text:
\section{Introduction}

With recent advances of vision technologies on smartphones and Head Mounted Devices such as Facebook Oculus~\cite{fb_oculus}, Samsung Gear VR~\cite{samsung_gear}, Microsoft Hololens~\cite{MS_hololens}, VR/\ang{360} video streaming is becoming increasingly popular. The major service providers such as YouTube and Facebook have already started streaming \ang{360} videos through optimized spherical video players~\cite{fb_360,yt_360}.

\ang{360} video frames are typically 4-6 times larger than normal videos ~\cite{qian2018flare} and it can grow up to 80 times~\cite{guan_pano_2019}, requiring more resources at every entity involved in the process, i.e. servers, networks and end-user devices. Viewport-adaptive streaming, \textit{a.k.a.} Field-of-View (FoV) aware streaming, has emerged as an efficient approach for \ang{360} video streaming \cite{qian_optimizing_2016, qian2018flare,he2018rubiks}. FoV-aware streaming is expected to significantly reduce the amount of data that has to be transferred by predicting the next viewport or FoV (visible area) of the user and sending only the selected portion of the panoramic frame to the client. Despite the great promise and research, viewport-adaptive streaming has not yet been adopted by streaming service providers like YouTube and Facebook. This is primarily due to lack of end-device software support in viewport-adaptive streaming and the lack of holistic understanding of how user viewports change from frame to frame and from video to video.

There have been scattered efforts to understand viewport characteristics of \ang{360} videos, but with small-scale experiments with limited number of videos~\cite{xie_cls_2018,rossi_spherical_2019,petrangeli_trajectory-based_2018}. This paper presents a comprehensive analysis of viewport trajectories of \ang{360} video users by analysing a large unified dataset of over 3700 viewport traces. \emph{To the best of our knowledge, this is the largest \ang{360} video user behavior analysis conducted to date.} We propose a viewport clustering mechanism to understand similarities of user behaviors that takes spatial, motion and other behavioral patterns into account. We particularly highlight the importance of adding temporal features such as head movement speed to the viewport clustering to ensure that subtle differences in highly dynamic viewport patterns can be captured.

Video categorizations are widely studied for normal video streaming~\cite{sun2016exploiting,jiang2017exploiting}, which
can support video indexing, storage management, popularity prediction, and efficient resource provisioning at both content and network service providers~\cite{sun2016exploiting, mcclanahan2017interplay}.
However, these traditional video categorizations will not be effective anymore for \ang{360} videos as they categorize entire video into one category or cluster~\cite{afzal_characterization_2017, carlsson_had_2020, nasrabadi_taxonomy_2019}. Due to the freedom of users to request different portions of the same frame and tile-based streaming of \ang{360} videos, user behavior can vary significantly even within a given video. In this paper, we propose a novel video segment categorization mechanism based on the dynamic viewport characteristics of users that cluster chunks of the same video into different groups.

The key contributions of this paper are;\vspace{-1mm}
\begin{itemize}
    \item An aggregated dataset with 88 videos with over 3700 viewport traces containing over 142 hours of viewing logs. We consolidate different formats of individual datasets, e.g. head orientation, data sampling rates, trace duration, into unified framework, which has been made publicly available\footnote{\url{https://github.com/theamaya/Viewport-Aware-Dynamic-360-Video-Segment-Categorization}}.
    \item A novel feature based viewport clustering algorithm that takes spatial, motional and other behavioral features of viewport patterns into account. We validate the effectiveness of the proposed clustering mechanism using the aggregated dataset compared to previously proposed clustering methods. The results show an average of 81.17\% of viewport overlap and limits the pairwise difference in angular speed of 0.47 rad/s for the traces within the resulting clusters.
    \item A novel dynamic video segment categorization algorithm which is based on different user behavior types induced by the corresponding video. The results show 28.32\% improvement in similarity in viewport distribution within the clusters when compared to the existing static genre-based video categorizations.
\end{itemize}

\section{Related Work}

\textbf{Viewport clustering}.
Majority of the existing algorithms for viewport clustering, are based on the hypothesis that users tend to exhibit similar viewing patterns for a given video. One notable clustering approach, proposed by \cite{rossi_spherical_2019} leverages the spatial arrangement of user viewpoints at a particular play-point or during a specific time window of the video. The clustering is done in the spherical domain using geodesic distance as the closeness metric. Another approach proposed by \cite{xie_cls_2018} extracts user fixations from viewport traces and performs density-based clustering using a spatial distance metric. The algorithm proposed by \cite{petrangeli_trajectory-based_2018} utilizes viewport trajectory data in the long term and clusters the viewports with a time and distance based metric.

\textbf{Video categorization}.
The literature surrounding video categorization is limited, with most studies focusing on video titles and genres. Afzal \textit{et al.}~\cite{afzal_characterization_2017} categorized 2285 \ang{360} videos based on their genre which is determined by keywords and found 14 different categories, ranging from documentaries to sports and driving. Another categorization approach, proposed by \cite{almquist_prefetch_2018} and \cite{carlsson_had_2020} used manual inspection of video content and expected behaviors of user FoV trajectories, resulting in categories such as \textit{Exploration}, \textit{Static focus}, \textit{Rides} etc. An approach proposed by \cite{nasrabadi_taxonomy_2019} categorizes videos based on the extent of camera motion and the number of moving targets.

\textit{In contrast to these methods, we aim to leverage a viewport-aware approach to the problem of \ang{360} video categorization. Our proposed solution aims to go beyond spatial features by considering temporal and behavioral patterns as metrics for viewport similarity modeling.}

\section{Dataset}\label{sec:dataset}
We created a unified dataset  combining 6 different \ang{360} video datasets from the literature. The selected datasets contain \ang{360} videos
and their corresponding user head orientation data. Table~\ref{tab:dataset} summarises the
datasets used. The aggregated dataset contains 88 videos with an average of 45 user traces per video. Each video is at least 30s long. We have made the aggregated dataset publicly available \href{https://github.com/theamaya/Viewport-Aware-Dynamic-360-Video-Segment-Categorization/}{\underline{here}}.

First,
we converted all head orientation traces to the same representation; Yaw and Pitch angles in radians (Roll angle variation was negligible, therefore was ignored in this study). Next,
to tackle the differences in sampling rates, we re-sampled the entire dataset at 10Hz.
For this study, we only considered the datasets collected using
HMDs, excluding
mobile and PC users to eliminate the impact of device on viewing patterns~\cite{xu_analyzing_2019}\footnote{We keep viewport patterns from different rendering devices and its impact on our categorization task as a future work}.

\vspace{-2mm}
\begin{figure}[h]
\centering
    \subfloat[Viewport fixations for 4 sample videos]{\includegraphics[trim=0 0 0 0, clip,width=0.7\columnwidth]{Motivation_FOV_added.pdf}%
    \label{fig:motivation_2_diff_videos}}

    \subfloat[Mean distribution: Yaw]{\includegraphics[width=0.35\columnwidth]{Motivation_feature_3.png}%
    \label{fig:motivation_f3_dist}}\quad

    \hspace{0.5mm}
    \subfloat[Max yaw movement]{\includegraphics[width=0.35\columnwidth]{Motivation_feature_11.png}
    \label{fig:motivation_f11_dist}}

    \vspace{-3mm}
    \caption{Viewport and two feature distribution between selected videos in two categories}
    \vspace{-4mm}
    \label{fig:motivation}
\end{figure}

\vspace{-2mm}
\subsection{Relationship of viewport and video types}

In our preliminary analysis, we studied how viewport distributions differ between different videos. Fig.~\ref{fig:motivation} shows four example videos analyzed in this study. In both \textit{i)} V1 (Rollercoster) \& V2 (Riding on a Hog): the camera is moving forward with a high speed whereas in \textit{ii)} V3 (Landscape) \& V4 (Panel): Camera movement is not significant, and contents are dispersed in the frame.

Viewport distributions in Fig.~\ref{fig:motivation_2_diff_videos} shows that, content of the video has an impact on the users' viewport distribution. We see that the viewports of V1 \& V2 are centered in the frame whereas, viewports are comparably dispersed in V3 \& V4. This indicates that,
viewport distributions can be used as a proxy to explain certain content patterns in \ang{360} videos
indicating that a viewport based categorization of videos can be effective. Fig.~\ref{fig:motivation_f3_dist} plots the spatial distribution of 2s viewport chunks in the yaw axis, and~\ref{fig:motivation_f11_dist} plots the maximum yaw angle moved by a viewport trace within a 2s chunk. We observe that patterns of raw viewport distributions in \ref{fig:motivation_2_diff_videos} are reflected in these features, indicating that carefully extracted features from viewport traces can result in an effective video categorization.

\section{Conclusion and Future work}

In this study, we conducted an in-depth analysis of user behaviors in \ang{360} video streaming using a large dataset of over 3700 viewport traces.
By extracting both spatial and temporal features from viewport traces, the proposed method can identify viewport similarities across different videos whilst achieving
81.17\% \textit{within-cluster} viewport overlap and outperforms the existing viewport clustering algorithms.
We extended the above solution to develop a novel \ang{360} video categorization which successfully captures temporal changes of content and user behaviors by independently processing 2s video chunks. The proposed categorization shows 28.32\% improvement in clustering videos with similar combinations of user behaviors over existing genre-based \ang{360} video categorizations.

In future work, we aim to extend our video categorization method for to develop a novel caching algorithm to enhance the \ang{360} video processing at edge servers. We plan to explore new approaches for edge resource optimization further analysing the content of the categorized videos.

\section{feature-based viewport clustering}\label{section 4}

In this section, we propose a feature representation for user viewport traces, which is used for a novel viewport clustering method. In Section \ref{section 5}, we extend this feature based viewport clustering to develop a novel dynamic video categorization.
We divided the duration of a video/ viewport trace into bins (chunks) of 2 seconds.
Table~\ref{tab:basic notation} briefly describes the notations used in our further explanations.

\begin{table}[h]
  \small
  \vspace{-2mm}
  \caption{Feature selection for viewport clustering}
  \vspace{-4mm}
  \begin{tabular}{l|l}
    \toprule
    Notation & Description\\
    \midrule
    $i$, $k$ &  video id: $i \in [ 0,87]$, chunk id: $k \in [ 0,13]$,  \\
    $n_i$ & num. of users for $i^{th}$ video\\
    $j$ & user id, $j\in [ 0,n_{j})$\\
    $\{V_{i}\}$ & Set of all videos $\forall i \in [ 0,87]$ \\
    $\{c_{k}\}$ & Set of all time chunks $\forall k \in [ 0,13]$ \\
    $\{u_{i,j}\}$ & Set of all users $\forall i \in [ 0,87], \forall j\in [ 0,n_{i})$ \\
    $\{v_{i,k}\}$ & Set of all video chunks $\forall i \in [ 0,87], \forall k \in [ 0,13]$\\
    $\{t_{i,k,j}\}$ & Set of all viewport trace chunks  $\forall i \in [ 0,87], \forall k \in [ 0,13],$\\
    & $\forall j\in [ 0,n_{i})$\\
    \bottomrule
\end{tabular}
\label{tab:basic notation}
\vspace{-2mm}
\end{table}

\vspace{-2mm}
\subsection{Feature extraction}\label{Section 4- feature extraction}

We observed that the
\textit{users who consume \ang{360} video content differ to each other in multiple aspects such as positioning, speed of head movement, extent of the sphere explored and the maximum angle moved within a defined window}.

We extracted features for every viewport trace chunk in $\{t_{i,k,j}\}$ as summarized in Table \ref{tab:Feature_selection_for_viewport_clustering}. Let $F^{( 1)}$ represent the feature extraction function for a viewport trace chunk. $\underline{f}^{( 1)}_{i,k,j}$ is a 15 dimensional feature vector that represent a viewport trace chunk in the dataset.

\vspace{-2mm}
\begin{equation}\label{eq:feature_extraction_func_vp_clust}
 F^{( 1)}( t_{i,k,j}) =\underline{f}^{( 1)}_{i,k,j}
\end{equation}
\vspace{-3mm}

\begin{table}[h]
  \small
  \caption{Feature selection for viewport clustering}
  \vspace{-4mm}
  \begin{tabular}{p{2.0cm}| p{5.5cm}}
    \toprule
    Feature & Measurements y--yaw, p--pitch\\
    \midrule
    Position & mean, 25\textsuperscript{th} and 75\textsuperscript{th} percentiles: y \& p \\
    Speed &  mean, 25\textsuperscript{th} and 75\textsuperscript{th} percentiles: y \& p \\
    Maximum angle &

    angular position relative to initial point: y \& p\\
    Area explored & \% coverage on the sphere by \ang{100}x\ang{100}  viewport\\
  \bottomrule
\end{tabular}
\label{tab:Feature_selection_for_viewport_clustering}
\end{table}

\vspace{-2mm}
\subsection{Clustering the viewport trace chunks}

Fig.~\ref{fig:view_port_based_cat} summarizes the complete procedure of generating feature vector representations for viewport traces
which are then used as input to  a K-Means algorithm that clusters the viewport trace chunks into \textit{M} clusters.
We extract the cluster to which a viewport trace chunk belongs to using Eq.~\ref{eq:cluster_alloction_func} (further used in Section \ref{section 5})

\begin{equation}\label{eq:cluster_alloction_func}
    C^{( 1)}( t_{i,k,j}) =m,\ m\in [ 0,M)
\end{equation}

We defined three pairwise metrics to quantify the similarity of two viewport trace chunks for this viewport cluster analysis.
\begin{itemize}
\item\textit{Pairwise viewport overlap- VPO-} \cite{rossi_spherical_2019} measures the spatial coherence of two traces. It calculates the geodesic distance between each two corresponding sample points of 2 viewport traces and uses it as a proxy for pairwise viewport overlap. In our representation, a viewport trace chunk has 20 points (10Hz, 2 seconds).
The average of geodesic distance between each pair of corresponding points is taken as pairwise VPO.

\item\textit{Pairwise difference in head movement speed-}The average angular speed is calculated for each viewport trace segment, and their pairwise absolute difference is measured.

\item\textit{Pairwise difference in percentage of sphere explored-}within the 2s duration from the features extracted as mentioned in \ref{tab:Feature_selection_for_viewport_clustering}.

\end{itemize}
\textit{maximization} of the Pairwise VPO, and \textit{minimization} of Pairwise difference in head movement speed and Pairwise difference in percentage of sphere explored are desirable within a cluster.

\vspace{-1mm}
\begin{figure}[h]
\centering
\includegraphics[width=0.9\columnwidth]{Blcok_Diagram_VP_2.pdf}%
\label{fig:viewport_clustering_based_categorization}
\vspace{-4mm}
\caption{Overview of viewport clustering}
\label{fig:view_port_based_cat}
\vspace{-4mm}
\end{figure}

\subsubsection{The effectiveness of the proposed feature representation}

Clustering similar user viewport traces is often used in viewport prediction for \ang{360} video streaming. In this experiment we prove that the feature representation introduced in Eq.~\ref{eq:feature_extraction_func_vp_clust} can be used to accurately cluster viewports in such an application.
The feature vectors $\underline{f}^{( 1)}_{i,k,j},\ \forall j\in [0,n_{i}]$ as input to the K-Means clustering to yield \textit{M} different clusters separately for different videos $i$ and different time bins $k$.
The results were compared with 3 other algorithms,

\textbf{Spherical Clustering\cite{rossi_spherical_2019}}- Models viewport traces as nodes of a unit distance graph. Edges are connected if the two viewport traces have high viewport overlap. Finds maximal cliques as clusters

\textbf{Trajectory based\cite{petrangeli_trajectory-based_2018}}- Represents viewport traces as graph nodes with edges weighted by their spatial similarity. Uses spectral clustering while minimizing the distortion score.

\textbf{DBSCAN\cite{xie_cls_2018}}- Maximizes spatial density of clusters by taking the mean positions of trajectories in yaw, pitch in the 2s interval

\begin{figure}[h]
\vspace{-2mm}
\centering
\subfloat[Pairwise viewport overlap]{\includegraphics[width=0.18\textwidth]{BOX_PLOT_VPalgoComparison-VPoverlap-_2_.pdf}%
    \label{fig:Pairwise viewport overlap}}

    \hspace{2mm}
\subfloat[Pairwise difference in head movement speed]{\includegraphics[width=0.18\textwidth]{BOX_PLOT_VPalgoComparison-Speeddif-_2_.pdf}%
    \label{fig:Pairwise difference in head movement speed}}
\vspace{-3mm}
\subfloat[Pairwise difference in percentage of sphere explored ]{\includegraphics[width=0.18\textwidth]{BOX_PLOT_VPalgoComparison-_sphere.pdf}%
    \label{fig:Pairwise difference in percentage of sphere explored }}

    \hspace{2mm}
\subfloat[Number of resulting clusters ]{\includegraphics[width=0.115\textwidth,angle=90 ]{BOX_PLOT_VPalgoComparison-nclusters-_2_.pdf}%
    \label{fig:Number of resulting clusters }}
\vspace{-4mm}
\caption{Within-cluster measurement comparison of T-Trajectory based\cite{petrangeli_trajectory-based_2018}, S- Spherical\cite{rossi_spherical_2019}, D- DBSCAN\cite{xie_cls_2018} with the P- proposed for the aggregated dataset.}
\vspace{-2mm}
\label{fig:VP clustering algorithm comparison}
\end{figure}

Fig.~\ref{fig:VP clustering algorithm comparison} contains the aggregated comparison of the proposed algorithm with \cite{rossi_spherical_2019, petrangeli_trajectory-based_2018, xie_cls_2018} for the task of viewport clustering. The analysis was done for all $\{v_{i,k}\}, \forall i \in [ 0,87], \forall k \in [ 0,13]$.
For a clear comparison, at each video and time chunk we used the resulting number of clusters from Spherical clustering\cite{rossi_spherical_2019} as the number of clusters for K-means in the proposed algorithm. It is clear that the proposed algorithm outperforms all others in speed and percentage exploration based metrics, and shows on par performance with Spherical clustering algorithm in \textit{VPO} metric.

Fig.~\ref{fig:Temporal VP clustering vs proposed} contains a temporal comparison of the 4 viewport clustering algorithms for two videos picked from the dataset; Rollercoaster and Timelapse.
Majority of \textit{Roller-coaster} users fixate on the center of the frame while \textit{Time-lapse} users explore the entire frame freely without a significant correlation. The number of clusters generated by each algorithm at each time point is indicated on the corresponding plot on Fig.~\ref{fig:VP overlap rollercoaster}~and~\ref{fig:VP overlap timelapse}. Since Spherical clustering\cite{rossi_spherical_2019} shows the best performance out of the three algorithms used for comparison, we used the same number of clusters yielded by Spherical clustering for our algorithm at each time bin.

\begin{figure}[h]
\centering
\subfloat[Viewport overlap - rollercoaster]{\includegraphics[width=0.244\textwidth]{VPAlgoCompareTemporal_VPOverlapRollercoaster.pdf}%
    \label{fig:VP overlap rollercoaster}}

\subfloat[Viewport overlap - timelapse]{\includegraphics[width=0.244\textwidth]{VPAlgoCompareTemporal_VPOverlapTimelapse-_2_.pdf}%
    \label{fig:VP overlap timelapse}}
\hfill
\subfloat[Pairwise Speed diff. -rollercoaster ]{\includegraphics[width=0.244\textwidth]{VPAlgoCompareTemporal_SpeeddiffRollercoaster.pdf}%
    \label{fig:Speed difference rollercoaster}}

\subfloat[Pairwise Speed diff. -timelapse ]{\includegraphics[width=0.244\textwidth]{VPAlgoCompareTemporal_SpeeddiffTimelapse.pdf}%
    \label{fig:Speed difference timelapse}}
\hfill
\subfloat[Pairwise \% sphere diff. -rollercoaster ]{\includegraphics[width=0.244\textwidth]{VPAlgoCompareTemporal__sphereRollercoaster.pdf}%
    \label{fig:percent sphere rollercoaster}}

\subfloat[Pairwise \% sphere diff. -timelapse ]{\includegraphics[width=0.244\textwidth]{VPAlgoCompareTemporal__sphereTimelapse.pdf}%
    \label{fig:percent sphere timelapse}}
\vspace{-4mm}
\caption{Comparison of the viewport (VP) clustering algorithms for Rollercoaster and Timelapse videos. }
\vspace{-2mm}
\label{fig:Temporal VP clustering vs proposed}
\end{figure}

Spherical clustering algorithm produces significantly high number of clusters as it is based on maximal clique clustering.
Nevertheless, for the same number of clusters, the proposed algorithm yields almost on par performance in terms of viewport overlap.
Furthermore, our proposed algorithm significantly outperforms all others in pairwise speed and pairwise \% sphere explored metrics by explicitly using those features to define the viewprot trace chunk. The literature suggests that having viewport traces with similar speeds, orientation and spatial distribution within a cluster facilitates more accurate and long term viewport prediction. \textit{Viewport prediction followed by clustering is not in the scope of this paper.}

\textit{\textbf{Takeaway-} Unlike other algorithms that rely primarily on the spatial details of the viewports, the proposed feature extraction and clustering method successfully identifies clusters of viewport trace chunks that not only attend to the same content, but are also similar in behavioral aspects such as the speed of head of movement, maximum angular displacement within the window, and the extent of the sphere viewed. Moreover, the proposed algorithm is fast and lightweight in comparison to Trajectory based\cite{petrangeli_trajectory-based_2018} and Spherical clustering\cite{rossi_spherical_2019} approaches that utilize algorithms with a high computational overhead.}

\vspace{-2mm}
\subsection{Viewport clustering for aggregated dataset}\label{subsection:Viewport clustering for the entire dataset}

As the first step of our dynamic video categorization, we generated clusters inside the set of all viewport trace chunks $\{t_{i,k,j}\}$ as introduced in Table\ref{tab:basic notation}. The same feature vectors derived in \ref{Section 4- feature extraction}, $\underline{f}^{( 1)}_{i,k,j}$ were used as inputs to the K-Means clustering algorithm.

The optimum number of clusters for the set $\{t_{i,k,j}\}$ was determined by a DB (Davies- Bouldin) Score analysis as indicated in Fig.~\ref{fig:DBScore analysis VP}. The graph gives 2 minimums; \textit{M}=3 and \textit{M}=10. According to the plot, \textit{M}=3 is not a stable minimum and as shown in Fig.~\ref{fig:Similarity measurement within clusters for M=3}~and~\ref{fig:Similarity measurement within clusters for M=10}, upon clustering the dataset to 3 clusters, 2.29\% of the datapoints were identified as outliers. Where as for 10 clusters the outlier percentage was only 1.12\%. \textbf{Therefore, we selected \textit{M}=10}.

\vspace{-2mm}
\begin{figure}[h]
\centering
\subfloat[.]{\includegraphics[width=0.235\textwidth]{DBScoreVPClusters_reduced_size.pdf}
    \label{fig:DBScore analysis VP}}

\subfloat[.]{\includegraphics[width=0.245\textwidth]{BehavioursPerVideoChunk_reduced_size.pdf}
    \label{fig:Behaviours per video chunk}}\\

\vspace{-4mm}
\caption{a)-DB-score analysis to determine the optimum number of clusters for viewport trace chunk clustering. b)-Number of different user behaviors available in video chunks and the CDF of their distribution}\vspace{-5mm}
\label{fig:Similarity measurement VP m=10}
\end{figure}

\begin{figure}[h]
\centering
\subfloat[M=3 ]{\includegraphics[width=0.16\textwidth, , angle=90]{3VPClustersWithinBOXPLOT2.pdf}
    \label{fig:Similarity measurement within clusters for M=3}}

\subfloat[M=10 ]{\includegraphics[width=0.34\columnwidth, , angle=90]{10VPClustersWithinBOXPLOT2.pdf}
    \label{fig:Similarity measurement within clusters for M=10}}\\

\vspace{-4mm}
\caption{Pairwise VPO and Pairwise difference in head movement speed within and between clusters for \textit{M=3, 10}}
\vspace{-4mm}
\label{fig:Similarity measurement VP m=10}
\end{figure}

Fig.~\ref{fig:Similarity measurement VP m=10} illustrates the results for similarity measurement within and between viewport clusters generated by the proposed algorithm.
We observe that for each cluster, the resulting percentage pairwise viewport overlap between the traces of a cluster is significantly higher than that between traces from different clusters.
The averaged pairwise viewport overlap within viewport clusters is 81.17\%, and that between different clusters is 47.80\%.
Moreover, the pairwise difference of viewport trace speeds within a cluster is significantly less than that between different clusters. While the averaged pairwise speed difference within clusters is 0.47 rad/s, that between different clusters is as high as 2.47 rad/s
It is evident that the proposed viewport clustering algorithm captures similarities in terms of both spatial and motion features.

\vspace{-2mm}
\begin{figure}[h!]
\centering
\subfloat[Viewport trace cluster 7]{\includegraphics[width=0.22\textwidth]{VPClusterHeatmap7.pdf}
    \label{fig:Viewport trace cluster 7}}

\subfloat[Viewport trace cluster 9]{\includegraphics[width=0.22\textwidth]{VPClusterHeatmap9.pdf}
    \label{fig:Viewport trace cluster 9}}
    \hfill

\subfloat[Cluster 7 features]{\includegraphics[width=0.18\textwidth]{VP_clusters_combined_reduced_fig_size_fig_1.pdf}
    \label{fig:features cluster 7}}

\subfloat[Cluster 9 features]{\includegraphics[width=0.26\textwidth]{VP_clusters_combined_reduced_fig_size_fig_2.pdf}
    \label{fig:features cluster 9}}
    \hfill
\vspace{-3mm}
\caption{Viewport distributions and corresponding features of clusters 7 and 9}
\vspace{-3mm}

\label{fig:Viewport distributions of different clusters}
\end{figure}

Fig.~\ref{fig:Viewport trace cluster 7} and \ref{fig:Viewport trace cluster 9} show example intermediate results of viewport clustering indicating viewport trace distribution on Equirectangular frame for clusters 7 and 9. Each colored dot indicates one sample of viewport centers of a given user in the cluster. A viewport trace for 2s is composed of 20 such points.
It is evident how different user behaviors are captured by different clusters. Cluster 7 contains user traces that are slowly moving or are stationary around the center of the frame while cluster 9 contains users that are moving fast in the equatorial axis biased to the right of the frame.
Each cluster can contain traces with different combinations of features describing position and motion as shown in in Fig.~\ref{fig:Viewport distributions of different clusters}.
Fig.~\ref{fig:Behaviours per video chunk} shows the number of different viewport trace clusters (different behaviors) to which the users of a particular video chunk belongs. To eliminate the effects of outliers, a behavior is counted only if there are two or more users belonging to that particular cluster. According to the CCDF, More than 75\% of all video chunks have users displaying more than 4 different user behavior types (out of 10).

\textit{\textbf{Takeaway-} Different users consuming different \ang{360} videos show similarities in terms of positioning, speed, and \% of sphere explored by their viewports. The novel feature definition successfully identifies such similar behaviors. Majority of the video chunks have users displaying a variety of user trace patterns. Hence, for a successful characterization of videos, all different user behaviors should be used }

\section{Dynamic Video chunk categorization} \label{section 5}

In this section, we present a novel algorithm to dynamically categorize video chunks. Inspired by the results of Section \ref{subsection:Viewport clustering for the entire dataset}, we statistically characterise a video chunk by the distribution of how the users perceive its content by using different viewport trace patterns. We start off with the hypothesis that \textit{for a large enough dataset, the aggregate set of all user viewport trace chunks can reasonably represent all practical types of short-term user behaviors in perceiving 360°videos.}
In the following subsections we define a feature representation for video chunks based on our results from section \ref{subsection:Viewport clustering for the entire dataset}, categorize the video chunks into optimum number of clusters, and evaluate the performance of the proposed categorization.

\vspace{-2mm}
\subsection{Feature engineering for video chunks}
Let ${F^{( 2)}( v_{i,k}) =\underline{f}^{( 2)}_{i,k}}$ be the feature extraction function for video chunks. .
For a particular video chunk $v_{i,k}$ it generates an \textit{M} dimensional feature vector $\underline{f}^{( 2)}_{i,k}$ as follows. (\textit{M} is the number of viewport trace clusters as explained in section \ref{subsection:Viewport clustering for the entire dataset}. We selected \textit{M}=10.)

\vspace{-2mm}
\begin{equation}\label{eq:vp_traces_vector_4_vid_clustering}
{\underline{f}^{( 2)}_{i,k}} =<\frac{|A^{0}_{i,k} |}{n_{i}},\frac{\ |A^{1}_{i,k} |}{n_{i}},\ ...,\frac{\ |A^{M-1}_{i,k} |}{n_{i}}>
\end{equation}
\vspace{-2mm}

such that $A^{m}_{i,k} =\left\{t_{i,k,j} \ |\ C^{( 1)}( t_{i,k,j}) =m,\ \forall j\in [ 0,\ n_{i})\right\}$

In a gist, the feature representation for a particular video chunk indicates the population distribution of its users that belongs to each of the  different viewport trace clusters (representing different user trace behaviors) resulting in section \ref{subsection:Viewport clustering for the entire dataset}. For this analysis we used 1232 unique \ang{360} video chunks. (88 videos, 14 time bins)

\vspace{-2mm}
\subsection{Video chunk categorization}\label{Video chunk categorization}

Next we generate clusters of similar video chunks in the defined \textit{M} dimensional feature space as shown in Fig.~\ref{fig:overview_dynamic_video_cat}. $\underline{f}^{( 2)}_{i,k} \ \forall i\in [ 0,87] ,\ \forall k\in [ 0,14)$ are taken as inputs to a K-Means clustering algorithm that yields \textit{Q} clusters. Similar to Eq.~\ref{eq:cluster_alloction_func}, corresponding cluster/category for a video chunk is given by $C^{( 2)}( v_{i,k}) =q,\ q\in [ 0,Q)$.

We define a category as a collection of video chunks that are similarly perceived by the users. i.e, videos with similar distributions of different user trace behaviors. The following distance metric for 2 video chunks $v_{i,k}$ and $v_{i',k'}$ quantifies the above definition.

\vspace{-2mm}
\begin{equation}\label{eq:similarity_metric2}
    S( v_{i,k} ,v_{i',k'}) =\sqrt{\sum\limits ^{M}_{m=0}\left( |A^{m}_{i,k} |-|A^{m}_{i',k'} |\right)^{2}}
\end{equation}
\vspace{-2mm}

It calculates the euclidean distance between the feature vectors of two video chunks, quantifying the difference in how the users of two videos are distributed among the 10 identified user trace patterns/behaviors.

We conducted a Davies-Bouldin score analysis to determine the optimum number of categories as shown in Fig.~\ref{fig:DB-Score_analysis_for_optimum_Q}, which yielded \textit{Q}=6.
Fig.~\ref{fig:overview_dynamic_video_cat} summarizes the feature extraction and categorization of video chunks.
Fig.~\ref{fig:Similarity measurement within and between video categories New Metric} represent the \textit{within-category} and \textit{cross-category} evaluation of the six dynamic video chunk categories. On average, the within-cluster pairwise feature distance is 0.27 and the cross-cluster pairwise feature distance is 0.56.

\vspace{-2mm}
\begin{figure}[h!]
\centering
\subfloat[DB-Score analysis for optimum \textit{Q} ]{\includegraphics[width=0.24\textwidth]{DBScoreVideoClusters_reduced_size.pdf}
    \label{fig:DB-Score_analysis_for_optimum_Q}}

\subfloat[Pairwise similarity]{\includegraphics[width=0.24\textwidth]{6VideoCategoryWithinBOXPLOTNewMetric_reduced_size.pdf}
    \label{fig:Similarity measurement within and between video categories New Metric}}
\vspace{-2mm}
\caption{(a)- DB-score analysis to determine the optimum number of categories, (b)- \textit{within-category} and \textit{cross-category} analysis of pairwise distance measurement calculated according to Eq.~\ref{eq:similarity_metric2} }\vspace{-4mm}
\label{fig:Optimal number of video categories}
\end{figure}

Fig.~\ref{fig:Video categories heatmaps and feature hist} shows heatmaps of equirectangular spatial viewport distribution for all six dynamic video categories obtained by combining the viewport traces of all of the users corresponding to its video chunks. A clear difference is observed between different spatial viewport distributions. In Fig.~\ref{fig:Video categories heatmaps and feature hist}, a histogram representation of the feature vectors of video chunks in each category is indicated below each corresponding heatmap. It is evident that the proposed dynamic video categorization algorithm successfully identifies different combinations of user trace types for video chunk clustering.

\textit{\textbf{Takeaway- }In viewing any \ang{360} video, there are different users who display different behaviors. It is advantageous to take all of these different behaviors into account, rather than generalizing it to the majority behavior when characterizing a video. The proposed video chunk categorization clusters the videos that induce similar types of behaviors in their users, irrespective of the video content.}

\vspace{-1mm}
\subsection{Static vs Dynamic video chunk clustering}

Majority of the existing categorizations of \ang{360} videos are static; the entire video belongs to a particular category. These static methods often fail since significant temporal variations in video content and corresponding user behavior can occur in long videos.
To the best of our knowledge, ours is the first proposed algorithm that dynamically categorizes \ang{360} videos by taking 2s video chunks. Fig \ref{fig:DynamicClustersPerVideo} shows how different chunks of the same video can belong to different categories according to the proposed algorithm.

We compared the performance of the proposed categorization with genre-based static categorization by \cite{afzal_characterization_2017}. The 88 videos in the dataset were assigned to 10 genres such as \textit{Sports, scenety etc.}
Fig.~\ref{fig:static vs dynamic new metric} shows the comparison results using \textit{within-category} and \textit{cross-category} feature distances as introduced in Eq.~\ref{eq:similarity_metric2}.

\vspace{-2mm}
\begin{figure}[h!]
\centering
\subfloat[\# of dynamic clusters per video  ]{\includegraphics[width=0.22\textwidth]{nDynamicClustersPerVideo.pdf}
    \label{fig:DynamicClustersPerVideo}}

    \hspace{0.5mm}
\subfloat[Static vs dynamic cat. ]{\includegraphics[width=0.22\textwidth]{GenreVsDynamicNewMetric.pdf}
    \label{fig:static vs dynamic new metric}}
    \vspace{-2mm}
\caption{a)- \# of dynamic clusters per a particular video b)- Comparison static vs dynamic categorizations using Eq \ref{eq:similarity_metric2}}\vspace{-2mm}
\label{fig:dynamic cat plots}
\end{figure}

\textit{\textbf{Takeaway- }Static categorizations of a long \ang{360} videos fail to take temporal variations of contents and user behaviors into account. Breaking the videos down to independant chunks enables more accurate categorizations. Quantifying the categorization in terms of user viewport patterns eliminates ambiguities and the requirement of semantic information for categorization.}

redpajama_set_name: RedPajamaArXiv


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text:
WoT XBOX/
Revision as of 00:46, 23 August 2014 by
ForcestormX:na
M3_Stuart (Stock)
37500 Price
220 Hit Points
12 / 12.5 kgWeight
38/25/25Hull Armor(front/sides/rear, mm)
38/25/25Turret Armor(front/sides/rear, mm)
245 h.p.Engine Power
61 km/hSpeed Limit
40 deg/secTraverse Speed
12 Standard Shell Damage
30 mmStandard Shell Penetration
7.7 Time for Complete Loading
40 deg/secTurret Traverse Speed
280 mView Range
265 mSignal Range
M5 StuartT82
The best-known light tank of World War II, with a total of 13,859 vehicles manufactured from 1941 through September 1943. The M3 Stuart was used in all theaters of war.
The tank has reasonable armor for its tier, and the upgraded 37mm gun is fast and accurate. However, when placed in higher-tier battles, it will face better armed and armored opposition. Speed and stealth are the keys to survival for light tanks; the M3 excels in both. Upgrade your suspension and engine and keep your movements unpredictable as you exploit the enemy's flanks, or race to a good hiding spot and help out your artillery and snipers by spotting the enemy as they advance. Avoid long drawn-out, stand-still shooting matches; the 37mm has a very high rate of fire so you can afford a few misses while shooting on the move.
M3E1
Weight (t)
Turret Armor (front/sides/rear, mm)
Gun Traverse Speed (deg/s)
View Range (m)
II D37812 1250 38/25/25 40 280
Average Penetration (mm)
Dispersion at 100 m
II 20 mm Hispano-Suiza Birgikt Gun 68 30/41 12/12 107.14 0.5 1.7
Engine Power (h.p.)
III Guiberson T-1020-4 331 245 12
Traverse Speed (deg/s)
II Stuart E1 3000 12.5 40
Signal Range (m)
II SCR 200 0 265
II 37 mm Gun M5 78 48/70/19 40/40/50 24 0.46 2.1
M3A1 M5 Stuart T82
III D39273 1700 38/32/32 48 330
II 37 mm Gun M5 78 48/70/19 40/40/50 26.09 0.46 1.7
III Continental W-670 256 262 20
III Stuart E2 3000 14.5 45
IV SCR 210 0 325
III 37 mm Gun M6 82 56/78/19 40/40/50 26.09 0.41 1.7
IV Continental R-975-C1 500 350 20
IX SCR 506 0 615
Great top speed and hull traverse.
Good rate-of-fire.
Above average accuracy, aim time, turret traverse speed, and gun elevation arc.
Very good frontal armor for light tank, immune to most machineguns and autocannons (except weak spots like driver's hatch; sh
Excellent radio.
Poor damage.
Mediocre gun penetration.
The M3 Stuart is a speedy, agile light tank. The M3 Stuart has both good straight line speed and decent traverse at high speeds. It has above average gun depression, mobility, and damage per minute. The tank can function as a scout, a sniper, or a flanker. The M3 Stuart is a good introduction to the American medium and light tank lines as the playstyles that work in the M3 carry over as a player advances up the two lines.
When top tier and fully upgraded the mobility and gun depression can be used to efficiently deal damage. Many opponents at tier will find it difficult to penetrate the 38mm frontal armor however that armor does have weak spots so it should not be relied upon. As with most light tanks the M3 Stuart relies on its mobility and camo rather than its armor. The gun of this tank is more than adequate at tier 3 with standard AP rounds. With premium APCR rounds it is able to penetrate most tanks in the same tier frontally.
When facing tier 4 tanks the M3 Stuart can be an effective scout or flanking tank. However several tanks at Tier 4, including the Matilda, B2, and AMX40, can be very difficult for a M3 to penetrate even if APCR rounds are used. Drivers of the M3 Stuart should engage with lower tier tanks or lightly armored light/medium Tier 4 tanks.
When facing tier 5 tanks the M3 Stuart can be an effective scout with proper equipment. An M3 with vents, camo net, and binoculars/coated optics can effectively match or exceed the vision ratings on many tier 5 tanks. The mobility of the tank will also allow it to spot tanks on the move and retreat back out of range of the enemy. The gun is unable to penetrate most tier 5 tanks frontally even with premium ammunition. Against highly armored tanks, such as the KV-1, it can only penetrate with very precise shots in the rear of the tank or by hitting weak spots with premium APCR rounds.
Early Research
The 20 mm Hispano-Suiza Birgikt Gun and the 37 mm Gun M5 carry over from the M2 Light Tank, as well as the SCR 506 radio if you researched it.
First research the Continental W-670 and the Continental R-975-C1 engines.
Next research the upgraded suspension for a large improvement in traverse speed.
Now research the second turret and the 37 mm Gun M6.
Be sure to research the SCR 506 if you haven't by now.
M3 light tank at the Aberdeen Proving Grounds, Maryland
The "M3 Stuart", formally "Light Tank M3", was an American light tank of World War II. It was supplied to the British and Commonwealth forces under the lend-lease program prior to the US entering the war and used thereafter by both US and Allied forces until the end of the war.
The name "General Stuart" or "Stuart" given by the British comes from the American Civil War Confederate General J.E.B. Stuart and was used for both the M3 and the derivative M5 Light Tank. In British service it also had the unofficial nickname of "Honey", after a tank driver remarked "She's a honey". To the United States Army, the tanks were officially known only as "Light Tank M3" and "Light Tank M5".
The "M3 Stuart" was the first US tank with an American crew to engage in tank versus tank combat during World War II.
Observing events in Europe, American tank designers realized that the Light Tank M2 was becoming obsolete and set about improving it. The upgraded design, with thicker armor, modified suspension, and new gun recoil system was called "Light Tank M3". Production of the vehicle started in March 1941 and continued until October 1943. Like its direct predecessor, the M2A4, the M3 was armed with a 37 mm M5 gun and five .30-06 Browning M1919A4 machine guns: coaxial with the gun, on top of the turret in an M20 AA mount, in a ball mount in the right bow, and in the right and left hull sponsons.
Internally, the radial engine was at the rear of the tank and the transmission to the driving sprockets was at the front. The prop shaft connecting the two ran through the middle of the fighting compartment. The radial engine compounded the problem by having it's crankshaft high off the hull bottom. When a turret floor was introduced, the crew had even less room. To relieve the demand for the radial airplane engines used in the M3, a new version was developed using twin Cadillac V-8 automobile engines and a Hydra-Matic transmission. Such installation produced a quieter, cooler and roomier variant, and it was easier to train with the automatic transmission. This new model was initially called "M4", but redesignated "M5" to avoid confusion with the "M4 Sherman[5]". It also featured a redesigned hull with sloped glacis plate and driver's hatches moved to the top. Although the main criticism from the units using it was that the Stuarts lacked firepower, the improved M5 series kept the same 37 mm gun. The M5 gradually replaced the M3 in production from 1942 and was, in turn, succeeded by the Light Tank M24 in 1944.
Combat history
War in North Africa and Europe
M3 Light Tank in Casablanca
The British Army was the first to use the Light Tank M3 as the "General Stuart" in combat. From mid-November 1941 to the end of the year, about 170 Stuarts (in a total force of over 700 tanks) took part in Operation Crusader during the North Africa Campaign, with poor results. Although the high losses suffered by Stuart-equipped units during the operation had more to do with better tactics and training of the Afrika Korps than the apparent superiority of German armoured fighting vehicles used in the North African campaign, the operation revealed that the M3 had several technical faults. Mentioned in the British complaints were the 37 mm M5 gun and poor internal layout. The two-man turret crew was a significant weakness, and some British units tried to fight with three-man turret crews. The Stuart also had a limited range, which was a severe problem in the highly mobile desert warfare as units often outpaced their supplies and were stranded when they ran out of fuel. On the positive side, crews liked it's relatively high speed and mechanical reliability. The high reliability distinguished the Stuart from cruiser tanks of the period, in particular the Crusader, which composed a large portion of the British tank force in Africa up until 1942.
From the summer of 1942, when enough US medium tanks had been received, the British usually kept Stuarts out of tank-to-tank combat, using them primarily for reconnaissance. The turret was removed from some examples to save weight and improve speed and range. These became known as "Stuart Recce". Some others were converted to armored personnel carriers and were known as "Stuart Kangaroo", and some were converted command vehicles and known as "Stuart Command". M3s, M3A3s, and M5s continued in British service until the end of the war, but British units had a smaller proportion of these light tanks than US units. The other major Lend-Lease recipient of the M3, the Soviet Union, was even less happy with the tank, considering it undergunned, underarmored, likely to catch fire, and too sensitive to fuel quality. The narrow tracks were highly unsuited to operation in winter conditions, as they resulted in high ground pressures under which the tank sank into the snow. Further, the M3's radial aircraft engine required high-octane fuel, which complicated Soviet logistics as most of their tanks used diesel. However, the M3 was superior to early-war Soviet light tanks such as the T-60, which were often underpowered and possessed even lighter armament than the Stuart. In 1943, the Red Army tried out the M5 and decided that the upgraded design was not much better than the M3. Being less desperate than in 1941, the Soviets turned down an American offer to supply the M5. M3s continued in Red Army service at least until 1944.
War in the Far East - CBI and Pacific
M3 light with seven sided welded turret and riveted hull.png
The US Army initially deployed 108 Stuart light tanks to the Philippines in September 1941, equipping the US Army's 194th and 192nd Tank Battalions. The first US tank versus tank combat to occur in World War II, began on 22 December 1941, when a platoon of five M3s led by Lieutenant Ben R. Morin engaged Type 95 Ha-Go north of Damaris. LT Morin maneuvered his Stuart off the road, but took a direct hit while doing so, and his tank began to burn. The other four Stuarts were also hit, but managed to leave the field under their own power. LT Morin was wounded, and he and his crew were captured by the enemy. M3s of the 194th and 192nd Tank Battalions continued to skirmish with the 4th Tank Regiment's Type 95 Ha-Go light tanks as they continued their retreat down the Bataan Peninsula, with the last tank verses tank combat occurring on 7 April 1942.
Due to the naval nature of the Pacific campaign, steel for warship production took precedence over tanks for the Imperial Japanese Army (IJA), creating by default an IJA light tank which performed admirably in the jungle terrain of the South Pacific. By the same measure, although the US was not hampered by industrial restrictions, the US M3 light tank proved to be an effective armored vehicle for fighting in jungle environments. With the IJA's drive toward India within the China-Burma-India Theater of Operations (CBI), the United Kingdom hastily withdrew their 2nd Royal Tank Regiment and 7th Hussars Stuart tank units (which also contained some M2A4 light tanks[15]) from North Africa, and deployed them against the Japanese 14th Tank Regiment. By the time the Japanese had been stopped at Imphal, only one British Stuart remained operational. Upon the American entry into the war in 1941, it had began to supply China with AFVs including the M3 Stuarts, and later M4 Shermans, and M18 Hellcats which trickled in through Burma and formed part of the several well-equipped, well-trained armies that the Chinese Nationalists could deploy. These units were responsible for stopping numerous Japanese attacks during the later phases of the war. Although the US light tanks had proven effective in jungle warfare, by late 1943, US Marine Corps tank battalions were transitioning their M3/M5 light tanks to M4 medium tanks. For the IJA, even though the US Marines had exchanged their light tanks for M4 medium tanks, they could not; and with the less common supplement of their Type 97 Chi-Ha medium tanks, the IJA was left to do battle against US Marine M4 Sherman medium tanks, with armor that had been designed and fielded in the 1930s.
M3 Light Tank purchased in July 1942, with war bonds by the community of Banning, California
When the US Army joined the North African Campaign in late 1942, Stuart units still formed a large part of it's armor strength. After the disastrous Battle of the Kasserine Pass, the US quickly followed the British in disbanding most of their light tank battalions and subordinating the Stuarts to medium tank battalions performing the traditional cavalry missions of scouting and screening. For the rest of the war, most US tank battalions had three companies of M4 Shermans and one company of M3s or M5/M5A1s. In Europe, Allied light tanks had to be given cavalry and infantry fire support roles since their main cannon armament could not compete with heavier enemy AFVs. However, the Stuart was still effective in combat in the Pacific Theater, as Japanese tanks were both relatively rare and were lighter in armor than even Allied light tanks. Japanese infantrymen were not well equipped with anti-tank weapons, and as such, had to use close assault tactics. In this environment, the Stuart was only moderately more vulnerable than medium tanks. In addition, the poor terrain and roads common to the theatre were unsuitable for the much heavier M4 medium tanks, and so initially, for both sides, it was advantageous to deploy light armor. Heavier M4s were eventually brought to overcome heavily entrenched positions, though the Stuart continued to serve in a combat capacity until the end of the war. Though the Stuart was to be completely replaced by the newer M24 Chaffee, the number of M3s/M5s produced was so great (over 25,000 including the 75 mm HMC M8) that the tank remained in service until the end of the war and well after. In addition to the United States, United Kingdom and Soviet Union, who were the primary users, it was also used by France, China (M3A3s and, immediately post-war, M5A1s) and Josip Broz Tito's Partisans in Yugoslavia (M3A3s and few M3A1). After the war, some countries chose to equip their armies with cheap and reliable Stuarts. The Republic of China Army, having suffered great attrition as a result of the ensuing civil war, rebuilt their armored forces by acquiring surplus vehicles left behind in the area by the US forces, including 22 M5A1s to equip two tank companies. They would have their finest hours during the Battle of Kuningtou, for which the tank came to be known as the "Bear of Kinmen".
The M5 played a significant role in the First Kashmir War (1947) between India and Pakistan. Most notably at the battle of Zoji-la Pass, which was fought at an elevation of nearly 12,000 feet. The vehicle remained in service in several South American countries until at least 1996. During the 1960s and 1970s, the Portuguese Army also used the M5 in the war in Angola, where its all terrain capability (compared to wheeled vehicles) was greatly appreciated. In 1967, The Portuguese Army deployed three M5A1 Light Tanks – nicknamed 'Milocas', 'Licas' and 'Gina' by their crews – in northern Angola, which served with the 1927th Cavalry Battalion stationed at Nambuangongo. The vehicles were employed mostly for convoy escort and recovery duties, and limited counter-insurgency operations against FNLA guerrillas, who dubbed them "Elefante Dundum". 'Milocas' was destroyed by an accidental fire in 1969 whilst 'Gina' and 'Licas' were withdrawn from active service in 1972, the former being sent to Luanda and the latter ended up in 1973 as an airfield security pillbox in the Portuguese Air Force' Zala airfield. Period photographs show some modifications in the basic design, namely the omission of the bow machine gun, re-installed on a pintle mount in the roof of the turret and a small searchlight fitted in front of the commander's copula.
The M3 Stuart is still on the active list in the Armed Forces of Paraguay where those elderly survivors are the only tracked armor used by that country.
M44 gun mount
M3 light tank's rounded homogeneous turret
M3 light tank's pistol port and protectoscope
M3 light tank with rounded homogeneous welded turret
M3 Light Tank during war games in Tennessee
M3 Light Tank captured by the Germans in North Africa
Sources and External Links
Crisp, Robert Major, Brazen Chariots (Ballantine Books, NY, NY 1961 Lib. Congress No. 60-5845)- Maj. Crisp's Story of British M3 Honeys in N.Africa campaign 1941 near Tobruk.
Hunnicutt, R. P. Stuart, A History of the American Light Tank. Vol. 1; 1992, Presidio Press. ISBN 0-89141-462-2.
Zaloga, Steven. M3 & M5 Stuart Light Tank. 1999; Osprey New Vanguard 33. ISBN 978-1-85532-911-9.
Zaloga, Steven. Japanese Tanks 1939-45. Osprey New Vanguard 137. ISBN 978-1-84603-091-8.
Zaloga, Steven. Armored Thunderbolt, The US Army Sherman in World War II. 2008; Stackpole Books. ISBN 978-0-8117-0424-3.
Hembree, Mark. "History You Can Model" FineScale Modeler February 2011..Page 74 (ISSN 0277-979X, USPS No. 679-590)
Light Tanks IT1 Cunningham • IIM2 Light Tank • IIT1E6-X • IIT1E6-X1 • IIT2 Light Tank • IIT7 Combat Car • IIIM22 Locust • IIIM3 Stuart • IIIMTLS-1G14 • IVM5 Stuart • VM24 Chaffee • VIT21 • VIT37 • VIIM41 Walker Bulldog • VIIT71 • VIIIM41B Brazilian Bulldog • VIIIT49
Medium Tanks IIT2 Medium Tank • IIIM2 Medium Tank • IVM3 Lee • VM4A2E4 Sherman • VM4A2E4 Ripper • VM4 Sherman • VM7 • VRam II • VIM4A3E8 Sherman • VISherman Fury • VIM4A3E2 Sherman Jumbo • VIIT20 • VIIT23E3 • VIIIM26 Pershing • VIIIT26E4 Super Pershing • VIIIT26E4 Freedom • VIIIT69 • VIIIT95E2 • IXM46 Patton • IXT54E1 • XM48A1 Patton
Heavy Tanks VT14 • VT1 Heavy Tank • VIM6 • VIIT29 • VIIIM6A2E1 • VIIIT32 • VIIIT34 • IXM103 • XT110E5 • XT57 Heavy Tank
Tank Destroyers IIT18 • IIIT82 • IVM8A1 • IVT40 • VM10 Wolverine • VT67 • VIM18 Hellcat • VIM36 Jackson • VIIT25/2 • VIIT25 AT • VIIIT28 • VIIIT28 Prototype • IXT30 • IXT95 • XT110E3 • XT110E4
Self-Propelled Artillery IIT57 • IIIM7 Priest • IIISexton I • IVM37 • VM41 • VIM44 • VIIM12 • VIIIM40/M43 • IXM53/M55 • XT92
USA IT1 Cunningham • IIM2 Light Tank • IIT1E6-X • IIT1E6-X1 • IIT2 Light Tank • IIT7 Combat Car • IIIM22 Locust • IIIM3 Stuart • IIIMTLS-1G14 • IVM5 Stuart • VM24 Chaffee • VIT21 • VIT37 • VIIM41 Walker Bulldog • VIIT71 • VIIIM41B Brazilian Bulldog • VIIIT49
UK IICruiser Mk. I • IICruiser Mk. III • IIICruiser Mk. IV • IIICruiser Mk. II • IVValentine • IVCovenanter • VCrusader
Germany ILeichttraktor • IIPz.Kpfw. 38H 735 (f) • IIPz.Kpfw. 35 (t) • IIPz.Kpfw. I • IIPz.Kpfw. II • IIIPz.Kpfw. 38 (t) • IIIPz.Kpfw. III Ausf. A • IIIPz.Kpfw. II Ausf. J • IIIPz.Kpfw. I Ausf. C • IIIPz.Kpfw. II Ausf. G • IIIT-15 • IVPz.Kpfw. 38 (t) n.A. • IVPz.Kpfw. II Luchs • VVK 16.02 Leopard • VIVK 28.01 • VIIAufklärungspanzer Panther • VIIISpähpanzer Ru 251
France IRenault FT • IID1 • IIHotchkiss H35 • IIIAMX 38 • IVAMX 40 • VELC AMX • VIAMX 12 t • VIF224 AMX Chaffee • VIIAMX 13 75 • VIIIAMX 13 90
USSR IMS-1 • IIBT-2 • IIT-26 • IIT-60 • IITetrarch • IIIBT-7 • IIIBT-SV • IIIM3 Light • IIIT-127 • IIIT-46 • IIIT-70 • IVA-20 • IVT-50 • IVT-80 • IVValentine II • VIMT-25 • VIILTTB • VIIIT-54 ltwt.
China IRenault NC-31 • IIVickers Mk. E Type B • IIIType 2597 Chi-Ha • IVM5A1 Stuart • VI59-16 • VIType 64 • VIIWZ-131 • VIIIWZ-132
Japan IRenault Otsu • IIType 95 Ha-Go • IIIType 98 Ke-Ni • IVType 5 Ke-Ho
ja:XBOX:M3 Stuart
Retrieved from "http://wiki.wargaming.net/en/index.php?title=XBOX:M3_Stuart&oldid=214721"
XBOX:USA Tanks
XBOX:Light Tanks
XBOX:Tier III Tanks

redpajama_set_name: RedPajamaCommonCrawl


University of Houston alum Brett Cullen isn't hard to find these days. Just turn on the TV, and there's a good chance you can spot him in a movie or television show.
This weekend, his latest project premieres. Original Lifetime series "Devious Maids" debuts at 9 p.m., June 23. The pilot also can be viewed online.
Skateland explores the rupture of charismatic Ritchie Wheeler, brother and sister Brent and Michelle Burkham, and wise-cracking lady-killer Kenny Crawford's seemingly complacent existence as they struggle with the collapse of their tumultuous lives, the alcohol-induced idling of reckless adolescence, and the wane of their hometown hangout, the local roller rink.
"Make It or Break It" is a one-hour scripted drama created and written by Holly Sorensen. Sorenson and Paul Stupin ("In Plain Sight") serve as executive producers. Set in the world of competitive gymnastics, "Make It or Break It" follows a group of teen Olympic hopefuls as they train and prepare for their day in the spotlight. Brett plays one of the Olympian hopeful's father, Mark Keeler.


text:
Magnificent Mustique
By Christina Valhouli
Mustique is unlike any other Caribbean island. Having frittered away most of his fortune, Scottish brewing heir Colin Tennant Colin Tennant , now Lord Glenconner, purchased the island in 1958 for $67,500. He founded The Mustique Company to develop the island, and limited the development to only 120 villas. In 1960, he gave a 10-acre parcel of land to the late Princess Margaret as a wedding present, and the island quickly became an exclusive playground for the ultra-wealthy and well-born.
Although financial woes induced Tennant to sell the company in 1976 (the villa owners now run it), his original conception of the island remains intact--more or less. While there may be fewer aristocrats and more celebrities these days--home owners include Mick Jagger Mick Jagger , designer Tommy Hilfiger Tommy Hilfiger and publisher Felix Dennis Felix Dennis --the island is still surprisingly casual despite the net worth of its residents.
Approximately 52 villas are available for rent (including Jagger's), and each varies wildly in design. Several were designed by Oliver Messel Oliver Messel , a popular English interior and set designer who died in the 1970s, and can be detected by his signature gingerbread trim, hardwood floors and stonework. In contrast, Toucan Place resembles a gilt Moroccan fantasy palace, while Sleeping Dragon has Chinese imperial antiques and pagodas. No matter what your taste is--Balinese, California casual or Tuscan farmhouse--there's a villa for every style on Mustique.
But "villa" is an understatement for the Mustique homes--compound is a better description. Properties range from two to nine bedrooms, and all come with a full staff, a butler and a "mule," the island term for a golf cart, which is how most people get around. Every golf cart is inscribed with the name of the villa to which it belongs, and it's not uncommon to be driving around the island and receive an impromptu lunch invitation from a fellow owner.
Anyone who doesn't want to stay in a villa can book a room at the Cotton House, sections of which were also designed by Messel. The hotel, a former sugar plantation, recently added a new spa and a beachside restaurant. Every Tuesday night, the Cotton House hosts an island-wide cocktail party, where guests mingle with villa owners and visiting movie stars or supermodels-- Elizabeth Hurley Elizabeth Hurley and Hugh Grant Hugh Grant have stopped by. Since the hotel is owned by The Mustique Company (which also manages the villas), anyone renting a villa has full use of the hotel. Kids will enjoy a weekly outdoor ice cream party and movie on the lawn.
This laidback attitude is one of the best aspects of Mustique. Since the island consists mostly of villas, with only a small local village, everyone knows each other. There are only a few bars and nightspots, and each night is designated to a different locale. Basil's is a Caribbean institution, and its Wednesday night "jump up," or dance party, is a must-do. It won't be hard to spot Basil himself: He's about 6 foot 2 and favors flowing robes. Every year in late January, Basil hosts the Mustique Blues Festival, with British blues icon Dana Guillespie Dana Guillespie , which raises funds for the children of nearby St. Vincent. Rumor has it that Mick Jagger may show up and play a few songs this year. Since this is Mustique, it just might happen.
Weekly villa rentals start at $7,000 during high season; the Cotton House starts at $630 per night. For more information, call The Mustique Company at (800) 747-9214.
Forbes Fact
In 2001, Britain's Channel 4 released a documentary about Colin Tennant called The Man Who Bought Mustique, which also features Mick Jagger, David Bowie David Bowie and Princess Margaret. In the film, Tennant travels back to Mustique to discuss how the island has changed since he lost control of it, and frequently has temper tantrums on screen. Tennant now lives on a plantation in nearby St. Lucia, where he once kept a pet elephant.


NEW ORLEANS (AP) — Saints safety Jairus Byrd turned toward cornerback Brandon Browner and put his hands up in apparent disgust over a coverage cross-up that led to an easy touchdown toss for Carolina's Cam Newton.
Although New Orleans' beleaguered defense came up with some big plays, there were also many mistakes in a 41-38 loss on Sunday to the unbeaten Panthers.
"We have to take it upon ourselves to know what we're doing," Byrd said. "It comes down to us as players, and we're making mistakes we can't be making. You can't say it's scheme. Us as players have to make sure that we're all locked in and doing our responsibility."
In addition to coverage mix-ups, the Saints were hurt by penalties, committing 11 for 104 yards — several of the drive-sustaining variety on third down.
Newton passed for 331 yards and five touchdowns, capped by his 15-yard scoring strike to Jerricho Cotchery with 1:05 to go.
"I expected us to win the football game," Newton said, adding he wasn't pleased by how tight the game got at the end against the reeling Saints (4-8), who've lost four straight.
"We've got to find ways moving forward not to put ourselves in this position. This was a wakeup call for us. And moving forward, we know we can't just win games by us showing up."
Drew Brees passed for 282 yards and three touchdowns. Saints linebacker Stephone Anthony returned a fumble 31 yards for a score, and also became the first NFL player to return a blocked extra-point kick for 2 points. The league made a rule change to allow such plays for the first time this season.
"There were a lot of good things, but still too many of the little things that get you beat," Brees said.
Coach Sean Payton also seemed angry about the officiating on several occasions, including a couple instances when he thought the Panthers should have been flagged for too many players on the field.
"I'm not going to get into the officiating. I said walking into this door (into the interview room) I was not going to get into it," Payton began. "And it happened twice — 12 men on the field, clearly. And they're getting that right (in high school games) on Friday nights."
Carolina led 27-16 after Newton's 13-yard pass to Devin Funchess, but New Orleans rallied to take a 31-27 lead on Brees' 54-yard TD to Brandin Cooks and 24-yard scoring pass to Brandon Coleman.
The Panthers went back in front on Newton's 45-yard pass to Ted Ginn Jr. But New Orleans answered with Mark Ingram's 9-yard run with 5:21 left, forcing Newton to put together one more touchdown drive.
He narrowly pulled it off, finding tight end Greg Olsen for a first down as he rolled left on a fourth-and-4. Olsen had to fall to the field to make a rolling fingertip grab of the underthrown pass, which was so close to hitting the turf that fans in the Superdome were roaring at replays as Payton asked for a review.
Olsen finished with nine catches for 129 yards.
Cooks had six catches for 104 yards for the Saints.
New Orleans' defense produced three turnovers and a touchdown in its second game under coordinator Dennis Allen, who took over when Rob Ryan was fired.
However, the Saints' offense was unable to convert cornerback Delvin Breaux's interception or Kenny Vaccaro's recovery of Olsen's fumble into points. One of those possessions ended in Kai Forbath's missed 38-yard field goal.
Anthony scored when he ripped the ball away from Jonathan Stewart as the running back tried to push through a pile of players. Carolina players seemed to think the play was over and watched as Anthony raced 31 yards for a 14-0 Saints lead.
"I even thought the play was over," Anthony said. "I didn't know whether I should keep running or not. I didn't hear a whistle, and I kind of waited, then I kept going."
Early in the second quarter, Carolina cut the lead in half on Mike Tolbert's 12-yard touchdown catch, set up by Newton's 30-yard bootleg on fourth-and-1 a couple plays earlier.
Stewart scored on a 5-yard run shortly before halftime, but Graham Gano's extra point kick was blocked by Kevin Williams and returned 82 yards by Anthony to give New Orleans a 16-13 lead.
NOTES: Breaux left the game in the first half with what Payton said was a hamstring injury. ... Panthers safety Kurt Coleman's interception of Brees was his sixth this season and fifth in his past six games.


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Biography News Uncategorized United States
Fly Tatted Autopsy – Was Bella Tatted Murderer?
Fly Tatted Autopsy Bella Tatted's death has been ruled a murder. Her fingerprints were found on the gun and her body was discovered. This shocking news shocked the entire world, especially as she was only at the very beginning of the film's "shocking" section. Her girlfriends Alex and Donna were still together at this point.
Fly Tatted Autopsy: Bella Tatted's murder was ruled a homicide
Fly Tatted Autopsy: Bella's fingerprints were found on the gun
Bella's essence during the shocking section
Alex and Donna are still together
Chris Nunez and Ami James are still good friends
While Bella Tatted died as a suicide at the time, Fly Tatted's murder was not. An autopsy revealed that Fly had died from blunt force trauma to his abdomen. Rumors of murder started to circulate after it was discovered that Bella was present at the event.
Mark Jenkins, Bella's stepfather was accused of Bella's murder and assault. He is facing 25 years of life imprisonment. The city Medical Examiner's Office determined that Bella's murder was a homicide.
Bella was at her home at the time of Bella's death. This is understandable. The fact that the victim left fingerprints on the gun suggest that he might have fired the bullets.
Fly Tatted, who was a Black Ink Crew Chicago cast member, died in controversy. There were many rumors that Bella Tatted was responsible for Tatted's death after he died. Bella has since cleared the air. She claimed that Fly died while she was there. She also left fingerprints on Fly's gun, which he used to kill him.
Initial reports stated that Fly Tatted had attempted suicide. He later died from homicide. There were many rumors about Bella being involved in Fly's death when the media reported her presence. However, this was not true.
Fly Tatted Autopsy, a member of the Black Ink Crew Chicago cast, died on February 21st, 2021. Although the cause of his death remains a mystery, speculation suggests that he tried suicide.
His attempt to suicide was documented by his fingerprints left on the gun he used. Bella is believed to have witnessed his death, leading to rumors of Bella's murder. His funeral was held on Monday, February 22nd.
Bella was dating Fly when he passed away, and she did the ominous-mimic's-might-be-the-moment-reported-to-have-happened-at-a-trial-scene-of-a-torture-case. She denies that she killed him.
Some media outlets took advantage of her presence to claim she was responsible for Fly's death. This is apparently a rumor since it wasn't investigated as a case of homicide.
Several cast members spoke out in the wake of Fly Tatted's passing. Bella Duran, the actress who played Alex was one of them. She said that she was present at Fly's funeral and that it wasn't suicide. This clarified rumors that Fly's suicide was a homicide.
Fly's sudden death had many ramifications. One of these was the confusion it caused viewers. They were not only unsure if Alex and Donna were still together but also felt a lot of sadness.
They couldn't focus on the present and future, which caused them to lose their ability to live happy life. They had to worry about how they would survive the loss.
According to some reports, Chris Nunez and Ami James are still close friends after the Fly-Tatted autopsy. They opened Love Hate Tattoo Studio near Miami Ink and have continued to grow the business. They decided they didn't want to work with Discovery ever again. After winning Miami Ink, they opened the shop.
Chris Nunez is a TV personality and was also a judge for Ink Master. He also owns a Fort Lauderdale art gallery and a handcrafted tattoo shop. Tatu Baby's son is Deniro Roman Soto.
Chris was a big fan of Tatu Baby during his time on Ink Master. He returned to season 3 after he was eliminated. He was kicked out of the show during the second season but was then reinstated when he returned. Tatu Baby won the vote, but his fans still showed a lot support.
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Bella Tatted's murder was ruled a homicide · Bella's essence during the shocking section · Bella's fingerprints were found on the gun · Fly Tatted Autopsy
Biography · News · Uncategorized · United States
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JPMorgan's Role In Not Reporting Suspicions Of Madoff Ponzi Scheme Has Some Feds Pressing For Criminal Guilty Plea [EXCLUSIVE]
By Murray Waas @murraywaas
Swindler Bernard Madoff enters the Manhattan federal court house in New York, March 12, 2009. REUTERS/Shannon Stapleton
Federal prosecutors in New York are pushing for a guilty plea from JPMorgan Chase for allegedly turning a blind eye to Bernard Madoff's Ponzi scheme, according to two law enforcement officials. They have informed their superiors in the Justice Department that they strongly oppose any settlement with the banking giant unless one of its subsidiaries pleads guilty to at least a single criminal charge.
JPMorgan Chase (NYSE:JM) had offered to settle the potential case regarding the work for Madoff in exchange for no formal criminal charges being brought against it. But top aides to Preet Bharara, the U.S. attorney for the Southern District of New York, as well as several senior career prosecutors in the Justice Department's Criminal Division, have been adamant that such a deal is unacceptable, according to the sources.
It is highly unusual for such a major bank to be pressured by prosecutors to admit criminal guilt. That pressure reflects the seriousness of what prosecutors consider senior JPMorgan officials to have done: Sources close to the investigation say investigators learned from the bank's own internal, confidential records that even though senior JPMorgan executives suspected Madoff was running a Ponzi scheme, they never informed federal regulators of their suspicions, as is required by federal law.
Such a warning might have tipped off regulators -- and more importantly, investors -- years earlier. Said one federal law enforcement official: "If they had stricter controls in place, if they had stricter money-laundering standards, if they simply informed regulators of their suspicions … perhaps someone in government would have given Madoff a look, and shut him down earlier." Billions of investors' dollars might very well have been saved.
The position taken by prosecutors who work for Bharara and others in the Criminal Division in Washington may once again pit them against the highest levels of the Justice Department, who have long argued that some banks that were once "too big to fail" are also too big to face criminal charges due to the potential impact on the economy. The long-running debate over the proper punishment for these misdeeds has long stymied the aides in the U.S. Attorney's Office in New York. "It does feel a lot like Groundhog Day," said one government official involved in the matter. This time, however, they have greater optimism that they — and to their mind, the public — will prevail.
Lanny Breuer, who headed the Criminal Division until March and was in charge of prosecuting Wall Street crimes, has said decisions as to which banks to charge were based on "sober predictions that a company or bank might fail if we indict, that innocent employees could lose their jobs, that entire industries may be affected, and even that global markets will feel the effects." His boss, Attorney General Eric Holder, has echoed those comments: "The impact on the stability of the financial markets around the world is something we take into consideration."
Bharara has at times appeared to openly signal defiance of that doctrine: "I don't think anyone is too big to indict — no one is too big to jail," he declared in a July 2013 speech. Prosecutors who work for Bharara and others in D.C. hope he will now powerfully advocate their position that JPMorgan must not escape criminal charges for its involvement with Madoff.
One reason many career federal law enforcement officials say they would be outraged if JPMorgan gets off lightly is the nature of the information uncovered in the investigation of the firm's involvement with Madoff. Madoff ran the largest Ponzi scheme in American history. He stole billions of dollars from unsuspecting investors. He is currently serving a 150-year prison term.
The bank where Madoff kept his stolen funds for two decades was JPMorgan. There was no evidence uncovered during the federal investigation that JPMorgan Chase or any of its employees knowingly aided and abetted the Ponzi scheme. What JPMorgan is in trouble for with federal authorities is not reporting its suspicions about Madoff to federal regulators. That seems like a minor offense, more a technical violation of law than something very serious. Indeed, that was the initial response of regulators as they first combed through the records, according to people close to the investigation.
But one federal law enforcement official explained to me the reasons why attitudes changed and what they discovered was a crime: Had the bank reported its suspicions about Madoff to regulators, as the law required, they might possibly have investigated him, shut him down, and prevented additional billions of dollars from being defrauded from investors.
Another aspect of the investigation revolved around whether JPMorgan violated federal money-laundering statutes as Madoff moved billions through his accounts at the bank without detection, sources say. It is unclear, however, whether prosecutors would insist that that be part of a guilty plea by the bank's subsidiary.
But it hasn't been decided whether to charge a JPMorgan subsidiary or demand a guilty plea as part of a settlement with the bank, sources close to the matter say.
"There has been a revolt of sorts taking place of late…," said a senior Justice Department official. "Some of us — though not everyone — want there to be more pleas, admissions of guilt. But it is an ongoing argument, and one in which it is unclear which side has won, and will be for some time." Bharara, as the U.S. attorney for Manhattan, has increasingly aligned himself with the prosecutors in the Criminal Division pressing for stiffer penalties.
In preliminary discussions with the Justice Department to settle the case, JPMorgan offered to enter a deferred prosecution agreement to settle potential charges of wrongdoing related to its time as Madoff's bank. Under such an agreement, the bank would pay a substantial fine, promise not to engage in such practices in the future, and also agree to let an outside monitor make sure it continues to comply with the law. In the meantime, any potential criminal charges would be deferred, and then would not be brought at all, if the bank were found to have complied with the law in the interim period. Crucially, JPMorgan would not have been required to make a formal admission of criminal wrongdoing.
The fact that corporations that enter deferred prosecution agreements do not plead guilty to a crime has drawn criticism from reformers, members of Congress, and even prosecutors and FBI agents. Bharara himself has been one critic of such settlements being overused without admissions of criminal wrongdoing.
Breuer said the department sometimes did not criminally prosecute large banks, financial institutions and Wall Street firms because they and financial regulators feared that such criminal charges might cause the firm to go out of business and put many innocent employees, who had nothing to do with the fraud, out of work. Breuer also argued that prosecuting large banks and financial institutions might significantly harm the economy at the time the nation is still trying to recover from the recession.
Breuer left office in March, providing an opening for Justice Department officials, like Bharara, who have advocated more aggressive investigations of Wall Street.
Attorney General Holder has made comments similar to Breuer's, although of late, he has signaled in other statements that such arguments might no longer hold as much sway with him personally, the Justice Department and the Obama administration — seemingly responding to criticism from both inside and outside the department.
There have been reasons for optimism.
JPMorgan recently agreed to pay a record $13 billion in fines and restitution for the role it played in the subprime mortgage crisis, which in part caused the 2008 financial crisis. In negotiations with the Justice Department to settle that matter, CEO Jamie Dimon, and the bank's attorneys, similarly argued that criminal charges might so severely damage the bank that innocent employees could lose their jobs and the economy would be damaged as a result.
During personal negotiations with Holder regarding JPMorgan's role in the subprime meltdown, Dimon repeatedly refused to settle with the government without a promise that there would be no criminal prosecution of the bank. Holder refused. Dimon and JPMorgan then agreed to pay more than $13 billion in fines and restitution to the government to settle the civil portions of the Justice Department's and regulators' case, but a criminal investigation by the Justice Department continues.
For its investigation of JPMorgan's work for Madoff, Bharara's office consulted with the Office of the Comptroller of the Currency to determine whether criminal charges against JPMorgan Chase would cause serious harm to the economy, according to people close to the investigation. The OCC responded that it did not oppose such criminal charges, the same sources said.
The Wall Street Journal reported on its website Thursday night that the comptroller had told the Justice Department that criminal charges against JPMorgan Chase could trigger a review of the bank's charter. The Journal reported that during a Sept. 6 meeting with the Justice Department, the OCC said it would be required to conduct a hearing on possibly revoking a bank's charter in the event of such a criminal conviction.
Not long ago, the possibility that criminal charges against a bank the size of JPMorgan Chase might have triggered a review of its charter or lead to layoffs probably would have caused the department to forgo criminal charges.
Before retiring, Breuer, then DOJ's Criminal Division chief, explained why he overrode the recommendations of career prosecutors to indict some on Wall Street:
"To be clear, the decision of whether to indict a corporation, defer prosecution, or decline altogether is not one that I, or anyone in the Criminal Division, take lightly. We are frequently on the receiving end of presentations from defense counsel, CEOs and economists who argue that the collateral consequences of an indictment would be devastating for their client. In my conference room, over the years, I have heard sober predictions that a company or bank might fail if we indict, that innocent employees could lose their jobs, that entire industries may be affected, and even that global markets will feel the effects. Sometimes – though, let me stress, not always – these presentations are compelling. In reaching every charging decision, we must take into account the effect of an indictment on innocent employees and shareholders. … In large multinational companies, the jobs of tens of thousands of employees can be at stake. And, in some cases, the health of an industry or the markets is a real factor. Those are the kinds of considerations in white-collar-crime cases that literally keep me up at night, and which must play a role in responsible enforcement."
Last December, the international bank HSBC agreed to plead guilty to civil charges of money laundering, paying a then-record $1.92 billion in fines and restitution. The bank was found to have laundered billions of dollars to people and corporations trying to evade taxes, rouge nations like Iran, the Mexican drug cartels, and even terrorists.
In doing so, the Justice Department ignored the calls of many of its own career prosecutors for criminal charges against the bank, saying it feared that charges against such a large financial institution might destabilize the entire global financial system.
That same month, the Justice Department took a tougher stance against UBS, the big Swiss bank, for having manipulated world interest rates. A subsidiary of the bank had to plead guilty as part of a settlement. Still, there was some criticism of the Justice Department for not taking stronger action.
During a press conference to announce the UBS settlement, Breuer said: "In the world today of large institutions where much of the financial world is based on confidence, one of the things we want to ensure … [is] that jobs are not lost, that there is not some world economic event that is disproportionate to the resolution we want."
Holder then stepped in and quickly added: "The impact on the stability of the financial markets around the world is something we take into consideration. We reach out to experts outside of the Justice Department to talk about what are the consequences of actions that we might take."
JPMorgan Linked To Madoff's $65B Ponzi Scheme
Tax Loophole May Mean JPMorgan Recoups Part Of Billion-Dollar Fines
Investment Jpmorgan chase
NYSE Glitch Sows Confusion, Prompts Investigation


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Mesa Verde Enterprises, Inc. was organized under its current name and incorporated in the State of New Mexico in October of 1993 by Randal, Jeffrey, and Timothy Rabon. The Rabons continue to be Mesa Verde's owner/operators and play an integral role in the daily operation of the business. Starting out as an underground utility work company, Mesa Verde entered into the heavy horizontal construction field in the mid-1990's in order to fill the need for a paving contractor in the local market. Since that time, Mesa Verde has grown substantially to include three asphalt plants, two concrete batch plants, and five crushers. Even though we are a small business entity, Mesa Verde is one of the largest, privately held construction firms in Otero County, New Mexico and the largest aggregate producer in the region. Employing between 100 to 150 people allows us to utilize our experienced personnel running quality equipment and materials to produce a superior product in a timely manner.
Mesa Verde Enterprises, Inc. has a strong commitment to excellence in performance, which has lead to past success and will continue to guide our operations in the future. We build quality into our services at the beginning, rather than relying on others to "inspect it in" after the fact. Mesa Verde has grown into a company with a reputation for fairness to our employees, subcontractors, and customers. We will continue to look toward the future with the advantage of years of experience and new innovative thinking to meet the needs of our customers.
Our New Mexico Contractor's License Number is 2967, and our classifications include GB98, GA98, GF98, GF07, GA02, GF09, GA01, GF01, MM98, GF06, GA04, GA05, GF04, GF08 and GA03.
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Notes From Parliament Hill
Marine Security
Ever increasingly the subject of marine security seems to dominate the regulatory agenda these days
The International Ship and Port Facility Security Code (the Code) was ratified in December 2002 by the International Maritime Organization as an amendment to the SOLAS Convention of 1974. The Code will be enforced worldwide by port states from July 1st, 2004 and at that time the Code will apply to all passenger and cargo ships of over 500 gross tons in addition to mobile offshore drilling units.
In short, the Code requires the completion of security assessments and security plans, and the designation of security officers.
The US Coast Guard have adopted the position that each foreign flag ship will receive a "verification boarding" the first time the ship calls at a US port after July 1st. Entry will be denied to all ships that lack a valid International Ship Security Certificate.
The Code, in effect, opens for scrutiny a ship's trading history and ports known to have lax security or those ports in countries that have not yet signed off on the Code are going to win ships extra attention from the regulatory authorities, and such attention will inevitably result in delays.
The port state is required to accept a valid International Ship Security Certificate "at face value" unless, that is there are clear grounds to believe that the ship is not in compliance with the Code. Ship owners, in this age of increased security awareness should be under no doubt that the regulatory authorities will seek "clear grounds" to lay the foundation for subsequent ship inspections by reviewing security records and also by observing and interviewing crew members after July 1st. Ship owners are therefore best advised to keep good security records and to prepare their crews for the inevitable interviews.
Canada is on course to meet the July 1st deadline. Marine Transportation Security Regulations implementing the requirements of the Code have been approved by the Governor in Council for publication in the Canada Gazette, Part II on June 2nd, 2004. In addition, the Government of Canada has recently established a Contribution Program to assist ports and port facilities with the added cost of security enhancements.
The Code and the impending regulations, in addition to enhancing marine security in Canada may also benefit the embattled Canadian Coast Guard. The Coast Guard is presently trying to break out of its cocoon at the Department of Fisheries and Oceans and transform itself into an independent agency reporting to the Minister of Transport. The Agency is seeking to be charged with primary responsibility for marine search and rescue, emergency environmental response and marine pollution prevention, in addition to marine security.
Marine Ship-Source Pollution
While the subject of security continues to denominate the marine regulatory agenda, thankfully it does not occupy the field exclusively. Canada is presently in the throes of an election with an expected outcome late next month (June 2004) of a minority liberal government. Before Parliament was dissolved earlier this month, the government introduced new legislation to amend the Migratory Birds Convention Act, 1994 and the Canadian Environmental Protection Act, 1999. The amendments were introduced in large part, to address the seemingly endless problem of the illegal discharge of oily waste at sea off the south coast of Newfoundland.
In recent years, the government has been buoyed by the prosecution of ships such as the Baltic Confidence and the CSL Atlas which resulted in fines as high as C$125,000. 00 (USD 86,000.00).
One of the world's busiest shipping routes and one of Canada's most sensitive seabird habitats converge off the south east coast of Newfoundland and recent data from Environment Canada would suggest that more than 300,000 birds die each year as a result of ships deliberately dumping bilge waste in the shipping channel.
The legislative amendments follow the example of the Canada Shipping Act and increase the maximum penalty available under the Migratory Birds Convention Act to C$ 1 million, extend the jurisdiction of both Acts to include the exclusive economic zone out to the 200 nautical mile limit and place an express reference in the Migratory Birds Convention Act to permit the prosecution of ships officers.
The subject of marine ship-source pollution tends to be non-partisan and the possibility of a minority liberal government after Canada's Election Day on June 28, 2004 bodes well for the reintroduction of this legislative initiative.


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Our Inspection Department also offers you access to all your inspection reports, deficiencies, and service reports on FLSA's secure Intranet. No longer will you scramble around looking for a report or proof of work performed when the local fire marshal pays you a visit. Also, if you would like, you can view our work schedule as it relates to your properties.
Our qualified inspection team provides inspection services to ensure that sprinklers, alarm systems, and other suppression systems and devices are functioning properly, are tested regularly, and meet all code requirements.


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Potential Landing Spots: Dez Bryant
Nick Peckham
PHILADELPHIA, PA - DECEMBER 31: Dez Bryant #88 of the Dallas Cowboys in action against the Philadelphia Eagles at Lincoln Financial Field on December 31, 2017 in Philadelphia, Pennsylvania. (Photo by Mitchell Leff/Getty Images)
Dez Bryant has been one of the best wide receivers in the league since he entered in 2010. Bryant has played his entire career on the Dallas Cowboys but over the past two seasons he has showed some decline. Over the past two seasons Dez has started and played in 29 games out of a possible 32 but has been targeted 228 times while only catching 119 passes for 1,607 yards which is 13.5 yards a reception.
In this past off-season, Dez was released by the Dallas Cowboys after he and the Cowboys failed to reach a deal on a restructured contract. Dez went through all of training camp and preseason without a deal and is still a free agent after week 1 of the NFL season. Dez received two offers from the Baltimore Ravens and the Cleveland Browns during training camp but didn't sign either. I will be breaking down 5 possible destinations for Dez Bryant this season.
1: Atlanta Falcons
The Atlanta Falcons offense looked horrible in their opening game against the Philadelphia Eagles. The Falcons moved the ball up and down the field all night but once they got into the red zone their offense stalled and they couldn't convert. Matt Ryan was all over the place and if it wasn't for Julio Jones having 10 receptions for 169 yards then they would have been in more trouble. Outside of Julio Jones the Atlanta Falcons recieving core of Mohammed Sanu and Calvin Ridley combined for 4 receptions and 18 yards which atrocious.
If the Falcons were to reach out to Dez Bryant then could make a lot of sense. Adding Bryant would have Sanu and Ridley play mainly in the slot position which would let Bryant and Jones play outside and demanding double teams. Bryant would be a major factor in the red zone with his heigh and his ability to win in one on one matchups. The Falcons could have one of the best offensives in the league and be a matchup nightmare for opposing defenses with the talent across the board on offense.
2: Cleveland Browns
The Cleveland Browns and Dez Bryant have been linked to each other since he was released by the Cowboys. The Cleveland Browns are headed in the right direction with some of the additions this off-season as well as there draft picks. The Browns added Jarvis Landry, Carlos Hyde, Tyrod Taylor, and Damarious Randall this off-season while drafting Baker Mayfield, Denzel Ward, and Nick Chubb.
The Browns have Jarvis Landry and Josh Gordon as their main wide-outs as well as Rashard Higgins but adding Dez Bryant would help them. Bryant would be paired with Josh Gordon on the outside while Jarvis Landry would be a match-up nightmare in the slot position. This would open up the playbook on the offensive side of the ball for the Browns and Dez would have less double teams and be able to play more one on one which is his bread and butter.
3: Pittsburgh Steelers
The Pittsburgh Steelers have one of the best offenses in the league with Antonio Brown, JuJu Smith-Schuster, James Washington, Justin Hunter, LeVeon Bell, James Conner, Jesse James, and Vance McDonald. The Steelers have a ton of depth at the receiver position but adding a guy like Dez Bryant could give them a whole new dimension.
Adding Dez Bryant to the outside and sliding JuJu and James Washington into the slot positions could open up the field for Ben Roethlisberger and the running game. Whether teams decide to double Antonio Brown or not will either give Bryant the one on one match-ups on the outside or that could mean less double teams on Brown. Bryant would be a huge addition to the red zone with his size and strength as well as him being a possession receiver.
4: Minnesota Vikings
The Minnesota Vikings made it to the NFC Championship game last season before losing to the eventual Super Bowl Champion Philadelphia Eagles. The Vikings our one of the top teams in the league and could be the team to beat in the NFC with the new additions. The Vikings added Kirk Cousins this off-season as one of their prized free agents.
Minnesota has Stefon Diggs and Adam Thielen as their wide receivers put after that they don't have much production with Kendall Wright and Laquon Treadwell. Dez Bryant would be a huge addition to the Vikings which would allow them to slide either Diggs or Thielen to the slot and have Dez be an outside receiver. Dez has shown that he can be the number one on a team and be very effective but having two more receivers who can be just as dominate would help Dez be successful.
5: Dallas Cowboys
Could we see a reunion in Dallas with Dez Bryant when it's all said and done ? Never count out the possibility but the odds are stacked against it. Dez Bryant was on the Dallas Cowboys for the first 7 years of his career but when the Cowboys came to him this off-season to restructure his contract, both sides didn't see eye to eye. Dez Bryant had some incredible seasons for the Dallas Cowboys but the past couple of seasons without the help of some other guys, Dez seemed to be fading away.
The Cowboys could use the services of Dez this season with the new receivers that they acquired during the off-season in Allen Hurns and Michael Gallup. Dez and owner Jerry Jones were just spotted together at a Beyonce concert at AT&T Stadium recently which brings speculation to a reunion in Dallas.
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Dez Bryant
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I had started my own sports website/blog to write articles and get my name out there for everyone to see and I got asked to write for Last Word on Sports. My interests are baseball, basketball, football, hockey. I am 25 years old and I am engaged and we have a 1 and a half year old son who has changed my life so much.


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