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Uptick in 'Canceling Amazon Prime' Searches Spotlights Some Consumers' Shop and Drop Plans
Why Recent Antitrust Regulation Isn't Really About Consumer Protection
CommentaryLeadership
4 ways to sell your risky business plan to skeptical colleagues
Ernest Gundling
Ernest Gundling, managing partner at Aperian Globalben krantz STUDIO www.benkrantz.com
Fortune Insider is an online community where the biggest names in business and beyond answer timely career and leadership questions. Today's answer for: How do you get buy in for a new idea? is written by Ernest Gundling, managing partner at Aperian Global.
Winning support for a new idea or direction has never been easy. For many reasons, groups don't readily embrace change, and whether we seek to impact employees in our own offices or remote team members on the other side of the world, there will always be resistance. This can stem from contrasting market environments and priorities, perceived risks that outweigh future benefits, entrenched interests, pressures to achieve existing goals, budgetary and resource limitations, or even the inertia of established habits.
In our own work in global talent development, for example, we have seen a trend in which products that we have previously offered to client companies in a business-to-business format are now moving to business-to-customer transactions. Rather than relying on their own employers for what they need, many people are sourcing these products and services directly online. We want to provide them with the solutions they need, but enabling large-scale B2C transactions requires real buy in from employees in several different geographic regions.
See also: The surprising thing that can help you sell a tough idea
Here's what we learned in rolling out this B2C initiative:
Create urgency
What looks like a valuable proprietary resource in one region may seem like more of a commodity in another. We've had to consider how to best balance the needs of a rapidly changing market in one location that appears to be hungry for these B2C offerings with the interests of other regions that don't want us to enable local competitors by making our intellectual property so widely available. When you're building a case for change, it is important to create a sense of urgency around a business model that is declining, and point to where the existing trend lines are headed.
Reflect on history
Compared to many of our B2B sales, the doubters say, most online transactions will be smaller. They're right. Much of what we're currently experimenting with is unproven and the volume is small — at first. Yet within our own history, we can point to a current product line that was once small and unproven but now provides almost a third of our revenue. Reminding your team of the company's history and creating a track record for the new product line based on structured experimentation are two ways to address such natural skepticism.
Reinforce persuasion with infrastructure
While gaining buy in requires persuading others to support a new direction, you might still be swimming upstream because your people have been effectively recruited, trained, equipped and rewarded to do other things. The change we are making is like starting a new business, but inside of an existing business. This can actually be more difficult because people need to unlearn previous patterns of success while learning new ones, and some may be most comfortable remaining within the old system — even in a dwindling business. We've found that we need to tackle the infrastructure issues one at a time: marketing methods, database links, software, pricing, payment methods, and so on. Only in this way can we begin to covert conceptual buy in into larger-scale customer engagement and actual business results.
See also: So your boss hates your new idea. Here's what to do next
Establish new priorities
We're currently in the mode of borrowing employees' time from other products and priorities. Even if they're convinced of the potential value of our new direction, employees who are on loan from other projects can only provide limited support. True buy in requires full commitment. The success of the new venture should be your primary goal and yardstick for your own success.
Based on past experience, at some point soon we will have to "go big or go home," or move from structured experimentation and modifications based on customer feedback to scaling up and making larger bets. That means changing from an organization that tolerates experimentation on the side to one that fully embraces the new business model across all of our geographies and functions. Otherwise, we'll need to pull the plug and call the experiment a failure. Although some arm-twisting may be necessary to get things rolling initially, buy in ultimately can't be forced. It must be earned based on customer feedback, a gradual accumulation of results, and our growing, shared conviction that this is the right way to go.
Ernest is coauthor of Leading Across New Borders: How to Succeed as the Center Shifts (Wiley, Sept. 28).
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How to make sure your new business idea isn't a total dud by Kathy Bloomgarden, CEO of Ruder Finn.
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Transtar Kicker Accelerator is designed to accelerate the curing, dust, tack and tape times of Transtar Acrylic Urethane Primers and Clear Coats with no sacrifice of pot life. When Kicker is used according to the directions, it can save time and cost, by improving shop productivity. TDS 700.


BrandIndex scores for the cruise line sector suffered due to a couple of incidents which occurred in late 2010 involving cruise lines, Royal Caribbean International and Carnival Cruise Lines, with recovery for the sector only beginning after the New Year.
On November 9 2010, Carnival Cruise Line's ship, the Carnival Splendor, was 200 miles south of San Diego, California when an engine room fire cut the ship's power. There were no casualties and the fire was extinguished, but the misfortunate incident left the Carnival Splendor with no air-conditioning, phone service or hot water. The cruise ship had to be towed slowly for more than a day into a Mexican port, with all 4,500 passengers aboard. The incident sent Carnival Cruise Lines' Buzz into a sharp tailspin, sending scores down from 43.1 to -48.2 by early December.
During this time, cruise line sector leader Royal Caribbean International was flying high with the introduction of its Allure of the Seas cruise ship, featuring the DreamWorks experience and coffee chain Starbucks on board. The ship also managed to make an appearance on NBC's 'Today' show. However, in mid-December, Allure of the Seas made US national news when the cruise ship was battered by huge storms in the eastern Mediterranean, throwing passengers and furniture around. Royal Caribbean's Buzz score plummeted from 66.1 in early November to 6.7 a month later.
Fortunately, both cruise lines have made perception recoveries in time for the start of the annual January cruise season with scores back up to nearly normal levels. Royal Caribbean International is back up to 53.2, and Carnival is at 23.9. Carnival's rise in Buzz could also be powered by a new campaign from Arnold Worldwide with the tagline 'Hey America, Didja Ever?'.


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\section{Introduction}

\begin{figure}[t!]
\begin{center}
\includegraphics[width=0.95\linewidth, trim={1cm 39.25cm 7.55cm 0cm},clip]{figures/wacv_np_pipeline.pdf}
\end{center}
\caption{\label{fig:neural rendering}
		{\bf Neural Rendering with Warping.} While a single frame method requires a deep neural network to synthesize a realistic head image from rough geometry, our implicit warping yields low latency for a new frame at $t+1$ with a shallow network.
	}
\end{figure}

Telepresence via photo-realistic 3D avatars promises to better connect people. Recent advances in neural rendering already enable near photo-realistic image quality, but the underlying deep neural networks limit the best possible latency with their sequential, layer-wise processing.
This is a problem for virtual reality applications as these require low latency upon head motion of the user to convey an immersive experience. For example, \cite{zhou2019evaluation} creates a high-fidelity system using 120Hz projectors and user viewpoint tracking with a tracker having 60Hz updates and 3ms latency to minimize users' perception of warping of the scene when they move.
However, none of the existing neural renderers reaches the required \emph{motion to photons latency}, i.e., the time it takes from the user input, such as moving the head in VR, until the display updates.
It is henceforth an open problem to find improved neural models that strike a better trade-off between speed and image quality.

We develop a parallel implementation, inspired by previous approaches for improving the frame rate for other video processing tasks \cite{carreira2018massively} by spreading the computation over several frames over multiple GPUs. By itself, parallel execution only improves frame rate, not the latency that is so crucial for VR, since the effective network depth of sequentially executed layers remains the same.
To nevertheless reach the desired latency reduction and frame rate, we combine parallel execution with a dedicated warp layer that is tailored for neural face synthesis and acts as a skip connection between consecutive frames.

This neural caching approach re-uses information from the previous time step to improve latency from the current one with a shallow network for image generation and a deep network for computing the cache while waiting for the next input frame. Note that this caching strategy by itself already improves latency on a single GPU and scales in frame rate on increasingly parallel hardware by breaking the sequential layer dependencies and offloading the image cache generation into multiple threads.

The difficulty lies in finding the right representation for the cache to succeed with a low-capacity network.
Our approach is inspired by classical low-latency rendering on VR displays~\cite{van2016asynchronous}, which compute only the position of objects for in-between frames and warp color from the previous. However, explicit warping does not apply well to neural renderers where the underlying geometry is approximate and the neural texture is high-dimensional making warping operations much more costly.

To warp the neural representation, we introduce an implicit warp that provides a skip connection that is tailored for neural rendering by taking into account the head model parameterization.
The result is a warp network that models the image-space motion from one frame to the next given the desired viewpoint and head model parameters. Figure~\ref{fig:neural rendering} outlines the main components.

Our design is geared towards \emph{novel-view-synthesis} of a talking head given a dynamically changing viewpoint, such as the user's head motion in VR. We build upon the deferred neural renderer (DNR)~\cite{thies2019deferred} that uses a neural texture learned at training time. Our contributions towards scaling frame rate and latency on parallel hardware are:
\begin{itemize}[noitemsep]
\item Demonstrating that the proposed neural caching can reduce latency by up to 70\% with minimal degradation in image quality (only 1\% PSNR).
    \item Extending a DNR to generate a high-resolution output with low latency via caching and an implicit warping.
    \item Developing a parallel scheduler that supports warping and synchronizes multiple threads using queues.
    \item Refining the representation of facial expression, head pose, and camera angle to improve the implicit warp.
    \item Adding head-stabilization and tweaking the backbone and training strategy for noisy real-world conditions.
\end{itemize}%
Our experiments highlight the importance of how to cache as well as what and how information from the current frame is passed to the shallow \emph{implicit warping network}.
Figure~\ref{fig:pref-v-lat} compares the most related methods. Ours strikes the best latency and FPS improvement with the least image quality trade-off.

\begin{figure}[t!]
\begin{center}
\includegraphics[height=0.45\linewidth, trim={0cm 0.5cm 0cm 0cm},clip]{figures/PSNRvsLatencyNew.pdf}
\includegraphics[height=0.45\linewidth, trim={0cm 0.5cm 0cm 0cm},clip]{figures/PSNRvsFPSNew.pdf}
\end{center}
\caption{\label{fig:pref-v-lat}
		{\bf Performance vs. latency (left) and fps (right)} for our models and the baselines on the \emph{beard} dataset. Optimal performance is in the top-left and top-right corners, indicated by the yellow star in each respective figure. The vertical blue line marks the latency and fps of \cite{zakharov2020fast}. Models suitable for parallel execution are run using two GPUs.}
\end{figure}
\section{Related Work}

In this section we discuss recent high-quality image generation methods and contrast with those that optimize runtime with a focus on face generation.

\parag{Photo-realistic synthesis.}
High-quality photorealistic rendering is booming, using either implicit scene representations such as Neural Radiance Fields (NeRFs) \cite{mildenhall2020nerf} or deep neural networks trained on GAN objectives~\cite{karras2018style,karras2020analyzing}, which can also be conditioned on viewpoint and pose changes~\cite{deng2020disentangled,tewari2020stylerig}. However, these implicit models all rely on very deep neural networks that do not run at high-enough frame rates or are limited to
static scenes via pre-computed acceleration structures~\cite{yu2021plenoctrees}.

\parag{Talking head models.} For dynamic faces, it is most common to start from a parametric face model that parameterizes identity and expression in the form of blend shapes that are linearly combined with the full mesh~\cite{blanz1999morphable,li2017learning}. The coefficients of these models are low-dimensional and therefore suitable for driving avatars in telepresence and for reenactment by mixing identity and pose information of two subjects. While earlier models focus on only modeling the facial region~\cite{vlasic2005face,thies2015real,thies2016face2face,suwajanakorn2017synthesizing,thies2019deferred} conditioning on a complete head model, such as FLAME~\cite{li2017learning}, enables novel view synthesis of side views~\cite{ghosh2020gif} suitable for view-dependent 3D systems\cite{zhou20173dps}.  We follow this line of work and extend it by reducing the rendering latency for interactive applications.

Another line of work developed subject-agnostic face synthesis~\cite{olszewski2017realistic,nagano2018pagan,geng2018warp,fried2019text} that is conditioned on single or multiple images instead of a subject-specific 3D model.
However, this comes at the price of reduced details and entanglement of expressions and head motion~\cite{wang2021one}, particularly if the reference and target poses differ largely, and may require explicit warping operations that are relatively costly and ill-defined for occluded regions~\cite{zakharov2020fast}. Although \cite{zakharov2020fast} is already extremely fast on medium image resolution with 4ms runtime, there is still room for improvement when rendering high-resolution images and medium resolution on embedded devices.

\parag{Caching approaches.} Re-using information from the previous time step has been used for many computer vision tasks, among them: object detection~\cite{malinowski2020sideways}, video action recognition \cite{feichtenhofer2020x3d}, and segmentation \cite{hu2020temporally}.
Carreira et al.~\cite{carreira2018massively} gives an excellent overview of different architectures for video processing, including:
\emph{i)} \emph{depth parallel} architectures that execute a deep neural network over several iterations, leading to a delay equal to the depth of the layers;
\emph{ii)} \emph{depth parallel + skip} in which the head (last couple of network layers) of a depth parallel network is updated with the new input via a skip connection; and,
\emph{iii)} \emph{multi-rate clock} architectures in which the input features for the head are not updated at every time step and the head and backbone operate at different clock rates.
Our caching approach follows the multi-rate clock pattern.
However, none of the existing parallel models has demonstrated image generation.
Our contribution is to tailor this general concept to the problem of novel-view-synthesis of faces via neural rendering by a suitable form of warping.

\section{Preliminaries}

Our goal is an efficient neural renderer that outputs an image $\mathbf{I} \in \mathbb{R}^{H \times W}$ of a face parameterized as a surface mesh with vertices $\mathbf{v} \in \mathbb{R}^{3 \times K}$, triangle indices $\mathbf{i} \in \mathbb{R}^{3 \times K}$, and associated texture coordinates $\mathbf{u} \in \mathbb{R}^{2 \times K}$ and neural texture $\mathbf{N} \in \mathbb{R}^{D \times H \times W}$ {\color{black}{\cite{thies2019deferred}}}. These are the same inputs that a normal renderer would expect, except that the texture $\mathbf{N}$ is $D$-dimensional instead of storing three color values.

\parag{Deferred neural rendering.} Our starting point is the deferred neural renderer introduced by Thies et al.~\cite{thies2019deferred}, which approximates the complex and computationally expensive rendering equation with a convolutional neural network $G$. Figure~\ref{fig:neural rendering} gives an overview including the differences of \cite{thies2019deferred} applied to our full model including caching.
Initially, a rasterizer renders UV maps $\mathbf{U} \in \mathbb{R}^{2 \times H \times W}$ of the textured mesh. These are of the same dimension as the output image and store for every pixel the corresponding texture coordinate. Sampling these locations from $\mathbf{N}$ gives the feature map $\mathbf{F} \in \mathbb{R}^{D \times H \times W}$. For classical deferred rendering, we would sample from a color texture and combine it with light position information to form the final image.
In the case of the neural renderer, the texture has more than three channels forming learnable features.
The network $G$ turns $\mathbf{F}$ into the final image, replacing geometric illumination computations and material shaders in classical rendering. This forward pass is traced with blue arrows in Figure~\ref{fig:neural rendering} while the backwards information flow during training is marked in green.

\parag{Training and face reconstruction.}
The parameters of the involved neural network $G$ as well as the neural texture itself are trained on a large dataset that has examples of the input 3D mesh and a high resolution image of the face---the desired output.
We use real videos of the person as input and reconstruct vertices $\mathbf{v} \in \mathbb{R}^{3 \times K}$, expression coefficients $\mathbf{e} \in \mathbb{R}^{50}$, PCA shape parameters $\vs \in \mathbb{R}^{100}$, and head pose $\theta \in \mathbb{R}^{6}$ of the FLAME parametric model~\cite{li2017learning} alongside camera position $\mathbf{p} \in \mathbb{R}^{3}$ using the off-the-shelf estimator DECA~\cite{DECA:Siggraph2021}. Internally, it is using the 2D keypoint detector from \cite{bulat2017far}.
The reconstructed face overlays well with the image when re-projected, but details, such as hair and ears are often misaligned, which puts a larger burden on the neural renderer to synthesize these.
This reconstruction step is marked with a red arrow in Figure~\ref{fig:neural rendering}.

The generator $G$ is a U-Net and the %
neural texture $\mathbf{N}$ is initialized at random and subsequently optimized by backpropagation to store details of the training object locally. The loss function is the L1 difference between the rendered and the reference image in the dataset. This backwards pass is traced with green arrows in Figure~\ref{fig:neural rendering}. Training is on cropped images, which speeds up training.

\parag{Base architecture.} We use the 10-layer U-Net as in \cite{pix2pix2017} and a multi-scale neural texture with four levels of detail as in \cite{thies2019deferred}. Furthermore, to better model viewpoint-dependent effects, the view direction is projected to 9 spherical harmonics coefficients which are subsequently multiplied to channels 4 through 12 of the feature map. This enables explicit encoding of view-dependent effects similar to positional encoding~\cite{vaswani2017attention}.

\parag{Real-time viewpoint-dependent rendering.} Our primary application fields is 3D teleconferencing, where a person must be rendered at a high frame rate, with low latency, from the viewpoint of the user that is roughly frontal.
Given a new view direction, e.g., from an eye-tracker, our focus is on generating a natural looking image of this novel view as quickly as possible to mitigate motion sickness, reduce warping artifacts~\cite{zhou2017error}, and avoid discomfort. The object motion capture could be performed offline or through a slower channel, as usually only the viewpoint-dependent rendering demands the low-latency. In telepresence applications,
bandwidth is dictated by the size of the FLAME model; estimated on the source side, transferred, and rendered by our system given a new view from the receiver.

\begin{figure}[t!]
\begin{center}
\includegraphics[width=0.8\linewidth]{figures/low_latency_pipeline_column-wacv.pdf}
\end{center}
\caption{\label{fig:warp_components}
		{\bf Our low latency pipeline} caches the previous frame (features from the generator (left) marked orange and red) and the pose of the person to generate the subsequent frames with only two additional up-convolution layers (right) as soon as a new view direction is available. The generator and warping networks are learned end-to-end and can be applied in parallel at inference time.
	}
\end{figure}

\section{Method}

In this section, we introduce our neural caching approach, and propose two variants that operate on single and multi-GPU systems and are respectively tuned for latency and frame rate.
We cache information from the immediately preceding frames. Thereby, the motion that must be bridged from the cached information to the current frame is small, which allows us to introduce an implicit warp that attains maximum performance.

\paragraph{Neural Cache.}
\label{sec:warping}
For our neural caching, we first run the deep and slow image generator $G(\mathbf{N}_t,\mathbf{U}_t,\allowbreak \mathbf{p}_t)$ that is conditioned on the neural texture $\mathbf{N}$, UV-map $\mathbf{U}_t$ and view direction (e.g., user's head motion in VR) of the current frame~$t$. Figure~\ref{fig:warp_components} provides a detailed overview of our pipeline.
We cache features $\mathbf{C}^{(3)}_t$, $\mathbf{C}^{(4)}_t$, and $\mathbf{C}^{(5)}_t$ from the last 3 layers of the generator together with camera position $\mathbf{p}_t$, and spherical harmonics (SH) encoding of the
 pose, $\mathbf{h}_t^\text{obj}$. Furthermore, we add the {\color{black}{UV map $\mathbf{U}_t$}}, expression $\mathbf{e}_t$, and
the pose $\theta_t$ that are specific to rendering faces.
Formally we write the combined cache $\mathbf{C}$ as

\begin{equation}
    \mathbf{C}_t := [\mathbf{C}^{(3)}_t, \mathbf{C}^{(4)}_t, \mathbf{C}^{(5)}_t, \theta_t, \mathbf{p}_t, \mathbf{e}_t, \mathbf{h}_t^\text{obj}, {\color{black}{\mathbf{U}_t}}].
\end{equation}

\parag{Implicit Warping.}
Previous work used an explicit warp operation from a reference frame, which requires preceding neural network layers to predict the warp and is implemented as a texture sampling step that is relatively slow due to random access patterns for every pixel.
We propose an implicit warp with a neural network $W$ that is composed of only two up-convolution layers. These two layers take in cached information $\mathbf{C}_t$,
rendered UV map and
the new camera, pose, and expression from the new frame $t+1$,
to reconstruct the image $I_{t+1}$. This network is intentionally kept shallow to decrease the latency of image generation.

In addition, we found in a detailed ablation study that giving as input the new camera position $\mathbf{p}_{t+1}$, object pose $\theta_{t+1}$, expression $\mathbf{e}_{t+1}$ and their differences to the previous frame, via a single-layer MLP, $M$, works best. This yields
\begin{equation}
    \bar{\theta}_{t+1} = M(\mathbf{p}_{t+1}, \Delta \mathbf{p}_{t+1}, \theta_{t+1}, \Delta \theta_{t+1}, \mathbf{e}_{t+1} \Delta, \mathbf{e}_{t+1}, \mathbf{h}_{t+1}^\text{obj}),
\end{equation}
where the $\Delta$ refers to the change in a quantity between two frames, here $\theta_{t+1} - \theta_{t}$. {\color{black}{Additionally, we also include the UV map $\mathbf{U}_{t+1}$.}}
Together with the cached features processing by the
first warping layer $W_1$ gives
\begin{equation}
    \mathbf{F}_1 = W_1(\mathbf{C}^{(3)}_t, \mathbf{C}^{(4)}_t \Delta \mathbf{h}_{t+1}^\text{obj}, \bar{\theta}_{t+1}, \Delta \mathbf{U}_{t+1})
\end{equation}
where $\Delta \mathbf{h}_{t+1}$ and $\bar{\theta}_{t+1}$ are broadcasted to the resolution of the cache and $\Delta \mathbf{h}_{t+1}$ modulates the feature map by multiplication, similar to how positional encoding works, but here for rotational changes.
These features are further processed to output the image with
$I_{t+1} = W_2(\mathbf{F}_1, \mathbf{C}^{(5)}_t \Delta \mathbf{h}_{t+1}^\text{obj})$.
Note that $W$ warps the previous to the current frame, but without a spatial transformation layer---it does so implicitly via a local approximation of how the image changes with respect to changes in pose.

\begin{figure}[t!]
\begin{center}
\includegraphics[width=0.8\linewidth]{figures/latency_small_new.pdf}
\includegraphics[width=0.8\linewidth, trim={0cm 19.5cm 2cm 0cm},clip]{figures/highframerate_small_new.pdf}
\end{center}
\caption{\label{fig:latency}
		{\bf Our rendering pipelines for sequential (Top) and parallel execution (Bottom).} A network $W$ warps a cache from the previous frame as soon as a new viewpoint $V$ is ready for rendering, producing the output image much quicker than the full generator $G$ could. Leveraging two threads further increases the output frame rate.
	}
\end{figure}

\subsection{Operation Modes and Parallel Execution}

Our approach scales easily between single and parallel GPU execution. When only a single GPU is available, Figure~\ref{fig:latency}-top visualizes the principle of reducing latency with a shallow warp network, compared to running the slower generator. Here, the cache is  updated in sequence with the warp, thereby reducing latency but not frame rate.

When multiple GPUs are available, we can combine the proposed implicit warp with parallel execution, thereby rendering at a two or more times higher frame rate %
by warping a single or multiple images $\mathbf{I}_t,\mathbf{I}_{t+1},\cdots$ while the generator runs on a separate GPU on a separate thread. Figure~\ref{fig:latency}-right showcases this scheme. Notably, even though the warping is an approximation, multiple warping does not reduce image quality when operating online on high-frame rate video streams. The higher processing speed of warping twice in parallel reduces the distance between frames that can be processed and thereby makes the two-frame warp as difficult as a single-frame warp operating at half the frame rate.

We experimented with different job assignment and synchronization schemes between the two threads. We found that threads alternating between image generation and warping is most efficient and eases implementation. In this model, the main thread distributes newly arriving viewpoint information to the two worker threads via a queue, each associated to one GPU. These in turn wait for new data in the queue. Upon receiving new data, they alternate between caching and warping as visualized in Figure~\ref{fig:latency}-bottom. Thereby, the warping is executed on the same GPU as the generator, such that the cache can remain on the same GPU. The alternative of using a dedicated warp and cache thread has a much lower performance since the cache would have to be moved from one GPU to CPU and then again from CPU to the target GPU.
The required synchronization of ques has a negligible overhead in our implementation on two RTX 2080s with only 0.25ms/frame.
Moreover, our preliminary attempt of using manual locks instead of queues was more complex without improving performance. We will make our implementation publicly available to facilitate further research.

\subsection{Improved Neural Head Rendering}
\label{sec:architecture}

Our starting point, DNR~\cite{thies2019deferred}, is a general rendering approach. In the following, we explain our architectural changes towards tuning it for face synthesis.

\parag{Head stabilization}
To reduce jitter and flicker of the head,
we ensure that the virtual camera that we use to generate our UV masks is always centered on the subject's head and has consistent scale when generating videos by centering the camera on the head midpoint and scaling by the projected ear-to-ear distance.
Because the driving motion capture signal is often unstable, we further smooth the global head position $\mathbf{p}_t$ with a delayed Gaussian filter of size five and fix the identity $\vs$ of the FLAME model to be the mean identity estimated by DECA on the training set. To maintain facial expressions and lip motion faithfully, the jaw orientation in $\theta$ and expression parameters $\mathbf{e}$ are left untouched.

\parag{Loss Function} For our baselines which only predict the image at time $t$ we define the loss function as
\begin{equation}
    L_\text{train} = \lambda_\text{tex} L_\text{tex} + \lambda_\text{img} L_\text{img} + \lambda_\text{p} L_\text{p} ,
\end{equation}
which measures the L1 distance between the first three channels of the sampled neural texture and the ground truth image ($L_\text{tex}$), the L1 photometric loss ($L_\text{img}$), and the perceptual loss \cite{johnson2016perceptual} between the predicted image and ground truth image ($L_{p}$). We weight these loss terms with coefficients 1, 1, and 0.1. For the warping we add $\lambda_\text{img}$ and $L_\text{p}$ on the future frame ${t+1}$ and down-weight the existing $L_\text{img}$ by 0.1 to put much more weight on the prediction for $I_{t+1}$ that is used at inference time.

\parag{Architectural Changes.} Based on the work of \cite{karras2021alias} we make the following improvements to the base DNR \cite{thies2019deferred} network to improve output image quality. First, we replace the transpose convolutions in the latter half of of the U-Net with a bilinear upsample layer followed by a 2D convolution (up-convolution). This has been shown to increase the final image quality of output and reduce grid-like noise in reconstructed images. {\color{black}{Furthermore, we apply a Gaussian low pass filter (LPF) on the smallest spacial features of the U-Net architecture.}}
We refer to the baselines that utilize all these improvements as DNR+.

\section{Experiments}
\label{sec:Experiments}
We evaluate our Neural Warping technique with respect to our goal of maximizing image quality and minimizing the latency by applying our Neural Warping. Figure~\ref{fig:pref-v-lat} summarizes our main results on the possible trade-offs between accuracy vs.~latency and accuracy vs. fps, comparing our two variants to the most related work and showing an improvement in fps of up to $300\%$ and a reduction in latency of $70 \%$. We also provide an ablation study to identify specific trade-offs with respect to individual components of our warping network. The supplemental material contains additional results.

\parag{Baselines.}
\textbf{DNR}~\cite{thies2019deferred} is the backbone we use as our reference.
\textbf{DNR+} improves DNR with recent neural network architecture improvements from~\cite{karras2021alias}. We furthermore add \textbf{DNR++} that uses the current and past frame as input. DNR+ and DNR++ both act as a theoretical upper bound for our model's image quality and therefore, provide a good measure of the effectiveness and efficiency of our approach.
We also compare against the recent method from \textbf{Wang et al.}~\cite{wang2021one}, using their online interface (\href{http://imaginaire.cc/vid2vid-cameo/}{http://imaginaire.cc/vid2vid-cameo/}) and in terms of runtime to \textbf{Zakharov et al.}~\cite{zakharov2020fast} using the same $512\times512$ image resolution.
We do not provide PSNR numbers as it was designed for a much smaller resolution of $256\times256$.
In addition, we create a \emph{naive} baseline where we shift input frames by one to emulate the delay that incures when running a large image generator without warping. It serves as an expected lower bound on accuracy.

\parag{Datasets.}
We use the talking head sequences from \cite{thies2019deferred} to compare against prior work as well as a \emph{beard} and a \emph{high-fps} sequence with more difficult facial hair that was recorded with ethics board approval.
Videos for the \textbf{\emph{beard}} and \textbf{\emph{high-fps}} dataset were recorded at 1920x1080 and split into, respectively, 2604/558/558 and 3600/500/1000 frames for training/validation/testing. \textbf{\emph{Trump}}~\cite{thies2019deferred} sequences is a 1280x720 of 431 frames.
The \textbf{\emph{Obama}}~\cite{thies2019deferred} sequence is a 512x512 of 2412 frames.
The \textbf{\emph{male}} and \textbf{\emph{female}}~\cite{thies2019deferred} sequences are both 768x768 with 2380 frames. The \emph{high-fps} recording has 60 fps, all others run at 30 fps.

\parag{Training Setup.} We train all our models for 150 epochs using Adam~\cite{kingma2014adam} with betas equal to \{0.9, 0.999\}, learning rate of 1e-4 for both the Generator, $G$ and Warp network $W$, and 1e-3 for the neural texture.

\begin{figure}[t!]
\begin{center}
\includegraphics[width=0.85\linewidth]{figures/errormaps_12_new.pdf}
\end{center}
\caption{\label{fig:errormap}
		{\bf Frames and their error maps} for our model (1x and 2x warp), DNR+, and naive baseline. Based on the L1 error we can see all models perform similarly, differences are visible in the error maps for high-frequency details.}
\end{figure}

\parag{Metrics.} %
We evaluate our image reconstruction accuracy using an L1 reconstruction error between the ground truth and reconstructed image as well as PSNR and SSIM. Furthermore, to show our increase in speed, we report the latency and frames-per-second (fps) of our models and baselines. For all methods, we only measure the time it takes to process the input UV maps and the corresponding skeleton (pose), expression, and camera (extrinsics) information; excluding the time it takes to render the UV map  because they are implementation dependent and with negligible overhead when implemented in a rasterizer. Similarly, we only account for the time the inference generator takes in \cite{zakharov2020fast}, ignoring the processing of the conditional keypoint image. All latency and fps metrics are computed on NVIDIA RTX 2080 GPUs. For our approaches we report metrics and timings when we cache every frame (1x warp) and every second frame (2x warp) to show our method is robust to varying deviations between input frames.

\subsection{Latency and Runtime Improvement}

The generator backbone has a runtime of 47.02ms and equivalent latency. Our warp net has a runtime of 14.62ms and therefore deduces latency by a factor of 3.2. Running on multiple GPUs improves frame rate from 28.5 to 67.6 while inducing only a negligible 0.25 ms increase in latency due to the required synchronization. Note that parallel execution across multiple GPUs can by itself not improve the latency of a sequential process. When processing even and odd frames on different GPUs, the time from input view to rendering output remains the same. The impact of different model configurations on latency is evaluated in Table~\ref{tab:ablation_quant}.

\subsection{Offline Reconstruction Quality}

\input{tables/main_quant}

To test image generation quality, we use a held-out test video and drive the trained models using the FLAME head model reconstructed on the reference video.
The results from Figure~\ref{fig:errormap} and Table~\ref{tab:quant_main} evaluate the difference to the reference video in offline processing mode.
When comparing individual frames between the baseline and our model, Figure~\ref{fig:errormap} shows that single (\VOOne{}) and multi-frame warping (\VTTwo{}) work nearly as well in terms of error. Table~\ref{tab:quant_main} reveals, somewhat surprisingly that \VOOne~ slightly outperforms the DNR+ baseline in terms of PSNR despite having to warp with a shallow network. This is possible since it has access to the current and past frame that helps to correct errors in the facial expression estimation. To this end, we introduced the DNR++ baseline as a new upper bound that also has access to the two previous frames. In summary, at a small reduction in accuracy compared to the baseline models, latency and frame rate are greatly improved by a factor of two or more.

\subsection{Online Reconstruction Quality}

Online reconstruction requires the algorithm to run at the native frame rate of the video. This has a significant influence on the performance of our algorithm as the warping operation becomes simpler for high-frame rate videos where the motion between two frames is reduced, leading to even higher performance gains than for the previous offline evaluation. We test this effect on the \emph{high-fps} sequence shown in Fig.~\ref{fig:qualitative_highfps}. Table~\ref{tab:ablation_highfps-compare} reveals that running online on 60 fps videos with Ours (2x warp) improves on Ours (1x warp), as the latter can only process every other frame requiring larger warps. Hence, warping multiple times is beneficial, jointly improving in latency, runtime, and image quality, when parallel hardware is available. The basic DNR baselines do not even run at 30 fps and are hence not comparable in the high-fps online setting.

\begin{figure}[t!]
 \begin{center}
 \includegraphics[width=0.95\linewidth, trim={0cm 0cm 0cm 0cm},clip]{figures/highfps_qualitative_main_new.pdf}
 \end{center}
 \caption{\label{fig:qualitative_highfps}
 		{\bf Online application.} When running at their native frame rate, high-fps models (\VTTwo~) improve as they have to bridge a smaller gap between frames.
 	}
 \end{figure}

\input{tables/highfps_compare}

Note that absolute PSNR numbers differ across subjects and scenes since faces are smaller/bigger and also contain more or less high-frequency details, including the texture on the shoulder region that is of little concern for the facial feature reconstruction fidelity. Hence, it does not make sense to relate absolute but only relative numbers between the \emph{beard} and \emph{high-fps} scores.

\subsection{Novel View Synthesis Quality}
\label{sec:novel-view}
Reproducing a pre-recorded sequence does not necessarily need low latency since the entire video could be cached. Yet, latency is crucial for rendering a face from a novel viewpoint to account for the user's head motion in VR and in general for viewpoint-dependent displays. We generate novel views of characters by rotating their underlying 3D mesh (which is used to generate our input UV maps) while holding other parameters fixed. Figure~\ref{fig:retarget_novel} shows the retargeting of poses and views across videos and \ref{fig:nvidia} shows synthetically generated views and compares it to the results from \cite{wang2021one}. Because we condition on a full 3D face model, our rotation is more precise and keeps the pose unchanged compared to the learned 3D features from Wang et al.~that lead to opening of the mouth and up-rotation. The quality by \cite{wang2021one} is expected to be slightly lower as it is not person-specific, which serves our motivation to train a person-specific model.

\begin{figure}[t!]
\centering
  \centering
    \includegraphics[width=0.8\linewidth, trim={0cm 0cm 0cm 0cm},clip]{figures/us_v_nvidia_new-wacv.pdf}
  \caption{\label{fig:nvidia}
		{\bf Comparison against Wang et al. \cite{wang2021one}}. While both models show similar levels of detail, ours is anchored in a 3D representation which gives us more fine-grained and independent control.
	}
\end{figure}

\begin{figure}[t!]
  \centering
    \includegraphics[width=0.8\linewidth, trim={0cm 0cm 0cm 0cm},clip]{figures/retarget_novel.pdf}
    \caption{\label{fig:retarget_novel}
		{\bf Novel views while retargeting}. Our model is capable of generating realistic, view-dependent novels views {\color{black}{(Section~\ref{sec:novel-view})}} while mimicking the source's facial expressions {\color{black}{(Section~\ref{sec:retarget})}}. These examples are generated using Ours (\VOOne~) using the target actors from \cite{thies2019deferred}.
	}
\end{figure}

\subsection{Retargeting}
\label{sec:retarget}
To show the flexibility of our approach, we retarget facial and head motions from one person to another on in-the-wild videos that were established as a benchmark in \cite{thies2019deferred}. In this setting we train the neural texture and renderer on our target subject and use these learned models at inference time, driven by the head motion reconstructed from a source sequence with a different actor. In Figure \ref{fig:retarget_main} we show that our approach is capable of retargeting with just transferring expressions (as in \cite{thies2019deferred}) and also when mapping the global head orientation. The original results, provided from \cite{thies2019deferred} do not include head stabilization. Nevertheless, this comparison shows that our approach (\VOOne~) is reproducing or even outperforming their image quality.

\begin{figure}[t!]
\begin{center}%
\includegraphics[width=0.85\linewidth,trim={0cm 0.1cm 0cm 0cm},clip]{figures/retarget_main_new.pdf}%
\end{center}%
\caption{\label{fig:retarget_main}
		{\bf Retargeting}, without and with transfer of the global head motion, including the comparison to~\cite{thies2019deferred}.}
\end{figure}

\input{tables/ablation_quant}

Furthermore, as we condition on a full 3D head model, we are able to generate novel views while performing the retargeting as shown in Figure~\ref{fig:retarget_novel}. Since our approach only approximates the background for each scene, we use a static background in our predictions.

\subsection{Ablation Study}

\parag{Network Backbones.}
To show our method's generality to other backbones, we compare our UNet to the widely successful ResNet-backbone using residual blocks. As expected, warping is effective in reducing latency from 113 ms to 26 ms, but the ResNet does not outperform the UNet architectures. On the \emph{beard} sequence, the ResNet backbone operating using single warps gives a PSNR of 23.70 (UNet 26.66) and 23.18 PSNR using two warps (UNet 26.45).

\parag{Model Components.} Adding the parameters and transformations used in \emph{Our} full model one-by-one increases image quality (L1, PSNR, and SSIM) while only incurring a slight decrease in latency and fps (see relative latency and relative fps column, the relative latency compared to our full model). Table~\ref{tab:ablation_quant} presents the results for the sequential operation mode on the \emph{beard} sequence over the following components:
\begin{itemize}[noitemsep]%
\item{\bf Concat~UV:} using the difference of the current and cached UV maps in $\mathbf{C}_3$.

\item{\bf Use $\mathbf{\theta}$:} concatenating the cached pose and camera extrinsics to $\mathbf{C}_3$.

\item{\bf Use MLP:} passing $\theta$, $\mathbf{e}$, $\mathbf{p}$ through an MLP $M$ before concatenation with~$\mathbf{C}_3$.

\item{\bf SH Pose:} includes the spherical harmonics $S^{obj}$ in $\theta$.

\item{\bf SH Skips:} 'rotationally' encode $\mathbf{C}_4,\mathbf{C}_5$ using $\Delta \mathbf{S}^{obj}$.

\item{\bf ExWarp:} explicit warping from a reference by sampling the learned neural texture with $\mathbf{U}_{t+1}$ and concatenating it with $\mathbf{C}_3$.

\item{\bf Exp:} cache and concatenate the expression $\mathbf{e}$ with $\theta$.%
\end{itemize}%
The explicit warping (\emph{ExWarp}, second-last row) adds a large latency increase while not improving the quality metrics consistently. Hence, we favour the implicit warp in our full model. When applied in parallel on multiple GPUs, these reduced latencies translate directly to improved frame rate. The synchronization overhead in the parallel implementation is only 0.25ms/frame, which we measured by running the sequential model with the same threading and queue synchronization as for the parallel mode and taking their latency difference.

\section{Limitations}

Because our generator, $G$, and warping network, $W$, learn how to generate an image without an explicit rendering equation, we require a diverse set of training views to ensure that we can perform novel view synthesis and accurate warping at inference time. We can see in Figure~\ref{fig:retarget_novel} that the novel views break at extreme angles around the ears of the target subject as these are not seen during our training videos. The limiting factor is here the face reconstruction algorithm that becomes unreliable when large parts of the face are occluded. Eyes and a wide-open mouths can also pose a problem since they are represented as holes in the underlying FLAME model and therefore the direction of the eye gaze and tongue cannot be modeled. Furthermore, because the FLAME model only estimates 3D models for the head, the network struggles in cases where users have glasses or expressions that it cannot express. Improving image quality in these directions is largely orthogonal to our contributions towards low latency and high frame rate.
\section{Conclusion}

We introduced an implicit warping method that reduces the latency and, if parallel hardware is available, increases the frame rate of neural face rendering. We believe that such parallel execution to reduce latency and increase frame rate will gain importance with VR and AR emerging on the consumer market at scale, as our caching approach is compatible with the deeper neural network architectures required to meet the ever-increasing demand for output resolution. Thus, our work makes an important step towards end-to-end VR and 3D telepresence using view-dependent displays.

\paragraph{Acknowledgements} This work was sponsored in part by the Natural Sciences and Engineering Research Council (NSERC), Huawei, and Compute Canada. We also want to thank Justus Thies for providing their dataset and baseline.

\section*{Appendix}
\section{Implementation details}

Meshes are generated using the forward function of the FLAME model implemented in python~\cite{li2017learning}. UV maps are generated by rendering the FLAME head model using the PyTorch3D rasterizer, which implements rasterization on the GPU sing custom cuda kernels but is not as fast as classical rasterization.
The explicit warp is implemented using the pytorch \emph{grid\_sample} function, which is the same as used by \cite{chu2021fast} for their explicit warp.

\paragraph{Generating Novel Views}
Our method relies on both the processed UV maps, initialized with the 3D mesh estimated by DECA \cite{DECA:Siggraph2021}. Therefore, to generate novel views for our method we require to modify these parameters. In our tests, we found that the best generalization to novel views is obtained by rotating the vertices outputed by the FLAME model, as modifying the joint angles in FLAME model to extreme poses may lead to parameter ranges not seen during training.

\paragraph{Multithreading Library}
Our multi-gpu operation modes are implemented using the PyTorch multithreading library and two threads. For synchronization and communication between threads we use the \emph{torch.multiprocessing.mp.Queue} ques: one input queue for each thread (two for two GPUs), one image output que, and one que to signal completion of caching. Listing~\ref{algo:parallelAlgorithm} provides pseudocode for how caching and warping threads are orchestrated from the main thread, making sure that the output images arrive in sequence and that the caching completes before warping on the same worker while also ensuring that the parallel workers are independent otherwise for maximal performance. For simplicity, the pseudocode is simplified for 2x GPU and 2x warping for every caching operation. A different number of GPUs and warp operations only requires to change the condition in line 4 and line 8 that distribute caching and warping operations over worker threads.
We will publish our multithreading code so others can build upon it with their own neural models.

\begin{algorithm*}[]
    \DontPrintSemicolon
    \SetKwBlock{DoParallel}{do in parallel}{end}

    \tcc{Main thread}
    Queue viewpoint\_queue[NGPU=2], cache\_queue, out\_queue \tcp*{Queues used for synchronization}
    cache\_tid $\leftarrow$ 0 \tcp*{Pointer to assign worker responsible for caching}
        \For{t=0; t++;}
        {
            \KwIn{viewpoint, expr}
        \If{t \% NUM\_WARPS == 0}{
            cache\_queue.get() \tcp*{Wait for caching to complete}
            cache\_tid $\leftarrow$ (cache\_tid+1) \% NGPU \tcp*{Switch role of threads}
            \tcp{Post viewpoint to worker and assign caching role}
            (viewpoint, expr, warp=False) $\rightarrow$
            viewpoint\_queue[cache\_tid]
         }
         \tcp{Post viewpoint to the other worker and assign warping role}
        (viewpoint, expr, warp=True) $\rightarrow$
        viewpoint\_queue[(cache\_tid+1) \% NGPU] \;
        \tcp{Display output image once available}
   \KwOut{out\_queue.get()}%
        }

    \tcc{Worker thread, one for each GPU}

    \DoParallel{
    NeuralCache cache \tcp*{variable local to each thread}
        (viewpoint, expression, warp) $\leftarrow$ viewpoint\_queue.get() \tcp*{Wait for new input to process }
        \If{warp = True}{
            image $\leftarrow$ runWarpNet(viewpoint, expression, cache)\tcp*{Generate the image}
            image $\rightarrow$
        out\_queue \tcp*{Return output image via queue}
        }
        \Else{
            mesh $\leftarrow$ runFaceModel(viewpoint, expression)\tcp*{Face mesh from expr. parameters}
            cache $\leftarrow$ runDecoder(viewpoint, expression, mesh)\tcp*{Cache neural representation}
            "caching done" $\rightarrow$
        cache\_queue \tcp*{Notify cache completion via queue}
        }
    }
    \caption{Parallel Implementation (2x GPUs, 2x warp)}
    \label{algo:parallelAlgorithm}
\end{algorithm*}

\paragraph{Algorithm Time Complexity} Using the code provided by \href{https://github.com/Lyken17/pytorch-OpCounter}{pytorch-OpCounter}, we measured the FLOPs for both the generator and warping network which are 364G and 87G FLOPs respectively. This factor of 4x corresponds well with the 3.2x latency improvement that we reported. Note that our approach decouples the complexity of the generator and warp networks, i.e., the warp keeps the same time complexity when coupled with deeper generators (c.f.~experiment with ResNet in ablation study).

\section{Limitations of Capture Algorithm}
\begin{figure}[t!]
\begin{center}
\includegraphics[width=1\linewidth, trim={0cm 21.5cm 0cm 0cm},clip]{figures/limitation.pdf}
\end{center}
\caption{\label{fig:limitation}
		{\bf Limitations of Head Reconstruction} We can see that at extreme angles and glasses present a problem to the capture algorithm and produces inaccurate UV maps and parameters.
	}
\end{figure}

Due to limitations with the DECA approach~\cite{DECA:Siggraph2021}, extreme head poses and cases when the subject is wearing glasses are not accurately reconstructed. Figure~\ref{fig:limitation} shows such a case with glasses and a perpendicular angle to the camera, leading to a misalignment of model and image around the mouth, ears, and forehead.

\section{Additional Ablation on Caching Layers}

In this additional ablation, we sequentially remove the caching layers (C3, C4, C5) and measure the reconstruction quality in terms of L1 loss, PSNR, and SSIM. Each configuration was trained on the \textit{beard} dataset for 50 epochs, and metrics were computed on the held-out test set. Table~\ref{tab:ablation-cs} shows that adding the C4 cache layer is the most impactful, but since performance increases after adding all layers, we include all cache layers in our final configuration.

\begin{table}[]
\begin{center}
\begin{tabular}{ccc|ccc}
C3 & C4 & C5 & L1 $\downarrow$     & PSNR $\uparrow$   & SSIM $\uparrow$         \\ \hline \hline
$\checkmark$  &    &    & 0.0261 & 26.13 & 0.9048 \\
$\checkmark$  & $\checkmark$  &    & 0.0247 & 26.48 & 0.9096 \\
$\checkmark$  & $\checkmark$  & $\checkmark$  & \textbf{0.0246} & \textbf{26.54} & \textbf{0.9102} \\ \hline
\end{tabular}
\caption{\label{tab:ablation-cs}
		{\bf Ablating caching layers} for our warping network using the sequential variant (1x warp), showing the improvement after adding each component on the \emph{beard} dataset.}
\end{center}
\end{table}

\section{Additional Ablation on the Warp Distance}

In the experiments in the main document, we present the results of our warping network by warping either 1 or 2 frames ahead. This ablation study measures the performance drop from warping more frames ahead.  Figure~\ref{fig:warp_distance} shows that the reconstruction quality decreases linearly with the warp distance on the \emph{beard} dataset, which was recorded at 30fps. Please note that this increase of error is expected as the proposed neural caching and warping is a local approximation designed for high-fps videos where the warp distance is small.

\begin{figure}
\begin{center}
\includegraphics[width=0.9\linewidth, trim={0cm 0cm 0cm 0cm},clip]{figures/psnrvswarpdistance.pdf}
\end{center}
\caption{\label{fig:warp_distance}
		{\bf Ablation on warp distance} as we increase the warp distance, or the number of warping operations per caching operation, the reconstruction quality decreases linearly.
	}
\end{figure}

redpajama_set_name: RedPajamaArXiv


Family Policy Alliance and our network of over 40 state family policy groups (check to see if you have one in your state!) care deeply about freedom. Our mission every day is to protect the freedom of families to make their own education choices for their children, to protect the right of parents (not government) to guide their children's medical care, and to ensure parents know what's happening in their children's schools. We advocate for people of faith in state capitols across the country, so that every child at school, every charity and ministry, every church, every business—every person—will have the right to freely live out their faith in every aspect of their life.
Because we care so deeply about our freedoms at home, we want to pause from our mission to honor our Veterans who fight and sacrifice to protect our freedoms from external threats. These men and women, and their families, have given so much, and we are incredibly grateful.
Our team is especially thankful for the Veterans in our families. We wanted to share some of our family photos with you. Will you share some of your photos of Veterans you hold dear with us on our Facebook page?
(L) Eric Teetsel, President of Family Policy Alliance of Kansas, with his father, Col. Robert Teetsel. (R) Col. Robert Teetsel with a child in Afghanistan. Col. Teetsel is retired from the Army after 29 years in the JAG Corps.
Family Policy Alliance's Vice President of Finance Rich Caldwell's daughter Laura with her husband Daniel Tucker at his Air Force Academy commissioning ceremony.
President of Family Policy Alliance of New Mexico Vince Torres's mother Yolanda who served over 20 years in the Army.
Family Policy Alliance's Dir. of Policy Communication Autumn Leva's grandfather William Carman after he graduated from Navy boot camp in 1954, with his father.
(L) Family Policy Alliance's Mobilization Manager John Paulton with his son Matthew at West Point. (R) Jack Paulton, John's father, fought in the Navy in World War II and dropped the depth charges that sunk a Japanese sub off Okinawa.
Family Policy Alliance's President & CEO Paul Weber (left) with his wife Lois and son Carlton who served in the Coast Guard.


text:
Marián ist ein slowakischer männlicher Vorname ursprünglich lateinischer Herkunft; weiteres zu Herkunft und Bedeutung des Namens siehe Marian.

Namensträger

 Marián Bielčik (* 1973), slowakischer Skispringer
 Marián Čalfa (* 1946), tschechoslowakischer Politiker
 Marián Chovanec (* 1957), slowakischer Weihbischof
 Marián Cisár (* 1978), slowakischer Eishockeyspieler
 Marián Čišovský (1979–2020), slowakischer Fußballspieler
 Marián Dávidík (* 1977), slowakischer Orientierungsläufer
 Marián Gáborík (* 1982), slowakischer Eishockeyspieler
 Marián Had (* 1982), slowakischer Fußballspieler
 Marián Hossa (* 1979), slowakischer Eishockeyspieler
 Marián Janušek (* 1960), slowakischer Politiker
 Marián Kelemen (* 1979), slowakischer Fußballspieler
 Marián Kočner (* 1963), slowakischer Unternehmer
 Marián Labuda (1944–2018), slowakischer Schauspieler
 Marián Lapšanský (* 1947), slowakischer Pianist
 Marián Masný (* 1950), slowakischer Fußballspieler
 Marián Palát (* 1977), tschechischer Fußballspieler
 Marián Šťastný (* 1953), tschechoslowakischer Eishockeyspieler
 Marián Šulko (* 1976), slowakischer Badmintonspieler
 Marián Varga (1947–2017), slowakischer Komponist und Musiker der Postmoderne

Einzelnachweise

Männlicher Vorname
Slowakischer Personenname
Tschechischer Personenname

redpajama_set_name: RedPajamaWikipedia


text:
Maciej Jabłoński  (ur. 2 maja  1969) – żołnierz, polski dowódca wojskowy, generał dywizji Wojska Polskiego, dowódca 12 Dywizji Zmechanizowanej (2018–2020), Inspektor Wojsk Lądowych w Dowództwie Generalnym Rodzajów Sił Zbrojnych od 2020.

Życiorys

Wykształcenie
Maciej Jabłoński ukończył Wyższą Szkołę Oficerską Wojsk Pancernych w Poznaniu (1992); Wyższy Kurs Doskonalenia Oficerów (1997); studia wyższe na wydziale wojsk lądowych kierunku dowódczo – sztabowym na Akademii Obrony Narodowej (2001); podyplomowe studia polityki obronnej w AON (2013).

Służba wojskowa
Służbę wojskową rozpoczął w 1992 r. jako dowódca plutonu czołgów w 1 pułku zmechanizowanym w Wesołej. W 1993 r. był na stanowisku dowódcy kompanii czołgów, tego pułku. W 1995 r. po przeformowaniu pułku został zastępcą dowódcy batalionu czołgów w 1 Warszawskiej Brygadzie Pancernej. W roku 1997 po ukończeniu WKDO został wyznaczony na stanowisko kierownika sekcji szkolenia w tejże brygadzie. W 2001 r. został mianowany specjalistą, a następnie został wyznaczony na zastępcę szefa oddziału szkolenia w Dowództwie Wojsk Lądowych w Warszawie. W roku 2007 w tymże oddziale objął stanowisko szefa oddziału operacyjnego.

Od 26 października 2011 do 18 kwietnia 2012 r. był na stanowisku szef sztabu – zastępcy dowódcy podczas X zmiany w Polskim Kontyngentcie Wojskowym w Afganistanie. W roku 2012 objął stanowiska głównego specjalisty w grupie organizacyjnej Dowództwa Generalnego Rodzajów Sił Zbrojnych, a pół roku później został wyznaczony na stanowisko szefa Zarządu Planowania Rozwoju (J-5) w tymże dowództwie.

1 lipca 2014 r. objął dowodzenie nad 10 Brygadą Kawalerii Pancernej im. gen. broni Stanisława Maczka. 1 sierpnia 2015 r. otrzymał awans na pierwszy stopień generalski. Od 24 listopada 2016 r. był na stanowisku szefa Zarządu Planowania Użycia Sił Zbrojnych i Szkolenia P3/P7 w Sztabu Generalnego Wojska Polskiego. W dniu 29 czerwca 2018 r. decyzją Ministra Obrony Narodowej został desygnowany na stanowisko dowódcy 12 Szczecińskiej Dywizji Zmechanizowanej.

15 sierpnia 2018 r. awansowany do stopnia generała dywizji. Akt mianowania odebrał z rąk Prezydenta Rzeczypospolitej Polskiej Andrzeja Dudy. Od 1 października 2020 jest Inspektorem Wojsk Lądowych w Dowództwie Generalnym Rodzajów Sił Zbrojnych.

Interesuje się kulturoznawstwem, motoryzacją, żeglarstwem. Jest żonaty i ma syna.

Awanse
 podporucznik – 1992
 porucznik – 1995
 kapitan – 1999
 major – 2004
 podpułkownik – 2005
 pułkownik – 2010
 generał brygady – 1 sierpnia 2015
 generał dywizji – 15 sierpnia 2018

Ordery i odznaczenia
 Srebrny Medal za Długoletnią Służbę
 Gwiazda Afganistanu – (Prezydent RP)
 Złoty Medal "Za zasługi dla obronności kraju" – (Minister ON)
 Srebrny Medal "Za zasługi dla obronności kraju"
 Brązowy Medal "Za zasługi dla obronności kraju"
 Złoty Medal "Siły Zbrojne w Służbie Ojczyzny" – (Minister ON)
 Srebrny Medal "Siły Zbrojne w Służbie Ojczyzny"
 Brązowy Medal "Siły Zbrojne w Służbie Ojczyzny"
 Army Commendation Medal

Wyróżnienia
 Odznaka Honorowa Wojsk Lądowych
 Złota Wojskowa Odznaka Sprawności Fizycznej – 1992
 Odznaka pamiątkowa 1 Pułku Zmechanizowanego – 1995
 Odznaka pamiątkowa 1 Warszawskiej Brygadzie Pancernej – 1997
 Odznaka absolwenta AON – 2001
 Odznaka pamiątkowa Dowództwa Wojsk Lądowych – 2007
 Odznaka pamiątkowa DG RSZ – 2012
 Odznaka absolwenta Podyplomowych Studiów Polityki Obronnej AON – 2013
 Odznaka pamiątkowa 10 Dywizji Kawalerii Pancernej – 2014
 Odznaka pamiątkowa Sztabu Generalnego Wojska Polskiego – 2016
 Odznaka pamiątkowa 12 Dywizji Zmechanizowanej – 2018
 Odznaka pamiątkowa 2 Stargardzkiego Batalionu Saperów – 2019

Przypisy

Linki zewnętrzne
 12 Dywizja Zmechanizowana

Urodzeni w 1967
Absolwenci Wyższej Szkoły Oficerskiej Wojsk Pancernych
Absolwenci Akademii Obrony Narodowej
Generałowie dywizji Sił Zbrojnych Rzeczypospolitej Polskiej
Odznaczeni Medalem za Długoletnią Służbę
Odznaczeni Brązowym Medalem "Siły Zbrojne w Służbie Ojczyzny"
Żołnierze Polskiego Kontyngentu Wojskowego w Afganistanie
Żołnierze Polskiego Kontyngentu Wojskowego w Iraku
Odznaczeni Medalem Pamiątkowym Wielonarodowej Dywizji Centrum-Południe w Iraku
Polacy odznaczeni Medalem Pochwalnym (USA)
Odznaczeni Brązowym Medalem "Za zasługi dla obronności kraju"
Dowódcy 12 Dywizji Zmechanizowanej


Now through Jan. 25, 2018, agencies and organizations affiliated with a Juvenile Detention Alternatives Initiative (JDAI™) site can apply to participate in a professional development program supported by the Annie E. Casey Foundation called Re-Imagining Juvenile Justice (RJJ).
This new program, taught by nationally recognized experts and innovators in the juvenile justice field, will consist of six daylong modules delivered over several months. RJJ participants will learn about embracing a developmental perspective when working with justice-involved youth. Frontline staff will also build skills needed to divert and redirect youth to appropriate and fair justice options, including options that require significant cross-system collaboration and coordination.
RJJ was successfully piloted with a cohort of 21 juvenile justice professionals in Massachusetts, a state actively engaged in JDAI. At its conclusion, participants were better equipped to support and develop the strengths of youth and their families.
On Jan. 10, 2018, at 3 p.m. EST, RJJ partners — the School & Main Institute and Wheelock College's Juvenile Justice and Youth Advocacy Department— are teaming up with the Casey Foundation to host an informational webinar about the 2018 program.
To RSVP for the webinar, log on to JDAIconnect.org. After being redirected to the Casey Foundation's Community Café page, users must click on the "Non-Casey Staff Login" link and follow the instructions.
access a link to the program's application.


text:
DHPA Supports USPSTF Recommendations to Begin CRC Screening at 45, Calls for More Research on Disparities in Communities of Color
Voices concern about recommendations for Stool-based tests vs. screening colonoscopy
Washington, DC – The Digestive Health Physicians Association (DHPA) voiced its support for a draft recommendation by the U.S. Preventive Services Task Force (USPSTF) that Americans begin screening for colorectal cancer at the age of 45, an update of 2016 guidelines that had recommended that adults without risk factors for CRC should begin screening at age 50 and continue periodically until 75.
In the USPSTF draft recommendation, screening for adults ages 50-75 years will remain an A grade recommendation, while adding the recommendation to begin screening at age 45 as a B grade recommendation, which indicates that "there is high certainty that the net benefit is moderate or there is moderate certainty that the net benefit is moderate to substantial."
"The decision to begin screening at age 45 provides an opportunity to screen more than 20 million additional Americans for colorectal cancer, which could prevent thousands of people from dying from the disease," said Dr. James Weber, president of DHPA. "The most effective way to prevent colorectal cancer is detecting and removing pre-cancerous polyps and providing screening colonoscopy at an earlier age will help in achieving that goal."
While supporting the draft recommendations overall, Dr. Glenn Littenberg, DHPA chair of health policy, expressed some concern about USPSTF's recommendations with respect to screening colonoscopy versus stool-based CRC screening.
"Colonoscopy is the only screening method that both detects and prevents colorectal cancer, but stool-based tests are appropriate for average risk patients when colonoscopy isn't possible, such as during the COVID-19 pandemic," said Dr. Littenberg. "Physicians should offer stool-based tests in accordance with guidelines set forth by the gastroenterology societies, and there needs to be appropriate follow-up to ensure that patients who have positive stool-based tests receive colonoscopy."
In 2017, the U.S. Multi-Society Task Force on Colorectal Cancer (MSTF), comprised of the American Gastroenterological Association (AGA), the American College of Gastroenterology (ACG) and the American Society for Gastrointestinal Endoscopy (ASGE), ranked screening options into three tiers based on performance features, costs, and practical considerations. The MSTF recommends offering colonoscopy first with annual fecal immunohistochemical testing (FIT) offered to patients who decline colonoscopy, followed by second-tier tests such as sDNA-FIT, CT Colonography and flexible sigmoidoscopy for patients who decline FIT.
DHPA supports the USPSTF's call for more research into increased colorectal cancer incidence and death rates in Black adults and calls for the research to examine all communities of color. Black Americans have lower screening rates and are more likely than white Americans to have late-stage disease at the time of diagnosis. Latino, Asian American, Native Hawaiian and Pacific Islander, and Native American and Alaskan Native populations also face disparities in colorectal cancer screening and death rates.
"We applaud the task force for calling for increased research into disparities faced by Black Americans, and this should be expanded to include all communities of color that face disparities in access, screening and treatment," said Dr. Aja McCutchen, DHPA chair of racial health equity. "Some of the disparity in outcome is due to lead time in detection, and we need to increase colorectal cancer screening initiatives in communities of color. This will become increasingly important as older Americans become a larger proportion of the population and we transition to a majority-minority nation over the next few decades."
Dr. Weber stated that DHPA looks forward to working with the USPSTF as the recommendations are finalized. The draft recommendation statement, evidence review and modeling report have been posted for public comment, which can be submitted until Nov. 23, 2020.
Filed Under: Diversity Equity and Inclusion, Newsroom, Press Releases, Remove Barriers to Colorectal Cancer Screening

redpajama_set_name: RedPajamaCommonCrawl


text:
#126691 – 1982 PRA Robert Fulton
- Fleetwood Commemorative Cover
This neat commemorative cover was canceled on Robert Fulton's 217th birthday near his birthplace in Pennsylvania. It features the 1965 Fulton bicentennial stamp. It'll make a fun and historic addition to your collection.
Birth Of Robert Fulton
Engineer and inventor Robert Fulton was born on November 14, 1765, in Little Britain, Lancaster County, Pennsylvania.
From a young age, Robert Fulton showed a great talent for art. After his father died, he moved to Philadelphia and painted portraits and landscapes and provided technical drawings for houses and machinery to provide for his mother and siblings. While in Philadelphia he met Benjamin Franklin and several businessmen who encouraged him to travel to Europe to further his art training.
Click this image to get all the stamps and covers on this page in one convenient order.
Fulton arrived in England in 1786 and lived with Benjamin West, who had been friends with his father. While Fulton received several commissions for portraits and landscapes, he liked to experiment with inventions in his free time. This led Fulton to develop his idea for tub-boat canals that used inclined planes instead of locks. He also began researching the use of steam for boats and patented a dredging machine.
Fulton moved to Paris in 1797, where he learned French, German, mathematics, and chemistry. He then started designing torpedoes and submarines. In 1800, he tested the first practical submarine, the Nautilus, which went under water for 17 minutes. Working with U.S. Ambassador to France Robert Livingston, Fulton began designing steamboats, but put that work aside for a time to design the first modern naval torpedoes and other weapons for England during their war with France.
Fulton returned to America in 1806 and resumed work on a steamboat with Livingston. While others had invented steamboats before him, they were often considered dangerous and nothing more than a novelty. But Fulton believed it could prove to be a successful business venture, and built a 150-foot-long ship that would make him famous. Critics dubbed the boat "Fulton's Folly," believing it wouldn't make the trip.
On the afternoon of August 17, 1807, Fulton and a group of passengers boarded his ship North River Steamboat (later named the Clermont) in New York City, bound for Albany, 150 miles up the Hudson River. Shortly after leaving the dock, the boat stopped suddenly. Passengers and spectators willingly shared their doubt in the boat's abilities. Fulton calmly went below the deck, found the problem, and easily fixed it. The boat then chugged along at a leisurely five miles per hour without any other incidents. They arrived in Albany (after a stopover at Livingston's home, Clermont) in a record 32 hours. While many had their doubts, Fulton proved the commercial viability of steam boats, which would rule American waterways for the next half-century.
After that success, Fulton was made part of the Erie Canal Commission. During this time he also worked on another boat, New Orleans, which he took on a long trip down the Ohio River. The journey was through relatively uncharted territory and the fact that he was able to get the boat down the river and back home was a major milestone.
In early 1815, Fulton was walking home on the frozen Hudson River when one of his friends fell through the ice. Fulton rescued his friend, but was soaked in the cold water and caught pneumonia. He then contracted consumption and died on February 24, 1815, in New York City.
A number of towns and other locations have been named for Fulton throughout the country. There have also been five U.S. Navy ships named for him and he was honored as part of the Hudson-Fulton Celebration in 1909.


text:
608.01(w) Copyright and Mask Work Notices [R-11.2013]
37 CFR 1.71 Detailed description and specification of the invention
(d) A copyright or mask work notice may be placed in a design or utility patent application adjacent to copyright and mask work material contained therein. The notice may appear at any appropriate portion of the patent application disclosure. For notices in drawings, see § 1.84(s). The content of the notice must be limited to only those elements provided for by law. For example, "©1983 John Doe" (17 U.S.C. 401) and "*M* John Doe" (17 U.S.C. 909) would be properly limited and, under current statutes, legally sufficient notices of copyright and mask work, respectively. Inclusion of a copyright or mask work notice will be permitted only if the authorization language set forth in paragraph (e) of this section is included at the beginning (preferably as the first paragraph) of the specification.
(e) The authorization shall read as follows:
A portion of the disclosure of this patent document contains material which is subject to (copyright or mask work) protection. The (copyright or mask work) owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all (copyright or mask work) rights whatsoever.
37 CFR 1.84 Standards for drawings
(s) Copyright or Mask Work Notice. A copyright or mask work notice may appear in the drawing, but must be placed within the sight of the drawing immediately below the figure representing the copyright or mask work material and be limited to letters having a print size of .32 cm. to .64 cm. (1/8 to 1/4 inches) high. The content of the notice must be limited to only those elements provided for by law. For example, " ©1983 John Doe" (17 U.S.C. 401) and "*M* John Doe" (17 U.S.C. 909) would be properly limited and, under current statutes, legally sufficient notices of copyright and mask work, respectively. Inclusion of a copyright or mask work notice will be permitted only if the authorization language set forth in § 1.71(e) is included at the beginning (preferably as the first paragraph) of the specification.
The U.S. Patent and Trademark Office will permit the inclusion of a copyright or mask work notice in a design or utility patent application, and thereby any patent issuing therefrom, which discloses material on which copyright or mask work protection has previously been established, under the following conditions:
(A) The copyright or mask work notice must be placed adjacent to the copyright or mask work material. Therefore, the notice may appear at any appropriate portion of the patent application disclosure, including the drawing. However, if appearing in the drawing, the notice must comply with 37 CFR 1.84(s). If placed on a drawing in conformance with these provisions, the notice will not be objected to as extraneous matter under 37 CFR 1.84.
(B) The content of the notice must be limited to only those elements required by law. For example, "©1983 John Doe"(17 U.S.C. 401) and "*M* John Doe" (17 U.S.C. 909) would be properly limited, and under current statutes, legally sufficient notices of copyright and mask work respectively.
(C) Inclusion of a copyright or mask work notice will be permitted only if the following authorization in 37 CFR 1.71(e) is included at the beginning (preferably as the first paragraph) of the specification to be printed for the patent:
A portion of the disclosure of this patent document contains material which is subject to (copyright or mask work) protection. The (copyright or mask work) owner has no objection to the facsimile reproduction by anyone of the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all (copyright or mask work) rights whatsoever.
(D) Inclusion of a copyright or mask work notice after a Notice of Allowance has been mailed will be permitted only if the criteria of 37 CFR 1.312 have been satisfied.
The inclusion of a copyright or mask work notice in a design or utility patent application, and thereby any patent issuing therefrom, under the conditions set forth above will serve to protect the rights of the author/inventor, as well as the public, and will serve to promote the mission and goals of the U.S. Patent and Trademark Office. Therefore, the inclusion of a copyright or mask work notice which complies with these conditions will be permitted. However, any departure from these conditions may result in a refusal to permit the desired inclusion. If the authorization required under condition (C) above does not include the specific language "(t)he (copyright or mask work) owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records,…" the notice will be objected to as improper by the examiner of the application. If the examiner maintains the objection upon reconsideration, a petition may be filed in accordance with 37 CFR 1.181.


BPCC SGA President Erica Lawson (front row, second from far left – blue/white dress) and SGA Secretary/Treasurer Jacob Tucker (front row, far left – red shirt) pose yesterday at the Veteran's Hospital with Ms BPCC Ali Leone and four Marine Reserves who serve with Jacob.
On this Veteran's Day 2016, Bossier Parish Community College announced it has earned the 2017 Military Friendly® School designation by Victory Media, publisher of G.I. Jobs®, STEM JobsSM, and Military Spouse, for the sixth consecutive year.
This semester, BPCC is honored to have three military students serving as its Student Government Association officers – President Erica Lawson; Vice President Samantha Edmonds; and Secretary/Treasurer Jacob Aaron Tucker.
Lawson served our country for five years in the U.S Army; Edmonds served in the Air Force Reserves for four years; and Tucker is currently a Lance Corporal E-3 in the Marine Reserves. He has been the Reserves for two years, seven months.
For more information about BPCC's commitment to attracting and supporting military students, visit the College's website at www.bpcc.edu/veteraneducationservices. BPCC will be showcased along with other 2017 Military Friendly® Schools in the annual Guide to Military Friendly® Schools, special education issues of G .I. Jobs® and Military Spouse Magazine, and on militaryfriendly.com.


text:
Family General Store Coming to the
Lifestyle, Sebastopol, Shopping, Things To Do in Sonoma
Family General Store Coming to the Barlow
by Karen Kizer
Scout West County, a general store selling clothing and wares of interest to the whole family, will open at the Barlow in mid-June. Photo: morganausterweil.com/photography
Oliver and Grace Entrada with their dog Scout, the namesake of their new store, "Scout West County," opening at the Barlow in mid-June.
Slide 10 of 11
Scout, Grace and Oliver Entrada's dog and the namesake of their new store, Scout West County, which opens at The Barlow in mid-June. Photo: morganausterweil.com/photography
Grace Estrada, a Chicago Art Institute graduate, opened Kitty Hawk Gallery on Sebastopol's Main Street in 2014 because she was "craving" the aesthetics from home, a style of arts and products she describes as "a little out there."
In April, Estrada closed Kitty Hawk Gallery and joined forces with her illustrator/artist husband Oliver Estrada to create their expanded retail vision: a general store selling clothing and wares of interest to the whole family.
In mid-June the Estradas will open Scout West County at The Barlow in Sebastopol. The store will feature an expansion of the art and women's clothing sold at Kitty Hawk, with the addition of men's and children's clothing.
Oliver's original screen printing work in his Sebastopol studio led him toward finding ethically source products: from method of manufacturing to treatment of employees. He looks forward to bringing lines of products made in this manner, including his own printed tees and well-tailored, American-milled Billiam jeans.
Grace previously exhibited her own and Oliver's work at Kitty Hawk, as well as that of fellow art institute grads and, of course, local artists. Grace and Oliver say their "out there" style of artwork was accepted by the community, although Oliver says there was "some pushback."
Grace who paints "in a narrative style" said people had this image of her being "from Marin," and Oliver said that if you don't paint "a farm, a landscape or a farm animal" some people don't see you as being a Sonoma County artist.
The Estradas are currently focused on launching the store, but hope to one day feature their own line of furniture in classic and mid-century styles with their inspiration being Grace's parents who've made a career of designing furniture.
In keeping with the family theme, Grace's mother is a behind-the-scenes influence on the store. "I trust her eye," says Grace. And the namesake of Scout West County is the couple's beloved dog Scout, a three-year-old Samoyed who will fill the role of store dog and lead greeter.
Scout West County is open Sunday – Monday (closed on Tuesday) at The Barlow, 6780 Depot St. Suite 140, Sebastopol, 707-827-6045, scoutwestcounty.com. Follow Scout West County's story at: instagram.com/scoutwestcounty
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Tags: family general store sebastopol, scout county west sebastopol
Everything You Need for Grillin' It Sonoma Style
Up your Sonoma County barbecue game this summer.


text:
Q: How divide a string into array If I have the following plain string, how do I divide it into an array of three elements?
{["a","English"],["b","US"],["c","Chinese"]}

["a","English"],["b","US"],["c","Chinese"]

This problem is related to JSON string parsing, so I wonder if there is any API to facilitate the conversion.

A: use DataContract serialization http://msdn.microsoft.com/en-us/library/bb412179.aspx

A: I wrote a little console example using regex there is most likely a better way to do it.
static void Main(string[] args)
    {
        string str = "{[\"a\",\"English\"],[\"b\",\"US\"],[\"c\",\"Chinese\"]}";
        foreach (System.Text.RegularExpressions.Match m in System.Text.RegularExpressions.Regex.Matches(str, @"((\[.*?\]))"))
        {
            Console.WriteLine(m.Captures[0]);
        }
    }

A: ASP.NET MVC comes with methods for easily converting collections to JSON format. There is also the JavaScriptSerializer Class in System.Web.Script.Serialization. Lastly there is also a good 3rd party library by James Newton called Json.NET that you can use.

A: Remove the curly braces then use String.Split, with ',' as the separator.
Unfortunately, I never did JSON stuff so don't know a parsing library. Can't you let WCF do this stuff for you ?

A: using String.Split won't work on a single token as the string in each token also contain a string (if I understood the requirements, the array elements should end up being:

*

*["a","English"]

*["b","US"]

*["c","Chinese"]

If you use string.Split and use a comma as the delimiter the array will be made up of:

*

*["a"

*"English"]

*["b"

*"US"]

*["c"

*"Chinese"]

A JSON parser that I've read about but never used is available here:
http://james.newtonking.com/pages/json-net.aspx

redpajama_set_name: RedPajamaStackExchange


Russia will head to Euro 2016 this summer in the hope they can overcome the psychological barrier that has prevented them succeeding consistently since the fall of the Soviet Union. As the USSR, the team lifted two European Championship titles yet, as Russia, they have fallen on harder times ever since then.
Dzyuba's goals have been crucial to Russia of late with the Zenit Saint Petersburg forward netting seven times in qualifying and he carries the hopes of the nation on his shoulders. His domestic form this season has been impressive, netting 15 goals in the Russian Premier League and if Russia are to do well they will need him to be at his best.
After Fabio Capello's acrimonious departure, Leonid Slutsky offered to step in and manage both CSKA Moscow and the national team. With the Russian team in disarray upon his arrival, he reorganised the squad and guided them to the tournament in France by winning four straight qualifying matches. He has brought confidence back into the team and there is a belief he can guide Russia to the latter stages of the competition.


text:
CBRE: prime office occupancy costs in San Francisco rise by 9.0% in year to September 2015
In its latest review of global prime occupancy costs, 'Office space gets pricier as global economy edges up', CBRE noted that San Francisco (Peninsula) was in 9th position in its ranking of centres by increases in local currency occupancy costs. Occupancy costs in San Francisco (Peninsula) increased by 9.0%. With September 2015 occupancy costs of US$93.02 per square foot per annum, San Francisco (Peninsula) was by that measure the 19th most expensive centre overall. San Francisco (Peninsula) rose by 10 places in CBRE's ranking of the most expensive centres over the six months to September 2015.
Source : CBRE/ February 2016/San Francisco/OFF


text:
Homepage > VICTIMS > 8-YEAR-OLD GIRL DIED IN WEDDING NIGHT
Girl, 8, dies from internal injuries after wedding night in Yemen
Eight-year-old bride suffered internal injuries, bleeding, after marriage to 40-year-old man in Yemen. Forcing Yemeni girls into arranged weddings has long, troubled history.
A young girl in Yemen died after having sexual relations with a 40-year-old man who became her husband in an arranged marriage, according to media reports.
The 8-year-old was identified as "Rawan" and died from torn genitals and severe bleeding in the northwest city of Hardh, Gulf News reported, citing Yemen media accounts.
RELATED: 1 IN 4 ASIAN MEN HAVE RAPED A WOMAN: SURVEY
The child lived in a tribal area that borders Saudi Arabia. Human rights organizations have called for the arrest of the husband, the news site Al Bawaba said.
Yemen has often been criticized for allowing girls to be forced into arranged marriages.
RELATED: BREAKING NEWS: INDIAN COURT CONVICTS 4 IN FATAL GANG RAPE CASE
Human Rights Watch reports that 14% of girls in Yemen are married before age 15, with 82% married before they are 18.
The country passed a law in 2009 setting the minimum age for marriage at 17. It was later repealed after conservative lawmakers said it went against Islamic teachings.
dhastings@nydailynews.com
Women in Yemen demonstrate in 2010, supporting a law banning marriage for girls under age 17. The legislation, designed to address a longstanding practice of forcing girls into arranged weddings, was ultimately repealed.
An 8-year-old little child!
Why her life had to have been taken? Just because an old paedophile pervert man to have fun a little bit!!!
Try to imagine that this little child how much feared, how much cried and how much suffered until she died!!!


Since its founding twenty years ago the "Journal of Social History" has made substantial contributions to altering the way American historians look at and interpret their subject. It has served as a central outlet for new and exciting scholarship in social history, particularly European and American history but also Asian and Latin American as well. Under the editorship of Peter N. Stearns, the journal has published innovative work by many major American historians. "Expanding the Past" commemorates and highlights the achievements of the journal by republishing a selection of the most excellent articles that have appeared in the journal and that especially illustrate key features and trends in social history.
These important essays cover issues such as illiteracy, work and gender roles, the police, kleptomania, immigration, and domesticity. Topics such as the history of old age, the social history of women, and working class history are explored. The volume reveals how historians define and deal with the most recent phenomena such as disease symptoms, the integration of subject matter to conventional issues like politics, and an enlargement of the past to embrace new elements. This book is an introduction to looking at the characteristic topics, methods, and particular insights of social history.
Collectively, the essays represent some of the most vigorous and important work in this dynamic field of American historical research. They serve as an ideal vehicle for those readers who wish to further their understanding of this distinct approach to the past.


Whether it be Sunday brunch, a casual work lunch, or dinner celebrating a special occasion, at CW's Restaurant, we offer a wide range of delicious menu options to tantalize your taste buds and satiate your hunger. Our goal is to provide the best American fare in Michigan. While enjoying our delicious food, don't forget to try one of our signature cocktails. Browse our menu and discover what our talented chefs have to offer you and your guests. Do not hesitate to get in touch with us today if you have any questions about our menu, including substitutions and allergy restrictions. We look forward to feeding you and your loved ones.
Dried tart cherries, Bleu cheese, walnuts, delicious apples shuffled in spring lettuce. Recommend: Balsamic Vinaigrette dressing.
Onion soup with provolone cheese.
Served with a house salad and your choice of one side.
Tender chicken breast, cavatappi, and broccoli in a creamy sauce. No side included.
4 pieces served with fries and your choice of dressing.
Shaved sirloin, peppers, sauteed onions, and fresh mushrooms, with white cheese sauce served on grilled Texas toast.
Shaved sirloin piled on a bun with melted provolone cheese on Texas toast, served with au jus.
Classic corned beef with sauerkraut, Swiss cheese, 1000 island dressing on grilled marble rye.


Today is the first working day after China's National Day holiday.
The country's railway network carried an average of 10 million passengers every day during the week. The peak was on Friday, where 12.5 million trips were made by train as people returned from their vacations. Some 650 trains were added nationwide to meet demand.
There were also over 82,000 commercial flights that carried close to 10 million people during the 7-day holiday. That's 11.6 % higher than that of last year. Hot spots included Beijing, Shanghai, Lhasa and Urumqi.