l \in L$. It transforms the original data set into $|L|$ data sets $D_l$ that contain all examples of the original data set, labeled as $l$ if the labels of the original example contained $l$ and as $\neg l$ otherwise. It is the same solution used in order to deal with a single-label multi-class problem using a binary classifier. Note that this classifier does not penalize per se non-overlapping partitions, as each target label is classified independently, and this property is requested to fairly confront overlapping algorithms such as \mbox{\it DEMON}\ and HLC, with the other non-overlapping algorithms. We used the Python implementation provided in the Orange software\footnote{http://orange.biolab.si/}. For time and memory constraints due to the BRL complexity, for IMDb we used as input only the biggest communities (with more than 15 nodes) and eliminating all nodes that are not part of any of the selected communities.

Multi-label classification requires different metrics than those used in traditional single-label classification. Among the measures that have been proposed in the literature, we use the multi-label version of the standard Precision and Recall measures. Let $D_l$ be our multi-label evaluation data set, consisting of $|D_l|$ multi-label examples $(x_i,Y_i), i=1..|D_l|, Y_i \subseteq L$. Let $H$ be our BRL multi-label classifier and $Z_i=H(x_i)$ be the set of labels predicted by $H$ for $x_i$. Then, we can evaluate Precision and Recall of $H$ as:

$$Precision(H,D_l)=\frac{1}{|D_l|} \sum_{i=1}^{|D_l|} \frac{|Y_i \cap Z_i|}{|Z_i|},$$

$$Recall(H,D_l)=\frac{1}{|D_l|} \sum_{i=1}^{|D_l|} \frac{|Y_i \cap Z_i|}{|Y_i|}.$$

\begin{table}
\centering
\tiny
\begin{tabular}{|l|rrrrr|}
\hline
Network & \mbox{\it DEMON} & HLC & Infomap & Modularity & Walktrap \\
\hline
Congress & \textbf{0.21275} & 0.14740 & 0.00535 & 0.00099 & 0.00725 \\
IMDb & \textbf{0.44252} & 0.43078 & 0.38470 & 0.10692 & 0.17488 \\
\hline
\end{tabular}
\caption{The F-Measure scores for Congress and IMDb dataset and each community partition.} \label{tab:fmeasure}
\end{table}

\begin{table*}
\centering
\begin{tabular}{|l|rr|rr|rr|rr|rr|}
\hline
\multirow{2}{*}{Network} & \multicolumn{2}{c|}{Demon} & \multicolumn{2}{c|}{HLC} &  \multicolumn{2}{c|}{Infomap} &  \multicolumn{2}{c|}{Modularity} &  \multicolumn{2}{c|}{Walktrap}\\
& $|\mathcal{C}|$ & $\bar{|c|}$ &  $|\mathcal{C}|$ & $\bar{|c|}$ & $|\mathcal{C}|$ & $\bar{|c|}$ & $|\mathcal{C}|$ & $\bar{|c|}$ & $|\mathcal{C}|$ & $\bar{|c|}$\\
\hline
Congress & 425 & 63.3671 & 1,476 & 4.5867 & 6 & 87.6667 & 3 & 175.3333 & 7 & 71.8571 \\
IMDb & 14,004 & 12.6824 & 88,119 & 8.3426 & 5,991 & 27.1574 & 4,746 & 11.9157 & 7,877 & 7.1781 \\
\hline
\end{tabular}
\caption{Statistics of the community set returned by the different algorithms.}
\label{tab:commset-stats}
\end{table*}

We then derive the F-measure from Precision and Recall. For alternatives multi-label evaluations, we refer to \cite{mleval-pakdd}. The results are reported in Table \ref{tab:fmeasure} and show how \mbox{\it DEMON}\   outperforms its competitors. We did not test Amazon network as HLC was not able to provide results due to its complexity and further the BRL classifier was not able to scale for the overall number of nodes and labels.

For IMDb dataset, HLC was able to score almost like \mbox{\it DEMON} . However, there is an important distinction to be made about the quantity of the results: if the community discovery returns too many communities, then it is difficult to actually extract useful knowledge from them. We reported in Table \ref{tab:commset-stats} the basic statistics about the partitions returned by the algorithms: number of communities ($|\mathcal{C}|$) and average community size ($\bar{|c|}$). For \mbox{\it DEMON} , we report the statistics of the communities extracted with $\epsilon = 0$. As we can see, not only  \mbox{\it DEMON}\ scores better results, but it does with 70-80\% less communities than HLC and with an average community size more manageable than Infomap.

\begin{figure}
\centering
\subfloat[Congress]{\includegraphics[width=.5\columnwidth]{congress_stats.pdf}}
\subfloat[IMDb]{\includegraphics[width=.5\columnwidth]{imdb_stats.pdf}}
\caption{Precision, Recall, F-Measure and number of communities for different $\epsilon$ values.}\label{fig:commset-stats}
\end{figure}

We report in Table \ref{tab:commset-stats} the results for $\epsilon = 0$. However, we vary the $\epsilon$ threshold and see what happens to the number of communities and to the quality of the results. We can see that for both Congress and IMDb the Precision, Recall and F-Measure scores remains constant (and actually F-Measure peaks at $\epsilon = 0.076$ and $\epsilon = 0.301$ for Congress and IMDb respectively) before falling for increasing $\epsilon$ values, while the relative number of communities dramatically drops. For Congress, we have the maximum F-Measure with only 175 communities; while for IMDb F-Measures peaks with 6,508 communities (in both cases, less than 50\% of the communities at $\epsilon = 0$ and than an order of magnitude of HLC).

\begin{figure}
\centering
\includegraphics{sizedistr.pdf}
\caption{The distribution of the community sizes for DEMON and Infomap in the Amazon network.}\label{fig:sizedistr}
\end{figure}

A final consideration is needed about the size distribution of the communities detected by \mbox{\it DEMON}\ and the other community discovery algorithms. In Figure \ref{fig:sizedistr} we depicted the community size distribution for \mbox{\it DEMON}\ and Infomap for the Amazon network. As we can see, Infomap returned, among the others, a giant community, one order of magnitude greater than the biggest one returned by \mbox{\it DEMON} . To analyze such a community has been proved impossible, and this is another reason why we are not able to provide an analitycal evaluation of the results extracted from the Amazon network.

We can conclude that \mbox{\it DEMON}\ with a manageable number of communities is able to outperform more complex methods and the choice of $\epsilon$ can make the difference in obtaining a narrower set of communities with the same (or greater) overall quality.

\subsection{Quality Evaluation via Community Cohesion}
As presented at the beginning of this section, the networks studied here possess \textit{qualitative attributes} that attaches a small set of annotations or tags to each node. Assuming that these qualitative attributes form a description of the node, beyond the network itself, we can reasonably state that ``similar'' nodes share more \textit{qualitative attributes} than dissimilar nodes. This procedure is not standard in community discovery results evaluation. Usually authors prefer to use the established measure of Modularity. However, Modularity is strictly (and exclusively) dependent on the graph structure. What we want evaluate is not how a graph measure is maximized, but how good is our community partition in describing real world knowledge about the clustered entities.

We quantify the matching between a community partition and the metadata by evaluating how much higher are on average the Jaccard coefficient of the set of \textit{qualitative attributes} for pair of nodes inside the communities over the average of the entire network, or:

$$
CQ(P) = \dfrac{\sum_{(n_1, n_2) \in P}\frac{|QA(n_1) \cap QA(n_2)|}{|QA(n_1) \cup QA(n_2)|}}{\sum_{(n_1, n_2) \in E}\frac{|QA(n_1) \cap QA(n_2)|}{|QA(n_1) \cup QA(n_2)|}},
$$

where $P$ is the set of node pairs that share at least one community, $QA(n)$ is the set of qualitative attributes of node $n$ and $E$ is the set of all edges. If $CQ(P) = 1$, then there is no difference between $P$ and the average similarity of nodes, i.e. $P$ is practically random. Lower values implies that we are grouping together dissimilar nodes, higher values are expected for an algorithm able to group together similar nodes.

To calculate the Jaccard coefficient for each pair of the network is computationally prohibitive. Therefore, for IMDb we chose a random set of 400k pairs. Moreover, CQ is biased towards algorithms returning more communities. For this reason, we just collected random communities from the community pool, trying to avoid too much overlap as we want also to maximize the number of nodes considered by CQ (i.e. we try not to consider more than one community per node). We apply this procedure for each algorithm and calculated the CQ value. We repeated this process for 100 iterations and we report in Table \ref{tab:cq} the average value of the CQ obtained. Also in this case, \mbox{\it DEMON}\ was able to easily outperform all the other algorithms.

\begin{table}
\centering
\tiny
\begin{tabular}{|l|rrrrr|}
\hline
Network & \mbox{\it DEMON} & HLC & Infomap & Modularity & Walktrap \\
\hline
Congress & \textbf{1.1792} & 1.1539 & 1.0229 & 1.0373 & 1.0532 \\
IMDb & \textbf{5.6158} & 5.1589 & 0.1400 & 1.4652 & 0.0211 \\
\hline
\end{tabular}
\caption{The Community Quality scores for Congress and IMDb dataset and each community partition.} \label{tab:cq}
\end{table}

\subsection{Interpretation of Discovered Communities}
In this Section we present a brief case study using the communities extracted for the previously exposed evaluation of \mbox{\it DEMON} . Aim of the section is to demonstrate that the extracted communities have practical applications in the extraction of knowledge from real world scenarios. In the Amazon network to have different communities for each item is very useful. A recommendation system is able to better discern if a user may be interested in a product or not given that he bought something else; however being part of one community of products does not mean that that particular community describes all aspects of a particular product.

Let us consider, as an example, the case of Jared Diamond's best selling book ``Guns, Germs, and Steel: The Fates of Human Societies''. Clearly, it is difficult to say that the people interested in this book are always interested in the same things. Checking the communities to which it belongs, we find two very different big communities (a depiction of the two communities is provided by Figure \ref{fig:amazon-examples}). These communities have some sort of overlap, however they can be characterized by looking at the products that appear exclusively in one or in the other. In the first one we find books such as: ``Beyond the State: An Introductory Critique'', ``The Econometrics of Corporate Governance Studies'' and ``The Transformation of Governance: Public Administration for Twenty-First Century America''. This is clearly a community composed mainly by purchases made by the people more interested in the socioeconomic aspects of Diamond's book. The second community hosts products such as: ``An Introduction to Metaphysics'', ``Aristophanes' Clouds Translated With Notes and Introduction'' and ``Being and Dialectic: Metaphysics and Culture''. This second communities is apparently composed by the purchases of customers more attracted by the underlying philosophical implications of Diamond's study. Products in one communities may have something in common, but they are part of two distinct and very well characterized groups, and the one in one group are not expected to be found in the other.

\begin{figure}
\centering
\includegraphics[scale=0.33]{ggs.pdf}
\caption{A representation of parts of the two communities surrounding our case study in the amazon network.}\label{fig:amazon-examples}
\end{figure}

This is of course one of the many cases. We report as an additional example the two communities around the historical novel ``The Name of the Rose'' by Umberto Eco: one community is characterized by history related products (such as ``Ancestral Passions : The Leakey Family and the Quest for Humankind's Beginnings''), the other by costume fiction (for example the 1932 Dreyer's movie ``Vampyr'').

\section{Conclusion and Future Works}\label{sec:conclusion}
In this paper we proposed a new method for solving the problem of detecting latent knowledge from significant communities in complex networks. We propose a democratic approach, where the peer nodes judge where their neighbors should be clustered together. This approach has robust theoretical properties, including correctness and completeness w.r.t.\ a precise community definition, determinacy, compositionality and incrementality, that make it amenable to challenge the conceptual and computational challenge to analyze networks with millions of nodes. We have shown in the experimental section that this method allows a discovery of communities in different real world networks collected from information rich datasets. The quality of the overlapping partition, a partition that allows nodes to be in different communities at the same time, is improved w.r.t state-of-the-art algorithms, evaluated using the communities to predict the metadata attached to the nodes, and according to a quantitative quality function, also metadata-based.

Many lines of research remain open for future work, such as an efficient parallel implementation that may make \mbox{\it DEMON}\  the first community discovery algorithm able to scale to billions of nodes; different merging strategies that may further improve the quality of the results; different hosted algorithms can be used instead of the Label Propagation algorithm in the inner loop of \mbox{\it DEMON}, to extract communities according to different definitions.\\

\textbf{Acknowledgments.} Michele Coscia is a recipient of the Google Europe Fellowship in Social Computing, and this research is supported in part by this Google Fellowship. This work has been partially supported by the European Commission under the FET-Open Project n. FP7-ICT-270833, DATA SIM -- DATa science for SIMulating the era of electric vehicles http://www.datasim-fp7.eu/.

\balance

\bibliographystyle{plain}

redpajama_set_name: RedPajamaArXiv


text:
Family Law Group Achieves Significant Victory
Earlier this month, we posted news on Facebook that GDWG family law attorneys David C. Gagne and Achille C. Scache achieved a significant victory in the Delaware Supreme Court when the Supreme Court ruled that the Family Court had abused its discretion in awarding grandparents supervised contact with their grandchildren over the parents' objection. Most of our friends on Facebook were very happy to hear that Mr. Gagne and Mr. Scache were able to win a victory on behalf of the parents in this case. Some, however, wondered in the comments section of our post, what could a couple grandparents do in order to get their visitation revoked from seeing their grand kids? For those interested, the decision can be found here and we should point out the pseudonyms are used in the decision to protect the identities of the parties. Without getting into all of the details, it was found that the grandparents (parents of father) had been at times intrusive, overbearing and downright nasty in their relationship with their son and his wife over many years
From the decision:
Here, in the case before us, grandparents petitioned for third-party visitation rights, and the parents objected. The Family Court essentially found the parents' objections were not unreasonable. The grandparents had attacked the parents on social media, disparaged their parenting abilities, demoralized their son (the father), and sought to meddle in the parents' relationships with their children. Nonetheless, the court awarded the grandparents the right to visitation in a "supervised, therapeutic setting."7 The court believed that visitation in such a controlled environment, and under the supervision of a trained professional, would minimize the parents' concerns and render their objections "clearly unreasonable."8
Understand that the Court in the State of Delaware effectively views a grandparent nearly the same as any other third party petitioning for visitation of a child.
Under Delaware's Third Party Visitation statute, 13 Del. C. § 2412 ("Section 2412"), when a parent objects to a third party's request for visitation, "the parent's determination of the child's best interest will prevail unless the nonparent seeking visitation proves that: (1) visitation is in the child's best interest; (2) the parent's objections are unreasonable by clear and convincing evidence; and (3) visitation will not substantially interfere with the parent-child relationship by a preponderance of the evidence."
In this case Mr. Gagne and Mr. Scache argued that the behavior of the grandparents had and would continue to interfere with the parent-child relationship. Furthermore, it was our contention that the parents' objections were not unreasonable regardless of supervised visitation.
However, the record provides no basis for supporting the conclusion that supervised, therapeutic visitation would adequately address the reasonable objections of the parents. Further, the grandparents here presented no evidence to support a finding that supervised, therapeutic visitation would not substantially interfere with the parent-child relationship […] Having reviewed this record, we cannot conclude that having someone in the room to referee Grandparents' actions and proclivity for engaging in inappropriate behavior would adequately address Parents' reasonable concerns about interference with their parent-child relationships.
It is rare for Family Court decisions to make it to the Delaware Supreme Court. There is no question that there is plenty of hurt feelings to go around in cases such as these. The most important piece is that the best interest of the children is protected. In this case, the Court found that third party visitation would have interfered with the parent-child relationship and thus would not have been in the best interest of the children involved.

redpajama_set_name: RedPajamaCommonCrawl


Each time I attend Stereophile's annual Home Entertainment Show, I look forward to two things. The first is the opportunity to perform classic and original jazz music with John Atkinson and Immedia's Allen Perkins. The second is to seek out the most exciting new, affordable speakers to review over the following year. Both buttons were pressed for me quite admirably during HE2005 at the New York Hilton, but this time it was not an affordable speaker that most impressed me.
I was just about to wrap up my British Invasion Tour of affordable speakers when I got a call from Fearless Leader John Atkinson.
The penultimate stop on Bob Reina's British Invasion Tour of Affordable Loudspeakers (footnote 1) brings us to the doors of KEF. Although KEF is a large and well-established British firm, I've noticed that their product lines have not been as visible in the US as those of, say, B&W, Wharfedale, or Mission. In fact, the last time I heard a KEF speaker, it was the company's then-flagship design, at a Consumer Electronics Show nearly 20 years ago! Before that, when I lived in London, KEFs were ubiquitous, down to the older, entry-level designs tacked to the walls of the ethnic restaurants I frequented. My strongest KEF memory is a cumulative one: Every KEF speaker I've ever heard, regardless of price, venue, or setup, has always produced good, convincing sound.
The last few years at our annual Home Entertainment Show, many readers have come up to me and asked: "How do you select which speakers to review?" In my case, most candidates are either new products that have impressed me when demonstrated at our HE Shows, or new products from manufacturers whose designs have impressed me in the past. Occasionally, editor John Atkinson gets wind of a speaker and asks if I'd like to review it. But once in a while, a manufacturer reads a rave review of a competing product that makes his or her blood boil.
It occurred to me recently that, after nearly a decade of specializing in reviewing affordable speakers, and with the exceptions of two entry-level Mission models, I'd never taken a look at recent designs from the large mainstream British speaker manufacturers. So with this review I embark on a Bob Reina "British Invasion" tour to seek out the most innovative and value-conscious designs from companies that have been household names in British stereo shoppes for decades.
No, folks, vinyl is not dead. And even though my colleague Mikey Fremer is beginning to sound like a broken record, the little guy is right: when it comes to the sound on offer, CD still doesn't come close. There are more turntables, phono cartridges, and tonearms on the market today than ever before. Moreover, with companies like Classic Records, Analogue Productions, and Mosaic offering a steady stream of ultra-high-quality reissues, there seems to be an increasing supply of quality vinyl at reasonable prices.


We are glad to introduce a new functionality for creating business cards.
You can create and edit information not only on the front side but also on the back side of the business card.
You can use the logotype, colour background and any text about your company. Try it!
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The album will be initially released digitally and on streaming services with plans for CD and vinyl to be announced soon.
It is surprising to know that David Duchovny only picked up a guitar a few years ago in a spell of free time, between projects, at first just to amuse himself but as he started playing, felt inspired to start writing songs. He grew up listening to classic late 60's early 70's rock: The Beatles, The Who, Rolling Stones, Yes etc… along with a lot of funk which he aspires to play, joking he'll need to become a better musician to do so.
David Duchovny is best known as an award winning actor, director and novelist starring in the smash TV series The X-Files (winning a Golden Globe for the role.) His first novel Holy Cow was released by Farrar Straus and Giroux in February 2015 and hit the New York Times Best Sellers' list. This was followed up by his second novel, Bucky F*cking Dent, which also hit the New York Times Best Sellers' list, and was released globally in 2016.
In 2015, David completed his first studio album for for ThinkSay Records titled Hell or Highwater touring both Europe and the US in support.
David reprised his award winning role as Fox Mulder January 3rd on The X-Files. His third book, Miss Subways, will be released globally in May of 2018, and David is planning on Directing and Producing his novel, Bucky F*cking Dent into a film summer of 2018. He also plans to hit the road in support of his new album, first in Australia and New Zealand in February with U.S. plans to be announced soon.


Two drug suspects led San Diego police on a high-speed chase during Thursday's after-work commute, at times flinging money from the windows of their pickup, police said.
The chase began south of downtown San Diego and covered surface streets and freeways, finally ending on northbound Interstate 5 near Del Mar when the truck ran over a spike strip.
The two were arrested at gunpoint. Police gathered more than $17,000 in bills spread over several miles as commuters gawked in amazement.
No one was hurt during the 45-minute drama, but traffic was snarled during the chase and arrest. Several commuters stopped their vehicles on the freeway and grabbed some of the bills, officials said.
The pursuit moved from highway to highway across the county. News crews following the pursuit spotted other drivers pulling to the side of Interstate 805 to pick up the money being tossed from the suspect's truck.
"People were jumping the median and stuffing dollar bills in their shirts," according to Cal Walker with KOGO traffic.
Finally, officers stopped the black Ford F-150 truck on northbound Interstate 5 in Del Mar Heights just before 5:30 p.m. Patrol cars shut down traffic heading north on I-5 while officers took the driver into custody and searched the car with a K-9 unit.
Money was picked up by police officers along Interstate 805. Some of the officers held thousands of dollars worth of $100 bills.
The chase started as a narcotics stop according to the CHP. The San Diego Police Department requested that CHP help in the pursuit.


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text:
Chorinaeus opacitas är en stekelart som beskrevs av Davis 1897. Chorinaeus opacitas ingår i släktet Chorinaeus och familjen brokparasitsteklar. Inga underarter finns listade i Catalogue of Life.

Källor

Brokparasitsteklar
opacitas

redpajama_set_name: RedPajamaWikipedia


text:
The Echigo mole (Mogera etigo) is an endangered species of mammal in the family Talpidae. It is endemic to Japan, being found only on the Echigo plain, Niigata prefecture.

References

Endemic mammals of Japan
Mogera
Taxonomy articles created by Polbot
Mammals described in 1991


WHEN moviegoers think of films from the developing world, they generally don't expect the kind of technical polish that's taken for granted in pictures from Western Europe, Japan, or the Hollywood studios. Movies from less wealthy nations may be fascinating and insightful, but even their admirers don't often look for stunning images and eye-dazzling montage. Like most prejudices, this one turns out to be pretty feeble when examined closely, and never more so than today. Several developing-world movies in the current New York Film Festival, including some that were also screened in the Toronto Festival of Festivals last month, are as visually rich as anything from the world's traditional motion-picture capitols. Reasons for this include the increasingly wide availability of first-rate technical training, and the experience that aspiring filmmakers often gain in local TV production. There is also an increase in coproductions with larger and more affluent ones.
Of all the developing-world films I've seen recently, the most flat-out gorgeous is ``Ju Dou,'' a new Chinese movie by Zhang Yimou, whose ``Red Sorghum'' played American theaters a couple of years ago. The film's visual appeal comes largely from its aggressive use of color; the story is set in a Chinese cloth-dying shop, which offers many opportunities for huge color fields to fill the screen, often waving in the air on fabrics or swirling and bubbling in chemical vats. Color is further enhanced by clever cinematography and energetic editing, and complemented by performances of great skill.
As for the story accompanied by all this richness, it's nothing if not surprising. Set in the 1920s and '30s, it centers on a young woman sold by her family to a nasty old man who wants a pretty wife and a male heir. Before long, she and the old man's nephew are lovers, and the old man is determined to kill either his nephew or the illegitimate baby that's just been born.
These characters skulk around the dye-works, dreaming up ever-more-ingenious ways of tormenting one another, until the baby grows up and joins in the craziness himself. It's not a gentle tale, but it's as suspenseful as anything Hollywood has concocted lately - even David Lynch's fantasies are no more startling - and Mr. Yimou's keen style gives it a sardonic fascination.
Burkina Faso, the African nation formerly known as Upper Volta, has a younger and more modest film industry than China's, but already an internationally celebrated filmmaker has emerged from it: Idrissa Ouedraogo, whose ``Yaaba'' was a critical and popular success in the United States last year. His new film, ``Tilai,'' begins when a young man returns to his village after a long absence, only to find that his father has taken his fianc'ee as a wife. The young couple are still in love, but are also governed by ``tilai,'' or the law of honor, which calls for death when they defy custom and continue their relationship in secret.
This story is more concise and absorbing than that of ``Yaaba,'' and Mr. Ouedraogo tells it through images of great beauty and simplicity; they testify to his mastery of motion-picture craft, as well as his deep-rooted understanding of the people, customs, and landscapes he explores. He is a major talent by any cinematic standard.
``Siddeshwari'' takes its name from Siddeshwari Devi, a singer of great renown in India, where the film was made. Its director is Mani Kaul, a unique cineaste whose work has more in common with certain avant-garde traditions than with the active but highly commercialized Indian film industry.
Exquisitely photographed, the movie weaves together fiction-film and documentary conventions as it probes the life and artistry of the singer, showing how she turned unpleasant life experiences into art that countless admirers found sublime.
The film moves at a leisurely pace that's achingly slow by Hollywood standards but precisely suits the timeless, meditative quality of Indian music. Its imagery is similarly evocative, making for a challenging but rewarding experience in improvisatory cinema.


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Hackathons. These marathon computer-programming competitions generally involve spending the day (or days) with strangers, forming teams around a problem or idea, and collaboratively coding a solution from scratch. These solutions take the shape of websites, mobile apps, or robots - and the teams then pitch the solution to judges to win prizes. Hackathons, at their core, are very entrepreneurial in spirit, as they encourage ideation and prototyping.
For companies like Digital Echidna, hackathons can also serve as a corporate marketing and recruiting event. And that is why I found myself on campus at Western University yesterday to attend my first hackathon -- SheHacks. SheHacks is an all-female hackathon, organized by Western University's Women in Technology Society (WiTS) club.
This year there were 500 registrants, of which 150 were accepted. That is quite an increase from last year's (still impressive) 80. Most hailed from Western University but other students were there from Waterloo University, Laurier University, York University, and University of Toronto. The Hackathon theme this year was: A Solution You Can Bank On.
We arrived early to set up our tradeshow booth in the atrium of North Campus Building. Registration followed and the teams met and formed. After the opening ceremonies, the hacking began and a series of workshops were available. The workshops were designed to help coach attendees with things they may need to know over the course of the day. as this event was created for first-time hackers to try something new. People from all backgrounds, disciplines, and majors were encouraged to attend.
Digital Echidna's Fatima Khalid delivered one such workshop -- Intro to Python. This brief presentation included a step by step how to use the libraries, call the API, get the data, and offered some examples of what you can do with the data. I learned a few new tricks.
And, just for that little added touch, at 4 p.m. therapy dogs were brought in for an hour to calm nerves, raise energy levels, and reset intentions.
At 9 p.m. hacking submissions deadline closed. At this point, each team had to have their presentation and solution entered to be considered for judging. This was a pretty intense part of the night as the competitive spirit was high in all.
In the end, $5,000 worth of prizes were given out in categories like best overall hack, best use of API, and best use of blockchain. Will any of the teams have created the next GroupMe, the popular social messaging app that sold to Skype for $85M, which was originally conceived at TechCrunch's Hackday hackathon? Maybe so! But even if it does, that is not the main objective of this event, says Michelle Chan, president of WiTS, and one of the organizers of SheHacks.
Congratulations to the WiTS organization and all SheHacks organizers, for such a well done event.
Digital Echidna is proud to sponsor SheHacks and to support the growth of the incredible talent in London's technology sector. I want to give a shout out to all the sponsors there yesterday -- Scotiabank, Techalliance, Coinsquare, Western Science, stdlib, Propel Western Entrepreneurship, Bach-Simpson, wattpad, ChainSafe, Zoom.ai, Deliotte Digital, and Communitech.
I know financial contributions enable events like this to be open and free to registrants and cover the cost of venue, breakfast, lunch, and dinner, and even transportation.
Are there any female-focused Hackathons?


These are three #23011 Right Hand Remote Control Switches, two #23010 Left Hand Remote Control Switches, the #5540 Crossover Track and the #12715 Illuminated Bumpers #260 (some missing pieces) that Lionel made a few years ago. Our Opinion is that they are generally in Excellent or better Condition. Original Boxes. All Original in our Opinion. Please add $35.00 for US-48 Shipping and Insurance.


text:
package android.graphics.cts;

import android.graphics.AvoidXfermode;
import android.graphics.AvoidXfermode.Mode;
import android.test.AndroidTestCase;

public class AvoidXfermode_ModeTest extends AndroidTestCase{

    public void testValueOf(){
        assertEquals(Mode.AVOID, Mode.valueOf("AVOID"));
        assertEquals(Mode.TARGET, Mode.valueOf("TARGET"));
    }

    public void testValues(){
        Mode[] mode = Mode.values();

        assertEquals(2, mode.length);
        assertEquals(Mode.AVOID, mode[0]);
        assertEquals(Mode.TARGET, mode[1]);

        //Mode is used as a argument here for all the methods that use it
        assertNotNull(new AvoidXfermode(10, 24, Mode.AVOID));
        assertNotNull(new AvoidXfermode(10, 24, Mode.TARGET));
    }
}

redpajama_set_name: RedPajamaGithub


Picture fold after fold of mountains and valleys, add lush green rice terrace cascading down the hills, nowdraw a sea of mist rolling from the peak of the mountains down to the valley with sparkling stream flowing through. Located in northwest Vietnam's amidst enchanting mountain ranges, Sapa is home to Vietnam's ethic minorities, including Hmong, Dzao, and Yao. It is a colourful and charming market town, but it is known to discerning adventurers as Vietnam's premier trekking base and the oasis of surreal sceneries.
Nested under the shadow of Fansipan Mountain, Vietnam's highest mountain, Sa Pa town amazingly command spectacular views of plunging valley and mountains towering above on all sides. Sa Pa was virtually non- existed on the map until the French arrived, fell in love with the town's natural beauty, and fresh and cool climate, and developed the town as a summer resort to escape the summer heat. Sadly, French colonial villas were damaged beyond repair during the resistant war against the French and the town went into a deep sleep, Sapa work up to welcome the first batch of tourists in early 1990s and it quickly developed into a large town to serve the booming tourism.
The town is well maintained in its own charms and the surroundings are harmoniously preserved. From the top of Ham Rong Mountains, a perfect panorama view of the town open wide with colourful local hill – tribe people and lovely architectures and buildings under the shades of pine trees which centered around the Church and market place. But you're not here to hang out in town because once you've stepped out into the lush fields you'll understand the Sa Pa's real charm.
Depending on your fitness, Sa Pa offers a wide range of trekking trip from a half day trekking to an overnight or a 3 days tour. A leisure half day tour to Cat Cat village, just 3 km south of Sa Pa, is a very popular activity, but if you really want to experience the ethnic minority group, Ta Phin village trail lies just 10 km from Sa Pa . Tourists usually catch a xe om, local motorbike taxi, to the starting point and trek through a 14 km route which passes through Black Hmong and Red Dzao villages, the 2 largest ethnic groups in Sa Pa. Try their much praised green tea and learn their rich traditions. To get the most out of the experience, hire a local guide, especially for an overnight trip.
For extraordinary views, Tram Ton Pass lies 1900 meters above sea level, making it the highest mountain pass in Vietnam. Often referred to as "Heaven Gate," Tram Ton pass boasts picturesque landscapes and magnificent views that resemble the gate to Heaven. You'll be surrounded by almost vertical rice terraces and stunning mountain peaks with regular mists hovering on them. Besides fantastic scenic point, the 100 meter tall Thac Bac Waterfall, or the Silver Waterfall, is also a popular en route attraction. Interestingly, Tram Ton Pass acts as the border between 2 weather fronts; on the Sapa side, it's often cold and foggy, but drop a few hundred meters onto the Lai Chau side, and it can be sunny and warm.


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text:
Reata's new CFO on M&A, weathering a recession and biotech's quirks
Manmeet Soni helped execute multi-billion dollar sales as CFO of Pharmacyclics and Ariad. Now, the exec is moving from Alnylam to a small, Texas-based biotech.
Elizabeth Regan / Industry Dive
Andrew Dunn @AndrewE_Dunn
Manmeet Soni is on the move again.
The 41-year-old executive has already had several short, but successful, stints serving as chief financial officer of biotech companies, earning respect on Wall Street.
He was the CFO for Pharmacyclics when AbbVie paid $21 billion to acquire the Imbruvica developer in 2015. Less than a year after he started at Ariad Pharmaceuticals in the same role, Takeda paid $5.2 billion to buy that business.
For the past two years, Soni was CFO of Alnylam as the company made the jump from clinical- to commercial-stage with its first drug, Onpattro.
About two weeks ago, he began his next gig: CFO of Reata Pharmaceuticals, a mid-sized biotech in Irving, Texas.
With four pivotal clinical studies ongoing — two of which are expected to read out data this year — Soni said he hopes Reata will take off in a similar fashion to Pharmacyclics, Ariad and Alnylam.
BioPharma Dive spoke with Soni about dealmaking, Reata and the role played by biotech CFOs.
This interview has been condensed and edited for clarity.
BIOPHARMA DIVE: For Reata, what are your top priorities in the short term?
SONI: The company is on the cusp of two pivotal data readouts. My number one priority is to make [Reata] commercial ready, to make sure they have all the resources, systems, process and people, [that] all are there in order to be commercial ready.
When you look back at the acquisitions of Pharmacyclics and Ariad, what did you learn about dealmaking?
SONI: With Pharmacyclics and Ariad, we were always running the company as if we are continuing the business forever. When the right time comes, because the decision is made by the board members, you should be ready and prepared to guide the board members.
I've always been proactive in keeping my books clean and making sure that we have a long-range plan that is always being refreshed. If you are prepared, then it's very easy for the board to assess if and when any offer comes. It all depends on if you are ready.
At least annually, you should talk to the board about what you think is the value of your company and how you see that in the next three to five years, the value accretion in the company so that the board is prepared.
Can M&A be a pitfall for biotechs? Particularly in the sense that it can distract from the day-to-day?
SONI: Where I think people trip up is, first of all, going out and saying that we are looking to sell, or we are looking for who can buy us. Shopping yourself: that's the worst thing you can do for the company.
It might give you a little pop in the interim, because M&A markets in biotech have been pretty lucrative and people have gotten significant jump-ups, so people get excited when they hear the rumors. But I've never seen a successful story where a company says that openly and has been able to do a good transaction.
There are so many examples where a company says they are looking, their shares will pop up, and then they are not able to find a buyer at a price they were thinking. And then shares will go back and shareholders lose much more value.
You should run the process super tight, super confidential, minimum number of employees and that's what we've done with both Pharmacyclics and Ariad.
Biotech seems unique in going years, if not decades, without revenue. Do you see comparisons to other industries for CFOs?
SONI: There is no other industry I can think of other than tech but, still, they have revenue.
That can only happen in biotech and some technology. Technology is the big thing.
Biotech is such a unique area. The success rate to find a drug and the level of investment needed is huge. I think the rewards are higher from that angle, but the success ratio is very small.
So you can't compare it with any other business, because, even in tech, you normally have huge revenues before you are valuable. This is only biotech where without revenues, only on the hope of Phase 1, Phase 2 data, and in some cases with gene therapy companies even before clinical data, before you put a drug into any human being, you are talking about a $1 billion or $2 billion market cap company.
In an economic climate that may turn downward, how do you think about financing future opportunities?
SONI: There are early signals right now predicting a slowdown or expected recession in the next couple of years. As a CFO or CEO of any company that has to raise money, they should always be prepared, doing scenario planning and looking at unique ways of how you can raise money.
There are some ways which everybody raises money, through equity financing and convertible debt, both of which are dilutive to the current shareholders. Then they have a few options of non-dilutive means, such as debt, collaborations with other companies by selling some rights to products or by structured royalty financing.
If you want to raise debt before you have product revenues or near to break even, the cost of debt is generally more expensive.
In the last few years, a lot of biotech companies have been doing structured royalty financing, selling the future royalty at some agreed percentage of their future product revenues in order to raise cash, which has been very lucrative as a measure of raising cash through non-dilutive means. Though this source generally is used when companies already have Phase 3 data or after their FDA approval, when the product revenue stream has some level of certainty.
Scenario planning [for] raising money through non-dilutive measures is very important for all CFOs and CEOs to consider.
Also, look into ways to control spend. I've seen companies where they go ahead of their skis in the sense of investing much earlier than they have known either the market size, market potential or the label. They invest much more. That's also an area where you have scenario planning to control the cost.
Follow Andrew Dunn on Twitter
Filed Under: Strategy & operations Risk management Leadership
Public Domain Free for commercial use No attribution required Pixabay
CFO KPI Close-up: Talent retention
With turnover more costly, talent retention has become a marker of a company's health and a primary CFO responsibility.
PwC releases ESG reporting app
Finance leaders are under pressure to measure their company's performance on environmental, social and governance values.
By Robert Freedman • Oct. 25, 2020
Latest in Strategy & operations
CFOs face challenging labor landscape under Biden administration
Companies experiencing financial, operational stress from COVID-19, survey finds
2021 Outlook: Why, and how, the CFO should lead ESG efforts


text:
Home / Blog / KMC KMC Commander In Action Matsui Nursery
KMC Commander in Action: Matsui Nursery
Article Date: March 6, 2020
By: Jason D. Mills, Director of Marketing and Communications
When Matsui Nursery, one of the largest orchid growers in the world, was looking to upgrade their greenhouse monitoring system, they turned to their technology provider – Alvarez Technology Group – and KMC Commander.
Headquartered in Salinas, California, the nursery grows millions of orchids each year inside 2.8 million square feet of greenhouse space. The plants have different requirements throughout the growing cycle, so Matsui relies on a variety of methods to keep conditions perfect.
"You need to monitor temperature, light, humidity and air flow," said Teresa Matsui, president and CEO. "We use different mechanical tools such as ventilation and curtains. We chalk the outside of the greenhouses during the summer months when the light is more intense. We wash that off during the winter months to allow more light to come in."
But weather conditions aren't always predictable. During Matsui's first year at the helm of the company her father founded in 1967, temperatures in January exceeded 80 degrees.
"If you start off expecting your winters to be cold then you have temperature spikes, you have to respond very quickly. We rely on both human monitoring and technological monitoring to manage and calibrate all of these different factors that go into producing a quality orchid," she said.
With greenhouses of Matsui's size, environmental inconsistencies and equipment malfunctions could have a major impact on their bottom line. With their larger greenhouses, they could lose a hundred thousand orchid plants in an hour if the vents don't open when the temperature reaches a certain point.
"We can struggle with climactic inconsistencies from one side of a greenhouse to another, especially in our larger greenhouses," said Michael Lopez, Matsui's agricultural engineer. "Higher temperature requirements for our tropical orchids means that we have to have an adequate heating system with fail-safes and backups. It requires a fair amount of maintenance."
According to Lopez, the ability to customize KMC Commander was crucial. He found that other automation systems were prepackaged, and commercial greenhouse growers had to work within set confines.
"Whereas the KMC Commander system, especially with the ControlBasic programming that I'm starting to play around with, I kind of see it as more as providing the foundations and a nice open area you can play around with," Lopez said. "KMC provides you the bricks and the tools and they say have at it, build whatever you like. I think that customization is going to be one of the best components that we could have to address our more troublesome parts of running a nursery of 85 acres in size."
Matsui worked with their long-standing technology provider – and KMC partner – Alvarez Technology Group to manage the install.
"The installation process with KMC Commander for me personally was pretty painless because I didn't have to think about it," Matsui said. "I can rely on my partners at Alvarez Technology Group to mastermind the process for me. As someone who's got a million things on her plate, I'm a huge believer in bringing in appropriate technology where it makes sense for us and for our operation, but I don't have the expertise"
Lopez was impressed with the ease and simplicity of the KMC Commander process from install, to setup and use.
"You plug, you situate your sensor, you situate your modules and connect it to the ethernet and you're good to go," Lopez said. "Having an application that's accessible from a web browser from anywhere is the most convenient thing about having an upfront monitoring system. We're not tied down to proprietary applications or software or hardware of any kind. It's just as simple as going to App.KMCCommander.com and putting in my login."
According to Matsui, KMC Commander allows the company to monitor with ease.
"We have a handful of people who are technically qualified to manipulate the mechanical systems and they can't be running everywhere at any given time," Matsui said. "A product like KMC Commander is going to be a real boon for the way we can react to changes in environment, changes in climate and also mechanical malfunctions."
Interested in seeing for yourself? Schedule a KMC Commander demo.


My daughter's godfather is a peripatetic Frenchman who I got to know at college in Australia in the early 'nineties. The Frenchman and I weren't especially close friends at college, but around the time my wife and I settled in St. Francisville, he landed a job with an airline and relocated to Dallas. Thus did each of us become the closest thing the other had to a familiar face for several thousand miles in any direction. Afflicted with chronic wanderlust, the Frenchman had taken the airline job for the travel perks and justified living in Dallas by vowing never to spend a weekend in Texas so long as he had the strength to drag himself to the airport. Since he could fly pretty much anywhere for next to nothing (and take friends along for the ride) we took to going places: either in Louisiana, when he would "pop" over after work on a Friday to spend the weekend, or if he could wrangle buddy passes for unsold seats, to other interesting locations in the Americas. So in our pre-kids, pre-saving, pre-taking-anything-seriously years, my wife and I had strange and marvelous adventures flying what we affectionately referred to as "Air Frog." At lunchtime on a random Thursday one of us would answer a phone to the Frenchman shouting "What are you doing zis weekend? Nozzing? OK! We going to … um … [pause while flight schedules are consulted] Venezuela!" Then would follow forty-eight hours that might or might not involve mountaineering, sleeping in airports, jungle navigation, dugout canoe travel, bribery, haggling, bouts of severe gastric distress, and on one occasion, the thrilling experience of being caught in a mob of angry men jumping up and down waving farm implements and chanting "Cha-VEZ! … Cha-VEZ! … Cha-VEZ!" Kaleidoscopic, unpredictable, and occasionally terrifying, what Air Frog Tours lacked in organization they made up for in improvisation. We visited places, saw ways of life, ate things we never would have encountered otherwise, and lived to tell the tales.
One Memorial Day weekend (in 1998, I think), the Frenchman called to announce that there were open seats on flights to Rio de Janeiro if anyone was interested. We went down on a Thursday—him flying from Dallas, me from New Orleans through Miami—and spent a colorful weekend doing the typical touristy things (riding the cablecar up Sugarloaf Mountain, visiting the Christ the Redeemer statue, swimming at Copacabana Beach), plus some inadvisable things (trying to join the dancefloor in a Samba club at 2 am, falling asleep on park benches) that you'd expect a couple of twenty-eight-year-olds to get up to when let loose in Brazil. Despite that, the whole adventure went off pretty well until we got to the Rio airport to come home. The Frenchman's Dallas flight left several hours before mine (through Miami) was due to depart, so after seeing him off I passed a pleasant hour or so deciding which taxidermied piranha to spend the last of my Brazilian reales on in the airport souvenir shops, secure in the knowledge that once aboard my flight I'd get something to eat and a few hours' sleep before landing back in the U.S. in time for work on Tuesday morning.
James's Escapes Issue travel tip: If leaving Brazil on the last flight of the day with no working financial instruments in your possession, do NOT spend the last of your cash on a stuffed piranha. Because if you do, and your flight develops engine trouble after takeoff, dumps its fuel in the sea, and lands back in Rio tailed by a fleet of fire trucks, you stand a good chance of spending the next twenty-four hours penniless, hungry, and alone, clutching your backpack and hiding behind a row of seats in a darkened corner of the (quite scary, at 2 am) Rio airport, with only a stuffed fish for company. Apparently the airline's policy of whisking paying passengers off to area hotels in the event of mechanical problems does not extend to cheapskates traveling on their employees' buddy passes. Live and learn. The piranha was confiscated at U.S. customs in Miami anyway. At that stage I didn't care; I've never been gladder to see a Burger King in my life.
For this annual Escapes issue of ours, that is my cautionary tale. You'll find some lovely, unusual, and thoroughly worthwhile adventures on the stories within. They're all within easy driving distance, too. So if things do go horribly wrong, home won't be so very far away.


text:
Q: MemSQL: I got error when inserting data with spark to the DB The Error:
Could not query S3: An error occurred (400) when calling the HeadObject operation: Bad Request
What is the cause of this error?
thanks!

A: Solved. It was a permission problem.

redpajama_set_name: RedPajamaStackExchange


text:
Illegal Workers Found at Wal-Mart Construction Site to Be Deported
An immigration raid at a Wal-Mart distribution center under construction in Pennsylvania led to the arrests of 125 illegal workers, all of whom will be deported, federal officials said Friday. The workers from Costa Rica, El Salvador, Guatemala, Honduras and Mexico were detained Thursday, according to immigration officials. Some of the 125 used fake documents to obtain employment with subcontractors, officials said.
By Michael Rubinkam | November 21, 2005 at 12:00 AM
An immigration raid at a Wal-Mart distribution center under construction led to the arrests of 125 illegal workers, all of whom will be deported, federal officials said Friday.


For those who like to eat chicken, I wanted to bring you this new and simple recipe of Chicken Marsala, and I recommend you to use the chicken breast in particular. Also for this occasion, I used this delicious Marsala wine, but if you do not have it at home, you can use a white wine as sweet as you can find.
In the same blog I have many other chicken recipes, but this one in particular that I bring here is simply fabulous. Besides, it took me a short time in its preparation and it uses very few ingredients.
I hope you enjoy it, as it is an excellent idea for lunch or dinner.
You may also be interested to see my recipes of Oven baked chicken parmesan, or Oven baked chicken fillet with parsley and lemon.
In a skillet, add some oil, and sauté the chicken (seasoned with salt and pepper) for about 4 minutes per side.
In the same pan, add a little more oil and sauté the sliced mushrooms and cook them for about 5 minutes. Then add the flour and stir constantly to avoid lumps. Next, add the chicken stock, Marsala wine and thyme. Cook for about 3 more minutes and add the tablespoon of butter. Remove from the heat and let stand a few minutes before serving.


STUDIO I AM is a creative fabrication and design studio providing engaged solutions for a diverse range of clients.
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We are able to work within a range of budgets and always aim to deliver a unique experience and are proud of the projects and ideas that leave our workshop.
Our facilities and knowledge cover a wide terrain from metal to wood fabrication, casting, sandblasting and screen-printing through to bespoke lighting, assembly, finishing and editioning. We are also able to provide complete installation, working with both interior and exterior sites, and have built shop-fits, gallery interiors, sets and exhibitions. We have installed projects and exhibitions in both corporate and domestic environments, here in the UK and internationally.
We have a dynamic and interesting workshop close to central London where our passion for making and materials comes alive.