diff --git a/poster.tex b/poster.tex
index b88b355642af89d9910fbdab3f6916aa03e8af1c..10cd17a7cd995637f2a58a68931931e2800d371b 100644
--- a/poster.tex
+++ b/poster.tex
@@ -39,7 +39,7 @@
 
 % Set project, title, and authors  (use \\ for multiple lines)
 \setproject{Project A08}
-\settitle{Lagrangian Coherence in Quasi-Stationary Atmospheric States}
+\settitle{Lagrangian Coherence in Atmospheric Blocking}
 \setauthors{\underline{Henry Schoeller}, Robin Chemnitz, Stephan Pfahl, P\' eter Koltai, Maximilian Engel}
 
 \begin{document}
@@ -54,7 +54,7 @@
 \posterbox[](\margin, -\headerheight-\margin)<0.95\boxwidth,13cm>{Lagrangian Coherence}{
 		Suppose we have $m$ sample trajectories of a flow $\Phi^t$ in $\mathbb{R}^n$, evaluated at $T$ time instances 
 $$ x_t^i:=\Phi^t x^i_{t_0} \in \mathbb{M}_t, \quad i\in \{1,\hdots, m\};\:t\in \{t_0, \hdots, t_{T-1}\}.$$
-We want to find \key{coherent sets}, which are regions in the phase space that keep their geometric integrity to a large extent 
+We want to find \key{coherent sets}, which are regions in state space that keep their geometric integrity to a large extent 
 during temporal evolution.\\ 
 $\rightarrow$ Find tight bundles of trajectories.
 %This is equivalent to finding \textcolor{red}{tight bundles of trajectories} if the points forming the trajectories are conceived as samples from such a set.
@@ -67,8 +67,8 @@ $\rightarrow$ Find tight bundles of trajectories.
 		%If $\Phi_{\epsilon, t}$ is the deterministic flow map plus some small random pertubation with variance $\epsilon$, \textcolor{red}{coherence implies robustness} in the sense of "$\Phi_{\epsilon, t}^{-1}(\Phi_{\epsilon, t} \mathbb{X}) \approx \mathbb{X}$", where $\mathbb{X} \subset \mathbb{R}^3$ is a coherent set at $t_0$. We make use of this by constructing a \textcolor{red}{diffusion operator on the data points}, whose eigenvectors give a low-dimensional representation which is used to extract coherent sets (\textcolor{red}{spectral clustering}) [1].
 }
 
-\posterbox[anchor=north east](\paperwidth-\margin, -\headerheight-\margin)<1.07\boxwidth,28.8cm>{Quasi-Stationary Atmospheric States}{
-    QSAS -- a.k.a.~high pressure blockings -- are \key{critical features of mid-latitude weather} and,  importantly, associated with extreme events. Forecasting skill is, however, still unsatisfying. Physically, QSAS are characterized by \key{high stability}. 
+\posterbox[anchor=north east](\paperwidth-\margin, -\headerheight-\margin)<1.07\boxwidth,28.8cm>{Atmospheric Blocking}{
+    High pressure blocking -- a.k.a.~Quasi-Stationary Atmospheric States(QSAS) -- are \key{critical features of mid-latitude weather} and,  importantly, associated with extreme events. Forecasting skill is, however, still unsatisfying. Physically, QSAS are characterized by \key{high stability}. 
 \vfill    
 \includegraphics[scale=1]{im/2016_00.pdf} \includegraphics[scale=0.8, viewport=0 0 375 450, clip]{im/blocktypes.png}\\
 {\footnotesize Left: Blocking example with PV field (shaded), wind $>30 \mathrm{\frac{m}{s}}$ (blue) and blocking region (purple). Right: Point vortex model idealizations of $\Omega$- and High-over-Low-Blockings (from [2]).}
@@ -88,13 +88,13 @@ $\rightarrow$ Find tight bundles of trajectories.
 
 \posterbox[anchor=north east](\paperwidth-\margin, -\headerheight-2\margin-28.8cm)<1.05\boxwidth,52.7cm>{Trajectory Density}{
 	Assume at time $t$ the points $x_t^i$ are sampled from some manifold $\mathbb{M}_t$. Then, the sum of the diffusion similarities can be interpreted as a \key{Monte Carlo integral approximation} [4]
-\begin{align*}S_{\epsilon, t} := \sum_{i,j}\mathbf{K}_{\epsilon, t}(i,j) \approx \frac{m^2}{\mathrm{vol}(\mathbb{M}_t)^2} \int_{\mathbb{M}_t}\int_{\mathbb{M}_t} \exp (-\epsilon^{-1} \|x-y\|^2) \dd x \dd y \approx \frac{m^2}{\mathrm{vol}(\mathbb{M}_t)} (\pi \epsilon)^{d/2}
+\begin{align*}S_{\epsilon, t} := \sum_{i,j}\mathbf{K}_{\epsilon, t}(i,j) \approx \frac{m^2}{\mathrm{vol}(\mathbb{M}_t)^2} \int_{\mathbb{M}_t}\int_{\mathbb{M}_t} \exp (-\epsilon^{-1} \|x-y\|^2) \dd x \dd y \approx \frac{m^2}{\mathrm{vol}(\mathbb{M}_t)} (2 \pi \epsilon)^{d(t)/2}
 \end{align*}
 %$S_{\epsilon, t}$ is a measure of the \textcolor{red}{density} of points, with $\lim_{{\epsilon \to 0}} S_{\epsilon, t}= m$ and $\lim_{{\epsilon \to \infty}}S_{\epsilon, t} = m^2$. Also $\log(S_{\epsilon, t}) \approx \frac{d}{2}\log(\epsilon) + \log(\frac{(2\pi)^{d/2} m^2}{\mathrm{vol}(\mathbb{M}_t)})$.
 For suited $\epsilon$, this yields a linear relationship between $\log(\epsilon)$ and $\log(S_{\epsilon, t})$.
-$$\log(S_{\epsilon, t}) = \frac{d}{2} \log(\epsilon) + \log(\rho(t)) + \log(m) + \frac{d}{2} \log(\pi); \quad \rho(t) := \frac{m}{\mathrm{vol}(\mathbb{M}_t)}.$$
+$$\log(S_{\epsilon, t}) = \frac{d(t)}{2} \log(\epsilon) + \log(\rho(t)) + \log(m) + \frac{d(t)}{2} \log(2 \pi); \quad \rho(t) := \frac{m}{\mathrm{vol}(\mathbb{M}_t)}.$$
 \vspace{16cm}\\
-The dimension $d$ and density $\rho(t)$ can be computed from this graph. We use $\ell(t):=\frac{1}{d} \log(\rho(t))$ as a measure of density. The trajectories tend to be \key{denser after passage} through the QSAS, which is in line with the established notion of blockings as regions characterized by high stability.	
+The dimension $d(t)$ and density $\rho(t)$ can be computed from this graph. We use $\ell(t):=\frac{1}{d(t)} \log(\rho(t))$ as a measure of density. The trajectories tend to be \key{denser after passage} through the QSAS, which is in line with the established notion of blockings as regions characterized by high stability.	
 %We obtain $d = \dim (\mathbb{M}_t)$ by maximizing $\frac{\dd \log (S_{\epsilon, t})}{\dd \log (\epsilon)}$ and calculate the point density measure $\rho (t) := \frac{1}{d}\log (\frac{m}{\mathrm{vol}(\mathbb{M}_t)})$.
 }