text:
\section{Our model\label{sec:our_model}}
\subsection{Decomposer network\label{sec:decomposer}}
The Decomposer network is trained to embed unlabeled shapes
into a factorized embedding space, reflecting the shared semantic structure of the shape collection. To allow for composite shape synthesis, the embedding space has to satisfy the following two properties: factorization consistency across input shapes, and existence of a simple shape composition operator to combine latent representations of different semantic factors.
We propose to model this embedding space $V$ as a \emph{direct sum of subspaces} $\{V_i\}_{i=1}^K$, where $K$ is the number of semantic parts, and each subspace $\{V_i\}$ corresponds to a semantic part $i$, thus satisfying the factorization consistency property. The second property is ensured by the fact that every vector $v \in V$ is given by a sum of unique $v_i \in V_i$ such that $V=V_1 \oplus ... \oplus V_k $, and part composition may be performed by part embedding summation. This also implies that the decomposition and composition operations in the embedding space are fully reversible.

A simple approach for such factorization is to split the dimensions of the $n$-dimensional embedding space into $K$ coordinate groups, each group representing a certain semantic part-embedding. In this case, the full shape embedding is a concatenation of part embeddings, an approach explored in~\cite{G2L18}. This, however, puts a hard constraint on the dimensionality of part embeddings, and thus also on the representation capacity of each part embedding subspace. Given that different semantic parts may have different geometric complexities, this factorization may be sub-optimal.

Instead, we perform a data-driven learned factorization of the embedding space into semantic subspaces. We use \emph{learned} part-specific projection matrices, denoted by $\{P_i\}_{i=1}^K \in \mathbb{R}^{n \times n}$. To ensure that the aforementioned two factorization properties hold, the projection matrices must form a \emph{partition of the identity} and satisfy the following three properties
\begin{align}
(1) & \,\,\, P_i^2 = P_i, \forall i,\nonumber\\
(2) & \,\,\, P_i P_j = \mathit{0} \text{ whenever } i \neq j,\nonumber\\
(3) & \,\,\, P_1 + ... + P_K = I,
\label{eq:part_of_identity}
\end{align}
where $\mathit{0}$ and $I$ are the all-zero and the identity matrices of size $n \times n$, respectively.

In practice, we efficiently implement the projection operators using fully connected layers without added biases, with a total of $K*n^2$ variables, constrained as in Equation~\ref{eq:part_of_identity}.
The projection layers receive as input a whole shape encoding, which is produced by a 3D convolutional shape encoder. The parameters of the shape encoder and the projection layers are learned simultaneously. The resulting architecture of the Decomposer network is schematically described in Figure~\ref{fig:wae}, and a detailed description of the shape encoder and the projection layer architecture is given in the supplementary material.

\subsection{Composer network}
\label{sec:cycle_consist}
The composer network is trained to reconstruct shapes with semantic part labels from \emph{sets} of semantic part embedding coordinates. The simplest composer implementation would consist of a single decoder mirroring the whole binary shape encoder (see Figure~\ref{fig:wae}), producing a semantically labelled reconstructed output shape. Such approach was used in~\cite{G2L18}, for instance. However, this straightforward method is known to fail in reconstructing thin volumetric shape parts and other fine shape details. To address this issue, we use a different approach, where we first separately reconstruct scaled and centered shape parts, using a \emph{shared part decoder}. We then produce \emph{per-part transformation parameters} and use them to deform the parts in a coherent manner, to obtain a complete reconstructed shape.

In our model, we make the simplifying assumption that it is possible to combine a given set of parts into a plausible shape by transforming them with per-part affine transformations and translations. While the true set of transformations which produce plausible shapes is significantly larger and more complex, our experiments demonstrate that the proposed simplified model is successful at producing geometrically and visually plausible results. This in-network part transformation is implemented using a 3D spatial transformer network (STN)~\cite{jaderberg2015spatial}. It consists of a localization net, which produces a set of 12-dimensional affine transformations (including translations) for all parts, and a re-sampling unit, which transforms and places the reconstructed part volumes at their correct locations in the full shape. The SNT receives as input both the reconstructed parts from the part decoder, and the sum of part encodings, for best reconstruction results. The resulting Composer architecture is schematically described in Figure~\ref{fig:wae}; its detailed description is given in the supplementary material.

We note that the proposed approach is related to the two-stage shape synthesis approach of~\cite{li2017grass}, in which a GAN is first used to synthesize oriented bounding boxes for different parts, and then the part geometry is created per bounding box using a separate part decoder. Our approach is similar, yet it works in a reversed order. Namely, we first reconstruct part geometry, and then compute per-part affine transformation parameters, which are a 12-dimensional equivalent of the oriented part bounding boxes in~\cite{li2017grass}.
Similarly to~\cite{li2017grass}, this two stage approach
improves the reconstruction of fine geometric details. However, unlike~\cite{li2017grass}, where the GAN and the part decoder where trained separately, in our approach the two stages belong to the same reconstruction pipeline, trained simultaneously and end-to-end.

\subsection{Cycle consistency\label{sec:cycle_augmentation}}
\begin{figure}[tb]
\begin{center}
\includegraphics[width=0.95\linewidth]{figures/cycle2.pdf}
\caption{Schematic description of the cycle consistency constraint. See Section~\ref{sec:cycle_augmentation} for details.\label{fig:cycle_consist}}
\end{center}
\vspace{-1em}
\end{figure}
Our training set is comprised of 3D shapes with ground-truth semantic part-decomposition; It does not include any training examples of synthesized composite shapes. Existing methods for such shape assembly task operate on 3D meshes with very precise segmentations, and often with additional knowledge about part connectivity \cite{xu2011photo,shen2012structure,kalogerakis2012probabilistic}. These methods cannot be applied to a dataset like ours, to produce a sufficiently large set of plausible new shapes (constructed from existing parts) to use for training a deep network for composite shape modelling.
In order to circumvent this difficulty, and train the net to produce non-trivial part transformations for geometrically and semantically plausible part arrangements, we use a \emph{cycle consistency} constraint. It has been previously utilized in geometry processing~\cite{nguyen2011optimization}, image segmentation~\cite{wang2013image}, and more recently in neural image transformation~\cite{pumarola2018ganimation, zhu2017unpaired}.

Specifically, given a batch of $M$ training shapes $\{X\}_{i=1}^M$, we map them to the factored latent space using the Decomposer, producing $K$ semantic part encodings per input shape. We the randomly mix the part encodings of the shapes in the batch, while ensuring that after the mixing each of the new $M$ encoding sets includes exactly one embedding coordinate per semantic part. We then reconstruct the shapes with correspondingly mixed parts using the Composer. After that, these new shapes are passed to the Decomposer-Composer pipeline once again, while de-mixing part encodings produced by the second Decomposer application, to re-store the original encoding-to-shape association. The cycle consistency requirement means that the final shapes are as similar as possible to the original $M$ training shapes. We enforce it using the cycle consistency loss described in the next section. The double application of the proposed network with part encoding mixing and de-mixing is schematically described in Figure~\ref{fig:cycle_consist}.

\subsection{Loss function\label{sec:loss}}
Our loss function is defined as the following weighted sum of several loss terms
\begin{align}
\label{eq:loss}
L = & \,\, w_{\text{PI}} \mathcal{L}_{\text{PI}} + w_{\text{part}} \mathcal{L}_{\text{part}} +  w_{\text{trans}} \mathcal{L}_{\text{trans}} +  w_{\text{cycle}} \mathcal{L}_{\text{cycle}}.
\end{align}
The weights compensate for the different scales of the loss terms, and reflect their relative importance.

\vspace{-1em}
\paragraph{Partition of the identity loss} \hspace{-0.8em} $\mathcal{L}_{\text{PI}}$ measures the deviation of the predicted projection matrices from the optimal projections, as given by Equation~\ref{eq:part_of_identity}.
\begin{align}
\label{eq:pi_loss}
\mathcal{L}_{\text{proj}}(P_1,...,P_k) = & \sum_{i=1}^K \|P_i^2-P_i\|_F^2 + \sum_{\substack{i,j=1,\\i \neq j}}^K \|P_i P_j\|_F^2 + \nonumber\\ & \|P_1+...P_K - I\|_F^2.
\end{align}

\vspace{-1em}
\paragraph{Part reconstruction loss} \hspace{-0.8em} $\mathcal{L}_{\text{part}}$ is the binary cross-entropy loss between the reconstructed centered and scaled part volumes and their respective ground truth part indicator volumes, summed over $K$ parts.

\vspace{-1em}
\paragraph{Transformation parameter loss} \hspace{-0.8em} $\mathcal{L}_{\text{trans}}$ is an $L2$ regression loss between the predicted and the ground truth 12-dimensional transformation parameter vectors, summed over $K$ parts. Unlike in the original STN approach~\cite{jaderberg2015spatial}, we found that direct supervision over the transformation parameters is critical for our network convergence.

\vspace{-1em}
\paragraph{Cycle consistency loss} \hspace{-0.8em} $\mathcal{L}_{\text{cycle}}$ is a binary cross-entropy loss between ground truth input volumes and their reconstructions, obtained using two applications of the proposed network, as described in Section~\ref{sec:cycle_augmentation}.

\subsection{Training details\label{sec:training}}
The network was implemented in TensorFlow~\cite{abadi2016tensorflow}, and trained for 500 epochs with batch size 32. We used Adam optimizer~\cite{kingma2014adam} with learning rate $0.0001$, decay rate of $0.8$, and decay step size of 40 epochs.
We found it was essential to first pre-train the binary shape encoder, projection layer and part decoder parameters separately for $150$ epochs, by minimizing the part reconstruction and the partition of the identity losses and using $w_{\text{trans}}=w_{\text{cycle}}\approx 0$, for improved part reconstruction results. We then train the parameters of the spatial transformer network for another $100$ epochs, while keeping the rest of the parameters fixed. After that we resume the training with all parameters and the cycle consistency loss to fine-tune the network parameters.
The optimal loss combination weights were empirically detected using the validation set, and set to be $w_{\text{PI}} = 0.1, w_{\text{part}} = 100, w_{\text{trans}} = 0.1, w_{\text{cycle}} = 0.1$. The network was trained on each shape category separately.

\section{Introduction}

Understanding, modeling and manipulating 3D objects are areas of great interest to the vision and graphics communities, and have been gaining increasing popularity in recent years.
Examples of related applications include semantic segmentation~\cite{yi2016scalable}, shape synthesis ~\cite{wu2016learning, achlioptas2017representation}, 3D reconstruction~\cite{choy20163d, fan2017point}, view synthesis~\cite{xia2018gibson}, and fine-grained shape categorization~\cite{achlioptas2019shapeglot}, to name a few.
The advancement of deep learning techniques, and the creation of large-scale 3D shape datasets~\cite{chang2015shapenet} enabled researchers to learn task-specific representations directly from the existing data, and led to significant progress in all the aforementioned areas.

\begin{figure}[t]
\centering
\includegraphics[width=1\linewidth]{figures/teaser2_3.pdf}
\caption{Given unlabeled shapes, the Decomposer maps them into a factorized latent space. The Composer can either reconstruct the shapes with semantic part labels, or
create new shapes
, for instance, by exchanging chair legs.\label{fig:teaser}}
\vspace{-1em}
\end{figure}

There is a growing interest in learning shape modeling and synthesis in a structure-aware manner, for instance, at the level of semantic shape parts.
This poses several challenges compared to approaches considering the shapes as a whole.
Semantic shape structure and shape part geometry are usually interdependent, and relations between the two must be implicitly or explicitly modeled and learned by the system.
Examples of such structure-aware shape representation-learning are \cite{nash2017shape,li2017grass,G2L18,wu2018structure}.

However, the existing approaches for shape modeling, while being part aware at the intermediate stages of the system, still ultimately operate on the low-dimensional representations of the \emph{whole} shape. For example, \cite{nash2017shape,G2L18} use a Variational Autoencoder (VAE)~\cite{kingma2013auto} to learn generative part-aware models of man-made shapes, but the latent spaces of the VAEs correspond to complete shapes, with entangled latent factors corresponding to different semantic parts.
Therefore, these and other existing approaches cannot perform part-level shape manipulation, such as single part replacement, part interpolation, or part-level shape synthesis.

Inspired by the recent efforts in image modeling to separate different image formation factors, to gain better control over image generation process and simplify editing tasks~\cite{pumarola2018ganimation,shu2018deforming,shu2017neural}, we propose a new semantic structure-aware shape modeling system.
This system utilizes an auto-encoder-based pipeline, and produces a factorized latent space which both reflects the semantic part structure of the shapes in the dataset, and compactly encodes different semantic parts' geometry. In this latent space, different semantic part embedding coordinates lie in \emph{separate linear subspaces}, and shape composition can naturally be performed by summing up part embedding coordinates. The latent space factorization is data-dependent, and is performed using learned linear projection operators. Furthermore, the proposed system operates on \emph{unlabeled} input shapes, and at test time it simultaneously infers the shape's semantic structure and compactly encodes its geometry.

Towards that end, we propose a Decomposer-Composer pipeline, schematically illustrated in Figure~\ref{fig:teaser}.
The Decomposer maps an input shape, represented by an occupancy grid, into the factorized latent space described above.
The Composer reconstructs a shape with semantic part-labels from a set of part-embedding coordinates. It explicitly learns the set of transformations to be applied to the parts, so that together they form a semantically and geometrically plausible shape. In order to learn and apply those part transformations, we employ a 3D variant of the Spatial Transformer Network (STN)~\cite{jaderberg2015spatial}.
3D STN was previously utilized to scale and translate objects represented as 3D occupancy grids in \cite{hu2018predictive}, but to the best of our knowledge, ours is the first approach suggesting an in-network affine deformation of occupancy grids.

Finally, to promote part-based shape manipulation, such as part replacement, part interpolation, or shape synthesis from arbitrary parts, we employ the cycle consistency constraint~\cite{zhu2017unpaired, pumarola2018ganimation, nguyen2011optimization, wang2013image}.
We utilize the fact that the Decomposer maps input shapes into a factorized embedding space, making it possible to control which parts are passed to the Composer for reconstruction. Given a batch of input shapes, we apply our Decomposer-Composer network twice, while randomly mixing part embedding coordinates before the first Composer application, and then de-mixing them into their original positions before the second Composer application. The resulting shapes are required to be as similar as possible to the original shapes, using a cycle consistency loss.

\vspace{-1em}
\paragraph{Main contributions}
Our main contributions are: (1) A novel latent space factorization approach which enables performing shape structure manipulation using linear operations directly in the learned latent space; (2) The application of a 3D STN to perform in-network affine shape deformation, for end-to-end training and improved reconstruction accuracy; (3) The incorporation of a cycle consistency loss for improved reconstruction quality.

\begin{figure*}[t]
\centering
\includegraphics[width=1\linewidth]{figures/arch_detailed_v2_3.pdf}
\caption{The proposed Decomposer-Composer architecture.\label{fig:wae}}
\vspace{-1em}
\end{figure*}

\section*{Acknowledgements}
\label{seq:ack}
L. Guibas acknowledges NSF grant CHS-1528025, a Vannevar Bush Faculty Fellowship, and gifts from the Adobe and Autodesk  Corporations.
A. Dubrovina and M. Shalah acknowledge the support in part by The Eric and Wendy Schmidt Postdoctoral Grant for Women in Mathematical and Computing Sciences. F.Xia acknowledges the support from Stanford Graduate Fellowship.

\section{Related work}
\paragraph{Learning-based shape synthesis}
Learning-based methods have been used for automatic synthesis of shapes from complex real-world domains; In a seminal work~\cite{kalogerakis2012probabilistic}, Kalogerakis \etal used a probabilistic model, which learned both continuous geometric features and discrete component structure,  for component-based shape synthesis and novel shape generation. The development of deep neural networks enabled learning high-dimensional features more easily;  3DGAN~\cite{wu2016learning} uses 3D decoders and a GAN to generate voxelized shapes. A similar approach has been applied to 3D point clouds and achieved high fidelity and diversity in shape synthesis~\cite{achlioptas2017representation}.

Apart from generating shapes using a latent representation, some methods generate shapes from a latent representation with \emph{structure}.
SSGAN~\cite{wang2016generative} generate the shape and texture for a 3D scene in a 2-stage manner. GRASS~\cite{li2017grass} generate shapes in two stages: first, by generating orientated bounding boxes, and then a detailed geometry within those bounding boxes. Nash and Williams~\cite{nash2017shape} use use point cloud shape representation and a VAE to learn a probabilistic latent space of shapes; however, they require all training data to be in point-to-point correspondence. In a related work \cite{G2L18}, Wang \etal introduced a 3D GAN-based generative model for 3D shapes, which produced segmented and labeled into parts shapes. Unlike the latter approach, our network does not use predefined subspaces for part embedding, but learns to project the latent code of the entire shape to the subspaces corresponding to codes of different parts.

In concurrent efforts, several deep architectures for part based shape synthesis were proposed  \cite{schor2018learning,li2019learning,pageSAGnet19,mo2019structurenet}. Schor \etal \cite{schor2018learning}
utilized point-base shape representation, while operating on input models with known per-point parts labels. Li \etal \cite{li2019learning} and \cite{pageSAGnet19} proposed two generative networks for part-based shape synthesis, operating on labeled voxelized shapes. Mo \etal \cite{mo2019structurenet} introduced a hierarchical graph network for learning structure-aware shape generation.

\vspace{-1em}
\paragraph{Spatial transformer networks}
Spatial transformer networks (STN)~\cite{jaderberg2015spatial} allow to easily incorporate deformations into a learning pipeline. Kurenkov \etal~\cite{kurenkov2018deformnet} retrieve a 3D model from one RGB image and generate a deformation field to modify it. Kanazawa \etal~\cite{kanazawa2016learning} model articulated or soft objects with a template shape and deformations. Lin \etal~\cite{lin2018st} use STNs iteratively, to warp a foreground onto a background, and use a GAN to constrain the composition results to the natural image manifold. Hu \etal~\cite{hu2018predictive} use a 3D STN to scale and translate objects given as volumetric grids, as a part of scene generation network. Inspired by this line of work, we incorporate an affine transformation module into our network. This way, the generation module only needs to generate normalized parts, and the deformation module transforms and assembles the parts together.

\vspace{-1em}
\paragraph{Deep latent space factorization} Several approaches suggested to learn disentangled latent spaces for image representation and manipulation. $\beta$-VAE~\cite{higgins2016beta} introduce an adjustable hyperparameter $\beta$ that balances latent channel capacity and independence constraints with reconstruction accuracy. InfoGAN~\cite{chen2016infogan} achieves the disentangling of factors by maximizing the mutual information between certain channels of latent code and image labels. Some approaches disentangle the image generation process using intrinsic decomposition, such as albedo and shading~\cite{shu2017neural}, or normalized shape and deformation grid~\cite{pumarola2018ganimation,shu2018deforming}.
The proposed approach differs from \cite{pumarola2018ganimation,shu2018deforming, shu2017neural} in that it maps both full and partial shapes into the same low dimensional embedding space, while in \cite{pumarola2018ganimation,shu2018deforming, shu2017neural}, different components have their own separated embedding spaces.

\vspace{-1em}
\paragraph{Projection in neural networks}
Projection is widely used in representation learning. It can be used for transformation from one domain to another domain~\cite{barnes2018projecting, poelitz2014projection, poerio2018dual}, which is useful for tasks like translation in natural language processing. For example, Senel \etal \cite{senel2018semantic} use projections to map word vectors into semantic categories.
In this work, we use a projection layer to transform a whole shape embedding into semantic part embeddings.

\section{Conclusions and future work}
\label{seq:conslusions}

We presented a Decomposer-Composer network for structure-aware 3D shape modelling. It is able to generate a factorized latent shape representation, where different semantic part embedding coordinates lie in separate linear subspaces. The subspace factorization allows us to perform shape manipulation via part embedding coordinates, exchange parts between shapes, or synthesize novel shapes by assembling a shape from random parts. Qualitative results show that the proposed system can generate high fidelity 3D shapes and meaningful part manipulations. Quantitative results shows we are competitive in the mIOU, connectivity, symmetry and classification benchmarks.

While the proposed approach makes a step toward automatic shape-from-part assembly, it has several limitations. First, while we can generate high-fidelity shapes at a relatively low resolution, memory limitations do not allow us to work with voxelized shapes of higher resolution. Memory-efficient architectures, such as OctNet~\cite{riegler2017octnet} and PointGrid~\cite{le2018pointgrid}, may help alleviate this constraint. Alternatively, using point-based shape representations and compatible deep network architectures, such as~\cite{qi2017pointnet}, may also reduce the memory requirements and increase the output resolution.

Secondly, we made a simplifying assumption that a plausible shape can be assembled from parts using per-part affine transformations, which represent only a subset of possible transformations. While this assumption simplifies the training, it is quite restrictive in terms of the deformations we can perform. In future work, we will consider general transformations which have higher degree of freedom, such as a 3D thin plate spline or a general deformation fields.
To promote better part connectivity, we will explore additional shape connectivity preservation losses, similar to \cite{G2L18}.
Finally, we have been using a cross-entropy loss to measure the shape reconstruction quality; it would be interesting to investigate the use of a GAN-type loss in this structure-aware shape generation context.

\section{Experiments}
\paragraph{Dataset}
In our experiments, we used the models from the ShapeNet 3D data collection~\cite{chang2015shapenet}, with part annotations produced by Yi \etal~\cite{yi2016scalable}.
The shapes were converted to $32\times32\times32$ occupancy grids using binvox~\cite{nooruddin2003simplification}. Semantic part labels were first assigned to the occupied voxels according to the proximity to the labeled 3D points, and the final voxel labels were obtained using graph-cuts in the voxel domain~\cite{boykov2001fast}.
We used the official ShapeNet train, validation and test data splits in all our experiments. Additional results for $64\times64\times64$ occupancy grids can be found in the supplementary material.

\subsection{Shape reconstruction}
\label{sec:reconstruction}
Figure~\ref{fig:volaestn_results} presents the results of reconstructing semantically labeled shapes from unlabelled input shapes, using the proposed network. Note that since our method performs separate part reconstruction with part decoders and part placement with an STN, it may produce less accurate part reconstruction, as compared to segmentation approaches - for example, the handles of the reconstructed rightmost chair in Figure~\ref{fig:volaestn_results}. But, as illustrated by our quantitative study in Section~\ref{sec:ablation}, this allows us to perform better part-based shape manipulation.

\begin{figure}[tb]
\begin{center}
    \includegraphics[width=0.15\linewidth]{figures_dec/batch3_gt0_gray.png}
    \hfill
    \includegraphics[width=0.15\linewidth]{figures_dec/batch3_dec0.png}
    \hfill\hfill\hfill\hfill
    \includegraphics[width=0.15\linewidth]{figures_dec/batch10_gt3_gray.png}
    \hfill
    \includegraphics[width=0.15\linewidth]{figures_dec/batch10_dec3.png}
    \hfill\hfill\hfill\hfill
    \includegraphics[width=0.15\linewidth]{figures_dec/batch26_gt3_gray.png}
    \hfill\hfill\hfill\hfill
    \includegraphics[width=0.15\linewidth]{figures_dec/batch26_dec3.png}
    \\
    \includegraphics[width=0.23\linewidth]{figures_dec/batch17_gt1_gray.png}
    \hfill
    \includegraphics[width=0.23\linewidth]{figures_dec/batch17_dec1.png}
    \hfill\hfill\hfill\hfill
    \includegraphics[width=0.23\linewidth]{figures_dec/batch21_gt0_gray.png}
    \hfill
    \includegraphics[width=0.23\linewidth]{figures_dec/batch21_dec0.png}
\vspace{-0.5em}
\caption{Reconstruction results of the proposed pipeline, for chair and table shapes. Gray shapes are the input test shapes; the results are colored according to the part label.}
\label{fig:volaestn_results}
\end{center}
\vspace{-1em}
\end{figure}

\subsection{Composite shape synthesis}
\label{sec:composite_synthesis}
\paragraph{Shape composition by part exchange}
In this experiment, we used our structured latent space to randomly swap corresponding embedding coordinates of pairs of input shapes (\eg, embedding coordinates of legs or seats of two chairs), and reconstruct the new shapes using the Composer. The results are shown in Figure~\ref{fig:swap}, and demonstrate the ability of our system to perform accurate part exchange, while deforming the geometry of both the new and the existing parts to obtain a plausible result. See the supplementary material for additional results using four shape classes.

\begin{figure}[tb]
\begin{center}
\rotatebox{90}{\hspace{1.5em}Legs}
\hfill
\includegraphics[width=0.15\linewidth]{figures_swap/pair10_gt0.png}
\hfill
\includegraphics[width=0.15\linewidth]{figures_swap/pair10_dec0.png}
\hfill
\includegraphics[width=0.15\linewidth]{figures_swap/pair10_swap0_partid2.png}
\hfill\hfill
\includegraphics[width=0.15\linewidth]{figures_swap/pair10_swap1_partid2.png}
\hfill
\includegraphics[width=0.15\linewidth]{figures_swap/pair10_dec1.png}
\hfill
\includegraphics[width=0.15\linewidth]{figures_swap/pair10_gt1.png}
\\
\rotatebox{90}{\hspace{1.5em}Back}
\hfill
\includegraphics[width=0.14\linewidth]{figures_swap/pair77_gt0.png}
\hfill
\includegraphics[width=0.14\linewidth]{figures_swap/pair77_dec0.png}
\hfill
\includegraphics[width=0.14\linewidth]{figures_swap/pair77_swap0_partid1.png}
\hfill\hfill
\includegraphics[width=0.14\linewidth]{figures_swap/pair77_swap1_partid1.png}
\hfill
\includegraphics[width=0.14\linewidth]{figures_swap/pair77_dec1.png}
\hfill
\includegraphics[width=0.14\linewidth]{figures_swap/pair77_gt1.png}
\\[0.2em]
\begin{minipage}{.05\linewidth} \centerline{ } \end{minipage}
\hfill
\begin{minipage}{.15\linewidth}
\centerline{{\small GT$_1$}}
\end{minipage}%
\hfill
\begin{minipage}{.15\linewidth}
\centerline{{\small REC$_1$}}
\end{minipage}%
\hfill\hfill
\begin{minipage}{.15\linewidth}
\centerline{{\small SWAP$_1$}}
\end{minipage}%
\hfill
\begin{minipage}{.15\linewidth}
\centerline{{\small SWAP$_2$}}
\end{minipage}%
\hfill\hfill
\begin{minipage}{.15\linewidth}
\centerline{{\small REC$_2$}}
\end{minipage}%
\hfill
\begin{minipage}{.15\linewidth}
\centerline{{\small GT$_2$}}
\end{minipage}%
\\
\caption{Single part exchange experiment. GT$_{1/2}$ denote ground truth shapes, REC$_{1/2}$ - reconstruction results, SWAP$_{1/2}$ - part exchange results. Unlabeled shapes were used as an input.\label{fig:swap}}
\end{center}
\vspace{-1em}
\end{figure}

\vspace{-1em}
\paragraph{Shape composition by random part assembly\label{sec:random_compose}}
In this experiment we tested the ability of the proposed network to assemble shapes from random parts using our factorized latent space. Specifically, we mapped batches of input shapes into the latent space using the Decomposer, and created new shapes by randomly mixing the part embedding coordinates of the shapes in the batch, and reconstructing new shapes using the Composer. The results are shown in Figure~\ref{fig:collect_chair}, for chairs and tables, and illustrate the ability of the proposed method to combine parts from different shapes, scale and translate them so that the resulting shape looks realistic. See the supplementary material for additional shape composition results.

\begin{figure}[t]
\begin{center}
    \begin{minipage}{0.02\linewidth}
    \rotatebox{90}{\hspace{1em}GT}
    \\[0.1em]
    \rotatebox{90}{\hspace{-1em}Composed}
    \end{minipage}
    \hfill\hfill\hfill
    \begin{minipage}{0.6\linewidth}
    \includegraphics[width=0.23\textwidth]{figures_collect_chair/batch88_gt0.png}
    \hfill
	\includegraphics[width=0.23\textwidth]{figures_collect_chair/batch88_gt1.png}
    \hfill
    \includegraphics[width=0.23\textwidth]{figures_collect_chair/batch88_gt2.png}
    \hfill
	\includegraphics[width=0.23\textwidth]{figures_collect_chair/batch88_gt3.png}
    \\
    \includegraphics[width=0.23\textwidth]{figures_collect_chair/batch88_collect0.png}
    \hfill
	\includegraphics[width=0.23\textwidth]{figures_collect_chair/batch88_collect1.png}
    \hfill
    \includegraphics[width=0.23\textwidth]{figures_collect_chair/batch88_collect2.png}
    \hfill
	\includegraphics[width=0.23\textwidth]{figures_collect_chair/batch88_collect3.png}
    \end{minipage}
    \hfill \hfill
    \begin{minipage}{0.35\linewidth}
    \includegraphics[width=0.48\textwidth]{figures_table/batch17_gt0.png}
    \hfill
	\includegraphics[width=0.48\textwidth]{figures_table/batch17_gt1.png}
    \\
    \includegraphics[width=0.48\textwidth]{figures_table/batch17_collect0.png}
    \hfill
	\includegraphics[width=0.48\textwidth]{figures_table/batch17_collect1.png}
    \end{minipage}
    \\
    \caption{Shape composition by random part assembly. The top row shows the ground truth (GT) shapes, and the bottom row - shapes assembled using the proposed approach (see Section~\ref{sec:random_compose}). Unlabeled shapes were used as an input.}
\label{fig:collect_chair}
\end{center}
\vspace{-1em}
\end{figure}

\vspace{-1em}
\paragraph{Full and partial interpolation in the embedding space}
In this experiment, we tested reconstruction from linearly interpolated embedding coordinates of complete shapes, as well as of a single semantic part. For the latter, we performed the part exchange experiment, described above, and interpolated the coordinates of that part, while keeping the rest of part embedding coordinates fixed. The results are shown in Figure~\ref{fig:full_ae_interp}. See the supplementary material additional interpolation results.

\begin{figure*}[thb]
\begin{center}
    \rotatebox{90}{\hspace{1.7em}Whole}
	\hfill
	\includegraphics[width=0.07\textwidth]{figures_interp/pair11_gt0.png}
    \hfill \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp/pair11_dec0.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp/pair11_interp1_alpha0.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp/pair11_interp1_alpha1.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp/pair11_interp1_alpha2.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp/pair11_interp1_alpha3.png}
    \hfill
 	\includegraphics[width=0.07\textwidth]{figures_interp/pair11_interp1_alpha4.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp/pair11_interp1_alpha5.png}
    \hfill
 	\includegraphics[width=0.07\textwidth]{figures_interp/pair11_interp1_alpha6.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp/pair11_interp1_alpha7.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp/pair11_dec1.png}
    \hfill \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp/pair11_gt1.png}

	\\[0.1em]

    \rotatebox{90}{\hspace{2em}Back}
	\hfill
	\includegraphics[width=0.07\textwidth]{figures_interp_part/pair9_gt1.png}
    \hfill \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp_part/pair9_dec1.png}
	\hfill
	\includegraphics[width=0.07\textwidth]{figures_interp_part/pair9_interp1_partid1_alpha0.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp_part/pair9_interp1_partid1_alpha1.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp_part/pair9_interp1_partid1_alpha2.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp_part/pair9_interp1_partid1_alpha3.png}
    \hfill
 	\includegraphics[width=0.07\textwidth]{figures_interp_part/pair9_interp1_partid1_alpha4.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp_part/pair9_interp1_partid1_alpha5.png}
    \hfill
 	\includegraphics[width=0.07\textwidth]{figures_interp_part/pair9_interp1_partid1_alpha6.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp_part/pair9_interp1_partid1_alpha7.png}
    \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp_part/pair9_dec0.png}
    \hfill \hfill
	\includegraphics[width=0.07\textwidth]{figures_interp_part/pair9_gt0.png}

    \\

    \begin{minipage}{0.03\linewidth} \centerline{ } \end{minipage}%
    \begin{minipage}{0.07\linewidth} \centerline{{\small GT$_1$}} \end{minipage}%
    \hfill \hfill
    \begin{minipage}{0.07\linewidth} \centerline{{\small REC$_1$}} \end{minipage}%
    \hfill
    \begin{minipage}{0.07\linewidth} \centerline{{\small $\alpha=\frac{1}{9}$}} \end{minipage}%
    \hfill
    \begin{minipage}{0.07\linewidth} \centerline{{\small $\alpha=\frac{2}{9}$}} \end{minipage}%
    \hfill
    \begin{minipage}{0.07\linewidth} \centerline{{\small $\alpha=\frac{3}{9}$}} \end{minipage}%
    \hfill
    \begin{minipage}{0.07\linewidth} \centerline{{\small $\alpha=\frac{4}{9}$}} \end{minipage}%
    \hfill
    \begin{minipage}{0.07\linewidth} \centerline{{\small $\alpha=\frac{5}{9}$}} \end{minipage}%
    \hfill
    \begin{minipage}{0.07\linewidth} \centerline{{\small $\alpha=\frac{6}{9}$}} \end{minipage}%
    \hfill
    \begin{minipage}{0.07\linewidth} \centerline{{\small $\alpha=\frac{7}{9}$}} \end{minipage}%
    \hfill
    \begin{minipage}{0.07\linewidth} \centerline{{\small $\alpha=\frac{8}{9}$}} \end{minipage}%
    \hfill
    \begin{minipage}{0.07\linewidth} \centerline{{\small REC$_2$}} \end{minipage}%
    \hfill \hfill
    \begin{minipage}{0.07\linewidth} \centerline{{\small GT$_2$}} \end{minipage}%
\caption{Example of a whole (top) and partial (bottom) shape interpolation. GT$_{1/2}$ denote original models, REC$_{1/2}$ - their reconstructions, and linear interpolation results are in the middle. Unlabeled shapes were used as an input.}
\label{fig:full_ae_interp}
\end{center}
\vspace{-0.7em}
\end{figure*}

\subsection{Latent space and projection matrix analysis}
The latent space obtained using the proposed method exhibits clear separation into subspaces corresponding to different semantic parts. The projection matrices, while not not being strictly orthogonal, as required for the partition of the identity (\ref{eq:part_of_identity}), have low effective ranks, which is in line with the clear separation into non-overlapping subspaces produced by them. See the supplementary material for the latent space and the projection matrices visualization.

\begin{table*}[tb]
\begin{center}
\begin{tabular}{|l||c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
\backslashbox{Method}{\begin{tabular}{@{}c@{}}Metric\end{tabular}}
& mIoU & \begin{tabular}{@{}c@{}}mIoU\\(parts)\end{tabular} &
\multicolumn{3}{c|}{Connectivity} & \multicolumn{3}{c|}{\begin{tabular}{@{}c@{}}Classifier\\accuracy\end{tabular}} & \multicolumn{3}{c|}{\begin{tabular}{@{}c@{}}Symmetry\\score\end{tabular}} \\
\cline{2-12}
& Rec. & Rec. & Rec. & Swap & Mix & Rec. & Swap & Mix & Rec. & Swap & Mix  \\
\hline\hline
Our method & 0.64 & 0.65 & \bf{0.82} & \bf{0.71} & \bf{0.65} & \bf{0.95} & 0.89 & \bf{0.83} & 0.95 & 0.95 & 0.95 \\
\Xhline{2\arrayrulewidth}
W/o cycle loss & 0.63 & 0.66 & 0.74 & 0.62 & 0.54 & 0.93 & 0.84 & 0.80 & 0.96 & 0.96 & 0.95 \\
\hline
Fixed projection & 0.63 & 0.65 & 0.72 & 0.61 & 0.58 & 0.94 & 0.86 & 0.77 & 0.94 & 0.95 & 0.95 \\
\hline
Composer w/o STN & \bf{0.75} & \bf{0.8} & 0.69 & 0.48 & 0.23 & \bf{0.95} & \bf{0.9} & 0.71 & 0.95 & 0.91 & 0.85 \\
\Xhline{2\arrayrulewidth}
Naive placement & - & - & - & 0.68 & 0.62 & 0.61 & 0.47 & 0.21 & - & \bf{0.96} & \bf{0.96} \\
\hline
ComplementM
& - & - & - & \bf{0.71} & 0.47 & - & 0.66 & 0.43 & - & 0.66 & 0.43 \\
\hline
Segmentation+STN & - & - & - & 0.41 & 0.64 & - & 0.64 & 0.36 & - & 0.77 & 0.77 \\
\hline
\end{tabular}
\end{center}
\vspace{-1em}
\caption{Ablation study results. The evaluation metrics are mean Intersection over Union (\emph{mIoU}), per-part mean IoU (\emph{mIoU (parts)}), shape \emph{connectivity} measure, binary shape \emph{classifier accuracy}, and shape \emph{symmetry score}. Rec., Swap and Mix stand for the shape reconstruction, part exchange and random part assembly experiment results, respectively (see Section~\ref{sec:composite_synthesis}).
See Section~\ref{sec:ablation} for a detailed description of the compared methods and the evaluation metrics. \label{tbl:ablation_eval}}
\end{table*}

\subsection{Ablation study and comparison with existing approaches}
\label{sec:ablation}
\subsubsection{Ablation study}
To highlight the importance of the different elements of our approach, we conducted an ablation study, where we used several variants of the proposed method, listed below.

\vspace{-1em}
\paragraph{Fixed projection matrices} Instead of using learned projection matrices in the Decomposer, the $n$-dimensional shape encoding is split into $K$ consecutive equal-sized segments, which correspond to different part embedding subspaces. This is equivalent to using constant projection matrices, where the elements of the rows corresponding to a particular embedding space dimensions are $1$, and the rest of the elements are $0$.

\vspace{-1em}
\paragraph{Composer without STN} We substituted the proposed composer, consisting of the part decoder and the STN, with a single decoder producing a labeled shape. The decoder receives the sum of part encodings as an input, processes it with two FC layers to combine information from different parts, and then reconstructs a shape with parts labels using a series of deconvolution steps, similar to the part decoder in the proposed architecture.

\vspace{-1em}
\paragraph{Without cycle loss} We removed the cycle loss component during the network training.

\subsubsection{Comparison with existing methods}
Most existing methods for composite shape modeling operate on triangulated meshes with precise part segmentation. Hence, they are not directly applicable to the large-scale ShapeNet dataset with less precise segmentation, preventing a fair comparison.
We therefore added the following comparisons with modern neural-net-based techniques: we combined the state-of-the-art ComplementMe method \cite{sung2017complementme} with a 3D-CNN segmentation network  \cite{qi2017pointnet}. From the former we used the \emph{component placement network}, which, given a partial shape and a complementary component, produces a 3-D translation to place the component correctly w.r.t. the partial shape. To produce the "to-be-added" component we used a 3D-CNN segmentation network, described in \cite{qi2017pointnet}, which achieved a state-of-the-art mean Intersection over Union (mIoU) of 0.91 on the test set. Together, these two networks replace our proposed Decomposer-Composer. Both networks were trained using the same training data as the proposed method.
This method is denoted by \emph{ComplementMe} in Table~\ref{tbl:ablation_eval}.

For an additional comparison, instead of the placement network of ComplementMe we utilized the spatial transformer network. Here, the STN was trained using the ground truth shape parts, and at test time it was applied to the results of the segmentation network, described above. This method is denoted by \emph{Segmentation+STN} in Table~\ref{tbl:ablation_eval}.

Finally, we compared the proposed method to a baseline shape composition network. Given ground-truth shape parts, it composes new shapes from these parts by placing them at their original locations in the source shapes they were extracted from. All the shapes in our dataset are centered and uniformly scaled to fill the unit volume, and there exist clusters of geometrically and semantically similar shapes. Thus, we can expect that even this naive approach without part transformations will produce plausible results in some cases. This method is denoted by \emph{Naive placement} in Table~\ref{tbl:ablation_eval}.

See the supplementary material for an additional qualitative comparison with 3D-GAN \cite{3dgan} and G2LGAN \cite{G2L18}, using $64\times64\times 64$ voxelized shapes.

\subsubsection{Evaluation metrics\label{sec:metrics}}
\paragraph{Mean Intersection over Union (mIoU)} is commonly used to evaluate the performance of segmentation algorithms~\cite{long2015fully}. Here, we use it as a metric for the reconstruction quality. We computed the mIoU for both actual-sized reconstructed parts, and scaled and centered parts (when applicable). We denote the two measures by \emph{mIoU} and \emph{mIoU (parts)} in Table~\ref{tbl:ablation_eval}.

\vspace{-1em}\paragraph{Connectivity} In part based shape synthesis, one pathological issue is that parts are often disconnected, or penetrate each other.
Here, we would like to benchmark the quality of part placement, in terms of part connectivity. For each $32\times 32\times 32$ volume, we compute the frequency of the shape forming a single connected component, and report it as \emph{Connectivity} in Table~\ref{tbl:ablation_eval}.

\vspace{-1em}\paragraph{Classification accuracy} To measure the shape composition quality of different methods, we trained a binary neural classifier to distinguish between ground-truth whole chairs (acting as positive examples) and chairs produced by naively placing random chair parts together (acting as negative examples). To construct the negative examples, we randomly combined ground-truth shape parts, by adding a certain semantic part only once, and placing the parts at their original locations in the source shapes they were extracted from. In addition, we removed negative examples assembled from parts from geometrically and semantically similar chairs, since such part arrangement could produce plausible shapes incorrectly placed in the negative example set. The attained classification accuracy on the test set was $\sim~88\%$. For a given set of chairs, we report the average classification score. Details of the network can be found in the supplementary material. The results are reported as \emph{Classifier accuracy} in Table~\ref{tbl:ablation_eval}.

\vspace{-1em}\paragraph{Symmetry} The chair shapes in the ShapeNet are predominantly bilaterally symmetric, with vertical symmetry plane. Thus, similar to \cite{G2L18}, we evaluated the symmetry of the reconstructed shapes, and defined the \emph{Symmetry score} as the percentage of the matched voxels (filled or empty) in the reconstructed volume and its reflection with respect to the vertical symmetry plane. We performed this evaluation using binarized reconstruction results, effectively measuring the global symmetry of the shapes. For the evaluation, we used the shapes in the test set (690 shapes), and conducted three types of experiments: shape reconstruction, single random part exchange between a pair of random shapes, shape composition by random part assembly. The experiments are described in more detail in Sections~\ref{sec:reconstruction} and \ref{sec:composite_synthesis}.

\subsubsection{Evaluation result discussion}
According to all metrics, our method outperforms or performs on par with all the baselines, and \emph{significantly} outperforms other existing methods. This shows that our design choices - the cycle loss, learned projection matrices and usage of the STN, help to achieve plausible results both when reconstructing shapes, and when performing composite shape synthesis. This is especially pronounced in the connectivity test results, illustrating that these design choices are necessary for achieving good assembly quality.

In the classifier accuracy test and the symmetry test, the proposed method performs slightly better or on par with all baselines considered in the ablation study. It seems that both these tests are less sensitive to disconnected shape components, and most advantage that the proposed method achieves over the baselines is in its composition robustness. As expected, the naive placement also achieves high symmetry score, since it preserves the symmetry of the ground-truth parts during shape assembly.

According to the mIoU and per-part mIoU metrics, the proposed method performs on par with all baselines, except when using the simple version of the Composer, without STN. This follows from the fact that the proposed system, while reconstructing better fine geometry features, decomposes the problem into two inference problems, for the geometry and the transformation, and thus does not produce as faithful reconstruction of the original model as the simple decoder. Notably, this version of the architecture achieves worst connectivity scores for all compared methods, which follows from the fact that such a Decomposer is unable to faithfully reconstruct fine shape details. Please see the supplementary material for a qualitative comparison of the results of all the compared methods.

redpajama_set_name: RedPajamaArXiv


New Perspective scholars have argued that justification is not the gospel and in fact, is not even dealing with matters of soteriology, but matters of ecclesiology. This assertion creates obvious differences in how we are to understand the doctrine of justification.
It seems to me that part of the attraction in redefining justification, as it marks those who are members of the Church, is its utility in breaking down the various barriers that separate Christendom. If justification is not about how you are saved, then why should Roman Catholics and Protestants be divided?
In others words, like Dunn, the new perspective on justification is about the rejection of exclusivism. His implication that all Christians should similarly oppose all exclusivism is unbiblical and goes beyond the Word of God5.
Moreover, the argument that our differences on justification are not important or unessential seems to be contradicted by Paul himself who argues that anyone who preaches another view of justification is in effect preaching another gospel. Paul's response (Gal 1:9) is emphatic, "As we have already said, so now I say again: If anybody is preaching to you a gospel other than what you accepted, let him be eternally condemned!"
How then can a less than clear understanding of justification by faith be a true basis for unity? Surely, lasting unity is found in our shared confession that we are saved by grace through faith in the work of Jesus Christ. Anything less than this confession is a flimsy basis for ecumenical progress.
In response to the assertions of the New Perspective scholars, Paul teaches that we have no basis of unity outside of the finished work of Jesus Christ. Paul's convictions about the efficacy of Christ's work can be seen in Romans 5:7 — "Since we have now been justified by his blood, how much more shall we be saved from God's wrath through him!" John builds upon this idea in 1 John 1:7, "But if we walk in the light, as he is in the light, we have fellowship with one another, and the blood of Jesus, his Son, purifies us from all sin."
Closely connected with the issue of justification by faith is the issue of assurance of salvation. The Reformation view of the forensic nature of justification lies as the bedrock of Christian assurance. To be justified is to have peace with God, and our personal assurance of salvation is therefore predicated upon Christ's acceptable, meritorious and atoning work.
Carson laments, that this is in contrast with those who hold to the new perspective and its categories of 'getting in' and staying in'.
Moreover, Wright, Dunn and Sanders all give (in varying degrees) the issue of sanctification or Christian obedience an instrumental role in the final eschatological vindication of God's people. That is, the New Perspective drives the believer to find comfort and assurance in their own obedience as evidence of the Spirit's work and as an instrumental part of their ultimate vindication. They take their eyes of the work of Christ and place their hope in the work of their own hands, at least in part, since "staying in" the covenant community is predicated on covenant obedience.
Clearly, the obedience of the Christian is instrumental in whether there will be a favorable eschatological verdict. Consequently, where justification by faith is at the periphery of Christian theology, Christian assurance is pushed to the periphery of Christian experience.13 According to the New Perspective, salvation is intrinsically uncertain (if not elusive) whilst it relies upon the instrument of our unpredictable obedience.
In contrast, if salvation is indeed the work of a sovereign God in seeking and saving sinners, then the Christian has in the work of Christ all the assurance they need to be confident on that great day when they shall be revealed as sons and daughters of the living God.
This orthodox position does not deny that obedience to God manifesting itself in the fruits of good works plays a secondary part in encouraging the believer that the Spirit is working in and through them.14 However, as the Reformers so clearly saw, it is the finished work of Jesus Christ that is the object of our faith and the foundation of our community, and hope of glory.
2. N.T Wright,	What St Paul Really Said (Eerdmans, Grand rapids, 1997) pg. 159.
5 .For instance, some differences are clearly God's will. The differences between men and women and their different economic roles whilst enjoying ontological equality (Gen. 1-2) would be one of many examples.
6.Robert S. Smith Justification and Eschatology: A dialogue with "The New Perspective on Paul". (RTR, Supplement Series#1, May 2001) pg 129.
7. D Carson,	Reflections on Christian Assurance, (WTJ 54 1992) pg 3.
9. Mark Thompson	Personal Assurance and the New Perspective (RTR, 53:2, 1994) pg. 84.
11. Robert S. Smith Justification and Eschatology: A dialogue with 'The New Perspective on Paul'. (RTR, Supplement Series#1, May 2001) pg. 134.
12.E.P. Sanders	Paul, The Law and the Jewish People (Fortress Press Minneapolis, 1983) pg. 111.
13. Mark Thompson	Personal Assurance and the New Perspective (RTR, 53:2, 1994) pg. 85.


text:
West Haven police release photos of woman wanted in hit-and-run crash
Peter Yankowski
Dec. 5, 2022 Updated: Dec. 5, 2022 5:31 p.m.
West Haven police said they looking to identify this woman suspected in a hit-and-run crash Sunday in the area of Post Road and Front Avenue that seriously hurt a pedestrian.
West Haven Police Department / Contributed PhotoShow MoreShow Less
West Haven police said they looking to identify this Toyota Camry that was involved in a hit-and-run crash Sunday in the area of Post Road and Front Avenue that seriously hurt a pedestrian.
The West Haven Police Department is seeking a driver involved in a hit-and-run crash Sunday evening that left a pedestrian with life-threatening injuries.
Arnold Gold / Hearst Connecticut Media file photoShow MoreShow Less
WEST HAVEN — Police say they are searching for a driver involved in a hit-and-run crash that left a pedestrian with life-threatening injuries over the weekend.
The crash happened around 6:50 p.m. Sunday in the area of Boston Post Road and Front Avenue. West Haven police did not immediately say on Monday whether the person was struck on Post Road or Front Avenue.
After striking the victim, the driver fled the scene, police said. The police department released photos of the suspect and are seeking help from the public to identify the woman. The woman was driving a Toyota Camry, possibly from 2007 to 2009, according to police.
The victim was treated at the scene by firefighters and transported to the hospital, "where he remains in their care," West Haven Police Sgt. Patrick Buturla said Monday.
Anyone with information regarding this incident is asked to call the West Haven Police Department's Traffic Division at 203-937-3900.
Reach Peter on
Peter Yankowski is a breaking news reporter for Hearst Connecticut Media Group. He was previously a reporter for the Danbury News-Times and the Ridgefield Press.

redpajama_set_name: RedPajamaCommonCrawl


This is one place where Instapage truly shines. Its editor is one of the Best around, and with it you can fix the fonts, colours, and designs to match your existing site or advertising materials.
Do note that AMP pages are designed for mobile, and may be a Fantastic path to go if you are designing a webpage specifically for mobile use. All these are designed to load incredibly fast, but you'll get this choice if you are signed up for the Enterprise program.
A custom domain name is really self-explanatory. Simply enter in the domain You'd like to print to. But beware, this one does need a modest technical know-how. Fortunately, Instapage comprises a video to help you through the process.
Instablocks — those are useful for times when you need to create multiple landing pages, but do not want to modify the style of the page. Rather than recreating your complete page, it will save your components for you to be reused. You will have access to blocks produced by Instapage or you can create your own.
A/B testing — In case you would like to test unique components of your page, you can split test using Instapage. Just duplicate your main copy and make a second copy with any changes to the text, images, CTAs, etc.. When you print your page, some will see the control version and others will see the variant. Info about how each performs will be kept on your Instapage analytics department.
Then, when you check our Analytics section after you page is pushed Live, you'll discover details about your webpage's conversion rate based on the targets you set.
While you can eliminate the Fundamental Core plan for just designing and Publishing (awesome) landing pages, the Optimizer gives you access to useful tools like heatmaps and A/B testing, which can help your overall conversion rate in the future.


Now, as an American citizen living in San Francisco, I can write about my experiences and perspectives in ways that many in Gaza cannot, fearing only that my parents, siblings and other family members who remain there are not held responsible for my opinions. My folks are sometimes jealous of my ability to speak my mind and remind me frequently to consider the implications for them of what I say.
While Israel continues to play a significant role in Gaza's affairs, the grim anniversary of the Hamas takeover warrants focusing less on Israel than the role that Palestinian political organizations have played in worsening the misery for Gaza's more than 2 million residents. And that has led me to conclude that the United Nations, for all its problems and the hate it incurs by Israelis, is perhaps Gazans' best hope for progress.
Read the Whole Thing: How to save Gaza: Palestinian American says it's time to stop blaming Israel – 7/12/2017.


text:
ACS Sets Up Accountability Office
Tara Jessup
New York, NY – Following several scandals and reports of chaos within the agency, the New York City Administration for Children's Services (ACS) has set up an ACS Accountability Office.
The Accountability Office is expected to oversee needed management improvements. Its hoped that the oversight and intensive monitoring will affect a positive change in operations. Eden Hauslaib, a former agency Senior Advisor of Strategic Initiatives, has been appointed Chief Accountability Officer to oversee the new division. With the continual reports of mismanagement and waste, Commissioner David Hansell committed to completing a top-to-bottom management review of ACS' programs and practices. The creation of the Accountability Office is an outgrowth of that review.
"Creating an Accountability Office is the next step in this agency's move towards greater organizational effectiveness," said ACS Commissioner David Hansell. "As we continue our internal assessment of agency practices and review the recommendations we receive from external oversights, we will remain focused on practical reform. We will continue to identify improved agency structures that best serve the children and families we work with, and that support our outstanding workforce. I am confident that Eden has the background and experience necessary to implement this new structure and achieve the goals of this new Office."
The Accountability Office will also conduct audits of case practices of ACS employees working within the two major divisions that provide direct services to the public: the Division of Child Protection (DCP), which employs close to 3,000 staff who conduct investigations throughout the five boroughs; as well as the Division of Youth and Family Justice (DYFJ) with 750 staff who serve young people in the juvenile detention and placement systems throughout the city
"The Office of Accountability will move the Administration for Children's Services forward as they work to strengthen services for families and build organizational capacity," said Deputy Mayor for Health and Human Services Dr. Herminia Palacio. "This Office will ensure that the many reforms that ACS has and will introduce will be implemented in a clear, consistent, and efficient manner, streamlining processes for the agency and, ultimately, improving the lives of families and children across New York City."
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I wound up spending quite a while exploring this beautiful, highly underrated country, so here's my Malaysia itinerary for 2 weeks.
Malaysia was actually a total surprise! I only went there because the cheapest flight from Bali went to Kuala Lumpur, and I thought I'd make a proper trip out of it.
I think Malaysia is still a bit overlooked among travelers, and people prefer to travel to neighboring countries like Thailand and Indonesia. Don't make that mistake! Malaysia is really a very beautiful country. It's also budget-friendly, travel throughout is pleasant, and Malaysians are both super friendly and speak English.
In my two weeks in Malaysia, I traveled through big cities, through jungles, around tea plantations, and out to tropical islands. It has a bit of everything, and because of this, I think it might just be one of my favorite Asian countries.
Here's my Malaysia itinerary along with some tips if you want to explore Singapore and Borneo as well!
In the summer, it's six hours ahead in Malaysia than in the Netherlands. In the winter, it's seven hours ahead.
Because Malaysia has a tropical climate, the weather is always nice, somewhere between 25C and 35C. It's a bit cooler in the Cameron Highlands, and the summer months have the least amount of precipitation. While it generally rains quite a bit in Malaysia, it's often only in the evening and not for very long.
It is best to make an appointment with the GGD to see which vaccinations you need exactly. In general, we recommend DTP, Hepatitis A and BMR (if you have never been vaccinated against measles). If you go to the jungles of Borneo you may have to take an emergency cure for Malaria. However, make sure you consult with actual doctors!
You'll start your itinerary in Kuala Lumpur, one of my favorite Asian metropolises. You may be surprised that it's actually very Western with its high skyscrapers and shiny shopping centers. It's also full of culture where you can explore places like Chinatown or get a taste of some Hindu culture in the famous Batu Caves.
If you do only have a day, check out this day tour from Kuala Lumpur.
If you are looking for something on a budget, many travelers recommend Orchid Haven, which is a more social guesthouse that offers different tours. If you just want luxury, and then stay at the Cameron Highlands Resort.
Penang is a state on the west coast of Malaysia. To be honest, the west coast is a much nicer side of the country. Its most famous area is its capital, George Town. Definitely stay here as it's a nice city with historic houses, trendy street art, and great food. The city center feels more like a village than a city, and it's even a UNESCO World Heritage site.
From George Town, you can easily visit the beaches of Batu Ferringhi or Penang Hill.
I stayed at The Frame Guesthouse, and I highly recommend it. It's housed in a beautiful, traditional building; the owner is lovely, and the rooms are comfortable. It's within walking distance of Love Lane on Chulia Street.
How does ending your vacation with a few days on a tropical beach sound? Sounds pretty perfect to me!
In that case, you can choose between two places: the Perhentian Islands or Langkawi. Because of its location, I chose Langkawi.
The island is full of beautiful beaches and waterfalls, and it has such a lovely, quiet vibe. From the SkyBridge, you have one of the most beautiful views ever.
For four days, I did almost nothing other than floating around in the pool, lying on the beach, and riding a scooter around. It was a vacation in the truest sense of the word, and I ended my trip feeling fully relaxed! *Side Note: Don't go swimming in the sea as there's a lot of jellyfish!
You can easily get to Langkawi from Penang via ferry, and the ride will be around 3 hours. You could also just fly, which is often around the same price, and only takes half an hour.
If you're on a budget I recommend staying at The Crowded House. It's a super cozy hostel with a relaxed vibe and a really nice swimming pool. A lot of the guests stay here for more than few days.
Jungle lovers will love to go to Taman Negara. This nature park claims to be the oldest rainforest in the world, even older than the Amazon. It is also the largest nature park in Malaysia. It's located about 4 hours from Kuala Lumpur and is easy to combine with a trip to the capital. Check to see if you'd want to do a day tour or an overnight tour.
These two islands are generally seen as the most beautiful islands in Malaysia and are ideal if you want to snorkel or a romantic getaway with your partner. My friends have been there and said that the islands are nicer than Langkawi, but there's a lot less to do and may be boring for some. If you go, make sure it's between March and October to avoid the rainy season.
The beautiful Malaysian Borneo is located on a different island than mainland Malaysia, and is known for its jungle, its enormous biodiversity, great snorkeling, and, of course, the world-famous orangutans! This can be combined with a trip through Kalimantan in Indonesia or a visit to Bruneï, the other two countries that own territory on the same island.
If you are in Malaysia, you definitely want to do at least 2 or 3 days in Singapore. A one-way bus journey from Kuala Lumpur takes about 5-8 hours. You could also take a night train that'll take 7-8 hours or fly over in an hour.
Anything I'm missing from this Malaysia itinerary?
great post! I was just recently in Penang, loved it!


Synovus Financial Corp. is a financial services company based in Columbus, Georgia, with approximately $32 billion in assets. We provide commercial and retail banking, investment, and mortgage services through 250 branches to customers all across the Southeast - in Georgia, Florida, Alabama, South Carolina, and Tennessee. Synovus Bank, a wholly owned subsidiary of Synovus, was recognized as one of America's �Most Reputable Banks� by American Banker and the Reputation Institute in 2018. Also in 2018, American Banker named Synovus one of the "Best Banks to Work For" in the United States. We're honored by those accomplishments, and we're proud of our team members who helped us achieve that recognition.


text:
Khaled Abdullah/Reuters
Women and children, displaced by the ongoing fighting in northwestern Yemen, collect water from a tank in al-Mazraq refugee camp near the Saada province, October 4.
At heart of Yemen's conflicts: water crisis
A recent report shows that 70 to 80 percent of rural conflicts are over water shortages in Yemen, already on the brink of becoming a failed state.
By Laura Kasinof Correspondent
November 5, 2009 | Sanaa, Yemen
While domestic insurgencies chip away at the control of Yemen's central government and an Al Qaeda branch gains strength in regions beyond the government's reach, another crisis — one that affects Yemen's entire population — has the potential to contribute to the country's instability and potential trajectory toward failure.
Yemen is running out of water – fast.
But the water crisis and the rise of militancy are not unrelated perils said Abdulrahman Al Eryani, Yemen's minister of Water and Environment, in an interview. Much of the country's rising militancy, he argues, is a conflict over resources.
"They manifest themselves in very different ways: tribal conflicts, sectarian conflicts, political conflicts. Really they are all about sharing and participating in the resources of the country, either oil, or water and land," said Minister Eryani. "Some researchers from Sanaa University had very alarming figures. They said that between 70-80 percent of all rural conflicts in Yemen are related to water."
Khalid Al-Thour, a geology professor at Sanaa University, adds that recent reports have indicated that Sanaa's wells will run dry by 2015 at current water-usage rates.
In their 2009 Failed States Index, Foreign Policy magazine topped their analysis with a sobering assessment of Yemen as "a perfect storm of state failure," including disappearing oil and water reserves.
The World Bank considers Yemen "one of the most water-scarce countries in the world" where only 125 cubic meters of water are available yearly per capita compared to the world average of 2,500 cubic meters. Just 46 percent of Yemen's rural population has direct access to an adequate water supply and the number is only slighter better in cities, according to the German Development Service (GDS), which is working with the Yemeni government to improve water management.
Public access to water is particularly sparse in Taiz, Yemen's third-largest city, where access to public water tanks is allowed only once every 45 days. When households run out of their personal water supply they have the option to buy water from private companies but in Yemen, where 42 percent of the population lives beneath the poverty line, many rely on charity from mosques to meet their needs, says Dierk Schlutter, coordinator for water and environment for GDS.
Soaring population, illegal wells, qat
Yemen's population of 23 million has almost tripled since 1975, according to UN statistics, and available water resources simply cannot cope.
The soaring population paired with poor management of water resources has led to what Dr. Thour calls a "disastrous situation." He says that the illegal drilling of wells into natural groundwater aquifers is increasing at an alarming rate and leaky pipes that waste up to 60 percent of water in urban areas are major culprits. Although water usage regulations are in place to tackle these issues, the government lacks the ability to enforce them.
"We have a war and we have all these troubles so the government doesn't want to think about water," says Thour.
Furthermore the production of qat – a mild narcotic that as a staple of Yemeni social gatherings is a driving force in Yemen's economy – uses up to 40 percent of available water in the capital Sanaa's water basin, according to the Ministry of Water and Environment. Qat farming has only increased in recent years.
"There is enough water for provision of a city of 2 million like Sanaa, if the people don't drill illegally in the outskirts and take the water for irrigation of a cash crop like qat," says Dr. Schlutter. "It's a very complicated social issue because of the social value of qat chewing in this society."
Yemenis reluctant to accept solutions
Experts say potential solutions to Yemen's water crisis include increasing the implementation of rainwater harvesting in rural areas and modern irrigation techniques that waste less water than current methods. However, the challenge is convincing Yemenis that these changes to the status quo will bring economic benefits as well.
"You don't need to tell people who are fighting everyday to get water that there is a crisis. What is lacking is providing them with alternatives and options about what they should do," said Eryani. "The main problem is that you have to create incentives, even if people understand what they should do if there is no incentive they wont do it ... and the best incentive of course is economic."
A khat-free wedding becomes big news in Yemen
Saudi-led coalition airstrikes in Yemen's capital kill 29
Houthi rebels dissolve Yemeni parliament
https://www.csmonitor.com/World/Middle-East/2009/1105/p06s13-wome.html


The documents in this section are meant to help you determine your hardware requirements and use the optimization potential of your server applications.
Using the web service API, the events and appointments maintained by means of Infopark Web Calendar can be published.
Search queries can be tested by means of a Verity commandline tool.
Infopark CMS Fiona comes with an XML client that can be used to delete documents from the seach engine index.
By means of a Tcl procedure, you can find CMS files that use much space.
By means of a Tcl procedure, all draft versions of a particular user can be assigned to a different user.
With user managers that do not support permissions, group assignments can be used instead.
A CMS file can be exported individually by means of a URL.
This article describes how content dependencies of export variables can be avoided to increase the export speed.
The Trifork Server can be hidden from external requests. This article explains how.
The Trifork server does not delete all its temporary files. From time to time, they should be removed manually or by means of a script.


INFOGRAPHIC: What Is Sales Enablement, Anyway?
Among the sometimes-vague sales terms that get tossed around, sales enablement may be one of the worst offenders. Sometimes, it's a code word for a sales operations group; other times, it's code for sales training. CRM can come into it, as can other technology. So what does "sales enablement" really mean?
In the research that led to our best-selling book, Cracking the Sales Management Code, we discovered how broadly people use this term. Then, in order to clarify it, we organized some of this information in the infographic below.
Basically, we found that sales enablement is a collection of tasks and tools that are intended to improve the execution of key sales activities—activities like making sales calls, pursuing opportunities, managing major accounts, and targeting top prospects. But our research also revealed that these tasks and tools all fall into one of four categories—categories that interestingly mirror the lifecycle of an employee.
First, there are Sales Enablement activities meant to improve the Recruiting and Hiring of a sales force. These are focused on ensuring that 'the right people are on the bus,' so to speak. In addition, sales enablement involves keeping all of the seats on the bus filled, so the sales force is operating at full capacity. These activities can be owned by sales, HR, consultants, or any one else, but the goal here is clear… Hire the right number of the right folks.
The next group of Sales Enablement activities falls into the category of Training and Coaching. Once the right sellers are onboard, a lot of effort is focused on improving their skills and knowledge to enable better execution of the sellers' key activities. Like Recruiting and Hiring, these activities could also be distributed across organizational boundaries; the goal is the same, too: improve the sellers' ability to sell.
The third bucket of Sales Enablement activities are focused on Equipping the sales force with supporting tools. Whether CRM, Internet portals, or other selling aids, an organization Equips its sellers with the tools to collect, structure, process, and report information that enables better selling. Whether high-tech or old school, Equipping the sales force to improve its efficiency and effectiveness is at the core of Sales Enablement.
And the final group of Sales Enablement activities is Assessment. These activities determine how well the salespeople and sales managers are performing their respective duties. Are the sellers skilled? Are they effective in their roles? Are they producing at their anticipated levels? Assessment activities essentially measure how well the sales force is performing across a range of performance criteria. And if the performance is sub-par… Back to the other Sales Enablement activities.
So 'sales enablement' is a term that gets a lot of use, but we're not certain it's been viewed with a lot of rigor. Our research revealed a simple definition of enablement—a collection of activities intended to enable better sales execution. And we start doing this by Recruiting and Hiring the right folks. Then we Coach and Train them to higher levels of proficiency. Then we Equip them with tools to support their selling efforts. And finally, we Assess their performance to ensure they're on track to succeed.


Our NEW bag for men.
Front flap closes with quality antique brass buckle. Cross shoulder strap (adjustable from 116 to126 cm) also has antique brass buckle.
Shown in Brown Relax Leather, also available in: Dark Brown relax leather, Black Mareno, Brown Mareno, Distressed Brown, Smooth Black, Smooth Navy and Smooth Forest Green Leathers.


Let's Do That. | Zut alors!
Sister: Have you ever taken those online tests that tell you how fast you can type?
Sister: Let's do that tonight.
We're pretty excited for it.
So here is to you this Thanksgiving, whether your living room is strewn with passed out family members, or you're in line at Best Buy waiting to brawl Black-Friday-Style, or you Googled "chicago thanksgiving dinner restaurants 2012" and ended up at Ann Sather meeting some pretty sweet strangers (by the way, our sweet strangers – Jerry and Julia – are pictured below).
Be thankful. Be Giving. Be well!
p.s. Yeah, I did wear that sweater again!
Love this post, Debbie – !


text:
Current News Today	 Jan 15, 2021 0
Goldie Hawn introduces her and Kurt Russell's newest family member — a puppy
Celebrities and their dogs: Why our pet pooches mean so much to us
Petpuls' Smart Collar Claims to Distinguish Your Dog's Bark | Digital Trends
Have you ever wondered what your pet is trying to tell you? This collar may hold the answer. It's one of the more intriguing pet-specific gadgets at CES 2021. CES has always been packed with pet-products that make caring for your pet more convenient, but few have had the potential impact of Petpuls' A.I.-powered smart collar. This collar uses…
The FDA Warns This Dog Food Is Fatal and Has Killed More Than 70 Pets
Even if you're a cautious pet owner, you might not put too much thought into what exactly is in your dog or cat's daily meals. But the U.S. Food and Drug Administration (FDA) has issued a warning that'll make you want to double-check. The FDA is alerting pet owners that…
Puppy Bowl and its furry competitors to return this year, despite pandemic
Even a pandemic can't stop one of the cutest days on television.The Puppy Bowl will be back on screens this year, Discovery announced this week. The three-hour, 17th annual event -- which encourages people to adopt the adorable, furry participants -- will air on Animal Planet and stream on the network's new platform, Discovery+, starting at 2…
Dog tries to spark friendship with cat, watch to know what happens next
The videos have prompted people to share tons of comments. By Trisha Sengupta PUBLISHED ON JAN 09, 2021 09:47 AM IST People love to see the videos capturing different antics of cats or dogs. However, do you know what they love even more? The videos that feature cats and dogs together. Just like these clips that show an interaction…
Service dog finally reuniting with owner after COVID-19 restrictions keep them
CROSSVILLE, Tenn. (WATE) — Hawaii has some of the toughest COVID-19 restrictions in the country. When Mary Mondello left Crossville five months ago and moved across the globe to the island state she never imagined it would take so long to get her service dog to her. Finally, service dog Pebbles has a ticket to fly. She's going to…
Justin Theroux rocks a chic fedora hat while taking a sunny stroll with his
Justin Theroux rocks a chic fedora hat while taking a sunny stroll with his adored pooch Kuma in New York CityBy Dan Heching For Dailymail.com Published: 23:11 EST, 7 January 2021 | Updated: 23:52 EST, 7 January 2021 Judging from most of his recent sightings as well as his Instagram account, his most prized current…
The Leftovers Route to Dog Domestication
Dr. Lahtinen and her colleagues take competition out of the equation. In winter, ice age humans would have had to forego plants, depending on hunting. But people can't survive on protein alone. Eventually they starve or get protein poisoning. They need fat, so they would have eaten primarily the fatty parts of prey, with some lean meat left…
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Dog stolen at pump A dog was stolen from a vehicle at a gas pump. BROOKHAVEN, Ga. - A family is desperately searching for their dog after police confirm it was stolen while inside a car from a QuikTrip Gas station. Police said the owner was just feet away with her back turned pumping gas. According to the police report, it happened at the QT…
Get your first look at next month's 'supersized' game
Not even a global pandemic can stop teams Ruff and Fluff. On Super Bowl Sunday, nearly six dozen mischievous mutts will hit the field for the 17th annual "Puppy Bowl" on the new Discovery+ streaming service and Animal Planet (2 EST/11 PST), which drafts pint-sized pooches for a playful "game" of football. Photos of next month's starting lineup…
St. Charles family reunited with lost dog after weeks-long search
ST. CHARLES, Mo. – After being dog gone lost since Christmas day, a dog and her owner are finally reunited. The reunion could not have happened without heroes, including another dog, that dog's owners and thousands of people. Video of the reunion Tuesday morning shows Dustin Clarke finally has his pup "Onyx" back in his arms. …


text:
Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review
Jonathan Huang, Nathan A. Shlobin, Sandi K. Lam, Michael DeCuypere*
Objective: Artificial intelligence (AI) has facilitated the analysis of medical imaging given increased computational capacity and medical data availability in recent years. Although many applications for AI in the imaging of brain tumors have been proposed, their potential clinical impact remains to be explored. A systematic review was performed to examine the role of AI in the analysis of pediatric brain tumor imaging. Methods: PubMed, Embase, and Scopus were searched for relevant articles up to January 27, 2021. Results: Literature search identified 298 records, of which 22 studies were included. The most commonly studied tumors were posterior fossa tumors including brainstem glioma, ependymoma, medulloblastoma, and pilocytic astrocytoma (15, 68%). Tumor diagnosis was the most frequently performed task (14, 64%), followed by tumor segmentation (3, 14%) and tumor detection (3, 14%). Of the 6 studies comparing AI to clinical experts, 5 demonstrated superiority of AI for tumor diagnosis. Other tasks including tumor segmentation, attenuation correction of positron emission tomography scans, image registration for patient positioning, and dose calculation for radiotherapy were performed with high accuracy comparable with clinical experts. No studies described use of the AI tool in routine clinical practice. Conclusions: AI methods for analysis of pediatric brain tumor imaging have increased exponentially in recent years. However, adoption of these methods in clinical practice requires further characterization of validity and utility. Implementation of these methods may streamline clinical workflows by improving diagnostic accuracy and automating basic imaging analysis tasks.
Dive into the research topics of 'Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review'. Together they form a unique fingerprint.
Artificial Intelligence Medicine & Life Sciences 100%
Brain Neoplasms Medicine & Life Sciences 70%
Neuroimaging Medicine & Life Sciences 68%
Pediatrics Medicine & Life Sciences 48%
Infratentorial Neoplasms Medicine & Life Sciences 15%
Patient Positioning Medicine & Life Sciences 13%
Huang, J., Shlobin, N. A., Lam, S. K., & DeCuypere, M. (2022). Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review. World neurosurgery, 157, 99-105. https://doi.org/10.1016/j.wneu.2021.10.068
Huang, Jonathan ; Shlobin, Nathan A. ; Lam, Sandi K. ; DeCuypere, Michael. / Artificial Intelligence Applications in Pediatric Brain Tumor Imaging : A Systematic Review. In: World neurosurgery. 2022 ; Vol. 157. pp. 99-105.
@article{0857ff344fe44b07b5f921b532cc522c,
title = "Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review",
abstract = "Objective: Artificial intelligence (AI) has facilitated the analysis of medical imaging given increased computational capacity and medical data availability in recent years. Although many applications for AI in the imaging of brain tumors have been proposed, their potential clinical impact remains to be explored. A systematic review was performed to examine the role of AI in the analysis of pediatric brain tumor imaging. Methods: PubMed, Embase, and Scopus were searched for relevant articles up to January 27, 2021. Results: Literature search identified 298 records, of which 22 studies were included. The most commonly studied tumors were posterior fossa tumors including brainstem glioma, ependymoma, medulloblastoma, and pilocytic astrocytoma (15, 68%). Tumor diagnosis was the most frequently performed task (14, 64%), followed by tumor segmentation (3, 14%) and tumor detection (3, 14%). Of the 6 studies comparing AI to clinical experts, 5 demonstrated superiority of AI for tumor diagnosis. Other tasks including tumor segmentation, attenuation correction of positron emission tomography scans, image registration for patient positioning, and dose calculation for radiotherapy were performed with high accuracy comparable with clinical experts. No studies described use of the AI tool in routine clinical practice. Conclusions: AI methods for analysis of pediatric brain tumor imaging have increased exponentially in recent years. However, adoption of these methods in clinical practice requires further characterization of validity and utility. Implementation of these methods may streamline clinical workflows by improving diagnostic accuracy and automating basic imaging analysis tasks.",
keywords = "Artificial intelligence, Brain tumors, Imaging, Pediatrics",
author = "Jonathan Huang and Shlobin, {Nathan A.} and Lam, {Sandi K.} and Michael DeCuypere",
note = "Funding Information: Conflict of interest statement: The authors declare that the article content was composed in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher Copyright: {\textcopyright} 2021 Elsevier Inc.",
Huang, J, Shlobin, NA, Lam, SK & DeCuypere, M 2022, 'Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review', World neurosurgery, vol. 157, pp. 99-105. https://doi.org/10.1016/j.wneu.2021.10.068
Artificial Intelligence Applications in Pediatric Brain Tumor Imaging : A Systematic Review. / Huang, Jonathan; Shlobin, Nathan A.; Lam, Sandi K.; DeCuypere, Michael.
In: World neurosurgery, Vol. 157, 01.2022, p. 99-105.
T1 - Artificial Intelligence Applications in Pediatric Brain Tumor Imaging
T2 - A Systematic Review
AU - Huang, Jonathan
AU - Shlobin, Nathan A.
AU - Lam, Sandi K.
AU - DeCuypere, Michael
N1 - Funding Information: Conflict of interest statement: The authors declare that the article content was composed in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher Copyright: © 2021 Elsevier Inc.
N2 - Objective: Artificial intelligence (AI) has facilitated the analysis of medical imaging given increased computational capacity and medical data availability in recent years. Although many applications for AI in the imaging of brain tumors have been proposed, their potential clinical impact remains to be explored. A systematic review was performed to examine the role of AI in the analysis of pediatric brain tumor imaging. Methods: PubMed, Embase, and Scopus were searched for relevant articles up to January 27, 2021. Results: Literature search identified 298 records, of which 22 studies were included. The most commonly studied tumors were posterior fossa tumors including brainstem glioma, ependymoma, medulloblastoma, and pilocytic astrocytoma (15, 68%). Tumor diagnosis was the most frequently performed task (14, 64%), followed by tumor segmentation (3, 14%) and tumor detection (3, 14%). Of the 6 studies comparing AI to clinical experts, 5 demonstrated superiority of AI for tumor diagnosis. Other tasks including tumor segmentation, attenuation correction of positron emission tomography scans, image registration for patient positioning, and dose calculation for radiotherapy were performed with high accuracy comparable with clinical experts. No studies described use of the AI tool in routine clinical practice. Conclusions: AI methods for analysis of pediatric brain tumor imaging have increased exponentially in recent years. However, adoption of these methods in clinical practice requires further characterization of validity and utility. Implementation of these methods may streamline clinical workflows by improving diagnostic accuracy and automating basic imaging analysis tasks.
AB - Objective: Artificial intelligence (AI) has facilitated the analysis of medical imaging given increased computational capacity and medical data availability in recent years. Although many applications for AI in the imaging of brain tumors have been proposed, their potential clinical impact remains to be explored. A systematic review was performed to examine the role of AI in the analysis of pediatric brain tumor imaging. Methods: PubMed, Embase, and Scopus were searched for relevant articles up to January 27, 2021. Results: Literature search identified 298 records, of which 22 studies were included. The most commonly studied tumors were posterior fossa tumors including brainstem glioma, ependymoma, medulloblastoma, and pilocytic astrocytoma (15, 68%). Tumor diagnosis was the most frequently performed task (14, 64%), followed by tumor segmentation (3, 14%) and tumor detection (3, 14%). Of the 6 studies comparing AI to clinical experts, 5 demonstrated superiority of AI for tumor diagnosis. Other tasks including tumor segmentation, attenuation correction of positron emission tomography scans, image registration for patient positioning, and dose calculation for radiotherapy were performed with high accuracy comparable with clinical experts. No studies described use of the AI tool in routine clinical practice. Conclusions: AI methods for analysis of pediatric brain tumor imaging have increased exponentially in recent years. However, adoption of these methods in clinical practice requires further characterization of validity and utility. Implementation of these methods may streamline clinical workflows by improving diagnostic accuracy and automating basic imaging analysis tasks.
KW - Artificial intelligence
KW - Brain tumors
KW - Imaging
KW - Pediatrics
Huang J, Shlobin NA, Lam SK, DeCuypere M. Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review. World neurosurgery. 2022 Jan;157:99-105. https://doi.org/10.1016/j.wneu.2021.10.068


text:
Mustafa Kemal "Muzzy" Izzet (; ; born 31 October 1974) is a former professional footballer who played as a midfielder for Chelsea, Leicester City, Birmingham City. Born in England, he represented the Turkey national team internationally.

Early life
His father is a Turkish Cypriot who came to England as a small boy, and his mother is English. His younger brother, Kemal, also became a professional footballer.

Club career

Leicester City
Izzet signed as a schoolboy for Chelsea, but never made a first team appearance during three years at the London club. He moved to Leicester City in March 1996 on loan for the rest of the season. He became a regular in the side, scoring one goal. When Leicester beat Crystal Palace in the play-offs, Izzet was able to make his move permanent for a fee of £800,000. He kept his place in the side for their Premier League season, which was successful for City. They came 9th in the Premier League, and won the League Cup. Izzet formed a partnership in midfield with Neil Lennon, which proved vital for Leicester.

In the 1998–99 season, Leicester came 10th in league, and reaching the League Cup final again, but lost out to Tottenham Hotspur. The following season, Leicester came 8th, and again got to the League Cup Final, this time coming up against Tranmere Rovers, of Division One. This year they won 2–1, with Matt Elliott getting both goals. Izzet had been a regular in the side through all of this time. After Martin O'Neill left, Izzet remained a Leicester player. His time at Leicester drew interest from West Ham and Middlesbrough, but he did not leave the club.

The following season started with a 5–0 home defeat to newly promoted Bolton Wanderers. Despite pledging to stay at Leicester during pre-season, Izzet handed in a transfer request in autumn of 2001, prior to the club's relegation, but he rejected a move to Middlesbrough as he did not want to be seen to be "jumping ship". He did issue a plea to O'Neill, who was now manager of Scottish Premier League side Celtic, to sign him in May 2002, remarking that "only an idiot wouldn't consider joining Celtic." Following their relegation at the end of the 2001–02 season, Izzet was again strongly touted for a move to Boro. However, Leicester insisted they would not accept a reduced offer from Middlesbrough for Izzet, who was priced at £6 million, and no deal came to fruition. He withdrew his transfer request in March 2003, after the club achieved automatic promotion by finishing in second place. In January 2004, Leicester rejected £500,000 bids from Aston Villa and Blackburn Rovers for Izzet. At the end of the 2003–04 season, Leicester were again relegated. Izzet was Leicester's standout player during the season, assisting on 14 of the club's 48 goals, or 29% of the goals the Foxes scored. Unable to afford Izzet's £30,000 per week wages, the club admitted he was leaving.

Birmingham City
Izzet joined Birmingham City in June 2004 on a free transfer, signing a three-year deal. Injury plagued his first season, with him making only a handful of appearances, though he did score his first league goal for Birmingham, an equaliser against Bolton Wanderers. His second season was worse, with a knee injury keeping him out for a long period of time. Izzet announced his retirement from football on 27 June 2006.

Thurmaston Town
On 28 November 2009, Izzet made a shock last minute return to Saturday football with Leicestershire Senior League side Thurmaston Town. However, he admitted days later that his knee was still very sore and was quoted saying "whether I can turn out for Thurmaston every week is another thing." He never played for them again.

International career
Izzet qualified for the Turkey national side through his Turkish-Cypriot father. He was included in the Turkey squad for Euro 2000, making his debut and sole appearance in the tournament in the group stage as Turkey drew 0–0 with Sweden. He was also part of the squad for the 2002 World Cup, featuring again just once, as a substitute in the 1–0 semi-final defeat to Brazil.

In total, Izzet made nine appearances for the national side.

Later career
Izzet now runs a football academy with fellow former professional footballer and former Leicester teammate Steve Walsh.
In September 2015 Izzet's autobiography was published, titled Muzzy: My Story. The autobiography was co-written with Leicester Mercury writer Lee Marlow.

Izzet is an ambassador for Dorothy Goodman School, a Special Educational Needs school in Hinckley, Leicestershire.

Career statistics

International

Honours
Leicester City
Football League Cup: 1996–97, 1999–2000; runner-up: 1998–99
Football League First Division play-offs: 1996

Turkey
FIFA World Cup third place: 2002

Individual
Premier League Player of the Month: September 1999
Most assists in the Premier League: 2003–04
PFA Team of the Year: 2002–03 First Division

References

External links

1974 births
Living people
Footballers from Mile End
English footballers
Turkish footballers
Turkey international footballers
Association football midfielders
Senrab F.C. players
Chelsea F.C. players
Leicester City F.C. players
Birmingham City F.C. players
English Football League players
Premier League players
UEFA Euro 2000 players
2002 FIFA World Cup players
Sportspeople of Turkish Cypriot descent
Turkish people of Cypriot descent
Turkish people of English descent
English people of Turkish Cypriot descent
English people of Turkish descent

redpajama_set_name: RedPajamaWikipedia


text:
If you are looking for information on the various brands within the Henkell Freixenet Group, you've come to the right place. Our press kits contain everything you need to know about the respective brand. Download the material for free with just a few clicks.
Mionetto Press kit
CIAO BELLA ITALIA! – APERITIVO, PROSECCO AND LA DOLCE VITA To Italians, 'aperitivo' signifies an enjoyable after-work transition to evening downtime. It is a moment that epitomises the uniquely…
Mangaroca Batida Press kit
Mangaroca Batida de Côco – that's the popular coconut liqueur with the famous slogan "The kiss of the coconut". The cult drink currently delights fans in more than 15 countries around the world.…
i heart WINES Press kit
I HEART WINES: THE FUN YOUNG BRAND FROM THE UK Enjoying life, celebrating love and seeing the lighter side to things ... that's what i heart WINES are all about: a trendy wine assortment with a…
Champagne Alfred Gratien Press kit
HANDCRAFTED AND UTTERLY UNIQUE In 1864, Alfred Gratien made his life's dream come true when he founded a winery in Épernay, in the heart of Champagne country, in whose vaults the finest…
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NUMBER ONE IN THE PREMIUM MARKET SEGMENT Fürst von Metternich has shaped the premium sparkling wine segment like no other brand with its superior quality and its image, making it a star of the…
Henkell Press kit
A GLAMOUROUS CLASSIC –FOR LIFE'S BEAUTIFUL MOMENTS At home in the spotlight, modern and innovative, and as full of charisma and character as any internationally loved star: These are the…
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Smooth, clear and pure – those are the three essential attributes that characterise Wodka Gorbatschow. Characteristics that vodka connoisseurs have valued for decades. Gorbatschow vodka has been…


Our comfortable and tastefully furnished guest rooms are located in the guesthouse. Perfect for business or private travelers who value a quiet, comfortable space. All rooms are non-smoking.
The breakfast was traditional German, all were excellent. The property was very clean and well kept.
Staff was very friendly. Wi-Fi working great.
Very welcoming staff, very good and cheap food, pretty surroundings.


In this section you will find everything related to your travel needs. We strongly advise you to stay at our affiliated hotels. We will pick up and deliver luggage to these affiliated hotels. We have also included information on airliners, car rental, bike shipping and added information related to AirBNB and Über.
Taxi, taxi … Uber? Moving to and from the airport has never been easier. Once you have claimed your baggage and bike box, exit any terminal and there will be taxis waiting in line to move you to your hotel of choice.
If you are interested in renting a car, Puerto Rico is serviced by the main national chain companies as well as several local operators.
If you are interested in shipping your bike to Puerto Rico, please contact us for some recommendations via cargo in certain airlines.