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0848e5dea6
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| 0848e5dea6 | |||
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| 606d68e2c1 | |||
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| 3b7618e1d1 |
@@ -224,25 +224,25 @@ construction for the [7,4,3]-Hamming code.
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}
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\begin{tikzpicture}
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\node[VN, label=above:$x_1$] (vn1) {};
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\node[VN, right=12mm of vn1, label=above:$x_2$] (vn2) {};
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\node[VN, right=12mm of vn2, label=above:$x_3$] (vn3) {};
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\node[VN, right=12mm of vn3, label=above:$x_4$] (vn4) {};
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\node[VN, right=12mm of vn4, label=above:$x_5$] (vn5) {};
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\node[VN, right=12mm of vn5, label=above:$x_6$] (vn6) {};
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\node[VN, right=12mm of vn6, label=above:$x_7$] (vn7) {};
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\node[VN, label=above:$x_0$] (vn1) {};
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\node[VN, right=12mm of vn1, label=above:$x_1$] (vn2) {};
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\node[VN, right=12mm of vn2, label=above:$x_2$] (vn3) {};
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\node[VN, right=12mm of vn3, label=above:$x_3$] (vn4) {};
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\node[VN, right=12mm of vn4, label=above:$x_4$] (vn5) {};
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\node[VN, right=12mm of vn5, label=above:$x_5$] (vn6) {};
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\node[VN, right=12mm of vn6, label=above:$x_6$] (vn7) {};
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\node[
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CN, below=25mm of vn4,
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label={below:$x_1 + x_3 + x_4 + x_6 = 0$}
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label={below:$x_0 + x_2 + x_3 + x_5 = 0$}
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] (cn2) {};
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\node[
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CN, left=40mm of cn2,
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label={below:$x_2 + x_3 + x_4 + x_5 = 0$}
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label={below:$x_1 + x_2 + x_3 + x_4 = 0$}
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] (cn1) {};
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\node[
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CN, right=40mm of cn2,
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label={below:$x_1 + x_2 + x_4 + x_7 = 0$}
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label={below:$x_0 + x_1 + x_3 + x_6 = 0$}
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] (cn3) {};
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\foreach \n in {2,3,4,5} {
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@@ -268,9 +268,9 @@ construction for the [7,4,3]-Hamming code.
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%
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Mathematically, we represent a \ac{vn} using the index $i \in
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\mathcal{I} := \left[
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1 : n \right]$ and a \ac{cn} using the index $j \in \mathcal{J}
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:= \left[ 1 : m \right]$.
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\mathcal{I} := \left[ 0:n-1 \right] := \left\{ 0,1,\ldots,n-1 \right\}$
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and a \ac{cn} using the index $j \in \mathcal{J}
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:= \left[ 0 : m-1 \right]$.
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We can then encode the information contained in the graph by defining
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the neighborhood of a variable node $i$ as
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$\mathcal{N}_\text{V} (i) = \left\{ j \in \mathcal{J} : \bm{H}_{j,i}
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@@ -41,7 +41,7 @@ address both.
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% Definition of fault tolerance
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We model the possible occurrence of errors during any processing
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stage as different \emph{error locations} $E_i,~i\in \{1,\ldots,N\}$
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stage as different \emph{error locations} $E_i,~i\in [1:N]$
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in the circuit.
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$N \in \mathbb{N}$ is the total number of considered error locations.
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The \emph{circuit error vector} $\bm{e} \in \{0,1\}^N$ is a vector
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@@ -861,7 +861,7 @@ can add the results from the previous round, as illustrated in
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\Cref{fig:detectors_from_measurements_general}.
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We thus have $D=n-k$.
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Concretely, we denote the outcome of
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measurement $\ell \in \{1,\ldots,n-k\}$ in round $r \in \{1,\ldots,R\}$ by
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measurement $\ell \in [1:n-k]$ in round $r \in [1:R]$ by
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$m_\ell^{(r)} \in \mathbb{F}_2$
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and define
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\begin{gather*}
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@@ -873,7 +873,7 @@ and define
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\end{pmatrix}
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.%
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\end{gather*}
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Similarly, we denote the outcome of detector $j\in\{1,\ldots,D\}$ in
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Similarly, we denote the outcome of detector $j\in[1:D]$ in
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round $r$ by $d_j^{(r)} \in \mathbb{F}_2$ and define
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\begin{gather}
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\label{eq:measurement_combination}
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File diff suppressed because it is too large
Load Diff
@@ -129,6 +129,24 @@ sp="/-\|"
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# done
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# done
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# Copy whole BP over max iter. results
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echo -e "\rCopying whole over max_iter simulation results..."
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echo -n ' '
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for decoder in "SyndromeMinSumDecoder"; do
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for p in 0.001 0.0025 0.004; do
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SRC_PATH="${BASE_PATH}+rust_exp=max_iter_bp,decoder.class_name=${decoder},simulation.phy_err_rate=${p}/"
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LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
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SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
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DEST_DIR="res/sim/max_iter/${decoder}/p_${p}/"
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mkdir -p ${DEST_DIR}
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DEST_FILE="${DEST_DIR}/LERs.csv"
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cp ${SRC_FILE} ${DEST_FILE}
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post_process_LERs ${DEST_FILE}
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printf "\b${sp:i++%${#sp}:1}"
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done
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done
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# # Copy BPGD decimation passing
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#
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# echo -e "\rCopying BPGD param exploration results..."
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@@ -151,20 +169,20 @@ sp="/-\|"
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# Copy BPGD with decimation info passing over max iter. results
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echo -e "\rCopying BPGD over max. iter. results..."
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echo -n ' '
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for pass_soft_info in "True" "False"; do
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for F in 1 2 3; do
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for W in 3 4 5; do
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SRC_PATH="${BASE_PATH}+rust_exp=max_iter_bpgd_pass_channel,decoder.class_name=WindowingSyndromeSpaGdDecoder,decoder.pass_soft_info=${pass_soft_info},simulation.phy_err_rate=0.0025,system.F=${F},system.W=${W}/"
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LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
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SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
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DEST_DIR="res/sim/max_iter/WindowingSyndromeSpaGdDecoderPassDecimation/p_0.0025/pass_soft_info_${pass_soft_info}/F_${F}/W_${W}"
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mkdir -p ${DEST_DIR}
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DEST_FILE="${DEST_DIR}/LERs.csv"
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cp ${SRC_FILE} ${DEST_FILE}
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post_process_LERs ${DEST_FILE}
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printf "\b${sp:i++%${#sp}:1}"
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done
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done
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done
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# echo -e "\rCopying BPGD over max. iter. results..."
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# echo -n ' '
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# for pass_soft_info in "True" "False"; do
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# for F in 1 2 3; do
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# for W in 3 4 5; do
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# SRC_PATH="${BASE_PATH}+rust_exp=max_iter_bpgd_pass_channel,decoder.class_name=WindowingSyndromeSpaGdDecoder,decoder.pass_soft_info=${pass_soft_info},simulation.phy_err_rate=0.0025,system.F=${F},system.W=${W}/"
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# LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
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# SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
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# DEST_DIR="res/sim/max_iter/WindowingSyndromeSpaGdDecoderPassDecimation/p_0.0025/pass_soft_info_${pass_soft_info}/F_${F}/W_${W}"
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# mkdir -p ${DEST_DIR}
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# DEST_FILE="${DEST_DIR}/LERs.csv"
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# cp ${SRC_FILE} ${DEST_FILE}
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# post_process_LERs ${DEST_FILE}
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# printf "\b${sp:i++%${#sp}:1}"
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# done
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# done
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# done
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@@ -0,0 +1,8 @@
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max_iter,num_trials,LER,LER_per_round,num_errors
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32,10528,0.0354293313069908,0.0030015014232951387,372.99999999999915
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128,68422,0.0029230364502645,0.00024391332035389457,199.99999999999764
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256,100000,0.00184,0.00015346279666106355,184.0
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512,100000,0.00122,0.00010172355962756452,122.0
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1024,100000,0.00084,7.002696447200307e-05,84.0
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2048,100000,0.00052,4.33436645435048e-05,51.99999999999999
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4096,100000,0.00042,3.5006739308451884e-05,42.0
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@@ -0,0 +1,8 @@
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max_iter,num_trials,LER,LER_per_round,num_errors
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32,5264,0.3575227963525836,0.03619728900635699,1882.0
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128,5264,0.1118920972644376,0.00983977212107956,588.9999999999995
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256,5264,0.0818768996960486,0.007093372523371166,430.99999999999983
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512,5264,0.0645896656534954,0.005548714177492475,339.9999999999998
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1024,5264,0.0524316109422492,0.004477957765848362,275.9999999999998
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2048,5264,0.0442629179331307,0.0037655941776483237,233.0
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4096,10528,0.0361892097264437,0.0030669771276242708,380.99999999999926
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@@ -0,0 +1,8 @@
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max_iter,num_trials,LER,LER_per_round,num_errors
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32,5264,0.8651215805471124,0.15375677320993897,4554.0
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128,5264,0.5987841945288754,0.0732807818579091,3152.0000000000005
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256,5264,0.5184270516717325,0.05907464062706547,2729.0
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512,5264,0.4591565349544073,0.04992921073125611,2417.0
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1024,5264,0.4209726443768997,0.04451268995716784,2216.0
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2048,5264,0.3865881458966565,0.03990837706831185,2035.0
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4096,5264,0.3563829787234042,0.036054914686007855,1875.9999999999995
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