text:
\section{Introduction}
Rendering plays a critically important role in various applications, \emph{e.g}\onedot} \def\Eg{\emph{E.g}\onedot virtual reality, interactive gaming, and movie production \emph{etc}\onedot} \def{\bm{s}}{\emph{vs}\onedot. High-quality and fast rendering techniques bring users realistic experience and make more applications possible. Recent neural rendering methods, represented by NeRF (neural radiance fields)~\cite{mildenhall2020nerf}, have shown great power for modeling 3D scenes with compact implicit representations and synthesizing high-quality novel-view images. However, conventional NeRF methods~\cite{mildenhall2020nerf,Barron_2021_ICCV} mainly focus on static scenes, while real-life scenarios usually involve object motions or topological changes. A series of subsequent NeRF works~\cite{pumarola2021d,park2021nerfies,li2021nsff,Tretschk_2021_ICCV,park2021hypernerf} improve radiance field construction towards dynamic scenes.

Besides, fast training and rendering speed of NeRF is needed in real-life applications. Conventional NeRF methods bear large time and computation cost to optimize the field networks, \emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot dozens of hours in general. Especially, most existing methods model dynamic scenes by introducing an additional deformation network with a similar scale of the radiance network, which maps point coordinates into a canonical space. This manner means much more cost for training and inferring dynamic fields. The cumbersome time cost impedes wide applications in real-life scenarios.

Representing scenes with explicit data structures shows great success in dramatically accelerating NeRF training and rendering \cite{Hedman_2021_ICCV,sun2021direct,yu_and_fridovichkeil2021plenoxels,muller2022instant}. However, it is challenging to represent dynamic scenes with explicit structures from two main aspects. On the one hand, these scenes involve complicated point motions where encoding temporal information is required. One direct and simple solution is to expand the voxel grids with an additional time dimension. However, this manner will undoubtedly increase memory cost significantly. On the other hand, there usually exist motions of different scales. Voxels with high resolutions locate in small grids, which fail to model large motions; voxels in large grids fail to capture details with small motions.

To tackle the above challenges, we propose a new dynamic radiance field method, named as TiNeuVox\@\xspace, by representing scenes with time-aware voxel features. To encode temporal information, we first build a highly compressed deformation network which maps 3D point coordinates into a coarse canonical space. Voxel features are queried with the transformed coordinates. We further enhance the temporal information by feeding time and coordinate embeddings into the latter radiance network. Thus deviation introduced by point mapping can be automatically suppressed by the neural network. Moreover, we propose a multi-distance interpolation method, where features are obtained from voxels with multiple distances. In this way, both small and large motions can be modeled even though only one single-resolution voxel features are constructed.

We summarize our contributions as follows.
\begin{itemize}
    \item We are the first to represent dynamic scenes with \textbf{optimizable} explicit data structures, which shows extremely high training efficiency.
    \item We encode coarse point motions with a tiny deformation network and enhance temporal information in the radiance network. A multi-distance interpolation technique is proposed to model both small and large motions with one single resolution of voxel features.
    \item We evaluate our method on both synthetic and real scenes, where our TiNeuVox\@\xspace achieves better or similar rendering quality with $8$-MB storage by taking only 8 minutes, $150\times$ faster than D-NeRF~\cite{pumarola2021d} and $192\times$ faster than Hyper-NeRF~\cite{park2021hypernerf}.
\end{itemize}

\section{Related Works}
\subsection{Neural Rendering for Dynamic Scenes}
The emergence of NeRF (neural radiance field)~\cite{mildenhall2020nerf} has greatly boosted the development of rendering techniques. A series of subsequent works improve NeRF from various aspects, \emph{e.g}\onedot} \def\Eg{\emph{E.g}\onedot anti-aliasing~\cite{Barron_2021_ICCV}, camera parameter optimization~\cite{wang2021nerf,lin2021barf}, rendering large-scale unbounded scenes~\cite{kaizhang2020}, and reconstructions from unstructured image collections~\cite{Martin-Brualla_2021_CVPR} \emph{etc}\onedot} \def{\bm{s}}{\emph{vs}\onedot.

Building radiance fields on dynamic scenes is one of the most important branch of improved NeRF which are tightly related to real-world scenarios. The key problem to solve dynamic-scene rendering lies in temporal information encoding. One stream of dynamic NeRF methods~\cite{li2021nsff,xian2020space,gao2021dynamic,du2021neural} model deformations in scenes by extending radiance fields with an additional time dimension. Due to prior knowledge lacking for structures in non-rigid scenes, additional geometry regularization and data modalities need to be introduced. Another class of methods~\cite{pumarola2021d,park2021nerfies,park2021hypernerf} introduce an additional deformation field to predict motions of points by mapping point coordinates into a canonical space, where large motions or geometry changes can be captured and learned.
Other methods improve dynamic neural rendering from various aspects, including distinguishing fore- and back-ground~\cite{tretschk2021non}, strengthening quality via depth information~\cite{attal2021torf} and producing sharper results by setting up key frames~\cite{li2021neural} \emph{etc}\onedot} \def{\bm{s}}{\emph{vs}\onedot. Most of current dynamic NeRF methods still bear cumbersome training cost. We dramatically accelerate the training speed by introducing optimizable explicit voxel features, while a compressed deformation network along with temporal information enhancement is designed. The overall framework achieves a good quality-speed trade-off via proper computation allocation on explicit and implicit representations.

\begin{figure*}[thbp]
    \centering
    \includegraphics[width=0.9\linewidth]{figs/frameworkv2.pdf}
    \vspace{-10pt}
    \caption{Overall framework of TiNeuVox\@\xspace. First, a deformation network $\Phi_d$ takes both point coordinates $\gamma(x, y, z)$ and encoded time embeddings $\Phi_t(\gamma(t_i)$ as input to obtain the shifted coordinates $(x', y', z')$. Then voxel features in grids with different sampling strides are queried and interpolated according to deformed coordinates. To enhance temporal information, coordinates $\gamma(x, y, z)$ and time embeddings $\Phi_t(\gamma(t_i)$ are further concatenated with interpolated voxel features, $\gamma(f_s)$, $\gamma(f_m)$, and $\gamma(f_l)$, which are finally fed into the radiance network to produce the density $\sigma$ and color ${\mathbf{c}}$.}
    \label{fig: framework}
\end{figure*}

\subsection{Neural Rendering Acceleration}
\paragraph{Rendering Acceleration}
Though conventional NeRF methods show high rendering quality, it bears high latency as a series of points along each ray need to be sampled and inferred for volume rendering. Some works propose to reduce inference times for acceleration by improving sampling strategies \cite{neff2021donerf,lindell2021autoint,piala2021terminerf,arandjelovic2021nerf,fang2021neusample} or introducing efficient rendering techniques \cite{sitzmann2021lfns}. \citet{Hedman_2021_ICCV,Yu_2021_ICCV,Garbin_2021_ICCV,Reiser_2021_ICCV,wizadwongsa2021nex} store properties like densities produced by pre-trained radiance fields into explicit data structures, \emph{e.g}\onedot} \def\Eg{\emph{E.g}\onedot voxel grids or MPIs (multiplane images). Only a few points need to be inferred by a small network for view-dependent color predictions. Though these methods have achieved real-time rendering performance, they still bear immense pre-training cost and cumbersome additional storage cost.

\paragraph{Convergence Acceleration}
Some methods explore to reduce training cost from the generalization perspective. \citet{Chen_2021_ICCV,yu2021pixelnerf,wang2021ibrnet,liu2022neuray,wang2022fourier} substantially pre-train NeRF on various scenes to obtain generalizable properties or features. \cite{deng2021depth} achieves faster training speed with external depth information. Some works~\cite{sun2021direct,yu_and_fridovichkeil2021plenoxels} propose to represent scenes with explicit voxel-grid features/properties and directly optimize these voxels for extremely fast convergence speed, reducing training time from hours to minutes. However, storage cost is significantly increased for storing voxel features. Recent works effectively reduce the storage cost via voxel hashing~\cite{niessner2013real,muller2022instant}, tensor decomposition~\cite{kolda2009tensor,chen2022tensorf} and bitrate dictionary lookup~\cite{vbnerf} while still maintaining surprisingly high training speed. These voxel-optimizable methods yet only focus on static scenes, where voxel features are direct to construct for only spatial information. We introduce optimizable explicit voxel features into dynamic scenes. Temporal information is encoded to obtain time-aware neural voxel features. Our TiNeuVox\@\xspace achieves similar or better rendering performance than previous dynamic NeRF methods with training time reduced from days to $8$ minutes.

\section{Method}
In this section, we first review methodologies of the original NeRF \cite{mildenhall2020nerf} in Sec~\ref{subsec: prel}. Second, we describe how we represent dynamic scenes with explicit voxel features in Sec.~\ref{subsec: neuvox}. Then we propose to encode temporal information along with voxel features in Sec.~\ref{subsec: temp_enc}. Finally, the overall framework and optimization procedures are presented in Sec.~\ref{subsec: frame_optim}.

\subsection{Preliminaries}
\label{subsec: prel}
Neural radiance fields are first proposed in \cite{mildenhall2020nerf}, which models 3D scenes by mapping the coordinate $(x,y,z)$ and view direction $(\theta, \phi)$ of each point in the space into its color ${\mathbf{c}}$ and density $\sigma$. The mapping function is usually instantiated as a neural network $\Phi_r$. This process can be formulated as
\begin{equation}
    {\mathbf{c}},\sigma = \Phi_{r}{(x,y,z,\theta, \phi)}.
\end{equation}

To get the expected color $C({\mathbf{r}})$ of the pixel in the image captured by the camera, a ray ${\mathbf{r}}(t)={\mathbf{o}}+t{\mathbf{d}}$ marching from the center of the camera to the pixel is involved, where ${\mathbf{o}}$ and ${\mathbf{d}}$ are the origin and direction of the ray respectively. $t$ denotes the distance from one point to the camera, which ranges from a pre-defined near bound $t_{n}$ to far bound $t_{f}$. The pixel color is rendered by sampling a series of points along the ray and performing the classical volume rendering~\cite{kajiya1984ray}:
\begin{equation}
\begin{aligned}
\label{eq: rendering}
    \hat{C}({\mathbf{r}}) &= \sum_{i=1}^N T_i(1 - \text{exp}(-\sigma_i \delta_i)){\mathbf{c}}_i,\\
    T_i &= \text{exp}(- \sum_{j=1}^{i-1} \sigma_j  \delta_j),
\end{aligned}
\end{equation}
where $\delta_i$ is the distance between the $i_\text{th}$ and $(i+1)_\text{th}$ sample point, $N$ denotes the number of sampled points. Eq.~\ref{eq: rendering} connects real 3D points with image pixels by accumulating colors ${\mathbf{c}}_i$ and densities $\sigma_i$ of sample points along the ray. Finally, radiance fields are optimized via gradient descent by minimizing the following loss:
\begin{equation}
    \label{eq: color_loss}
    \mathcal{L} = \lVert\hat{C}({\mathbf{r}}) - C({\mathbf{r}})\rVert^2_2 ,
\end{equation}
where $C({\mathbf{r}})$ denotes the groundtruth color of the pixel.

Besides, NeRF~\cite{mildenhall2020nerf} finds that details cannot be depicted by merely inputting $x,y,z$ coordinates and $\theta, \phi$ view directions. A positional encoding is introduced to map the input into a periodic formulation.
\begin{equation}
    \label{eq: pe}
    \gamma(x) =(sin(2^0x),cos(2^0x),...,sin(2^{L-1}x),cos(2^{L-1}x)),
\end{equation}
where $L$ is a hyperparameter that controls the highest frequency of the input.

\begin{figure}[t!]
  \centering
  \includegraphics[width=0.7\linewidth]{figs/interp.pdf}
  \caption{Illustration of multi-distance interpolation. Voxel features are interpolated from grids sampled with different strides.}
  \vspace{-10pt}
  \label{fig: interp}
\end{figure}

\subsection{Multi-Distance Interpolation with Neural Voxels}
\label{subsec: neuvox}
Conventional NeRF methods \cite{mildenhall2020nerf,Barron_2021_ICCV,pumarola2021d,park2021nerfies,park2021hypernerf}  build radiance fields with pure implicit representations, \emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot neural networks. Though this manner achieves promising rendering quality with high storage efficiency, it usually takes non-negligible time cost to optimize the fields, \emph{e.g}\onedot} \def\Eg{\emph{E.g}\onedot dozens of hours or even several days. To accelerate the convergence of radiance fields, we propose to represent scenes with explicit data structures except for implicit ones. To this end, \textbf{neural voxels} are introduced, which are a set of features organized as morphology of voxel grids. These features are designed to be further queried and inferred by neural networks to obtain certain properties, such as radiance and transmittance. As shown in Fig.~\ref{fig: framework}, a scene is represented with grids of neural voxels ${\mathbf{V}} \in {\mathbb{R}}^{C_v \times N_x \times N_y \times N_z}$, where $C_v$ denotes the channel number of each voxel feature, and $N_x$, $N_y$ and $N_z$ denote the length of three spatial dimensions.

To predict the property of one 3D point, neural voxels stored in eight vertices of the grid this point lies in are queried and interpolated trilinearly. The interpolated feature is then inferred by neural networks to predict the expected properties. Considering points in dynamic scenes may move with a dramatic motion trajectory, small grids of neural voxels have limited capacity to model these point movements. We propose a multi-distance interpolation method to model point motions with various scales. As shown in Fig.~\ref{fig: interp}, besides the smallest grid, voxel features are also interpolated from vertices of larger grids. This means the final features for inference not only come from nearest voxels, but also from sub- and subsub-nearest voxels. In this way, small motions can be modeled via near voxels while motions in a large region are perceived with farther voxels.

We implement the above process as follows. When performing interpolation, we sample neural voxels from the pre-built grids ${\mathbf{V}}$ with different strides $s_1, s_2, s_3, \dots$. Then features are trilinearly interpolated with several sampled voxel grids respectively. Finally, these features are concatenated and fed into neural networks. This process can be formulated as
\begin{equation}
\begin{aligned}
\label{eq: interp}
    {\bm{f}}   &= {\bm{f}}_1 \oplus \cdots {\bm{f}}_m \cdots \oplus {\bm{f}}_M,\\
    {\bm{f}}_m &= \text{interp} (x, y, z, {\mathbf{V}}[::s_m]),
\end{aligned}
\end{equation}
where $M$ denotes the total number of defined sampling strides, ${x, y, z}$ denote the coordinates of the 3D point.

Moreover, before being fed into the neural network for inference, these interpolated voxel features are first positional-encoded as in Eq.~\ref{eq: pe}. This manner is of critical importance for compressing neural voxels into small sizes while maintaining strong performance for modeling details, which is evaluated in experiments (Sec.~\ref{subsec: abla}).

\subsection{Temporal Information Encoding}
\label{subsec: temp_enc}
Dynamic scenes involve complicated point motions in the space. We propose to encode temporal information from two perspectives as follows.

\paragraph{Coarse Coordinate Deformation}
Like most previous implicit NeRF methods for dynamic scenes~\cite{pumarola2021d,park2021nerfies,park2021hypernerf}, we introduce a deformation network to shift coordinates of points which simulates the movement of points, but compress the network into a very small one. Denoting coordinates of one point as $(x, y, z)$ and the deformation network as $\Phi_d$, the coordinates are mapped into new ones according to encoded time embeddings ${\bm{t}}_i$:
\begin{equation}
\label{eq: deform}
    x', y', z' = \Phi_d(x, y, z, {\bm{t}}_i).
\end{equation}

We use only 3-layer MLPs (multilayer perceptrons) as the deformation network, which is much smaller than ones adopted in previous dynamic-scene methods. As this deformation network is applied on every sample point which accounts for a large potion of computation cost, we compress this network from both widths and depths for accelerating optimization and rendering processes.

\paragraph{Temporal Information Enhancement}
As the aforementioned deformation network is severely compressed in our method, it may introduces unavoidable deviation for coordinate shifting due to its limited capacity. Besides, this deviation will be aggravated as neural voxels are queried according to point coordinates. Thus, final error not only comes from interpolation weights but also from the queried vertices. As shown in Fig.~\ref{fig: framework}, to alleviate this deviation/mismatch, we propose to further enhance the temporal information by concatenating interpolated features in Eq.~\ref{eq: interp} with positional-encoded coordinates and neural-encoded temporal embeddings. All the concatenated features and embeddings are further fed into neural networks, where the above deviation will be automatically suppressed.

\begin{table*}[thbp]
\newcommand{\ding{55}}{\ding{55}}
\centering
\caption{Comparisons about training/memory cost and rendering quality on synthetic scenes.}
\label{tab: synth-comp}
\begin{tabular}{l|cc|cc|ccc}
\toprule\textbf{Method} & \textbf{w/ Time Enc.} & \textbf{w/ Explicit Rep.} &\textbf{Time}&\textbf{Storage}&\textbf{PSNR} $\uparrow$ & \textbf{SSIM} $\uparrow$ & \textbf{LPIPS} $\downarrow$ \\
\midrule
NeRF~\cite{mildenhall2020nerf} & \ding{55} & \ding{55} & $\sim$ hours &  5 MB &19.00&  0.87& 0.18 \\
DirectVoxGO~\cite{sun2021direct} & \ding{55} & \checkmark & 5 mins & 205 MB & 18.61 & 0.85 & 0.17  \\
Plenoxels~\cite{yu_and_fridovichkeil2021plenoxels} & \ding{55} & \checkmark & 6 mins & 717 MB & 20.24 & 0.87 & 0.16 \\
\midrule
T-NeRF~\cite{pumarola2021d} & \checkmark & \ding{55}& $\sim$ hours & -- &29.51 &0.95 & 0.08\\
D-NeRF~\cite{pumarola2021d} & \checkmark & \ding{55} & 20 hours& 4 MB& 30.50 &0.95 & 0.07\\
\midrule
TiNeuVox\@\xspace-S (ours) & \checkmark & \checkmark & 8 mins  & 8 MB  &30.75& 0.96&0.07 \\
 TiNeuVox\@\xspace-B (ours) & \checkmark &\checkmark & 28 mins & 48 MB & \textbf{32.67} & \textbf{0.97}  & \textbf{0.04} \\
\bottomrule
\end{tabular}
\end{table*}

\begin{figure*}[thbp]
    \centering
    \includegraphics[width=\linewidth]{figs/syth_3imgs.pdf}
    \vspace{-20pt}
    \caption{Qualitative comparisons between D-NeRF~\cite{pumarola2021d} and our TiNeuVox\@\xspace on synthetic scenes. ``GT'' denotes groundtruth.}
    \label{fig: syth-imgs}
\end{figure*}

\subsection{Overall Framework and Optimization}
\label{subsec: frame_optim}
Our overall framework is illustrated in Fig.~\ref{fig: framework}. First, the time stamp is encoded by a two-layer MLPs $\Phi_t$, and then fed into a compressed deformation network $\Phi_d$ along with coordinates of the sampled point $(x, y, z)$ to obtain shifted coordinates $(x', y', z')$ as in Eq.~\ref{eq: deform}. The shifted coordinates are used for querying and interpolating neural voxels with a multi-distance manner as in Eq.~\ref{eq: interp}. Then, interpolated neural voxels ${\bm{f}}$, encoded time embeddings ${\bm{t}}$ and original coordinates $(x, y, z)$ are all concatenated to be fed into a narrow and shallow radiance network $\Phi_r$ to obtain the final density and color:
\begin{equation}
\begin{aligned}
\label{eq: radiance_net}
    {\mathbf{c}},\sigma = \Phi_r(\gamma({\bm{f}}), {\mathbf{t}}, \gamma(x', y', z'), \gamma({\bm{d}})),
\end{aligned}
\end{equation}
where ${\bm{d}}$ represents the ray direction and is fed into $\Phi_r$ in the latter stage.

For each ray, we sample points evenly from the near bound to the far bound. By performing the above computation on each sampled point along a ray, the final predicted color can be obtained via volumne rendering as Eq.~\ref{eq: rendering}.
Parameters of all the neural voxels and networks can be optimized by minimizing the distance between predicted colors and groundtruth colors of image pixels as Eq.~\ref{eq: color_loss}. Besides, following \citep{sun2021direct} we adopt two additional loss functions for regularization. One supervises predicted colors of all the sampled points along the ray with the groundtruth image pixel color for stablization. The other one builds a cross-entropy loss on $T_{N+1}$ to distinguish fore- and back-ground, where $T_{N+1}$ denotes the accumulated transmittance for an additional point as computed in Eq.~\ref{eq: rendering}.

Besides, once we obtain the predicted density values with the radiance network, we filter points for the rest part of neural network inference with a pre-defined density threshold. This manner can effectively reduce cost of view-dependent color prediction but rarely affect the rendering quality.

\vspace{-2pt}
\section{Experiments}
In this section, we first provide our implementation details in Sec.~\ref{subsec: implem}. Then we show evaluation results and compare TiNeuVox\@\xspace with other related methods in Sec.~\ref{subsec: eval}. We further perform a series of ablation studies on the key components of TiNeuVox\@\xspace and provide detailed results and analysis in Sec.~\ref{subsec: abla}.

\subsection{Implementation Details}
\label{subsec: implem}
We implement our framework as Fig.~\ref{fig: framework} mainly with PyTorch~\cite{paszke2019pytorch} and provide two versions, \emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot TiNeuVox\@\xspace-S (small) and TiNeuVox\@\xspace-B (base). For TiNeuVox\@\xspace-S, neural voxels are constructed with a resolution of $100^3$ and a channel number of $4$; neural voxels in TiNeuVox\@\xspace-B are at $160^3\times6$. For acceleration, we set the initial resolutions of voxel grids as $\frac{1}{8}$ of the given ones, which are doubled after $2k$, $4k$, and $6k$ iterations during training. The channel dimension $C_h$ of hidden layers is set as $64$ for TiNeuVox\@\xspace-S and $256$ for TiNeuVox\@\xspace-B. The dimension $C_t$ of time embeddings is set the same as each positional-encoded voxel feature, \emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot $20$ for TiNeuVox\@\xspace-S and $30$ for TiNeuVox\@\xspace-B. The frequency number $L$ of positional encoding (Eq.~\ref{eq: pe}) is set as $10$ for coordinates $(x,y,z)$, $4$ for view direction ${\bm{d}}$, $8$ for the time stamp $t$, and $2$ for neural voxels. Points are sampled with a step of half the voxel size along each ray for volume rendering.

\begin{figure*}[thbp]
    \centering
    \includegraphics[width=\linewidth]{figs/vrig_imgs.pdf}
    \vspace{-15pt}
    \caption{Qualitative comparisons between TiNeuVox\@\xspace and other methods on real dynamic scenes.}
    \label{fig: real-imgs}
\end{figure*}

For optimization, an Adam~\cite{kingma2015adam} optimizer is used with $(0.9, 0.99)$ $\beta$ values. In each iteration, $4096$ rays are randomly sampled from the whole dataset to form a batch. The initial learning rate is set as $8\times10^{-2}$ for all voxels features, $6\times10^{-4}$ for parameters of the deformation network $\Phi_d$, and $8\times10^{-4}$ for parameters of the other MLPs, which finally decays by $0.1$ with an exponential schedule. The color regularization loss and background cross-entropy loss are weighted by $10^{-2}$ and $10^{-3}$ respectively. To further compress the neural voxel storage, we convert them into the half-precision floating-point format for the last $1k$ iterations. It takes $20k$ iterations in total on one single GeForce RTX 3090 GPU for every scene evaluated in this paper unless specified.

\vspace{-5pt}
\subsection{Evaluation}
\label{subsec: eval}
In this section, we evaluate our method on both synthetic and real dynamic scenes for novel view synthesis. Experimental results are compared with other state-of-the-art (SOTA) methods both quantitatively and qualitatively.

\paragraph{360$^\circ$ Synthetic Scenes}
We adopt the dataset provided by D-NeRF \cite{pumarola2021d} for synthetic-scene evaluation, containing $8$ scenes with dynamic objects under large motions and realistic non-Lambertian materials. Each scene contains $50 - 200$ images for training and $20$ images for testing. To fairly compare with D-NeRF~\cite{pumarola2021d}, each image is trained and rendered at $400 \times 400$ pixels.

\begin{table}[t!]
\setlength{\tabcolsep}{2pt}
\centering
\caption{Quantitative comparisons on real dynamic scenes.}
\label{tab: real-comp}
\begin{tabular}{l|c|cccccccc|cc}
\toprule
\textbf{Method} & \textbf{Time} & \textbf{PSNR$\uparrow$}  & \textbf{MS-SSIM$\uparrow$} \\
\midrule
    NeRF~\cite{mildenhall2020nerf} & $\sim$ hours & 20.1&0.745 \\
    NV~\cite{lombardi2019neural} & $\sim$ hours & 16.9&0.571 \\
    NSFF~\cite{li2021nsff} & $\sim$ hours & 26.3& 0.916\\
    Nerfies~\cite{park2021nerfies} & $\sim$ hours & 22.2& 0.803\\
    HyperNeRF~\cite{park2021hypernerf} & 32 hours$^\dagger$
    & 22.4& 0.814\\
    \midrule
    TiNeuVox\@\xspace-S (ours) & 10 mins & 23.4  & 0.813 \\
    TiNeuVox\@\xspace-B (ours) &30 mins &24.3  &0.837  \\
\bottomrule
\end{tabular}
\begin{tablenotes}
\footnotesize
\item[] \textdagger \; Time cost of HyperNeRF~\cite{park2021hypernerf} is estimated according to descriptions in their paper but on TPUs.
\end{tablenotes}
\end{table}

As shown in Tab.~\ref{tab: synth-comp}, we provide two versions of our TiNeuVox\@\xspace as described in Sec.~\ref{subsec: implem}. Three metrics are used for evaluation, \emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot peak signal-to-noise ratio (PSNR), structural similarity (SSIM)~\cite{wang2004image} and
learned perceptual image patch similarity (LPIPS)~\cite{zhang2018unreasonable}. Conventional NeRF~\cite{mildenhall2020nerf} and the fast-convergence method DirectVoxGO~\cite{sun2021direct} Plenoxels~\cite{yu_and_fridovichkeil2021plenoxels} are all targeted at static scenes, which are unable to model point motions and show bad results. T-NeRF~\cite{pumarola2021d} and D-NeRF~\cite{pumarola2021d} are two dynamic NeRF methods, which model time information via additional input dimension or deformation fields. D-NeRF shows promising rendering quality but it takes about 20 hours on one GPU for per-scene training\footnote{We test this time cost by reproducing D-NeRF~\cite{pumarola2021d} on one single RTX 3090 GPU for fair comparison.}. In comparison, our TiNeuVox\@\xspace-S takes only $8$ minutes to finish one scene learning and achieves similar rendering performance with three evaluation metrics. For the larger version TiNeuVox\@\xspace-B, a quite better rendering quality can be achieved ($32.67$ average PSNR) and the optimization can still be finished with only $28$ minutes. It is worth noting that though explicit representations are introduced for acceleration, storage cost of TiNeuVox\@\xspace is pretty small. TiNeuVox\@\xspace-S takes only $8$ MB, even similar with pure-implicit D-NeRF; TiNeuVox\@\xspace-B takes $48$ MB, much smaller than previous explicit methods~\cite{sun2021direct,yu_and_fridovichkeil2021plenoxels}.
Moreover, we provide qualitative comparisons in Fig.~\ref{fig: syth-imgs}, where our TiNeuVox\@\xspace shows finer and more accurate details than D-NeRF though with much less training time.

\paragraph{Real Scenes}
We further evaluate our method on real non-rigidly deforming scenes provided by HyperNeRF~\cite{park2021hypernerf}. To obtain images from these scenes, a multi-view data capture rig is built with 2 Pixel 3 phones rigidly attached roughly 16cm apart. More details can be referred to in \citet{park2021nerfies,park2021hypernerf}. We perform experiments on four scenes released by \citet{park2021hypernerf}, \emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot Broom, 3D Printer, Chicken, and Peel Banana. Following \citet{park2021hypernerf}, PSNR and MS-SSIM~\cite{wang2003multiscale} are used as evaluation metrics\footnote{We find LPIPS~\cite{zhang2018unreasonable} values reported in \cite{park2021hypernerf} hard to reproduce so we omit this evaluation metric.}. Each image is trained and rendered at half of 1080p resolutions, \emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot $960 \times 540$ pixels, for quantitative evaluation. To fairly compare with HyperNeRF~\cite{park2021hypernerf}, qualitative results are obtained at full-HD with roughly $1920\times1080$ pixels, taking $40k$ iterations while HyperNeRF takes $1M$ iterations.

As shown in Tab.~\ref{tab: real-comp}, we compare with several highly related neural rendering works towards real dynamic scenes. Taking $192\times$ less training time, our TiNeuVox\@\xspace achieves similar rendering performance with the previous SOTA method HyperNeRF~\cite{park2021hypernerf}. Noting that though NSFF~\cite{li2021nsff} achieves higher evaluation metrics, our TiNeuVox\@\xspace and HyperNeRF shows better qualitative rendering quality as in Fig.~\ref{fig: real-imgs}. This phenomenon is also observed in \citet{park2021hypernerf} as quantitative metrics are usually sensitive to small shifts which are yet not obvious to humans. However, our rendered images are slightly more blurred than HyperNeRF. We deduce that far more iterations ($1M$) and additional regularization losses for real dynamic scenes in HyperNeRF matter. We would like to further explore these regularization techniques in future, which are compatible with our frameworks.

\subsection{Ablation Study}
\label{subsec: abla}
In this section, we perform a series of experiments to study key components and factors involved in our method to better understand the mechanism and demonstrate the effectiveness. Following experiments are performed on all the synthetic dynamic scenes \cite{pumarola2021d} and averaged metric values are reported.

\paragraph{Temporal Information Encoding}
We study three components for encoding time information, \emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot coordinate deformation $\Phi_d$, temporal information enhancement as in Fig.~\ref{fig: framework}, and encoding time embeddings with the neural network $\Phi_t$. As shown in Tab.~\ref{tab: abla-key-comps}, all of the three components is of critical importance to the final rendering performance. Besides, we visualize rendered images produced by several ablated settings in Fig.~\ref{fig: compts-imgs}. Removing any of these components causes evident damages for modelling details.

\begin{table}[t!]
\newcommand{\ding{55}}{\ding{55}}
\centering
\caption{Ablation study about components for encoding time information with TiNeuVox\@\xspace-B, \emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot deforming coordinates, enhancing temporal information, and encoding neural time embeddings.}
\setlength{\tabcolsep}{1pt}
\label{tab: abla-key-comps}
\begin{tabular}{ccc|ccc}
\toprule
 \begin{tabular}{c} \textbf{Deform}\\\textbf{Coords.}\end{tabular}& \begin{tabular}{c}\textbf{Enhance}\\\textbf{Temp. Info.} \end{tabular} &
 \begin{tabular}{c}\textbf{Enc. Neural}\\
\textbf{Time Embeds.}\end{tabular}&
 \textbf{PSNR}  $\uparrow$ & \textbf{SSIM} $\uparrow$ & \textbf{LPIPS} $\downarrow$ \\
\midrule
\checkmark & \checkmark & \checkmark & \textbf{32.668} & \textbf{0.971} & \textbf{0.041} \\
\midrule
\ding{55}& \checkmark & \checkmark & 29.684& 0.956 & 0.065\\
\midrule
\checkmark &  \ding{55} &\checkmark & 31.473 &0.968 & 0.045 \\
\midrule
\checkmark & \checkmark &\ding{55} &  32.384 & 0.971 & 0.044\\
\bottomrule
\end{tabular}
\end{table}

\begin{figure}[t!]
    \centering
    \includegraphics[width=\linewidth]{figs/comp.pdf}
    \caption{Qualitative comparisons by ablating three time-encoding components of TiNeuVox\@\xspace. ``DC'' denotes deforming coordinates, ``TIE'' denotes temporal information enhancement, and ``NTE'' denotes neural time embeddings.}
    \label{fig: compts-imgs}
\end{figure}

\begin{table}[t!]
\newcommand{\ding{55}}{\ding{55}}
\centering
\caption{Ablation study on neural voxels with various resolutions and sampling strides for interpolation. ``Res.'' denotes the resolution of neural voxels.}
\setlength{\tabcolsep}{3pt}
\begin{tabular}{cc|cc|ccc}
\toprule
\textbf{Res.} & \textbf{Strides} & \textbf{Time} & \textbf{Storage} & \textbf{PSNR} $\uparrow$ & \textbf{SSIM} $\uparrow$ & \textbf{LPIPS} $\downarrow$ \\
\midrule
$100^3$ & 1 & 7 mins & 8 MB& 30.279&0.953 &0.072 \\
$100^3$ & 1, 2, 4 &8 mins & 8 MB&30.746& 0.956& 0.067 \\
\midrule
$160^3$ & 1 &10 mins &  32 MB& 30.624& 0.960& 0.072\\
$160^3$ & 1, 2, 4 &12 mins &32 MB & 31.498 & 0.964 & 0.062 \\
\midrule
$256^3$ & 1 &15 mins&  128 MB&30.100& 0.961& 0.082 \\
$256^3$ & 1, 2, 4 & 21 mins& 128 MB&31.693 & 0.968 & 0.061  \\
$256^3$ & 1, 2, 4, 8 & 23 mins& 128 MB & 31.792 & 0.969 & 0.056 \\
\bottomrule
\end{tabular}
\label{tab: abla-interp}
\end{table}

\paragraph{Multi-distance Interpolation on Different Resolutions}
We study effectiveness of the proposed multi-distance interpolation (MDI) and show results in Tab.~\ref{tab: abla-interp}. Experiments with a single $1$ sampling stride equals to interpolation without the multi-distance manner. For the small resolution setting with $100^3$, MDI brings a $0.467$ PSNR promotion. It can be observed the larger resolutions are, the bigger advantages of MDI are, \emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot $0.874$ for $160^3$ and $1.593$ for $256^3$. This is because larger the resolution is, a smaller region each grid can represent. Noting that for $256^3$, each grid is too small to capture complete motions without MDI. It is proved that MDI can effectively promote the capacity of modeling different scales of motions while taking non-negligible time and storage cost.

\begin{table}[t!]
\newcommand{\ding{55}}{\ding{55}}
\centering
\caption{Quantitative study about positional encoding on neural voxels with TiNeuVox\@\xspace-B. $\#$Freqs. denotes the frequency number of positional encoding. $C_v$ denotes the channel number of neural voxels.}
\setlength{\tabcolsep}{4pt}
\label{tab: abla-pe}
\begin{tabular}{cc|cc|ccc}
\toprule
\textbf{\#Freqs.} & $\bm C_v$ & \textbf{Time} & \textbf{Storage} & \textbf{PSNR} $\uparrow$ & \textbf{SSIM} $\uparrow$ & \textbf{LPIPS} $\downarrow$\\
\midrule
2 & 6 & 28 mins & 48 MB & \textbf{32.668} & \textbf{0.971} & \textbf{0.041}\\
0 & 6 &  25 mins &48 MB  &32.255& 0.970& 0.051\\
0 & 12 &  28 mins & 94 MB& 32.440&0.971 & 0.045 \\
4 & 6 &  31 mins & 48 MB &32.111 & 0.970 & 0.047\\
\bottomrule
\end{tabular}
\end{table}

\begin{figure}[t!]
    \centering
    \includegraphics[width=\linewidth]{figs/pe.pdf}
    \caption{Qualitative studies about positional encoding on neural voxels.}
    \label{fig: pe-imgs}
\end{figure}

\paragraph{Positional Encoding on Neural Voxels}
We observe that applying positional encoding (PE) as in Eq.~\ref{eq: pe} on neural voxels plays an important role in compressing voxel channel dimensions $C_v$. Removing PE on neural voxels results in dramatic performance degradation as in Tab.~\ref{tab: abla-pe} and details missing as in Fig.~\ref{fig: pe-imgs}. We then enlarge $C_v$ to $12$. Even though the storage cost increases to $94$ MB, the rendering performance is still worse than the smaller $C_v = 6$ with $2$-frequency PE. Above experiments demonstrate that PE on neural voxels has great power on modelling details with limited channel dimensions, which requires no additional parameters. We further experiment with a larger frequency number $4$ which leads to a worse result.

\section{Conclusion and Discussion}
In this paper, we propose a fast neural rendering framework TiNeuVox\@\xspace targeted at dynamic scenes. Different from static scenes, dynamic ones involve complex point/object motions. We construct time-aware neural voxels to represent scenes, where temporal information is encoded with a highly compressed deformation network and enhanced in the radiance network. A multi-distance interpolation method is proposed to model motions of various scales. TiNeuVox\@\xspace can achieve extremely fast training speed with low storage cost.

We believe our work can shed light on neural rendering acceleration for dynamic scenes. How to equip pure-spatial neural voxels with temporal information is an interesting and valuable question. Our framework is a preliminary exploration and has achieved promising performance on most previous benchmarks. There still leave some critical topics for future work, \emph{e.g}\onedot} \def\Eg{\emph{E.g}\onedot regularization techniques for complicated real scenes, applications on higher-resolution scenarios, and further introducing compressing/pruning techniques like voxel hashing~\cite{niessner2013real,muller2022instant} and tensor decomposition~\cite{chen2022tensorf} \emph{etc}\onedot} \def{\bm{s}}{\emph{vs}\onedot.

\begin{acks}
The authors would like to thank Prof. Angela Dai for her voice recorded in the presentation video and Liangchen Song for his valuable comments and discussions.
\end{acks}

\bibliographystyle{ACM-Reference-Format}

redpajama_set_name: RedPajamaArXiv


Formerly titled "The Original Weekender"
Nice weight, wear it all year.
For over 85 years, McGregor Industries has been crafting high-quality socks and legwear. As a family operated company, McGregor is rooted in a tradition of premium craftsmanship and innovation. These guiding principles have allowed McGregor to become the number one brand of better socks and legwear in Canada, as well as a marketer of socks and legwear in 75 countries around the world.
McGregor has evolved into a global organization with sales and distribution centres around the world, and offices in Toronto, New York, London and Hong Kong. Mansour's is the best place to purchase McGregor Socks.


Help children to pursue their dreams.
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Imagine waking up in a motel room, bleary-eyed and yawning, to see a strange shadow cast on the curtains. Opening them, you find yourself eyeball to glassy eyeball with a blood-matted deer hanging from the second-floor balcony.
"It was very few and far in-between when it happened," says Michael Pouliot, 41, whose family owns the hunter/fisherman-friendly Terrace Bay Inn. "Half the time, they pack socks in their gun cases. They are here to relax and party with friends . . . it's more about getting away from the house. Maybe a third of them go out to hunt."
Whether Pouliot's guests are packing firearms, socks or fishing tackle, however, his 71-unit cream-colored motel hosts a range of amenities to keep antlered beasts off the balconies and fish scales out of the drains. And his business isn't alone in its in-house wild game facilities. A fish-cleaning room and angler icebox behind the front desk are not only en vogue, but a necessary competitive asset.
"Today, you have to have a fish-cleaning room, especially if you're on the south end [of town]. Within the last 10 or 15 years, it's become quite a walleye fishing area up here. People come up a long way to fish these kinds of waters," Pouliot says. "This is a fishing mecca here."
In Michigan's Upper Peninsula on Green Bay, outdoors enthusiasts of the plaid-and-fluorescent-orange set are a seasonal source of income for mom and pop motels. Autumn's hunting season, flanked by fishing seasons in the summer and winter, bring a flood of visitors each year, Pouliot says.
In particular, the last five years has brought more than a 20 percent increase in angler traffic, says Mike Furmanski, president of Bay de Noc Great Lakes Sports Fishermen.
"This area is getting to be pretty well known for its walleye fishing," Furmanski says. "It's more a wilderness area, undeveloped. . . . Opening Day [of hunting season] is a big deal up here too. It's a big draw up here."
And that means friendly competition among the innkeepers to attract visitors with the amenities they value most.
Buck poles, for example. The crossbar apparatus for hanging game goes up at the south end of the motel during hunting season. They are positioned right outside the front office, so desk clerks can keep an eye out for thieves.
To lure anglers, Pouliot has opened his own boat launch, an enhancement that he spent years planning and building. Guest rooms in the Terrace Bay Inn already prominently display photocopied instructions telling guests about the fish-cleaning room at the north end of the motel and the freezer behind the checkout desk.
"We have butcher blocks and sinks in an enclosed and heated room, so ice fishermen can come in and warm up," Pouliot says. "That's important."
Five years ago, Pouliot says, they had problems with customers cleaning fish in the bathroom sinks and bringing boat supplies such as gas cans into the rooms to thwart sticky-fingered passersby. Others brought in charcoal grills.
"It was just dangerous stuff to store in the room," he says. "You have to spell everything out for them."
And when fish stains and odors get into the carpet and linens, Pouliot says, you have to start from scratch and "dry clean everything," so it made sense to build convenient fish-cleaning facilities and nudge guests to use them.
A few miles south, Dan Kotowski's 22-unit Bay View Motel also hosts fishermen and provides an outdoor fish-cleaning shack, as he calls it, with electricity and running water. "When I bought the Bay View nine years ago, [the fish-cleaning facility] was one of the first things I put in," he says.
As a fishing enthusiast himself, not to mention vice president of the Bay De Noc Great Lakes Sport Fishing Club, Kotowski has a particular appreciation of details that can make an angler's day.
"It's the little things," he says. "If they forget their Ziplock storage bags, we keep some behind the counter -- they can use ours free of charge."


text:
High Profile Settlements, Strengthened Data Security Orders, and COPPA: The FTC's 2019 Privacy and Data Security Update
February 28, 2020 By Kathleen Benway and Privacy, Cyber & Data Strategy Team
Each year the Federal Trade Commission (the "FTC" or "Commission") publishes a report on its activities with respect to consumer privacy and data security during the prior year. On February 25, 2020, the Commission released its 2019 Privacy and Data Security Update. The update contains a summary of the FTC's enforcement, advocacy, and rulemaking actions as well as its activities with respect to its privacy and security-related workshops, consumer education and business guidance, and international engagement. The update is a useful way to see what the FTC focused on in the prior year and where to expect continued interest. Some highlights from the update are provided below.
In the enforcement space, the FTC update spotlights its two most high-profile settlements to date: Facebook and Equifax. First, in July 2019 the FTC and the Department of Justice's announced a joint settlement with Facebook based on allegations that the company's misrepresentations and consumer privacy failures violated its 2012 order. The 2019 settlement order imposed a record-setting $5 billion penalty and included a number of provisions designed to change Facebook's overall approach to privacy. The settlement is currently pending approval by the United States District Court for the District of Columbia. Also, in July 2019, the FTC announced a settlement with Equifax for alleged data security violations that affected 147 million people. The settlement included a payment of up to $700 million to help consumers affected by the breach and was part of a global resolution with a consumer class action, the Consumer Financial Protection Bureau, and 50 states and territories.
The FTC's enforcement actions over the past year with respect to data security incidents also highlight the Commission's efforts to strengthen its data security orders, including through increased specificity, increased accountability of third-party assessors, and improved corporate governance on data security issues. Each category of improvement is reflected in seven data security orders issued by the FTC over the past year against companies in a range of industries: ClixSense (pay-to-click survey company), i-Dressup (online games for kids), DealerBuilt (car dealer software provider), D-Link (Internet-connected routers and cameras), Equifax (credit bureau), Retina-X (monitoring app), and InfoTrax (service provider for multilevel marketers).
The FTC's update also makes clear the FTC's continued focus on the Children's Online Privacy Protection Act ("COPPA") in 2019 and beyond. In September 2019, the FTC and New York Attorney General settled with Google, and its subsidiary YouTube over allegations it collected personal information, including in the form of persistent identifiers, from viewers of child-directed channels without first notifying parents and getting their consent. The $170 million judgment is the largest civil penalty under COPPA. In 2019 he FTC also settled charges against Musical.ly, now known as TikTok, for $5.7 million for illegally collecting personal information from children on a child-directed app. The FTC also announced it was seeking comments on the effectiveness of the 2013 amendments to the COPPA Rule and hosted a workshop in October 2019 to discuss whether additional changes are needed.
The FTC update describes other areas of focus, including credit reporting and financial privacy, Do Not Call and telemarketing, and international enforcement. You can read the entire update here.
About Kathleen Benway
Kathleen Benway is a partner with Alston & Bird's Litigation & Trial Practice Group. She concentrates her practice on government investigations and corporate compliance related to consumer protection issues, including privacy, security, advertising, and marketing. She is a former chief of staff at the FTC's Bureau of Consumer Protection.
[Read Bio]

redpajama_set_name: RedPajamaCommonCrawl


text:
Company News and Events • News • News and Events
First Microsoft Dynamics NAV 2016 Live in the World – Here in Singapore!
Published On: November 13th, 2015 / Categories: Company News and Events, News, News and Events / Tags: 2016, First, First Microsoft Dynamics NAV 2016 Live in the World in Singapore, Live, Microsoft Dynamics NAV, Singapore, World /
NaviWorld Singapore is proud to successfully implement the first Microsoft Dynamics NAV 2016 project. The project went live on 2 Nov 2015, just 3 weeks after this version was released on the 5 Oct 2015.
This is probably the first Microsoft Dynamics NAV 2016 Live Customer in the world after it was officially released.
About NaviWorld Singapore Project Policy
NaviWorld Singapore has a policy to provide the latest ERP version to our customers.
We implement our ERP projects to be easy to upgrade, minimum risk and maximum benefits to our customers.
We will suggest to customer to go live with the latest version whenever it is feasible, by explaining the pros and cons of the new version. This is despite the fact that the customers may have signed the implementation contract with us for the older version of the ERP.


text:
Tourism in Republika Srpska
EU integration
The Srpska Times
Home Balkan Thaci accuses Belgrade of "arming Serbs in north"
Thaci accuses Belgrade of "arming Serbs in north"
TheSrpskaTimes
Kosovo President Hashim Thaci has said that Serbia was "arming Serbs in the north, as part of a plan to partition Kosovo."
He added that Kosovo's institutions had information that what was referred to as "the Russian train" – a Russian-manufactured Serbian Railways train on the Belgrade-Kosovska Mitrovica line that was prevented from reaching its destination earlier this month – transported "arms and members of paramilitary formations whose goal was to enter Kosovo and destabilize it."
"At the same time, we know of a Serb plan to partition Kosovo and we also know that Serbia is arming Serbs in northern Kosovo," said Thaci.
As proof for this claims, Thaci said there was "evidence that Serbia is using the model used by Russia in Ukraine."
"They are sending civilians but also people in uniforms to the north, and are arming Serbs with firearms and other weapons, so they can be ready to defend themselves from alleged Albanian attacks," he said.
Thaci then called on Kosovo Serbs "not to fall under the influence" and also "not to become the victims of Belgrade's nationalist policy which caused great pain to the Serbs in the past."
"Instead of calls for war from Serbia, I'm calling the Kosovo Serbs to peace and dialogue. Instead of ethnic and territorial partition I call on them to join NATO and the EU," Thaci told Deutsche Welle.
Speaking on Tuesday in Brussels, where he was taking part in a new round of the EU-sponsored Kosovo talks, Serbian President Tomislav Nikolic dismissed the claims that Serbs in northern Kosovo were being armed – previously presented in some Albanian language media – and called them "nonsensical."
"That is absolutely not correct, because that's impossible to do in a territory that is completely under control of EULEX, UNMIK, KFOR, and a police where Albanians are represented. That's impossible also because all crossings are under full control," said Nikolic.
He added that he "doesn't know why Albanians came up with this story – unless it's to find excuses for attacking that territory."
"I don't recommend to them to endanger peace and security anywhere in Kosovo and Metohija, especially not in the north," Nikolic said.
Source: B92
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The Srpska Times is the first English language magazine originating from Republika Srpska.
Council of Europe to Croatia: Cyrillic Alphabet Insufficiently Present
Serbia: The First Doses of the Vaccine Will Arrive by the End of the Year and Will Be Free
Indictment Confirmed, Thaci Goes to the Hague
Coronavirus in Srpska: 240 New Cases
TheSrpskaTimes - 25.12.2020
Since the last report on the epidemiological situation in the Republic of Srpska, in the last 24 hours, at the Institute of Public Health...
Šeranić: The First Vaccines in the First Quarter of 2021.
Coronavirus in Srpska: 233 New Cases Today
"Ivo Andrić Grand Prix" to Peter Handke
Mićo Mićić Passed Away
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text:
Trending Now in News & Politics
DC Metro
Heroes of the Crisis: A Helping Hand for Hungry Families
To Martha's Table's Kim R. Ford, "the resilience and the optimism in the community is so inspiring."
Written by Rob Brunner
| Published on October 15, 2020
Photograph by Jeff Elkins
This article is part of Washingtonian's feature "Heroes of the Crisis." From medical professionals to social-justice activists to culinary stars, here are some of the people who have helped get us through these most challenging of times. Read about the 15 people making a difference during the pandemic here.
Kim R. Ford
President and CEO, Martha's Table
How she's helping:
Covid and the resulting economic crisis have been devastating for communities served by the Ward 8–based nonprofit. After Martha's Table's education centers moved to virtual learning, the organization helped sustain affected families with grocery gift cards; diapers and formula; and a cash assistance program. And its food-distribution efforts have ramped up significantly. "The need has just drastically jumped," says Ford, who estimates that the nonprofit went from handing out 500 bags of groceries a day to more than 2,000.
What gives her hope:
The outpouring of support for what the nonprofit does. "Folks have been volunteering in droves. They're like, 'I don't have any way to support you financially right now, but I want to give back.' There is a lot of death, there's a lot of despair, violence—domestic violence is jumping, physical violence is jumping. You've got a health pandemic that's taking people out, you've got an economic pandemic and racial-injustice pandemic going on. So the resilience and the optimism in the community is so inspiring."
Toughest challenge:
"Personally, it's just been trying to lead in a compassionate way when people are going through so much that you don't know about. You just have to come to everything with compassion and grace. A crisis brings out all kinds of stuff in people. You just try to be there for people."
Biggest win:
Connecting with the community. Two years ago, Martha's Table moved from its longtime home on 14th Street, Northwest, to Southeast DC. As a result of the crisis, "far more folks in our direct community know us now—know who we are and that we're going to stand alongside them. We're going to respond when things get tough."
How she unwinds:
Playing basketball by herself early every morning. "I shoot baskets and run up and down the court, and that clears my mind for the day."
Who's inspiring her:
The people she interacts with daily while doing her job. "It's very humbling. To know that they are going through everything that everyone else is, plus X plus Y plus Z, and still keep getting up and coming back—that, to me, is inspiring. I feed off their energy. It's amazing because they're optimistic. They just know that, one way or another, we're going to get through this."
This article appears in the October 2020 issue of Washingtonian.
More: Heroes of the Crisis
Rob Brunner
Politics and Culture Editor
A DC native, Rob Brunner moved back to the city in 2017 to join Washingtonian. Previously, he was an editor and writer at Fast Company and other publications. He has also written for the New York Times Magazine, New York, and Rolling Stone, among others. He lives with his family in Chevy Chase DC.
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text:
Gadila singaporensis är en blötdjursart som beskrevs av Sharp och Henry Augustus Pilsbry 1898. Gadila singaporensis ingår i släktet Gadila och familjen Gadilidae. Inga underarter finns listade i Catalogue of Life.

Källor

Tandsnäckor
singaporensis

redpajama_set_name: RedPajamaWikipedia


This item is not currently available for purchase. It may be a few weeks up to several months before they are back in stock. If you would like to be notified when this item returns please send a blank eMail to backorders@kultofathena.com with subject ''GB0205'' and we'll let you know as soon as it is back.
The Crusader`s surcoat provided a ready distinction between friend and foe in a melee. This reconstruction is distinctive in its black and white contrasts. Crafted in lined heavy linen, the surcoat is designed to avoid excessive extra weight and heat retention. Sleeveless. Fits up to 52'' chest measured over clothing. 55'' Long.


text:
Reflection and Choice
A blog of the School of Humanities at Houston Baptist University
What's A Constitution Among Friends?
March 6, 2015 By Chris Hammons in Politics Tags: boehner, Constitution, Iran, Netanyahu, Obama	1 Comment
The press has blown a gasket the past few days crowing about the breach of protocol related to Benjamin Netanyahu's address to Congress. The charge, and it's not without merit, is that "Ambassadors and other public ministers" must be received by the president, as stated in Article Two, Section Three of the Constitution. As head of state, the argument goes, it's the president's right to meet with other heads of state. By inviting Netanyahu to speak and bypassing the White House, critics contend that Speaker Boehner ignored the Constitution.
The Constitution is rather vague on this issue. The language of the relevant section states that presidents "shall receive ambassadors and other public ministers." In the context of how Article Two is framed, Section Three lists what might be considered duties of the president rather than prerogatives (which are dealt with in the previous section). In this sense, while protocol may have been violated by not clearing Netanyahu's visit with the White House, the refusal of the president to meet with the head of a foreign nation could also be construed as a violation of constitutional duty. As read, the president is required in his symbolic capacity as our leader to meet with foreign heads of state. He might refuse, but such action can only be interpreted as a diplomatic statement, and an antagonistic one at that.
As much as we look to the president as our Commander-in-Chief in foreign affairs, the Founder's didn't preclude Congress from involvement in foreign policy. The president is required by the Constitution to seek Senate approval for treaties, and the power to declare war is a Congressional power. In fact, the power to receive foreign dignitaries was originally vested with the legislature under our first constitution, the Articles of Confederation. It was moved to the executive branch largely out of a sense of efficiency, it being much easier to organize a meeting between two people rather than a meeting of a large body. Hamilton, in Federalist #69, calls the move a matter of convenience more than anything else, and the entire protocol for such receptions more a demonstration of "dignity than authority."
Nonetheless, Boehner's actions are unprecedented, and the breach of etiquette has created an opportunity for those who treat the Constitution lightly to harangue those who take it seriously. The Obama administration is not known for its reverence of constitutionalism and the rule of law. President Obama's past remarks regarding the burden of separation of powers, his extensive use of executive orders, and his growing use of executive agreements to bypass the legislative branch have clearly struck a nerve with Congressional Republicans. Only a few days back, the White House said it will not seek Congressional approval of its nuclear deal with Iran. When pressed by a reporter as to why not, the White House responded that "we can't put in place an additional hurdle for that agreement to overcome here at the 11th hour." In essence, following the Constitutional process is just too much trouble.
Politics doesn't happen in a vacuum. Congressional Republicans have come to the point where they've grown tired of what they see as blatant dismissal of the rule of law by the president. The impact of inviting a foreign head of state to address Congress without presidential approval was intentional. Congress was trying to send a message.
Boehner has a phone and a pen as well, and he's willing to use them.
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It has been a blessing for us at Charismatic Renewal Services for the Archdiocese of Boston to welcome Brendan Walsh.
Brendan is a Catholic Evangelist. His message is rooted deeply in the teaching of the Church. He helps people to know they can encounter the Living God, in the life of the Church, particularly in the Sacraments. Brendan has been blessed with a gift of healing, through Brendan's prayer the Lord confirms the proclamation of the Word by blessing people with emotional, spiritual and physical healing.


I have a two kicker competition 12" subwoofers in a box in my trunk, connected to a 1200 watt, 4 ohm sony xplod amp...and I do not believe either of these to be the problem in the rumbling(exhaust like) sound coming from my speakers, even when the volume is down. Music plays as normal, this extra noise is just bothering me...I believe it may be the ground wire...I have the ground wire screwed down in my trunk underneath the carpet...would ground wire placement be a good thing to try?
What watt amp would be best for my 1100 watt 10 inch xplod?
Most likely cause is heat. The clearances behind the radio are often not sufficient to provde the proper air coolling that the higher power radios need. The heat sinks on the rear of the radio require a certain amount of air flow to dissipate the heat generated under high load conditions. If you add summer heat to the equation, you can have the radio going into thermal cutout to protect it circuity.
Bottom Mount Depth (inches) 10"
Cutout Diameter or Length (inches) 12"
First are they in the same box? If so and they are sharing the same air space running one amp to both is best andwill perform better. One woofer could be over powering the other and or you could have a woofer problem. A lso your wiring coul be problem. When amp is bridged it changes the OHM reading and output your other woofer may not be rated at or able to handle a lower Ohm rating also it coul blow that woofer if it hasn't already hapened . Be careful. check woofers independently for operation and check the non performing woofer on an unbridged power source or amp. Hope this helps. Write back if not.
off the same amp? you have two way different sounding speakers man. you can buy 2 identical speakers from the same company and brand and series and they will play differently every time. you have 2 speakers with nothing in common so you will hear a difference and maybe interference because of the different ohm levels.
Bridging your amplifier @ 4 ohm will yield the most power.... with the subwoofers you have.
You must wire them in Parallel.
Positive on both subwoofers to + on amplifier.
Negative on both subwoofers to - on amplifier.
The lowest impedence possible with 8 ohm woofers is 4 ohms.
Thank you for using FixYa - a FixYa rating is appreciated for taking the time to answer your FREE question.
That's not a 1000w amp. It's a 400 watt amp when loaded to it's lowest rated load. If you have one speaker per channel (the only safe load if you have single 4 ohm coil speakers), you're only getting 165 watts/speaker. It's probably distorted because you're driving it to clipping.
I have 2 kicker comp 12" subs in a ported box and i noticed my left sub was rattling alot and when i disconnected it i found that it was that the sub does not work but the other one is making it vibrate. when i tried testing just the one with the problem nothing came out no vocals or vibrations. my wiring on my amp is good but the sub wont work at all.
Have a manual for Kicker 05C104 Car Subwoofer?


Posted on May 8, 2010 by Jerry McDaniel | Comments Off on L.A. Teacher Calls for Mexican Revolt in the U.S.
As you watch this video, keep in mind, this man is a teacher in the California education system.
Comments Off on L.A. Teacher Calls for Mexican Revolt in the U.S.


text:
Health & Pharmaceuticals›
State of Health›
Heart transplant list in the United Kingdom (UK) 2018
Number of individuals on the heart transplant list in the United Kingdom (UK) as of March 2018
by Conor Stewart, last edited Jun 11, 2019
As of March 31 2018, there were 277 patients on the active transplant list in the United Kingdom (UK) waiting for a heart transplant. Most of those waiting were residents in England, with a total of 228 patients, while Scotland had 30 patients on the heart transplant list, followed by ten and nine individuals waiting in Wales and Northern Ireland respectively.
Heart transplants carried out in the UK
In the period 2017/18, 191 heart transplants were carried out in the United Kingdom as a whole. The number of heart transplants in the UK has not increased from the previous year. The majority of these (164), were carried out in England. A further sixteen in Scotland were performed, the second highest in the UK.
Comparisons with Europe
The UK has a low rate of heart transplant procedures compared to other European nations at 2.9 heart transplants per million population. This ranked as 19th in the EU and was significantly lower than the rate of, for example, Slovenia which at 11.4 per million population was the highest.
Number of individuals on the waiting list
Northern Ireland 9
Year ending March 31, 2018
Financial year ending March 31, 2018
Organ donation in the United Kingdom
State of Health
Global number of organ transplantations 2016
Deceased organ donor rate in selected countries 2017
Patient deaths on organ transplant waiting list in the United Kingdom (UK) 2018
Liver transplants in the United Kingdom (UK) 2017/18
Everything On "Organ donation in the United Kingdom" in One Document: Edited and Divided into Handy Chapters. Including Detailed References.
Statistics on "Organ donation in the United Kingdom"
Donors in the UK
Donors by demographics
Organ transplant list
Deceased organ donors
Organ transplants
Estimated number of organ transplantations worldwide in 2016Global number of organ transplantations 2016
Distribution of deceased and living liver donors worldwide in 2015, by regionGlobal deceased and living liver donors by region 2015
Rate of deceased organ donors in select countries in 2017 (per million population)Deceased organ donor rate in selected countries 2017
Rate of patients with transplanted organs in selected countries in 2017 (per million population)Global number of patients with organs transplanted in select countries 2017
Total number of deceased donor organ transplants in the United Kingdom (UK) from 2010 to 2018Number of organ transplants in the United Kingdom (UK) 2010-18
Number of deceased donor organ transplants in the United Kingdom (UK) from 2017 to 2018, by organ donatedNumber of deceased donor organ transplants in the UK 2018, by organ donated
Number of patients who died while on the organ transplant waiting list in the United Kingdom (UK) from 2015 to 2017, by organ type Number of patient deaths on organ transplant waiting list in the United Kingdom 2017
Proportion of opt-in registrations on the NHS organ donation register (ODR) in the United Kingdom (UK) from 2010 to 2018Share of people registered on organ donation register in the UK 2010-2018
Number of organ transplants in the United Kingdom (UK) from 2005 to 2018Organ transplants in the United Kingdom (UK) 2005-2018
Number of deceased donors in the United Kingdom (UK) from 2005 to 2018Deceased donors in the United Kingdom (UK) 2005-2018
Number of living organ donors in the United Kingdom (UK) from 2005 to 2018Living organ donors in the United Kingdom (UK) 2005-2018
Number of potential and actual organ donors in the United Kingdom (UK) in 2018Organ donor population in the United Kingdom (UK) 2018
Number of patient deaths on the organ transplant waiting list in the United Kingdom (UK) in 2018, by organPatient deaths on organ transplant waiting list in the United Kingdom (UK) 2018
Share of organ donors in the United Kingdom (UK) in 2018, by ageOrgan donation population in the United Kingdom 2018, by age
Share of organ donors in the United kingdom (UK) in 2018, by body mass indexOrgan donation population in the United Kingdom (UK) 2018, by body mass index
Share of organ donors in the United kingdom (UK) in 2017/18, by cause of deathOrgan donation population in the United Kingdom 2017/18, by cause of death
Share of organ donors in the United Kingdom (UK) in 2017/18, by ethnicityOrgan donation population in the United Kingdom (UK) 2017/18, by ethnicity
Share of organ donors in the United Kingdom (UK) in 2017/18, by blood groupOrgan donation population in the United Kingdom (UK) 2017/18, by blood group
Share of organ donors in the United kingdom (UK) in 2017/18, by gender and donation type*Organ donation population in the United Kingdom (UK) 2017/18, by gender
Total number of patients on transplant list in the United Kingdom (UK) from 2011 to 2018Number of patients on organ transplant list in the United Kingdom (UK) 2011-2018
Number of individuals on the kidney transplant list in the United Kingdom (UK) as of March 2018Kidney transplant list in the United Kingdom (UK) 2018
Number of individuals on the pancreas transplant list in the United Kingdom (UK) as of March 2018Pancreas transplant list in the United Kingdom (UK) 2018
Number of individuals on the heart transplant list in the United Kingdom (UK) as of March 2018Heart transplant list in the United Kingdom (UK) 2018
Number of individuals on the lung transplant list in the United Kingdom (UK) as of March 2018Lung transplant list in the United Kingdom (UK) 2018
Number of individuals on the liver transplant list in the United Kingdom (UK) as of March 2018Liver transplant list in the United Kingdom (UK) 2018
Number of kidney and pancreas patients on the active transplant waiting list in the United Kingdom (UK) in 2018Kidney and pancreas patients: active waiting list in the United Kingdom (UK) 2018
Number of cardiothoracic patients on the active transplant waiting list in the United Kingdom (UK) in 2018Cardiothoracic patients: active waiting list in the United Kingdom (UK) 2018
Number of deceased kidney donors in the United Kingdom (UK) in the year to March 2018, by countryDeceased kidney donors in the United Kingdom (UK) 2017/18
Number of deceased pancreas donors in the United kingdom (UK) in the year to March 2018, by countryDeceased pancreas donors in the United Kingdom (UK) 2017/18
Number of deceased heart donors in the United Kingdom (UK) in the year to March 2018Deceased heart donors in the United Kingdom (UK) 2017/18
Number of deceased lung donors in the United Kingdom (UK) in the year to March 2018, by countryDeceased lung donors in the United Kingdom (UK) 2017/18
Number of deceased liver donors in the United Kingdom (UK) in the year to March 2018, by countryDeceased liver donors in the United Kingdom (UK) 2017/18
Number of deceased intestinal donors in the United Kingdom (UK) in the year to March 2018, by countryDeceased intestinal donors in the United Kingdom (UK) 2017/18
Number of kidney transplants in the United Kingdom (UK) in the year to March 2018*Kidney transplants in the United Kingdom (UK) 2017/18
Number of pancreas transplants in the United Kingdom (UK) in the year to March 2018Pancreas transplants in the United Kingdom (UK) 2017/18
Number of heart transplants in the United kingdom (UK) in the year to March 2018*Heart transplants in the United Kingdom (UK) 2017/18
Number of lung transplants in the United Kingdom (UK) in the year to March 2018*Lung transplants in the United Kingdom (UK) 2017/18
Number of liver transplants in the United Kingdom (UK) in 2017/18Liver transplants in the United Kingdom (UK) 2017/18
Number of heart transplants in Spain in 2016, by autonomous community
Number of heart transplants in Catalonia 2012-2015
Leading Spanish hospitals by heart transplants performed in 2016
Number of heart transplants in Cantabria 2012-2015
Number of heart transplants in Murcia 2012-2015
Number of heart transplants in Asturias 2012-2015
Intestinal transplants in the United Kingdom (UK) 2017/18
Organ transplants: donation after brain death in the United Kingdom 2017/18, by organ
Organ transplants: donation after cardiac death in the United Kingdom 2018, by organ
Organ transplant: number reported as functioning in the United Kingdom 2018, by organ
Number of heart transplants performed in the Netherlands 2014-2018
Number of kidney transplants in Murcia 2012-2015
Donors for corneal tissue in Spain 2011-2015
Number of lung transplants in the Region of Valencia 2012-2015
Leading Spanish hospitals by intestinal transplants performed in 2016
Number of patient deaths on the organ transplant waiting list in Spain 2015-2017
Organ transplantation activity per million population in Russia 2015-2017
Number of patients active on organ transplant waiting list in Belgium 2015-2017
UK construction Key Performance Indicators client value satisfaction 2015
Austria: enterprises in the manufacture of games & toys industry 2008-2015
Organ donations and transplants
Organ transplantation in Europe
Organ Donation and Transplantation Activity Report 2017/18
GODT - Organ Donation and Transplantation Activities 2016
Organ transplants in Italy
ONT - International Figures on Donation and Transplantation - 2017
Donate Life America 2018 Annual Update
Cardiovascular disease in Sweden
Cardiovascular disease in the U.S.
Estimated number of organ transplantations worldwide in 2016
Distribution of deceased and living liver donors worldwide in 2015, by region
Rate of deceased organ donors in select countries in 2017 (per million population)
Rate of patients with transplanted organs in selected countries in 2017 (per million population)
Total number of deceased donor organ transplants in the United Kingdom (UK) from 2010 to 2018
Number of deceased donor organ transplants in the United Kingdom (UK) from 2017 to 2018, by organ donated
Number of patients who died while on the organ transplant waiting list in the United Kingdom (UK) from 2015 to 2017, by organ type
Proportion of opt-in registrations on the NHS organ donation register (ODR) in the United Kingdom (UK) from 2010 to 2018
Number of organ transplants in the United Kingdom (UK) from 2005 to 2018
Number of deceased donors in the United Kingdom (UK) from 2005 to 2018
Number of living organ donors in the United Kingdom (UK) from 2005 to 2018
Number of potential and actual organ donors in the United Kingdom (UK) in 2018
Number of patient deaths on the organ transplant waiting list in the United Kingdom (UK) in 2018, by organ
Share of organ donors in the United Kingdom (UK) in 2018, by age
Share of organ donors in the United kingdom (UK) in 2018, by body mass index
Share of organ donors in the United kingdom (UK) in 2017/18, by cause of death
Share of organ donors in the United Kingdom (UK) in 2017/18, by ethnicity
Share of organ donors in the United Kingdom (UK) in 2017/18, by blood group
Share of organ donors in the United kingdom (UK) in 2017/18, by gender and donation type*
Total number of patients on transplant list in the United Kingdom (UK) from 2011 to 2018
Number of individuals on the kidney transplant list in the United Kingdom (UK) as of March 2018
Number of individuals on the pancreas transplant list in the United Kingdom (UK) as of March 2018
Number of individuals on the lung transplant list in the United Kingdom (UK) as of March 2018
Number of individuals on the liver transplant list in the United Kingdom (UK) as of March 2018
Number of kidney and pancreas patients on the active transplant waiting list in the United Kingdom (UK) in 2018
Number of cardiothoracic patients on the active transplant waiting list in the United Kingdom (UK) in 2018
Number of patients on the active organ waiting list in the United Kingdom (UK) in 2018, by organ
Number of deceased kidney donors in the United Kingdom (UK) in the year to March 2018, by country
Number of deceased pancreas donors in the United kingdom (UK) in the year to March 2018, by country
Number of deceased heart donors in the United Kingdom (UK) in the year to March 2018
Number of deceased lung donors in the United Kingdom (UK) in the year to March 2018, by country
Number of deceased liver donors in the United Kingdom (UK) in the year to March 2018, by country
Number of deceased intestinal donors in the United Kingdom (UK) in the year to March 2018, by country
Number of kidney transplants in the United Kingdom (UK) in the year to March 2018*
Number of pancreas transplants in the United Kingdom (UK) in the year to March 2018
Number of heart transplants in the United kingdom (UK) in the year to March 2018*
Number of lung transplants in the United Kingdom (UK) in the year to March 2018*
Number of liver transplants in the United Kingdom (UK) in 2017/18
Number of heart transplants performed in Spain in 2016, by autonomous community
Number of heart transplants performed in Catalonia from 2012 to 2015
Leading Spanish hospitals by number of heart transplants performed in 2016
Number of heart transplants performed in Cantabria from 2012 to 2015
Number of heart transplants performed in Murcia from 2012 to 2015
Number of heart transplants performed in Asturias from 2012 to 2015
Number of intestinal transplants in the United Kingdom (UK) in the year to March 2018
Number of organ transplant donations after brain death in the United Kingdom (UK) in 2017/18, by organ
Number of organ transplant donations after cardiac death in the United Kingdom (UK) in 2018, by organ
Number of organ transplants reported as functioning in the United Kingdom (UK) as of March 2018, by organ*
Total number of heart transplants performed in the Netherlands from 2014 to 2018
Number of kidney transplants performed in Murcia from 2012 to 2015
Number of corneal donors in Spain from 2011 to 2015
Number of lung transplants performed in the Region of Valencia from 2012 to 2015
Leading Spanish hospitals by number of intestinal transplants performed in 2016
Number of patients who died while on the organ transplant waiting list in Spain from 2015 to 2017, by organ type
Rate of organ transplantation activity per million population in Russia from 2015 to 2017, by organ type
Total number of patients on an organ transplant waiting list in Belgium from 2015 to 2017, by organ*
Satisfaction of clients with value for money in the United Kingdom (UK) construction industry 2001-2015
Number of enterprises in the manufacture of games and toys industry in Austria from 2008 to 2015


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text:
Q: Auto-vectorization of loop containing comparisons I'm trying to use the Visual C++ 2013 auto-vectorizer to make the following loop vectorized (/arch:AVX2) but the compiler refuses and gives the following message:
info C5002: loop not vectorized due to reason '1100'

This reason code means
Loop contains control flow—for example, "if" or "?".

I have tried to split the comparisons and the final assignment into a separate loop but that seems inefficient when there are intrinsics available for performing comparisons on floating point values.
Why should the compiler treat comparisons as flow control, and what can I change in the implementation so that the compiler will vectorize this function?
void triplets_positive(
    const std::uint64_t count,
    double * const a,
    double * const b,
    double * const c,
    std::uint64_t * const all_positive)
{
    for (std::uint64_t i = 0; i < count; ++i)
    {
        // These >= operations make the loop not vectorisable because
        // they introduce control flow.
        std::uint64_t a_pos = (a[i] >= 0.0);
        std::uint64_t b_pos = (b[i] >= 0.0);
        std::uint64_t c_pos = (c[i] >= 0.0);

        all_positive[i] = a_pos & b_pos & c_pos;
    }
}

A: Unfortunately, this appears to be either a bug or limitation in the Visual C++ 2013 compiler. Other compilers make use either of the CMPPD instruction (AVX/AVX2) or CMP*PD instructions (SSE2).
Compilers that successfully vectorise this loop include:

*

*Visual C++ 2017

*Visual C++ 2015

*Clang + LLVM (Apple LLVM version 8.1.0 (clang-802.0.42))

While it's theoretically possible to write the comparison as bitwise operations, that's counterproductive and the best option is to upgrade to another compiler.

redpajama_set_name: RedPajamaStackExchange


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text:
English Conversation Practice - Earthquake
SPEAK WITH JIM
SPEAK WITH SUE
JIM: "Honey, are you ok?"
SUE: "Yes, are you ok?"
JIM: "Yeah. I've never felt an earthquake before?"
SUE: "Me too. That was scary. We've experience hurricanes, but never an earthquake."
JIM: "I'm going to look around and see how bad the damage is."
SUE: "Be careful. There could be aftershocks."
JIM: "I know. Don't go outside. The power is down. There could be power lines on the ground."
SUE: "Ok. Where are the dogs?"
JIM: "I found them. They're both under the bed."
SUE: "They must be scared."
JIM: "I wish we could watch the news. I want to know how much damage this earthquake caused."
SUE: "Let me find the radio. We can listen to the news."
JIM: "I think it's in the garage."
SUE: "Do we have any batteries for it?"
JIM: "Yeah. Let me get them."
SUE: "I knew the big one was coming one day, but we weren't prepared."
JIM: "Yeah, that was a big mistake."
SUE: "We better pack an emergency kit for next time."
1. What did Jim and Sue experience for the first time?
A hurricane
An earthquake
A tornado
A house fire
2. Why can't they watch the news?
They don't have a TV
The power is down
The news is not on right now
Their TV is broken
3. What do they need for their radio?
4. What did they not prepare for an emergency?
An emergency kit
1. An earthquake
2. The power is down
3. Batteries
4. An emergency kit


text:
Forty billionaires must think otherwise.
Forty billionaires have just pledged to give away at least half their wealth to charity, concerning which a few observations.
Scripture says it is easier for a camel to pass through the eye of a needle than for a rich man to enter the kingdom of Heaven (capitalized because, as Ralph de Toledano pointed out, it's a place — you know, like Scarsdale). We could note in passing, but will not, that there is no suggestion that a rich woman would be similarly challenged.
Viewed from Scripture's vantage point, the forty billionaires may simply be taking the necessary steps to make the needle's eye larger or the camel, and their riches, smaller.
Is it fair — is it accurate — to say that these people who are giving away billions are being generous?
During the 1968 presidential campaign, William F. Buckley Jr. remarked that the Democratic candidate, Hubert Humphrey, had already promised the American people everything and that, since beyond everything there is only nothing, Humphrey was now promising the people exactly: nothing.
There's a bit of that here too. If we assume that even half a billion dollars is more than enough to live on, indeed more than enough to satisfy the dreams of avarice, then any additional wealth can mean to him that possesses it only nothing; in which case what these generous people are giving away is also: nothing.
At least nothing that means anything to them. Which again raises the question: What is generosity?
But perhaps that's just being churlish. How is a rich man supposed to be generous? What can he do? Is it possible for him to sacrifice? Perhaps it is best just to understand that that's his business.
There are other miscellaneous points. One of them is that the donors will get tax deductions for their gifts, the meaning of which is unclear since they have so much money that taxes have no impact on their lives — which, not incidentally, tends to diminish their resistance to high taxes, which is not good for the rest of us.
Another point is that it is almost certain that the country would be better served if these billionaires kept their money and continued to invest it. Investing is an activity at which they have proved to be exceptionally skilled, and their investments would be far more likely to produce jobs and products — real benefits — than anything any charitable institution could possibly do with the funds.
A third point is the curious collectivity of the operation. Why did they get together to make a joint announcement? Why not just make their decisions in the privacy of their privy chambers? Were they looking for glory (which would suggest that having billions is not satisfying), or setting an example (for whom?)?
There is at least one potential significant benefit of the gifts: the funds will be deployed by private people, not government, which would surely waste it. But this is only a potential benefit: most of the donors are liberals, and it is possible, even likely — perhaps certain — that the money will go to the usual left-wing causes. With good luck, it will be wholly wasted; with bad luck it will do significant damage to limited constitutional government (the only real hope of the poor). The relevance of either scenario to generosity being — what?
Still, the gifts do focus on one aspect of modern democratic life that the big-government types like to avoid. The donors are private people who will be fulfilling their own obligations to the poor.
It is part of liberal dogma that "we must look after the poor," by which, of course, liberal politicians mean they will take our money by force and deploy in ways they see fit.
Joe Biden, Exhibit A, spoke in 1988 of "changing the attitude of Americans about what their responsibilities are to the poor, about what their responsibilities are to other people."
What is this man talking about? Americans are an exceedingly generous people, as has been amply documented by Arthur Brooks, in his book Gross National Happiness and in a piece he wrote for the American Enterprise Institute, of which he is the president.
Here are some of the points he made:
• "In 2006, Americans gave about $295 billion to charity."
• "Charitable giving has generally risen faster than the growth of the American economy for more than half a century."
• "Most estimates place the percentage of American households that make monetary contributions each year at 70 to 80 percent, and the average American household contributes more than $1,000 annually."
• "About a third of individual gifts go toward sacramental activities, primarily supporting houses of worship. The rest goes to secular activities, such as education, health, and social welfare."
• "No developed country approaches American giving. For example, in 1995 (the most recent year for which data are available), Americans gave, per capita, three and a half times as much to causes and charities as the French, seven times as much as the Germans, and 14 times as much as the Italians."
• "Tax deductibility is actually irrelevant for most people. IRS records show that only about a third of people who file tax returns itemize their deductions."
• "When we measure monetary giving as a percentage of income in order to ascertain the level of one's 'sacrifice,' we find a surprising result: it is low-income working families that are the most generous group in America, giving away about 4.5 percent of their income on average. This compares to about 2.5 percent among the middle class, and 3 percent among high-income families."
• "Self-described 'conservatives' in America are more likely to give — and give more money — than self-described 'liberals.'" (Isn't that delicious!?)
It is apparent that Americans have not needed the example of billionaires to teach them how to be generous.
If we think there are questions about the generosity of billionaires' giving away their own money, what are we to make of politicians giving away our money?
The question becomes increasingly urgent as the ability of government to spend money diminishes relative to the budgets of the faux-eleemosynary programs the politicians concoct.
It is not possible to count quickly the government programs that give money to the poor; they are, roughly speaking, innumerable: food stamps; children's health insurance; the Women, Infants, and Children Program; college student aid; Temporary Assistance for Needy Families; federal foster care; et cetera; et cetera; et cetera.
What we have discovered is that it is as difficult to stop the politicians from taking money from the productive sector and giving it to the nonproductive sector — in the name of charity — as it is for a camel to pass through the eye of a needle. That is becoming, or has become, the central problem of our time.
Of course, "in the name of charity" (or any similar formulation, such as meeting one's "responsibilities to the poor") is Congressional nonsense. The recipients of federal grants can be charities, but the taxpayers aren't being charitable because they are acting under coercion; and Congress isn't being charitable because the funds don't belong to Congress.
If the acts of the forty billionaires serve to remind us of the inescapably individual nature of charity, their gifts will be worth trillions, and our debt to them inestimable.