text:
\section{Introduction}

In the recent decade, deep learning has demonstrated its power in many different cognitive tasks that were historically believed challenging to conceptualize and quantify with mathematical models, such as image recognition, object detection, semantic segmentation, machine translation, and art generation. With the help of massive datasets \cite{imagenet_cvpr09}, specially designed network architectures \cite{He_resnet} have been very efficient in learning how to perform complex nonlinear feature engineering, capture and master the intrinsic stochastic nature of those tasks, and approximate the human cognitive learning process.

Despite the abundance of success stories in the world of cognitive tasks, the other world of tasks filled with problems and systems that are driven by mathematical logics (i.e., first principle laws, well-defined governing differential equations/models, deterministic or stochastic), still relies on conventional analytical and numerical simulation including finite-difference, finite-volume and finite-element methods. These methods discretize space and time domains into small cells and intervals, transform the partial differential equations into linear and non-linear algebraic problems, and solve those problems numerically. Consider for instance the discrete static 2D Poisson equation which uses simple 3x3 2D kernels $\bigl( \begin{smallmatrix}4 & -1 & 0 \\ -1 & 4 & -1 \\ 0 & -1 & 4\end{smallmatrix}\bigr)$ and $\bigl( \begin{smallmatrix}1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{smallmatrix}\bigr)$ to convert the second-order partial differential equation into coupled linear systems that can be solved at O($n^3$) time complexity (where $n\times n$ is the size of the discretized 2D grid). In a dynamic system, the time dimension also needs to be discretized and solved sequentially. In computational fluid dynamics (CFD), the time step must be very small to satisfy the CFL condition \cite{CFL_paper}, making CFD time consuming and numerical errors accumulate with time.

In this paper, we show a novel approach of using a physics-based finite-volume simulator to teach domain-lacking deep learning models to accurately simulate 2D subsurface two-phase fluid dynamics in heterogeneous porous media. The governing equations are Navier-Stokes equations plus Darcy's Law
\begin{equation}\label{eqn:reservoir_pde}
    \frac{\partial}{\partial t}(\phi \rho_i S_i) - \nabla\cdot(\rho_i\lambda_i\mathbf{K}\cdot\nabla p_i) = q_i^w,\ \ i=o, w
\end{equation}
where the subscript $i \in \{o,w\}$ identifies phase as being oil or water, $\rho_i$ is the phase density, $\lambda_i$ is the relative phase mobility, $\mathbf{K}$ and $\phi$ are the permeability and porosity distribution of the porous medium, and $q_i^w$ are the sink/source rates at the locations of producing/injecting wells. The phase saturation $S_i$ (i.e., volumetric percentage of the phase) and pressure $p_i$ are the physics quantities we are trying to solve. Since the two phases are directly connected, we will by default choose the water phase pressure and saturation for discussion.

When all parameters are given, the dynamics are determined and can be forward simulated using a finite-volume method. In real-world problems, however, the exact form of the equation is not yet determined due to the uncertainty of subsurface rock properties like permeability $\mathbf{K}$. Meanwhile, the system can be measured/observed at some locations (such as wells). A more important and challenging problem is to, in reverse fashion, estimate the physics parameters $\mathbf{K}$ based on the observed data. In CFD approaches, a forward simulation is conducted with an initial guess of the permeability map and simulated behavior at observable locations is compared with real measurements. The initial guess of permeability is then revised in order to yield new behavior that better aligns with real data. This iterative revision process is called history-matching and is an example of an inverse problem wherein one wishes to infer the exact form of the governing laws based on limited observed data. Forward evaluation is itself expensive, inverse problems, which require many forward evaluations, are much more demanding. We will show that the deep-learning-based surrogate model and its interpretation offer unprecedented advantages to solve the inverse problem.

There have been several recent efforts to capture the physics of a system using fast deep learning surrogates. Examples appear in rainfall prediction, animation, aerodynamics design and of particular interest to us, reservoir simulation \cite{shi_convolutional_nodate,ladicky_data-driven_2015,guo_convolutional_2016,zhu_bayesian_2018}. These efforts may be summarized according to two broad strategies.

\begin{enumerate}
\item Data-Driven Approaches; the network observes the system and derives the physics from data. Our approach is data-driven in nature.
\item Physics-Embedded Approaches; a scientist supplies the governing physics equations directly to the network and the network is trained to adhere to them.
\end{enumerate}

Inspired by the success of CNN architectures on image translation problems like pix2pix \cite{isola_pix2pix_2016}, \citeauthor{zhu_bayesian_2018} \shortcite{zhu_bayesian_2018} introduce a fully convolutional encoder-decoder network (DenseED) to model the behavior of fluid in heterogeneous media. Encoder-decoder networks are often motivated by an intuition that input and output images share an underlying structure. However, though no such structure is readily apparent, \citeauthor{zhu_bayesian_2018} \shortcite{zhu_bayesian_2018} find that DenseED successfully translates permeability to steady-state velocity fields. \citeauthor{mo_integration_2019} \shortcite{mo_integration_2019} extend DenseED to incorporate GAN loss to tackle highly non-linear outputs (for other examples of GANs applied to proxy particle shower simulations and heat conduction see \citeauthor{paganini_accelerating_2018} \shortcite{paganini_accelerating_2018} and \citeauthor{farimani_deep_2017} \shortcite{farimani_deep_2017} respectively).

Time dependence is captured using a variety of strategies. Without altering the architecture of DenseED, \citeauthor{mo_deep_2019} \shortcite{mo_deep_2019} model time-dependence by broadcasting time across an additional input channel. By treating time in this manner, \citeauthor{mo_deep_2019} are able to furnish predictions at arbitrary time instances. However, fluid flow is highly autoregressive in nature; the state of the system at time $t$ is a function of the system's state at $t-1$ and \citeauthor{mo_deep_2019} do not explicitly exploit this structure available in the data. In contrast \citeauthor{wiewel_latent-space_2018} \shortcite{wiewel_latent-space_2018} combine an encoder-decoder with a long short-term memory network (LSTM) and task the LSTM with learning transitions in the encoder-derived latent space. \citeauthor{shi_convolutional_nodate} (2015) and \citeauthor{wang_predrnn:_nodate} (2017) further customize LSTMs for spatiotemporal data by innovating layers that respectively include convolution operations in transition functions and construct a shared memory pool across LSTM layers.

Alternatively, physics-embedded approaches take an unsupervised approach and utilize the governing equations of a system directly. \citeauthor{raissi_physics_2017} \shortcite{raissi_physics_2017} prescribe a framework for embedding physics into a neural network by expressing the loss as the sum of two terms, one that describes the dynamics of the system and another that describes boundary conditions. As \citeauthor{raissi_physics_2017} minimize this loss, they find a function approximation that satisfies known dynamics and boundary conditions and so solve the PDE directly.  \citeauthor{zhu_physics-constrained_2019} \shortcite{zhu_physics-constrained_2019} demonstrate how spatial gradients can be computed using Sobel filters and therefore extend the framework pioneered in \citeauthor{raissi_physics_2017} to convolutional architectures.

The paper is organized as follows. First, we formulate the problem and describe deep neural network architectures that have successfully learned from a CFD simulator. We compare two different approaches to dealing with time dependence and discuss how accuracy and computation time of the deep learning surrogate scale with more training data. Then we show how training with adversarial loss is helpful for solutions with large spatial gradient. Finally, we demonstrate how the model explanation can convert the inverse problem into a gradient-enabled optimization problem that can be solved 4-5 orders of magnitude faster than traditional numerical methods.

\begin{figure*}[t!]
\centering
\includegraphics[width=5in]{architecture_compressed.PNG}
\caption{DenseED models time by broadcasting it across a 50x50 input channel (purple) so that it receives two-channel input. DenseED-LSTM receives a single permeability channel as input and outputs $T$ saturation fields. We train both models using Adam as our choice of optimizer, MSE loss and an initial learning rate of $3e^{-3}$ which we reduce using a scheduler over 200 epochs.}
\label{fig:architecture_compressed}
\end{figure*}

\section{Method}

\subsection{Problem Formulation}
We wish to develop a fast, accurate surrogate to a CFD simulator that solves a collection of PDEs (Eqn \ref{eqn:reservoir_pde}) given a permeability distribution $\mathbf{K}$ on a uniform 2D Cartesian grid. Since $\mathbf{K}$ is the input of the system, we represent $\mathbf{K}$ by 2D tensor $X \in \mathbb{R}^{1\times H\times W}$ where $H$ and $W$ denote the height and width of the grid. In general, we can extend $X$ to 3D and include multiple inputs by expanding the tensor $X \in \mathbb{R}^{n_x\times H\times W\times D}$ where $n_x$ counts the number of input types and $D$ denotes depth.

The output of the simulator, $Y$, is spatiotemporal and describes the path of two maps; a saturation map that describes the relative composition of water in each cell, and a pressure map. Again, we represent $Y \in \mathbb{R}^{T\times n_y\times H\times W}$ as a tensor, where $n_y$ counts the total number of output maps ($n_y=2$) and $T$ is the total number of time steps. The simulator can be represented as a function that maps $f:X \rightarrow Y$. We cast the problem as an image-to-image regression treating $n_x$ and $n_y$ as channels and $H\times W$ as the shape of the input and output images.

We nondimensionalize Eqn \ref{eqn:reservoir_pde} and choose $H=W=50$. Initially, the entire 50 by 50 region is filled with oil, and hence water saturation starts as zero everywhere. An injector placed at the center of the region injects water at a constant rate (source term in Eqn \ref{eqn:reservoir_pde}), replacing and pushing the liquid towards four constant-rate producers at the four corners. This problem setting describes a waterflood, a practice of secondary recovery of crude oil extraction, in which water, less viscous and immiscible with crude oil, is injected into a reservoir to achieve higher long-term ultimate oil recovery as well as maintain subsurface pressure.

We create a prior permeability map using a sequential Gaussian method and divide the region into six sub-regions based on its value and pixel proximity. We run 400 CFD simulations to time step $T=30$ wherein each simulation is characterized by a different permeability map $X$ generated by multiplying six randomly sampled independent multipliers in the range $[e^{-2},e^{2}]$ to the six sub-regions of the prior permeability map. We effectively obtain a 400-case dataset filled with synthetic data that correspond to different physics input. $336$ cases are used as the training set, and the remaining $64$ cases are used for testing. The goal is to train networks with the training simulation data that yield reliable predictions of pressure and saturation on the hitherto unseen permeabilities in the test set.

As we later discuss, we recognize that pressure is a more global property than saturation, demanding a larger receptive field. Furthermore, relative to saturation pressure exhibits less variation over time. Owing to these diverging properties, we decouple the problem and separately predict saturation and pressure.

\subsection{Architecture}

We employ the network architecture developed by \citeauthor{zhu_bayesian_2018} \shortcite{zhu_bayesian_2018} (DenseED) and incorporate an LSTM as an alternative treatment of time and GAN loss to better simulate shocks. The design presented in \citeauthor{zhu_bayesian_2018} \shortcite{zhu_bayesian_2018} is a combination of two structures; \textbf{convolutional encoder-decoders} and \textbf{Densenet}. Encoder-decoder networks have successfully been applied to image-image translation problems such as image segmentation and pattern infilling \cite{long_fully_nodate,badrinarayanan_segnet_2015}. Encoder-decoders subject an image to a coarse-refine process. With each successive encoding layer, the network coarsens, extracting higher-level features from the image at lower spatial resolution. Ultimately, the encoder finds an underlying representation of the image in a latent space. With each successive decoding layer, this underlying representation is refined to construct the output.

DenseNets \cite{huang_densely_2016} choose convolutions that preserve identical dimensions between inputs and outputs, thereby permitting their concatenation. The input to any one layer is the last layer's output concatenated with all previous inputs. This structure is particularly valuable when analyzing objects across multiple spatial scales. Each successive convolution reflects an expanding receptive field. A 1x1 convolution combines feature maps of varying receptive fields resulting in a network that readily adapts to local versus global phenomena. We present the structure of DenseED in Panel A of Figure \ref{fig:architecture_compressed} and detail the specific design of the encoder and decoder in Figures \ref{fig:encoder_v2} and \ref{fig:decoder_v2}.

\citeauthor{mo_deep_2019} \shortcite{mo_deep_2019} propose a strategy for modeling time-dependent outputs without altering the design of DenseED by broadcasting time across an input channel. We experiment with incorporating an LSTM \cite{hochreiter_long_1997} to model transitions in the encoder-derived latent space henceforth referred to as DenseED-LSTM (Panel A of Figure \ref{fig:architecture_compressed}). We hypothesize that an LSTM, with its memory and transition properties, is particularly apt to model a simulator's dynamic behavior. The persistent cell state will house information about the permeability grid, which is relevant to prediction at every time step. Meanwhile, the hidden state-to-state transitions will receive a strong signal due to the autoregressive nature of the governing physics. Compared to DenseED, the encoder in DenseED-LSTM outputs twice as many feature maps (96 vs. 48). Half of those feature maps are used to initialize the first hidden state and half the first cell state. The LSTM outputs hidden state vectors for $T$ time steps which we decode to predict the saturation path.

\begin{figure}
\centering
\includegraphics[width=2.4in]{encoder_v2.png}
\caption{Encoder Design. We represent the arguments of the convolution operations in parentheses. Conv2d (k3s1p1, 48, 16) denotes a 2D convolution with kernel size 3, stride 1, padding 1, which receives 48 features maps as input and outputs 16. DenseED is modularized using encoding and decoding block units which consist of a dense block and a transition block. Each encoding (decoding) block halves (doubles) the dimensions of the output feature map. We find that encoding down to 12x12 features maps works best for saturation and encoding down to 6x6 works best for pressure.}
\label{fig:encoder_v2}
\end{figure}

\begin{figure}
\centering
\includegraphics[width=2.4in]{decoder_v2.png}
\caption{Decoder design. See encoder for details.}
\label{fig:decoder_v2}
\end{figure}

\section{Saturation Solutions and Alternative Treatment of Time-Dependence}

\begin{table}
\centering
  \caption{Goodness of fit}
  \label{table:gof}
\resizebox{0.75\columnwidth}{!}{
  \begin{tabular}{lcc}
    \toprule
     & Train $R^2$ & Test $R^2$ \\
    \midrule
     DenseED & 0.991 & 0.840 \\
     DenseED-LSTM & 0.941 & 0.847 \\
  \bottomrule
\end{tabular}}
\end{table}

We evaluate the performance of the surrogate in predicting the path of saturation with $R^2 = 1-\sum_{i=1}^{N} (\hat{y_i}-y_i)^2 / (y_0-\bar{y})^2$, where $y_i$ is a tensor describing the true saturation path for simulation $i$ and $\hat{y_i}$ is the corresponding model prediction. The null model, from which we derive $\bar{y}$, predicts the mean image for all time steps. In Table \ref{table:gof}, we record the test performance of DenseED and DenseED-LSTM which achieve similar degrees of success.

We further characterize the model's performance by exploring how error varies in time and space. We expect time will influence error because the difficulty of the prediction task is non-uniform. Initially easy, prediction becomes difficult and then reverts to being easy again over short-, mid- and long-time horizons. The first images are easy to predict because water is initially only concentrated around the injector. As water floods the field, saturation enters a period of rapid flux. In this period, saturation at $t-1$ bears the least resemblance with its state at $t$, and prediction is commensurately more difficult. Once saturation steadies, the model can issue a prediction at time $t$ at least as accurately as it did at $t-1$. Therefore we expect performance to plateau at long time horizons.  We observe that for both DenseED and DenseED-LSTM, $R^2$ follows this trend (Figure \ref{fig:r2}). Performance is highest when $t$ is near zero and deteriorates to a floor.

\begin{figure}
\centering
\includegraphics[width=2.6in]{r2.png}
\caption{Test $R^2$ evaluated at each time step. Both DenseED and DenseED-LSTM exhibit initially strong performance that deteriorates to a floor.}
\label{fig:r2}
\end{figure}

We also inspect individual test predictions to examine the spatial distribution of error. In Figure \ref{fig:sat_path}, we present one such prediction. We observe that for both DenseED and DenseED-LSTM error tends to be concentrated around the saturation front.

\begin{figure}
\centering
\includegraphics[width=3in]{sat_path.png}
\caption{Path of saturation and predictions for a single simulation in the test set. A conventional solver provides ground truth. DenseED and DenseED-LSTM are trained using 334 simulations. Saturation is a proportion that takes values between 0 (blue) and 1 (red). We plot error using a diverging color palette and enforce symmetry by setting vmin, vmax = -0.5 (brown), 0.5 (blue).}
\label{fig:sat_path}
\end{figure}

Once trained, the neural network can make very fast forward predictions. The CFD simulator took 10 seconds to finish each simulation when creating the synthetic dataset on an Intel Xeon 2.4GHz 128GB RAM Nvidia K80 workstation. For comparison, the neural network makes batch predictions on 1000 different permeability maps in less than a second on the same machine. The main time overhead of the neural network is in training. Still, it only takes 2 minutes to train DenseED for 200 epochs to predict a specific time instance with a Nvidia K80. In Figure \ref{fig:scaling}, we show how test performance and training time vary with the number of simulations provided in training. Unsurprisingly the more simulations are provided in training, the better the model's performance on the test set.

\begin{figure}
\centering
\includegraphics[width=3in]{scaling.png}
\caption{Scaling. DenseED achieves high performance with limited training data.}
\label{fig:scaling}
\end{figure}

\section{Global properties and Adversarial Effect}
Unlike saturation which evolves at the speed of mass motion, pressure is a "global" physics property established at the speed of sound, several orders of magnitude faster than the fluid's convection speed. Therefore, the pressure solution is affected by the permeability of the entire region. In other words, the receptive field of pressure is global. Indeed, we find that we obtain the best validation results by adding one additional encoding and one additional upsampling block to the DenseED structure, which shrinks the bottleneck of the autoencoder from $12\times12$ to $6\times6$. The smaller bottleneck further contains six $3\times3$-kernel convolutional layers, effectively resulting in a global receptive field of each pixel at the end of the bottleneck.

Fluid dynamic systems are known to have sharp spatial gradient and discontinuity like shocks. For example, the pressure solution scales as $\log(r)$ near the sources and sinks in 2D or cylindrical 3D space. This can lead to enormous spatial gradients close to the wells. Vanilla generative neural networks have difficulty in capturing such behavior because the regular distance-based losses lead to smoother, blurry results in an attempt to resemble many possible solutions. In the case of a fluid dynamic system, although the solution is unique for a given permeability map, we still found large-gradient results are more difficult to generate (see the 2nd row of Figure \ref{fig:pressure_gan}).

\begin{figure*}
\centering
\includegraphics[width=5.5in]{pressure_gan_nogan_comparison.png}
\caption{Pressure solutions scaled to $[0,1]$ at time $t=25$ of six randomly sampled permeability maps from the test dataset. Top row: True solution from numerical CFD. 2nd row: Outputs from DenseED trained with L1 loss only. 3rd row: Outputs from the same DenseED architecture trained with L1 and GAN loss. Both the L1-only and L1+GAN cases are trained using an initial learning rate of $5\times10^{-4}$ that is halved every $30$ epochs over $250$ total epochs. The relative weight $\alpha$ for the GAN loss term is $50$. The discriminator is trained using soft labels ($0.9$ and $0.1$). The median absolute errors are listed for each of the L1-only and L1+GAN solutions.  Bottom row: Cross-sectional view of the three solutions at $y=0$.}
\label{fig:pressure_gan}
\end{figure*}

Generative Adversarial Networks (GAN) \cite{goodfellow_gan} are well-known for creating sharp and visually realistic results by introducing a smart loss term learned by comparing the real and generated results. An additional discriminator $D$ is trained simultaneously to penalize the generator if it produces results that the discriminator easily discerns as fake. Similarly to \citeauthor{isola_pix2pix_2016} \shortcite{isola_pix2pix_2016},  we adopt the final objective as
\begin{equation}\label{eqn:gan_goal}
    G^* = \mathcal{L}(G) + \alpha \cdot \arg \min_G\max_D \mathcal{L}_{GAN}(G,D),
\end{equation}
where $G$ is the DenseED autoencoder (generator), $D$ is the discriminator which has the same architecture as the encoder of DenseED plus a fully-connected layer to output binary prediction, and $\mathcal{L}$ is the distance-based loss (L1-loss by default) between pixels of generated results and CFD results (true label). The adversarial loss term is given by
\begin{equation}\label{eqn:gan_loss}
    \mathcal{L}_{GAN}(G,D) = \mathbb{E}_{y}[\log D(y)] + \mathbb{E}_x [\log(1-D(G(x))],
\end{equation}
where $x$ is the permeability map input to DenseED, $y$ is the CFD result, and $G(x)$ is the generated result. In \citeauthor{isola_pix2pix_2016} \shortcite{isola_pix2pix_2016}, a conditional discriminator is utilized to judge if an image is real and also relevant to the input $x$. In our case, although we do not find the conditional adversarial mechanism enhances the results notably, it does help stabilize the GAN training process. A fundamental difference between Eqn \ref{eqn:gan_loss} and regular GAN is the absence of a randomly sampled latent vector. Once the permeability map $x$ is given, the fluid system's dynamics are determined. Therefore, unlike a traditional GAN, we do not feed a random latent vector $z$ as input to the generator. The term $\alpha$ in Eqn \ref{eqn:gan_goal} is a weight quantifying the relative importance of GAN loss and is chosen to be $50$ so that the GAN loss is about 50\% of L1 loss at the conclusion of the training cycle.

Figure \ref{fig:pressure_gan} compares the pressure solutions from CFD, DenseED trained with L1 loss only and the same DenseED architecture trained with both L1 and GAN loss (Equation\ref{eqn:gan_loss}). GAN loss helps significantly in capturing the large pressure gradient near the central injector. In comparison, L1-only DenseED produces consistently smoother results. Although the overall pixel-wise error of L1-only DenseED is smaller in many cases, the L1+GAN results are scientifically more accurate near the sink/source locations. This is critical because, in the real world, the sink/source locations are where the observable data are sampled.

\section{Model Explanation and the Inverse Problem}
Interpreting complex machine learning models such as deep neural networks is important because it helps humans understand the behavior of the model, vet whether the prediction is made based on the correct reasoning, and build even better models. Pixel-wise sensitivity and/or spatial attention maps have been used to help understand convolutional neural networks \cite{Bach2015,smoothgrad_cnn,SHAP,Zagoruyko2016_attention}. Given that the deep learning models have been proven capable of accurately mimicking CFD numerical simulations, one important application is to explain the neural network model and uncover its underlying logic.

Considering saturation $S$ as a function of permeability $\mathbf{K} = K(x',y')$ and time $t$, the "pixel-wise" sensitivity of saturation to the permeability distribution can be quantified by
\begin{equation}\label{eqn:sensitivity_def}
    X(x,y,t,x',y',\mathbf{K}) \equiv \frac{\partial S(x,y,t,\mathbf{K})}{\partial K(x',y')}.
\end{equation}
The partial in Equation\ref{eqn:sensitivity_def} represents how much saturation at location and time $(x,y,t)$ would change if the permeability at $(x',y')$ were a bit higher. In real cases, $(x,y,t)$ are the locations/time where the system is observed/measured. Given a fixed location of interest $(x_0, y_0)$ and a time $t_0$, the local saturation sensitivity to permeability is a 2D slice of Eqn \ref{eqn:sensitivity_def}:
\begin{equation}\label{eqn:sesitivity_local}
  \mathbf{X}_{x_0,y_0,t_0,\mathbf{K}} \equiv X(x',y')_{x_0,y_0,t_0,\mathbf{K}} =  \frac{\partial S(x_0,y_0,t_0,K)}{\partial K(x',y')}.
\end{equation}

The sensitivity map helps us understand what consequences a slight alteration of the input physics laws may have on the system. However, the sensitivity map $\mathbf{X}$ is extremely difficult to estimate using traditional CFD approaches because there is no explicit formula of $S(x,y,t,\mathbf{K})$. Instead, sensitivity must be calculated by forward computation at very high cost. In the given case, in order to numerically estimate $\mathbf{X}$ on a 50x50 grid at time $t=25$, $2500$ simulations have to be run till this time step for each slightly perturbed input permeability map, which would take 7 hours on the same workstation. When the permeability distribution $\mathbf{K}=K(x',y')$ has to be updated iteratively, say, 100 times, in the inversion problem, the CFD-based approach would take more than one month.

Thanks to the deep-learning surrogate model, $\mathbf{X}$ can be computed directly via backward propagation. Since DenseED and the convLSTM takes $\mathbf{K}$ as input, the derivative of the output of the networks w.r.t. the "pixels" of the input permeability map is the same as Eqn \ref{eqn:sesitivity_local}. We can also compute $\mathbf{X}$ easily via numerical differentiation by running a batch of $2500$ forward prediction at low cost. For the same example, evaluating six 2D $\mathbf{X}$ at six different $(x_i,y_i,t_i)$ on the same workstation took only 619 ms, a 40,000-fold acceleration.

Figure \ref{fig:sensitivity} shows the saturation sensitivity map at six locations at time $t=25$ for a given permeability map from the test set. The particular chosen case is more permeable on the left half of the region, resulting in earlier water breakthrough in locations 1 and 3 than locations 2 and 4. As a result, the upper right and lower right corners have higher sensitivity to the permeability distribution between them and the central injector. The central point has zero sensitivity to the permeability map because it is always flooded immediately regardless. Location 6, which is located in a low permeability channel, is most sensitive to its upstream, the small region between itself and the central injector. In some sensitivity maps, certain regions have negative impact, indicating that an increase of permeability in those regions will actually result in a lower saturation; this is because those regions with a higher permeability will redirect some water to other directions and consequently give lower saturation at the locations of interest. The sensitivity maps, effectively obtained by locally linearizing the deep-learning-based surrogate model, honor the underlying physics very well, confirming the deep-learning model's ability to capture the physics by learning from data.

\begin{figure}
\centering
\includegraphics[width=3in]{partial_dependence.png}
\caption{Top rows: Input permeability map (left) and the saturation solution given by DenseED at time $t=25$ (mid). Top right shows the saturation solution with a slightly perturbed permeability. Row 2-3: Pixel-wise sensitivity at six observation sites to the input permeability. A value $\beta$ at coordinate $(x,y)$ in the sensitivity map for Location-\textbf{i} means that a $\Delta k$ increase in the permeability map at $(x,y)$ will result in a $\beta\Delta k$ increase in the saturation at location \textbf{i} at time $t=25$. The partial derivatives are computed by directly backpropagating DenseED and averaged via a $5\times 5$ kernel.}\label{fig:sensitivity}
\end{figure}

The interpretation of the deep learning model lends itself to a powerful application. In the inverse problem, a dynamic system is governed by certain but not-yet-fully-determined laws. The goal is to find out the exact form of the governing laws so that the resulting dynamics satisfy all the observed data. In our case, the saturation at $(x_i,y_i,t_i)$ is observed and certain; however, the permeability distribution, which gives the exact form of the fluid dynamic equations, is not yet determined. The solution should be an optimal $\mathbf{K}^* = K(x',y')$ so that the resulting $S$ has minimized total discrepancy with all the observed data, i.e.,
\begin{equation}
\mathbf{K}^* = \underset{\mathbf{K}}{\mathrm{argmin}} \mathcal{L}_{\text{IP}},\ \
\mathcal{L}_{\text{IP}} = \Sigma_i || S(x_i,y_i,t_i,\mathbf{K}) - S_i||
\end{equation}

where $\{S_i\}$ are the set of real measurements at locations/times $(x_i,y_i, t_i)$. With L2 norm, the loss of the inverse problem is differentiable w.r.t. $\mathbf{K}$:
\begin{equation}
\nabla_{\mathbf{K}}\mathcal{L}_{\text{IP}} = 2 \Sigma_i ( S(x_i,y_i,t_i,\mathbf{K}) - S_i)\cdot \mathbf{X}_{x_i,y_i,t_i,\mathbf{K}}
\end{equation}
Given that $\mathbf{X}$ can be readily evaluated with the deep-learning surrogate model, the above problem can be solved using gradient-based optimization methods iteratively via
$$\mathbf{K}\leftarrow \mathbf{K} - \gamma\cdot\nabla_{\mathbf{K}}\mathcal{L}_{\text{IP}},$$
where $\gamma$ is the learning rate in the above gradient-descent iteration. In other words, in every iteration of the inversion optimization, the input permeability map is added/subtracted by a linear combination of the sensitivity maps shown in Figure \ref{fig:sensitivity}, and then the sensitivity maps are recomputed to adjust for the change of the input permeability distribution. For instance, we perturbed the input permeability by adding $0.1$ times the sensitivity map 2 and subtracting $0.3$ times the sensitivity map 3, to obtain a desired saturation map (upper right corner of Figure \ref{fig:sensitivity}) in which location 2 has higher saturation and location 4 has lower saturation.

We show in this section that model explanation offers an unprecedented advantage in solving the inverse problem, the true challenge that is not yet achievable with the traditional CFD method due to the limitation of computation power.

\section{Summary and Discussion}
We have presented a novel method of modeling a special fluid dynamic system with deep learning. We show that, by sampling a good distribution of exact form of the physics laws, it is possible to teach one unique deep learning model to capture the underlying physics and accurately simulate the dynamic system under each of the sampled physics law, and make prediction at high accuracy to new physics that were not explicitly provided in the training data; moreover, the deep learning approach provides a significantly faster way to forward evaluate the system, potentially helping solve the motivating inverse problem. The nonlinear partial dependence of the system behavior to the imposed physics can be quantified using the techniques of neural network explanation. This provides a much easier way to solve the inverse problem that would otherwise be infeasible using  traditional numerical methods due to their high cost.

\nocite{*}

redpajama_set_name: RedPajamaArXiv


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Focusa2z Connects World
On Your Service from 2008
Emayatzy Corinealdi Plays the Ambitious Politician with a Complicated Past in 'The Red Line'
You are here: Focusa2z Connects World > Blog > Multiple Choices > Emayatzy Corinealdi Plays the Ambitious Politician with a Complicated Past in 'The Red Line'
Actress Emayatzy Corinealdi is most notably known for her work in the Ava DuVernay film Middle Of Nowhere.
On Sunday nights on CBS, you can catch her playing the role of Tia Young, the ambitious politician with a complicated past in the new series The Red Line. Produced by both Ava DuVernay and Greg Berlanti, The Red Line is an eight-episode original limited series that follows three very different Chicago families as they journey toward hope and healing after a tragedy causes them all to consider how race and racial biases affect their lives.
Tia Young discovers her daughter whom she gave up for adoption as a teen, is also the daughter of an innocent man killed in a fatal shooting by the police. Tia has to tackle her political career and reconnecting with her daughter who has come back into her life. Emayatzy chats about working with Ava DuVernay again and drawing from real life politicians to get into character.
THE RED LINE two-hour series premiere airs Sunday, April 28 (8:00-10:00 PM, ET/PT) on the CBS Television Network. ©2018 CBS Broadcasting, Inc. All Rights Reserved
Can you briefly tell our readers who is Tia Young in The Red Line?
Tia Young is a young mother living in Chicago with her husband and her young son. There's a tragedy that happens. There's a Black man who was killed by a white officer and she is affected by that. The show, Tia's role in that is she decides to make a change. She decides to do something about that. She decides to do what she thinks is really the only thing that she can do to find some kind of justice for what has happened, so she decides to run for political office. And in doing so, bad things from her past that come up and then threaten to either derail everything she has going on if she doesn't find a way to reconcile.
I know that this character has a lot of political ambitions. Did you draw from any real-life politicians or civil rights activists while preparing to play Tia?
I did because one of the things that I loved about Tia's political operation is that it really mirrors what was happening right now in our country. She really captured all the women who are running for Congress. For other women, there's a woman named Lauren Underwood in Illinois who is a Black woman, and she ran and to see her in a way that no one ever thought would happen. And she ended up being elected.
Then, of course, we have Laurie Lightfoot. She came after, we were almost finished rolling by that time. But there are so many examples of women going into Congress and women running for office around the Country that I feel all of that energy. I really did try to view Tia with all of that.
Isn't that beautiful that there are so many examples of women going into Congress that you can draw from?
Oh my gosh. You know what I mean? It is exciting in itself.
What scene or scenes resonated with you the most while filming?
Oh boy. Well, there is one but I can't say it because that would kind of spoil the show. So, I will just say, in a general sense, what resonated with me the most in doing the show was being able to have an opportunity with this character, with the show, to speak to something that is pertinent to what is happening to us right now. Black men really are being killed at a big rate, and I feel like every time that we think that we're making some kind of progress, something else happens. And so, we have an issue here. So, this show was hoping to allow people to, from the comfort of your living room, engage in a conversation with other people. When you go to work the next day, when you have dinner with your family, let's have a conversation about what is happening.
So, for me, that's at the heart of the show and that's what my goal is for the show. That's what my hope is, is that people would walk away from this looking to examine themselves. Look at their own implicit bias and see how that is affecting our lives every day, especially when you talk about law enforcement. When people's lives are hanging in the balance. So, there's a need for us to stop. Take a second. See this other person here. Think for a second before we make these kinds of moves. So, that's what the heart of the show is.
I really hope that people will watch this show and see something inside themselves or reflect a little bit more about what's happening in our Country.
I know you've worked with Ava DuVernay before in Middle Of Nowhere, and Ava's also an executive producer on this show. Did she bring you in because of your history together? And what was it like working together again?
You know, working with Ava this time, is just really different. That it's a completely different role. With Middle of Nowhere, we had a director and this has an executive producer so I don't see her like I did as before. A very different role so in that sense, it was a different experience. So, anytime she and I get together, always a lot of fun. She and I have a thing for doing Middle of Nowhere. So, this was … I'm looking forward to it for that reason.
You play a very significant role in this series and your story arc is the vehicle to this story, really. How is Tia unique from any of the other characters that you've played?
I think because she's somewhat in the middle, I'll say, of the other two families, so she's able to have a bit of a different perspective. She's attached but not quite attached, so I think that's given her a different lens that's she's viewing the whole picture from. So, she's able to assess the situation a little bit differently and start to make moves from there.
And I know that this is a limited mini-series but do you see the story going further if this mini-series gets picked up for another season?
I mean, if it were to get picked up, because we shot it just as you see it, with it being this very pressing conversation that we want to have, this event. But is it worth to get picked up and go for a second season? Absolutely there would be more places for these characters to go, especially Tia's character. Given where she is this season, there are just miles and miles of roads in terms of where she can go. Especially when you think of the Mayor of Chicago right now, Laurie Lightfoot, and how her election, how that impact politics of Chicago. There's just a lot of room for that.
And we do have with the other families, the Calder family, even just dealing with the issues of adoption and things like that. There's so much room for that to play out. And then you have on the other end, you have this officer. Now he has to deal with the aftermath of what he's done. There's an opportunity to see how his bias is affected as time goes on. But there's a lot of room for all of the characters to grow if given that opportunity.
What's next for you? Are there any new projects that we should keep on our radar?
I do have a couple of things that are brewing right now for which I can see really get into. I'm working on that on Ballers to do with our 50's and that's just always such a fun set to be on. I'm working with Dwayne Johnson. He's just so much fun. So, I'm looking forward to that.
Thank you so much. I really appreciate your time.
The Red Line airs Sunday nights at 8pm ET on CBS.
The post Emayatzy Corinealdi Plays the Ambitious Politician with a Complicated Past in 'The Red Line' appeared first on Black Girl Nerds.
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redpajama_set_name: RedPajamaCommonCrawl


When we approached this project, the building was two flats that the owner wanted to transfer into one. We completely renovated the house both internally and externally and knitted the two flats into one house. We extended the ground floor outwards and sideways.
We completed extensive remodelling of both front and rear gardens and built a large studio, used as a TV den, which is at the end of the garden.
Completion Date July 2013. Contract Period 36 weeks.


Centennial Resource Development, Inc. (NASDAQ: CDEV) is currently seeking a Treasury Analyst. Reporting to the Director of Corporate Finance/Assistant Treasurer, the Treasury Analyst will perform a wide variety of tasks in support of the treasury and risk functions. The ideal candidate will be able to successfully operate in a fast-paced environment while maintaining strong attention to detail and excellent communication skills.


Welcome to part 3, the final segment to our Leave Management paper. This article follows 'More Tips and Tricks' and we'll delve into 'Data'.
If you have a large or complex organisation, some of the best insights can be captured from the data held within your payroll database. This is where having the right technology really helps simplify things. We've found the following measures provide useful insights to the business, allowing managers and HR practitioners to spot trends, compare groups or identify outliers. The data provides the starting point to investigate why the differences exist and to track compliance to agreed company norms.
· Track the average number of leave requests per employee by department each month. There should be a relatively consistent average across the organisation. You're looking for the teams that have a significantly lower or higher number of requests over a period of time.
· Track the average balance of annual leave per employee by department. Look for the significant outlier. If there isn't a good commercial reason why they have so much leave, there could be mismanagement by the manager, an overworked team, or leave requests aren't being processed and you're effectively paying employees twice. Looking at the leave management graph below, "site a" (shown as the blue bar) is indicating that something happening in the department and needs more investigation.
· Track the average balance of annual leave per employee by department over the last 12 months. This is a really simple one to see if you are getting leave information from your teams. It's natural to expect the average balance to drop after key holiday periods (around Christmas and Easter) and in the following example you can see how only one team had their balance decrease between December and January (dark red). You can graph this information in excel, but having the right dashboard or analytics functionality can really help make this an easy task.
o A significantly higher level of sick/personal leave taken than other departments could indicate a level of disengagement or cultural issues within the team. This could be investigated further by looking at employee turnover within the department compared to others.
Effective leave management is a mix of having great organisational processes and great technology to support this. Most of these tips can be handled manually, but with automation and simplification, administrative time can be reallocated to more value adding activities.
Datacom is here to help, as a market leading provider of cloud based payroll and HR services and consulting we're able to help minimise your leave issues and associated costs through providing great technology and supporting your organisation to establish better processes.


1. India has overtaken the US to become the third-largest steel producer in the world with a production of 14.56 million tonnes (MT) in first two months of the year. India has been the fourth-largest steel producer for the past five years, behind China, Japan and the US.
2. Sania Mirza and Swiss legend Martina Hingis began their partnership with a title win as they overcame Ekaterina Makarova and Elena Vesnina 6-3 6-4 in the summit clash of the BNP Paribas Open, in Indian Wells.
3. Novak Djokovic of Serbia won his fourth Indian Wells Open Tennis Men's Singles title. In the final clash, Djokovic defeated Roger Federer of Switzerland 6-3, 6-7 (5), 6-2. This was his career's 50th ATP Title.
4. Lee Kuan Yew, the founding father and first prime minister of Singapore who transformed that tiny island outpost into one of the wealthiest and least corrupt countries in Asia, passed away due to severe pneumonia. He was 91.
5. The Union Cabinet has approved the Pradhan Mantri Kaushal Vikas Yojana (PMKVY) to provide skills training to youth across the country. 1500 crore rupees have been alloted to this scheme. The PMKVY Scheme will be implemented by the Union Ministry of Skill Development and Entrepreneurship through the National Skill Development Corporation (NSDC). The target for skilling under the scheme will be associated with Union Government's flagship programmes such as Make in India, Digital India, Swachh Bharat Abhiyan and National Solar Mission.
6. New Zealand's Martin Guptill made the highest score in World Cup history with 237 not out against West Indies to send his team into the semi-finals of ICC World Cup 2015. He made 237 runs notout on 163 balls. He is the fifth batsman to hit double century in One Day match.
7. Satwant Atwal Trivedi, Himachal Pradesh's first woman IPS officer, is all set to open Border Security Force (BSF)'s door to the gender seniority in India. Inspector General of Police (Crime and cyber security) Satwant,1996-batch police officer, got her orders for central deputation to the BSF– country's leading para-military organisation. She will be first IPS officer to join the BSF.
8. Rajendra Singh of India dubbed as "the Water Man of India" is the 2015 Stockholm Water Prize Laureate for innovative water restoration efforts in rural India, and extraordinary determination in his quest to improve the living conditions for those most in need. This award also known as "the Nobel Prize for water" has been given to this Indian campaigner who has brought water to 1,000 villages. The judges of the Stockholm Water Prize say his methods have also prevented floods, restored soil and rivers, and brought back wildlife.
9. The SEBI approved a new set of norms for listing and trading of municipal bonds on stock exchanges. Municipal bonds, also to be known as "Muni Bonds", would allow authorities to raise funds including for setting up of smart cities, by raising funds from the public and from the institutional investors. The municipal authorities, desiring of issuing these bonds, would need to have a strong financial track record and such bonds would be listed on the stock exchanges.
10. Former Sri Lankan Army Chief Sarath Fonseka was conferred the highest military rank of Field Marshal by the Sri Lankan Government. He was awarded this title by President Maithripala Sirisena at a state ceremony held in the Colombo. With this Fonseka became the first Sri Lankan to receive this highest military title which is honorary in nature.


The Name of the 12th chapter of Haalim is سلطان ساز (The King Maker). Read Haalim Episode 12 by Nimrah Ahmed Pakistani one of the Best and Famous novelist Nimra Ahmed has introduced this very Novel for Khawateen Digest specially liked and loved by ladies but most of all ONLY ladies Read Haalim Episode 12 Download version as well as readable version for the Month of April..
Upload ho gai hai page ko refresh karain. or tamam pages mojood hain, last main PDF link bhi dia hua hai.
Ab tou sham bhi hogai hmri sabar ka imtihan na lijye.


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Q: How to add a List of Parent objects to a List of Child Objects Consider a parent class named PowerHost. Consider a child class named PowerHostHistory which is like this:
PowerHostHistory extends PowerHost

I have a list of child class defined like this which is constantly filled with PowerHostHistory objects.
List<PowerHostHistory> excludedHosts = new LinkedList<PowerHostHistory>();

Now, in a bit different way, I am going to add a list of PowerHost objects to my excludedHosts.
I want to add a list of parent objects to a list of child objects. Is that possible, and how?

A: It is possible by simply casting away the generic type:
PowerHost powerHost = ...
((List)excludedHosts).add(powerHost);

but it does not make sense: You declared excludedHosts to be of type List<PowerHostHistory> and then you add some non PowerHostHistory objects.
Everyone who is using the list elements will be surprised.
You should decide if you want a list of PowerHostHistory: then don't add PowerHost objects to it. Or you want a list of PowerHost and then of course you can add the PowerHost objects.

A: You can add parent object by downCasting. You can do like :
excludedHosts.add((PowerHostHistory)new PowerHost());

But it will give RunTime exception
Instead why don`t you do like this :
List<PowerHost> excludedHosts = new LinkedList<PowerHost>();

        excludedHosts.add(new PowerHost());
        excludedHosts.add(new PowerHostHistory());

Then you can add both.
Hope it helps !!

redpajama_set_name: RedPajamaStackExchange


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Trial Attorney
Our office places a high value on diversity of experiences and perspectives and encourages applications from all qualified men and women from all ethnic and racial backgrounds, veterans, LGBT individuals, and persons with disabilities.
As a Trial Attorney (Chapter 11), GS-0905-14/15, duties and responsibilities will include, but are not limited to:
Serving as a member of a cohesive and collegial team of experienced chapter 11 bankruptcy attorneys based in OGC reporting to the Associate General Counsel.
Developing litigation strategies, conducting legal research, and drafting and reviewing pleadings, memoranda, briefs, and other legal documents for a broad spectrum of chapter 11 litigation.
Providing legal advice and litigation support for field office trial attorneys involved in challenging or novel chapter 11 issues arising in a variety of chapter 11 cases-from small individual chapter 11s to the largest, most complex business reorganizations-often on a short deadline.
Providing legal advice and counsel on a broad range of chapter 11 issues to senior management.
Developing and implementing chapter 11 policies on matters of USTP significance, as assigned.
Preparing and presenting training sessions, both live and distance, for USTP personnel on a variety of chapter 11 issues.
Assisting and advising a team of dedicated appellate lawyers on the defense and prosecution of appeals relevant to chapter 11 issues.
Assisting in the drafting, review, and preparation of public presentations and articles.
Cooperating with and supporting other departments in EOUST on special projects, as assigned, related to the chapter 11 practice, including media, congressional, and public inquiries and information requests.
Identifying emerging issues and developing trends in chapter 11 practice and recommending USTP responses, as appropriate.
Participating in working groups and other collaborative efforts relevant to chapter 11 practice.
Consulting and advising on cases under other chapters of the Bankruptcy Code, where issues may affect or intersect with chapter 11.
Performing other duties as assigned.
See Qualifications below.
Required Qualifications : Applicants must have excellent academic and professional credentials; superior oral and written communication skills; outstanding legal research, analytical, and legal reasoning skills; thoughtful judgment; and the ability to function with minimal guidance in a demanding legal environment. Further, an applicant must have a J.D. degree from an accredited law school; be an active member of the bar (any U.S. jurisdiction) in good standing; and have at least four (4) years post-J.D. professional legal experience interpreting, applying, and advocating under chapter 11 of the United States Bankruptcy Code, the Federal Rules of Bankruptcy Procedure, and related laws, rules, and regulations that arise in bankruptcy cases.
Preferred Qualifications : In addition to the required qualifications listed above, the ideal candidate will have:
A demonstrated record of analyzing complex legal problems, interpreting statutes, rules, and regulations, and developing litigation strategies;
The ability to be a forceful advocate for transparency and accountability in the bankruptcy process and to act as a watchdog for the public's interest in the integrity and efficiency of the bankruptcy system;
Outstanding organizational skills and the ability to address a broad array of issues arising in a large volume of cases;
The ability to articulate accurately and precisely the critical issues in a wide variety of cases and projects;
The ability to set, manage, and balance diverse, evolving, and challenging projects and priorities;
Demonstrated initiative and creativity;
Strong communication skills;
Strong interpersonal skills, including the ability to work in a professional manner with staff and managers at every level in high-pressure, time-sensitive situations;
Strong information technology skills, including using data bases and IT systems to manage and track work; and,
A commitment to the highest ethical and professional standards.
Please Note: Your years and type of experience will determine at which grade-level to set your starting salary.
See the "Qualifications" section of this vacancy announcement.
DEPARTMENT OF JUSTICE POLICIES
Equal Employment Opportunity: The U.S. Department of Justice is an Equal Opportunity/Reasonable Accommodation Employer. Except where otherwise provided by law, there will be no discrimination because of color, race, religion, national origin, political affiliation, marital status, disability (physical or mental), age, sex, gender identity, sexual orientation, protected genetic information, pregnancy, status as a parent, or any other nonmerit-based factor. The Department of Justice welcomes and encourages applications from persons with physical and mental disabilities. The Department is firmly committed to satisfying its affirmative obligations under the Rehabilitation Act of 1973, to ensure that persons with disabilities have every opportunity to be hired and advanced on the basis of merit within the Department of Justice. For more information, please review our full EEO Statement .
Reasonable Accommodations: This agency provides reasonable accommodation to applicants with disabilities where appropriate. If you need a reasonable accommodation for any part of the application and hiring process, please notify the agency. Determinations on requests for reasonable accommodation will be made on a case-by-case basis.
Outreach and Recruitment for Qualified Applicants with Disabilities: The Department encourages qualified applicants with disabilities, including individuals with targeted/severe disabilities to apply in response to posted vacancy announcements. Qualified applicants with targeted/severe disabilities may be eligible for direct hire, non-competitive appointment under Schedule A (5 C.F.R. § 213.3102(u)) hiring authority. Individuals with disabilities are encouraged to contact one of the Department's Disability Points of Contact (DPOC) to express an interest in being considered for a position. See list of DPOCs .
Applicants should note that SF 15 requires supporting documentation associated with service- connected disabilities or receipt of nonservice-connected disability pensions to be dated 1991 or later except in the case of service members submitting official statements or retirement orders from a branch of the Armed Forces showing that his or her retirement was due to a permanent service-connected disability or that he/she was transferred to the permanent disability retired list (the statement or retirement orders must indicate that the disability is 10% or more).
Continuation from above,
Suitability and Citizenship: It is the policy of the Department to achieve a drug-free workplace and persons selected for employment will be required to pass a drug test which screens for illegal drug use prior to final appointment. Employment is also contingent upon the completion and satisfactory adjudication of a background investigation. Congress generally prohibits agencies from employing non-citizens within the United States, except for a few narrow exceptions as set forth in the annual Appropriations Act (see, https://www.usajobs.gov/Help/working-in-government/non-citizens/ ). Pursuant to DOJ component policies, only U.S. citizens are eligible for employment with the Executive Office for Immigration Review, U.S. Trustee's Offices, and the Federal Bureau of Investigation. Unless otherwise indicated in a particular job advertisement, qualifying non-U.S. citizens meeting immigration and appropriations law criteria may apply for employment with other DOJ organizations. However, please be advised that the appointment of non-U.S. citizens is extremely rare; such appointments would be possible only if necessary to accomplish the Department's mission and would be subject to strict security requirements. Applicants who hold dual citizenship in the U.S. and another country will be considered on a case-by-case basis. All DOJ employees are subject to a residency requirement. Candidates must have lived in the United States for at least three of the past five years. The three-year period is cumulative, not necessarily consecutive. Federal or military employees, or dependents of federal or military employees serving overseas, are excepted from this requirement. This is a Department security requirement which is waived only for extreme circumstances and handled on a case-by-case basis.
Veterans: There is no formal rating system for applying veterans' preference to attorney appointments in the excepted service; however, the Department of Justice considers veterans' preference eligibility as a positive factor in attorney hiring. Applicants eligible for veterans' preference must include that information in their cover letter or resume and attach supporting documentation (e.g., the DD 214, Certificate of Release or Discharge from Active Duty and other supporting documentation) to their submissions. Although the "point" system is not used, per se, applicants eligible to claim 10-point preference must submit Standard Form (SF) 15, Application for 10-Point Veteran Preference, and submit the supporting documentation required for the specific type of preference claimed (visit the OPM website, www.opm.gov/forms/pdf_fill/SF15.pdf for a copy of SF 15, which lists the types of 10-point preferences and the required supporting document(s).
Noncritical-Sensitive (NCS)/Moderate Risk
See the "How To Apply" section of this vacancy announcement.
Lawyer and Attorney Federal jobs in Washington D.C.


Our double award-winning game! Creative Child Magazine chose this game as their 2012 Top Toy of the Year Award AND their 2012 Classic Game of the Year. One board - two games!
"At Maple Landmark, we have been making eco-friendly, educational wooden toys, games and gifts since 1979 at our shop here in Middlebury, Vermont. We pride ourselves in being a local company that supports other local companies who operate in a responsible and sustainable manner. We employ about 40 local folks year around. As a result of these practices we produce products that have always been safe and of superior quality."


Real Gains is a powerful mass gainer that is addressed to you, a top athletic man who wants to receive support when it comes to mass-cultivating. With Real Gains, you will only receive the things that you need, such as calories, protein, premium fats with fiber and an enhanced taste. On the other hand, you will not receive sugar, which is not beneficial.
If you want to gain muscle mass, you need protein, that is found in each scoop of Real Gains. You also want to cut down the amount of fat you receive. Real Gains makes use of only premium whey protein and micellar casein so that you receive top ingredients within the mix. The protein complex inside the product features a high-level amino profile and requires no blender in order to be mixed in a tasty shake.
Complex carbs are also added in high amounts within the product so that you can receive the needed fuel in order to expand your muscles. Carbs provide high amounts of energy and they are ideal if you want to exercise and train hard. Universal also packed inulin in the mix, a dietary fiber that will make sure every other ingredient in the blend is carefully digested. Flaxseed oils and MCT are also found within the mix and they support hormone production, as well as cell protection and growth. However, if you want to benefit the most from Real Gains, you need to combine it with a good diet and a serious workout program.
Directions For Real Gains: Use one serving between meals and at bedtime. May also be used to replace 1 meal per day. Mix 3.5 scoops with 12 to 16 oz of liquid. This formula is highly concentrated, and due to its potency you may want to consider using 1/2 serving two or three times per day.
Warnings: This product is not for use by any individual under the age of 18. Please consult with a physician before using this product. Do not take this product if you have or are at risk for any medical condition or disease including but not limited to diabetes, asthma, depression, recurrent headaches, glaucoma, difficulty urinating, prostate enlargement, seizure disorder, high blood pressure, high cholesterol, arthritis, heart disease, stroke, are pregnant, or are suffering from any inflammatory diseases. Be sure to talk to your physician before using this product if you are using any prescription drug, over-the-counter medication, or supplements. Not for use by children, patients, pregnant or nursing women. Immediately discontinue use and consult your physician if dizziness, sleeplessness, tremors, nervousness, agitation, headache, heart palpitations, or any side effects occur. Discontinue use two weeks prior to surgery. The use of this product may be banned by some athletic associations. Athletes should consult with their sanctioning authority before use. Exercise good judgment and keep this out of reach of children. Store this product in a cool, dry place, away from heat, moisture and sunlight. Do not exceed recommended dose.
Maltodextrin, Real Gains Protein Complex™ [Ultra Filtered Whey Concentrate (Milk), Micellar Casein, Whey Protein Isolate], Dutch Processed Cocoa, Inulin, MCT, Calcium Caseinate, Natural & Artificial Flavors, Lecithin (Soy), Chocolate Fudge Flavor, Sodium Chloride, Flaxseed Oil, Sucralose, Acesulfame Potassium.
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In the past, I've voiced that the non-pervasiveness of Flash (rather an average user's ability to install plug-ins) is one reason website designers should consider before going for Flash. I officially scrap that as a point no longer valid.
Millward Brown, one of the world's leading market research agencies, recently closed a survey commissioned by Adobe Systems, Inc. with a goal "to track and compare Plug-In technology used to view content on the web." This study, conducted quarterly, placed Adobe Flash Player at a comfortable 99% as the most pervasive software platform. Java (85%), Microsoft Windows Media Player (79.3%), Apple QuickTime Player (65.5%) and Adobe Shockwave Player (59.2%) round up the top five. The maximum margin of error was reportedly +/-6%.
Pervasiveness, or penetration, is defined based on the presence of a platform on Internet-enabled desktops in mature markets (US, UK, Canada, France, Germany and Japan). The study has results from countries like mainland China, South Korea, Russia, India, and Taiwan as well. However, nothing explicit was mentioned about Philippines, Brazil and Romania which according to my latest trend check are among the top 9 regions searching for RIA technologies.
To me, the biggest surprise is the high position of QuickTime. Can anybody explain it?
Visit the methodology page for in-depth details about the study, and the version penetration page for statistics by version.


The following are links to our 2018 FRC Chairman's Award essay as well as our 2018 video. The Chairman's award recognizes a team that exemplifies FIRST values: a team that other teams can look up to. This team embodies all the ideals of FIRST including gracious professionalism. The award application includes an essay in addition to short answer responses and a video, all of which explain how our team exemplifies the ideas of FIRST.


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handysize bulk carrier dimensions
Handy Bulk Carriers. 1 day of time charter Unit of Trading. Cargo Capacity 98% INCL. Compared to larger bulk carriers, handysizes carry a wider variety of cargo types. Helm - the steering wheel of a ship which steers the ship by turning its rudder. Panamax. Their small size allows these vessels to enter smaller ports to pick up cargoes, and because in most cases they are 'geared' - i.e. They are mainly used in transporting finished petroleum products and for bulk cargo. HANDYSIZE. Diese Website benutzt Cookies. Tufton Oceanic Assets said the yield during the charter was in line with targets it expressed in its 25 September 2018 prospects. Ship Chartering. Chinamax vessels are classified under Very Large Carriers (VLOC) and happen to be one of the largest bulk carrier ships in the world. But while measuring a Chinamax, even its length is taken into consideration. Handysize vessels have a carrying capacity of 15,000 to 35,000 DWT and range in length from 130m to 150m with a 10m draught. Handysize is also sometimes used to refer to the span of up to 60,000 tons, with the vessels above 35,000 tonnes *Numbers of vessels may vary in real time. Ship owner Tufton Oceanic Assets said it had acquired a handysize bulk carrier for $10.6 million, taking its fleet to 19 vessels. Handymax and Supramax are naval architecture terms for the larger bulk carriers in the Handysize class. Capesize. Handysize is a naval architecture term for smaller bulk carriers or oil tanker with deadweight of up to 50,000 tonnes, although there is no official definition in terms of exact tonnages. Phone: +971 4 2046500 SLOPS (M3/BBLS) - 41,858 / 263,284 ; Cargo Capacity 98% EXCL. The larger dry cargo vessels, Baby Capes, mainly transport iron ore and coal, while the Post Panamax and Panamax vessels also carry grains. One cent ($0.01) per Charter Day Last Trading Day. Handysize is numerically the most common size of bulk carrier, with nearly 2000 units in service totalling about 43 million tons. Most of the cargo ships are measured in terms of size. fitted with cranes - they can often load and discharge cargoes at ports which lack cranes or other cargo handling systems. Gearless container carriers 8 They typically contain five cargo holds for diversified storage with four on-deck cranes. CARGO. Consider the case of Handysize bulk carrier vessel with the following specifications: • About 33,00 DWT • Grain/Bale: 46,000/45,000 (m3) • 1,625,000/1,590,106 cubic feet (1 cubic foot = 0.0283) The ship has the option to load the following three alternative cargoes: 1. Handysize is also sometimes used to refer to the span of up to 60,000 tons, with the vessels above 35,000 tonnes referred to as Handymax or Supramax. Many translated example sentences containing "Handysize bulk carrier" – French-English dictionary and search engine for French translations. On the basis of a case study of a 30,000 dwt Handysize bulk carrier in compliance with IMO Tier II emission rules, this paper shows the influence on fuel consumption when choosing the new G40ME-B engine compared with existing Handysize bulk carrier engines. These vessels have a large variation in size and characteristics. More; Global Directory. 2. US Dollars and cents Trading Price. Dry bulk carriers. Handysize Bulk Carriers. The scope of the €2m ($2.1m) contract includes providing basic and detailed design, procurement handling and site assistance for the two vessels. Ship Operator. Caption: Bulk carrier La Loirais - Image courtesy of Olaf LDA has declared an option to sell La Loirais to an unrelated third party and the transaction was completed today. These vessels are ideal for small as well as large ports, and so make up the majority of ocean cargo vessels in the world. TCH Contract Size. Bulk Carriers. The Green Award Foundation is active all over the world with the certification scheme for dry bulk carriers and oil tankers that go above and beyond the set standards in cleanliness and safety. These vessels are typically … OLDENDORFF CARRIERS Middle East Ltd. Burj Daman – Office 1005, DIFC P.O. Find the right specialists from across the world. Our vessels make more than 6,000 port calls per year and move over 200 million tons of 100 different dry bulk commodities. Handymax. Handysize is also sometimes used to refer to the span of up to 60,000 tons, with the vessels above 35,000 tonnes referred to as Handymax or Supramax. Handysize class consists of Supramax (50,000 to 60,000 DWT), Handymax (40,000 to 50,000 DWT), and Handy (<40,000 DWT).The ships are used for less voluminous cargoes, and different cargoes can be carried in different holds. Download our brochure. Polish Bulk Carrier Kociewie in the Port of Hamburg. 3 cranes … The "Panamax" size gets its name from being the largest size bulk freighter that can routinely pass through the Panama … Taking a cue from the other dry bulk indices, the Handies pushed through our 450 – 475 resistance ideas from Week 21. This is the maximum size ship that can pass through the locks of the Panama Canal. 6 Months Derivatives Data. Any multiple of 1 day of time charter Currency. Geared tweendecker. Our global fleet translates into the possibility of optimizing your supply chain and stocks by scaling up or down in shipment size. Major bulk carrier size categories: Name Size in DWT Ships Traffic New price Used price; Handysize: 10,000 to 35,000: 34%: 18%: $25M: $20M: Handymax: 35,000 to 59,000: 37%: Panamax: 60,000 to 80,000: 19%: 20%: $35M: $25M: Capesize: 80,000 and over : 10%: 62%: $58M: $54M: Bulkers are segregated into six major size categories: small, handysize, handymax, panamax, capesize, and very large. $1.00 per Charter Day Settlement Price. Initially, these vessels were designed for the Chinese port facilities (as the name suggests) and Brazil in South Africa. Sizing for bulk freighters is as follows: Approximately 20,000 DWT - Handysize Approximately 45,000 DWT - Handymax size Approximately 80,000 DWT - Panamax size 80,000 - 200,000 DWT - Capesize Handysize ships often operate coastal routes that don't take them very far to sea. • Bulkcarriers were remotely originated in the ships for bulk cargo that appeared in Great Lakes of the EUA for the carriage of iron ore. Chartering. Our Fleet - Dry Bulk Carriers. 70 1 minute read. Dubai. More; Expanding Horizons. with large dimensions. Handysize Contract Symbol. WORLD BULK CARRIER FLEET (*) PROFILE AS OF JANUARY 1, 2016 SIZE GROUP BY DWT AVERAGE DIMENSIONS, SPEED IN SERVICE ON ORDER LENGTH (Feet) BEAM (Feet) DRAFT (Feet) SPEED (Knots) SHIPS DWT (000s) Average DWT SHIPS DWT (000s) Average DWT Handy Size (10,000-29,999 dwt) 10,000-19,999 455.1 70.2 26.9 13.1 614 9,031 14,708 17 214 12,588 20,000-24,999 519.1 81.0 31.2 … Handysize is also sometimes used to refer to the span of up to 60,000 tons, with Handymax being a subclassification, rather than a larger category. Nova Marine Carriers adds handysize bulker pair. Handysize (15-35 000 tons) 41; Supramax (35-60 000 tons) 43; Panamax bulker (60 000 - 80 000mt DWT) 15; Sea going dry cargo vessels 238; Sea-River Dry Cargo Vessels 28; Sea-River Tankers 8; Ferries 37. Handysize (15-35 000 tons) 41; Supramax (35-60 000 tons) 43; Panamax bulker (60 000 - 80 000mt DWT) 15; Sea going ... Craned bulk carrier of 28685 mt DWT built in 2000 in Japan with box type holds. Box 507058, Dubai, United Arab Emirates. 228m Kamsarmax Size Bulk Carrier 81812 DWT - 2015: 748' 105' 43' 2015: 81812: NK: Non US: Japan: POR: 8402: 228m Kamsarmax General Cargo Bulk Carrier 2011 - DWT 82165: 748' 105' 66' 2011: 82165: 13205: NK: Non US: UAE: POR: 7645: 228m Kamsarmax General Cargo Bulk Carrier 81955 DWT - 2014: 748' 105' 2014: 81955: NK: Non US: China: POR: 6685: 225m Panamax Bulk Carrier Built 1999 - DWT … The main dimensions for this en-gine type, and for other existing Handy-size bulk carrier (and tanker) engines, are shown in Fig. Top-quality vessels. 17299 tons DNV GL. 4 cranes x 30.5 tons. OK Upon request. Bulk Carrier Time Charter Rates. The main dry bulk carriers we operate are Handysize, Supramax, Panamax, Post Panamax and Baby Cape. Dry bulk carriers Supramax Great Lakes only Seawaymax >Suez >Suez Size category Small Handysize Large Handysize Handymax Large Handymax Panamax New Panamax Mini Capesize Capesize DWT-range 10,000 - 24,999 dwt 25,000 - 39,999 dwt 40,000 - 49,999 dwt 50,000 - 59,999 dwt60,000 - 99,999 dwt 85,000 - 129,999 dwt 130,000 dwt > Nicola Capuzzo January 17, 2020. SLOPS (M3/BBLS) - 40,804 / 256,631; Segregation # - Seven Taking another surge for Week 29, the Handysize Index rose 20 points to a 492 fix. One cent ($0.01) per Charter Day Minimum Price Fluctuation . Our Locations . All efforts are made to benefit the marine environment, including cleaner seas. These vessels fall into roughly eight different sizes. La Loirais is a Handysize dry bulk vessel built in 2018 and employed on a long-term charter to Louis Dreyfus Armateurs (LDA). B.Delta26LNG handysize bulk carrier design and features. These are a larger version of the Handysize vessels and popular for both bulk and crude carriers. Geared tweendecker of 17299 mt DWT with ice class 1A built in 2008 in China. The carrier had a fixed-rate time charter for 10-to-13 months to a large maritime transport and logistics company. Geared tweendecker TBN0640. Ro-Pax 16; Passenger Ferries 6; Ro-Ro 22; Container Carriers 36. To get your credentials for the first time please go to the subscription … Handysize Bulk Carrier - are small-sized ships with a capacity ranging between 15,000 and 35,000 DWT. HANDYSIZE BULK CARRIER FEATURES . The Supramax and Handysize vessels additionally transport steel, cement, sugar, fertilizers and other types of cargo. Panamax Size: 50,001 dwt - 80,000 dwt. Dry bulk carriers transport large quantities of dry goods. Even in 1900 these ships attained lengths of about 150 m. • The current configuration dates from the years 1960s. Wenn du die Website weiter nutzt, gehen wir von deinem Einverständnis aus. Handysize. Shipowners. US Gulf Dry Bulk Handysize Routes to rest of the world, voyage price in $/mT, charter price in $/day.. Because you are not logged, data on display are delayed by 60 days. Handysize is a naval architecture term for smaller bulk carriers or oil tanker with deadweight of up to 50,000 tonnes, although there is no official definition in terms of exact tonnages. Handysize is a naval architecture term for smaller bulk carriers or oil tanker with deadweight of up to 50,000 tonnes, although there is no official definition in terms of exact tonnages. HANDYSIZE HS2 to HS5 in $/mT. 2008 . Handymax Size: 30,001 - 50,000 dwt. Handysize is a naval architecture term for smaller bulk carriers or oil tanker with deadweight of up to 50,000 tonnes, although there is no official definition in terms of exact tonnages. Handysize can refer either to a bulk carrier or tanker. Iron ore SF: 14 cubic feet per ton / … More; Free Charts. 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handysize bulk carrier dimensions 2021


text:
Fancy Some Classic Carson? TCM Licenses 50 Interviews From 'The Tonight Show Starring Johnny Carson'
Fancy Some Classic Carson? TCM Licenses 50 Interviews From 'The Tonight Show Starring Johnny Carson'
Alison Willmore
@alisonwillmore
Here's Johnny… on Turner Classic Movies. The network, best known for its commercial-free programming of older films from the Turner Entertainment library, has licensed 50 interviews from "The Tonight Show Starring Johnny Carson," many of which have not aired since they first premiered, for broadcast in the summer of 2013 under the title "Carson on TCM."
Among the interviewees: Bette Davis, Henry Fonda, William Holden, Gregory Peck, James Stewart, Elizabeth Taylor, Mel Brooks, Chevy Chase, Steve Martin, Burt Reynolds, Robin Williams, a seven-year-old Drew Barrymore, Truman Capote, Sally Field, a pre-"Annie Hall" Diane Keaton, Ronald Reagan (almost a decade before his presidency) and Susan Sarandon.
"Carson on TCM" is the brainchild of producer Peter Jones, the writer and co-director of the "Johnny Carson: King of Late Night" episode of PBS's "American Masters." "What Johnny did better than anyone was to engage his guests in wonderful conversation," said Jones. "I heard from many of the people who appeared on 'The Tonight Show' over the years that Johnny had the ability to bring out the best in the people he interviewed. He listened carefully and made his guests feel as though they were simply talking to a friend."
In 2006, TCM set up a similar licensing deal with select episodes of "The Dick Cavett Show," including interviews with Katharine Hepburn, Alfred Hitchcock and Groucho Marx.
Take a look at a promo for "Carson on TCM" below.
This Article is related to: Television and tagged TCM, Television, The Tonight Show, TV News


text:
in Game Video and Trailers, Video Games
X-Men: Destiny launch trailer before quiet Xbox 360, PS3 & PC release
by Chris Cook September 28, 2011, 11:56
Several big name titles are set to release in the up-coming quarter this year, such as Modern Warfare 3, Battlefield 3 and Batman: Arkham City to name but a few, each of which has been well documented by us and other websites. One game that is also set to release which has seemingly been trying to keep off the radar is X-Men: Destiny.
The game is developed by Silicon Knights, the team behind Xbox 360 exclusive Too Human and we first brought you news about it back in December last year. Since then it has been keeping a relatively low profile, until it was announced by Activision, the game's publisher, recently to be coming out this week, Friday to be exact. To mark the launch, the team has also released a trailer, which can be viewed below.
X-Men: Destiny is an RPG that allows players to take control of one of three exclusive characters, Aimi, Grant or Adrian. After selecting the character, players must build their own legacy by customizing their powers and increasing their reputation as a legendary mutant. They can choose to side with either the Brotherhood or the X-Men. As they progress through the levels, they can acquire various other X-Genes, the strand of gene that gives mutants their powers, which they can then use for their own.
CVG were able to give a full account of what they thought of the game based on their hands-on time they had with it. Despite being a little skeptical, given the reputation of the developers, they remained optimistic about X-Men: Destiny as they are self-confessed 'die-hard Marvel fans.' You can check out their full review by clicking the link above.
Will you be getting X-Men: Destiny this Friday?
Written by Chris Cook
Chris enjoys reading most types of news, which includes gaming for the PS3, Xbox 360, and other popular gaming devices. His passion for sports, music, and the latest technology is shown in the news he reports. While the Internet keeps everyone connected, Chris has a keen interest to view the world first hand. This aim is made more possible thanks to being able to report news online from anywhere in the world.
Activisionsilicon knightsX-Men Destiny
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text:
'Little Women' Trailer: Watch It Here
Timothée Chalamet & Saoirse Ronan Reunited in First Trailer for 'Little Women'
Writer-director Greta Gerwig calls on two of her Lady Bird castmates — Timothée Chalamet and Saoirse Ronan — to star in her adaptation of Louisa May Alcott's iconic 1868 novel, Little Women. With the film due out later this year, today we receive the official trailer for the anticipated remake.
Gerwig's Little Women is told from the perspective of Alcott's alter ego, Jo March. The movie centers around the March sisters, four young women who are determined to live life on their own terms. Jo, Meg, Amy, and Beth March are played by Saoirse Ronan, Emma Watson, Florence Pugh, and Eliza Scanlen, respectively, as Timothée Chalamet stars as their neighbor Laurie. Laura Dern then portrays Marmee, and Meryl Streep is Aunt March.
Written and directed by Greta Gerwig, 2019's Little Women comes to theaters December 25.
Timothée Chalamet Feels the Weight of the Crown in First Trailer for Netflix's 'The King'
MoviessponsoredAugust 27, 2019
Lupita Nyong'o Stars in First Trailer for Zombie Comedy 'Little Monsters'
MoviessponsoredJuly 31, 2019
Timothée Chalamet Battles Robert Pattinson in Final Trailer for Netflix's 'The King'
MoviessponsoredOctober 24, 2019
Gal Gadot Rides Lightning in First 'Wonder Woman 1984' Trailer & Twitter Is Obsessed
MoviessponsoredDecember 9, 2019
'Charlie Says' Follows the Women Who Killed for Charles Manson
MoviessponsoredMarch 13, 2019


text:
require "spec_helper"

describe Mongoid::Findable do

  describe ".distinct" do

    before do
      Band.create(name: "Tool")
      Band.create(name: "Photek")
    end

    it "returns the distinct values for the field" do
      Band.distinct(:name).should eq([ "Tool", "Photek" ])
    end
  end

  describe ".each" do

    let!(:band) do
      Band.create
    end

    it "iterates through all documents" do
      Band.each do |band|
        band.should be_a(Band)
      end
    end
  end

  describe ".each_with_index" do

    let!(:band) do
      Band.create
    end

    it "iterates through all documents" do
      Band.each_with_index do |band, index|
        index.should eq(0)
      end
    end
  end

  describe ".find_and_modify" do

    let!(:person) do
      Person.create(title: "Senior")
    end

    it "returns the document" do
      Person.find_and_modify(title: "Junior").should eq(person)
    end
  end

  describe ".find_by" do

    context "when the document is found" do

      let!(:person) do
        Person.create(title: "sir")
      end

      context "when no block is provided" do

        it "returns the document" do
          Person.find_by(title: "sir").should eq(person)
        end
      end

      context "when a block is provided" do

        let(:result) do
          Person.find_by(title: "sir") do |peep|
            peep.age = 50
          end
        end

        it "yields the returned document" do
          result.age.should eq(50)
        end
      end
    end

    context "when the document is not found" do

      context "when raising a not found error" do

        before do
          Mongoid.raise_not_found_error = true
        end

        it "raises an error" do
          expect {
            Person.find_by(ssn: "333-22-1111")
          }.to raise_error(Mongoid::Errors::DocumentNotFound)
        end
      end

      context "when raising no error" do

        before do
          Mongoid.raise_not_found_error = false
        end

        after do
          Mongoid.raise_not_found_error = true
        end

        context "when no block is provided" do

          it "returns nil" do
            Person.find_by(ssn: "333-22-1111").should be_nil
          end
        end

        context "when a block is provided" do

          let(:result) do
            Person.find_by(ssn: "333-22-1111") do |peep|
              peep.age = 50
            end
          end

          it "returns nil" do
            result.should be_nil
          end
        end
      end
    end
  end

  describe ".first_or_create" do

    context "when the document is found" do

      let!(:person) do
        Person.create
      end

      it "returns the document" do
        Person.first_or_create.should eq(person)
      end
    end

    context "when the document is not found" do

      context "when providing a document" do

        let!(:person) do
          Person.create
        end

        let(:from_db) do
          Game.first_or_create(person: person)
        end

        it "returns the new document" do
          from_db.person.should eq(person)
        end
      end

      context "when not providing a block" do

        let!(:person) do
          Person.first_or_create(title: "Senorita")
        end

        it "creates a persisted document" do
          person.should be_persisted
        end

        it "sets the attributes" do
          person.title.should eq("Senorita")
        end
      end

      context "when providing a block" do

        let!(:person) do
          Person.first_or_create(title: "Senorita") do |person|
            person.pets = true
          end
        end

        it "creates a persisted document" do
          person.should be_persisted
        end

        it "sets the attributes" do
          person.title.should eq("Senorita")
        end

        it "calls the block" do
          person.pets.should be_true
        end
      end
    end
  end

  describe ".first_or_initialize" do

    context "when the document is found" do

      let!(:person) do
        Person.create
      end

      it "returns the document" do
        Person.first_or_create.should eq(person)
      end
    end

    context "when the document is not found" do

      context "when providing a document" do

        let!(:person) do
          Person.create
        end

        let(:found) do
          Game.first_or_initialize(person: person)
        end

        it "returns the new document" do
          found.person.should eq(person)
        end

        it "does not save the document" do
          found.should_not be_persisted
        end
      end

      context "when not providing a block" do

        before do
          Person.delete_all
        end

        let!(:person) do
          Person.first_or_initialize(title: "esquire")
        end

        it "creates a non persisted document" do
          person.should_not be_persisted
        end

        it "sets the attributes" do
          person.title.should eq("esquire")
        end
      end

      context "when providing a block" do

        let!(:person) do
          Person.first_or_initialize(title: "Senorita") do |person|
            person.pets = true
          end
        end

        it "creates a new document" do
          person.should_not be_persisted
        end

        it "sets the attributes" do
          person.title.should eq("Senorita")
        end

        it "calls the block" do
          person.pets.should be_true
        end
      end
    end
  end

  describe ".pluck" do

    let!(:depeche) do
      Band.create(name: "Depeche Mode", likes: 3)
    end

    let!(:tool) do
      Band.create(name: "Tool", likes: 3)
    end

    let!(:photek) do
      Band.create(name: "Photek", likes: 1)
    end

    context "when field values exist" do

      let(:plucked) do
        Band.pluck(:name)
      end

      it "returns the field values" do
        plucked.should eq([ "Depeche Mode", "Tool", "Photek" ])
      end
    end

    context "when field values do not exist" do

      let(:plucked) do
        Band.pluck(:follows)
      end

      it "returns an empty array" do
        plucked.should be_empty
      end
    end
  end

  Origin::Selectable.forwardables.each do |method|

    describe "##{method}" do

      it "forwards the #{method} to the criteria" do
        Band.should respond_to(method)
      end
    end
  end
end

redpajama_set_name: RedPajamaGithub