text:
\section{Introduction}
\label{sec:intro}

Visualization
is recognized as an essential component of learning and education~\cite{gilbert2005visualization}.
In the last few years, the impact of visualization in \textit{edutainment} (i.e., educational entertainment) has also been investigated---for example within the context of augmented~\cite{stefan2014ar} and virtual reality~\cite{cai2006bio}, or in comparison to serious games~\cite{charsky2010edutainment}.
\textit{Physicalization} is a subdomain of visualization, which revolves around creating physical objects to represent and explore data~\cite{djavaherpour2021data}.
The field has recently seen a resurgence, also for education and edutainment purposes~\cite{perin2021students}.
Generated physical objects are tangible and employ more senses than plain vision, to enhance our cognition and perception about displayed information~\cite{Embodiment, jansen2015opportunities}.
Physicalizations provide a high degree of engagement, supporting understanding and memorability~\cite{jansen2013evaluating,SS15}, while also making the entire process interactive and entertaining.

In education, physicalizations may be used either for \emph{demonstration and presentation} purposes with pre-made data models or for \emph{engaging students} in the learning processes by letting them handcraft their representations.
In edutainment,
physicalizations would rather focus on the latter.
So far, the generation of physicalizations has mainly focused on \emph{digital fabrication methods} (e.g., 3D printing, laser cutting, moulding, etc.)~\cite{bickel2018state}, \emph{augmentations} (e.g., projections on passive props and augmented perspectives), and \emph{active approaches} (e.g., data sculptures that use materials to communicate data, robots, and screens)~\cite{djavaherpour2021data}.

In biology and anatomy education, physical models have always been used~\cite{markovicDevelopment}, as opposed to
on-screen educational applications that emerged only in the last few decades~\cite{blume2011google, halle2017open, preim2018survey}.
Nowadays, biological and anatomical physicalizations are mainly restricted to model kits or contextual 3D printed models.
\textcolor{black}{Although consumer-oriented 3D printers are becoming more and more popular, they can use a limited amount of colors and their maintenance remains costly and technically demanded for private usage~\cite{3Dprintingbasedonimagingdata}.}
Recently, a series of works proposed computer-aided pipelines for the easy generation of data sculptures, using affordable and readily available materials~\cite{Vol2velle, raidou2020slice, schindler2020anatomical, pahr2021}.
The core motivation around such approaches relates to the notions of \textcolor{black}{\textit{constructivism}~\cite{huang2010investigating} and \textit{embodiment}~\cite{jang2017direct}.
The former supports that active learning facilitates knowledge construction with less cognitive load, the latter claims that learning can benefit from the involvement of motorics and physical interaction.}

Among all possible types of data sculptures, \textit{papercrafts} are popular both as an art or recreational form~\cite{paczkowski2018papercraft3d} and as a tool serving educational purposes~\cite{eisenberg1998shop}.
Papercrafts are 2D or 3D objects from paper or cardboard that have been created by a 3D mesh unfolding into 2D patches, which can be printed and reconstructed back to 3D.
Papercraft generation has proven to be a difficult task, given the often occurring overlaps and distortions~\cite{haenselmann2012optimal}, as well as the complexity of the reconstruction process~\cite{takahashi2011optimized}.
\textcolor{black}{A discussion with a professional papercraft designer~\cite{Chan:2022} revealed to us that even ``simple'' models (i.e., with less than 100 polygon faces) require approximately a week for their creation and around ten days for the papercraft generation.}

\textcolor{black}{Creating a papercraft with nested structures would add further to this complexity.
\textit{Nested papercrafts} would, for example, be needed for the investigation of a biological or anatomical model, which often include multiple, nested structures.}
\textcolor{black}{For example, a plant cell includes a nucleolus, which is nested within the nucleus.
Subsequently, the nucleus is nested within the cytoplasm together with other structures, such as mitochondria, ribosomes, chloroplasts and the vacuole.}
Representing all these structures within one papercraft and ensuring their visibility, while maintaining the necessary level of interactivity and engagement without complicating the unfolding or the reconstruction, poses significant \textcolor{black}{fabrication} challenges.

In this work, we are investigating a new workflow for the computer-aided generation of \textcolor{black}{nested} papercraft physicalizations that represent models from anatomy and biology.
\textcolor{black}{Figure~\ref{fig:teaser} shows an example of our nested papercrafts}.
Our proposed workflow targets specifically the representation of nested substructures within a 3D model and ensures their visibility.
In our approach, we employ hierarchical structuring to detect nested levels within a model, viewpoint calculation to ensure that outer levels can be cut for an unobstructed view on the inner levels, and a stability calculation at all levels (Figure~\ref{fig:teaser} (a--b)).
The final step is to unfold the nested levels of the model into 2D patches (Figure~\ref{fig:teaser} (c)), which can be reconstructed and examined (Figure~\ref{fig:teaser} (d)).
As an additional visibility mechanism, we propose a smart strategy that takes advantage of the optical properties of our physical world by unveiling different channels of information through the use of different color channels (Figure~\ref{fig:teaser} (e)), inspired by our previous work~\cite{schindler2020anatomical}.
Our approach supports 3D models of varying nesting complexity, by exploiting the interactivity of nested papercraft models, and the projection of additional structures on their surface.

The overall \emph{contribution} of this work is the design and realization of a workflow for the computer-aided generation of nested papercrafts to represent complex models from anatomy and biology.
\textcolor{black}{Our \emph{goal} is to support the generation of nested papercrafts in an easy (to manufacture and reconstruct), accessible (in resources and technologies) and affordable way.
Our papercrafts can be used for tangible, interactive edutainment, where the user is engaged in the creation and assembly of the model's papercraft.}
The main \emph{components} of our approach include:
\begin{itemize}
    \item The \textit{automated detection and representation of nested configurations} in anatomical and biological models, as well as corresponding papermesh synthesis.
    \item A dual (i.e., topology- and texture-based) approach to guarantee \textit{optimal visibility on the inner levels}.
    \item A strategy to ensure that the generated nested papercrafts are \textit{realizable} (i.e., assemblable and stable) and \textit{engaging}.
\end{itemize}

\vspace{-5pt}
\section{Related Work}
\label{sec:related}

\noindent\textbf{Anatomical and Biological Education and Edutainment:}
Preim and Saalfeld~\cite{preim2018survey} presented a survey on virtual anatomy education systems, where they review a big corpus of techniques targeting the education of medical students.
These span from surface and volume visualization~\cite{pommert2001creating, lichtenberg2016sline, vazquez2008interactive} to animations~\cite{bauer2014interactive}, and from anatomical labeling~\cite{ bruckner2005volumeshop} to virtual and augmented reality~\cite{saalfeld2016semi, pohlandt2019supporting, john2016use, Messier2016AnI3}.
Popular examples include VOXEL-MAN~\cite{pommert2001creating} and open anatomy browser~\cite{ halle2017open}.
Applications for the general public include the ZygoteBody~\cite{ blume2011google}.
For biological data, learning bio-molecular structures is achieved through gaming in VR~\cite{cai2006bio, cai2006immersive}.
Other previous work focuses on teaching through hands-on graphics experiences that facilitate the exploration of structural aspects of macromolecular systems~\cite{canning2001teaching}.
Animation is also a fundamental concept that is often assessed to determine its suitability for educational purposes in biology~\cite{mnguni2021assessment}, as well as storytelling approaches~\cite{goodsell2021molecular}.
\textcolor{black}{All the aforementioned approaches are on-screen solutions, which have proven to significantly contribute to anatomical and biological education, while also serving edutainment approaches either through gaming or storytelling.
In our work, instead, we investigate how ``hands-on'' approaches involving physical models are applied to such scenarios.}

\noindent\textbf{Anatomical and Biological Physicalizations:}
Medical and biological data physicalizations are mainly 3D printed physical objects~\cite{Hybrid3Dprinting, 3Dprintingbasedonimagingdata, CardiacBloodFlowPhys}.
Recently, Ang et al.~\cite{CardiacBloodFlowPhys} developed a cardiac blood flow physicalization based on 3D printing that allows the user to explore 4D MRI data in a slice-based manner.
On the biology side, Gillet et al.~\cite{gillet2004computer} combine AR and 3D printing in an application for structural biology education.
Approaches that do not involve 3D printing are limited.
Historical approaches include the use of wax, wood, ivory, cardboard, and fabric models~\cite{markovicDevelopment}.
Computer-assisted approaches involve the use of volvelles, i.e., interactive wheel charts of concentric, rotating disks that support the physical fine-tuning of transfer functions~\cite{Vol2velle}, or sliceform papercrafts that support the representation of volumetric or mesh data using an octree-based partitioning~\cite{raidou2020slice}.
Both approaches target the visual and physical representation of the entire volume of a structure.
However, the former focuses more on transfer function definition.
The latter provides a holistic slice-based view of the data, but for higher resolution, more slices are needed, which increases the complexity of the approach.
Interaction is also not possible without compromising the stability of the sliceforms.
Pahr et al.~\cite{pahr2021} proposed an interactive slice-based alternative for the physicalization of medical data that resembles holograms of volumetric medical data.
\textcolor{black}{In our work, we also focus on largely available materials (i.e., paper), as this design choice can support the generation of physical models at home without the need for sophisticated solutions. Still, we focus on providing an illustrative physical representation of the data. }

\noindent\textbf{Papercraft Generation:}
The computer-assisted generation of papercrafts has been tackled before, for example for creating paper pop-ups from 3D meshes~\cite{ruiz2014multi, xiao2018computational} or origami architecture papercrafts~\cite{le2013surface}, iris papercrafts~\cite{igarashi2016computational},  \textcolor{black}{cardboard-based papercrafts~\cite{zhang2016cardboardizer}}, and animated pop-ups that show motions of articulated characters~\cite{ruiz2015generating}.
This has also been investigated for large-scale models~\cite{sass2016embodied, chen2017generative} and entire 3D scenes through developable surfaces~\cite{paczkowski2018papercraft3d}.
Mesh unfolding has different applications, such as papercraft models~\cite{takahashi2011optimized,straubcreating} and models from self-folding materials~\cite{felton2013self,an2018thermorph}.
Some techniques employ mesh deformation~\cite{chang2017improved,mitani2004making} to relax the unfolding problem.
The simultaneous handling of unfolding and glue tabs has been explored in our previous work~\cite{Korpitsch:2020:WSCG}.
An optimization model is formulated to search for an unfolding solution with optimal number of glue tabs needed for papercraft reconstruction.
However, to the best of our knowledge, none of \textcolor{black}{the} aforementioned approaches research generating physical models that contain nested structures or the stability of such composite papercrafts.
This problem is not trivial, as it involves the ability to unfold individual meshes and the composition of the submeshes within a model.
\textcolor{black}{In our work, we investigate thoroughly this aspect, applying it in the context of anatomical and biological edutainment.}

\vspace{-5pt}
\section{Requirements and Conceptual Choices}
\label{sec:overview}

\begin{figure*}[t]
    \centering{
    \setlength{\tabcolsep}{0pt}
    \begin{tabular}{cccccccc}
        \includegraphics[width=0.125\linewidth]{figures/workflow-1-input.pdf} &
        \includegraphics[width=0.125\linewidth]{figures/workflow-2-hull.pdf} &
        \includegraphics[width=0.125\linewidth]{figures/workflow-3-tree.pdf} &
        \includegraphics[width=0.125\linewidth]{figures/workflow-4-viewpoint.pdf} &
        \includegraphics[width=0.125\linewidth]{figures/workflow-5-cut.pdf} &
        \includegraphics[width=0.125\linewidth]{figures/workflow-6-stability.pdf} &
        \includegraphics[width=0.125\linewidth]{figures/workflow-7-projection.pdf} &
        \includegraphics[width=0.125\linewidth]{figures/workflow-8-unfolding.pdf} \\
        (a) Input  &
        (b) Approximation &
        (c) Hierarchy &
        (d) Best viewpoint &
        (e) Cutting &
        (f) Stability &
        (g) Projection &
        (h) Unfolding \\
    \end{tabular}
    \caption{Conceptual diagram depicting our workflow. We start by (a) taking as input a set of 3D meshes and (b) simplifying each mesh to create a coarse approximation. (c) We create a hierarchical structure that represents the nested configuration and (d) we perform an entropy-based viewpoint calculation to obtain the optimal view on each inner level, which is used to (e) cut the model open. \textcolor{black}{(f) Stability calculations ensure that the constructed model does not fall apart and stands, and (g) our projection method renders textures on the surface of each level. Finally, (h)} we unfold each 3D model into a single 2D patch. \vspace{-5pt}}
    \label{fig:overview}
    }
\end{figure*}

The basic \textit{concept} of our approach is to provide a workflow for the cost-effective and easy generation of physical models that support nested structures, which are often present in biological and anatomical models.
Building and exploring physical models aims to make the experience more engaging and, thus, more memorable or understandable~\cite{Embodiment}.
Although papercrafts are not a new approach for education or edutainment~\cite{eisenberg1998shop}, the use of papercrafts to represent nested structures is novel and challenging.
\noindent Our main \textit{requirements} are:
\begin{enumerate}
\setlength{\itemindent}{12pt}
    \item[\textbf{R1.}] \textcolor{black}{The user does not need to have any knowledge of} the domain of application (i.e., anatomy or biology).
    \item[\textbf{R2.}] \textcolor{black}{The approach requires a 3D mesh model as input} with multiple registered nested structures (submeshes). Examples could be a plant cell or an anatomical model of a human head.
    \item[\textbf{R3.}]  \textcolor{black}{The user does not need to perform any complex interaction with our proposed pipeline (i.e., beyond few clicks) or to provide any other special input to it}.
    \item[\textbf{R4.}] The output of the workflow \textcolor{black}{needs to} be an easy-to-assemble and engaging physical twin of the virtual model.
    \item[\textbf{R5.}] The assembly of the physical model \textcolor{black}{needs to} be intuitive, and time- and cost-efficient.
    \item[\textbf{R6.}] The employed resources, i.e., materials and technologies, \textcolor{black}{need to} be easily affordable and accessible.
\end{enumerate}

\noindent Based on these requirements, our \textit{conceptual choices} are:
\begin{enumerate}
\setlength{\itemindent}{12pt}
    \item[\textbf{C1.}] \textbf{Supported data and structures:}
    Our data are meshes representing anatomical and biological structures, which contain nested substructures, \textcolor{black}{as dictated by \textbf{R2}}.
    These substructures are not ordered, i.e., there is no prior information about their topological hierarchy.
    Also, we do not restrict the number of nested levels and we do not pose any limits to the number of children at each nested level.
    \item[\textbf{C2.}] \textbf{Nested papercrafts:}
    Our physicalizations are papercrafts with multiple nested levels, \textcolor{black}{according to \textbf{R2}}.
    The choice is made given the popularity of papercrafts, as well as the affordability and availability of materials \textcolor{black}{(see \textbf{R6})}.
    \textcolor{black}{We abstract the input meshes, so the papercraft can be assembled in a reasonable time.
    Given that the substructures are not ordered, we also detect the nested configuration and employ viewpoint selection to cut the outer levels and reveal the inner ones.}
    This approach poses several challenges concerning the unfolding and reconstruction of the physicalization.
    \textcolor{black}{First of all, this step should not require the user to have prior knowledge of the domain \textcolor{black}{(see \textbf{R1})}, nor to heavily interact with the framework to create the papercraft \textcolor{black}{(see \textbf{R3})}.
    Furthermore, to} ensure that our papercraft is always feasible, i.e., can be unfolded and stable in reconstruction \textcolor{black}{(see \textbf{R4})}, we abstract the structures of distinct levels to close-to-convex polygonal meshes.
    \item[\textbf{C3.}] \textbf{Texture projections:}
    Conceptual choice \textbf{C2} does not account for three special cases:
    \textbf{(a)} the mesh abstraction might introduce distortions, if the shape of the structure is inherently concave;
    \textbf{(b)} the size of (inner) substructures might be prohibiting for printing and reconstruction, if these are too small; and
    \textbf{(c)} structures with genus higher than $0$ or with complex topology, e.g., the surface of a Golgi apparatus might be problematic.
    For these cases, we choose to use a smart visibility mechanism that projects textures from inner structures as additional information on the surfaces of the papercraft \textcolor{black}{(see \textbf{R2})}.
    This allows us to show correctly the shape of the inner structures on the approximated surfaces, even for small or intricate objects, while it can serve as ``\textcolor{black}{X}-ray vision'' to reveal the underlying levels.
    Our projection is based on our previous work~\cite{schindler2020anatomical} and reveals up to three different inner structures with the use of colored filters.
    \item[\textbf{C4.}] \textbf{Mesh unfolding into single connected patch:}
    Unfolding a 3D mesh into a single patch facilitates the reconstruction, as opposed to using multiple patches, but dealing with overlaps and distortions is a difficult task~\cite{takahashi2011optimized}.
    In our previous work~\cite{Korpitsch:2020:WSCG}, we proposed a method for unfolding a 3D mesh into a single connected 2D patch by searching the best unfolding variant, which is represented by a minimum spanning tree (MST). A simulated annealing optimization is used to find an optimal unfolding.
    The additional introduction of glue tabs supports the reconstruction process.
    We make use of this approach, and we adapt it to accommodate choices \textbf{C2} and \textbf{C3}. \textcolor{black}{This choice relates to \textbf{R5}}.
    \item[\textbf{C5.}] \textbf{Required materials and technologies:}
    The reason for selecting papercrafts, as opposed to other digital fabrication methods, is that they are easy to manufacture \textcolor{black}{(\textbf{R4})} and reconstruct \textcolor{black}{(\textbf{R5})}, as they use affordable resources (paper and colored filters) and technologies (\textcolor{black}{full-color 2D printers}), \textcolor{black}{according to \textbf{R6}}.
\end{enumerate}

\vspace{-5pt}

\section{Nested Papercrafts Pipeline}
\label{sec:method}

\subsection{\textcolor{black}{Pipeline Overview}}
\label{sec:overview_pip}
Figure~\ref{fig:overview} depicts the workflow of our approach.
Our approach takes as input a set of \textcolor{black}{registered} meshes that composes the 3D model of an anatomical or biological structure (Figure~\ref{fig:overview}(a), \textbf{C1}).
In a preprocessing step, we simplify each mesh to create a feasible papermesh, i.e., a coarse approximation, as a basis for the approach (Figure~\ref{fig:overview}(b), Section~\ref{ssec:input}, \textbf{C2}).
In the next step, we use an iterative strategy to detect the nested levels within the model and to create a hierarchical structure that represents the nested configuration in the data  (Figure~\ref{fig:overview}(c), Section~\ref{ssec:hierarchical}, \textbf{C2}).
After obtaining the distinct nested levels, we perform an entropy-based viewpoint calculation step~\cite{vazquez2001viewpoint}, which returns the optimal viewpoint that provides an unobstructed view of each inner level (Figure~\ref{fig:overview}(d), Section~\ref{ssec:viewpoint}, \textbf{C2}).
Then, \textcolor{black}{we cut each parent level} based on the optimal viewpoint on the child level, to reveal at opening time the best possible view on the inner level (Figure~\ref{fig:overview}(e), Section~\ref{ssec:cutting}, \textbf{C2}).
A stability estimation ensures that the cutting configuration at each level results in a feasible and stable physical model, i.e., the individual levels do not fall apart (Figure~\ref{fig:overview}(f), Section~\ref{ssec:stability}, \textbf{C2}).
Subsequently, a projection method creates textures to be rendered on the surface of the papermesh that represent a detailed version of the structure and/or additional inner levels or smaller structures, such as vessels (Figure~\ref{fig:overview}(g), Section~\ref{ssec:projection}, \textbf{C3}).
For this, we take advantage of the subtractive color printing properties of light~\cite{schindler2020anatomical}, i.e., \textcolor{black}{we use cyan, magenta, and yellow inks} to denote individual structures.
\textcolor{black}{We can reveal individual structures on demand} under appropriate colored filters (red, magenta, and blue, respectively).
The final step is to unfold the multiple nesting levels of the 3D model with the additional texture projections into multiple single 2D patches~\cite{Korpitsch:2020:WSCG} (\textbf{C4}), which \textcolor{black}{we can later print and reconstruct} (Figure~\ref{fig:overview}(h), Section~\ref{ssec:unfolding}, \textbf{C5}).
Our approach exploits the interactivity of nested papercrafts, as well as the projection of additional structures on their surface to allow the examination of 3D models of varying complexity.
\vspace{-5pt}

\subsection{Input and Data Approximation}
\label{ssec:input}

Our input data are \textcolor{black}{registered} meshes from anatomical and biological models with a nested configuration \textbf{R2}.
No other prior information is required about them, as discussed in Section~\ref{sec:overview}, \textbf{C1}.
Also, as discussed in \textbf{C2} and \textbf{C3}, our fundamental idea is to use an approximation of the input mesh to create a papercraft model that represents the nested configuration, while \textcolor{black}{we project} additional structures as textures on the surfaces of the papercraft.
\textcolor{black}{In our previous work we already proved that projection can be an effective strategy}~\cite{schindler2020anatomical}, \textcolor{black}{while we also demonstrated the need for following more organic shapes for the crafts~\cite{raidou2020slice}---even if these are simplified}.
We need two types of minimal user input (\textbf{R1,3}) for our papercraft physicalization: \textcolor{black}{which meshes to group together onto a papercraft and which color to use for each.}
In our current implementation, users choose which structures are particularly interesting for them to use in the projections.

In this paper, we use the terms ``\textit{papercraft}'' to denote the final output of the physicalization workflow and ``\textit{papermesh}'' to denote the virtual twin of the papercraft.
\textcolor{black}{Each of our papermeshes is a manifold triangular mesh with genus $0$~\cite{munkres2000topology}.}
Before we can create our nested papercraft, we need to approximate the fine input mesh to a reasonably foldable papermesh \textbf{(R4,5)}.
Takahashi et al.~\cite{takahashi2011optimized} showed that the participants could construct meshes with around $150$ faces, while meshes over $300$ faces were less feasible.
Although, theoretically, we support papermeshes with a high face number,
we follow Takahashi et al. and limit the total face number to $150$.
\textcolor{black}{The upcoming sections provide details on the achieved sizes for several anatomical and biological models.}

In this preprocessing step, \textcolor{black}{we create our papermeshes} in an iterative manner.
We start by initializing a bounding box that wraps each loaded structure, as shown in Figure~\ref{fig:input}.
Subsequently, we subdivide the bounding box into a fine triangular mesh
\textcolor{black}{(e.g., $3,000$ faces for a large anatomical model such as the head)},
and we move each vertex of the bounding box to the corresponding closest vertex on the structure.
This process allows us to create a compact wrapper that retains better the shape of the structure.
Once we have such an \textcolor{black}{approximating} mesh, we apply the mesh simplification algorithm by Lindstrom and Turk~\cite{cgal:sm-simplification} to significantly reduce the mesh face number and to create a papermesh that \textcolor{black}{we can unfold later}.

\begin{figure}[b]
    \centering{
    \includegraphics[width=.8\linewidth]{figures/figure_hull2.pdf}
    \caption{Conversion of input structures to a set of papermeshes.}
    \label{fig:input}
    }
\end{figure}
\vspace{-5pt}

\subsection{Hierarchy Detection}
\label{ssec:hierarchical}

Once \textcolor{black}{we create the papermesh approximations}, we need to detect the hierarchy within the nested configuration of the entire model to ensure we can open up outer levels to show inner ones \textbf{(C2, R2)}.
We identify meshes that are encapsulated in others, and we use this information to build a tree $T$ representing this relationship. Figure~\ref{fig:tree} shows an example of a tree constructed for a given nested model.
\textcolor{black}{We solve this by using a tree insertion algorithm.}
As input we consider an unsorted set of papermeshes $M = {m_a, m_b, ..., m_n}$.
The first mesh $m_a$ is the root of $T$.
\textcolor{black}{We need to check all following meshes} for their hierarchical relationship to the first one.
Consider the second mesh to insert $m_b$.
\textcolor{black}{If $m_a$ encapsulates $m_b$, we assign $m_b$ as a child node of node $m_a$ in $T$.
Otherwise, if $m_b$ encapsulates $m_a$, we assign $m_b$ as the new root and $m_a$ as a child of $m_b$.
We continue this process and traverse $T$ (Breadth-First Search, BFS or Depth-First Search, DFS) to find the right position of each papermesh.}

\textcolor{black}{We conduct the \textit{mesh-inside test} in two steps.}
First, we test if all vertices on $m_b$ are inside $m_a$~\cite{cgal:atw-aabb-21b}, and then we perform dD Iso-oriented box intersection tests~\cite{cgal:lty-pmp-21b} on the edges to detect edge--edge intersections.
If the first step is valid and \textcolor{black}{we find no intersection, we settle the relationship between $m_a$ and $m_b$.}
Note that the algorithm runs under the assumption that no papermeshes intersect, as this is the case for anatomical and biological structures.

\begin{figure}[b]
    \setlength{\tabcolsep}{10pt}
    \centering{
    \includegraphics[width=.8\linewidth]{figures/figure_tree.pdf} \\
    \caption{Conceptual diagram of a hierarchical relationship in a nested model using a tree data structure.}
    \label{fig:tree}
    }
\end{figure}
\vspace{-5pt}

\subsection{Viewpoint Calculation}
\label{ssec:viewpoint}

\begin{figure}[b]
    \centering{
    \includegraphics[width=\linewidth]{figures/viewpoint.pdf}
    \caption{Example of the viewpoint calculation within the nested structure. The viewpoint at level $l$, where $l=1,2,3$ with maximum entropy $H_l$ is used to cut the parent level $l-1$. }
    \label{fig:viewpoint}
    }
\end{figure}

After capturing the hierarchy within the nested structure, we need to find a suitable configuration to cut it open \textbf{(C2, R2)}.
A suitable cutting plane must fulfill two conditions.
First, when opening an outer nested level, we need to provide the \textit{best view on the inner levels}.
We define the best view, as a view that reveals the inner structure in the most recognizable manner, as soon as the outer structure is open.
Second, cutting the outer structure should \textit{not affect the stability} of the entire nested model.
Although the best view needs to take the stability into account, we deal with this further together with the next two steps of our workflow (Sections~\ref{ssec:cutting}--~\ref{ssec:stability}).

Optimal viewpoint calculation is a heavily researched topic, relevant for several applications within computer graphics and visualization.
We refer \textcolor{black}{our readers} to a recent comparative survey on viewpoint calculation methods by Bonaventura et al.~\cite{bonaventura2018survey}.
Our definition of the best view within our specific application is the view that supports the recognizability of the inner levels when opening the outer level.
Reviewing existing literature~\cite{bonaventura2018survey, dutagaci2010benchmark, secord2011perceptual, polonsky2005s} points to entropy-based methods, such as the maximum viewpoint entropy calculation by Vazquez et al.~\cite{vazquez2001viewpoint}.
This robust measure optimizes in a balanced way the projected area and the number of faces that are visible.
Measures that target only area (or silhouette) optimization would not be sufficient, as these measures do not tell us about the amount of detail we can see in our scene.
Measures that target only the number of faces are also insufficient, as scenes with a high number of small faces would be inadequately described.
The maximum viewpoint entropy indicates that a certain viewpoint balances the number of visible faces with respect to the relative projected area.
Although Sbert et al.~\cite{sbert2005viewpoint} found that this method is sensitive to discretization, this is not an issue for our approach, while we discarded other approaches~\cite{viola2006importance} due to high complexity.

Figure~\ref{fig:viewpoint} shows the steps of our viewpoint calculation.
We conduct a recursive calculation based on viewpoint entropy, as defined by Vazquez et al.~\cite{vazquez2001viewpoint} at each level of our nested model (except for the outermost level $l=0$).
The viewpoint entropy $H$ at each level $l\geq1$ assumes as probability distribution the relative area of the projected faces over a sphere of directions centered in the viewpoint, and is defined as: $H_{l} = -\sum_{i=0}^{^{N_l}}\frac{A_{i,l}}{A_{t}}log\frac{A_{i,l}}{A_{t}}$, where $N_l$ is the number of faces of the scene at a specific level $l$, $A_i,l$ is the projected area of face $i$ of level $l$ over the sphere, and $A_t$ is the total area of the sphere.
The maximum viewpoint entropy $H_l$ at each level $l$ is used as input to cut the outer level $l-1$ (Figure~\ref{fig:viewpoint}), to reveal the structure of the inner level.
For example, \textcolor{black}{we use} the viewpoint at level $1$ that gives the maximum entropy $H_1$ (Figure~\ref{fig:viewpoint}, see ``1st iteration'', red) to cut the parent level (black).
\textcolor{black}{We cut the other levels similarly} (Figure~\ref{fig:viewpoint}).
\vspace{-5pt}

\subsection{Optimal Cutting Plane}
\label{ssec:cutting}

\begin{figure}[b]
    \centering{
    \setlength{\tabcolsep}{0pt}
    \begin{tabular}{cc}
        \includegraphics[width=0.5\linewidth]{figures/figure_cutting.pdf} &
        \includegraphics[width=0.5\linewidth]{figures/figure_stability.pdf} \\
        (a) Cutting plane &
        (b) Stability testing \\
    \end{tabular}
    \caption{Example of (a) a cutting plane (perpendicular to the best viewpoint direction and passing through the center of mass $c_m$ of the inner level) and (b) testing for a stable papercraft composition.}
    \label{fig:cutting}
    }
\end{figure}

Figure~\ref{fig:cutting} shows the steps of the optimal cutting based on the viewpoint calculation.
To provide the optimal cutting plane \textbf{(C2, R2)}, we need to conduct the following steps.
First, we subtract the meshes of the inner level from the outer level to create an ``envelope'' mesh that carries the difference operation between the inner and outer level.
Then, we compute the center of mass $c_m$ of the meshes of the inner level and we place at this location the origin of our cutting plane.
\textcolor{black}{We retrieve the normal \textbf{$n$} from the viewpoint calculation}, as described in the previous section (Figure~\ref{fig:cutting} (a)).
Subsequently, \textcolor{black}{we clip the envelope mesh (between the outer and inner level)} using the cutting plane as an implicit function.
This returns two meshes with two boundary edge loops, each.
\textcolor{black}{We, then, stitch these together with triangles to ensure continuity.}
\vspace{-5pt}

\subsection{Papercraft Stability Test}
\label{ssec:stability}

One important factor for a nested papercraft is the stability of the constructed model in the physical world \textbf{(C2, R2,4)}.
The assembled nested papercraft should not fall apart, and the subparts should stay connected withing the entire physical model.
We examine the papercraft stability using physical simulation.
Note that the stability cannot account for errors introduced by the users during crafting, which cannot be easily controlled as they strongly depend on the user's experience.
\textcolor{black}{We conduct the stability test iteratively} in conjunction with the viewpoint and the cutting plane calculation (Sections~\ref{ssec:viewpoint}--~\ref{ssec:cutting}).
Each cut should guarantee that the decomposed subparts will integrate well with each other.
Otherwise, an unstable cut will cause the model to fall apart (Figure~\ref{fig:cutting} (b), left column), while a slightly adapted cut ensures a stable composition based on gravitational and frictional forces (Figure~\ref{fig:cutting} (b), right column).
The articulated body algorithm by Featherstone~\cite{featherstone1987aba} for physics simulation is sufficient for our simple scenes.
This is a forward dynamics approach with computational complexity $O(n)$.
In our implementation, we use gravity as an external force, we assign a mass to each individual papermesh, and simulate if papermeshes will fall apart in a physical environment.
If so, the algorithm will return an \emph{unstable} flag back to the cutting plane algorithm.
The cutting plane algorithm will look for the next best plane until we reach stability.
\vspace{-5pt}

\subsection{Texture Projection}
\label{ssec:projection}

One papermesh can contain multiple substructures (e.g., an anatomical mesh contains bones, muscles and vessels) \textbf{(C2--3, R2)}.
To ensure the visibility of the different substructures, we use a strategy, adapted from our previous work~\cite{schindler2020anatomical}.
This strategy allows us to use up to three channels to render different substructures on the surface \textbf{(C3)}, which \textcolor{black}{we can bring forward} through the use of red, green, and blue colored filters or light \textbf{(C5)}.
These colored filters isolate (groups of) structures rendered respectively in cyan, magenta, and yellow, taking advantage of the properties of visible light.
\textcolor{black}{We show an example of this in Figure~\ref{fig:teaser} (e).}
For more details on the color filter mechanism, we refer to our previous work.

\begin{figure}[b]
    \centering{
    \setlength{\tabcolsep}{0pt}
    \begin{tabular}{ccc}
        \includegraphics[width=0.33\linewidth]{figures/inflate.png} &
        \includegraphics[width=0.33\linewidth]{figures/clipping.png} &
        \includegraphics[width=0.33\linewidth]{figures/cube.png} \\
        (a) &
        (b) &
        (c) \\
    \end{tabular}
    \caption{Different modes of our projection technique~\cite{schindler2020anatomical}: (a) inflation, (b) clipping, and (c) cube mode.}
    \label{fig:proj_modes}
    }
\end{figure}

For each structure, we iterate over the triangles of the respective papermesh and we project on them a texture.
Before starting the projection, the user can adapt the papermesh to fit better the individual structures using one of the following three modes:
\textit{inflation mode} wraps the papermesh around a single structure, projects and then, inflates it back to the original;
\textit{clipping mode} removes features that are more distant from the texture without changing the papermesh;
and \textit{cube mode} projects the papermesh faces onto the faces of the bounding cube along the face normals of the cube.
\textcolor{black}{Figure~\ref{fig:proj_modes} summarizes these modes.}
For papermeshes that have multiple pieces, the bounding cube is also cut into multiple pieces, using the same cutting plane.
To construct the texture for each triangle, we place a camera at the center of the triangle facing towards the structure.
\textcolor{black}{We map the values for each texture to a single hue} (magenta, cyan, or yellow) sequential colormap, where higher values are mapped to higher luminance, i.e., $[t_{min}, t_{max}] \rightarrow (h,s,v)=(200,240,120...240)$, as $(h,s,v)=(200,240,120)$ corresponds to pure magenta and $v=240$ to white for all values of $h$ and $s$.
Subsequently, \textcolor{black}{we normalize each texture to the range $[0,1]$ and we multiply with the other textures of this papermesh, if available}.

\vspace{-5pt}
\subsection{Papermesh Unfolding}
\label{ssec:unfolding}

The last step of our workflow generates manifold papermeshes to be unfolded individually.
To follow up on \textbf{(C4, R4--5)}, we do not allow mesh deformation to retain the shape approximation, as described in Section~\ref{ssec:input}.
We also aim to keep the unfolded patches as single connected components, so that the reader gains an overview on the number of individual papercrafts.
To achieve this, we adapt our previous approach~\cite{Korpitsch:2020:WSCG}.

\textcolor{black}{We handle the mesh unfolding problem using properties} from graph theory, i.e., we use a spanning tree to represent an unfolding patch~\cite{takahashi2011optimized}.
\textcolor{black}{We create the spanning tree by building a dual graph of the input papermesh.}
Then, we find the minimum spanning tree of this dual graph.
\textcolor{black}{We use the weight assigned to the edges in the spanning tree search algorithm to control the priority of a boundary edge to be cut. }
For example, if an edge $e$ is a sharp mountain or valley fold, we avoid cutting this edge since it increases the complexity during construction.
In this case, the edge $e$ is likely to be added in the tree.
For more details about the full criteria, we refer \textcolor{black}{our readers} to our previous work~\cite{Korpitsch:2020:WSCG}.
Mathematically, \textcolor{black}{we use the spanning tree to describe variations of unfolded patches. The tree also serves as a backbone to control the unfolding process from 3D to 2D.}
In addition, \textcolor{black}{we introduce glue tabs} to facilitate stitching.
This adds another complexity to the optimization, as the triangles of the input mesh should not intersect with the glue tabs when unfolded.
For this, we use a simulated annealing optimization.

In our previous work, we did not consider textures on the meshes and used only indicators to guide the users during reconstruction.
In the current approach, we consider textures as rendered from the previous projection step, but we still need the reconstruction indicators.
For this, we take the advantage of double-sided printing.
On one side of the paper we render the glue tabs, and on the other side we render the corresponding position to be glued on the back side of the patch.
In this way, \textcolor{black}{we can render additional indicators on the back of the paper} without interfering with the texture of the structure projected on the front side.
Figure~\ref{fig:result_real_1} (c-1) shows an example of the front side and (c-2) an example of the back side.

\vspace{-5pt}
\subsection{Nested Papercraft Construction and Interaction}
\label{ssec:papercraft}

Once \textcolor{black}{we generate the unfolded patches, we print out these} on A4 or A3 papers and follow the glue tab instructions to create the papercraft physicalization \textbf{(C5, R4--6)}.
The assembly requires double-sided tape or glue.
An advantage of unfolding all papermeshes to connected patches (one patch for each level of the papermesh) is to provide the users with an overview of the total number of papermeshes in this 3D model \textbf{(C4, R4--5)}.
Once \textcolor{black}{we assemble the papercraft model, we can open up} the subcomponents and explore the information projected on the surface with the use of colored filters \textbf{(C5, R6)} to bring forward different structures (Figure~\ref{fig:teaser}(e)).
\vspace{-5pt}

\subsection{Implementation}
\label{ssec:implementation}
\textcolor{black}{The main source code is in C++17 and Python 3.9.
Major libraries include VTK
for fundamental computer graphics management, PyQt5
for the user interface, and CGAL
for computational geometry.
\textcolor{black}{We compiled the code} on a Windows 10 machine using MSVC
and CMake.
\textcolor{black}{We used Pybullet~\cite{coumans2021}} for the physical simulation, and OpenGL for rendering the final results to an off-screen frame buffer using a camera facing the center of the unfolding using parallel projection.
Our implementation, along with examples and a demo of this work, is available at our repository~\cite{repo}.
}

\section{Experimental Results}
\label{sec:result}

In this section, we demonstrate several nested papercraft examples generated with our approach.
We first employ a set of synthetic models, where we experiment with nested structures of different topologies and hierarchies (Section~\ref{ssec:synthetic}).
Then, we demonstrate several cases with anatomical and biological models (Section~\ref{ssec:real_data}).
Table~\ref{table:meshstats}
shows the number of vertices and faces for the input data and for the papermeshes (in parentheses), used in our examples.

\begin{table*}
\caption{
The total vertex and face count for input meshes and approximate papermeshes (in parentheses) of each 3D model.
}
\label{table:meshstats}
\centering{
\begin{tabular}{|c|cccccc|}
\hline
Models & Head (Fig.~\ref{fig:teaser}) & Synthetic 1 (Fig.~\ref{fig:result_synthetic_12}(a)) & Synthetic 2 (Fig.~\ref{fig:result_synthetic_12}(b)) & Head (Fig.~\ref{fig:result_real_1}) & Plant cell (Fig.~\ref{fig:result_real_2}) & Torso (Fig.~\ref{fig:result_real_3}) \\
\hline
Vertices & 1,051,155 (102) & 252 (109) & 210 (152) & 1,051,155 (84) & 2,396 (60) & 1,722,665 (98)\\
Faces & 2,091,807 (196) & 480 (210) & 400 (296)  & 2,091,807 (156) & 2,316 (112) & 3,418,171 (188) \\
\hline
\end{tabular}
\vspace{-5pt}}
\end{table*}
\vspace{-5pt}

\subsection{Synthetic Models}
\label{ssec:synthetic}
Synthetic meshes are used to showcase the theoretical possibilities of the pipeline, disregarding the possibility of reconstruction due to the limited paper size, when printing unfoldings.
Figure~\ref{fig:result_synthetic_12}
shows two different complex configurations of nested meshes.
In these cases, we focus on the hierarchy detection, the viewpoint calculation, the identification of the optimal cutting plane, and the stability calculation (i.e., steps (a)--(f) in Figure~\ref{fig:overview}).
Our two examples demonstrate that the first part of the workflow is possible and gives solutions within reasonable times.
The reconstruction of such complex models might not be possible, due to the limited size of printed patches which makes the nested structures too small to assemble.
\vspace{-5pt}

\subsection{Anatomical and Biological Nested Models}
\label{ssec:real_data}
Figure~\ref{fig:teaser} shows the workflow steps to obtain results of a nested papercraft for a \textit{human head}, with only two nested structures, i.e., the outer head and the inner brain.
Figure~\ref{fig:result_real_1} shows a human head papercraft with a more complex nested configuration.
Here, the inner level is split into two subparts, as shown in the tree and cutting configuration (Figure~\ref{fig:result_real_1} (a)).
The outer model consists of muscles (in magenta), bones (in cyan), and veins (in yellow), projected onto the papermesh (Figure~\ref{fig:result_real_1} (b)), the unfolding (Figure~\ref{fig:result_real_1} (c-1,3)) and papercraft (Figure~\ref{fig:result_real_1} (d,e)) surface.
The inner model consists of two meshes approximating the forebrain (in magenta), and the midbrain together with the hindbrain as nested structures of the head.
These are also projected on the papermesh (Figure~\ref{fig:result_real_1} (b)), the unfolding (Figure~\ref{fig:result_real_1} (c-1,3)) and papercraft (Figure~\ref{fig:result_real_1} (d,e)) surface.
This shows the typical use case for projecting structures that cannot be turned into papermeshes, while enabling the exploration of bigger structures and their relation to each other as papermeshes.

Figure~\ref{fig:result_real_2} shows a nested papercraft example for a \textit{plant cell}.
The model is split into three levels in the tree configuration (Figure~\ref{fig:result_real_2} (a)).
Various small or complex structures, i.e., plasmodesmata, amyloplast, peroxisome and the Golgi apparatus (either in magenta or in cyan) are projected on the outer level that represents in yellow the cytoplasm (Figure~\ref{fig:result_real_2} (b,c)).
The nucleus and nucleolus can be unfolded as individual nested papermeshes (see respectively cyan and magenta in Figure~\ref{fig:result_real_2} (b,c)), and assembled (Figure~\ref{fig:result_real_2} (d)).
\textcolor{black}{Assembling the outer level, visible in Figure \ref{fig:result_real_2} (c) took 28 min 49 s, excluding the time for cutting out the template.}

Figure~\ref{fig:result_real_3} shows a nested papercraft of the \textit{human upper body}.
The tree configuration results into three levels for this model (Figure~\ref{fig:result_real_3} (a)).
The outer level shows the veins (in cyan), muscles (in magenta) and bones (in yellow) projected onto the surface (Figure~\ref{fig:result_real_3} (b,c,f)). \textcolor{black}{The papercraft for the outer level, visible in Figure \ref{fig:result_real_3} (g) took 41 min 58 s to assemble.}
The middle level contains the intestines (in yellow), bladder (in magenta) and kidneys (in cyan) together, and separately the lungs (in yellow) with the bronchial tree (in magenta) (Figure~\ref{fig:result_real_3} (d,e)).
The last level contains the heart (Figure~\ref{fig:result_real_3} (e)). In this case the nested meshes also have multiple projected structures.
Projecting all of the present structures onto the same surface would lead to visual clutter, making it harder to understand the distinct relation between different structures.
Instead, generating multiple papermeshes for important organs leads to less visual clutter without losing information (Figure~\ref{fig:result_real_3} (g)).
All of the real world examples were printed on A3, and the relative scale is indicated on the figures for each of the models.
\vspace{-5pt}

\section{Evaluation and Discussion}
\label{ssec:discuss}

In this section, we assess the feasibility of the presented approach.
This includes a performance test of the components of the workflow (Section~\ref{ssec:performance}) and a feasibility test for papercraft reconstruction (Section~\ref{ssec:feasibility}).
This is followed by an informal interview with users
(Section~\ref{ssec:interview}).
\textcolor{black}{We conducted also a second study to evaluate the impact of mountain-valley folds on the papercraft assembly (Section~\ref{ssec:mountain-valley-folds}).}
We include also a discussion of the current limitations and intuitive solutions thereof (Section~\ref{ssec:limit}).

\vspace{-5pt}
\subsection{Quantitative Evaluation}
\label{ssec:performance}

Our system was tested on a Windows 10 machine with a 3.8 GHz, 64MB L3 Cache Processor and 32GB of RAM.
Table~\ref{table:runtime} gives an overview of the running time in seconds of each component of our workflow.
The first steps (components (a)--(f) of Figure~\ref{fig:overview}, as discussed in Sections~\ref{ssec:input}--~\ref{ssec:stability}) do not take too much time.
The primary computational bottlenecks come from the projection (Section~\ref{ssec:projection}, Figure~\ref{fig:overview}(g)) and unfolding (Section~\ref{ssec:unfolding}, Figure~\ref{fig:overview}(h)).
The former is an iterative approach~\cite{schindler2020anatomical} and the latter is an optimization approach~\cite{Korpitsch:2020:WSCG}, which justifies their performance times.

\begin{table*}
\vspace*{0mm}
\small
\caption{
\vspace{-5pt}The running time (in $s$) of the results showcased in this paper. \textcolor{black}{The last column indicates the total time (in $s$ and in $mm:ss$).}}
\label{table:runtime}
\centering{
\begin{tabular}{|p{22pt}|ccccccc|c|}
\hline
&
\includegraphics[width=0.08\linewidth]{figures/workflow-2-hull.pdf} &
\includegraphics[width=0.08\linewidth]{figures/workflow-3-tree.pdf} &
\includegraphics[width=0.08\linewidth]{figures/workflow-4-viewpoint.pdf} &
\includegraphics[width=0.08\linewidth]{figures/workflow-5-cut.pdf} &
\includegraphics[width=0.08\linewidth]{figures/workflow-6-stability.pdf} &
\includegraphics[width=0.08\linewidth]{figures/workflow-7-projection.pdf} &
\includegraphics[width=0.08\linewidth]{figures/workflow-8-unfolding.pdf} &
\textcolor{black}{\textbf{Total time}}\\
Step & Approximation & Hierarchy & Best viewpoing & Cutting & Stability & Projection & Unfolding & \textcolor{black}{in $s$ (and in $mm:ss$)}\\
\hline \hline
Fig.~\ref{fig:teaser}       & 2.04 & 0.05 & 0.95 & 0.3 & 0.484 & 461.1 & 6.69 & \textcolor{black}{ 471.614 (7:52)} \\
Fig.~\ref{fig:result_real_1} & 2.23 & 0.1 & 1.07 & 0.54 & 0.486 & 482.41 & 40.11 & \textcolor{black}{ 526.946 (8:47)} \\
Fig.~\ref{fig:result_real_2} & 9.85 & 0.05 & 1.22 & 0.56 & 0.502 & 727.25 & 19.78 & \textcolor{black}{ 759.212 (12:39)} \\
Fig.~\ref{fig:result_real_3} & 2.87 & 0.12 & 1.17 & 1.23 & 0.501 & 863.28 & 627.01 & \textcolor{black}{ 1496.181 (24:56)} \\
\hline
\end{tabular}
\vspace{-5pt}}
\end{table*}

\vspace{-5pt}
\subsection{Feasibility of Mesh Construction}
\label{ssec:feasibility}

We conducted a user study to investigate the feasibility of constructing the nested papercrafts.
We recruited $10$ participants ($3$ females and $7$ males) with ages ranging from $25$ to $39$.
\textcolor{black}{The participants were mostly colleagues and students.}
Only one participant (User 4, female) has no computer graphics background and one participant (User 7, male) is colorblind.
We prepared the head model shown in Figure~\ref{fig:teaser}, being a model of adequate complexity and an interesting structure to learn anatomy.
This model includes three components: the left and right half of the head, and the brain inside the head.
It is adequately challenging to construct, due to the concavity of half head papercraft, which contains several sharp angles in the papermesh.
The patches were printed double-sided on multiple \textcolor{black}{A3 (220 GSM)} papers.
For each participant, we started by explaining the process of building individual papercrafts and pointed out that a single connected component corresponds to an enclosed papercraft.
We moved on to explaining the meaning of the numbers on the glue tabs and that these glue tabs should be attached to the corresponding number on the back side using tape.
\textcolor{black}{We instructed the participants to start with the small papercraft of the brain (28 patches), as this is easier and could serve also as an introduction to the assembly of the other, more complex structures.}
The participants were allowed to observe the patches for as long as they needed and to ask questions regarding the renderings before reconstruction.
This part was not included in the timings.
We note that once the time measurement began, the participants could use their own strategy to explore and construct the papercraft, as they would do in a real-life scenario.
Table~\ref{table:constructtime} summarizes the total construction time \textcolor{black}{(excluding cutting time)} of the nested papercraft for each participant.
All participants managed to build the papercraft within less than two hours (average time was 90 min 41 s and standard deviation 21 min 22 s).

\begin{table*
\vspace*{0mm}
\small
\caption{
The assembly time (in $mm$:$ss$) of the head model (Figure~\ref{fig:teaser}) for each participant (F: female and M: male) \textcolor{black}{for the two studies (A: without and B: with mountain-valley folds), excl. cutting time. We denote average (avg), standard deviation (sd), and no participation (N/A).}}
\label{table:constructtime}
\centering{
\begin{tabular}{|l|cccccccccc|c|}
\hline
User & 1 (M) & 2 (M) & 3 (F) & 4 (F) & 5 (F) & 6 (M) & 7 (M) & 8 (M) & 9 (M) & 10 (M) & avg $\pm$ sd\\
\hline
Time A (mm:ss) & 99:38 & 95:04 & 112:32 & 99:12 & 59:04 & 53:02 & 98:04 & 103:07 & 117:04 & 67:32 & 90:26 $\pm$ 22:23 \\
\hline
\textcolor{black}{Time B (mm:ss)} & \textcolor{black}{85:52} & \textcolor{black}{92:21} & \textcolor{black}{99:56} & \textcolor{black}{89:33} & \textcolor{black}{48:52} & \textcolor{black}{40:06} & \textcolor{black}{86:23} & \textcolor{black}{N/A} & \textcolor{black}{N/A} & \textcolor{black}{N/A} & \textcolor{black}{77:35 $\pm$ 23:13 }\\
\hline
\end{tabular}
\vspace{-5pt}}
\end{table*}

\subsection{Interview with Evaluation Participants}
\label{ssec:interview}

After completing the reconstruction of the papercrafts, we asked the evaluation participants about their experience with the physicalization.
When asked about whether the papercraft was easy to build or not, most of the participants indicated that they had difficulties---especially, when building the ``negative'' side of the head level, i.e., the head--brain interface.
Reconstructing just the brain was considered easy by most, and all reconstructed it fast.
Most people would have appreciated additional indicators for the reconstruction.
One person commented that they ``\textit{would like to build one with a single texture, to see if it would be easier}''.
We also asked the participants what they consider to be an upper limit in number of meshes for a feasible reconstruction.
\textcolor{black}{Most participants commented that 3--5 meshes would be the upper limit for them.}
We also asked them about a comparison to traditional on-screen visualizations.
One participant commented that ``\textit{it is interesting to be able to take the brain out with [their] own hands}'', and another one mentioned that ``\textit{[..] the scope [i.e., between physicalization and visualization] is different}''.
Two people were clearly in favor of an on-screen visualization.
When asked about particular findings,
one person commented that ``\textit{you can see a very surprising scale of how big the brain is in comparison to the skull and that is more noticeable than a 3D rendering}''
We finally asked them about potential applications, and half of the participants commented about children education.
One commented that the model is ``\textit{very fragile to use for anything else than entertainment [and] would seem unprofessional to use in patient communication}''.

\vspace{-5pt}
\subsection{\textcolor{black}{Effect of Mountain-Valley Folds on Assembly}}
\label{ssec:mountain-valley-folds}

\textcolor{black}{In a second study, we assess the effect of mountain-valley folds on the papercraft assembly.
Two months after the first study, we asked the same people to participate in a second session.
Participants 8--10 were not available, but participants 1--7 underwent the same process as described in Section~\ref{ssec:feasibility}.
The difference in this case was that the papercrafts had an additional encoding of the mountain-valley fold~\cite{takahashi2011optimized}, supporting the users in folding the patch towards the ``outside'' or the ``inside''}.
\textcolor{black}{Again, we measured the assembly time for all participants.
The results are summarized in the last row of Table~\ref{table:constructtime}, and they confirm that the assembly time decreases with the use of mountain-valley folds by more than about 13 minutes (average time was 77 min 35 s and standard deviation 23 min 13 s).
Other smart guidance techniques to support the efficient assembly of the papercrafts should be further investigated, while coupling with informative aids could make the process more engaging.}
\vspace{-5pt}

\subsection{Limitations}
\label{ssec:limit}

Our evaluation brought forward some limitations of our approach, opening opportunities for future improvements.
Some limitations are inherited from the employed computer graphics solutions, while others were introduced by the specifics of our implementation.
\textcolor{black}{The papermesh after the mesh approximation should be \textit{quasi-convex and manifold}.}
This implies that the papermeshes cannot self-intersect, as they need to fit together in a physicalization.
If \textcolor{black}{intersection} happens after the mesh approximation, we have to slightly scale or translate the meshes to avoid this situation.
Additionally, only \textit{manifold meshes with genus $0$} are currently supported.
Whether a mesh with a genus higher than $0$ can always be unfolded into a single connected patch remains a general open topic.
\textcolor{black}{As an alternative option, we can cut meshes with higher genus values to a set of meshes with genus $0$ and apply our approach.}
Note that the shapes of the approximated meshes influences whether an inner mesh can be \textit{taken out of} its outer level.
\textcolor{black}{A precise shape analysis scheme could be used to optimally cut the branches of a mesh, instead.}
It remains to be investigated if a stable solution can be reached---and even, if a possible solution can be found---with an increasing number of constraints.
For the same reason, \textit{simple implicit functions} are used, and more complex configurations should be investigated in the future.
\textcolor{black}{Another straightforward solution would require to employ a user-defined cutting plane.}

\textcolor{black}{Our initial} user study (Section~\ref{ssec:feasibility}) also showed that the combination of concave and convex patches on the meshes can lead to \textit{difficulties during the folding process}.
\textcolor{black}{The assembly of the head papercraft employed in the user study was in average 1.5 hour,
which can is a considerable amount of time of classroom education.}
\textcolor{black}{The construction and assembly time of our papercraft is reasonable based on our discussion with a professional designer~\cite{Chan:2022}.
As also mentioned in Section~\ref{sec:intro}, a simple 3D model with $66$ polygonal faces requires a week for the creation and $10$ working days for the generation of the corresponding papercraft, while assembling it requires one hour.}
\textcolor{black}{Our early investigations on advanced guidance mechanisms (Section~\ref{ssec:mountain-valley-folds}) indicate ways to simplify the construction, but we will pursue this further to identify adequate ways of reducing the user's overhead (e.g., assembly order, paper size).}

\vspace{-5pt}
\section{Conclusion and Future Work}
\label{sec:conclude}

Our paper presents a novel workflow to support the computer-aided generation of paper-based physicalizations.
The nature of nested papercrafts allows us to map anatomical or biological models to submeshes that physically represent their hierarchical topology.
The approach combines playfulness with insight, by supporting the easy design of papercrafts and data navigation through a combination of unfolding and texture projections that can be explored through colored filters or light.
\textcolor{black}{As shown in our user study, the constructed papercrafts can be used for edutainment purposes.
It would be interesting to evaluate whether this could be also used outside of the domain of anatomy or biology.}
As most prominent future improvements, a constrained-based cutting algorithm can be investigated to minimize the number of cutting planes, while extending our input to non-manifold meshes remains an open and challenging problem.
When thinking about human factors, an interesting direction would be to consider machine-assisted recommendations in the workflow in order to ease the papercraft construction process.
\textcolor{black}{Finally, an evaluation that covers the entire fabrication process (and potential ways of supporting the users better, e.g., with different papers or different scorings) needs to be conducted.}

\noindent \textbf{Acknowledgments}
\vspace{-3pt}

\noindent \textcolor{black}{We thank Amao Chan~\cite{Chan:2022} for sharing his knowledge on papercraft design and production, and his thoughts on future challenges.}
\section*{Supplementary Material}
\label{sec:appendix}

In the appendix, we provide the unfolded patches of Figure~\ref{fig:teaser} (c) with better image resolution.
\textcolor{black}{Our readers} can print them out and build their own head papercraft.
First of all, the \textcolor{black}{three} pages need to be printed double-sided with long-edge binding.
It is recommended to print on A3 paper or other larger formats.
Once the patches are printed, they need to be cut out.
Before reconstruction, it is recommended to bend the triangles on the solid lines and observe the patches in advance.
Once everything is prepared, double-sided tape to the glue tabs can be applied.
Then, the numbers on the glue tabs need to be matched with the numbers on the matching area on the back side of the paper.
We recommend to start with the last page that contains the smallest structure (i.e., the brain).
The other two include the two halves of the skull.
After the papercraft is built, the projected structures can be investigated with colored filters (i.e., simple colored foils) or under colored light (red, green, blue).
Enjoy!

\section{The Head Dataset}
\label{app:head1}
\clearpage
\includepdf[pages={-}, angle=0]{appendix/print_head_userstudy_new.pdf}

redpajama_set_name: RedPajamaArXiv


The GDP figure for Q3 FY 2013 has come in at a shocking 4.5%, as against expectations of 5%. The figure for the previous quarter was 5.3 per cent.
The poor GDP numbers are likely to worry economists and analysts who are only grappling with a mediocre Union Budget 2013.
Q3 construction growth has come in at 5.8 per cent vs 6.7 per cent quarter on quarter. Electricity growth has come in at 4.5 % vs 3.4% quarter on quarter.
Poor GDP numbers are now likely to put pressure on the RBI to cut interest rates to push growth.


text:
Research program awarded $3 million for biomedical careers
John Michael Simko
Dr. Steven B. Oppenheimer is one of the leaders of the MARC program. He has been involved in the program since its inception. Sundial File Photo
CSUN is the recipient of a $3 million, five-year grant from the National Institutes of Health (NIH) for the Minority Access Research Careers (MARC) program to continue providing support for underprivileged minority college students in the biomedical field.
The MARC Undergrad Science Training and Academic Research (U-STAR) program is a NIH fundeds program designed to mentor minority students and give them research experience to compete at the graduate level. According to the NIH, CSUN is one of 18 California universities that participate in the MARC U-STAR program.
"It's important that everyone has access to higher education," said Dr. Maria Elena Zavala, director of the MARC program and biology professor. "I do what I do because of the lack of opportunities for my parents. It's for everyone who was never provided with an opportunity and doesn't have the financial means."
Zavala said the MARC U-STAR program was started in 1990 with four CSUN students and has developed with the NIH ever since. The money helps participants involved with the program and allows improvement in the College of Sciences and Mathematics.
Dr. Carrie Saetermoe, chair of the Psychology Department said Maria Elena Zavala has done more to diversify the sciences than the vast majority, if not all other sciences.
Students involved with MARC are offered opportunities to participate on various research projects, receive mentoring, professional guidance, graduate school preparation, attend special seminars and present their research results at scientific conferences.
"CSUN has an excellent reputation for providing well trained students to excel," Zavala said. "CSUN has a very high rate of Ph.D.s for MARC students. The average for most schools is something like 50 percent, but CSUN MARC students have an 89 to 90 percent success rate with people graduating and attending places like UC Berkley, UCLA, UC Irvine, UC San Diego, Harvard and John Hopkins University."
Dr. Steven B. Oppenheimer, a biology professor and MARC faculty member, said the MARC program is mostly comprised of biology and psychology majors, but the program is available for students trying to obtain any biomedical behavioral or basic science degree who plan on pursuing a Ph.D. or combined M.D. and Ph.D. degree.
These biology, chemistry, mathematics, psychology, and kinesiology majors are encouraged to go directly from undergraduate studies to Ph.D. programs, Oppenheimer said.
As a result, it is a very rigorous process to be considered for the MARC program, Oppenheimer added. There are usually 300 to 400 eligible students, but only a few are invited to compete for spots.
After an intense summer orientation, about a dozen students are accepted into the program, he added. Those students receive partially reduced tuition fees, stipends, travel support to conferences and research for each individual.
"Without the NIH, there is no program," Oppenheimer said. "Students receive fellowship stipends to advance themselves academically. They wouldn't be able to do all the research for biomedical, chemistry, and health science."
Zavala said most of the grant will probably be spent on stipends, but has hope with one proposal.
With the new grant, there is a proposal to initiate science 100 classes that are specifically tailored for each science major by having relevant activities and readings rather than a basic broad spectrum science class, she said. Most science majors do not take the regular science 100 class because it is "too general" and "students don't believe they won't get enough out of it."
Zavala said she is hopeful that the first batch of students will try the newly proposed science classes and see the appealing advantages in the Fall of 2011.
MARC has had a huge impact on the campus by creating enhanced research environments, she added.
Quality research has impacted the campus and encouraged professors to publish more. As a result, more grants and financial contributions have led to newer, expensive equipment and the creation of additional courses and class sections.
Zavala and Oppenheimer lead the MARC program and have been involved since the inception. They were CSUN's first and second recipients of the Presidential Award for Excellence in Science, Mathematics, and Engineering Mentoring.
John Michael Simko, Author

redpajama_set_name: RedPajamaCommonCrawl


The Jet Propulsion Laboratory will present officials of the city of Pasadena with checks totaling $1.125 million March 7, in connection with the contamination of four water wells in the nearby NASA facility.
The amount will help pay the city's past costs incurred by the contamination of four wells and is a first payment toward installation of a temporary treatment plant to decontaminate the water from those wells.
It is part of a total of more than $3 million JPL will pay the city over three years under terms of a recently negotiated agreement.
Contamination by volatile organic chemicals was found in the city's water wells in the early 1980s. The city has taken the position that JPL was the source of the contamination, believed to have occurred during the 1940s and 1950s as the result of the disposition of solvents and other cleaning materials while JPL was a U.S. Army ballistic missile facility.
JPL has not accepted liability for the contamination but has worked with the city to build an appropriate plant and has agreed to contribute significantly to its costs.
and negotiations concerning a long term solution will be postponed until the temporary plant has been demonstrated.
This plan will give Pasadena and JPL time to learn more about the contamination and JPL's responsibility and will avoid having to design a permanent plant while the problems are not yet fully understood.
The agreement for the treatment plant is the successful culmination of negotiations between the City of Pasadena and JPL and it will permit the affected wells to be returned to service and supply safe water to the community.


text:
Countering chaos
A Marine Corps general's strategy for peace in an unstable world
Retired Gen. Tony Zinni is the proverbial E.F. Hutton of defense advice. When he speaks, people tend to listen — and rightfully so. His insights on the intricate dynamics of the Middle East, honed over several tours as a Marine officer, commander at U.S. Central Command and presidential envoy have been sought out regularly. Sometimes that advice was accepted, other times it was not. Far more often than not, people should have listened. His track record, especially with regard to the current debacle in Iraq, is unerringly prescient.
That is what makes this book so irritating.
In "The Battle for Peace," the pragmatic retired Marine general outlines a strategy that will match America's power and purpose to better address tomorrow's security challenges. With his co-author, Tony Koltz, Zinni explains the chaotic nature of today's insecurity and argues for a strategy centered on countering instability. Based on his four decades of government service, he believes the U.S. must do all it can to create stability or quell impending disorder. The alternative is to do nothing, to hunker down and gamble again that chaos and disorder will not visit our shores. That is a false hope, Zinni contends. Too many societies are too close to instability and "the problems out there will come to affect all of us in the stable part of the world, and this is happening with ever-increasing frequency." The sinews of this strategy are built upon selective early engagement, international organizations, regional development and today's Pentagon mantra of "building partnership capacity."
While the ends of strategy are cogently laid out and appealing, the means are not. As the 9/11 Commission noted several years ago, "As presently configured, the national security institutions of the U.S. government are still the institutions constructed to win the Cold War." The commission urged the government to adapt its traditional bureaucracies for a new world that requires fast, creative and agile responses. Zinni is especially critical of the organizational reforms to date, particularly the homeland security and intelligence reforms. These are dismissed as "creating additional stand-alone bureaucracies." This will surprise anyone familiar with the national response plan for domestic disasters or the true nature of intelligence reform, which added an integrating layer but not a stand-alone institution. Zinni goes on to propose altered and new institutions to better integrate, coordinate and collaborate on the fly.
Rather than create single-mission bureaucracies, which suffer from a stovepiped outlook, he strongly urges the U.S. government to embrace a vertical flattening and decentralized approach to adapt to the fast-breaking nature of today's complex era. Not surprisingly, given his background, the general is comfortable with military structures and approaches. This may confuse the reader. If you're looking for agile, lean and flattened structures, the Pentagon is hardly a model worthy of emulation. Those familiar with the sclerotic nature of the Pentagon and the deliberate nature of the joint planning process may find its pathologies turned into virtues somewhat disconcerting.
At the strategic level, the authors define a need for a new organization to integrate the relevant national-level agencies, using a small cadre of permanent personnel and members drawn from every department to ensure direct input and involvement. They propose the creation of the National Monitoring and Planning Center (NMPC). Its responsibilities include monitoring unstable areas, destabilizing conditions and emerging threats. It is to serve as an integrating organization, providing all views from each relevant agency, all of whom would be represented. Knowledgeable readers will note that the NMPC has the same task as the National Security Council staff. The authors fail to make this distinction or offer criticisms of the existing system. The proposal has merit, but readers are left short-changed.
At the operational level, the existing joint military command structure is presented as a paragon of organizational efficiency. The military model is described as "far more efficient, far more flexible, and far more responsive to … fast moving changes and new challenges." To enhance the ability of the country to conduct the kind of multi-agency operations that today's contingencies call for, Zinni recommends the creation of four standing joint interagency task forces to support each combatant commander. There is a huge presumption here that the Pentagon's command structure and doctrine remain the optimal means of applying all instruments of American power.
At the tactical level, Zinni urges the establishment of the Inter-Agency Field Force — "a deployable, civilian capability that would join military forces in the field to handle the nonmilitary dimensions of program implementation or postconflict recovery and reconstruction." The general envisions a modular force but not a specific design or structure. This force would be administered and supported by military units, with the civilians retaining their identity and chain back to their parent agencies, paralleling military lines of command. Without a hint of irony, Zinni compares the Inter-Agency Field Force as "a CPA-like capability," referring to the Coalition Provisional Authority that governed Iraq until June 2004. There is no discussion of the Provincial Reconstruction Teams that are being used in Afghanistan or the Advanced Civilian Team construct being promoted by the U.S. State Department's Coordinator for Stability and Reconstruction.
Zinni's proposals are less detailed and far less original than initiatives put forward more than a year ago by the administration and by influential studies issued by the Center for Strategic and International Studies. These and other studies have offered detailed organizational designs, as well as the requisite doctrine, training and education programs required to make them effective. Zinni would do well to study the planning, intelligence and cultural flaws that have hampered U.S. efforts in Iraq to inform his proposals. It is not clear whether proposals would generate the common vision, unity of effort, fused intelligence and integrated multidimensional operations required for today's complex contingencies. Following his model could institutionalize the separate and poorly integrated "lines of operation" that presently characterize a military-dominated approach and outdated operational art.
All told, "The Battle for Peace" reflects a lot of common sense and a well-balanced future strategy for the U.S. Readers dissatisfied with ideological approaches will find the author's strategy free from blind adherence to academic theory. This will make the book worth acquiring, if only as an antidote to the pedantic musings passed on as revealed wisdom from the chattering class in Washington. The authors' arguments for taking on potentially disruptive sick cases is a viable strategy. This book easily outpaces fanciful theories about "flat worlds" and dysfunctional gaps in describing the nature of today's dynamics.
But, despite its subtitle, this is not a front-line perspective on today's challenges. Quite the contrary, it suffers from a tinge of rear-view-mirror driving. Readers looking for a more comprehensive and true interagency approach are encouraged to look elsewhere.
Frank Hoffman is a retired Marine officer and a nonresident fellow with the Foreign Policy Research Institute in Philadelphia.


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I have replaced my high tech contact form with a more direct and human communication here. This means that I am not, presently compiling a database. So if you like what you have seen and read, please subscribe to my blog or follow me on Facebook.
Click on the subscribe icon at the top right-hand corner of any website page. It is the last icon and looks like a quarter slice of concentric circles. Follow any Facebook page topic by clicking on 'Follow' on that page. Thank you. I look forward to connecting and talking with you.
Each page does have a contact form for your convenience. So you have a choice of a personal, old-fashioned way of connecting as well as a high tech version.
"Be Careful Of The Maven In The Hat . . .
Who is this Maven in the Hat anyway? This hyper-energetic woman from New York? She's an inventive marketer, one who will stir you up. If you want complacency, if you want the old-style, do-nothing marketing… then run from her – she definitely is NOT your kind of person. But if you want some new out of the box ideas, she's the one for you.


With four locations located throughout the Tampa Bay area, Orthopaedic Associates of West Florida (OAWF) makes it convenient for you to get the quality Orthopaedic care you expect. OAWF's commitment to EXCELLENCE IN ORTHOPAEDICS sets us apart from other Orthopaedic groups in the area with in-house Digital X-ray, MRI, EMG, Bone Density(dexascan) Physical Therapy, Hand Therapy and Orthotics and Prosthetics department.
Note: Please refer to your physician coordinator to determine which forms you will need to fill out.
Our office hours are Monday through Friday 8:00am to 5:00pm. New patients are always welcome. To schedule an appointment at any of our offices, please call (727) 461-6026 Ext 1950 or email patient@orthowestfl.com.
Please Download the Records Request form and either fax or bring it to our Clearwater Office.


text:
Home/ The Advocate/
faculty and staff news Fall-2020
/ SCOTUS Strikes Down Nonunanimous Jury Convictions in Criminal Cases, Cites Professor Aliza Kaplan in Opinion
SCOTUS Strikes Down Nonunanimous Jury Convictions in Criminal Cases, Cites Professor Aliza Kaplan in Opinion
Professor Aliza Kaplan, director of the Criminal Justice Reform Clinic,
Steve Hambuchen
U.S. Supreme Court Justice Brett Kavanaugh cited Lewis & Clark Professor Aliza Kaplan, director of the Criminal Justice Reform Clinic, in his concurring opinion in Ramos v. Louisiana. The case, which was decided on April 20, 2020, strikes down nonunanimous jury convictions as unconstitutional.
"Oregon adopted the nonunanimous jury practice in 1934—one manifestation of the extensive 19th- and early 20th-century history of racist and anti-Semitic sentiment in that state," Kavanaugh wrote in his opinion. The statement cited Kaplan's article, coauthored with Amy Saack '17, "Overturning Apodaca v. Oregon Should Be Easy: Nonunanimous Jury Verdicts in Criminal Cases Undermine the Credibility of Our Justice System," which was published in the Oregon Law Review in 2016.
"Aliza Kaplan has tirelessly advocated against nonunanimous juries in Oregon," stated John Parry, associate dean of faculty. "Her scholarship and teaching on this topic have had far-reaching impacts, including being recognized by The New York Times, the Oregonian, and news outlets all over the nation." Clinic students working with Kaplan have been researching the history of nonunanimous jury laws in Oregon for the past five years. In addition to the law review article, the clinic filed two amicus briefs with SCOTUS in different cases.
"Students have participated in every aspect of getting rid of Oregon's unjust and unconstitutional nonunanimous jury law—they conducted research and helped draft court briefs, articles, testimony, materials, editorials, and conducted presentations," said Kaplan. The professor and clinic students also worked directly with colleagues in Louisiana to bring attention to this issue through legal work and the media.
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email jasbury@lclark.edu
We welcome correspondence from readers. Please be sure to include your name and location. Submissions are subject to editing.
Judy Asbury, Assistant Dean, Communications and External Relations


These days it seems everyone wants to know what everyone else is talking about: What's trending on Twitter or Facebook or Google Search, etc.
Well I wanted to know what our world-wide community of customers and developers were talking about in regards to our DataFlex products and their own development work. And I figured our company's management and development team - along with our entire community - would too.
It shows the top 10 subjects being discussed on our public community forums within four different slices of time: 24-hours, 7-days, 30-days, and 90-days. Our forums are utilized by many as an historical knowledgebase and the message threads are viewed repeatedly over a long period of time, so it's not unusual to see a top-trending subject appear in more than one of these slices of time - a useful indicator of long-lived topics that developers are interested in, indeed!
At a glance you can see in the web reports what subjects (forum threads) are the most viewed over each of these time slices including how many replies were posted in the subject threads. Clicking on any subject shows who posted the message and when. From there you can click on the row icon to display all messages in the thread in the date-time order they were posted, and see the first couple lines of the message text. From there a link lets you jump into the actual complete thread in our vBulletin forum system.
So the dashboard consists of four report grids, one for each of the time slices mentioned. No programming was required to create the report presentations, or to combine the four into a single dashboard presentation, though I did write some formula expressions in the reports to define the time slice condition and generate the hyperlinks into our actual forums. Essentially I created one report and then cloned it three more times, changing only the title and the query condition applied in each report to the created date-time stamp on the thread. Each of those reports' final drill-down path calls a fifth report that displays the actual message postings, automatically constraining that report to whatever specific subject thread was clicked on – all without any programming.
Tip: I used Dynamic AI to discover just where the data resided and how it was structured in the vBulletin database, for which I could find no documentation. In fact, I use Dynamic AI on virtually every development project in order to very quickly and efficiently learn about the database structure and data distribution in whatever database is used by a given application. It's an invaluable tool and time-saver, but that's a topic for a future blog!
Report design: totals, drill-down path, etc.
Styling and combining 4 reports into 1 dashboard.
The database is MySQL, and the main table needed is called vb_thread. Among its 32 columns are: ThreadID, Title, ForumID, ReplyCount, PostUserID, PostUserName, Views, and DateLine (the DateTime stamp the thread was created. These columns can be used to join to other tables such as vb_Forum, to get other descriptive information, etc. The ThreadID column value is used to allow making a drill-down report into the actual messages which live in the vb_Post table.
Note all of the datetime values in the database are in Unix format which stores date-times as integer values according to its own counting scheme. That integer needs to be converted to a recognizable date value using the built-in MySQL function: from_UnixTime(date-time_col_name). This can be done easily as a formula column in the report design, or in a view, which is simply a select statement saved in Dynamic AI's repository and becomes a datasource available for any number of reports.
2) Report Design: Totals, Drill-down path, etc.
As examples of business intelligence reports, these are about as simple as it gets. Essentially the reports are simply reporting the top 10 detail rows from the data source based on the view count value in each row in descending order. A top 10 report in Dynamic AI requires a group total. The report is grouping on the ThreadID column and captioned with the Title column. The Views and Replies columns are defined as totals with the descending order applied to the Views total. So in this case each grouping consists of a single row and drilldown from that grouping is to the same detail row, but showing different columns.
The detail row provides two links for further drill-down. There is a column labeled Open DAW Thread and clicking on the data row thread below it that shows Open Thread will open the actual vBulletin thread inside the DAW forum application. Clicking on the row number cell, the unlabeled first column, will call a related report that will display all of the messages in the thread.
One of the nicest, most elegant and powerful tools in Dynamic AI is something called Dyn-Relations. These are simple metadata objects that allow defining that the data values in one or more columns from any data source in one database is related to the data in corresponding columns in any other data source. Those sources can even live in different databases and reside on different servers – anywhere. Those sources can be tables, back-end views, dyn-views, or dyn-joins.
In report design when defining what the final lowest level drill-down object will be, it can either stop at the detail row, proceed to a form, or proceed to a related report based on common data values in the single row used for drill-down, and the related report's data source. In report design, Dynamic AI will present a drop down list of all of the reports that exist that were built using related data sources as defined in the dyn-relation object. Think of it as using the data values from a detail row in one report as providing dynamic runtime values to dynamically generated filters applied to a different report – automatically – with no programming involved.
Dynamic AI defines various CSS classes and applies a style sheet to every report, form or dashboard. It comes with a library of CSS that can be modified and saved as a new style sheet. The class names are documented and it was simple trial and error that allowed me to tweak the right classes with color codes and fonts to achieve the results you see. Below the CSS style sheet used by this report is linked to. Within Dynamic AI's repository you can enter additional parameters on how to use the style sheet for such things as a logo file name to use, adding rules to header and footer sections, etc.
A dashboard in Dynamic AI is simply a package of any combination of other existing report objects in its repository. You can click on any existing report in the repository to select it for inclusion in a new dashboard, and then click new/dashboard. So literally with 5 mouse clicks I created the final dashboard. Then I added some header comments to instruct users how to use the dashboard. I didn't need to change any other available settings to force positioning such as wrap versus float. There are some powerful, creative behaviors available settings will activate for various kinds of reports or dashboards, but that's for a future blog.


Get rid of wrinkles: Wash your face with this amazing homemade soap!
Commercial soaps or shower gels contain a lot of chemicals which, if you read the list of ingredients, you would never wish to have them on your skin. But the good news is you can make a homemade soap based on coconut oil to wash your face in the morning and in the evening to clean your skin and to get rid of wrinkles.
Put ingredients in a jar. Place the jar in a saucepan filled with water (bain marie). Boil the water until all ingredients melt, stirring constantly.
You can add your favorite essential oils to give the soap a nice scent. Pour the mixture into silicone molds. You can use the muffin ones. Let the resulted paste to cool and harden, then remove the soaps.
Wash your face daily with this soap and you'll see how wrinkles begin to disappear.


text:
Faculty Positions in Computer Science
Multiple Faculty Positions in Computer Science
The Department of Computer Science (CS) at The University of Texas at San Antonio (UTSA) invites applications for one tenure-track Assistant Professor and two open-rank positions (Assistant, Associate or Full Professor) starting in Fall 2020 in the general areas of algorithms/theory, computer systems/big data, and data science/artificial intelligence. Internationally recognized outstanding candidatesin other areasare also encouraged to apply. The CS department also invites applications fortwo cluster hiring inaugmented and virtual realityand inquantum computing and information,with strong collaborative potential with other disciplines.For Spring 2020, the department is hiring for multiple non-tenure-track positions including a Professor of Practice, two Lecturers, and part-time instructors.We seek faculty members who embrace excellence and diversity with a strong commitment to teaching, mentoring, and collaboration. UTSA especially seekstogrow its faculty ranks of women and underrepresented minorities. The CS department is well-known for its cybersecurity programs and will play a significant role in the shaping of the newly formed School of Data Science (also hiring a school academic director and a chief data strategist) and the National Security Collaboration Center, a combined $100M initiative. Annual research expenditures in the department average $4M to $5M. UTSA's CS department fosters an excellent research and mentoring environment for faculty, in particular for early career faculty. This has resulted in nine NSF CAREER awards since 2005, among a strong portfolio of external awards. The University, a Hispanic Serving Institution (HSI), is on a significant growth trajectory and is embarking on an ambitious plan toward becoming a model for student success, a great public research university, and an exemplar for strategic growth and innovative excellence backed up by significant resources and multipronged initiatives. Therefore, salary and start-up packages for the positions are highly competitive.
Review of applications will begin on Oct 21, 2019 and are continuing until the positions get filled. For enquiries and nominations, please contact the department chair at sushil.prasad@utsa.edu.
Applicants for an Assistant Professor position must earn a Ph.D. by September 1, 2020, in Computer Science or in a related field and must demonstrate a strong potential for excellence in research and teaching.
Applicants for an Associate/Full Professor position must have a Ph.D. in Computer Science or in a related field, established research program in their area of specialization, and extensive experience in graduate and undergraduate teaching. Tenure is contingent on Board of Regents approval.
Ideal candidates include those who demonstrate evidence of a commitment to collaboration, diversity, equity, and inclusion through research, teaching, and service endeavors.For senior candidates, preference is for those with transferrable grants. NSF CAREER awardees are especially welcome to apply. UTSA is advancing transdisciplinary research through cluster hires and other significant investments in the areas of analytics & data science, artificial intelligence, augmented reality / virtual reality, brain health, cybersecurity, human performance, Latin American studies, and quantum communication & computing. Candidates with interest and experience in any of these areas are of particular interest.
Responsibilities include research (individual and collaborative), teaching at the graduate and undergraduate levels, and program development. Successful candidates must demonstrate their ability to work with and be sensitive to the educational needs of diverse urban populations and support the University's commitment to thrive as a Hispanic Serving Institution and a model for student success.
The Department of Computer Science currently has 21 tenured/tenure-track faculty members, 7 professors of practice, and 12 lecturers and offers B.S., M.S., and Ph.D. degree programs, supporting 1,500+ undergraduates, 90 master's students, and 77 Ph.D. students. Faculty in the Department of Computer Science conduct numerous research projects with funding from a variety of federal and state agencies, and their graduate students are very well placed at leading academic and research institutions. Research is conducted in areas such as artificial intelligence, big data, bioinformatics, cloud computing, computer vision, cyber security, data analytics, database, game development, human computer interaction, image processing, machine learning, networks, operating systems, parallel, distributed and high performance computing, and software engineering.
Major research units in the department include: the Institute for Cyber Security (ICS), which operates FlexCloud and FlexFarm laboratories dedicated to cyber security research; and the Center for Infrastructure Assurance and Security (CIAS), which focuses on cyber security maturity of cities and communities and also conducts nation-wide Cyber Security Defense Competitions for college and high school students. The research activities and experimental facilities have been well supported by several federal research and infrastructure grants. ICS recently received a 5-year NSF CREST grant on investigating cloud security and privacy. CIAS recently received a 5-year grant from the Department of Homeland Security to set up a national standards organization for information sharing and analysis. The Department also recently received a 5-year grant from the Army Research Laboratory to investigate the modeling and prediction of cyber-attacks as well as several grants from other funding agencies. UTSA Computer Science was ranked #68 in the U.S. by Times Higher Education and #119 by U.S. News in 2018. UTSA undergraduate cyber security programs are collectively ranked #1 in a 2014 survey; the graduate programs are ranked #2 in a 2016 listing. The recently established Army Research Laboratory—South Cyber office at UTSA provides additional opportunities for Cyber Security research and funding. See http://www.cs.utsa.edu for additional information on the Department of Computer Science and its faculty.
The University of Texas at San Antonio, with nearly 32,100 students, is the largest university in South Texas. The City of San Antonio has a population of over one million and is known for its rich Hispanic culture, historic attractions, affordable housing, and excellent medical facilities. The Austin-San Antonio corridor is a high-tech center that is home to many major computer companies. Nearby higher education and research institutions include UT Health San Antonio and Southwest Research Institute.
San Antonio is a recognized cyber-analytics hub. Given the complexity of problems in transdisciplinary fields, and the opportunity for greater government and industry partnerships, UTSA has established The National Security Collaboration Center (NSCC) to support national security and global defense research in cybersecurity and cloud computing, cyber-attack/deterrence, critical infrastructure protection, analytics, high performance computing, AI, machine learning and visualization. These positions will facilitate outstanding collaborative research to further connect partners in the NSCC ecosystem.
Required documents and submission process:
Visit https://cs.utsa.edu/people/fac-staff-search for details on these positions and submission process.
Applications must include a letter of application (with research, teaching, and diversity aspectsbrieflydiscussed) and a curriculum vitae. Assistant professor candidates must also include a statement of research interests, a statement of teaching interests with discussion of the role of diversity and inclusion in academic environment, and the names, addresses (postal and e-mail), and telephone numbers of 3-6 references (these will be required of all candidates selected for on campus interview). Applicants who are selected for on campus interviews must be able to show proof that they will be eligible and qualified to work in the US by the time of hiring.
The University of Texas at San Antonio is an Affirmative Action/Equal Opportunity Employer. Women, minorities, veterans, and individuals with disabilities are encouraged to apply.
RE: Faculty Search
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