text:
\section{Optimized Coding Algorithms}
\label{sec:algorithms}

In the following, we present two algorithms for the encoding process, both based on the knowledge of the recovery status of the neighboring nodes. In order to increase the speed of information dissemination, our algorithms make coding decisions that by design allow the neighboring nodes to recover another information unit immediately upon reception of a new coded packet.

\subsection{Greedy algorithm}

The first algorithm gives priority to the symbols that are rarest within the neighborhood. The key is to find the combination of original symbols that maximizes the number of neighbors that are able to decode a new information unit.

\begin{small}
\begin{algorithm}[Greedy algorithm]
\caption{Greedy algorithm}
$_{}$\\
$_{}\ \ \ \ \ \ \ \ C=\emptyset$\\
$_{}\ \ \ \ \ \ \ \ \textrm{choose } s^* =\arg \max \limits_{s\in B_x} |R(\{s \})|$\\
$_{}\ \ \ \ \ \ \ \ q=0$\\
$_{}\ \ \ \ \ \ \ \ \textrm{while } |R(C\cup\{s^*\})|\geq q$\\
$_{}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ q=|R(C\cup\{s^*\})|$\\
$_{}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \textrm{add } s^* \textrm{ to } C$\\
$_{}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \textrm{choose } s^*=\arg \max \limits_{s\in B_x \backslash C} |R(C\cup\{s\})|$\\
$_{}\ \ \ \ \ \ \ \ \textrm{end}$\\
$_{}\ \ \ \ \ \ \ \ p=\bigoplus \limits_{s\in C} s$\\
$_{}\ \ \ \ \ \ \ \  \textrm{transmit $p$.}$
\end{algorithm}
\end{small}

As shown in Algorithm 2, the choice of the first symbol is very different from the Opportunistic algorithm ({\it Algorithm
\ref{oppor}}). Instead of a random choice, the Greedy algorithm selects the symbol that maximizes the number of nodes that are able to decode a new symbol if a packet of degree one is sent. This corresponds to maximizing $|R(\{ s\})|$. If there are
multiple symbols that satisfy this condition, the algorithm chooses one of
them randomly. As we will see later on, a proper choice of the first
symbol is crucial for a good performance. In fact, we will show that,
if the nodes send packets of degree one (i.e. plain symbols) and use the selection criteria of our protocols, the resulting performance is already quite close to the performance of the Opportunistic algorithm.

Taking a closer look at the loop of this algorithm, we realize that after choosing the
first symbol, the algorithm proceeds by selecting among the symbols yet to be
chosen the element that maximizes the number of neighbors
able to decode a new symbol from the packet, which is obtained by XORing this
new symbol with all the symbols selected so far. This can be written as
$|R(C\cup \{s \})|$. After choosing this candidate symbol (a symbol $s^*=\arg \max \limits_{s\in B_x \backslash C}
|R(C\cup\{s\})|$), the algorithm will check if there is a gain in
adding this candidate symbol to the set of symbols to be mixed in the output packet. Notice that, for the algorithm to continue, we do not require that neighbors that could previously recover a new symbol will continue to be able to do so; the algorithm continues as long as the number of neighbors able to recover a new symbol does not decrease from one
step to the next one.

Denote by $p^*$ the packet obtained by XORing all the symbols chosen so far (i.e., all the symbols in the current set $C$) and denote by $s^*$ the candidate symbol. If the number of neighbors that are able to decode a new symbol from $p^*\oplus s^*$ (which is
represented by $|R(C\cup \{s^* \})|$) is less than the number of
neighbors that are able to decode a new symbol from $p^*$ (which is
represented by $q$), i.e. if $|R(C\cup \{s^* \})|\geq q$, the
algorithm stops and produces a packet that combines all of the symbols selected thus far.

Going back to the scenario illustrated in Fig.~\ref{fig:example}, we see that the algorithm starts by choosing the symbol $s^*$ that maximizes the size of $R(\{s\})$ over all $s$ in the buffer of node $X$, i.e. that maximizes the number of neighbors that do not have the symbol $s^*$. Clearly, $s^*$ is the {\it rarest symbol in the neighborhood}. It follows that the algorithm ends up choosing $s_1$ or $s_3$, since each of them is present in the buffer of only one of the neighbors. If the algorithm chooses symbol $s_1$, we have that $|R(\{s_1\})|=2$. In the first iteration, the algorithm sets $q=2$ and $C=\{s_1\}$. Next, the algorithm selects the symbol $s^*$ as the one that maximizes the size of $R(C\cup \{s\})$ over all $s\neq s_1$. More specifically, it will choose the symbol that maximizes the number of neighbors that are able to recover a new symbol from the packet obtained when XORing this candidate symbol with all the symbols in $C$. In this case, since $C=\{s_1\}$ and all $3$ neighbors can recover a new symbol from $s_1\oplus s_2$, this candidate symbol is $s^*=s_2$. Now, the algorithm checks if the number of neighbors that can recover a new symbol increases when compared to the previous step. In this case, since $q=2$ neighbors recovered a new symbol and adding the candidate symbol increases this number to $3$ (i.e. $|R(C\cup \{s_2\})|\geq 2$), the algorithm continues by updating $q$ to $q=3$ and adding the candidate symbol to the packet: $C=\{s_1,s_2\}$. Now, the algorithm chooses the next candidate symbol using the same rule, i.e. to maximize the number of neighbors that are able to recover a new symbol. In this case, this symbol can be $s_3$ or $s_4$. In either case, we have that only one neighbor will be able to recover a new symbol if the candidate symbol is added, thus we will have $|R(C\cup \{s^*\})|=1$. In the subsequent step, since $|R(C\cup \{s^*\})|<3=q$, the algorithm stops and outputs the packet $p=s_1\oplus s_2$, which is an ideal packet.

Notice that in the first choice we had two options: $s_1$ and $s_3$. We saw that if $s_1$ is chosen, the algorithm outputs the ideal packet $p=s_1\oplus s_2$. Using analogous arguments, it is easy to see that if $s_3$ is chosen in the first step, the algorithm outputs the packet $p=s_2\oplus s_3$, which is also an ideal packet. Thus, we have that in this example, with probability $1$, the Greedy algorithm outputs an ideal packet.

Similarly to the Opportunistic algorithm, the Greedy algorithm evolves in each iteration by selecting a symbol to be added to the set of symbols that will form the output packet.  After the choice of the first symbol, the algorithm ensures that the number of neighbors that are able to decode new symbols does not decrease with the next decisions. Beyond the choice of the first symbol (which has a significant impact on the performance as we will see
latter on), the selection procedure targets the symbol that will maximize the number of neighbors that are able to decode, whereas the Opportunistic algorithm make this selection in a purely random fashion.

\subsection{Equalizing algorithm}

The Greedy algorithm presented in the previous section is prone to lead to an uneven distribution of information. In the worst case, some nodes that are not well connected to the rest of the network might receive mostly packets they cannot decode, since they lack some of the information units that all the other nodes already have. These nodes would be served by the greedy algorithm only after all other nodes have decoded all of the information, leading to a high worst case delay. The way to prevent this from happening is to equalize the recovery level among the neighbors instead of maximizing it in a greedy fashion.

The so called Equalizing algorithm pursues mainly the goal of giving new decodable information to the neighbors that have recovered the fewest information units, thus increasing the minimum number of recovered packets per node.

\begin{small}
\begin{algorithm}[Equalizing algorithm]
\caption{Equalizing algorithm}
$_{}$\\
$_{}\ \ \ \ \ \ \ \ C=\emptyset$\\
$_{}\ \ \ \ \ \ \ \ B=B_x$\\
$_{}\ \ \ \ \ \ \ \ R^*(C)=\{j: C \subseteq B_j \}$\\
$_{}\ \ \ \ \ \ \ \ \textrm{while } |B|>0 \textrm{ and } |R^*(C)|>0$\\
$_{}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \textrm{choose } J=\arg \min \limits_{j\in R^*(C)} |B_j|$\\
$_{}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ S=B\cap \overline{B}_J$\\
$_{}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \textrm{choose } s^*=\arg \max \limits_{s\in S} |R  (C\cup \{s \})|$\\
$_{}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \textrm{add } s^* \textrm{ to } C$\\
$_{}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ B=B \cap B_J$\\
$_{}\ \ \ \ \ \ \ \ \textrm{end}$\\
$_{}\ \ \ \ \ \ \ \ p=\bigoplus \limits_{s\in C} s$\\
$_{}\ \ \ \ \ \ \ \  \textrm{transmit $p$.}$
\end{algorithm}
\end{small}

IN Algorithm 3, $R^*(C)=\{j: C \subseteq B_j \}$ represents the set of
neighbors that have all the symbols in $C$. In each step, the
algorithm chooses the neighbor that has the least recovered packets
among those not yet considered.  Then, the algorithm selects one of
the symbols that this particular neighbor has not yet recovered (and
that all the previously chosen neighbors did recover, thus ensuring
that the previously chosen neighbors can still decode the
packet). This symbol is added to the packet to be sent.

The algorithm needs to keep track of the symbols that neighbors chosen so far have already recovered. This is captured by set $B$. One condition to stop the loop of the algorithm is precisely the existence of symbols in $B$. If there are no symbols $B$, i.e. if there is no symbol that has been recovered by all the nodes chosen up
to a certain iteration, no symbol can be added to the packet to be sent without rendering at least one of the neighbors unable to decode. The other condition for the loop to stop is $|R^*(C)|>0$, which means that the loop only continues if there are still neighbors that have recovered all the symbols in the output packet constructed so far. If there are no neighbors in this condition, no further nodes will be able to recover a new symbol irrespective of which symbol is added to the packet.

In each iteration, the algorithm starts by inspecting all nodes that have recovered all the symbols in the packet constructed so far (i.e. neighbors in $R^*(C)$ which implies that no neighbor can be chosen twice) and finding the one that recovered the least number of symbols. More specifically, we choose the neighbor $J$ that satisfies $J=\arg \min \limits_{j\in R^*(C)} |B_j|$. After making this selection, the algorithm calculates the set of symbols that can still be added to the packet. These symbols must have been recovered by all the previously chosen neighbors(i.e., symbols in $R$) and cannot have been recovered by the neighbor that was chosen in the current iteration (i.e., symbols not in $B_J$). Thus, the set of candidates is defined by $S=R\cap \overline{B}_J$.

Next, from this set of candidate symbols, the algorithm selects the one that maximizes the number of neighbors that are able to decode a new symbol, assuming that the output packet results from the XOR of all symbols in $C$.
After this choice, the algorithm adds the symbol to the set $C$ and updates the set $R$. The new set $R$ will be the set of symbols shared by all the neighbors that were chosen before the current iteration (namely $R$) and possessed by the new chosen neighbor ($B_J$), i.e. $R=R\cap B_J$. When the loop is completed, the algorithm computes the packet to be sent by XORing all the symbols in $C$.

Once again, we will use the scenario in Fig.~\ref{fig:example} to clarify the main steps of the algorithm. The algorithm starts by setting $C=\emptyset$, $B=\{s_1,s_2,s_3,s_4\}$ and $R^*(C)=\{N_1,N_2,N_3\}$ (recall that $R^*(C)$ represents the set of neighbors that have already recovered {\it all} of the symbols in $C$). In the first iteration, the algorithm starts by checking which neighbor has the smallest buffer, i.e. the one with the smallest number of recovered symbols. In this case, the chosen neighbor is $N_2$, since it only recovered symbol $s_2$. Then, the algorithm computes the set of symbols that this node does not have in its buffer: $s=\{s_1,s_3,s_4\}$. The goal is to provide a new symbol to this particular neighbor. Hence, the first chosen symbol is a symbol from $S$ and, since we also want to provide (if possible) new symbols to other neighbors, the algorithm chooses the symbol that is more rare within the neighborhood, among all the symbols in $S$. In this case, we have two options: $s_1$ or $s_3$. Suppose that the algorithm chooses $s^*=s_1$. We have that $C=\{s_1\}$ and $B=\{s_2\}$, i.e. $B$ is the set of symbols that node $N_2$ has already recovered. It is necessary to keep track of the symbols that all the neighbors chosen by the algorithm have already recovered to ensure that the neighbors with the smaller number of recovered symbols will be able to recover a new symbol from the resulting output packet.

Next, in the second iteration, the algorithm chooses the neighbor that
has the smallest number of recovered symbols among all the neighbors
that have all the symbols in the current instance of set $C$,
i.e. among the neighbors in $R^*(C)$. In this case, $R^*(C)=\{N_1\}$
and thus the chosen neighbor is $N_1$. Now, the set of symbols that
can be added to $C$ is the set of all symbols that all the previously
chosen neighbors have in their buffers, $B$, and that the neighbor
chosen in the current iteration does not have in its buffer,
$\overline{B}_1$. Thus, in this case, we have that $S=\{s_2\}$ and,
hence, $C=\{s_1,s_2\}$. Notice that there are no further symbols that
have been recovered by all the chosen neighbors, i.e. $B=\{s_2\} \cap
\{s_1,s_3,s_4\}=\emptyset$. Thus, the algorithm cannot continue and
consequently outputs the packet $p=s_1\oplus s_2$, which is an ideal
packet.

In the first iteration, we could have chosen symbol $s_3$ instead of
$s_1$. Using similar arguments, it is easy to see that, if $s_3$ is
chosen, the algorithm outputs the packet $p=s_2\oplus s_3$, which is
also an ideal packet. Therefore, we again have that with probability
$1$ the Equalizing algorithm outputs an ideal packet in our example.

\section{Conclusions}
\label{sec:conclusions}

In this paper we proposed two coding algorithms which exploit
feedback information on the recovery status of neighboring nodes. Through the
analysis of a wide range of settings in our simulations, we show that
the Greedy algorithm consistently outperforms all other algorithms in
terms of number of immediately decodable packets, which is fundamental
for delay-sensitive applications in wireless networks such as
real-time media streaming. Moreover, satisfactory results of the
Greedy algorithm are already obtained using just a simple decoder,
whereas for the Equalizing algorithm the use of Gaussian elimination
improves the performance significantly. However, using the Equalizing
algorithm is beneficial in some inhomogeneous (clustered) topologies,
where the worst case delay is lower than that of the Greedy algorithm
at a similar recovery rate.

The algorithms proposed in this paper focus on immediate decodability,
and hence take the decoding delay as the sole optimization criterion. As a next step, we
intend to explore the design tradeoff between delay and throughput in
more detail. The perceived quality of a video transmissions is largely dependent on
the right balance between the two. An algorithm which imposes slightly
less stringent delay requirements and allows decoding after reception
of a fixed number of packets (as opposed to just one packet), will provide higher
throughput, which may improve the overall perceived quality. We
further assumed instantaneous feedback in the evaluation of the
algorithms. The analysis of the impact of imperfect and delayed
feedback on the performance of network coding algorithms is left as an important
item for future work.

\section{Introduction}

The basic idea of network coding \cite{inftheory,practical_coding}, by
which nodes transmit packets that result from joint encoding of
multiple original information units, has led to communication
protocols that are applicable in a wide range of wireless
communication scenarios \cite{Fragouli2006Primer}. The gains brought
by network coding are most evident in applications involving multicast
or broadcast sessions (in which messages are intended for multiple
destination nodes) in combination with physical layer broadcast (in
which neighboring nodes can overhear potentially useful information).

For delay-sensitive applications such as media streaming, it is not
desirable for receivers to have to wait for the arrival of several
packets until they are able to decode the sent data. Instead, we would
like for a packet to be immediately decodable given only the
information units already available at those nodes. Moreover, each
packet should be useful for as many neighbors as possible, thus
minimizing the required number of transmissions. Similar
considerations hold for distributed systems with highly constrained
nodes, such as sensor networks, where it may well be impossible to
store a large number of coded packets and to decode them using
Gaussian elimination.

In general, there is a tradeoff between delay, throughput and
end-to-end quality, and different codes can try to optimize either one
of these performance metrics. Priority Encoded
Transmission~\cite{albanese1996pet} provides graceful degradation by
specifying different levels of coding (and consequently different
minimum numbers of packets required for decoding) depending on the
content and the priority of the underlying information units. Fountain
codes (e.g.~Raptor codes~\cite{shokrollahi2006rc}) offer very low
coding overhead and are (asymptotically) rate optimal when
transmitting over erasure channels.  However, decoding $n$ packets is
only possible \emph{after} $n+\epsilon$ coded packets have been
received. A fraction of the encoded symbols can be decoded earlier
albeit at the cost of significant overhead~\cite{sanghavi2007ipr}.

We focus our attention on algorithms that allow \emph{immediate}
decoding of each incoming packet.  This stringent delay requirement
comes at the cost of a reduction in throughput in the sense that
broadcasting coded packets brings new information to fewer neighbors
than fountain coding or priority encoded transmission would allow.
Fostering early or immediate decoding requires an algorithm that
decides which and how many information units or symbols\footnote{These
  two expressions shall be used interchangeably throughout the paper.}
should be combined in each new packet that is to be transmitted.

Adequate design of such an algorithm is highly dependent on
the state information available at the nodes. Complete lack of state
information is likely to occur in highly dynamic networks, such as
mobile sensor networks, where the overhead of tracking a changing
neighborhood would be prohibitive. In case a node has no information
about the packets that have already been recovered by its neighbors,
the algorithm can only optimize how many information units to combine
(i.e., the codeword degree~\cite{GC}).  Each node simply combines
randomly chosen information received from other nodes with its own
information units, until the desired codeword degree is reached. The
algorithm needs to find the right tradeoff between a high codeword
degree that ensures that coded packets bring new information to many
of the neighbors, and a low codeword degree that allows packets to be
decoded immediately using only the information that is locally
available. An analysis of optimum degree distributions with respect to
network dynamics and topology was carried out
in~\cite{Resilient_Coding}.

When information about the data recovery status of neighboring nodes
is available, it is possible to employ more sophisticated coding
algorithms. One such instance is presented in \cite{nc_xor}, which
proposes a protocol for unicast routing in wireless mesh
networks. Routers combine packets opportunistically from different
sources in order to increase the diversity of the information content
of each transmission. A node chooses the symbols to combine based on
the content of the neighbors' buffers. This form of state information
is piggybacked onto data packets and also extrapolated from past loss
rate measurements and overheard packets. The procedure ensures that
coded packets are immediately decodable at the next hop with very high
probability.

Although the protocol in \cite{nc_xor} targets unicast traffic, very
similar considerations also apply to broadcast.
\cite{Online_broadcasting_with_NC} analyzes a number of simple
heuristics for the online and offline version of the problem.

Intrigued by the behavior of network coding protocols for one-to-all
or all-to-all information dissemination, we compare the performance of
several existing coding algorithms with various levels of buffer state
information on neighboring nodes. We further propose two new
protocols, one that maximizes the number of immediately decodable
packets in a greedy fashion, and one that attempts to equalize the
number of recovered information units among neighbors. The proposed
schemes operate under the assumption that neighboring nodes exchange
information about which symbols they have already recovered, for
example by appending this information every time they send a data
packet.

We show that the proposed schemes outperform existing algorithms in
various scenarios of interest.  We first consider a simple decoder
that discards all incoming packets that cannot be decoded
immediately. We then proceed with a characterization of the
performance gains induced by a more complex decoder that buffers all
received packets and uses Gauss-Jordan elimination to recover the sent
data.

The remainder of the paper is structured as follows. In
Section~\ref{sec:related} we discuss related coding algorithms that
shall serve as a baseline for the subsequent
comparison. Section~\ref{sec:algorithms} describes our two network
coding algorithms, whose performance is highlighted in
Section~\ref{sec:results}, where simulation results are used to
compare their performance to that of existing
solutions. Section~\ref{sec:conclusions} concludes the paper.

\subsection{Degree distribution based algorithm}

Network coding schemes that combine all packets in the buffer are
not always optimal in terms of performance, as observed in \cite{GC}.
The authors show that depending on the number of recovered packets $r$
at a specific node, there exists an optimal number of packets to
combine to maximize the number of decodable packets at each instant in
time. More precisely, defining the {\it codeword degree} as the number
of original symbols which are jointly encoded to form a coded
packet, the authors of \cite{GC} determine the optimum \emph{degree
  distribution}.  For a prescribed number of recovered packets $r$, the
degree distribution $D(r)$ returns the degree of the next output packet.

The scenario studied in \cite{GC} is a random encounter scenario, in
which each node meets independently at random one neighbor at a
time. The optimum degree distribution depends on the dynamics of the
underlying network and in~\cite{Munaretto2007Persistence} the authors
show the deficiencies of such an algorithm in scenarios beyond the
specific model they were designed for.  Since \cite{GC}, some other
coding algorithms based on a pre-defined degree distribution were
proposed. As a comparison algorithm, we use the following instance.\\

\textbf{Adaptive Network Coding (ANC)} \cite{Resilient_Coding}:
\emph{When a transmission opportunity occurs, the node randomly
  combines a specific number of packets in its buffer, which ensures
  that the degree of the resulting output packet is as high as
  possible and less than or equal to $D(r)$ (as defined above).}

\subsection{Opportunistic Algorithm}

The previous algorithms do not make use of feedback information in
terms of the recovery state of neighboring nodes. However, if
available, this additional information can be be used to make more
efficient coding decisions and can bring significant improvements to
the overall performance.  The work presented
in~\cite{Online_broadcasting_with_NC} analyzes how it can be used and
proposes a number of heuristic algorithms designed for the scenario of
a source node broadcasting to $n$ receivers over independent erasure
channels, i.e. a one-to-all broadcast scenario. Among the algorithms
proposed in \cite{Online_broadcasting_with_NC}, we consider the so
called Opportunistic algorithm, because it is the only one that can be
used in conjunction with a simple decoder. The basic idea is that each
node uses the feedback received from its neighbors to compute a queue
of symbols that have not yet been received by at least one node. The
first symbol is chosen randomly and further symbols are added under
the condition that the packet remains immediately decodable by all
neighbors that were previously able to decode it (in other words, the
number of nodes that can decode the packet can only increase).

Before defining a different set of algorithms capable of exploiting
the knowledge of the recovery status of neighboring nodes, we must
introduce some basic notation. In the following, we will denote the
node performing the coding algorithm by $x$. The set of neighbors of
node $x$ is denoted by $N_x=\{1,\dots,m \}$ and
$B_x=\{s_1,\dots,s_n\}$ denotes the set of symbols in the buffer of
node $x$. $B_j$ represents the set of symbols that are in the buffer
of node $j$, with $j\in N_x$. The set of symbols that are in the
buffer of $x$ and that are not in the buffer of neighbor $j$ is
denoted by $\overline{B}_j$ and can be computed as
$\overline{B}_j=B_x \backslash (B_j\cap B_x)$.

The algorithms presented next start by choosing the set of symbols $C$ that will be combined in the output packet by means of an XOR operation. The set $C$ is constructed iteratively, such that in each step of the algorithm one symbol is added to the previously constructed set. $\overline{C}$ denotes the set of symbols that node $x$ has in its buffer and that are not in $C$ (it can be calculated as $\overline{C}=B_x \backslash C$).

Finally, we will denote by $R(C)$ the set of neighbors of node $x$ that are able to recover a new symbol from the XOR of all symbols in a given set $C$. This means that, if there are $y$ symbols in set $C$, the neighbor must have already recovered exactly $y-1$ of the symbols in $C$. Therefore, we have that $R(C)=\{j: |C|-|B_j\cap C|=1 \}$.

We are now ready to give a formal definition of the Opportunistic scheme, presented in \cite{Online_broadcasting_with_NC}.
\begin{small}
\begin{algorithm}[Opportunistic Algorithm \cite{Online_broadcasting_with_NC}]
\caption{Opportunistic algorithm \cite{Online_broadcasting_with_NC}}
\label{oppor}
$_{}$\\
$_{}\ \ \ \ \ \ \ \ C=\emptyset$\\
$_{}\ \ \ \ \ \ \ \ S=\{s \in B_x: |R(\{s \})|>0 \}$\\
$_{}\ \ \ \ \ \ \ \ \textrm{while } |S|>0$\\
$_{}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \textrm{choose } s^*\in S$\\
$_{}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \textrm{add } s^* \textrm{ to } C$\\
$_{}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ S=\left(\bigcap \limits_{j\in R(C)} (B_j\cap B_x) \right) \cap \overline{C}$\\
$_{}\ \ \ \ \ \ \ \ \textrm{end}$\\
$_{}\ \ \ \ \ \ \ \ p=\bigoplus \limits_{s\in C} s$\\
$_{}\ \ \ \ \ \ \ \  \textrm{transmit $p$.}$
\end{algorithm}
\end{small}
As shown in Algorithm 1, the set $C$ is initialized to the empty
set. Throughout the algorithm, set $S$ represents the set of symbols
that can be added to the current configuration of set $C$. Initially,
all the symbols that are in the buffer of node $x$ and that are not in
the buffer of at least one neighbor are deemed to be possible
candidates. Thus, because $R(\{s\})$ is the set of neighbors that has
not recovered symbol $s\in B_x$, $S$ is initialized according to
$S=\{s \in B_x: |R(\{s \})|>0 \}$.

Next, we focus on the loop in the algorithm, which will only continue
while $S$ is non-empty. As long as there are symbols in $S$, the
algorithm chooses one of them randomly and adds it to $C$. After this
step, it is necessary to update $S$, from which the algorithm chooses
new symbols to add to $C$.

The new set $S$ is defined as the set of symbols that satisfy the
following conditions: (1) they are present in the buffers of the
neighbors in $R(C)$ (i.e. those neighbors that are able to recover a
new symbol from the current set $C$), (2) they are stored in the
buffer of node $x$; and (3) they were not chosen up to this
step. Thus, the new set $S$ can be determined according
to $$S=\left(\bigcap \limits_{j\in R(C)} (B_j\cap B_x) \right) \cap
\overline{C}.$$

When the loop finishes, all the symbols in set $C$ are XORed together
and the corresponding packet is sent to the neighbors of node $x$.

\begin{figure}[t]
  \includegraphics[width=0.45\textwidth]{example_rui.eps}
  \caption{An example of a network, in which node ${\bf X}$ has to make coding decisions based on the buffer state of ${\bf 3}$ neighbors: ${\bf N_1}$, ${\bf N_2}$ and ${\bf N_3}$. For each neighbor, the figure depicts only that part of the buffer which contains symbols that are also stored in the buffer of node ${\bf X}$. }
  \label{fig:example}
  \vspace{-1.5em}
\end{figure}

The ideas behind the algorithm are best described by the example shown
in Fig.~\ref{fig:example}. Node $X$ (the node performing the coding
algorithm) has in its buffer symbols $s_1$, $s_2$, $s_3$ and
$s_4$. From Fig.~\ref{fig:example} it is clear that some of these
symbols can also be found in the buffers of the three neighbors of
$X$. It follows that there are only two possible output packets which
are optimal in the sense that they maximize the number of neighbors
able to recover a new symbol immediately upon reception: $p=s_1\oplus
s_2$ or $p=s_2\oplus s_3$. In both these cases, all neighbors are able
to recover a new symbol from the received packet, since they have one
and only one of the symbols therein. Moreover, no other combination of
symbols can provide a packet that immediately provides a new symbol to
every neighbor.

We now analyze the behavior of the Opportunistic algorithm. The
initial set $S$ (from which we can choose a symbol to be mixed in the
packet) is $S=\{s_1,s_2,s_3,s_4 \}$, due to the fact that none of the
neighbors has recovered all the symbols in the buffer of node
$X$. Thus, in the first iteration, each symbol in $S$ can be chosen
with probability $1/4$. Suppose that the algorithm chooses $s^*=s_1$
(again with probability $1/4$). Then, we have that $C=\{s_1\}$ and
$R(C)=\{N_2,N_3\}$. Recall that $R(C)$ is the set of neighbors that
have recovered all but one of the symbols in $C$ (in this case, it is
the set of neighbors who have not yet recovered symbol $s_1$). Since
$S$ is the set of symbols that (a) all the neighbors in $R(C)$ have
already recovered and (b) have not yet been chosen, we have that
$S=\{s_2\}$. In the second iteration, since $S=\{s_2\}$, the algorithm
chooses $s^*=s_2$ and sets $C=\{s_1, s_2\}$. Thus, $R(C)$ is
equivalent to the entire set of neighbors and, since there are no more
symbols recovered by the ensemble of neighboring nodes, we have that
$S=\emptyset$. Hence, the algorithm stops and outputs the packet
$p=s_1\oplus s_2$, which can be classified as an ideal packet.

We have seen that the algorithm outputs an ideal packet if the first
chosen symbol corresponds to $s_1$ (and that this happens with
probability $1/4$). Analogously, if the algorithm chooses $s_3$ first,
then $C=\{s_3\}$ and $R(C)=\{N_2,N_3\}$, yielding $S=\{s_2\}$. Hence,
in the next step the algorithm chooses symbol $s_2$ which will lead to
$S=\emptyset$. It follows that if the algorithm starts by choosing
symbol $s_3$, then we get the ideal output packet $p=s_2\oplus s_3$.

Suppose now that the algorithm starts by choosing symbol $s_2$. In
this case, we have that $C=\{s_2\}$ and $R(C)=\{N_1\}$. Since $S$ is
the set of unselected symbols recovered by the ensemble of neighbors
in $R(C)$, we have that $S=\{s_1,s_3,s_4\}$. Hence, in the second
iteration, the algorithm has a probability $1/3$ of choosing each of
the symbols in $S$. If the algorithm chooses symbol $s_1$
(respectively, symbol $s_3$), based on the same arguments as in the
previous cases, we deduce that the output packet will be $p=s_1\oplus
s_2$ (respectively, $p=s_2\oplus s_3$), which is an ideal packet. In
case the algorithm chooses $s_4$, the output will not be an ideal
packet.  Thus, the probability that the algorithm outputs an ideal
packet is given $$
\frac{1}{4}+\frac{1}{4}+\frac{1}{4}\left(\frac{1}{3}+\frac{1}{3}
\right)=\frac{2}{3}. $$

It is worth noting that in this algorithm, the sole criterion for the
choice of symbols to be mixed in the output packed is to ensure that a
node which is able to recover a new symbol from the current set $C$
(constructed up to the given iteration), will continue to be able to
recover a new symbol from the instances of $C$ that are constructed
after that iteration. In other words, after the choice of the first
symbol (which is performed randomly), the algorithm simply ensures
that the number of neighbors that are able to recover a new symbol
does not decrease with the next decisions.
\section{Simulation Results}
\label{sec:results}

In this section we present and discuss the performance of the
aforementioned coding algorithms in various scenarios. The main
part of the analysis assumes the simple decoding
algorithm.

We discuss the enhancement of performance provided by the use of a
full decoding scheme at the end of this section.  The following
performance metrics are of interest for the analysis of the
algorithms. The recovery rate of a node is the number of original
packets recovered by the node as a function of the total number of
packets received by the node. It measures the speed of the
dissemination process and thus the overall efficiency of the
protocol. This metric is crucial in communication networks, especially
for delay-sensitive applications such as real-time media streaming,
where the disseminated segments must be recovered by the receivers
within strict time intervals.  We average the recovery rates over all
nodes and repeated the simulations several times so as to get tight
confidence intervals. For the recovery rate shown in the next plots,
the confidence intervals are all within $\pm 2 \%$. These intervals
are omitted from the figures for the sake of readability.

The codeword degree is measured as the number of original symbols
which are combined to form a (coded) packet and we calculate
the {\it average codeword degree} over all nodes.  For the analysis of
the single-hop scenario we also consider {\it packet delay} as defined
in Definition 2 of \cite{Online_broadcasting_with_NC}. The delay that
a receiver experiences is the total number of received packets that do
not allow immediate recovery of a new original symbol. Again we
consider this delay averaged over all nodes.  Finally, the {\it
information potential} is the number of original symbols that are
available at a given neighboring node, but not at the node itself. This
metric is useful for analyzing the overlap between the information
recovered by a target node and by its neighbors. It provides a measure
of the efficiency of the coding process and gives an insight into the
degree of freedom available for making coding decisions.

The simulation results have been obtained using a custom C++
simulator. It provides an ideal (collision-free) MAC layer, with a
sequential or random scheduling of packets. All transmissions use
physical layer broadcast.

\subsection{Single-hop Scenario}

In this setting, a single source node broadcasts $100$ original
symbols to its $100$ neighbors over independent erasure channels and,
as in \cite{Online_broadcasting_with_NC}, perfect feedback is
available from the receivers to the source. For all the algorithms in
our analysis, i.e. Greedy, Equalizing, Opportunistic and ANC, the
source node starts by sending out all the original symbols in uncoded
form. It is only after this initial stage that the source node sends
encodings of the original symbols as described in Sections
\ref{sec:related} and \ref{sec:algorithms}. The erasure probability is
set to $0.5$.

\begin{figure}[t]
  \includegraphics[width=0.51\textwidth]{paper_1hop_camera_ready_IMMDEC_2.eps}
  \vspace{-1.5em}
  \caption{Recovery rate (top) and average codeword degree (bottom) for the single-hop scenario.
  }
  \label{1hop}
  \vspace{-1.5em}
\end{figure}

In Fig.~\ref{1hop}, the Greedy algorithm shows the best performance in
the single-hop scenario. With this algorithm, all nodes achieve the
full recovery of the $100$ original symbols within $150$ information
packets received. From the first 100 uncoded packets the receivers
miss around 50 original packets and they differ from node to node due
to the random erasure pattern.  This allows the Greedy algorithm to
increase the degree of the coded packets compared to the Opportunistic
and ANC algorithms between 50 received packets and 120 received
packets. When the process approaches the full recovery state, the
number of nodes still missing some packets decreases and low
degree codewords are sufficient to serve these nodes. Given the diversity of
missing information among all the receivers and the large amount of
information potential of the neighbors (due to the erasure pattern),
the degree of freedom for making coding decisions lets the source node
perform the coding that best allows a large number of receivers to
immediately recover a new symbol from the sent packet.

The Equalizing algorithm has a considerably worse recovery rate than
the other algorithms. The reason is visible in Fig.~\ref{1hop},
bottom, where Equalizing starts using higher and higher codeword
degrees quite early on. Since it is designed to provide an immediately
decodable packet to the neighbor(s) which recovered the least number
of original symbols, many other neighbors are not able to decode the packet
--- their composition of recovered packets differs from those poor
nodes. Focusing only on the poor nodes results in a packet that despite
its high degree is useful only for few receivers.

Surprisingly, the performance of ANC and of the Opportunistic
algorithm is almost the same for most of the simulation. The
Opportunistic algorithm, which allows the source node to use the
neighborhood status information to make the coding decisions, performs
just slightly better than ANC.  Due to the large information potential
of the neighbors and to the huge diversity of missing information
among the receivers, the choice of which symbols to encode is not
crucial, as long as the number of symbols that are combined is the
same. Only at the very end, the information obtained from the
receivers by the Opportunistic algorithm allows the source node to
send the last few missing symbols without wasting time sending packets
that carry encodings of randomly picked symbols.

Focusing the analysis on the {\it packet delay}, we note that the
Greedy algorithm achieves the lowest packets delay of only $30$ not
useful packets (averaged over all receivers), followed by the
Opportunistic algorithm with $75$, ANC with $90$ and Equalizing with
$100$, as could be expected from the previous analysis.

\subsection{Multi-hop Scenario}

In the multi-hop scenarios, each of the $100$ nodes generates one
original symbol that is intended to be delivered to every other node
in the network.

\subsubsection{Static grid}

\begin{figure}[t]
\includegraphics[width=0.51\textwidth]{paper_grid_wrap_camera_ready_IMMDEC_2.eps}
     \vspace{-1.5em}
  \caption{Recovery rate (top) and average codeword degree (bottom) for 100 nodes on a static grid.
  }
  \label{grid_wrap}
   \vspace{-1.5em}
\end{figure}

In this setting, nodes are placed on a static grid (that wraps around)
and each node has eight neighbors to communicate with. In
Fig.~\ref{grid_wrap}, top, the algorithm with the best performance is
the Greedy one (as in the previous scenario), but now the difference to the
performance of the Equalizing algorithm is much smaller. From the
analysis of their respective average codeword degrees, in
Fig.~\ref{grid_wrap}, bottom, we see that the coding degree of our two
algorithms is very similar, except for the very end where the Equalizing
algorithm takes longer than the Greedy algorithm to increase
the coding degree for recovery of the last missing symbols.

The high degree of correlation of the information recovered by the
neighbors, due to the wrap around and the symmetrical topology, and
the consequently minor diversity of information stored by the
neighbors compared to the single-hop setting, makes the use of packets
with high degree ineffective. Moreover, choosing which original
packets to combine has a huge impact on the performance of the
dissemination process. To visualize this, we also show the recovery
rates achieved by the algorithms corresponding to Greedy and
Equalizing when we limit the codeword degree to one, i.e., only an
uncoded packet is sent. In the top graph of Fig.~\ref{grid_wrap}, we
plot only Greedy with codeword degree one, but both of the algorithms
perform the same. Even in this limited case, the recovery rates of our
algorithms are very close to the recovery rate of the Opportunistic
algorithm \emph{with} coding. The few degrees of freedom for making
coding decisions, typical of this setting, limit the performance of
the Opportunistic algorithm, where the first symbol is chosen
randomly.

Finally, the impact of using neighborhood recovery status in the
coding decisions is obvious when we compare the recovery rate of the
ANC algorithm with the recovery rates of the other algorithms.  For
instance, the total number of received packets necessary to achieve
full recovery is, in the case of ANC, several times larger than in the
case of Greedy, while the average codeword degree is quite similar for
most of the values of received packets.

\subsubsection{Static random and clustered networks}

\begin{figure}[t]
  \includegraphics[width=0.51\textwidth]{paper_random_imm_camera_ready_IMMDEC_2.eps}
   \vspace{-1.5em}
  \caption{Recovery rate (top) and average codeword degree (bottom) for the static random network, 100 nodes.
  }
  \label{random}
   \vspace{-1.5em}
\end{figure}

In this section, we consider two different scenarios: static random
topologies with an average density of 8 nodes per communication range,
and clustered networks. These static networks are relatively sparse,
which means that the information potential of neighbors is smaller
than in the grid network and that the diversity of information stored
by the neighbors is lower. The high degree of correlation among the
original symbols recovered by the nodes explains why the Greedy,
Equalizing and Opportunistic algorithms perform similarly
(Fig.~\ref{random}). No degree of freedom for making specific coding
decisions is left to these algorithms, so that the differences are
small. Also in such settings, ANC cannot perform well given the
extremely low level of diversity of information among nodes.  In
Fig.~\ref{overlap}, top, we show the low information potential.  Only
Greedy and Equalizing increase the diversity of information among
neighbors at the beginning of the simulations, as expected from the
description of their coding mechanism in Section
\ref{sec:algorithms}. After ~$75$ packets received, all algorithms
experience the same neighborhood information potential (due to the
natural progressive lowering of the diversity of information over
time).  The differences of the performance in terms of recovery rate,
among Greedy, Equalizing and Opportunistic algorithms increase
slightly for larger node densities (not shown here). Such settings are
closer to the characteristics of a grid topology concerning the
degrees of freedom for making coding decisions.

The delay experienced by nodes using the Greedy and Equalizing
algorithms in static sparse random and clustered networks shows an
interesting result. For each setting in analysis, the average delay is
practically the same for the two algorithms, however the worst delay
(i.e. the delay experienced by the node with the highest delay) of the
Greedy algorithm is up to $5$\% higher than the worst delay of the
Equalizing algorithm.  Since the Equalizing algorithm will always try
to provide an immediately decodable packet to the neighbor with the
lowest number of recovered original symbols, this will obviously
improve worst case delay.

\begin{figure}[t]
\includegraphics[width=0.51\textwidth]{paper_overlap_final_camera_ready_IMMDEC_2.eps}
   \vspace{-1.5em}
  \caption{Neighborhood information potential for static random network (top) and moderate mobility scenario (bottom).}
  \label{overlap}
   \vspace{-1.5em}
\end{figure}

\subsubsection{Moderate mobility}

In this scenario, we consider nodes moving according to a random
waypoint mobility model with speeds uniformly distributed in the
interval $[2,4]\: m/s$. Again, the node density  allows on average for eight
neighbors per node.  We assume perfect information about the neighbor recovery status.

\begin{figure}[t]
  \includegraphics[width=0.51\textwidth]{paper_mob2to4_camera_ready_IMMDEC_2.eps}
   \vspace{-1.5em}
  \caption{Recovery rate (top) and average codeword degree (bottom) for the mobile scenario, 100 nodes.
  }
  \label{mobile}
   \vspace{-1.5em}
\end{figure}

In Fig.~\ref{mobile}, top, we notice that the performance of the
algorithms under consideration in terms of recovery rate is somewhat
similar to the one observed in the static grid setting (Fig.
\ref{grid_wrap}).  Due to the mobility of the nodes, the correlation
among the original symbols recovered by neighbors is much lower in the
case of moderate mobility than in the case of a static grid or static
random networks. An insight into the differences of information
potential of neighbors for two extreme cases is given in
Fig.~\ref{overlap}: on the top the random static network shows a lower
diversity of information among neighbors than in the mobile case,
bottom, where the information potential with our algorithms can
achieve very high values of up to $40-50$, whereas for the other
protocols the measured values are close to $0$. As seen in the
previous section, our algorithms allow nodes to maintain a high
diversity of information in the neighborhood and thus a high degree of
freedom for the coding decisions.

It is also important to notice that the coding degree of the
Equalizing algorithm is always higher than the coding degree of the
Greedy algorithm. This observation and the fact that the recovery rate
of the Greedy algorithm is higher than the recovery rate of the
Equalizing algorithm let us conclude that the Equalizing algorithm
builds packets with a too high codeword degree, rendering these
packets not immediately decodable. We will see later on in this paper
that, if we allow the use of a full decoding process, i.e., all the
packets in the buffer (decoded and undecoded) are considered for the
decoding algorithm, the recovery rate of the Equalizing algorithm can
actually surpass the recovery rate of the Greedy algorithm.

\subsection{Performance gains using a complete buffer decoding mechanism}

In this section, we investigate the benefits of full decoding, which
is more efficient (in the sense that it does not discard useful packets)
but also more costly in terms of energy, memory
requirements, and processing. Up to now, we were considering a scheme
were only the immediately recovered original symbols were considered
for the simple decoding process. Here, all the packets received
(decoded and undecoded) are taken into consideration when performing
the decoding of the received packets.  In the following figures, we
omit the plot of the average codeword degree for the sake of
readability of the recovery rate. Also, the average codeword degree of
the algorithms using a full decoding scheme is almost the same as the
one with the simple decoder, except for a slight increase of the
average codeword degree.

As we already mentioned in the previous analysis, it is expected that
the recovery rate significantly increases with the full decoding
scheme, since the algorithms often produce packets that are not
immediately decodable for some neighbors but that are innovative. The
node is not able to recover a new original symbol from the received
information packet since it did not yet recover the required other
original symbols that form the coded information packet. By storing
these not immediately decodable but innovative packets in the buffers
and considering these packets in the decoding process, nodes can find
these packets helpful later on, when more and more (innovative or
immediately decodable) packets are received, increasing the recovery
rate of the algorithm. This benefit can be observed in
Fig.~\ref{1hop_gauss}, where the recovery rate of the algorithms using
a full decoding mechanism (including the Systematic Random Network
Coding algorithm) is plotted for the single-hop scenario.

\begin{figure}[t]
\includegraphics[width=0.51\textwidth]{paper_1hop_gauss_camera_ready_GAUSS_2.eps}
   \vspace{-1.5em}
  \caption{Recovery rate for the single-hop scenario with simple and full decoding schemes.
  }
  \label{1hop_gauss}
   \vspace{-1.5em}
\end{figure}

Comparing the results obtained with this full decoding scheme to the
results obtained with a simple decoding scheme, we can observe a major
improvement of the recovery rate of the Equalizing algorithm.  With
the full decoding scheme, the Equalizing algorithm has a recovery rate
that slowly increases after the initial phase (where the nodes first
send the original symbols uncoded once) and, at around $80$ packets
received, shows a ``smooth'' step behavior that is typical of the
random network coding algorithms (e.g.  Systematic Random Network
Coding). The Equalizing algorithm reaches the full recovery state
before the Greedy algorithm. However, the recovery rate of the Greedy
algorithm is higher than the one of the Equalizing algorithm before
the step behavior takes place.

We also consider the packet delay when using a full decoding
mechanism. The delay presented by Greedy with this decoding scheme is
$10$ lower than the delay obtained when using the simple decoder, with
a total averaged delay of $20$. In \cite{Online_broadcasting_with_NC},
none of the algorithms proposed for the single-hop scenario is able to
achieve such a low delay (in the same conditions as used here). The
Equalizing algorithm experiences only an average delay of $40$
packets, ANC of $70$ and the Systematic Random Network Coding of $50$
as in \cite{Online_broadcasting_with_NC}.

After analyzing the performance enhancements achieved by using a full
decoding scheme in the single-hop scenario, we now discuss the results
obtained for the multi-hop scenario with moderate mobility.  We have
chosen this particular setting of the multi-hop scenario because the
performance of the algorithms in the other settings is similar to the
one presented in Fig.~\ref{grid_wrap}, although there are some
differences that should be pointed out. With a full decoding scheme,
the Greedy and Equalizing algorithms have quite similar
performance. Moreover, the enhancements achieved by the other
algorithms when using a full decoding scheme are negligible except for
the ANC algorithm, for which the performance is still far from the
performance achieve by our algorithms.

\begin{figure}[t]
\includegraphics[width=0.51\textwidth]{paper_mob_gauss_camera_ready_GAUSS_2.eps}
   \vspace{-1.5em}
  \caption{Recovery rate for the multi-hop mobile network, with simple and full decoding schemes.
  }
  \label{mobile_gauss}
   \vspace{-1.5em}
\end{figure}

In Fig.~\ref{mobile_gauss}, we can again see that in the moderate
mobility scenario and with a full decoding scheme, there is a major
improvement of performance of the Equalizing algorithm. The recovery
rate of the Equalizing algorithm comes very close to the recovery rate
of the Greedy algorithm until around $80$ packets received (similar to
the behavior in the single-hop scenario). After this value, the
Equalizing algorithm is faster in recovering new original packets,
reaching the full recovery state $10$ packets before the Greedy
algorithm.  It is also interesting to notice that there is no
significant difference in terms of recovery rate between the
two decoding mechanisms in combination with the Greedy
algorithm. This behavior was
expected, since the Greedy algorithm was designed for immediate
decoding. Few not immediately decodable packets mean that a complete
buffer decoding mechanism can just slightly outperform a simple
decoder.

redpajama_set_name: RedPajamaArXiv


text:
Marines Armor-Up Amphibious Assault Vehicles
byWarriorMaven
The Marines are moving along with building an upgraded, stronger, more high-tech Amphibious Assault Vehicles
By Kris Osborn
The Marine Corps is revving up its fleet of 1970s-era Amphibious Assault Vehicles to integrate the latest technology and make them better able to stop roadside-bombs and other kinds of enemy attacks, service officials said.
The existing fleet, which is designed to execute a wide range of amphibious attack missions from ship-to-shore, is now receiving new side armor (called spall liner), suspension, power trains, engine upgrades, water jets, underbelly ballistic protections and blast-mitigating seats to slow down or thwart the damage from IEDs and roadside bombs, Maj. Paul Rivera, AAV SU Project Team Lead, told Scout Warrior
"The purpose of this variant is to bring back survivability and force protection back to the AAV P-variant (existing vehicle)," he said.
The classic AAV, armed with a .50-cal machine gun and 40mm grenade launcher, is being given new technology so that it can serve in the Corps fleet for several more decades.
"The AAV was originally expected to serve for only 20-years when it fielded in 1972. Here we are in 2016. In effect we want to keep these around until 2035," John Garner, Program Manager for Advanced Amphibious Assault," said in an interview with Scout Warrior.
The new AAV, called AAV "SU" for survivability upgrade, will be more than 10,000 pounds heavier than its predecessor and include a new suspension able to lift the hull of the vehicle higher off the ground to better safeguard Marines inside from being hit by blast debris. With greater ground clearance, debris from an explosion has to farther travel, therefore lessening the impact upon those hit by the attack.
The AAV SU will be about 70,000 pounds when fully combat loaded, compared to the 58,000-pound weight of the current AAV.
"By increasing the weight you have a secondary and tertiary effects which better protect Marines. We are also bringing in a new power train, new suspension and new water jets for water mobility," Rivera said.
A new, stronger transmission for the AAV SU will integrate with a more powerful 625 HP Cummins engine, he added.
The original AAV is engineered to travel five-to-six knots in the water, reach distances up to 12 nautical miles and hit speeds of 45mph on land – a speed designed to allow the vehicle to keep up with an Abrams tank, Corps officials said.
In addition, the new AAV SU will reach an acquisition benchmark called "Milestone C" in the Spring of next year. This will begin paving the way toward full-rate production by 2023, Rivera explained.
The new waterjet will bring more speed to the platform, Rivera added.
"The old legacy water jet comes from a sewage pump. That sewage pump was designed to do sewage and not necessarily project a vehicle through the water. The new waterjet uses an axial flow," Rivera said.
The new, more flexible blast-mitigating seats are deigned to prevent Marines' feet from resting directly on the floor in order to prevent them from being injured from an underbelly IED blast.
"It is not just surviving the blast and making sure Marines aren't killed, we are really focusing on those lower extremities and making sure they are walking away from the actual event," Rivera said.
The seat is engineered with a measure of elasticity such that it can respond differently, depending on the severity of a blast.
"If it's a high-intensity blast, the seat will activate in accordance with the blast. Each blast is different. As the blast gets bigger the blast is able to adjust," Rivera said.
In total, the Marines plan to upgrade roughly one-third of their fleet of more than 900 AAVs.
The idea with Amphibious Assault Vehicles, known for famous historical attacks such as Iwo Jima in WWII (using earlier versions), is to project power from the sea by moving deadly combat forces through the water and up onto land where they can launch attacks, secure a beachhead or reinforce existing land forces.
Often deploying from an Amphibious Assault Ship, AAVs swim alongside Landing Craft Air Cushions which can transport larger numbers of Marines and land war equipment -- such as artillery and battle tanks.
AAVs can also be used for humanitarian missions in places where, for example, ports might be damaged an unable to accommodate larger ships.
Alongside this ongoing effort to modernize the existing fleet of AAVs, the Corps is also constructing a new, wheeled Amphibious Combat Vehicles, or ACVs. These new platforms will include a wide range of next-generation technologies, travel much faster, deploy from much farther distances and perform at a much higher level across the board compared with the existing fleet.
Navy Arms Destroyers With New High-Powered Laser - Changes War Tactics
China Likely Tested Missiles That Can Kill Aircraft Carriers in the South China
Navy Fast-Tracks New Large "Strike Warfare" Attack Surface Drones
Why Didn't the U.S. Navy Build More Seawolf-Class Submarines?
Video Special: Navy Captain Describes Building a Nuclear-Armed Submarine- Fast
Video: US Missile Defense Breakthrough: Navy Ships to Destroy Enemy ICBMs
These U.S. Navy Patrol Boats Would Be the First to Fight Iran in a War
A Simulated War Game Shows How China Could Win in the South China Sea
Could the U.S. Military Lose a Carrier in a War with Iran?

redpajama_set_name: RedPajamaCommonCrawl


Pour the milk in blender. Add cottage cheese and blend at medium speed until smooth. About 45 seconds.
Add the pudding mix and blend on high until thick and creamy about 45 more minutes. Stopping blender to stir with spoon and scrape down sides as needed.
6 popsicle molds. Insert wooden sticks and place in freezer for about 3 hours.


Tom Wilson is in the penalty box.
Music to my ears. Capitals fell 4-2 to the Montreal Canadiens in what has been a shaky preseason, but at least Tom Wilson is in midseason form.
Green Bay's O-line led by Bryan Lulaga and David Bakhtiari, have given up 4 sacks in each of the two games this year. Meanwhile, only Matt Ioannidis and Ryan Anderson are the only Redskins to record a sack thus far. Kerrigan and Preston Smith have remained quiet with 5 tackles between the two of them. One of these units is going to have to win this battle, and it could very well be the Skins' pass rush. Kerrigan is only a year removed from back to back dougle digit sack and will be hoping to find a groove, as he usually starts the season off pretty fast. Add in the fact that Aaron Rodgers is playing on one leg (which takes him from God-like to slightly less God-like), and it looks like a game where Manusky's unit can come out on top.
Davante Adams, Geronimo Allison, and Randall Cobb all have over 100 yards and are averaged at least 10 yards per catch. They also have scored 5 TDs between the three. Adams is the biggest threat as Rodgers has targeted him 20 times and owns 5 of the teams 10 red zone targets. Since Adams primary plays on the left side, that means he will match up most of the game against the surprisingly impressive Quinton Dunbar. Dunbar has stood out on defense this season with 5 passes defended and 1 INT. The match up Green Bay would love to exploit is Randall Cobb in the slot vs Fabian Moreau, which is the most advantageous for them.
Alex Smith has been exactly what he's been expected. He wasn't to blame for the loss to Indy, but he hasn't wow'd anyone. Green Bay gave up 425 yards and 4 TDs to Kirk Cousins at home last week. Davon House replaced injured starter Kevin King(groin) but is currently nursing a bicep injury, leaving Green Bay with Tramon Williams and two rookies at CB. Paul Richardson and Josh Doctson should win these battles, but they've been less than stellar. Green Bay plays a lot of Cover 1 with a single high safety, so Smith could exploit the young corners who play on the right side. This is a tough match up to call, but if Smith gets into a rhythm early, we may see a Doctson coming out party.
Not exactly inspiring hope with that answer, Coach. But I will give him the benefit of the doubt here and say he was just being honest. Floyd has produced in the past but has short tenures with the Patriots, Vikings, and Saints since 2016. Perriman is only 25 but a series of knee injuries and setbacks have cost him time. There is no guarantee either player will fit in Gruden's passing attack, but at this point, it couldn't hurt to see if they can muster up any sort of spark in the air.
The first look at Washington's new signing at practice.
Jay Gruden: Paul Richardson missed practice with a shoulder injury. MRI on knee was negative.
PRich: Knee, huh? Nah, nothing there. Did have test on the shoulder. We'll see.
I guess they were signed as insurance for Richardson's sho-…. kn-…. whatever he's dealing with.
Ale to the Redskins! Get it? These beers will be on sale at FedEx Field at all the Devil's Backbone stands this season.
I need a gif of T.J. Oshie drinking one of these through his shirt while Tom Wilson cheers him on.
Skins tied for first. I am the point where I feel dangerously over optimistic about this team. We're on to Indianapolis!
Starting the season off right is correct. In a huge road win with a new quarterback, Jay Gruden won a season opening game for the first time. Every unit on this team seemed to be firing on all cylinders. Alex Smith went 21-30 for 255 yards and 2 TDs. He was boring, and that's a good thing. Boring doesn't throw a bad interception when the team is driving. Adrian Peterson racked up 166 yards and 1 rushing TD on 28 total touches while Chris Thompson had 128 yards and 1 receiving TD on just 11 touches. This duo is exciting, Peterson is the workhorse he wanted to be in New Orleans and Arizona while Thompson continues to be the change of pace back who can hit the home run at any time. If these two are healthy, the Skins will have a dangerous backfield. Jordan Reed scored a touchdown as well, he would finish with 4 rec and 48 yards.
*Ahem: Jesse James take notes.
Defensively, there was not much to complain about. The Cardinals only ran 49 plays netting 213 yards. David managed 37 yards on 9 carries with a touchdown on the goaline. Quinton Dunbar was the stand out performance to me. The Skins went out and signed FA CB Orlando Scandrick then subsequently cut him during training camp, effectively naming Dunbar the starter opposite Josh Norman. Dunbar finished the day with 5 tackles, 3 passes defended, and 1 int. Pretty impressive numbers for a guy who was playing wide receiver just three seasons ago.
Quinton Dunbar is feeling it today.
All in all, Skins fan have good reason to feel optimistic. The run game is lethal, Alex Smith is leading the passing attack, the front seven on defense looks stout. Next week will be a big test for the secondary as they face Andrew Luck and the Colts.
Still a no-go on Dez Bryant. I don't hate that either. As much as I wanted a big name WR this offseason, I never wanted it to be Dez because I never thought he would fit in. I do hope Dez lands a gig somewhere. Maybe Cleveland.
Welp. There it is. I thought Perine might have gotten the ax with Bibbs a suitable back up for Chris Thompson. Bibbs could be a candidate to return as Kelley and Perine are on short leashes.


text:
int main(int arg, char * argv[])
{
        int distance = 100;
        float power = 2.345f;
        double superpower = 56789.4532;
        char initial = 'P';
        char first_name[] = "Sam";
        char last_name[] = "Davies";

        printf("You are %d miles away.\n", distance);
        printf("You have %f levels of power.\n", power);
        printf("You have %f awesome superpowers.\n", superpower);
        printf("I have an initial %c.\n", initial);
        printf("I have a first name %s.\n", first_name);
        printf("I have a last name %s.\n", last_name);
        printf("My whole name is %s %c. %s.\n", first_name, initial, last_name);
        printf("");

        return 0;
}

redpajama_set_name: RedPajamaGithub


text:
QUIET HEROES
Janelle Hanson
During the 2018 Sundance Film Festival, a documentary will premiere depicting the courageous story of physician Kristen Ries, a retired U professor, and her partner (both in life and work) Maggie Snyder. "Quiet Heroes" depicts Ries' selfless career as the first doctor in Utah to treat HIV/AIDS patients at a time when others cruelly turned them away. The film was produced and directed by U graduates, Jared Ruga, Jenny Mackenzie and Amanda Stoddard.
Ries came to Salt Lake City in the early 1980's with the hope and ambition to set up a medical practice of her own. By chance, she arrived on the exact same day the Centers for Disease Control released a report about a new infectious disease affecting populations of gay men. This intrigued Ries and she launched her long career as the premier specialist who treated HIV/AIDS patients. What started as hospice care for highly stigmatized individuals became a model for compassionate care at the height of the AIDS epidemic. When families, churches, social circles, politicians and even other doctors abandoned the affected individuals, Ries and Snyder created a safe haven amidst a sea of hate and fear for those who had nowhere else to turn.
"Ries and Snyder created a safe space and extended family for their maligned patients, many of whom were exiled from their entire social networks and had nowhere else to go," said Ruga. "Over the course of their decades-long practice, they treated thousands of HIV/AIDS patients whom everyone else seemed willing to just let die — particularly in the terrifying early years when survival was the exception."
The film was inspired by a multidisciplinary project started by Terry Kogan, U professor of law, to preserve the work of Ries and Snyder in honor of their heroic contributions to the state. The project, housed in the U's Marriott Library includes oral histories, scrapbooks, letters, obituaries, personal materials and more. Elizabeth Clement, U associate professor of history, processed the archival materials and the oral history component and Leslie Frances, U professor of law, advised on issued related to medial ethics.
"Historians have studied HIV in liberal places like San Francisco and New York, but no work has been done on what happened in conservative places like Utah," said Clement, who also served as a historical consultant on the film. "But of course far more places in the U.S. in the 1980s looked like Salt Lake City, than like New York. Understanding their story will go a long way towards exploring and explaining the national story,"
For more information about the film and screening times, please visit the website.
Photo Caption (from left to right): Maggie Snyder and Kristen Ries.
Updated masking guidelines
Johnson & Johnson vaccine once again available
Keeping each other safe with quarantine and isolation
Explore U of U Health discoveries and innovations


V Touch Solutions (VTS) is a Lucknow (India) based Digital Marketing Company. We Provide Digital Marketing Services like WhatsApp Marketing, DND Bulk SMS, Bulk Voice Call and Bulk Emailing Service. In Other terms you can say we provide WhatsApp SMS, Promotional Bulk SMS, Voice Call Service and Bulk Email Service. We have many years of experience in Digital Marketing and a very huge Client base.
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Creativity, Honesty, Reliability, Integrity and Dedication many more to add value to your BUSINESS. Excellence is not just a catchword, it is an ethics in V Touch Solutions. Everyday at V Touch Solutions is a step towards realizing the goal of customer satisfaction, as we deeply value customer satisfaction.


Discussion of auditory hallucinations in the normal population.
One barrier to understanding or accepting Jaynes's theory for some is the notion that auditory hallucinations played such a prominent role in the bicameral mental model. People tend to keep their voice hearing experiences to themselves. However when people learn of my interest in the subject, they are often more willing to share their personal experiences. As a result, I've heard many interesting voice-hearing stories over the years. I thought it would be interesting to have people post their personal voice-hearing experiences here, both for those who don't hear voices (so that they might better understand the experience) as well as for those who do (to read about the experiences of others).
So, if you are willing, please post your personal voice hearing experiences here. You can of course remain anonymous, or even register a separate account for this forum than the one you normally use to post messages.
1) Whether or not you have shared your experiences before.
2) Whether or not you have sought help or been "officially" diagnosed (to provide a better idea of how many people are not included in "official" estimates).
3) What medications you take (if any) and how helpful (or unhelpful) they have been for you.
4) Alternative therapies you have tried.
Thanks in advance to everyone who participates.
I've been in the full grip of paranoid schizophrenia for a number of years now. The most intense period is now over but I still get softer echoes of previous auditory hallucinations which were quite marked, sharp, and blatant at the height of the initial period of the "illness". Whilst the voices used to be "short, sharp shock" barking orders, it has now receded to the "gallery of whispers" as very accurately portrayed in the TV series "Lost", where individuals on the island are enveloped by a seemingly 360 degree surround sound effect of distinct whispering voices in concert.
At the initial "break point" from reality when the psychosis was in full swing there were complete visual hallucinations. I'm still convinced they were real, except how surreal they were - lone motorcyclists in black, unidentified, always with helmets following me everywhere, and finding me absolutely wherever I went no matter what (this was basically the exact same motorcyclist really,on an identical bike, in identical get up), and also seeing things that would appear and then disappear after I looked away. Quite normal, everyday things like cars for instance. One second there, next second gone.
That only lasted a matter of about three days though when the psychotic break was the most violent.
1. These experiences have been shared previously.
2. "Officially diagnosed", yes. It took about a year and a half for a diagnosis though.
3. I've been on a variety of medications. Though while they alleviate the positive and negative symptoms to greater and lesser degrees respectively, many have quite spectacular side-affects. Some make you even more "ill" than the illness they profess to treat.
Thank you Winston for getting the ball rolling with your very interesting and personal account. I was wondering if, looking back, you feel there were any signs during your childhood that might have foreshadowed the onset of schizophrenia? I had a childhood friend who, although we lost touch many years ago, I've heard now suffers from schizophrenia. He was for the most part normal in childhood although there were some occasional odd behaviors that are hard to describe. For example, while walking, he would occasionally veer from his path and lightly tap his fingers (in almost a petting motion) on a table for no apparent reason. On other occasions, he would crouch down and run his fingers on the ground. On one of these occasions another person asked what he was doing, and it appears his brain confabulated a reason for the (perhaps unconscious) behavior and he responded: "I'm looking for ants." I imagine this specific behavior is unique to this individual but I wonder if looking back you now see any possible signs that there was trouble ahead.
I did indeed display behaviour that foreshadowed the onset of full blown schizophrenia. The similarity in symptoms between schizophrenia and autism I had been aware of particularly as the result of a single newspaper article back when I was a teenager. The main thing that sparked realisation that I was quite possibly displaying signs of schizophrenia was this piece that detailed the differences between these conditions that displayed often enough in a very similar manner.
I can't remember the exact items listed but it struck a chord enough for me to tell my mother that I believed I had "latent" schizophrenia. She dismissed the notion out of hand. The article hit me hard enough for the memory to come to mind quite often, usually after some social malfunction.
The main thing is that I'd always known that there were problems with my intra-personal and inter-personal relations that I couldn't square myself with. Whilst being relatively intelligent and possessing a degree of emotional sensitivity I was often unable to communicate effectively, often leaving other people with a lingering doubt as to my state of mind.
This was a totally unconscious mechanism. I have always viewed myself as "out of synch" with the prevailing social structure, specifically the areas of "unwritten laws" governing things like "small talk" and other such relatively minor aspects to -.
I could never fully put my finger on it, but this feeling of complete incompatibility in relation to the world lingered with me all my life.
It then manifested to being thought of as "awkward" and "uncooperative" in workplace environments when an adult. I was thought of as a disruptive influence. This was a perspective not entirely without some basis in reality. The term "anti-social" was bandied about a lot.
I also would hear people say things which in reality they hadn't uttered. I would ask them: "What did you say just now?"
They would reply, with an incredulous look: "Nothing. What are you talking about?"
And of real significance to me, more than the minor auditory hallucinations, or imaginings, was my inability to hear what people would say to me, when they were right in front of my face. Their mouths would form words, and no sound would come out. This happened a lot, especially under stress of rather simple social interactions that I couldn't cope with without being under tension.
This, more than anything else, seemed to contribute to my "weirdo" status in the community. After all, who "pretends" to not be able to hear what someone says right in front of them? When I had no hearing difficulties whatsoever? My hearing and sight had checked just fine with my GP. Always had done.
There's a bit more to it, but I'll leave it at that for the time being.
Possibly off topic, but the comments of Jaynes about deafness are of note. I am hard of hearing, and also am probably neuro diverse - dyspraxic, aspergic spectrum.
I do wonder if the psychoses and autisms are symptoms of right brain issues, and the neuroses left brain, Damage to both sides - and being left handed - would lead to all sorts of complications!
And yes, I am trying to put psychiatry where it belongs, as a subset of neurology!


text:
Sport & Charity
Marham is a monthly publication. See what is in the latest edition.
Marham's Rally Team
© Pro-Rally photography
This month saw the end of a dream season for the Marham Rally Team.
Having travelled all over the UK competing in the top amateur British Tarmac rally Championship, again the team have brought a collection of honours back to Marham. The Team made up of Wg Cdr Gordon McNair (Team Principal), Sqn Ldr Sally Berris (Team Manager) Driver, Sgt Chris Daykin, Co-driver: SAC Dan Megannity. Mechanics: SAC Neil Beaumont, SAC Brad Carter, SAC Nathan Evans, Cpl Pete Gwilliam, SAC Andy Leonard and SAC Nicci Nelson have finished 8th out of the 116 crews that started the Championship back in February and have collected three top 3 class awards in the process. After a new build of a Subaru Impreza alongside the Chesterfield based "Scooby Clinic" and official support from "Help the Heroes" not only did the team compete against the top teams but raised money for the well known Charity. The Team also supported RAF Recruitment at shows including the famous "Goodwood Festival of Speed" and then by invitation competed against the American driving Star "Ken Block". Heading into the winter season, now sees the car undergoing a complete overall and upgrade ready for the 2013 challenge. Always looking for new Team members, if any personnel are interested in joining the Team for 2013 season please contact Sgt Chris Daykin in E&LW. The Team would like to thank the Station Commander, all line Managers of Team personnel, MT and PE'd Flight for the invaluable assistance over the year.


Currently unavailable. New crop expected December 2019.
Originally a Dutch variety brought to Ontario in 1799 by Mennonite immigrant Urias Martin. Used as a looseleaf variety. Beautiful green leaves tinged with red.


Multiple Sclerosis MS Treatment eBook Cure By Dr. Gary M. Levin - Why Can't Doctors Cure Your MS? The inside story on Dr. Gary M. Levin's remarkable Multiple Sclerosis Cure "Read on to discover what really causes your multiple sclerosis!"
TJ Holmes - What a bargain!
Did Rand's marketing department make a mistake? If you buy this book, you'll get one million random digits for the ridiculously low price of $64.74. That means each digit only costs 0.00006474 cents, and they throw in the deviates for free!
Laurie Wandell - Excellent results.
Excellent product. I will definitely buy more. I sure helped get my tea stained teeth back in shape without having to resort to my bleaching regime which is a pain and makes my nerves all sensitive. Don't accidentally drop your open jar in the bathroom like I did. Even after losing half of it on my cabinet doors, rug and shoes I still have enough to last me a long time because you don't use very much. Definitely worth the price.
This is my second book that talks about meditation, I can say that this is more concise and easy to understand, however, same with the last one, short but more comprehensive.. I got this books because I believed that through yoga and meditation, I can clear my mind and relieve myself from stress.. Nice book.


I received my Ph.D. in biostatistics from Harvard University in 2015, advised by Jun S. Liu and Tianxi Cai. Before joining Carnegie Mellon University in 2017, I was a postdoctoral researcher in the Department of Operations Research and Financial Engineering at Princeton University, working with Han Liu. I have a B.S. degree in applied mathematics from Sofia University.
I have diverse interests in statistics and machine learning. My current research focuses on problems of high dimensional nonparametric estimation, variable selection, inference, and graph property testing. In addition to my theoretical interests, I am also interested in statistical applications in medical settings such as electronic medical records (EMRs) and personalized medicine.


text:
SARAH VINE: Help! My home has been hit by a teenbomb just like Nicky Campbell's
By Sarah Vine for the Daily Mail
TV presenter Nicky Campbell brought us all a little light relief this week when, in a fit of frustration, he 'room-shamed' his teenage daughter on Twitter by posting a snap of her clothes and make-up-strewn floor.
'This is completely unacceptable,' he captioned it. 'You are nearly 19 years old and I am bloody sick of it. I don't care that you were going to tidy it up. It just really p***** me off.'
He has since claimed it was all part of an elaborate internet joke . . . though it looked real enough to me. After all, what parent hasn't been there?
You go into your child's room only to discover that it appears to have been ransacked by burglars. Strange . . . the thieves don't seem to have touched anything else? The TV's still there, as is the hi-fi and the cat. It leaves only one possible explanation: teenbomb.
Pictured: The tweet from Nicky Campbell that sparked criticism online, with social media users accusing him of a 'very unfair' power imbalance
What is it about the teenage brain that simply cannot seem to process the concept of putting things away? A brain that is apparently pathologically incapable of hanging up a towel, turning out a light or putting a plate in a dishwasher?
My mother, who knows everything and is wise, says it's because their frontal cortex — aka the seat of higher cognitive functions in the brain such as emotional expression, problem-solving, judgment and the ability to replace the top on the toothpaste — is not yet fully developed.
They are instead disproportionately governed by the reptilian brain which, judging by the state of Ms Campbell's room (and those of my own dear teenagers') is perfectly content to sleep in a bed of its own dirty pants and half-eaten pizza.
The only comfort is we are not alone: Campbell's post prompted a flurry of pictures from similarly frustrated parents, many of the opinion that, compared to their offspring, Ms Campbell was a veritable model of domestic virtue.
BBC presenter and father-of-four Nicky Campbell finally... What my husband's drugs confession has taught me about...
Indeed, the whole incident seems to have struck a chord with the nation's long-suffering mothers and fathers, many of whom gleefully detailed tales of two-week-old scrambled eggs and fermenting cereals festering inside drawers, posting endless snaps of piles of detritus recovered from beneath beds and behind chests of drawers.
Others weren't so charitable, accusing Campbell of the modern equivalent of putting her in the stocks. Even Labour MP Helen Goodman weighed in, writing: 'Really very, very unfair use of power imbalance. Most inappropriate.' Who says Lefties have no sense of humour, hey?
Nicky Campbell admonished daughter Breagha (far-right, pictured in 2011) in a post on Twitter in which he said her messy room 'really p*****' him off
So if, for example, I write that one of my children (I won't say which one, but it's not my son) managed to attract a small colony of mice to her bedroom, such was the level of rotting comestibles secreted in her room, I might find myself in very hot water indeed.
Personally, then, I'm rather grateful to Campbell for reminding me that I'm not alone in my parental plights. Not that it is a peculiarly modern problem.
As a teenager I once left a ham sandwich under the bed for about a month — in Italy, in summer. After that my mother adopted a simple but effective policy of sweeping away anything she found on the floor into a black bin liner, until eventually my bedroom was as spartan as a nun's. The only way I could get any of it back was by doing chores.
I quickly learned to put things away.
But perhaps the best solution to sloppy teenage behaviour she ever devised was when my brother was 16.
Having recently acquired a girlfriend, he was more than usually confined to his bedroom. That Christmas, I remember, after the whole family exchanged presents, she brought out one beautifully wrapped box and gave it to him.
We were all on tenterhooks as he opened it, expecting some rare wonder. Inside, in tissue paper, was a very small, very padded, very pink bra she'd found amid the detritus under his bed.
His room was immaculate ever after.
It's so card-shop cliche, Harry
I'm always delighted to see pictures of small royals. In fact, I love the shots that the Duke and Duchess of Cambridge periodically release of their brood, many of which are taken by Kate.
But while the Cambridge family album looks fundamentally like any other (albeit slightly better laundered), the Sussexes have decided to pursue a weirdly naff Clintons card aesthetic when it comes to theirs. I'm sorry, but the picture they released of baby Archie on Father's Day was a catalogue of cliches. The arty sepia tones, the tiny fist clutching his father's finger, the glint of the wedding ring.
It's a baby, for heaven's sake, not an advertising campaign. This is pure Instagram influencer pretentiousness.
Prince Harry and Meghan posted an adorable photograph of Archie, six-weeks-old, in a touching tribute on Father's Day (pictured)
The worst part is the coyness of it: we don't really get to see Archie, more an idea of him. It's an image designed to manipulate, not inform. And, like so much about Instagram and social media in general, it holds a false promise of intimacy when in reality it is little more than an act of vanity.
It also puts Harry and Meghan on a par with every other attention-grabbing celebrity whose success is measured by the numbers of followers and 'likes' they attract.
Call me old-fashioned, but royalty should be above such things.
How dismaying that ITV is to ban all-male writing teams in the pursuit of gender equality. Comedy writers should not be judged on gender, rather on whether they are funny. The same is true in other walks of life, from politics to business: what matters is ability, not whether you tick a box.
Which bikini is the cheekiest?
Mass internet outrage over the notion of an online fashion retailer selling a £1 bikini, as worn by a former Love Island contestant whose name no one can be bothered to remember.
Of course fast fashion is crass and exploitative and leads to increased pollution and landfill, all of which are distinctly sub-optimal.
But when, on closer examination, it transpires that said £1 bikini is almost identical in size and scope to the one promoted by Gwyneth Paltrow and priced via her website at £285, it's hard to decide what's most outrageous: selling it for £1 or charging a small fortune.
And while I'm on the subject, I only have a limited time on this planet, which is why I really cannot bring myself to follow Love Island.
I am, however, aware that someone in a bikini has been crying a lot, while someone else, also in a bikini, has been flirting in a predatory manner. For some reason, all this has prompted hundreds of complaints to Ofcom. But isn't this the same format as every series — in which case why are people so up in arms about it? Perhaps pretending to be upset is simply their way of justifying their tawdry voyeurism.
Warning ... stupidity ahead
I was astounded to read about the cyclist ordered to pay compensation to a woman he knocked down after she stepped onto a road without looking because she was too busy staring at her mobile phone. Especially since he had come through a green light and even sounded a bike-horn to warn her.
All road users must be alive to the possibility of carelessness — but surely we can't also be held accountable for gross stupidity.
If you've ever wondered what kind of idiot spends thousands of pounds on so-called 'sports-luxe' fashion (aka overpriced tracksuits), now you have the answer: Madonna, seen here in the kind of ensemble not even a toddler would wear. Let's hope that smile means she's wearing it for a bet.
As someone who regularly finds herself staring into three sets of soulful brown eyes, especially at mealtimes, I must confess I feel slightly cheated to discover far from being an expression of undying love and loyalty, my dogs' expressions are actually a cunning evolutionary strategy designed to trigger a nurturing response in humans. Next thing you know we'll discover they've found a way to turn humans into their slaves without us even noticing. Oh, wait . . .
I can't fathom why Rory Stewart's opponents are targeting him on the basis that he won't confirm or deny working for British intelligence in his 20s. Everyone is entitled to a misspent youth, and if Rory chose to spend his working in defence of his country, then who are we to question that decision?


Barlow on Brazil, our police state, and Delta Airlines CAPS plan in action.
question whether I will be able to remain in America.
This is not how it was at Laguardia.
"That's a pen. Here. Let me show you."
"That's a battery for my laptop. Look, it has Apple's logo on it."
"Sir, could you tell me why you have three cigarette lighters in your bag?"
"I didn't know I had any cigarette lighters at all." I didn't either.
a lovely little pair of silver scissors.
see their point. It has been nice being in a George Bush-free zone.
whether personal, cultural, or political.
is to believe that they deserve it from him.
people were crying at the sight of you."
for raconteureal purposes, to some other moment already passed.
singer/composer Caetano Veloso, about whom I knew little then.
having heard of the Rolling Stones.
of Brazilian Portuguese, I have enjoyed watching it being spoken.
sounding tongue should at least ease its acquisition a little.
mess with them anyway. They're much too nice.
The central feature of Salvadorian Carnival is the "trio electrico,"
generators driving eardrum-bruising speaker banks and light shows.
and they are a kind of club organized to celebrate Carnival together.
pharmaceutical-quality LSD, and a Terry Gilliam film.
I'm not having fun now.
to the Williamsburg Bridge down to one lane.
11 months. They were the only thing that has made this day tolerable.
pay for, but surely it doesn't have to be so starkly fair as this.
presence of this horror simply defeat me?
dissident - would relate to this tragedy. I think I know.
beyond a certain point, I simply have to let it be."
I wonder if I can let it be. Particularly since it appears I have no choice.
I will re-engage this enterprise as soon as I get some time.
Meanwhile, I'm going to do my best to let it be.


OpenMandriva, an association formed to create a community-based Linux distro sponsored by Mandriva SA and ROSA, is now a registered non-profit organization in France. The progress over at OM is going pretty well, and now they need a logo for the association.
Yesterday a logo contest was launched by OM, which will accept user-submitted designs that'll be later put up for voting, and the best submission will be selected as OM's official logo.


text:
Shawmut and Wesely Thomas Anno...
Shawmut and Wesely Thomas Announce Strategic Partnership
Tags: Shawmut News, Education, Healthcare, Los Angeles, Greg Skalaski, Irvine, Safety
Shawmut and Wesely Thomas, a leading healthcare, life science and education contractor, jointly announced the formation of a new strategic partnership in the Southern California market.
The Shawmut and Wesely Thomas partnership will increase collective building capabilities and expand market presence in the healthcare, life science and education sectors marked by Shawmut's world-class construction management services and Wesely Thomas' deep sector expertise. The partnership will offer clients a winning combination of leading-edge building technologies, an added level of certainty of outcome and unparalleled sector-driven experience.
"We are thrilled to announce this joint venture," said Greg Skalaski, executive vice president, Shawmut West. "Wesely Thomas has built a solid, three decades-long reputation in Southern California and their experience perfectly complements Shawmut's delivery of exceptional client service powered by precision, care and best-in-class expertise. Together, we'll be able to provide clients with the experience and resources of two powerful contractors fueled by the shared values of flawless execution, transparent communication and safety."
"Our team is very enthusiastic around the possibilities that the Shawmut and Wesely Thomas partnership will provide clients," said Terry Hillsten, president, Wesely Thomas Inc. "The ability to combine our expertise with Shawmut's resources and experience offers a unique and powerful combination for successful projects across our core market sectors."
Both firms' emphasis on safety will remain a top priority and every jobsite will feature enhanced safety protocols to mitigate the risk of infectious disease spread. New procedures, jobsite innovation and an exhaustive COVID-19 risk assessment and response plan has been integrated on Shawmut sites nationwide since the onset of the pandemic, including Shawmut Vitals, a custom technology platform which allows team members to self-certify daily health screenings by scanning a job-specific QR code and completing a health survey.
Historically known as a luxury retail and restaurant builder in Southern California, Shawmut has steadily expanded share in new markets, and most recently launched a hotel division. The firm boasts an impressive portfolio of recent projects, including Silver Lake Pool & Inn, The Hoxton Hotel and Soho Warehouse in Downtown Los Angeles. Partnering with Wesely Thomas on healthcare, life science and education sectors represents the latest move in a series of successful growth and service offerings for the firm.
Wesely Thomas' growth over three decades is founded on two important elements—focusing on the clients and projects they do best and excelling at delivering those projects to the most exacting standards of quality and safety. The firm's focused approach on healthcare, life science, manufacturing and higher education has provided solid, steady growth year after year and has established Wesely Thomas as an important resource for both existing and new clients. This strategic partnership with Shawmut will bring their expertise to a broader range of projects.


text:
Records,
1839-1863 and undated
Letter to F. Markor, Esq, from A. Goldsborough Bruff dated August 14, 1843 containing Rebus characters. (Image nos. SIA2012-1138 and SIA2012-1139)
11.43 cu. ft. (21 document boxes) (2 half document boxes) (1 12 x 17 box)
This collection consists of records, mostly 1840-1844, concerning the founding and operation of the National Institute by Joel Roberts Poinsett, Peter Force, John James Abert, Francis Markoe, Jr., and others, particularly efforts to obtain federal subsidies, to collect specimens of natural history and manufactures, to provide space for housing its collections, and to gather support for its programs from the general scientific community at a special meeting in 1844; correspondence among officers of the Institute about administration of its affairs; much incoming correspondence from members; minutes of meetings and committee reports; records dealing with a part of the collections of the U. S. Exploring Expedition; bills and accounts; and publications of the Institute.
These records display some operational confusion, due in part to John Varden, who worked, often simultaneously, for the National Institute, for Captain Charles Wilkes in the Exploring Expedition collections, and for Henry Leavitt Ellsworth in the National Gallery of the Patent Office, often keeping his records on these different jobs together.
Researchers should consult the Peter Force Papers in the Library of Congress, especially for information on the Institute's share in the Executive Committee, which organized American participation in the Great Exhibition of 1851 in London.
The National Institute (1840-1862) was first organized as the National Institution for the Promotion of Science at Washington, D.C., in 1840 as a voluntary society interested in promoting study of diverse subjects, particularly natural history and the physical sciences. In 1842 Congress granted the body a federal charter, and it was known as the National Institute for the Promotion of Science thereafter until its dissolution in 1862. In fact, the National Institute could trace its origins to two earlier organizations. The Columbian Institute, founded in 1816, lost its federal charter in 1838 and joined the Institute in 1841, and the American Historical Society, created in 1835, attached itself to the Institute in 1840.
The National Institute was probably formed with a view to gaining control of the bequest of James Smithson, and it certainly pursued that goal until the Smithsonian Institution was created in 1846. However, despite its chronic lack of funds, the Institute did not wait for the Smithson legacy before pursuing its interest in science and the arts. An active program of collecting specimens of natural history and of corresponding with scholars and societies at home and abroad was begun immediately and soon created problems.
Joel Poinsett, the Institute's first president, arranged in 1841 for his organization to act as custodian for the advance collections of the Wilkes Expedition, and many other items were also received. To deal with this flood of specimens, the Institute had only a small space--in the Patent Office--and even less money for preservation, since it could not obtain government appropriations. The government's lack of interest in the Institute was further displayed when, in 1842, custody of the collections of the Wilkes Expedition was transferred from the Navy Department to the Joint Library Committee, which had no sympathy for the Institute's ambitions.
The Institute tried to improve its deteriorating position in 1844 by promoting a gathering of the country's leading men of science at Washington. From the gathering the Institute hoped to obtain resolutions of support which would influence the government to offer it financial aid. The meeting was held and a memorial adopted urging the Institute's claims upon Congress. The Congress remained unmoved and the Institute continued its decline, hastened in good part by the indifference of prominent scientists like Joseph Henry. Even though the Institute was responsible for organizing the American contribution to the Great Exhibition of 1851, its revival was short-lived. Finally, in 1862 the Institute transferred its remaining collections to the Smithsonian Institution and quietly expired.
Personal Papers
Smithsonian Institution Archives, Record Unit 7058, National Institute, Records
Poinsett, Joel Roberts, 1779-1851 Personal Name Search Smithsonian Collections Search ArchiveGrid
Force, Peter, 1790-1868 Personal Name Search Smithsonian Collections Search ArchiveGrid
Abert, John James, 1788-1863 Personal Name Search Smithsonian Collections Search ArchiveGrid
Markoe, Francis, Jr. Personal Name Search Smithsonian Collections Search ArchiveGrid
National Institute Corporate Name Search Smithsonian Collections Search ArchiveGrid
National Institution for the Promotion of Science (U.S.) Corporate Name Search Smithsonian Collections Search ArchiveGrid
Smithsonian Institution Corporate Name Search Smithsonian Collections Search ArchiveGrid
United States Exploring Expedition (1838-1842) Corporate Name Search Smithsonian Collections Search ArchiveGrid
Great Exhibition (1851 : London, England) Corporate Name Search Smithsonian Collections Search ArchiveGrid
Natural history Topic Search Smithsonian Collections Search ArchiveGrid
Science -- Societies, etc. Topic Search Smithsonian Collections Search ArchiveGrid
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