text:
\section{Introduction}
\label{sec1}
Road accidents remain one of the pressing communal welfare concerns. Regardless of notable advancements in the field of vehicle technology and road engineering, on a global scale, traffic accidents are one of the leading causes of premature death and injury. According to official statistics from the World Health Organization (WHO), more than 1.25 million people die every year due to traffic accidents. Besides, traffic accidents cost many countries up to 3\% of their GDP \citep{world2015global}. Therefore, minimizing the road accidents is a worldwide challenge and can benefit a majority of the nations in different ways. Towards this, it is important to understand which road segments are potentially dangerous or safe.

Few works have shown the influence of environmental factors like weather, light condition on road accidents \citep{tamerius2016precipitation,chung2018framework,black2018effects}.
However, gathering such data is costly and laborious. Further, due to lack of resources and technology, such data is not maintained properly in most low and middle-income nations and unfortunately, these are the nations which suffer dreadfully from traffic accidents \citep{schmucker2010road}. Hence, there is a need for an efficient approach which can work well with easily available and affordable data. To this end, satellite images are used for road safety mapping \citep{najjar2017combining}. However, the dataset is mostly imbalanced, and percentage of the safe class is far more compared to the dangerous class. Further, the images are misaligned. Thus, using such data for training efficient supervised models is another challenge.

\begin{figure} [htbp]
	\centering
	\includegraphics[width=0.46\textwidth]{teaser_diagram}
    \caption{A deep learning framework to predict city-scale safety maps using raw satellite images for Domain A (Source city) and Domain B (Target city) such that labels are available only for Domain A.}
    \label{fig:teaser_diagram}
\end{figure}

In addition, it has been observed that models trained using classical machine learning techniques for one region do not perform well if tested on regions that differ immensely in terms of traffic regulations, city planning, architecture, etc. \citep{najjar2017combining}. Also, it is quite inconvenient to obtain traffic accident data for every different region and train a model for the same.
Therefore, the difference in domains is yet another challenge in the effective road safety mapping.

To address these challenges, we propose a deep region guided attention network as shown in Figure \ref{fig:teaser_diagram}. We use a base network (ResNet-50) to extract global features. We further use a sub-network which can attend to individual subregions. Towards this, we extract the conv 2 layer features and divide them into $N$ non-overlapping regions. Region or part based networks have shown good accuracy in re-identification tasks \citep{zhu2017part}. We further augment this network to adapt to different domains. We use a loss function based on the covariance matrix to minimize the gap between the source and target domains. Our contributions are:

\begin{itemize}
  \item We propose a deep learning framework that uses the region guided attention network to predict accurate city-scale safety maps from satellite images.
  \item We propose a domain adaptation network with a training loss which minimizes the Frobenius norm of the difference of \textit{within} class covariance matrices of source and target, as well as the difference of \textit{between} class covariance matrices of source and target.
 \end{itemize}

The rest of this paper is organized as follows. The related work is discussed in section \ref{rw}. Section \ref{overview} presents the data sources and collection technique and also introduces the formulation of the problem. Our approach is explained in section \ref{dan}. Section \ref{us} shows a user-study followed by Section \ref{ex}, which describes our experiments. Finally, the paper is concluded in section \ref{conc}.

\section{Related Work}
\label{rw}
In this section, we briefly outline an overview of studies on accident hotspot detection (also known as, black spots and black zones), understanding road accidents, and lastly, the city-scale road-safety mapping that exploits recent advancements in the field of deep learning.

There are few studies on the detection of high-density accident hotspots. Kernel density estimation and clustering have been used to create classifiers to identify accident hotspots \citep{anderson2009kernel,bil2013identification}. Another interesting work includes the use of Bayesian statistics to predict accidents \citep{deublein2013prediction}. \citeauthor{fawcett2017novel} presented a Bayesian hierarchical model to rank accident hotspots in line with their possibility to surpass a threshold accident count in some future time period. Researchers have developed models to estimate the total number of crashes, the number of injury crashes, and the number of property damages and proved that the models are statistically meaningful and closer to real-world data \citep{pulugurtha2013traffic}. In addition, association rules have been used for the identification of accident situations that frequently occur together \citep{geurts2003profiling,geurts2005understanding,das2018factors}. \citeauthor{gomes2013influence} proposed a model for accident frequency estimation which takes the influence of the road characteristics into account.

\begin{table*}
    \caption{Sample traffic accident reports for London.}
    \label{t:tab1}
    \centering
  \begin{tabular}{|c|c|c|c|c|c|c|c|}
    \hline
    ID & Date & Time & Day of the week & Latitude & Longitude & No. of Vehicles & No. of Casualties\\
    \hline
1 & 01/11/2016 & 2:30:00 AM & 3 & 51.5847 & 0.2793 & 2 & 1 \\ \hline
2 & 21/11/2016 & 6:00:00 PM & 2 & 51.5092 & 0.0472 & 2 & 2 \\ \hline
3 & 20/05/2016 & 7:00:00 PM & 6 & 53.8126 & -2.9323 & 1 & 1 \\ \hline
4 & 11/01/2016 & 7:07:00 AM & 2 & 54.9785 & -1.6203 & 2 & 3 \\
    \hline
    \end{tabular}
\end{table*}

In the recent past, the remarkable progress in deep learning has contributed significantly to the field of computer vision. Spatio-temporal data have been used by researchers to predict the number of accidents in a given area with the help of ConvLSTM \citep{yuan2018hetero}. \citeauthor{chen2016learning} developed a Stacked Denoising Autoencoder for prediction of traffic accident risk level at the city-scale using real-time GPS data of users. In a similar study, \citeauthor{najjar2017combining} demonstrate that visual attributes captured in satellite image can be used as a proxy signal of road safety. They proposed a deep-learning based mapping framework that exploits open data to predict city-scale safety maps at bearable costs. However, the distinction among the above frameworks resides in their application itself. Both \citep{chen2016learning,yuan2018hetero} are interested in the real-time prediction of traffic accidents, whereas \citep{najjar2017combining} is inclined towards assisting in informed decision-making for city-planning and policy formulation when heterogeneous data is not accessible or bearable. Our work is closer in spirit to \citeauthor{najjar2017combining}; however, there are three major differences. First, we only consider safe and dangerous classes compared to an additional neutral class defined by \citeauthor{najjar2017combining} This is because a neutral road segment may not assert a strong decision making during road safety planning. Second,
the authors extract features from AlexNet, whereas, we propose a novel architecture. Third, we also perform domain adaptation for the unlabeled target domain. Our two major goals are as follows. First, we propose
a region guided attention network to generate a city-scale safety map. Second, we propose a domain adaptation technique to generate safety maps where traffic accident data is not available.

Domain Adaptation is a branch of machine learning in which we aim at learning a well performing network from a source data that can generalize well on a different but related target data distribution \citep{ben2010theory}. It has been widely studied in visual applications \citep{csurka2017domain,wu2018dcan}. One of the popular class of algorithms is discrepancy based methods \citep{tzeng2014deep,long2015learning,sun2016deep}. In particular, \citeauthor{sun2016deep} propose to minimize the Frobenius norm of the difference between the feature covariance matrix of source and target. In our case, we also make use of the labels available for source and pseudo-labels for the target. These pseudo-labels are determined by the model trained on the source data. We then compute the feature covariance matrix for safe and dangerous classes respectively. Further, we compute our domain adaptation loss function based on \textit{within} and \textit{between} class covariance matrices of source and target.

\section{Overview}
\label{overview}
In this section, we present the data that we use at various stages of the work. We also introduce the formulation of our problem.
\subsection{Data}
We select two cities, London (UK) and New York City (USA) for the purpose of this study. Our reason for selecting these two cities is two-fold. First, the availability of sufficient data to work on and second, the extreme difference in traffic regulations, city planning, architecture etc. Figure \ref{fig:spatial_difference} shows the difference in the street network orientation of both the cities \citep{boeing2018urban}. All the data that we use in this work are available as \textit{open data}. Open data is the scheme under which some datasets are freely available to use and republish, without any restrictions \citep{dietrich2009open}. We also present results for Denver (USA) which is altogether a different domain.

\begin{figure}[htbp]
\centering
\subcaptionbox{London\label{sfig:london}}{\includegraphics[width=2.7cm,height=2.7cm]{london.png}}\hfill
\subcaptionbox{Manhattan, NYC\label{sfig:manhattan}}{\includegraphics[width=2.7cm,height=2.7cm]{manhattan.png}}\hfill
\subcaptionbox{Denver\label{sfig:denver}}{\includegraphics[width=2.7cm,height=2.7cm]{denver}}\\
\caption{The spatial differences in terms of street network orientation. All the locations have visual difference in their infrastructure orientation which makes learning a single network for all different domains a challenging task.}
\label{fig:spatial_difference}
\end{figure}

\textbf{Traffic Accident Data:} We collected 99,516 traffic accident records for London from 2013 to 2016.\footnote{https://www.europeandataportal.eu/data/en/dataset/road-accidents-safety-data} We gathered 1,256,205 traffic accident reports for New York City (NYC) from 2012 to 2017.\footnote{https://opendata.cityofnewyork.us} Similarly, we collected 143,776 traffic accident reports for Denver from 2013 to 2017.\footnote{https://www.denvergov.org/opendata/dataset/city-and-county-of-denver-traffic-accidents} Each record has numerous attributes such as latitude, longitude, date, time, vehicle type etc. However, there is a difference in the datasets. Table \ref{t:tab1} presents sample records from London data.
Information such as junction type, weather etc. is only available in London dataset. Therefore, we restrict ourselves to common attributes only.

\textbf{Satellite Images:} We collect satellite images using Google's Static Maps API.\footnote{https://cloud.google.com/maps-platform/}

\subsection{Problem Formulation}
We divide the whole study area by imposing a grid \textit{G}, where each grid $g_i$ is a square region of $s \times s$. Here, $s$ is equal to $30$ meters which is a sufficient area for traffic accident analysis \citep{najjar2017combining}. In this work, our objective is to produce a model that learns features such that it can predict the safety level of a given grid $g_i$ using its raw satellite image.

In order to curate a training dataset, we plot each accident on the grid using the locations from the Traffic Accident data. Each grid $g_i$ is given a safety score $S_i$ which is equal to the number of accidents in the grid. The higher $S_i$ corresponds to higher number of accidents.

We obtain satellite image for each of these grids. To obtain the image label for each $g_i$, we apply the classical k-means algorithm to bin $S_i$ into two clusters. Hence, we get two clusters of \textit{safe} and \textit{dangerous} locations. We observe that the obtained set of labels are highly imbalanced. Over 88\% of the regions are labeled as safe. Therefore, we re-sample the dataset by down-sampling the majority class such that both classes are balanced out.

\begin{figure*}[ht]
	\centering
	\includegraphics[width=\textwidth, height = 13cm]{nd}
    \caption{Architecture of our proposed network. There are three 2-way switches that enable the network to run in two modes. When all the switches are connected to \textit{red} nodes, DAM mode is enabled whereas if switches are connected to green nodes, DAM-DA mode is enabled. The solid and dashed lines represent DAM and DAM-DA mode respectively. It consists of a global and a local network. ResNet50 acts as a base to the global network. The ROI pooled features from conv 2 layer are passed through the local network to identify the subregion with maximum prediction probability to guide the global network. $L_C$ is used for training DAM and is also used along with $L_{DA}$ for training DAM-DA (Best viewed in color).}
    \label{fig:arch_dia}

\end{figure*}

\section{Deep Attention-Network Approach}
\label{dan}
In this section, we begin by introducing the network architecture. Then we elaborate
the technique used to implement the unsupervised domain adaptation for satellite images that belong to different domains.

\subsection{Network Architecture} \label{na}
The model consists of a global and a local network, with the global network borrowing layers from ResNet50 \citep{he2016deep}. We show the architecture of the proposed model in Figure \ref{fig:arch_dia}.
There are three 2-way switches in the architecture which enables the network to run in two different modes. When all the switches are connected to \textit{red} nodes, the Deep Attention Model (DAM) mode is enabled whereas if switches are connected to all the green nodes, the Deep Attention Model with Domain Adaptation (DAM-DA) mode is enabled. The solid and dashed lines represent DAM and DAM-DA mode respectively. We discuss the DAM-DA mode in the next section.
As shown in Figure \ref{fig:arch_dia}, the global network is augmented with a local network. In our experiments, we notice that the global network based on ResNet only, gives significant false positives, where false positives are defined as dangerous regions being predicted as safe. A possible reason behind such false positives could be that the satellite images are usually misaligned. To overcome misalignment and improve the performance of ResNet, we design the network such that it can exploit local regions in addition to the global region. We perform \textit{Region of Interest} (ROI) pooling on conv 2 layer of the model with the subregions being $N$ non-overlapping blocks. We pass features of each subregion through a local network. The local network consists of conv$_{1}^{l}$ and conv$_{2}^{l}$ layers. Each subregion of size 7x7 acts as an input to the local network to extract features and to identify which of them is useful for the classification task. Thus, this network attends to the regions which can significantly contribute towards enhancing the classification accuracy. We perform average pooling on the output of conv$_{2}^{l}$ layer. We obtain a feature vector of size 4096x1x1 that is passed through $fc_l$. The subregion with the maximum prediction probability of either of the two classes (Safe and Dangerous) is concatenated to the feature-map of the global network. We perform concatenation after the conv 4 layer of the global network. \textit{Thus, we can see that only certain subregions can guide the global feature vector for classification.} Once average pooling is performed, we pass the feature vector through $fc$ to obtain the final $d$-dimensional vector. We train the network using cross entropy loss $(L_C)$.

\subsection{Domain Adaptation}
In this section, we discuss our proposed Domain Adaptation (DA) technique for the target domain where there is no annotated data available. In our experiments, we consider London as the source domain for which labeled data is present, whereas, NYC as the target domain for which labels are not given. There are significant differences in both the cities.
Although NYC is nearly 20\% smaller in terms of area, it's population density is almost double than that of London.
According to \citeauthor{nieminen2002population}, higher the population density, higher are the chances of occurrence of accidents. Due to above-mentioned reasons, these cities would be a good extreme example for performing DA and can give us an idea of lower bound of model's performance.

\begin{table}[ht]
\caption{The comparison of the London and NYC in terms of area and population density.}
\label{t:differences_in_cities}
\centering
\begin{tabular}{|c|c|c|}
\hline
 & London & NYC \\ \hline
\begin{tabular}[c]{@{}c@{}}Area \footnotesize{(mi\textsuperscript{2})} \end{tabular} &607 & 468.5 \\ \hline
\begin{tabular}[c]{@{}c@{}}Population Density\\ \footnotesize{(people per mi\textsuperscript{2})} \end{tabular} &14,550 & 28,491 \\ \hline
\end{tabular}
\end{table}

First, we train DAM network using data from the source domain as explained in the section \ref{na}. Using this trained model, we generate \textit{pseudo labels} for data from the target domain. Thus, we have labels for the source domain (London) and pseudo labels for the target domain (NYC). Now, we use data from both source and target domain to train the augmented DA (DAM-DA) network as shown in Figure \ref{fig:arch_dia}. There are three differences when compared to DAM.
First, this network has two additional convolutional layers in the global network, conv$_{1}^{g}$ and conv$_{2}^{g}$ layers of dimensions 512x7x7 and 256x7x7 respectively, to reduce the dimensions of the feature maps. This augmentation is necessary to be able to compute the covariance matrix efficiently. We explain the utility of the covariance matrix in the later part of this section. Second, instead of conv$_{1}^{l}$ and conv$_{2}^{l}$ layers in the local network, we use conv$_{1}^{l'}$ and conv$_{2}^{l'}$ layer of size 512x7x7 and 256x7x7 respectively. Again, this is done to reduce the dimensions for covariance matrix calculation. Third, in DAM mode, we train the network using $L_C$ only, but in DAM-DA mode we use domain adaptation loss ($L_{DA}$) along with $L_C$. We explain $L_{DA}$ next.

Let $x_i$ denote the feature for \textit{i}-th sample classified as dangerous in source domain. Similarly, let $y_i$ be the feature for \textit{i}-th sample classified as safe in source domain. These features are obtained from the final layer $(fc)$ of the network as shown in Figure \ref{fig:arch_dia}. Now, we can obtain the \textit{within} class covariance matrix $\sum_{SW}$ $\in \mathcal{R}^{d\times d}$ as,

\begin{equation}
\begin{split}
\sum\nolimits_{SW} = \sum\nolimits_{i,j \neq i} (x_{i}-x_{j}) (x_{i}-x_{j})^T  \\ + \sum\nolimits_{i,j \neq i} (y_{i}-y_{j}) (y_{i}-y_{j})^T
\end{split}
\end{equation}

Similarly, we can compute the \textit{between} class covariance matrix $\sum_{SB}$ $\in \mathcal{R}^{d\times d}$ as,
\begin{equation}
\sum\nolimits_{SB} = \sum\nolimits_{i,j}(x_{i}-y_{j}) (x_{i}-y_{j})^T
\end{equation}

We can compute the \textit{within} and \textit{between} class covariance matrices for target domain in a similar manner. Let these be denoted by $\sum_{TW}$ and $\sum_{TB}$ respectively. Then, we use the following loss function to adapt to the target domain,

\begin{equation} \label{eq:loss}
L_{DA} = \norm{ \sum\nolimits_{SW} - \sum\nolimits_{TW} }_F^2 +  \norm{\sum\nolimits_{SB} - \sum\nolimits_{TB} }_F^2
\end{equation}
where $\norm{.}_F$ denotes the Frobenius norm. Though the pseudo labels are noisy, it is still beneficial to use them. In our experiments, we demonstrate that
using the loss to minimize the distance between feature covariance matrices between source and target, which is agnostic of source labels, achieves sub-par performance when compared to $L_{DA}$.

\section{User Study}
\label{us}
We conduct a study with an aim to observe that how accurately humans can classify raw satellite images as safe or dangerous. It's first of its kind user study. We develop an annotation portal and provide the raw satellite images from our test-data to the users.
The portal has two sections. In the first section, we provide four sample images from each category. In the second section, an image is presented to the user along with three options to choose from. A user can mark an image as \textit{dangerous, safe or unsure}. We show the same image to three different users and record their response to maintain the confidence in the user's response. We also add sixteen more sample images from each class to help the user. We record users' response for 1,000 images having 500 images from each category, i.e., safe and dangerous classes.

In this study, 38 users participated. All the participants are above 18 years of age. To check inter-annotator agreement, we compute Fleiss' kappa \citep{fleiss1973equivalence}. We achieve 0.2743 which depicts a fair agreement. To assign a final label to the image, we calculate the mode of all three responses. If all the responses are different, we discard that response. In our study, we found 0.03\% of such responses and discarded them. Finally, 67.22\%, 27.94\%, and 4.84\% images are marked as \textit{safe, dangerous,} and \textit{unsure} respectively by users. In Table \ref{t:user_study_data}, we provide a complete description of the user-study results. With 80.32\% accuracy, users are able to detect \textit{safe} images whereas the accuracy drops to 45.02\% for \textit{dangerous} images. Therefore, in this case, we find that humans can identify safe locations with better accuracy than dangerous locations.

\begin{table}[htbp]
\caption{The complete description of the user-study outcome (in \%).}
\label{t:user_study_data}
\centering
\begin{tabular}{c|c|c|c|c|}
\multicolumn{2}{c}{}
            &   \multicolumn{3}{c}{Predicted} \\
            \cline{2-5}
    &       &   Safe &   Dangerous     & Unsure       \\
    \cline{2-5}
\multirow{2}{*}{\rotatebox[origin=c]{90}{Actual}}
    & Safe   & 40.41   & 05.56      &  4.32        \\ \cline{2-5}
    & Dangerous    & 26.80    & 22.37   & 0.51            \\
    \cline{2-5}
    \end{tabular}
\end{table}

\section{Experiments}
\label{ex}
We initially performed the experiments with AlexNet, VggNet, DenseNet, and ResNet and found that ResNet gives the best accuracy.
We now prove the efficacy of the proposed technique by assessing the classification accuracy of the predicted road safety maps in multiple scenarios. It is also important to note the non-triviality of learning from satellite data, as it is seen in the user study, and can also be seen in the examples shown in Figure \ref{fig:non_trivial} and Figure \ref{f:cams}.

\begin{figure}[htbp]
\centering
\subcaptionbox{Dangerous location, might be perceived as \textit{safe.}\label{sfig:ds-1}}{\includegraphics[width=3.5cm,height=3.5cm]{dangerous-sample-nontrivial.jpg}} \hspace{4mm}
\subcaptionbox{Safe location, might be perceived as \textit{dangerous.}\label{sfig:sd-1}} {\includegraphics[width=3.5cm,height=3.5cm]{safe-sample-nontrivial.jpg}}\hfill
\caption{Image Samples to show the non-triviality of learning from satellite data.}
\label{fig:non_trivial}
\end{figure}

\textbf{Training Parameters:} We train our network on London dataset. There are 4,517 training samples and 903 validation samples in each class. The models are trained with a batch size of 4, with learning rate as 0.0001 and a learning rate decay of 0.5 per 10 epochs. We train it over 50 epochs on a Nvidia GeForce TITAN X GPU.

\textbf{Evaluation of DAM:}
To assess the model, we test on unseen data from London. As shown in Table \ref{t:tab2}, we test the models in multiple scenarios such as with horizontal (DAM + HS), vertical (DAM + VS), and square boxed (DAM + SQ) subregions and with the combinations of them. We test another variation of the model where the activation maps are produced based on an intermediate convolutional layer. We experiment with two conv layers; conv 2 and conv 3 layer. We finally use conv 2 layer as we achieve better results with it. We find that the DAM using HS+VS+SQ subregions outperformed ResNet50, VGG19, and its other variants on the same dataset as shown in Table \ref{t:tab2}.
\begin{table}[htbp]
\caption{Comparison of classification accuracy (in \%) for ResNet50, VGG19, and variants of the DAM both for original dataset (London) and cross dataset (NYC and Denver).}
\label{t:tab2}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
Model& Original Data & \multicolumn{2}{c|}{Cross Data} \\ \cline{2-4}
 & London & NYC & Denver \\ \hline
ResNet50 & 85.77  & 69.16  & 70.00  \\ \hline
VGG19 & 85.83  & 64.60  & 70.00  \\ \hline
DAM (HS) & 85.81  & 72.28  & \textbf{76.20}  \\  \hline
DAM (VS) & 85.52  & \textbf{74.77}  & 75.00  \\ \hline
DAM (SQ) & 85.86  & 70.70  & 70.00  \\ \hline
DAM (HS+VS) & 85.34  & 70.37  & 70.01  \\ \hline
DAM (HS+VS+SQ) & \textbf{86.21}  & 67.23  & 69.86 \\  \hline
\end{tabular}
\end{table}

\textit{One of our goals is to minimize the false positives.} This is necessary as the cost of predicting safe as dangerous may only lead to overly conservative approaches for planning, whereas, having high false positive rate with more dangerous locations being marked safe can lead to fatal accidents. Therefore, we consider the classification of dangerous images as safe to be more costly than vice-versa. We test ResNet50 and DAM on London, NYC, and Denver test-set of 7,228, 8,342, and 500 images respectively. From Table \ref{t:tab4}, we can see that DAM gives fewer false positives in comparison to ResNet50 for every domain.
\begin{table}[htbp]
\centering
\caption{A comparison of false positive rate between DAM and ResNet50. All results are in percentage.}
\label{t:tab4}
\begin{tabular}{|c|c|c|c|}
\hline
Model & London & NYC & Denver \\ \hline
DAM & \textbf{07.60}  & \textbf{22.05}  & \textbf{29.20} \\ \hline
ResNet50 & 12.73  & 39.05  & 40.80  \\ \hline
\end{tabular}
\end{table}

\textbf{Cross Dataset Testing:}
We also perform cross-data testing and test our model on NYC and Denver dataset. From Table \ref{t:tab2}, we can see that DAM + VS and DAM + HS performs the best for NYC and Denver dataset respectively. For DAM (HS+VS+SQ), the base model trained on London with three sub-regions is over-fitting, and the network does not generalize well on Denver. When we decrease sub-regions to 2, the accuracy increases for NYC and Denver, and the model performs the best when used with only one sub-region.

\begin{figure*}[ht]
\centering
\rotatebox{90}{\hspace{0.7cm}Dangerous Image}
{\includegraphics[width=4cm,height=4cm]{dangerous-incorrect-original.png}}\hspace{4mm}
{\includegraphics[width=4cm,height=4cm]{dangerous-safe-r.jpg}} \hspace{3mm}
{\includegraphics[width=4cm,height=4cm]{dangerous-safe-a1.jpg}} \hspace{3mm}
{\includegraphics[width=4cm,height=4cm]{dangerous-safe-a2.jpg}}\\
\vspace{3mm}
\rotatebox{90}{\hspace{12mm}Safe Image}
\subcaptionbox{Original Images\label{sfig:Safe-incorrect-original}}
{\includegraphics[width=4cm,height=4cm]{safe-incorrect.png}}\hspace{4mm}
\subcaptionbox{ResNet50\label{sfig:CAM-Resnet2}}
{\includegraphics[width=4cm,height=4cm]{safe-dang-r.JPG}}\hspace{4mm}
\subcaptionbox{DAM-Global Network\label{sfig:CAM-DAM-11}}{\includegraphics[width=4cm,height=4cm]{safe-dang-a1.JPG}}\hspace{4mm}
\subcaptionbox{DAM-local Network\label{sfig:CAM-DAM-21}}{\includegraphics[width=4cm,height=4cm]{safe-dang-a2.JPG}}\\
\caption{A comparison of activated features of a \textit{dangerous} and a \textit{safe} location among ResNet50, DAM-Global Network, and DAM-Local Network. Top row and bottom row represent dangerous and safe locations respectively. DAM-Global Network contains activated features in the global network before the concatenation of local network features. DAM-Local Network contains activated features from the local network alone (Best viewed in color).}
\label{f:cams}
\end{figure*}

\textbf{Qualitative Analysis:}
In order to understand the behavior of the network qualitatively, we generate Class Activation Maps (CAM) \citep{zhou2016learning}. In Figure \ref{f:cams}, the images in the top row correspond to a dangerous image and its CAMs for ResNet50, DAM-Global Network, and DAM-Local Network. Similarly, the images in the bottom row correspond to a safe image and its corresponding CAMs. From the top row in Figure \ref{sfig:CAM-DAM-11} and Figure \ref{sfig:CAM-DAM-21}, we can establish that the DAM not only identifies the green region around the divider on the road but also identifies the cars and roads in the surrounding areas as well, which positively contributes towards the identification of this image as dangerous. This image contains a dangerous location, but, ResNet50 mis-classifies it as safe. As from top row in Figure \ref{sfig:CAM-Resnet2}, ResNet50 seems to have identified the green region and used that to classify the sample as safe, whereas our proposed DAM is efficient enough to correctly identify it as a dangerous location. Similarly, from the CAM in the bottom row, we observe that ResNet50 recognizes only roads whereas the DAM identifies houses and trees in the image. Therefore, DAM correctly identifies this sample as safe whereas ResNet50 mis-classifies it as dangerous.

\textbf{Evaluation of DAM-DA Network:}
We train our DAM-DA network with 2,085 images of each class, i.e., Safe and Dangerous. We use 4,170 images each from the source (London) and target (NYC) domain. We use a batch size of 16, a learning rate of 0.0001 and a decay of 0.5 per 10 epochs. It is trained for over 50 epochs. We test the DAM-DA network on 3,336 images from NYC. As shown in Table \ref{t:tab6},
DAM-DA network gives an accuracy of 75.75\% in comparison to DAM network which gives an accuracy of 71.94\%.

In another setting, we replace $L_{DA}$ with the loss \citep{sun2016deep} in Equation \ref{lda},

\begin{equation} \label{lda}
L_{DA}^{ST} = \frac{1}{d}\norm{C_{S} - C_{T} }_F^2
\end{equation}

where $C_{S}$ and $C_{T}$ denotes the feature covariance matrix of source and target samples in a training batch, respectively. When compared to $L_{DA}$, $L_{DA}^{ST}$ does not take the class labels into account. As shown in Table \ref{t:tab6}, we obtain an accuracy of 74.73\%. Similarly, DAM$-$DA$-L_{DA}$ results in the least false positives rate (18.94\%) in comparison to its counterparts. Thus, we can see that the class wise covariance loss works better in this scenario.

\begin{table}[htbp]
\caption{A comparison of DAM, DAM-DA with $L_{DA}$, and DAM-DA with $L_{DA}^{ST}$ network with batch-size of 16.}
\label{t:tab6}
\centering
\begin{tabular}{|c|c|c|}
\hline
Model & Accuracy (\%) & FPR(\%)\\ \hline
DAM & 71.94 & 30.03\\ \hline
DAM$-$DA$-L_{DA}$ & \textbf{75.75} & \textbf{18.94} \\ \hline
DAM$-$DA$-L_{DA}^{ST}$ & 74.73 & 30.69\\ \hline
\end{tabular}
\end{table}

\textbf{Comparison of DAM and Humans:} We test DAM on the set of same 1000 images that we use for user study. We present these results in Table \ref{t:user_study_comp}.
We can see that DAM is more accurate than humans by a significant difference of 23.12\%. Moreover, there is a substantial difference of 30.77\% in precision. Therefore, this proves that DAM performs better than humans in this case.

\begin{table}[htbp]
\caption{A comparison of the performance of our proposed deep learning model (DAM) and humans.}
\label{t:user_study_comp}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
Model & Acc(\%) & Precision & F1\\ \hline
Humans & 62.78  & 0.601 & 0.687\\  \hline
DAM & \textbf{85.90}  & \textbf{0.909} & \textbf{0.850}\\ \hline
\end{tabular}
\end{table}

\section{Conclusion}
\label{conc}
In this paper, we address the challenge of learning efficient embeddings to classify the road segments as dangerous or safe using easily available and inexpensive data. We leverage open data and satellite images to predict city-scale road safety maps. We propose a deep learning based model that uses a region guided attention network. It consists of a global and a local network. The local network attends to features in the subregions and the features with maximum prediction score are used to guide the global features to enhance the accuracy. We evaluate our network on the public dataset of London and achieve the accuracy of 86.21\%. We experiment with the cross-datasets of NYC and Denver and achieve significant results with the accuracy 74.77\% and 76.20\% respectively. In addition, we propose a covariance loss based domain adaptation for the scenario where target domain labels are missing. In our experiments, we show that with the domain adaptation network, the accuracy of NYC increases by 4\% and the network also achieves the lowest false positives. We also conduct a user study and find that our model outperforms human by 23.12\%. In the future, we would like to explore Places trained models instead of ImageNet trained models.

\section{Acknowledgments}
We would like to thank Precog at IIIT-Delhi for supporting this research work.

\section{Introduction}
\label{sec1}
Road accidents remain one of the pressing communal welfare concerns. Regardless of notable advancements in the field of vehicle technology and road engineering, on a global scale, traffic accidents are one of the leading causes of premature death and injury. According to official statistics from the World Health Organization (WHO), more than 1.25 million people die every year due to traffic accidents. Besides, traffic accidents cost many countries up to 3\% of their GDP \citep{world2015global}. Therefore, minimizing the road accidents is a worldwide challenge and can benefit a majority of the nations in different ways. Towards this, it is important to understand which road segments are potentially dangerous or safe.

Few works have shown the influence of environmental factors like weather, light condition on road accidents \citep{tamerius2016precipitation,chung2018framework,black2018effects}.
However, gathering such data is costly and laborious. Further, due to lack of resources and technology, such data is not maintained properly in most low and middle-income nations and unfortunately, these are the nations which suffer dreadfully from traffic accidents \citep{schmucker2010road}. Hence, there is a need for an efficient approach which can work well with easily available and affordable data. To this end, satellite images are used for road safety mapping \citep{najjar2017combining}. However, the dataset is mostly imbalanced, and percentage of the safe class is far more compared to the dangerous class. Further, the images are misaligned. Thus, using such data for training efficient supervised models is another challenge.

\begin{figure} [htbp]
	\centering
	\includegraphics[width=0.46\textwidth]{teaser_diagram}
    \caption{A deep learning framework to predict city-scale safety maps using raw satellite images for Domain A (Source city) and Domain B (Target city) such that labels are available only for Domain A.}
    \label{fig:teaser_diagram}
\end{figure}

In addition, it has been observed that models trained using classical machine learning techniques for one region do not perform well if tested on regions that differ immensely in terms of traffic regulations, city planning, architecture, etc. \citep{najjar2017combining}. Also, it is quite inconvenient to obtain traffic accident data for every different region and train a model for the same.
Therefore, the difference in domains is yet another challenge in the effective road safety mapping.

To address these challenges, we propose a deep region guided attention network as shown in Figure \ref{fig:teaser_diagram}. We use a base network (ResNet-50) to extract global features. We further use a sub-network which can attend to individual subregions. Towards this, we extract the conv 2 layer features and divide them into $N$ non-overlapping regions. Region or part based networks have shown good accuracy in re-identification tasks \citep{zhu2017part}. We further augment this network to adapt to different domains. We use a loss function based on the covariance matrix to minimize the gap between the source and target domains. Our contributions are:

\begin{itemize}
  \item We propose a deep learning framework that uses the region guided attention network to predict accurate city-scale safety maps from satellite images.
  \item We propose a domain adaptation network with a training loss which minimizes the Frobenius norm of the difference of \textit{within} class covariance matrices of source and target, as well as the difference of \textit{between} class covariance matrices of source and target.
 \end{itemize}

The rest of this paper is organized as follows. The related work is discussed in section \ref{rw}. Section \ref{overview} presents the data sources and collection technique and also introduces the formulation of the problem. Our approach is explained in section \ref{dan}. Section \ref{us} shows a user-study followed by Section \ref{ex}, which describes our experiments. Finally, the paper is concluded in section \ref{conc}.

\section{Related Work}
\label{rw}
In this section, we briefly outline an overview of studies on accident hotspot detection (also known as, black spots and black zones), understanding road accidents, and lastly, the city-scale road-safety mapping that exploits recent advancements in the field of deep learning.

There are few studies on the detection of high-density accident hotspots. Kernel density estimation and clustering have been used to create classifiers to identify accident hotspots \citep{anderson2009kernel,bil2013identification}. Another interesting work includes the use of Bayesian statistics to predict accidents \citep{deublein2013prediction}. \citeauthor{fawcett2017novel} presented a Bayesian hierarchical model to rank accident hotspots in line with their possibility to surpass a threshold accident count in some future time period. Researchers have developed models to estimate the total number of crashes, the number of injury crashes, and the number of property damages and proved that the models are statistically meaningful and closer to real-world data \citep{pulugurtha2013traffic}. In addition, association rules have been used for the identification of accident situations that frequently occur together \citep{geurts2003profiling,geurts2005understanding,das2018factors}. \citeauthor{gomes2013influence} proposed a model for accident frequency estimation which takes the influence of the road characteristics into account.

\begin{table*}
    \caption{Sample traffic accident reports for London.}
    \label{t:tab1}
    \centering
  \begin{tabular}{|c|c|c|c|c|c|c|c|}
    \hline
    ID & Date & Time & Day of the week & Latitude & Longitude & No. of Vehicles & No. of Casualties\\
    \hline
1 & 01/11/2016 & 2:30:00 AM & 3 & 51.5847 & 0.2793 & 2 & 1 \\ \hline
2 & 21/11/2016 & 6:00:00 PM & 2 & 51.5092 & 0.0472 & 2 & 2 \\ \hline
3 & 20/05/2016 & 7:00:00 PM & 6 & 53.8126 & -2.9323 & 1 & 1 \\ \hline
4 & 11/01/2016 & 7:07:00 AM & 2 & 54.9785 & -1.6203 & 2 & 3 \\
    \hline
    \end{tabular}
\end{table*}

In the recent past, the remarkable progress in deep learning has contributed significantly to the field of computer vision. Spatio-temporal data have been used by researchers to predict the number of accidents in a given area with the help of ConvLSTM \citep{yuan2018hetero}. \citeauthor{chen2016learning} developed a Stacked Denoising Autoencoder for prediction of traffic accident risk level at the city-scale using real-time GPS data of users. In a similar study, \citeauthor{najjar2017combining} demonstrate that visual attributes captured in satellite image can be used as a proxy signal of road safety. They proposed a deep-learning based mapping framework that exploits open data to predict city-scale safety maps at bearable costs. However, the distinction among the above frameworks resides in their application itself. Both \citep{chen2016learning,yuan2018hetero} are interested in the real-time prediction of traffic accidents, whereas \citep{najjar2017combining} is inclined towards assisting in informed decision-making for city-planning and policy formulation when heterogeneous data is not accessible or bearable. Our work is closer in spirit to \citeauthor{najjar2017combining}; however, there are three major differences. First, we only consider safe and dangerous classes compared to an additional neutral class defined by \citeauthor{najjar2017combining} This is because a neutral road segment may not assert a strong decision making during road safety planning. Second,
the authors extract features from AlexNet, whereas, we propose a novel architecture. Third, we also perform domain adaptation for the unlabeled target domain. Our two major goals are as follows. First, we propose
a region guided attention network to generate a city-scale safety map. Second, we propose a domain adaptation technique to generate safety maps where traffic accident data is not available.

Domain Adaptation is a branch of machine learning in which we aim at learning a well performing network from a source data that can generalize well on a different but related target data distribution \citep{ben2010theory}. It has been widely studied in visual applications \citep{csurka2017domain,wu2018dcan}. One of the popular class of algorithms is discrepancy based methods \citep{tzeng2014deep,long2015learning,sun2016deep}. In particular, \citeauthor{sun2016deep} propose to minimize the Frobenius norm of the difference between the feature covariance matrix of source and target. In our case, we also make use of the labels available for source and pseudo-labels for the target. These pseudo-labels are determined by the model trained on the source data. We then compute the feature covariance matrix for safe and dangerous classes respectively. Further, we compute our domain adaptation loss function based on \textit{within} and \textit{between} class covariance matrices of source and target.

\section{Overview}
\label{overview}
In this section, we present the data that we use at various stages of the work. We also introduce the formulation of our problem.
\subsection{Data}
We select two cities, London (UK) and New York City (USA) for the purpose of this study. Our reason for selecting these two cities is two-fold. First, the availability of sufficient data to work on and second, the extreme difference in traffic regulations, city planning, architecture etc. Figure \ref{fig:spatial_difference} shows the difference in the street network orientation of both the cities \citep{boeing2018urban}. All the data that we use in this work are available as \textit{open data}. Open data is the scheme under which some datasets are freely available to use and republish, without any restrictions \citep{dietrich2009open}. We also present results for Denver (USA) which is altogether a different domain.

\begin{figure}[htbp]
\centering
\subcaptionbox{London\label{sfig:london}}{\includegraphics[width=2.7cm,height=2.7cm]{london.png}}\hfill
\subcaptionbox{Manhattan, NYC\label{sfig:manhattan}}{\includegraphics[width=2.7cm,height=2.7cm]{manhattan.png}}\hfill
\subcaptionbox{Denver\label{sfig:denver}}{\includegraphics[width=2.7cm,height=2.7cm]{denver}}\\
\caption{The spatial differences in terms of street network orientation. All the locations have visual difference in their infrastructure orientation which makes learning a single network for all different domains a challenging task.}
\label{fig:spatial_difference}
\end{figure}

\textbf{Traffic Accident Data:} We collected 99,516 traffic accident records for London from 2013 to 2016.\footnote{https://www.europeandataportal.eu/data/en/dataset/road-accidents-safety-data} We gathered 1,256,205 traffic accident reports for New York City (NYC) from 2012 to 2017.\footnote{https://opendata.cityofnewyork.us} Similarly, we collected 143,776 traffic accident reports for Denver from 2013 to 2017.\footnote{https://www.denvergov.org/opendata/dataset/city-and-county-of-denver-traffic-accidents} Each record has numerous attributes such as latitude, longitude, date, time, vehicle type etc. However, there is a difference in the datasets. Table \ref{t:tab1} presents sample records from London data.
Information such as junction type, weather etc. is only available in London dataset. Therefore, we restrict ourselves to common attributes only.

\textbf{Satellite Images:} We collect satellite images using Google's Static Maps API.\footnote{https://cloud.google.com/maps-platform/}

\subsection{Problem Formulation}
We divide the whole study area by imposing a grid \textit{G}, where each grid $g_i$ is a square region of $s \times s$. Here, $s$ is equal to $30$ meters which is a sufficient area for traffic accident analysis \citep{najjar2017combining}. In this work, our objective is to produce a model that learns features such that it can predict the safety level of a given grid $g_i$ using its raw satellite image.

In order to curate a training dataset, we plot each accident on the grid using the locations from the Traffic Accident data. Each grid $g_i$ is given a safety score $S_i$ which is equal to the number of accidents in the grid. The higher $S_i$ corresponds to higher number of accidents.

We obtain satellite image for each of these grids. To obtain the image label for each $g_i$, we apply the classical k-means algorithm to bin $S_i$ into two clusters. Hence, we get two clusters of \textit{safe} and \textit{dangerous} locations. We observe that the obtained set of labels are highly imbalanced. Over 88\% of the regions are labeled as safe. Therefore, we re-sample the dataset by down-sampling the majority class such that both classes are balanced out.

\begin{figure*}[ht]
	\centering
	\includegraphics[width=\textwidth, height = 13cm]{nd}
    \caption{Architecture of our proposed network. There are three 2-way switches that enable the network to run in two modes. When all the switches are connected to \textit{red} nodes, DAM mode is enabled whereas if switches are connected to green nodes, DAM-DA mode is enabled. The solid and dashed lines represent DAM and DAM-DA mode respectively. It consists of a global and a local network. ResNet50 acts as a base to the global network. The ROI pooled features from conv 2 layer are passed through the local network to identify the subregion with maximum prediction probability to guide the global network. $L_C$ is used for training DAM and is also used along with $L_{DA}$ for training DAM-DA (Best viewed in color).}
    \label{fig:arch_dia}

\end{figure*}

\section{Deep Attention-Network Approach}
\label{dan}
In this section, we begin by introducing the network architecture. Then we elaborate
the technique used to implement the unsupervised domain adaptation for satellite images that belong to different domains.

\subsection{Network Architecture} \label{na}
The model consists of a global and a local network, with the global network borrowing layers from ResNet50 \citep{he2016deep}. We show the architecture of the proposed model in Figure \ref{fig:arch_dia}.
There are three 2-way switches in the architecture which enables the network to run in two different modes. When all the switches are connected to \textit{red} nodes, the Deep Attention Model (DAM) mode is enabled whereas if switches are connected to all the green nodes, the Deep Attention Model with Domain Adaptation (DAM-DA) mode is enabled. The solid and dashed lines represent DAM and DAM-DA mode respectively. We discuss the DAM-DA mode in the next section.
As shown in Figure \ref{fig:arch_dia}, the global network is augmented with a local network. In our experiments, we notice that the global network based on ResNet only, gives significant false positives, where false positives are defined as dangerous regions being predicted as safe. A possible reason behind such false positives could be that the satellite images are usually misaligned. To overcome misalignment and improve the performance of ResNet, we design the network such that it can exploit local regions in addition to the global region. We perform \textit{Region of Interest} (ROI) pooling on conv 2 layer of the model with the subregions being $N$ non-overlapping blocks. We pass features of each subregion through a local network. The local network consists of conv$_{1}^{l}$ and conv$_{2}^{l}$ layers. Each subregion of size 7x7 acts as an input to the local network to extract features and to identify which of them is useful for the classification task. Thus, this network attends to the regions which can significantly contribute towards enhancing the classification accuracy. We perform average pooling on the output of conv$_{2}^{l}$ layer. We obtain a feature vector of size 4096x1x1 that is passed through $fc_l$. The subregion with the maximum prediction probability of either of the two classes (Safe and Dangerous) is concatenated to the feature-map of the global network. We perform concatenation after the conv 4 layer of the global network. \textit{Thus, we can see that only certain subregions can guide the global feature vector for classification.} Once average pooling is performed, we pass the feature vector through $fc$ to obtain the final $d$-dimensional vector. We train the network using cross entropy loss $(L_C)$.

\subsection{Domain Adaptation}
In this section, we discuss our proposed Domain Adaptation (DA) technique for the target domain where there is no annotated data available. In our experiments, we consider London as the source domain for which labeled data is present, whereas, NYC as the target domain for which labels are not given. There are significant differences in both the cities.
Although NYC is nearly 20\% smaller in terms of area, it's population density is almost double than that of London.
According to \citeauthor{nieminen2002population}, higher the population density, higher are the chances of occurrence of accidents. Due to above-mentioned reasons, these cities would be a good extreme example for performing DA and can give us an idea of lower bound of model's performance.

\begin{table}[ht]
\caption{The comparison of the London and NYC in terms of area and population density.}
\label{t:differences_in_cities}
\centering
\begin{tabular}{|c|c|c|}
\hline
 & London & NYC \\ \hline
\begin{tabular}[c]{@{}c@{}}Area \footnotesize{(mi\textsuperscript{2})} \end{tabular} &607 & 468.5 \\ \hline
\begin{tabular}[c]{@{}c@{}}Population Density\\ \footnotesize{(people per mi\textsuperscript{2})} \end{tabular} &14,550 & 28,491 \\ \hline
\end{tabular}
\end{table}

First, we train DAM network using data from the source domain as explained in the section \ref{na}. Using this trained model, we generate \textit{pseudo labels} for data from the target domain. Thus, we have labels for the source domain (London) and pseudo labels for the target domain (NYC). Now, we use data from both source and target domain to train the augmented DA (DAM-DA) network as shown in Figure \ref{fig:arch_dia}. There are three differences when compared to DAM.
First, this network has two additional convolutional layers in the global network, conv$_{1}^{g}$ and conv$_{2}^{g}$ layers of dimensions 512x7x7 and 256x7x7 respectively, to reduce the dimensions of the feature maps. This augmentation is necessary to be able to compute the covariance matrix efficiently. We explain the utility of the covariance matrix in the later part of this section. Second, instead of conv$_{1}^{l}$ and conv$_{2}^{l}$ layers in the local network, we use conv$_{1}^{l'}$ and conv$_{2}^{l'}$ layer of size 512x7x7 and 256x7x7 respectively. Again, this is done to reduce the dimensions for covariance matrix calculation. Third, in DAM mode, we train the network using $L_C$ only, but in DAM-DA mode we use domain adaptation loss ($L_{DA}$) along with $L_C$. We explain $L_{DA}$ next.

Let $x_i$ denote the feature for \textit{i}-th sample classified as dangerous in source domain. Similarly, let $y_i$ be the feature for \textit{i}-th sample classified as safe in source domain. These features are obtained from the final layer $(fc)$ of the network as shown in Figure \ref{fig:arch_dia}. Now, we can obtain the \textit{within} class covariance matrix $\sum_{SW}$ $\in \mathcal{R}^{d\times d}$ as,

\begin{equation}
\begin{split}
\sum\nolimits_{SW} = \sum\nolimits_{i,j \neq i} (x_{i}-x_{j}) (x_{i}-x_{j})^T  \\ + \sum\nolimits_{i,j \neq i} (y_{i}-y_{j}) (y_{i}-y_{j})^T
\end{split}
\end{equation}

Similarly, we can compute the \textit{between} class covariance matrix $\sum_{SB}$ $\in \mathcal{R}^{d\times d}$ as,
\begin{equation}
\sum\nolimits_{SB} = \sum\nolimits_{i,j}(x_{i}-y_{j}) (x_{i}-y_{j})^T
\end{equation}

We can compute the \textit{within} and \textit{between} class covariance matrices for target domain in a similar manner. Let these be denoted by $\sum_{TW}$ and $\sum_{TB}$ respectively. Then, we use the following loss function to adapt to the target domain,

\begin{equation} \label{eq:loss}
L_{DA} = \norm{ \sum\nolimits_{SW} - \sum\nolimits_{TW} }_F^2 +  \norm{\sum\nolimits_{SB} - \sum\nolimits_{TB} }_F^2
\end{equation}
where $\norm{.}_F$ denotes the Frobenius norm. Though the pseudo labels are noisy, it is still beneficial to use them. In our experiments, we demonstrate that
using the loss to minimize the distance between feature covariance matrices between source and target, which is agnostic of source labels, achieves sub-par performance when compared to $L_{DA}$.

\section{User Study}
\label{us}
We conduct a study with an aim to observe that how accurately humans can classify raw satellite images as safe or dangerous. It's first of its kind user study. We develop an annotation portal and provide the raw satellite images from our test-data to the users.
The portal has two sections. In the first section, we provide four sample images from each category. In the second section, an image is presented to the user along with three options to choose from. A user can mark an image as \textit{dangerous, safe or unsure}. We show the same image to three different users and record their response to maintain the confidence in the user's response. We also add sixteen more sample images from each class to help the user. We record users' response for 1,000 images having 500 images from each category, i.e., safe and dangerous classes.

In this study, 38 users participated. All the participants are above 18 years of age. To check inter-annotator agreement, we compute Fleiss' kappa \citep{fleiss1973equivalence}. We achieve 0.2743 which depicts a fair agreement. To assign a final label to the image, we calculate the mode of all three responses. If all the responses are different, we discard that response. In our study, we found 0.03\% of such responses and discarded them. Finally, 67.22\%, 27.94\%, and 4.84\% images are marked as \textit{safe, dangerous,} and \textit{unsure} respectively by users. In Table \ref{t:user_study_data}, we provide a complete description of the user-study results. With 80.32\% accuracy, users are able to detect \textit{safe} images whereas the accuracy drops to 45.02\% for \textit{dangerous} images. Therefore, in this case, we find that humans can identify safe locations with better accuracy than dangerous locations.

\begin{table}[htbp]
\caption{The complete description of the user-study outcome (in \%).}
\label{t:user_study_data}
\centering
\begin{tabular}{c|c|c|c|c|}
\multicolumn{2}{c}{}
            &   \multicolumn{3}{c}{Predicted} \\
            \cline{2-5}
    &       &   Safe &   Dangerous     & Unsure       \\
    \cline{2-5}
\multirow{2}{*}{\rotatebox[origin=c]{90}{Actual}}
    & Safe   & 40.41   & 05.56      &  4.32        \\ \cline{2-5}
    & Dangerous    & 26.80    & 22.37   & 0.51            \\
    \cline{2-5}
    \end{tabular}
\end{table}

\section{Experiments}
\label{ex}
We initially performed the experiments with AlexNet, VggNet, DenseNet, and ResNet and found that ResNet gives the best accuracy.
We now prove the efficacy of the proposed technique by assessing the classification accuracy of the predicted road safety maps in multiple scenarios. It is also important to note the non-triviality of learning from satellite data, as it is seen in the user study, and can also be seen in the examples shown in Figure \ref{fig:non_trivial} and Figure \ref{f:cams}.

\begin{figure}[htbp]
\centering
\subcaptionbox{Dangerous location, might be perceived as \textit{safe.}\label{sfig:ds-1}}{\includegraphics[width=3.5cm,height=3.5cm]{dangerous-sample-nontrivial.jpg}} \hspace{4mm}
\subcaptionbox{Safe location, might be perceived as \textit{dangerous.}\label{sfig:sd-1}} {\includegraphics[width=3.5cm,height=3.5cm]{safe-sample-nontrivial.jpg}}\hfill
\caption{Image Samples to show the non-triviality of learning from satellite data.}
\label{fig:non_trivial}
\end{figure}

\textbf{Training Parameters:} We train our network on London dataset. There are 4,517 training samples and 903 validation samples in each class. The models are trained with a batch size of 4, with learning rate as 0.0001 and a learning rate decay of 0.5 per 10 epochs. We train it over 50 epochs on a Nvidia GeForce TITAN X GPU.

\textbf{Evaluation of DAM:}
To assess the model, we test on unseen data from London. As shown in Table \ref{t:tab2}, we test the models in multiple scenarios such as with horizontal (DAM + HS), vertical (DAM + VS), and square boxed (DAM + SQ) subregions and with the combinations of them. We test another variation of the model where the activation maps are produced based on an intermediate convolutional layer. We experiment with two conv layers; conv 2 and conv 3 layer. We finally use conv 2 layer as we achieve better results with it. We find that the DAM using HS+VS+SQ subregions outperformed ResNet50, VGG19, and its other variants on the same dataset as shown in Table \ref{t:tab2}.
\begin{table}[htbp]
\caption{Comparison of classification accuracy (in \%) for ResNet50, VGG19, and variants of the DAM both for original dataset (London) and cross dataset (NYC and Denver).}
\label{t:tab2}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
Model& Original Data & \multicolumn{2}{c|}{Cross Data} \\ \cline{2-4}
 & London & NYC & Denver \\ \hline
ResNet50 & 85.77  & 69.16  & 70.00  \\ \hline
VGG19 & 85.83  & 64.60  & 70.00  \\ \hline
DAM (HS) & 85.81  & 72.28  & \textbf{76.20}  \\  \hline
DAM (VS) & 85.52  & \textbf{74.77}  & 75.00  \\ \hline
DAM (SQ) & 85.86  & 70.70  & 70.00  \\ \hline
DAM (HS+VS) & 85.34  & 70.37  & 70.01  \\ \hline
DAM (HS+VS+SQ) & \textbf{86.21}  & 67.23  & 69.86 \\  \hline
\end{tabular}
\end{table}

\textit{One of our goals is to minimize the false positives.} This is necessary as the cost of predicting safe as dangerous may only lead to overly conservative approaches for planning, whereas, having high false positive rate with more dangerous locations being marked safe can lead to fatal accidents. Therefore, we consider the classification of dangerous images as safe to be more costly than vice-versa. We test ResNet50 and DAM on London, NYC, and Denver test-set of 7,228, 8,342, and 500 images respectively. From Table \ref{t:tab4}, we can see that DAM gives fewer false positives in comparison to ResNet50 for every domain.
\begin{table}[htbp]
\centering
\caption{A comparison of false positive rate between DAM and ResNet50. All results are in percentage.}
\label{t:tab4}
\begin{tabular}{|c|c|c|c|}
\hline
Model & London & NYC & Denver \\ \hline
DAM & \textbf{07.60}  & \textbf{22.05}  & \textbf{29.20} \\ \hline
ResNet50 & 12.73  & 39.05  & 40.80  \\ \hline
\end{tabular}
\end{table}

\textbf{Cross Dataset Testing:}
We also perform cross-data testing and test our model on NYC and Denver dataset. From Table \ref{t:tab2}, we can see that DAM + VS and DAM + HS performs the best for NYC and Denver dataset respectively. For DAM (HS+VS+SQ), the base model trained on London with three sub-regions is over-fitting, and the network does not generalize well on Denver. When we decrease sub-regions to 2, the accuracy increases for NYC and Denver, and the model performs the best when used with only one sub-region.

\begin{figure*}[ht]
\centering
\rotatebox{90}{\hspace{0.7cm}Dangerous Image}
{\includegraphics[width=4cm,height=4cm]{dangerous-incorrect-original.png}}\hspace{4mm}
{\includegraphics[width=4cm,height=4cm]{dangerous-safe-r.jpg}} \hspace{3mm}
{\includegraphics[width=4cm,height=4cm]{dangerous-safe-a1.jpg}} \hspace{3mm}
{\includegraphics[width=4cm,height=4cm]{dangerous-safe-a2.jpg}}\\
\vspace{3mm}
\rotatebox{90}{\hspace{12mm}Safe Image}
\subcaptionbox{Original Images\label{sfig:Safe-incorrect-original}}
{\includegraphics[width=4cm,height=4cm]{safe-incorrect.png}}\hspace{4mm}
\subcaptionbox{ResNet50\label{sfig:CAM-Resnet2}}
{\includegraphics[width=4cm,height=4cm]{safe-dang-r.JPG}}\hspace{4mm}
\subcaptionbox{DAM-Global Network\label{sfig:CAM-DAM-11}}{\includegraphics[width=4cm,height=4cm]{safe-dang-a1.JPG}}\hspace{4mm}
\subcaptionbox{DAM-local Network\label{sfig:CAM-DAM-21}}{\includegraphics[width=4cm,height=4cm]{safe-dang-a2.JPG}}\\
\caption{A comparison of activated features of a \textit{dangerous} and a \textit{safe} location among ResNet50, DAM-Global Network, and DAM-Local Network. Top row and bottom row represent dangerous and safe locations respectively. DAM-Global Network contains activated features in the global network before the concatenation of local network features. DAM-Local Network contains activated features from the local network alone (Best viewed in color).}
\label{f:cams}
\end{figure*}

\textbf{Qualitative Analysis:}
In order to understand the behavior of the network qualitatively, we generate Class Activation Maps (CAM) \citep{zhou2016learning}. In Figure \ref{f:cams}, the images in the top row correspond to a dangerous image and its CAMs for ResNet50, DAM-Global Network, and DAM-Local Network. Similarly, the images in the bottom row correspond to a safe image and its corresponding CAMs. From the top row in Figure \ref{sfig:CAM-DAM-11} and Figure \ref{sfig:CAM-DAM-21}, we can establish that the DAM not only identifies the green region around the divider on the road but also identifies the cars and roads in the surrounding areas as well, which positively contributes towards the identification of this image as dangerous. This image contains a dangerous location, but, ResNet50 mis-classifies it as safe. As from top row in Figure \ref{sfig:CAM-Resnet2}, ResNet50 seems to have identified the green region and used that to classify the sample as safe, whereas our proposed DAM is efficient enough to correctly identify it as a dangerous location. Similarly, from the CAM in the bottom row, we observe that ResNet50 recognizes only roads whereas the DAM identifies houses and trees in the image. Therefore, DAM correctly identifies this sample as safe whereas ResNet50 mis-classifies it as dangerous.

\textbf{Evaluation of DAM-DA Network:}
We train our DAM-DA network with 2,085 images of each class, i.e., Safe and Dangerous. We use 4,170 images each from the source (London) and target (NYC) domain. We use a batch size of 16, a learning rate of 0.0001 and a decay of 0.5 per 10 epochs. It is trained for over 50 epochs. We test the DAM-DA network on 3,336 images from NYC. As shown in Table \ref{t:tab6},
DAM-DA network gives an accuracy of 75.75\% in comparison to DAM network which gives an accuracy of 71.94\%.

In another setting, we replace $L_{DA}$ with the loss \citep{sun2016deep} in Equation \ref{lda},

\begin{equation} \label{lda}
L_{DA}^{ST} = \frac{1}{d}\norm{C_{S} - C_{T} }_F^2
\end{equation}

where $C_{S}$ and $C_{T}$ denotes the feature covariance matrix of source and target samples in a training batch, respectively. When compared to $L_{DA}$, $L_{DA}^{ST}$ does not take the class labels into account. As shown in Table \ref{t:tab6}, we obtain an accuracy of 74.73\%. Similarly, DAM$-$DA$-L_{DA}$ results in the least false positives rate (18.94\%) in comparison to its counterparts. Thus, we can see that the class wise covariance loss works better in this scenario.

\begin{table}[htbp]
\caption{A comparison of DAM, DAM-DA with $L_{DA}$, and DAM-DA with $L_{DA}^{ST}$ network with batch-size of 16.}
\label{t:tab6}
\centering
\begin{tabular}{|c|c|c|}
\hline
Model & Accuracy (\%) & FPR(\%)\\ \hline
DAM & 71.94 & 30.03\\ \hline
DAM$-$DA$-L_{DA}$ & \textbf{75.75} & \textbf{18.94} \\ \hline
DAM$-$DA$-L_{DA}^{ST}$ & 74.73 & 30.69\\ \hline
\end{tabular}
\end{table}

\textbf{Comparison of DAM and Humans:} We test DAM on the set of same 1000 images that we use for user study. We present these results in Table \ref{t:user_study_comp}.
We can see that DAM is more accurate than humans by a significant difference of 23.12\%. Moreover, there is a substantial difference of 30.77\% in precision. Therefore, this proves that DAM performs better than humans in this case.

\begin{table}[htbp]
\caption{A comparison of the performance of our proposed deep learning model (DAM) and humans.}
\label{t:user_study_comp}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
Model & Acc(\%) & Precision & F1\\ \hline
Humans & 62.78  & 0.601 & 0.687\\  \hline
DAM & \textbf{85.90}  & \textbf{0.909} & \textbf{0.850}\\ \hline
\end{tabular}
\end{table}

\section{Conclusion}
\label{conc}
In this paper, we address the challenge of learning efficient embeddings to classify the road segments as dangerous or safe using easily available and inexpensive data. We leverage open data and satellite images to predict city-scale road safety maps. We propose a deep learning based model that uses a region guided attention network. It consists of a global and a local network. The local network attends to features in the subregions and the features with maximum prediction score are used to guide the global features to enhance the accuracy. We evaluate our network on the public dataset of London and achieve the accuracy of 86.21\%. We experiment with the cross-datasets of NYC and Denver and achieve significant results with the accuracy 74.77\% and 76.20\% respectively. In addition, we propose a covariance loss based domain adaptation for the scenario where target domain labels are missing. In our experiments, we show that with the domain adaptation network, the accuracy of NYC increases by 4\% and the network also achieves the lowest false positives. We also conduct a user study and find that our model outperforms human by 23.12\%. In the future, we would like to explore Places trained models instead of ImageNet trained models.

\section{Acknowledgments}
We would like to thank Precog at IIIT-Delhi for supporting this research work.

redpajama_set_name: RedPajamaArXiv


text:
Le chlorobenzaldéhyde est un composé aromatique de formule C7H5ClO. Il est constitué d'un
noyau benzénique substitué par un groupe aldéhyde  (-CHO) et un atome de  chlore (Cl). Comme tous les benzènes disubstitués, il existe sous la forme de trois isomères structuraux, les composés ortho, méta et para, selon la position relative des deux substituants sur le cycle.

Par abus de langage, le chlorure de benzoyle est parfois aussi appelé « alpha-chlorobenzaldéhyde », sans pour autant être un aldéhyde mais un chlorure d'acyle, le chlore se substituant à l'atome d'hydrogène de l'aldéhyde. La suite de cet article ne concernera que les « vrais » isomères du chlorobenzaldéhyde.

Propriétés

Le 4-chlorobenzaldéhyde qui a la symétrie la plus élevée a le plus haut point de fusion. C'est le seul qui est solide à température ambiante.

Synthèse

La synthèse du 4-chlorobenzaldéhyde peut être faite à partir de 4-chlorotoluène, qui est mis à réagir d'abord avec du pentachlorure de phosphore et du chlore sous irradiation UV. L'hydrolyse de l'intermédiaire avec le groupe méthyle doublement chloré, le 4-chloro-(dichlorométhyl)benzène ainsi obtenu, avec de l'acide sulfurique concentré conduit au 4-chlorobenzaldéhyde.

Utilisation

Les chlorobenzaldehydes sont utilisés dans la synthèse de colorants triphénylméthane et de médicaments. Le 4-chlorobenzaldéhyde est une matière première pour la synthèse de pesticides chlorobenzilate, coumachlore, paclobutrazol, triticonazole et uniconazole. Partant du 2-chlorobenzaldéhyde, le clomazone est obtenu.

Le 4-chlorobenzaldéhyde peut également être utilisé pour la préparation du 4-fluorobenzaldéhyde.

Notes et références

Isomérie
Chlorobenzène
Benzaldéhyde
Benzène disubstitué

redpajama_set_name: RedPajamaWikipedia


Indie Art Photoblog: get a load of her!
get a load of her!
I love you work. Seriously you are amazing. I also love the way you use the light! AMAZING!
Well, you both pulled it off without a hitch! lol Tights or no tights, knots and all... fab!
beautiful, anna! but i REALLY want to know how you did her hair in those knots. i love em but i've never been able to get them to look quite the way i want them to...?
You are so creative! I love the tights and knots! Where on earth did you shoot this? I love the windows in the back. Still hoping to do a mentoring session with you... just have to come up with the mulahh (money)!
Anna, where in the world are you? You are missed!!!!
P.S. The next time you are in town I have a favor to ask! Lol, hope all is well!
OH. MY. GOSH! I haven't been on here in a while and just saw YOUR family pics. You have a seriously adorable family! And I LOVE YOUR HAIR!
Hey, just checking to see if you are receiving my e-mails. I didnt know if you were just really busy or if there was a problem with them.


This clear glass pillar candle holder is perfect for displaying your pillar candle (D-4" or smaller) and it's ideal for both wick and LED candles. Dual purpose candle holder can be used for pillar or taper candles. You will find a slight lip and an indented center for potential wax spillage. This piece is great for weddings, restaurants, and churches use.


text:
Home / Products / Detroit: The Dream Is Now: The Design, Art, and Resurgence of an American City
Detroit: The Dream Is Now: The Design, Art, and Resurgence of an American City
"Detroit: The Dream Is Now: The Design, Art, and Resurgence of an American City" by Michel Arnaud
Detroit: The Dream Is Now is a visual essay on the rebuilding and resurgence of the city of Detroit by photographer Michel Arnaud, co-author of Design Brooklyn. In recent years, much of the focus on Detroit has been on the negative stories and images of shuttered, empty buildings—the emblems of Detroit's financial and physical decline. In contrast, Arnaud aims his lens at the emergent creative enterprises and new developments taking hold in the still-vibrant city. The book explores Detroit's rich industrial and artistic past while giving voice to the dynamic communities that will make up its future. The first section provides a visual tour of the city's architecture and neighborhoods, while the remaining chapters focus on the developing design, art, and food scenes through interviews and portraits of the city's entrepreneurs, artists, and makers. Detroit is the story of an American city in flux, documented in Arnaud's thought-provoking photographs.
Detroit Graffiti $50.00
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Canvas Detroit $34.99
Canvas Detroit
"Canvas Detroit" by Julie Pincus & Nichole Christian Detroit's unique and partly abandoned cityscape has scarred its image around the world for decades. But in the last several years journalists have...

redpajama_set_name: RedPajamaCommonCrawl


The Ecto-1AG is as simple as it gets. It's an AG inspired distortion with volume and gain controls. "Listen, you smell something?" It features an unorthodox toggle on/off switch with a 10bar graph led indicator and red pilot. I really went into detail with some of the enclosures features. It runs on a standard 9v power supply but does not have the option for battery. Each pedal is hand made in Chicago Illinois. Build times can take up to several months.


As expected, Apple has just unveiled the new OS X 10.10 at the WWDC 2014 in San Francisco. When Craig Federighi took the stage, he explained how Apple shifted from "cat names" to "beautiful places in California" for its OS X naming and this time, they arrived at 'Yosemite'. Once again as expected, Yosemite comes with a new look, moving away from the classic 'aqua' design theme to the all new iOS 7-inspired colourful, translucent design.
To begin with, Yosemite comes with a dark mode, so users can have dark gray toolbars instead of the white translucent framing of previous versions. With OS X 10.10, Notification Center is also getting a refresh with a today view that shows users their full day ahead along with a notifications pane. Calendar also has a new day view, along with a right-side bar that gives users the details of their appointments.
Next comes the all new Spotlight, which now shows up as a big search bar n the middle of the screen, with results popping up beneath it as users search. Additionally, it can do conversions such as for distance (feet to meters), and can also give you food recommendations and movie times, depending on what you're looking for.
OS X Yosemite also brings the all new 'iCloud Drive'. With the new feature, all of your iCloud drive files are available in folders from Finder. Those files are auto-synced across devices and are fully searchable and taggable.
Another brand new feature introduced in OS X 10.10 is 'Mail Drop', a new feature that'll allow users to email attachments up to 5GB in size. Mail Drop automatically separates the file from the email, uploads it into iCloud and forwards a link to the file along with the email. Mac users will get the file without clicking, while other clients can get to it through the link.
The updated Safari has a much cleaner look. It replaces default visibility of Favorites bar with Favorites popping up as soon as you click on the address bar. Other enhancements include RSS feeds in Safari sidebar, and one-click social sharing with a single button in the right-hand corner. Similarly, a new image-handling feature in Mail allows users to add annotations, markups (similar to Skitch) before emailing.
The most amazing new features of Yosemite is perhaps what Apple calls 'Continuity', which is basically a collection of features that streamline the workflow between iOS and OS X. First of all, AirDrop now works between iOS and Mac. Then comes 'Hand-off', which allows for different iOS and Mac devices to be aware of what the others devices on the same network are doing. Craig demonstrated the feature with an email being composed on his iPhone, and the dock on the Mac populated an icon prompting him to finish things up on the MacBook. Awesome!
Additionally, if a Mac is currently without WiFi, it'll sense when user's iPhone is around and will provide that iPhone as an option in its list of network, making tethering super easy. Furthermore, users can now receive and make calls from their Mac or their iPhone (a notification pops up onscreen to ask if you'd like to answer.) And, if a phone number shows up on a webpage, users can highlight and dial it directly from the Mac.
OS X 10.10 Yosemite will be available to download for developers starting today, with everyone else getting it for free in the fall. Apple has announced it will also open Yosemite to some non-devs this summer through a beta program.


text:
Q: How does declaring variables in swi prolog work? Sorry for the inconvenience, I'm still new to Prolog's syntax, Im just asking how would one declare/instantiate and manipulate a variable X (if possible) and be able to write/print it out similar to other languages. For example:
int x = 5;
print(x);

or even swapping variables
int x = 5, y = 10, z;
z = y;
x = z;
y = x;

Is it possible to implement these in prolog? If so, how? If not, why?

A: You are not the first person to ask this exact question. Even searching around on Stackoverflow would have been easier. By now I suspect there is a CompSci professor out there just trolling their students with this question.
Your example looks like C or a C-like language. Other very popular languages do not require declarations, for example Python. Declaring variables is an artifact of early compiled languages. This is a very broad topic that belongs to a university-level lecture on compiler design, programming language design and so on.
You "swap variables" when your variables are handles to registers in the processor or memory locations. Prolog is on a very different level, to the point that I feel like saying "unask the question" despite not being too wise.
So, let's start again: what are you trying to achieve by swapping variables?
If you want to print a 5, write:
?- X = 5.
X = 5.

or maybe:
?- write(5).
5
true.

and so on and so forth. Long story short, there is more to this question than meets the eye, but I am not convinced this is the right place to ask it.

redpajama_set_name: RedPajamaStackExchange


Wall-mounted mirror television with minimum installation required allows you to integrate a functional and versatile solution.
Mobile self-standing mirror TVs that rest on the floor or extendable wall-mounted versions that can be tilted in any direction in order to watch TV from different parts of the room.
Need something more specific? If you're looking for special creation, need to create a custom wardrobe with a built-in mirror TV or turn any other of your ideas into reality, this is your solution.
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We work with monitors that range from 32" to 75". Choose the TV that best fits your mirror.
* The minimum size of the mirror depends on the size of the TV screen.
Decide where you want to place the screen. Different positions are recommended depending on the location of the mirror TV and the purpose of the room.
All our designs are customisable. Mirror TVs can work with a frameless mirror or with the frame of your choice. Special creations can be made with an endless range of finishes and details. You will always find the design that fits your project.
Depending on the geographical area, our mirror TVs will be installed by our own installation service or by a specialised team we select. Any problem that you might have once the TV is installed will be handled by the TV brand's technical service.
The quality of Oculting mirror, with its smoky tonality obtained through the latest technology, represents a great evolution in common mirrors since it allows polarization, creating the mirror effect and screen viewing when the technology is connected.
Just like a TV or projector, the most ideal lighting to see images and contents in a TV Mirror is a soft and subdued ambient light. It is convenient to be able to filter the natural light that penetrates the room through the windows with curtains, blinds or, even, to be able to lower the blinds if the daylight is too intense.
Both artificial lighting and sunlight, in general, should be adequately treated to minimize reflections in the glass when the screen is connected, especially in bright environments.
The optimal viewing distance with respect to the size of the device is in relation to 50/80 cm for every 10 inches of screen. In this way, for its correct visualization in long periods, without tiring the eye, a 40″ screen should be placed between 2 and 3.2m away and a 60″screen, for example, between 3 and 5.6 meters.


text:
Chronic congestive heart failure. Description and survival of 190 consecutive patients with a diagnosis of chronic congestive heart failure based on clinical signs and symptoms
Eur Heart J. 1994 Mar;15(3):303-10. doi: 10.1093/oxfordjournals.eurheartj.a060495.
B K Madsen 1 , J F Hansen, K H Stokholm, J Brøns, D Husum, L S Mortensen
1 Department of Cardiology, Hvidovre Hospital, University of Copenhagen, Denmark.
DOI: 10.1093/oxfordjournals.eurheartj.a060495
The prognosis, and clinical findings related to prognosis, were examined in a consecutive series of 190 patients under 76 years of age (mean 64 years) with congestive heart failure (CHF). The aetiology of CHF was ischaemic heart disease in 66%, hypertension in 11% and cardiomyopathy in 23%. The 2-year mortality was 32%. Median left ventricular ejection fraction (LVEF) was 0.30, range 0.06 to 0.74. Eight per cent were in New York Heart Association (NYHA) class I, 46% in II, 44% in III and 2% in IV. Multivariate analysis, excluding exercise test variables, revealed seven variables with independent, significant prognostic information, (hazard ratios for death in brackets): ln (natural logarithm) (LVEF) (3.19), NYHA class III+IV (2.72), plasma urea > 7.6 mmol.l-1 (2.22), serum creatinine > 121 mumol.l-1 (2.05), serum sodium < or = 137 mmol.l-1 (2.03), pulmonary congestion on X-ray (1.86), and age > 65 years (1.86). Multivariate analysis, including exercise testing, showed the following variables to contain independent, significant prognostic information: increase in heart rate during maximal exercise < or = 35 min-1 (3.5), ln (LVEF) (3.7), serum creatinine > 121 mumol.l-1 (2.9), maximal exercise time < or = 4 min (2.3), serum sodium < or = 137 mmol.l-1 (2.5), ischaemic heart disease (2.0) and plasma urea > 7.6 mmol.l-1 (1.9). In conclusion, patients with CHF have a high risk of death despite intensive medical treatment. LVEF is a strong predictor of mortality. Both NYHA class and exercise variables have strong independent prognostic information as regards mortality in combination with LVEF, but are mutually exclusive.
Exercise Test
Heart Failure / diagnosis
Heart Failure / drug therapy
Heart Failure / etiology
Heart Failure / mortality
Heart Failure / physiopathology*
Stroke Volume
Ventricular Function, Left*


text:
New country, new faith
Some rights reserved by hector melo
Over the past five years, migrants and refugees have flowed into Europe in unprecedented numbers, arousing deep concerns about their impact on host countries. What is not clear, though, is how much impact the new arrivals will have on Europe's religious alignments.
The main effect will be to swell the Muslim population. But alongside the Muslim influx are Christians of various kinds. Their actual numbers are open to much debate. A limited number of the new arrivals come from ancient churches in Syria and Iraq, but far more common are the African Christians who are not fleeing any particular conflict, but who are migrating in search of a better life.
Their presence has found an evocative material symbol in the makeshift tent church erected in the camp at Calais, in France, among the many thousands of desperate migrants struggling to reach Britain. Many of the church's attenders are Ethiopians and Eritreans, from Christian communities that were already well established when France itself was still pagan soil.
Philip Jenkins teaches at Baylor University. He is the author of The Great and Holy War and The Many Faces of Christ.
Aug 31, 2016 issue
Another intriguing category of believers has attracted a great deal of media coverage in recent months: there is a remarkable number of new converts to Christianity, to the point that some urban churches are reporting mass baptisms. The standard picture of a convert is of a young Iranian or Afghan who might have had very limited exposure to Christianity in his own country—barely even hearing the name—but who now publicly avows his faith. (Given the overwhelmingly male character of recent arrivals, I consciously say "his.") Such decisions often arise from gratitude for the pastoral care extended by churches: not just the feeding and clothing of newcomers, but more generally the intense efforts by Christian groups to make migrants and refugees feel welcome. Kindness pays dividends, all the more so when deliberate proselytizing forms no part of the picture.
For Americans especially, such conversions are wonderful news, fulfilling as they do the hope that Muslims are indeed open to evangelization, provided that they are free to hear the gospel. And might those new Christians open the way to successful missions in their old homelands? Might Europe's Muslim migrants provide the foundation for a whole new Christian explosion across the Middle East and North Africa?
Maybe, but certainly not yet. Those new converts undoubtedly exist, but the phenomenon needs to be treated cautiously. The reported numbers are actually not that large when set alongside the total picture of the migration. So far, the number of Muslim converts to Christianity does not approach the traffic in the other direction—that of formerly Christian Europeans accepting Islam.
Also rarely asked in news reports is what the converts are converting from. Western media and government often speak of "Muslim nations" in monolithic terms, as if every one of their citizens was a devout and knowledgeable member of that faith. In fact, many of those people are Muslim only in the notional and cultural sense in which millions of nonparticipating members of Western national churches might describe themselves as Christians. They are thus quite open to accepting new creeds in new countries, all the more so if they had some family connection with one of the ancient churches. Significantly, many of the reported converts are Iranian, from a country long deeply disenchanted with official Islamic rule and clerical power.
One thing we can be certain of is that any Christian expansion will take place without the slightest assistance from European governments, which are more likely to react with bureaucratic hostility. For decades, Middle Eastern Christians have been fleeing to Western countries in search of sanctuary from persecution in their home countries, the bitter reality of which seems too obvious to be worth spelling out. Repeatedly though, European (and some North American) immigration authorities have demanded that asylum seekers prove their Christian credentials by a series of absurd tests. In Britain, notoriously, these have included being asked to recite the Ten Commandments or to state how many books there are in the Bible (presumably all non-Protestants fail that one outright). "What is Pentecost?" is another favorite.
Such an approach is ludicrous enough when applied to members of established Christian communities, who might have some dim recollections of Sunday school. It is absolutely inappropriate for new believers who grasped shreds of doctrine and scripture while they were living in deeply hostile societies and who hope for a new life in historically Christian Europe.
With all these caveats and concerns, these emerging Christian communities will repay watching, and especially the new converts. If those emerging churches endure and grow, they could mark an important new departure for the continent's religious story.
middle eastern christians
European Muslims
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Research counters nativist claims about Europe's Muslim population
A report from the Pew Research Center looks at projections depending on rates of welcoming refugees and immigrants.
by Emily McFarlan Miller
Europeans warned on female trafficking: Churches urged to step up efforts to fight practice
Whose holy ground?
"You are here to kneel," wrote Eliot, "where prayer has been valid." But which prayers are valid at the Mezquita Catedral, or at Hagia Sophia?
In Europe, religious minorities face mounting hostility, harassment
Nationalism and anti-immigrant movements have gained ground.
by Tom Heneghan
In Europe, even occasional prayer is on the way out
It's not just disaffection with particular state churches. People's religious orientation itself is gone.


text:
A Murder of Innocence
Based on a true story, a community is rocked by the double-homicide of a prominent couple and the local police struggle to find the killer. As a darkness settles over the town, Albert, the new resident pastor must help his church and family overcome the aftershocks by providing stability, healing and hope while his wife Aimee must come to grips with her own fear through her faith.
Director: Shawn Justice
Actors: Ava Petruna, Cody Leo, Ella Cortese, Frank Chiesurin, Jeremy McLaughlin, Katie O'Grady, Lily Haug, Marc Steele, Rachel MacMillan, Scott Galbraith, Xander Steel
Keywords:A Murder of Innocence
Lone Wolf McQuade
The archetypical renegade Texas Ranger wages war against a drug kingpin with automatic weapons, his wits and martial arts after a gun battle leaves his partner dead. All of this…
Genre: Action, Crime, Drama, Romance, Thriller, Western
Once Upon a Time in Mumbai Dobaara!
Once Upon A Time In Mumbai Dobaara is a sequel of Once Upon A Time In Mumbai
A supernatural tale set on death row in a Southern prison, where gentle giant John Coffey possesses the mysterious power to heal people's ailments. When the cellblock's head guard, Paul…
Genre: Crime, Drama, Fantasy, Mystery
Stray Bullets
In upstate New York, two teenage boys are tasked with cleaning out their father's old mobile home on an abandoned property, but the boys are in for a surprise when…
L.A., I Hate You
The lives of an action star wannabe, a frustrated family man, and a guilt-ridden boyfriend, intertwine in three tales of sex, violence, redemption, murder, and unthinkable twists all taking place…
Genre: Action, Crime, Drama, Horror, Thriller
An incorruptible IT officer Amay Patnaik (Ajay Devgn) gets an anonymous tip about a political leader Tauji's (Saurabh Shukla) illegal assets. Patnaik plans an elaborate Raid on Tauji's home and…
Snabba cash – Livet deluxe
JW now lives in exile and is more than ever determined to find out what happened to his missing sister Camilla. Every trace leads him to the world of organized…
A 19 year old Brooklyn boy who is torn between two worlds when his photography portfolio wins him a partial scholarship to NYU. He must figure out how to balance…
Bad Boy Bubby
Bad Boy Bubby is just that: a bad boy. So bad, in fact, that his mother has kept him locked in their house for his entire thirty years, convincing him…
French Connection II
"Popeye" Doyle travels to Marsailles to find Alain Charnier, the drug smuggler that eluded him in New York.
An ex-soldier, a teen and a cop collide in New Orleans as they hunt for the source behind a dangerous new pill that grants users temporary superpowers.
Genre: Action, Crime, Drama, Mystery, Sci-Fi, Science Fiction, Thriller
Stop Over in Hell
In a stagecoach stop far away in the Wild West the unexpected visit of a man called 'Colonel', will change their peaceful lives .
Genre: Crime, Horror, Thriller, Western
One False Move
Following a series of drug deals and murders, three criminals — Fantasia, Ray Malcolm and Pluto — travel from Los Angeles to Houston, finally arriving in a small Arkansas town…
Operation Varsity Blues: The College Admissions Scandal
An examination that goes beyond the celebrity-driven headlines and dives into the methods used by Rick Singer, the man at the center of the shocking 2019 college admissions scandal, to…
Genre: Crime, Documentary, Drama
Before the Devil Knows You're Dead
When two brothers organize the robbery of their parents' jewelry store, the job goes horribly wrong, triggering a series of events that send them and their family hurtling towards a…
The Treacherous
The story of Joseon's tyrant king Yeonsan who exploits the populace for his own carnal pleasures, his seemingly loyal retainer who controls him and all court dealings, and a woman…
Genre: Crime, Drama, History, Thriller
A disc jockey, a pimp and an Italian tourist escape from jail in New Orleans.
A police officer suspended and now accused of murder is forced to join forces with his court-appointed attorney to assemble the pieces of a deadly puzzle to find the missing…
Genre: Crime, Mystery, Romance, Thriller


The maszakdi fever has gotten to some Mzansi musos including "Titi Mboweni" hitmaker Cassper and "Usezondibona" mastermind Vusi Nova.
Indeed experimenting makes for variety and engagement. And variety is kosher in the world today. Yes?
Mainstream artistes are looking deeper into their roots for inspiration and musical projection. They gotta make something appetizing and refreshing.
Cassper Nyovest, who recently addressed the "fake" humility of which he was accused days back, has announced a new collaboration with Maskandi artiste Shwi Nomtekhala.
He made the announcement in a tweet, highlighting the fact that all the songs he made with music legends have become classics. So collaborating with Shwi Nomtekhala is yet another way to a musical milestone.
Mufasa, who Ghanaian rapper Shatta Wale recently dedicated a mixtape to, took to Twitter to announce his collaboration with Shwi Nomtekhala and urged his fans to look forward to it.
"All the songs I made with legends are classics. From the legendary Mahotella queens , Ntate Tsepo Tshola and now the legendary Shwi Nomtekhala which we are premiering tomorrow on .@ukhozi_fm between 3-5 !!!! Tune in!!!!" his tweet reads.
The muso share a video of him and Maskandi artiste Igcokama Elisha (Mthandeni) jamming to a song, which has seen fan screaming in delight.
Other artiste who have caught the Maskandi fever are DJ Melon and Nesiliwe Sibiya. "I'm really excited for everything that is happening and I thank God. I am doing afro-kandi, which is a very unlikely fusion between afro-pop and maskandi," Nesiliwe Sibiya had told TshisaLIVE.
At the rate the Maskandi fever is spreading, Mzansi might find itself contending with a Maskandi epidemic. Or what do you think? Share your thoughts with us in the comment section.


There are many of us who had to deal with annoying people. When they get your number they might send you inappropriate messages or pictures on WhatsApp, fill up your feed with useless information or they might even start calling you non-stop. You can stop this nonsense completely by blocking them. If you want to find out how, then keep on reading.
The app was launched in 2009 and it slowly became one of the most popular ways of keeping in touch with friends or family. With over 1 billion users right now, WhatsApp is popular in 180 countries. It became so popular, in fact, that Facebook became interested and spent $20 billion to acquire it.
With so many people using it you are bound to come across some that don't know anything about boundaries or politeness. If you don't want to hear from such people again then all you have to do is block them.
Really, it is not too difficult to figure out how to block a number on WhatsApp. Let's figure out together how to do it, either on Android or iOS.
Open the app and on the bottom right of the screen you will see 'Settings'. Go there and select 'Account', 'Privacy', then 'Blocked'. Here is a list filled with people you don't want to hear from again. If you want to add another one to the collection, tap 'Add New…' and it will send you to your contact list. From here, select those who annoy you and silence them.
On the top right corner of the Android WhatsApp, you will see three dots. Open that menu, then follow this path 'Settings>Account>Privacy>Blocked Contacts'. Then, tap the icon on the top right corner and add whomever you want to banish from your contact list.


Pop music lovers and Troye Sivan fans are sure to love the synth dominated pop melody that is "UFO ft. Allday" from Mallrat. The 19-year-old singer from Brisbane combines the lightheartedness of pop music with an R&B twist.
The track features layers of synthy goodness, hypnotic melodies, and the smooth flow of Mallrat's vocals. The dense production show the growth the singer has had since the release of her debut EP in 2016. A quick feature from Allday brings the track to the next level with the smoothest verse of his career.
2018 is reving up to be a major year for Mallratt. Set to attend SXSW in Austin, Texas this March and a few shows across the US, she'll will be spreading her love even further around the globe.