text:
\section{Introduction}

The task of 3D reconstruction from monocular video is a longstanding task in computer vision. The state-of-the-art algorithm for dense monocular 3D reconstruction involves steps including Simultaneous Localization and Mapping (SLAM) or Structure from Motion (SfM) to get a semi-dense or sparse 3D reconstruction and camera pose estimates. Subsequently multi-view stereo (MVS) methods are used to get dense 3D reconstructions. Despite the progress in current visual SLAM \cite{LSDSLAM,ORBSLAM,DTAM}, SfM \cite{COLMAP,wu2013towards,agarwal2011building,frahm2010building} and MVS algorithms \cite{schonberger2016pixelwise,furukawa2010towards,furukawa2010accurate}, this reconstruction pipeline still has some inherent limitations; it can only work in static scenes with rigid objects; it requires a sufficient motion baseline for the cameras; and it assumes static lighting condition and Lambertian surface reflection. Our algorithm addresses all three limitations to enable highly flexible, video-based joint camera motion and dense geometry estimation.

Recently, convolutional neural networks (CNN) \cite{liu2015deep,eigen2015predicting,garg2016unsupervised,zhou2017unsupervised,ummenhofer2017demon} have began to produce results of comparable quality to traditional geometric computer vision methods for depth estimation. However, most methods can take only a single frame or pair of frames as input, or report no benefit from additional frames. For example, Zhou \textit{et al.} \cite{zhou2017unsupervised} report that adding more frames for their technique does not improve the estimation accuracy, as their CNN can only capture the spatial relationships of the input. When their network receives stacked images as input, the temporal ordering is lost.

\begin{figure}[h]
    \centering
    \includegraphics[width=12.5cm]{teasor.png}
    \caption{Our proposed DenseSLAMNet takes successive video frames as input and outputs a high quality depth map and camera pose for every input frame.}
\end{figure}

We have developed a recurrent neural network (RNN) for dense visual SLAM that simultaneously estimates the camera poses and dense depth maps from a video sequence taken by a monocular camera. In an RNN, the input to each layer includes information about the previous prediction, and thus explicitly takes temporal information into account. As far as we know, this is the first learning-based dense SLAM method that can estimate camera motion and dense depth maps in an unconstrained multi-view environment. We have improved upon existing deep single- and two-view stereo depth estimation methods by interleaving Long Short-Term Memory (LSTM) units with convolutional layers to effectively utilize multiple previous frames in each estimated depth maps.

In this paper, we present DenseSLAMNet, a network that can sequentially estimate depths and camera motion from monocular video. Our primary innovation is to incorporate LSTM units, commonly used in natural language processing, into a depth estimation network. These LSTM units allow the depth and camera motion estimation to become a multi-view process. We evaluate our network on several 3D benchmark datasets (SUN3D, RGBD-SLAM, NYUDepthV2, KITTI, and Make3D) and real patient endoscopic data. We analyze the effectiveness of our method on both deformable and rigid scenes. We summarize our contributions as follows:
\begin{itemize}
    \item We introduce a new RNN architecture for depth estimation from multiple views.
    \item We show that our multi-view depth estimation outperforms existing single-view methods.
    \item We demonstrate the successful application of our framework on endoscopic videos, a particularly challenging data modality for depth estimation.
\end{itemize}

\section{Related work}

SfM and SLAM are the two most prevalent frameworks for sparse 3D reconstruction of rigid geometry from images. SfM is typically used for offline 3D reconstruction from unordered image collections, while visual SLAM aims for a real-time solution using a single camera \cite{MONOSLAM,PTAM}. Sch\"{o}nberger and Frahm \cite{COLMAP} review the state-of-the-art in SfM and propose an improved incremental SfM method. More recent works on sparse SLAM systems include ORB-SLAM \cite{ORBSLAM} and DSO \cite{DSO}.

While sparse methods use detected feature points for reconstruction, dense (or semi-dense) methods attempt to reconstruct all pixels from the 2D image. LSD-SLAM \cite{LSDSLAM} is a semi-dense SLAM method that operates directly on image intensities both for tracking and mapping. The DTAM framework \cite{DTAM} creates a dense 3D surface model through direct dense image registration and immediately uses it for camera tracking. Our DenseSLAMNet falls into this category of dense reconstruction methods. Multi-view stereo (MVS) \cite{schonberger2016pixelwise,furukawa2010towards,furukawa2010accurate} is another dense reconstruction method that generates dense depth maps using camera poses and raw image data. Frequently, MVS is used together with SfM. All above sparse and dense reconstruction methods require a static scene, constant illumination, and sufficient camera motion baseline for accurate reconstruction. In this paper, we present a method that can perform single- and multi-view dense 3D reconstruction for both static and deformable scenes with either constant or inconsistent light conditions.

Recently, researchers have started to apply CNNs to the 3D reconstruction problem. Eigen \textit{et al.} \cite{eigen2015predicting} and Liu \textit{et al.} \cite{liu2015deep} propose end-to-end networks, while other work has used CNNs for components of the pipeline, including correspondence matching \cite{yi2016lift,ilg2017flownet},
camera pose estimation \cite{kendall2015posenet}, and stereo \cite{luo2016efficient,kendall2017end}. Common output representations include depth maps, point clouds, and voxels. The advantage of these learning-based methods over the classical SfM-MVS pipeline is that we can leverage semantic supervision during the training process. This can lead to better reconstructions of texture-less or occluded surfaces and very thin structures, both of which are challenging for purely geometric techniques.

A particular case of dense geometry estimation is monocular depth estimation. Monocular depth estimation has gained interest because regressing the depth representation is similar to the segmentation problem and thus the structure of CNNs can be easily adapted to the task of depth estimation \cite{FCN}. Eigen \textit{et al} \cite{eigen2015predicting} proposed an early multi-scale, end-to-end, per-pixel depth estimation framework. Laina \textit{et al}. \cite{laina2016deeper} extended Eigen's work with a deeper residual network. More recently, incorporating elements of view synthesis \cite{zhou2016view} and Spatial Transform Networks \cite{jaderberg2015spatial}, Gordard \textit{et al}. \cite{godard2017unsupervised}, Garg \textit{et al}. \cite{garg2016unsupervised}, Zhou \textit{et al}. \cite{zhou2017unsupervised}, have trained end-to-end monocular depth estimation networks without ground-truth. This was done by transforming the depth estimation problem into an image reconstruction problem where the depth is the intermediate product that integrates into the image reconstruction loss. Despite the fact that these unsupervised depth estimation methods eliminate the complication of obtaining ground-truth depth, none outperform the traditional SfM or SLAM methods \cite{COLMAP,agarwal2011building,DTAM,LSDSLAM}.

Two-view or multi-view stereo methods have traditionally been the most common techniques for dense depth estimation. For the interested reader, Scharstein and Szeliski \cite{scharstein2002taxonomy} give a comprehensive review on two-view stereo methods. Newcombe \textit{et al.} \cite{DTAM} demonstrates that estimated depth accuracy becomes more precise as the number of views increases, even with small baseline motion. We leverage this result, explicitly learning correspondences between nearby frames which results in a similar multi-view benefit.  Recently, Ummenhofer \textit{et al}. \cite{ummenhofer2017demon} formulated two-view stereo as a learning problem. They showed that by explicitly incorporating dense correspondences estimated from optical flow into the two-view depth estimation, they can force the network to utilize stereo information on top of the single view priors. There is currently a very limited body of CNN based multi-view reconstruction methods. Choy \textit{et al}. \cite{choy20163d} use an RNN to reconstruction the object in the form of a 3D occupancy grid from multiple viewpoints. Rezende \textit{et al}. \cite{rezende2016unsupervised} introduced a family of generative models of 3D structures and recover these structures from 2D images via probabilistic inference. They learn the complex 3D to 2D projection through a generative model in an unsupervised way. However, these methods target single object reconstruction and fail for deformable objects.

Our approach is most closely related to dense visual SLAM in that camera motion and depth maps are estimated from multiple views in a sequential manner.  Tanteno \textit{et al}.\cite{tateno2017cnn} proposed CNN-SLAM, which predicts a depth map as an initial guess and subsequently refines it with a direct SLAM scheme relying on small-baseline stereo matching. Our DenseSLAMNet, as shown in Figure \ref{refine}, implicitly performs the small-baseline refinement via the information preserved across time-steps by the hidden layers of the LSTM.

 \begin{figure}[h]
    \centering
    \includegraphics[width=11cm]{eg3}
    \caption{An example of the small-baseline refinement using LSTM layers.}
    \label{refine}
\end{figure}

\section{Network architecture}

\begin{figure}[h]
    \centering
    \includegraphics[width=12cm]{overall}
    \caption{Overall network architecture of DenseSLAMNet.}
    \label{overall}
\end{figure}

Our DenseSLAMNet can simultaneously estimate dense depth maps and camera poses from a monocular video sequence under different scenarios (indoor, outdoor, endoscopy). We incorporate recurrent units into a CNN to leverage temporal information in our depth estimation, making it more accurate for continuous video sequences. However, unlike DeMoN \cite{ummenhofer2017demon} which is restricted to two-view input, our DenseSLAMNet takes a single frame at a time as input, but can operate over longer image sequences. It can also perform single-view depth estimation when required. Although our network incorporates temporal information through the recurrent units, it operates on each individual frame independently during training. This is contrary to Zhou \textit{et al}. \cite{zhou2017unsupervised}, Godard \textit{et al}. \cite{godard2017unsupervised}, and dense SLAM methods \cite{LSDSLAM,ORBSLAM,DTAM} which utilize relative geometry between frames. Therefore, our method is not restricted to static scenes or constant scene illumination. Figure \ref{endo} (a) shows an example of method estimating depth from endoscopic videos, where the scene frequently deforms and the light source moves with the camera, changing the scene illumination throughout the video sequence.

The overall architecture of our network is shown in Figure \ref{overall}. It takes a single RGB frame $I_t$ and the hidden states $h_{t-1}$ from the previous time step as input. The hidden states are transmitted internally through the LSTM units. The output of our network is the depth map $z_t$ and the camera pose $\{R_t, T_t\}$ of the current frame. Similar to single-view depth estimation networks, our DenseSLAMNet takes only a single frame at a time as input. Therefore, our network can perform both single-frame and multi-frame depth estimation. This makes our DenseSLAMNet more flexible than both CNN-based single-view depth estimation methods and visual SLAM methods.

\begin{figure}[h]
    \centering
    \includegraphics[width=12.5cm]{detail.png}
    \caption{(Best viewed in color) Our network architecture at a single time step. We use the DispNet architecture. The width and height of each rectangular block indicates the size and the number of the feature map at that layer. Each increase and decrease of size represents a change factor of 2. The first convolutional layer has 32 feature maps. The kernel size for all convolution layers is 3, except for the first two convolution layers, which are 7 and 5, respectively.}
    \label{detail}
\end{figure}

Figure \ref{detail} shows our network at a single time step in more detail. Different colors encode the different units: yellow is a convolutional layer, red is an LSTM block, dark gray is a deconvolutional layer, and blue is and input/output layer. Our network uses a U-shape network architecture similar to  DispNet \cite{mayer2016large}. The height of each rectangle in Figure \ref{detail} represents the size of its feature maps, where each smaller feature map is half the size of the preceding feature map. The down-sampling from a previous layer to the next is done by a stride-2 convolution instead of max-pooling. The lines connecting corresponding layers in the encoder and decoder are skip-connections.

We denote the size of our temporal window by $N$. In all experiments, we use $N=10$ as the length of our temporal sequence. Hence, the network in Figure \ref{detail} is replicated 10 times as shown in Figure \ref{overall}, with the temporal information being passed between the three LSTM blocks at each time-step.

\section{Training procedure}

For the ease of training and data preparation, we use a temporal window size of $N=10$, but ideally, similar to natural language processing, the network should take an arbitrary length sequence as input for training. During training, we feed frames to the network and compute losses from all frames in a temporal window. However, there is no input length constraint at test time. Even though the network can only store information from up to ten frames, longer sequences can still yield better results because the each prior frame has already been boosted by its previous ten frames. Figure \ref{training} shows a example of our training data.

\begin{figure}[h]
    \centering
    \includegraphics[width=12.5cm]{training.png}
    \caption{Example training data with a temporal window size of ten.}
    \label{training}
\end{figure}

\subsection{Loss function}

Our loss function is a composition of a point-wise depth loss, a camera pose loss, and a scale-invariant gradient loss. Similar to DeMoN \cite{ummenhofer2017demon}, we use disparity, the reciprocal of depth, $\xi=\frac{1}{z}$ as our direct estimation because it can represent points at infinity and account for the localization uncertainty of points at increasing distance. For camera pose, we use the Euler angle $R$ and the translation vector $T$. In total there are 6 parameters in the pose parameterization.
Our point-wise depth loss is formulated as follows:
\begin{equation}
L_{depth} = \sum_{t}^N\sum_{i,j}|\xi_t(i,j)-\hat{\xi_t}(i,j)|
\end{equation}
where $i,j$ is the pixel location in a depth map and $t$ represents the time-step. In this work, we use temporal window is $N=10$, so $t \in [0,9]$.
For depth we use an $L_1$ loss due to its robustness to noise. The overall depth loss integrates over all pixels as well as all frames in a temporal window.

To ensure the smoothness and sharpness of the estimated depth, we have adopted a loss on a scale-normalized gradient-like measurement, as introduced by Ummenhofer \textit{et al.} \cite{ummenhofer2017demon}. This loss is defined as
\begin{equation}
L_{grad} = \sum_t\sum_{h\in\{1,2,4,8,16\}}\sum_{i,j}||g_{h,t}(i,j)-\hat{g}_{h,t}(i,j)||_2
\end{equation}
where $h$ is a spatial step size for computing $g_{h,t}$ at different scale. The vector $g_{h,t}$ is a scale-normalized, discretized measurement of the local changes of $\xi_t$. The measurement is defined as
\begin{equation}
g_{h,t} = (\frac{\xi_t(i+h,j)-\xi_t(i,j)}{|\xi_t(i+h,j)|+|\xi_t(i,j)|}, \frac{\xi_t(i,j+h)-\xi_t(i,j)}{|\xi_t(i,j+h)|+|\xi_t(i,j)|})^T
\end{equation}

$L_{grad}$ in Eq. (2) emphasizes the depth discontinuities, such as occlusion boundaries and sharp edges, as well as the smoothness in homogeneous regions. This property encourages the estimated depth map to preserve more details and reduce noise. Therefore, we put highly weight on this component of the loss.

For the camera poses, the losses are weighted separately for rotation $R$ and translation $T$.
\begin{equation}
\begin{aligned}
L_{rot} &= \sum_t||r_t-\hat{r_t}||_2\\
L_{trans} &= \sum_t||t_t-\hat{t_t}||_2
\end{aligned}
\end{equation}

The overall loss is a weighted sum of $L_{depth}$, $L_{grad}$, $L_{rot}$, and $L_{trans}$, where the weights are chosen empirically.

\textbf{Training details.} We set the weights for depth loss, scale-invariant gradient loss, camera rotation loss, and camera translation loss to 500, 1000, 500, and 100, respectively. We use the Adam \cite{kingma2014adam} optimizer with $\beta_1=0.9$, $\beta_2=0.999$. The initial learning rate is 0.0002 and decays exponentially every 10,000 steps by a factor of 0.9. For indoor scenes and endoscopic data, we resized the images to 192$\times$256. For outdoor scenes we resize the images to 128$\times$416. The image sizes are chosen for both computational efficiency and to be consistent with existing methods. We trained and evaluated our network on indoor and outdoor scenes separately. Different datasets have different camera intrinsic parameters, so we explicitly crop and resize images to ensure uniform intrinsic parameters. This step assures that the non-linear mapping between color and depth is consistent across all training datasets.

\section{Experiments}
We evaluate our method on multiple datasets and compare against the state-of-the-art for learning-based, depth estimation methods.

\subsection{Training datasets}

\textbf{Indoor}. We use two publicly available datasets for indoor scenes. The first one is \textbf{SUN3D} \cite{xiao2013sun3d}, which is a large dataset with ground truth depth maps and camera poses. We selected 192 scenes from a total of 354 scenes as our training data. Then we randomly selected 30 scenes from the remaining 162 scenes as for validation and testing. The second dataset is \textbf{RGBD-SLAM} \cite{sturm12iros}, which is a smaller dataset but with higher camera pose accuracy. RGBD-SLAM provides a training and validation split of their dataset, so we directly use their split. In addition, we used the \textbf{NYUDV2} \cite{Silberman:ECCV12} dataset for generalization evaluation.

\textbf{Outdoor}. We use the KITTI dataset \cite{Geiger2013IJRR} for outdoor scenes. To perform a consistent comparison with existing methods, we used the \textbf{Eigen Split} \cite{eigen2015predicting} to train and evaluate our network. The \textbf{Make3D} \cite{saxena2009make3d} dataset is used to evaluate generalization.

\textbf{Endoscopy} (challenge dataset). We also explore the 3D reconstruction of humans' inner body surfaces from endoscopic videos. For qualitative evaluation and training, we generated an endoscopic dataset containing 65,235 frames of video from 16 patients. We generate depth maps and camera poses using the SFMS method \cite{wang2017improving}. We train our model on 14 patients and test on 2 patients.

\subsection{Evaluation metrics}

At test time, our DenseSLAMNet runs in real-time, at approximately 40 frames-per-second on a machine with a GeForce GTX1080 GPU.

We evaluate DenseSLAMNet using five error metrics:

\begin{equation}
sc-inv(z,\Hat{z})  = \sqrt{\frac{1}{n}\sum_id^2_i-\frac{1}{n^2}(\sum_id_i)^2}
\end{equation}
where $d_i=log_{10}(z_i)-log_{10}(\Hat{z_i})$. $Sc-inv$ is a scale invariant error \cite{eigen2015predicting} that can evaluate depth regardless of scale.

\begin{equation}
Abs-rel(z,\Hat{z}) = \frac{1}{n}\sum_i\frac{|z_i-\Hat{z_i}|}{\hat{z_i}},
Abs-inv(z,\Hat{z}) = \frac{1}{n}\sum_i|\frac{1}{z_i}-\frac{1}{\Hat{z_i}}|
\end{equation}
$Abs-rel$ measures the relative difference of output predictions and the ground truth depth. It emphasizes close objects in the ground truth.
$Abs-inv$ also measures this relative difference, but even further emphasizes closer objects.
\begin{equation}
RMSE(z,\Hat{z})  = \sqrt{\frac{1}{n}\sum_i(z_i-\Hat{z_i})^2},
RMSE-log(z,\Hat{z})  = \sqrt{\frac{1}{n}\sum_i(d_i)^2}
\end{equation}
$RMSE$ and $RMSE-log$ are two of the most commonly used error measurements \cite{eigen2015predicting,zhou2017unsupervised,godard2017unsupervised,garg2016unsupervised,kuznietsov2017semi}, the first measuring absolute depth error and the second measuring absolute log-depth error.

\subsection{Comparison with Existing Methods}\label{sec:compare}

\begin{figure}[h]
    \centering
    \includegraphics[width=12.5cm]{compare1.png}
    \caption{Visual comparison of ours vs. DeMoN's results \cite{ummenhofer2017demon} on SUN3D \cite{xiao2013sun3d} dataset. As can be seen in row (a) and (d), our DenseSLAMNet performs better at large distance.}
    \label{compare}
\end{figure}

We compared our DenseSLAMNet to state-of-the-art CNN-based, single- and two-view depth estimation methods.

We compared to Eigen \textit{et al.} \cite{eigen2015predicting}, Liu \textit{et al.} \cite{liu2015deep}, and DeMoN \cite{ummenhofer2017demon}. Eigen \textit{et al.} and Liu \textit{et al.} are single-frame depth estimation methods, and DeMoN is a two-view depth estimation method. We take their publicly available pre-trained models and test on our prepared testing data. Eigen \textit{et al.} and Liu \textit{et al.} methods are trained on NYUDV2 dataset. DeMoN is trained on several indoor, outdoor, and synthetic datasets, including SUN3D and RGBD-SLAM. In order to evaluate the full capability of our network on single-frame depth estimation, we feed DenseSLAMNet video sequences of size $N=10$ during testing and report results on the last frame. When evaluating against DeMoN, we report their result as the pair of frames within each temporal window that gives best score for their method. Table \ref{table:1} shows that our method outperforms the existing state-of-the-art across every quantitative metric for indoor scenes.

Figure \ref{compare} shows a visual comparison of our DenseSLAMNet with other methods. It can be seen that our DenseSLAMNet produces sharper results than DeMoN, the second best performing method. Rows (a) and (d) in Figure \ref{compare} also demonstrate that we perform significantly better for larger distances. Figure \ref{range} shows a detailed comparison between DeMoN and our method at different depth ranges. We measure the average $sc-inv$ error at different depth ranges, eg. 0 to 1 meters, 1 to 2 meters and so on, across all testing images. As can be seen, our method consistently performs better at all ranges and especially at large distances.

\begin{figure}[h]
    \centering
    \includegraphics[width=10cm]{range_plot.png}
    \caption{Comparison with DeMoN \cite{ummenhofer2017demon} at different ranges. We outperform DeMoN in all ranges, especially at large distance.}
    \label{range}
\end{figure}

\begin{table}[h!]
\centering
\begin{tabular}{ |p{3cm}||p{2cm}|p{2cm}|p{2cm}| }
 \hline
 Methods & Sc-inv & Abs-inv & Abs-rel \\
 \hline
 Eigen \textit{et al.} \cite{eigen2015predicting} &0.190 & 0.068&  0.177\\
 Liu \textit{et al.} \cite{liu2015deep} &0.215 & 0.070&  0.212\\
 DeMoN \cite{ummenhofer2017demon} &0.128 & 0.047&  0.109\\
 \textbf{DenseSLAMNet} & \textbf{0.112} & \textbf{0.039}&  \textbf{0.091}\\
 \hline
\end{tabular}
\caption{Quantitative comparison of our DenseSLAMNet with the state-of-the-art CNN-based methods on SUN3D \cite{xiao2013sun3d} dataset. Lower numbers are better.}
\label{table:1}
\end{table}

\begin{table}[h!]
\centering
\begin{tabular}{ |p{3cm}||p{1.2cm}||p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|  }
 \hline
 Methods & Dataset & Abs-rel & Sq-rel & RMSE& RMSE-log\\
 \hline
 Eigen \textit{et al}. \cite{eigen2015predicting} & K  & 0.203  & 1.548 & 6.307 & 0.282 \\
 Liu \textit{et al}. \cite{liu2015deep} & K &   0.202  &  1.614  & 6.523 & 0.275 \\
 Kuznietsov \textit{et al.} \cite{kuznietsov2017semi} & K & 0.113 & 0.741 & \textbf{4.621} & 0.189\\
 Zhou \textit{et al.} \cite{zhou2017unsupervised} & CS+K & 0.198 & 1.836 & 6.565 & 0.275 \\
 Godard \textit{et al.} \cite{godard2017unsupervised} & CS+K & \textbf{0.097} & 0.896 &  5.093 & \textbf{0.176} \\
 \textbf{DenseSLAMNet} & K & 0.129 & \textbf{0.704}&  4.743 & 0.199 \\
 \hline
 \hline
 \textbf{DenseSLAMNet} & K & \textbf{0.058} & \textbf{0.205}&  \textbf{2.538}& \textbf{0.087} \\
 \hline
\end{tabular}
\caption{Quantitative comparison of our DenseSLAMNet with other state-of-the-art CNN-based methods on KITTI \cite{Geiger2013IJRR} dataset using the Eigen Split \cite{eigen2015predicting}. The last row is our performance on continuous sequences. Lower numbers are better. K and CS stand for KITTI and Cityscapes, \cite{Cordts2016Cityscapes} respectively. All results are capped at 80m depth.}
\label{table:2}
\end{table}

\begin{figure}[h]
    \centering
    \includegraphics[width=12.5cm]{compare2.png}
    \caption{Visual comparison between the results of Eigen \textit{et al}.\cite{eigen2015predicting} and ours on KITTI dataset \cite{Geiger2013IJRR}. Groundtruth depth is interpolated for visualization purpose.}
    \label{KITTI}
\end{figure}

Table \ref{table:2} shows a quantitative comparison on outdoor scenes. We compare to Eigen \textit{et al}. \cite{eigen2015predicting}, Garg \textit{et al}. \cite{garg2016unsupervised}, Godard \textit{et al}. \cite{godard2017unsupervised}, Liu \textit{et al}. \cite{liu2015deep}, Zhou \textit{et al.} \cite{zhou2017unsupervised}, and Kuznietsov \textit{et al.} \cite{kuznietsov2017semi}. To perform a consistent comparison to state-of-the-art methods, we use the 697 test images from the Eigen Split \cite{eigen2015predicting} for evaluation. However, these 697 images are randomly selected from 28 scenes and do not form a continuous sequence. Therefore, they do not fully demonstrate the capability of our network. Despite this fact, Table \ref{table:2} shows that our method performs similarly to the state-of-the-art on this test set. In the last row of the table, we show an evaluation result of our method on continuous sequences that we randomly selected from the KITTI test dataset. It can be seen that our network gets a significant performance boost when dealing with continuous sequences, which we explore in more depth in Section \ref{sec:ablation}.

We demonstrate DenseSLAMNet's ability to handle non-static scenes, moving light sources, and non-Lambertian surface reflections by training and testing it on an endoscopic dataset. Figure \ref{endo} shows a visual result of our DenseSLAMNet on this data modality. In order to perform a quantitative evaluation, we 3D printed a textured phantom throat model using geometry extracted from CT scans and performed an endoscopy process on it to capture video. DenseSLAMNet obtains 0.271 in $sc-inv$, 0.216 in $abs-rel$, and 0.010 in $abs-inv$. Figure \ref{endo} (b) shows a visual result on phantom dataset.

\begin{figure}[h]
    \centering
    \includegraphics[width=12.5cm]{endo.png}
    \caption{Visual results on real patient and phantom endoscopic data. The vocal cord is visibily closing in the image sequence in (a). In both datasets, one can see the illumination changes due to the moving light source.}
    \label{endo}
\end{figure}

\subsection{Pose Estimation}

We provide a qualitative evaluation of the pose estimation task by plotting the predicted poses together with the ground truth camera poses. As can be seen from Figure \ref{cam}, our DenseSLAMNet can handle smooth and small camera motions very well, but fails on large sudden jumps and random camera motions. This is expected because, in the training data, the camera motions are small and smooth causing the network adapts to this specific type of camera motion. To handle random camera motion and large magnitude motion between frames, we suspect we might apply weights to different types of camera motion, or explicitly generate a balanced set of different types of camera motion sequences for training. We see this as an opportunity for future work.

\begin{figure}[h]
    \centering
    \includegraphics[width=12.5cm]{cam_pose.png}
    \caption{Camera pose estimation evaluation. Red cameras represent the ground truth camera positions and blue cameras represent our estimated camera positions. Here we only plot the point cloud of the last camera.}
    \label{cam}
\end{figure}

\subsection{Generalization to new data}

\begin{table}[h!]
\centering
\begin{tabular}{ |p{3cm}||p{2cm}|p{2cm}|p{2cm}|  }
 \hline
 Methods & Sc-inv & Abs-inv & Abs-rel\\
 \hline
 DeMoN \cite{ummenhofer2017demon} &0.203 & 0.079&  0.201\\
 \textbf{DenseSLAMNet} & \textbf{0.181} & \textbf{0.071}&  \textbf{0.171}\\
 \hline
\end{tabular}
\caption{Generalization comparison between the depth estimation of DeMoN and our DenseSLAMNet on NYUDV2 dataset \cite{Silberman:ECCV12}.}
\label{table:3}
\end{table}

We evaluate the generalization ability of our DenseSLAMNet on both indoor and outdoor scenes. For indoor scenes, we use the NYUDV2 \cite{Silberman:ECCV12} dataset. The NYUDV2 does not provide the ground truth camera poses, so we cannot use it for training. Table \ref{table:3} shows the quantitative comparison results, and Figure \ref{compare} shows the visual results. Again our method outperforms DeMoN across every quantitative metric.

\begin{table}[h!]
\centering
\begin{tabular}{ |p{3.5cm}||p{2cm}|p{2cm}|p{2cm}|p{2cm}|}
 \hline
 Methods & Sq-rel & Abs-rel & RMSE& $log_{10}$ \\
 \hline
 Godard \textit{et al}. \cite{godard2017unsupervised} &11.990 & 0.535&  11.513& 0.156\\
 zhou \textit{et al}. \cite{zhou2017unsupervised} &5.321 & 0.383&  10.47& 0.478 \\
 Kuzenietsov \textit{et al}. \cite{kuznietsov2017semi} & - & 0.421&  8.237& 0.190\\
 \textbf{DenseSLAMNet} & \textbf{2.404} & \textbf{0.275}&  \textbf{6.476}& \textbf{0.102}\\
 \hline
\end{tabular}
\caption{Generalization comparison on Make3D dataset \cite{saxena2009make3d}. All results are capped at 70 meters depth.}
\label{table:4}
\end{table}

\begin{figure}[h]
    \centering
    \includegraphics[width=12.5cm]{general.png}
    \caption{Our prediction on unseen outdoor dataset (Make3D).}
    \label{make3d}
\end{figure}

For outdoor scenes, we evaluate our DenseSLAMNet on the unseen Make3D dataset \cite{saxena2009make3d}. The Make3D dataset is very different from KITTI (used for training) in that its image resolution is 2272$\times$1702, whereas KITTI's is 375$\times$1280. Table \ref{table:4} shows the quantitative comparison results. Our method generalizes best among the state-of-the-art methods that are not trained on the Make3D dataset. From Figure \ref{make3d} we can see that our predicted depth map preserves fine details like trees, cars, and pillars.

\subsection{Ablation studies}\label{sec:ablation}
In order to justify the effectiveness of the different components of our network architecture, we did a series of ablation studies using the test dataset of SUN3D.

\begin{table}[h!]
\centering
\begin{tabular}{ |p{3cm}||p{2cm}|p{2cm}|p{2cm} | }
 \hline
 Methods & Sc-inv & Abs-inv & Abs-rel \\
 \hline
CNN-SINGLE  & 0.131    & 0.049 & 0.103  \\
 CNN-STACK &   0.144  & 0.060   & 0.124 \\
 \textbf{DenseSLAMNet} & \textbf{0.112} & \textbf{0.039}&  \textbf{0.091}\\
 \hline
\end{tabular}
\caption{The use of LSTM in DenseSLAMNet gives the best depth estimation accuracy. Simply stacking up frames for training actually leads to worse performance than using just a single frame.}
\label{table:5}
\end{table}

We compared 3 types of networks. We trained a CNN-SINGLE network that uses the network architecture in Figure \ref{detail} but without LSTM (RNN) units. Then we trained another network, CNN-STACK, that uses the same network architecture as CNN-SINGLE. Instead of taking a single image as input, CNN-STACK takes a stack of ten images as input. Table \ref{table:5} shows the quantitative results of our analysis. These results demonstrate that the LSTM units make an important contribution to preserving temporal information across a video sequence, which leads to better depth maps.

\section{Conclusions}

In this paper, we presented a real-time, RNN-based, multi-view dense SLAM method for depth and camera pose estimation from single or multiple frames. Our method effectively utilizes the temporal relationships between neighboring frames through LSTM units, which we show is more effective than simply stacking multiple frames together as input. Our DenseSLAMNet outperformed nearly all of the state-of-the-art CNN-based, single-frame depth estimation methods on both indoor and outdoor scenes and showed better generalization ability. It also predicted more accurate depth at large distance compared to the existing state-of-the-art. In addition, we demonstrated its capability to estimate depth from especially difficult data: endoscopic videos with dynamic scene geometry and illumination. In the future, we would like to further investigate the camera pose estimation component to make our network robust to highly varied camera motion, as well as explore the possibility of training on variable-length temporal sequences.

\clearpage

\bibliographystyle{splncs}

redpajama_set_name: RedPajamaArXiv


Warning over holiday deposit rules Firms cannot automatically keep a large deposit if a customer cancels owing to unavoidable circumstances.
Unemployment across UK shows slight fall Unemployment fell by 27,000 in the three months to February to 1.34 million.
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"Trust yourself, that you can be a part of something."
After completing our flagship course, The NEXT Project (now called Psychology for Change), in 2014, Tonya decided to embark on a Level 3 qualification to work professionally in the drug and alcohol field, having discovered a skill and a passion that drew on her lived experience.
Despite dedicated voluntary work to further her skills and knowledge in order to be eligible for paid roles, pressure from the Job Centre to get back to work – any work – began as soon as "treatment" ended leaving Tonya feeling unsupported, undervalued and defeated. She explains how a combination of determination and resilience meant neither her recovery nor her hope ended up getting derailed.
Tonya joins us as our newest member of staff, our feminism lead, responsible for delivering our Feminism for Change course.
What did you discover about yourself, doing The NEXT Project?
I was holding onto a lot of awful stuff that wasn't mine, most of all. I had internalized all these negative core beliefs about myself. They came from what I knew and grew up with but all that was put upon me, it wasn't actually me. No one in therapy or treatment had told me about core beliefs – how they work unconsciously to drive negative thinking. I'd never heard of them until we did the day on Cognitive Behavioural Therapy (CBT). That's the power of knowledge; once I understood how these internalised beliefs fuelled constant negative thought patterns, I was able to let go of all this bitterness I had towards my family, my mum, the father of my children who was a heroin addict…It took time, obviously, but once I developed understanding of what was going on the bitterness just left me. It's gone now, all that heavy sh*t weighing me down, and made way for a lightness.
The course brought up so much for me. I didn't share in group necessarily – I was uncomfortable with that, it was very powerful just listening to others doing so. Being in group means you get to have your mind opened. I became more accepting of ideas – that if these other people could, perhaps I might be able to let go of the bitterness eating me up. That allowed me to share and be honest in my one to one tuition with Liz. There was trust between us and I knew I wouldn't be judged.
It was only after NEXT that I became strong. Or rather, I felt unafraid to BE strong. I probably was strong all along but was scared to use my voice.
What changes did your family and friends notice in you?
Before I didn't used to think very much before doing things. Now I am a lot more considered, more organized, more adult. For my friends, I am the one that's stable, that they come to, I think because they can see that I am OK with myself – with my own company, with my household, my family…I am grounded, happy, content with my lot and that shines out. My kids are so proud of me. We wouldn't have the relationship we have if I had not let go of all the sh*t I was holding onto – I'd have passed it straight down onto them. We have a very healthy family dynamic. Talking about things is normalized. We ask each other advice. My two brothers are amazed; they are very different to me but have suffered a lot too and in some ways stayed the same. What's amazing is that my teenage son can go around theirs and spend time with them and bring a whole new way of being into their household – the changes I've been able to make have a ripple effect on those around me.
For me the most important thing for my kids was to have them grow up in one household, go to one school, have a consistent group of friends – to have roots and a sense of belonging, which is what I missed out on. I wanted them to feel as though they know where they've come from, rather than have to go and look, and I think they have that.
I've noticed that I deal with things differently. I am not angry inside or bitter anymore. I've let go of a lot of sorrow relating to my mum – to the things we never got to say or talk about before her death, the relationship we never had. I know I'm not going to get answers to the questions I have about why she brought me up in the way that she did, which means that I can let it go. I can recognise what's HER stuff doesn't need to be mine. She didn't know how to 'own' being a mum.
My ex says I think I'm "too good" for everyone else (ie him), a bit above myself or something. I can rationalise him saying things like that – in many ways I am, my problem was I couldn't see it! He spent 15 years trying to bring me down. Now I stand taller and have got stronger and what he says or thinks about me just doesn't affect or bother me anymore. I don't internalise it or batter myself with it. I don't feel anger or resentment, in situations where I used to feel such bitterness. I let it glide over. I'm content with myself!
At the beginning I had no interest in working in the field. I did the course because it was recommended by my key-worker. It was purely for my own recovery – I needed to look after myself. I had had lots of therapy and was sceptical about whether a psychology course would benefit me but gave it a go anyway.
Graduating was so exciting. We got all dressed up and were awarded our certificates at the House of Lords! Afterwards I started volunteering at my kids' school. I did it without really thinking about what I wanted or what I was good at – I was just used to being in "mum mode". I didn't particularly want to work with kids as I was with them all day! But it got me showing up every day in a professional capacity, and seeing myself as part of a workforce, which was a new and valuable experience.
What brought you to the decision to go for a professional qualification?
At the House of Lords event Liz had mentioned the certified practitioner-training course and said I should do it. I trusted her I suppose, and was willing to go with anything she would throw at me. Doing that course completely changed how I saw myself. When you are training to work in the field you cover a lot of the psychological theory and material in more depth, but more importantly you have to consider it in relation to working with others, rather than how it relates to your own experience, so you have to learn to separate and own your own stuff.
How did you begin gaining experience working in the health & social care field?
My placement was at the drug and alcohol charity WDP and I absolutely loved it. There was me there and one other person from the course, although we worked in different departments. I was a Family & Carer's Support Worker; I organised the parent and carer's weekly groups and had 121s with women mostly, working with families, children, that side of things…it was perfect for me. The role was about empowering women – I wouldn't say "help", because people help themselves. I threw myself into it full time and knew then that my forte was guiding people to discover their hidden voice, to gain confidence. When you have lived experience it gives you a completely open mind.
What obstacles did you encounter or have to overcome in order to find work that was meaningful for you?
Before I finished the placement my son Ryan turned three. Immediately the Job Centre took me off Income Support and put me onto Job Seekers Allowance. I had to sign on every few weeks and be looking for work, even though I was completing a placement as part of my training that would hopefully result in paid work on a career path. Their attitude was "well that's your choice to be volunteering in your spare time" – completely just "computer says NO" kind of thing, and insisted I apply for and take any work going. The life path I was on was irrelevant. WDP were amazing and tried to help me find part-time work so that I would be able to continue with them but in the end I just caved into the pressure and got a cleaning job.
I have to say it brought back all those old feelings of being defeated. I was so angry at the system again – that I'd just wasted all that time and still, it felt like "I've ended up here and I can't get there".
And I got the job! It was working in a hostel for people with complex needs. It was incredibly hard shift-work but I shone through it. The people I was working with shouldn't have been there really; they'd been going round-and-round the [mental health, drug & alcohol and homelessness] systems like a washing machine, passed between services where no one had been able to help them in at least the last 10 years and suddenly I was expected to come up with something.
I built a great rapport with them though. Some of them would just laugh at the patronising questions I was supposed to ask for their 'support plan' and just say, "Tonya, you've gotta be kiddin' me?!". I'd laugh right back and say "I know, I know! Shall we just have a chat?!" That's what worked, just being real, building a relationship with people. I hated the whole treatment model. I didn't want to issue them "warnings" about their behaviour or threats about terminating their support if they didn't comply because it felt like that was punishing, and setting up people with difficulties to fail…I would just explain "look, this is what they want to do if you don't stick to the rules, I don't agree or like it, but if you go along you might stay out of trouble".
In some ways it was frowned upon, the way I worked with my heart on my sleeve, but I got great feedback from the people I worked with. They said I was the best support worker.
Towards the end, not only was I exhausted, my family was really suffering; I was working almost every weekend and had no time with my kids, who were practically looking after themselves. My shifts were often 7.30am – 4pm so I wouldn't even get to see my youngest when I got home at night or before leaving in the morning. The balance was all wrong and something had to give. I asked to pop in for a chat with Liz again, and she told me they were looking for someone to work on the Feminism for Change course full time. It was such an incredible opportunity, I had to go for it.
That must have been a big leap?
If you were giving advice to someone trying to make a big change – start a new career, rebuild their lives – what would it be?
Go for it. You have to do something you don't think you can do. Don't stay comfortable but oppressed. You will be scared – that never goes away! The only way to overcome fear is just to do it anyway. You've got to, for your own peace of mind. Trust yourself, that you can be a part of something. There is no need to feel 'less than'. Before I worked at WDP I'd never even used a computer! I felt way out of my depth! It was surreal at the beginning being sat there at a desk with a PC and a phone – not me! You've got to fake it 'til you make it a bit.
When I was talking to a potential participant for the Feminism for Change course the other day she was looking at me as though I was someone with power, who's in control, who has their sh*t together – that's how I used to look at my key worker, all suited and booted and professional. It's not like that any of that comes naturally to me! I was with a heroin addict 20 years older than me, 3 kids and no self worth. I didn't know I was capable of anything, until my eyes were opened to it.
What makes you optimistic about the future?
Life is still scary and challenging. But not so much that I can't walk through it. I have to keep riding through my fears. I would like to stay within the field of guiding people. I'm not creating plans that are too fixed or too rigid, but rather feel as though I am following a path.
Support more people like Tonya by making a donation to us.


text:
#include <stdio.h>
#include <stdlib.h>
#include <sys/time.h>

#include "mdp.h"
#include "imm-reward.h"
#include "sparse-matrix.h"

#define DOUBLE_DISPLAY_PRECISION                  4

#define EPSILON  0.00001  /* tolerance for sum of probs == 1 */

/* To indicate whether we are using an MDP or POMDP.
   */
Problem_Type gProblemType = UNKNOWN_problem_type;

/* The discount factor to be used with the problem.
   */
double gDiscount = DEFAULT_DISCOUNT_FACTOR;

char *value_type_str[] = VALUE_TYPE_STRINGS;

Value_Type gValueType = DEFAULT_VALUE_TYPE;

/* These specify the size of the problem.  The first two are always required.
   */
int gNumStates = 0;
int gNumActions = 0;
int gNumObservations = 0;   /* remains zero for MDPs */

/*  We need two sets of variable for the probabilities and values.  The first
    is an intermediate representation which is filled in as the MDP file
    is parsed, and the other is the final sparse reprsentation which is
    found by converting the interemediate representation.  As aresult, we
    only need to allocate the intermediate memory while parsing. After parsing
    is completed and we are ready to convert it into the final sparse
    representation, then we allocate the rest of the memory.
    */

/* Intermediate variables */

I_Matrix *IP;  /* Transition Probabilities */

I_Matrix *IR;  /* Observation Probabilities (POMDP only) */

I_Matrix IQ;  /* Immediate action-state pair values (both MDP and POMDP) */

/* Sparse variables */

Matrix *P;  /* Transition Probabilities */

Matrix *R;  /* Observation Probabilities */

Matrix Q;  /* Immediate values for state action pairs.  These are
	    expectations computed from immediate values. */

/* Normal variables */

/* Some type of algorithms want a place to start off the problem,
   especially when doing simulation type experiments.  The belief
   state is for POMDPs and the initial state for an MDP */

double *gInitialBelief;
int gInitialState = INVALID_STATE;

/***************************************************************************/
double *newBeliefState(  ) {

  return( (double *) calloc( gNumStates, sizeof( double )));
}  /* *newBeliefState */
/***************************************************************************/
int transformBeliefState( double *pi,
                         double *pi_hat,
                         int a,
                         int obs ) {
   double denom;
   int i, j, cur_state, next_state;

   if( gProblemType != POMDP_problem_type )
      return ( 0 );

   /* zero out all elements since we will acumulate probabilities
      as we loop */
   for( i = 0; i < gNumStates; i++ )
      pi_hat[i] = 0.0;

   for( cur_state = 0; cur_state < gNumStates; cur_state++ ) {

      for( j = P[a]->row_start[cur_state];
	  j < P[a]->row_start[cur_state] +  P[a]->row_length[cur_state];
	  j++ ) {

         next_state = P[a]->col[j];

         pi_hat[next_state] += pi[cur_state] * P[a]->mat_val[j]
            * getEntryMatrix( R[a], next_state, obs );

      } /* for j */
   }  /* for i */

   /* Normalize */
   denom = 0.0;
   for( i = 0; i < gNumStates; i++ )
      denom += pi_hat[i];

   if( IS_ZERO( denom ))
      return( 0 );

   for( i = 0; i < gNumStates; i++ )
      pi_hat[i] /= denom;

   return( 1 );
}  /* transformBeliefState */
/**********************************************************************/
void copyBeliefState( double *copy, double *pi ) {
/*
*/
   int i;

   if(( pi == NULL) || (copy == NULL ))
      return;

   for( i = 0; i < gNumStates; i++ )
      copy[i] = pi[i];

}  /* copyBeliefState */
/**********************************************************************/
void displayBeliefState( FILE *file, double *pi ) {
   int i;

   fprintf( file, "[%.*f", DOUBLE_DISPLAY_PRECISION, pi[0] );
   for(i = 1; i < gNumStates; i++) {
      fprintf(file, " ");
      fprintf( file, "%.*f", DOUBLE_DISPLAY_PRECISION, pi[i] );
   }  /* for i */
      fprintf(file, "]");
}  /* displayBeliefState */
/***************************************************************************/
int readMDP( char *filename ) {
/*
   This routine returns 1 if the file is successfully parsed and 0 if not.
*/

   FILE *file;

   if( filename == NULL ) {
      fprintf( stderr, "<NULL> MDP filename: %s.\n", filename );
      return( 0 );
   }

   if(( file = fopen( filename, "r" )) == NULL ) {
      fprintf( stderr, "Cannot open the MDP file: %s.\n", filename );
      return( 0 );
   }

   if( readMDPFile( file ) == 0 ) {
      fprintf( stderr,
              "MDP file '%s' was not successfully parsed!\n", filename );
      return( 0 );
   }

   fclose( file );

   /* After the file has been parsed, we should have everything we need
      in the final representation.
      */
   return( 1 );
}  /* readMDP */
/**********************************************************************/
void allocateIntermediateMDP() {
/*
   Assumes that the gProblemType has been set and that the variables
   gNumStates, gNumActions, and gNumObservation have the appropriate
   values.  It will allocate the memory that will be needed to store
   the problem.  This allocates the space for the intermediate
   representation representation for the transitions and observations,
   the latter for POMDPs only.
*/

  int a;

  /* We need an intermediate matrix for transition probs. for each
     action.  */
  IP = (I_Matrix *) malloc( gNumActions * sizeof( *IP ));

  for( a = 0; a < gNumActions; a++ )
    IP[a] = newIMatrix( gNumStates );

  /* Only need observation probabilities if it is a POMDP */
  if( gProblemType == POMDP_problem_type ) {

    /* We need an intermediate matrix for observation probs. for each
       action.  */
    IR = (I_Matrix *) malloc( gNumActions * sizeof( *IR ));

    for( a = 0; a < gNumActions; a++ )
      IR[a] = newIMatrix( gNumStates );

    /* Note that the immediate values are stored in a special way, so
       we do not need to allocate anything at this time. */

    /* For POMDPs, we will keep a starting belief state, since many */
    /* type of algorithms use a simulation approach and would want to */
    /* start it in a particular place. This is not kept in a sparse */
    /* way, so it is just a vector of the number of states. We */
    /* initialize it to be all zeroes.  */

    gInitialBelief = (double *) calloc( gNumStates, sizeof( double ));

  }  /* if POMDP */

  /* Regardless of whether there is an MDP or POMDP, the immediate
     rewards for action-state pairs will always exist as an expectation
     over the next states and possibly actions.  These will be computed
     after parsing from the special immediate reward representation.
     */

  IQ = newIMatrix( gNumActions );

} /* allocateIntermediateMDP */
/************************************************************************/
int verifyIntermediateMDP() {
/*
   This routine will make sure that the intermediate form for the MDP
   is valid.  It will check to make sure that the transition and
   observation matrices do indeed specify probabilities.

   There is a similar routine in the parser.y file, which is nicer
   when parsing a POMDP file, but this routine is needed when we
   are creating the POMDP through a program.  In this case there
   will be no parsing and thus no logging of errors.
*/
   int a,i,j;
   double sum;

   for( a = 0; a < gNumActions; a++ )
      for( i = 0; i < gNumStates; i++ ) {
	 sum = sumIMatrixRowValues( IP[a], i );
         if((sum < ( 1.0 - EPSILON)) || (sum > (1.0 + EPSILON))) {
	   return( 0 );
         }
      } /* for i */

   if( gProblemType == POMDP_problem_type )
     for( a = 0; a < gNumActions; a++ )
       for( j = 0; j < gNumStates; j++ ) {
	 sum = sumIMatrixRowValues( IR[a], j );
         if((sum < ( 1.0 - EPSILON)) || (sum > (1.0 + EPSILON))) {
	   return( 0 );
         } /* if sum not == 1 */
       }  /* for j */

  return( 1 );
}  /* verifyIntermediateMDP */
/************************************************************************/
void deallocateIntermediateMDP() {
/*
   This routine is made available in case something goes wrong
   before converting the matrices from the intermediate form
   to the final form.  Normally the conversion routine convertMatrices()
   will deallocate the intermediate matrices, but it might be desirable
   to get rid of them before converting (especially if something
   has gone wrong) so that things can be started over.
*/
  int a;

  for( a = 0; a < gNumActions; a++ ) {

    destroyIMatrix( IP[a] );

    if( gProblemType == POMDP_problem_type ) {
      destroyIMatrix( IR[a] );
    }

  }

  free( IP );

  if( gProblemType == POMDP_problem_type ) {
    free( IR );
    free( gInitialBelief );
  }

  destroyIMatrix( IQ );

}  /* deallocateIntermediateMDP */
/**********************************************************************/
void computeRewards() {
  int a, i, j, z, next_state, obs;
  double sum, inner_sum;

  /* Now do the expectation thing for action-state reward values */

  for( a = 0; a < gNumActions; a++ )
    for( i = 0; i < gNumStates; i++ ) {

      sum = 0.0;

      /* Note: 'j' is not a state. It is an index into an array */
      for( j = P[a]->row_start[i];
	  j < P[a]->row_start[i] +  P[a]->row_length[i];
	  j++ ) {

	next_state = P[a]->col[j];

	if( gProblemType == POMDP_problem_type ) {

	  inner_sum = 0.0;

	  /* Note: 'z' is not a state. It is an index into an array */
	  for( z = R[a]->row_start[next_state];
	      z < (R[a]->row_start[next_state] +  R[a]->row_length[next_state]);
	      z++ ) {

	    obs = R[a]->col[z];

	    inner_sum += R[a]->mat_val[z]
	      * getImmediateReward( a, i, next_state, obs );
	  }  /* for z */
	}  /* if POMDP */

	else /* it is an MDP */
	  inner_sum = getImmediateReward( a, i, next_state, 0 );

	sum += P[a]->mat_val[j] * inner_sum;

      }  /* for j */

      addEntryToIMatrix( IQ, a, i, sum );

    }  /* for i */

}  /* computeRewards */
/************************************************************************/
void convertMatrices() {
/*
   This routine is called after the parsing has been succesfully done.
   It will assume that the intermediate representations for the transition
   and observation matrices have been allocated and had their values set.
   It also assumes that the special immediate reward representation
   has been set.

   This routine will do two functions.  It will convert the intermediate
   sparse representations for the transitions and observations to the
   actual true sparse representation.  It will also compute the action-state
   immeidate reward pairs as an expectation over next states and possibly
   observations from the special immediate reward representation.  This
   will be the final step toward the use of the MDP/POMDP model in
   computation.
   */

  int a;
  struct timeval startTime, endTime;

  /* Allocate room for each action */
  P = (Matrix *) malloc( gNumActions * sizeof( *P ) );
  R = (Matrix *) malloc( gNumActions * sizeof( *R ) );

  /* First convert the intermediate sparse matrices for trans. and obs. */

  for( a = 0; a < gNumActions; a++ ) {

    if (zmdpDebugLevelG >= 1) {
      printf("pomdp_spec: transforming transition matrix [a=%d]\n", a);
    }

    P[a] = transformIMatrix( IP[a] );
    destroyIMatrix( IP[a] );

    if (zmdpDebugLevelG >= 1) {
      printf("pomdp_spec: transforming obs matrix [a=%d]\n", a);
    }

    if( gProblemType == POMDP_problem_type ) {
      R[a] = transformIMatrix( IR[a] );
      destroyIMatrix( IR[a] );
    }

  }

  free( IP );

  if( gProblemType == POMDP_problem_type )
    free( IR );

  /* Calculate expected immediate rewards for action-state pairs, but
     do it in the sparse matrix representation to eliminate zeroes */

  if (zmdpDebugLevelG >= 1) {
    printf("pomdp_spec: computing rewards\n");
    gettimeofday(&startTime, NULL);
  }

  computeRewards();

  if (zmdpDebugLevelG >= 1) {
    gettimeofday(&endTime, NULL);
    printf("  (took %lf seconds)\n",
	   endTime.tv_sec - startTime.tv_sec + 1e-6 * (endTime.tv_usec - startTime.tv_usec));
    printf("pomdp_spec: transforming reward matrix\n");
  }

  /* Then convert it into the real representation */
  Q = transformIMatrix( IQ );
  destroyIMatrix( IQ );

}  /* convertMatrices */
/**********************************************************************/
int writeMDP( char *filename ) {
  FILE *file;
  int a, i, j, obs;

  if( (file = fopen( filename, "w" )) == NULL )
    return( 0 );

  fprintf( file, "discount: %.6f\n", gDiscount );

  if( gValueType == COST_value_type )
    fprintf( file, "values: cost\n" );
  else
    fprintf( file, "values: reward\n" );

  fprintf( file, "states: %d\n", gNumStates );
  fprintf( file, "actions: %d\n", gNumActions );

  if( gProblemType == POMDP_problem_type )
    fprintf( file, "observations: %d\n", gNumObservations );

  for( a = 0; a < gNumActions; a++ )
    for( i = 0; i < gNumStates; i++ )
      for( j = P[a]->row_start[i];
	  j < P[a]->row_start[i] +  P[a]->row_length[i];
	  j++ )
	fprintf( file, "T: %d : %d : %d %.6f\n",
		a, i, P[a]->col[j], P[a]->mat_val[j] );

  if( gProblemType == POMDP_problem_type )
    for( a = 0; a < gNumActions; a++ )
      for( j = 0; j < gNumStates; j++ )
	for( obs = R[a]->row_start[j];
	    obs < R[a]->row_start[j] +  R[a]->row_length[j];
	    obs++ )
	  fprintf( file, "O: %d : %d : %d %.6f\n",
		  a, j, R[a]->col[obs], R[a]->mat_val[obs] );

  if( gProblemType == POMDP_problem_type )
    for( a = 0; a < gNumActions; a++ )
      for( i = Q->row_start[a];
	  i < Q->row_start[a] +  Q->row_length[a];
	  i++ )
	fprintf( file, "R: %d : %d : * : * %.6f\n",
		a, Q->col[i], Q->mat_val[i] );

  else
    for( a = 0; a < gNumActions; a++ )
      for( i = Q->row_start[a];
	  i < Q->row_start[a] +  Q->row_length[a];
	  i++ )
	fprintf( file, "R: %d : %d : * %.6f\n",
		a, Q->col[i], Q->mat_val[i] );

  fclose( file );
  return( 1 );

}  /* writeMDP */
/**********************************************************************/
void deallocateMDP() {
  int a;

  for( a = 0; a < gNumActions; a++ ) {
    destroyMatrix( P[a] );

    if( gProblemType == POMDP_problem_type )
      destroyMatrix( R[a] );
  }  /* for a */

  free( P );

  if( gProblemType == POMDP_problem_type ) {
    free( R );
    free( gInitialBelief );
  }

  destroyMatrix( Q );

  destroyImmRewards();

}  /* deallocateMDP */
/**********************************************************************/
void displayMDPSlice( int state ) {
/*
   Shows the transition and observation probabilites (and rewards) for
   the given state.
*/
   int a, j, obs;

   if(( state < 0 ) || ( state >= gNumStates ) || ( gNumStates < 1 ))
      return;

   printf( "MDP slice for state: %d\n", state );

   for( a = 0; a < gNumActions; a++ )
      for( j = P[a]->row_start[state];
          j < P[a]->row_start[state] +  P[a]->row_length[state];
          j++ )
         printf( "\tP( s=%d | s=%d, a=%d ) = %.6f\n",
                P[a]->col[j], state, a, P[a]->mat_val[j] );

   if( gProblemType == POMDP_problem_type )
      for( a = 0; a < gNumActions; a++ )
         for( obs = R[a]->row_start[state];
             obs < R[a]->row_start[state] +  R[a]->row_length[state];
             obs++ )
            printf( "\tP( o=%d | s=%d, a=%d ) = %.6f\n",
                   R[a]->col[obs], state, a, R[a]->mat_val[obs] );

   for( a = 0; a < gNumActions; a++ )
      printf( "\tQ( s=%d, a=%d ) = %5.6f\n",
             state, a, getEntryMatrix( Q, a, state ));

}  /* displayMDPSlice */
/**********************************************************************/

redpajama_set_name: RedPajamaGithub


text:
id: 5900f5021000cf542c510014
challengeType: 5
title: 'Problem 405: A rectangular tiling'
videoUrl: ''
localeTitle: 'Задача 405: прямоугольная черепица'
---

## Description
undefined

## Instructions
undefined

## Tests

```yml
tests:
  - text: <code>euler405()</code> должен вернуть 237696125.
    testString: 'assert.strictEqual(euler405(), 237696125, "<code>euler405()</code> should return 237696125.");'

```

## Challenge Seed

```js
function euler405() {
  // Good luck!
  return true;
}

euler405();

```

## Solution

```js
// solution required
```


text:
GreekReporter.comEnvironmentAnimalsThe Adorable Native Cat Breeds of Greece and Cyprus
The Adorable Native Cat Breeds of Greece and Cyprus
By Anna Wichmann
An Aegean cat. The breed is beloved in Greece. Credit: Anna Wichmann/Greek Reporter
Anyone who has visited Greece or Cyprus knows that both beautiful countries are home to many, many cats. Yet some people may be unaware that there are two distinct cat breeds, the Aegean cat and the Cyprus Cat, that are native to Greece and Cyprus.
The Greek islands are particularly known for their adorable four-footed residents. Cats can be seen all across the Greek isles, but are most commonly spotted lounging around fishing boats down at the local port, hoping to secure a nice seafood meal.
Most visitors to the Greek islands are shocked when they discover how sweet and friendly the majority of cats there truly are. That's because they're likely Aegean cats, the only breed of cat native to Greece and the Greek islands.
Aegean cats are known to be playful, vocal, friendly, and very loving. They're particularly comfortable around water and love to go fishing.
The Aegean cat breed in Greece is thought to be one of the oldest in the world
In terms of appearance, they are always bicolor, or rarely tricolor, and one of their colors is almost always white. The most common color combinations are white and black, white and ginger, and white and tabby.
Aegean cats are best known for their beautiful, piercing eyes. Their large eyes are almond shaped, and often a striking shade of green, yellow, or blue.
Believed to be descendants of the ancient cats that inhabited the Greek islands throughout antiquity, Aegean cats have bred naturally, without human intervention, for thousands of years.
It is thought to be one of the oldest domesticated breeds in the world.
This natural breeding process means that these cats are extremely resistant to the genetic diseases that are more commonly found in cat breeds that were created from human intervention.
Although kept as pets, many Aegean cats are feral. Regardless of whether they have a home or not, felines of this breed are beloved in Greece.
Aegean cats have medium-sized paws that have adapted well to grasping at fish swimming along in the sea, and their fur can be short yet full in the summer and a bit longer and thicker in the winter.
Although common in Greece, especially on the islands, Aegean cats are a particularly rare breed outside of the country.
Despite the fact that they are a well-documented breed with a long history, the Aegean cat is not yet recognized by the Cat Fanciers Association.
This means that, if you're enchanted by the Aegean cat, you'll have to visit Greece and adopt one!
Cyprus Cats, the breed brought by St. Helen
A Cyprus cat lounging at Limassol Castle. This breed of feline has a long history in Cyprus. Credit: Citrus flower talk/ Wikimedia Commons/ CC BY-SA 4.0
Cyprus cats, also known as St. Helen's cats, are a breed of domesticated felines found on the island nation of Cyprus.
The breed has a illustrious history — according to historical sources, St. Helen, the mother of Roman Emperor Constantine the Great, brought the forefathers of the breed to the island herself.
It is said that St. Helen filled her ship up with cats from Egypt — although some sources say Palestine — and brought them with her to Cyprus to help curb the snake problem there in the fourth century.
The cats were brought to the Monastery of St. Nicholas, where the venomous snake population was extremely large and threatening. According to legend, the cats were called to hunt snakes on the monastery grounds by a bell that was rung each day.
The cat population at the monastery, which is now a convent, is still vibrant. Greek poet Georgos Seferis wrote a poem about the felines there, entitled "The Cats of St. Nicholas."
There is actually archaeological evidence of cats living alongside humans in Cyprus in antiquity, although they are likely not the ancestors of the Cyprus cat breed.
Excavations at a Neolithic site called Shillourokampos in Cyprus showed that ancient people there truly cared for their feline companions, and even dug out a grave with care for their pet cat.
The Cypriot breed is linked to other feline groups in Egypt and Palestine, so the story of St. Helen likely has some truth to it.
The Cyprus cat has very much in common with the Aegean cat breed of Greece.
Much like the Aegean cat, Cyprus cats have been left to breed naturally, so they have less inclination to genetic diseases.
Cyprus cats, unlike most Aegean cats, tend to be extremely energetic and athletic. They have a thick coat, which can be short or semi-long.
They are extremely playful and social, and love to be around humans. They're also great hunters. Cyprus cats are commonly tabby with a mix of white, but can come in all colors, ranging from ginger to black.
Like Aegean cats, Cyprus cats are common in their native country but very rare in the rest of the world.
Currently, cat breeder and fancier organizations are beginning to develop qualifications for the breed in order for it to be recognized internationally.
It is recognized already by the World Cat Federation under the name "Aphrodite's Giant."
Aegean Cat
Cyprus Cat
Greek cat breed
Greek cats
A Turkish Harem on the Acropolis: Fact or Fiction?
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redpajama_set_name: RedPajamaCommonCrawl


17 bits of Singapore | Photo Gallery | Hey! What's the way?
When starting our long trip around Southeast Asia, we were quite sure Singapore would end up being one of our stops. Because, you know, all that talk about this tiny little country which grew up to become one of the world's top economies can make you quite curious.
..well, at least if you're into big cities, which we pretty much are.
Now, what we didn't expect was to spend much more than four or five days there, what with the sky high cost of living and all.
But that's when a beautiful Workaway opportunity showed up and we actually ended up landing a placement there for three weeks (plus one week more on our second coming to the city-state 😮 ).
On this post, we will give you a taste of Singapore, both through some photos we took and through some of the things we learned about this huge but small country.
You're very welcome to tag along this small journey. Just check your bubblegum at the door, please.
A small island at the very southern tip of the Malay Peninsula, the now super rich and developed Singapore was not that long ago just a fishing village waiting to be found. Which happened when Sir Thomas Raffles arrived, in 1819, setting off its modern history by making it an important trade post for the British Empire.
In 1963-65, while independence from its colonial grasp was still quite fresh, a short attempt at merging Singapore with the Federation of Malaysia was made. However, it didn't take long before conflicting ideologies became apparent between leaders on both parties, mostly relating to social politics and racial inequality, resulting in Singapore being expelled and what we know today as the independent countries of Malaysia and Singapore.
By the hand of a government which has been named by some as authoritarian and restricting of democracy and its citizens' political rights, in the last 50 years Singapore has become one of the most developed countries in the world, often complimented for its low levels of corruption and crime, as well as low unemployment and overall high living standards.
Soon after touching down, Singapore doesn't take long to leave its lasting impression on you, both from the sheer number of tall and modern skyscrapers at the heart of its financial district, as from the unexpected fusion between these giants of metal and glass and the leafy trees, bushes and flowers that dominate the entire island, even in the most urbanized parts of it.
The small city-state does justice to its nickname "Garden City" through the use of public incentives and urban development that prioritizes a strong "green" component (besides an aesthetic one). It's common then to find vertical gardens and wooded terraces spread all across the city, and even buildings which positively burst with greenery — Oasia Hotel Downtown being the obvious example.
From all the economic development rankings, you can tell right away how Singapore loves growing. That is also true in more concrete and easily perceptible ways, such as the size of its territory, which has increased in a whopping 20% (!) since Singapore has gained its independence (or a little over 50 years ago). This is a result of the immense land reclamation projects of which the popular Gardens by the Bay are a part.
Singapore has one of the world's busiest ports, which played a major role on the economic rise of the small country in the last decades. Just hop on an elevator in one of the many tall buildings near the coast to gaze upon a sea filled with huge tankers and container ships.
Some of the supertrees in the Gardens by the Bay reach as high as 50 meters. Also, they're built in a way that allows them to harness solar energy and to collect rainwater, basically making them somewhat similar to actual trees!
Even if it is one of the richest countries in the world, Singapore still suffers from significant problems regarding wage disparity. Also because of that, it's easy to find "hawker markets" everywhere around the city. These are public food courts where you can find a wide array of local food choices at prices which are accessible to most wallets, including citizens with lesser means (and travellers on a budget).
Almost 75% of the population is of Chinese descent , with citizens of Malay and Indian descent making most of the remaining 25%. An Ethnic Integration Policy based on quotas has been put in place to "promote racial integration and harmony", making sure the percentage of residents from each ethnicity living in every building or block reflects the percentages at a national level.
Being quite a young country, and due to the fact that its population is characterized by a diversity of languages, religions and cultures (and immensely rich and ancient ones too), it can sometimes be challenging to describe what's the culture and identity of Singapore.
One of the most distinct forms of purely Singaporean identity is Singlish. Though denounced by the government, this can be considered the truly local language, a Creole language born from the mix between English and the many languages spoken by the inhabitants of Singapore, including of course the remaining official languages (Malay, Mandarin and Tamil).
The ethnic neighbourhoods of Little India, Chinatown and Arab Street are some of the top tourist attractions in Singapore, besides holding their special place as comercial and cultural hotspots for citizens of each respective ethnic background. If you ever decide to visit, expect to find the Singaporean-style (quite tidy and organized) version, though of course they're definitely a sight to behold and experience, especially on holidays.
All the cultural and ethnic diversity that is deeply rooted in Singapore also brings religious diversity, of course, which means it's quite easy to come upon temples and churches of many creeds, from Buddhism to Taoism, Christianity and Islam. Some of them are spectacular or interesting enough to be considered must-sees on a visit to Singapore, such as the Hindu Temple Sri Mariamman, the Buddha Tooth Relic Temple and the Masjid Sultan Mosque.
With what is probably one of the highest concentrations of shopping malls in the world — over 22, from what we heard —, Orchard Road gives a "small" taste of one of the things that makes Singapore tick — shop, shop, shop.
The merlion — kind of like a mermaid in which the "maid" was swapped for a "lion" — is the symbol Singapore has adopted as its "national identity", reflecting the original meaning of its Sanskrit name, "Lion City". This was the largest Merlion we found during our stay. It lives in Sentosa, a smaller island where you can also find theme parks, beaches and a ton of resorts. In short, a lot of stuff we couldn't afford.
Singapore is often referred to as the cleanest, most organized and safest country (or city) in the world. And that's all out there for everyone to see. What's not as talked about is the price that comes with all the cleanliness, organization and safety — a highly controlled society. It's common to see public notices stating the strict laws to abide by, as well as the hefty fines to be paid in case of non-compliance. Or the punishments which include, of course, the infamous caning.
Graphics used The Watercolor Branding Kit and Mammoth Watercolour.


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"Clear Browsing Data" from the menus, then tick the "Cookies and site data" and clear data.


text:
21-30 results from 1000 for query «crew»
12 December, 2018 9:24 AM EPL: Manchester City agree deal to sign U.S. goalkeeper Zack Steffen
Manchester City have agreed a deal that will see United States international goalkeeper Zack Steffen join them from MLS side Columbus Crew next July, the Premier League champions said. The exact financial details of the four-year contract were not revealed but Crew said the transfer fee was the largest in their history and the highest received by an MLS side for a goalkeeper 163
10 December, 2018 2:45 PM US-Bangla Airlines Flight Hit By Bird While Landing In Kolkata
After landing, an inspection was carried out and no damage to the aircraft was found. The pilot of a US-Bangla Airlines flight from Dhaka to Kolkata reported a bird hit while landing at the NSCBI Airport in Kolkata today, Airports Authority of India (AAI) officials said. The Dhaka-Kolkata flight BS 201, with 163 passengers and seven crew on board, landed 140
7 December, 2018 5:24 AM Game of Thrones Season 8 Teaser Gives Us a Glimpse of the Ice and Fire Battle
Game of Thrones season 8, which is the final season of the popular HBO show, has received its first teaser. The 39-second clip gives us a mere glimpse of what all we can expect from the final season of Game of Thrones. The teaser starts off with ice and fire sweeping across the map of fictional world Westeros, possibly a take on the "A Song of Ice and Fire" book series 107
4 December, 2018 7:45 AM Siddhant Karnick is tripping on 'THIS'
MUMBAI: Siddhant Karnick, who is currently shooting for Kaal Bhairav Rahasya 2, on Star Bharat, has an interesting adventure planned for the new year. Recently, the actor gifted himself a new Honda dirt bike and has planned to go on a solo trip to different places in India. Siddhant Karnick is a passionate bike rider and loves solo trips. This is the first 155
4 December, 2018 7:33 AM YouTube's Highest-Paid Stars Are Led by a 7-Year-Old Multi-Millionaire: Forbes
With the year coming to a close, American business magazine Forbes has revealed its list of the world's highest-paid YouTube stars in 2018, which is headed by a 7-year-old named Ryan who reviews toys. His channel, aptly named Ryan ToysReview, reportedly pulled in $22 million (about Rs. 154.84 crore) during the 12-month period ending June 1, 2018. That is exactly 116
29 November, 2018 7:00 AM Air India flight hits airport building in Stockholm, aircraft wing damaged
The Air India flight from Delhi to the Swedish capital was grounded following the incident. It was an unpleasant experience for 179 passengers travelling onboard Air India flight AI 167 as the aircraft hit an airport terminal building in Stockholm. The flight from Delhi to the Swedish capital was grounded following the incident. According to news agency 186
26 November, 2018 11:46 AM Nimai Bali and other 65 crew members of Sony TV's Chandragupta Maurya go BALD! Here's why..
MUMBAI: Seen in historical and mythological shows such as Mahakali, Porus, Suryaputra Karn and daily soaps such as Balika Vadhu, actor Nimai Bali is currently seen in the historical drama Chandragupta Maurya on Sony TV as Amatya Rakshas. Nimai was required to shave his head for the character and much to our surprise; we found a lot of people go bald following 302
24 November, 2018 5:53 PM Body Of Indian Pilot Who Flew Doomed Indonesian Plane Identified
Indonesian authorities have identified the body of Indian pilot Bhavye Suneja who captained the ill-fated Indonesian plane that crashed into the sea on October 29. External Affairs Minister Sushma Swaraj, in a tweet, announced about identification of Captain Suneja's body. "Indonesian authorities have confirmed identification of the body of Capt.Bhavya 104
23 November, 2018 3:30 PM Kalank: Varun Dhawan Announces 'Crazy' Schedule Wrap, Shares Pics With Alia Bhatt And Others
Varun Dhawan shared pictures from the schedule wrap on Instagram featuring himself with the cast and crew of Kalank Varun Dhawan announced the schedule wrap of Abhishek Verman's Kalank on Friday aur isi baat pe ek pizza party toh banti hai. The 31-year-old actor shared pictures from the schedule wrap on Instagram featuring himself with the cast and crew of the film including 100
23 November, 2018 6:54 AM 'Housefull 4' is four times funnier, says Riteish
MUMBAI: Actor Riteish Deshmukh, who has just wrapped up shooting for "Housefull 4", says every shoot in the upcoming comedy film has been fun, adding that it was four times funnier. Riteish on Thursday night tweeted a photograph of himself along with the cast and crew of the film and captioned it: "Wrapped up 'Housefull 4'... Every shoot in this series 137


text:
E-ISSN: 2148-5402 | Contact
Instructions for the Authors
Instructions for Reviewers
Ethics & Policies
Volume: 24 Issue: 3 Year: 2022
Copyright Transfer Form Author Contribution Form ICMJE COI Form
In titles In abstracts In keywords In authors
Manuscripts submitted to Eurasian Journal of Pulmonology will go through a double-blind peer-review process. Each submission will be reviewed by at least two external, independent peer reviewers who are experts in their fields in order to ensure an unbiased evaluation process. The editorial board will invite an external and independent editor to manage the evaluation processes of manuscripts submitted by editors or by the editorial board members of the journal. The Editor-in-Chief is the final authority in the decision-making process for all submissions. The language of the journal is English.
All articles submitted for publication are strictly reviewed by the reviewersfor their;
originality,
importance,
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suitability for the journal
Eurasian Journal of Pulmonology uses a well-constructed scheme for the evaluation process. The journal peer review flow chart is seen as below (for more information, please read to "Instructions to Author"). The reviewers should consider below points for the evaluation of papers;
1. Does the manuscript contain new and significant information to justify publication?
2. Does the abstract clearly and accurately describe the content of the article?
3. Is the topicsignificant and concisely stated?
4. Are the methods described comprehensively?
5. Are the interpretations and conclusions justified by the results?
6. Are adequate and up-to-date references used in the article?
7. Is the length of the article appropriate?
8. Are the numbers of table/ figure adequate?
9. Is the language acceptable?
10. Is the manuscript prepared in according with the journal's guidelines?
11. Please rate the priority for publishing this article (10 is the highest priority. 1 is the lowest priority?)
Editorial Board of the Eurasian Journal of Pulmonology encourages reviewers to comment on possible research or publication misconduct such as unethical research design, duplication, plagiarism, etc. Plagiarism is a serious problem and the most common ethical issue afflicting medical writing. The Eurasian Journal of Pulmonology does not allow any form of plagiarism. In accordance with our journal policy, submitted manuscripts are screened with plagiarism software to detect instances of overlapping and similar text (iThenticate and others) at least two times (during the evaluation process and after acceptance). If the reviewers have any suspect, the editors can provide them information obtained by plagiarism screening tools.
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The journal thanks to the reviewers and publishes the reviewer list every year in the last issue and on the website after each issue.
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text:
Emeka's Money (book)
Onyinye Ough's debut children's book about corruption, 'Emeka's Money' is available online now. Emeka was a good man, and tried to do good things for the people he liked. He worked for a State Governor in Nigeria and had good intentions of using his power to help his friends and family. Yet, Emeka soon learns that using public office to do so can actually harm the people of his State. As a result, he decides that things need to change and takes action against corruption. This is the first in a series of children's books about corruption written by Onyinye Ough.
Designed for children aged six to ten, the book aims to help them understand the impact corruption has within society and the importance of integrity. Written by anti-corruption expert Onyinye Ough and illustrated by Adeniyi Odeleye. Through this book and others (Halima's Vote and Tosin's Story), Onyinye aims to encourage a new generation of leaders who are willing to take action against corruption across Nigeria and through-out Africa.
To Buy this Book …
US sales from Amazon
Nigeria sales from Roving Heights
Electronic book available through Google Play
We are grateful for the MacArthur Foundation's support. As a result of their partnership we now have an audio-book version of Emeka's Money and are producing a short animated film version.
The Catch Them Young Initiative
With thanks to backing from donors such as the United Nations Office on Drugs and Crime (UNODC) and MacArthur Foundation, Step Up Nigeria has reached over 8,000 children with story books and class-room engagements. Over the next two years, we will have reached more than 20,000.
As a result of our engagements, we have identified young anti-corruption champions who are speaking out against corruption . One of our young anti-corruption champions is 13 year old Naomi Oloyede, who got the attention of the world in 2019, with her speech at the UNODC Education for Justice conference
Naomi's speech was focused on creating a fair society and tackling corruption. Onyinye's dream is that she will be able to roll out this type of engagement to every Nigerian child. "We want to create thousands of Naomi's. Only if we catch them young can we hope to change public attitudes towards corrupt behaviour." Onyinye's children's books on corruption and her range of creative content under development puts them in a strong position to take on this challenge. If they can change attitudes, Onyinye strongly believes there is hope of properly tackling corruption in Nigeria.
See some of the news coverage generated by the release of Onyinye's first anti-corruption children's book, for example, in the links below.
Transparency International Blog Article 'It starts with our children.'
Mail Online 'Tackling Corruption with a children's book'
Wall Street Journal: Nigerian woman fights corruption with a children's book.
FranceInfo: Nigeria: Corruption explained to children by Onyinye Ough.


Will you invest lots of time thinking of your the teeth? You need to, because men and women observe your tooth over it may seem. In case your dental hygiene online game needs tips, this post will assist you to. We've collected a list of dental care ideas to provide you with the most brilliant grin at any time.
Just use mouthwash clear of alcoholic beverages. Liquor being an ingredient can dry up the tissues inside your oral cavity. This creates a reproduction ground for the extremely bacteria you really want to prevent. Alcoholic beverages-cost-free mouthwash is also less of a temptation for kids and teens planning to try when bored stiff.
Floss, floss, floss! Sure every person brushes their the teeth like they're designed to, but what percentage of them in fact floss. Flossing cleans the area between the teeth exactly where food items could get caught up and harmful bacteria can spread out. This may also avoid the beginning of feasible infection that will arise when you don't floss.
If you feel your tooth should be whitened, you must timetable a scheduled appointment together with your dental practitioner. Your dentist will advise you on the way to brush and floss your the teeth proficiently and advocate some products you can use. In some cases, getting your the teeth cleansed by your dental professional is going to be adequate to ensure they are seem whiter.
Whenever you buy tooth paste, you need one particular with fluoride whether it is normal or not. This can develop teeth power. Powerful tooth ultimately are healthier ones.
Make sure you change your teeth clean frequently. Replace a minimum of every single three or four months. Even if your tooth brush nevertheless appears to be new, the bristles are likely frayed. An older toothbrush will not be as good as more recent toothbrushes. Swapping your brush on a regular basis is a crucial part of your oral health.
You shouldn't simply remember to brush the surface areas of your the teeth. It's important to remember to brush the gums at the same time, acquiring beneath these to clear out anything at all which is situated there. Oral cavaties which are underneath the gums are definitely the most severe as a result, you need to do all you are able to wash this place.
Do you know that the series that you just take in your food items in can be a factor in whether or not you develop oral cavaties? Eating sweet foods including food alone is significantly worse for the the teeth, than if you eat this cake following a meal. Don't take in sugary meals on your own, always try to eat all of them with other food items.
To avoid severe and possible long-lasting problems for your teeth, never utilize them for any action other than biting the meal which you take in. You manage the chance of chipping or cracking your pearly whites if you make use of your teeth to look at a package, draw a thing that is trapped or split wide open nutshells.
There are several methods to far better your teeth's health, only one is vital. That may be keeping semi-yearly appointments with the dentist. These sessions are employed to thoroughly clean your pearly whites and execute examinations. If weakened areas are located inside your the teeth, your dentist might load these locations to give your teeth more security.
After learning more about tips on how to take care of your pearly whites, you must be able to implement a number of modifications. Enable the ideas be your manual, and give on your own the smile you generally needed. Continue to keep discovering what you can, along with your pearly whites can look better as time passes.


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Afghan Dam Diplomacy:…
Afghan Dam Diplomacy: India's Aid, Taliban & The Peace Process
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