diff --git a/inst/appdir/visualizationAcrossTasks.Rmd b/inst/appdir/visualizationAcrossTasks.Rmd
index ba81e4c..d42cf0a 100644
--- a/inst/appdir/visualizationAcrossTasks.Rmd
+++ b/inst/appdir/visualizationAcrossTasks.Rmd
@@ -1,131 +1,131 @@
 # Visualization of cross-task insights
 
 Algorithms are ordered according to consensus ranking.
 
 
 
 
 ## Characterization of algorithms
 
 ### Ranking stability: Variability of achieved rankings across tasks
 <!-- Variability of achieved rankings across tasks: If a -->
 <!-- reasonably large number of tasks is available, a blob plot -->
 <!-- can be drawn, visualizing the distribution -->
 <!-- of ranks each algorithm attained across tasks. -->
 <!-- Displayed are all ranks and their frequencies an algorithm -->
 <!-- achieved in any task. If all tasks would provide the same -->
 <!-- stable ranking, narrow intervals around the diagonal would -->
 <!-- be expected. -->
-Blob plot similar to the one shown in Section \ref{blobByTask} substituting rankings based on bootstrap samples with the rankings corresponding to multiple tasks. This way, the distribution of ranks across tasks can be intuitively visualized.
+Algorithms are color-coded, and the area of each blob at position $\left( A_i, \text{rank } j \right)$ is proportional to the relative frequency $A_i$ achieved rank $j$ across multiple tasks. The median rank for each algorithm is indicated by a black cross. This way, the distribution of ranks across tasks can be intuitively visualized.
 
 
 \bigskip
 
 ```{r blobplot_raw,fig.width=9, fig.height=9}
 #stability.ranked.list
 stability(object,ordering=ordering_consensus,max_size=9,size=8,shape=4)+
   scale_color_manual(values=cols)
 ```
 
 
 
 
 
 ```{r, child=if (isMultiTask && bootstrappingEnabled) system.file("appdir", "characterizationOfAlgorithmsBootstrapping.Rmd", package="challengeR")}
 
 ```
 
 
 
 ## Characterization of tasks
 
 
 ```{r, child=if (isMultiTask && bootstrappingEnabled) system.file("appdir", "characterizationOfTasksBootstrapping.Rmd", package="challengeR")}
 
 ```
 
 ### Cluster Analysis
 <!-- Quite a different question of interest -->
 <!-- is to investigate the similarity of tasks with respect to their -->
 <!-- rankings, i.e., which tasks lead to similar ranking lists and the -->
 <!-- ranking of which tasks are very different. For this question -->
 <!-- a hierarchical cluster analysis is performed based on the -->
 <!-- distance between ranking lists. Different distance measures -->
 <!-- can be used (here: Spearman's footrule distance) -->
 <!-- as well as different agglomeration methods (here: complete and average).  -->
 
 
 Dendrogram from hierarchical cluster analysis} and \textit{network-type graphs} for assessing the similarity of tasks based on challenge rankings. 
 
 A dendrogram is a visualization approach based on hierarchical clustering. It depicts clusters according to a chosen distance measure (here: Spearman's footrule) as well as a chosen agglomeration method (here: complete and average agglomeration). 
 \bigskip
 
 
 ```{r dendrogram_complete, fig.width=6, fig.height=5,out.width='60%'}
 if (length(object$matlist)>2) {
   dendrogram(object,
              dist = "symdiff",
              method="complete")
 } else cat("\nCluster analysis only sensible if there are >2 tasks.\n\n")
 ```
 
 \bigskip
 
 
 ```{r dendrogram_average, fig.width=6, fig.height=5,out.width='60%'}
 if (length(object$matlist)>2)   
   dendrogram(object,
              dist = "symdiff",
              method="average")
 
 ```
 
 <!-- An alternative representation of -->
 <!-- distances between tasks (see Eugster et al, 2008) is provided by networktype -->
 <!-- graphs. -->
 <!-- Every task is represented by a node and nodes are connected -->
 <!-- by edges. Distance between nodes increase exponentially with -->
 <!-- the chosen distance measure d (here: distance between nodes -->
 <!-- equal to 1:05d). Thick edges represent smaller distance, i.e., -->
 <!-- the ranking lists of corresponding tasks are similar. Tasks with -->
 <!-- a unique winner are filled to indicate the algorithm. In case -->
 <!-- there are more than one first-ranked algorithm, nodes remain -->
 <!-- uncoloured. -->
 
 
 In network-type graphs (see Eugster et al, 2008), every task is represented by a node and nodes are connected by edges whose length is determined by a chosen distance measure. Here, distances between nodes are chosen to increase exponentially in Spearman's footrule distance with growth rate 0.05 to accentuate large distances.
 Hence, tasks that are similar with respect to their algorithm ranking appear closer together than those that are dissimilar. Nodes representing tasks with a unique winner are colored-coded by the winning algorithm. In case there are more than one first-ranked algorithms in a task, the corresponding node remains uncolored.
 \bigskip
 
 ```{r ,eval=T,fig.width=12, fig.height=6,include=FALSE, fig.keep="none"}
 if (length(object$matlist)>2) {
   netw=network(object,
                method = "symdiff", 
                edge.col=grDevices::grey.colors,
                edge.lwd=1,
                rate=1.05,
                cols=cols
                )
   
   plot.new()
   leg=legend("topright",  names(netw$leg.col), lwd = 1, col = netw$leg.col, bg =NA,plot=F,cex=.8)
   w <- grconvertX(leg$rect$w, to='inches')
   addy=6+w
 } else addy=1
 
 ```
 
 ```{r network, fig.width=addy, fig.height=6,out.width='100%',dev=NULL}
 if (length(object$matlist)>2) {
   plot(netw,
        layoutType = "neato",
        fixedsize=TRUE,
        # fontsize,
        # width,
        # height,
        shape="ellipse",
        cex=.8
        )
 }
 
 ```
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