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Researchers Teach Soy Tricks To Appeal To Indian Tastes : Shots - Health News Salty snacks are increasingly the bane of health experts looking to head off childhood obesity in the U.S. But in India, researchers are trying to get more salty snacks into kids' hands – snacks that from the outside look a lot like Cheetos.
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Health Inc.
Policy-ish
Researchers Teach Soy Tricks To Appeal To Indian Tastes
October 22, 20108:00 AM ET
Eliza Barclay
Scientists hope a soy-based, curry-flavored snack food will appeal to malnourished Indian kids. Courtesy of Dr. Soo-Yeun Lee hide caption
Courtesy of Dr. Soo-Yeun Lee
Salty snacks of the junk food variety are increasingly the bane of health experts looking to head off the stubborn childhood obesity epidemic in the U.S.
But in India, researchers are looking at ways to get salty snack foods into kids' hands – snacks that are secretly healthy, vegetarian, and protein-rich.
Why? Because In 2009, the United Nations World Food Program found that a fourth of the world's hungry, or 230 million people, live there. And some 43 percent of the country's children under five are underweight.
Think of it as an Indian version of a cheese puff-- a crunchy, fluffy, tube-shaped snack that delivers a big hit of salt -- with a couple of twists.
While Cheetos are made mainly with cornmeal, the Indian snack has a critical ingredient: protein-rich soy, something not often found in Indian foods. It's also covered with spices like cumin and red chili pepper to appeal to local tastes, instead of electric orange cheese-flavored power.
The project is the brainchild of Soo-Yeun Lee, a professor of sensory science at the University of Illinois at Urbana-Champaign. She works a lot with soy, making functional foods - foods fortified with particular nutrients -- more palatable. After traveling to India, she was shocked by the number of hungry kids there, and thought she might be able to lend a hand.
Indians, an estimated 40 percent of whom are vegetarian, are especially hurting for protein – it makes up only nine percent of the average per capita caloric intake. (The U.S. Institute of Medicine recommends that 10 to 35 percent of daily caloric intake come from protein.) But the country is also the fifth largest soybean producer in the world, most of which is exported. Dr. Lee sees this as an opportunity.
"Soy is a high-quality protein and it's cheap, so they have all the right reasons to use it," says Dr. Lee. The problem was, she found, the flavor.
Dr. Lee teamed up with the Akshaya Patra Foundation in Bangalore to test out the product she developed in her lab.
It got high marks from a panel of 67 taste-testers in Bangalore, as well as from a panel of 72 Indians living in Illinois. She published results in the Journal of Food Science and the Journal of Sensory Science this summer.
Akshaya Patra plans to distribute the product with its school meal plan and sell the curry snack in local stores.
Public Health & Prevention


Last week, I was joined by 18 senior and junior enthusiastic delegates from across the professions for an update on digital marketing and social media in the professions at a PM Forum training session http://www.pmforum.co.uk/training/ . Thanks to Dentons in the City for hosting.
Whilst most delegates acknowledged use – at least personally – of major platforms such as Facebook, LinkedIn, Twitter and Yammer (internal Twitter) some of the others mentioned included: Instagram, Mootis (social legal community), Pinterest, Flipboard (personal magazine), Snapchat (mobile conversation), Vine (video), Jaba (proximity networking), Pulse (analytics) and Passle (content marketing).
There were also mentions for inward profile LinkedIn integration within InterAction (CRM) through PeopleLinx during a discussion about integrating social media into CRM generally and the subsequent Data Protection challenges.
There was also discussion of the content publishing and content marketing facilities within the collaboration platform HighQ. The tools (Google Adwords), value and some examples of remarketing generated some interest.
As always, this was a major topic of discussion as it remains a key challenge. There were some useful insights into the differences in the way that marketers use social media (particularly in a central, co-ordinating role) and how lawyers, accountants and surveyors use social media in their daily roles to connect with and communicate with clients and contacts.
The need for internal segmentation, champions, sponsors, training, support (from drop in centres to desk side hand-holding), encouragement, creation of watch lists to monitor key clients and personalising the benefits were among the helpful tips shared.
As the preferred tool for the professions, there was considerable discussion of LinkedIn. There were some interesting examples of it being used for tender and bid research. A major concern related to the creation and maintenance of groups (online communities) – and the limitations of the group administrator functionality.
On a more pragmatic level, the use of checklists and templates was mentioned. And seeking help from LION (LinkedIn Open Networker) individuals. The new Elevate platform to help with content distribution and personal analytics generated some interest from the more advanced users.
The need for an integrated campaign where social media is included in all aspects of the planning, research, profile raising, lead generation, conversion and relationship management was recognised. Some commented that during the early adoption and transitional phases, there was a need to distinguish between "learning use" of social media compared to "business use".
Establishing realistic objectives at the outset could only really be achieved within the context of a campaign and this, in turn, was critical for meaningful return on investment (or engagement) calculations if we are to extend beyond process measurement models and "vanity reporting" of followers and interactions.


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\subsection{Atrous}

\subsection{Special cases}
\subsubsection{Ground truth analysis}

\subsubsection{Non-Manhattan}

\subsection{Qualitative Results}
\subsubsection{SUN360 dataset}

\subsubsection{Stanford 2D-3D dataset}
\section{Corners for Layout}
Here we describe our end-to-end approach for recovering the layout, \textit{i.e.} the main structure of the room, from single $360^\circ$ images. After introducing some details about the target data, we describe the proposed network architecture and how we directly transform the output into the 3D layout. The network architecture is adapted for Standard Convolutions and for our proposed Equirectangular Convolutions{} implementation, the latest being explained in Section~\ref{sectionequiconv}.

\begin{figure}
\centering
\includegraphics[width=0.98\linewidth]{paper/sections/images/architecture3.pdf}
\caption{\textbf{CFL architecture}. Our network is built upon ResNet-50, adding a single decoder that jointly predicts edge and corner maps. Here we propose two network variations: on top, the network applies StdConvs on the equirectangular panorama, whereas the one on the bottom applies EquiConvs{} directly on the sphere.}
\label{fig:Archit}
\vspace{-3mm}
\end{figure}

\subsection{Ground truth}
The ground truth (GT) for every panorama consists of two maps, $m$, one represents the room edges ($m=e$), \ie intersections between walls, ceiling and floor, and the other encodes the corner locations ($m=c$). Both maps are defined as $\mathcal{Y}^m=\{y_1^m, \hdots, y_i^m, \hdots\}$, with pixel values $y_i^m\in\left\{0,1\right\}$. $y_i^m$ has a value of $1$ if it belongs to an edge or a corner, and $0$ otherwise. We do line thickening and Gaussian blur for easier convergence during training since it makes the loss progression continuous instead of binary. The loss is gradually reduced as the prediction approaches the target.

Notice here that our target is considerably simpler than others that usually divide the ground truth into different classes. This contributes to the small computational footprint of our proposal. For example, \cite{Mallya:2015,zhao2017physics} use independent feature maps for background, wall-floor, wall-wall and wall-ceiling edges. A full image segmentation into left, front and right wall, ceiling and floor categories is performed in \cite{dasgupta2016delay}. In \cite{RoomNet}, they represent a total of 48 different corner types by a 2D Gaussian heatmap centered at the true keypoint location. Here, instead, we only use two probability maps, one for edges and another one for corners -- see \textit{outputs} in the Figure~\ref{fig:Archit}.

\subsection{Network architecture}
The proposed FCN follows the encoder-decoder structure and builds upon ResNet-50 \cite{he2016deep}. We replace the final fully-connected layer with a decoder that jointly predicts layout edges and corners locations already refined. We illustrate the proposed architecture in Figure~\ref{fig:Archit}.

\textbf{Encoder.}
Most of deep-learning approaches facing layout recovery problem have made use of the VGG16 \cite{simonyan2014very} as encoder \cite{Mallya:2015,dasgupta2016delay,RoomNet}. Instead, \cite{zhao2017physics} builds their model over ResNet-101 \cite{he2016deep} outperforming the state of the art. Here, we use ResNet-50 \cite{he2016deep}, pre-trained on the ImageNet dataset \cite{russakovsky2015imagenet}, which leads to a faster convergence due to the general low-level features learned from ImageNet. Residual networks allow us to increase the depth without increasing the number of parameters with respect to their plain counterparts. This leads, in ResNet-50, to capture a receptive field of $483 \times 483$ pixels, enough for our input resolution of $256 \times 128$ pixels.

\textbf{Decoder.}
Most of the recent work \cite{Mallya:2015,zou2018layoutnet,ren2016coarse} builds two output branches for multi-task learning, which increases the computation time and the network parameters. We instead propose a unique branch with two output channels, corners and edge maps, which helps to reinforce the quality of both map types.
In the decoder, we combine two different ideas. First, skip-connections \cite{ronneberger2015u} from the encoder to the decoder. Specifically, we concatenate ``up-convolved'' features with their corresponding features from the contracting part. Second, we do preliminary predictions at lower resolutions which are also concatenated and fed back to the network following the spirit of \cite{dosovitskiy2015flownet}, ensuring early stages of internal features aim for the task.
We use ReLU as non-linear function except for the prediction layers, where we use Sigmoid.

We propose two variations of the network architecture for two different convolution operations (Figure~\ref{fig:Archit}).
 The first one, \textit{CFL StdConvs}, convolves the feature maps with Standard Convolutions and use up-convolutions to decode the output. The second one, \textit{CFL EquiConvs}, uses Equirectangular Convolutions{} both in the encoder and the decoder, using unpooling to upsample the output.
 Equirectangular Convolutions{} are deformable convolutions that adapt their size and shape depending on the position in the equirectangular image, for which we propose a new implementation in Section~\ref{sectionequiconv}.
 %

\begin{figure}
\centering
\includegraphics[width=1\linewidth]{paper/sections/images/layout4.pdf}
\caption{\textbf{Layout from corner predictions}. From the corner probability map, the coordinates with maximum values are directly selected to generate the layout.}
\label{fig:Layout}
\vspace{-3mm}
\end{figure}

\subsection{Loss functions}
Edge and corner maps are learned through a pixel-wise sigmoid cross-entropy loss function. Since we know a priori that the natural distribution of pixels in these maps is extremely unbalanced ($\thicksim95\%$ have a value of $0$), we introduce weighting factors to make the training stable. Defining as $1$ and $0$ the positive and negative labels, the weighting factors are defined as $w_t = \frac{N}{N_t}$, being $N$ the total number of pixels and $N_t$ the amount of pixels of class $t$ per sample. The per-pixel per-map loss $\mathcal{L}_{i}^m$ is as follows:
\begin{eqnarray}
\mathcal{L}_{i}^m  &=&
 w_1 \big( y_i^m\big(-\log(\hat{y}_i^m)\big)\big) + \nonumber \\
 & + & w_0 \big( (1-y_i^m) \big(-\log(1-\hat{y}_i^m)\big) \big),
\end{eqnarray}
\noindent where $y_i^m$ is the GT for pixel $i$ in the map $m$ and $\hat{y}_i^m$ is the network output for pixel $i$ and map $m$. We minimize this loss at $4$ different resolutions $k=\{1, \hdots, 4\}$, specifically in the network output ($k=4$) and 3 intermediate layers ($k=\{1, \hdots, 3\}$). The total loss is then the sum over all pixels, the $4$ resolutions and both the edge and corner maps
\begin{eqnarray}
\mathcal{L}  &=& \sum_{k=\{1, \hdots, 4\}} \sum_{m=\{e,c\}} \sum_{i} \mathcal{L}_{i}^m\left[k\right].
\end{eqnarray}

\subsection{3D Layout}

Aiming to a fast end-to-end model, CFL avoids post-processing and strong scene assumptions and just follow a natural transformation from corners coordinates to 2D and 3D layout. The 2D corners coordinates are the maximum activations in the probability map. Assuming that the corner set is consistent, they are directly joined, from left to right, in the unit sphere space and re-projected to the equirectangular image plane. From this 2D layout, we infer the 3D layout by only assuming ceiling-floor parallelism, leaving the wall structure unconstrained --\textit{i.e.}, we do not force the usual Manhattan perpendicularity between walls. Corners are projected to floor and ceiling planes given a unitary camera height (trivial as results are up to scale). See Figure~\ref{fig:Layout}.

\begin{figure}
\centering
    \includegraphics[width=0.77\linewidth]{paper/sections/images/SPHERICAL_FILTER_v5.pdf}
     %
\caption{\label{fig:equiconv} \textbf{Spherical parametrization of EquiConvs{}}. The spherical kernel, defined by its  angular size ({\color{cAlphas}$\alpha_w \times \alpha_h$}) and resolution ({\color{cRes}$r_w \times r_h$}), is convolved around the sphere with angles {\color{cPhi}$\phi$} and {\color{cTheta}$\theta$}.}
\vspace{-3mm}
\end{figure}

\noindent\textbf{Limitations of CFL:}
We directly join corners from left to right, meaning that our end-to-end model would not work if any wall is occluded because of the convexity of the scene. In those particular cases, the joining process should follow a different order. \cite{fernandez2018layouts} proposes a geometry-based post-processing that could alleviate this problem, but its cost is high and it needs the Manhattan World assumption. The addition of this post-processing into our work, in any case, could be done similarly to \cite{fernandez2018panoroom}.

\section{Experiments}

\begin{table*}[t!]
\scriptsize
\centering
\begin{tabular}{@{\extracolsep{4pt}}ccc ccccc c ccccc}
\multicolumn{3}{c}{}&\multicolumn{5}{c}{Edges}&&\multicolumn{5}{c}{Corners}\\
\cline{4-8} \cline{10-14}
\textbf{Conv. Type} & \textbf{IntPred}& \textbf{Edges}& $IoU$ & $Acc$ & $P$ & $R$ & $F_1$ && $IoU$ & $Acc$ & $P$ & $R$ & $F_1$\\
\hline
 & & & $:1$ & $:1$ &$:1$&$:1$&$:1$ && $:1$ & $:1$ &$:1$&$:1$&$:1$\\
StdConvs & {\color{cRes}-} & {\color{cRes}-} & - & - & - & - & - && $0.433$ & $0.971$ & $0.802$ & $0.484$ & $0.600$\\

StdConvs & {\color{cRes}-} & {\color{cTheta}\checkmark} &$0.564$ & $0.926$ & $0.751$ & $0.681$ & $0.713$ && $0.453$ & $0.973$ & $0.850$ & $0.493$ & $0.621$ \\

StdConvs & {\color{cTheta}\checkmark} & {\color{cTheta}\checkmark} &$\textbf{0.588}$ & $\textbf{0.933}$ & $\textbf{0.782}$ & $\textbf{0.691}$ & $\textbf{0.733}$ & & $\textbf{0.465}$ & $\textbf{0.974}$ & $\textbf{0.872}$ & $\textbf{0.498}$ & $\textbf{0.632}$

\Bstrut\\
\hline
EquiConvs & {\color{cRes}-} & {\color{cRes}-} & - & - & - & - & - && $0.437$ & $0.970$ & $0.784$ & $0.496$ & $0.604$ \Tstrut\\

EquiConvs & {\color{cRes}-} & {\color{cTheta}\checkmark} & $0.548$ & $0.920$ & $0.718$ & $\textbf{0.686}$ & $0.700$ && $0.444$ & $0.972$ & $0.822$ & $\textbf{0.491}$ & $0.611$\\

EquiConvs & {\color{cTheta}\checkmark} & {\color{cTheta}\checkmark} & $\textbf{0.575}$ & $\textbf{0.931}$ & $\textbf{0.789}$ & $0.667$ & $\textbf{0.722}$ && $\textbf{0.460}$ & $\textbf{0.974}$ & $\textbf{0.887}$ & $0.488$ & $\textbf{0.627}$\\

\cline{4-8} \cline{10-14}
\multicolumn{3}{c}{}&\multicolumn{5}{c}{\textit{bigger is better}{}}&&\multicolumn{5}{c}{\textit{bigger is better}{}}\\
\end{tabular}
\caption{\label{tab:ablationStd}\textbf{Ablation study on SUN360 dataset.} We show results for both Standard Convolutions (StdConvs) and our proposed Equirectangular Convolutions{} (EquiConvs{}) with some modifications: Using or not intermediate predictions (IntPred) in the decoder and edge map predictions (Edges).}
\vspace{-5mm}
\end{table*}

We present a set of experiments to evaluate CFL using both Standard Convolutions (StdConvs) and the proposed Equirectangular Convolutions{} (EquiConvs{}). We do not only analyze how well it predicts edge and corner maps, but also the impact of each algorithmic component through ablation studies. We report the performance of our proposal in two different datasets, and show qualitative 2D and 3D models of different indoor scenes.

\subsection{Datasets}
We use two public datasets that comprise several indoor scenes, SUN360 \cite{Xiao2012} and Stanford (2D-3D-S) \cite{2017arXiv170201105A} in equirectangular projection (360$^\circ$). The former is used for ablation studies, and both are used for comparison against several state-of-the-art baselines.

\noindent\textbf{SUN360 \cite{Xiao2012}}: We use $\thicksim$500 bedroom and livingroom panoramas from this dataset labeled by Zhang \textit{et al.} \cite{PanoContext}. %
We use these labels but, since all panoramas were labeled as box-type rooms, we hand-label and substitute 35 panoramas representing more faithfully the actual shapes of the rooms. We split the raw dataset in 85$\%$ training scenes and 15$\%$ test scenes randomly by making sure that there were rooms of more than 4 walls in both partitions.

\noindent\textbf{Stanford 2D-3D-S \cite{2017arXiv170201105A}}: This dataset contains more challenging scenarios like cluttered laboratories or corridors. In \cite{zou2018layoutnet}, they use areas 1, 2, 4, 6 for training, and area 5 for testing. For our experiments we use same partitions and the ground truth provided by them.

\vspace{-1mm}
\subsection{Implementation details}
\vspace{-1mm}
The input to the network is a single panoramic RGB image of resolution $256\times128$.
The outputs are, on the one hand, the room layout edge map and on the other hand, the corner map, both of them at resolution $128\times64$.
A widely used strategy to improve generalization of neural networks is data augmentation. We apply random erasing, horizontal mirroring as well as horizontal rotation from $0^\circ$ to $360^\circ$ of input images during training. The weights are all initialized using ResNet-50 \cite{he2016deep} trained on ImageNet \cite{russakovsky2015imagenet}. For \textit{CFL EquiConvs} we use the same kernel resolutions and field of views as in ResNet-50. This means that for a standard 3$\times$3 kernel applied to a W$\times$H feature map, {\color{cRes}$r$}$=3$ and {\color{cAlphas}$\alpha$}$= ${\color{cRes}$r$}$ \frac{fov}{W}$, where $fov=360^\circ$ for panoramas.
We minimize the cross-entropy loss using Adam \cite{kingma2014adam}, regularized by penalizing the loss with the sum of the L2 of all weights. The initial learning rate is $2.5e^{-4}$ and is exponentially decayed by a rate of $0.995$ every epoch. We apply a dropout rate of $0.3$.%

The network is implemented using TensorFlow \cite{abadi2016tensorflow} and trained and tested in a NVIDIA Titan X. The training time for StdConvs is around $1$ hour and the test time is $0.31$ seconds per image. For EquiConvs{}, training takes $3$ hours and test around $3.32$ seconds per image. %

\subsection{FCN evaluation}

We measure the quality of our predicted probability maps using five standard metrics: intersection over union of predicted corner/edge pixels \textit{IoU}, precision \textit{P}, recall \textit{R}, F1 Score \textit{$F_{1}$} and accuracy \textit{Acc}.
Table~\ref{tab:ablationStd} summarizes our results and allows us to answer the following questions:

\noindent\textbf{What are the effects of different convolutions?}
As one would expect, EquiConvs{}, aware of the distortion model,  learn in a non-distorted generic feature space achieving accurate predictions, like StdConvs on conventional images \cite{RoomNet}. However and counterintuitively, StdConvs, ignoring the distortion model, rely on image patterns that this generates obtaining similar performance -- see Table~\ref{tab:ablationStd}. Distortion understanding, nonetheless, gives the network other advantages. While StdConvs learn strong bias correlation between features and distortion patterns (\textit{e.g.} ceiling line on the top of the image or clutter in the mid-bottom), EquiConvs are invariant to that. For this reason, the performance of EquiConvs{} does not degrade when varying the camera 6DOF pose -- see Section~\ref{RobustSect}. Additionally, EquiConvs allow a more direct use of networks pre-trained on conventional images. Specifically, this translates into a faster convergence, which is desirable as, to date, $360^\circ$ datasets contain far less images than datasets with conventional images. Moreover Tateno \textit{et al.} demonstrate in their recent work \cite{tateno2018distortion} that other tasks like depth prediction, panoramic monocular SLAM, panoramic semantic segmentation and panoramic style transfer can also benefit from this type of convolutions.

\noindent\textbf{How can we refine predictions?} There are some techniques that we can use in order to obtain more accurate and refined predictions. Here, we make pyramid preliminary predictions in the decoder and iteratively refine them, by feeding them back to the network, until the final prediction. Also, although we only use the corner map to recover the layout of the room, we train the network to additionally predict edge maps as an auxiliary task. This is another representation of the same task that ensures that the network learns to exploit the relationship between both outputs, \textit{i.e.}, the network learns how edges intersect between them generating the corners. The improvement is shown in the Table~\ref{tab:ablationStd}.

\begin{table*}[ht!]
\scriptsize
\centering
\begin{tabular}{@{\extracolsep{3pt}}c c ccc c ccc}
\multicolumn{2}{c}{}&\multicolumn{3}{c}{Edges}&&\multicolumn{3}{c}{Corners}\\
\cline{3-6} \cline{7-9}
 &  &  $F_{1}$ & $Acc$ & $IoU$ && $F_{1}$ & $Acc$ & $IoU$ \\
\hline
 & & $\%$ & $\%$ & $\%$ && $\%$ & $\%$ & $\%$ \\
\multirow{2}{*}{\color{trasla}\textbf{\small Translation} (-0.3h:+0.3h)} & StdConvs & $63.00 \pm 5.85$ & $89.70 \pm 1.89$ & $46.25 \pm 6.20$ && $43.97 \pm 5.70$ & $97.79 \pm 0.25$ & $28.35 \pm 4.71$\\
 & EquiConvs & $\textbf{64.25} \pm \textbf{2.36}$ & $\textbf{90.16} \pm \textbf{0.8}$ & $\textbf{47.37} \pm \textbf{2.57}$ && $\textbf{44.75} \pm \textbf{5.34}$ & $\textbf{97.88} \pm \textbf{0.20}$ & $\textbf{28.97} \pm \textbf{4.34}$\\
\noalign{\smallskip}
\hline
\noalign{\smallskip}
 %
\multirow{2}{*}{\color{rota}\textbf{\small Rotation} ($-30^\circ$:$+30^\circ$)} & StdConvs & $54.99 \pm 11.8$ & $86.83 \pm 4.7$  & $38.88 \pm 11.7$ && $33.47 \pm 12.9$ & $97.38 \pm 0.6$  & $20.84 \pm 9.7$\\
 & EquiConvs & $\textbf{59.51} \pm \textbf{9.2}$ & $\textbf{88.64} \pm \textbf{3.5}$ & $\textbf{42.97} \pm \textbf{9.4}$ && $\textbf{35.82} \pm \textbf{12.4}$ & $\textbf{97.66} \pm \textbf{0.4}$ & $\textbf{22.53} \pm \textbf{9.5}$\\
\end{tabular}
\caption{\label{tab:Robustness}\textbf{Robustness analysis}. Values represent the mean value (\textit{bigger is better}) $\pm$ standard deviation (\textit{smaller is better}). We apply two types of transformations to the panoramas: {\color{trasla}\textbf{translations}} in $y$ dependant on the room height, $h$, and {\color{rota}\textbf{rotations}} in $x$. We do not use these images for training but just for testing in order to show the generalization capabilities of EquiConvs.}
\vspace{-5mm}
\end{table*}

\begin{figure}
\begin{center}
     \includegraphics[width=1\linewidth]{paper/sections/images/occl.pdf}
\end{center}
\vspace{-2mm}
\caption{\textbf{Augmenting the data with virtual occlusions.} Left: Image with erased pixels. Right: Input panorama and predictions without and with pixel erasing. Notice the improvement by random erasing.}
\label{fig:occlusions}
\vspace{-3mm}
\end{figure}

\noindent\textbf{How can we deal with occlusions?}
We do Random Erasing Data Augmentation. This operation randomly selects rectangles in the training images and removes its content, generating various levels of virtual occlusion. In this manner we simulate real situations where objects in the scene occlude the corners of the room layout, and force the network to learn context-aware features to overcome this challenging situation. Figure~\ref{fig:occlusions} illustrates this strategy with an example.

\begin{figure}
\centering
\includegraphics[width=1\linewidth]{paper/sections/images/nonmanh2.pdf}
\caption{\label{fig:nonMan} \textbf{Relaxation of assumptions.} The figure shows two CFL predictions of non-Manhattan/not box-like rooms.}
\vspace{-5mm}
\end{figure}

\noindent\textbf{Is it possible to relax the scene assumptions while keeping a good performance?} Our end-to-end approach overcomes the Manhattan assumption as well as the box-type simplification (four-walls rooms).
On the one hand, although we label some panoramas more accurately to their actual shape, we still have a largely unbalanced dataset. We address this problem by choosing a batch size of $16$ and forcing it to always include one non-box sample. This favors the learning of more complex rooms despite having few examples.
On the other hand, while recent works \cite{zou2018layoutnet,fernandez2018layouts,PanoContext} use pre-computed vanishing points and posterior optimizations, here we directly obtain the corner coordinates from the FCN output without applying geometric constraints. In Figure~\ref{fig:nonMan} we show two examples where CFL predicts more than $4$ walls. Notice also the non-Manhattan ceiling in the left image.

\subsection{Robustness analysis}
\label{RobustSect}
With the motivation of exploiting the potential of EquiConvs, we test our model with previously unseen images where the camera viewpoint is different from that in the training set. The distortion in equirectangular projection is location dependent, specifically, it depends on the polar angle {\color{cTheta}$\theta$}. Since EquiConvs are invariant to this distortion, it is interesting to see how modifications in the camera extrinsic parameters ({\color{trasla}\textbf{translation}} and {\color{rota}\textbf{rotation}}) affect the model performance using EquiConvs against StdConvs.  When we generate translations over the vertical axis and rotations, the shape of the layout is modified by the distortion, losing its characteristic pattern (which StdConvs use in its favor).%

Since standard datasets have a strong bias when referring to camera pose and rotation, we synthetically render these transformations along our test set. The {\color{rota}\textbf{rotation}} is trivial as we work on the spherical domain. As the complete 3D dense model of the rooms is not available, the {\color{trasla}\textbf{translation}} simulation is performed by using the existing information, ignoring occlusions produced by viewpoint changes. Nevertheless, as we do not work with wide translations the effect is minimal and images are realistic enough to prove the point we want to highlight (see Figure \ref{fig:rot&trans}). %

\begin{figure}[t!]
\begin{center}
{\includegraphics[width=0.46\linewidth,cfbox=trasla 2pt 0pt 0pt]{paper/sections/images/axoqUp.png}}\hspace*{0.005\linewidth}
{\includegraphics[width=0.46\linewidth,cfbox=rota 2pt 0pt 0pt]{paper/sections/images/pano_axoqpxdavwohvd-20.jpg}}
\caption{\label{fig:rot&trans} \textbf{Synthetic images for robustness analysis.} Here we show two examples of panoramas generated with upward {\color{trasla}\textbf{translation}} in $y$ and {\color{rota}\textbf{rotation}} in $x$ respectively.}
\end{center}
\vspace{-9mm}
\end{figure}

For both experiments, we uniformly sample from a minimum to a maximum transformation and calculate the mean and standard deviation for all the metrics. What we see in Table~\ref{tab:Robustness} is that we obtain higher mean values while smaller standard deviation by using EquiConvs. This means that this EquiConvs make the model more robust and generalizable to real life situations, not covered in the datasets, \eg panoramas taken by hand, drones or small robots. This effect is highlighted especially in the evaluation of the edges since it is their appearance that is highly modified by these changes of the camera.

\subsection{3D Layout comparison}%

We evaluate our layout predictions using three standard metrics, 3D intersection over union $3DIoU$, corner error $CE$ and pixel error $PE$, and compare ourselves against four approaches from the state of the art \cite{PanoContext,zou2018layoutnet,fernandez2018layouts, yang2018dula}. Pano2CAD \cite{Pano2cad} has no source code available nor evaluation of layouts, making direct comparison difficult.
The pixel error metric given by \cite{zou2018layoutnet} only distinguishes between ceiling, floor and walls, $PE^{SS}$. Instead our proposed segmented mask distinguish between ceiling, floor and each wall separately, $PE^{CS}$, which is more informative since it also has into account errors in wall-wall boundaries.
For all experiments, only SUN360 dataset is used for training.
Table~\ref{tab:Layoutresults} shows the performance of our proposal testing on both datasets, SUN360 and Stanford 2D-3D. Results are averaged across all images. It can be seen that our approach outperforms the state of the art clearly, in all the metrics.

It is worth mentioning that our approach, not only obtains better accuracy but also it recovers shapes more faithful to the real ones, since it can handle non box-type room designs with few training examples.
In Table~\ref{table:CTime} we show that, apart from achieving better localization of layout boundaries and corners, our end-to-end approach is much faster. Our full method with EquiConvs{} takes $3.47$ seconds to process one room and with StdConvs just $0.46$ seconds, which is a major advantage considering the aforementioned applications of layout recovery need to be real-time (robot navigation, AR/VR).%

\begin{table}
\footnotesize{}%
\centering
\begin{tabular}{@{\extracolsep{0pt}}ccc ccc}
\multicolumn{6}{c}{}\\
\textbf{Test} & \textbf{Method}& $3DIoU$ & $CE$ & $PE^{SS}$ & $PE^{CS}$\\
\hline
 & & $\%$ & $\%$ & $\%$ & $\%$\\
\multirow{5}{*}{SUN360} & PanoContext \cite{PanoContext}&  $67.22$ & $1.60$  & $4.55$ & $10.34$ \\
&Fernandez \cite{fernandez2018layouts} & - & - & - & $7.26$\\
&LayoutNet \cite{zou2018layoutnet} & $74.48$ & $1.06$ & $3.34$ & -\\
&DuLa-Net \cite{yang2018dula} & $77.42$ & - & - & -\\
& CFL StdConvs  &  $\textbf{78.79}$ & $0.79$ & $\textbf{2.49}$ & $\textbf{3.33}$   \\
& CFL EquiConvs   &  $77.63$ & $\textbf{0.78}$ & $2.64$ & $3.35$   \\
\noalign{\smallskip}
\hline
\noalign{\smallskip}
\multirow{3}{*}{Std.2D3D} & Fernandez \cite{fernandez2018layouts} & - & - & - & $12.1$\\
& CFL StdConvs  &  $65.13$ & $\textbf{1.44}$ & $\textbf{4.75}$ & $\textbf{6.05}$ \\
& CFL EquiConvs   &  $\textbf{65.23}$ & $1.64$ & $5.52$ & $7.11$\\
\cline{4-6}
\multicolumn{2}{c}{}&\multicolumn{1}{c}{}&\multicolumn{3}{c}{\textit{smaller is better}{}}\\
\end{tabular}
\caption{\label{tab:Layoutresults} \textbf{Layout results} on both datasets, training on SUN360 data. \textit{SS}: Simple Segmentation (3 categories): ceiling, floor and walls \cite{zou2018layoutnet}. \textit{CS}: Complete Segmentation: ceiling, floor, wall$_{1}$,..., wall$_{n}$ \cite{fernandez2018layouts}. Observe how our method outperforms all the baselines in all the metrics.}
\vspace{-2mm}
\end{table}

\begin{table}
\small
\begin{center}
\begin{tabular}{cc}
\hline\noalign{\smallskip}
\textbf{Method} &\textbf{Computation Time (s)} \\
\noalign{\smallskip}
\hline
\noalign{\smallskip}
PanoContext \cite{PanoContext} & $>300$\\
LayoutNet \cite{zou2018layoutnet} &  $44.73$ \\
DuLa-Net \cite{yang2018dula} &  $13.43$ \\
CFL EquiConvs  &  $3.47$\\
CFL StdConvs  &  $\textbf{0.46}$\\
\hline
\end{tabular}

\caption{\textbf{Average computing time per image.} Every approach is evaluated using NVIDIA Titan X and Intel Xeon 3.5 GHz (6 cores) except DuLa-Net, evaluated using NVIDIA 1080ti GPU. Our end-to-end method is more than 100 times faster than other methods.}
\label{table:CTime}
\end{center}
\vspace{-7mm}
\end{table}

\begin{figure}
\begin{center}
     \includegraphics[width=1\linewidth]{paper/sections/images/finalLayouts3.pdf}
\end{center}
\vspace{-2mm}
\caption{Layout predictions ({\color{magenta}light magenta}) and ground truth ({\color{dmagenta}dark magenta}) for \textbf{complex room geometries}.}
\label{fig:experi2}
\vspace{-5mm}
\end{figure}

\section{Conclusions}

In this work we present CFL{}, the first end-to-end algorithm for layout recovery in $360^\circ$ images. %
Our experimental results demonstrate that our predicted layouts are clearly more accurate than the state of the art. Additionally, the removal of extra pre- and post-processing stages makes our method much faster than other works. Finally, being entirely data-driven removes the geometric assumptions that are commonly used in the state of the art and limits their usability in complex geometries. We present two different variants of CFL{}. The first one, implemented using Standard Convolutions, reduces the computation in 100 times and it is very suitable for images taken with a tripod. The second one uses our proposed implementation of Equirectangular Convolutions{} that adapt their shape to the equirectangular projection of the spherical image. This proves to be more robust to translations and rotations of the camera making it ideal for panoramas taken by a hand-held camera.

\section{Related Work}

The layout of a room provides a strong prior for other visual tasks like depth recovery \cite{Eigen2015}, realistic insertions of virtual objects into indoor images \cite{karsch2011rendering}, indoor object recognition \cite{bao2011toward,song2016deep} or human pose estimation \cite{fouhey2014people}. A large variety of methods have been developed for this purpose using multiple input images \cite{tsai2011real,flint2011manhattan} or depth sensors \cite{zhang2013estimating}, which deliver high-quality reconstruction results. For the common case when a single RGB image is available, the problem becomes considerably more challenging and researchers need very often to rely on strong assumptions. %

The seminal approaches to layout prediction from a single view were \cite{Delage2006,Lee2009}, followed by \cite{Hedau2009,schwing2013box}. They basically model the layout of the room with a vanishing-point-aligned 3D box, being hence constrained to this particular room geometry and unable to generalize to others appearing frequently in real applications.
Most recent approaches exploit CNNs and their excellent performance in a wide range of applications such as image classification, segmentation and detection. \cite{Mallya:2015,ren2016coarse,Mallya2,zhao2017physics}, for example, focus on predicting the informative edges separating the geometric classes (walls, floor and ceiling). Alternatively, Dasgupta \etal \cite{dasgupta2016delay} proposed a FCN to predict labels for each of the surfaces of the room. All these methods require extra computation added to the forward propagation of the network to retrieve the actual layout. In \cite{RoomNet}, for example, an end-to-end network predicts the layout corners in a perspective image, but after that it has to infer the room type within a limited set of manually chosen configurations. %

While layout recovery from conventional images has progressed rapidly with both geometry and deep learning, the works that address these challenges using omnidirectional images are still very few.
Panoramic cameras have the potential to improve the performance of the task: their 360$^\circ$ field of view captures the entire viewing sphere surrounding its optical center, allowing to acquire the whole room at once and hence predicting layouts with more visual information. PanoContext \cite{PanoContext} was the first work that extended the frameworks designed for perspective images to panoramas. It recovers both the layout, which is also assumed as a simple 3D box, and bounding boxes for the most salient objects inside the room. Pano2CAD \cite{Pano2cad} extends the method to non-cuboid rooms, but it is limited by its dependence on the output of object detectors. Motivated by the need of addressing complex room geometries, \cite{fernandez2018layouts} generates layout hypotheses by geometric reasoning from a small set of structural corners obtained from the combination of geometry and deep learning. The most recent works along this line are LayoutNet \cite{zou2018layoutnet}, that trains a FCN from panoramas and vanishing lines, generating the layout models from edge and corner maps, and DuLa-Net \cite{yang2018dula}, that predicts Manhattan-world layouts leveraging a perspective ceiling-view of the room. All of these approaches require pre- or post-processing steps like line and vanishing point extraction or room model fitting, that increase their cost.

In addition to all the challenges mentioned above, we also notice that there is an incrongruence between panoramic images and conventional CNNs. The space-varying distortions caused by the equirectangular representation makes the translational weight sharing ineffective.
Very recently, Cohen \etal \cite{cohen2018spherical} did a relevant theoretical contribution by studying convolutions on the sphere using spectral analysis. However, it is not clearly demonstrated whether Spherical CNNs can reach the same accuracy and efficiency on equirectangular images. Our EquiConvs have more in common with the idea of \cite{tateno2018distortion}, that proposes distortion-aware convolutional filters to train their model using conventional perspective images and then use it to regress depth from panoramic images. We propose a novel parameterization and implementation of the deformable convolutions \cite{dai2017deformable} by following the idea of adapting the receptive field of the convolutional kernels by deforming their shape according to the distortion of the equirectangular projection.

\section{Introduction}

Recovering the 3D layout of an indoor scene from a single view has attracted the attention of computer vision and graphics researchers in the last decade.
The idea is going beyond pure geometrical reconstructions and provide higher-level contextual information about the scene, even in the presence of clutter.
Layout estimation is a key technology in several emerging application markets, such as augmented and virtual reality and robot navigation. But also for more traditional ones, like real estate \cite{Liu_2015_CVPR}.%

Layout estimation, however, is not a trivial task and there are several major problems that still remain unsolved. For example, most existing methods are based on strong assumptions on the geometry (\eg Manhattan scenes) or the over-simplification of the room types (\eg box-shaped layouts), often underfitting the richness of real indoor spaces. The limited field of view of conventional cameras leads to ambiguities, which could be solved by considering a wider context. For this reason it is advantageous to use wide fields of view, like 360$^\circ$ panoramas. In these cases, however, the methods for conventional cameras are not suitable due to the image distortions and new ones have to be developed.

In the last years, the main improvements in layout recovery from panoramas have come from the application of deep learning. The high-level features learned by deep networks have proven to be as useful for this problem as for many others. Nevertheless, these techniques entail other problems such as the lack of data or overfitting. State-of-the-art methods require additional pre- and/or post-processing. As a consequence they are very slow, and this is a major drawback considering the aforementioned applications for real-time layout recovery.

\begin{figure}
\centering
\includegraphics[width=1.\linewidth]{paper/sections/images/teaserIm4.pdf}
\caption{\label{fig:teaserIM}\textbf{Corners for Layout{}}: The first end-to-end model from the sphere to the 3D layout.}
\vspace{-4mm}
\end{figure}

In this work, we present Corners for Layout{} (CFL{}) the first end-to-end neural network that recovers the 3D layout from a single $360^\circ$ image (Figure~\ref{fig:teaserIM}).
CFL{} predicts a map of the corners of the room that is directly used to obtain the layout without further processing. %
This makes \textbf{CFL{} more than 100 times faster} than the state of the art, while still \textbf{outperforming the accuracy of current approaches}.
Furthermore, our proposal is not limited by typical scene assumptions, meaning that it can predict complex geometries, such as rooms with more than four walls or non-Manhattan structures.
Additionally, we propose a novel implementation of the convolution for $360^\circ$ images \cite{tateno2018distortion,cohen2018spherical} in the
equirectangular projection. %
We deform \cite{dai2017deformable} the kernel to compensate the distortion and make CFL{} more \textbf{robust to camera rotation and pose variations}.
Hence, it is equivalent to applying directly a convolution operation to the spherical image, which is geometrically more coherent than applying a standard convolution on the equirectangular panorama.
We have extensively evaluated our network in two public datasets with several training configurations, including data augmentation techniques to address occlusions by enforcing the network to learn from the context. %
Our \textbf{code} and labeled \textbf{dataset} can be found here: \href{https://cfernandezlab.github.io/CFL/}{CFL webpage}.

\section{Equirectangular Convolutions{}}
\label{sectionequiconv}

Spherical images are receiving an increasing attention due to the growing number of omnidirectional sensors in drones, robots and autonomous cars.
A na\"ive application of convolutional networks to a equirectangular projection, is not, in principle, a good choice due to the space-varying distortions introduced by such projection.

In this section we present a convolution that we name EquiConv, which is defined in the spherical domain instead of the image domain and it is implicitly invariant to equirectangular representation distortions. The kernel in EquiConvs{} is defined as a spherical surface patch --see Figure~\ref{fig:equiconv}. We parametrize its receptive field by the angles {\color{cAlphas}$\alpha_w$} and {\color{cAlphas}$\alpha_h$}. Thus, we directly define a convolution over the field of view. The kernel is rotated and applied along the sphere and its position is defined by the spherical coordinates ({\color{cPhi}$\phi$} and {\color{cTheta}$\theta$} in the figure) of its center. Unlike standard kernels, that are parameterized by their size $k_w \times k_h$, with EquiConvs{} we define the angular size ({\color{cAlphas}$\alpha_w \times \alpha_h$}) and resolution ({\color{cRes}$r_w \times r_h$}). In practice, we keep the aspect ratio, $\frac{{\color{cAlphas}\alpha_w}}{\color{cRes}r_w}=\frac{{\color{cAlphas}\alpha_h}}{\color{cRes}r_h}$, and we use square kernels, so we will refer the field of view as {\color{cAlphas}$\alpha$} ({\color{cAlphas}$\alpha_w=\alpha_h$}) and the resolution as {\color{cRes}$r$} ({\color{cRes}$r_w=r_h$}) respectively from now on.
\begin{figure}
\centering
\includegraphics[width=1\linewidth]{paper/sections/images/atrouslike.pdf}
\caption{\label{fig:sphericalatrous} \textbf{Effect of changing field of view {\color{cAlphas}$\alpha$} (rad) and resolution {\color{cRes}$r$} in EquiConvs{}}. \nth{1} column shows a narrow field of view {\color{cAlphas}$\alpha=0.2$}. \nth{2} column shows a wider kernel keeping its resolution (atrous-like), {\color{cAlphas}$\alpha=0.5$}. \nth{3} column shows an even larger field of view for the kernel, {\color{cAlphas}$\alpha=0.8$}. Notice how the kernel adapts to the equirectangular distortion. Rows are resolutions {\color{cRes}$r=3$} and {\color{cRes}$r=5$}.}
\vspace{-5mm}
\end{figure}
As we increase the resolution of the kernel, the angular distance between the elements decreases, with the intuitive upper limit of not giving more resolution to the kernel than the image itself. In other words, the kernel is defined in a sphere, being its radius less or equal to the image sphere radius.
EquiConvs can also be seen as a general model for spherical Atrous Convolutions \cite{chen2018deeplab,chen2017rethinking} where the kernel size is what we call resolution, and the rate is the field of view of the kernel divided by the resolution. An example of the differences of EquiConvs{} by modifiying {\color{cAlphas}$\alpha$} and {\color{cRes}$r$} can be seen in Figure~\ref{fig:sphericalatrous}.

\subsection{EquiConvs{} Details}

In \cite{dai2017deformable}, they introduce deformable convolutions by learning additional offsets from the preceding feature maps. Offsets are added to the regular kernel locations in the Standard Convolution enabling free form deformation of the kernel.

\begin{figure}
\begin{center}
\includegraphics[width=\linewidth]{paper/sections/images/equiconv_deformable.pdf}
\caption{\label{fig:kerneloffsets} \textbf{Effect of offsets on a $3\times3$ kernel}. Left: Regular kernel in Standard Convolution. Center: Deformable kernel in \cite{dai2017deformable}. Right: Spherical surface patch in EquiConvs{}.}
\end{center}
\vspace{-6mm}
\end{figure}

Inspired by this work, we deform the shape of the kernels according to the geometrical priors of the equirectangular image projection. To do that, we generate offsets that are not learned but fixed given the spherical distortion model and constant over the same horizontal locations. Here, we describe how to obtain the distorted pixel locations from the original ones.

Let us define $(u_{0,0},v_{0,0})$ as the pixel location on the equirectangular image where we apply the convolution operation (\emph{i.e.} the image coordinate where the center of the kernel is located). First, we define the coordinates for every element in the kernel and afterwards we rotate them to the point of the sphere where the kernel is being applied. We define each point of the kernel as
\begin{equation}
    \hat{p}_{ij}=
    \begin{bmatrix}
        \hat{x}_{ij}\\
        \hat{y}_{ij}\\
        \hat{z}_{ij}
    \end{bmatrix}=\begin{bmatrix}
        i\\
        j\\
        d
    \end{bmatrix},
\end{equation}
where $i$ and $j$ are integers in the range $[-\frac{{\color{cRes}r}-1}{2},\frac{{\color{cRes}r}-1}{2}]$ and  $d$ is the distance from the center of the sphere to the kernel grid. In order to cover the field of view ${\color{cAlphas}\alpha}$,
\begin{equation}
d = \frac{{\color{cRes}r}}{2\tan(\frac{{\color{cAlphas}\alpha}}{2})}.
\end{equation}
We project each point into the sphere surface by normalizing the vectors, and rotate them to align the kernel center to the point where the kernel is applied.
\begin{equation}
    p_{ij} = \begin{bmatrix}
        {x}_{ij}\\
        {y}_{ij}\\
        {z}_{ij}
    \end{bmatrix}=R_y({\color{cPhi}\phi_{0,0}})R_x({\color{cTheta}\theta_{0,0}})\frac{\hat{p}_{ij}}{|\hat{p}_{ij}|},
\end{equation}
where $R_a(\beta)$ stands for a rotation matrix of an angle $\beta$ around the $a$ axis. ${\color{cPhi}\phi_{0,0}}$ and ${\color{cTheta}\theta_{0,0}}$ are the spherical angles of the center of the kernel --see Figure~\ref{fig:equiconv}, and are defined as
\begin{equation}
{\color{cPhi}\phi_{0,0}} = (u_{0,0}-\dfrac{W}{2})\dfrac{2\pi}{W} \quad ;\quad  {\color{cTheta}\theta_{0,0}}  = -(v_{0,0}-\dfrac{H}{2})\dfrac{\pi}{H},
\end{equation}
where $W$ and $H$ are, respectively, the width and height of the equirectangular image in pixels. Finally, the rest of elements are back-projected to the equirectangular image domain.
First, we convert the unit sphere coordinates to latitude and longitude angles:
\begin{equation}
\phi_{ij} =  \arctan{(\dfrac{x_{ij}}{z_{ij}})} \quad ;\quad \theta_{ij}  = \arcsin{(y_{ij})}.
\end{equation}
And then, to the original 2D equirectangular image domain:
\begin{equation}
u_{ij}  = (\dfrac{\phi_{ij} }{2\pi} + \dfrac{1}{2})W
\quad ;\quad
v_{ij}  = (-\dfrac{\theta_{ij} }{\pi} + \dfrac{1}{2})H.
\end{equation}
In Figure~\ref{fig:kerneloffsets} we show how these offsets are applied to a regular kernel; and in Figure~\ref{fig:convsSph} three kernel samples on the spherical and on the equirectangular images.

\begin{figure}
\centering
\includegraphics[width=1\linewidth]{paper/sections/images/equiconv_sphere2.pdf}
\caption{\label{fig:convsSph} \textbf{EquiConvs{} on spherical images.} We show three kernel positions to highlight the differences between the offsets. As we approach to the poles (larger $\color{cTheta}\theta$ angles) the deformation of the kernel on the equirectangular image is bigger, in order to reproduce a regular kernel on the sphere surface. Additionally, with EquiConvs{}, we do not use padding when the kernel is on the border of the image since offsets take the points to their correct position on the other side of the $360^\circ$ image.}
\vspace{-3mm}
\end{figure}

redpajama_set_name: RedPajamaArXiv


text:
Home » Health News » Wait, Did Gypsy Rose Blanchard Actually Suffer From Any Real Illnesses?
Wait, Did Gypsy Rose Blanchard Actually Suffer From Any Real Illnesses?
The Act on Hulu raises a lot of questions about Gypsy Rose Blanchard's actual health—including which diseases she suffered from, and which ones she didn't.
When Gypsy was a child, her mom Dee Dee told her that she suffered from leukemia and a host of other health issues.
Gypsy revealed in a 20/20 interview that the only medical condition she actually has is a lazy eye.
The Act, Hulu's new series based on the true story of Gypsy Rose Blanchard and her mother Dee Dee, raises a lot of questions. But one mystery stands out among the rest: Did Gypsy actually suffer from any real medical illnesses?
First, a little background: Dee Dee Blanchard suffered from factitious disorder imposed on another (FDIA), formerly known as Munchausen syndrome by proxy, a condition that led her to pretend Gypsy Rose was ill and disabled in order to get attention, sympathy, and money from charities.
Gypsy eventually learned that she wasn't as sick as her mother led her to believe, and tried to escape Dee Dee. After meeting a boyfriend, Nicholas Godejohn, online, she planned to murder Dee Dee, and Godejohn carried out the stabbing. She's currently serving a ten-year sentence; Godejohn is serving life in prison.
So what illnesses did Gypsy believe she suffered from?
Throughout The Act, Gypsy's told she suffers from a laundry list of conditions, and this happened IRL too. In a 20/20 special, Gypsy revealed that Dee Dee told people she had leukemia, asthma, vision and hearing impairment, muscular dystrophy and seizures.
Gypsy believed most her mother's imposed illnesses to be true, but eventually began to figure out that many of her disabilities weren't real. "There are certain illnesses I knew I didn't have. I knew I didn't need the feeding tube; I knew that I could eat. And I knew that I could walk," she said.
Well, did Gypsy suffer from any illnesses at all?
Gypsy says she believed one of her mother's illness claims: "I did believe my mother when she said that I had leukemia," she said in her interview with 20/20, but without any real medical records confirming that diagnosis, no one knows for sure (Dee Dee claims that most of Gypsy's medical records were lost in Hurricane Katrina).
In reality, the only health issue Gypsy suffers from is minor vision impairment in her left eye. "I have a little bit of a lazy eye, not all the time," she said. Other than that, Gypsy has a perfectly clear bill of health.
When asked why she believed her mother and agreed to using a wheelchair and feeding tube when she knew she didn't need them, she explained that she looked up to her mom. For most of her life, she was her only friend. "I was so young," Gypsy said. "I believed that she knows best. I didn't question it."
Gypsy added that her mother was able to convince doctors of her illnesses with her Southern charm, friendliness, and convincing lies.
Dee Dee also had Gypsy see hundreds of doctors in order to keep them from seeing patterns. In one instance, a neurologist doubted Gypsy's muscular dystrophy and told Dee Dee she had been misdiagnosed. Instead of being happy, Dee Dee stormed out and refused to return to that doctor, according to the 20/20 special.
According to Gypsy, Dee Dee hurt her because she was mentally ill. "She was constantly seeking attention for herself because she didn't feel loved," Gypsy told 20/20. "So let's make this baby girl sick so it forever needs you."
You can watch episodes one through five of The Act now on Hulu; new episodes air every Wednesday at 3 a.m., EST.
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redpajama_set_name: RedPajamaCommonCrawl


text:
Sparks1524
Think independently, experience wonder, always explore…go and DO GREAT THINGS!
Photo Finish – Unusual Views
Museum ships are popular attractions in the United States. Few artifacts are as impressive, imposing, and imminently capable of transporting a visitor back in time as is a ship from a bygone age. Panoramic photos of such vessels are always favorite souvenirs, but what happens if you take a closer look? What facets of the ship's personality are revealed by looking at an unusual view of the vessel? What connection can one make with the daily life of sailors and technology past by focusing one's attention on a single detail?
Here is a brief tour of some of the United States' more popular museum ships, but each one is examined from a single, unique perspective. Sometimes the shot is used to emphasize the sheer size of the vessel; other times the perspective provides a glimpse into the daily life of the sailors who used to live aboard the ship.
Never underestimate the power of an unusual view (be it an extreme close-up or an usual angle or perspective) to tell your readers a powerful story.
The "cat head" is a large beam jutting from the bow of an old sailing vessel that was used to support the raising and lowering of the anchor. Cat heads were often finished by carving an actual cat's head on the end. This cat head is aboard the frigate USS Constitution. Launched in 1797, Constitution entered service ("commissioned" in today's terminology) in 1798. She is the oldest commissioned warship afloat in the world, and is, along with the World War II carrier USS Enterprise (CV 6), one of the two most decorated surface ships in Navy history. Today she is permanently berthed as a living Navy museum at the Boston Navy Yard. The ship has seen four centuries (18th, 19th, 20th, 21st), and served under 44 of the 45 Presidents of the United States. Charlestown, Massachusetts. (Nathanael Miller, 21 July 2018)
Battleship USS North Carolina (BB55) dwarfs a maintenance worker. Lead ship of the North Carolina-class, she was in service from 1941 – 1947, and came to Wilmington to become a museum ship in 1961. Wilmington, North Carolina (Nathanael Miller, 2 Feb. 2018)
Protected cruiser USS Olympia (C 6) on display at the Independence Seaport Museum. Olympia was commissioned in 1895 and reactivated several times, finally decommissioned in 1922. Most famously, she was Commodore Dewey's flagship during the 1898 Battle of Manila Bay. A cursory glance shows a typical steel warship, but a closer look reveals numerous details that show Olympia's status as a turn-of-the-century era vessel, most notably the portholes on either side of the ship's name. Philadelphia, Pennsylvania. (Nathanael Miller, 9 May 2018)
Most people would not expect an escalator on a U.S. aircraft carrier, but the concept was experimented with a few times. The idea was to reduce the physical stress on aircrew ascending to their planes on the flight deck after they were decked out in all their survival gear. The plan never took hold, partly due to difficulty keeping the escalators working. This rare example is aboard USS Lexington (CV 16), on display in Texas. Named for the previous carrier Lexington, which was sunk at the 1942 Battle of the Coral Sea, Lexington was commissioned in 1943 and served until 1991. It was the last Essex-class carrier in service, and the last U.S. carrier in service from World War II. The ship became a floating museum in Corpus Christi in 1992. Corpus Christi, Texas. (Nathanael Miller, 31 Oct. 2018)
Take a moment and see if you can recognize this device. Hint: There's a small water outlet on the right, and a drain on the left. It's an early 20th-century scuttlebutt (drinking fountain) aboard the New York-class battleship USS Texas (BB 35). Texas was in commission from 1914 – 1948. It is one of the few American ships (and only battleship left) that served in both World Wars I and II. The ship has been a museum since 1948. La Porte, Texas. (Nathanael Miller, 01 Nov. 2018)
Get up close and personal to a ship, and the shape of the hull takes on very unusual proportions. Standing under the bow of the Iowa-class battleship USS Wisconsin (BB 64) reveals just how gracefully streamlined the ship's hull really is. Wisconsin is on permanent display at the Nauticus National Maritime Center. Wisconsin was in service from 1944 – 1991 and launched the first strikes of the Jan. 1991 Gulf War. Norfolk, Virginia. (Nathanael Miller, 26 Jan 2018)
Not all museum ships were warships. There are a tiny handful of cargo vessels from World War II that survive, telling the incredible story of the men who kept the supply lines moving despite the danger. Launched in 1945, SS American Victory saw service through the end of World War II, Korea, and Vietnam. Today a museum ship, she is one of only four operational WWII-era merchants ships left in the country. The "Victory Ships" were the successor design to the "Liberty Ships" of early World War II. Tampa, Florida. (Nathanael Miller, 21 Feb. 2018)
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Posted on November 5, 2020 November 5, 2020 by sparks1524Posted in Navy, Photo Finish, photography, photos, Ship, warship, warshipsTagged #military, #museumship, #museumships, #nathanaelmiller, #Navy, #novelist, #photographer, #photojournalist, #sparks1524, #warship.
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Think Independently, experience wonder, explore always…go DO GREAT THINGS
Currently an independently-published novelist and blogger, I have enjoyed a remarkably unusual and varied career. I'm a retired U.S. Navy photojournalist and writer, a former instructor, F-14 Tomcat fighter technician, museum curator's assistant, and lover of *really* corny puns. The human experience is the greatest adventure story ever told, and we all write part of it every day.
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Should Yale rename itself? Founding benefactor Elihu Yale was slave trader
Yale University recently announced the creation of a "Committee to Establish Principles on Renaming." It's charged with articulating "a set of principles that can guide Yale in decisions about whether to remove a historical name from a building or other prominent structure or space on campus."
Yale has been embroiled in a huge brouhaha over its Calhoun College, named after the seventh vice president of the United States of America, who also happened to be a slavery advocate. The name, according to hundreds of professors, creates an oppressive and offensive atmosphere. They want it scrubbed. Yale at first said no, now it's saying maybe.
In an op-ed in The Wall Street Journal, Roger Kimball cleverly suggests while the Committee on Renaming is at it, they mind as well throw their namesake Yale in the mix:
I have unhappy news for Mr. Salovey. In the great racism sweepstakes, John Calhoun was an amateur. Far more egregious was Elihu Yale, the philanthropist whose benefactions helped found the university. As an administrator in India, he was deeply involved in the slave trade. He always made sure that ships leaving his jurisdiction for Europe carried at least 10 slaves. I propose that the committee on renaming table the issue of Calhoun College and concentrate on the far more flagrant name "Yale."
Kimball then goes on to liken Salovey's committee to that of the French Revolution's Robespierre's Committee of Public Safety, created in April 1793. "On July 28, 1794, Robespierre, the man who oversaw the murder of so many, was himself guillotined. Thus do revolutions consume their abettors."
Read the full op-ed in The Wall Street Journal.


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Sexual assault involves any unwanted physical contact that is sexual in nature. My clients tell me it is a terrifying experience. Survivors often find themselves experiencing a range of fears and other emotions. Many survivors are understandably reluctant to talk about their experience and want to put it behind them as soon as possible.
Sexual assault can have long-term physical and psychological effects, as well as affecting everything from your family life to your job. Compensation may be available to help relieve the financial burden this can cause, and our sexual assault injury lawyers are available to help. Please contact Grosso Hooper Law for more details. Your information will be kept strictly confidential.
The person who assaulted you - In order to get compensation, you have to prove liability – that the person you are suing sexually assaulted you. This could include someone who breached their fiduciary duty to protect you and maintain a strictly professional relationship with you, for example a doctor, teacher or coach.
The employer of the person who assaulted you - If you can prove the employer was negligent (for example, a school board allowing a teacher to continue in his or her post despite complaints from students), you may be entitled to compensation.
The person who put you in a relationship with the assaulter - You could be entitled to compensation if you can prove that a person or organization was negligent in putting you in a relationship with the assaulter, or in letting that relationship continue.
Timing is important. You must start a lawsuit within two years of the time of the sexual assault. There are exceptions to this rule, and the facts of your case may allow you to make a claim after the two year limit.
Proving liability does not guarantee payment of compensation if the person you sue has no money or insurance to pay. Grosso Hooper Law will be able to advise you before you begin your lawsuit, and will fight for you to get payment once it is successful.
Pain and suffering and loss of quality of life (general damages) - This is a claim for your past, present and future pain and suffering, both physical and psychological. In Ontario, the maximum compensation is approximately $358,000. In sexual assault cases the range is from $10,000 to $225,000.
Pecuniary damages (loss of wages) - This is a claim for loss of wages in the past, present and future. If you are unable to work (temporarily or for the rest of your life), return to work but make less money, or fail to get employment or promotion because of the assault, you may be entitled to compensation.
Out-of-pocket expenses and future care costs - This compensates you for any expenses caused by ongoing physical or psychological difficulties, including over-the-counter prescription medication, rehabilitation devices, or parking and mileage for doctors' appointments.
K.T. v. Denis Vranich, Elixir and Paradise lane Developments, Hamilton Inc.
A case in which the defendant, Vranich sexually assaulted a young employee of his nightclub, Elixir. Vranich had used his financial control over her as an employer to isolate her from her peers and block any path of escape while he sexually violated her. Vranich was found liable and Elixir vicariously liable for the conduct of Vranich. There was no liability found with respect to Paradise Lane Developments. The Judge awarded the Plaintiff (represented by Rob Hooper) $125,000 for pain and suffering, $25,000 for punitive damages, $75,000 for loss of earning capacity, $26,000 for future therapy costs and $48,753 for legal costs. The judgment stands out because the award for pain and suffering is among the highest level in Ontario. Also, punitive damages being awarded against the corporation (who were vicariously liable) is a first in Ontario.


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Lambda Chi Alpha fraternity at Butler University suspended
INDIANAPOLIS, Ind. – The Lambda Chi Alpha fraternity at Butler University has been suspended.
It's unclear why, but the fraternity's national board of directors made the decision Monday, Fox59 reported.
Under the suspension terms, the members of the Alpha-Alpha Zeta chapter cannot represent Lambda Chi Alpha or gather in its name.
Butler officials say they're providing alternative housing for any member who is currently living in the chapter house. The Office of the Vice President for Student Affairs is working closely with the national Lambda Chi Alpha organization to facilitate this action.
A return of a chapter of the Alpha-Alpha Chapter to the Butler campus will require a joint agreement between the international offices of Lambda Chi Alpha and Butler University. The university says an agreement will not be considered prior to the spring of 2021.
The suspension of Lambda Chi Alpha comes a week after IU's Delta Tau Delta fraternity was suspended for hazing.
This story originally appeared at Fox59.com.


I have returned from my vacation in the USA and will soon be experiencing the wonders of the Legion pre-patch. Because of jetlag and wife aggro I am unable to play much between now and Sunday so there will be no show this weekend. However friends fear not for there will be much time this week for me to see the patch content and, more importantly have a go at priesting Legion style so there will be a show next weekend.
So for now farewell and please get in touch with your views on the new style either by mail to podcast@evangelysm.com or tweeting me @EvangelysmCast.
← Evangelysm Podcast: Season 3, Show 53.


Here at One80, we realize that we are in the service industry; to truly serve others and put someone else's interests above your own definitely requires a servant's heart.
We have a simple rule here: to work at One80, you have to be involved in community service. From helping throw charity fundraisers to hosting kids events, we are always looking for ways serve our community. We also hold numerous volunteer positions with a variety of non-profit institutions. Several of our REALTORS® contribute to our profession to enrich our community by serving on the Board of Directors and committees of our local, state and national real estate associations.
Our actions speak louder than our words. We live by our principles.


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Collaborative proposals to work on India's Urban Challenge: CISOTT and CITIURI
CISOTT: Civil Society Think Tank on India's Urban Challenge
Building on INHAF's earlier efforts during Habitat 3 that led to the Delhi Declaration, it was suggested to conceive CISOTT: Civil Society Think Tank on India's Urban Challenge. Considering that the cities are the undeniable future of the country with 820 million estimated to live there by 2050 and 70 percent of the GDP coming from the urban centres, the response of cities be that investment, institutions, technology or governance, is far behind and removed from what is required. It would appear that the government, professionals and institutions are failing to do justice to its multiple complex demands. Meeting the challenge squarely and exploit the potential creatively much innovative and out of box thinking is necessary. CISOTT—a think tank created and manned by the civil society, to generate a new, innovative response to the urban challenge. Not working against the government, working closely with it but from outside and in collaboration with potential individuals and groups in education and the professional community.
'CITIURI': Citizen's Urban Initiative
Following this and on the back of continuing efforts to set up the National Commission on Urbanisation-2, the Gujarat State Commission on Urbanisation and against the backdrop of the National urban policy framework drafted by the GoI in 2018, Inhaf reached out to different academic institutions and individuals to work with us on 'reassessing India's urban challenge, rethinking Indian cities and reformulating the response in the context of the national development challenge'.
In mid-2019, INHAF drafted a proposal to rethink Indian urbanization and named the initiative 'CITIURI': Citizen's Urban Initiative. This initiative is about organising a multi-level and multi-disciplinary systematic societal effort, spearheaded by some of India's leading urban and related professionals, thinkers and practice-ners to prepare a blueprint, making a roadmap, that outlines the country's response to the complex urban challenge in a way that ensures efficient, productive, inclusive, sustainable, liveable and humane cities for a happy people and an equitable and harmonious society.
Through CITIURI, we seek to do the following: (a) Reassess India's Urban Challenge (b) Rethink Indian Cities, and (c) Reformulate the Response in the context of National Development Challenges.
Letter to initiate CISOTT
CITIURI proposal
Letter to set up CITIURI
Rethinking Webinar Series
National Urban Policy Framework: A Response
CISOTT and CITIURI
Campaigns for Commissions on Urbanisation
Habitat III: A Civil Society Response
How Inclusive are Our Cities


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Tag: American League Championship Series
Sports	 October 14, 2012 October 14, 2012
THE TIGERS vs THE YANKEES – 1961
The Detroit Tigers and the New York Yankees are meeting in the playoffs for the third time since 2006. The two previous times were in the divisional series, but this is the American League Championship Series. In 2006 and 2011 the Tigers were successful in vanquishing the hated Yankees but this series harkens back to a time past when the teams were battling neck and neck for the American League Pennant. I'm talking about 1961. That was a truly historic season for a lot of reasons. The two teams battled the entire season and when September came around there was only 1 1/2 games separating them. I was a young pup then, 14 years old, and I had a 14 year-long hatred for those "Damned Yankees."
The Yankees represented the elite of Major League baseball. The Tigers were, well, Detroit. They were us. While New York had Wall Street, Broadway, Madison Square Garden and Yankee Stadium, Detroit was Dodge Main, Fleetwood, it was the Olympia, Woodward Avenue and it was Briggs Stadium. I remember my father taking me, and my two brothers, to a double-header there, sitting in the lower deck in right field, behind Al Kaline. It was May of 1959 and if I remember correctly, Billy Hoeft was the pitcher. That was a day when Kaline made one of his patented leaping catches, back to the fence, glove extending high over the barrier. Charley Maxwell (nicknamed "Paw Paw," by former Tiger announcer Van Patrick), was the Tiger's left-fielder. Maxwell was famous for his Sunday heroics. He had four homers that day including 3 in one game.
By the start of the 1961 season the Yankees had been to the World Series 25 times to the Tigers 7 trips. Now other than the Yankees, only the Giants, the Cardinals, the Dodgers, and the Athletics had been to the series more than the Tigers, and though the Tigers hadn't been since 1945, the rivalry between the two teams was as intense as any in sports, at least to this young sports fan.
1961 turned out to be a truly magical year. The year before the Tigers had a 71-83 record and finished 26 games behind the Yankees. Somehow though, there was always hope, you see, the Tigers had traditionally been successful, against the Yankees. We had an acknowledged Yankee killer in Frank Lary, we had "the kid" Al Kaline, the Tiger's answer to "the Mick," Mickey Mantle. It's not talked about a lot, but did you know that Kaline won the batting average title in 1955, with a .340 batting average, the year before Mantle? He (Kaline) was the youngest batting champion in major league history.
The previous year we had obtained "The Rock," Rocky Colavito (42 home runs) in a trade for another batting title winner Harvey Keunn (.353 BA), the first time that a batting champion had been traded heads-up for a home run title winner in league history. That same off-season we managed to get first baseman Norm Cash from the Chicago White Sox. Add to the mix, in 1961, rookie Jake Woods at 2nd base and that age-old song wait until next year started having a little bass in it. Along with Lary, the pitching staff headed by ace Jim Bunning, who in 1958 threw a no-hitter at Ted William's Boston Red Sox, also included Don Mossi, Paul Foytack, Hank Aguirre, Phil Regan and one of my personal favorites Hal Woodeshick (Come on now, you know you remember him. Don't act like I'm the only one.)
The Yankee lineup for 1961 included, Mantle, Roger Maris, Yogi Berra, Tony Kubek, Clete Boyer, Bobby Richardson and Bill (Moose) Skowron. They had a young Elston Howard and Tom Tresh. The pitching staff had Whitey Ford, Bob Turley, Ralph Terry, Duke Maas and who could ever forget Ryne Duren (he, of the coke bottle bottom glasses).
As fans we always had great expections about our Tigers, but, not even in our wildest dreams, while playing catch on the side of the house, or, our favorite game "backyard baseball" (we used to emulate entire teams, down to the stances they took at the plate), did we believe what, ultimately, took place that summer.
When we weren't outside re-enacting the games, or playing our own games, we were huddled around a radio. Sometimes my father would bring one from inside, or we would follow the game on a transistor radio (remember those?). As the season went on, we found ourselves listening intently to Ernie Harwell and George Kell as they described the action for us.
The teams went back and forth the entire summer. From June 20th until the end of the season October 1st the Yankees were either in 1st or 2nd place. For the Tigers the streak went all the way back to tax day, April 15th. I can't remember, nor, can I find a season, where 2 teams went at each other in the standings that way.
The Tigers held first place from April 29th to June 8th. And then again from June 14th to July 6th. The Yankees from July 25th until the end of the season. From June 20th until the end of the season either the Tigers or the Yankees occupied first place with the other second.
All the while that this FANtastic race was going on, the stars on each team were having unbelievable seasons. For the Yankees, Mantle and Maris were involved in what was an unparalleled assault on Babe Ruth's 60 home run record. Maris, who hit his first home run of the year against the Tigers ended then season breaking the magic number with 61, on the last day of the regular season. Maris also won the MVP for the American League, that year. Mantle was right with him most of the way finishing the season with 54 home runs. They had 141 and 128 RBI respectively, and Mantle batted .317. The Yankees ended the season with a team total of 240 home runs to lead the league.
The Tigers were led by Norm Cash, Rocky Colavito, and Al Kaline. Just check out Cash's season. He led the league in batting average at .361 (and hits 193), he hit 41 home runs and batted in 132 runs. He also scored 119 runs. All this and he didn't even come close, other than his batting average, to winning the triple crown. In fact, Rocky Colavito led the team in both home runs (45) and RBI (140), while scoring 129 runs. Al Kaline finished 2nd in batting average at .324, along with 19 home runs, 82 RBI and 116 runs scored.
For five months these two teams battled each other as no other teams had, and this 14-year-old dreamer was captured and enraptured by what was occurring. My team the, Detroit Tigers, was taking on the mighty New York Yankees and we were right there with them. 1 1/2 games out at the start of September with a series with the Yankees at Yankee Stadium. That night it was two "port-siders"" going at one another. Don Mossi for the Tigers, and for the Yankees, Whitey Ford. Ford, I should mention won the Cy Young Award in 1961 with a 25-4 won-loss record, a 3.21 ERA and 209 SO. And that was when they only gave out one award for both leagues.
Don Mossi (15-7, 2.96 ERA) battled Ford through 8 innings of a 0-0 game. The tension was unbelievable. The bottom of the ninth began with Mossi getting Maris out on a flyball to right field. He followed that by striking out "the Mick" looking. "He stood there like a house on the side of the road and watched that one go by." (Ernie Hawell) Up next was Elston Howard, the first African-American to were the famed pin-stripes. Howard lined a single to center field. Yogi Berra followed with a single to right field sending Howard to third. There were 2 outs, runners on first and third, and it was up to Bill Skowron. Moose grounded a single to left field, Howard scored and that was the ball game. Ultimately that was the season as well.
The Tigers were swept by the Yankees that week-end and split the final 4 games between them. They finished September with a 14-14 record while the Yankees went 21-8. They both won on the final day.
The Tigers finished the season with a 101- 61 record. Their most wins for a season in their history and still finished 8 games behind the Yankees. They had a winning record against every team in the league except Baltimore (9-9) but, alas, it wasn't enough.
But this story is not really about what the Tigers lost that year. It's about the gift these two teams gave to me, my father and brothers, and fans around the country. What we all ended up with is the memory of perhaps the greatest head-to-head season any two teams, in any of the major sports leagues, has ever had.
Thanks to both teams for that.
I want to thank http://www.baseball-reference.com/ and to wikipedia.org for their remarkable collection of stats and other data. Their information was the thread I use to stitch together these fabulous memories. Memories of what was a truly FANtastic season.
Tigers vs Yankees ALCS Tickets Now Available (prweb.com)
Match-Up: Tigers Vs. Yankees (detroit.cbslocal.com)
ALCS Preview: Tigers vs. Yankees (hardballtalk.nbcsports.com)
Bats: A.L.C.S. Game 1: Tigers vs. Yankees (bats.blogs.nytimes.com)
You: Historic World Series Rematch Almost Guaranteed as LCS Begins (bleacherreport.com)
Blog Post: ALCS Game 1 Detroit Tigers at New York Yankees: Start Time, Odds, Free Pick (pregame.com)
Filed under: Al Kaline, American League, American League Championship Series, Detroit Tigers, Elston Howard, Jake Wood, Mickey Mantle, New York Yankees, Norm Cash, Rocky Colavito, Roger Maris, Tiger, Whitey Ford, Yankee, Yogi Berra


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Vincent Cianni (American, born 1952). Nelson, Bedford Avenue, Williamsburg, Brooklyn, 1998. Gelatin silver photograph, Sheet: 20 x 16 in. (50.8 x 40.6 cm). Brooklyn Museum, Gift of the artist, 2003.19.2. © artist or artist's estate (Photo: Brooklyn Museum, 2003.19.2.jpg)
Nelson, Bedford Avenue, Williamsburg, Brooklyn
Vincent Cianni
ARTIST Vincent Cianni, American, born 1952
MEDIUM Gelatin silver photograph
DIMENSIONS Sheet: 20 x 16 in. (50.8 x 40.6 cm) Image: 17 3/4 x 13 1/4 in. (45.1 x 33.7 cm) (show scale)
SIGNATURE Signed in graphite, lower center verso: "Vincent C."
COLLECTIONS Photography
ACCESSION NUMBER 2003.19.2
EDITION Edition: 3/25
CREDIT LINE Gift of the artist
RIGHTS STATEMENT © Vincent Cianni
The Brooklyn Museum holds a non-exclusive license to reproduce images of this work of art from the rights holder named here. The Museum does not warrant that the use of this work will not infringe on the rights of third parties. It is your responsibility to determine and satisfy copyright or other use restrictions before copying, transmitting, or making other use of protected items beyond that allowed by "fair use," as such term is understood under the United States Copyright Act. For further information about copyright, we recommend resources at the United States Library of Congress, Cornell University, Copyright and Cultural Institutions: Guidelines for U.S. Libraries, Archives, and Museums, and Copyright Watch. For more information about the Museum's rights project, including how rights types are assigned, please see our blog posts on copyright. If you have any information regarding this work and rights to it, please contact copyright@brooklynmuseum.org. If you wish to contact the rights holder for this work, please email copyright@brooklynmuseum.org and we will assist if we can.
CAPTION Vincent Cianni (American, born 1952). Nelson, Bedford Avenue, Williamsburg, Brooklyn, 1998. Gelatin silver photograph, Sheet: 20 x 16 in. (50.8 x 40.6 cm). Brooklyn Museum, Gift of the artist, 2003.19.2. © artist or artist's estate (Photo: Brooklyn Museum, 2003.19.2.jpg)
IMAGE overall, 2003.19.2.jpg. Brooklyn Museum photograph, 2003
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