text:
\subsection{Ablation Analysis}
\label{sec:ablation}
Our experiments in \secref{sec:experiments} demonstrate the qualitative and quantitative advantages of modeling relationships for 3D estimation using our method. We use the SUNCG dataset to analyze some architecture design choices.\\
{\bf Effect of Spatial Coordinates.} Our method appends spatial coordinates (following ~\cite{santoro2017simple}) to improve the final prediction. In Table~\ref{tab:ablation-spatial-and-mask}, we study the effect of adding these spatial coordinates on our method and the baselines. We show the results of our method without spatial coordinates in row 1.\\
{\bf Effect of Object Location Masks.} Our method also appends the mask of the object pair to the input to the relative prediction network, and in Table \ref{tab:ablation-spatial-and-mask} we analyze the effect of removing mask. Comparing the results of our method (row 4) to when we remove the location masks (row 2), we note that location masks improve the translation and scale predictions.
\vspace{-5pt}

\section{Appendix}
\subsection{Metrics}
We use the metrics from~\cite{tulsiani2018factoring} and summarize them below.
\begin{itemize}[leftmargin=*]
    \item Translation ($\mathbf{t}$): Euclidean distance between prediction and ground-truth $\| \mathbf{t}_{\mathrm{p}} - \mathbf{t}_{\mathrm{gt}} \|$. $\delta_\mathbf{t} \le 0.5$m.
    \item Scale ($\mathbf{s}$): We measure the average unsigned difference in log-scale, \ie, $\Delta(\mathbf{s}_{\mathrm{p}}, \mathbf{s}_{\mathrm{gt}}) = \frac{1}{3}\sum_{i=1}^{3} |\log_2(\mathbf{s}_{\mathrm{p}}^{i}) - \log_2( \mathbf{s}_{\mathrm{gt}}^{i}) |$. We threshold at $\delta_\mathbf{s} \le 0.2$.
    \item Rotation ($\mathbf{q}$): Geodesic distance between rotations $\frac{1}{\sqrt{2}} \| \log(R_{\mathrm{p}}^{\intercal}R_{\mathrm{gt}}) \|$. $\delta_\mathbf{q} \le 30^{\circ}$. For objects that exhibit rotational symmetry, we use the lowest error across the different possible values of $R_{\mathrm{gt}}$.
    \item Shape ($\mathbf{V}$): Following~\cite{choy20163d}, we measure the intersection over union (IoU) and use threshold $\delta_V = 0.25$. As a higher IOU is better, so we use $\delta_V \ge 0.25$ for true positive.
    \item Bounding Box overlap ($\mathbf{b}$): The bounding box overlap is measured using IOU. $\delta_b \ge 0.5$.
    \item Detection: A prediction is considered a true positive when it satisfies the thresholds for each of the above components
    $(\delta_\mathbf{t}, \delta_\mathbf{s}, \delta_\mathbf{q}, \delta_V, \delta_\mathbf{b})$.
    We use Average Precision (AP) to measure the final detection performance.
\end{itemize}

\subsection{Training Details}
\paragraph{Unary Loss Functions.} We use the following loss functions to train the unary predictors
\begin{itemize}[leftmargin=*]
    \item Loss Translation. $L_{t} = \|t_{p} - t_{gt}\|^{2}$
    \item Loss Scale. $L_{s} = \|\log(s_{p}) - \log(s_{gt})\|^{2}$
    \item Loss Rotation. $L_{r} = -\log(q_{gt})$, we minimize the NLL of the $gt$ bin. $q$ represents a probability distribution over the 24 bins.
    \item Loss Shape. $L_{v} = \sum_{n} V_{n} \log(\hat{V_{n}}) + (1-V_{n})\log(1- \hat{V_{n}}) $. $V_{n}$ are the ground-truth voxels, and $\hat{V_{n}}$ are the predicted voxels
\end{itemize}
\paragraph{Relative Loss Functions.} We use the following loss functions to train the relative predictors
\begin{itemize}[leftmargin=*]
    \item Loss Translation. $L_{rt} = \|t_{(ij), p} - t_{(ij), gt}\|^{2}$, for objects $t_{(ij)}$ represents relative translation between $i$,$j$.
    \item Loss Scale. $L_{rs} = \|\log(s_{(ij), p}) - \log(s_{(ij), gt})\|^{2}$, for objects $s_{(ij)}$ represents relative scale between $i$,$j$.
    \item Loss Direction. $L_{d} = -\log(d_{gt})$, we minimize the NLL of the $gt$ bin. $d$ represents a probability distribution over the relative directions.
\end{itemize}
\paragraph{Joint Relative Losses} We impose a loss on the joint prediction by combining unary and relative predictions
\begin{itemize}[leftmargin=*]
    \item Loss Translation. $L_{jt} =  \|t^{*} - t_{gt}\|^{2}$, where $t^{*}$ is the joint prediction computed using Equation 1
    \item Loss Scale. $L_{js} =  \|\log(s^{*}) - \log(s_{gt})\|^{2}$, where $s^{*}$ is the joint prediction computed using Equation 1
\end{itemize}

\paragraph{Optimization.}
We train our network in two stages. In the first stage of training we use ground truth boxes. We train for 8 epochs by using adam optimizer with a learning rate of $10^{-4}$. During the first 4 epochs of the training we train for relative and object specific predictions independently and during next 4 epochs of the training we optimize the whole model jointly by combining the relative and object specific estimates.  In the next stage we consider overlapping proposals with IOU of over 0.7 with respect to ground truth boxes and the ground truth boxes as positive proposals to further make the model robust in the detection setting.
\par In the NYUv2 setting we start with a network trained on the SUNCG dataset and finetune the network for 16 epochs on the NYU train + val split and evaluate method on the test split.
\paragraph{Rotation Prediction.}
We defined $\Delta(R, d, t)$ as a measure of how inconsistent a predicted rotation $R$ is w.r.t the predicted relative direction distribution $d$ and relative translation $t$. Given a predicted rotation $R$, we would expect the predicted direction to align with the vector $\bar{R}~\hat{t}$, where $\hat{t}$ is unit-normalized. Note that the predicted $d$ is a probability distribution over possible directions, and let $d^{*}$ denote the bin that aligns maximally with $\bar{R}~\hat{t}$. We measure $\Delta(R, d, t)$ by combining measures of how likely this bin is with how well it agrees with the rotation and translation: $\Delta(R, d, t) = - \log~p(d^{*}) + (1 - \cos(d^{*}, \bar{R}~\hat{t}~)) $.
\paragraph{Relative Importance.} We use lambda for unary importance to get $\trans^{*}$ and $\scale^{*}$ as 1. In case of rotation we use we weight for the relative predictions as $\min(5.0/n,1)$ where $n$ represents number of neighbours of the object. In the detection setting we create set of valid objects which are allowed to influence the final predictions for other objects based upon the detection score. We consider objects with a score above 0.3 to be part of the valid set and only use them to get final predictions for other objects.

\subsection{Additional Visualizations and Results}
We visualize the precision-recall curves in the detection setting using the SUNCG dataset in \figref{fig:pr-curves}. We also visualize predictions for randomly sampled images in the setting with known bounding boxes in \figref{fig:random-qual}.
\input{results/pr_curves.tex}

\subsection{Visualization on NYU in Detection Setting}
We visualize sample from NYU in the detection setting, and show comparisons with respect to the baseline. Please refer to Figure \ref{fig:nyu-detection-vis}.
\input{results/nyu_det_vis.tex}

\subsection{Factored3D + CRF Details}
We implement the CRF model by creating statistical models for relative translation, relative scale, and relative direction for every pair of object categories. We fit a mixture of 10 Gaussian to the data from each pair and modality. At test time we optimize using this prior assuming access to ground truth class labels to choose the appropriate prior. For optimization we use LBFGS \cite{liu1989limited} and we optimize for 1000 iterations for every example.
We visualize the outputs for CRF + Factored3D model and compare against the baseline in Figure \ref{fig:crf-gt-vis}
\input{results/crf_vis_2.tex}
\begin{table*}[!ht]
\setlength{\tabcolsep}{0.2em}
\centering
  \scalebox{1.0}{
\begin{tabular}{cccc@{\hskip 0.2in}cccc}
\toprule
Image & GT & Factored3D & Ours  & Image & GT & Factored3D & Ours\\
\midrule
\addpic{figures/vis_bbox_random/0_img.jpg} & \addpic{figures/vis_bbox_random/0_gt.png}  & \addpic{figures/vis_bbox_random/0_baseline.png} & \addpic{figures/vis_bbox_random/0_ours.png} & \addpic{figures/vis_bbox_random/1_img.jpg} & \addpic{figures/vis_bbox_random/1_gt.png}  & \addpic{figures/vis_bbox_random/1_baseline.png} & \addpic{figures/vis_bbox_random/1_ours.png}\\
\addpic{figures/vis_bbox_random/2_img.jpg} & \addpic{figures/vis_bbox_random/2_gt.png}  & \addpic{figures/vis_bbox_random/2_baseline.png} & \addpic{figures/vis_bbox_random/2_ours.png} & \addpic{figures/vis_bbox_random/3_img.jpg} & \addpic{figures/vis_bbox_random/3_gt.png}  & \addpic{figures/vis_bbox_random/3_baseline.png} & \addpic{figures/vis_bbox_random/3_ours.png}\\
\addpic{figures/vis_bbox_random/4_img.jpg} & \addpic{figures/vis_bbox_random/4_gt.png}  & \addpic{figures/vis_bbox_random/4_baseline.png} & \addpic{figures/vis_bbox_random/4_ours.png} & \addpic{figures/vis_bbox_random/5_img.jpg} & \addpic{figures/vis_bbox_random/5_gt.png}  & \addpic{figures/vis_bbox_random/5_baseline.png} & \addpic{figures/vis_bbox_random/5_ours.png}\\
\addpic{figures/vis_bbox_random/6_img.jpg} & \addpic{figures/vis_bbox_random/6_gt.png}  & \addpic{figures/vis_bbox_random/6_baseline.png} & \addpic{figures/vis_bbox_random/6_ours.png} & \addpic{figures/vis_bbox_random/7_img.jpg} & \addpic{figures/vis_bbox_random/7_gt.png}  & \addpic{figures/vis_bbox_random/7_baseline.png} & \addpic{figures/vis_bbox_random/7_ours.png}\\
\addpic{figures/vis_bbox_random/8_img.jpg} & \addpic{figures/vis_bbox_random/8_gt.png}  & \addpic{figures/vis_bbox_random/8_baseline.png} & \addpic{figures/vis_bbox_random/8_ours.png} & \addpic{figures/vis_bbox_random/9_img.jpg} & \addpic{figures/vis_bbox_random/9_gt.png}  & \addpic{figures/vis_bbox_random/9_baseline.png} & \addpic{figures/vis_bbox_random/9_ours.png}\\
\addpic{figures/vis_bbox_random/10_img.jpg} & \addpic{figures/vis_bbox_random/10_gt.png}  & \addpic{figures/vis_bbox_random/10_baseline.png} & \addpic{figures/vis_bbox_random/10_ours.png} & \addpic{figures/vis_bbox_random/11_img.jpg} & \addpic{figures/vis_bbox_random/11_gt.png}  & \addpic{figures/vis_bbox_random/11_baseline.png} & \addpic{figures/vis_bbox_random/11_ours.png}\\
\addpic{figures/vis_bbox_random/12_img.jpg} & \addpic{figures/vis_bbox_random/12_gt.png}  & \addpic{figures/vis_bbox_random/12_baseline.png} & \addpic{figures/vis_bbox_random/12_ours.png} & \addpic{figures/vis_bbox_random/13_img.jpg} & \addpic{figures/vis_bbox_random/13_gt.png}  & \addpic{figures/vis_bbox_random/13_baseline.png} & \addpic{figures/vis_bbox_random/13_ours.png}\\
\addpic{figures/vis_bbox_random/14_img.jpg} & \addpic{figures/vis_bbox_random/14_gt.png}  & \addpic{figures/vis_bbox_random/14_baseline.png} & \addpic{figures/vis_bbox_random/14_ours.png} & \addpic{figures/vis_bbox_random/15_img.jpg} & \addpic{figures/vis_bbox_random/15_gt.png}  & \addpic{figures/vis_bbox_random/15_baseline.png} & \addpic{figures/vis_bbox_random/15_ours.png}\\
\addpic{figures/vis_bbox_random/16_img.jpg} & \addpic{figures/vis_bbox_random/16_gt.png}  & \addpic{figures/vis_bbox_random/16_baseline.png} & \addpic{figures/vis_bbox_random/16_ours.png} & \addpic{figures/vis_bbox_random/17_img.jpg} & \addpic{figures/vis_bbox_random/17_gt.png}  & \addpic{figures/vis_bbox_random/17_baseline.png} & \addpic{figures/vis_bbox_random/17_ours.png}\\
\addpic{figures/vis_bbox_random/18_img.jpg} & \addpic{figures/vis_bbox_random/18_gt.png}  & \addpic{figures/vis_bbox_random/18_baseline.png} & \addpic{figures/vis_bbox_random/18_ours.png} & \addpic{figures/vis_bbox_random/19_img.jpg} & \addpic{figures/vis_bbox_random/19_gt.png}  & \addpic{figures/vis_bbox_random/19_baseline.png} & \addpic{figures/vis_bbox_random/19_ours.png}\\
\addpic{figures/vis_bbox_random/20_img.jpg} & \addpic{figures/vis_bbox_random/20_gt.png}  & \addpic{figures/vis_bbox_random/20_baseline.png} & \addpic{figures/vis_bbox_random/20_ours.png} & \addpic{figures/vis_bbox_random/21_img.jpg} & \addpic{figures/vis_bbox_random/21_gt.png}  & \addpic{figures/vis_bbox_random/21_baseline.png} & \addpic{figures/vis_bbox_random/21_ours.png}\\
\addpic{figures/vis_bbox_random/22_img.jpg} & \addpic{figures/vis_bbox_random/22_gt.png}  & \addpic{figures/vis_bbox_random/22_baseline.png} & \addpic{figures/vis_bbox_random/22_ours.png} & \addpic{figures/vis_bbox_random/23_img.jpg} & \addpic{figures/vis_bbox_random/23_gt.png}  & \addpic{figures/vis_bbox_random/23_baseline.png} & \addpic{figures/vis_bbox_random/23_ours.png}\\
\addpic{figures/vis_bbox_random/24_img.jpg} & \addpic{figures/vis_bbox_random/24_gt.png}  & \addpic{figures/vis_bbox_random/24_baseline.png} & \addpic{figures/vis_bbox_random/24_ours.png} & \addpic{figures/vis_bbox_random/25_img.jpg} & \addpic{figures/vis_bbox_random/25_gt.png}  & \addpic{figures/vis_bbox_random/25_baseline.png} & \addpic{figures/vis_bbox_random/25_ours.png}\\
\bottomrule
\end{tabular}
}
\vspace{-0.1in}
\captionof{figure}{We visualize predictions for \emph{randomly sampled images} in the setting with known ground-truth boxes for the SUNCG dataset.}
\label{fig:random-qual}
\end{table*}

\section{Approach}

Our goal is to predict the 3D pose and shape for all the objects in a scene.
We observe that in addition to the visual cues per object, reasoning about relationships across them can further help our predictions, in particular for the 3D pose -- a chair would be in front of a table, and of a compatible relative size, and therefore even if we are uncertain about the pose of one of these objects, \eg, due to occlusion, these relationships can enable us to make accurate predictions.

We operationalize this insight in our method (see \figref{fig:approach}) that leverages both - independent per-object predictions alongwith predictions regarding relationships between them. We infer the final estimates for all the objects in the scene by integrating these two. We first formally describe the object-centric representations pursued and briefly review a recent per-instance prediction approach
in \secref{sec:background}. We then introduce the relative representations in \secref{sec:relrep} and present our network architecture that enables predicting these in \secref{sec:relnet}. In \secref{sec:reltest} we discuss how these relative predictions are combined with the independent per-object predictions to yield the final 3D estimates for the objects. We show that optimizing the combination of these estimates (\ie, final estimate) in a differentiable end-to-end framework helps improve the final per-object predictions.

\subsection{Instance Specific Representations and Inference}
\label{sec:background}
We output the 3D pose of an object by predicting its shape in a canonical frame, and its scale, translation and rotation in camera frame.
The shape is parametrized as a $32^3$ volumetric occupancy grid in a canonical space where the objects are upright, front-facing, normalized to a unit cube. The translation $\mathbf{t} \in \mathbb{R}^3$ and (logarithm of) anisotropic scale $\mathbf{s} \in \mathbb{R}^3$, and normalized quaternion $\mathbf{q}$ indicate the position, size and orientation of the object respectively. Following prior work~\cite{tulsiani2018factoring}, we parametrize the rotation prediction as a classification task among fixed bins, hence $\mathbf{q}$ represents a probability distribution over those fixed bins.

We build upon the recent work by~\cite{tulsiani2018factoring}: they pursue a similar per-object representation, but make independent predictions across objects. We use their approach to obtain the independent predictions for each object.
We briefly review their prediction framework but refer the reader to the paper for details about the representation and architecture. Their method uses an architecture similar to Fast R-CNN~\cite{girshick2015fast}, where the input image is encoded via convolutional layers, and for each object bounding box, an RoI pooling layer crops the corresponding features. These per-object features, in conjunction with coarse image feature and an encoding of the bounding box coordinates, are encoded to an instance-specific bottleneck representation from which the corresponding shape and pose are predicted.

We adopt a similar architecture, illustrated in \figref{fig:approach-detail}, with the introduction of spatial coordinates as additional feature channels (see \secref{sec:relnet}) to obtain per-object (unary) predictions. We note that these predictions are obtained independently across objects, and do not incorporate reasoning across them. However, unlike previous work, these are subsequently integrated with relative predictions (\secref{sec:reltest}) to obtain final estimates.

\subsection{Relations as Relative Representations}
\label{sec:relrep}
Given an image of a scene, we can infer that two chairs nearby might be of a similar size, a laptop kept above a desk, and a television facing the bed \etc. Thus, relative pose between objects captures an important aspect of their relationship in the scene.

Concretely, given two objects, say $A$ and $B$,
we infer the relative pose from $A$ to $B$. The relative pose, akin to the absolute 3D pose, is factored into the relative translation, scale, and direction. The relative translation is defined as the difference of the absolute locations of the object in the camera frame $\mathbf{t}_{AB} = \mathbf{t}_{B} - \mathbf{t}_{A}$. Similarly, the relative scale is simply the ratio of the two object sizes, or equivalently a difference in logarithms $\mathbf{s}_{AB} = \mathbf{s}_{B} - \mathbf{s}_{A}$. Finally, we predict a relative \emph{direction} $\hat{\mathbf{d}}_{AB}$, \ie, the direction of object $B$ in the frame of object $A$: $\hat{\mathbf{d}}_{AB} \propto \text(\bar{R}_A)~\mathbf{t}_{AB}$, where $\hat{\mathbf{d}}_{AB}$ is normalized to unit norm. Here $\bar{R}_A$  denotes the rotation corresponding to $q_{A}$. We note that this parametrization, unlike relative rotation, helps us overcome some ambiguities due to symmetries, \eg, the relative direction from a chair to a table in front of it is unambiguous, even if the table is symmetric.

\subsection{Network Architecture}
\label{sec:relnet}

Our network has two branches -- a per-object (unary) prediction module, and a relative prediction module. The architecture of both these branches is shown in \figref{fig:approach-detail}. The former, as described in \secref{sec:background}, simply computes a per-object encoding and subsequently makes per-object predictions. The relative prediction module is then tasked with inferring the relative pose (\secref{sec:relrep}) for every pair of objects in the scene.

As depicted in \figref{fig:approach-detail}, the relative prediction module takes as input: a) the encoding of both objects, and b) an encoding of the larger (union) bounding box containing both objects. Since this larger box may contain several additional objects beyond the ones of interest, we additionally give as input two channels with binary masks indicating the source and target bounding box extents respectively (denoted as `object location image'). We also find it beneficial to concatenate the normalized per-pixel spatial coordinates as additional features, as it allows the network to easily reason about the absolute spatial location of the bounding box(es) under consideration. Finally, similar to the parametrization in \secref{sec:background}, we frame the relative direction prediction as a classification task among 24 bins, where the bins are computed by clustering relative directions across the training instances like in \cite{tulsiani2018factoring}.

\subsection{Combining Per-object and Relative Predictions}
\label{sec:reltest}
We saw in \secref{sec:background} that we can obtain independent per-object predictions for the 3D shape and pose, and introduced the relative pose predictions and architecture in \secref{sec:relrep} and \secref{sec:relnet}.
We denote by $(\trans_n, \scale_n, \quat_n)$ the per-object (unary) predictions for the translation, log-scale, and rotation respectively for the $n^{th}$ object, and similarly $(\trans_{mn}, \scale_{mn}, \direction_{mn})$ denote the relative pose predictions from the $m^{th}$ to the $n^{th}$ object. Using these, we show that we can obtain final per-instance predictions $(\transFinal_n, \scaleFinal_n, \quatFinal_n)$ that incorporate both, the unary and relative predictions.

\vspace{1mm}
\noindent \textbf{Translation and Scale Prediction.}
The relative predictions give us linear constraints that the final predictions should ideally satisfy. As an example, we would want $\mathbf{t}^*_n - \mathbf{t}^*_m = \mathbf{t}_{mn}$. This can equivalently be expressed as $A_{mn} \mathbf{t}^* = \mathbf{t}_{mn}$, where $A_{mn}$ is a sparse row-matrix with the $m^{th}$ and $n^{th}$ entries as $(-1, 1)$, and $\mathbf{t}^*$ denoting the final translations for all the $N$ objects. We can similarly express all pairwise linear constraints as $A \mathbf{t}^* = \trans_{\mathrm{rel}}$, where $\trans_{\mathrm{rel}}$ denotes all the relative predictions, and $A$ is the appropriate sparse matrix.

In addition to satisfying these linear constraints, we would also like the final estimates to be close to the unary predictions. We can therefore incorporate both, the relative and the unary constraints via a system of linear equations, and solve these to obtain the final estimates.
\begin{gather}
     \begin{bmatrix}
        \lambda~I \\
        A\\
     \end{bmatrix}~\trans^{*} =
      \begin{bmatrix}
        \lambda~\mathbf{t} \\
        \trans_{\mathrm{rel}}\\
     \end{bmatrix}; ~~~~
     \trans^{*} =  \begin{bmatrix}
        \lambda~I \\
        A\\
     \end{bmatrix}^+
      \begin{bmatrix}
        \lambda~\mathbf{t} \\
        \trans_{\mathrm{rel}}\\
     \end{bmatrix}
\end{gather}
Here $X^+$ denotes the Moore-Penrose inverse of matrix $X$, and $\lambda$ indicates the relative importance of the unary estimates. We can therefore obtain the final translation predictions $\trans^{*}$ that integrate both, unary and relative estimates. We note that the final estimates are simply a linear function of the unary predictions $\mathbf{t}$ and the relative predictions $\trans_{\mathrm{rel}}$, and it is therefore straightforward to propagate learning signal from supervision for $\trans^{*}$ to these predictions. While the description here focused predicting translation, as we represent scale using logarithm of the sizes, similar linear constraints apply. We can therefore similarly compute final predictions for the scales across objects $\scale^{*}$.

\vspace{1mm}
\noindent \textbf{Rotation Prediction.}
While the incorporation of unary and relative predictions can be expressed via linear constraints in the case of translation and scale, a similar closed form update does not apply for rotation because of the framing as a classification task and non-linearity of the manifold. Instead, we update the likelihood of the unary predictions based on how consistent each rotation bin is with the relative estimates.

We denote as $R^{b}$ the rotation matrix corresponding to the ${b}^{th}$ rotation bin, and use $\Delta(R, \mathbf{d}, \mathbf{t})$ to measure how inconsistent a predicted rotation $R$ is w.r.t the predicted relative direction distribution $\mathbf{d}$ and relative translation $\mathbf{t}$ (see appendix for details). Using these, we can compute the (unnormalized) negative-log likelihood distribution over possible rotations as follows:
\begin{gather}
    \quat^{*}_m(b) = \mathbf{q}_m(b) + \underset{n}{\sum} \Delta(R^b, \mathbf{d}_{mn}, \mathbf{t}_{mn})
\end{gather}
This update to compute $\quat^{*}$ can equivalently be viewed as a single round of message passing, with the messages being of an explicit rather than an implicit form.

\vspace{1mm}
\noindent \textbf{Training Details.}
We described above how the independent per-object predictions are analytically combined with the relative pose predictions to obtain the final estimates, and note that this integration process allows us to propagate learning signal from supervision on the final estimates back to the unary and relative predictions.
Our training happens in two steps. In the first step, we train both unary and relative predictions independent of each other. We formulate the loss-function for each network similar to ~\cite{tulsiani2018factoring}. Specifically, we use regression losses for shape encoding, the (absolute and relative) translation and scale, and classification losses for the rotation and relative direction prediction. Note that as some objects might be rotationally symmetric, we allow multiple `correct' bins for these and maximize the maximum probability across these. In the second step, after a few epochs, we train the whole model in joint manner. We add similar losses for the final pose predictions that are computed using both, unary and relative estimates.  During inference, given the unary and relative predictions, we simply compute the final pose predictions via the optimization process described above. Additional details on optimization are provided in the appendix.
\section{Discussion And Future Work}
We proposed a method to incorporate relationship based reasoning in the form to relative pose estimates for the task of 3D scene inference. While this allowed us to significantly improve over existing approaches that reason independently across objects, numerous challenges still remain to be addressed. In our approach, we only  leveraged pairwise relations among the objects in a scene, and it would be interesting to pursue incorporating higher order relations. We also relied on a synthetic dataset with full 3D supervision to train our prediction networks, thereby limiting direct applicability to datasets without 3D supervision. Towards overcoming this, it  might be desirable to combine our approach with parallel efforts in the community to use 2D reprojection losses~\cite{garg2016unsupervised} or leverage domain adaptation techniques~\cite{hoffman2017cycada}.

\section{Experiments}
\label{sec:experiments}

\subsection{Experimental Setup}
\label{sec:experimental-setup}
\par \noindent \textbf{Datasets:} We use the SUNCG dataset~\cite{song2017semantic} which provides many diverse and detailed 3D models for houses. Following~\cite{zhang2017physically,tulsiani2018factoring}, we use the 2D renderings of the houses and the corresponding parsed 3D house model information to get roughly 550k image and 3D supervision pairs. We follow the setup in~\cite{tulsiani2018factoring} and split this data into train (70\%), val (10\%) and test (20\%); with objects classes - bed, chair, desk, sofa, table, and tv.

We also use the NYUv2~\cite{silberman2012indoor} dataset which consists of 1449 real world images, and use the annotations by ~\cite{guo2013support} to finetune the network trained on SUNCG using the same subset of object categories. This dataset has lower resolution images and serves well to check the generalization of our approach to real data. As the NYUv2 annotations use the same small set of 3D shapes across train and test images, we do not evaluate shape prediction.

During inference, the input to our system is always a 2D image along with 2D bounding boxes indicating the objects in the scene. We present results in both scenarios: a) when the 2D boxes correspond to ground-truth locations, and b) when the 2D boxes are obtained via a detector. Given this input, our method outputs a 3D prediction for each of the boxes. We summarize this in Table \ref{tab:exp_overview} and provide pointers to the results under the two settings.

\vspace{1mm}
\par \noindent \textbf{Metrics:}~\cite{tulsiani2018factoring} propose different metrics to measure the quality of various prediction components -  detection, rotation, translation and scale. They also propose different thresholds $\delta$ for each of these components which are used to count a prediction as a true positive in the detection setting. We use these metrics and refer the reader to the appendix for a summary review.

\vspace{1mm}
\par \noindent \textbf{Baselines:} We use the following baseline methods:
\begin{itemize}[leftmargin=*]

    \item \emph{Factored3D}: This method from ~\cite{tulsiani2018factoring} reasons about each object independently and serves as a baseline to see how our relationship reasoning can improve performance.

    \item \emph{Factored3D + CRF}: We optimize the independent predictions from  ~\cite{tulsiani2018factoring} using a CRF\cite{lafferty2001conditional} with unary and binary terms. The binary potential terms correspond to log-likelihood of the relative pose for each pair, where the likelihood is modelled using a mixture of gaussian model for each category pair. Note that this baseline is allowed to use the ground-truth object classes during inference. See appendix for details.
    \item \emph{GCN}: We use Graph Convolutional Networks~\cite{kipf2016semi} to perform implicit relational reasoning. We use the Object Encoder (Figure~\ref{fig:approach-detail}) to obtain object embeddings $o_i$ for each object and then use a 2-layer GCN to obtain the final object embeddings for each object. These are then used to predict the 3D pose and shape for the objects.

    \item \emph{InteractionNet}: We use the method from~\cite{battaglia2016interaction} as an alternate way to perform implicit reasoning over the object embeddings.  We compute an `effect' embedding $e_{AB}$ for each ordered tuple $(o_A, o_B)$ via a learned MLP, and update the object embeddings by aggregating these as $o_A+\underset{B}{\max}(e_{AB})$, and use these for per-object predictions.
\end{itemize}
Note that while the latter two baselines can implicitly reason about relationships, they ignore the underlying structure (how relative translations affect translation \etc). In contrast, while the CRF baseline leverages this structure, it ignores the image when reasoning about relationships -- the relative pose prior is image agnostic, whereas in our approach the relative pose is predicted.

\begin{table}[!t]
    \centering
    \setlength{\tabcolsep}{0.2em}
    \scalebox{0.75}{
    \begin{tabular}{l|cc}
    \toprule
    \bf Section & \bf Input & \bf Output \\
    \midrule
    Sec~\ref{sec:gt-boxes} & 2D Images, ground truth boxes & 3D Pose for each box\\
    & Table~\ref{tab:gt-boxes}, Fig~\ref{fig:qual-results},~\ref{fig:qual-results-nyu} & \\
    \midrule
    Sec~\ref{sec:detection} & 2D Images, detection boxes & 3D Pose for each box\\
    & Table~\ref{tab:detection-SUNCG} Fig~\ref{fig:qual-results-detection} \\
    \midrule
    Sec~\ref{sec:ablation-optimization} & 2D Images, ground truth boxes & 3D Pose for each box\\
    & Tables~\ref{tab:ablation-optimize},~\ref{tab:ablation-spatial-and-mask} & \\
    \bottomrule
    \end{tabular}}
    \caption{We provide an overview of the \textbf{test time} experimental settings we use in Section~\ref{sec:experiments}. We define the datasets, inputs and outputs.}
    \label{tab:exp_overview}
\end{table}

\input{results/suncg_gt_box_vis.tex}
\input{results/nyu_gt_vis.tex}

\subsection{Evaluation Using Ground Truth Boxes}
\label{sec:gt-boxes}
We first analyze all methods in the setting where we are given the ground truth bounding boxes. In this setting, we can analyze just the 3D prediction quality without the additional variance introduced due to imperfect detection. During training, we train all the methods on ground-truth boxes as well as object proposals (obtained using~\cite{zitnick2014edge}) which have an IOU $\ge 0.7$. Each method is trained to predict the 3D pose of the objects.

At test time, we evaluate all the methods by providing the image and the ground truth boxes as input. All methods predict the translation, rotation and the scale for each of the ground truth boxes. As shown in Table~\ref{tab:gt-boxes}, our method generates higher quality predictions across both translation and scale where it can outperform the baselines on the mean, median and \% error. First we observe the performance of the CRF \cite{lafferty2001conditional} model and conclude that there is not a significant gain over the baseline and minor improvements to translation. It is also worth noting that adding any form of relationships modeling, like in GCN or InteractionNet, gives a boost over doing per-object predictions like in Factored3D. Our structured reasoning and inference about object relationships provides a boost over other pairwise models such as GCN and InteractionNet. Our performance gain over the baseline holds across SUNCG and NYUv2 datasets demonstrating the generalization of our method.
\vspace{1mm}
\par \noindent \textbf{Qualitative Results:} In \figref{fig:qual-results}, we show a few results of our method and the baseline (Factored3D). We see that our method can correct many error modes (relative positions and poses of objects) compared to the baseline. We observe similar trends on the NYUv2 dataset (\figref{fig:qual-results-nyu}).

\input{results/gt_boxes_results.tex}
\input{results/suncg_det_vis.tex}

\subsection{Evaluation Using Detections}
\label{sec:detection}
In this setting, we test the learned models using detections from a pre-trained object detector (taken from~\cite{tulsiani2018factoring}). Thus, we can now evaluate the robustness of these methods when the input object boxes are not pristine. In Table~\ref{tab:detection-SUNCG}, we see the mean Average Precision values for different criteria used to define true positives (see appendix for metrics). As an example, in the first column a true positive satisfies IOU (box2d) threshold with the ground truth, is close in scale, rotation, translation, shape thresholds. Each subsequent column examines one criteria so we can analyze their accuracy with detections. We see (in the first column) that our method provides a significant gain of \textbf{6 points} in mAP over the baseline. Relationship modeling (GCN and InteractionNet) provides benefit in the detection setting too. However, our structured reasoning outperforms these methods, with most of the gains coming from predicting higher quality scale and translation. We visualize some predictions showing the difference between our approach and the baseline in \figref{fig:qual-results-detection}.
Furthermore, in Table \ref{tab:detection-SUNCG} we evaluate the our method on the NYUv2 in the detection setting, and we achieve similar trends in performance with respect to the baseline. Due to increased difficulty of the task on NYU we observe relative lower mAP scores.

\input{results/ablation_io_jo.tex}
\input{results/ablation_arch.tex}
\input{results/pr_results.tex}

\subsection{Effect of Combination and Optimization}
\label{sec:ablation-optimization}
\vspace{-4pt}
Combining the unary and relative predictions provides benefit at the training time because it allows the model to modify its unary and relative predictions so that they are more `compatible' with each other. We quantify this in Table~\ref{tab:ablation-optimize} using the SUNCG dataset. We report the performance of a purely multi-task version (`MT') that only predicts the unaries and relative pose values, without ever combining them. The `MT + combine test only' combines these during inference (but not training). Finally, the `MT + combine train only', where we also compare to a method that only uses this combination at train time, and finally report our full method as a reference.
We see that combining the relative predictions at either train or test alone performs better than pure multi-task learning. This shows the importance of the unary and relative predictions interacting with each other. Combining these at both train and test time, and jointly optimizing like in our method performs the best.

\section{Introduction}
A single 2D image can induce a rich 3D perception. When we look at an image, we can reason about its 3D layout, the objects in the image, their shape, extent, relationships \etc. This is really surprising given that going from a 2D projection to a 3D model is inherently ill-posed. How are we able to solve this problem? Humans rely on regularities in the 3D world in order to do so -- this helps us discard many improbable solutions in 3D and reason about more likely ones. This regularity exists at the scene level - indoor scenes have roughly perpendicular walls; object level - chairs have similar shapes; and in local object relationships - chairs are close to tables, monitors are on top of tables \etc. A decade ago, a lot of work in computer vision focused on using all the three levels of regularities. For example, a lot of work focused on object-centered 3D prediction~\cite{fidler20123d}, scene-level 3D prediction~\cite{hedau2009recovering}, and multi-object 3D reasoning~\cite{jiang2012learning}. However, in recent years, since the advent of ConvNets, a vast majority of computer vision approaches do not leverage these object-object relationships, and instead reason about each object independently.

In this paper, we attempt to take a holistic view of the 3D prediction problem and note that solving the 3D prediction problem would require incorporation of all the three cues. We believe there are three fundamental questions that need to be answered to design this holistic architecture: (a) What is the right representation for object level 3D prediction?; (b) How do we represent object-object relationships and how do we predict them from pixels?; (c) Finally, how to incorporate object-object relationships with object-level modules. This paper builds upon the recent success in (a) and investigates how to model relationships and incorporate them into our 3D prediction framework.

So, how do we model relationships and estimate them from pixels? There is a whole spectrum of possible approaches. On one end of the spectrum is a complete end-to-end approach. Some examples of these include Interaction Networks~\cite{battaglia2016interaction} or Graph Convolutional Networks~\cite{kipf2016semi}. Both these methods provide a mechanism for object features in the scene to effect each other, thereby allowing an implicit modeling of relationships among them. However, as we show in our experiments, these end-to-end approaches disregard the structural information which might be crucial for modeling the relationships. The other end of spectrum is to use category-based image-agnostic pairwise priors~\cite{zhao2011image} to model relationships. A drawback is that these priors are often too strong to generalize and it is better to learn them~\cite{jiang2012learning}.
When it comes to the final question of how does one incorporate relationships to improve 3D prediction, the answer is even murkier. One classical approach is to use graphical models such as CRFs~\cite{lafferty2001conditional,schwing2012efficient}. However, these classical approaches have usually provided little improvements over object-based approaches. Our key insight is to incorporate structural information in end-to-end systems. Specifically, we model and predict pairwise relationships in the translation, rotation and scale space. One advantage of using this structured relationship space is that the incorporation of relationships into object-level estimates is simple yet effective. But how do we predict these pairwise relationships from pixels? Our paper investigates several design choices and proposes a simple architecture. Our method demonstrates significant improvement in performance across multiple metrics and datasets. As we show in our experiments, this modeling of relationships in this structured space provides a huge {\bf 6 point AP} improvement in detection settings over current state-of-the-art 3D approaches. We will release our code for reproducibility.

\section{Related Work}

Dating back to the first thesis written on computer vision~\cite{roberts1963machine}, inferring the underlying 3D structure of an image has been a long-standing goal in the field.
While we have explored several 3D scene representations over the years, \eg,  depth~\cite{saxena2009make3d,eigen2014depth}, qualitative 3D~\cite{hoiem2005geometric,Gupta2010}, manifolds~\cite{osadchy2007synergistic} or volumetric 3D~\cite{choy20163d,Girdhar16b,song2017semantic}
, the prevalent paradigm is still the one followed in Roberts' seminal work -- that of inferring a 3D scene in terms of the shape and pose of the underlying objects.

The initial attempts under this paradigm~\cite{guzman1968decomposition,huttenlocher1990recognizing} focused on placing known object instances to match image evidence, relying on matching edges, corners \etc to fit the known shape templates to images. Subsequent approaches have focused on a more general setting of reconstructing scenes comprising of novel objects, and leverage either explicit or implicitly learned category level priors for pose and shape estimation, typically relying on a deformable shape space~\cite{cashman2013shape,shapesKarTCM15} or template CAD models~\cite{lim2013parsing,aubry2014seeing,li2015joint,bansal2016marr,izadinia2016im2cad} for the latter inference. Current CNN based incarnations of these approaches, driven by the abundant success of deep learning and availability of annotated data, have further improved the results for pose estimation~\cite{vpsKpsTulsianiM15,pavlakos20176}, and have also been extended to joint shape and pose inference of the objects present in a scene~\cite{3DRCNN_CVPR18,tulsiani2018factoring}.

A common characteristic amongst these approaches is the reasoning at a per-object level. While the object-centric nature is certainly desirable as a representation, we argue that reasoning independently for each object to infer this representation is not, as it does not allow leveraging the relationships between the entities in a scene. We propose a method that also uses object-centric representations but goes beyond independent reasoning per object.

We are of course not the first to pursue reasoning about relationships between entities in a scene. Several previous approaches focus on the goal of predicting various relations e.g. human-object interactions~\cite{gupta2015visual,gkioxari2017interactnet}, object-object interactions~\cite{Gupta2010, lu2016visual}, object-scene interactions~\cite{GuptaNIPS2010} \etc While these works pursue relation inference as the end goal, we instead aim to leverage these for a per-instance prediction task. In the context of incorporating relations for such per-instance prediction, there are two alternate ideologies. On the one hand, approaches pursuing 3D scene inference or generation~\cite{zhao2011image,lin2013holistic,choi2013understanding, fisher2012example, fisher2011characterizing,huang2018holistic,  jiang2018configurable} typically incorporate pairwise (or higher order) relations via explicit class-based priors regarding possible configurations and optimize predictions to adhere to these. This approach of explicitly modeling relations as a prior imposes the same constraints across all scenes, independent of the structure in the image, and is therefore not flexible enough and has difficulties in scaling up to arbitrary relations across arbitrary objects.

The alternate ideology for incorporating relationships is to eschew any explicit structure for these relations, and instead implicitly capture these  via architectural changes to the CNNs, thereby allowing the features of objects to influence one another~\cite{battaglia2016interaction,kipf2016semi}. While this a generally applicable mechanism, it does not leverage several aspects regarding the structure of the problem -- for 3D inference, specific relations like relative position, orientation are very relevant, and can be used in specific ways to influence per-instance predictions. Our approach leverages some aspects of both these ideologies -- unlike the classical prior based approaches, we learn and infer these relations in a image-dependent context via a CNN, and unlike the purely implicit methods, we are more explicit about the structure and meaning of these relations.

\subsection{Corrected Error Modes}
\begin{table*}[!ht]
\setlength{\tabcolsep}{0.15em}
\centering
  \scalebox{0.95}{
\begin{tabular}{cccc@{\hskip 0.2in}cccc}
\toprule
Image & GT & Factored3D & Ours  & Image & GT & Factored3D & Ours\\
\midrule
\addpic{figures/cherry_results/0_img.jpg} & \addpic{figures/cherry_results/0_gt.png}  & \addpic{figures/cherry_results/0_baseline.png} & \addpic{figures/cherry_results/0_ours.png} & \addpic{figures/cherry_results/1_img.jpg} & \addpic{figures/cherry_results/1_gt.png}  & \addpic{figures/cherry_results/1_baseline.png} & \addpic{figures/cherry_results/1_ours.png} \\
\addpic{figures/cherry_results/2_img.jpg} & \addpic{figures/cherry_results/2_gt.png}  & \addpic{figures/cherry_results/2_baseline.png} & \addpic{figures/cherry_results/2_ours.png} &
\addpic{figures/cherry_results/7_img.jpg} & \addpic{figures/cherry_results/7_gt.png}  & \addpic{figures/cherry_results/7_baseline.png} & \addpic{figures/cherry_results/7_ours.png}
\\
\addpic{figures/cherry_results/4_img.jpg} & \addpic{figures/cherry_results/4_gt.png}  & \addpic{figures/cherry_results/4_baseline.png} & \addpic{figures/cherry_results/4_ours.png} & \addpic{figures/cherry_results/5_img.jpg} & \addpic{figures/cherry_results/5_gt.png}  & \addpic{figures/cherry_results/5_baseline.png} & \addpic{figures/cherry_results/5_ours.png} \\
\addpic{figures/cherry_results/6_img.jpg} & \addpic{figures/cherry_results/6_gt.png}  & \addpic{figures/cherry_results/6_baseline.png} & \addpic{figures/cherry_results/6_ours.png} &

\addpic{figures/cherry_results/3_img.jpg} & \addpic{figures/cherry_results/3_gt.png}  & \addpic{figures/cherry_results/3_baseline.png} & \addpic{figures/cherry_results/3_ours.png}
 \\

\bottomrule
\end{tabular}
}
\vspace{-0.1in}
\captionof{figure}{\textbf{3D Output using Ground truth 2D Boxes:} In Section~\ref{sec:gt-boxes}, we evaluate only the 3D output of all methods by using ground truth boxes at test time. We visualize the 3D predictions for our method and the baseline. Our method estimates 3D better  than the baseline, \eg, bottom right where the distance between the chairs and table is predicted correctly, and the pose of the yellow chair is corrected. Best viewed in color.
}
\label{fig:qual-results}
\end{table*}

redpajama_set_name: RedPajamaArXiv


text:
The Year in Closed Government
This eight-month investigation into the use of private email by New Mexico government officials uncovered an extensive network of political and financial influences over state government, out of the public eye. We specifically focused on public officials' use of private emails to conduct public business, in an apparent attempt to hide that business from the public record. Our reporting resulted in the resignation of a high-profile lobbyist, Republican National Committeeman and board member of the New Mexico Foundation for Open Government, as well as a formal complaint upheld by the New Mexico Attorney General.
cover story (12.18.12)
additional reporting (2012-2013)
coverage by Columbia Journalism Review (Feb. 2013)
2012 elections reporting
SFR's coverage of the 2012 elections exposed the unprecedented influx of money into state and local races, as well as the influence of local and national super-PACs and out-of-state donors. Our reporting contributed to the New Mexico Secretary of State's decision to fine one of the state's key super PACs.
news articles: 6.20.12, 10.02.12, 11.07.12, 11.14.12, 12.01.12, 1.08.13
Santa Fe Public Schools
More than a year's worth (2010-2012) of investigative reporting on Santa Fe Public Schools' improper use of student testing data, among other problems, resulted in the school board's decision to terminate the superintendent's contract.

redpajama_set_name: RedPajamaCommonCrawl


text:
Gibson Kamau Kuria
Dr. Gibson Kamau Kuria, "the recipient of both the Robert F. Kennedy 1988 Human Rights Award and the American Bar Association's Rule of Law Award. Dr. Kuria, a distinguished Kenyan lawyer, has for decades been at the center of Kenya's struggle for constitutionalism and the rule of law. He has been repeatedly honored – for his defense of dissidents, for his mobilization of members of the Kenyan bar in resisting anti-democratic initiatives and practices, for his distinguished serve as a judge, and for his role in the campaign to restore political pluralism in Kenya. Recently, following the election of President Kibaki, Dr. Kuria was appointed to a tribunal established to inquire into allegations of judicial corruption; in addition, he is currently serving as assisting counsel to the commission enquiring into the infamous Goldenberg scandal." [1]
1988 - awarded Robert F. Kennedy Human Rights Award; however, the Kenya government which had confiscated Dr. Kuria's passport refused to allow him to travel to the US to receive the award; also received the Lawyers Committee for human Rights award; Hon. Paul Muite (now member of Parliament and Chairman of the Parliament Select Committee on Constitutional Reform), traveled to the USA and received the award on Dr. Kuria's behalf and his passport was confiscated upon his return to Kenya,
August 1990 – Dr. Kuria was awarded the Rule of Law Award by the American Bar Association for my work in human rights in Kenya,
December 1990 - received Human Rights Watch's award as a human rights monitor,
May 1991 – Dr. Kuria was awarded the Doctorate of Laws by Lewis Clark Law School, Oregon, USA because of his human rights work; served as Commencement Speaker at the Law School's graduation,
1993 – Dr. Kuria was awarded the Jurists of the Year Award by the International Commission of Jurists (Kenya Chapter),
July 1995 - elected Secretary General, East Africa Law Society; in this capacity, Dr. Kuria devised programs for promoting regional integration with a view to establishing an East African federal state to be served by a Federal bar Association; published two articles on how to establish an East African federal state and enforce a Bill of Rights; served until April 1998,
Between 1997 and 1998; Dr. Kuria served as the vice-Chairman of the Law Society of Kenya; this bar association has been in the forefront in the struggle to restore constitutionalism,
March 1999-2001 – Dr. Kuria Served as Chairman, Law Society of Kenya; in that capacity, Dr. Kuria promoted the rule of law, constitutionalism, and economic reforms, and human rights,
Between September 2001 and May 2002 – Dr. Kuria was involved in litigation aimed at removing gerrymandering from the country's electoral process (the Michuki case); the court ruled that the Constitution mandated the one-person - one-vote principle; between May and October 2002, jointly with the RFK Memorial Centre for Human Rights, Dr. Kuria worked on a project to persuade Kenyans to engage in the drawing of the electoral districts on the basis of one-person - one-vote principle; in late October, the Centre sent a delegation to Kenya in support of this project, promotion of constitutionalism, human rights, and the rule of law,
Since the formation of the new Government on December 30, 2002, Dr. Kuria has been actively involved in the press in the promotion of democracy, rule of law, human rights, and a competent and independent judiciary [2]
"Gibson Kamau Kuria", (Biographical note), Undated, Accessed June 2007.
Smith Hempstone, Jr.
Retrieved from "https://www.sourcewatch.org/index.php?title=Gibson_Kamau_Kuria&oldid=549802"


Colombo High Court Judge Lalith Jayasuriya said today that Vass Gunawardana case is not an extraordinary case to the Court.
The Judge made this statement while considering two petitions submitted by Vass Gunawardana's wife Shamali Gunawardana Perera to obtain bail for husband and her son Ravindu Gunawardana.
Vass Gunawardana and son Ravindu Gunawardana have been in remand custody in connection with the millionaire businessman Mohammad Siyam murder case.
Appearing for the Petitioner, Attorney-at-law Wasantha Pitigala said that Vass Gunawardana was a former high ranking police officer, and would not abscond the Court orders.
However from the prosecuting party, the CID and the Attorney General had not filed objections.
Having informed to file objections on the 27th of January, High Court Judge Lalith Jayasuriya further said that there are no different spoons such as tea spoons and table spoons in his possession.
The judge said in the open Court that he uses 'one spoon' for all when it comes to mete-out justice.


text:
Carrie Fisher and Debbie Reynolds - An Illuminati Bloodline Family
Thanks to ascendingstarseed.wordpress.com for posting a link about the Reynolds Bloodline family. I thought this link and post would be a good follow up to Kent Dunn's incredible information about the recent deaths of Star Wars actress Carrie Fisher and her mom's death Debbie Reynolds and how they were both somehow related to the notorious dark cabal. Here are some notes I took from Kent Dunn's intel update.
Kent Dunn continues to make some incredible claims about how Star War's actress Carrie Fisher and her famous mother Debbie Reynolds were part of the cabal and had many dirty secrets, suggesting that both Fisher and Reynolds were murdered this past week. Apparently Fisher's daughter Billie Lourd is also "in on it" and Kent Dunn does not know how Mrs. Lourd will be dealt with.
Now we have this data point that exposes the Reynolds Illuminati family bloodline which now will help researches connect more dots into the vast web of the global elite families and famous personnel.
Here is a Reddit channel discussing the MKULTRA connection between Carrie Fisher and Debbie Reynolds.
Debbie Reynolds and Carrie Fisher (Illuminati Family)
http://www.bibliotecapleyades.net/bloodlines/reynolds.htm
The Reynolds Bloodline
THE REYNOLDS ARE A VERY ELITE ILLUMINATI FAMILY
The Reynold's family is not one of the 13 primary bloodlines, but they are such a prominent Illuminati family within the 13 bloodlines that I have decided to single them out for another article on Illuminati bloodlines. Although the Reynolds are allied with many of the major Satanic bloodlines, including the Rothschilds, the DuPonts, the Rockefellers, the Graces and the Grays, they are especially close and intertwined to the Duke and Cullman families. (One example of a Duke/Reynold joint project is the Research Triangle Foundation.) It should be remembered that The Temporary National Economic Committee of Congress in 1937 which studied the super rich found that the Dukes and the Reynolds were among the top 13 wealthiest families in America. Further, my research at one place or another has shown that the Reynolds of the Illuminati variety have interacted with all of the other major Satanic bloodlines. These elite birds of a feather all flock together.
MY NEWSLETTER HAS REPEATEDLY EXPOSED MEMBERS OF THIS FAMILY
During the course of putting out this newsletter in '92-'95, I have had repeated opportunities to include various members of the Reynold's bloodline in my exposes. It must always be borne in mind that a large share of each of these bloodlines are hidden behind other last names.
Within this area, my newsletter has exposed Reynold family members of the Illuminati from Astoria, Oregon (such as Scottish Rite Librarian/Mortician Reynolds) and Bend, Oregon (such as Mayor Paul Reynolds who also runs a mortuary, and his son Mark). One of the first Illuminati Monarch slaves in the Portland area to seek her freedom was Mary D. Reynolds. She was from the Mother of Darkness level. She wanted to find treatment for the programming that goes with the Niother of Darkness level, and since wanting her freedom it is anybody's guess what happened to her.
A LOCAL OCCULT NEWSPAPER "EXCALIBER" ADVERTISES LAMOYNE REYNOLDS
OCCULT BOOKS
Some of the Reynolds family have written occult books. I don't know how all the people mentioned in this section are related, but some or all of these people may relate to the Satanic Reynold's bloodline. One of the important ecumenical Christian leaders is Frank E. Reynolds who is an American Baptist Minister (ordained 1955, Program dir. Student Christian Ctr. Bangkok, Thailand, Minister for Chicago Ecumenical Ministries '61-'64.) Frank E. Reynolds has been trying to introduce Buddhism to America. He and his wife Mani have written a number of books to introduce and teach Buddhism to Americans. Many of the Reynolds have been Episcopalians. Remember the Episcopalian Church is run by Freemasons and is thoroughly sold out to the occult. One of my Monarch survivors vividly recalls a human sacrifice done at the altar of an Episcopalian church. Anyway, getting back to the names of some Reynolds who have written occult books, here is a list of some:
Barrio Gordon Robert Reynolds--author of Magic. Divination & Witchcraft among the Barotse of Northern Rhodesia (1963)
Charles R. Reynolds w/ Regina Reynolds--authors of One Hundred Years of Magic Posters (1977)
David K. R. Reynolds--author of Naikan Psychotherapy: Meditation for Self-Development pub. by Chicago Press.
Denise AR. Reynolds--Sensitive Thoughts for the New Wave Soul.
Mani R Reynolds w/ her husband--Three Worlds According to King Ruana: A Thai Buddhist Cosmology
Frank E. Reynolds--Guide To Buddhist Religion
Jane Reynolds--Cosmobiology
Lloyd J. K.-- Mv Dear Runemeister: A Voyage Through the Alphabet
OVERVIEW OF FINANCIAL INTERESTS OF THE REYNOLDS
Before we get into the nitty-gritty let's take a quick overview of the Reynold's financial interests. Much of the family's money has been well-hidden behind fronts, holding companies, etc. The Satanists of the Reynolds family have been involved in high level drug dealing during this century. They have also had some important and major interests in Banking, Tobacco, and Aluminum, it seems like a fair number have gotten into the mortuary/cremation business, which is a great assistance for cremation after rituals involving human sacrifice. The financial interests of the Reynolds seem to be strongest in the Middle South, and strong in both Virginia and North Carolina. The Reynold's financial interests are so tightly woven in with the Cuilmans and Dukes that I will have to deal with all three families together. A few years ago the financial heads of the three families were as follows: Angier Biddle Duke, Richard S. Reynolds, Jr., and Joseph F. Cullman, 3rd. All were members of the Pilgrim Society. It is possible that the family also has sonic type of secret spiritual hierarchy too. If so, it might follow along the lines of the top 13 families which have kings and princes, and princesses, etc. of their bloodlines.
Richard S. Reynolds, Jr. has been a board chairman of Robertshaw Controls Company, which has a stranglehold monopoly on manufacturing car thermostats and other car parts. The big three American auto manufacturers all buy from Robertshaw Controls Company; but then since all 3 of the American auto manufacturers are Illuminati controlled, they likely don't care that the Reynolds family has such a stranglehold on them. Cadence which own theaters and has published occult comic books like the series, "Journey into Mystery With The Mighty Thor" is also tied to the Reynolds family. Cadence promotes the occult while making money. What American doesn't know what 'Reynold's wrap" is. The Reynold's name is a well-know household name just like DuPont paints, or the Rockefeller Center, or the Waldorf Astoria are well known American items named after Illuminati families. The Reynolds family controls several Aluminum companies which form a large part of the Illuminati-controlled Aluminum cartel. The Mellon family works with the Reynolds in this Aluminum cartel, and various Illuminati families also have their hands in the management of the Aluminum industry.
THE TOBACCO-DRUG CONNECTION
The Reynolds family is behind the RJ. Reynolds Tobacco Co. C. Boyden Gray of the Satanic Grey/Gray family is heir to the RJ. Reynolds Tobacco Co. But don't let that sidetrack you, the Reynolds family still has financial interests and power in the company. If readers remember previous articles on top 13 Illuminati families--specifically, the Astor family article, the Onassis family article and the Li family article you will remember some details that will help you understand the secret illegal drug trade that the Reynold's got involved in. The British elite got involved in shipping opium. The elite families got monopolies on the opium trade.
The British empire's military might and political clout was used to force China to allow the opium trade- Before the communists took over China, the British Illuminati families hid their opium trade behind the cover of the British American Tobacco Co. Later the Red Chinese would hide their opium trading behind the same front tobacco, with their state-run People's Republic of China Tobacco Bureau. In fact, the Red Chinese opium trade was controlled by another Illuminatus, the P.R. President Li Xiannian. Li Xiannian is from the occult U family who are proud that they are the leading oriental Satanic family.
President Li, a drug lord was finance minister of Red China from '57-'75. He sold so much opium to the west that he was able to help Red China pay off her debts, and he was nicknamed 'the money god.' RJ. Reynolds was a partner with British American Tobacco Co. and was also involved in trading in opium for many years. R.J. Reynolds was also involved with the rigidly controlled tobacco industry. I have reasons to suspect that the family later got involved with running cocaine. Onassis also hid his early drug smuggling behind tobacco importing. The Dukes controlled Liggett & Nlyers Tobacco via their Duke Endowment, a family foundation. The Cuilmans are known for their Miller Brewing Company. The Culiman fortune was also originally derived via a tobacco company. The Duke family via underhanded tactics put together a monopoly of the tobacco industry called the American Tobacco Company.
Later they merged and bought out 88 tobacco companies, and the capitalization of the company was $235 million prior to 1911. The Supreme Court made the American Tobacco Company dissolve since it had created an illegal monopoly. However, the Dukes rearranged things and kept on going. The Dukes intermarried with the Biddies who worked with the Rothschilds. The Dukes also intermarried with the Drexels, who in turn intermarried with the Harrimans and Goulds. Nicholas Biddle (1879-1923) was a trustee of the Illuminatus William Astor of the top 13 Illuminati family of Astors. Some of the Biddies intermarried with the Astors Illuminati family too! It's a small world at the top. Researcher Ferdinand Lundberg in his book America's 60 Families states on p. 36, "The Morgan firm and its affiliated commercial banks act, broadly, on behalf of such tremendous accumulations as those of the Vanderbilts, Goulds, Drexels, Wideners, Berwinds, Phippses, Hills, Dukes, Ryans, McCormicks, Bakers, DuPonts, Fishers, Jameses, and Others.' Isn't it interesting that my writings have exposed the Morgans, Vanderbilts, Goulds, Drexels, Phippses, Dukes, Bakers, DuPonts as families with members in the Illuminati?
CONNECTIONS TO ILLUMINATI ORGANIZATIONS
Many of the Illuminati organizations which this newsletter has exposed have had members from the Reynolds family. Some examples of this follow:
COSMOS CLUB
Joseph Melvin Reynolds
Lloyd George Reynolds
COUNCIL OF FOREIGN RELATIONS (CFR).
A. William Reynolds
PILGRIM SOCIETY:
Richard S. Reynolds, Jr.
33° MASONS.
Harman Gansvort Reynolds,
Marshall S. Reynolds
32° MASONS:
Arthur Rowley Reynolds,
Benn Phillips Reynolds,
Charles Phillip Reynolds,
Charles Shaw Reynolds,
Erwin James Reynolds,
Isaac Reynolds,
Harry Edwards Reynolds,
Henry James Reynolds, John C. Reynolds (Deputy Grand Secy of Grand Lodge of Illinois)
KNIGHTS TEMPLAR MASONS. (These 3 KT names all come from just one Lodge in NY--Jerusalem Chapter No. 8)
Alex Reynolds,
J. Hyatt Reynolds,
T. Spaulding Reynolds
John Charles Reynolds, who worked for the Graces as a communications executive, trustee of the Presb. Hospital in San Juan.
FOUNDATIONS & CONNECTIONS
The Dukes and Reynolds use tax-free foundations as a vehicle to escape taxes and hid their wealth while maintaining their financial power. Other Illuminati families also used the foundations as a scheme to remain powerful and to resist paying taxes. The controlled press builds them up as charitable, when most of the grants by these foundations are self-serving for the benefit of the elite. Some of these foundations are the Z. Smith Reynolds Foundation (est. '36 in North Carolina), the Kate B. Reynolds Foundation (est. '46 in North Carolina), the Richard S. Reynolds Foundation (est. '55 in Virginia), and the Mary Reynolds Babcock Foundation, and the Duke Endowment (address at Rockefeller Plaza, NY). All of these foundations work for the Illuminati.
The Donald W. Reynolds Foundation (est. '54 in Nevada and then moved to Arkansas) might also be connected to the elite--but I don't know. The Kate B. Reynolds Health Care Trust provides another example of a liaison between the Duke and Reynolds families. John A. McMahon, a Duke University grad, served as BOTH chairman of the board of trustees of Duke University and since 1971 as a member of the advisory board to the Kate B. Reynolds Health Care Trust. Here is an example of a single man working for the interests of both the Dukes and Reynolds. Edwin C. Whitehead, chairman of Technicon Corp. serves as a similar example of a man working for both Duke and Rockefeller interests. Whitehead is a trustee of both Duke University and Rockefeller University. He also has been in charge of several NWO organizations promoting a one-world, for instance he was Governor of the U.N. Association.
This article will now show you the reader some of the grants that these foundations make. I ask you, "Do you see any possibility for any mischief behind these grants?! What I noticed about the Reynolds grants is that they control all the crisis centers in the North Carolina area via their money. This means any survivor of SRA calling a hot line is going to get tapped into the Illuminati's net for catching escapees! I also noticed that all the colleges and universities got money, and a variety of different denominations incl. the Baptists, the Methodists, the Non-denominationalists, the Presbyterians, and the Ecumenicalists. What surprised me was that a number of government agencies get money from the Reynold Foundations, especially those dealing with children like Social Services, and even Police Departments get money from the Reynolds Foundations.
IN 1984 the Z. SMITH REYNOLDS FOUNDATION in North Carolina gave money to these following organizations (plus some others not listed):
Catawba College - $75,000
Chapel Hill Police Department - $37,500
Child Watch - $25,000
Contact of Winston-Salem (a crisis counseling service) - $15,000
Council on Drug Abuse - $27,000
Elizabeth City State University - $50,000
Family & Children's Service of Greater Greensboro (they deal with Rape & Family Abuse) - $5,000
Friends of Public Radio - $25,000 (Note: they even want to control public radio!)
General Baptist State Convention of North Carolina - $85,000 (The love of money is the root...)
Mars Hill College - $30,000
Meredith College - $9,360
Montreal-Anderson College - $17,280
Moses H. Cone Memorial Hospital - $31,000
North Carolina Center for Public Policy Research - $225,200 (Wow!)
North Carolina Department of Human Resources (foster homes) - $12,000
North Carolina Department of Natural Resources - $30,000
North Carolina Department of Public Instruction - $103,000
North Carolina Hospital Education & Research Foundation - $50,213
North Carolina Justice Academy - $10,000
North Carolina School Boards Association - $7,500
Outer Banks Hotline (crisis Counseling) . $5,000
Pitt County Memorial Hospital - $35,000
Planned Parenthood of Greater Raleigh - $15,000 (The Illuminati foundations have been the big support behind
Planned Parenthood. The Van Duyn family was tied to Planned Parenthood in NY.)
Planned Parenthood of Orange Co. - $15,000
Religious Coalition for Abortion Rights Education Fund - $10,500
Rowan Cooperative Christian Ministry - $15,000
St. Mary's Episcopal Church - $10,000
Salvation Army of Waynesville - $20,000
University of North Carolina - $233,265
Western North Carolina Amer. Meth.-Episco. Zion Church - $25,000
YMCA of Winston-Salem - $300,000
In 1989 there were more grants of a similar nature. The following are by just the Z. Smith Reynolds Foundation in 1989. I have picked (Jut a few to list so that the reader can get an idea of the breadth of their grants to religious bodies and child protective groups.
Advocates for Children in Court - $25,000
Child Care Resources - $40,000
Duke-UNC Center for Research on Women - $60,000
Ebenezer Baptist Church - $8,000
First Baptist Church - $15,000
God's Harvest House of Deliverance Church $10,004)
Metropolitan A.M.E. Zion Church - $27,100
National Conference of Christians & Jews $5,000 (The Illuminati have always well financed this organization.)
Rape Crisis Center, Asheville, NC - $20,000
Religious Coalition for Abortion Rights Education Fund - $7,500
St. Andrews Presbyterian College - $50,000
Women's Abuse Shelter - $15,000
I don't want to take more time and space with listing foundation figures, 1 hope the reader gets the picture of how these foundations are able to control society for the Illuminati. We see government agencies, especially the ones that the Illuminati would want to make sure they controlled are well supported financially. People who live in the Carolinas & Virginia report that the corruption in those states in absolute. These statistics give some facts behind those statements. One organization receiving money above deserves exposure. The Drug Abuse Council (Council on Drug Abuse) has been headed by an Illuminatus & Pilgrim Society member Bethuel M. Webster.
R.J. REYNOLDS INDUSTRIES
R.J. Reynolds Industries of Winston-Salem, North Carolina had an annual sales of over $6 billion and 37,000 employees. I have the 1976 profit figure for the company was $353 million. I don't know what the current figures are. The public thinks that the shares are widely held by the public, while in reality the control is very narrow. The Reynolds were careful when their stocks went on the market to retain secret control. R.J. Reynolds Industries chairman was Colin Stokes. Colin Stokes is an interesting person who works for the Reynolds. He was a member of Kiwannis International, and a director of several key things, dir. of Winston-Salem Savings & Loan, Integon Corp., and NCNB Corporation which is the bank holding company of North Carolina National Bank. The NCNB, which at one time had assets of $4.647 billion (it may be more now) has been the largest of the Middle South banks and is a key component of the Reynold/Duke/Cuilman clique.
From 1958 to 1964, Stokes was a director of the William and Kate B. Reynolds Memorial Park, and further serves the Reynolds as trustee of the Wake Forest Univ. Another interesting liaison person for the Reynolds has been J.P. Sticht, a director of R.J. Reynolds, who has acted as a liaison between the Reynolds, Duke, and Rockefeller dynasties. Sticht is a member of Rockefeller University Council, and a member of the board of visitors to Duke University. Leighton Hammond Coleman, who is in the Illuminati from the Satanic Coleman family, also sits as emeritus director of RJ. Reynolds Industries. His father and him both belonged to the Pilgrim Society, which is a level above the CFR in the Illuminati political section. His father was a director of the corrupt American-Russian Chamber of Commerce which has been previously exposed in my writings. Now readers can see why RJ. Reynolds Industries has been one of the top companies trading with Communist Russia in the past.
Another director of RJ. Reynolds Industries is John D. Macomber, a 1950 Yale graduate. (I don't know if he belonged to one of the Illuminati's 5 fraternities at Yale or not.) Macomber married into the Illuminati Morgan family, and is a member of the Pilgrim Society. Besides being a director for Li. Reynolds, he serves the Rockefellers as a director of Chase Manhattan. He is also dir. of Norlin Corp. Gordon Gray, of the satanic Gray family, also sits on the Li. Reynolds board. Both Gordon Gray and John Macomber belong to the CFR.. Gray also went to Yale ('33 grad.) and was with Triangle Broadcasting run by Pilgrim Society member Walter H. Annenberg (who is close to being a billionaire). As you can see, some heavy duty Illuminati men direct Li. Reynolds Industries. Gray, a member of the CFR., also served as a liaison to the Corcoran financial interests. William W. Corcoran was a corrupt Washington banker who had a lily white front. Corcoran bribed the chairman of the House Ways & Means Committee so that he could illegally profit a half million dollars for being the middle man of the transaction of the U.S. paying off its debt to Mexico for lands taken during the Mexican War. (See Reports of Committees, Thirty-third Congress, First Session, Volume iii, # 354:4.) The U.S. did not need a middle man to pay off Mexico, but due to corruption in Congress, Corcoran was able to make a bundle of money with no effort. Riggs Bank is associated with the Corcoran fortune. CFR & Yale Grad & Pilgrim Soc. member William McChesney Martin, Jr. has worked for Riggs National Bank as their advisor. The multitude of connections of people serving the Reynolds to the elite are mind-boggling. Many of the institutions in the Middle South connect back to the Illuminati and their front organizations such as the Freemasons and the Chambers of Commerce, the CFR etc. etc. Some of the companies that R.J. Reynolds Industries interlocks with are Arista Co., Avon Products,
Charlette, NC branch of the Federal Reserve Bank of Richmond, Dun & Broadstreet Companies, Foremost-McKesson Inc., Hatteras Income Securities, Hayes-Albion Corp., Jefferson Pilot Corp., McClean Industries, Northwestern Mutual Life Insurance, Perkin Elmer Corp., Richardson-Merrell Inc., S.C. Johnson & Sons Inc.,
Southern Broadcasting Inc., Standard Oil Co. of Indiana, Standard Savings & Loan, Stauffer Chemical Co., United States Filter Corp., United States Steel Corp., Wachovia Corp. I doubt if this is current information, but needless to say, Li. Reynolds Industries today interlocks with many other corporations.
OTHER REYNOLD/DUKE COMPANIES
The Reynolds Metals Company- notice I wrote "Metals Company"- interlocks with a large number of companies too. The Reynolds Metals Company has had most of its directors from the Reynold's family. Recently, four were on the board of directors and one was the company treasurer. One of companies that the Reynolds Metals Company interlocks with is the Bank of Virginia. The Bank of Virginia in turn interlocks with a long list of companies. A large share of these companies have directors that tie back to the elite. Some of these corporations have directors who are members of the Pilgrim Society, others are with other Illuminati organizations. If the entire set of interlocks and there directors were exposed it might have an emotional impact on the reader, but it gets wearisome to the eyes to be stressed with so many lists, so I will dispense with listing other interlocks. Terry Sanford has worked for the Duke family as a director of the American Arbitration Association, which is an Illuminati front.
The Astor family is represented in the AAA by David W. Peck. Terry Stanford has been an FBI agent, is the former governor of North Carolina, and president of Duke University. Stanford is also chairman of the Cordell Hull Foundation for International Education which had another Duke/Reynolds henchman Dean W. Colvard as its trustee. Colvard also has been an advisor to the Illuminati controlled Kellogg Foundation of Battle Creek, MI. Reynolds Securities in New York has been run by William Christian Miller, a member of the Pilgrim Society. William Christian Miller, an Illuminatus, has sat on the board of many corporations. Richard S. Reynolds, Jr. of the Illuminati has sat on the board of Central National of Richmond Corp. which is a holding company for the Central National Bank of Richmond. His younger brother David has sat on the board of United Virginia Bancshares Board. Hugh Culiman is a director of United Virginia Bancshares.
Hugh's cousin is in the Pilgrim Society. Hugh is also president of Philip Moms Inc., Richmond Corp., and Foreign Policy Association, and the International Chamber of Commerce. Also working with him on the board of United Virginia Bancshares has been Herbert Stein, a director of Reynolds Metals Company. Stein also worked for L.F. Rothschild and Company as a consultant from 1976 onward. Another director working with Stein, David Reynolds, and Hugh Cullman is Kenneth A. Randall, a Mormon. Kenneth A. Randall is a trustee of the Rockefellers Council of the Americas and works with various Illuminati members at other places too. Some of the Reynolds are Mormons. George Reynolds wrote Commentary on the Book of Mormon and Commentary on the Pearl of Great Price. Eskimo Pie Co. is just one of many that the Reynolds have controlled. Julian Louis Reynolds is on both the board of the Metals Co. and the Eskimo Pie Co.
REVIEW ABOUT REYNOLD COMPANIES
This article has only touched on what the Reynold family operates. However, the thrust of what this part of the article is saying is that the Reynold family works with many other elite Illuminati families, and is active in banking, aluminum, and tobacco. SUMMARY OF PART 1.--The Reynolds family is an elite Illuminati family which ranks right up there with the top 13 Illuminati families. They have a very extensive power base but are strongest in the Middle South.
SOME REYNOLDS OF NOTE -- KNOWN ILLUMINATI MEMBERS ARE GIVEN 3 ASTERIKS ***
Mrs. Mn. Reynolds Forsyth- Officer of Richard S. Reynolds Foundation
A. William Reynolda*** - CFR
Billy I. Reynolds-script writer for television and radio, Methodist, lives in VA
Dana Drummond Reynolds-worked w/ UN symposium for New Inter. Econ. Order in 1979, mmbr World Future Society, advisor for USOM to Libya, extension training advisor to Afghanistan, worked for ABC network '47-48, mmbr Planetary Citizens (A New Age globalist group), Kappa Tau Alpha, Ctr, for Study Democracy, went to George Wash. University
David Parham Reynolds-dir. Reynolds Metal Co., Reynolds Alum. Sales Co., Eskimo Pie Co., Reynolds Jamaica Mines, Ltd. Presbyterian living in Richmond, Virginia.
Debbie Reynolds-original name Mary Francis Reynolds, movie actress, had the Debbie Reynolds show
Donald Worthington Reynold-owner of 6 radio stations, and a newspaper/book publisher, lives in ARK
J Berkley Reynolds-Sd of Directors Billy Graham Evang. Association in Canada.
John W. Reynolds-Fed. Judge
Joseph Allen Reynolds, Jr-banker, mmbr several fraternities, Episco.
Joseph Melvin Reynolds-mmbr Cosmos Club, physicist, univ. official, grad. Vanderbilt Univ. '48 & Yale '47
Jullan Lewis Reynolds--Libby Holman Reynolds-chief donor for the Christopher Reynolds Foundation
Lloyd George Reynolds***-economist, eco. professor Yale '46-47, Guggenheim Fellow 54-55, lives at Yale Univ. mmbr Cosmos Club
Norman Eben Reynolds, Jr-lawyer, b. Muskogee, OK & lives in Okia. City, belongs to several fraternities
Randolph Nicholas Reynolds-son of William Gray Reynolds, has a son Robert Gray Reynolds, banker, dir Dominion Nat Bank, VA. Episco.
Richard S. Reynolds, Jr.*** - Pilgrim Society
Russell Seaman Reynolds, Jr.- pres. Russell Reynolds Assoc., Yale 54, important figure in Republican party, on Adv. bd. for Greater
NY's Salvation Army, worked for at least two Morgan companies (J.P. Morgan, Morgan Guaranty), likes Yachting, Society of Colonial Wars (an elite organization)
William Francis Reynolds-b. Boston, married a Fitzgerald, Harvard 51, instruc at MIT,
William Glasgow Reynolds-lawyer, Judge, government employee, married Nancy Bradford duPont, worked for E I. duPont de
Nemours & Co. from 1935-71, Mem. vis. com. Law School, Vanderbilt Univ., Episcopalian, wrote a history book on the Reynolds.
Posted by enerchi at 1/02/2017 01:29:00 AM
Labels: Assassination, Illuminati, Intel Report


KAMPALA, Uganda (AP) — The Ugandan government filed a charge of treason Thursday against a pop star-turned-lawmaker who has emerged as a major critic of the long-serving president and whose jailing drew outrage from top musicians around the world.
Kyagulanyi Ssentamu, popularly known as Bobi Wine, was visibly weak and struggled to walk into court from what his lawyer said was a severe beating while in custody. The government denied it.
It was the first time Ssentamu had been seen in public since his detention, and he clenched his fists as he greeted supporters. A colleague wrapped a scarf in the colors of Uganda's national flag around his shoulders.
The 36-year-old Ssentamu was arrested with other lawmakers while campaigning for a candidate Aug. 13. He was charged with illegal possession of firearms for his alleged role in an incident in which the motorcade of President Yoweri Museveni was pelted with stones.
The arrest sparked protests in the capital, Kampala, and elsewhere demanding his release, with scores of people detained as security forces cracked down on demonstrators. Dozens of top musicians, including Angelique Kidjo, Chris Martin, Chrissie Hynde and Brian Eno, signed a letter demanding Ssentamu's release, and a social media campaign to #FreeBobiWine was launched.
In Kampala's Kisekka Market area on Thursday, protesters held posters of Ssentamu, praising him as the "People's Voice." Demonstrations also occurred in Nairobi, the capital of neighboring Kenya, and in London.
A military court in the northern town of Gulu dropped the weapons charges, but he was quickly re-arrested, taken to a magistrate's court and charged with treason, which carries the death penalty.
He needed help to walk into court and appeared to cry at one point. He sat in the dock in magistrate's court, with his lawyers saying he was unable to stand on his own.
Ssentamu was arrested with four other opposition lawmakers, three of whom also face treason charges. A fifth legislator has been hospitalized with injuries allegedly sustained during detention.
The magistrate ruled Ssentamu should be allowed to see his own doctors, and he was remanded into custody until Aug. 30.
Medical treatment abroad is desirable, but first he needs to go to a private Ugandan health facility, said one of Ssentamu's attorneys, Medard Sseggona.
"As to whether they can do it is a different matter," he said.
Other opposition figures expressed concern about being targeted by security forces as Museveni's government has come under pressure in the Ssentamu case.
"Every way out of my home is barricaded since very early today," tweeted Kizza Besigye, a four-time presidential challenger who has been jailed many times.
Ssentamu, who was elected to parliament last year, has emerged as an influential critic of Museveni, especially among many educated youth who cannot find work in Uganda, where hospitals often lack basic medicines, and main roads are dangerously potholed.
He has protested an unpopular social media tax as well as a change to the constitution that removed the presidential age limit of 75. That provision allows the 74-year-old Museveni, who has been in power since 1986, to seek re-election in 2021.
Ssentamu's arrest came after he was campaigning for a lawmaker in the northwestern town of Arua. Museveni also was in Arua at the time, campaigning for a rival candidate who eventually lost.
In a statement late Wednesday, Museveni accused "unprincipled politicians" of luring youth into rioting. Addressing calls on social media to free Ssentamu, he said he had no power to do so.
"Let us therefore wait for the courts and see what they decide," Museveni said.
Museveni, a U.S. ally on regional security, has been elected five times. Although he has campaigned on a record of establishing peace and stability, some worry that those gains are being eroded the longer he stays in power.


Posted by Ted Guarnero admin on Tuesday, July 8th, 2014 at 4:08pm.
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text:
The Old West
"Remember the Alamo'" 1836
Buffalo Hunt,
The Tragic Fate of
the Donner Party,
California Gold Rush,
Vigilante Justice,
The Sack of
Lawrence, Kansas,
Pony Express Rider, 1861
The Stage, 1861
Crossing the Plains, 1865
Completing the Transcontinental Railroad, 1869
Shoot Out with "Wild Bill" Hickok, 1869
Buffalo Bill Entertains a Russian Grand Duke, 1872
Battle With The Apache, 1872
Captured by the Arapaho, 1875
Encounter with the Texas Rangers, 1876
Custer's
Traveling on an Emigrant Train, 1879
The Death
Of Billy The Kid, 1881
Ranchers and Farmers Collide in Nebraska, 1884
A Cowboy in Dodge City
Massacre At Wounded Knee, 1890
The Dalton
Gang's Last Raid, 1892
Land Rush, 1893
Train Robbery, 1899
Vigilante Justice, 1851
Printer Friendly Version >>>
In many areas of the burgeoning West, the absence of established institutions of law and order led the local community to literally take the law into its own hands and dispense justice through Vigilante Committees.
San Francisco vigilantes
hang a murderer,
In San Francisco, for example, the news of the discovery of gold to its north depleted its police force while simultaneously triggering an explosion in its population. (see The California Gold Rush, 1849) The resulting increase in crime and violence prompted the establishment of a Vigilante Committee to maintain law and order. The Committee was made up of 600 local volunteers, most of whom were prominent members of the business community. During its first year (1851), the Committee hanged four law breakers, whipped one, deported 20 and released 41 after trial. As a result, violent crime was reduced in the city. The Committee was disbanded within a year after its creation. It was revived five years later and disbanded the same year.
The remoteness of mining camps, often in politically unorganized territories, put them beyond the reach of the law. In this unruly environment, volunteers formed Committees of Vigilance that established basic rules of conduct and assured at least a minimum level of order. The community thus entrusted the Vigilante Committee with the combined responsibilities of judge, jury and executioner.
"They granted him a respite of three hours to prepare for his sudden entrance into eternity."
Mrs. Louise Clappe was the wife of a physician and lived in the mining area known as Indian Bar that bordered the Feather River in Northern California. In the period from 1851 to 1852 she wrote a number of letters to her sister in Massachusetts describing her experience. These letters were originally published in Pioneer Magazine (1854-55) and then as a book in 1922. A copy of this book resides in the Library of Congress.
In a letter written on December 14, 1851, Louise describes how the mining community established its own form of law and order:
"The facts in this sad case are as follows. Last fall, two men were arrested by their partners on suspicion of having stolen from them eighteen hundred dollars in gold-dust. The evidence was not sufficient to convict them, and they were acquitted. They were tried before a meeting of the miners, as at that time the law did not even pretend to wave its scepter over this place.
The prosecutors still believed them guilty, and fancied that the gold was hidden in a coyote-hole near the camp from which it had been taken. They therefore watched the place narrowly while the suspected men remained on the Bar. They made no discoveries, however, and soon after the trial the acquitted persons left the mountains for Marysville.
A few weeks ago, one of these men returned, and has spent most of the time since his arrival in loafing about the different barrooms upon the river. He is said to have been constantly intoxicated. As soon as the losers of the gold heard of his return, they bethought themselves of the coyote-hole, and placed about its entrance some brushwood and stones in such a manner that no one could go into it without disturbing the arrangement of them. In the mean while the thief settled at Rich Bar, and pretended that he was in search of some gravel-ground for mining purposes.
A few mornings ago he returned to his boarding-place, which he had left some hour earlier, with a spade in his hand, and, as he laid it down, carelessly observed that he had been out prospecting. The losers of the gold went, immediately after breakfast, as they had been in the habit of doing, to see if all was right at the coyote-hole. On this fatal day they saw that the entrance had been disturbed, and going in, they found upon the ground a money-belt which had apparently just been cut open. Armed with this evidence of guilt, they confronted the suspected person and sternly accused him of having the gold in his possession. Singularly enough, he did not attempt a denial, but said that if they would not bring him to a trial (which of course they promised) he would give it up immediately. He then informed them that they would find it beneath the blankets of his bunk, as those queer shelves on which miners sleep, ranged one above another somewhat like the berths of a ship, are generally called. There, sure enough, were six hundred dollars of the missing money, and the unfortunate wretch declared that his partner had taken the remainder to the States.
A mining camp
By this time the exciting news had spread all over the Bar. A meeting of the miners was immediately convened, the unhappy man taken into custody, a jury chosen, and a judge, lawyer, etc., appointed. Whether the men who had just regained a portion of their missing property made any objections to the proceedings which followed, I know not. If they had done so, however, it would have made no difference, as the people had taken the matter entirely out of their hands.
At one o'clock, so rapidly was the trial conducted, the judge charged the jury, and gently insinuated that they could do no less than to bring in with their verdict of guilty a sentence of death! Perhaps you know that when a trial is conducted without the majesty of the law, the jury are (sic) compelled to decide not only upon the guilt of the prisoner, but the mode of his punishment also. After a few minutes' absence, the twelve men, who had consented to burden their souls with a responsibility so fearful, returned, and the foreman handed to the judge a paper, from which he read the will of the people, as follows: That William Brown, convicted of stealing, etc., should, in one hour from that time, be hung by the neck until he was dead.
By the persuasions of some men more mildly disposed, they granted him a respite of three hours to prepare for his sudden entrance into eternity. He employed the time in writing, in his native language (he is a Swede), to some friends in Stockholm. God help them when that fatal post shall arrive, for, no doubt, he also, although a criminal, was fondly garnered in many a loving heart.
He had exhibited, during the trial, the utmost recklessness and nonchalance, had drank many times in the course of the day, and when the rope was placed about his neck, was evidently much intoxicated. All at once, however, he seemed startled into a consciousness of the awful reality of his position, and requested a few moments for prayer.
The execution was conducted by the jury, and was performed by throwing the cord, one end of which was attached to the neck of the prisoner, across the limb of a tree standing outside of the Rich Bar graveyard, when all who felt disposed to engage in so revolting a task lifted the poor wretch from the ground in the most awkward manner possible. The whole affair, indeed, was a piece of cruel butchery, though that was not intentional, but arose from the ignorance of those who made the preparations. In truth, life was only crushed out of him by hauling the writhing body up and down, several times in succession, by the rope, which was wound round a large bough of his green-leaved gallows. Almost everybody was surprised at the severity of the sentence, and many, with their hands on the cord, did not believe even then that it would be carried into effect, but thought that at the last moment the jury would release the prisoner and substitute a milder punishment.
A vigilante court
in a mining camp
It is said that the crowd generally seemed to feel the solemnity of the occasion, but many of the drunkards, who form a large part of the community on these bars, laughed and shouted as if it were a spectacle got up for their particular amusement. A disgusting specimen of intoxicated humanity, struck with one of those luminous ideas peculiar to his class, staggered up to the victim, who was praying at the moment, and, crowding a dirty rag into his almost unconscious hand, in a voice broken by a drunken hiccough, tearfully implored him to take his "hankercher," and if he were innocent (the man had not denied his guilt since first accused), to drop it as soon as he was drawn up into the air, but if guilty, not to let it fall on any account.
The body of the criminal was allowed to hang for some hours after the execution. It had commenced storming in the earlier part of the evening, and when those whose business it was to inter the remains arrived at the spot, they found them enwrapped in a soft white shroud of feathery snow-flakes, as if pitying nature had tried to hide from the offended face of Heaven the cruel deed which her mountain-children had committed."
Clappe, Louise Amelia Knapp Smith, The Shirley Letters from California Mines in 1851-52, ed. by Thomas C. Russell (1922); Davis, William C. and Joseph Rosa (eds.), The West (1994); Grafton, John, The American West in the Nineteenth Century (1992).
"Vigilante Justice, 1851" EyeWitness to History, www.eyewitnesstohistory.com (2006).
The author of this account, Louise Clappe, and her husband moved to San Francisco where he set up his medical practice and she became a school teacher. She died in 1906.