text:
\section{Room Navigation using Amodal Semantic Maps}
\label{sec:approach}

\begin{figure*}[t]
\centering
\includegraphics[width=\textwidth]{figs/maps.pdf}
\caption{ \textbf{Egocentric crops of Semantic Top Down Maps.} We show the regions in a $26m \times 26m$ crop of the ground truth semantic maps \wrt agent's location and orientation. Agents are trained to predict these semantic maps, including regions they have not seen yet, based on regions of the house they have seen so far.
Regions colored \protect\includegraphics[height=0.3cm]{figs/icon1.pdf} lie inside the specified room. \protect\includegraphics[height=0.3cm]{figs/icon2.pdf} denotes regions inside the house and not in the room and \protect\includegraphics[height=0.3cm]{figs/icon3.pdf} denotes regions outside the house.}
\label{fig:egocentric_crops}
\end{figure*}

We develop a room navigation framework with an explicit mapping strategy to predict amodal semantic maps by learning underlying correlations in houses, and navigating using these maps. Our approach assumes there are architectural and stylistic regularities in houses which allow us to predict regions of a house which lie beyond our field of view. For instance, if we are in the kitchen we can guess that the dining room is adjacent to it and we would be right in most cases.\footnote{We acknowledge that these regularities likely vary across geographies and cultures.} We first verify the existence of such correlations by scaling and aligning all the homes in the Matterport 3D\cite{chang2017matterport3d} dataset such that the kitchen is in the bottom-left corner. As shown in Fig.~\ref{fig:priors}, we can observe that the concentration of dinning rooms is close to the kitchens and the bedrooms are in the opposite corner, away from the kitchens. We believe there exist similar and more subtle correlations (\eg size of the kitchen could be indicative of the number of bedrooms in a house) which our agent can automatically learn while predicting amodal semantic top down maps of regions. We now provide an overview of our approach followed by a detailed explanation of each of its sub-components.

\noindent\textbf{Overview}. Our room navigation framework is outlined in Fig.~\ref{fig:overview}. The agent is spawned in a random location and is asked to navigate to a specified target room, $tr \in \mathcal{R}$, where $\mathcal{R}$ is the set of all possible target rooms. With each step $t$, the agent receives \texttt{RGB}\xspace image $I_{t}$, \texttt{Depth}\xspace $D_{t}$ and GPS+Compass\xspace information. The agent predicts egocentric crops of top down semantic maps indicating the rooms which lie in and beyond its field of view. An example of these maps is shown in~\figref{fig:egocentric_crops}. To generate the maps, the agent uses a sequence to sequence (Seq2Seq) network which takes as input the \texttt{RGB}\xspace Image from the current time-step $I_{t}$, the predicted semantic maps from the previous time step $M^\textrm{pred}_{t-1,r} \forall r \in \mathcal{R}$, and the previous action $a_{t-1}$ and predicts semantic maps $M^\textrm{pred}_{t,r}$ for the current time step. The predicted semantic maps $M^\textrm{pred}_{t,r}$ are fed to a point prediction network which predicts a target point $P^\textrm{pred}_{t}$ lying inside the target room. The agent navigates to the predicted point using a  point navigation policy $\pi_{nav}(P^\textrm{pred}_{t}, D_{t})$. The agent updates its beliefs of the predicted semantic maps and the predicted point as it steps around the environment. The episode is deemed successful if the agent stops inside the target room before 500 steps.

Next, we describe the three main components of our model architecture: Map Generation, Point Prediction and Point Navigation, as shown in~\figref{fig:overview}.

\subsection{Map Generation}
\label{sec:mapper}
We model the correlations in houses by learning to predict amodal semantic top-down maps. The agent uses the information it has gathered so far to determine where the different rooms in the house are present even before visiting these regions. The maps are crops of top-down maps indicating the type, location, size and shape of rooms and are egocentric to the agent. \figref{fig:egocentric_crops} shows an example of ground truth maps. The maps have 3 classes: (1) Regions which lie outside the house, (2) Regions which lie in the house but outside the target room, (3) Regions which lie in the target room. We hypothesize that the agent will learn architectural regularities in houses and correlations between the \texttt{RGB}\xspace images and layouts in order to predict these maps. We train a map generation network $f_{map}$, to predict egocentric crops of top down semantic maps for each room $r \in \mathcal{R}$. $f_{map}$ consists of a sequence to sequence network $f_{seq}$ and a decoder network $f_{dec}$. At each time step $t$, $f_{seq}$ takes in as input a concatenation of learned representations of the current \texttt{RGB}\xspace frame $f_{i}(I_{t})$, the previous action $f_{act}(a_{t-1})$, and the semantic map of the previous time step $f_{m}(M_{t-1,r})$. $f_{seq}$ outputs a latent representation $h_{t,r}$. $h_{t,r}$ is passed through a parameterized decoder network $f_{dec}$ that resembles the decoder in~\cite{ronneberger2015u}. $f_{dec}$ upsamples the latent representation using multiple transpose convolutions layers to produce output $M^\textrm{pred}_{t,r} \forall r \in \mathcal{R}$. Following~\cite{bengio2015scheduled}, during training, we uniformly choose to set $M_{t,r}^\textrm{input}$ to be $M^\textrm{pred}_{t-1,r}$ (predicted map from previous time step) 50\% of the time and $M^\textrm{GT}_{t-1,r}$ (ground truth map from the previous time step) the rest of the time. At each step, we use the ground truth semantic map $M_{t,r}^\textrm{GT}$ to train $f_{seq}$ and $f_{dec}$ with cross entropy loss, $\mathcal{L}_{map}$. Eqn.~\ref{eq:map_start}-\ref{eq:map_end} describe the exact working of $f_{map}$. The agent continuously generates maps at each step until it calls \texttt{stop} or reaches end of the episode.

\vspace{-1.50em}
\begingroup
\allowdisplaybreaks
\begin{align}
  \label{eq:map_start}
  \mu&~\sim~\text{Uniform}(0, 1)\\
  M_{t,r}^\textrm{input} &=
  \begin{cases}
      M_{t-1,r}^\textrm{GT}, \text{if } \mu > 0.5~\text{or t =}~0,\\
      M^\textrm{pred}_{t-1,r}, \text{otherwise }
  \end{cases}\\
  h_{t,r} &= f_{seq}(f_m(M^\textrm{input}_{t}), f_{act}(a_{t-1}), f_i(I_t)) \\
  M^\textrm{pred}_{t,r} &= f_{dec}(h_{t,r}) \\
  \mathcal{L}_{map} &= \sum_{r \in \mathcal{R}} \text{CrossEntropy}(M^\textrm{pred}_{t,r}, M_{t,r}^\textrm{GT})
  \label{eq:map_end}
\end{align}
\endgroup

During inference, we feed a random image as the input semantic map for the first time step and use $M_{t,r}^\textrm{pred}$ as $M_{t,r}^\textrm{input}$ for all consecutive steps.

\subsection{Point Prediction}
\label{sec:point_pred}
The maps predicted by $f_{map}$ are amodal -- the agent predicts regions that it has not seen yet. They are however \emph{crops} -- the agent does not predict the layout of the entire house. These crops are egocentric to the agent and the target room may not always appear in these maps. For \eg, consider~\figref{fig:egocentric_crops}, there exists a bathroom in the house but this region does not appear inside the crop as it does not fall inside the crop \wrt the agent's current location. Inspired by the recent progress in point navigation~\cite{savva2019habitat, wijmans2019decentralized}, we reduce the room navigation problem to point navigation.
We train a network $f_{point}$ to predict a target point $P^\textrm{pred}_{t}=(x'_{t}, y'_{t})$ that lies in the target room $tr$, at each step $t$ of the agent. Similar to Sec.~\ref{sec:mapper}, we learn representations $f_{i}(I_{t})$ of the \texttt{RGB}\xspace image $I_t$, $f_m(M_{t,r}^\textrm{pred}) \forall r \in \mathcal{R}$ of the predicted semantic maps $M_{t,r}^\textrm{pred}$, and $f_{emb}(tr)$ which is a one-hot embedding of the target room ID $tr$. The predicted semantic map representations for the different rooms are combined using Eq.~\ref{eq:combine_maps}, %
\be
g_{M} = f_m(M_{t,r}^\textrm{pred}) \odot f_{emb}(tr)
\label{eq:combine_maps}
\ee
where $\odot$ represents element-wise multiplication. These are then
concatenated with the target room ID $tr$ and fed to a multilayer perceptron (MLP) $f_{point}$ which outputs $P_t^\textrm{pred}$ as described in Eqn.~\ref{eq:b_point}. $f_{point}$ is trained using mean square loss \wrt a ground truth target point in the target room, $P^\textrm{GT}$, as shown in Eqn.~\ref{eq:e_point}. Sec.~\ref{sec:dataset} describes how this point is chosen.
\begingroup
\allowdisplaybreaks
\begin{align}
\label{eq:b_point}
  P_t^\textrm{pred} = (x'_t, y'_t) &= f_{point}(g_{M}, f_{i}(I_{t}), f_{emb}(tr)) \\
  \mathcal{L}_{point} &= \text{MSELoss}(P^\textrm{pred}_t, P^\textrm{GT})

\label{eq:e_point}
\end{align}
\endgroup

During inference, the agent predicts a point every $k=6$ steps. Once the agent completes $K=60$ steps, the target point is simply fixed and no longer updated. The episode terminates if the agent calls \texttt{stop} or reaches $N=500$ steps.

\subsection{Point Navigation}
\label{sec:point_nav}
At this stage, we have reduced room navigation to point navigation where the agent needs to navigate to the predicted target point $P^\textrm{pred}_{t}$. Following the approach in~\cite{wijmans2019decentralized}, we train a point navigation policy using Proximal Policy Optimization (PPO)~\cite{schulman2017proximal} on the dataset of point navigation episodes in~\cite{savva2019habitat}. The policy, described in Eqn.~\ref{Eq:pointnav}, is parameterized by a $2$-layer LSTM with a $512$-dimensional hidden state. It takes three inputs: the previous action $a_{t-1}$, the predicted target point $P_t^\textrm{pred}$, and an encoding of the \texttt{Depth}\xspace input $f_{d}(D_t)$. We only feed \texttt{Depth}\xspace input to the  point navigation policy as this was found to work best~\cite{savva2019habitat}. The LSTM's output is used to produce a softmax distribution over the action space and an estimate of the value function.
\be
a_t = \pi_{nav}(P_t, f_{d}(D_t), a_{t-1})
\label{Eq:pointnav}
\ee
\noindent
The agent receives terminal reward $r_T = 2.5$, and shaped reward $r_t (a_t) = -\Delta_{\text{geo\_dist}} - 0.01$, where $\Delta_{\text{geo\_dist}} = d_t - d_{t-1}$ is the change in geodesic distance to the goal by performing action $a_t$. We then use this pre-trained policy, $\pi_{nav}$, to navigate to the predicted point, $P^\textrm{pred}_t$. We also fine-tune $\pi_{nav}$ on the points predicted by our model and this improves the performance.

To recap, $f_{map}$ generates the semantic map of the space, $f_{point}$ acts as a high-level policy and predicts a point, and the low level point navigation controller $\pi_{nav}$ predicts actions to navigate to this point.

\subsection{Implementation Details}
\label{sec:implementation}

\noindent\textbf{Map Generation.}
The image and map representations, $f_{i}(I_{t})\in\mathbb{R}^{256}$ and $f_m(M_{t,r}^\textrm{input})\in\mathbb{R}^{256}$, are obtained by first embedding the input \texttt{RGB}\xspace image $I_{t}$ and semantic map $M_{t}^\textrm{input}$ using a ResNet50~\cite{he2016deep} pre-trained on ImageNet~\cite{deng2009imagenet} followed by a fully connected layer. The action representation $f_{act}(a_{t})\in \{0,1\}^{32}$ is a one-hot embedding of the action $a_{t}$.

All three are concatenated to form a 544 dimensional vector which is reduced to a vector of size 512 using a linear layer. This is fed through $f_{seq}$, a 2 layer LSTM~\cite{hochreiter1997long} which outputs a hidden state $h_{t,r} \in\mathbb{R}^{512}$. This is passed through $f_{dec}$ which consists of 5 transpose convolutions interleaved with BatchNorm~\cite{ioffe2015batch} and followed by ReLU.
The map generation network is trained using data collected from shortest path trajectories between source and target points in the house. We experimented with multiple semantic map crop sizes ranging from 20m to 40m and found 26m to work the best.

\noindent\textbf{Point Prediction.}
The input \texttt{RGB}\xspace image $I_{t}$ and semantic maps $M_{t,r}^\textrm{input}$ are embedded using a pre-trained ResNet50~\cite{he2016deep}, same as before, but $f_m(M_{t,r}^\textrm{input})$ is now a 32 dimensional vector using a fully-connected layer. The target room embedding $f_{emb}(tr)\in\mathbb{R}^{32}$ is a 32 dimensional one-hot encoding of the target room $tr$. The generated maps for each room type $f_m(M_{t,r}^\textrm{input})$ are multiplied with target embedding $f_{emb}(tr)$ and concatenated to form $g_M\in\mathbb{R}^{160}$. This is concatenated with the \texttt{RGB}\xspace image to form a 416 dimensional vector which is passed through $f_{point}$ to obtain $P^\textrm{pred}_{t}$. We compute a goal vector relative to the agent's current location which is used by the  point navigation module. Similar to map generation, the point prediction network is also trained using data collected from a shortest path trajectories.

\noindent\textbf{Point Navigation Policy.} The \texttt{Depth}\xspace encoding $f_d(D_t)$ is based on ResNeXt~\cite{xie2017aggregated} with the number of output channels at every layer reduced by half. As in ~\cite{wijmans2019decentralized}, we replace every BatchNorm layer~\cite{ioffe2015batch} with GroupNorm~\cite{wu2018group} to account for highly correlated inputs seen in on-policy RL. As in ~\cite{wijmans2019decentralized}, we use PPO with Generalized Advantage Estimation (GAE)~\cite{schulman2015high} to train the policy network. We set the discount factor $\gamma$ to $0.99$ and the GAE parameter $\tau$ to $0.95$. Each worker collects (up to) 128 frames of experience from 4 agents running in parallel (all in different environments) and then performs 2 epochs of PPO with 2 mini-batches per epoch. We use Adam~\cite{kingma2014adam} with a learning rate of $2.5 \times 10^{-4}$. We use DD-PPO \cite{wijmans2019decentralized} to train 64 workers on 64 GPUs.

\section{Conclusion}

In this work, we proposed a novel learning-based approach for Room Navigation which models architectural and stylistic regularities in houses. Our approach consists of predicting the top down belief maps containing room semantics beyond the field of view of the agent, finding a point in the specified target room, and navigating to that point using a point navigation policy. Our model's improved performance (SPL) compared to the baselines confirms that learning to generate amodal semantic belief maps of room layouts improves room navigation performance in unseen environments. Our results using ground truth maps indicate that there is a large scope for improvement in room navigation performance by improving the intermediate map prediction step. We will make our code and dataset publicly available.

\section{Room Navigation Dataset}
\label{sec:dataset}

\xhdr{Simulator and Datasets.}
We conduct our experiments in Habitat~\cite{savva2019habitat}, a 3D simulation platform for embodied AI research. We introduce the room navigation task in the Habitat API and create a dataset of room navigation episodes using scenes from Matterport 3D~\cite{chang2017matterport3d}. We use Matterport 3D as it is equipped with room category and boundary annotations and hence is best suited for the task of Room Navigation. It consists of 61 scenes for training, 11 for validation, and 18 for testing. We only use the subset of 90 buildings which are houses and exclude others such as spas as those locations do not have common room categories with the majority of the dataset. We extract a subset of these scenes which contain at least one of the following room types: Bathroom, Bedroom, Dining Room, Kitchen, Living Room on the first floor. We only use the first floor of the house because, (1) In Matterport3d, the bounding boxes of rooms on different floors overlap at times, \eg the box for a room on the first floor often overlaps with the room right above it on the second floor, making it hard to sample points which lie on the same floor, (2) The floors are uneven, making it difficult to distinguish between the different levels of the house.

Our dataset is comprised of 2.6 million episodes in 32 train houses, 200 episodes in 4 validation houses, and 500 episodes in 10 test houses.

\xhdr{Episode Specification.} An episode starts with the agent being initialized at a starting position and orientation that are sampled at random from all navigable positions of the environment~\cite{savva2019habitat}. The target room is chosen from $R$ if it is present in the house and is navigable from the starting position. We ensure the start position is not in the target room and has a geodesic distance of at least 4m and at most 45m from the target point in the room. During the episode, the agent is allowed to take up to 500 actions. After each action, the agent receives a set of observations from the active sensors. Statistics of the room navigation episodes can be found in the supplementary.

\begin{wrapfigure}{R}{0.5\textwidth}
\centering
\includegraphics[width=0.45\textwidth]{figs/roomnav-spl-figure.pdf}
\caption{\textbf{SPL for Room Navigation.} We compute the geodesic distance from source point to every point inside the room, within 0.2m of the bounds of the room. The point with the smallest geodesic distance is chosen as the \emph{ground truth target point} and the SPL is computed \wrt this point.}
\label{fig:roomspl}
\end{wrapfigure}
\xhdr{Evaluation Metric.} Similar to ~\cite{anderson2018evaluation}, we design two evaluation metrics for room navigation - RoomNav Success weighted by (normalized inverse) Path Length (\emph{\textbf{RoomNav SPL}}) and \emph{\textbf{Success}}. An episode is considered a success if the agent stops 0.2m inside the bounds of the specified target room. We use 0.2m as the room boundaries in Matterport 3D sometimes lie outside the room and this factor ensures the agent has indeed stepped inside the room. As shown in ~\figref{fig:roomspl}, we compute the geodesic distance from the source point to all the navigable points that lie 0.2m within the bounds of the room and choose a ground-truth target point $P^{GT}$ that is closest to the agent's start position, \ie has the shortest geodesic distance. RoomNav SPL is similar to the SPL defined in~\cite{anderson2018evaluation}. Let $S$ indicate `success', $l$ be the length of the shortest geodesic distance between start point and $P^{GT}$ defined above, and $p$ be the length of the agent's path, then
$
\text{RoomNav SPL} = S \frac{l}{\max(l, p)}
$.
To achieve an SPL of 1, the agent must enter the nearest target room, and call stop when it has stepped 0.2m into the room.

\xhdr{Agent.} As in \cite{savva2019habitat}, the agent is modeled as a cylinder with diameter 0.2m and height 1.5m. The actions and the sensors are the same as in~\cite{savva2019habitat}.

\section{Results}
\label{exp}

We design baselines to evaluate the effectiveness of each component of our proposed room navigation framework and to validate our approach. We also report oracle results using ground-truth annotations to establish an upper-bound on the scores that can be achieved by our model.

Table~\ref{tab:results} shows the RoomNav SPL and Success scores on the room navigation validation and test sets (for selected baselines).
Our room navigation framework described in Sec.~\ref{sec:approach} achieves an SPL of 0.31 on validation and 0.29 on the test set. Fine-tuning the point navigation policy on points predicted by the point prediction network improves the SPL to 0.35 on validation and 0.33 on test, making this our best performing model.

\vspace{-0.5mm}
\input{sections/tables/main.tex}

\xhdr{Vanilla Room Navigation Policy.}
Here we compare to an approach that does not use semantic maps to model correlations and does not use point navigation. We ablate both the map prediction and point generation components by training room navigation policy from scratch using PPO, similar to the point navigation policy in Sec.~\ref{sec:point_nav}. Instead of a target co-ordinates relative to current state, it takes in the target room ID as input. The agent receives terminal reward $r_T = 2.5~\text{RoomNav-SPL}$, and shaped reward $r_t (a_t) = -\Delta_{\text{geo\_dist}} - 0.01$, where $\Delta_{\text{geo\_dist}} = d_t - d_{t-1}$ is the change in geodesic distance to the target room by performing action $a_t$.
The SPL using this baseline is 0.10 on validation and 0.10 on test, significantly worse compared to our approach (SPL 0.35). \textbf{\emph{This reinforces the effectiveness of our model, specifically the need to generate maps and use point navigation.}} Note that this approach mimics an approach that tries to solve room navigation via vanilla (``brute force'') reinforcement learning.

\xhdr{Vanilla Room Navigation Policy with Map Generation.}
We ablate the point prediction model in Sec.~\ref{sec:roomnav} and train a room navigation policy to navigate to the target room using \texttt{RGB}\xspace images and semantic maps. We use the map generator to predict semantic maps for each room type. We then train a policy to navigate to the target room. The policy is similar to the room navigation policy described above and takes four inputs: the previous action, the target room represented as an ID, the predicted semantic maps for all rooms embedded as in Eq.~\ref{eq:combine_maps} and the \texttt{Depth}\xspace encoding. It is trained the same way as the room navigation policy.

This baseline achieves an SPL of 0.16, which is worse by a large margin of ~0.2 when compared to our best performing model (room navigation using Map Generation + Point Prediction). \emph{\textbf{The improved performance of our best method emphasizes the significance of the point prediction and point navigation modules in our best performing model.}}

 \begin{table}[t]
    \centering
\caption{Ablation study of our mapping model and point prediction model.}
\begin{subtable}[t]{0.625\linewidth}
    {
    \centering
    \caption{\small{\textbf{Map Generation Models Performance.} Map Generation is a three-way classification problem. Class-1 consists of regions lying outside the house, Class-2 is regions lying inside the house but not in the room and Class-3 is regions lying in the room.}}
    \setlength{\tabcolsep}{1pt}

    \resizebox{\textwidth}{!}{%
    \begin{tabular}{lc cc cc cc cc cc}
    \toprule
    \multicolumn{1}{c}{\multirow{2}{*}{\bf Mapper}} &&
    \multicolumn{1}{c}{\multirow{2}{*}{\bf mIoU}} &&
    \multicolumn{7}{c}{\bf Pixel Accuracy \%} \\
    \cmidrule{5-11}
    && && Class-1 && Class-2 && Class-3 && Avg \\
    \midrule
    CNN && 25.66 && 31.84 && 36.97 && 5.63 && 24.81 \\
    LSTM(no maps) && 32.92 && 41.44 && 48.92 && 10.40 && 33.59\\
    LSTM(ours) && 41.45 && 56.13 && 60.92 && 13.13 && 43.39\\
    \bottomrule
    \end{tabular}
    }
    \label{tab:mapper}
    }

\end{subtable}
\hspace{0.0125\linewidth}
\begin{subtable}[t]{0.325\linewidth}
     {
     \centering
     \caption{\small{\textbf{Point Prediction error using Predicted Maps and Ground Truth Maps.} With ground truth maps, the prediction error is lower, suggesting scope for improvements.}}
     \setlength{\tabcolsep}{1pt}
    \resizebox{\textwidth}{!}{%
     \begin{tabular}{l c}\toprule
         {} & {\bf Error \%} \\
         \midrule
         Pred Maps + MLP & 39.13 \\
         GT Maps + MLP & 22.91 \\\bottomrule
     \end{tabular}
    }
     \label{table:point_prediction}
    }
\end{subtable}
\end{table}

\xhdr{Point Prediction and Point Navigation Policy.}
We perform room navigation using only the high-level point prediction network and the low-level point navigation controller.
We ablate the map generation module and train a modified version of the point prediction network without maps as input. Similar to $f_{point}$ in  Eq.~\ref{eq:b_point}, it generates $P_t = (x_t, y_t)$ but by using only the image representation $f_i(I_t)$ and target room embedding $f_{emb}(tr)$. The agent then navigates to $P_t$ using the pre-trained point navigation policy as in Sec.~\ref{sec:point_nav}.

This method achieves an SPL of 0.17 when the policy is trained from scratch and an SPL of 0.21 when the policy is fine tuned with points predicted by the point prediction network. Our best model surpasses this by a large margin of $\sim$0.15, which \emph{\textbf{shows the advantage of using supervision to learn amodal semantic maps that capture correlations.}} It also indicates the effectiveness of our map generation network. Since the environments in validation are different from train, we can also conclude that predicting semantic maps allows for better generalization to unseen environments as the RoomNav SPL is a direct indicator of how ``quickly'' an agent can reach a target room.

\xhdr{Using Ground Truth (GT) Maps.} To get a better sense of how well our models can do if we had perfect map generation, we train a few of our baselines with ground truth maps instead of generated maps and report results in Table~\ref{tab:results}. With GT maps, Vanilla Room Navigation Policy with Map Generation achieves an SPL of 0.54. Adding GT maps to our best model, with and without fine-tuned point navigation policy we achieve SPL of 0.61 and 0.67 respectively. This suggests that there is still a large room for improvements in the Map Prediction module to perform room navigation more effectively. Table~\ref{table:point_prediction} reports the prediction error of point prediction model when using generated maps and ground truth maps.

\xhdr{Random.} We evaluate using a random agent that takes action randomly among
\texttt{move\_forward}, \texttt{turn\_left}, and \texttt{turn\_right} with uniform distribution. It calls \texttt{stop} after 60 steps, which is the average length of an episode in our dataset. This achieves a RoomNav SPL of 0 on both test and validation, which implies that our task is difficult and cannot be solved by random walks.

\xhdr{Using GT Point Selection} We use the pre-trained point navigation policy defined in Sec.~\ref{sec:point_nav} to navigate to the ground truth target points $P^\textrm{GT}$ in the target room defined in Sec.~\ref{sec:dataset}. This achieves an SPL of 0.82 and 0.79 on validation and test respectively. It provides an ``upper-bound'' on the performance that can be achieved by the room navigation policy, as this indicates the maximum RoomNav SPL that can be achieved by the framework in Sec.~\ref{sec:approach} if the error on point prediction were 0. These numbers are comparable to the SPL values for point navigation on the Matterport-3D dataset in ~\cite{savva2019habitat}, thus indicating our episodes are at least as difficult as the point navigation episodes in~\cite{savva2019habitat}.

\xhdr{Map Generation Ablations.} We also experimented with different semantic map generation models. The results in Table~\ref{tab:mapper} show that the LSTM map generation model described in Sec.~\ref{sec:approach} performs best with a mean Intersection-over-Union (mIoU) of 41.45 and an Average Pixel Accuracy of 43.39\%. The CNN only approach predicts a semantic map from each \texttt{RGB}\xspace image without maintaining a memory of the previous maps. This performs poorly and has a mIoU of 25.66 and an Average Pixel Accuracy of 24.81\%. We train another LSTM model which doesn't use the semantic maps as input at each time step. This has a mIoU of 32.93 and Average Pixel Accuracy of 33.59.

\xhdr{Trajectory Videos.} Qualitative results of our model can be found \href{https://youtu.be/5rjvw8dxUw4}{here}. The first image in the first row shows the \texttt{RGB}\xspace input. The second and third maps in the first row show the location of the agent in allocentric and egocentric views respectively. The last figure on the first row shows two dots, red indicating the ground truth target point in the target room and green showing the predicted point. When only one dot is visible it indicates the predicted and ground-truth points overlap. There are 5 ground-truth semantic maps for each of the 5 room types we consider. The labels at the bottom indicate the room type being predicted. The second row shows the ground truth semantic maps indicating the location of the rooms in the house. The third row shows the maps predicted by our agent. The target room is mentioned at the very bottom, in this case, ``Dining Room". As seen in the video, the model dynamically updates the semantic belief maps and predicts the target point with high precision. The agent is able to detect the room its currently present in and also develop a belief of where other rooms lie. The RoomNav-SPL for this episode is 1.0 as the agent successfully reaches the target room following the shortest path. Additional videos can be found \href{https://www.youtube.com/playlist?list=PLzNOWIcHk2AsUW35ECtPCvkzyi4NsNrHD}{here}.

\section{Room Navigation Task}
\label{sec:roomnav}
The agent is spawned at a random starting location and orientation in a \emph{novel} environment and is tasked with navigating to a given target room -- \eg, ``Kitchen.'' If there exist multiple rooms of same type, the agent needs to navigate to the room closest to its starting location. We ensure that the agent never starts in a room of the target room type \ie if the agent is in a bedroom the target room cannot be a bedroom. Similar to~\cite{savva2019habitat}, with each step the agent receives an \texttt{RGB}\xspace image from a single color vision sensor, depth information from the depth sensor and GPS+Compass\xspace that provides the current position and orientation \emph{relative} to the start position. When there's no GPS information available we only need egomotion, which most robotics platforms provide via IMU sensors or odometry. As in~\cite{savva2019habitat, wijmans2019decentralized}, the agent does not have access to any ground truth floor plan map and must navigate using only its sensors. Unlike point navigation~\cite{savva2019habitat}, room navigation is a semantic task, so GPS+Compass\xspace is insufficient to solve the task and only helps in preventing the agent from going around in circles.

\begin{figure*}[t]
\centering
\includegraphics[width=\textwidth]{figs/priors.pdf}
\caption{\textbf{Architectural patterns in Matterport 3D.} All homes are scaled and aligned such that the kitchen is in the bottom left corner. The maps confirm the existence of structure in house layouts which can be leveraged by an agent trying to navigate to a room in a new environment.}
\label{fig:priors}
\end{figure*}

\begin{figure*}[t]
\centering
\includegraphics[width=\textwidth]{figs/approach.pdf}
\caption{\textbf{Room Navigation using Amodal Semantic Maps:} Our room navigation framework consists of 3 components (1) \textbf{Map Generation:} We predict egocentric crops of top down maps indicating rooms in a house. We use a sequence-to-sequence network which takes as input the current image $I_{t}$, the previous semantic map $M'_{t-1},$ and previous $a_{t-1}$ and predicts the current semantic map $M'_{t}$. (2) \textbf{Point Prediction:} We feed the predicted maps ($M'_{t}$) along with $I_{t}$ and target room ID $tr$ to a network to predict a target point $P^\textrm{pred}_{t}=(x_{t}, y_{t})$ that lies in the target room. The predicted and ground truth points are shown in {\color{green}green} and {\color{red}red} respectively on the top down map. (3) \textbf{Point Navigation:} We use a pre-trained point navigation policy to navigate to $P^\textrm{pred}_{t}$. The process is repeated for $N$ steps until the agent calls \texttt{stop} or $N=500$, whichever happens first.}
\label{fig:overview}
\end{figure*}

\section{Introduction}
Humans have an uncanny ability to seamlessly navigate in unseen environments by quickly understanding their surroundings. Consider the example in \figref{fig:main}. You're visiting a friend's home for the first time and you want to go to the kitchen. You're in the entryway and you look around to observe your surroundings. You see a bedroom in one direction and the dining room in the other direction. A possible, but tedious solution is to head in a random direction and exhaustively search the space until you end up in the kitchen. Another option, and most probably the one you'd pick, is to walk towards the dining room as you are more likely to find the kitchen near the dining room rather than the bedroom. We believe that there are underlying architectural principles which govern the design of houses and our prior knowledge of house layouts helps us to navigate effectively in new spaces. We also improve our predictions of how the rest of the house is laid out as we walk around and gather more information. The goal of this work is to elicit a similar behaviour in embodied agents by enabling them to predict regions which lie beyond their field of view through learned scene priors. The agent models correlations between the appearance and architectural layout of houses to efficiently navigate in unseen scenes.

Currently, there exist two paradigms for the navigation problem (1) Classical path planning based methods: SLAM-based approaches which first build a geometric map and then use path planning with localization for navigation \cite{hartley2003multiple, thrun2005probabilistic, lavalle2006planning}. (2) Learning-based methods: A policy is learned for a specific task in an environment \cite{zhu2017target, gupta2017cognitive, yang2018visual}. In this work, we introduce a learning-based method that unlike previous approaches, predicts an intermediate representation that captures the model's current belief of the \emph{semantic layout of the house}, beyond the agent's field of view. \cite{wu2019bayesian, yang2018visual} represent priors in the form of knowledge graphs or probabilistic relation graphs which capture room location priors. However, we also want to learn other correlations such as estimating the shape of a room by observing parts of it. We choose to model correlations as semantic maps indicating the location, shape, and size of rooms which lie beyond the agent's field of view. This offers a more flexible representation.

In this work, we develop a novel technique to dynamically generate amodal semantic top-down maps of rooms by learning architectural regularities in houses and to use these predicted maps for room navigation. We define the task of room navigation as navigating to the nearest room of the specified type. For e.g., navigating to the bedroom closest to the starting point. We train an agent using supervision to predict regions on a map that lie beyond its field of view which forces it to develop beliefs about where a room might be present before navigating to it. The agent constantly updates these beliefs as it steps around the environment. The learned beliefs help the agent navigate in novel environments.

\textbf{Contributions}
(1) We introduce a novel learning-based approach for room navigation via amodal prediction of semantic maps. The agent learns architectural and stylistic regularities in houses to predict regions beyond its field of view.
(2) Through carefully designed ablations, we show that our model trained to predict semantic maps as intermediate representations achieves better performance on unseen environments compared to a baseline which doesn't explicitly generate semantic top-down maps.
(3) To evaluate our approach, we introduce the room navigation task and dataset in the Habitat platform~\cite{savva2019habitat}.
\section{Related Work}
\label{rel}

\textbf{Navigation in mobile robotics.}
Conventional solutions to the navigation problem in robotics are comprised of two main steps: (1) mapping the environment and simultaneously localizing the agent in the generated map (2) path planning towards the target using the generated map. Geometric solutions to the mapping problem include (i) structure from motion and (ii) simultaneous localization and mapping (SLAM) \cite{durrant2006simultaneous, bailey2006simultaneous, hartley2003multiple, fuentes2015visual, thrun2005probabilistic, cadena2016past}. Various SLAM algorithms have been developed for different sensory inputs available to the agent. Using the generated map, a path can be computed to the target location via several path planning algorithms \cite{lavalle2006planning}. These approaches fall under the passive SLAM category where a human navigates around the environment beforehand to generate the maps. On the other hand, active SLAM research focuses on dynamically controlling the camera for building spatial representations of the environment. Some works formulate active SLAM as Partially Observable Markov Decision Process and use either Bayesian Optimization~\cite{martinez2009bayesian} or Reinforcement Learning~\cite{kollar2008trajectory} to plan trajectories that lead to accurate maps. \cite{carlone2014active} and \cite{stachniss2005information} use Rao-Blackwellized Particle Filters to choose the set of actions that maximize the information gain and minimize the uncertainty of the predicted maps.

A less studied yet actively growing area of SLAM research is incorporating semantics into SLAM \cite{walter2013learning, pronobis2012large, bowman2017probabilistic, wang2019computationally}. \cite{wang2019computationally} and \cite{bowman2017probabilistic} use semantic features for improved localization performance and for performing SLAM on dynamic scenes. However, all of the aforementioned SLAM techniques rely on sensor data which is highly susceptible to noise~\cite{chen2019learning}. They also have no mechanism to learn and update semantic beliefs which can be transferred across environments. \cite{aydemir2011plan} combines a classical continual planner with a decision theoretic
planner for active object search. The planner leverages conceptual spatial knowledge in the form of object co-occurrences and semantic place categorisation. On the other hand, our work learns semantic maps of room locations for the task of Room Navigation. \cite{crespo2020semantic} summarizes the various ways of representing semantic information and using it for indoor navigation. The limitations and open challenges in SLAM have been outlined in~\cite{cadena2016past}. This motivates learning based methods for navigation, which we describe next.

\noindent\textbf{Learning based methods for navigation.} With the motivation of generalizing to novel environments and learning semantic cues, a number of end-to-end learning based approaches have been developed in the recent past~\cite{savinov2018semi, gupta2017cognitive, zhu2017target, mirowski2016learning, fang2019scene}. \cite{savinov2018semi} use a topological graph for navigation and \cite{fang2019scene} propose a memory based policy that uses attention to exploit spatio-temporal dependencies. \cite{mirowski2016learning} jointly learn the goal-driven reinforcement learning problem with auxiliary depth prediction tasks. \cite{wu2018building} introduce the RoomNav task in the House3D simulation platform and train a policy using deep deterministic policy gradient~\cite{lillicrap2015continuous} to solve the same. \cite{chen2019learning} focus on building a task-agnostic exploration policy and demonstrate that this helps for downstream navigation tasks. Most relevant to our work is Cognitive Mapping and Planning~(CMP)~\cite{gupta2017cognitive}. It uses a differentiable mapper to learn a spatial memory that corresponds to an egocentric map of the environment and a differentiable planner that uses this memory alongside the goal to output navigational actions. The maps constructed using this approach only indicate free space and contain no semantic information. On the other hand, we predict semantic top-down maps indicating the location, shape, and size of rooms in the house. Furthermore, unlike CMP where the map corresponds to a top-down view of what the agent is currently seeing, our maps predict regions which lie beyond the agent's field of view by learning architectural regularities in houses. \cite{yang2018visual} uses prior knowledge about spatial and visual relationships between objects represented as a knowledge graph~\cite{krishna2017visual} for the task of semantic navigation. The main disadvantage of such an approach is that the agent cannot modify the graph to learn new priors or update existing beliefs during training. \cite{wu2019bayesian} estimates priors at training time by constructing probabilistic relationship graphs over semantic entities and uses these graphs for planning. However, their graphs don't capture information regarding size and shape of rooms and patterns in houses. Contrary to previous approaches, our work does not rely on pre-constructed maps or knowledge graphs representing priors. We dynamically learn amodal semantic belief maps which model architectural and stylistic regularities in houses. Further, the agent updates its beliefs as it moves around in an environment.

\noindent\textbf{Vision-Language Navigation (VLN).} A different but related task, language guided visual navigation was introduced by~\cite{anderson2018vision}. In VLN, an agent follows language instructions to reach a goal in a home. For \eg, ``Walk up the rest of the stairs, stop at the top of the stairs near the potted plant''. There are multiple works which attempt to solve this problem ~\cite{fried2018speaker,wang2019reinforced,wang2018look}. The room navigation task introduced here is different in that the agent doesn't receive any language based instructions, just the final goal in the form of a room type. In~\cite{anderson2018vision}, the agent "teleports"  from  one  location  to  another  on  sparse pre-computed navigation graph and can never collide with anything. Our Room  Navigation  task  on  the  other  hand,  starts  from  the significantly  more  realistic  setting  as  in  Point  Navigation~\cite{savva2019habitat}  where  the  agent takes the low-level actions such as move-forward (0.25m), turn-left/right (10 degrees) and learns to avoid collisions. In~\cite{anderson2018vision}, paths have an average length of 3.5m whereas the ground truth paths in our task have average length of 125. Overall, compared to VLN the navigation in our task is significantly more challenging. Relative to point navigation where the goal is specified as coordinates, the goal specification in our work is more semantic and closer to language – name of a room. The room navigation task allows us to move towards complex goal specification (e.g., follow an instruction) while keeping the navigation realistic. We'd like to highlight that the methods developed for VLN aren't directly applicable to room navigation as they all rely on intermediate goals in the form of language based instructions which aren't a part of our task specification.

\section{Acknowledgements}

We thank Abhishek Kadian, Oleksandr Maksymets, and Manolis Savva for their help with Habitat, and Arun Mallya and Alexander Sax for feedback on the manuscript. The Georgia Tech effort was supported in part by NSF, AFRL, DARPA, ONR YIPs, ARO PECASE, Amazon.  Prof. Darrell's group was supported in part by DoD, NSF, BAIR, and BDD. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government, or any sponsor.

redpajama_set_name: RedPajamaArXiv


text:
关于柯翌
柯翌中国
国际团队
柯翌业务
柯翌动态
自营品牌
The Guangpu European industry investment guide fund is officially signed
The Guangpu European Industry Investment Guidance Fund has now been established and a signing ceremony was held for the Nanjing Chic Global Commodity Headquarters Base and related industrial chain projects. The event was held at the Italian Embassy in China. Attending the event was Xie Guo Yi, ambassador of Italy to China , Margherita Barberis, chairman of the Italian Foundation, Charles Fischer, chairman of Chic Group, Giuseppe Tamburi, a member of the Tamburi family of the Italian food industry, and Corrado Negri, chairman of Emotion Park of IRGENRE Group. At the ceremony, district leader Wang Lei signed on behalf of the district.
In Italy, witnessed by Ambassador Xie Guoyi and all the guests, Italy China foundation, Nanjing Pukou District People's government, CHIC group and other business representatives on the European light PU industry investment guidance fund and the establishment of Nanjing Chic global commodity headquarters base project contract. At the same time, the industry chain project of Italy Rinaldi food company and IRGENRE group automobile industry as the first European Project Group officially signed with chic.
It is reported that Guangpu European Industrial Investment Guide has an initial fund scale of one billion yuan. It aims to assist small and medium European enterprises in entering China, as well as for the establishment of the "Nanjing Global Commodity Headquarters Project" enterprises. Guangpu European Industrial Investment Guide provides investment, finance, trade, information, technology and other support, to promote the European small and medium-sized enterprises in China. This is done through wholesale, retail, exhibitions, services and other channels for the development of new business model projects. The goal is to set a standard for a new model of enterprise project cooperations, in order to promote the economic and cultural exchanges of European countries
It is understood that the Sino-Italian Foundation is the only designated semi-official executive body for the "Belt and Road"between Italy and the China The Sino-Italian Foundation was established in Milan, Italy, on November 11, 2003. It aims to promote exchanges and cooperation in political, economic, cultural, educational, health and other areas between the two governments and enterprises, through support and coordination of activities. In the past few decades, through its continuous efforts, it has successfully hosted and co-sponsored exchanges among large and medium-sized countries such as the "China Tibet Development Forum", "Milan-Expo Joint Pavilion", "Italian Prime Minister's to visit China", and others .
Chic Group was founded by the Henry Fischer family. The Henry Fischer family has been engaging in business and financial activities since 1883, and is involved in fashion, gold jewelry, watches, finance and other industries. Its trade and related industries have invested in Europe, China, the United States, Dubai, and other parts of the world. After entering the Chinese market in 2008, and after eight years of continuous development in China, it has taken the lead in positioning a free trade zone and experiential projects, created a new modern industrial model that features bonded international famous product discounts, and which coexists in various business models. Chic Group is committed to free trade zone bonded direct sales of new industrial models for investment, development and operational management.
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redpajama_set_name: RedPajamaCommonCrawl


text:
Yellow Faced Tiger (, originally released in the United States as Slaughter in San Francisco) is a 1974 Hong Kong-American martial arts action film directed by Lo Wei, and starring Wong Tao, Sylvia Chang and Chuck Norris.

Summary
In this kung-fu vehicle Chuck Norris plays the biggest drug king in San Francisco who owns everyone, the police department included. He finds his match when a young police officer stands up to the drug lord's power and must fight him and the entire system. A former cop in the San Francisco Bay Area goes after the goons who murdered his ex-partner.

Plot
San Francisco police officers John (Robert Jones) and Wong (Wong Tao) arrest two men raping a girl named Sylvia (Sylvia Chang), utilizing their knowledge of martial arts to take down the assailants, but at the station Sylvia refuses to press charges claiming that she knew the men and that the altercation was all in fun, in clear contradiction of what had truly taken place. In retribution for their arrest, the rapists kidnap John in broad daylight and take him to the beach, where he is beaten by numerous assailants. Wong manages to come to his partner's rescue, killing one of them. For this, he is kicked out of the force by his superior Captain Newman (Dan Ivan) and soon imprisoned, but John still keeps in touch with his old friend. After serving his sentence, Wong takes a job as a waiter.  While waiting tables he meets Chuck Slaughter (Chuck Norris), a criminal mastermind. Slaughter offers Wong a job in his organization but Wong refuses, even as Slaughter threatens to have him killed.

John witnesses some men running away from a bank robbery one morning. While John is chasing some bank robbers. He pursues the robbers, but there are too many for him and one of them pulls a gun. John runs but is caught in the backyard of the Chu family, where the robbers kill him. The next morning when the police find his body the captain, Newman, accuses Mr. and Mrs. Chu of being in on the murder and the bank robbery. Newman has the Chu's arrested and locked up without bail despite Mr. Chu's claims that he had nothing to do with the crime and did not even see the men killing John in his backyard. Wong becomes involved because he wants to avenge John's death and free the Chu's. He begins shaking down Chinese criminals looking for clues, and they lead him to Captain Newman. Newman, who was working for Mr. Slaughter, intended to frame the Chu's for the crime committed by Slaughter's own men.

Wong fights Newman and kills him. After Wong kills Newman, he attempts to warn Chu's defending lawyer, but the boss of the gang has already murdered the lawyer (Joy Sales). Wong is confronted by Slaughter's men on the street, and fights them all off, eventually confronting Slaughter himself. Wong defeats Slaughter after a heated battle, and almost kills him when the police burst onto the scene. The new police captain stops Wong from delivering the lethal blow, informing him that he knows the whole story. Wong is reinstated to the police force, but refuses to carry a pistol saying "(He) has no need of guns".

Cast

 Don Wong Tao as Officer Don Wong (Yellow Faced Tiger)
 Sylvia Chang as Sylvia Clu
 Chuck Norris as Chuck Slaughter / Himself
 Dan Ivan as Captain Newman

Release

Theatrical
The film played theatrically in the US in 1981 as Slaughter in San Francisco.

Home Video
Videocassette releases in the 1980s and 90s used the Slaughter in San Francisco title as well as Karate Cop.

Reception

Critical response
American critics noticed that it was a low budget martial arts film, and an early Norris acting credit, taking advantage of his current fame.

See also
 Chuck Norris filmography

References

External links

1974 films
1970s action thriller films
American action thriller films
Hong Kong action thriller films
American martial arts films
Hong Kong martial arts films
Cantonese-language films
1970s Mandarin-language films
Golden Harvest films
Films shot in San Francisco
Films set in San Francisco
Films with screenplays by Chang Yung-hsiang
1970s American films
1970s Hong Kong films

redpajama_set_name: RedPajamaWikipedia


text:
Q: Return to calling page instead of dashboard after Login (Laravel 8) In my Laravel 8 Application link to login route is provided on different locations, i.e., user can login from multiple pages.  By default, after login user is redirected to the dashboard. I am looing for a way to return the user to same page from where he clicked on the login button.
I am using Laravel with Jetstream and Livewire.  The documentation says that I can do this by changing "public const HOME = '/ dashboard';" to the desired destination, but in my case the destination can be more than a single url.
I tried 'redirect()->back()' in place of '/ dashboard' but it does not work.  Please help what change is required.

A: Save previous url when you redirect user to login page and after authentication of user redirect him on same page with the session you saved. Try this:
public function showLoginForm()
{
     session(['link' => url()->previous()]);
     return view('auth.login');
}

protected function authenticated(Request $request, $user)
{
     return redirect(session('link'));
}

redpajama_set_name: RedPajamaStackExchange


They Are In A Pretty Good Condition Except One Is A Little Peeled , Which You Can See In The Picture.
Designed with leatherette upper, criss cross form, open toe, extra long lace up front and zipper closure.
Designed with leatherette upper, open toe, criss cross form with adjustable bukles and zipper closure.


text:
QUESTIONS?CONTACT US
Anja Lambrecht
London Business School
Anja's research focuses on digital marketing and the digital economy, emphasizing online targeting and advertising, as well as promotion and pricing. Professor Lambrecht has examined how firms can use retargeting to reach out to consumers and how firms can advertise on Twitter to early trend propagators. Her work on digital pricing has examined, among others, freemium pricing for online content sites and mobile apps. Her current work also focuses on how spillovers between different economic actors in digital markets can lead to apparent algorithmic bias.
Her work has been published in leading marketing journals, including the Journal of Marketing Research, Marketing Science and Management Science. In 2014, she received the Paul E. Green Award, in 2016 and 2017 she was a finalist for the INFORMS Society of Marketing Science Long Term Impact Award and in 2018 she received the William F. O'Dell Award. She is an Associate Editor at Marketing Science and the Journal of Marketing and a member of the Editorial Board of the Journal of Marketing Research.
Before joining London Business School, she was a Visiting Assistant Professor at the UCLA Anderson School of Management and a Visiting Scholar at Stanford Graduate School of Business. Prior to her academic life, Anja Lambrecht worked as consultant at McKinsey & Company in Frankfurt. Anja has a Ph.D. from Goethe University, Frankfurt. She also holds a Diplom-Kauffrau from Goethe University, Frankfurt and a Maîtrise des Sciences de Gestion from Université Paris-Dauphine, Paris.
David C. Parkes
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Karim Lakhani
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Keystone Strategy uses cookies to improve functionality and performance of this site. More information can be found in our PRIVACY POLICY. By continuing to browse this site, you consent to the use of cookies.ACCEPT


text:
Home | Contact Us | Booking Form | Travel & Accommodation
What's On Get Tickets Speakers News
About Animex	Partners Downloads Register
Disney Assistant Art Director debut at Talk!
Karen deJong, currently working on Disney's latest feature, Meet The Robinsons, will feature in an amazing line up at Animex in February 2007.
Karen's credits include work on a wide range of feature films from live action to animation including Lord of the Rings Return of the King, Chicken Little, Dinosaur, Gone in 60 Seconds, Contact and Batman & Robin.
She has worked in a variety of roles including visual development, background painter, matte painter, texture painter and now as assistant art director for Disney Feature Animation. Karen will undoubtedly bring her vast experience to an Animex crowd and ensure that everybody fortunate enough to be there is inspired and energised.
Book your festival pass


We believe anyone with a great idea that makes impact should have the opportunity of trying to become a successful entrepreneur within or outside of a company. That's why we support both startup founders and corporates to be entrepreneurial by helping in developing the necessary skills, learning the best method and using the right tools.
The HighTechXL Accelerator is an elite program designed exclusively for advanced technology and hardware entrepreneurs. We know that hardware is tough. This is why we translated years of product development and experience in the high-tech hardware industry into a practical blueprint that supports hardware founders from A to Z.


GameStop announced last month that.
Crew assignments feel more important than ever as you face bigger and more varied foes while exploring numerous alien environments.
The Xbox 360 runs Netflix, Hulu and Amazon in addition to Microsoft's own "movies and TV" service, and so this could serve as a free alternative to a Roku or other streaming device for someone with a bit of space available in their entertainment system.
Read our full, the Elder Scrolls V: Skyrim review, buy it now from: Amazon.In other GameStop deals news, the store is selling.7: Do you have a Steam Group?Afterward, it kills you again for good measure.The gameplay has been cleaned up, and it feels much smoother and quicker than the original.Meme, status, submission, year 2007, origin 4chan, tags trolling, xbox 360, call, consoles, why, do, they, it, a, additional References.Full of epic, frustration-filled boss battles and find-your-own-way exploration aided only by short messages left by other players, Souls is equally punishing to all who dare to play.But this is gaming: things move fast.The voucher will have a unique 20-digit code that will expire on Dec. .
Grand Theft Auto V You know what to expect with Grand Theft Auto : drug dealers, cars, and a colorful cast of characters.
With just a week to go before Christmas, GameStop has rolled out a new nationwide promotion where it's giving away.
And on January 12th, 2010, Redditor vladikov377 posted an image.The result hb studio discount code was a massive success, and the innovative.A.T.S.Usually, they'll add 10 for store credit.Before its release in 2011, The Elder Scrolls: Skyrim became one of the most hyped games of all time.The story starts off with a bang, as the protagonist Shepard is brutally killed, only to be rebuilt by Cerberus a sort of paramilitary organization with a strong political agenda.The enthralling story pulls you in from the moment you hear Mark Hamill as the Joker, only to follow it up with superb puzzles and fighting mechanics that take advantage of a slew of iconic weapons from Batmans arsenal.Spend the 15 (which makes 200) and add the 10, you'll get a total of 220.Check out r/badkarma or the, shared Ban did bradley cooper win a tony List.Contact us after completing a quality giveaway.If you have an extra wireless john wayne spice rub gift set controller, they'll probably give you another.This could be a great "buy" for someone tangentially interested in video games that's just never gotten up enough curiosity to actually go buy a console.
You can explore your own way, or follow the path of the Dragonborn as you learn to speak the language of dragons, which have returned after lying dormant for thousands of years.
But close if you buy a lot of used games and accessories at GameStop.


text:
package net.firejack.platform.core.config.meta.element.resource;

public class TextResourceVersionElement extends ResourceVersionElement {

    private String text;

    /**
     * @return
     */
    public String getText() {
        return text;
    }

    /**
     * @param text
     */
    public void setText(String text) {
        this.text = text;
    }

    @Override
    public boolean equals(Object o) {
        if (this == o) return true;
        if (o == null || getClass() != o.getClass()) return false;
        if (!super.equals(o)) return false;

        TextResourceVersionElement that = (TextResourceVersionElement) o;

        if (text != null ? !text.equals(that.text) : that.text != null) return false;

        return true;
    }

    @Override
    public int hashCode() {
        int result = super.hashCode();
        result = 31 * result + (text != null ? text.hashCode() : 0);
        return result;
    }

}

redpajama_set_name: RedPajamaGithub


Welcome to 8tracks radio: free music streaming for any time, place, or mood. tagged with indie, chill, and sad. You can also download one of our free apps to enjoy internet radio on your favorite device.
The star studded night sky.
Crying is good sometimes .
They said we're gonna make it.


text:
Celebrate Hong Kong Special Administrative Region Establishment Day!
2018-07-01 MET Column
Mong Kok (旺角), one of the major shopping areas in Hong Kong. *Photo from Wikipedia
July 1 is Hong Kong Special Administrative Region Establishment Day, or Establishment Day, which is celebrated annually since 1997.
The holiday commemorates the transfer of the sovereignty of Hong Kong from the United Kingdom to the People's Republic of China and the establishment of the Hong Kong Special Administrative Region.
Hong Kong's territory was acquired by United Kingdom from China through three separate treaties: the Treaty of Nanking in 1842, the Treaty of Beijing in 1860, and The Convention for the Extension of Hong Kong Territory in 1898, which gave the UK the control of Hong Kong Island, Kowloon (area south of Boundary Street), and the New Territories (area north of Boundary Street and south of the Shenzhen River, and outlying islands), respectively. Although Hong Kong Island and Kowloon had been ceded to the United Kingdom in perpetuity, the control on the New Territories was a 99-year lease.
The Sino-British Joint Declaration was signed by the Prime Ministers of the People's Republic of China and the United Kingdom governments on December 19, 1984 in Beijing. The Declaration entered into force with the exchange of instruments of ratification on May 27, 1985. In the Joint Declaration, the PRC (the Republic of China) Government stated that it had decided to resume the exercise of sovereignty over Hong Kong (including Hong Kong Island, Kowloon, and the New Territories) with effect from July 1, 1997, and the UK Government declared that it would restore Hong Kong to the PRC with effect from July 1, 1997. In the document the PRC Government also declared its basic policies regarding Hong Kong.
In accordance with the "One country, two systems" principle agreed between the UK and the PRC, Hong Kong would become a special administrative region where the socialist system of PRC would not be practized, and Hong Kong's capitalist system and its way of life would remain unchanged for a period of 50 years from the date of handover. The Hong Kong handover ceremony officially marked the transfer of sovereignty from the United Kingdom. It was an internationally televised event with the ceremony commencing on the night of June 30, 1997 and finishing on July 1, 1997.
In commemoration of the day, we introduce you to lovely people from Hong Kong and a Japanese who is related to the area.
*Click the photo below and read their stories!
"There are really so many treasures in every place in Japan." – Chiu Yin Mei, Catering Chef/Food and Beverage Public Relations Expert
Happy Birthday to DR Congo!
Happy Canada Day!
Small place, large vision – You will find something spectacular even in small spaces
Interviewed & written by Isao Tokuhashi Mail to: info@myeyestokyo.com Chiu Yin Mei (Hong
I always want to know something that I don't know. I couldn't have come this far without curiosity.
Interviewed & written by Isao Tokuhashi Mail to: info@myeyestokyo.com Tomoko Komatsuzaki
Japan is a place where innovation and consideration live together.
Interviewed & written by Isao Tokuhashi Mail to: info@myeyestokyo.com Chan Wing Kong (Hon


Spend the whole day fishing with a friend and bring home dinner in the Coleman Colorado Fishing Kayak. This durable vessel is constructed with an 18-gauge PVC to get into those out-of-the-way fishing spots without worry of puncture.
Paddle in comfort or add the feature of a trolling motor on this inflatable kayak and store the paddles away in the built in storage space.
Enjoy hands free fishing with the Berkley Quick Set Rod Holders. Sit back in your adjustable seats, enjoy the water, and bring in your daily catch with ease in the Coleman Colorado.
Rugged design: This inflatable fishing kayak has a weight capacity of 470lbs. It is constructed from 18-gauge PVC with a 1000D tarpaulin bottom. Its 840D nylon cover provides extra protection from punctures in case you run into obstacles. But just in case, the multiple air chambers allow you stay afloat if one is punctured. The patented Airtight System is guaranteed not to leak.
Rod Holders: The Coleman Colorado fishing kayak is designed with adjustable Berkley Quick Set Rod Holders for hands-free fishing.
Trolling Motor Fittings: Expand your fishing capabilities with the option of using a trolling motor on this kayak. Use the conveniently designed paddle holders to secure them out of the way while using the motor.
Storage: Mesh storage pockets are perfect for keeping snacks, sunscreen, water, etc. handy while you are fishing. The Coleman Colorado fishing kayak also features D-rings so you can attach other equipment.
Portable: Carry handles allow you to pull this inflatable kayak in and out of the water with ease, and to carry over shallow areas if the need arises. The kayak folds down into an included carry bag making transport from vehicle to water quick and easy. It weighs about 40 lbs.
Other Features: NMMA certified, adjustable seats, pressure gauge, included skeg, and a one year limited warranty.
Spacious: The Coleman Colorado fishing kayak offers enough space for two adults plus gear. Customers reported filling this inflatable kayak to its weight limit and being perfectly comfortable. There is enough space for 6 fishing rods and spots to keep the paddles out of the way while using a trolling motor.
Easy Set-Up and Transport: Using a dual action pump and the included pressure gauge, this kayak takes about 10 minutes each to inflate and deflate. The lightweight construction and carry bag make for a comfortable and easy walk on longer trails to the water.
Trolling Motor Option: Customers love the option of adding the Coleman trolling motor to their fishing experience when using the Coleman Colorado. This item is highly recommended for finding those fishing spots that larger boats cannot go. It's perfect for those all day fishing trips.
Sturdy and Well Built: Reviewers are pleasantly surprised by how sturdy this inflatable kayak is. One customer compares the craftsmanship to that of the Navy's version of this kayak and raves about its solid foundation. People feel very safe in this kayak. It's Airtight System guarantees not to leak.
Versatile: Not only do people love the option of trolling or paddling but also to be able to go out alone or with someone. This kayak is easy to maneuver and remains just as sturdy whether you have one or two people onboard.
No Pump or Paddle: You will need to factor in the cost of a paddle and pump when purchasing this inflatable kayak. Customers recommend 96" inch paddles and a dual action air pump for the Coleman Colorado fishing kayak.
It was tough to find a downside to the Coleman Colorado fishing kayak. This fishing vessel proves to be durable and sturdy for fisherman and leisure paddlers alike.
It has the versatility to hold one or two paddlers and gear without compromising maneuverability.
The feature that allows a trolling motor to be attached adds a whole new element to the fishing experience.
This product is cleverly designed to hold fishing rods making this watercraft a fisherman's dream. It's nice to be able to keep this kayak in the trunk of your car for spur of the moment fishing trips.
The inflatable adjustable seats allow you to be out on the water all day in comfort.
Quietly sneak into the most remote fishing spots that you could not reach in a hard-shelled boat.
If you are looking for a other options please see our homepage.
The ArmyBassAnglers & the Coalition are not satisfied with just fishing the hardest team tournaments in Texas. More importantly, they use their ability to fish in national team tournaments and the support of some nationally recognized sponsors to raise the awareness and generate assistance for some very note worthy charitable causes and events. More details at http://armybassanglers.com.


1666 Callowhill Street has a Walk Score of 96 out of 100. This location is a Walker's Paradise so daily errands do not require a car.
1666 Callowhill Street is an eight minute walk from the BSL NRG Station to Fern Rock TC at the Race Vine Station - BSL stop.
This location is in the Fairmount - Art Museum neighborhood in Philadelphia. Nearby parks include Franklintown Park, Logan Circle and JFK Plaza.
Explore how far you can travel by car, bus, bike and foot from 1666 Callowhill Street.
1666 Callowhill Street is a Rider's Paradise which means world-class public transportation. Car sharing is available from Zipcar and RelayRides.
1666 Callowhill Street compared to all of Philadelphia.
How do you feel walking near 1666 Callowhill Street?
1666 Callowhill Street is in the Fairmount - Art Museum neighborhood. Fairmount - Art Museum is the 19th most walkable neighborhood in Philadelphia with a neighborhood Walk Score of 91.


text:
Q: Java - Obtain text within script tag using Jsoup I am using the Jsoup library to read a URL. This url has text within a few <script> tags. Is it possible for me to obtain the text within each <script> tag? Please note that I am not asking to parse a Javascript file as I am already aware JSoup does not allow that. The actual source code of the URL has text within a script tag, I need that.
doc = Jsoup.connect("http://www.example.com").timeout(10000).get();

Element div = doc.select("script").first();
for (Element element : div.children()) {
System.out.println(element.toString());
}

This is what one of the script tags look like from the source code:
<script type="text/javascript">
(function() {
...
})();
</script>

A: Document doc = Jsoup.parse(html);
Elements scripts = doc.getElementsByTag("script");
for (Element script : scripts) {
    System.out.println(script.data());
}

A: According to your case the solution will be as below.
Document doc = Jsoup.connect("http://www.example.com").timeout(10000).get();
Elements scripts = doc.select("script");

for (Element script : scripts) {
    String type = script.attr("type");
    if (type.contentEquals("text/javascript")) {
        String scriptData = script.data(); // your text from the script
        break;
    }
}

A: Alternatively, you could use the Element#html() method that returns the inner html of an element.
Since 1.11.1: Use efficient Element#selectFirst() method to find the script element.

Document doc = Jsoup.connect("http://www.example.com").timeout(10000).get();
Element scriptElement = doc.selectFirst("script");

// Don't forget to check scriptElement is not null...

String jsCode = scriptElement.html();

Up to Jsoup 1.10.3: Combine Element#select() and Elements#first() calls to find the script element.

Document doc = Jsoup.connect("http://www.example.com").timeout(10000).get();
Element scriptElement = doc.select("script").first();

// Don't forget to check scriptElement is not null...

String jsCode = scriptElement.html();

A: Yes. You can use Element#getElementsByTag() to get all the script tag . Each script tags will be represented by the DataNode.
 Document doc =Jsoup.connect("http://stackoverflow.com/questions/16780517/java-obtain-text-within-script-tag-using-jsoup").timeout(10000).get();
 Elements scriptElements = doc.getElementsByTag("script");

 for (Element element :scriptElements ){
        for (DataNode node : element.dataNodes()) {
            System.out.println(node.getWholeData());
        }
        System.out.println("-------------------");
  }


What would your friends say if you invited them over and then told them they were going to be doing all the floor and carpet cleaning in your house? It may be a good way to determine who your real friends are, but it's just as easy to get help by calling us. And we'll still talk to you afterwards. ServiceMaster Restoration and Cleaning Services by Skip exists precisely for this purpose. We'll take care of your residential cleaning services in Somonauk, IL, when you don't want to tackle the job. You could sacrifice an entire weekend of precious free time on hard surface floor cleaning, carpet cleaning, and tile and grout cleaning and still not achieve the same level of clean as a professional cleaning service.
Just how many free weekends do you have? If you're like many of us, much of the week is already filled with work, commuting to and from work, and social and school commitments. And how often do you spend extra hours at work or take assignments home from the office? You need your weekends for fun and relaxation. When you weigh the value of free time against the value of using a residential cleaning service, you can see why so many find it an easy choice. The work is done to a high standard of quality by trained technicians using the best equipment, so you don't lift a finger and you no longer have to worry about how and when to fit house cleaning into your schedule.
We'll do your tile floor cleaning, grout cleaning, carpet cleaning, and even clean your area rugs, too. If you have vinyl floor cleaning needs, any kind of composite or laminate flooring, or stone, marble, or hardwood surfaces, we can handle it. We'll also help keep your environment clean with HVAC duct cleaning, from the vents to the furnace and back again. We can set up times that meet your needs and do the job while you're at work so you come home to a fresh, clean house. Don't use your valuable time to clean and don't expect your friends to either. Let ServiceMaster Restoration and Cleaning Services by Skip be your home cleaning choice in Somonauk, IL.


How did I do that?
Well, it should have grown and I thought I didn't do too bad this week, but, I confess, I can't see much of a change from last week. It is beginning to blur a little in terms of where I was and where I am because it's the same.
Despite a bit of a scare yesterday when the cable snapped and a large bunch of stitches made a break for freedom, I'm glad to report reasonable progress, and ordering of replacement cable.
Had a op on my knee Thursday, it's been really sore, knitting wasn't really on the cards much for a couple of days. But here's the latest pic.


This page displays sold house prices for Dorchester Close in Aylesbury.
Dorchester Close in Stoke Mandeville, Aylesbury consists predominantly of detached houses. Properties on Dorchester Close typically have values around £400,000 - £550,000, but larger detached houses can command upwards of £550,000.
Map showing Dorchester Close in Aylesbury.


Home → Articles → MIP™ Colostrum – a natural resource for your body's Glutathione requirements?
Free Radicals: What can it do to my Health?
Having contained an unpaired electron in an atomic orbital, a free radical is independently existent in human body, and shares similar characteristics with other radicals. These radicals are highly reactive and mostly unstable; however, their capability to overwhelm the body's coping mechanism against disease is surprising. According to an article, oxidative stress is produced in the presence of a growing number of free radicals combined with major cellular deterioration. Bruce Ames, a famous scientist in anti-oxidative studies, assessed the rate of cellular collision with free radicals by showing how just one cell of the human body hits about 10,000 free radicals per day. A damage or loss in bodily lips, proteins and nucleic acids, according to research, results in too many free radicals with the ability to produce oxidative stress on a daily basis.
Oxidative Stress and Ageing Process: Am I at risk?
Human body disruptions take place significantly the moment oxidative stress exceeds controllable limits, thereby causing cardiovascular diseases, ulcers, musculoskeletal disorders, cancers, autism, Parkinson's and Alzheimer's diseases, arthritis and other long-term health issues. It is also commonly cited as the free radical theory of aging associating free radical evolution with the ageing process in which age is taken as a factor giving birth to more eruptions of free radicals in human bodies. When free radicals cause deterioration to DNA, resulting cellular diseases lead to untimely aging. Research around free radical proliferation suggests dietary measures for re-balancing the anti-oxidants of the body with their presence by taking in antioxidants as part of routine meals. Recommended dietary allowance (RDA) suggests the use of antioxidant vitamins and minerals for controlling the excessive cell-radical collisions with the use of vitamins and beta-carotene that is not a common dietary component for most individuals. Think again if you thought you are still not at risk!
What is Glutathione? Do I really need it?
Preventing inflammation through protecting polyunsaturated fats.
Think about some nutrients that can strengthen the immune system and develop healthy body mechanisms to fight back angry free radicals?
Well, recent researches support intake of oysters, nuts, seafood, dairy products, beans, red meat and poultry being zinc-rich sources which helps immune systems and provide anti-disease defence against free radicals. However, often individuals have reported taking too much to boost immunity overnight, which often results in otherwise. While these nutrients and minerals scientifically exposes human bodies to major defence mechanism against diseases and bacteria, taking too much of these elements can result in exhaustion of enzymic processes which slows down recovery from disease and affects immunity in an adverse manner. A moderate approach is therefore, recommended by practitioners and medical experts who suggested that minerals, zinc and vitamins are not too helpful in building immunity if taken excessively in high amounts. This means while individuals should keep a track of their intake of defensive antioxidants, excessive of anything should be avoided, as well.
Research in antioxidant therapy thrives on the use of glutathione as an antioxidant therapy for reducing the formation of free radicals and oxidizing sensitive molecules in human body. Additionally, glutathione's ability to react with non-organic and organic body substances also helps in detoxification.
What does MIP Colostrum offer to me?
Apart from increasing your health-related knowledge on this sensitive topic of medical interest, MIP Colostrum offers much more than you may have initially imagined. It brings to you, a unique solution to fulfil all your Glutathione needs!
MIP™ Colostrum is a proven success for providing beneficial nutrients for optimal health and vitality for long-term bodily sustenance against damaging free radicals. The very first reason why you should choose MIP™ Colostrum is for its contribution to biological stability and bodily health using better and faster coping nutrients in moderate amounts. This does not only provide you with the results in a faster and better manner, it also ensures that your supply of antioxidants does not exceed bodily requirements. Recommended by top athletes, guarantees vitality and bodily strength, fights bacteria, viruses and parasites, helps with better digestion, improves epigastric health and bowel function, provides faster recovery against injury and sport accidents, and builds up stronger tissues.
For all this and more, MIP™ Colostrum should be a confident choice for a healthier 'you'!
Bast, A., & Goris, R. J. A. (1989). Oxidative stress. Pharmaceutisch Weekblad, 11(6), 199-206.
Di Domenico, F., Barone, E., Perluigi, M., & Butterfield, D. A. (2015). Strategy to reduce free radical species in Alzheimer's disease: an update of selected antioxidants. Expert review of neurotherapeutics, 15(1), 19-40.
Ghezzi, P. (2011). Role of glutathione in immunity and inflammation in the lung. Int J Gen Med, 4, 105-113.
Knight, J. A. (2000). Review: Free radicals, antioxidants, and the immune system. Annals of Clinical & Laboratory Science, 30(2), 145-158.
Raha, S., & Robinson, B. H. (2000). Mitochondria, oxygen free radicals, disease and ageing. Trends in biochemical sciences, 25(10), 502-508.
Santo, A., Zhu, H., & Li, Y. R. (2016). Free Radicals: From Health to Disease.Reactive Oxygen Species, 2(4), 245-263.


It is no astonishment that I subsequent to to mix things happening re here. One thing however that I never untouched out, one that took me continually to pull the put into action on, was our associates room media console. The paint color house misrepresented that however; and is our perfect sideboard. A stunning modern, coastal choose for our space. It was correspondingly worth the wait.
I searched and searched for months and could not locate the just right replacement. They were every too dark, too bulky, too traditional, too modern and later I finally found what I thought was the perfect fit. sadly later in the room I realized the wood heavens was too golden for the sky compared to the other woods in the room. Thankfully I realized that this sideboard looked amazing in the dining room, in view of that it was not a loss but did send me encourage upon the hunt for our perfect media console.
Then I found The paint color house and it was adore at first sight. I was appropriately nervous after ordering that I would end up having the similar wood way of being dilemma taking into account this piece as the last, but unquestionably optimistic this one would work and function it did!! I more or less cried taking into consideration I saying how absolute it was for our needs. Finding the just right weathered coastal fragment that still complimented our enlightened and minimal aesthetic had proven bordering to impossible. The paint color house came into my energy and misrepresented that. Im in love.
This piece can of course be active as a sideboard in your dining room we just opted to utilize as a media console instead. For us it works for that reason well as a media console is because of the rattan doors. The rattan is thick and substantial plenty to conceal the components from view. yet yet possesses passable gapping in the middle of the weave that you can keep the doors closed and law all the tech behind a remote. WIN. Especially for us subsequent to little avid kiddo hands roaming just about the house.
Overall this is just a great piece that could conscious harmoniously along with many designs styles. From modern, to coastal, eclectic, or minimal even in a more conventional home. It has correspondingly many elements that lend itself to in view of that many styles. The clean lines, the texture of the rattan, the weathered finish alongside to the smooth black legs. Its just a astonishing fragment that we adore and are thrilled to have in our space.


text:
Getting to Know the Cynthia & George Mitchell Foundation's Texas Grantmaking
June 22, 2016 / Alyssa Ochs
We recently profiled the Cynthia and Georgia Mitchell Foundation's localized giving as it relates to Texas nonprofits. This is a funder that we've previously brought up because of its science research funding, but it's also squarely focused on addressing the challenges of sustainability in Texas.
So I connected with Marilu Hastings, Mitchell's vice president of the sustainability program, to get a better sense of this funder's grantmaking. We always like to ask foundations what their theory of change is, and sometimes what comes back goes beyond the standard canned response that you might expect. Here's how Hastings answered:
The Mitchell Foundation's theory of change is that only well-informed decision-makers, armed with the latest and most robust scientific research and data available— information that is communicated effectively—will be able to make decisions today to protect generations of Texans to come.
Scientific knowledge on complex issues must be useful to end users to inform their decision-making. In fact, applied research should be co-designed with the end user to ensure its value. Research must be cutting edge to solve tomorrow's problems—ideally preemptively, before problems become intractable. Thus the foundation funds "the next thing," which includes solutions to a number of issues that we help to identify and define. If science is not accessible and understandable to influencers, leadership, and decision-makers, its findings will be inconsequential even if their importance is critical.
Grounded in the rigor of scientific knowledge, the foundation achieves impact by placing that knowledge in the context of political, social, and cultural dynamics. We call this "knowledge to action," and it represents a fundamental tenet of our theory of change. Our grantmaking centers on ensuring that community leaders, advocates, and decision-makers are armed with rigorous research, data, and knowledge. Q: What is the theory of change behind your grantmaking? (continued) If we identify gaps in knowledge, we do our best to fill them with strategically focused research funding. If our advocates aren't communicating vital information effectively, we fund an improved outreach strategy. If decision-makers do not have relevant information in the form and time they need it, we convene meetings with experts to help provide the knowledge that leaders need to make timely decisions. If outdated policies need to be revisited in light of emerging scientific information, we work with grantees to help educate leaders about new knowledge.
Fortunately, the Mitchell Foundation is looking for new grantees. More specifically, it typically looks for grantees that are connected to state and federal government agencies, scientific organizations, and nonprofit networks. Groups that can bridge emerging scientific knowledge and policy options that work are given priority.
However, it is of key importance how prospective grantees demonstrate their understanding of sustainability, policy, and scientific construct within the state of Texas. Since Texas is a conservative "red" state in every sense of the word, strategies that work in other parts of the country don't necessary work here.
Hastings said:
Our grantees, however, can win here if they have a sophisticated understanding of the political landscape and strong ties to influencers at both the grassroots and leadership levels, are knowledgeable of the complex science of the Texas environment, and are capable of developing robust and innovative strategies that are well communicated.
Right now, the foundation is undergoing a transition and is in the midst of a vigorous planning process that will take a few years to develop. Stay tuned to learn more about two new grantmaking programs: one that's focused on land conservation and the other about community development in Galveston, Texas. We expect to start seeing the first grants awarded for these new programs in 2019.
When I asked the foundation for a piece of advice that they'd offer to prospective grantees, Hastings suggested that nonprofits should be able to clearly demonstrate the following four things to the program staff:
Your work's point-of-differentiation with respect to current research or advocacy;
How your work will contribute to the achievement of the foundation's mission;
How well prepared you are to work effectively in the Texas policy landscape; and
How your work relates to and complements the work of current grantees.
For a list of the types of projects that Mitchell does and doesn't fund, see the foundation's website. Learn more about this funder in our full profile, Cynthia and George Mitchell Foundation: Southwest (Texas) Grants.
June 22, 2016 / Alyssa Ochs/
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The Orix Foundation Has $3 Million ...
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The client needed a carefully branded website that they could easily update. I provided them with a custom designed theme and WordPress installation. The visuals and typesetting are a blend of classic and modern – reflecting the content of the website.


SINGAPORE, Dec 31 (Reuters) - Oil prices edged higher on the last trading day of the year on Monday, taking a cue from firmer stock markets, but remain on track for the first yearly decline in three years amid concerns of a supply glut.
Brent crude futures - the international benchmark for oil prices - rose 17 cents, or 0.3 percent, to $53.38 a barrel by 0115 GMT. Brent has shed about 20 percent in 2018 following two years of successive growth.
U.S. West Texas Intermediate (WTI) crude futures were at $45.52 a barrel, up 19 cents, or 0.4 percent, from their last close. WTI is down nearly 25 percent this year.
"Investors are looking for bargains in an illiquid market (today)... If Trump gets over trade issues with China expect economic demand to surge," said Jonathan Barratt, chief investment officer at Probis Securities in Sydney.
"Also, drivers on Iran with waivers ending in May are still not been put to bed. Hence, it could get ugly at any time and I expect this is a diversion for Trump when needed," Barratt added.
"Investors will require substantive indications from global economic fundamentals and oil inventories to move in positively on oil prices in 2018," said Benjamin Lu Jiaxuan, commodities analyst at Singapore-based brokerage firm Phillip Futures.
Earlier this month, the Organization of the Petroleum Exporting Countries (OPEC) and its allies including Russia, agreed to curb output by 1.2 million bpd starting in January in a bid to clear a supply overhang and prop up prices.


PC was all over UNH in the 3rd. That shot that hit the crossbar was one of several good opportunities they had. If they can come out and play for 60 minutes the way they did over the second half of today's game, I think they've got a good shot. Anything less than that, though, and it's a whole other matter.
If Mount finishes off LaSalle maybe Meehan will be available.
So was I. That was a rough one to give up at that point in time too.
I think we got to hit a bit more and slow UNH down a bit, also hope Gillies bounces back....maybe, giving what in the other thread you decribed as his worst goal of the year (and I agree), he'll be extra motivated to carry the team.
I obviously haven't seen as many games as you cav but I would imagine given his rep and what I've seen of him, that was a really atypical goal. And it was definitely a bad one in a bad spot to give up.
Totally agree -- his performance tomorrow is another huge factor.
The lineup tonight was puzzling to me. Can someone give me some insight as to why all the changes?
Would be interesting to know why....I thought the Rooney line played pretty well Friday night. I guess not.
The Rooney thing is as you describe. In Divver's pre game tweets he first noted line up changes, then said Demopoulous is out day to day, then corrected and said all healthy scratches. Obviously don't have his information but I wonder if Demo is not 100% and he wanted to protect if possible for later use.
Didn't Adams get injured real early in the year? Can he get a redshirt?
I agree with you on Goss. The worst part of Harvey's game is that he screws up when he is not being pressured.
I believe Adams was hurt in the Friday night game (November 2nd) at Vermont. He played in the Friday game, but was not in the lineup Saturday night going forward.
I think he is eligible if he desired a medical red shirt season and PC wants him for a 5th season. On his part remember he is drafted and might want to give it a go, I guess what happens next year tells us if PC wants him.
I'm not sure who I'd want to see to be honest. Not Bunyon.
"I have this reeeealllly shiny whistle that I just got. See? SEE?! It is so cool. With it, I can make people do what I want. I just blow this thing whenever I want...so COOL!"


text:
LOAN: GOALKEEPER JONAS LÖSSL JOINS TOWN
David Threlfall-Sykes
@htafcdotcom
Danish international signs for the season
- Jonas Lössl signs on loan for Town
- Goalkeeper joins from Bundesliga side 1. FSV Mainz 05
- Town has option to buy at the end of the loan
Huddersfield Town has tonight completed the signing of Danish international goalkeeper Jonas Lössl on a season-long loan from Bundesliga club 1. FSV Mainz 05.
The 28 year-old custodian joins the Terriers on loan until 30 June 2018, at which point Huddersfield Town has the option to complete a permanent transfer.
Standing at an imposing 6' 5" tall, Lössl arrives at the John Smith's Stadium with over 250 top flight appearances under his belt in Denmark, France and Germany, as well as European and international experience.
Huddersfield Town Head Coach David Wagner commented:
"As everyone knows, we were very successful with our loans last season and now we have had the chance to bring another high-quality player to the Club in this way.
"He was part of a club in Mainz that had a difficult season in 2016/17 and that is why we have this rare chance to take a player of his quality on loan. I trust and believe in his talent and potential.
"Jonas has all the skills that you want from a modern goalkeeper; he is a good shot-stopper, he is a real presence and he is good with the ball at his feet too.
"I also know he is also a good character; a real family man, who speaks fluent English. He will fit in perfectly with our squad."
More reaction to Town's latest arrival of the summer transfer window will follow on htafc.com shortly!
LOAN: RYAN SCHOFIELD JOINS CRAWLEY TOWN
Huddersfield Town goalkeeper Ryan Schofield joins Crawley Town on loan
TRANSFER: JON RUSSELL JOINS BARNSLEY
Jon Russell joins Sky Bet League One side Barnsley on a permanent transfer subject to EFL approval
LOAN: GIOSUE BELLAGAMBI JOINS SALFORD CITY
Ugandan international goalkeeper joins Salford City on loan for remainder of 2022/23 season
LOAN: SORBA THOMAS JOINS BLACKBURN ROVERS
Winger joins Blackburn Rovers for remainder of 2022/23 Sky Bet Championship season


Marie Fiala, Director of Portfolio Marketing for Blue Planet at Ciena The catalysts and ROI of AI-powered network analytics for automated operations were the focus of discussion for service providers at the recent FutureNet conference in London. Blue Planet's Marie Fiala details the conversation. D..
Last week at Hannover Messe, Microsoft and German carmaker BMW announced a partnership to build a hardware and software technology framework and reference architecture for the industrial internet of things (IoT), and foster a community to spread these smart-factory solutions across the automotive an..
This is the first in a 3-part blog series that will detail how deploying a business-driven SD-WAN edge platform can help enterprises successfully accelerate multi-cloud digital transformation initiatives. For a growing number of enterprises, a migration to the cloud is not a simple matter of deployi..
VeloCloud is now a Business Unit within VMware since being acquired in December 2017. The two companies have had sufficient time to integrate their operations and fit their technologies together to build a cohesive offering. In January, Neal Weinberg provided an overview of where VMware is headed wi..
Building and deploying lightweight apps is becoming an easier and more reliable process with the emergence of unikernels. While limited in functionality, unikernals offer many advantages in terms of speed and security. What are unikernels?A unikernel is a very specialized single-address-space machin..
BGP (Border Gateway Protocol) is considered the glue of the internet. If we view through the lens of farsightedness, however, there's a question that still remains unanswered for the future. Will BGP have the ability to route on the best path versus the shortest path? There are vendors offering perf..
Companies have found that IoT partners well with a host of other popular enterprise computing technologies of late, and blockchain – the innovative system of distributed trust most famous for underpinning cryptocurrencies – is no exception. Yet while the two phenomena can be complementary in certain..
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As part of its massive data center event on Tuesday, Intel formally launched the Optane persistent memory product line. It had been out for a while, but the current generation of Xeon server processors could not fully utilize it. The new Xeon 8200 and 9200 lines take full advantage of it. And since ..
The quest to find more efficient methods for cooling computers is almost as high on scientists' agendas as the desire to discover better battery chemistries. More cooling is crucial for reducing costs. It would also allow for more powerful processing to take place in smaller spaces, where limited pr..
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