text:
\section{Introduction}
Quantum annealing is a meta-heuristic for addressing discrete (or combinatorial) optimization problems that are intractable in the realm of classical computing.
While simulated annealing (a.k.a. thermal or classical annealing) uses adjustable thermal fluctuations to jump over the energy mountains, quantum annealing applies adjustable quantum fluctuations for tunneling through the (narrow-enough) energy barriers \cite{amara1993global,finnila1994quantum,kadowaki1998quantum,das2008colloquium,ohzeki2011quantum,nishimori2017exponential}.
Quantum annealers are a special case of the adiabatic quantum computers (i.e., stoquastic open-system) that provide a hardware implementation for finding the ground state (or minimum energy configuration) of (Ising) Hamiltonians \cite{albash2018adiabatic,ayanzadeh2020reinforcement}.
To solve a problem using the quantum annealers, therefore, we must define a Hamiltonian whose ground state represents the optimum solution of the original problem of interest \cite{lucas2014ising,mcgeoch2020theory,ayanzadeh2020leveraging}.

We can form an Ising Hamiltonian whose ground state represents the optimum solution of any given problem of interest—which can be nontrivial in many real-world applications \cite{ayanzadeh2019quantum,ayanzadeh2020leveraging}.
In practice, however, executing the corresponding quantum machine instruction (QMI) on a physical quantum annealer does not guarantee achieving the global optimum \cite{albash2018adiabatic,ayanzadeh2019quantum_assisted,ayanzadeh2020reinforcement}.
In addition to thermal noise and diabatic transitions \cite{albash2018adiabatic}, examples of control error sources include sparse connectivity between qubits \cite{cai2014practical,vinci2015quantum}, confined annealing schedule \cite{nishimori2017exponential}, coefficients' range and precision limitations \cite{pudenz2015quantum,dorband2018extending}, and noise and decoherence \cite{lidar2008towards,deng2013decoherence,pudenz2014error,gardas2018defects,gardas2018quantum}. These error sources lower the quality of results, i.e., the energy value of the drawn samples is higher than the energy value of the ground state \cite{king2014algorithm}.

Modifying some aspects of the Hamiltonian by adapting (and better selecting) initial and final Hamiltonians, optimizing the schedule/path function, adding a catalyst Hamiltonian (i.e., a Hamiltonian that is present only in intermediate time), or adding non-stoquastic term to the Hamiltonian can circumvent certain drawbacks of the adiabatic quantum computers \cite{albash2018adiabatic}.
Nevertheless, the majority of these techniques are mainly suitable for closed systems, or current generations of the physical quantum annealers cannot (fully) accommodate them.
Acknowledging that adiabatic quantum computing has some inherent resistance to noise and decoherence, we need error correction and mitigation mechanisms for ensuring the scalability of adiabatic quantum computers just as we do with other quantum information processing models \cite{jordan2006error,sarovar2013error,mizel2014fault,vinci2016nested}.
In spite of several error correction proposals for adiabatic quantum computing and quantum annealing \cite{jordan2006error,lidar2008towards,sarovar2013error,young2013adiabatic,young2013error,ganti2014family,mizel2014fault,pudenz2014error,bookatz2015error,pudenz2015quantum,vinci2015quantum,matsuura2016mean,mishra2016performance,vinci2016nested,matsuura2019nested},
an accuracy-threshold theorem for adiabatic quantum computing, unlike its gate model counterpart \cite{aliferis2005quantum}, remains elusive \cite{pudenz2014error,vinci2016nested}.
Besides, most error correction schemes (e.g., nested quantum annealing correction method \cite{vinci2016nested,matsuura2019nested}) utilize multiple physical qubits for coding every qubit that notably reduces the capacity of current quantum annealers.

From an application perspective, problem-solving with a physical quantum annealer has two drawbacks: (1) quantum annealers can yield excited states rather than the ground state of the given Hamiltonian, and (2) the results/samples attained by the physical quantum annealers are not reproducible over time.
Applying classical postprocessing techniques to (raw) samples attained by the physical quantum annealers can mitigate these drawbacks to some extent.
For example, one may apply evolutionary algorithms (e.g., genetic algorithms) or swarm intelligence techniques (such as particle swarm optimization) as a postprocessing method for quantum annealers \cite{engelbrecht2007computational}.
Nevertheless, these heuristics and meta-heuristics are stochastic techniques that can result in a different solution in each try and lessen the robustness of results in the application domain.
Moreover, most of these techniques require hyper-parameter optimization—e.g., the number of iterations, the probability of crossover and mutation, and selection method in evolutionary algorithms—that can significantly impact the quality of ultimate results \cite{shahamatnia2011adaptive}.
To this end, we devise a lightweight and deterministic postprocessing method that can improve the quality and robustness of results attained by physical quantum annealers.
It is worth highlighting that, in this study, we did not aim to demonstrate quantum speed-up.

We view quantum annealers as a Gibbs sampler that allows diabatic transitions in an open system and present \emph{multi-qubit correction} (MQC) as a novel postprocessing technique for quantum annealers.
Unlike most studies that try to identify different types of errors and mitigate/correct them, we try to recognize the pattern(s) among the incorrect observations (or lower quality samples compared to the ground state of the given Hamiltonian) and leverage it to achieve a better solution.
Measurement is the most error-prone operation on superconducting noisy intermediate-scale quantum (NISQ) \cite{preskillNISQ} machines \cite{tannu2019mitigating}—here, we look at the quantum annealers as the NISQ model of adiabatic quantum computers.
Therefore, in this study, we mainly focus on bit-flips that mostly occur due to measurement errors on superconducting quantum devices.
MQC compares pairs of samples and recognizes groups of qubits that we can flip their values simultaneously, and tunnels through these groups to converge to a (notably) better solution.
Our experiments using the D-Wave 2000Q quantum annealers show that MQC utilizes fewer samples and finds better solutions compared to recent software/hardware advances in the realm of quantum annealing.

\section{Results}
Quantum annealers, like the quantum processing units (QPU) by D-Wave Systems, are single-instruction (quantum) computing machines that can only sample from the ground state of the following problem Hamiltonian (denoted by $H_p$):
\begin{equation}
	\label{eqn:ising_energy}
	H_p := E_{\text{Ising}} \left( \mathbf{z} \right) = \sum_{i=1}^{N}{\mathbf{h}_i \mathbf{z}_i} + \sum_{i=1}^{N}{\sum_{j=i+1}^{N}{J_{ij} \mathbf{z}_i \mathbf{z}_j}},
\end{equation}
where $N$ denotes the number of quantum bits (qubits), spin variables $\mathbf{z} \in \{-1,+1\}^N$, and $\mathbf{h}$ and ${J}$ represent local fields  and couplers, respectively \cite{johnson2011quantum,mcgeoch2020theory,ayanzadeh2020leveraging}.
Quantum annealers can efficiently recognize the region of the ground state(s) of the given Hamiltonians; however, they generally fail to get to the global minimum, regardless of how close they are to the ground state.
In other words, unlike classical annealing that always converges to a local (or sometimes the global) optimum, quantum annealers generally yield an excited state(s) that are not necessarily a local optimum \cite{dorband2018method,ayanzadeh2019quantum_assisted,ayanzadeh2020reinforcement}.
Hence, we can expect that applying optimization heuristics and meta-heuristics on samples attained by a quantum annealer to result in new (or synthetic) sample(s) with lower energy value, specifically on systems with glassy landscapes \cite{das2008colloquium}.
In this section, we start with a local optimization heuristic, called \emph{single-qubit correction} (SQC), and then extend it to introduce \emph{multi-qubit correction} (MQC) scheme for mitigating errors in quantum annealers \cite{dorband2018method}.

\subsection{Single-Qubit Correction}
Measuring qubits after the annealing process results in an eigenstate that is not necessarily the ground state or even a local optimum of the given problem Hamiltonian \cite{das2008colloquium}.
Owing to hardware limitations such as limited precision of coefficients, the Hamiltonian that is minimized on a physical quantum annealer can be (slightly) different from the problem Hamiltonian of interest (in application domain); therefore, the evolution is not guaranteed to result in an eigenstate of the problem Hamiltonian \cite{albash2018adiabatic}.
We start by adopting a hill-climbing \cite{engelbrecht2007computational,russell2016artificial} algorithm to present a postprocessing approach for quantum annealers, called \emph{single-qubit correction} (SQC).
SQC has a zero-temperature simulated annealing scheme that can relax a raw sample to a new/synthetic sample with a lower energy value.
In each iteration of SQC, we individually toggle the value of every qubit and keep all of the changes that result in a state with a lower energy value.
Algorithm \ref{algo:SQC} shows how SQC exploits the neighborhood of an excited state, denoted by $\mathbf{z}.$

\begin{algorithm}[!ht]
	\DontPrintSemicolon
	\KwIn{$\mathbf{z}, \mathbf{h}$ and $J$}
	\KwOut{$\mathbf{z}$}
	$N \gets \lvert\mathbf{z}\rvert$\;
	$lowestEnergy \gets E_{\text{Ising}}(\mathbf{z}, \mathbf{h}, J)$\;
	$terminate \gets \text{True}$\;
	\Do{$\mathrm{\mathbf{not\;}} terminate $}{
		\For{$i \gets 1$ \KwTo $N$}{
			$\mathbf{z}_i \gets -\mathbf{z}_i$ \tcp*{Flip bit and check its influence}
			\eIf{$lowestEnergy > E_{\text{Ising}}(\mathbf{z}, \mathbf{h}, J)$}{
				$lowestEnergy \gets E_{\text{Ising}}(\mathbf{z}, \mathbf{h}, J)$\;
				$terminate \gets \text{False}$\;
			}{
				$\mathbf{z}_i \gets -\mathbf{z}_i$ \tcp*{Flip bit back since it did not result in a sample with lower energy}
			}
		}
	}
	\Return{$\mathbf{z}$}\;
	\caption{
Single-qubit correction (SQC) heuristic for exploiting the neighborhood of excited states to find a sample with lower energy value.
}
	\label{algo:SQC}
\end{algorithm}

From an optimization point of view, SQC is very likely to result in a meta-stable state (i.e., SQC is sensitive to the input sample) when the input sample is not a local optimum, which is trivial for systems with the glassy landscape.
To solve a problem on a quantum annealer, we generally draw many samples, e.g., up to 10,000 samples/reads per QMI on a D-Wave quantum annealer.
Therefore, in practice, SQC explores a broader area, i.e., the neighborhood of all excited states attained by the quantum annealers.
Figure \ref{fig:benchmarking_QA1_QA1+SQC} illustrates the impact of applying SQC to raw samples drawn by the D-Wave quantum annealers.
For uniform and normal benchmark problems, SQC always finds a better sample, and the corresponding $p-$value is $8.8818 \times 10^{-16}$ which indicates that results are statistically significant (i.e., $p<0.05)$.
However, for Binary problems, both SQC and the baseline method demonstrated a similar performance (i.e., results are not statistically significant).
For more information about the benchmarking, see the section Method.

It is worth highlighting that we do not propose SQC as a postprocessing method for quantum annealers since more efficient techniques,  such as simulated annealing, can outperform SQC in terms of finding samples with lower energy values.
Indeed, we will extend SQC to introduce a novel postprocessing approach that can notably improve the quality and reproducibility of results attained by the quantum annealers.

\begin{figure}[!ht]
	\begin{center}
	\includegraphics[scale=1]{./figures/fig_benchmarking_QA1_QA1+SQC.pdf}
	\end{center}
	\vspace{-0.6cm}
	\caption{
Performance comparison between quantum annealing ($\text{QA}^1)$ and applying SQC to raw samples attained by quantum annealers ($\text{QA}^1+SQC)$ where $s$ denotes the number of samples.
}
	\label{fig:benchmarking_QA1_QA1+SQC}
\end{figure}

\subsection{Multi-Qubit Correction}
SQC is an optimization heuristic that (1) neglects the interactions between spins of the given problem Hamiltonian; and (2) entirely depends on one excited state as its initial state.
Instead of processing one qubit at a time on samples individually, we introduce \emph{multi-qubit correction} (MQC) method that treats groups of qubits as tunnels (or units) and compares pairs of samples to find these tunnels.
In this study, the term "tunnel" is analogous to the concept of quantum tunnels \cite{denchev2016computational} and refers to a group of qubits that a quantum annealer simultaneously flips their values to change an excited state to a sample with a lower energy value.

Let $\mathbf{h}$ and ${J}$ denote linear and quadratic coefficients of a problem Hamiltonian that we aim to find its ground state, respectively.
Also, let $\mathbf{z}^{1}$ and $\mathbf{z}^{2}$ be two (excited) states, with ${N}$ spin variables, attained by a quantum annealer.
We define two sets of qubits as
\begin{equation}
	\label{eqn:MQC_S}
	S=\{ \mathbf{z_i} | \mathbf{z}_{i}^{1} = \mathbf{z}_{i}^{2}\}
\end{equation}
and
\begin{equation}
	\label{eqn:MQC_D}
	D=\{ \mathbf{z_i} | \mathbf{z}_{i}^{1} \neq \mathbf{z}_{i}^{2}\}
\end{equation}
where $i=1, 2, \dots, N.$
The set ${D}$ is a tunnel that represents the transformation of sample $\mathbf{z}^{1}$ to sample $\mathbf{z}^{2}$, and vice versa.
From a problem-solving viewpoint, finding $D$ is rather useless.
However, we can use $D$ to find sub-tunnels that may reduce $\mathbf{z}^{1}$ (or $\mathbf{z}^{2})$ into a new sample with a lower energy value.
A sub-tunnel of ${D},$ denoted by ${T},$ is a subset of ${D}$ where
\begin{equation}
	J_{ij} =
	\begin{cases}
		\mathbb{R}_{\neq0}	&	\mathbf{z}_i, \mathbf{z}_j \in T;\\
		0	&	\mathbf{z}_i \;\text{or}\; \mathbf{z}_j \notin T.
	\end{cases}
\end{equation}
In this sense, ${T}$ is the closure of a set of qubits connected transitively to each other but not connected to other qubits in ${D}.$
Hence, we can represent ${D}$ as a partition of sub-tunnels as
\[
	D=\bigcup_k{T^k}
\]
where
\[
	T^i \cap T^j = \{\}, \quad\quad \forall i,j.
\]
We define the influence (or energy contribution) of $T^k$ to $\mathbf{z}^{1}$ as follows:
\begin{equation}
	\label{eqn:MQC_influence_subtunnel}
	I^k_{\mathbf{z}^{1}} = {\sum\limits_{i \in T^k} \mathbf{h}_i\mathbf{z}^{1}_i + \sum\limits_{i \in T^k} \sum\limits_{j \in S} J_{ij} \mathbf{z}^{1}_i \mathbf{z}^{1}_j }.
\end{equation}
Note that we have omitted the term
\[
	\sum\limits_{i,j \in T^k} J_{ij} \mathbf{z}^{1}_i \mathbf{z}^{1}_j
\]
from Eq. \eqref{eqn:MQC_influence_subtunnel} since flipping values of all qubits in $T^k$ does not effect $I^k_{\mathbf{z}^{1}}.$
Finally, we reduce $\mathbf{z}^{1}$ to a new sample, denoted by $\mathbf{z}^*,$ via flipping the qubit values of all sub-tunnels that have a positive influence (or energy contribution) value.
Note that $I^k_{\mathbf{z}^{1}} = -I^k_{\mathbf{z}^{2}};$ thus, applying the abovementioned process on $\mathbf{z}^{2}$ will result in a new sample that is identical to $\mathbf{z}^*.$
Algorithm \ref{algo:MQC_reduce} shows how we reduce two input samples, denoted by $\mathbf{z}^1$ and $\mathbf{z}^2$, to a new sample (denoted by $\mathbf{z}^*)$ whose energy value is guaranteed to be less than or equal to energy values of $\mathbf{z}^1$ or $\mathbf{z}^2.$

\begin{algorithm}[!ht]
	\DontPrintSemicolon
	\SetKwFunction{FReduce}{Reduce}
	\SetKwProg{Fn}{Function}{:}{}
	\Fn{\FReduce{$\mathbf{z}^1$, $\mathbf{z}^2$, $\mathbf{h}$, $J$}}{
		$\mathbf{z}^* \gets \mathbf{z}^1$\;
	      $N \gets \lvert\mathbf{z}^*\rvert$\;
		$S \gets \{\}$\;
		$D \gets \{\}$\;
		\For{$i \gets 1$ \KwTo $N$}{
			\eIf{$\mathbf{z}^1_i = \mathbf{z}^2_i $}{
				$S \gets S \cup \{i\}$ \tcp*{Identical bits between $\mathbf{Z}^1$ and $\mathbf{Z}^2$}
			} {
				$D \gets D \cup \{i\}$ \tcp*{Different bits between $\mathbf{Z}^1$ and $\mathbf{Z}^2$}

		$Adj \gets [\,]$ \tcp*{Adjacency of bits in $D$ based on $J$}
		\For{$i \in D$}{
			$Adj_i =\{\}$\;
		}
		\For{$i,j \in J$}{
			\If{$J_{ij}\neq0$ $\&$ $i \in D$ $\&$ $j \in D$}{
				$Adj_i \gets Adj_i \cup \{j\}$\;
				$Adj_j \gets Adj_j \cup \{i\}$\;
			}
		}
		\SetKwFunction{Fcc}{ConnectedComponents}
		$T \gets \Fcc{$Adj$}{}$\tcp*{Sub-tunnels (groups of isolated bits in $D)$}
		\For{$k \gets 1$ \KwTo $|T|$}{
			$I^k_{\mathbf{z}^1} = \sum\limits_{i \in T^k} \mathbf{h}_i \mathbf{z}^1_i + \sum\limits_{i \in T^k} \sum\limits_{j \in S} J_{ij} \mathbf{z}^1_i \mathbf{z}^1_j$ \tcp*{Influence of sub-tunnels on $E_{\text{Ising}}$}
			\If{$ I^k_{\mathbf{z}^1} > 0$}{
				\For{$l \in T^k$}{
					$\mathbf{z}^*_l \gets -\mathbf{z}^*_l$ \tcp*{Flip all bits of sub-tunnels with positive influence}
				}

			}
		}
	}
	\Return{$\mathbf{z}^*$}\;
	\caption{Reducing two input samples $\mathbf{z}^1$ and $\mathbf{z}^2$ to a new sample ($\mathbf{z}^*)$ with lower energy value based on virtual tunnels.}
	\label{algo:MQC_reduce}
\end{algorithm}

The Reduce procedure presented in this paper is analogous to the crossover operation in evolutionary algorithms that acts on two potential solutions, known as parent chromosomes, and yields new solution(s), known as offspring \cite{engelbrecht2007computational,shahamatnia2011adaptive}.
The Reduce procedure, shown in Algorithm \ref{algo:MQC_reduce}, is the extended version of SQC method, shown in Algorithm \ref{algo:SQC}, that acts on a group of qubits simultaneously rather than flipping the values of qubits individually.
Besides, the Reduce procedure acts on two excited states and is less sensitive to a single initial point when compared to SQC that depends entirely on a single excited state.

When we employ physical quantum annealers, we generally request many samples/reads, i.e., repeating the annealing process with different initial eigenstates to improve the probability of achieving the ground state of the given Hamiltonian.
Algorithm \ref{algo:MQC} illustrates the \emph{multi-qubit correction} (MQC) method that receives a sample set as input and tries to iteratively reduce it to a new sample with a lower energy value.
For a sample set with ${n}$ samples/reads, MQC takes $\log_2(n)$ steps, and in each iteration, the size of the sample set is divided by two.

\begin{algorithm}[!ht]
	\DontPrintSemicolon
	\SetKwFunction{Reduce}{Reduce}
	\SetKwFunction{Pop}{Pop}
	\SetKwFunction{Append}{Append}
	\KwIn{$Z=\left[\mathbf{z}^1, \mathbf{z}^2, \dots, \mathbf{z}^n\right], \mathbf{h}$ and $J$}
	\KwOut{$\mathbf{z}^*$}
	\While{$|Z| >1$}{
		$\hat{Z} \gets Z$\;
		$Z \gets \{\}$\;
		\While{$|\hat{Z}| >1$}{
			$\mathbf{z}^A \gets \hat{Z}.\Pop{}{}$\;
			$\mathbf{z}^B \gets \hat{Z}.\Pop{}{}$\;
			$\mathbf{z}^{AB} \gets \Reduce{$\mathbf{z}^A, \mathbf{z}^B, \mathbf{h}, J$}{}$\;
			$Z.\Append{$\mathbf{z}^{AB}$}{}$\;
		}
		\If{$|\hat{Z}| >0$}{
			$Z.\Append{$\hat{Z}.\Pop{}{}$}{}$\;
		}
	}
	$\mathbf{z}^* \gets Z.\Pop{}{}$\;
	\caption{Multi-qubit correction (MQC) method for reducing a set of samples to a new sample whose energy value is less than all input samples or (in the worst case) is equal to the lowest energy value of input samples.}
	\label{algo:MQC}
\end{algorithm}

Figure \ref{fig:benchmarking_QA1+SQC_QA1+MQC} shows that for normal and uniform benchmark problems MQC always outperforms SQC (i.e., finds a better sample with lower energy value), and the corresponding  $p-$value is $8.8818 \times 10^{-16}.$
For Binary benchmark problems, the success rate of MQC (i.e., MQC outperforms SQC) ranges from 60\% to 66\%, and the corresponding $p-$values ranges from $9.3132 \times 10^{-10}$ to $1.1641 \times 10^{-10}$ which indicate that results are statistically significant.
We remark that SQC was not able to outperform MQC in any arrangement.

Figure \ref{fig:benchmarking_QAi_QA1+MQC} demonstrates that applying MQC on raw samples, attained by the D-Wave quantum processors in sampling from the ground state of benchmark Ising models, outperforms recent software/hardware advances in the field of quantum annealing, such as spin-reversal transforms, optimization, and sampling postprocessing methods, and increased inter-sample delay between successive measurements.
For normal benchmark problems, MQC always outperforms all baselines (i.e., finds a better sample with lower energy value), and the corresponding  $p-$value is $8.8818 \times 10^{-16}.$
For uniform benchmark problems, the success rate of MQC (i.e., MQC outperforms the baseline method) ranges from 98\% to 100\%, and the corresponding $p-$values ranges from
$1.7764 \times 10^{-15}$ to $8.8818 \times 10^{-16}.$
For Binary benchmark problems, the success rate of MQC (i.e., MQC outperforms the baseline method) ranges from 46\% to 100\%, and the corresponding $p-$values ranges from
$1.1921 \times 10^{-7}$ to $8.8818 \times 10^{-16}.$
We remark that none of the baseline methods was not able to outperform MQC in any arrangement, and results are statistically significant (i.e., $p<0.05)$ in all arrangements.
These results explain that MQC requires notably fewer samples to visit the ground state of the given Hamiltonian with a high enough probability compared to recent software and hardware advances in the realm of quantum annealing.
For more information about the benchmarking, see the section Method.

\begin{figure}[!ht]
	\begin{center}
	\includegraphics[scale=1]{./figures/fig_benchmarking_QA1+SQC_QA1+MQC.pdf}
	\end{center}
	\vspace{-0.6cm}
	\caption{
	Performance comparison between applying MQC and SQC to raw samples attained by quantum annealers, denoted by $\text{QA}^1+MQC$ and $\text{QA}^1+SQC,$ respectively $(s$ denotes the number of samples).
	}
	\label{fig:benchmarking_QA1+SQC_QA1+MQC}
\end{figure}

\begin{figure}[!ht]
	\begin{center}
	\includegraphics[scale=1]{./figures/fig_benchmarking_QAi_QA1+MQC.pdf}
	\end{center}
	\vspace{-0.6cm}
	\caption{
	Comparing the performance of applying MQC to raw samples attained by a quantum annealer ($\text{QA}^1+MQC)$ with recent software/hardware advances—namely quantum annealing with five spin-reversal transforms $(\text{QA}^2),$ quantum annealing with longer inter-sample delay $(\text{QA}^3),$ quantum annealing with optimization postprocessing $(\text{QA}^4),$ quantum annealing with sampling postprocessing $(\text{QA}^5),$ and quantum annealing with five spin-reversal transforms, longer preparation time and optimization postprocessing $(\text{QA}^6)$ where $s$ denotes the number of samples.
	}
	\label{fig:benchmarking_QAi_QA1+MQC}
\end{figure}

From an application perspective, problem-solving with a physical quantum annealer has two drawbacks: (1) quantum annealers can yield excited states rather than the ground state of the given Hamiltonian, and (2) the results/samples attained by the physical quantum annealers are not reproducible over time.
According to Anderson localization \cite{altshuler2010anderson}, as an example, the energy gap between the ground and first excited states is shrunk close to the end of the annealing.
The landscape of glassy Hamiltonians generally includes many excited states, and a physical quantum annealer is likely to relax to one of these excited states.

In conclusion, problem-solving with quantum annealers results in a distribution of (potential) ground states, and the variance of the corresponding energy values is large enough to lessen the reproducibility of results.
We repeated the aforementioned methods 50 times, and Table \ref{tbl:benchmarking_robustness} includes the variance of the energy values of these repeated experiments.
Figure \ref{fig:benchmarking_QAi_QA1+MQC} explains that applying the sampling postprocessing on raw samples attained by the D-Wave quantum annealers ($\text{QA}^5$) can significantly lower the quality of results.
In other words, applying the sampling postprocessing method can result in a sample with a higher energy value.
On this basis, we omitted $\text{QA}^5$ from Table \ref{tbl:benchmarking_robustness}.
These results reveal that applying MQC on (raw) samples attained by the D-Wave quantum processors can notably improve the reproducibility of results.

\begin{table}[!ht]
	\begin{center}
		\caption{
	Comparing the robustness (or reproducibility of results) of applying MQC to raw samples attained by a quantum annealer ($\text{QA}^1+MQC)$ with recent software/hardware advances—namely quantum annealing with five spin-reversal transforms $(\text{QA}^2),$ quantum annealing with longer inter-sample delay $(\text{QA}^3),$ quantum annealing with optimization postprocessing $(\text{QA}^4),$ and quantum annealing with five spin-reversal transforms, longer preparation time and optimization postprocessing $(\text{QA}^6).$ Each element represents the variance of energy values from repeating the corresponding method 50 times.
}
		\label{tbl:benchmarking_robustness}
		\begin{tabular}{cccccccc}
			Coefficients		&	Samples	&	$\mathrm{QA^1}$	&	$\mathrm{QA^2}$	&	$\mathrm{QA^3}$	&	$\mathrm{QA^4}$	&	$\mathrm{QA^6}$	&	$\mathrm{QA^1+MQC}$\\
			\hline
			\multirow{4}{*}{Binary}
				&	100	&	1.3696	&	2.8304	&	1.4544	&	2.7264	&	5.2416	&	0.9984\\
				&	200	&	0.9936	&	3.4576	&	0.9664	&	1.4656	&	8.1936	&	0.8704\\
				&	500	&	0.7696	&	2.3936	&	0.9104	&	1.3584	&	3.9184	&	0.6400\\
				&	1000	&	0.4816	&	1.9584	&	0.9984	&	0.8464	&	3.3936	&	0.2944\\
			\hline
			\multirow{4}{*}{Uniform}
				&	100	&	1.6558	&	1.1323	&	0.6401	&	0.6606	&	1.1116	&	0.0704\\
				&	200	&	0.8674	&	0.9988	&	0.7189	&	0.4792	&	1.2079	&	0.0171\\
				&	500	&	0.5471	&	0.9898	&	0.5596	&	0.4471	&	0.7513	&	0.0092\\
				&	1000	&	0.6006	&	0.5860	&	0.5792	&	0.2757	&	0.7606	&	0.0049\\
			\hline
			\multirow{4}{*}{Normal}
				&	100	&	5.9365	&	4.0989	&	2.5668	&	0.4009	&	1.0033	&	0.0563\\
				&	200	&	3.8940	&	3.5666	&	2.4667	&	0.3343	&	0.5284	&	0.0071\\
				&	500	&	2.8559	&	2.0883	&	2.0424	&	0.4017	&	0.4056	&	0.0000\\
				&	1000	&	2.7226	&	2.3799	&	1.5881	&	0.2467	&	0.2899	&	0.0000\\
			\hline

		\end{tabular}
	\end{center}
\end{table}

\subsection{Randomized MQC}
MQC is a postprocessing heuristic that iteratively reduces a set of samples to a smaller sample set whose energy values are lower than the previous iteration(s).
Therefore, the performance of MQC mostly depends on samples that we obtain from the physical quantum annealers.
When we repeat the annealing process on a D-Wave quantum annealer, successive measurements are correlated to each other due to limited preparation time.
Hence, successive measurements generally form clusters of samples, i.e., groups of identical states.
Since applying the Reduce procedure on identical input samples yields the same sample, early iterations of applying MQC on raw samples attained by the physical quantum annealers can become ineffective.
Figure \ref{fig:benchmarking_QA1+MQC_QA6+MQC} compares the performance of applying MQC to samples attained by standard quantum annealing (i.e., raw samples attained by the D-Wave quantum annealers) and enhanced quantum annealing (i.e., quantum annealing with spin-reversal transforms, longer inter-sample delay, and classical optimization postprocessing).
For normal benchmark problems, results are statistically significant when we draw fewer than 5,000 samples.
$p-$values of uniform and Binary problems are less than 0.05 only when we draw 10,000 and 5,000 samples, respectively.

\begin{figure}[!ht]
	\begin{center}
	\includegraphics[scale=1]{./figures/fig_benchmarking_QA6+MQC_QA1+MQC.pdf}
	\end{center}
	\vspace{-0.6cm}
	\caption{
	Comparing the performance of applying MQC to raw samples attained by a quantum annealer $(\text{QA}^1+MQC)$ with applying MQC to samples attained by the quantum annealing with five spin-reversal transforms, longer preparation time and optimization postprocessing $(\text{QA}^6+MQC)$ where $s$ denotes the number of samples.
	}
	\label{fig:benchmarking_QA1+MQC_QA6+MQC}
\end{figure}

Figure \ref{fig:benchmarking_QA1+MQC_QA6+MQC} suggests that the quality of the input sample set can impact the performance of MQC.
We propose the  \emph{randomized multi-qubit correction} (RMQC) scheme, presented in Algorithm \ref{algo:RMQC}, that repeats MQC on shuffled sample sets.
In RMQC, we repeat the MQC method $r$ times.
We start with the raw sample set (similar to MQC) and then shuffle the sample set in each iteration.
Finally, we apply MQC on $r$ samples (results from applying MQC on shuffled sample set) to obtain the final solution.
Note that when $r=1$ MQC and RMQC are identical.
For $r>1$, RMQC is guaranteed to outperform MQC—albeit $r$ times more (classical) computation time/overhead.

\begin{algorithm}[!ht]
	\DontPrintSemicolon
	\SetKwFunction{MQC}{MQC}
	\SetKwFunction{Append}{Append}
	\SetKwFunction{Shuffle}{Shuffle}
	\KwIn{$Z=\left[\mathbf{z}^1, \mathbf{z}^2, \dots, \mathbf{z}^n\right], r>0, \mathbf{h}$ and $J$}
	\KwOut{$\mathbf{z}^*$}
	$Z^* \gets \{\}$\;
	\Do{$r>0$}{
		$r \gets r-1$\;
		$Z^*.\Append{$\MQC{$Z$}{}$}{}$\;
		$Z \gets \Shuffle{$Z$}{}$\;
	}
	$\mathbf{z}^* \gets \MQC{$Z^*$}{}$\;
	\Return{$\mathbf{z}^*$}\;
	\caption{Randomized multi-qubit correction (RMQC).}
	\label{algo:RMQC}
\end{algorithm}

Figure \ref{fig:benchmarking_QA1+MQC_QA1+RMQC51_101} illustrates the performance of RMQC for $r=5$ and $10$, and compares it with applying MQC to raw samples attained by the D-Wave quantum annealers.
For normal and uniform benchmark problems, results are always statistically significant (i.e., $p<0.05).$
More specifically, when $r=5,$ $p \in \left[{0.0078, 1.8626 \times 10^{-9}} \right]$
and $p \in \left[{0.0313, 0.001} \right]$ for normal and uniform benchmark problems, respectively.
When $r-10,$  $p \in \left[{0.004, 4.6566 \times 10^{-10}} \right]$
and $p \in \left[{0.002, 0.0003} \right]$ for normal and uniform benchmark problems, respectively.
For Binary problems, however, results of MQC and RMQC are not statistically significant—for sufficient number of samples MQC and RMQC demonstrate similar performance on Binary benchmark problems.

\begin{figure}[!ht]
	\begin{center}
	\includegraphics{./figures/fig_benchmarking_QA1+MQC_RMQC51_101.pdf}
	\end{center}
	\vspace{-0.6cm}
	\caption{
	Performance comparison between MQC (denoted by $\text{QA}^1+MQC)$ and RMQC with $r=5$ and 10, denoted by $\text{QA}^1+RMQC$ $(s$ represents the number of samples).
	}
	\label{fig:benchmarking_QA1+MQC_QA1+RMQC51_101}
\end{figure}

\section{Discussion}
Owing to various technological barriers such as diabatic transitions, thermal noise, and a vast range of control errors, quantum annealing in a real device (i.e., open-system and stoquastic) is necessarily susceptible to errors \cite{albash2018adiabatic,matsuura2019nested}.
While several studies have proposed various error correction approaches for adiabatic quantum computers, most of them are not applicable to quantum annealers \cite{albash2018adiabatic}.
Moreover, error correction techniques such as the nested quantum annealing correction method \cite{vinci2016nested,matsuura2019nested} use multiple physical qubits for representing logical qubits that notably reduce the capacity of current quantum annealers.
Quantum annealers can draw many high-quality samples in near-constant time. In other words, the annealing time does not depend on the number of qubits.
However, they generally fail to find the global minimum, specifically when the energy gap between the ground and the first excited state(s) is small.

While most studies try to recognize specific types of errors and mitigate/correct them, we show that one can recognize the pattern(s) of incorrect observations (or lower quality samples) and leverage it to improve the fidelity of the quantum annealers.
In this sense, we view the open-system quantum annealing process as a Gibbs distribution sampler \cite{vinci2016nested} and exploit the neighborhood of the drawn samples to find a better solution.
We first examined the impact of applying a local search heuristic, called \emph{single qubit correction} (SQC), on raw samples drawn by a D-Wave 2000Q quantum annealers.
Figure \ref{fig:benchmarking_QA1_QA1+SQC} reveals that applying SQC on raw samples for a given Ising Hamiltonian with normal and uniform coefficients always results in a sample with a lower energy value.
In other words, none of the drawn samples for normal and uniform problems were a local optimum because SQC was able to perform a local search and find another sample with a lower energy value.
On the other hand, roughly all drawn samples for binary problems were a local (or global) optimum, and applying SQC could not improve the quality of the attained samples.

There are two possibilities for this observation:
(1) Ising Hamiltonians with binary (or discrete) coefficients are easier problems, and sampling with a D-Wave quantum annealer is very likely to result in the ground state;
or (2) owing to the precision limitations (e.g., 8–9 bits precision on the D-Wave quantum annealers), the Ising Hamiltonian that is being minimized by a physical quantum annealer is different from the given Ising Hamiltonian (with double precision).
Previous studies have mentioned that random Ising problems might not be hard-enough problems for quantum annealers (and even classical optimization techniques) \cite{katzgraber2014glassy,marshall2016practical}.
In practice, nevertheless, we see that applying SQC/MQC can notably improve the fidelity of the D-Wave quantum annealers.
More specifically, while for most random Binary problems applying SQC/MQC does not result in a better solution—the D-Wave quantum annealer can itself find the best (achievable) solution—applying MQC to random Normal and Uniform Ising Hamiltonians always results in a better solution. Hence, we can conclude that (in practice) easy to solve random Ising problems can be challenging for NISQ machines—we look at quantum annealers as the NISQ model of adiabatic quantum computers.
Indeed, not only does SQC exploits the neighborhood of an input sample for finding a sample with lower energy, but it can also remediate the precision limitations of physical quantum annealers.

We extended SQC to introduce a novel postprocessing method, called \emph{multi-qubit correction} (MQC).
The first premise behind MQC is the idea that quantum annealers can draw high-quality samples from the ground state of the given problem Hamiltonian.
In other words, all samples attained by a quantum annealer partially represent the ground state of the given problem Hamiltonian, although they can contain bits in error.
The second premise is that we must simultaneously flip subsets of multiple bits to relax any excited state to a ground state.
From another perspective, flipping bits individually (like how SQC tries to exploit the neighborhood of the measured samples) is (very) likely to result in a local optimum.

For every reduction, MQC takes two samples and bitwise compares them to determine which bits are the same and which are different.
Although identical bits are more likely to be correct, we are not interested in them because we do not know whether they are correct.
On the other hand, if a bit is different between the two samples, one of the samples has the correct bit value.
The objective of MQC is to find groups of isolated bits such that simultaneously flipping them can yield a sample with a lower energy value.
If there is only one isolated group, then flipping all the bits in the group will only change one of the two samples into the other.

Figure \ref{fig:benchmarking_QA1+SQC_QA1+MQC} explains that applying MQC outperforms SQC.
More specifically, for random normal and uniform Ising problems, applying MQC to raw samples always (i.e., in 100\% of the employed benchmark problems) results in a sample with lower energy than applying SQC to the same set of raw samples.
On binary problems, nevertheless, MQC was able to outperform SQC in about 63\% of cases and they were a tie in approximately 37\% of the problems.
Figure \ref{fig:benchmarking_QAi_QA1+MQC} demonstrates that MQC outperforms recent software/hardware advances in the realm of quantum annealing—namely increasing the inter-sample delays and applying classical pre/post-processing methods that are available in Ocean SDK.
More specifically, for normal and uniform random Ising problems, MQC always (100\% of the used benchmark problems) finds a sample with a lower energy value.

For binary problems, MQC almost results in a sample with a lower energy value.
However, it is worth noting that applying spin-reversal transforms, longer inter-sample delay, and optimization postprocessing resulted in a better solution in about 2.3\%, 1\%, and  2\% of random binary problems, respectively.
Besides, employing all enhancements (denoted by $\text{QA}^6)$ was able to outperform MQC in about 5\% of random binary problems.
From an application viewpoint, near-term quantum processors provide a (noisy) distribution of the ground state(s).
Hence, the results of the physical quantum annealers are not well reproducible, mostly due to thermal noise.
Table \ref{tbl:benchmarking_robustness} reveals that applying MQC notably improves the robustness of the D-Wave quantum annealers.

MQC might be considered related to sample persistence \cite{karimi2017boosting} or meta-heuristics (such as evolutionary algorithms and swarm intelligence techniques) \cite{shahamatnia2011adaptive}.
MQC is a deterministic postprocessing method—i.e., for a fixed set of input samples, it will always yield the same solution—but most postprocessing schemes are stochastic. Hence, applying MQC (notably) improves the reproducibility of results attained by the quantum annealers.
Moreover, most meta-heuristics require hyperparameter optimization (e.g., number of iterations, and probabilities of crossover and mutation in evolutionary algorithms); however, run-time and performance of MQC mainly depend on the size and quality of the input sample set, respectively.
More specifically, for ${n}$ samples, MQC performs $\log_2{n}$ iterations, and in iteration $i$ it compares $n/2^i$ pairs of samples.

Successive measurements on the D-Wave quantum annealers are correlated to each other due to limited preparation time.
From another perspective, successive measurements generally form clusters of samples (i.e., groups of identical states).
Consequently, technological barriers (e.g., the limited delay between successive reads and the thermal noise) can lessen the performance of MQC.
Figure \ref{fig:benchmarking_QA1+MQC_QA6+MQC} shows that applying MQC to samples attained by enhanced quantum annealing (denoted by $\text{QA}^6)$—i.e., using five spin-reversal transforms, increasing the preparation time, and performing optimization postprocessing—mostly results in better solutions, compared to applying MQC on raw samples (denoted by $\text{QA}^1).$
It is worth highlighting that, nevertheless, increasing the number of reads/samples shrinks the gap between the performance of applying MQC to $\text{QA}^1$ to and $\text{QA}^6.$
In this sense, we extended MQC and introduced \emph{randomized MQC} (RMQC) that re-applies MQC on a shuffled sample set.
RMQC is guaranteed to outperform MQC (for $r>1),$ albeit notably more (classical) computations.

\section{Method}
This paper presents a novel postprocessing method for quantum annealers, called \emph{multi-qubit correction} (MQC), that notably improves quantum annealers' performance in terms of reproducibility of results and finding solutions with lower energy values.

\subsection{Quantum Hardware}
For all evaluations, we used a D-Wave 2000Q quantum annealer by D-Wave Systems Inc. (located at Burnaby, British Colombia, Canada).
The annealing time for all experiments was 20 microseconds.

\subsection{Study Cases}
Generating random Hamiltonians is a common practice for benchmarking quantum annealers \cite{das2008colloquium,
pudenz2015quantum,
king2016degeneracy,
ayanzadeh2019quantum_assisted}.
Hence, we generated three different types of Ising Hamiltonians as follows:
\begin{itemize}
	\item Binary—random Ising Hamiltonians whose linear and quadratic coefficients (denoted by $\mathbf{h}$ and ${J},$ respectively) were randomly drawn from $\{-1,+1\}$, based on a Bernoulli distribution with equal probabilities for -1 and +1;
	\item Uniform—random Ising Hamiltonians whose linear and quadratic coefficients (denoted by $\mathbf{h}$ and ${J},$ respectively) are (double-precision) uniform random numbers in $[-1,+1];$
	\item Normal—random Ising Hamiltonians whose linear and quadratic coefficients (denoted by $\mathbf{h}$ and ${J},$ respectively) are (double-precision) Normal random numbers that follow the standard Gaussian distribution, i.e., average and standard deviation are 0 and 1, respectively.
\end{itemize}

For each problem type, we generated 50 instances (random problems) and adopted the finite-range Ising model, a.k.a. EA model (Edward—Anderson) \cite{das2008colloquium}, to generate benchmark problems.
More specifically, all randomly generated benchmark problems were compatible with the D-Wave 2000Q quantum processor's working graph—in Chimera topology, every qubit is connected to at most six other qubits.
Therefore, until the next maintenance that can change the quantum annealer's working graph, one can directly execute them without embedding problems to a target graph.

We remark that random Ising problems might not be hard-enough problems for quantum annealers (and even classical optimization techniques) \cite{katzgraber2014glassy}.
To demonstrate the quantum speed-up, one need to use proper benchmarks \cite{marshall2016practical}.
It is worth highlighting that our objective in this study was not to show/claim quantum speed-up.

\subsection{Baselines}
Since executing a quantum machine instruction (QMI) on a physical quantum annealer is not guaranteed to achieve the ground state of the corresponding Ising Hamiltonian, even if we request many samples/reads, several studies have proposed software and hardware advancements to improve the performance of the quantum annealers.
As an example, recent studies have revealed that using spin-reversal transforms (also known as gauge transforms)—i.e., flipping qubits randomly without altering the ground state of the original Ising Hamiltonian—can reduce analog errors of the quantum annealers \cite{pelofske2019optimizing}.
Similarly, applying classical postprocessing heuristics on raw samples (attained by the quantum annealers) can result in samples with lower energy values \cite{borle2019post,golden2019pre}.
Furthermore, when we submit a problem to a D-Wave quantum annealer, it is a common practice to request several samples/reads (i.e., up to 10,000 per QMI on the current D-Wave quantum processors).
For every read (or measurement), the D-Wave QPU initializes all qubits and repeats the annealing process.
Therefore, when we repeat the annealing process, successive measurements are correlated to each other due to limited preparation time.
From another perspective, successive measurements generally form clusters of samples (i.e., groups of identical states).
Hence, increasing the preparation time can reduce the inter-sample correlations.
In this study, we used the following arrangements for evaluating the performance of the proposed postprocessing methods:
\begin{itemize}
	\item $\text{QA}^1$—raw samples attained by a D-Wave quantum annealer;
	\item $\text{QA}^2$—applies five spin-reversal transforms on $\text{QA}^1;$
	\item $\text{QA}^3$—puts a longer delay between successive reads/samplings to reduce the sample-to-sample correlation, albeit longer run-time;
	\item $\text{QA}^4$—performs the optimization postprocessing, available from the D-Wave's Ocean SDK, to all raw samples ($\text{QA}^1);$
	\item $\text{QA}^5$— performs the sampling postprocessing, available from the D-Wave's Ocean SDK, to all raw samples ($\text{QA}^1);$
	\item $\text{QA}^6$—applies five  spin-reversal transforms, puts a longer delay between successive reads, and performs the optimization postprocessing to raw samples (attained by $\text{QA}^1).$
\end{itemize}

\subsection{Evaluations}
To compare MQC with baseline methods, we count how many times the \emph{expected to win} method outperforms the baseline (i.e., finds a sample with lower energy value), ignoring cases where both methods can find an identical solution (in terms of energy value).
Since case studies used here are all random problems, of interest is how statistically significant such results are.
Hence, we performed hypothesis testing.
Our null hypothesis in all evaluations was that the \emph{expected to win} method and the baseline are not different, and the alternate hypothesis was that the \emph{expected to win} method outperforms the baseline (i.e., can always find a sample with lower energy value).
To this end, we calculate the $p-$value as:
\begin{equation}
	\label{eqn:p-value}
	p=\frac{1}{2^{n_b+n_w}}\sum^{n_b+n_w}_{k=n_b} \binom{n_b+n_w}{k} \, .
\end{equation}
where $n_b$ represents the number of times that the \emph{expected to win} method was able to outperform the baseline and $n_w$ is the number of times that the baseline was able to outperform the \emph{expected to win} method.
This represents the probability of the null hypothesis is true.
By convention, $p < 0.05$ disproves the null hypothesis (i.e., the \emph{expected to win} indeed outperforms the baseline) and is considered to be statistically significant,
and $0.05 \leq p \leq 0.95$ indicates that results are not statistically significant.
While $p \geq 0.95$ also disprove the null hypothesis (i.e., results are statistically significant), it also rejects the alternate hypothesis and indicates that the expected winner has been chosen incorrectly \cite{chen2021performance}.

\section*{Acknowledgements}
This research was carried out when the first author was a student member and research faculty member at the University of Maryland, Baltimore County. He is currently a postdoctoral fellow at the Georgia Institute of Technology.
This research has been supported by NASA grant (\#NNH16ZDA001N-AIST 16-0091), NIH-NIGMS Initiative for Maximizing Student Development Grant (2 R25-GM55036), and the Google Lime scholarship. We would like to thank the D-Wave Systems management team for granting access to the D-Wave 2000Q quantum processor.

\section*{Author contributions statement}
R.A. and J.D. conceived algorithms, and R.A. carried out implementations. M.H. and T.F. performed analysis.

\section*{Additional information}
Authors declare no competing interests.

redpajama_set_name: RedPajamaArXiv


text:
Home > Brexit, Europe	> The EU will have to be faced with implementing their hard border before they will compromise in the way that a reasonable organisation would
The EU will have to be faced with implementing their hard border before they will compromise in the way that a reasonable organisation would
August 24, 2019 Adam Tugwell	Leave a comment Go to comments
To say that Brexit and negotiation with the EU has been mishandled since the Referendum in 2016 would be a considerable understatement indeed.
What Margaret Thatcher called being Frit, has been the overwhelming modus operandi of the Government up until this Summer. It is behaviour that has, is and until we have a General Election, will continue to set us up for fall after fall.
Whilst Boris' impact in the past month has really started to make the UK think, it has also began to filter out a lot of the noise that continually comes from the EU and now suggests the pathway for Brexit this Autumn that is most likely to come.
During the past week, it has become openly clear that the EU is working on the basis that only two very defined options for the basis of Brexit after the UKs departure exist. That's a relationship which our politicians are free to call whatever they want, but will always include the unrescindable elements and characteristics of Remain, or to accept that the only alternative is a hard border between Northern Ireland and the Irish Republic.
The EU has always banked on the premise that a hard border would never be acceptable to the UK and that as such, we would never do it. Ergo, the UK would have no choice but to accept arrangements with the EU that retain most if not ultimately all of those that our relationship does with the EU now.
What they have overlooked is the fundamental reality that the standards and regulations which the EU holds so dear – and deems either of these binary options as being essential to guaranteeing the integrity of what they are supposed to do, are one of the key – if not overwhelming reasons that a majority of British people voted to Leave.
To Remainers – that's those who are really committed to the EU cause, rather than most people who simply want to avoid anything dressed up as change – that free-flowing border on the EUs terms is the only way they believe that it can operate and that a relationship with the EU is not available to the UK in any form.
But a hard border isn't necessary in the minds and eyes of Leavers either. And whilst the Frit or rather the Frightened Remainers who cannot see any other way forward for the UK than being under ru control look set to impale themselves on their own ideas as the fight for something that is not theirs to own, we know that free flowing trade is not a problem for us, that we continue to hold our relationship with the Irish dear, and that a so-called hard border and the imposing infrastructure that goes with it, is not something the UK is set to impose.
The EU has counted on those Amongst us who are committed to their sins and ideals to see their own version of Brexit through. And with the Brexiteer Prime Minister now in situ that we really should have had in the incumbency before, the stark reality that both he, We and the EU faces is it is only through their actions in the coming weeks that it could ever still be argued that there is any need to Remain.
Boris' 'negotiations ' with the EU in the coming weeks is not going to work. Any concessions from Brussels will be aesthetic at best. And it is only when they can accept that a) the UKs departure is inevitable and b) it is only they that will implement a hard border, that the EU will accept that their relationship with the UK is not ever going to be the same again.
Categories: Brexit, Europe Tags: Brexit, EU, Hard Border, Northern Ireland, The Backstop
There's nothing wrong with Boris doing everything necessary to stop undemocratic MPs wrecking our Brexit – so long as he's not going to do the same thing Ditching the Backstop is the priority. Without it we have the exit clause that means everything else is actually up for negotiation

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: How to perform simple zooming with calabash-android I have a simple application consisting of a custom chart with enabled zooming. What I want to do is to write a test which will perform some zooming of the chart and will check that the app doesn't crash after the zooming. Is that possible (I looked over the performAction method but the only zoom I found was for the google maps) ?

A: Without knowing how your app is made.
You might be able to solve this using recorded touch events.
So record an event, in your steps play it, and after you have played it verify some part of the UI is still active. Then you will know if the app did not crash.

redpajama_set_name: RedPajamaStackExchange


My first time over at Premiera Hotel, located at Jalan Tuanku Abdul Rahman, in the vicinity of SOGO. In fact the hotel is located in the same building as Menara MARA where the new MARA 'Lowyat' is located. I was there that evening to savour their Ramadan fares. Check out what they have to offer below.
The dinner buffet will be held at their Main Hall on the sixth floor of Premiera Hotel, with a maximum capacity of 1000 guests. More than 100 varieties of traditional Malay cuisine is offered on their buffet spread.
Among the dishes available were the gulai pedas, gulai kawah, kambing panggang, sup ekor berempah, ketam masak lemak cili api, laksa Johor, rojak buah, roti John, Malay Kuih Muihs, lots of different types of drinks and many more.
I was told there will be durians up for grabs too when the actual day comes. We didn't have a chance to savour it that evening though, but rest assure, it will be in the "menu" this Ramadan, as promised by Hotel Premiera Kuala Lumpur.
Guests will be entertained by a live music performance. In fact if you have your dinner here during Ramadan, you will stand a chance to win their 'Umrah Package" for two. More lucky draws will be held daily. Book your reservations early, with a price of RM68 nett for adults, and RM40 nett for child age 6-12 years. The normal price would be RM78 nett per adult. Check out Ramadan Buffet Dinner @ Premiera Hotel Kuala Lumpur if you're in the vicinity this Ramadan.


text:
Product Recalls: Consumer Safety Tip from McNabola Law Group
From airbags to baby sleepers to food, product recalls seem like everyday occurrences. With thousands of items recalled in the United States each year, many consumers wonder, "how do my family and I stay safe?"
First, be proactive, McNabola Law Group founder Mark McNabola recommends. If you're shopping for anything from a car seat to a new vehicle, do your research. Know which products have been linked to recalls or safety issues. The U.S. Consumer Product Safety Commission and U.S. Food and Drug Administration are great resources.
When it comes to healthcare, make sure the drugs or medical devices you've been prescribed — such as hip or knee replacements — are not associated with defects. Don't be afraid to ask your doctor or pharmacist questions.
If you hear of a product recall on the news, pay attention and act accordingly.
Finally, if you do become ill from a recalled food item or injured when your vehicle or medical device didn't operate properly, seek medical help and consult a lawyer to protect yourself.
"The public has profound trust in the companies that produce what we eat, drive and consume in every aspect of our day-to-day lives," McNabola said. "This is particularly true when there is a minimum stamp of approval by a government agency like the Food and Drug Administration. When those companies fail, attorneys can serve as victims' advocates to ensure accountability and change."
McNabola obtained a $1.8 million result in a product liability lawsuit filed on behalf of the family of a 3-year-old Chicago boy who died after tipping backward in an unstable chair. The high-back chair was part of a five-piece dinette set his mother bought at a well-known discount furniture store. The chairs were known to be unsteady from the start by the manufacturer, and tilted back easily if coats were set on the backs.
An autopsy determined the fall contributed to the boy's death. He had an arterial venous malformation (AVM) at the base of his brain that ruptured when his neck snapped forward and back upon hitting the floor.
"Grieving families are often plagued by questions about how and why a tragedy occurred," McNabola said. "We can never bring back a loved one, but we can try to prevent other families from experiencing the same heartbreak by always being overly cautious, asking questions and holding companies accountable before tragedy strikes."
By: McNabola Journal


text:
Viral Communication
Final Event | Wednesday, 11 May 2022, 18:00 CEST
PSY Symposium | Opponents or supporters of COVID-19 vaccination: Decision-making beyond black and white
Over two years ago, on March 11th 2020, the World Health Organization declared COVID-19 a pandemic. Since then, more than 470 million Covid-19-cases were registered and more than 6 million people have died. The measures required to contain the pandemic have brought to the fore the conflict between the power of the state and the individual freedom of citizens.
Vaccination and the rights and obligations associated with it have been at the centre of this debate. Since the beginning of the pandemic, vaccinations have been seen as a key factor in ending the pandemic – but who agrees and who opposes? Who has changed their mind over time?
In this symposium, we will take a closer look at attitudes and vaccination behaviour based on the data collected by the Understand_ELSED project. How have these changed over the course of the pandemic (late 2020 to late 2021)? What are the influencing factors? To what extent can we identify polarisation of vaccination opponents and supporters? What role do conspiracy theories play? Under what conditions is mandatory vaccination advocated or opposed?
After a short introduction of the overall project, six short presentations will provide insights into the different subprojects, which approach the above mentioned questions from different perspectives. The program is as follows:
Understand_ELSED: An introduction (Project Team, 5 min)
Representative Online Survey and Diary Study: Vaccination willingness: Development over time and influencing factors (Axel Pfleger, 10 min)
Representative Online Survey: Social biases and vaccination willingness: An experiment (Eric Jensen, 10 min)
Social Media Analysis: Pandemic or infodemic? Social media platforms as a (mis)information source for vaccinations (Lars Lorenz, 10 min)
Questions and break
Interview Study: "But what about the side effects!?" – Lines of argumentation in the context of the COVID-19 vaccines (Lisa Herbig, 10 min)
Interview Study: Identity Management and Vaccination Willingness: Who am I and who are "the others"? (Meike Watzlawik, 10 min)
Representative Online Survey & Interview Study: Vaccination, Polarization and Conspiracy: Longitudinal case studies (Brady Wagoner, 10 min)
Conclusion and Final Discussion (all)
We invite the audience to discuss the results, implications for practice as well as (the lack of) ethical and global perspectives in the vaccination debate with us.
The presentation and discussion will be hosted online via Zoom. Please join us on May 11th at 6 p.m. at (local time Berlin/UTC + 2 hours):
Zoom Link: https://us02web.zoom.us/j/85774044436
We welcome registration via e-mail: meike.watzlawik@sfu-berlin.de
Axel Pfleger graduated in Science Communication and Bionics with distinction. His experience in science communication evaluation and social research includes international events such as the Cheltenham Science Festival and The Best-Of InScience, as well as his influential research thesis analysing the European Space Agency's strategic communication and publics' attitudes toward the Agency. Recently, Axel has worked on the Understand_ELSED project, focusing on public attitudes and science communication during the COVID-pandemic in Germany.
Brady Wagoner received his PhD from the University of Cambridge on a Gates Cambridge scholarship. He is currently Professor at Aalborg University. His research focuses on how people construct meaning in their lives in communication with others within cultural frameworks. His recent books include The Constructive Mind: Bartlett's Psychology in Reconstruction (Cambridge University Press, 2017) and Remembering as a Cultural Process with Ignacio Bresco and Sarah H. Awad (Springer 2019). He has received several major awards, including the Humboldt Research Prize in 2021.
Eric Jensen is an Associate Professor of Sociology at the University of Warwick, where he teaches on public engagement with science, audience research and survey research methodology. He is a highly experienced social scientist, internationally recognised evaluator and science communication consultant and scholar with 20 years of professional experience in this field. He is the author of Doing Real Research: A Practical Guide to Social Research (SAGE). Jensen's PhD is in sociology from the University of Cambridge.
Lars Lorenz is specialised in managing the research process while keeping client requirements closely aligned with project deliverables and deadlines. He gained extensive experience with RRI as work package leader in NUCLEUS. More recent work includes other projects (RRING, GRRIP or TeRRIFICA) in research operations, project management and quantitative analysis and reporting. He worked as a scientific associate at Rhine-Waal University after graduating with a first degree in Science Communication and Bionics.
Lisa Herbig is a PhD candidate in Political Science at the Amsterdam Institute for Social Science Research (AISSR) of the University of Amsterdam and the Duitsland Instituut Amsterdam (DIA) where she studies the impact of the COVID-19 pandemic on support for the European Union. Previously, Lisa has worked on the Understand_ELSED project, focusing on the support for preventive measures, vaccination willingness, trust in political and scientific actors as well as conspiracy beliefs. Her research integrates quantitative and qualitative research methods. Lisa holds a Master in Intercultural Psychology from Osnabrück University and a Bachelor in Psychology from the Goethe University Frankfurt.
Meike Watzlawik is Professor for Development, Education and Culture at the Sigmund Freud University Berlin. She is the scientific director of the Institute for Psychological Research at the SFU. The combination of different methodologies is her preferred approach when examining identity development in different population. As part of an international network, Watzlawik has worked with scholars from all over the world, presented the collaborative work at conferences or in recognized publications like the Oxford Handbook of Culture and Psychology.
The project Understand_ELSED*, funded by the Federal Ministry of Education and Research, investigates how people in Germany experience the pandemic, what challenges they face in the context of the pandemic and what ways they find to cope with them. Central to this is the information that is available to the public about the COVID 19 pandemic, or which is made available to them, and how they handle this information over time. Reception of scientific misinformation, risk perceptions and attitudes to mitigation behaviors were assessed. Insights from this research will inform recommendations about how to improve public resilience to misinformation and/or its negative effects on public health and solidarity in society.
Understand-ELSED is an acronym for Understanding the Ethical, Legal, and Socio-Economic Dimensions of the COVID-19 Outbreak.
Viral Communication: Public Responses to the COVID-19 Pandemic
In this project, we are researching exposure to and use of information relevant to the coronavirus/COVID-19 crisis. Through this research, we have been collecting survey data relating to how people draw on, understand and share information. This involves exploring phenomena such as reception of scientific misinformation, risk perceptions and attitudes to mitigation behaviors. Insights from this research will inform recommendations about how to improve public resilience to misinformation
and/or its negative effects on public health and solidarity in society.
Viral outbreaks often see heightened public anxiety, concerns, different levels of perceived risk, misinformation, and stigmatization. At the same time, there is limited information about critical social dimensions and countermeasures. These dimensions have crucial consequences for the ways in which German residents make certain social, ethical, and legal decisions based on their own socio-economic situations which collectively have critical consequences for the way in which
the social context of the pandemic takes shape.
The goal of this project is to inform tailored and effective strategies to minimise the impact of the pandemic. Providing a robust knowledge base of German residents' risk perceptions, attitudes and behaviours related to COVID-19 can enhance societal resilience by implementing bottom-up approaches which empower citizens and foster pro-social behaviour.
Objective 1
Gain insights on respondents' demographic and socio-economic backgrounds, as well as the development of their understanding of the pandemic, perceptions of risk, and attitudes toward mitigation measures. This will be done by means of a nationally representative survey repeated a total of three times with the same individuals over 12 months.
Assess how individuals deal with ambiguities, ambivalences, and uncertainty through qualitative interviews with a subsample of survey participants, following up on each of the three survey measures.
Understand Germans' online interactions and content creation related to COVID-19 through an online ethnography over six months. This includes monitoring participants' social media feed and interactions, taking notes of issues potentially requiring further attention, and qualitative interviews. The interviews help capture information on reasoning behind the social media discourse, and meta-discursive features that correspond with, or diverge from conventional offline speech
Parallel to the online ethnography, gain insight into respondents' thoughts, feelings, and experiences related to the pandemic by means of a diary survey. Here, they fill out a short questionnaire every two weeks.
Understand-ELSED
This research project is funded by the Federal Ministry of Education and Research, and is officially named Understand-ELSED. The acronym stands for Understanding the Ethical, Legal, and Socio-Economic Dimensions of the COVID-19 Outbreak.
For more information about the funding, please visit the funder's project page
© Viral Communication
For more information contact axel.pfleger@sfu-berlin.de


text:
Bombers Schedule
SJHL Schedule
The Future of Flin Flon
The Reminder
October 4, 2013 03:00 AM
The Reminder is making its archives back to 2003 available on our website. Please note that, due to technical limitations, archive articles are presented without the usual formatting.
Day on the lake leads to big money for Trout Fest derby winner
'Superbob' one celebrated artist featured on Main Street
Leaner, meaner Handivan hits the road
Thomas H. Heine of Flin Flon has written a five-part essay entitled 'The Future of Flin Flon.' Today we present the fifth and final part. *** A Plan for the Future Let's have a look at what we have right now. We have a decent highway system connecting us to major centres in the south (Winnipeg, 750 km) and west (Prince Albert, 400 km). We have access to railroad infrastructure. This efficient mode of transportation used to bring people and supplies into Flin Flon. Now it is used only to transport zinc concentrate and propane into the community, and copper concentrate and zinc ingots to outside markets. Our municipal airport is in good shape. Our community has numerous cultural groups. These include the Flin Flon Community Choir, Ham Sandwich (theatre), Flin Flon Bombers, the Northern Visual Arts centre (NorVA) and the Central Canada Film Group. We also have adequate recreational facilities: the aging Whitney Forum / Uptown curling rink, an older aquatic centre, 26 km of excellent cross-country ski trails and several within-town walking trails. We have a full range of active service clubs. We have numerous large lakes on which we enjoy various water sports. Fishing in the area is second to none. We have a workforce with a wealth of experience in a variety of professions, particularly those related to the mineral exploration and mining industry. Over the years, various ideas have come forward for some modicum of regional economic diversification: using the South Main shaft as a zero-gravity research facility; establishing an oriented strandboard plant; using burbot livers for pharmaceuticals; growing flowers and pharmaceutical crops underground; establishing an underground nuclear waste storage facility. I remember a comment that a mines minister made a few years ago while on a visit to Flin Flon. Even then he was aware that our economy should be diversified, and he mused that a copper cookware manufacturing facility could be set up here. Copper pots are a high-value item, and apparently we had a ready supply of copper metal that could be used in their manufacture. He did not realize that we didn't actually have easy access to refined copper, and that the copper coming out of the smelter could not be directly used in manufacturing cooking utensils. Or other products. It still needed to be refined. It was a good idea but had a few shortcomings. But that's a moot point now: after decades of service, the copper smelter is permanently closed. Other communities of our region have seen the expansion of educational facilities due to the largesse of the provincial government. Some $82,000,000 went to Thompson and $15,000,000 was allotted to The Pas. An incarceration facility is under construction in The Pas, and $5,000,000 has been spent so far. Thus, the province is actively working to diversify the economies of these communities through the establishment and support of educational and other institutions. Flin Flon has been virtually ignored. A relatively modest amount has been provided for the construction of the Northern Manitoba Mining Academy. Figures supplied by the provincial government indicate that the total cost of the facility was $4,639,000 in the form of cash, in-kind, and internal resource allocations. No money was provided from the Mining Community Reserve Fund. Instead, funding was provided by the following: ÊCanada-Manitoba Economic Partnership Agreement ($1,720,000) ($950,000 from the province, $770,000 from the federal government) ÊGovernment of Canada Western Economic Diversification Community Adjustment Fund ($920,000) ÊInnovation, Energy and Mines ($850,000) ÊManitoba Entrepreneurship Training and Trade ($200,000 - Industry Workforce Development / Apprenticeship Manitoba) University College of the North ($500,000) HudBay ($294,000) ($200,000 cash and $94,000 in-kind as land donation) ÊCity of Flin Flon ($155,000, as utilities connection work) Economic diversification does not happen instantly but involves years of work combined with a long-term vision and plan. In Flin Flon, a variety of economic development officers have passed through the doors of Community Futures Greenstone over the years, with little to be seen for their efforts. The same can be said for similar individuals working in other communities in our region. The reasons are manifold, but one fact has become apparent: beyond funding the economic development offices and officers, little financial support appears to be forthcoming to support real economic diversification. The route followed to attain some modicum of diversification, at least to this writer, is fairly straightforward. First, it cannot happen overnight. Once an idea is presented, it must be tested and evaluated to see if it is viable. This involves funding feasibility studies. It is at this point where government funding may prove critical. If these studies prove positive, modest funds can be expended to see if the idea works. These decisions must be made by people who are familiar with all aspects of our community. A gradual and prudent build-up may be required before full-scale implementation of a proposal bears fruit. In its efforts to diversify Flin Flon's economy, Greenstone has repeatedly tried to gain access to money held under the Mining Community Reserve, to no avail. By design or accident, we now have the Northern Manitoba Mining Academy. The curriculum of the Mining Academy is being developed slowly. An innovation, one that has been common for centuries in Europe, is providing workforce training on an as-needed basis. So far this has included exploration technician training, underground equipment operator training and Workplace Hazardous Materials Information System (WHMIS) training. Plans for future offerings include first-year university geology courses, to take advantage of the superb exposures of the volcanic stratigraphy in Flin Flon. When the Academy opened, provincial politicians suggested that the academy would attract students from across Canada and, eventually, internationally. A second initiative planned for the academy is offering formal geology courses to be tied into the curricula of universities in southern Manitoba. As part of this plan, it is envisaged that the facility could be used as a base for multidisciplinary research undertaken in the region. The third initiative for this institution is that it functions as an outreach and coordination centre with other educational institutions using an inter-agency cooperative approach. Its entire approach is designed to stay flexible in the programs that it offers. So far, at least, it appears that no thought has been given as to where these countless students will reside. And the former Flin Flon Hotel remains empty and undeveloped. The extensive tailing pond behind the mining and smelting complex provides another opportunity. Mine remediation is a growing business both in Canada and internationally. Much research still needs to be done on methods to stabilize existing tailing for the long term. See 'Letter' on pg. Continued from pg. There is no reason this mining waste cannot be used as a real-world laboratory to examine tailing behavior in the non-laboratory environment. Other diversification proposals have been suggested and, to some degree, are being pursued. At this point in our history, no proposal can be dismissed out-of-hand. The bottom line in this discussion is that our community has been entering a crisis era for some time. We have a declining population that is finding it increasingly difficult to pay for the services that we have come to expect. Municipal tax reform can help the situation in the short term. The possibility of annexing adjacent communities to raise municipal funds is less palatable for a variety of reasons. I wish that I could offer a definite solution to the crisis. I have neither the knowledge nor the expertise to lead us into the future. There are, however, several things that I do know. Solutions imposed from outside of our community are often based on ignorance and, although well-intentioned, often do little to serve their intended purposes. Solutions must come from within the community and be supported by it. Past ideas / initiatives have been small scale with, at best, only limited effects on our economy. Many remember the promotion of Flin Flon as the Science Fiction Capital of the World, the proposal of painting murals on the rocks overlooking Ross Lake to attract tourists, the study suggesting ways of improving the appearance of Main Street. Tourism is seasonal and can provide only secondary support for a diversified economy. But now it is decision time in a real sense. We can continue on as we are, in apparent ignorant bliss, to suffer the fates of other mining communities in northern Manitoba. Or we can come together, as a community, to develop a vision and plan to implement a full-scale diversification of the economy for our town. Time is short. We have to start now. Thomas H. Heine Flin Flon
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The Flin Flon Reminder welcomes your opinions and comments. We do not allow personal attacks, offensive language or unsubstantiated allegations. We reserve the right to edit comments for length, style, legality and taste and reproduce them in print, electronic or otherwise. For further information, please contact the editor or publisher, or see our Terms and Conditions.
One Foot Outdoors: Tips, tricks and hints for cooking in the woods
One Foot Outdoors: Tips, tricks and hints for cooking in ...
Disaster planning POLL
Do you have a plan in case of natural disaster?
Yes - we're prepared in case something happens
We have parts of a plan but we'll probably just panic
We don't have a plan but probably should have one
If something bad happens, my goose is cooked
2019 Tourism Guide
Read the new 2019 Flin Flon & area tourism guide here!
Popular Reminder Letters
In Other Words: Growing protests a front for intolerance
Letter to the Editor: School boards hold vital role
MLA Report: Looking back and forward
Letter to the Editor: time to get serious after mine shutdown
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I wrote one that grabs more info but I couldn't figure out how to get the columns since there could 1 to N.
Nice. Definately more than bit more sophisticated than mine. I'm going to work on it some more just to see what I can do with it. My Xquery is so shaky though it takes me forever to figure out what to do. But the practice is good.
Yah, it is definitely something I have found useful. Here is another one that finds scans that may be because of implicit conversions. Although, xquery makes my head hurt.
Another nice one. I'm going to have to explore this more thoroughly.
Nice. I've been meaning to write something like this for a while.
Guess I need to learn XQuery sometime.
Thanks. I think it's pretty obvious that I haven't learned XQuery yet either. More to come on this one I think. We've got quite a few Microsoft Dynamics development projects coming our way. They're all going to need indexing added after they're developed. Having a mechanism to more quickly identify indexes will be helpful.
Thanks for the latest link, looks good.
I find this XQuery stuff really interesting.


The former CEO of defunct Japanese Bitcoin exchange Mt. Gox, Mark Karpeles, who may face a ten-year sentence over alleged embezzlement, has maintained his innocence in the closing arguments in a Tokyo court, Japanese public broadcaster NHK reported.
The infamous Mt. Gox hack of 2014 resulted in the loss of 850,000 Bitcoins (BTC) which were worth US$ 473 million at that time and it has become the first notable bitcoin theft in history. Since then, legal battles have raged between the exchange and its creditors.
According to an indictment against Karpeles in a Tokyo court earlier this month, claiming he stole funds worth 340 million yen ($3 million) from the exchange and manipulating data to inflate its cash balance between September and December 2017. The prosecution has asked for a ten-year prison sentence for Karpeles.
On Thursday, Karpeles admitted and apologized for his failure in protecting user's digital assets from hacking, but denied his guilt on all charges in court.
Karpeles's defense team also argued that the so-called "embezzled funds" were not client funds but the exchange's revenues and the transfer of money to his own account was part of the exchange's business. According to prosecutors, Karpeles took the funds to invest in a software development business.
The Mt. Gox trail has been taking place at the Tokyo District Court since last July. A verdict of this trial is expected on March 15, 2019.
According to a statement released in September by Mt. Gox's trustee, Nobuaki Kobayashi claimed that he has liquidated almost 26 billion yuan ($230 million) in Bitcoin and Bitcoin Cash in four months. He also informed the public that he has sold 24,658 BTC and 25,331 BCH since the third quarter of 2017.


Crianza creamy, ripe black fruit and sweet layers of sumptuous coffee, chocolate, nutmeg and licorice that watch upon standing. Elegant, delicate and good acidity to accompany exquisite dishes of red meat.
100% Peter Sisseck project. Pingus's little brother, it must also centenarian vineyards, which results in an elegant, fresh, lively, firm wine. According to the magazine "Wine Advocate" is "a triumph over nature.


Below I listed Hitachi India contact information like technical support and customer support info, customer service phone number and contact email address, if you are the customer of Hitachi India and are you looking information for how to get support from Hitachi India. If any problem or doubt occurs please make a call to below I mentioned Hitachi India Customer Service & Support Number.
Hitachi 24/7 Customer Care Numbers: 3532-4848*.


Start by accepting that your opinions and needs are important and you have the right to express them.
Use an assertive style of behaviour – This means standing up for your own rights without violating those of others. Expressing your own needs, wants, feelings and beliefs in a direct, honest and appropriate way.
The aim of assertive behaviour is to satisfy the needs and wants of both parties, in other words a 'win-win' situation.
Broken record – In a calm voice quietly repeat what it is you want until the other person listens and responds to your need. Stick to your point and keep it short. This will avoid the need to rehearse arguments until you run out of steam and give in.
Make the most of your listening skills. Ask open questions and check out your assumptions and understanding.
Take control of your inner voice. Listen to the positive enabling voice and turn off the critic.
Think about the language you use. Move from 'I should' 'I must' to 'I choose', a much more motivating position.
Think about how you manage your time. If you are constantly responding to other people's crisis then learn to say 'no' or ' not now' without feeling guilty.
Practice a couple of quick stress management techniques that you can use when you are feeling challenged.
Identify your strengths and promote them. Identify your learning or development needs and make a SMART plan to address them.
Measurable – can I measure the success or failure of my objective?
Achievable – realistically, do I have enough resources and time to carry out this objective successfully?
Relevant – does this objective help me achieve progress for my project?
Time-bounded and trackable – do I have a firm end date for my project and milestones along the way to help me check on progress?
Get used to change by challenging yourself with small changes that place you outside your comfort zone.
What agenda they might have?
What they might need from you?
What are the weaknesses in your idea they'll try to exploit?
What are the strengths they'll try to avoid?
Then prepare your response. Consequently, you will feel much more confident going into a negotiation or influencing situation. When you feel confident you will behave in a confident manner and people will see a confident person.


Hartgen offers a range of architectural documentation and preservation services which are described in detail on the individual pages listed on the left. These services include histories, existing conditions studies, and planning documents aimed at preservation. As required, these services can be offered in conjunction with our archeological services.
To find out if we can provide the services you need, please contact Walter Wheeler by email or by telephone at (518) 283-0534.


Product Description. Rupture Valve. TECHNICAL INFORMATION The rupture valve R 10 can be mounted in any position directly onto the cylinder inlet connection Z.
roller sand screening machine offers 226 roller sand screening machine products. About 83% of these are vibrating screen, 1% are other machinery amp; industry equipment. A wide variety of roller sand screening machine options are available to you, such as circular, linear.


Whenever a renewable energy project is planned and implemented in partnership with the local community we create a significant win / win situation.
Increasingly Renewable Energy Co-ops are seen to enter into partnerships with developers of renewable energy projects and members of the respective community, a model that makes environmental and economic sense.
The local economy wins because with community ownership the likelihood for local contractors to be included in the competitive process during the construction and maintenance phase of the project significantly increase. Infrastructure work, excavation, construction, electrical connection and equipment rental are more likely to be awarded to contractors that are located in the community because their work practices and reliability are known by the community.
The environment wins when a community chooses renewable energy over the alternative of fossil fuel burning energy production. It is significantly easier for a community to make that choice when the community owns the project in partnership with the developer. For example Woodstock Hydro has been successfully leading the energy conservation effort which was and continues to be only possible with the support of all members of the Oxford County Community and that same community spirit can put Oxford County on the map as a model for environmental change. Community ownership in the energy production from Wind, Solar, and Bio fuels can support the County in this effort.
The Renewable Energy Project Developer wins because they receive first hand feedback from the local community about the community's needs, its concerns, and solution suggestions on how to address those needs. The information flow is fast tracked in both directions and many misunderstandings can be either avoided altogether or addressed quickly.
Most importantly the Community wins when its members can attain ownership in these renewable energy projects. Owners educate themselves about the product they consider investing in. A broader knowledge about the environmental impact of centralized conventional energy production and a shared understanding of the opportunity to curb these negative impacts with the implementation of renewable energy projects is an important first step. Owners monitor the development of their investment and ensure that the community interest is maintained and concerns are addressed. The partnership of member-owned Renewable Energy Cooperatives in these projects ensures that the community's interests are represented throughout the project's life. Co-operatives successfully negotiate with the developer to provide support to small scale community initiatives such as playgrounds or nature trails in the project areas to support their partnership attitude.


jin loves to eat: Help Save PJ!
Do you know Pithecophaga Jefferyi?
I call him PJ or Jfryi. He's a really handsome and powerful dude.
In case you don't know, he's also our Philippine National Bird and he needs our help.
All proceeds go to the Philippine Eagle Foundation.
Of course, the best is to actually adopt a Philippine Eagle and you get to give him or her a really cool name. Or just name him or her after you. Hehehe.


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text:
Flu vaccine is 48% effective this season, CDC says
By Susan Scutti, CNN
Updated 2:23 PM EDT, Mon April 10, 2017
Seven questions about the flu shot
Influenza A (H3N2) viruses dominate this season, the CDC says
Doctors say unvaccinated people should still get a flu shot this season
The flu shot has been reducing the risk of infections in the United States by about half this season, according to reports issued Thursday by the Centers for Disease Control and Prevention, but the season is expected to continue for several more weeks.
The predominant virus strain this season is influenza A (H3N2) strain; the estimated effectiveness of the vaccine in preventing illness caused by that strain was 43%. The vaccine's estimated effectiveness against the influenza B virus was 73%. That amounts to overall protection of 48%, the CDC said.
More about 'Unseen Enemy'
Go to CNN.com/unseenenemy for more stories and resources about epidemics, pandemics and the CNN Films documentary, "Unseen Enemy."
The CDC's interim findings are based on data from November 28 through February 4 for 3,144 children and adults who are enrolled in the United States Influenza Vaccine Effectiveness Network. The CDC uses information from this network to routinely estimate effectiveness of the flu vaccine as the season progresses.
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Flu spreads across 40 states
During the 2015-16 season, vaccine effectiveness was 47% – about the same as this season. During the 2014-15 season, effectiveness was just 19%, according to the CDC.
"The viruses in the vaccine are a good match for the circulating viruses this season," said Brendan Flannery, lead investigator for the US Flu Vaccine Effectiveness Network. "The prediction for the H3N2 virus was right on in terms of that particular virus continuing to be a dominant virus."
As Flannery explained, the H3N2 was not seen much during last season, but it's the same virus that first appeared in the 2014-15 season. "This vaccine is a much better match for the circulating virus than the vaccine we had two years ago."
In a separate report, the CDC noted that flu activity in the US began to increase in mid-December, remained elevated through early February and is expected to continue for several more weeks. Influenza A (H3N2) viruses have predominated, though the CDC also identified influenza A (H1N1)pdm09 and influenza B viruses.
Gemma Botelho, age 4
Courtesy Botelho Family Photos
Girl awake after nearly deadly flu case
From October 2 through February 4, 38,244 specimens tested positive for flu virus in the US, according to the new report. Age was reported for 13,306 patients: 7.9% were younger than 4 years old, 30.4% were between 5 and 24 years old, 30.3% were between 25 and 64 years old, and 31.5% were 65 or older. Fifty-one of 54 US states and territories are experiencing elevated levels of flu and flu-like illnesses. There have been 20 pediatric deaths reported this season.
The CDC recommends that unvaccinated people who are 6 months old or older still get a shot this year.
"Influenza activity may have peaked where there were early peaks in the Northwest and on the West Coast," Flannery said. "Activity is increasing in some of the Midwest and the East Coast."
However, it's "unpredictable where it's going to peak," he said, and after what appears to have been a peak, "there can still be an increase in activity in some areas." For example, it seemed like a peak occurred in early January, but afterward, activity rose in several areas.
"We were a bit surprised by how much the influenza activity has increased recently," Flannery said, "and I think that strengthens the message for people to still get vaccinated."
Building a vaccine
The flu shot is an inactivated influenza vaccine. Each February, the Advisory Committee on Immunization Practices, a group of medical and public health experts, makes the final decision about which virus strains will go into vaccines, which will be sold beginning in September to protect people during the forthcoming flu season.
Dr. Jennifer Caudle urges everyone to get their flu shot before the season is over.
Mario Tama/Getty Images file
Why you can get the flu even if you've had the flu shot
The committee's decision is based on information from more than 100 countries, where influenza-monitoring centers conduct surveillance of circulating viruses. The committee members consider which viruses are making people sick, where those viruses are spreading and how well the previous season's vaccine protects against them.
After the committee selects the strains to be used for the vaccine, manufacturers will produce trivalent formulations with three strains and quadrivalent formulations with four strains for the upcoming season; both trivalent and quadrivalent shots are equally recommended, according to Flannery.
Thimerosal is a preservative used in some vaccines. It was eliminated from most childhood vaccines in 2001.
Thimerosal: Everything you need to know about this vaccine preservative
Generally, vaccine effectiveness varies and not only based on what type of viruses are circulating.
"How well the vaccine matches the circulating virus is certainly a big factor," Flannery said. "But that's not the whole story, and sometimes there are differences by age or … the immune status of the individual. So there are individual factors as well as the circulating viruses that influence vaccine effectiveness."
The pattern this season is very similar to previous H3N2 seasons, according to Flannery, with higher hospitalization rates seen in the 65 and older population. "Young kids also are having severe illness," he said.
"Right now, it looks on track to be a moderately severe H3N2 season," he said. "It seems like it's similar to the 2012-2013 season, which was a season with millions of cases and about 60,000 hospitalizations and 2,000 deaths."
'If you haven't gotten vaccinated … run'
Infectious disease specialist Dr. William Schaffner freely admits that "the flu vaccine is an imperfect vaccine. It often cannot prevent flu completely."
This negative-stained transmission electron micrograph (TEM) depicts the ultrastructural details of an influenza virus particle, or "virion". A member of the taxonomic family Orthomyxoviridae, the influenza virus is a single-stranded RNA organism.
Your flu risk may be linked to the year you were born
"But if you get vaccinated and get the flu, your disease is going to be in all likelihood much milder than it might have been. You won't have to be admitted to the hospital, you won't have to go to the ICU, and you won't die."
"So half a loaf is better than none," Schaffner said. "In the meantime, we support the people who are working in laboratories to make an even better influenza vaccine, which we hope will be available a few years down the road.
"Still, we can do a lot of good with a pretty good vaccine available today."
With flu continuing to occur through this month and into March, there's still flu that can be prevented or made less severe, Schaffner said. "So that's why we always say it's not too late – but it is very late.
"If you haven't gotten vaccinated and you haven't gotten sick yet, run – do not walk – this afternoon. You should get your flu vaccine, because it does take 10 days to two weeks for maximum protection."
See the latest news and share your comments with CNN Health on Facebook and Twitter.
If you do come down with the flu, it is important to call a health care provider who can give you a prescription for an antiviral drug.
"And what that will do is also make the disease less severe, and you're likely not to be ill for as long," Schaffner said. "It shortens the duration of the influenza, and it also makes it less likely you will spread that virus to the people around you."
Specifically, the CDC advises early treatment with oseltamivir for anyone at risk for suffering flu complications, a group that includes "65 and older, young children, people with underlying compromising health conditions" and those who are hospitalized, according to Flannery.
"If influenza is suspected, it doesn't have to be confirmed," Flannery said. "It should be started as early as possible and shouldn't depend on whether someone was vaccinated or not."


text:
Adolescents and Young Adults
Young people with cerebral palsy aged 10 to 30 years
CP-Achieve NEEDS YOU
Are you passionate about making a difference to the health and life outcomes for young people with cerebral palsy in Australia?
You could advise researchers how to do research about cerebral palsy!
CP-Achieve invites you to join a project – we would like to work together to make research better for adolescents and young adults with cerebral palsy.
CP-Achieve stands for Australian Centre for Health Independence, Economic Participation and Value Enhanced Care for adolescents and young adults with cerebral palsy (CP). We do research with and about adolescents and young adults with cerebral palsy aged 10 to 30 – a life stage when young people are becoming independent and figuring out who they are and want to become. Our vision is for people with cerebral palsy in Australia to receive the best health care throughout their lives and participate in, and contribute to, supportive communities.
In each project you will work together with the team to develop and carry out the project and how to use the findings to help people with cerebral palsy.
If you choose to be involved in one of our projects, we will:
Help you to get to know the team
Make sure you know what the project is about
Give you support all along the way, the team will make sure you are involved
Be flexible in how you are involved
Pay you for your time
Benefits to you. Young people who join research teams tell us that they:
Extend their social and work networks
Benefit from being part of a passionate team
Feel a sense of satisfaction
Enjoy helping others
Like to share their experiences of cerebral palsy
Develop skills and knowledge that are helpful in other aspects of their life
Find it useful to have the experience on their résumés
Read about each of our projects:
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Gyms and Physical Activity Research Team
This research is to work out the most important ways of helping young people to work out in gyms in their own communities.
The Consumer Evaluation Research Team
CP-Achieve aims to involve young people with cerebral palsy and their families in all our research. This research team will be working out how well we are doing this, what strategies work well for involving consumers, and how we can do better.
Transition to Adulthood Research Team
This team will be finding out the important life areas for young people as well as what will prepare and support young people to be involved in the life areas they choose, as they move through adolescence into adulthood.
The Drooling Research Team
We want to learn how drooling affects the health and well-being of young people with cerebral palsy and how to best help young people to manage drooling.
The Mentor Program Research Team
The advisory group of young adults with cerebral palsy for CP-Achieve is called CP Unite. Some CP Unite Advisors have a mentor to work with to develop skills and confidence about being an Advisor. This research is to work out how well the mentor program is working for the Advisors and for the mentors, and how we could be doing better.
The Adolescent Advisors Working Group
We would like to start a group of adolescents who are expert advisors for CP-Achieve. We would like adolescents to work with us to advise about how best to find adolescents for the advisory group and how best to work with and support them to be advisors.