text:
\section{Introduction}
\label{sec:introduction}
\begin{figure*}[!ht]
    \centering
    \includegraphics[width=1.\linewidth]{figures/graph_extraction.pdf}
    \caption{An illustration of \textit{scene-graph} extraction using the \textit{Real Image Pipeline}. In this process, the first step is to detect a list of objects on each frame of a clip.
    Then, we project each frame to its bird's-eye view to better approximate the spatial relations between objects.
    Finally, we construct a \textit{scene-graph} using the list of detected objects and their attributes.}
    \label{fig:network_archi}
\end{figure*}
Autonomous Driving Systems (ADSs) have advanced significantly in recent years.
However, navigation is still challenging in complex urban environments since the scenarios are highly variable and complex~\cite{montgomery2018america, yurtsever2019survey, paden2016survey}.
The continued reports of autonomous vehicle crash only highlight these challenges~\cite{lavrinc2014bad, davies2016google, mcfarland2016, tblee2019}.
A risk-based approach for autonomous driving has the potential to address this challenge and better assure driving safety.
Within this context, the effectiveness of understanding the driving scenes and quantifying the risk of driving decisions becomes particularly crucial for ADSs.

In prior research,  the problem of risk assessment for autonomous driving has been tackled by modeling either the \textit{objective risk} or the \textit{subjective risk}~\cite{grayson2003risk, fuller2005towards, bao2019personalized}.
The \textit{objective risk} is defined as the objective probability of an accident occurring and is usually determined by statistical analysis~\cite{grayson2003risk}.
Some works have focused on minimizing the \textit{objective risk} by modeling the trajectories of vehicles~\cite{6025208, 8569759} to guarantee safe driving.

\textit{Subjective risk} refers to the driver's own perceived risk and is an output of the driver's cognitive process~\cite{fuller2005towards, bao2019personalized}.
Studies suggest that modeling the \textit{subjective risk} can be a better option as drivers typically avoid taking a risky driving action based on their subjective perception~\cite{bao2019personalized}.
For this reason, our objective in this work is to build a model for subjective risk assessment.

Several papers have leveraged state-of-the-art deep learning architectures for modeling subjective risk~\cite{yurtsever2019survey, yurtsever2019risky}.
Such methods typically apply Convolutional Neural Networks (CNNs) and Long-Short Term Memory Networks (LSTMs) and have been proven to be effective at capturing features essential for modeling subjective risk in both spatial and temporal domains~\cite{yurtsever2019risky}.
However, it is unclear whether these methods can capture critical higher-level information, such as the relationships between the traffic participants in a given scene.
Failure in capturing these relationships can result in poor ADS performance in complex scenarios.

Also, training these networks requires large datasets covering a wide range of ``corner cases'' (especially risky driving scenarios), which are expensive and time-consuming to generate~\cite{dosovitskiy2017carla}.
Many researchers resort to using synthesized datasets containing many examples of these corner cases to address this issue.
However, for these to be valuable, a model must be able to transfer the knowledge gained from simulated training data to real-world situations.
A standard method for measuring a model's ability to generalize is \textit{transferability}, where a model's accuracy on a dataset different from the training dataset is evaluated.
If a model can transfer the knowledge gained from a simulated training set to a real-world testing set effectively, it is likely that it will perform better in unseen real-world scenarios.

Even if these existing methods can transfer knowledge well, the predictions of such methods lack \textit{explainability}, which is crucial for establishing trust between ADSs and human drivers~\cite{bao2019personalized, bahdanau2014neural, adadi2018peeking}.
\textit{Explainability} refers to the ability of a model to effectively communicate the factors that influenced its decision-making process for a given input, particularly those that might lead the model to make incorrect decisions~\cite{adadi2018peeking, knyazev2019understanding}.
If a model can give attention to the aspects or entities in a traffic scene that make the scenario risky or non-risky, it can improve its decision and its decisions become more explainable~\cite{vaswani2017attention}.

Overall, designing a risk assessment system for ADSs using data-driven approaches presents the following challenges:
\begin{itemize}
    \item Designing a reliable method that can handle a wide range of complex and unpredictable traffic scenarios.
    \item Building a model that is transferable from the simulation setting to the real-world setting because the real-world datasets for supervised training are limited.
    \item Building a model that can provide explainable decisions.
\end{itemize}

In this paper, we present our work on the modeling of \textit{subjective risk} for a specific driving maneuver: lane change.
This task by itself is crucial, given that 7.62\% of all traffic accidents between light vehicles can be attributed to improper execution of lane change~\cite{najm2007pre}.
We propose a \textit{scene-graph} augmented data-driven approach for assessing the subjective risk of driving maneuvers, where the \textit{scene-graphs} serve as intermediate representations (IR) as shown in Figure~\ref{fig:network_archi}.
The key advantage is that using \textit{scene-graphs} as IRs allows us to model the relationships between the participants in a traffic scene, thus potentially improving the model's understanding of a scene.

Our architecture consists of three major components: (i) a pipeline to convert the images of a driving clip to a sequence of \textit{scene-graphs}, (ii) a Multi-Relational Graph Convolution Network (MR-GCN) to convert each of the \textit{scene-graphs} to an embedding (a vectorized representation), and (iii) an LSTM for temporally modeling the sequence of embeddings of the respective \textit{scene-graphs}.
Our model also contains multiple attention layers: (i) a node attention layer before the embedding of a \textit{scene-graph} is computed, and (ii) an attention layer on top of the LSTM, both of which can further improve its performance and the explainability.
For training the model, we propose formulating the problem of subjective risk assessment as a supervised \textit{scene-graph} sequence classification problem.

Our key contributions are as follows:
\begin{itemize}
    \item We present a novel \textit{scene-graph} augmented data-driven approach for assessing the risk of driving actions in autonomous vehicles.
    \item We demonstrate that our approach outperforms existing methods at risk assessment across a wide range of scenarios using lane change as a use case.
    \item We demonstrate that the use of multi-level attention in our proposed approach provides better explainability.
    \item We demonstrate that our \textit{scene-graph} based approach can better transfer knowledge gained from simulated environments to real-world risk assessment tasks.
\end{itemize}

The rest of the paper is structured as follows:
In Section~\ref{sec:related_work}, we discuss related works. In Section~\ref{sec:approach} we introduce our \textit{scene-graph} augmented approach.
In Section~\ref{sec:experimental_results} we discuss our experimental results.
Finally, in Section~\ref{sec:conclusion} we present our conclusions.
\section{Related Work}
\label{sec:related_work}
\subsection{Design Philosophies in ADSs}
Two broad approaches for designing ADSs are (i)  modular design , (ii)  end-to-end design~\cite{yurtsever2019survey}.
Most modular approaches comprise a pipeline of separate components from the sensory inputs to the actuator outputs, while end-to-end approaches generate output directly from their sensory inputs~\cite{pomerleau1989alvinn, bojarski2016end}.
One advantage of a modular design approach is the division of a task into an easier-to-solve set of sub-tasks that have been addressed in other fields such as robotics~\cite{laumond1998robot}, computer vision~\cite{jain1995machine} and vehicle dynamics~\cite{rajamani2011vehicle}.
Therefore, prior knowledge from these fields can be leveraged when designing the components corresponding to the sub-tasks.
However, one disadvantage of such an approach is the complexity of the whole pipeline~\cite{yurtsever2019survey}.
End-to-end approaches can achieve good performance with a smaller network size because they perform feature extraction from sensor inputs implicitly through the network's hidden layers~\cite{bojarski2016end}.
However, the authors in~\cite{chen2015deepdriving} point out that the needed level of supervision is too weak for the end-to-end model to learn critical controlling information (e.g., from image to steering angle), so it can fail to handle complicated driving maneuvers.

A third approach was first proposed by DeepDriving~\cite{chen2015deepdriving}, called the \textit{direct perception} approach.
In their approach, a set of \textit{affordance indicators}, such as the distance to the lane markings and the distance to cars in the current and adjacent lanes, are extracted from an image and serves as an IR for generating the final control output.
They prove that the use of this IR is effective for simple driving tasks such as lane following and for generalizing to real-world environments.
Authors in~\cite{bansal2018chauffeurnet} use a collection of filtered images, each representing a piece of distinct information, as the IR.
They state that the IR used in their approach allows the training to be conducted on real or simulated data, facilitating testing and validation in simulations before testing on a real car.
Moreover, they show that it is easier to synthesize perturbations to the driving trajectory at the mid-level representations than at the level of raw sensors, enabling them to produce non-expert behaviors such as off-road driving and collisions.
The authors in~\cite{yurtsever2019risky} use Mask-RCNN~\cite{he2017mask} to color the vehicles in each input image, producing a form of IR.
In contrast to the works mentioned above, our approach uses a \textit{scene-graph} IR that encodes the spatial and semantic relations between all the traffic participants in a frame.

\subsection{Graph-Based Driving Scene Understanding}

Several papers have applied graph-based formulations for driving scene understanding~\cite{mylavarapu2020towards, li2019learning, mylavarapu2020understanding}.
In~\cite{li2019learning}, the authors propose a 3D-aware egocentric Spatio-temporal interaction framework that uses both an \textit{Ego-Thing} graph and an \textit{Ego-Stuff} graph, which encode how the ego vehicle interacts with both moving and stationary objects in a scene, respectively.
In~\cite{mylavarapu2020towards, mylavarapu2020understanding}, the authors propose a pipeline using a multi-relational graph convolutional network (MR-GCN) for classifying the driving behaviors of traffic participants.
The MR-GCN is constructed by combining spatial and temporal information, including relational information between moving objects and landmark objects.
Our work is primarily inspired by~\cite{mylavarapu2020towards, mylavarapu2020understanding} but differs in the application and network architecture.
These papers focus on predicting each object's behavior in the \textit{scene-graph} while our work focuses on assessing the subjective risk of the entire scenario.
Consequently, we propose a network architecture that implements more components such as node-attention, graph pooling layers, and readout operations.

\subsection{Risk Assessment}
\label{subsec:risk}
Several works have studied subjective risk assessment for autonomous driving systems~\cite{fuller2005towards, bao2019personalized, yurtsever2019risky, yurtsever2018integrating}.
In~\cite{yurtsever2018integrating}, Hidden Markov Models (HMMs) and Language Models are used to detect unsafe lane change events.
The approach taken in~\cite{yurtsever2019risky} is the most related to our work as it infers the risk-level of overall driving scenes with a deep Spatio-temporal neural network architecture.
By using Mask-RCNN~\cite{he2017mask} to generate an IR for each image, their approach achieves a 3\% performance gain in risk assessment.
They show that the architecture with Semantic Mask Transfer (SMT) + CNN + LSTM can perform 25\% better than the architecture with Feature Transfer (FT) + Frame-by-Frame (FbF), indicating that capturing the spatial and temporal features just from a monocular camera can be useful in modeling subjective risk.
However, this approach only considers the spatial features (the latent vector output of the CNN layers) of a frame instead of the relations between all the traffic participants.
Our work uses \textit{scene-graphs} as IRs to capture the high-level relationships between all the traffic participants of a scene.
\section{Scene-Graph Augmented Approach for Risk Assessment}
\label{sec:approach}
\begin{figure*}[!ht]
    \includegraphics[width=1.0\linewidth]{figures/architecture.pdf}
    \caption{An illustration of our model's architecture.
    It first converts each image $I_t \in \mathbf{I}$ to a \textit{scene-graph} $G_t$ via the \textit{Scene-Graph Extraction Pipeline}. Next, it converts each $G_t$ to its corresponding \textit{scene-graph} embedding $\mathbf{h}_{G_t}$ with layers in Spatial Modeling. Then, it temporally models these \textit{scene-graph} embeddings to acquire the spatio-temporal representation $\mathbf{Z}$ for a \textit{scene-graph} sequence. Finally, the risk inference $\hat{y}$ of a clip is calculated from $\mathbf{Z}$ using an MLP with a \textit{Softmax} activation function.}
    \label{fig:archi}
\end{figure*}

In this section, we discuss our proposed approach for \textit{scene-graph} augmented risk assessment.
In our work, we make the same assumption used in~\cite{yurtsever2019risky} that the set of driving sequences can be partitioned into two jointly exhaustive and mutually exclusive subsets: risky and safe.
We denote the sequence of images of length $T$ by $\mathbf{I} = \{I_1, I_{2}, I_{3}, ..., I_T\}$.
We assume the existence of a spatio-temporal function $f$ that outputs whether a sequence of driving actions $x$ is safe or risky via a risk label $y$, as given in Equation~\ref{formular1-v2}.
\begin{equation}
\label{formular1-v2}
    y = f(\mathbf{I}) = f(\{I_1, I_2, I_3, ..., I_{T-1}, I_T\}),
\end{equation}
where
\begin{equation}
    y=\left \{
    \begin{array}{ll}
         (1,0), &  \text{if the driving sequence is safe}\\
         (0,1), &  \text{if the driving sequence is risky}.
    \end{array}
    \right.
\end{equation}

In this section, we propose a suitable model for approximating the function $f$.
In the model we propose, the first step is the extraction of the \textit{scene-graph} $G_t$ from each image $I_t$ of the video clip $\mathbf{I}$.
This is achieved by a series of processes that we collectively call the \textit{Scene-Graph Extraction Pipeline}, described in Section~\ref{subsec:extraction}.
In the second step, these \textit{scene-graphs} are passed through graph convolution layers and an attention-based graph pooling layer.
The graph-level embeddings of each \textit{scene-graph}, $\mathbf{h}_{G_t}$, are then calculated using a graph readout operation.
Next, these \textit{scene-graph} embeddings are passed sequentially to LSTM cells to acquire the \textit{spatio-temporal} representation, denoted as $\mathbf{Z}$, of each \textit{scene-graph} sequence.
Lastly, we use a Multi-Layer Perceptron (MLP) layer with a \textit{Softmax} activation function to acquire the final inference, denoted as $\hat{y}$, of the risk for each driving sequence $\mathbf{I}$.
We describe more details regarding each of these components of our model in Section~\ref{subsec:model}.

\subsection{Scene-Graph Extraction Pipeline}
\label{subsec:extraction}
Several approaches have been proposed for extracting scene-graphs from images by detecting the objects in a scene and then identifying their visual relationships~\cite{xu2017scene, yang2018graph}.
However, these works have focused on single general images instead of a sequence of images as it arises in autonomous driving, where higher accuracy is demanded.
Thus, we adopted a partially rule-based process to extract objects and their attributes from images called the \textit{Real Image Pipeline}.
Besides, to evaluate how our approach performs with \textit{scene-graphs} containing ground truth information, we use the \textit{Carla Ground Truth (GT) Pipeline} as a surrogate for the ideal situation where the attributes for each object can be correctly extracted.
After the objects in a scene and their attributes have been extracted, the \textit{scene-graphs} are constructed as described in \ref{subsec:constructgraph}.
We discuss these two approaches in detail below.

\subsubsection{Real Image Pipeline}
\label{subsec:real}
In this pipeline, object attributes and bounding boxes are extracted directly from images using state-of-the-art image processing techniques.
As Figure~\ref{fig:network_archi} shows, we first convert each image $I_t$ into a collection of objects $O_t$ using Detectron2, a state-of-the-art object detection model based on Faster RCNN~\cite{wu2019detectron2, ren2015faster}.
Next, we use OpenCV's perspective transformation library to generate a top-down perspective of the image, commonly known as a "birds-eye view" projection~\cite{opencv_library}.
This projection allows us to approximate each object's location relative to the road markings and the ego vehicle.
Next, for each detected object in $O_t$, we use its estimated location and class type (cars, motorcycles, pedestrians, lanes, etc.) to compute the attributes required in building the \textit{scene-graph}.

\subsubsection{Carla Ground Truth Pipeline}
\label{subsec:carla}
Object detection and location estimation with solely monocular camera can be unstable because of factors such as weather and camera position~\cite{caesar2020nuscenes}, which can impact the correctness of our image-based \textit{scene-graph} construction pipeline and thus our approach's performance.
To evaluate our methodology under the assumption that object attributes can be extracted without error, we build our \textit{scene-graphs} using the ground-truth location and class information for each vehicle in the \textit{Carla GT Pipeline}.
We extract this information directly from Carla simulator~\cite{dosovitskiy2017carla} without using any image processing steps.

\subsubsection{Graph Construction}
\label{subsec:constructgraph}
After collecting the list of objects in each image and their attributes, we begin constructing the corresponding \textit{scene-graphs} as follows.
For each image $I_t$, we denote the corresponding \textit{scene-graph} by $G_t = \{O_t, A_t\}$ and model it as a directed multi-graph where multiple types of edges connect nodes.
The nodes of a \textit{scene-graph}, denoted as $O_t$, represent the objects in a scene such as lanes, roads, traffic signs, vehicles, pedestrians, etc.
The edges of a \textit{scene-graph} are represented by the corresponding adjacency matrix $A_t$, where each value in $A_t$ represents the type of the edges.
The edges between two nodes represent the different kinds of relations between them (e.g., near, Front\_Left, isIn, etc.).

In assessing the risk of driving behaviors, traffic participants' relations that we consider to be useful are the distance relations and the directional relations.
The assumption made here is that the local proximity and positional information of one object will influence the other's motion only if they are within a certain distance.
Therefore, in this work, we extract only the location information for each object and adopt a simple rule to determine the relations between the objects using their attributes (e.g., relative location to the ego car), as shown in Figure~\ref{fig:network_archi}.
For distance relations, we assume two objects are related by one of the relations $r\in$ \{\textit{Near\_Collision} (4 ft.), \textit{Super\_Near} (7 ft.), \textit{Very\_Near} (10 ft.), \textit{Near} (16 ft.), \textit{Visible} (25 ft.)\} if the objects are physically separated by a distance that is within that relation's threshold.
In the case of the directional relations, we assume two objects are related by the relation $r \in$ \{\textit{Front\_Left}, \textit{Left\_Front}, \textit{Left\_Rear}, \textit{Rear\_Left}, \textit{Rear\_Right}, \textit{Right\_Rear}, \textit{Right\_Front}, \textit{Front\_Right}\} based on their relative positions if they are within the \textit{Near} threshold distance from one another.

In addition to directional and distance relations, we also implement the \textit{isIn} relation that connects vehicles with their respective lanes.
For the \textit{Carla GT Pipeline}, we extract the ground-truth lane assignments for each vehicle from the simulator directly.
For the \textit{Real Image Pipeline}, we use each vehicle's horizontal displacement relative to the ego vehicle to assign vehicles to either the \textit{Left Lane}, \textit{Middle Lane}, or \textit{Right Lane} based on a known lane width.
Our abstraction only includes these three-lane areas and, as such, we map vehicles in all left lanes to the same \textit{Left Lane} node and all vehicles in right lanes to the \textit{Right Lane} node.
If a vehicle overlaps two lanes (i.e., during a lane change), we assign it an \textit{isIn} relation to both lanes.
Figure~\ref{fig:network_archi} illustrates an example of resultant \textit{scene-graph}.

\subsection{Scene-graph Sequence Based Risk Assessment Model}
\label{subsec:model}

The model we propose consists of three major components: spatial model, temporal model, and risk inference.
The spatial model outputs the embedding $h_{G_t}$ for each scene-graph $G_t$.
The temporal model processes the sequence of \textit{scene-graph} embeddings $\mathbf{h}_I = \{h_{G_1}, h_{G_2}, ..., h_{G_T}\}$ and produces the spatio-temporal embedding $\mathbf{Z}$.
The risk inference component outputs each driving clip's final risk assessment, denoted as $\hat{Y}$, by processing the Spatio-temporal embedding $\mathbf{Z}$.
The overall network architecture is shown in Figure~\ref{fig:archi}. We discuss each of these components in detail below.

\subsubsection{Spatial Modeling}
The spatial model we propose uses MR-GCN layers to compute the embedding for a \textit{scene-graph}.
The use of MR-GCN allows us to capture multiple types of relations on each \textit{scene-graph} $G_t = \{O_t, A_t\}$.
In the \textit{Message Propagation} phase, a collection of node embeddings and their adjacency information serve as the inputs to the MR-GCN layer.
Specifically, the $l$-th MR-GCN layer updates the node embedding, denoted as $\mathbf{h}^{(l)}_v$, for each node $v$ as follows:
\begin{equation}
    \mathbf{h}^{(l)}_v = \mathbf{\Phi}_{\textrm{0}} \cdot
        \mathbf{h}^{(l-1)}_v + \sum_{r \in \mathbf{A_{t}}} \sum_{u \in \mathbf{N}_r(v)}\frac{1}{|\mathbf{N}_r(v)|} \mathbf{\Phi }_r \cdot \mathbf{h}^{(l-1)}_u,
\end{equation}
where $N_r(v)$ denotes the set of neighbor indices of node $v$ with the relation $r\in A_{t}$.
$\mathbf{\Phi }_r$ is a trainable relation-specific transformation for relation $r$ in MR-GCN layer.
Since the information in $(l-1)$-th layer can directly influence the representation of the node at $l$-th layer, MR-GCN uses another trainable transformation $\mathbf{\Phi }_0$ to account for the self-connection of each node using a special relation~\cite{schlichtkrull2018modeling}.
Here, we initialize each node embedding $\mathbf{h}^{(0)}_{v}$, $\forall v \in O_t$, by directly converting the node's type information to its corresponding one-hot vector.

Typically, the node embedding becomes more refined and global as the number of graph convolutional layers, $L$, increases.
However, the authors in~\cite{xu2018powerful} also suggest that the features generated in earlier iterations might generalize the learning better.
Therefore, we consider the node embeddings generated from all the MR-GCN layers.
To be more specific, we calculate the embedding of node $v$ at the final layer, denoted as $\mathbf{H}^{L}_v$, by concatenating the features generated from all the MR-GCN layers, as follows,
\begin{equation}
\mathbf{H}^{L}_v = \textbf{CONCAT}(\{\mathbf{h}^{(l)}_v\}|l=0, 1, ...,L).
\end{equation}
We denote the collection of node embeddings of \textit{scene-graph} $G_t$ after passing through $L$ layers of MR-GCN as $\mathbf{X}^{prop}_{t}$ ($L$ can be 1, 2 or 3).

The node embedding $\mathbf{X}^{prop}_{t}$ is further processed with an attention-based graph pooling layer.
As stated in~\cite{knyazev2019understanding}, such an attention-based pooling layer can improve the explainability of predictions and is typically considered as a part of a unified computational block of a graph neural network (GNN) pipeline.
In this layer, nodes are pooled according to the scores predicted from either a trainable simple linear projection~\cite{gao2019graph} or a separate trainable GNN layer~\cite{lee2019self}.
We denote the graph pooling layer that uses the \textbf{SCORE} function in~\cite{gao2019graph} as \textit{TopkPool} and the one that uses the \textbf{SCORE} function in~\cite{lee2019self} as \textit{SAGPool}.
The calculation of the overall process is presented as follows:
\begin{equation}
\mathbf{\alpha} = \textbf{SCORE}(\mathbf{X}^{prop}_{t}, \mathbf{A_{t}}),
\end{equation}
\begin{equation}
\label{eq:pool}
\mathbf{P} = \mathrm{top}_k(\mathbf{\alpha}),
\end{equation}
where $\mathbf{\alpha}$ stands for the coefficients predicted by the graph pooling layer for nodes in $G_t$ and $\mathbf{P}$ represents the indices of the pooled nodes which are selected from the top $k$ of the nodes ranked according to $\alpha$. The number $k$ of the nodes to be pooled is calculated by a pre-defined pooling ratio, $pr$, and using $k = pr \times |O_t|$, where we consider only a constant fraction $pr$ of the embeddings of the nodes of a scene-graph to be relevant (i.e., 0.25, 0.5, 0.75).
We denote the node embeddings and edge adjacency information after pooling by $\mathbf{X}^{pool}_{t}$ and $\mathbf{A^{pool}_{t}}$ and are calculated as follows:
\begin{equation}
\mathbf{X}^{pool}_{t}= (\mathbf{X}^{prop}_{t} \odot\mathrm{tanh}(\mathbf{\alpha}))_{\mathbf{P}},
\end{equation}
\begin{equation}
\mathbf{A^{pool}_{t}} = \mathbf{A^{prop}_{t}}_{(\mathbf{P},\mathbf{P})}. \\
\end{equation}
where $\odot$ represents an element-wise multiplication, $()_{\mathbf{P}}$ refers to the operation that extracts a subset of nodes based on $P$ and  $()_{(\mathbf{P},\mathbf{P})}$ refers to the formation of the adjacency matrix between the nodes in this subset.

Finally, our model aggregates the node embeddings of the graph pooling layer, $\mathbf{X}^{pool}_{t}$, using a graph \textbf{READOUT} operation, to produce the final graph-level embedding $\mathbf{h}_{G_t}$ for each \textit{scene-graph} $G_t$ as given by
\begin{equation}
    \mathbf{h}_{G_t} = \textbf{READOUT}(\mathbf{X}^{pool
    }_{t}),
\end{equation}
where the \textbf{READOUT} operation can be either summation, averaging, or selecting the maximum of each feature dimension, over all the node embeddings, known as \textit{sum-pooling}, \textit{mean-pooling}, or \textit{max-pooling}, respectively.
The process until this point is repeated across all images in $\mathbf{I}$ to produce the sequence of embedding, $\mathbf{h}_I$.

\subsubsection{Temporal Modeling}
\label{subsec:temporal_modeling}
The temporal model we propose uses an LSTM for converting the sequence of scene-graph embeddings $\mathbf{h}_I$ to the combined spatio-temporal embedding $\mathbf{Z}$.
For each timestamp $t$, the LSTM updates the hidden state $p_t$ and cell state $c_t$ as follows,
\begin{equation}
    p_t, c_t = \mathbf{LSTM}(\mathbf{h}_{G_t}, c_{t-1}),
\end{equation}
where $\mathbf{h}_{G_t}$ is the final \textit{scene-graph} embedding from timestamp $t$.
After the LSTM processes all the scene-graph embeddings, a temporal readout operation is applied to the resultant output sequence to compute the final Spatio-temporal embedding $Z$ given by
\begin{equation}
    \mathbf{Z} = \textbf{TEMPORAL\_READOUT}(p_1, p_2, ..., p_T)
\end{equation}
where the $\textbf{TEMPORAL\_READOUT}$ operation could be extracting only the last hidden state $p_T$ (LSTM-last), or be a temporal attention layer (LSTM-attn).

In~\cite{bahdanau2014neural}, adding an attention layer $b$ to the encoder-decoder based LSTM architecture is shown to achieve better performance in Neural Machine Translation (NMT) tasks. For the same reason, we include \textit{LSTM-attn} in our architecture.
\textit{LSTM-attn} calculates a context vector $q$ using the hidden state sequence $\{p_1, p_2, ..., p_T\}$ returned from the LSTM encoder layer as given by
\begin{equation}
\label{eq:temporalattn}
q = \sum^{T}_{t=1} \beta_t p_t
\end{equation}
where the probability $\beta_t$ reflects the importance of $p_t$ in generating $q$.
The probability $\beta_t$ is computed by a {\it Softmax} output of an energy function vector $e$, whose component $e_t$ is the energy corresponding to $p_t$. Thus, the probability $\beta_t$ is formally given by
\begin{equation}
    \beta_t = \frac{\text{exp}(e_t)}{\sum_{k=1}^T \text{exp}(e_k)},
\end{equation}
where the energy $e_t$ associated with $p_t$ is given by $e_t = b(s_0, p_t)$.
The temporal attention layer $b$ scores the importance of the hidden state $p_t$ to the final output, which in our case is the risk assessment.
The variable $s_0$ in the temporal attention layer $b$ is computed from the last hidden representation $p_T$. The final Spatio-temporal embedding for a video clip, $Z$, is computed by feeding the context vector $q$ to another LSTM decoder layer.

\subsubsection{Risk Inference}
The last piece of our model is the risk inference component that computes the risk assessment prediction $\hat{Y}$ using the spatio-temporal embedding $\mathbf{Z}$. This component is composed of a MLP layer followed by a \textit{Softmax} activation function. Thus, the prediction $\hat{Y}$ is given by
\begin{equation}
    \hat{Y} = Softmax(\mathbf{MLP}(Z))
\end{equation}
The loss for the prediction is calculated as follows,
\begin{equation}
    \argmin \mathbf{CrossEntropyLoss}(Y, \hat{Y})
\end{equation}
For training our model, we use a mini-batch gradient descent algorithm that updates its parameters by training on a batch of \textit{scene-graph} sequences.
To account for label imbalance, we apply class weighting when calculating loss.
Besides, several dropout layers are inserted into the network to reduce overfitting.
\section{Experimental Results}
\label{sec:experimental_results}
In this section, we provide extensive experimental results to illustrate the accuracy of our model and the ability of our model to transfer knowledge (transferability).
We do this by providing comparisons between our model and a state-of-the-art SMT+CNN+LSTM based risk assessment model~\cite{yurtsever2019risky}; we refer to this model as the \textit{Baseline}.
Besides, we provide results for our model's best hyper-parameter setting and perform an ablation study to evaluate the contribution of each major component in our model.

\subsection{Dataset Preparation}
\label{subsec:dataset}
We prepared two types of datasets for our experiments (i) real-world driving datasets and (ii) synthesized datasets. We generated the real-world dataset by extracting lane change clips from the Honda Driving Dataset (HDD)~\cite{Ramanishka_behavior_CVPR_2018}.
To create the synthesized datasets, we developed a tool to generate lane changing clips using the Carla\footnote{\url{https://github.com/carla-simulator/carla}} and Carla Scenario Runner\footnote{\url{https://github.com/carla-simulator/scenario_runner}}.

Carla is an open-source driving simulator~\cite{dosovitskiy2017carla} that allows users to control a vehicle in either manual mode or autopilot mode.
The Carla Scenario Runner contains a set of atomic controllers that enable users to control a car in a driving scene and perform complex driving maneuvers.
We modified the user script in Carla so that it can (i) select one autonomous car randomly and switch its mode to manual mode, and then (ii) utilize Scenario Runner's function to force the vehicle to change lanes.

The data generating tool allows us to fabricate lane changing clips directly instead of extracting them from long driving clips.
We generated a wide range of simulated lane changes using the various presets in Carla that allowed us to specify the number of cars, pedestrians, weather and lighting conditions, etc.
Also, through the APIs provided by the Traffic Manager (TM) of the Carla simulator, we were able to customize the driving characteristics of every autonomous vehicle, such as the intended speed considering the current speed limit, the chance of ignoring the traffic lights, or the chance of neglecting collisions with other vehicles.
Overall, this allowed us to simulate a wide range of very realistic urban driving environments and generate synthesized datasets suitable for training and testing a model.

To label the lane change clips in both the real-world and synthesized datasets, we performed an annotation process similar to the one used in~\cite{yurtsever2019risky}.
First, in this process, human annotators were asked to assign a risk score to each clip that ranges from -2 and 2, where 2 implies a highly risky lance change and -2 means the safest lane change.
Then, for each lane change clip, all the risk labels of annotators were averaged and converted to the binary label $y$ as follows: if the average is $\leq$ 0, then assign $y=0$ (safe); else assign $y=1$ (risky).

We generated two synthesized datasets: a \textit{271-syn} dataset and a \textit{1043-syn} dataset, containing 271 and 1,043 lane changing clips, respectively.
In addition, we sub-sampled the \textit{271-syn} and \textit{1043-syn} datasets further to create two balanced datasets that have a 1:1 distribution of risky to safe lane changes: \textit{96-syn} and \textit{306-syn}.
We call the real-driving dataset as \textit{571-honda} as it contains 571 lane changing video clips.

We randomly split each dataset into a training set and a testing set by the ratio 7:3 such that the split is stratified, i.e., the proportion of risky to safe lane change clips in each of the splits is the same.
The models are evaluated on a dataset by training on the training set and evaluating their performance on the testing set.
The final score of a model on a dataset is computed by averaging over the testing set scores for ten different stratified train-test splits of the dataset.

\begin{figure*}
\begin{subfigure}{.5\textwidth}
    \centering
    \includegraphics[trim=300 165 300 160, clip, width=0.98\linewidth]{figures/acc_plots.pdf}
    \label{fig:my_label1}
\end{subfigure}
\begin{subfigure}{.5\textwidth}
    \centering
    \includegraphics[trim=300 165 300 160, clip, width=0.98\linewidth]{figures/auc_plots.pdf}
    \label{fig:my_label2}
\end{subfigure}
    \caption{Accuracy and AUC comparison between our approaches (Real Image and Carla GT) and \cite{yurtsever2019risky} on different datasets. In these experiments, we trained the model using the hyper-parameter settings reported in Section~\ref{subsec:exp1}. }
    \label{fig:risk_result_1}
\end{figure*}

\subsection{Training and Model Specification}
\label{subsec:training}

Our models were implemented using \textit{PyTorch} and \textit{PyTorch-Geometric}~\cite{paszke2017automatic, Fey_Lenssen_2019}.
We used the ADAM optimizer for the training algorithm.
We considered three learning rates: $\{0.0005, 0.0001, 0.00005\}$, and a weight decaying rate of $5\times 10^{-4}$ per epoch.
We used a batch size of 16 sequences for each training epoch.
In our experiments, we trained each model for 200 epochs.
Regarding the setting of hyper-parameters, we considered the options described in Section~\ref{subsec:model}.
To ensure a fair comparison between our model and the baseline model, we reported the model's performance with the lowest validation loss throughout the training for scoring.

All the experiments were conducted on a server with one NVIDIA TITAN-XP graphics card and one NVIDIA GeForce GTX 1080 graphics card.
For implementing the baseline model \cite{yurtsever2019risky}, we used the source code available at their open-source repository\footnote{\url{https://github.com/Ekim-Yurtsever/DeepTL-Lane-Change-Classification}}.
Our model implementation is also available at \url{https://github.com/louisccc/av_av}.

\subsection{Experiments on Risk Assessment}
\label{subsec:exp1}
We evaluate each model's performance by measuring its classification accuracy and the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) for each dataset.
The classification accuracy is the ratio of the number of correct predictions on the test set of a dataset to the total number of samples in the testing set.
AUC, sometimes referred to as a balanced accuracy measure~\cite{sokolova2009systematic}, measures the probability that a binary classifier ranks a positive sample more highly than a random negative sample.
This is a more balanced measure for measuring accuracy, especially with imbalanced datasets (i.e. \textit{271-syn}, \textit{1043-syn}, \textit{571-honda}).

From our experimentation, we found that the best option for the hyper-parameters of our model is a mini-batch size of 16 sequences, a learning rate of 0.00005, two MR-GCN layers with 100 hidden units, a SAGPool pooling layer with ratio 0.5, \textit{sum-pooling} for graph readout operation and \textit{LSTM-attn} for temporal modeling.

Figure~\ref{fig:risk_result_1} shows the comparison between our model's performance and the baseline model~\cite{yurtsever2019risky} for all the synthetic datasets. The results show that our approach consistently outperforms~\cite{yurtsever2019risky} across all the datasets in terms of both classification accuracy and AUC.
Particularly, on the \textit{1043-syn} dataset, our Image-based and GT pipelines outperform~\cite{yurtsever2019risky} in classification accuracy by 4.4\% and 5\% respectively (i.e., an accuracy of 95.8\% and 96.4\% compared to 91.4\% for the baseline).

We found that the performance difference between our approach and the baseline increased when the training datasets were smaller.
Figure~\ref{fig:risk_result_1} shows that the difference in the accuracy between our approach using the GT pipeline and the baseline~\cite{yurtsever2019risky} is 5\% for the \textit{1043-syn} dataset and 8.7\% for the  \textit{271-syn} dataset.
This indicates that our approach can learn an accurate model even from a smaller dataset.
We postulate this is a direct result of its use of a scene-graph based IR.

We also found that our approach performs better than the baseline on balanced datasets.
Among the datasets used for evaluation of the models, the datasets \textit{271-syn} and \textit{306-syn} contain roughly the same number of clips but differ in the distribution of safe to risky lane changes (2.30:1 for \textit{271-syn} vs. 1:1 for \textit{306-syn}).
We found that the performance difference between our image-based approach and the baseline on these datasets is 12.9\% on the \textit{306-syn} dataset compared to 7.8\% on the \textit{271-syn} dataset, indicating that our approach can discriminate between the two classes better than the baseline.

We also evaluated the contribution of each functional component in our proposed model by conducting an ablation study.
The results of the study are shown in Table~\ref{tbl:ablation}.
From Table~\ref{tbl:ablation} we find that the simplest of the models, with no MR-GCN layer (replaced with an MLP layer) and a simple average of the embeddings in $\mathbf{h}_I$ for the temporal model (denoted as \textit{mean} in Table~\ref{tbl:ablation}), achieves a classification accuracy of 75\%.
We find that replacing \textit{mean} with an LSTM layer for temporal modeling yields a 10.5\% increase in performance.
We also find that including a single MR-GCN with 64 hidden units and \textit{sum-pooling} to the simplest model results in 14.8\% performance gain over the simplest model.
The performance gain achieved just by including the MR-GCN layer clearly suggests the effectiveness of modeling the relations between the objects.
Finally, we find that the model with one MR-GCN with 64 hidden units and \textit{sum-pooling} plus the LSTM layer for temporal modeling yields the maximum gain of 18.1\% over the simplest model.
These results clearly demonstrate the importance of each component in the model we propose.
\begin{table}
\begin{center}
\begin{tabular}{p{35pt} p{75pt} p{40pt} p{20pt} p{20pt}}
    \hline
     & Spatial Modeling & Temporal Modeling & Avr. Acc. & Avr. AUC\\
    \hline
    \hline
    \multirow{4}{8pt}{Ablation Study}&No MR-GCN & \textit{mean} & 0.762 & 0.823 \\\cline{2-5}
    &No MR-GCN & \textit{LSTM-last} & 0.867 & 0.929\\\cline{2-5}
    &1 MR-GCN & \textit{mean} & 0.910 & 0.960\\\cline{2-5}
    &1 MR-GCN & \textit{LSTM-last} & 0.943 & 0.977\\
    \hline
    \hline
    \multirow{4}{8pt}{Temporal Attention}&No MR-GCN & \textit{LSTM-last} & 0.867 & 0.929\\\cline{2-5}
    &No MR-GCN & \textit{LSTM-attn} & 0.868 & 0.928\\\cline{2-5}
    &1 MR-GCN & \textit{LSTM-last} & 0.943 & 0.977 \\\cline{2-5}
    &1 MR-GCN & \textit{LSTM-attn} & 0.950 & 0.977 \\
    \hline
    \hline
    \multirow{3}{8pt}{Spatial Attention}&1 MR-GCN & \textit{mean} & 0.910 & 0.960\\\cline{2-5}
    &1 MR-GCN, \textit{TopkPool} & \textit{mean} & 0.886 & 0.930 \\\cline{2-5}
    &1 MR-GCN, \textit{SAGPool} & \textit{mean} & 0.937 & 0.968 \\
    \hline
\end{tabular}
\end{center}
\caption{The results of the Carla GT approach on \textit{1043-syn} dataset with various spatial and temporal modeling settings. In these experiments, we used MR-GCN layers with 64 hidden units and \textit{sum-pooling} as the graph readout operation.}
\label{tbl:ablation}
\end{table}

\begin{figure}[!ht]
    \centering
    \includegraphics[trim=300 160 295 160, clip, width=\linewidth]{figures/transfer_plots.pdf}
    \caption{The results of comparing transferability between our Real Image model and \cite{yurtsever2019risky}. In this experiment, we trained our model using our best hyper-parameters on both \textit{271-syn} dataset and \textit{1043-syn} dataset.
    Then we tested the accuracy of our approach on both original dataset and \textit{571-honda} dataset. We followed the same procedure to train and test \cite{yurtsever2019risky}.}
    \label{fig:transfer}
\end{figure}

\subsection{Evaluation of Attention Mechanisms on Risk Assessment}
In this section, we evaluate the various attention components of our proposed model.
To evaluate the benefit of attention over the spatial domain, we tested our model with three different graph attention methods: no attention, \textit{SAGPool}, and \textit{TopkPool}.
To evaluate the impact of attention on the temporal domain, we tested our model with the following temporal models: \textit{mean}, \textit{LSTM-last}, and \textit{LSTM-attn}.
The detailed results that elucidate the effectiveness of these different attention mechanisms are presented in Table~\ref{tbl:ablation}.

For evaluating the benefits of graph attention, we start with an attention-free model: one MR-GCN layer with \textit{sum-pooling} + \textit{mean}.
In comparison, the model that uses \textit{SAGPool} for attention on the graph shows a 2.7\% performance gain over the attention-free model.
This indicates that the use of attention over both nodes and relations allows \textit{SAGPool} to better filter out irrelevant nodes from each \textit{scene-graph}.
We found that the model using \textit{TopkPool} as the graph-attention layer became relatively unstable, resulting in a 2.4\% performance drop compared to the attention-free model.
This can be because \textit{TopkPool} ignores the relations of a node when calculating $\alpha$.
Another reason for this instability could be the random initialization of weights in \textit{TopkPool}, which can exponentially affect the overall performance as stated in~\cite{knyazev2019understanding}.

For evaluating the impact of attention on the temporal model, we evaluated the effects of adding a temporal attention layer to the following two models: (i) with no MR-GCN layers and no temporal attention and (ii) with one MR-GCN layer and no temporal attention.
Compared to the model with no MR-GCN layer and no temporal attention, the performance of the model with no MR-GCN and \textit{LSTM-attn} was found to be 0.1\% higher.
We also found that adding \textit{LSTM-attn} to the model with one MR-GCN layer increases its performance by 0.7\% over the same model with no temporal attention.
These results demonstrate that the inclusion of temporal attention does improve the performance, though only marginally.
The reason why we only see a marginal improvement can be that the temporal attention layer is less relevant with the dataset that our model was trained on.
When preparing these datasets, we manually removed the frames that are irrelevant to a lane change, exactly the set of frames that temporal attention would have given less attention to, thus minimizing its effect.

The primary benefit of using the graph-attention and the temporal attention is that it improves the explainability of the model's risk assessment.
We demonstrate this capability using the visualization of both graph attention and temporal attention provided in Figure~\ref{fig:attention}.
Figure~\ref{fig:attention} shows the trend of the attention scores $\beta_1, \beta_2, ... \beta_T$ for a risky lane changing clip.
Intuitively, the frame with a higher attention score $\alpha_t$ contributes more to the context vector $c$ (shown in Equation~\ref{eq:temporalattn}), thus playing a more critical role in calculating $h_{G_t}$ and contributing to the final risk assessment decision.
In this risky lane changing example, the temporal attention scores progressively increase between frames 19 and 32 during the lane change; and the highest frame attention weights appear in frames 33 and 34, which are the frames immediately before the collision occurs.
Figure~\ref{fig:attention} also shows the projection scores for the node attention layer, where a higher score for a node indicates that it contributes more to the final decision of risk assessment.
As shown in this example, as the ego car approaches the yellow vehicle, the node attention weights for the ego car and the yellow vehicle are increased proportionally to the scene's overall risk.
In the first few frames, the risk of collision is low; thus the node attention weights are low; however, in the last few frames, a collision between these two vehicles is imminent; thus the attention weights for the two cars are much higher than for any other nodes in the graph.
This example clearly demonstrates our model's capability to pinpoint the critical factors in a \textit{scene-graph} that contributed to its risk assessment decision.
This capability can be valuable for debugging edge cases at design time, thus reducing the chances of ADS making unexpected, erroneous decisions in real-world scenarios and improving human trust in the system.

\begin{figure*}[!ht]
\centering
\includegraphics[width=1.0\linewidth]{figures/attention.pdf}
\caption{The visualization of attention weights in both spatial ($\alpha$) and temporal ($\beta$) domains using a risky lane changing clip as an example. We used a gradient color from light yellow to red for visualizing each node's projection score that indicates its importance in calculating a \textit{scene-graph} embedding. We also used a gradient colored (white to red) bar chart to visualize the temporal attention coefficients $\beta_1, \beta_2, ... \beta_{36}$ used for calculating the context vector $c$.}
\label{fig:attention}
\end{figure*}

\subsection{Transferability from Virtual To Real Driving}
In this section, we demonstrate our approach's capability to effectively transfer the knowledge learned from a simulated dataset to a real-world dataset.
To demonstrate this capability, we use the model weights, and parameters learned from training on the \textit{271-syn} dataset or the \textit{1043-syn} dataset directly for testing on the real-world driving dataset: \textit{571-honda}.
We also compare the transferability of our model with that of the baseline method~\cite{yurtsever2019risky}.
The results are shown in Figure~\ref{fig:transfer}.

As expected, the performance of both our approach and the baseline degrades when tested on \textit{571-honda} dataset.
However, as Figure~\ref{fig:transfer} shows, the accuracy of our approach only drops by 6.7\% and 3.5\% when the model is trained on \textit{271-syn} and \textit{1043-syn}, respectively, while the baseline's performance drops drastically by a much higher 21.3\% and 14.9\%, respectively.
The results categorically show that our proposed model can transfer knowledge more effectively than the baseline.
\section{Conclusion}
\label{sec:conclusion}
Subjective risk assessment is a challenging, safety-critical problem that requires a good semantic understanding of many possible road scenarios.
Our results show that our scene-graph augmented approach outperforms state-of-the-art techniques at risk assessment tasks in terms of accuracy (95.8\% vs. 91.4\%) and AUC (0.978 vs. 0.958).
We also show that our approach can learn with much less training data than these techniques, as our approach achieves 91.8\% accuracy on the \textit{96-syn} dataset compared to 78.2\% accuracy achieved by \cite{yurtsever2019risky}.
Additionally, our results show that our approach can better transfer knowledge gained from simulated datasets to real-world datasets (5.0\% avg. acc. drop for our approach vs. 18.1\% avg. acc. drop for \cite{yurtsever2019risky}).
We also show that the spatial and temporal attention components used in our approach improve both its performance and its explainability.
\section*{Acknowledgment}
The authors would like to show gratitude to the colleagues of the Embedded \& Cyber-Physical Systems (AICPS) Lab, from University of California, Irvine, particularly to Emily Sing Yen Lam and Aung Myat Thu for the contribution during the course of this research.
This work was partially supported by NSF under award CMMI-1739503 and the award ECCS-1839429.
Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the funding agency.

redpajama_set_name: RedPajamaArXiv


text:
100 new candidates for next generation probiotics
Translating groundbreaking science into new microbial solutions to improve health and prevent or treat disease
Jul 05. 2017 14:17 GTM
Only last month June 2017 Chr. Hansen announced that we had expanded our strain collection for next generation probiotics from the human microbiome by 100 well-characterized strains – safety assessed and amendable for production and formulation into commercial products.
This constitutes a major milestone in the advancement of science in this exciting field.
Bacteria from the human microbiome - the trillions of microbes that flourish in and on the human body – have a profound impact on our physiology. It can be part of the reason why people get sick or, conversely, what helps them stay healthy.
Entering into a disruptive period in healthcare
"We keep learning more about how the human microbiome actively influences our physical health and potentially even our mental health. There is great potential in obtaining a better understanding of the beneficial bacteria that reside in our gut and in creating the proper conditions for them to work in our favor," explains Johan van Hylckama Vlieg, VP, Microbiome & Human Health Innovation, Chr. Hansen.
"I truly believe we are entering into a disruptive period in healthcare at large, both in terms of how we think about preventive solutions but also treatments and cures. Maybe one day we'll look at healthcare before and after the human microbiome – in the same way as when antibiotics completely changed the scene!"
Solving safety and industrialization challenges
Developing human microbiome bacterial strains as next generation probiotics for Food, Dietary Supplement, Infant and Pharma applications, presents unique challenges. Many of the relevant species have no history of commercialization. They were until recently described as difficult to cultivate and require radically new techniques for cultivation and production since they cannot tolerate oxygen.
The 100 new strains in the Chr. Hansen strain collection have been screened for safety and their ability to grow in production media [a substance designed to support the growth of microorganisms].
"Our safety assessment includes screening for absence of antibiotic resistance and virulence factors [molecules produced by bacteria that increase their capacity to infect or damage human tissues].
We also looked for their ability to grow in industrial media and sensitivity to oxygen. And we also screened for the ability of strains to impact the immune system," explains Gemma Henderson, Senior Scientist.
"Now we have a shortlisted selection of strains that we believe will be of major interest to innovators in the microbiome space, who are looking to develop next generation probiotics and live biotherapeutics. It is our goal to help our customers and partners move these new documented strains from the lab to the clinic," concludes Henderson.
The work has been carried out in partnership between Chr. Hansen and three academic institutes – Gut Health, The Rowett Institute, University of Aberdeen; Host-Microbe Interactomics, Wageningen University and Research; and Medical Microbiology, UMCG, University of Groningen.
Chr. Hansen is a global, differentiated bioscience company that develops natural ingredient solutions for the food, nutritional, pharmaceutical and agricultural industries. At Chr. Hansen we are uniquely positioned to drive positive change through microbial solutions. We have worked for over 145 years to enable sustainable agriculture, better food and healthier living for more people around the world. Our microbial and fermentation technology platforms, including our broad and relevant collection of around 40,000 microbial strains, have game-changing potential. Matching customer needs and global trends we continue to unlock the power of good bacteria to respond to global challenges such as food waste, global health and the overuse of antibiotics and pesticides. As one of the world's most sustainable companies, we touch the lives of more than 1 billion people every day. Driven by our legacy of innovation and curiosity to pioneer science, our purpose – To grow a better world. Naturally. – is at the heart of everything we do.
News Human health Gut health Infant health Probiotics
Jun 16. 2022 09:18 GTM
Chr. Hansen focuses on HMOs, probiotics and the infant microbiome at the ESPGHAN 2022...
Human health HMOs Probiotics Oral health Gut health Woman's health Immune health Infant health Products R&D
Mar 21. 2022 09:43 GTM
Chr. Hansen focuses on HMOs, probiotics and the human microbiome at Probiota 2022 in...
Human health HMOs Probiotics Woman's health Gut health Immune health
Feb 28. 2022 09:43 GTM
New study of consumer understanding of probiotics points to significant opportunities for...
Food cultures and enzymes Human health Probiotics Fresh dairy Gut health Immune health

redpajama_set_name: RedPajamaCommonCrawl


text:
PCPS4Top
Akiba's Trip: Hellbound & Debriefed coming west for PS4, PC
The original Akiba's Trip comes west.
Sal RomanoJun 6, 2019 at 2:28 PM EDT0 Comment3
Akiba's Trip: Hellbound & Debriefed, which originally launched as Akiba's Trip Plus for PSP in Japan in 2012, is coming west for PlayStation 4 and PC, publisher XSEED Games announced.
Here is an overview of the game, via XSEED Games:
Take a trip back to the origin of a series that will knock more than just your socks off! In Akiba's Trip, players expose vampiric creatures called Shadow Souls to deadly sunlight by liberating them of their clothes. Now, a fully remastered version of the original "Strip Action RPG" is coming to the West for the first time. Following the success of Akiba's Trip: Undead & Undressed, which was released as Akiba's Trip 2 in Japan and has sold over 500,000 copies worldwide, Akiba's Trip: Hellbound & Debriefed completely rebuilds the first title in a modern engine, bringing back the beat-'em-up, strip-'em-down gameplay fans know and love while accurately rendering Akihabara, Japan's geek paradise, from circa 2012 in glorious HD.
Developed by Acquire Corp. and published in North America by XSEED Games, Akiba's Trip: Hellbound & Debriefed is in development with the launch date to be announced at a later time.
View a set of screenshots at the gallery.
visit gallery »
Akiba's Trip: Hellbound & Debriefed
Destiny 2: Shadowkeep expansion, free-to-play version, Steam and Stadia versions, and cross-save support announced Previous post
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SUPERCAST CORBEL LATEX MOULD & PLASTIC SUPPORT JACKET. Please note this sale is for the Latex Mould & Plastic Jacket only. The casting shown in the image is for reference only. Make your own Corbels or make to sell them at Car Boots, Markets, Shops, etc.
Suitable for casting with Plaster, Concrete or Polyester Resin. Dimensions Height 17cm x Width 12cm x Depth 8.5cm 6 3/4 x 4 3/4 x 3 3/8 inches. Any questions or for advice contact me through My Messages. The item "SUPERCAST ORNATE CORBEL REUSABLE LATEX MOULD & JACKET" is in sale since Friday, July 16, 2010. This item is in the category "Home, Furniture & DIY\Home Decor\Ceiling Roses".


Do you have a son or daughter who is interested in getting some extra touches and work over the summer? Take a minute to look at the available options provided by Century. All of the info is located under the CAMPS tab to the left.


text:
Food industry challenges validity of panel for junk food sale in schools
Health activists say court petition an attempt to scuttle process of ensuring healthy food in schools
By Jyotsna Singh
Published: Saturday 05 October 2013
The All India Food Processors' Association ( AIFPA ) has filed a review petition in the Delhi High Court, claiming that the committee set up by the Central government to look into junk food sale in schools is not legal. The first hearing of the petition was held on Friday. Date for next hearing is October 25, when a decision on the validity of the committee is expected.
The issue is being seen as of great importance because the industrialists' lobby promoting packaged food and aerated beverages is at loggerheads with activists campaigning for ban on junk food in school canteens and within 50 metre radius of the institutions. The nodal agency for food safety in the country, the Food Safety and Standards Authority of India (FSSAI), had submitted guidelines to the court on August 4 but these came under attack from various quarters as they lacked substance.
The expert committee was formed by the Ministry of Health and family Welfare of the Central government following a high court order of September 4 in Uday Foundation v/s Union of India. The 14-member committee includes government officials, eminent doctors, scientists and representatives from the industry and health activists.
In the first meeting of the committee, held on September 25, representatives of AIFPA and National Restaurants of India (NRAI) did not appear. Instead, the two bodies went to the court questioning the validity of the committee. It is to be noted that companies like Nestle, PepsiCo, Coca-Cola and Dabur are part of AIFPA. A large sum of their finances is at stake in this case.
Abhishek Manu Singhvi, lawyer of the industry, argued before the court that the committee has not been formed according to the Food Safety and Standards Act, 2005. Under the section 13 of the Act, the Food Safety and Standards Authority of India has the authority to form committees related to matters of food and beverage. The section says, "The Food Authority shall establish scientific panels, which
shall consist of independent scientific experts. The Scientific Panel shall invite the relevant industry and consumer representatives in its deliberations." The FSSAI can establish as many panels as it considers necessary.
The present committee, however, has been formed by the health ministry.
Health activists emphasised that this is nothing but an attempt by the interested bodies to scuttle the process of ensuring healthy food in schools. They said that the industry wants to limit the matter within the ambit of the FSSAI, while the health ministry wants to take matter in its own hands. The Central government is allowed to form committees for matters it deems fit.
"Everyone knows that the committee is not illegal or invalid. But attempts are being made to try to dissolve it because of the strong presence of well-meaning people who do not side with the industry," said an activist.
Global access to nutrition index 2013
Food marketing towards children: Brand logo recognition, food-related behavior and BMI among 3–13-year-olds in a south Indian town
The impact of transnational "Big Food" companies on the south: A view from Brazil
Affidavit filed in the Delhi High Court by Food and Safety Standards Authority of India (Union Health Ministry) regarding petition of Uday Foundation to ban junk food in schools, dated July 2011
Fast-food marketing and children's fast-food consumption: Exploring parents' influences in an ethnically diverse sample
Web edition Web edition News News News PepsiCo PepsiCo PepsiCo Nestle Food Safety And … Food Safety And … Food Safety And … Down to earth Delhi High Court Dabur Committee on junk … Coca-Cola Coca-Cola Coca-Cola All India Food … Abhishek Manu Singhvi
Children's health affected by marketing of unhealthy foods
Mind your diet
FSSAI draft guidelines do not propose ban on junk food in schools
Expert committee to look into junk food in schools


More than at any other time in history, today's parents of young children expect to regularly receive meaningful data on their child from the people who take care of them. Whether using a teacher or nanny, parents ask for photos, videos, and details of their child's day, yet traditional methods of photo-sharing are cumbersome. Child care providers have a tall order; keep parents happy and well-informed while coping with a multitude of operational details. Program information is often frustratingly disorganized due to a lack of integrated software systems. This administrative burden takes time away from important face-to-face activities. EduKids Connect Systems is a cloud-based application that answers this need on both fronts. First, child care providers use this elegant, one-screen app on tablets and smart phones to capture individualized information for each child. Parents stay informed in real-time through instant notifications and also get a customized summary at days' end. Secondly, administrators increase their efficiency through a comprehensive back-end dashboard that streamlines tedious processes through digitized transactions, freeing up more time to invest in building relationships with families, mentoring the staff, and interacting with the children. Check out the people behind this startup, a part of the StartED edtech accelerator; they'll be presenting during NY Edtech Week on December 20th at NYU. Learn more in the video above.


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The project was well planned ahead of time and carried out. We are thankful we went with this company as the other companies we called never even considered how they would complete the challenging areas. The workmanship is PERFECT. Our screened porch looks fantastic and the bugs are frustrated. I would highly recommend this company. Joe and Carol P.
These guys are like Superman, Batman & Robin. Great Service, Experience & Quality. We Love the Sunroom they built for us. We are now hiring them again to put siding on the house.
Our overall experience with Chesapeake Home Improvement was very positive. Quality -- The QUALITY IS TOP NOTCH..!! Even my (Engineer) father-in-law was highly impressed..!! Professional & Courteous -- The crew of Dave & Keith were always polite and willing to explain everything they were doing. Customer Service - - Dave was willing to modify the design (a couple of times) to suit our needs. He discussed alternatives to help us make the best decisions based his input. Dave will always take your call or return your call the same day. Timing -- The multi-level deck was built within the time Dave estimated. Investment -- Chesapeake Home Improvement will give you a competitive quote. It probably will not be the cheapest, but you get what you pay for and you WILL get quality work. I am not quick or comfortable giving recommendations; however, in the case of Chesapeake Home Improvement I will make an exception. We truly enjoy working with Dave Gordon and his guys, Dave & Keith. We will absolutely call them back for our next project.
Hired to replace our deck and stairs with new lumber. Dave and Keith are were very professional in their approach to the job. Their quality of work and demeanor met or exceeded my high expectations as to who I hire to work on my home. These two gentlemen completed the project on time and left a clean work site. Chesapeake Home Improvement and Dave Gordon are now my first call for any future projects.
I really like the the service. These guys know what they are doing. Great job. I would hire them again.
Very professional, accommodating, Explain the work to be done , materials being use. Quality work without a doubt. Enjoy the experience and no hiccups along the way. We truly enjoy working with Chesapeake and they made sure that we were happy with the results and we are! Will definitely call them back for our next project. Recommend highly.
Services Offered by Chesapeake Home Improvement Group, Inc.


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Q: Haskell type classes - difference between div and /? When I query the type of div infix operator and the / operator in Ghci, I find that div has the signature (div) :: Integral a => a -> a -> a while / has the signature (/) Fractional a => a -> a -> a. While they accomplish pretty much the same thing, what is the difference between type classes Fractional and Integral?

A: There is a conceptual difference between those operations:

*

*div is division with remainder (see euclidean domain)

*(/) is fractional division - this is the inverse operation to (*)
think 5 / 2 = 2.5 vs 5 `div` 2 = 2
The difference between the classes is just that

*

*Integral is for numbers you can do division with remainder with

*and Fractional is for rational-like numbers in the form of a/b - think of 1/2 or 2/7, etc.

Indeed this is a common problem if you mix both Fractional and Integral operations like div and (/) as just within the prelude there is no type that is an instance of both of these classes and so you end up with an well-typed expression that you often cannot evaluate.
The usual fix for this is using fromIntegral to go convert into the common Num instance you are targeting.

redpajama_set_name: RedPajamaStackExchange


text:
Restrict zooming between specific scale ranges.

![Image of min max scale](set-min-max-scale.png)

## Use case

Data may only appear at a certain scale on a map, and may be visually lost if zooming too far in or out. Setting the minimum and maximum scales ensures the zoom extents are appropriately limited for the purposes of the map.

## How to use the sample

Zoom in and out of the map. The zoom extents of the map are limited between the given minimum and maximum scales.

## How it works

1. Instantiate an `ArcGISMap` object.
2. Set min and max scales of map with `setMaxScale()` and `setMinScale()`.
3. Set the map to a `MapView` object.

## Relevant API

* ArcGISMap
* BasemapStyle
* MapView
* ViewPoint

## Tags

area of interest, level of detail, maximum, minimum, scale, viewpoint

redpajama_set_name: RedPajamaGithub


At the end of 2016, I faced losing the ride on the best horse I'd ever sat on. At the end of 2017, I face, for the first time in my life, regular and serious training on a horse who can go all the way, as well as starting another talented baby. And nobody but God can take either of them from me because it's my name on the papers. It truly should not be possible, not for an anxious overachiever in the middle of nowhere on a shoestring budget, but God pays no attention to the possible.
When I have finally upgraded my membership (gotta pay Dressage SA first *gulp*), I will share what's going on in my heart about the upcoming year of dressage, as well as my adventures riding Coach J's super ultra fancy horses yesterday (squee!). But for today we're going to be a bit intellectual and look at the things we want to work on this year in terms of the four babies I show myself.
The man himself went from strength to strength in 2017, starting off by coming second in his first classes ever with mid-60s scores. He never scored less than 63% through a year at Prelim, then debuted at Novice with scores in the low 70s. We finished by scoring 66s again at CHG Champs despite riding in a thunderstorm with me as sick as a dog.
School all the Novice work, ready to compete next year. We have schooled everything. It's not all polished yet, but he can go into an arena and do any Novice test without totally embarrassing himself, I'm sure. We have not polished all of it, but the movements are installed.
Jump 70cm courses with confidence.
Do our pre-flatwork short hacks calmly. He's sometimes a little tense, but there has been no bolting. Yay!
With Coach J's help, Thunny's schooling is pretty much on track to continue scoring steadily at Novice at the very least. Our greatest bugbear is tension in the show ring. If he's relaxed he unfailingly scores in the 70s. He has never been disobedient in the show arena, but he locks up through his back and neck, going from being connected and through (for the level) to simply being rhythmic and on the bit. This is just something that we'll have to keep competing to resolve, and I'm willing to be patient with him. If it takes him five years to be consistent at competitions, then so be it. The horse is far too good for me to lose sleep over losing points at Novice because he saw a butterfly.
With this in mind, my focus is shifting to schooling correctly rather than winning competitions next year. Of course I would like to compete him at least once a month to work on resolving that issue and, obviously, earn grading points, but we probably won't be doing CHG Series. We will probably end up going to bigger shows, though, for both of us to get our heads around the atmosphere. (Also because Coach J might be there).
I'll be real honest, I kind of have no idea what goals I should be setting. In yesterday's visit Coach J was talking about doing EM this year. I have my doubts, but the oracle knows best. Mostly I'll keep chugging along doing my thing and watching to see what God does, because He seems bent on blowing my pathetic little expectations out of the water lately.
Improve all of our downward transitions.
Improve the halts, specifically staying connected in halt and immobility.
Keep working on quiet little hacks.
Arwie had another year of doing everything from showjumping to working riding to a whole lot of dressage. She took me to my first ever Elementary and tried her guts out in every single test despite mediocre riding and simply poor schooling, even scraping up a few placings as we went. Most of all, she was the dragon who relit my fire when I needed it, which is what she does best. Someday, if God wills it, I'll ride the big grades and nobody will remember her – but I will, because she made it possible.
Get points for Elementary Medium. Sooooooo close. We would totally have done this if it wasn't for the issue with her feet that ruined the last few months of the show season. It was God's will, so I don't mind admitting that this goal didn't happen. Still, we have nine points and we need ten. She's still registered with DSA so I might just drag the beast to one more show, get the last pesky point and then be done.
Do some cross-country lessons and/or go drag hunting. So we didn't do this either, although not for lack of trying. Still, it's no biggie.
Arwen tried her heart out for me this year, but we've been stuck in a rut for months, making almost no progress on our Elementary work. It's not surprise, considering that all of her basics are lacking. It got worse when Thunder started to play with some of the movements and they were all so easy on him, which caused frustration with myself every time I schooled Arwie, knowing I could have done better if I'd had her as a youngster now. So, apart from maybe popping out to get that last grading point, dressage is on the shelf for Arwen for now.
Instead, we'll be making our first serious foray into the world of showing. We've done bits, but this year I'm signing up with Showing SA (which is ridiculously cheap compared to DSA) and we'll be hitting some of the bigger ones. The expensive classes do mean that I won't be competing her as often, but she's not a baby, she doesn't need it that much anymore. I really look forward to it.
Take at least one showing lesson or clinic.
Get over my phobia of all showing judges. Show at least once with one of the horrible ones and learn to deal.
Improve her rein-back and lengthenings.
Get points to go out of Novice.
Last year this time, it was just days after I'd met Faithy for the first time. On the second of January, she came home – completely unhandled and so fresh off the veld that she didn't even know what concentrates were and politely declined to eat them.
The little unicorn spent most of the year chilling in a field. The basic ground work was effortless, although loading went a lot better once I discovered that treats can bribe Faith into doing practically anything. I brought her in for backing in November, but it's been slow progress, mostly because I hit a nasty burnout and non-competing horses went onto the back burner.
Show in-hand. Boo. There were no shows we could really do this year, as well as the minor disadvantage that she spent most of it looking like something like an adorable grey camel. Still, she has all the in-hand skills required, so that's something.
In spring, lunge. I planned to start lunging in September/October and back shortly after her third birthday, but she was still such a baby then. I had to wait for the front end to catch up first, and I'm glad I did. She's a sweetie, but very much immature for her age.
She has worn a saddle, but that's about it. Smart and loyal she is, but mature, not so much. I'm taking it slow. She could do with some growth and muscle tone before being asked to really do anything just yet.
Faithy is still only a very babyish three-year-old, so 2018 will still be very much relaxed. Our main goal is to be ready for YDHS in 2019 since I absolutely loved it on Nell last year, and since four-year-olds only do Prelim, I'm in no hurry. The main thing this year will be to establish rock-solid basics, and I'm taking the most basic of the basics: obedience, rhythm and relaxation.
introduce free walk and stretchy trot figures.
compete at least twice at Prelim.
I didn't even show Jamaica until February 2017, where we popped around something ridiculous like 50cm. We quickly made our way to 80cm, then stayed there forever as I tried to scrape my nerve together. Our move up to 90cm was easy, and I feel nicely set up for learning to jump Module 5 in mid to end 2018.
Showjump 90cm at available training shows.
Compete at equitation 90cm at SANESA. Score 70% (that's a 28) or more, if not at the first qualifier, then at least at the last two. 70% is the pass mark for Module 5.
Introduce all the flatwork required at Module 5: leg-yield, turn on the forehand, turn on the haunches, a little shoulder-in, a little travers. (The exam specifies only "lateral work" but I don't expect to have to do half-pass).
In May/June, jump a 1.00m showjumping round. (I would love to do the 1.00m equitation, but it requires swapping horses, and that just ain't happening, thankyouverymuch. Ask me in a decade).
Jump at least one more clear 1.00m round at an appropriate pace. It doesn't have to be fast, just flowing.
Jump Module 5 in September.
If we do fail it, jump it again in December.
I didn't actually set personal goals in 2017, but I did improve my position a LOT. I had a terrible chair seat for years and I feel like this year I finally fixed it. It's still not perfect, especially not in my current dressage saddle, but yesterday when I sat down in Coach J's proper dressage saddle my alignment was suddenly perfect without any effort on my part. My hands are also a lot better, as is my core.
I do still have a very long way to go. Riding Coach J's big horses really impressed on me the amount of strength and suppleness required to keep it up, and I know I'm still severely crooked.
In terms of jumping, my high Module 4 mark shows that my position is pretty adequate. Still, my nerves tend to show when I grab mane and my lower leg tends to swing back on landing, so there is lots to do.
Have regular chiro. If my medical aid covers it, I don't have an excuse not to go, and it doesn't help that I care about Thunny's back when my own back is stiff and crooked on top of him. I'm almost permanently body sore, and that's a disservice to the horses who get to carry that sack of potatoes around.
Take conditioning classes twice a week throughout the year. No excuses. My sister is a dance teacher, and nothing in the world compliments dressage like ballet does: it will be excellent for my core strength, body control, and awareness of how I use my muscles. Ballet is kinda savage so it'll build my cardio for jumping at the same time. Even if my sister has to miss some classes, I have to find somewhere that I can cross-train. Volunteers get discounted membership at the local gym, so I really do not have excuses. I need to be just as strong and supple as Thunny.
I'm pretty good about eating and sleeping well. (This week does not count. One of the many perks of being a friendly teacher is large amounts of sweets at Christmastime – children's love language). Keep it up, especially when I'm on volunteer duty.
Find out what's going on with my shoulders – probably a chiro issue, but my left shoulder always starts stinging about midway through a ride. I also hang on my left rein a LOT and my left shoulder blade can't go flat like Ms Ballet Teacher Sister wants it to. Literally, it can't, not even with her manipulating it. (My chiro will be horrified).
Improve on my bad habit of bracing the lower back as soon as I feel tension. That tension is not limited to nervousness – even if I'm just concentrating, the moment I try harder than usual, my lower back arches and braces, and I lose my connection to Thunny's back.
Improve on my bad habit of tipping forward at the hip, especially in canter. It's related to the back and shoulders issues, but it needs focused attention to fix.
Improve on my super bad habit of looking down.
Improve my tendency for my heels to come up on landing. Lots of light seat for me!
Improve my tendency to try and jump ahead on takeoff.
I've never been so genuinely and childishly excited for a show season. God has blown me away this year, and I can't wait to see what He does next. Yet, as always, there is so much possibility for things to not go according to plan. Horses go lame. Money runs out. These things happen. But my Abba, He loves me, regardless of what happens. So I lay down my anxiety, I look forward with joy, and I look up with peace, knowing that whatever lies ahead is part of His plan.
And I do not dare to dream, for my dreams are foolish. But as He has proven time and time again, He dreams for me.


After yesterday's presentation of the new MacBook Air and iPad Pro, Apple without announcement and explanation raised the price of similar products in your online store in Russia.
13-inch MacBook Pro 2-Core, 2.3 GHz, 128 GB: 94 990 109 990 RUB RUB +15 thousand.
13-inch MacBook Pro 4-Core 2.3 GHz, 256 GB: 152 144 990 rubles 990 rubles + 8 thousand.
As you know, at the October event, Apple unveiled updated MacBook Air, which got major changes in design and powerful specifications. A day later, the network began to appear the first video unboxing of the new MacBook Air.
First received news YouTube-bloggers. While we are talking only about unboxing the 13-inch MacBook Air with Retina display, Intel Core i5, fingerprint reader, keyboard with butterfly mechanism 3 generation Apple chip T2.
30 Oct 2018 Apple held a presentation of new devices, among them iPad Pro with Face ID feature and the updated MacBook Air and Mac Mini. After the event, CEO Tim cook thanked the guests for attention.
Many people in the transition from the previous generation MacBooks, which were issued before 2015, the new models face a shortage of the usual connectors for connecting peripherals. But it's easy to solve with the help of USB hubs.
Today, October 18, the company Mophie has announced the release of a new external Powerstation battery USB C 3XL, which has been specially designed to charge all new models of MacBook.
PowerStation USB-C-3XL are equipped with a battery capacity of 26,000 mAh battery and provides up to 45 watts of power through which you can get up to 18 hours of additional battery life 12-inch MacBook. It is the most powerful external battery pack from Mophie.
Apple has updated the page with the recovered products and added MacBook Pro 2018. The range consists of 13-inch model with a starting price of $ 1529. On stock 15-inch models no information.
The cheapest restored 13-inch MacBook Pro 2018 is worth $ 1529. This price is for a configuration with the Touch Bar, 256 GB SSD, 8 GB RAM and Quad-core Intel Core i5 processor. New laptop with similar features on the Apple website costs $ 1,799.


Nobody expected President Sam Nujoma or his ruling South West African People's Organisation to lose the elections but SWAPO's victory was impressive. Putting down a marker was the new Congress of Democrats and its leader, Ben Ulenga (AC Vol 40 No 7), a former High Commissioner to Britain, trades unionist and SWAPO guerrilla fighter; the CoD overtook the DTA of Namibia (formerly Democratic Turnhalle Alliance) to become the official opposition. The first act of Nujoma's next five years in office was to affirm direct support for the Angolan government's drive against the União Nacional para a Independência Total de Angola.


Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.
Whether you're analyzing megabytes or terabytes, Cloud Datalab has you covered. Query terabytes of data in BigQuery, run local analysis on sampled data and run training jobs on terabytes of data in Cloud Machine Learning Engine seamlessly.
Use Cloud Datalab to gain insight from your data. Interactively explore, transform, analyze, and visualize your data using BigQuery, Cloud Storage and Python.
Go from data to deployed machine-learning (ML) models ready for prediction. Explore data, build, evaluate and optimize Machine Learning models using TensorFlow or Cloud Machine Learning Engine.
Datalab is free. You may incur compute, storage and other cloud services costs based on usage.
Cloud Datalab simplifies data processing with Cloud BigQuery, Cloud Machine Learning Engine, Cloud Storage, and Stackdriver Monitoring. Authentication, cloud computation and source control are taken care of out-of-the-box.
Cloud Datalab combines code, documentation, results, and visualizations together in an intuitive notebook format.
Only pay for the cloud resources you use: Google Compute Engine VMs, BigQuery, and any additional resources you decide to use, such as Cloud Storage.
Use Google Charting or matplotlib for easy visualizations.
Supports TensorFlow-based deep ML models in addition to scikit-learn. Scales training and prediction via specialized libraries for Cloud Machine Learning Engine.
Datalab is based on Jupyter (formerly IPython) so you can use a large number of existing packages for statistics, machine learning etc. Learn from published notebooks and swap tips with a vibrant IPython community.
Developers wishing to extend Datalab can fork and or submit pull requests on the GitHub hosted project.


Yarrow is a herb with feathery leaves and small, delicate white flowers that are arranged in clusters. It's traditionally brewed as a tea for fevers and colds, as well as to soothe diarrhea. However, its most common usage is for stopping bleeding and hastening recovery from wounds.
Yarrow's scientific name, Achillea millefolium, attests to its legendary status – literally – as a treatment for wounds. The name comes from the half-god Greek hero Achilles of The Iliad fame, the very one who inspired the term "Achilles heel" thanks to his body's only vulnerable spot. Legend has it that Chiron, centaur and infamous teacher of Greek heroes, taught Achilles to use yarrow to treat wounds, which the warrior followed to staunch his comrades' wounds. Yarrow eventually became known as "herba militaris" for its prominent use in the battlefield.
The herb's ability to staunch bleeding isn't merely an old wives' tale. Laboratory tests have confirmed that achilleine, an alkaloid component of the plant, has powerful styptic properties. Because of this though, yarrow must not be administered to pregnant or nursing women – as well as those who are prone to excessive blood clotting – especially in tea form.
You can pick several stems of yarrow and tie them together into a sort of bouquet. Ideally, a bundle can have three to five plants. Remember to leave some, especially the hardiest ones, so they can propagate and refill the area. Take better care of the leaves than the flowers – you'll need the former and not the latter.
It's a natural antiseptic – Yarrow and its essential oils are among the most potent natural antiseptics on the planet. This is one of the reasons it's so great for wounds, apart from its styptic properties. What's more, it works not just for external infections, but for internal ones as well.
It's great for the skin – If you have skin inflammation and eczema, yarrow extract and essential oil may offer relief. The herb also has effective anti-scarring capability, which makes it great for acne and wounds. Drinking yarrow tea reportedly enhances skin cell regeneration, which may give your skin a healthy and youthful glow.
It addresses hormonal imbalance – The menstrual cycle makes women prone to hormonal ups-and-downs that may affect mood and general health. Yarrow belongs to a group of plants called emmenagogues which increase menstrual flow. This makes yarrow effective for regulating your hormones and ensuring a regular menstrual cycle.
It helps with asthma – Yarrow has relaxing properties that affect both the circulatory and respiratory systems, calming and opening up your bronchial tubes for easier breathing, as well as ensuring proper blood and oxygen circulation.
It lowers blood pressure – The relaxing properties of yarrow help dilate the blood vessels, which lowers blood pressure. The herb also calms your nerves to make you feel less agitated.
Discover the many benefits of yarrow and other herbs at Herbs.news.


This two bedroom apartment, in the award winning Triptych Building, offers elegance, style and lifestyle opportunities. Enjoy the best of Melbourne with restaurants, gardens, galleries and public transport only a short stroll away. Entertain easily with a beautifully appointed Poliform kitchen with Miele appliances and excellent storage, spacious open plan living and dining area with stunning eastern views, large master bedroom with BIRs and ensuite with double basin, shower and separate bath. Second bedroom with BIR is serviced by sparkling main bathroom. Walk in laundry, a private, secure entrance with extra storage options, a carspace and access to all of Triptych's high end facilities make this one not to miss out on.


I grew up forty-five minutes from the ocean. Most of my summers were spent going to the beach. I would hitch rides with my family, our church youth group, public transportation, or my friends. One afternoon, after my freshman year of college, my best friend and I drove to our favorite beach in North County, outside San Diego.
I am forever indebted to Jim.
A short time later, I nervously, yet expectantly, sat outside of the breaking surf waiting for a wave. A smaller set of waves approached. I paddled, felt the wave begin to catch me, and jumped to my feet. Though I probably looked like a kook (more on that word later in the book), I felt like I was the best surfer in the world. I rode the wave for perhaps four or five seconds, kicked out, and immediately looked around to see if anyone had seen my wave (unfortunately, no one did).
Photo taken from the book of world record holder Aaron Gold. To read Gold's thoughts on the book see the endorsements section of this website. Photo by Aaron Lynton.
That feeling is what a surfer refers to as stoke.
Ask a surfer what stoke means and you will likely receive various answers: happiness, fulfillment, pure joy, excitement, fun. In John 10:10, Jesus Christ speaks of what I like to refer to as spiritual stoke. He says, "My purpose is to give them a rich and satisfying life." In other translations, the word abundant is used to express what Jesus wants to give his children in life.
I love that. Jesus Christ came to the world to give people a rich, abundant, and satisfying life. To be spiritually stoked is to experience the abundance, the richness, and the satisfaction God desires for you. This stoke, whether you consider yourself a surfer or not, is what God wants for you.
We are meant to live with purpose. We are meant to live with connection. We are meant to live with impact. This simple and practical book will show you how. What surfing represents, and many surfers have experienced, is a feeling called "stoke." It's that happiness, that thrill and pure joy that comes when you catch and ride that perfect wave.
Believe it or not, God intends for you to have this kind of life. And yet so often we settle for much less. Dropping In will help you open your heart, mind, and soul to the possibility of living differently.
Dropping In is a book about surfing and following Jesus. This is a book about so much more than looking for and dropping in the perfect wave. I trust Dropping In will not just point you to good waves but to the good God who desires to bless you with even more than the joy of surfing.
Dropping In means stepping out in faith and obedience, not knowing exactly where the wave will take you, and allowing God to shape and grow you in a relationship with him. Dropping In means taking the words of Jesus seriously, experiencing spiritual stoke, and walking on water with him throughout life. I trust this book will be a resource that helps shape and grow your relationship with Jesus.
Thanks for considering Dropping In. Let me know what you think!


TACC (Texas Advanced Computing Center) has published a science highlight of the SoyKB project. Pegasus is used to orchestrate the computations running on TACC Wrangler and automatically retrieving and storing data in the CyVerse data store. Also highlighted is how the XSEDE ECSS (Extended Collaborative Support Service) can be used get scientific workflow support on XSEDE.


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Tanger Declares Dividend for Second Quarter 2019
GREENSBORO, N.C., July 11, 2019 /PRNewswire/ -- Tanger Factory Outlet Centers, Inc. (NYSE: SKT), announced today that its Board of Directors declared a quarterly dividend of $0.355 per share for the second quarter ending June 30, 2019. A cash dividend of $0.355 per share will be payable on August 15, 2019 to holders of record on July 31, 2019.
Since becoming a public company in May 1993, the company has paid a cash dividend each quarter and has increased its dividend each year.
About Tanger Factory Outlet Centers
Tanger Factory Outlet Centers, Inc. (NYSE: SKT), is a publicly-traded REIT headquartered in Greensboro, North Carolina that presently operates and owns, or has an ownership interest in, a portfolio of 39 upscale outlet shopping centers. Tanger's operating properties are located in 20 states coast to coast and in Canada, totaling approximately 14.3 million square feet, leased to over 2,900 stores which are operated by more than 510 different brand name companies. The Company has more than 38 years of experience in the outlet industry. Tanger Outlet Centers continue to attract more than 181 million shoppers annually. For more information on Tanger Outlet Centers, call 1-800-4TANGER or visit the Company's website at www.tangeroutlets.com.
Cyndi Holt
Vice President of Investor Relations
View original content to download multimedia:http://www.prnewswire.com/news-releases/tanger-declares-dividend-for-second-quarter-2019-300882734.html
SOURCE Tanger Factory Outlet Centers, Inc.
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Christmas is an exciting time of the year. Many of us decorate a Christmas tree, share a big meal with family and friends, build a snowman, sample the cakes, cookies, and other sweets of the season, and, when it's not too cold, we enjoy being outside and taking advantage of all that winter has to offer.
Many of these holiday traditions, however, are based on legends and ancient festivities that date back to a time long before Christmas was ever known as Christmas. Have a look at some of the photographs in the Whitby Archives and learn a little about the history of some of our most treasured Christmas traditions.
The ancient Roman festival of Saturnalia honoured the god of the harvest and agriculture, Saturn. It was celebrated from 17-25 December and it involved a reversal of social roles in which the lower classes switched places with the upper classes. People also dressed up as animals and paraded through the streets as it was believed that the evening of 25 December was a magical time when animals could speak.
Modern Christmas seems to have retained some of the magical atmosphere of Saturnalia. Some people believed that at the stroke of midnight on Christmas Eve, farm animals could speak to each other. Indeed, Christmas Eve is a wondrous time and children of all ages use their imaginations to conjure up visions of Santa, reindeer, and sugar plum fairies.