text:
\section{Introduction}

Camera relocalization, or image-based localization is a fundamental problem in robotics and computer vision. It refers to the process of determining camera pose from the visual scene representation and it is essential for many applications such as navigation of autonomous vehicles, structure from motion (SfM), augmented reality (AR) and simultaneous localization and mapping (SLAM). Due to importance of these problems various relocalization approaches have been proposed. Point-based localization approaches find correspondences between local features extracted from an image by applying image descriptors (SIFT, ORB, etc ~\cite{SURF,SIFT,ORB}) and 3D point clouds of the scene obtained from SfM. In turn, such set of 2D-3D matches allows to recover the full 6-DoF (location and orientation) camera pose. However, this low-level process of finding matches does not work robustly and accurately in all scenarios, such as textureless scenes, large changes in illumination, occlusions and repetitive structures.

Recently, various machine learning methods \cite{DSAC,scorfShotton,scorfValentin}, such as scene coordinate regression forest (SCoRF)~\cite{scorfShotton,scorfValentin}, have been successfully applied to camera localization problem. SCoRF utilize predicted 3D location of four pixels of an input image to generate an initial set of camera pose hypotheses which are subsequently refined by a RANSAC loop.
However, all these methods require depth maps associated with input images at training time, thus the applicability of such approaches is restricted.

Inspired by the success in image classification~\cite{ResNet,ImagenetKrizh}, semantic segmentation~\cite{SegmentationHong,SegmentationNoh} and image retrieval~\cite{ImageRetrievalBabenko,retrievalGordo}, convolutional neural networks (CNNs) have also been used to predict camera pose from visual data  \cite{Posenet2,Posenet}. They cast camera relocalization as a regression problem, where camera location is directly estimated by CNN pre-trained on image classification data~\cite{ImageNet}. Although learning-based approaches overcome many disadvantages of point-based methods, they still have certain limitations. Directly regressing the absolute camera pose constrains the current machine learning models to be trained and evaluated scene-wise when the scenes are registered to different coordinate frames.
 The reason for this is that the trained model learns a mapping from image (pixels) to pose which is dependent on the coordinate frame of the training data belonging to a particular scene. This causes complications, especially if one is interested in localization across several scenes simultaneously, and also prevents transferring learnt knowledge of geometric relations between scenes. The second problem is the obviously limited scalability to large environments since a finite neural network has an upper bound on the physical area that it can learn, as pointed out by \cite{Posenet}.

In this paper, we propose to decouple the learning process from the coordinate frame of the scene. That is, instead of directly regressing absolute pose like \cite{Posenet2,Posenet,HourglassPose}, we train a Siamese CNN architecture to regress the relative pose between a pair of input images and propose a pipeline for computing the absolute pose from several relative pose estimates between the query image and  database images. This approach is flexible and has several benefits: \emph{(a)} our CNN can learn from image pairs of any scene thereby being able to improve towards generic relative pose estimator; \emph{(b)} a single network can be trained and used for localization in several disjoint scenes simultaneously, even in scenes whose training images are scarce or not available during the training time of the network; and \emph{(c)} the approach is scalable because a single CNN can be used for various scenes and the full scene-specific database (\ie training) images are not needed in memory at test time either as compact feature descriptors and fast large-scale image retrieval techniques can be utilized instead.

To summarize, we make the following contributions:
\begin{itemize}
\item
 We propose a new deep learning based approach for camera relocalization. Our approach is general and scalable alternative to previous models and
compares favourably to other CNN-based methods.
\item We show through extensive evaluation the generalization capability of the approach to localize cameras in scenes unseen during training of the CNN.
\item We introduce a new challenging indoor dataset with accurate ground truth pose information and evaluate the proposed method also on this data.
\end{itemize}

The rest of the paper is organized as follows. Section~\ref{sec:related_work} discusses related work in image-based localization. Section~\ref{sec:pipeline} describes the network structure and the whole pipeline of our approach. The details of a new large indoor dataset and evaluation results of our method are provided in Section~\ref{sec:dataset} and Section~\ref{sec:experiments} accordingly. Conclusion and some suggestions for future work are given in Section \ref{sec:conclusion}.

We will make the source code and the dataset publicly available upon publication of the paper.

\section{Related work}\label{sec:related_work}

Camera relocalization approaches largely belong to two classes: visual place recognition methods and 3D model-based localization algorithms. Visual place recognition methods cast image-based localization problem as an image retrieval task and apply standard techniques such as image descriptors (SIFT, ORB, SURF~\cite{SURF,SIFT,ORB}), fast spatial matching~\cite{Oxford5k}, bag of visual words~\cite{BOWCula, BOWVarma} to find a representation of an unknown scene (a query image) in a database of geo-tagged images. Then, the geo-tag of the most relevant retrieved database image is considered as an approximation of a query location. The major limitation of visual recognition methods is that the images in database are often sparse, so that in situations where the query is far from database images the estimate would be inaccurate~\cite{scorfValentin}.

Structure-based localization methods utilize a 3D scene representation obtained from SfM and find correspondences between 3D points and local features extracted from a query image establishing a set of 2D-3D matches. Finally, the camera pose is established by applying RANSAC loop in combination with a Perspective-n-Point algorithm~\cite{PnPBujnak}. However, descriptor matching is expensive and time-consuming procedure making camera relocalization complicated problem for large scale scenes such as urban environment. In order to accelerate this stage,~\cite{PrioritizedSearchSnavely,PrioritizedSearchSattler} eliminate correspondence search as soon as enough matches have been found, and~\cite{SattlerTopDbSearch2,SattlerTopDbSearch1} propose to perform matching with the 3D points of top-retrieved database images

Sattler~\etal~\cite{sattler_CVPR2017} demonstrate that combining visual place recognition approaches with local SfM leads to better localization performance compared with 3D-based methods. However, the localization process itself is still very time-consuming.

It has also been shown that machine learning methods have potential for providing efficient solutions to the pose estimation problem. Similar to structure-based localization approaches, Shotton~\etal~\cite{scorfShotton} utilize a regression forest to predict a 3D point location for each pixel of an input RGB-D image. Thus, the method establishes 2D-3D matches which are then used to recover 6-DoF camera pose by applying RANSAC. Rather then finding point correspondences, Valentin~\etal\cite{scorfValentin} propose to exploit the uncertainty of the predicted 3D point locations during pose estimation. Brachmann~\etal~\cite{DSAC} propose a differentiable RANSAC method for camera localisation from an RGB image. However, it still requires dense depth maps in the training stage.

Recently proposed CNNs-based approaches have shown great success in image-based localization. Originally, utilizing CNNs to directly regress camera relocalization was proposed by Kendall~\etal~\cite{Posenet}. Their method, named PoseNet, adapts GoogLeNet~\cite{GoogLeNet} architecture pre-trained on large-scale image classification data to reconstruct 6-DoF camera pose from an RGB image. In the recent paper~\cite{Posenet2}, Kendall~\etal propose a novel loss function based on scene reprojection error and show its efficiency in appearance-based localization. Contrary to~\cite{Posenet2,Posenet},  HourglassPose~\cite{HourglassPose} utilizes a symmetric encoder-decoder network structure with skip connections which leads to improvement in the localization accuracy outperforming PoseNet.
Partly motivated by advances of Recurrent Neural Networks (RNN) in text classification, Clark~\etal and Walch~\etal apply LSTM networks to determine the location from which a photo was taken~\cite{VidLoc,LSTMPose}. Following \cite{Posenet}, both of these methods, called VidLoc \cite{VidLoc} and LSTMPose \cite{LSTMPose}, utilize GoogLeNet to extract features from input images to be localized, then feeding these features to a block of LSTM units. The regression part consisting of fully-connected layers utilizes output of LSTM units to predict camera pose. The major drawback of VidLoc is that it requires a sequence of adjacent image frames as input and is able to estimate camera translation only.

The proposed approach is related to all previously discussed CNN-based methods, but it is the first one solving image-based localization problem via relative camera pose. Inspired by~\cite{MelekhovRelative,OliveiraRelative,Demon}, we apply Siamese neural network to predict relative orientation and relative translation between two views. These relative translation estimates are then triangulated to recover the absolute camera location. Compared with~\cite{VidLoc,Posenet2,Posenet,HourglassPose,LSTMPose}, our study provides more general and scalable solution to image-based localization task. That is, the proposed approach is able to estimate 6-DoF camera pose for scenes registered to different coordinates frames, unlike existing CNN-based methods. Finally, compared with traditional machine learning approaches, our method does not require depth maps for training, thus it is applicable for outdoor scenes as well. Further details of our approach will be given in the following sections.

\begin{figure*}[t!]
		\centering

        \includegraphics[width=0.9\textwidth]{figures/system_pipeline_m.pdf}
		\caption{Pipeline of the proposed system. A Siamese based CNN network consisting of two branches pre-trained on ImageNet~\cite{ImageNet} is trained to directly regress relative pose between a pair of cameras (top). Once the training process is finished, we employ the branch as a descriptor to compute feature representations of database and query images. Then, the dot product is applied to these representations as a part of retrieval system and database descriptors are ranked according to higher similarity score. Consequently, query descriptor and its top N ranked database representations are concatenated and fed to the regression part of the network to predict pairwise relative pose. Finally, the proposed fusion algorithm naturally coalesces relative pose estimates and ground truth absolute poses to produce the full 6-DoF query location.}\label{fig:pipeline}
\end{figure*}

\section{Proposed approach}\label{sec:pipeline}
This section introduces the proposed localization approach for predicting camera pose. The method consists of two modules: a Siamese CNN network for relative pose computation and the localization pipeline. The input to the system is an RGB query image to be localized, and a database of images with their respective poses. At the first stage, we construct a set of training image pairs and use it to train a Siamese CNN to predict relative camera pose of each pair (Section~\ref{subsec:est_rel_pose}). It should be noted that the training image pairs can be independent of the scenes present in the localization database.Then, each trained branch of the network is considered a feature extractor and the extracted feature vectors can be utilized to identify the database images that are nearest neighbours (NN) to the query image in the feature space. Finally, relative pose estimates between the query and its neighbours are computed and then coalesced with ground truth absolute location of the corresponding database images in a novel fusion algorithm (Section~\ref{subsec:localization_system}) producing the full 6-DoF camera pose.

\subsection{Pairwise Pose Estimation}\label{subsec:est_rel_pose}

The first part of the proposed approach aims to directly estimate relative camera pose from an RGB image pair. The problem of regressing rigid body transformation between a pair of images has been well studied in recent years~\cite{deepHomography,MelekhovRelative,Demon}. Following~\cite{MelekhovRelative}, we construct a Siamese neural network with two representation branches connected to a common regression part as shown in Fig.~\ref{fig:pipeline}. The branches share the same set of weights and have ResNet34 architecture~\cite{ResNet} truncated at the last average pooling layer. The weights are initialised from a network pre-trained for large-scale image classification task~\cite{ImageNet}, and later fine-tuned for the relative pose estimation task as described below. The outputs of the representation branches are concatenated and passed through the regressor which consists of three fully-connected layers (FC), namely $FC_i$, $FC_r$ and $FC_t$, where the latter two predict relative orientation and translation, respectively. Fig.~\ref{fig:pipeline} shows a detailed description. The fully-connected layers are initialized randomly.

The output of the regression part is parameterized as 4-dimensional quaternion for relative orientation $ \Delta r $ and 3-dimensional $ \Delta t $ for relative translation ~\cite{MelekhovRelative,OliveiraRelative}. As the quaternions lie on a unit sphere, enforcing unit norm constraint on any 4-D vector outputs a valid rotation. Also the distance between two quaternions $s(r_i,r_k)$ can be measured by the Euclidean $l_2$ norm $||r_i - r_k||_2$, unlike other rotation parameterizations such as rotation matrices that lie on a manifold and distance computation requires finding an Euclidean embedding. For a more elaborate discussion we guide the reader to \cite{Hartley_IJCV13,Posenet2}. To regress relative camera pose, we train our CNN with the following objective criterion:

\begin{equation}\label{eq:eq_loss_function}
\mathcal{L} = \left\|\Delta t_{gt} - \Delta t\right\|_2 + \beta\left\|\Delta r_{gt} - \Delta r\right\|_2
\end{equation}
where $\Delta r_{gt}$ and $\Delta t_{gt}$ are the ground-truth relative orientation and translation respectively. To balance the loss for orientation and translation we use the parameter $\beta>0$ \cite{MelekhovRelative,OliveiraRelative}. The details of the training are described in Section \ref{sec:experiments}.

At test time, a pair of images is fed to the regression neural network, consisting of two branches, which directly estimates the real-valued parameters of the relative camera pose vector. Finally, the estimated quaternion and translation vectors are normalized to unit length. The normalized translation vector gives the translation direction from the database image to the query camera location. Although the translation vector predicted by our network contains scale information, we found that in practice recovering the scale using the approach discussed in Section~\ref{subsec:localization_system} is more accurate and reliable.

\subsection{Localization Pipeline}\label{subsec:localization_system}
\noindent\textbf{Retrieving the nearest neighbours} In order to find the nearest database images to the query,
it is important to obtain a suitable representation for both the query $q$ and the database images $D$.
Considering recent advances of CNN-based approaches in the field of image retrieval~\cite{retrievalGordo,retrievalRadenovich}, we use the fine-tuned network (one branch of the model architecture) from the first stage (Section~\ref{subsec:est_rel_pose}) as a feature extractor to encode the query and database images to fixed global representations (\ie 512 dimensional feature vectors, see Fig.~\ref{fig:pipeline}). In turn, the dot product of the query and database feature vectors is computed to obtain image similarity. Consequently, the database images are ranked according to similarity scores. Finally, the top $N$ ranked database images, $d = \{d^j| d^j \in D,j=1...N\}$ are selected as the nearest neighbours to the query image, $q$.

Although the retrieval stage is an important component in our pipeline, we adopted the simple approach above in this work and postpone deeper discussion and analysis to future work. The primary focus of our paper is not image retrieval, but camera localization. However, in Section~\ref{sec:experiments}, besides evaluating the performance of the complete pipeline, we also experimentally study how the system would perform with perfectly accurate retrieval stage.

\begin{figure}[t!]
		\centering

        \includegraphics[width=0.3\textwidth]{figures/triang_m.pdf}
		\caption{Estimation of query camera translation.

        }\label{fig:hyp_filtering}
\end{figure}

\noindent\textbf{Compute Relative Pose}
In the next stage of the pipeline the Siamese CNN is used to regress relative camera pose for the image pairs $Q = q \times d$, $ \Delta R = \{ \Delta R^1, \Delta R^2...\Delta R^N \}$ and $ \Delta \hat{t} = \{\Delta \hat{t}^1, \Delta \hat{t}^2,... \Delta \hat{t}^N\} $. Here, $ \Delta R^j $ represents the relative orientation between the $j^{th}$ NN database image, $ d^j \in d $ and the query $q$. Similarly, $ \Delta \hat{t}^j $ is the relative translation direction.

\noindent\textbf{Pose hypotheses filtering}
The final part of the localization system is a novel fusion algorithm
recovering the absolute query camera
pose from these $N$ relative pose estimations. This process is illustrated schematically in Fig.~\ref{fig:hyp_filtering}.

From the shortlisted top ranked database images
$d$, select a pair $p^s = \{ d^k, d^m \}$, where $p^s \subset d$ and $s = 1,2,\ldots, {{N}\choose{2}}$. Now, the translation direction predictions to the query $q$ from the images $ p^s $ are triangulated to obtain the location/translation parameter of query camera, $t^s$. This gives us ${{N}\choose{2}}$ hypotheses for the query translation, $\tilde{t}_q = \{t^s|s=1,2,\ldots, {{N}\choose{2}}\}$. Now, for each $t^s \in \tilde{t}_q$ the direction rays from the camera centers of the remaining images in $d$, $p^r = d$ \textbackslash $p^s$ to this triangulated camera location $t^s$ is obtained. If the direction ray associated with an image in $ p^r $ is within a pre-defined angular distance to the direction vector predicted by our network, then it is considered an inlier ($d_2$ and $d_N$ in Fig.~\ref{fig:hyp_filtering}) to the estimate $t^s$. The translation estimate $t^s \in \tilde{t}_q$, $s = 1,2,\ldots, {{N}\choose{2}}$ with the highest inlier count is assigned to the query camera. If two or more translation estimates have the same inlier count, then they are averaged to obtain the final query translation estimate.

Estimating rotation for the query camera is much simpler as the following equation can be used to obtain a hypothesis:
\begin{equation}
\Delta R^j = R_{j}^TR_{q}^j
\end{equation}
where $R_{j}$ is the orientation of the $j^{th}$ camera in $d$, $d^j \in d $ available as input to our system, and $R_{q}^j$ is the $j^{th}$ hypothesis of the query camera orientation. These results in $N$ hypotheses for query orientation, $\tilde{R}_q = \{R_{q}^1...R_{q}^N \}$. Instead of naively averaging the estimations, we apply a consensus based filtering similar to the process of estimating query translation. For each hypothesis, $R_{q}^j \in \tilde{R_q} $ a consensus set is formed from the remaining hypotheses in $\tilde{R_q}$ that lie within a pre-defined angular distance to $R_{q}^j $.
The number of elements in the consensus set is defined as the inlier count for $R_{q}^j $. The hypothesis with the highest inlier count is assigned as the orientation estimate for the query camera. When two or more hypotheses share the same inlier count, a robust rotation averaging algorithm \cite{Hartley_CVPR11} is applied to obtain the final query camera rotation.

\begin{figure*}[t!]
		\centering
		\includegraphics[width=0.9\textwidth]{figures/sys.pdf}
		\caption{The University dataset. The proposed large-scale indoor localization dataset consists of 5 different scenes (segments) registered to a common global co-ordinate system. }\label{fig:university_dataset}
\end{figure*}

\section{Datasets}\label{sec:dataset}
We evaluate the proposed approach on two different dataset for indoor localization.

\noindent\textbf{7Scenes} Microsoft 7-Scenes dataset contains RGB-D images covering 7 different indoor locations~\cite{7ScenesDataset}. The dataset has been widely used for image-based localization~\cite{VidLoc,Posenet2,Posenet,HourglassPose} exhibiting significant variation in camera pose, motion blur and perceptual aliasing. In our experiments we utilize the same train and test datasets for each scene as provided in the original paper.

\noindent\textbf{University} The scenes in  \textit{7Scenes} dataset have their own coordinate system without being linked to each other in a global coordinate system.  Therefore, all existing machine learning models are trained and evaluated scene-wise.
This fundamental limitation restricts to widely apply these approaches to real life scenarios where a large-scale environment consists of multiple sub-scenes.

We release a challenging indoor localization dataset, \textit{University}, with different locations that are all registered to a common global coordinate frame. For this paper, we consider a segment of the whole dataset, consisting of image sequences of 5 scenes, \textit{Office, Meeting, Kitchen, Conference, and Coffee Room}. The scenes are structured in a similar way to \textit{7Scenes}, with multiple traversals through each of the scenes.
The training and test split of the sequences are provided. Overall, the proposed dataset contains 9694 training and 5068 test images respectively. Some challenging test cases of \textit{University} dataset are presented in Figure 1 of the Supplementary material.

Ground-truth localization data of the dataset was generated using \textit{Google Project Tango's} tablet and high-resolution image sequences were collected by iPhone 6S mounted on top of the tablet. The device outputs two types of odometry estimations: Start of Service (SOS), which is the raw odometry and thus suffers from drift, and Area Learning (AL), which uses device's drift correction engine. As the AL engine fails sometimes~\cite{Laskar_ICIP16}, we use the odometry estimates from SOS and manually generated location constraints in a pose-graph optimization framework to generate a globally consistent map.

\section{Experiments}\label{sec:experiments}
In this section we quantitatively demonstrate the effectiveness of the proposed system on \textit{7Scenes} and \textit{University} datasets. We compare our approach with the current state-of-the-art CNN-based methods, such as PoseNet~\cite{Posenet}, HourglassPose~\cite{HourglassPose}, LSTMPose~\cite{LSTMPose}, VidLoc~\cite{VidLoc}, and PoseNet with reprojection loss~\cite{Posenet2}, dubbed PoseNet2.

\begin{table*}[t!]
\begin{center}
\resizebox{1\textwidth}{!}{%
	\begin{tabular}{l | l | l l l l l | l l}
	\multirow{2}{*}{Scene} & \multicolumn{1}{c|}{Spatial} & \multicolumn{1}{c}{PoseNet}  & \multicolumn{1}{c}{LSTM-Pose} & \multicolumn{1}{c}{VidLoc} & \multicolumn{1}{c}{Hourglass-Pose} & \multicolumn{1}{c|}{PoseNet2} & \multicolumn{1}{c}{ResNet34-Pose} & \multicolumn{1}{c}{Ours} \\
    & \multicolumn{1}{c|}{Extent} & \multicolumn{1}{c}{\cite{Posenet}} & \multicolumn{1}{c}{\cite{LSTMPose}} & \multicolumn{1}{c}{\cite{VidLoc}} & \multicolumn{1}{c}{\cite{HourglassPose}} & \multicolumn{1}{c|}{\cite{Posenet2}} & \multicolumn{1}{c}{(baseline)} & \\
    \hline
    \hline
    Chess & $3\times 2 \times 1$m & $0.32$m, $8.12^{\circ}$ & $0.24$m, $5.77^{\circ}$ &  $0.18$m, N/A & $0.15$m, $6.53^{\circ}$ & $0.13$m, $4.48^{\circ}$ & $0.12$m, $6.69^{\circ}$ & $0.13$m, $6.46^{\circ}$\\
    Fire & $2.5\times 1 \times 1$m & $0.47$m, $14.4^{\circ}$ & $0.34$m, $11.9^{\circ}$ & $0.26$m, N/A & $0.27$m, $10.84^{\circ}$ & $0.27$m, $11.3^{\circ}$ & $0.31$m, $13.36^{\circ}$ & $0.26$m, $12.72^{\circ}$ \\
    Heads & $2\times 0.5 \times 1$m & $0.29$m, $12.0^{\circ}$ & $0.21$m, $13.7^{\circ}$ & $0.14$m, N/A & $0.19$m, $11.63^{\circ}$ & $0.17$m, $13.0^{\circ}$ & $0.16$m, $13.78^{\circ}$ & $0.14$m, $12.34^{\circ}$\\
    Office & $2.5\times 2 \times 1.5$m & $0.48$m, $7.68^{\circ}$ & $0.30$m, $8.08^{\circ}$ & $0.26$m, N/A & $0.21$m, $8.48^{\circ}$ & $0.19$m, $5.55^{\circ}$ & $0.21$m, $8.78^{\circ}$ & $0.21$m, $7.35^{\circ}$  \\
    Pumpkin & $2.5\times 2 \times 1$m & $0.47$m, $8.42^{\circ}$ & $0.33$m, $7.00^{\circ}$ & $0.36$m, N/A & $0.25$m, $7.01^{\circ}$ & $0.26$m, $4.75^{\circ}$ & $0.25$m, $7.89^{\circ}$ & $0.24$m, $6.35^{\circ}$ \\
    Red Kitchen & $4\times 3 \times 1.5$m & $0.59$m, $8.64^{\circ}$ & $0.37$m, $8.83^{\circ}$ & $0.31$m, N/A & $0.27$m, $10.15^{\circ}$ & $0.23$m, $5.35^{\circ}$ & $0.22$m, $9.35^{\circ}$ & $0.24$m, $8.03^{\circ}$\\
    Stairs & $2.5\times 2 \times 1.5$m & $0.47$m, $13.8^{\circ}$ & $0.40$m, $13.7^{\circ}$ & $0.26$m, N/A & $0.29$m, $12.46^{\circ}$ & $0.35$m, $12.4^{\circ}$ & $0.37$m, $14.45^{\circ}$ & $0.27$m, $11.82^{\circ}$ \\
    \hline \hline
    \multicolumn{2}{l|}{Average} & $0.44$m, $10.4^{\circ}$ & $0.31$m, $9.85^{\circ}$ & $0.25$m, N/A & $0.23$m, $9.53^{\circ}$ & $0.23$m, $8.12^{\circ}$ & $0.23$m, $10.61^{\circ}$ & $0.21$m, $9.30^{\circ}$
	\end{tabular}
    }
\end{center}
\vspace{-2mm}
\caption{Camera localization performance of the proposed method and existing RGB-only CNN-based approaches for \textit{7Scenes} datasets. We follow original notation presented in~\cite{Posenet} and provide median translation and orientation errors. In terms of localization error, our approach is superior to other methods utilizing similar loss (\ref{eq:eq_loss_function}) such as PoseNet~\cite{Posenet}, LSTM-Pose~\cite{LSTMPose}, VidLoc~\cite{VidLoc} and Hourglass-Pose~\cite{HourglassPose} for the all scenes. Furthermore, the proposed method outperforms PoseNet2~\cite{Posenet2} in 4 scenes and achieves better localization accuracy in general. However, it is important to note that both methods are not directly comparable, due to more advanced loss function optimized in~\cite{Posenet2}.}\label{lbl:table_results_7scenes}
\end{table*}

\begin{table}[t!]
\begin{center}
\resizebox{.4\textwidth}{!}{%
  \begin{tabular}{l c | c c}

   Scene & Baseline & Proposed  \\ \hline \hline
   Office  & $2.76$m, $17.08^{\circ}$ & $0.57$m, $17.09^{\circ}$\\
   Meeting  & $2.13$m, $14.13^{\circ}$ & $2.30$m, $12.27^{\circ}$\\
   Kitchen  & $1.65$m, $12.92^{\circ}$ & $0.70$m, $11.72^{\circ}$\\
   Conference  & $4.97$ m, $17.18^{\circ}$ & $2.74$m, $15.00^{\circ}$\\
   \hline \hline
   Average &  $2.88$ m, $15.33^{\circ}$&  $1.58$ m, $14.02^{\circ}$ \\
 \end{tabular}
  }
\end{center}
\caption{Camera relocalization performance of the proposed approach and the baseline on \textit{University} dataset presented as median orientation and translation errors. Training is done using the training images of all scenes for both approaches. Evaluation is performed scene-wise.}\label{lbl:performance_on_university}
\end{table}

\begin{table}[t!]
\tiny
\begin{center}
\resizebox{.45\textwidth}{!}{%
  \begin{tabular}{l| c| c}

  \multicolumn{1}{l|}{Removed} & \multicolumn{2}{c}{Median error} \\
  \multicolumn{1}{l|}{scene} & \multicolumn{1}{c|}{position [m]} & \multicolumn{1}{c}{orientation [deg]}\\
  \hline\hline
   Chess & 0.27 & 13.05  \\
   Heads & 0.23 & 15.03  \\
   Red Kitchen & 0.36 & 12.60  \\

 \end{tabular}
  }
\end{center}
\vspace{-2mm}
\caption{Generalization performance of the proposed approach. Localization accuracy of the proposed method on unseen scenes of \textit{7Scenes} dataset.}\label{tbl:generalization_removed_scenes}
\end{table}

\begin{table}[t!]
\tiny
\begin{center}
\resizebox{.45\textwidth}{!}{%
  \begin{tabular}{l| c| c}

  \multirow{2}{*}{Scene} & \multicolumn{2}{c}{Median error} \\
                          & \multicolumn{1}{c|}{position [m]} & \multicolumn{1}{c}{orientation [deg]}\\
  \hline\hline
   Chess       & 0.31 & 15.05  \\
   Fire        & 0.40 & 19.00  \\
   Heads       & 0.24 & 22.15  \\
   Office      & 0.38 & 14.14  \\
   Pumpkin     & 0.44 & 18.24  \\
   Red Kitchen & 0.41 & 16.51  \\
   Stairs      & 0.35 & 23.55  \\
   \hline \hline
   Average     & 0.36 & 18.38
 \end{tabular}
  }
\end{center}
\vspace{-2mm}
\caption{Generalization performance of the proposed approach. The network is trained on only \textit{University} and evaluated on \textit{7Scenes} dataset.}\label{tbl:generalization_trained_on_aalto}
\end{table}

According to Fig.\ref{fig:pipeline}, representation part of the proposed system is based on a Siamese architecture. In this work, we initialize our model using original ResNet34 model truncated at the last average pooling layer and pre-trained on ImageNet~\cite{ImageNet} as a structure of each branch.

\noindent\textbf{Training data} We start experiments by evaluating the system on \textit{7Scenes} dataset. As it is mentioned in Section~\ref{sec:dataset}, the dataset consists of different indoor scenes and each of them provides training and testing image sequences. Since the system requires an image pair to learn relative pose, the following strategy is applied to obtain training dataset. For every image in the training set of each scene, we find a corresponding image from the same set so that they have sufficiently overlapping field of view. Total number of training pairs of a scene is equal to the number of images in the training sequence of this scene in the original dataset.
Finally, the training pairs from all the scenes are merged into a single training set for training the CNN in our approach.

\noindent\textbf{Training details} As a preprocessing step, the training images are resized to 256 pixels on the smaller side while maintaining the aspect ratio of the resized image. The training images are further mean-centered and normalized using standard deviation computed over the whole training set. To ensure fixed sized input to our network, we use random crops (224 $ \times$ 224) during training and perform the evaluation using central crops at test time. The network is trained for 200 epochs with an initial learning rate of 0.1 which is gradually decreased by 10 times after every 50 epochs. The loss (\ref{eq:eq_loss_function}) is minimized using stochastic gradient descent with a batch size of 64 training samples. The scale factor $\beta$ is set to 1 after empirical evaluation for all our experiments. The weight decay is set to $10^{-5}$ and no dropout was used in our experiments. All experiments were evaluated on two NVIDIA Titan X GPUs using Torch7~\cite{Torch7} machine learning framework.

\noindent\textbf{Evaluation stage}
The input to the system is a database containing the list of images from the training set of all scenes (for a given dataset) and their respective camera poses. The combined list of test images from all scenes constitute the query set. For each query, we retrieve its top 5 NN ($N$ = 5) from the database images using neural representations from our trained representation branch.

The query and its NN are then fed sequentially to our Siamese model to obtain the relative camera pose estimates. From a practical standpoint it is not necessary to feed the query and its NN images through the full network model. The only component of our network that requires pairwise input is the regression part, which takes in input from the representation part of each branch of the Siamese model. Also, both the representation branches share the same parameters and the output of the representation part is already used in the first stage of our pipeline to compute image similarity. Thereby, to compute relative pose, we simply feed the representations of the query and its NN in a pairwise manner to the regression component.

The relative pose estimations are then robustly fused to obtain the query camera pose. The angular distance threshold of inliers for both translation and rotation is 20 degrees.

\begin{comment}
\begin{figure}[t!]
 	\centering
	\includegraphics[width=.4\textwidth]{figures/hm_fire.png}
   \includegraphics[width=.4\textwidth]{figures/hm_chess.png}
   \includegraphics[width=.4\textwidth]{figures/hm_office.png}

 \caption{Attention maps of input image pairs produced by our approach for several locations of \textit{7Scenes} dataset (from top to bottom: Fire, Chess, Office).
 }\label{fig:fig_correspondance}
 \end{figure}
\end{comment}

\subsection{Quantitative Results}\label{ssec:results}
We compare our proposed system with the existing CNN based localization methods on~\textit{7Scenes}, while for~\textit{University} dataset we provide an evaluation of our proposed system and a baseline method. The results are shown in Table~\ref{lbl:table_results_7scenes} and Table~\ref{lbl:performance_on_university}. For both datasets, the localization performance is measured as the median orientation and translation error over each scene.

For several scenes in the \textit{7Scenes} dataset we outperform other CNN-based methods in camera relocalization.
In particular, we perform favourably  to the current best performing method, PoseNet2 on several scenes. However, PoseNet2 is not directly comparable to our work as it uses a more sophisticated loss function during training and a different CNN architecture.
\begin{comment}
To get better understanding how the system estimates relative pose, we visualize a set of attention maps (Fig.~\ref{fig:fig_correspondance}) of image pairs representing different scenes by adopting a method proposed by~\cite{GradCam}. The system focuses on the same regions even with high viewpoint changes between images.
\end{comment}

For a fairer evaluation we compare our system with a baseline model consisting of pre-trained convolutional layers of ResNet34 architecture and a regression part replicating the one utilized in the proposed approach (but without the Siamese architecture). We entitle this model {ResNet34-Pose}. Following~\cite{Posenet2,HourglassPose}, the baseline is trained and evaluated scene-wise. Table~\ref{lbl:table_results_7scenes} shows that our proposed system has a consistent improvement over the baseline for both rotation and translation across all the scenes. Although the margin of improvement is not large, it is to be noted that all the existing methods (including the baseline) are trained in a scene-specific manner whereas our system was designed to inherently overcome this fundamental limitation and allows us to train and test our model jointly on all the scenes. That is, our approach uses the same network for all 7 scenes whereas other approaches of Table \ref{lbl:table_results_7scenes} have one network per scene (\eg in total ResNet34-Pose has thus 7 times more parameters that are learnt).

The performance increase of our system compared to the baseline can be attributed to a number of factors: \emph{i)} representation sharing across scenes during training, \emph{ii)} generating multiple hypothesis for query camera pose followed by robust pose filtering, \emph{iii)} larger training set.  These factors, although plausible have not been experimentally validated in this paper and we leave it for future work.

For the \textit{University} dataset, our system and the baseline are trained jointly on the scenes: \textit{Office, Meeting, Kitchen} and \textit{Conference}. The scene \textit{Coffee Room} is a recent addition to the database, and due to time constraints we could not train and evaluate our proposed system and {ResNet34-Pose} on this scene. However, we use it to evaluate our trained model in Section \ref{sec:generalize}. The performance evaluation is presented in Table~\ref{lbl:performance_on_university}. The results show that the margin of translational error between the baseline and our system has increased significantly. In particular, it has increased from 2cm in \textit{7 Scenes} (all scenes combined) to 130cm in \textit{University}. Although it does demonstrate that the proposed system performs better even under similar training setup, it also provides additional insight on the scalability of our system. As mentioned in Section~\ref{sec:dataset}, the \textit{University} dataset consists of several scenes spread over an area of 2500 $m^2$. Absolute pose prediction models like the baseline model need to maintain a track of the spread or scale of the map, while models like our system are not much affected by scale. Our system essentially removes the influence of scale by finding the NN and solving the relative pose problem which does not depend on the scale of the map/dataset.

\vspace{-1mm}
\subsection{Generalization Performance} \label{sec:generalize}
\vspace{-1mm}

Current machine learning models for camera localization are not only restricted to scene-wise training and evaluation, but also limited in their applicability to previously unseen scenes. In this section we experimentally demonstrate the generalization capability of our pipeline to data previously unseen during training.

We hold out one of the scenes in \textit{7Scenes} dataset for evaluation and train our model on the remaining 6 scenes. In particular, we held out \textit{Chess, Heads,} and \textit{RedKitchen} separately as evaluation sets. Table~\ref{tbl:generalization_removed_scenes} shows a graceful drop in performance on the held out test scene compared to the case where our model was trained on all the 7 scenes (Table \ref{lbl:table_results_7scenes}). In \textit{University} dataset, we evaluate on \textit{Coffee Room} using the model trained on the remaining 4 scenes. The median position and orientation error were 1.44 m and 19.22 degrees.

We further evaluate the performance on the \textit{7Scenes} dataset using our model trained on the \textit{University}  dataset (excluding \textit{Coffee Room}). According to Table~\ref{tbl:generalization_trained_on_aalto}, the performance drop is not drastic, with the mean of the median error over all the scenes drop by 9 degrees and 15 cm for rotation and translation respectively.

\begin{table}[t!]
 \begin{center}
 \resizebox{.5\textwidth}{!}{%
 	\begin{tabular}{l | l l l }
 	Scene  & Viewpoint 0  & Viewpoint 3 & Viewpoint 7 \\
     \hline
     \hline
     Chess  & $0.19$m, $7.48^{\circ}$ & $0.16$m, $7.26^{\circ}$ &  $0.16$m, $7.61^{\circ}$ \\
     Fire   & $0.13$m, $6.61^{\circ}$ & $0.10$m, $6.45^{\circ}$ & $0.11$m, $6.32^{\circ}$  \\
     Heads  & $0.25$m, $8.74^{\circ}$ & $0.25$m, $8.54^{\circ}$ & $0.25$m, $8.71^{\circ}$ \\
     Office & $0.21$m, $11.13^{\circ}$ & $0.19$m, $11.14^{\circ}$ & $0.19$m, $11.95^{\circ}$ \\
     Pumpkin  & $0.24$m, $9.39^{\circ}$ & $0.26$m, $9.50^{\circ}$ & $0.25$m, $9.35^{\circ}$ \\
     Red Kitchen  & $0.21$m, $7.59^{\circ}$ & $0.19$m, $7.41^{\circ}$ & $0.19$m, $7.45^{\circ}$ \\
     Stairs  & $0.23$m, $7.92^{\circ}$ & $0.23$m, $8.26^{\circ}$ & $0.23$m, $8.56^{\circ}$ \\
     \hline \hline
     Average & $0.21$m, $8.41^{\circ}$ & $0.20$m, $8.37^{\circ}$ & $0.20$m, $8.44^{\circ}$
 	\end{tabular}
     }
 \end{center}
 \vspace{-4mm}
 \caption{Camera relocalization accuracy of the proposed system for different viewpoint changes between the query and the database image.}\label{lbl:results_perfect_retrieval}
 \vspace{-1mm}
 \end{table}

\vspace{-1mm}
\subsection{Ideal Retrieval Results}\label{subsec:perfect_retrieval}
We now evaluate the scenario when the retrieval stage of our pipeline returns only the true nearest neighbours to the query. We further evaluate the effect of image spacing between the query and the retrieved NN. These experiments are evaluated on \textit{7Scenes} dataset.

Due to low image spacing between training images across all the scenes in \textit{7Scenes}, a query often has more than 300 true NN. Now, for a given query we use ground truth to sort all the training images from the corresponding scene using a metric similar to \eqref{eq:eq_loss_function}. We then create a sublist consisting of the top 355 images from this sorted list. From this sublist, we further select 8 sets of 5 images each at an interval of 50 ranks. That is, the first set (Viewpoint 0 in Table \ref{lbl:results_perfect_retrieval}) contains images ranked 1-5, the next set (Viewpoint 1 in Supplementary) consisting of images ranked 51-55 and so on.
We then evaluate our proposed system using these sets of true NN instead of the one obtained using neural representations. Table \ref{lbl:results_perfect_retrieval} (and Table in Supplementary) shows that the proposed system has a consistent performance across wide viewpoint variation in the true NN. This is an indication that the pipeline is robust to the quality of the nearest neighbours. They do not necessarily need to be the database images that have most overlap with the query. On the other hand, the result also shows that with \textit{7Scenes} our choice of $N=5$ might not be the optimal choice. Increasing $N$ will increase the chances of retrieving the true NN, and the consistent performance across all scenes and viewpoint changes suggests that the true NN have a higher likelihood of forming a consensus set (\cf Section \ref{subsec:localization_system}).

\vspace{-1mm}
\section{Conclusion} \label{sec:conclusion}
We addressed some of the challenges and limitations of the current setup in which machine learning models are trained and evaluated for camera localization. By leveraging the training images both at training and test time, we are able to mitigate these limitations and achieve competitive results on challenging datasets. Results demonstrate that the scope of the proposed system is easily extendable to scenes without prior training.

As future work, possible directions include training the network simultaneously with both relative pose and image similarity objectives \cite{zamir2016generic}. Also, learning a better generic relative camera pose estimator~\cite{Demon} can improve the generalization performance of the proposed system.

 {\small
 \bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


NEW DELHI: In a relief to Uttar Pradesh chief minister Mayawati, the Delhi high court on Wednesday dismissed an appeal filed by I-T department challenging a tax tribunal order exempting from tax gifts received by her during the assessment year 2003-04.
A division bench of justices AK Sikri and Suresh Kait upheld the order passed by Income Tax Appellate Tribunal (ITAT) that gifts received by the BSP supremo from party supporters were not taxable under the Income Tax Act.
ITAT had accepted Mayawati's claim that immovable property worth Rs 62.72 lakh and cash payments of Rs 2 lakh were gifts given by her supporters out of love and affection.
The government in its appeal had held her claim as unacceptable and contended that cash and property given to Mayawati should be included in her annual income tax return.


999lazer: The adrenaline fuelled Arenacross tour returns for another action packed season of dirt bike racing in 2019, and once again we are going to be along for the drama filled ride! Buckle up for AX MEN season 2, we'll have new episodes premiering every Thursday for the next 6 weeks. In S2E1, we go behind the scenes and talk with the men that make the show happen, we get the lowdown on the format shake up and set the stage for the epic season to come. Sit back, relax and enjoy THE GREAT INDOORS.
This race series looks really awesome and this show could be cool too. I like that they just implement ideas and run with them.


Education is your first priority, and your network of workstations and servers is an invaluable tool to provide quality education. Consequently, protecting this learning environment is also one of your most important challenges. Reliable data protection enables you to focus on education by ensuring reliable solutions throughout your organization.
With the rapidly increasing amounts of stored data and the addition of virtualization technologies, data protection software is evolving from file based backup technologies to block level technologies to better accommodate data volume, backup speed, and system recovery.
E-RateForSchools recommends Data Protection solutions that feature integrated source and target based deduplication, comprehensive data protection for virtual environments, and advanced monitoring and reporting across domains.
Many of the mysteries of equipment failures downtime, software failures, and data corruption are the result of a problematic source of power. It is important that mission-critical equipment such as servers, routers, switches, and access points be provided with a high quality, always-on power source and the ability to gracefully shut down during power outages.
E-RateForSchools recognizes power standards and bodies like the IEEE govern good engineering disciplines to ensure power quality and prevent threats like transients, overvoltage, undervoltage, power interruptions, waveform distortion, voltage fluctuations, and frequency variations. We understand power and would welcome the opportunity to help you obtain the right sized solution for your school.


The Vanderbilt University All Over Fairway Cover is created using the highest-quality Pendleton® Woolen Mills fabric. The exterior has a supple texture with a Black base and the Vanderbilt University "Star V" mark embroidered in an all-over fashion. Each head cover is hand cut and sewn by our in-house artisans. The Vanderbilt University All Over Fairway cover is lined with a black fleece interior.


We hold expertise in offering an excellent quality Mens Royal Blue Athletic Lowers. These athletic lowers are made up of 100% breathable fabric and advanced technology. This athletic lower is known for its sweat absorbent, skin friendliness, c smooth texture and comfortable fitting. This athletic lower gives comfort to the player while in the ground. By keeping in mind customer's specific needs, we are delivering the offered Mens Royal Blue Athletic Lowers in different colors and sizes. Clients can avail this athletic lower at reasonable rates.


text:
Томас Холланд () — английский писатель и . Написал восемь работ по античной, средневековой истории и истории ислама, а также является автором нескольких хоррор-романов.

Биография
Томас родился в деревне  вблизи Солсбери, графство Уилтшир, Англия. Обучался в Куинз-колледже Кембриджского университета.

Книги

Романы
На английском

На русском

Нон-фикшен (документальная, научная проза)
На английском
 Rubicon: The Last Years of the Roman Republic. New York, 2003. — (Random House). — ISBN 978-1-400-07897-4.
 Persian Fire: The First World Empire and the Battle for the West. New York, 2005. — (Random House). — ISBN 978-0-307-27948-4.

 In the Shadow of the Sword: The Battle for Global Empire and the End of the Ancient World. New York, 2012. — (Random House). — ISBN 978-1-408-70007-5.

 Dominion: The Making of the Western Mind (U.S. edition subtitled 'How the Christian Revolution Remade the World'). — L., 2019. — (Little, Brown). — ISBN 978-1-408-70695-4.
На русском

Примечания

Выпускники Кембриджского университета
Выпускники Куинз-колледжа (Кембридж)
Писатели Англии
Авторы научно-популярной литературы
Авторы литературы ужасов
Англоязычные писатели-фантасты

redpajama_set_name: RedPajamaWikipedia


text:
Home » Answer » PAD 525 WEEK 6 DISCUSSIONs Employment Law
PAD 525 WEEK 6 DISCUSSIONs Employment Law
WEEK 6 DISCUSSION 1
"Employment Law." Please respond to the following:
Analyze Garcetti v. Ceballos, to determine whether or not the Pickering balancing test is applicable to this case and state why or why not.
Many states are facing budget constraints that are shining a light on public employees' pay and benefits, which have led to debates about state employees' collective bargaining rights. From a public administrator perspective, speculate to the short-term and long-term effects if limited collective bargaining is adopted. Hint: Look at all the stakeholders involved.
This law governs the rights and duties between workers and their employers. It is also referred to as the labor law. This rule ensures that the rights of employees are protected by fair treatment, and also protects the interests of the employers as well. These laws also deal with the minimum wage, overtime pay, discrimination at the workplace among others.
In the case of Garcetti v. Ceballos, the Pickering balancing test is very much applicable. The test is applied when the interests of a public employer need to balance with the right of the employee to free speech. There are a number of considerations that have to be made which include the following: whether the individual demonstrated that his/her speech addresses a matter or matters of public concern and interest. Whether the individual demonstrated that his/her speech was significant or motivating factor in the decision of the employer. If the court balanced the individual's interest as regards commenting on matters of public interest and the interest of the public employer in promoting the efficiency of public service. An individual's First Amendment rights have to be respected in this process.
In this case (Garcetti v. Ceballos), the Supreme Court dealt with a challenge of the First Amendment of a deputy district attorney. The attorney had alleged receiving retaliatory actions from his employer when he brought to attention concerns regarding potential misconduct by the police to his supervisors through a memorandum. The focus was on whether his speech was made on the official duties of the district attorney whereby the powers of the employer were expanded over his speech. It was found out that the memo was done on official capacity; it was found that Ceballo was not raising the concerns as a citizen on matters of public concern and therefore was not protected by the First Amendment. The Pickering test applies here because he was speaking on official job capacity.
Collective bargaining is made up of negations involving the employer and a group of employees in order to determine employment conditions. It allows employees to join a trade union that bargains on their behalf. It is important that these rights are not limited as they may have both long-term and short term effects. The short-term effects of limiting this rights is that employers will be able to control the amount pay and benefits offered to employees which means that they will be able to to offset their budgets. Long-term effects however, include loss of arbitration as a solution to employee upheavals, increased strikes from employees and a lot of court cases due to violation of the employee rights.
Feda, M. (2016). Labor and Employment. Illinois State Bar Association. Retrieved 7 November 2016, fromhttps://www.isba.org/sections/laboremploymentlaw/newsletter/2011/12/publicemployeesandfreespeech
"Tort Law." Please respond to the following:
Examine the Federal Tort Claims Acts (FTCA). Discuss whether these laws reduce the risk to citizens or does it serve a compelling state interest. Analyze Dolan v. United States Postal Service.
As a public administrator, discuss which view you find more persuasive – that sovereign immunity should be narrowly or broadly construed. State why with examples or evidence to support your position.
I think that the Federal Tort Claims Act (FTCA) does not reduce the risk to citizens but serves a compelling state interest. This is evidenced by the case of Dolan v. United States Postal Service. In this case, the petitioner Barbara Dolan had sustained serious injuries as a result of tripping over a stack of letters, packages and other mail that had been negligently left by an employee of the USPS at her home. She sued the federal government and the postal service for the injuries under the Federal Tort Claims Act. Her case was dismissed by the district court as well as by the Supreme Court who cited that her claim was not covered or included by the law as it was under the statutory exception to the FTCA. According to the exception, actions caused by "negligent transmission" of mail by their employees cannot be compensated.
The question here is whether the exception for "negligent transmission" of mail by employees of the postal service applies to claims of physical harm to individuals as a result of employee negligence in delivering mail, or is it limited to claims of mail damaged by negligence of employees (Feda, 2016). These laws therefore do not reduce the risks to citizens but only serves their interests. By not clearly defining what the "negligent transmission" entailed, the state refused to compensate Dolan for her injuries that were as a result of negligence by the state's employees. So the state was only protecting their own interests due to lack of clarity on the law.
Sovereign immunity
I think that sovereign immunity should be broadly interpreted. This doctrine holds that the UNITED States government cannot be sued without its consent (Feda, 2016). In this regard, Congress passed the law of FTCA to forego the powerful protection shielding the government from liability and enable the states to be liable for monetary damages resulting from harm of individuals by federal employees. Some exceptions were made to this law such that there are some cases where the government will not be liable especially in areas where the government is very vulnerable.
Sovereign immunity needs to be broadly interpreted to that it is well understood and clear to the areas where individuals are able to seek for damages and in the cases where they cannot. This means that certain sections such the "negligent transmission" needs to be clearly indicated as to what it means and what the citizens are protected from. This will also protect citizens from injuries caused by federal employees due to their negligence as in the case of Dolan.
Dolan v. United States Postal Service (04-848). (2016). LII / Legal Information Institute. Retrieved 7 November 2016, fromhttps://www.law.cornell.edu/supct/cert/04-848
PAD 525 WEEK 6 DISCUSSIONs Employment Law.docx

redpajama_set_name: RedPajamaCommonCrawl


text:
Moich Abrahams
Area: Battersea
Type: Artist / Individual
Art form: Art, Ceramics, Drawing, Painting, Print
Biography / Artist statement
Moich Abrahams is an award-winning artist painter based in Battersea. He obtained a first from St Martins (Byam Shaw) and an MA from Goldsmiths when they were first offered (four years before Damian Hirst). In 2013 Moich was a prize winner at the Rootstein Hopkins Drawing Exhibition at the Morley Gallery and more recently was a winner several times, including a global art competition based in New York. His work has been selected for many shows at institutions such as the Institute of Contemporary Art, the Whitechapel Gallery, the Discerning Eye at the Mall Galleries, Mauger Modern Art in Bath, and The British Drawing Show at the Hayward Gallery. Moich has shown many times with the London Group since 1975 and was elected member in 1977.
Moich enjoys combining spontaneity and playfulness while exploring deeper aspects of the unconscious. He is fascinated with both unwrapping the mysterious and reinventing childlike expressiveness. Central to this process is the art of 'letting go'.
Art Manifesto:
"Since everything is none other
than exactly as it is
one may well just break out in laughter."
(Long Chen Pa)
Moich lives in Battersea and has his studio in Streatham.
Associated with: London Group and Espacio Artists Collective
Why do you love Wandsworth?
I love the cosmoplitan vibrancy of Battersea where I have lived for 40 years, and the whole of Wandsworth. It has fabulous atmosphere, awesome cafes, restaurants and shops; wonderful parks, and great access to the best culture in the world.
And receive arts news, information on happenings, and opportunities in the Borough
© 2021 Wandsworth Art
Wandsworth Art
Wandsworth Town Hall,
Wandsworth High St
London SW18 2PU United Kingdom
arts@wandsworth.gov.uk


Although I'm ready to move out and away from my parents home, I'm absolutely terrified that I won't feel safe in my new home. I don't know what it is, but I hate being alone at the thought of leaving the fire on or leaving my front door unlocked.
When my parents go away on holiday I'm usually left alone in the house – and it scares the heck out of me. I have my dog with me, and he's massive, but I still feel like I've done something that could go wrong. I'm not sure where this fear comes from…I mean, it could come from that time I burnt a hole in our kitchen counter with incense, but I digress. Here are some ways that you can feel safe in your own home.
In our home, we have an open fire and a gas cooker. Now, it's nice to look at, but the thought of leaving the fire on accidentally or leaving the stove on fills me with dread sometimes. I also light candles before bed and my palms will sweat until I blow them out! Now, there are fire protection systems you can buy to ensure your safety – and I can imagine I'll buy a system when I have my own home. I know that I turn everything off, but if someone terrible happens, I know I'll be prepared.
Now, you could also install an alarm in your home. We used to have to one, but our dog tends to set it off – so we leave it now and use him as our little deterrent. But alarms can be really helpful in putting your mind at ease. You can also set alarms on your phone to do a nightly check before you go to bed. Make sure that your doors and windows are shut – that your appliances are switched and that you have no candles burning.
My friend recently experienced a break in, and I can't even imagine how horrifying it must have been for her. Since the incident, she's installed a home camera system. This monitors the home and gives you any notification if the camera detects movement. Now, as she has two cats, she gets a few notifications, but it's quite cute because she can talk to them through the microphone! If you're looking to invest in a home monitoring camera, take a look at the hive camera. I saw an advert for it the other day and it looks so good!


text:
Q: Equal sign in function prototype I was checking the reference for the STL vector container's constructor, and I see one of the prototypes listed is this:
explicit vector(size_type n, const value_type& val = value_type(),
             const allocator_type& alloc = allocator_type());

Link: http://www.cplusplus.com/reference/vector/vector/vector/
Could anyone explain what the = in the second and third parameters mean?

redpajama_set_name: RedPajamaStackExchange


text:
Home > List of families > Fabaceae subfamily Papilionoideae > Crotalaria > recta
Crotalaria recta Steud. ex A. Rich.
Selected images: Click on each image to see a larger version and details of the record View all images (5)
Kaperepeza road, Nyika National Park.
Synonyms: Crotalaria recta Steud. ex A. Rich. subsp. simplex (A. Rich.) Baker f.
Crotalaria recta Steud. ex A. Rich. var. katangensis Robyns ex R. Wilczek
Crotalaria simplex A. Rich.
Perennial, usually with several erect, hollow, ribbed, appressed pubescent stems, usually 1–2 m tall. Leaves 3-foliolate (rarely 5); leaflets linear-lanceolate, oblanceolate-elliptic, elliptic or obovate, mostly 2–14 cm long, pubescent beneath; petiole 2–6 cm long; stipules linear-lanceolate, up to 9 mm long. Racemes many-flowered, 15–50 cm long; bracts linear-lanceolate, 6–11 mm long. Calyx 9–12 mm long, hairless or pubescent with triangular lobes more or less as long as the tube or longer. Standard subcircular, yellow, veined and suffused purplish at the base inside, hairless or with a few hairs at the apex outside; wings longer than the keel with a dark mark at the base; keel 1.2–1.5 cm long, with a short incurved twisted beak. Pod on a short stipe, broadly oblong-club-shaped, 4.5–6.5 cm long, hairless, c. 26–34-seeded.
Habitat: In upland grassland, high-rainfall miombo, along forest margins, streams and dambos, sometimes in disturbed places.
Altitude range: (metres) 1050 - 2000 m
Worldwide distribution: From Nigeria to Ethiopia, Kenya, Tanzania, Malawi, Mozambique, Zambia, Zimbabwe, Angola, Angola, southeastern DRC, Eswatini and Limpopo, Mpumalanga, South Africa.
Zimbabwe distribution: C,E
Images last updated: Wednesday 17 July 2019
Baumann, G. (2005). Photographic Guide to Wildflowers of Malawi Wildlife and Environmental Society of Malawi Pages 30 - 31. (Includes a picture).
Da Silva, M.C., Izidine, S. & Amude, A.B. (2004). A preliminary checklist of the vascular plants of Mozambique. Southern African Botanical Diversity Network Report No. 30 Sabonet, Pretoria Page 67.
Drummond, R.B. (1972). A list of Rhodesian Legumes. Kirkia 8(2) Page 218.
Fabian, A. & Germishuizen, G. (1997). Wild Flowers of Northern South Africa. Fernwood Press, Vlaeburg. Pages 182 - 183. (Includes a picture).
Martins, E.S. et al. (2003). Papilionoideae Flora Zambesiaca 3(7) Pages 167 - 168.
Phiri, P.S.M. (2005). A Checklist of Zambian Vascular Plants Southern African Botanical Diversity Network Report No. 32 Page 59.
Other sources of information about Crotalaria recta:
Flora of Malawi: Crotalaria recta
Flora of Mozambique: Crotalaria recta
Flora of Zambia: Crotalaria recta
African Plants: A Photo Guide (Senckenberg): Crotalaria recta
African Plant Database: Crotalaria recta
BHL (Biodiversity Heritage Library): Crotalaria recta
EOL (Encyclopedia of Life): Crotalaria recta
ePIC (electronic Plant Information Center): Crotalaria recta
Flora Zambesiaca web site: Crotalaria recta
GBIF (Global Biodiversity Information Facility): Crotalaria recta
GRIN (Germplasm Resources Information Network): Crotalaria recta
iNaturalist: Crotalaria recta
IPNI (International Plant Names Index): Crotalaria recta
JSTOR Plant Science: Crotalaria recta
Kew Herbarium catalogue: Crotalaria recta
Mansfeld World Database of Agricultural and Horticultural Crops: Crotalaria recta
Plants of the World Online: Crotalaria recta
Tropicos: Crotalaria recta
Wikipedia: Crotalaria recta
Hyde, M.A., Wursten, B.T., Ballings, P. & Coates Palgrave, M. (2022). Flora of Zimbabwe: Species information: Crotalaria recta.


text:
Home : Ethics : General : Moral,Ethical Reasoning : Rounding the Horn with the Principle of Double Effect
Rounding the Horn with the Principle of Double Effect
Sean Murphy *
Administrator, Protection of Conscience Project
April, 1805: Napoleon is master of Europe.
Only the British fleet stands before him.
Oceans are battlefields.
Thus opens the movie Master and Commander: The Far Side of the World.1
The audience finds British Captain Jack (Lucky Jack) Aubrey and the crew of his man o'war on the north coast of Brazil, hunting the French privateer Acheron. The film follows the hunt down the east coast of South America, around Cape Horn and into the Pacific.
"Rounding the Horn" in the days of sail was often a treacherous business, and the film makes the most of the tradition with a roaring gale, mountains of roiling water, blinding spray and the sepulchral moan of the wind in the rigging.2 Aubrey, his quarry in sight, is pushing his ship to its limits when the ship's carpenter shouts a warning: "I'll not vouch for this mast, sir! Not around the Horn!"
But Aubrey presses on, and the result is tragedy in the full sense of the word.
For the carpenter is right about the mast. Unable to withstand the fury of the wind, the top of it snaps off and blows into the sea, carrying with it a sail and boom and a hapless sailor. When the broken mast and sail crash into the waves, the rigging connecting them to the ship instantly pulls taut, turning the wreckage into a deadly sea anchor. The ship, crippled, begins to heel over, while the desperate sailor thrashes through the water to reach the rigging, his only hope of getting back to the ship.
Aubrey and the men above instinctively urge him on, but the camera reveals seasick men below deck, suddenly terrified when they feel the lurch of the ship as it begins to go over. A shout from a ship's officer brings the danger home to Aubrey and the choice before him. He must cut loose the rigging to save the ship and crew, leaving the man in the water to drown, or wait for the man to reach the rigging and pull himself to the ship, risking the loss of the ship and all hands.
Three hatchets are produced, and the Captain, an officer and crewman hack at the lines even as the floundering sailor nears the rigging and his comrades aloft call hope to him. With a final look at the doomed man, Aubrey cuts the last rope. The ship, suddenly freed, rights herself and lifts once more in the surge, leaving the gasping victim to his fate. The men below break into cheers; Aubrey, the officer and the crewman sink in grief. Afterward, the ship's surgeon, trying to console Aubrey, suggests that he had to choose the lesser of two evils. In fact, the incident was a fine cinematic rendering of the principle of double effect.
Aubrey's act - cutting loose the wrecked mast and rigging - had two inseparable effects, one intended (saving the ship and crew) and one unintended (the death of the man overboard). The intended effect was unquestionably good, and the unintended effect unquestionably evil.
But the act - cutting the lines - was not, in itself, wrong. And the good effect was accomplished by this act, not by the death of the sailor, which was an unintended and unavoidable consequence. Finally, the evil effect (the death of one sailor) was not disproportionate to the good effect (saving the ship and crew).
These are the essential elements of the principle of double effect. The man overboard scene in the film, only a few minutes in length, dramatically illustrates the nature of the choice: the good and evil effects, their inseparability and proportionality, that the good is intended, the evil, unintended. Especially praiseworthy from an ethical perspective is the portrayal of the death of the sailor as not just 'unintended' in a technical sense, but something clearly unwanted, a source of profound sorrow.
It is remarkable to find a principle that is sometimes mocked as ethical sleight-of-hand so vividly illustrated by acting, music and script in a popular film.
1. 20th Century Fox, Universal Pictures and Miramax Films, 2004. The DVD version of the film, with its scene-selection option, is particularly convenient for classroom use.
2. Scene 14 in the DVD version.
CMDA- Moral Complicity with Evil
CMDA- Double Effect


text:
Helen Reichert (November 11, 1901 – September 25, 2011) was an American talk show personality, New York University professor, founder of The Round Table of Fashion Executives, and the oldest living alumnus of Cornell University at the time of her death at age 109, just seven weeks before she would have become a supercentenarian.

Early life
Helen Faith Kahn was born on November 11, 1901, in the Lower East Side, Manhattan to Polish Jewish immigrants. As a young girl, she participated in the Girl Scouts of the United States of America (GSUSA) and had the distinction of being a member of the first Girl Scout Troop to sell World War I war bonds.

In 1925, Reichert graduated Phi Beta Kappa with a degree in English from the College of Arts and Sciences at Cornell University. She experienced anti-Semitism in Ithaca as a Cornell student and had to change her name from Kahn to Keane due to Collegetown realtors refusing to rent an apartment to a Jewish woman. During her undergraduate career, she lived in Risley Hall on East Hill.  According to her caretaker Olive Villaluna, Reichert worked in the cafeteria because "she was ashamed of asking her parents for money to buy cigarettes." She also participated in the women's crew team and is, according to IvyLeagueSports.com, "arguably the program's most well-known alumna."

In 1931, Reichert earned a master's degree in psychology from Teachers College, Columbia University.

Career in fashion
In 1947, Reichert began her thirty-year-long teaching career at The Graduate School of Retailing at New York University, where she taught "Fashion Co-ordination" and a self-created course called "Costume History." She has been credited with co-founding the Round Table of Fashion Executives in 1949.  As a result of the growing Fashion Group International, it became increasingly difficult to network with other women in the industry. In addition, for six years, she worked as a copywriter for Bloomingdale's, ultimately rising to the position of Fashion Coordinator. She later transferred to Montgomery Ward, a mail-order catalog and retail company, and worked as a fashion coordinator.

Broadcasting career
In 1951, she hosted the award-winning TV talk show, FYI: The Helen Faith Keane Show, airing on New York City's Channel 5.  The show resulted from Reichert's dissatisfaction with a male commentator from a fashion show broadcast over the DuMont network who she believed was "uninformed and condescending on the topic." She telephoned the company and was told that if she felt she could do a better job, she should come down to the station and try it herself. She agreed. Over lunch, the producer, Keith Thomas, offered her a show.

The viewing audience sent in questions or problems to the show.  Reichert along with the production staff would construct a show around popular topics sent in by viewers; each episode featured experts in the relevant field. Examples of topics covered by the daily program were cooking, housekeeping advice, how to play piano, narcotics, and talking to one's doctor about breast cancer. Because the show was designed to benefit the community, it performed a great deal of public outreach, including promoting the League of Women Voters and garnering donations for the Volunteers of America.

In 1951, her show was awarded the McCall's Golden Mike Award for Women in Radio and Television. According to the description on the cover of the program of the award ceremony, "The McCall's Awards to Women in Radio and Television […] are the only awards given exclusively to women Broadcasters and Executives for public service accomplishments in the communications field. Judged solely on evidence submitted by the contestants, [the award ...] set a publishing precedent. McCall's is the only magazine with a national circulation which has ever paid tribute to the public service record of any group in radio and television."

One of the judges, Senator Margaret Chase Smith of Maine, stated "The material was inspiring evidence of what women can do, and are doing, for our country and our people."

Personal and family life
In 1939, fourteen years after graduating from Cornell University, Reichert married cardiologist Dr. Philip Reichert, M.D. (Weill Cornell Medical College '23). Her husband was a founding member, trustee, and governor of The American College of Cardiology (founded in 1949), and served as its executive director from 1952 to 1962. Much of his rare diagnostic medical equipment and personal archives (correspondence, publications, notebooks, biographical information, minutes and transactions of the Board of Trustees of the American College of Cardiology, research, and other miscellaneous literature) were donated by his wife to Weill Cornell and are on permanent display in the college's faculty room. Dr. Reichert died on March 19, 1985, at the age of 87. The couple had
no children.

Helen Reichert founded the Helen F. Reichert Scholarship, awarded to medical students of Weill Cornell, in honor of her husband, who resolved to become a doctor after watching a doctor tend to his dying father. In order to put himself through school, her husband needed to work multiple jobs but ended with a lifelong, happy career. She stated that her hope was to "help someone else reach that dream."

Helen Reichert had three siblings: sister Leonore (Lee) Reichart and brothers Irving Kahn, the noted value investor, and Peter Keane, a Hollywood cinematographer and film executive, who also changed his last name from Kahn to Keane to avoid anti-Semitism. The four siblings were all centenarians.

Death
Helen Reichert died of natural causes on September 25, 2011, in her Park Avenue apartment in New York City at the age of 109. Olive Villaluna, her caretaker for more than 11 years, said that Reichert died "as she had wanted to, comfortably in her chair with a smile." She was survived by her two centenarian brothers, Irving and Peter, aged 105 and 101, respectively, at the time of her death, as well as many nieces and nephews. She rewrote her will under undue influence and disinherited many of her devoted family members.

In her will, she bequeathed her body to research at Weill Cornell Medical College. In addition, she bequeathed a $100,000 gift to the Residential Initiative in support of Cornell's West Campus House System. The Reichert Suite in Carl Becker House is named in her honor. In lieu of flowers, she requested that donations be made to Cornell Medical College.

As a subject of genetic research
Because Helen Reichert and her siblings were all centenarians (her younger sister, Leonore Kahn Reichart, who was born May 25, 1903, died on February 18, 2005, at the age of 101, researchers have been studying them to see if longevity is correlated to genetics. Geneticist Nir Barzilai, a researcher who studied the biology of aging for over a decade, included the Kahn siblings and other Ashkenazi Jews in his research. He asked the pool about the details of their living habits: nutrition, alcohol consumption, smoking, physical activity, sleep, education, status, and spirituality. Barzilai and the team of researchers at Einstein's Institute for Aging Research discovered three genes common to the centenarians that may be the key to their long life. The first is a gene that increases the amount of HDL (HDL cholesterol) to two to three times higher than average, which Barzilai believes is part of the reason for the siblings' mental acuity at an old age.  The second is a gene that slows metabolism as a result of a mildly underactive thyroid gland.  The last is a mutation in axis, a human growth hormone that could possibly protect against age-related diseases, such as cancer.

Trivia

 Reichert confessed that there is no reason why she should have lived as long as she did. She hated vegetables, getting up early, and most things associated with living a healthy lifestyle.  She loved rare hamburgers, chocolate, cocktails, and nightlife in New York, including exotic restaurants, Broadway shows, movies, and the Metropolitan Opera. She smoked for over eighty years.
 Reichert saw Albert Einstein eating Jell-O in a Princeton dining hall.

References

External links
 https://www.nytimes.com/keyword/montgomery-ward
 http://www.med.cornell.edu/archives/pdf/personal_aids/Reichert.pdf
 The Helen Faith Keane collection at the University of Maryland Libraries

1901 births
2011 deaths
Television personalities from New York City
Cornell University alumni
New York University faculty
American broadcasters
American centenarians
Teachers College, Columbia University alumni
Women centenarians


Welcome to LDS Business College! We are so excited to have you join our campus family and help you reach your academic, personal, professional, and spiritual goals. The following BC PREP course will help you know what you need to be prepared to succeed here at LDS Business College.
The first part of the BC Prep Course is a new student form. This will help us get to know you a little better and confirm your attendance to the in person Orientation.
We look forward to seeing you at Orientation! Enjoy the rest of the BC Prep Course.
Welcome to LDS Business College! Below you will find a brief intro video to help you understand the importance of Orientation and what you can expect. Enjoy!
Welcome to LDS Business College!
Spencer Taggart (Director of Marketing and Micro Courses) shares some reasons of why you are a genius for coming to LDS Business College.
What are the four reasons you are "a genius" for coming to LDSBC?
We are so excited to have you here! We look forward to connecting with you and helping you get ahead in your educational, spiritual, and professional life!


text:
Biomedical Scientist (12)
Care Worker (26)
Custody Nurse (19)
Dental Hygienist (10)
Dental Nurse (15)
Dentist (16)
Dietitian (12)
Healthcare Assistant (42)
Homecare Worker (27)
Occupational Therapist (22)
Pharmacist (23)
Physiotherapist (24)
Podiatrist (12)
Prison GP (14)
Qualified Social Worker (22)
Radiographer (19)
Speech & Language Therapist (20)
Mental Health in Society: What Support is Available for Healthcare Professionals?
There is little doubt that, as a society, our attitude towards mental illness has improved significantly in recent years. The support on offer to those suffering from mental health issues has increased dramatically, while stigma and discrimination continue to plummet. But there is still much to be done.
As time has moved on, our understanding and compassion towards promoting and maintaining mental health within the workplace has somewhat improved, but also comprehending there is still much to be done. In this sense, both support and training are worthwhile in their own right, but also the importance of what is a meaningful life associated with a productive one.
As a society, we are now far more understanding and accepting of mental illness. Our outlook towards people with mental health problems has improved, while a record number of people in England admit they would now be willing to live, work or have a relationship with someone who has experienced such a problem.
As our comprehension improves to what it means to possess good mental health, so do our relations with significant others, both in the work place and at home.
Despite this, it is worth noting that the mental well-being of healthcare professionals in the UK remains at risk. With increasing pressure being placed on the healthcare industry, doctors, nurses and allied health professionals face a daily struggle to meet the ever-increasing demands of their roles.
Yes, our attitudes towards mental health may have changed, but there is still a long way to go.
How do we feel about mental illness?
Our outlook towards mental health is certainly improving. Society, in general, is becoming far better equipped and more open to discussing mental health issues than ever before. More people are likely to know someone who suffers from a mental illness. This has resulted in a decrease in stigma and increased levels of openness and acceptance surrounding mental health.
There are clear and positive signs that the larger society is far more tolerant of mental health issues and open-mindedness with understanding on the rise.
However, the ways with which people are coping with mental health problems is creating cause for concern. The number of cases of self-injury and suicidal thoughts or ideation is increasing despite the number of mental health support services available.
Despite the colossal strides we have taken towards eradicating mental health discrimination in society, it seems mental illness is still considered a taboo subject. This is a concerning fact given that, according to the mental health charity Mind, around one in four people in the UK experience mental illness each year.
Many find the idea hard-to-discuss, particularly in the workplace. This is especially concerning given that nearly half of all long-term absences from work are down to mental health issues.
People who have been out of work as a result of mental illness are often worried about going back. Common fears include returning to work too soon and becoming unwell again and facing discrimination or bullying upon their return. Indeed, a report from the Royal College of Psychiatrists warns: "Many people with mental health problems fear that, no matter how good a recovery they have made, their symptoms will be made worse by going back to work."
If handled properly, a return to work after mental illness could actually have a hugely beneficial effect. Paid employment can lend people a sense of purpose, increase the size of their social circle, and decrease the amount of time they spend alone. These factors can reduce feelings of loneliness that could prompt further mental health problems.
It should also be pointed out that it's in the best interests of employers to address – and, if necessary, enhance – their approach to mental illness. According to the Organisation for Economic Co-operation and Development, mental health costs the UK economy £70 billion a year, equivalent to 4.5 per cent of GDP.
Furthermore, issues surrounding mental health are estimated to cost employers an annual total of £26 billion, or an average of £1,035 per employee. Put simply, we can't afford not to improve our attitude toward mental health.
The mental health of doctors
According to findings by the British Medical Association (BMA), around 40 per cent of doctors surveyed reported experiencing a mental health disorder. Depression, anxiety, burnout and stress, were amongst the most common complaints, with factors such as work and training being affected.
The report even revealed that an alarming 80 per cent of doctors were at risk of burnout with junior physicians most at risk. What's more, less than half of the doctors quizzed said they had been offered mental health support.
These findings are deeply worrying, especially considering the United Kingdom's ageing population rellies so heavily on healthcare.
The mental health of nurses
As with doctors, nurses are also susceptible to the negative effects of mental health. Factors that accompany the roles – such as long hours and stress - can certainly take their toll on the mental well-being of any nurse.
Calls have been made in the past for healthcare managers to introduce systems for assessing and monitoring the mental well-being of nurses so that risks caused by working conditions can be quickly addressed. Such ideas include regular attitude surveys and further background about absence rates and
staff turnover.
So what mental health support services are available for doctors and nurses?
Mental health support for doctors and nurses
Thankfully, mental health support services are available for doctors and nurses in the UK. Specialist services are provided by organisations like the Doctors' Support Network (DSN), the BMA and the Royal College of Nurses (RCN). These are accessible and provide much-needed help and supportive resources to doctors and nurses who require a helping hand when it comes to improving their mental health.
As well as delivering support, these organisations aim to improve awareness around mental health and address the stigma around accessing mental health support. They also offer online mental health support and champion positive mental health in the workplace.
Of course, the support on offer doesn't necessarily have to come from a specialist source. Mental health support is available in the form of organisations such as Mind, the Samaritans, Time to Change and Turning Point, whilst the NHS also offers support to those in need - including the Practitioner Health
Programme, a confidential service for doctors suffering from mental health issues.
How can we improve our outlook and view towards and to mental health?
It's extremely positive to see the improvements we've made as a society when it comes to supporting and understanding mental health. But it's also clear that there are ways in which stigma is still attached to mental illness.
Employment is the biggest area in which our approach towards mental illness and health still needs to improve. For instance, a significant proportion of people have concerns about discussing mental illness in a working environment. While some people would feel comfortable talking to their employer about their mental illness, a greater proportion may feel uncomfortable. This is an attitude that still needs addressing in modern society.
If you are interested in helping others by furthering your career in mental health, contact one of our dedicated recruitment teams.
Find out more about mental health nursing with Medacs Healthcare or discover more about our specialist mental health opportunities.
Alternatively, learn more about our wide range of locum psychiatry jobs.
Previous: How Relationships Promote Good Mental Health
Next: Dental Nurse Career Development: What Opportunities are Available?


text:
Home Construction Building Materials Brutalist Architecture; All you need to know
Brutalist Architecture; All you need to know
ConstructionBuilding MaterialsEngineering
There was probably no single architectural style (and whether or not that was one style that was yet being discussed) that was demonized and hated as much as brutalism, especially in the 70s, 80s, and even 90.
From different sources comes the reasons for this unsolicited crucifixion of seemingly harmless, like the styles of a building. Some of them are mediated by the human inability to separate social circumstances from the physical event, which reluctantly becomes their symbol. Others rely on universal canons of beauty, and the seemingly rough concrete wasn't exactly the prettiest sight of its time.
However, as society continued to move forward and shift its perspective, a strange phenomenon struck us in the 2010s: Brutalist Architecture became popular. Here we are in a new era, watching the style finally achieve its long-awaited recognition, even as it is fetishized and replicated through collectible vinyl toys and similar consumer goods.
So how did the ultimate villain suddenly become a hero?
Start of brutalist architecture
Stylistically, brutalism probably came from the eminent modernist architect Le Corbusier and his Unité d'Habitation project in 1952. The style was adopted relatively quickly by British architects and gradually became easily identifiable with the capital itself. Even it was rather strange, given that modern architecture arrived in Britain quite late and was soon "replaced" by the term brutalism.
Although it is thought to be first implemented by Le Corbusier, brutalism cannot be fully equated with the term modernism. Perhaps we could describe it as a kind of alternation: modern architecture with a more lively character, perhaps. Some would even say that brutalism appears as a cross between modernism and postmodernism in the history of architecture.
Brutalism: an explanation of the term
However, the term brutalism does not probably come from Le Corbusier. Several other versions of the story make it difficult to determine the truth, but none of them involve the first thing that comes to anybody's mind.
Brutalism has nothing related with brutality; as such, at least that was not their intention. It was derived from the French term concrete-brut, which means very well raw concrete, or it was adopted from the Swedish architect Hans Asplund, who used the term New Brutalism to describe Villa Göth in Uppsala (English critic Reyner an also used New Brutalism Banham, the guy who wrote the book named as the New Brutalism: Ethic or Aesthetic? which is now considered a precursor of the movement). The house itself, however, has little to do with Brutalist architecture as we know it in the form today.
According to the 3rd version of the story, according to which Peter Smithson's nickname Brutus was the one that decided the name of the Brutalist. It was probably a blend of all of these three versions.
What is called the brutalist architecture?
Villa Göth
Peter Smithson, the architect, mentioned above, and his wife Alyson Smithson, was one of the leading figures of brutalism in Britain. Like Erno Goldfinger, the Hungarian-born architect, who designed the famous Trellick Tower and the Balfron Tower, which are often mentioned as the most iconic buildings representing the brutalist movement. When you look at their designs, there's no question that all Brutalist buildings have one thing in common: concrete, of course. But there is more to its design than merely using a classic building material, which was not chosen by accident by the way.
Concrete is mostly used in Brutalist architecture because it is a flexible and straightforward material that responds to authentic architectural expression, but still very clear and noticeable when dry. Interestingly, concrete takes on the properties of the mold it was poured into and then directly conveys honesty – a word commonly used to describe the sheer essence of brutalist ideology. Since the word "honesty" sounds attractive on its own, let's try to explain why architects and theorists use it confidently.
While this was not the first time concrete was used in architecture, it was the first time concrete was used for the facade. Before brutalism, the ordinary raw material was generally hidden below the surface, whether it was a decorative floral facade or made of steel and glass. Since uncovered cement was for once the genuine hero, it indicated its sculptural characteristics, typifying monumentality and accomplishing formalism with the goal that the choice could be viewed as a sort of transparency of the material.
But on the other hand, brutalism is often associated with "architectural ethics," honesty was necessary because of the idea that nothing is "ordered" or hidden under the mask. This is what it is, and Brutalist architecture relies on the people who live or work there to understand and appreciate it.
This is also one of the other reasons why the word "style" is sometimes avoided, as the general term style seems in one way or another to degrade the initial effort of the brutalist ideology, which was supposed to carry things on a level.
Brutalist buildings victims of modern society
Given that most of this architecture emerged in the 1960s and continued into the 1970s, you can probably imagine the mentality surrounding brutalism. It was based in part on social equality and hope, especially in communist countries (where it also played an important role). Unity and shared space were somewhat better transformed into shape thanks to brutalist suburban blocks, with plenty of open space and mid-rise buildings and houses that can accommodate and have a lot of people.
Brutalist architecture was also at times associated with Futurism, a brilliant vision of the future, as presented initially, close to how people imagined utopia. But as you know, utopia quite quickly turns into dystopia, especially under the influence of the 1968 revolution and among young people influenced by Orwell.
Thanks to all the unsolicited political and social connotations, brutalism suffered the consequences of the transmutation of communism into totalitarianism, causing popular culture to portray brutalist architecture differently. Shared spaces turn into dangerous terrain, "honest" buildings turn into concrete monstrosities, and before you know it, brutalism is associated with violence, which was crowned once and for all in Kubrick's A Clockwork Orange. Now you can find a host of movies that use brutalist architecture as a backdrop to violence.
A great designer platform YUGEN is very famous for their piece on Brutalist Architecture. They have a lot of cool content as well so worth checking out the rest of there content HERE
Durango, Colorado Is The Ultimate Winter Road Trip Destination For Families
Previous articleSinker EDM – What it is and How it Works
Next article5 Home Repairs You Can't Afford to Put Off
Concrete Floors Cleaning Tips – Do It Like the Professionals
Want to know some concrete floors cleaning tips? Undoubtedly, we have the best up our sleeves. So, read on to know all about them in detail!
A Whole Guide To Landscaping In Moncton & Gardening
The staff at Tree Service Moncton consists of, licensed panorama designers, project estimators, landscape construct technicians, and design-build supervisors and lead hands. So –...
Choosing a Topic on Architecture: History Essay Guide for Students
Whether you like history or not, you need to study it in order to get your degree. And studying doesn't mean just reading and...