text:
\section{Approach} \label{sec:approach}
The input to this work's architecture (\cref{fig:system_architecture}) is a RGB-D camera stream with semantic mask, which is used to produce a partial, top-down, semantic gridmap.
An image-to-image translation model is trained to estimate the planning cost-to-go, given the semantic gridmap.
Then, the estimated cost-to-go is used to inform a frontier-based exploration planning framework.

\subsection{Training Data}\label{sec:training_data}

\begin{figure*}[t]
	\centering
	\includegraphics [trim=0 230 0 0, clip, width=0.8\textwidth, angle = 0, page=1]{figures/cost_to_go_diagram.pdf}
	\caption{Training data creation. A satellite view of a house's front yard is manually converted to a semantic map (top left), with colors for different objects/terrain. Dijkstra's algorithm gives the ground truth distance from the goal to every point along drivable terrain (bottom left)~\cite{dijkstra1959note}. This cost-to-go is represented in grayscale (lighter near the goal), with red pixels assigned to untraversable regions. To simulate partial map observability, 256 observation masks (center) are applied to the full images to produce the training set.}
	\label{fig:training_data}
\end{figure*}

A typical criticism of learning-based systems is the challenge and cost of acquiring useful training data.
Fortunately, domain-specific data already exists for many exploration environments (e.g., satellite images of houses, floor plans for indoor tasks); however, the format differs from data available on a robot with a forward-facing camera.
This work uses a mutually compatible representation of gridmaps segmented by terrain class.

A set of satellite images of houses from Bing Maps~\cite{bing_maps} was manually annotated with terrain labels.
The dataset contains 77 houses (31 train, 4 validation, 42 test) from 4 neighborhoods (3 suburban, 1 urban); one neighborhood is purely for training, one is split between train/val/test, and the other 2 neighborhoods (including urban) are purely for testing.
Each house yields many training pairs, due to the various partial-observability masks applied, so there are 7936 train, 320 validation, and 615 test pairs (explained below).
An example semantic map of a suburban front yard is shown in the top left corner of~\cref{fig:training_data}.

\cref{alg:training} describes the process of automatically generating training data from a set of semantic maps, $\mathbf S$.
A semantic map, $\mathcal S \in \mathbf S$, is first separated into traversable (roads, driveways, etc.) and non-traversable (grass, house) regions, represented as a binary array, $\mathcal S_{tr}$~(\cref{alg:training:traversable}).
Then, the shortest path length between each traversable point in $\mathcal S_{tr}$ and the goal is computed with Dijkstra's algorithm~\cite{dijkstra1959note}~(\cref{alg:training:c2g}).
The result, $\mathcal S_{c2g}$, is stored in grayscale (\cref{fig:training_data} bottom left: darker is further from goal).
Non-traversable regions are red in $\mathcal S_{c2g}$.

This work allows the robot to start with no (or partial) knowledge of a particular environment's semantic map; it observes (uncovers) new areas of the map as it explores the environment.
To approximate the partial maps that the robot will have at each planning step, full training maps undergo various masks to occlude certain regions.
For some binary observation mask, $\mathcal M$: the full map, $\mathcal S$, and full cost-to-go, $\mathcal S_{c2g}$, are masked with element-wise multiplication as $\mathcal S^M = \mathcal S \circ \mathcal M$ and $\mathcal S^M_{c2g} = \mathcal S_{c2g} \circ \mathcal M$ (\cref{alg:training:mask_obs,alg:training:mask_c2g}).
The $(\textrm{input, target output})$ pairs used to train the image-to-image translator are the set of $(\mathcal S^M, \mathcal S^M_{c2g})$~(\cref{alg:training:input_output_pairs}).

\begin{algorithm}[t]
	\textbf{Input:} semantic maps, $\mathbf{S}$; observability masks, $\mathbf{M}$\\
	\textbf{Output:} training image pairs, $\mathbf{T}$ \\
	\
	\ForEach{$\mathcal S \in \mathbf S$}{
		$\mathcal S_{tr} \leftarrow$ Find traversable regions in $\mathcal S$\label{alg:training:traversable}\\
		$\mathcal S_{c2g} \leftarrow$ Compute cost-to-go to goal of all pts in $\mathcal S_{tr}$\label{alg:training:c2g}\\
		\ForEach{$\mathcal M \in \mathbf M$}{
			$\mathcal S_{c2g}^{M} \leftarrow$ Apply observation mask $\mathcal M$ to $\mathcal S_{c2g}$\label{alg:training:mask_c2g}\\
			$\mathcal S^{M} \leftarrow$ Apply observation mask $\mathcal M$ to $\mathcal S$\label{alg:training:mask_obs}\\
			$\mathbf T \leftarrow \mathcal \{(S^{M}, S_{c2g}^{M})\} \cup \mathbf T$\label{alg:training:input_output_pairs}
		}
	}
	\caption{Automated creation of NN training data}
	\label{alg:training}

\end{algorithm}

\subsection{Offline Training: Image-to-Image Translation Model}

The motivation for using image-to-image translation is that i) a robot's sensor data history can be compressed into an image (semantic gridmap), and ii) an estimate of cost-to-go at every point in the map (thus, an image) enables efficient use of receding-horizon planning algorithms, given only a high-level goal (``front door'').
Although the task of learning to predict just the goal location is easier, a cost-to-go estimate implicitly estimates the goal location and then provides substantially more information about how to best reach it.

The image-to-image translator used in this work is based on~\cite{pix2pix2017,pix2pix-tensorflow}.
The translator is a standard encoder-decoder network with skip connections between corresponding encoder and decoder layers (\mbox{``U-Net''})~\cite{ronneberger2015u}.
The objective is to supply a 256x256 RGB image (semantic map, $\mathcal S$) as input to the encoder, and for the final layer of the decoder to output a 256x256 RGB image (estimated cost-to-go map, $\mathcal{\hat{S}}_{c2g}$).
Three U-Net training approaches are compared: pixel-wise $L_1$ loss, GAN, and a weighted sum of those two losses, as in~\cite{pix2pix2017}.

A partial map could be associated with multiple plausible cost-to-gos, depending on the unobserved parts of the map.
Without explicitly modeling the distribution of cost-to-gos conditioned on a partial semantic map, $P(~\mathcal S_{c2g}^{M}~\mid~\mathcal{S}^M)$, the network training objective and distribution of training images are designed so the final decoder layer outputs a most likely cost-to-go-map, $\mathcal{\bar{S}}_{c2g}^{M}$, where
\begin{equation}
\mathcal{\bar{S}}_{c2g}^{M} = \argmax_{\mathcal S_{c2g}^{M}\in\mathbb{R}^{256\times256\times3}} P(\mathcal S_{c2g}^{M}\mid\mathcal S^M).
\end{equation}

\subsection{Online Mapping: Semantic SLAM}

To use the trained network in an online sense-plan-act cycle, the mapping system must produce top-down, semantic maps from the RGB-D camera images and semantic labels available on a robot with a forward-facing depth camera and an image segmentation algorithm~\cite{he2018mask}.
Using~\cite{semantic_slam}, the semantic mask image is projected into the world frame using the depth image and camera parameters to produce a pointcloud, colored by the semantic class of the point in 3-space.
Each pointcloud is added to an octree representation, which can be converted to a octomap (3D occupancy grid) on demand, where each voxel is colored by the semantic class of the point in 3-space.
Because this work's experiments are 2D, we project the octree down to a 2D semantically-colored gridmap, which is the input to the cost-to-go estimator.

\subsection{Online Planning: Deep Cost-to-Go}

This work's planner is based on the idea of frontier exploration~\cite{yamauchi1998frontier}, where a frontier is defined as a cell in the map that is observed and traversable, but whose neighbor has not yet been observed.
Given a set of frontier cells, the key challenge is in choosing $\textit{which}$ frontier cell to explore next.
Existing algorithms often use geometry (e.g., frontier proximity, expected information gain based on frontier size/layout); we instead use context to select frontier cells that are expected to lead toward the destination.

\begin{algorithm}[t]
	\textbf{Input:} current partial semantic map $\mathcal{S}$, pose $(p_x, p_y, \theta)$\\
	\textbf{Output:} action $\mathbf{u}_t$ \\
	$\mathcal S_{tr} \leftarrow$ Find traversable cells in $\mathcal S$~\label{dc2g:traversable}\\
	$\mathcal S_{r} \leftarrow$ Find reachable cells in $\mathcal S_{tr}~\label{dc2g:reachable}$ from $(p_x, p_y)$ w/ BFS\\
	\If{goal $\not\in \mathcal S_{r}$}{~\label{dc2g:if_goal_reachable}
		$\mathcal F \leftarrow$ Find frontier cells in $\mathcal S_r$~\label{dc2g:frontier}\\
		$\mathcal F^e \leftarrow$ Find cells in $\mathcal S_r$ where $f \in \mathcal F$ in view~\label{dc2g:frontier_expanding}\\
		$\mathcal F_r^e \leftarrow$ $\mathcal S_r \cap \mathcal F^e$: reachable, frontier-expanding cells~\label{dc2g:frontier_reachable_expanding}\\
		$\mathcal{\hat{S}}_{c2g} \leftarrow$ Query generator network with input $\mathcal S$~\label{dc2g:query}\\
		$\mathcal C \leftarrow$ Filter and resize $\mathcal{\hat{S}}_{c2g}$~\label{dc2g:filter_and_resize}\\
		$(f_x, f_y) \leftarrow$ $\argmax_{f\in\mathcal F_r^e} \mathcal C$~\label{dc2g:min_cost}\\
		$\mathbf{u}_{t:\infty} \leftarrow$ Backtrack from $(f_x, f_y)$ to $(p_x, p_y)$ w/ BFS\label{dc2g:backtrack_to_frontier}\\
	}
	\Else{
		$\mathbf{u}_{t:\infty} \leftarrow$ Shortest path to goal via BFS~\label{dc2g:backtrack_to_goal}\\
	}
	\caption{DC2G (Deep Cost-to-Go) Planner}
	\label{alg:DC2G}

\end{algorithm}

The planning algorithm, called Deep Cost-to-Go (DC2G), is described in~\cref{alg:DC2G}.
Given the current partial semantic map, $\mathcal S$, the subset of observed cells that are also traversable (road/driveway) is $\mathcal S_{tr}$~(\cref{dc2g:traversable}).
The subset of cells in $\mathcal S_{tr}$ that are also reachable, meaning a path exists from the current position, through observed, traversable cells, is $\mathcal S_r$~(\cref{dc2g:reachable}).

The planner opts to explore if the goal cell is not yet reachable~(\cref{dc2g:if_goal_reachable}).
The current partial semantic map, $\mathcal S$, is scaled and passed into the image-to-image translator, which produces a 256x256 RGB image of the estimated cost-to-go, $\mathcal{\hat{S}}_{c2g}$~(\cref{dc2g:query}).
The raw output from the U-Net is converted to HSV-space and pixels with high value (not grayscale $\Rightarrow$ estimated not traversable) are filtered out.
The remaining grayscale image is resized to match the gridmap's dimensions with a nearest-neighbor interpolation.
The value of every grid cell in the map is assigned to be the saturation of that pixel in the translated image (high saturation $\Rightarrow$ ``whiter'' in grayscale $\Rightarrow$ closer to goal).

To enforce exploration, the only cells considered as possible subgoals are ones which will allow sight beyond frontier cells, $\mathcal F_r^e$, (reachable, traversable, and frontier-expanding), based on the known sensing range and FOV.
The cell in $\mathcal F_r^e$ with highest estimated value is selected as the subgoal~(\cref{dc2g:min_cost}).
Since the graph of reachable cells was already searched, the shortest path from the selected frontier cell to the current cell is available by backtracking through the search tree~(\cref{dc2g:backtrack_to_frontier}).
This backtracking procedure produces the list of actions, $\mathbf{u}_{t:\infty}$ that leads to the selected frontier cell.
The first action, $\mathbf{u}_{t}$ is implemented and the agent takes a step, updates its map with new sensor data, and the sense-plan-act cycle repeats.
If the goal is deemed reachable, exploration halts and the shortest path to the goal is implemented~(\cref{dc2g:backtrack_to_goal}).
However, if the goal has been observed, but a traversable path to it does not yet exist in the map, exploration continues in the hope of finding a path to the goal.

A key benefit of the DC2G planning algorithm is that it can be used alongside a local collision avoidance algorithm, which is critical for domains with dynamic obstacles (e.g., pedestrians on sidewalks).
This flexibility contrasts end-to-end learning approaches where collision avoidance either must be part of the objective during learning (further increasing training complexity) or must be somehow combined with the policy in a way that differs from the trained policy.

Moreover, a benefit of map creation during exploration is the possibility for the algorithm to confidently ``give up'' if it fully explored the environment without finding the goal.
This idea would be difficult to implement as part of a non-mapping exploration algorithm, and could address ill-posed exploration problems that exist in real environments (e.g., if no path exists to destination).

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

\subsubsection{Planning \& Exploration}
Classical planning algorithms rely on knowledge of the goal coordinates (A*, RRT) and/or a prior map (PRMs, potential fields), which are both unavailable in this problem.
Receding-horizon algorithms are inefficient without an accurate heuristic at the horizon, typically computed with goal knowledge.
Rather than planning to a destination, the related field of exploration~\cite{yamauchi1998frontier,stachniss2005information} is a conservative search strategy, and pure exploration algorithms often estimate \textit{information gain} of planning options using geometric context.
However, exploration and search objectives differ, meaning the exploration robot will spend time gaining information in places that are useless for the search task.

\subsubsection{Context for Object Search}
Leveraging scene context is therefore fundamental to enable object search that outperforms pure exploration.
Many papers consider a single form of context.
Geometric context (represented in occupancy grids) is used in~\cite{6907760,10.1007/978-3-319-50115-4_34,Richter2018,bhardwaj2017learning}, but these works also assume knowledge of the goal location for planning.
Works that address true object search usually consider semantic object relationships as a form of context instead.
Decision trees and maximum entropy models can be trained on object-based relationships, like positions and attributes of common items in grocery stores~\cite{joho2011learning}.
Because object-based methods require substantial domain-specific background knowledge, some approaches automate the background data collection process by using Internet searches~\cite{samadi2012using,kollar2009utilizing}.
Object-based approaches have also noted that the spatial relationships between objects are particularly beneficial for search (e.g., keyboards are often \textit{on} desks)~\cite{kunze2014using,kunze2014bootstrapping,lorbach2014prior}, but are not well-suited to represent geometries like floorplans/terrains.
Hierarchical planners improve search performance via a human-like ability to make assumptions about object layouts~\cite{aydemir2013active,hanheide2017robot}.

To summarize, existing uses of context either focus on relationships between objects or the environment's geometry.
These approaches are too specific to represent the combination of various forms of context that are often needed to plan efficiently.

\subsubsection{Deep Learning for Object Search}
Recent works use deep learning to represent scene context.
Several approaches consider navigation toward a semantic concept (e.g., go to the kitchen) using only a forward-facing camera.
These algorithms are usually trained end-to-end (image-to-action) by supervised learning of expert trajectories~\cite{brahmbhatt2017deepnav,DBLP:journals/corr/abs-1801-02209,blukis2018following} or reinforcement learning in simulators~\cite{zhu2017icra,zhu2017visual,gordon2018iqa,gupta2017cognitive,embodiedqa}.
Training such a general input-output relationship is challenging; therefore, some works divide the learning architecture into a deep neural network for each sub-task (e.g., mapping, control, planning)~\cite{gupta2017cognitive,blukis2018following,gordon2018iqa}.

Still, the format of context in existing, deep learning-based approaches is too general.
The difficulty in learning how to extract, represent, and use context in a generic architecture leads to massive computational resource and time requirements for training.
In this work, we reduce the dimensionality (and therefore training time) of the learning problem by first leveraging existing algorithms (semantic SLAM, image segmentation) to extract and represent context from images; thus, the learning process is solely focused on context utilization.
A second limitation of systems trained on simulated camera images, such as existing deep learning-based approaches, is a lack of transferability to the real world.
Therefore, instead of learning from simulated camera images, this work's learned systems operate on semantic gridmaps which could look identical in the real world or simulation.

\subsubsection{Reinforcement Learning}
Reinforcement learning (RL) is a commonly proposed approach for this type of problem~\cite{zhu2017icra,zhu2017visual,gordon2018iqa,gupta2017cognitive,embodiedqa}, in which experiences are collected in a simulated environment.
However, in this work, the agent's actions do not affect the static environment, and the agent's observations (partial semantic maps) are easy to compute, given a map layout and the robot's position history.
This work's approach is a form of model-based learning, but learns from a static dataset instead of environment interaction.

\subsubsection{U-Nets for Context Extraction}
This work's use of U-Nets~\cite{ronneberger2015u,pix2pix2017,pix2pix-tensorflow} is motivated by experiments that show generative networks can imagine unobserved regions of occupancy gridmaps, suggesting that they can be trained to extract significant geometric context in structured environments~\cite{pronobis2017learning}.
However, the focus of that work is on models' abilities to encode context, not context utilization for planning.

\section{Conclusion} \label{sec:conclusion}
This work presented an algorithm for learning to utilize context in a structured environment in order to inform an exploration-based planner.
The new approach, called Deep Cost-to-Go (DC2G), represents scene context in a semantic gridmap, learns to estimate which areas are beneficial to explore to quickly reach the goal, and then plans toward promising regions in the map.
The efficient training algorithm requires zero training in simulation: a context extraction model is trained on a static dataset, and the creation of the dataset is highly automated.
The algorithm outperforms pure frontier exploration by 189\% across 42 real test house layouts.
A high-fidelity simulation shows that the algorithm can be applied on a robot with a forward-facing camera to successfully complete last-mile deliveries.
\section{Introduction} \label{sec:intro}
A key topic in robotics is the use of automated robotic vehicles for last-mile delivery.
A standard approach is to visit and map delivery environments ahead of time, which enables the use of planning algorithms that guide the robot toward a specific goal coordinate in the map.
However, the economic inefficiency of collecting and maintaining maps, the privacy concerns of storing maps of people's houses, and the challenges of scalability across a city-wide delivery system are each important drawbacks of the pre-mapping approach.
This motivates the use of a planning framework that does not need a prior map.
In order to be a viable alternative framework, the time required for the robot to locate and reach its destination must remain close to that of a prior-map-based approach.

Consider a robot delivering a package to a new house's front door (\cref{fig:rover}).
Many existing approaches require delivery destinations to be specified in a format useful to the robot (e.g., position coordinates, heading/range estimates, target image), but collecting this data for every destination presents the same limitations as prior mapping.
Therefore the destination should be a high-level concept, like ``go to the front door.''
Such a destination is intuitive for a human, but without actual coordinates, difficult to translate into a planning objective for a robot.
The destination will often be beyond the robot's economically-viable sensors' limited range and field of view.
Therefore, the robot must explore~\cite{yamauchi1998frontier,stachniss2005information} to find the destination; however, pure exploration is slow because time is spent exploring areas unlikely to contain the goal.
Therefore, this paper investigates the problem of efficiently planning beyond the robot's line-of-sight by utilizing \textit{context} within the local vicinity.
Existing approaches use context and background knowledge to infer a geometric understanding of the high-level goal's location, but the representation of background knowledge is either difficult to plan from~\cite{joho2011learning,samadi2012using,kollar2009utilizing,kunze2014bootstrapping,kunze2014using,lorbach2014prior,vernaza2014learning,aydemir2013active,hanheide2017robot}, or maps directly from camera image to action~\cite{brahmbhatt2017deepnav,wu2018building,blukis2018following,zhu2017icra,zhu2017visual,gordon2018iqa,gupta2017cognitive,embodiedqa}, reducing transferability to real environments.

\begin{figure}[t]
	\centering
	\begin{subfigure}{0.33\columnwidth}
		\centering
		\includegraphics [trim=0 0 0 0, clip, width=\textwidth, angle = 0]{figures/robot_delivery_marked}
		\caption{Oracle's view}
		\label{fig:robot_delivery_scene}
	\end{subfigure}
	\begin{subfigure}{0.31\columnwidth}
		\centering
		\includegraphics [trim=0 0 0 0, clip, width=\textwidth, angle = 0]{figures/robot_delivery_camera}
		\caption{Robot's view}
		\label{fig:robot_delivery_camera}
	\end{subfigure}
	\begin{subfigure}{0.31\columnwidth}
		\centering
		\includegraphics [trim=0 0 0 0, clip, width=\textwidth, angle = 0]{figures/world1}
		\caption{Semantic Map}
		\label{fig:semantic_map}
	\end{subfigure}
	\caption{Robot delivers package to front door. If the robot has no prior map and does not know where the door is, it must quickly search for its destination. Context from the onboard camera view (b) can be extracted into a lower-dimensional semantic map (c), where the white robot can see terrain within its black FOV.}
	\label{fig:rover}
\end{figure}

This work proposes a solution to efficiently utilize context for planning.
Scene context is represented in a semantic map, then a learned algorithm converts context into a search heuristic that directs a planner toward promising regions in the map.
The context utilization problem (determination of promising regions to visit) is uniquely formulated as an image-to-image translation task, and solved with U-Net/GAN architectures~\cite{ronneberger2015u,pix2pix2017} recently shown to be useful for geometric context extraction~\cite{pronobis2017learning}.
By learning with semantic gridmap inputs instead of camera images, the planner proposed in this work could be more easily transferred to the real world without the need for training in a photo-realistic simulator.
Moreover, a standard local collision avoidance algorithm can operate in conjunction with this work's global planning algorithm, making the framework easily extendable to environments with dynamic obstacles.

The contributions of this work are
i) a novel formulation of utilizing context for planning as an image-to-image translation problem, which converts the abstract idea of scene context into a planning metric,
ii) an algorithm to efficiently train a cost-to-go estimator on typical, partial semantic maps, which enables a robot to learn from a static dataset instead of a time-consuming simulator,
iii) demonstration of a robot reaching its goal 189\% faster than a context-unaware algorithm in simulated environments, with layouts from a dataset of real last-mile delivery domains,
and
iv) an implementation of the algorithm on a vehicle with a forward-facing RGB-D + segmentation camera in a high-fidelity simulation.

\section*{Acknowledgment}
This work is supported by the Ford Motor Company.
\balance
\bibliographystyle{IEEEtran}

\section{Results} \label{sec:results}

\begin{figure}[t]
	\centering
	\begin{subfigure}{0.49\columnwidth}
		\centering
		\includegraphics [trim=80 80 60 60, clip, width=0.9\linewidth, angle = 0]{figures/full_maps_panel_small.png}
		\caption{Full Semantic Maps}
		\label{fig:map_result_panels_full}
	\end{subfigure}
	\centering
	\begin{subfigure}{0.49\columnwidth}
		\centering
		\includegraphics [trim=80 80 60 60, clip, width=0.9\linewidth, angle = 0]{figures/partial_maps_panel_small.png}
		\caption{Partial Semantic Maps}
		\label{fig:map_result_panels_partial}
	\end{subfigure}
	\caption{Qualitative Assessment. Network's predicted cost-to-go on 10 previously unseen semantic maps strongly resembles ground truth. Predictions correctly assign red to untraversable regions, black to unobserved regions, and grayscale with intensity corresponding to distance from goal. Terrain layouts in the top four rows (suburban houses) are more similar to the training set than the last row (urban apartments). Accordingly, network performance is best in the top four rows, but assigns too dark a value in sidewalk/road (brown/yellow) regions of bottom left map, though the traversability is still correct. The predictions in \cref{fig:map_result_panels_partial} enable planning without knowledge of the goal's location.}
	\label{fig:gan_qualitative}

\end{figure}

\begin{figure}[t]
	\centering
	\includegraphics [trim=0 0 0 0, clip, width=\linewidth, angle = 0]{figures/network_training_small.png}
	\caption{Comparing network loss functions. The performance on the validation set throughout training is measured with the standard $L_1$ loss, and a planning-specific metric of identifying regions of low cost-to-go. The different training loss functions yield similar performance, with slightly better precision/worse recall with $L_1$ loss (green), and slightly worse $L_1$ error with pure GAN loss (magenta). 3 training episodes per loss function are individually smoothed by a moving average filter, mean curves shown with $\pm1\sigma$ shading. These plots show that the choice of loss function has minimal impact on this work's dataset of low frequency images.}
	\label{fig:network_losses}

\end{figure}
\begin{figure}[t]
	\centering
	\includegraphics [trim=0 0 0 0, clip, width=\linewidth, angle = 0]{figures/image_perf_final.png}
	\caption{Cost-to-Go predictions across test set. Each marker shows the network performance on a full test house image, placed horizontally by the house's similarity to the training houses. Dashed lines show linear best-fit per network. All three networks have similar per-pixel $L_1$ loss (top row). But, for identifying regions with low cost-to-go (\nth{2}, \nth{3} rows), GAN (magenta) has worse precision/better recall than networks trained with $L_1$ loss (blue, green). All networks achieve high performance on estimating traversability (\nth{4}, \nth{5} rows). This result could inform application-specific training dataset creation, by ensuring desired test images occur near the left side of the plot.}
	\label{fig:gan_quantitative}
\end{figure}

\subsection{Model Evaluation: Image-to-Image Translation}

Training the cost-to-go estimator took about 1 hour on a GTX 1060, for 3 epochs (20,000 steps) with batch size of 1.
Generated outputs resemble the analytical cost-to-gos within a few minutes of training, but images look sharper/more accurate as training time continues.
This notion is quantified in~\cref{fig:network_losses}, where generated images are compared pixel-to-pixel to the true images in the validation set throughout the training process.

\subsubsection{Loss Functions}
Networks trained with three different loss functions are compared on three metrics in~\cref{fig:network_losses}.
The network trained with $L_1$ loss (green) has the best precision/worst recall on identification of regions of low cost-to-go (described below).
The GAN acheives almost the same $L_1$ loss, albeit slightly slower, than the networks trained explicity to optimize for $L_1$ loss.
Overall, the three networks perform similarly, suggesting the choice of loss function has minimal impact on this work's dataset of low frequency images.

\subsubsection{Qualitative Assessment}
\cref{fig:gan_qualitative} shows the generator's output on 5 full and 5 partial semantic maps from worlds and observation masks not seen during training.
The similarity to the ground truth values qualitatively suggests that the generative network successfully learned the ideas of traversability and contextual clues for goal proximity.
Some undesirable features exist, like missed assignment of light regions in the bottom row of~\cref{fig:gan_qualitative}, which is not surprising because in the training houses (suburban), roads and sidewalks are usually far from the front door, but this urban house has quite different topology.

\subsubsection{Quantitative Assessment}
In general, quantifying the performance of image-to-image translators is difficult~\cite{salimans2016improved}.
Common approaches use humans or pass the output into a segmentation algorithm that was trained on real images~\cite{salimans2016improved}; but, the first approach is not scalable and the second does not apply here.

Unique to this paper's domain\footnote{As opposed to popular image translation tasks, like sketch-to-image, or day-to-night, where there is a distribution of acceptable outputs.}, for fully-observed maps, ground truth and generated images can be directly compared, since there is a single solution to each pixel's target intensity.
\cref{fig:gan_quantitative} quantifies the trained network's performance in 3 ways, on all 42 test images, plotted against the test image's similarity to the training images (explained below).
First, the average per-pixel $L_1$ error between the predicted and true cost-to-go images is below 0.15 for all images.
To evaluate on a metric more related to planning, the predictions and targets are split into two categories: pixels that are deemed traversable (HSV-space: $S<0.3$) or not.
This binary classification is just a means of quantifying how well the network learned a relevant sub-skill of cost-to-go prediction; the network was not trained on this objective.
Still, the results show precision above 0.95 and recall above 0.8 for all test images.
A third metric assigns pixels to the class ``low cost-to-go'' if sufficiently bright (HSV-space: $V>0.9$ $\land$ $S<0.3$).
Precision and recall on this binary classification task indicate how well the network finds regions that are close to the destination.
The networks perform well on precision (above 0.75 on average), but not as well on recall, meaning the networks missed some areas of low cost-to-go, but did not spuriously assign many low cost-to-go pixels.
This is particularly evident in the bottom row of~\cref{fig:map_result_panels_full}, where the path leading to the door is correctly assigned light pixels, but the sidewalk and road are incorrectly assigned darker values.

To measure generalizability in cost-to-go estimates, the test images are each assigned a similarity score $\in [0,1]$ to the closest training image.
The score is based on Bag of Words with custom features\footnote{The commonly-used SIFT, SURF, and ORB algorithms did not provide many features in this work's low-texture semantic maps}, computed by breaking the training maps into grids, computing a color histogram per grid cell, then clustering to produce a vocabulary of the top 20 features.
Each image is then represented as a normalized histogram of words, and the minimum $L_1$ distance to a training image is assigned that test image's score.
This metric captures whether a test image has many colors/small regions in common with a training image.

The network performance on $L_1$ error, and recall of low cost-to-go regions, declines as images differ from the training set, as expected.
However, traversabilty and precision of low cost-to-go regions were not sensitive to this parameter.

Without access to the true distribution of cost-to-go maps conditioned on a partial semantic map, this work evaluates the network's performance on partial maps indirectly, by measuring the \textit{planner's} performance, which would suffer if given poor cost-to-go estimates.

\subsection{Low-Fidelity Planner Evaluation: Gridworld Simulation}

The low-fidelity simulator uses a 50$\times$50-cell gridworld~\cite{gym_minigrid} to approximate a real robotic vehicle operating in a delivery context.
Each grid cell is assigned a static class (house, driveway, etc.); this terrain information is useful both as context to find the destination, but also to enforce the real-world constraint that robots should not drive across houses' front lawns.
The agent can read the type of any grid cell within its sensor FOV (to approximate a common RGB-D sensor: $90^{\circ}$ horizontal, 8-cell radial range).
To approximate a SLAM system, the agent remembers all cells it has seen since the beginning of the episode.
At each step, the agent sends an observation to the planner containing an image of the agent's semantic map knowledge, and the agent's position and heading.
The planner selects one of three actions: go forward, or turn $\pm 90^{\circ}$.

\begin{figure}[t]
	\centering
	\includegraphics [trim=0 0 0 0, clip, width=\linewidth, angle = 0]{figures/planner_perf_small.png}
	\caption{Planner performance across neighborhoods. DC2G reaches the goal faster than Frontier~\cite{yamauchi1998frontier} by prioritizing frontier points with a learned cost-to-go prediction. Performance measured by \% steps beyond optimal path given a complete prior map. The simulation environments come from real house layouts from Bing Maps, grouped by the four neighborhoods in the dataset, showing DC2G plans generalize beyond houses seen in training.}
	\label{fig:planner_quantitative}
\end{figure}

Each gridworld is created by loading a semantic map of a real house from Bing Maps, and houses are categorized by the 4 test neighborhoods.
A random house and starting point on the road are selected 100 times for each of the 4 neighborhoods.
The three planning algorithms compared are DC2G, Frontier~\cite{yamauchi1998frontier} which always plans to the nearest frontier cell (pure exploration), and an oracle with a prior map.
The optimal path is computed by an oracle with full knowledge of the map ahead of time; DC2G and Frontier performance is therefore presented in~\cref{fig:planner_quantitative} as percent of \textit{extra} time to the goal beyond the oracle's path, $\% t^e_{goal} = \frac{t^{alg}_{goal} - t^{oracle}_{goal}}{t^{oracle}_{goal}} \geq 0$.

\cref{fig:planner_quantitative} is grouped by neighborhood to demonstrate that DC2G improved the plans across many house types, beyond the ones it was trained on.
In the left category, the agent has seen these houses in training, but with different observation masks applied to the full map.
In the right three categories, both the houses and masks are new to the network.
Although grouping by neighborhood is not quantitative, the test-to-train image distance metric (above) was not correlated with planner performance, suggesting other factors affect plan lengths, such as topological differences in layout that were not quantified in this work.

Across the test set of 42 houses, DC2G reaches the goal within 63\% of optimal, and 189\% faster than Frontier on average.

\subsection{Planner Scenario}

\begin{figure*}[t]
	\centering
	\includegraphics [trim=0 260 100 0, clip, width=\linewidth, angle = 0, page=3]{figures/cost_to_go_diagram.pdf}
	\caption{Sample gridworld scenario. The top row is the agent's observed semantic map (network input); the middle is its current estimate of the cost-to-go (network output); the bottom is the trajectory so far. The DC2G agent reaches the goal much faster (98 vs. 304 steps) by using learned context. At DC2G (green panel) step 20, the driveway appears in the semantic map, and the estimated cost-to-go correctly directs the search in that direction, then later up the walkway (light blue) to the door (red). Frontier (pink panel) is unaware of typical house layouts, and unnecessarily explores the road and sidewalk regions thoroughly before eventually reaching the goal.}
	\label{fig:result_trajectory}
\end{figure*}

A particular trial is shown in~\cref{fig:result_trajectory} to give insight into the performance improvement from DC2G.
Both algorithms start in the same position in a world that was not seen during training.
The top row shows the partial semantic map at that timestep (observation), the middle row is the generator's cost-to-go estimate, and the bottom shows the agent's trajectory in the whole, unobservable map.
Both algorithms begin with little context since most of the map is unobserved.
At step 20, DC2G (green box, left) has found the intersection between road (yellow) and driveway (blue): this context causes it to turn up the driveway.
By step 88, DC2G has observed the goal cell, so it simply plans the shortest path to it with BFS, finishing in 98 steps.
Conversely, Frontier (pink box, right) takes much longer (304 steps) to reach the goal, because it does not consider terrain context, and wastes many steps exploring road/sidewalk areas that are unlikely to contain a house's front door.

\subsection{Unreal Simulation \& Mapping}

\begin{figure}[t]
	\centering
	\begin{subfigure}[t]{0.8\linewidth}
		\centering
		\includegraphics [trim=0 0 0 0, clip, width=\columnwidth, angle = 0]{figures/unreal_render_small.jpg}
		\caption{Unreal Engine Rendering of a Last-Mile Delivery}
		\label{fig:unreal_rendering}
		\vspace{0.1in}
	\end{subfigure}
	\begin{subfigure}[t]{0.32\columnwidth}
		\centering
		\includegraphics [trim=0 0 0 0, clip, width=\columnwidth, angle = 270]{figures/unreal_map.png}
		\caption{Semantic Map}
		\label{fig:unreal_map}
	\end{subfigure}
	\begin{subfigure}[t]{0.32\columnwidth}
		\centering
		\includegraphics [trim=0 0 0 0, clip, width=\columnwidth, angle = 270]{figures/unreal_c2g.png}
		\caption{Predicted Cost-to-Go}
		\label{fig:unreal_c2g}
	\end{subfigure}
	\begin{subfigure}[t]{0.32\columnwidth}
		\centering
		\includegraphics [trim=0 0 0 0, clip, width=\columnwidth, angle = 270]{figures/unreal_plan.png}
		\caption{Planned Path}
		\label{fig:unreal_path}
	\end{subfigure}
	\caption{Unreal Simulation. Using a network trained only on aerial images, the planner guides the robot toward the front door of a house not seen before, with a forward-facing camera. The planned path (d) starts from the robot's current position (cyan) along grid cells (red) to the frontier-expanding point (green) with lowest estimated cost-to-go (c), up the driveway.}
	\label{fig:unreal}
\end{figure}

The gridworld is sufficiently complex to demonstrate the fundamental limitations of a baseline algorithm, and also allows quantifiable analysis over many test houses.
However, to navigate in a real delivery environment, a robot with a forward-facing camera needs additional capabilities not apparent in a gridworld.
Therefore, this work demonstrates the algorithm in a high-fidelity simulation of a neighborhood, using AirSim~\cite{shah2018airsim} on Unreal Engine.
The simulator returns camera images in RGB, depth, and semantic mask formats, and the mapping software described in~\cref{sec:approach} generates the top-down semantic map.

A rendering of one house in the neighborhood is shown in~\cref{fig:unreal_rendering}.
This view highlights the difficulty of the problem, since the goal (front door) is occluded by the trees, and the map in~\cref{fig:unreal_map} is rather sparse.
The semantic maps from the mapping software are much noisier than the perfect maps the network was trained with.
Still, the predicted cost-to-go in~\cref{fig:unreal_c2g} is lightest in the driveway area, causing the planned path in~\cref{fig:unreal_path} (red) from the agent's starting position (cyan) to move up the driveway, instead of exploring more of the road.
The video shows trajectories of two delivery simulations: \url{https://youtu.be/yVlnbqEFct0}.

\subsection{Discussion \& Future Work}
It is important to note that DC2G is expected to perform worse than Frontier if the test environment differs significantly from the training set, since context is task-specific.
The dataset covers several neighborhoods, but in practice, the training dataset could be expanded/tailored to application-specific preferences.
In this dataset, Frontier outperformed DC2G on $2/77$ houses.
However, even in case of Frontier outperforming DC2G, DC2G is still guaranteed to find the goal eventually (after exploring all frontier cells), unlike a system trained end-to-end that could get stuck in a local optimum.

The DC2G algorithm as described requires knowledge of the environment's dimensions; however, in practice, a maximum search area would likely already be defined relative to the robot's starting position (e.g., to ensure operation time less than battery life).
Future work will explicitly model the uncertainty in the network's cost-to-go predictions to inform a probabilistic planner, and will involve hardware experiments.

redpajama_set_name: RedPajamaArXiv


text:
JIBE Goes SaaS To Make All Job Applications Mobile-Friendly
New York City-based startup JIBE first appeared on the scene in late 2009 as LocalBacon, a job board that required job-seekers to pay 99 cents to apply for positions, weeding out less serious candidates. Several months later in March 2010, the company smartly re-positioned itself as JIBE — a recruiting platform to help people find jobs through their social networks.
The pivot was a smart one: A $6 million Series A round followed in mid-2011, and JIBE grew to have a staff of 18 across three offices in the US.
Despite the decent traction, JIBE recently tweaked its mission once again — and this could be its savviest move yet. JIBE has shifted its core technology to be a software-as-a-service (SaaS) platform that plugs into company job boards and makes any job posting capable of receiving applications through mobile devices.
JIBE believes that in the near future many people will be applying to jobs through smartphones and tablets, not traditional desktop or laptop computers. It may seem far-fetched right now — personally, applying for jobs has always been something that's required my full keyboard-and-screen attention. But then again, it wasn't so long ago at all that people thought digital job applications would never seriously take off. Remember paying extra at Kinko's to print your resume on really nice paper?
So I sat down with JIBE CEO and founder Joe Essenfeld to hear first-hand the details about his company's shift to a SaaS strategy. Watch the video embedded below to hear him discuss why mobile could own the future of job applications, how JIBE sells its platform to huge Fortune 500 companies as a relatively small startup, what were the signs that have led JIBE to tweak its strategy, and more.

redpajama_set_name: RedPajamaCommonCrawl


Exercising the "right to be forgotten" should be addressed as an interesting field in Remedial Law, Court of Appeals Associate Justice Eduardo B. Peralta Jr. told members of the Judicial and Bar Council (JBC).
The "right to be forgotten" became popular because of the 2014 case about the removal of articles published online by La Vanguardia (Google v. Costeja Gonzáles), which was allowed by a court in Europe.
In the Philippines, such right may have been included in the Data Protection Act, which provides for the right to suspension and blocking, allowing data subjects to order the blocking, removal or destruction of his or her personal information from a personal information controller's filing system subject to limitations.
Peralta is one of the 20 applicants vying for the Supreme Court post that will become vacant upon the retirement of Associate Justie Noel Tijam.
He is the only applicant with a Doctor of Civil Law degree.
Peralta started his career in the judiciary in 1989 as a staff member in the office of then Court of Appeals Associate Justice Jose Melo.


There is no relationship in life longer than that of siblings. And, like today's families, sibships come in all shapes and sizes. The classic nuclear family has diminished in the past decades, due to divorce, remarriage and parenting out of wedlock. So now we have more half-sibs, step-sibs and sibs by adoption. In addition, second and third marriages have created large gaps in sibling ages.
How does the fact that siblings share common parents in some form affect a family business? Here are some suggestions to make sibling management teams effective.


Back from Spring Break at Mammoth Mountain and trying to catch up on the usual backlog of things I'm supposed to be doing. Let's start by talking about Eastern Sierra backcountry conditions, south edition: things are looking thin. Very, very thin.
By officially-measured snowpack I know we're hovering around normal for this time of year, but don't be fooled by those measurements for one minute: this is a thin year, particularly if you count snowpack at lower elevations, even more so as you go south.
At Mammoth Lakes snowpack is basically nonexistent below about 8000 feet. Heading south past Big Pine, the Eastside looks pretty dire: some good high-altitude snowfields, yes, but even these are wind-stripped and erratic.
Staying at the Mammoth Mountain Inn, I spent a lot of time looking over the many vintage photos of the Mammoth area. Wow, has winter changed over the past fifty years. You see the same story looking at old photos of the High Sierra and Owens Valley at the Laws Museum.
I would argue over even the past ten years we've basically seen the level of persistent snowpack rise two to three thousand feet higher in southern and central California—a huge step increase. This is especially evident looking at Birch Mountain, or the White Mountains, both of which seem practically unable to hold snow these days.
On that unhappy note, for good So. Sierra backcountry skiing, you'll want to go deep and you'll want to go high. Or mine the Mammoth Lakes area above 8K, where you'll find much better conditions.
Not one but two pairs of boots arrived this past week, both Scarpa: the (2017) F1, which eschews the tronic mechanism in its North American edition, and the updated Maestrale, which is currently available. When I think about the Maestrale, I think about all the spectacular and huge Sierra tours I did on that boot.
The new Maestrale adds a touch of extra stiffness plus some incremental improvements, and remains a ski mountaineering standout. The new F1 drops over a pound per pair on the Maestrale while keeping much of that boot's climbing and skiing chops, admittedly sans instep buckle. Watch for full reviews of both sometime in April.
Meanwhile at Mammoth I continued to be delighted skiing with my kids (a few overzealous lift attendants notwithstanding), and I continued puzzling over Anselme Baud's steep skiing technique.
Possibly I've come up with a bit of a breakthrough in that regard, though we've seen that picture before. Let me try to break the turn the next time I'm up north. If I can't, then I will be happy to proclaim I've once and for all decoded the uphill-weighted pedal turn, and why I (and some but not all skiers) seem to have so much trouble trying to replicate it.
Finally, a note on content: as mandated by law, I do a lot of microposting on my facebook account these days, rather than putting everything here at SierraDescents. You're welcome to follow or friend me there if you want to be sure you're getting every last drop of my hiking and skiing (plus non-threatening photos of my kids, who are of course much better than your kids).
The climate is changing. Snow levels have risen in my lifetime. Old timers in Bishop recall ice skating the pond in the city park. No body would ever do that now.
I lived in Mammoth Lakes in the late 70s early 80s. We used to cross country ski in the hills around Hot Creek. When was the last time that was possible?


Networking – what does it mean?
The word "networking" is undoubtedly overused. Every interaction — face-to-face or virtual — is considered networking; every online community is a network; every meeting is a networking event.
But think of networking as more than a cliché or all-purpose panacea. The very heart of networking is relationship building, and the investment you make in developing a profound connection with someone will pay dividends, regardless of whether you are on the giving or receiving end. Networking is more than a transaction; it is a lasting bond with a natural ebb and flow. Some of these relationships move to the forefront during certain periods while others retreat to the background, but there is always a sense of reliance, dependability and trust at the foundation.
It is my belief that the best relationships cannot be sustained in a simply one-dimensional communication medium, and I have found my association to others deepens when there is dialogue beyond our initial connection point. A LinkedIn invite augmented by a personal email; a Twitter posting followed by a direct message; an in-person meeting fortified by email; or reacquainting with someone from the past with a phone call all seem to solidify my attachment to my "network.
Microsoft Virtual TechDays: Have you registered yet?


text:
Cambrian Confusion: Some answers, more questions
Brian Switek
Yesterday I questioned the path by which Simon Conway Morris and James Valentine, two evolutionary biologists, came to appear in the new creationist film Darwin's Dilemma. Created by Illustra Media, a film company that has worked closely with the Discovery Institute, the film claims that the "Cambrian Explosion" provides evidence for intelligent design. I now […]
Yesterday I questioned the path by which Simon Conway Morris and James Valentine, two evolutionary biologists, came to appear in the new creationist film Darwin's Dilemma. Created by Illustra Media, a film company that has worked closely with the Discovery Institute, the film claims that the "Cambrian Explosion" provides evidence for intelligent design. I now have more information.
Simon Conway Morris was interviewed for the film. In an e-mail by Lad Allen, the producer of Darwin's Dilemma, the filmmaker provided evidence that he had interviewed Conway Morris on November 14, 2006. The questions focused on Conway Morris' expertise about Cambrian fossils. Said Allen;
I want to assure you that you [Conway Morris] are, in no way, represented as a proponent or supporter of intelligent design. Instead, your role in the film is that of one of the world's foremost experts on the Burgess Shale. You explain how the Burgess fossils were discovered, why they are so unique, and what they reveal about the biological explosion of complex animal life on earth. I thought you did an outstanding job.
Conway Morris stated that, at the time of the interview, he was unaware that he was participating in a creationist documentary. He also did not know of the relationship between Illustra Media and the Discovery Institute. (As Allen notes in his e-mail, Illustra Media is a separate legal entity from the Discovery Institute. Even so, Illustra has worked closely with Discovery Institute fellows to create the films The Privileged Planet, Unlocking the Mystery of Life, and Darwin's Dilemma, all of which also feature interviews with Discovery Institute fellows. Fellow Stephen C. Meyer was integral to the development of Darwin's Dilemma.) To reiterate, Conway Morris has stated that he did not know that his interview was going to be used for a creationist documentary until the release of the film this week.
How Illustra obtained the James Valentine video has yet to be confirmed. At the present time rumor has it that Valentine's segment came from an interview filmed by an outfit other than Illustra. It may be that previously-existing video of Valentine was simply co-opted for the new project. Expect more on this in the near future.
The question on where Conway Morris' interview came from has now been resolved, but the details of Valentine's are still unknown. Both paleontologists were interviewed about scientific subjects by creationist film outfits and it appears that they were unaware of the ends to which their interviews would be used. This is a shame. Such tactics on the part of creationist filmmakers make scientists reluctant to give out interviews, a tragedy during a time when we need more scientists reaching out to the public.
I have also been made aware that others are looking into Darwin's Dilemma. Expect to hear more about it soon.
Post-script: Something appears to have gone wrong with the commenting system (I have been trying to respond since last night), so I will respond here.
Granted, I do not have as much experience with the media as some of the esteemed commentators below, but I decided to go ahead with this story after Conway Morris said he could not recall being interviewed (and would not have given permission for footage of himself to appear in a pro-ID documentary). Valentine knew that an interview with him had been used in creationist documentaries but did not know that the film crew that interviewed him was going to use the footage in that way. Once I wrote the previous post, however, I had to write a second post to update the story with the new information presented above. It would have been irresponsible not to.
I had expected that it was standard practice for film crews to tell scientists what programs their interviews would end up in. Perhaps I was mistaken, and Conway Morris should have checked up on what Illustra Media was beforehand. (I know I would have looked into the previous work of people coming to interview me if I were in the same position.) Even so, I still think that it is dishonest for a creationist film crew to conduct an interview with a scientist for a project that the scientist might not agree to be in if they knew what it was. Furthermore, it may be that Valentine's interview has been recycled from a previous source. This has yet to be confirmed, but I think it is a bit shady for a film company to include an old interview into their film (which they said they conducted themselves) in order to give their product more clout, especially without notifying the scientist in that interview.
As I said, perhaps this is all "standard practice", but if that is so it is a bit frightening. Either way, I think (at the least) Illustra withheld information from one interviewee so that they could get him to appear in a film he might otherwise refuse to participate in. Conway Morris is not blameless in this, he should have been aware of who he was being interviewed by, but I would still think (hope?) that media companies would be forthright with what they were going to do with the interviews they film.
Brian Switek is a freelance science writer and a paleontology volunteer with the Natural History Museum of Utah. He is also author of the books My Beloved Brontosaurus and Written in Stone.
Wired Science Blogger
TopicscreationismMoviesPaleontology


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The exact boundaries of the metro area can differ in different contexts. Hillsborough County and Pinellas County (including the cities of Tampa, St. Petersburg, Clearwater, and several smaller communities) make up the most limited definition. The United States Census Bureau defines the Tampa–St. Petersburg–Clearwater Metropolitan Statistical Area (MSA) as including Hillsborough and Pinellas Counties along with Hernando and Pasco Counties to the north.


text:
FCS Championship: 2021 season matchup, how to watch
By Kevin Kelley - January 7, 2022
Photo: Troy Babbitt-USA TODAY Sports
The FCS Championship for the fall 2021 season features the No. 2 North Dakota State Bison taking on the No. 8 Montana State Bobcats.
The game is set for Saturday, Jan. 8, 2022 and will be played at Toyota Stadium in Frisco, Texas. The game will be televised by ESPN2 and the ESPN app at noon ET/11:00am CT, with Dave Flemming calling the play-by-play, Rod Gilmore and Jay Walker providing analysis, and Stormy Buonantony reporting from the sideline.
Additionally, a Spanish-language presentation of the game will be available on ESPN3 and ESPN Brasil with Matheus Pinheiro and Weinny Eirado on the call.
In their most recent matchup back in 2019, North Dakota State defeated Montana State 42-14 at home at the Fargodome in Fargo, N.D. The Bison have won three consecutive games against the Bobcats, but Montana State still leads the overall series 21-15.
North Dakota State is playing in their ninth FCS Championship. The Bison have won the championship game in each appearance (2011, 2012, 2013, 2014, 2015, 2017, 2018, and 2019 seasons).
NDSU finished the fall 2021 regular-season 10-1 and ranked third in the STATS Perform FCS Top 25 Poll. The Bison also claimed the Missouri Valley Conference championship and the automatic bid to the FCS Playoffs, where they were named the second-seed.
The Bison advanced to the FCS Championship with victories over Southern Illinois 38-7 in the Second Round, East Tennessee State 27-3 in the Quarterfinals, and James Madison 20-14 in the Semifinals. All three contests were played at the Fargodome.
Montana State is making their second appearance in the FCS Championship. In their first appearance back in 1984 when the game was known as the Division I-AA Championship, the Bobcats defeated Louisiana Tech 19-6.
The Bobcats wrapped up the fall 2021 regular-season with a 9-2 overall record No. 7 ranking in the STATS Perform FCS Top 25 Poll. Montana State did not win the Big Sky championship, but earned the eighth-seed and a first-round bye in the FCS Playoffs.
Montana State opened the FCS Playoffs with a 26-7 win at home over UT Martin in the Second Round. The Bobcats then upset top-ranked Sam Houston 42-19 on the road in the Quarterfinals before taking down South Dakota State at home in the Semifinals, 31-17.
The FCS Championship is scheduled to remain in Frisco through the 2025 season with an option for 2026.
Prior to relocating to Frisco in 2010, the FCS Championship was played at Finley Stadium on the campus of UT Chattanooga in Chattanooga, Tenn., from 1997 through 2009.


Have you ever experienced serious mind numbing pains in your feet that requires the examination and care of a foot expert? A foot clinic can offer you extensive medical solutions for your foot and ankle problems so that you can eradicate any pain or discomfort as quickly as possible. Proper foot care can not only provide the confidence that you need to have a healthy life, but it can also help you to attend to deeper health problems that are critical in nature but might not be as noticeable. Foot clinics on Harley Street are equipped with the right kind of medical infrastructure for treating the vast spectrum of foot & ankle disorders.
A foot clinic has typically has in-house medical professionals, such as podiatrists, chiropodists, pedorthists, physical therapists, sports medicine specialists, or orthopedic surgeons who can examine and diagnose the problem that you are having. Based on these initial assessments, a foot specialist can discuss the different types of treatment options that are available for your case. The foot experts may also make use of different types of techniques for understanding the specific nature of your foot problem. Some of these diagnostic procedures include bone scans, ultrasounds, computed tomography or CT scans, X-rays and MRIs.
A foot experts may recommend a wide range of solutions to ease pain and help you to get back in good shape. Some of the different types of foot problems that are treated in these clinics include plantar fasciitis, Morton's neuroma, ingrown toenails, shin splints, ankle injuries, heel spurs, corns and calluses, soft tissue masses, stress fractures, high arched feet or flat feet, arthritis, fungal and bacterial infections, metatarsalgia, debridement of ulcers, skin growths and rashes, as well as foot diseases associated with diabetes and gout. The treatments that are carried out in these foot clinics can continue for a short or a long while, depending on the severity of the case.
Whatever the nature of the problem, a foot expert will recommend a number of non-surgical measures to manage the condition. These measures may include rest, shoe selection, immobilization, stretching exercises, metatarsal cushions, heel cups, arch supports and other forms of clinical treatment. In case these measures fail to produce the desirable results, surgery may be recommended as a last option. Injections also form a critical part of surgery and sometimes they may be used for managing painful sensations. An effective way to resolve many types of foot problems is the use of foot orthotics or arch supports, which are actually specially designed insoles that you need to wear inside your shoes. They can help to manage abnormal foot function, help ease the pain and also cure various biomechanical problems.


Rest is defined as to cease work or movement in order to relax, refresh oneself, or recover strength. So I wound up asking myself after a day of "rest", am I refreshed, do I have my strength back? The answer was "no". So the "rest" that was recommended to me, wasn't actually doing anything, therefore it wasn't rest.
-Rev. Alfred H Ells, M.C.
This was section of the first thank you letter we received in 2007. Since then, we have spoken with hundreds of pastors, who, in one form or another, went through this same process. We identified that the process itself is critical to healing, and yet, does not always have to take three or more days. There is a trend of common misconceptions about rest, our ministries, and our positions that can lead to some erroneous and damaging paths. We have developed a workshop called Rest is Best. This teaching is designed to help expedite getting you to a better place of rest and listening, without giving up the needed process described above. The three sections: Fundamental, Overflow, and Bespoke, have short teachings followed by self-led workshops. The workshops can take anywhere from a couple of minutes to a day, all depending on barriers or misconceptions one has brought with them to a time of rest. If you are about to enter a time of rest, for a day or a month, we encourage you to watch the video and complete the workshops. We believe it will make a huge difference as you listen for His voice. You can request the video by simply sending an email.
En Gedi Retreat is Open-use, which means we don't have any programs for you to follow. None. However, we do have a heart for helping you hear from God during your time of rest. As such, we have put together a short teaching that you may participate in if you wish. This has come out of listening to hundreds of pastors about their experience during their time with us.


text:
The Performing Arts at WCSFIII
by David Eaton-NYC
The World Culture and Sports Festival in Washington, D.C. was undoubtedly one of the most significant events that our movement in America has produced. As director of the cultural programs of WCSF III I was presented with the challenge of fashioning a series of events that would reflect the spirit of True Parent's vision and values.
The following is a synopsis of the week's events.
New York City Symphony and Un Mundo at The Washington Times' Arbor Ballroom.
This concert was created in order to highlight the work of the Washington Times in South America and its sister publication in Washington, Tiempos del Mundo. The concert featured two outstanding musicians from Venezuela, flutist Marco Granados and harpist Annette Leon in a performance of Mozart's Concerto for Flute, Harp and Orchestra with members of the New York City Symphony, David Eaton conducting. Both soloists are of notable stature in there country (Miss Leon is the principal harpist with the Simon Bolivar Symphony Orchestra in Caracas) and they performed with great panache and virtuosity as the caliber of their musicianship was extremely high.
The second half of the program feature Mr. Granados and his quartet Un Mundo in a program of Latin Jazz and Folk music. The quartet was of exceptional quality and they received a long standing ovation.
There were several dignitaries from South American Embassies in attendance and a reception followed the concert. Caroline Betancourt of the Washington Times and Matthew Goldberg of Tiempos del Mundo were co-producers of this event.
Spyro Gyra at the Omni Shoreham Ballroom.
Spyro Gyra is one of the pioneering groups of what is now known as "cool jazz." Their performance was of extremely high caliber as well. We expected that, given their reputation as one of America's leading instrumental ensembles. The production aspects (handled by Kashmere Productions, Ltd. and New World Productions) were first rate and the reception of the audience was totally enthusiastic.
The MC for this event was radio personality Lee Hamilton of FM 105.9, the "cool jazz" station in DC.
Aaron Rosand at the Corcoran Gallery's Frances & Armand Hammer Auditorium
Aaron Rosand is on the last great exponents of the "Romantic" style of violin playing in the manner of Heifetz, Milstein, Oistrakh and Menhuin. His performance was completely masterful and he is a very giving performer.. He is a long time friend of our movement (he visited Father in Danbury) and was very happy to be part of the festival.
International Music Concert at Union Station's East Hall.
The East Hall at Union Station is a great venue to hold an event. The majesty of the hall lent itself perfectly to our concert. The quality of the three ensembles; Alhambra (Middle Eastern Music) Music From China (Traditional Chinese instrumental music) and Hassan Hakmoun (music from Africa) was very high and the audience was very enthusiastic. This concert highlighted the multicultural aspect of the overall Festival.
Kirov Academy of Ballet at Tawes Hall, University of Maryland.
The Kirov Academy presented a completely first rate performance featuring two solo dancers of the Kirov Ballet in Russia, Yvgeni Ivanchenko and Irina Amosova. The students of the Kirov academy were totally charming. There was a very fine turn out as well with nearly 1000 people in attendance. Michael Beard, Executive Director of the Kirov Academy and his staff did a great deal to generate an audience and their efforts paid off. ¨
Shin Sekai Chamber Ensemble at the Corcoran Gallery
This ensemble is comprised of soprano Yoshimi Kadota, Violinist Makiko Tachugi, guitarist Isamu Nakashio and pianist Rikako Asanuma. Their performances were of a very high level and their heartistic commitment to this event was evident. Having worked with them on many other occasions I was especially proud of their efforts in this concert.
Flutist Marco Granados made a guest appearance in a performance of Astor Piazzolla's "History of the Tango" for Flute and Guitar, a piece that has become very popular in the classical music repertory. He and Isamu Nakashio performed this piece with a refined sense of sensitivity.
New York City Symphony at Union Station
This concert was a free in the main hall of Union Station. The program was comprised of light classical and seasonal music and featured singers Raoul Joseph, Miyuki Harley and Yoshimi Kadota with the orchestra. As soon as the orchestra began playing hundreds of people gathered around to listen. Singers Raoul, Miyuki and Yoshimi conveyed a kind of heart that is very attractive and they were warmly received by the large audience.
Vickie Winans and Mavis Staples at the Lincoln Theater.
These two signers represent the new and old guard in the realm of Gospel Music. Though Vickie was working with us for the first time she was very gracious and totally professional. She sang to tracks and the audience really loved her performance. Mavis is a friend of our movement having performed at WFWP conferences and for Parents on one of our church Holy Day performances at the Manhattan Center. Performing with a band, Mavis had people dancing in the aisles by the end of her show.
The MC was Allison Wilson, a radio personality in the DC area and she did a fabulous job and really got the crowd into the spirit of the evening. The turn out for the concert was quite good; 900 people with the Mayor's office requesting 200 tickets as well.
"Images of Eden" Art Exhibit
The "Images of Eden" Art Exhibit was a highlight of the Festival. It was the largest visiting art show in Washington this year featuring 200 works of art by 60 artists representing 20 countries. The works ranged from 16th century Dutch masters Pieter Brueghel and Jacob Jordeans to contemporary artists Marc Chagall and Tom Nakashima, a well known Washington artist.
There were nearly 30 Unificationist artists represented and the media coverage seems to have been be quite substantial. Over 2000 people attended the exhibit which was coordinated by Michael and Elizabeth Ennis.
RFK Stadium Production
In addition to the Festival events there was considerable effort made on the stadium production. Steve Marks and David Carlin King of Carlin Enterprises were instrumental in fulfilling our talent, production and ground transportation needs. Their company was able to secure the talents of the Chinese Ribbon Dancers, the Eastern High School Band, the West African Dancers and the Apollo Karate Team.
Soprano Jamie Baer Peterson presented a heart rendering performance of "Morning of An Age" (composed by Kevin Pickard with the text by Randolph Remmel), a piece composed for the Assembly of the World Religions conference in 1985.
The contributions of Raoul Joseph and Miyuki Harley in the stadium production should not be overlooked. They performed "Your Heart will Open the Way" which was composed by Kevin Pickard for the Blessing '97 event. They also sang Eternal Love" and World of Love, tribute songs from the 1992 and 1995 Blessings. Raoul and Miyuki work so well as a duet and I believe the music from the previous WCSF's was most complimentary to the occasion. Their set was just right to bring the Holy Spirit into the stadium environment.
Kevin Pickard recorded much of the ceremonial music that was utilized during the RFK proceedings. Linda Eisenberg and Larry Moffitt created the script for the Stadium show and their tireless efforts in making the day a cohesive event should not be underestimated.
In Hindsight -- A reflection.
I feel that the quality of performances and production/technical support was consistently high as there were no technical problems and the artistic integrity of each event was never compromised. Being in the Performing Arts for over 21 years has taught me a great many lessons. A reoccurring theme, one that Father himself has reiterated, is that the arts are like the face of our movement. They reflect our values and beliefs -- our culture.
Over? the years we haven't always had the proper circumstances in which to present our cultural expressions and this cultural festival was a challenge in that regard. I feel that the quality was never compromised and as a result we could be very proud of the quality of that which was presented during the week of WCSF III.
For me personally WCSF was a very rewarding experience. Having been in the Performing Arts in our movement for over 21 years, WCSF III represented a culmination of sorts. This was a harvesting of many of the seeds that Parent's have planted in the artistic world. I'm sorry there are not more fruit to bring to the altar-artists who can fully appreciate the Father's commitment to marriage and fidelity as a way to build the ideal of world peace.
Obviously, Father has hope and a certain faith that we can eventually achieve our innermost goals as people (and artists). I remember Father saying that the criteria for becoming a great artist was to become a grandparent. Only after experiencing all realms of family love could one qualify to express the most profound emotions via ones creative endeavors. Obviously we all have a way to before can meet that criteria.
I feel very strongly that with the proper spiritual attitude and external circumstances, many of our more talented members can produce significant art. This was evident in the "Images of Eden" art exhibit and the work of Kevin Pickard (Music Director of the RFK production) Miyuki Harley, Raoul Joseph, Linda Eisenberg (Director of the RFK show) Larry Moffitt and the Shin Sekai Ensemble. WCSF provided an unique opportunity to display the talents of our members in a very positive fashion.
I thank God and True Parents for their support in making the Cultural events of WCSF III as successful as they were and pray for even greater success in the future.
This is the letter of introduction I wrote for the WCSFIII arts program.
In my thirty years as a professional musician I've had numerous occasions to perform for audiences throughout the world. Wherever my concert experiences have taken me, I have always been fascinated by how much people from diverse cultural backgrounds have in common. The universality of the human spirit is indeed a palpable reality, thus the artistic expressions of any given culture can provide unique insights to the heart and soul of humankind.
When asked by the World Cultural and Sports Festival to construct a series of events that would reflect world culture, I felt that expressing the universality of the human spirit through the arts would be a most felicitous way to pay tribute to the ideals of love and family; central themes of the Festival and elemental values cherished by people of all ethnic backgrounds.
I have always believed that artists could, and in fact should play significant role in creating conditions conducive to world peace and harmony. Obviously, the notion that art has persuasive affections is not new. Confucius, Plato, Aristotle, Boethius and St. Augustine shared the belief that art possessed the power to impact the moral and ethical standards of society. Twentieth century German composer Paul Hindemith believed that once an artist believed that particular assertion, one should then use that power "with the severest sense of moral responsibility."
As we move toward the beginning of the new millennium, the arts will continue to have a profound influence on the attitudes and values of contemporary culture. It is my hope that by discovering commonality among various cultures through the efforts of the talented artists participating in this festival, a spirit of altruism can emerge and thereby stimulate our global consciousness to aspire for a world culture that embodies the noble and godly attributes of truth, beauty and goodness.
David Eaton was the Director of Cultural Events for WCSF III.


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Q: Does every arbitrary covariant functor $F:\mathcal{C} \to \mathcal{D}$ maps an iso into an iso?? Let $F:\mathcal{C} \to \mathcal{D}$ a  covariant functor between  two categories I claim that if $f:X \to Y$ is an isomorphism in $\mathcal{C}$ then $F(f)$ is an isomorphism.
As $f:X \to Y$ is an isomorphism in $\mathcal{C}$ then there is another morphism $f':Y \to X$ in $\mathcal{C}$ such $f'f=1_{X}$ and $ff'=1_{Y}$. Then
$$F(f)F(f')=F(ff')=F(1_{Y})=1_{F(Y)},$$
and
$$F(f')F(f)=F(f'f)=F(1_{X})=1_{F(X)}.$$
Proving $F(f):F(X) \to F(Y)$ is an isomorphism in $\mathcal{D}$.
Is this right? Im surprised is just needed just the definition to prove this and every functor preserves isos.

A: You're right and the proof is correct. It follows because an isomorphism is just an arrrow with an inverse and functors preserve composition and identities, and hence map the inverse to the inverse. This works in basically any "algebraic" morphism setting that you already know: a ring morphism maps units to units too etc. Categories are sort of "algebraic" in flavour: composition as operation and functors preserve the operation and the identities..

A: Yes.
If we think about algebraic topology,  where functors first arose, we should be able to see that this basic property is very important. That is, isomorphic (homeomorphic) topological spaces would need to have isomorphic homology groups and homotopy groups,  as well.
For the attached groups are used to categorize the (topological) spaces.

redpajama_set_name: RedPajamaStackExchange


Form Field Templates are reusable layers of fields that can be applied to any document. Templates are ideal if you have one field layout that works for multiple documents, or if you have a document that can be sent several different ways.
Similar to library documents, Form Field Templates can be edited and shared to your group or account.
Choose a Form Field Template from the Select Form Field Template drop-down list.
In the drag-and-drop Authoring Environment, click the Field Templates drop-down in the upper-left corner.
If necessary, enter the page the template should start on.


In Brazil, UNICA reports that sugarcane milling in the Center-South totaled 12.6 million tons in the first half of December. In the accumulated harvest of 2018/2019 until December 16, processing reached 556.85 million tons, down 4.10% over the same period of the previous cycle (580.68 million tons).
In the first half of December, sugar production fell by 20.1% in relation to the same half of 2017, totaling only 408.3 thousand tons. In the case of ethanol, there was a 34.7% increase in production, with 681.5 million liters produced (601.4 million hydrated and 80.1 million anhydrous).
From the beginning of the 2018/2019 crop to December 16, the amount of sugar produced totaled 26.17 million tons, down 26.72% when compared to the same period of 2017. Conversely, ethanol production is high of 18.93%, reaching 29.77 billion liters (9.06 billion anhydrous and 20.71 billion hydrated).
The total volume of ethanol sold in the Center-South totaled 1.38 billion liters in the first half of December, 65.74 million of which were destined for export and 1.32 billion for the domestic market.


text:
Q: How to use Google Api to upload to a single Youtube channel on a User-less app I'm building an app that will allow users to upload videos to my Youtube channel. I have checked online about using Google Api to access the data/upload videos, but all references and examples seem to assume videos will be uploaded to the user's youtube channel, hence the process always involves using OAuth to gain approval from the user.
However, in my case the only youtube channel all users will access is mine. And I'm not even asking them to create an account on my app to upload the videos. Is there a way to do this directly from the app.
My app is an iOS app, but if you have a solution in any other language, I welcome your suggestion.
Thanks in advance

A: I had the same problem not so long ago. Youtube Documentation doesnt make it easy. Even though you getting access to your own Youtube channel, You still need to be authenticated using OAuth. Google makes hard for you to be vulnerable to attacks.
My solution is using a server side(API) application. What you can then do is make your iOS app hit the API.
So the tricky thing is getting the refresh token. Its hardly documented anywhere. What you can then do is follow documentation of your preferred language for the API.
The documentation can be found here https://developers.google.com/youtube/v3/guides/auth/server-side-web-apps
Get the Refresh token:
Step 1 : Hit this url in the browser https://accounts.google.com/o/oauth2/auth?client_id=ClientID_Here&redirect_uri=urn:ietf:wg:oauth:2.0:oob&scope=https://gdata.youtube.com&response_type=code&access_type=offline
It will make you log in using your youtube credential which is your google account.
You will then have to agree for google to access your account by clicking allow.
At the end it will give you a code.
Step 2 : You should then authenticate using a clientless platform.
(eg instead of browser) Use curl instead. (There plenty of 3 minute tutorials on how to use curl)
Hit this url endpoint :
curl https://accounts.google.com/o/oauth2/token -d "code=CodeFromGoogleOAuth&client_id=ClientIDHere&client_secret=ClientSecretHere&redirect_uri=urn:ietf:wg:oauth:2.0:oob&grant_type=authorization_code"
You should then get a refresh token in the response.
There are alot of examples of how to upload from then on like
https://developers.google.com/youtube/v3/code_samples/dotnet
In the example above you have to avoid using the GoogleWebAuthorizationBroker use Googles one instead. Look for RefreshToken Field

A: It is not possible to allow third parties to upload videos to a single YouTube account with the YouTube Data API, and that is by design. See this Google Blog, explaining "It's tempting to design a system in which all videos are uploaded to a single "master" YouTube account, but this is always the wrong approach."
A potential workaround is to use YouTube Direct Lite, which allows you to easily build and moderate a playlist on your YouTube account comprised of videos uploaded by your users on their accounts.


I'm gonna post a few links regarding the content of the middle story. The links are not spam.
See if you can put together whether or not this is forward or backward looking advice that the CIA is seeking.


This summer World Vets had the opportunity to work at an elephant sanctuary located in northern Thailand. Our volunteers took part in routine daily tasks involving the general care, feeding and maintenance of elephants. They also provided ongoing veterinary attention that various elephants require due to injures suffered prior to their re-location to the sanctuary. What's more, we also used this experience as a teaching opportunity for students to learn more about elephant anatomy, medicine and treatments.
A Successful Summer in the Balkans!
In August World Vets had two veterinary teams working in the Balkans region of Europe. Our teams provided high volume sterilization campaigns in Albania and Romania over a two week period. They also provided other needed veterinary services for animals, all of which were provided free of charge. Hundreds of animals benefited for these efforts where many came to us in baskets, bags and other forms of "transport" to receive attention and care.
We greatly appreciate and thank our dedicated volunteers and local partners for an outstanding job in making this a successful summer effort!
During the month of July World Vets sent a veterinary team to India to carry out our first campaign ever held on the subcontinent. Over four days our team worked alongside local veterinarians to provide free sterilizations as well as health consultations for the small animal population of Mysuru (formerly known as Mysore).
We'd like to thank our local partners as well as our dedicated group of volunteers who made this pilot project a great one!


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Home › Business › How The Proposed Tax Changes Will Affect Business Owners
How The Proposed Tax Changes Will Affect Business Owners
By Ottawa Board of Trade on July 20, 2017 • ( 0 )
On July 18, 2017 the federal government announced proposed tax changes which will have a significant impact on business owners. The main premise of the proposed changes is that the government feels it is unfair that an employed person with a T4 cannot do the same tax planning as a business owner; the proposed changes will limit what a business owner can do to save or defer tax.
Here is a brief summary of the impact of the proposed changes for business owners:
The concept of the "kiddie tax", the income splitting rule that currently applies to minors, will apply to adult relatives as well. The highest rate of tax will apply to dividends, certain types of interest, and capital gains that are paid to/realized by adult relatives unless the amount can be justified as "reasonable" in the circumstances. The reasonable test will be based on level of participation in the business and/or capital invested in the business. There appears to be a more stringent reasonable test for 18 to 24 year olds, but essentially it will eliminate income splitting with all relatives, regardless of age, unless they are active in the business. This applies whether or not the adult relative owns shares or is a beneficiary of a trust that owns the shares.
These rules will apply to distributions in 2018 and subsequent years.
Capital Gain Exemption (CGE)
The CGE can be claimed when a business owner sells the shares of their small business company. In 2017, this would result in the ability to exempt from tax the first $835,000 of capital gain arising from the share sale. It has been common tax planning to multiply access to the CGE through family ownership of shares, either directly or through a family trust.
For dispositions arising in 2018 or subsequent years, the CGE can neither be claimed by a person under the age of 18, nor be claimed after they turn 18 in respect of value increases prior to turning age 18. For adult relatives, if the reasonableness test noted above applies in the case of a realized capital gain, not only is the capital gain subject to the highest rate of tax, but no CGE may be claimed.
In addition, starting in 2018, if the shares are held by a trust (other than an alter ego or joint partner trust), any value increase during the holding period will not qualify for the CGE. There will be the ability to make a one-time election in 2018 to increase the adjusted cost base (ACB) of the shares and claim the CGE, subject to some easing of the restrictions on qualifications for the CGE claim.
Between the restrictions on income splitting and the loss of the CGE, these rules will negate the use of a family trust for tax planning purposes.
Pipeline Planning
Pipeline planning is a method to extract tax paid cost basis of small business company shares by transferring the shares to a different company in return for a promissory note. Essentially, as tax has been paid once it should not have to be paid again. Under the current rules, if son arranges for his company to buy from his mother, shares of her company, mother has to report a dividend instead of a capital gain. If son (not his company) buys the shares from mother, she reports a capital gain (not a dividend). Then, provided mother does not claim the CGE, such that she has paid tax on the capital gain, son can sell the shares to his own company for a promissory note, thereby accessing the tax paid cost basis (mother paid the tax on that value already) so son does not have to pay the tax again.
The government's position is that a distribution of value from a small business company within the related group should always be taxed as a dividend, never as a capital gain. So the rule change – first, under the current rules, in the above example, note that the "pipeline" of the tax paid cost basis works provided mother did not claim the CGE. If she claims the CGE, it is not "tax paid" cost basis so son will have a dividend if he transfers the shares to his company for a promissory note. The change – reference to claiming the CGE will be eliminated from the rules. The rule will now apply if any person related to son (e.g. mother) reported a capital gain when they sold the shares, such that when son transfers the shares to his company in return for a promissory note, son will be required to report a dividend. This will result in double tax, wherein mother reports a capital gain and son reports a dividend on the same value. To avoid this double tax, mother will have to report a dividend or son will have to hold the shares personally until he sells to someone else.
The government acknowledges that a true intergenerational transfer of shares to an adult child's corporation should be treated in the same manner as a sale to an arm's-length corporation (mother should be able to report a capital gain and claim the CGE and son should be able to use a company to acquire the shares just like he could if he purchased the shares from anyone else). However, the government has claimed the inability to distinguish between a true intergenerational transfer and planning for mother to extract retained earnings at capital gains tax rates.
There was no mention of post-mortem pipeline planning, wherein mother dies and son inherits the shares, but it does appear that the new rules will also apply in this case. This could be a real problem for existing estates that have assumed that these rules would not be changed; there is a real possibility of a double tax problem.
These changes will apply to shares disposed of, and amounts received or that become receivable, on or after July 18, 2017.
Investment of Retained Earnings
The government feels that the tax deferral achieved by way of corporate tax rates, whether small business or general rates, being lower than the top personal tax rates should only be of benefit to the business owner if the after-tax retained earnings are reinvested in the business. The proposal is such that if invested in passive investments, there will be no tax deferral available. The justification is based on the perceived injustice that an employed person with a $220K T4 who wants to invest $20K only has $10K after tax to invest, while a business owner would have $17K ($20K less the 15% small business tax rate) to invest.
The government has proposed multiple options to address the aforementioned situation. One suggestion is that an additional tax be paid by the company to the extent that the retained earnings are not reinvested back into the business, tracking pools of income taxed at different rates. Another looks at an election based method that would cause the company to pay tax at high rates on all sources of income. In addition, it could be that the "connected company"/Part IV tax system would be replaced by a refundable tax to be paid on all inter-corporate dividends.
The issues of and proposed solutions to the investment of retained earnings have not been issued in draft legislation form, but are merely musings by the government that the rules should achieve the stated objective; it is now a question of how to get there.
These proposed rule changes are far reaching and will have a significant impact on small business owners/entrepreneurs, the very people often recognized for driving the economy and employment. The government has requested commentary from all stakeholders, but the consultation period is short – comments must be received by October 2, 2017.
Don Scott, FCPA, FCA is a Partner & Director of Tax Services at Welch LLP
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