text:
\section{Introduction}
\vspace{-2mm}
\input{tex/intro.tex}\label{sec:intro}

\vspace{-2mm}
\section{Related Work}
\vspace{-2mm}
\input{tex/relatedwork.tex}\label{sec:relatedwork}

\vspace{-2mm}
\section{Problem Definition}
\vspace{-2mm}
\input{tex/prodef}\label{sec:prodef}

\vspace{-2mm}
\section{Technical Approach}
\vspace{-2mm}
\input{tex/tech.tex}

\vspace{-2mm}
\section{Experiments}\label{sec:exp}
\vspace{-2mm}
\input{tex/exp_final.tex}

\vspace{-2mm}
\section{Conclusion}
\vspace{-2mm}
\label{sec:conclusion}
\input{tex/con.tex}

\clearpage
\acknowledgments{We would like to thank the UnitX company~(\href{https://www.unitxlabs.com}{https://www.unitxlabs.com}) for providing us the UR5 robot arm used in the real robot experiments. We thank Shenli Yuan for helping us set up the customized two-fingered gripper, and Hongzhuo Liang for helping us set up the network configuration.}
\section{Appendix}
\input{tex/appendix_arxiv}

\subsection{Object Stable Placement}\label{sec:exp_placing}
\vspace{-1mm}

We evaluate our proposed framework on learning stable placement poses in this subsection.
\begin{table}[ht]
\vspace{-3mm}
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
\hline
method & \begin{tabular}[c]{@{}c@{}}bowl-\\ fork\end{tabular} & \begin{tabular}[c]{@{}c@{}}bowl-\\ spoon\end{tabular} & \begin{tabular}[c]{@{}c@{}}box-\\ hammer\end{tabular} & \begin{tabular}[c]{@{}c@{}}box-\\ wrench\end{tabular} & \begin{tabular}[c]{@{}c@{}}box-\\ spatula\end{tabular} & \begin{tabular}[c]{@{}c@{}}mug-\\ fork\end{tabular} & \begin{tabular}[c]{@{}c@{}}mug-\\ spoon\end{tabular} & \begin{tabular}[c]{@{}c@{}}mug-\\ corkscrew\end{tabular} & mean \\ \hline
\emph{Random} & 0.000 & 0.001 & 0.109 & 0.039 & 0.000 & 0.000 & 0.000 & 0.012 & 0.020 \\ \hline
\emph{~\citet{jiang2012learning}} & 0.037 & 0.080 & 0.201 & 0.113 & 0.047 & 0.018 & 0.125 & 0.092 & 0.089 \\ \hline
\emph{~\citet{jiang2012learning} + heuristic} & 0.175 & 0.041 & 0.437 & 0.852 & 0.603 & 0.013 & 0.128 & 0.000 & 0.281 \\ \hline
\emph{Ours} & \textbf{0.869} & \textbf{0.848} & \textbf{0.894} & \textbf{0.911} & \textbf{0.905} & \textbf{0.674} & \textbf{0.834} & \textbf{0.829} & \textbf{0.845} \\ \hline
\end{tabular}
}
\caption{Overall comparison of the stable placement accuracy. We report the accuracy of our method and different baseline methods for each category and the average accuracy over all the categories. Our method significantly outperforms these baseline methods.}
\label{tab:place_acc}
\vspace{-4mm}
\end{table}
\vspace{-2mm}
\begin{table}[bh]
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
\hline
method & \begin{tabular}[c]{@{}c@{}}bowl-\\ fork\end{tabular} & \begin{tabular}[c]{@{}c@{}}bowl-\\ spoon\end{tabular} & \begin{tabular}[c]{@{}c@{}}box-\\ hammer\end{tabular} & \begin{tabular}[c]{@{}c@{}}box-\\ wrench\end{tabular} & \begin{tabular}[c]{@{}c@{}}box-\\ spatula\end{tabular} & \begin{tabular}[c]{@{}c@{}}mug-\\ fork\end{tabular} & \begin{tabular}[c]{@{}c@{}}mug-\\ spoon\end{tabular} & \begin{tabular}[c]{@{}c@{}}mug-\\ corkscrew\end{tabular} & mean \\ \hline
\emph{~\citet{jiang2012learning} + heuristic} & 2.500 & 1.000 & 28.000 & 28.333 & 10.333 & 2.000 & 4.250 & 0.000 & 9.552 \\ \hline
\emph{Ours} & \textbf{16.900} & \textbf{18.100} & \textbf{45.850} & \textbf{49.100} & \textbf{52.133} & \textbf{14.500} & \textbf{15.750} & \textbf{12.450} & \textbf{28.098} \\ \hline
\end{tabular}}
\caption{Overall comparison of the predicted stable placement poses' diversity.}
\label{tab:comp_diversity}\vspace{-4mm}
\end{table}

\begin{wrapfigure}{r}{6cm}
\vspace{-4mm}
\centering
\includegraphics[width=\linewidth]{imgs/pose_gen_diversity.pdf}
\vspace{-6mm}
\caption{Point cloud visualization of sampled stable poses from our proposal network. Our proposal network can generate diverse stable placements of the candidate object (colored in green). We also mark the predicted stable scores.}\label{fig:pose_gen_diversity}
\vspace{-6mm}
\end{wrapfigure}

\vspace{-1mm}
\paragraph{Evaluation of Object Placement Accuracy.}
Our pipeline samples and refines 128 poses for each object and supporting item pair. We use the pose classifier to estimate the scores for these 128 poses and use a threshold of 0.8 to filter out the poses with low scores. To verify the remaining poses, we run a forward simulation in PyBullet~\cite{coumans2019} to check whether the objects at these poses are stable. The results of the stable placement accuracy for each category are reported in Table~\ref{tab:place_acc}.

We first adopt a baseline~(\emph{Random}) which is based on a random pose sampling strategy. The rotation value is sampled from SO(3) uniformly, and translation value is uniformly sampled within this region of [$\pm 0.05$, $\pm 0.05$, $z$+0.02] in the supporting item's local frame, where $z$ is determined by the maximal height of each supporting item. We use the meter as the unit of translation value throughout the experiment section.

We slightly modify the approach in ~\cite{jiang2012learning} to work in our setting and use it as another baseline~(\emph{~\citet{jiang2012learning}} ), in which a stable pose classifier is learned based on hand crafted features. We randomly sample 128 poses in the local frame of the supporting environment, and classify these poses using the stable pose classifier. We evaluate the accuracy of these stable poses in PyBullet~\cite{coumans2019} and report the results in Table~\ref{tab:place_acc}. The results suggest that even with random sampled poses, a simple classifier with hand-craft feature can improve the stable placement accuracy. We also design another baseline that adopts heuristic rules for pose sampling but with the same classifier from~\cite{jiang2012learning}. For this baseline (\emph{~\citet{jiang2012learning} + heuristic}), the object translation in the horizontal dimensions (XY dimensions) is sampled in the radius of $0.9 * r$ and the translation in the gravity dimension (Z dimension) is determined by $z+0.01$, where $r$ and $z$ are the radius and maximal height of supporting item respectively. We additionally rotate the object to make the longest axis of the object horizontally. While this baseline can achieve some reasonable results in easy situations when the supporting item has a nearly flat horizontal surface (i.e., box-wrench, box-spatula), it perform worse with supporting items with complicated geometry (i.e., bowl, mug). The results demonstrate that a good pose proposal algorithm is also of great importance to the success of stable object placement. With carefully designed stable pose proposal network and stable pose classifier network, our framework achieves the best results over all the approaches.

\vspace{-1mm}
\paragraph{Evaluation of Object Placement Diversity.} We evaluate whether the pose proposal network can generate a reasonable distribution of the stable poses for placing the object over the supporting items. A diversifying stable pose distribution is crucial for the success of finding desirable regrasping solutions. For each input object and supporting item pair, we draw 128 poses from the proposal network, and transform the candidate object to the proposed stable poses. The qualitative results are presented in Fig.~\ref{fig:pose_gen_diversity}. Our visualization demonstrates that our proposed stable poses yield diverse object placements.

To quantitatively measure the diversity of the stable object placements, we define two poses as two different poses if their translation difference is larger than $\tau_1$ and the rotation angle between them is larger than $\tau_2$. In our experiment, $\tau_1$ and $\tau_2$ are set to be $0.03m$ and $30^{\circ}$ respectively. We report the average number of different stable object placements in Table~\ref{tab:comp_diversity}. The results showing that although random uniform sampling combined with heuristic rules can generate diverse stable poses for some easier environment (i.e., boxes with flattened surface), it can not handle complicated geometry. Our proposed method outperforms the baseline by a large margin.

\vspace{-1mm}
\subsection{Object Regrasping Performance}
\vspace{-1mm}

\begin{figure*}[t!]
\centering
\includegraphics[width=0.85\linewidth]{imgs/real_pose.pdf}
\vspace{-2mm}
\caption{Demonstration of regrasping tasks on real world data and visualization of stable poses generated by our model.}\label{fig:real_pose}
\vspace{-4mm}
\end{figure*}

We evaluate the performance of our model in real-world experiments on everyday objects. We first test the pose proposal network on point clouds from Intel RealSense RGB-D camera. Utilizing the camera matrix, we can reconstruct a
3D point cloud. The point clouds are fed into our model to predict stable placement poses. We then run Unigrasp~\cite{shao2020unigrasp} to generate a list of valid grasp poses and search for the desired target poses based on the pipeline described in Sec. 4. The generated stable placements and several key frames of the regrasping process are visualized in Fig~\ref{fig:real_pose}.
For more results on the regrasping experiments, please refer to our supplementary material.

\subsection*{A. Additional Experiments on Object Stable Placement}
\paragraph{A1. Does Multi-task Learning Help?}
\begin{table}[ht]
\centering
\begin{tabular}{c|c c c c}
\hline
\hline
Task Combination & Contact Acc. & Contact Prec. & Stable Acc. & Stable Prec. \\ \hline
stable & N/A & N/A & 0.888 & 0.890  \\ \hline
stable + contact & 0.925 & 0.977 & \textbf{0.918} & 0.906 \\ \hline
stable + contact + offset (ours) & \textbf{0.945} & \textbf{0.981} & 0.912 & \textbf{0.910} \\ \hline
\end{tabular}
\vspace{1mm}
\caption{We report the performance of our model trained with different loss variants. Our multi-task joint learning strategy leverages the correlation between tasks to enhance the feature learning, which results in better generalization.}
\vspace{-4mm}
\label{tab: diff_loss}
\end{table}

To verify the usefulness of the multi-task joint learning framework, we adopt the same network architecture but with different combinations of task losses. The quantitative results on the test set are shown in Table~\ref{tab: diff_loss}. We use the accuracy and precision metrics to measure the performance of each model. While the model trained with only stable loss reaches a reasonable performance, adding auxiliary losses improves the stable precision from 0.906 to 0.910. Our experiments demonstrate that the contact prediction and contact offset regression are beneficial to the stable pose learning task. This is reasonable because a strong correlation between each subtask eases the joint learning process: 1) for all stable poses, the placed object must have contact points with the supporting environment, and 2) the offset from the support and the object should be consistent with each other.

\paragraph*{A2. Different Score Threshold.}
We evaluate how different score threshold will affect the accuracy of the proposed stable placements, the results are presented in Fig~\ref{fig:diff_thresh_acc}. While observing that filtering out the generated poses using higher thresholds will generally lead to better accuracy of stable placement, we do not consider a too harsh threshold because it will remove all the poses. We choose the threshold to be 0.8 for best practice.
\begin{figure*}[ht]
\centering
\includegraphics[width=0.85\linewidth]{imgs/diff_thresh_acc.pdf}
\caption{The accuracy of the proposed stable placements under different score thresholds.}
\vspace{-3mm}
\label{fig:diff_thresh_acc}
\end{figure*}

\subsection*{B. Dataset}
We construct a dataset containing 50 objects (i.e., spoon, fork, hammer, wrench, etc.) and 30 supporting items (i.e., mug, box, bowl). We then split these objects and supporting items into training and test sets. We generate 249 pairs from the training set of objects and supporting items, and 38 pairs from the test. For each pair of object-supporting item, the initial placement poses are generated by sampling different object poses with respect to the supporting item in PyBullet, and running a forward simulation to see if it remains stable. We collect the placement data for supporting items by randomly placing the supporting items with respect to the initial environment (i.e., the ground), and running the simulation to check whether the supporting items keep static. To make the stable placement robust to variant dynamics and geometry, we randomize the dynamic parameters including friction, mass, and external forces. For each round of simulation, we randomly scale the object mass by [0.9, 1.1], and we randomly select the gravity in the range [$\pm 0.1$, $\pm 0.1$, -9.81 $\pm 0.1$] for approximating the perturbations in the real environment, we also sample the lateral friction, spinning friction, and rolling friction in the range of [0.3, 0.7]. In total, we generate 1,050,884 poses for training items and 15,105 poses for testing items. Some stable object placements from our dataset are visualized in Fig~\ref{fig:dataset}.

\begin{figure*}[ht]
\centering
\includegraphics[width=\linewidth]{imgs/dataset.png}
\caption{Visualization of random sampled stable placements in our dataset.}
\vspace{-3mm}
\label{fig:dataset}
\end{figure*}

\subsection*{C.~Regrasping Simulation Experiments}
Leveraging the stable pose prediction model and object grasping model, we evaluate the regrasping performance of our proposed system. We randomly select multiple objects from our dataset when creating each initial manipulation scene. These objects are placed stably, except that the robot might grasp one object. In each scene, there is one regrasp query, which is a target grasp pose of one object, for our system to accomplish.

In total, we have 30 scenes as the test set. Our system generates the regrasp graph by predicting the stable poses of each object and checking whether there are edges connecting two nodes. The regrasp task is considered successful if our system finally finds the
target regrasp poses. We report the average success rate among the test scenes. We adopt a baseline approach by replacing our object placement prediction
model with the baseline method~(\emph{Random}) and keeping the same rest settings. Our proposed pipeline achieves 83.3\% success rate while the baseline does not have any successful case.

\subsection*{D.~Regrasping Real World Experiments}
We mount a two-fingered customized gripper onto a ur5 shown in the video on the project webpage~\href{https://sites.google.com/view/regrasp}{https://sites.google.com/view/regrasp}. A realsense camera is mounted at the robot's wrist and its relative transformation to the end-effector is calibrated. We gather raw point clouds from the realsense camera leveraging its calibrated intrinsic camera parameters. In the current setting, we use the black color cloth to get object segmentation. Other segmentation approaches could also be adopted. These point clouds are fed into our model to predict stable poses. We run position control to move and place the object at its predicted pose. The video currently plays at 4x speed.

\subsection*{E.~Generalization and Robustness}
Our pipeline assumes the object and support can be segmented from the background using off-the-shelf methods~\cite{he2017mask, long2015fully, tremeau1997region}. Considering the noise induced by the perceptual algorithm and real sensor, we also add gaussian noise on point clouds during training to make the models more robust to outliers. For evaluation of the tolerance on segmentation errors, we first test our pipeline under different IoUs using simulation data (the ground-truth segmentation mask is randomly expanded or cropped), the performance is shown in Fig~\ref{fig:iou_noise}. To quantify the robustness of our models to sensor noise, we add Gaussian noise with different standard deviation to the input point clouds. The results suggest that our trained models can resist segmentation noise and sensor noise in a reasonable range.

To mitigate the uncertainties related to the object dynamics, we add various perturbations when annotating the stable poses during the data generation stage. In the real-world execution, we would prefer the stable object placement object that a small neighborhood of the specific stable pose remains stable according to the stable pose classifier.  This specific stable pose is helpful during the real-world execution when the noise in robot actuation may lead to alignment errors between the object and the surrounding environments.
\begin{figure}
  \centering
  \includegraphics[scale=0.5]{imgs/iou.pdf}
  \hspace{0.2in}
  \includegraphics[scale=0.5]{imgs/noise2.pdf}
  \caption{The accuracy of the proposed stable placements under different segmentation IoUs and noise.}
  \label{fig:iou_noise}
\end{figure}

\begin{figure*}[ht!]
\centering
\includegraphics[width=0.6\linewidth]{imgs/fail.pdf}
\caption{Failure cases visualization.}\label{fig:fail}
\end{figure*}

\subsection*{F.~Failure Cases and More Result Analysis}
\paragraph{F1.~Stable Pose Prediction Module.}
Our model takes raw point clouds of the object and surrounding environment along with random variables as inputs, and outputs a list of stable poses. However, these poses can not be 100\% accuracy, the failure cases are shown in Fig.~\ref{fig:fail}. We analyze that some predicted poses might be sensitive to tiny perturbation, or missing parts of the depth scans, these predicted poses might result in slight collisions between the object and the surrounding environments. These failure cases could be mitigated if force/tactile sensors are leveraged to produce closed-loop manipulation policies.

\paragraph{F2.~Entire Regrasping Pipeline.}
The regrapsing task is quite challenging. A typical regrasping process includes the object placement stage and the object regrasping stage.

During the object placement stage, when the robot is placing the object (for example, a spoon) into the supporting item (for example, a bowl), the final object pose might be stable but different from the predicted desired pose. The 6D pose of the supporting item might also change when receiving external forces in the process. These changes in the object and supporting item's 6D poses might ruin the following regrasping stage. For example, the spoon might fall into the bottom of the bowl, which makes the spoon's regrasping process impossible.

In the object regrasping stage, the robot might fail to find a feasible collision-free approach plan.  Given that the robot finds a collision-free approach, the grasping process might still fail due to sensor noise, friction. In the regrasping stage, although the object is already at a stable pose, it might still be sensitive to external perturbations resulting in failure grasps. The most failure cases are caused by the false stable object pose predictions.

\subsection*{G.~Computational Costs of the ReGrasping Pipeline}
Our system creates and maintains a regrasp graph to tackle the regrasping problem. Each node represents a list of all objects' stable poses in the scene, and each edge indicates that there is one regrasping operation to change from a list of stable poses to another list of stable poses.

In daily tasks such as regrasping spoons and forks, the regrasping process usually happens between two or three objects. In our cases, there are two or three objects in the scene. It takes one or two second to generate 128 predicted stable poses per object and 0.5 to 1 second to predict each object's grasping parameters. It takes 5 to 10 seconds to construct the nodes in our cases and around 30 seconds to generate the edges. Since the regrasp graph in our experiments is relatively small, it takes less than one second to search the graph.

\subsection{Regrasp Graph Construction and Searching}
\vspace{-1mm}

A regrasping problem solves a sequence of valid grasping $\mathscr{G}$ and placement $\mathscr{P}$ operations to reach a target grasp pose $\mathcal{G}_*^t$ of the object $\mathcal{O}_t$.
We also allow robots to change other objects' poses $\{\mathcal{T}_g^{i\ne t}\}$ by applying several intermediate grasping-and-placing operations, since changing the poses of other objects rearranges the environment and may provide extra supports that can increase the number of valid grasping operations for the target object $\mathcal{O}_t$.
Below, we describe in details how to construct a regrasp graph and run a searching algorithm over it to figure out a plan.

\vspace{-2mm}
\paragraph{Graph Construction.}
Our system creates and leverages a regrasp graph to solve the regrasping problem.
Each node $\mathcal{N}$ in the regrasp graph includes stable poses $\{\mathcal{T}^i_g\}$ for all objects $\{\mathcal{O}_i\}$.
An edge $\mathcal{E}_{uv}$ connects two nodes $\mathcal{N}_u$ and $\mathcal{N}_v$, if there is one and only one object $\mathcal{O}_j$ whose stable pose is changed between the two nodes, and there exists a valid pair of grasping operation $\mathscr{G}_k^j$ and placement operation $\mathscr{P}_k^j$ that transform the object's stable pose. The edge indicates that the robot can first grasp the object $\mathcal{O}_j$ from its stable pose under one node and then place it at the stable pose in the other node.

Initially, all objects $\{\mathcal{O}_i\}$ are stably placed in the scene. All objects' initial poses define the first node in the regrasp graph.
For each object $\mathcal{O}_j$, we use a deep-learned stable object placement pose prediction model (Sec.~\ref{sec:placeing}) to estimate the object's alternative stable poses under the current surrounding environment.
Each alternative stable pose of the object $\mathcal{O}_j$, along with other objects' stable poses, leads to a new node in the regrasp graph.
Whenever a new node is created, our system checks whether there are edges connecting it to the rest nodes and add these edges to the graph.

\vspace{-2mm}
\paragraph{Graph Searching.}
Leveraging the constructed regrasp graph, the solution of a regrasp problem is a path from a starting node $\mathcal{N}_0$ to a target node $\mathcal{N}_t$ that contains a stable object placement pose $\mathcal{T}_{g}^t$ allowing a valid grasping operation $\mathscr{G}_l^t$ to achieve the target grasp pose $\mathcal{G}_*^t$ for the target object $\mathcal{O}_t$. We use depth-first-search algorithm to search for a valid path.

\begin{figure*}[t!]
\centering
\includegraphics[width=0.85\textwidth]{imgs/learning_to_place.pdf}
\caption{Our proposed framework for predicting stable object placement poses.
As shown in the left figure, we first learn a stable pose proposal network that takes as inputs the partial point clouds of the object and the surrounding environment, and outputs a diverse set of rough pose estimations. We formulate it as a generative model and feed in different Gaussian noise vectors $z$'s to query diverse pose prediction outputs.
On the right figure, we then show our stable pose classification and refinement network that learns to classify if an object 6D pose leads to a stable placement.
We then apply \emph{CEM}~\cite{CEM} to perform local optimization for the pose refinement.
We also leverage multi-task training by predicting the contact points between the object and environment as we find it beneficial.
}
\label{fig:placing}
\vspace{-2mm}
\end{figure*}

\vspace{-2mm}
\subsection{Stable Object Poses Prediction}\label{sec:placeing}
\vspace{-1mm}

We train a neural network to predict stable object placement poses under diverse surrounding environments.
Estimating stable placement poses for a wide variety of objects with diverse and complicated geometry is a challenging task.
It requires very accurate pose predictions, since a slight error may easily result in an undesired object falling or penetration between the object and its surrounding environment.
Hence, we design a two-stage pipeline (Fig.~\ref{fig:placing}) that first predicts diverse rough poses and then refines to obtain more accurate results.
We describe the detailed network designs below.

\vspace{-1mm}
\paragraph{Stable Poses Proposal.}
Given an object $\mathcal{O}_i$ and a static surrounding environment $\mathcal{S}=\mathfrak{O}-\{\mathcal{O}_i\}$, we first train a generative model that learns to propose a diverse set of poses $\{\mathcal{T}_k^i\}$ for the object $\mathcal{O}_i$ to be stably placed in the environment $\mathcal{S}$.

Fig.~\ref{fig:placing} (left) illustrates the inputs, outputs, and design overview for this network.
In our implementation, we represent both the partial observations of the object $X_i\in\mathbb{R}^{m\times3}$ and surrounding environment $X_\mathcal{S}\in\mathbb{R}^{m\times3}$, captured by the robot's RGB-D camera, as 3D point clouds consisting of $m=1,024$ points.
We first employ two PointNet++ encoders~\cite{qi2017pointnet++} to extract global point cloud features {$f_i\in\mathbb{R}^{128}$ and $f_\mathcal{S}\in\mathbb{R}^{128}$}.
To propose multiple solutions to the stable poses of the object $\mathcal{O}_i$, we sample a random Gaussian noise {$z\in\mathbb{R}^{3}$} as an additional input and use an Multilayer perceptron (MLP) to extract a feature {$f_z\in\mathcal{R}^{64}$}.
We concatenate $f_i$, $f_\mathcal{S}$, and $f_z$ to produce a single joint feature $f_{i,\mathcal{S},z}$ and finally employ another MLP to decode a final 6-DoF pose ${\hat{\mathcal{T}}}_{g,z,\mathcal{S}}^i$ for the object $\mathcal{O}_i$.
For brevity, we omit the subscripts $g$ denoting the global world coordinate, $\mathcal{S}$ indicating the environment, and the superscript $i$ representing the shape $\mathcal{O}_i$ in the following paragraph.
The 6-DoF pose is composed of a 3D translation and a 3D orientation. For the orientation part, we adopt the axis-angle representation, which has been shown to be effective for pose prediction task~\cite{8411477}.

Sampling different Gaussian noises $\{z_l\}_{l=1}^r$, we obtain a diverse set of pose predictions $\{\hat{\mathcal{T}}_{z_l}\}_{l=1}^r$.
Provided with a ground-truth list of stable poses $\{\mathcal{T}_{z_l}\}_{l=1}^{r_*}$ generated by running several random interaction trials in physical simulation, we define a loss $\mathcal{L}_{M}$ to train our pose prediction outputs.
\begin{equation}\label{eqn:matchloss}
\mathcal{L}_M(\{\hat{\mathcal{T}}_{z_l}\}_{l=1}^{r}, \{\mathcal{T}_{z_{l'}}\}_{l'=1}^{r_*}) = \sum_{l=1}^r \min_{l'} L(\hat{\mathcal{T}}_{z_l}, \,\mathcal{T}_{z_{l'}}) + \sum_{l'=1}^{r_*} \min_l L(\hat{\mathcal{T}}_{z_l}, \,\mathcal{T}_{z_{l'}})
\end{equation}
where $L$ measures the distance between two 6-DoF poses. We implement $L$ as the average {$L_2$} distance between the corresponding points of the object point clouds after applying the two poses.

\vspace{-1mm}
\paragraph{Stable Pose Classification and Refinement.}
We train a model to classify the stability of the sampled poses. For each predicted pose $\hat{\mathcal{T}}_{z_l}$, we first concatenate the transformed object point cloud $\hat{\mathcal{T}}_{z_l}(X_i)$ with the surrounding environment point cloud $X_\mathcal{S}$. Then, we augment this combined point cloud with one extra 1D arrays of ones and zeros, to indicate if a point belongs to $X_i$ or $X_\mathcal{S}$, along the XYZ dimension to form a tensor of shape $(M+M, 3+1)$. With this augmented input, a straightforward method for stable classification would be using a PointNet++~\cite{qi2017pointnet++}. However, we find that this naive approach does not provide satisfying results due to the intrinsic difficulty of analyzing stability among two separate geometry components.

To facilitate learning, we propose to solve this problem via a multi-task supervised learning approach. After obtaining the feature from the PointNet++ backbone, we have three task branches that perform stable classification, contact classification, and contact point regression simultaneously. For contact point regression, we use a PointNet++ decoder to regress the 3D offset $v_i$ from each point $p_i$ of the input point cloud to its nearest ground-truth contact point with a loss defined as:
\begin{equation}
\mathcal{L}_{offset} = \sum_{p_i \in X} L(p_i + v_i, \argmin_{c \in C}\left\|c-p_i\right\|_2)
\end{equation} Here $X$ denotes the input point clouds including the transformed object point cloud $\hat{\mathcal{T}}_{z_l}(X_i)$ and the environment point cloud $X_\mathcal{S}$. Based on the predicted $\hat{\mathcal{T}}_{z_l}$ pose, we calculate the nearest ground truth stable pose from Eqn.~\ref{eqn:matchloss} and collect the corresponding contact point sets between the object and the environment in the simulation denoted as $C$. We implement $L$ as smooth $L1$ loss. We also adopt a variance loss $L_{variance}$ similar to \cite{8411477} to reduce the variance of the group \{$p_i + v_i$\} that correspond to the same contact point. We train two MLP classifiers for stable pose and contact classification with losses denoted as $\mathcal{L}_{stable}$ and $\mathcal{L}_{contact}$ respectively. Our total loss is defined as:
\begin{equation}
\mathcal{L}_{C} = \mathcal{L}_{stable} + \lambda_1 \mathcal{L}_{offset} + \lambda_2\mathcal{L}_{contact} + \lambda_3\mathcal{L}_{variance}
\end{equation}
where $\mathcal{L}_{stable}$ and $\mathcal{L}_{contact}$ are standard binary cross entropy losses.

Leveraging the above stable pose classifier function denoted as $\mathcal{V}$, we apply \emph{CEM}~\cite{CEM} to search for a locally optimal goal pose starting from the initial predicted poses. We first apply the predicted transformation $\hat{\mathcal{T}}$ to the object point cloud $X_i$ to get a point cloud $\tilde{X}_i$. A point $a$ in the \emph{CEM} searching action space is a 6D transformation $\mathcal{T}_a$ which transforms the object point cloud $\tilde{X}_i$ into $\mathcal{T}_a(\tilde{X}_i)$. A point $s$ in the CEM state space is the transformed object point cloud along with the surrounding environment point cloud $(\mathcal{T}_a(\tilde{X}_i),X_\mathcal{S})$.  When selecting the action $a$, we run a derivative-free optimization method CEM~\cite{CEM} to search within the 6D pose space to find a 6D transformation $\mathcal{T}_a$ associated with the highest score in the value model $\mathcal{V}(s)$.
\begin{equation}
    a^* = \argmax_a{\mathcal{V}(\mathcal{T}_a(\tilde{X}_i),X_\mathcal{S})}
\end{equation}
The 6D pose associated with $a^*$ is the final stable placement pose produced by our model.

\vspace{-1mm}
\subsection{Object Grasping}
\vspace{-1mm}

In this subsection, we describe how our system performs valid grasping operations.
Recall that a grasp pose $\mathcal{G}_k^i$ of the object $\mathcal{O}_i$ is parameterized by two contact points on the object surface $(\mathbf{p}^i_k,\mathbf{q}^i_k)$ and an approaching direction of the robotic hand $\mathbf{d}^i_k$.
We adopt UniGrasp~\cite{shao2020unigrasp}, an efficient data-driven grasp synthesis method, to generate a list of valid grasp poses $\{\mathcal{G}_k^i\}$ of the object $\mathcal{O}_i$.
UniGrasp learns a deep neural network to select a set of contact points $(\mathbf{p}^i_k,\mathbf{q}^i_k)$ from the input point cloud of the object $\mathcal{O}_i$.
We refer to \citet{shao2020unigrasp} for more detailed description of the method.
Given two contact points $\mathbf{p}^i_k$ and $\mathbf{q}^i_k$, we calculate a plane perpendicular to the line segment of the two contact points intersecting at the middle point of the line segment.
We then consider 36 approaching directions evenly distributed within the plane, which produces 36 potential grasp poses.
For each grasp pose $\mathcal{G}_k^i$, we solve the inverse kinematics of the robot.
If there is no valid inverse kinematic solution or there are collisions in all possible approaching directions, we drop the contact point pair and continue checking for the next pair of contact points.
We keep running this process until we find a valid grasping operation if there exists one.

\subsection{Object Placement}
\vspace{-2mm}

Pick-and-place is one of the most common robotic manipulation tasks. Picking objects, or grasping, has attracted great attention in robotics. For a broader review of the field on data-driven grasp synthesis, we refer to~\cite{sahbani2013,bohg2014}. In contrast, object placement, which is the process of deciding where and how to place an object, has received considerably less attention. \citet{HARADA20141463} presented an object placement planner to find object placement poses by matching planar surface patches on the object with planar surface patches in the environment. \citet{8967732} introduced a motion planning algorithm for robots to place a grasped object in a cluttered environment. \citet{jiang2012learning} employed Support Vector Machines with hand-designed features to score the placement suitability of candidate poses. Unlike these works either requiring full observability or relying on hand-crafted feature engineering, we propose an end-to-end approach with deep-learned features extracted directly from the raw point clouds.

\vspace{-1mm}
\subsection{Regrasp for Object Reorientation}
\vspace{-2mm}

Early works~\cite{1087910,10.5555/130121} build Grasp-Placement~(GP) to search for regrasp sequences, by solving the inverse kinematics, checking all collisions to remove invalid grasp-placement pairs, filling them in the GP table, and finally searching within the table to find a sequence of pick-and-place motion. There are many works following this direction. \citet{619165} improved the efficiency of regrasp planning using an evaluated breadth-first-search, rated grasp, and placement quantities. \citet{769979} replaced the GP table with a space of compatible grasp-placement-grasp triplet and searches this space to find a sequence of pick-and-place motion. Regrasp is also studied in the context of task and motion planning~(TAMP). \citet{6943079} presented a framework where a symbolic planner plans a sequence of high-level sub-tasks and a constraint satisfaction problem solver plans low-level operations. A regrasp plan is composed of a symbolic pick-and-place sequence and a set of geometrically feasible placements, grasps, and paths, which requires a precise full observability of the entire system.

\citet{wan2019regrasp} presented a regrasp planning component for object reorientation. Given the initial and goal pose of an object, the regrasp planning component finds a sequence of robot postures and grasp configurations that reorient the object from the initial pose to the goal pose. \citet{raessa2021planning} presented a hierarchical motion planner for planning the manipulation motion to re-pose long and heavy objects. They developed a graph-based planning system that combines both regrasp and in-hand manipulation motion considering external support surfaces. \citet{cao2016analyzing} increased the reorientation capability of a pick-and-place regrasp by adding a vertical pin on the working surface and using it as the intermediate location for regrasping. The work most related to ours is~\cite{ma2018regrasp} where they developed a regrasp planning algorithm considering intermediate stable states on fixture such as box baskets. Unlike these planning based approaches, our learning-based approach do not require full observability and learn features directly from raw sensory data. Our approach also allows robots to actively change other objects in the environment to create extra supports. Besides regrasping, object reorientation can also be achieved through in-hand manipulation~\cite{4626569, 8560381, rollergrasperV2} or dual-arm manipulation~\cite{cruciani2019dual, saut2010planning}.
\subsection{Object Stable Placement}\label{sec:exp_placing}

We evaluate our proposed framework on learning stable placement poses in this subsection.
\begin{table}[ht]
\vspace{-3mm}
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
\hline
method & \begin{tabular}[c]{@{}c@{}}bowl-\\ fork\end{tabular} & \begin{tabular}[c]{@{}c@{}}bowl-\\ spoon\end{tabular} & \begin{tabular}[c]{@{}c@{}}box-\\ hammer\end{tabular} & \begin{tabular}[c]{@{}c@{}}box-\\ wrench\end{tabular} & \begin{tabular}[c]{@{}c@{}}box-\\ spatula\end{tabular} & \begin{tabular}[c]{@{}c@{}}mug-\\ fork\end{tabular} & \begin{tabular}[c]{@{}c@{}}mug-\\ spoon\end{tabular} & \begin{tabular}[c]{@{}c@{}}mug-\\ corkscrew\end{tabular} & mean \\ \hline
Random & 0.000 & 0.001 & 0.109 & 0.039 & 0.000 & 0.000 & 0.000 & 0.012 & 0.020 \\ \hline
~\citet{jiang2012learning} & 0.037 & 0.080 & 0.201 & 0.113 & 0.047 & 0.018 & 0.125 & 0.092 & 0.089 \\ \hline
~\citet{jiang2012learning} + heuristic & 0.175 & 0.041 & 0.437 & 0.852 & 0.603 & 0.013 & 0.128 & 0.000 & 0.281 \\ \hline
Ours & \textbf{0.869} & \textbf{0.848} & \textbf{0.894} & \textbf{0.911} & \textbf{0.905} & \textbf{0.674} & \textbf{0.834} & \textbf{0.829} & \textbf{0.845} \\ \hline
\end{tabular}
}
\caption{Overall comparison of the stable placement accuracy. We report the accuracy of our method and different baseline methods for each category and the average accuracy over all the categories. Our method achieves the best performance.}
\label{tab:place_acc}
\vspace{-3mm}
\end{table}

\begin{wrapfigure}{r}{7cm}
\vspace{-4mm}
\centering
\includegraphics[width=\linewidth]{imgs/pose_gen_diversity.pdf}
\vspace{-2mm}
\caption{Point cloud visualization of sampled stable poses from our proposal network. Our proposal network can generate diverse stable placements of the candidate object (colored in green). We also mark the predicted stable scores.}\label{fig:pose_gen_diversity}
\vspace{-4mm}
\end{wrapfigure}

\vspace{-1mm}
\paragraph{Evaluation of Pose Proposal Network.} We first evaluate whether the pose proposal network can generate a reasonable distribution of the stable poses for placing the object over the supporting items, as a diversifying stable pose distribution is crucial for the success of finding desirable regrasping solutions. For each input pair, we draw 128 poses from the proposal network, and we transform the candidate object to the proposed stable poses, the qualitative results are presented in Fig.~\ref{fig:pose_gen_diversity}. Our visualization demonstrates that our proposed stable poses yield a diversifying object placement.

\vspace{-1mm}
\paragraph{Evaluation of Pose Classifier.}
To evaluate how accurate the pose classifier is, we estimate the scores for the sampled and refined 128 poses using the pose classifier. We use a threshold of 0.8 to filter out all the unsure poses. For verifying the remaining poses, we run a forward simulation in PyBullet~\cite{coumans2019} to check whether the pose keep static, and the accuracy of stable placement for each category are reported in Table~\ref{tab:place_acc}.

We first adopt a baseline which is based on a random sampling strategy that draw the rotation from SO(3) uniformly, and translation is sampled from [$\pm 0.05$, $\pm 0.05$, $z$+0.02] in the supporting item's local frame, where $z$ is determined by the maximal height of each supporting item.

We also compare our approach with~\citet{jiang2012learning} in Table~\ref{tab:place_acc}. Followed their method, we randomly sample 128 poses in the local frame of the supporting environment, and filter out all the unstable poses using the learned classifier, the remaining ones are used for evaluating the accuracy in PyBullet~\cite{coumans2019} through running a forward simulation. The results suggest that even with random sampled poses, a simple classifier with hand-craft feature can improve the stable placement accuracy. We also test another baseline that adopt heuristic rules for pose sampling but with the same classifier from~\cite{jiang2012learning}. For this baseline, the object translation in XY dimension is sample in the radius of $0.9 * r$ and the translation in Z dimension is determined by $z+0.01$, both are in support's local frame, where $r$ and $z$ are the radius and maximal height of supporting item respectively. We additionally rotate the object to make the longest axis of the object to be parallel with the XY plane. While this baseline can achieve some reasonable results in some easier pairs that with a nearly flat surface (i.e., box-wrench, box-spatula), it perform worse with supporting items with complicated geometry (i.e., bowl, mug). The results demonstrate that a good pose proposal algorithm is also of great importance to the success of stable object placement. With carefully designed stable pose proposal network and stable pose classifier network, our framework achieves the best results over all the approaches.

\vspace{-1mm}
\subsection{Object Regrasping Performance}
\vspace{-1mm}

\begin{wrapfigure}{r}{6cm}
\centering
\includegraphics[width=\linewidth]{imgs/vis.pdf}
\vspace{-4mm}
\caption{Visualization of two regrasping tasks. Top row: regrasp a hammer; Bottom row: regrasp a spoon.}\label{fig:more}
\vspace{-2mm}
\end{wrapfigure}

Leveraging the stable pose prediction model and object grasping model, we evaluate the regrasping performance of our proposed system. We randomly select multiple objects from our dataset when creating each initial manipulation scene. These objects are placed stably, except that the robot might grasp one object. In each scene, there is one regrasp query, which is a target grasp pose of one object, for our system to accomplish. In total, we have 30 scenes as the test set.

Our system generates the regrasp graph by predicting the stable poses of each object and checking whether there are edges connecting two nodes. The regrasp task is considered successful if our system finally finds the target regrasp poses. We report the average success rate among the test scenes. We adopt a baseline approach by replacing our object placement prediction model with the baseline method described in Sec.~\ref{sec:exp_placing} and keeping the same rest settings. Our proposed pipeline achieves 73.3\% success rate while the baseline does not have any successful case. More visualization results are available in the supplementary material.

\vspace{-1mm}
\subsection{Ablation Study: Does Multi-task Learning Help?}
\vspace{-1mm}

\begin{table}[]
\centering
\begin{tabular}{c|c c c c}
\hline
\hline
Task Combination & Contact Acc. & Contact Prec. & Stable Acc. & Stable Prec. \\ \hline
stable & N/A & N/A & 0.888 & 0.890  \\ \hline
stable + contact & 0.925 & 0.977 & \textbf{0.918} & 0.906 \\ \hline
stable + contact + offset (ours) & \textbf{0.945} & \textbf{0.981} & 0.912 & \textbf{0.910} \\ \hline
\end{tabular}
\vspace{1mm}
\caption{We report the performance of our model trained with different loss variants. Our multi-task joint learning strategy leverages the correlation between tasks to enhance the feature learning, which results in better generalization.}
\vspace{-4mm}
\label{tab: diff_loss}
\end{table}

To verify the usefulness of the multi-task joint learning framework, we adopt the same network architecture but with different combinations of task losses. The quantitative results on the test set are shown in Table~\ref{tab: diff_loss}. We use the accuracy and precision metrics to measure the performance of each model. While the model trained with only stable loss reaches a reasonable performance, adding auxiliary losses improves the stable precision from 0.906 to 0.910. Our experiments demonstrate that the contact prediction and contact offset regression are beneficial to the stable pose learning task. This is reasonable because a strong correlation between each subtask eases the joint learning process: 1) for all stable poses, the placed object must have contact points with the supporting environment, and 2) the offset from the support and the object should be consistent with each other.

redpajama_set_name: RedPajamaArXiv


For those in search of new sensations, we have a range of infusions from the furthermost and most exotic locations so you can enjoy a range of varieties presented in silk pyramids.
Enjoy our Green Tea, Red Tea and Linden tisanes.
Enjoy our Tea, Peppermint and Chamomile tisanes.


text:
New Worgen and Goblin Character Models Coming in WoW 8.2.5
by Magistrate - 1 year ago show comments
The long-awaited Goblin and Worgen model updates are at least on the horizon. As the most recently-added new races, they lagged behind the vanilla race model update that began years ago. Players can expect the models to hit with World of Warcraft patch 8.2.5. Little else is yet known about the far-off patch, but we do know one thing: female Worgen look hella better.
We'll look more at future patches 8.1, 8.1.5, 8.2, and 8.2.5 as we continue to dive into the Day 1 World of Warcraft: What's Next panel. Click here for more from the panel (it's a lot), and don't miss everything Warcraft from BlizzCon 2018!
posted in News Tags: Blizzcon 2018, Blizzcon 2018 What's Next Panel, character models, Goblin, new character models, worgen
Seth Harkins
PC gamer and lover of (most) things Blizzard. In his off time, he writes bad fan fiction, tends to his growing number of house plants, and enjoys a love-hate relationship with two cats.
Follow @Seth_Harkins
© 2013-2017 BlizzPro All rights Reserved. World of WarCraft, StarCraft, Diablo, Hearthstone and Heroes of the Storm content and materials are trademarks and copyrights of Blizzard Entertainment or its licensors. All rights reserved. This site is a part of the BlizzPro.com network of websites and is not directly affiliated with Blizzard Entertainment

redpajama_set_name: RedPajamaCommonCrawl


text:
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
// Code generated by Microsoft (R) AutoRest Code Generator.

package com.azure.resourcemanager.iothub.models;

import com.azure.core.annotation.Fluent;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.time.Duration;

/** The properties of the feedback queue for cloud-to-device messages. */
@Fluent
public final class FeedbackProperties {
    /*
     * The lock duration for the feedback queue. See:
     * https://docs.microsoft.com/azure/iot-hub/iot-hub-devguide-messaging#cloud-to-device-messages.
     */
    @JsonProperty(value = "lockDurationAsIso8601")
    private Duration lockDurationAsIso8601;

    /*
     * The period of time for which a message is available to consume before it is expired by the IoT hub. See:
     * https://docs.microsoft.com/azure/iot-hub/iot-hub-devguide-messaging#cloud-to-device-messages.
     */
    @JsonProperty(value = "ttlAsIso8601")
    private Duration ttlAsIso8601;

    /*
     * The number of times the IoT hub attempts to deliver a message on the feedback queue. See:
     * https://docs.microsoft.com/azure/iot-hub/iot-hub-devguide-messaging#cloud-to-device-messages.
     */
    @JsonProperty(value = "maxDeliveryCount")
    private Integer maxDeliveryCount;

    /**
     * Get the lockDurationAsIso8601 property: The lock duration for the feedback queue. See:
     * https://docs.microsoft.com/azure/iot-hub/iot-hub-devguide-messaging#cloud-to-device-messages.
     *
     * @return the lockDurationAsIso8601 value.
     */
    public Duration lockDurationAsIso8601() {
        return this.lockDurationAsIso8601;
    }

    /**
     * Set the lockDurationAsIso8601 property: The lock duration for the feedback queue. See:
     * https://docs.microsoft.com/azure/iot-hub/iot-hub-devguide-messaging#cloud-to-device-messages.
     *
     * @param lockDurationAsIso8601 the lockDurationAsIso8601 value to set.
     * @return the FeedbackProperties object itself.
     */
    public FeedbackProperties withLockDurationAsIso8601(Duration lockDurationAsIso8601) {
        this.lockDurationAsIso8601 = lockDurationAsIso8601;
        return this;
    }

    /**
     * Get the ttlAsIso8601 property: The period of time for which a message is available to consume before it is
     * expired by the IoT hub. See:
     * https://docs.microsoft.com/azure/iot-hub/iot-hub-devguide-messaging#cloud-to-device-messages.
     *
     * @return the ttlAsIso8601 value.
     */
    public Duration ttlAsIso8601() {
        return this.ttlAsIso8601;
    }

    /**
     * Set the ttlAsIso8601 property: The period of time for which a message is available to consume before it is
     * expired by the IoT hub. See:
     * https://docs.microsoft.com/azure/iot-hub/iot-hub-devguide-messaging#cloud-to-device-messages.
     *
     * @param ttlAsIso8601 the ttlAsIso8601 value to set.
     * @return the FeedbackProperties object itself.
     */
    public FeedbackProperties withTtlAsIso8601(Duration ttlAsIso8601) {
        this.ttlAsIso8601 = ttlAsIso8601;
        return this;
    }

    /**
     * Get the maxDeliveryCount property: The number of times the IoT hub attempts to deliver a message on the feedback
     * queue. See: https://docs.microsoft.com/azure/iot-hub/iot-hub-devguide-messaging#cloud-to-device-messages.
     *
     * @return the maxDeliveryCount value.
     */
    public Integer maxDeliveryCount() {
        return this.maxDeliveryCount;
    }

    /**
     * Set the maxDeliveryCount property: The number of times the IoT hub attempts to deliver a message on the feedback
     * queue. See: https://docs.microsoft.com/azure/iot-hub/iot-hub-devguide-messaging#cloud-to-device-messages.
     *
     * @param maxDeliveryCount the maxDeliveryCount value to set.
     * @return the FeedbackProperties object itself.
     */
    public FeedbackProperties withMaxDeliveryCount(Integer maxDeliveryCount) {
        this.maxDeliveryCount = maxDeliveryCount;
        return this;
    }

    /**
     * Validates the instance.
     *
     * @throws IllegalArgumentException thrown if the instance is not valid.
     */
    public void validate() {
    }
}

redpajama_set_name: RedPajamaGithub


Duke University announced in April 2018 that it is delaying indefinitely plans to allow Duke Energy to build a fracked gas power plant on campus.
and thank him for choosing a cleaner path forward for Duke University.
Update: There have been reports that Duke Energy and Duke University are considering instead building a plant that would burn biogas from hog waste. Read our July 2018 letter pointing out some pitfalls of this plan and urging them to prioritize the health of safety of hog farm neighbors.
In May 2016, Duke University announced it was considering a partnership with Duke Energy to build a 21-megawatt natural gas-burning power plant on the Duke campus. As our October 2016 press statement explains, the campus would be a guinea pig for a national scheme to build fracked gas power plants with climate-wrecking emissions on campuses.
NC WARN met with Duke University officials beginning in July 2016, presenting them with information on the dangers of natural gas and discussing alternatives. In October, we sent a report titled Analysis of and Alternatives to the Proposed Duke University Natural Gas-Fired CHP Plant to Duke University President Richard Brodhead.
We followed that up in November with a letter refuting misinformation about the project, and a request in December to be included in the stakeholder process.
In January 2017, we sent a supplemental letter and technical analysis showing that the pollution control equipment proposed by Duke Energy for the plant would allow 10 times as much nitrogen oxide (NOx) to be emitted as best-in-class NOx control equipment used at other gas plants.
Duke Energy has filed for approval of the plan by the NC Utilities Commission, but the details of the contract remain to be worked out and approved by the University. NC WARN will intervene in the case at the Commission (see our Motion to Intervene).
The Commission had scheduled a public hearing and an evidentiary hearing on the plant for January 2017, but on December 1, Duke Energy asked the Commission to delay the hearings to early summer. The University requested more time after receiving significant student and public objections to the plan.
In March, four former deans of Duke's Nicholas School of the Environment published an op-ed opposing the plant and NC WARN called "flagrant foul" on Duke for its secretive decision-making process.
On April 11, the University announced it would not put the plant to a vote by its Board of Trustees in May as had been planned.
NC WARN applauds Duke University President Richard Brodhead's decision on April 11 to delay consideration by the Board of Trustees of the proposed fracked gas power plant that Duke Energy wants to build on campus. Read the University's announcement here and media coverage here. The announcement came after a special subcommittee of the campus sustainability committee submitted its report on the plant proposal to the University administration.
When it appeared in 2018 that attempts to build the plant had resumed, another community effort was launched, and Duke University announced on April 6, 2018 that it was suspending plans for a gas-burning power plant on campus.
Duke Energy has plans to build such plants at other universities as well. A similar proposal at Clemson University is also encountering resistance. We are glad that Duke University declined the opportunity to become the poster campus for an industry scheme to create profit centers at universities. As it becomes clear that the climate crisis is being driven by methane emissions from the fracking boom, humanity badly needs leadership from Duke University and other academic institutions.
Letter to Dr. Vincent E. Price, President Duke University, and Lynn Good, President & CEO Duke Energy: Amid prodigious PR by the two Dukes since the spring about creating biogas from hog waste, there apparently has been little or no technology breakthrough in several years.
Duke University has delayed indefinitely plans to build a freestanding Combined Heat and Power (CHP) plant and will instead focus its attention on expanding opportunities to use biogas and other environmentally friendly fuels for its growing energy needs, university officials announced on Friday.


text:
Turkish Journal of Field Crops is een Turks, aan collegiale toetsing onderworpen open access wetenschappelijk tijdschrift op het gebied van de landbouwkunde. De naam wordt in literatuurverwijzingen meestal afgekort tot Turk. J. Field Crops. Het eerste nummer verscheen in 2009.

Landbouwkundig tijdschrift
Open access-tijdschrift

redpajama_set_name: RedPajamaWikipedia


The PAS Vr is a hand-held, rapid alcohol detection and screening instrument using a platinum electrochemical fuel cell sensor of high alcohol specificity, accuracy and stability. Certified by NHTSA as an Alcohol Screening Device (ASD) when used with a mouthpiece in active/direct mode.
The PAS Vr may also be used passively (without mouthpiece) to quickly screen large numbers of subjects for the presence of breath alcohol simply by having a subject speak. The user can screen a subject in as little as 15 seconds to determine if alcohol is present on a subject's breath. The PAS Vr can also be used to screen an open container for the presence of alcohol.
Function Combines both direct and passive testing for detecting low levels of alcohol in exhaled breath or the environment.
Cell Heater Built-in heater regulates fuel cell temperatur at 104F (40C).
Specificity Fuel cell detects only alcohol. It is unaffected by acteone, paint or glue fumes, foods, confectionary, methane, and practically any other substance likely to be found in the breath.
Breath Sample Pump runs for 5 seconds and draws in a 1 cu. in. (15ml) air sample (nominal figures).
Display Color-coded 9-element LED bargraph and numeric display of alcohol level.
Peak Reading 2-10 seconds at 104F (40C). Longer at lower temperatures unless fuel cell heater is on.
Recovery Time A few seconds to minutes after a positive reading; longer if fuel cell is overloaded. No recovery time if no alcohol is detected.
Environmental Operating temperature range: 0 to 104F (-18 to +40C). The PAS Vr housing is weather resistant.


text:
Home News Africa Can a tooth settle the mystery over Congo's independence hero?
Can a tooth settle the mystery over Congo's independence hero?
Belgium's federal court said on Friday it would hand over a tooth, suspected to be the only remains of the murdered Congolese independence hero Patrice Lumumba, to his family, a move that could finally unlock the secret behind his death.
Lumumba, a firebrand political leader, became the Democratic Republic of Congo's first democratically-elected prime minister after independence from Belgium in 1960, but alarmed the West with overtures to Moscow at the height of the Cold War.
His government lasted just three months before he was overthrown and assassinated by firing squad, a killing for which the Belgian government took partial responsibility in 2002.
But the exact circumstances of his death have retained an element of mystery as his body was never found. His supporters and some historians accuse the CIA of having ordered his killing.
The tooth was reportedly taken from Lumumba's body by a Belgian policeman, Gerard Soete, who claimed in a BBC documentary to have dissolved much of Lumumba's body in acid, and burned the rest.
"The tooth is historically traced to Mr Lumumba by somebody who approached his body after his death," a spokesperson for Belgium's federal prosecutor told Reuters.
"This person is now deceased but had given the tooth to his daughter who gave it to the justice."
A DNA test of the tooth would give Soete's account greater credibility. But the court has not approved a test because it would have destroyed the tooth, leaving nothing to give the family, the spokesman said.
In July, Lumumba's daughter Juliana sent a letter to the Belgian king asking for her father's remains to be returned. She wants to give the tooth a symbolic burial in Congo on Jan. 17, the anniversary of his death.
"We've been waiting for this moment 60 years to bury our father," Juliana said by telephone.
Asked whether she believed the tooth belonged to her father, she said: "maybe it's true, maybe it's not. Even if it's symbolic, we have to end this painful and dramatic episode."
"A lot of questions remain."
Previous articleUnilever South Africa takes further steps after 'racist' TRESemmé advert
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Additional Reporting
Assassinated
Bate Felix
Belgian King
Belgian Policeman
Belgium's Federal Court
Belgium's Federal Prosecutor
Congolese Independence Hero
Firebrand Political Leader
Fiston Mahamba
Gerard Soete
Hereward Holland
Independence Hero
Marine Strauss
Overthrown
Partial Responsibility
Soete
Symbolic Burial
Ugandan opposition party says it will challenge presidential election result
Death toll in Sudan Darfur clashes rises to 48 – state media
Uganda election: Bobi Wine 'fearful for life' after Yoweri Museveni win
UN troops retake rebel-controlled city in Central Africa Republic
U.S. vows action against election riggers in Uganda
Tribal clashes kill two in Sudan's West Darfur – state media
Mali army probes jihadist prisoners death
Yoweri Museveni clings on as one of Africa's longest-serving leaders
Guinea president Alpha Conde receives coronavirus vaccine jab
UNIABUJA signs MoU with Diabetes Africa
University of Abuja, Gwagwalada, FCT, has signed a Memorandum of Understanding (MoU) with Diabetes Africa, a network of health professionals, to establish a top-class educational programme on Diabetes Education in the institution.
Napoli confirm Victor Osimhen will retake coronavirus test
Details of how Osimhen contracted the virus was revealed on social media as the forward did not follow Covid-19 rules when he celebrated his birthday on December 29, which he has apologized for.
AGF: 774,000 jobs to address unemployment rate among youths
The Attorney General of the Federation and Minister of Justice, Abubakar Malami, (SAN) has said that, the extended Federal Government Special Public Works Programme of 774,00 jobs opportunities to Nigerians was part of deliberate efforts and conscious strategies to address the ballooning population of unemployed youths and rising insecurity in some parts of the country.
Kano council polls: Governor Ganduje optimistic of APC victory
Kano State Governor Abdullahi Umar Ganduje has expressed optimism that the All Progressives Congress (APC) would emerge victorious at the local government polls in the state.
Neymar thought about quitting football but has learned to ignore haters
The Brazil international is regarded as one of the most divisive stars in the game. The 28-year-old became the most expensive player in world football when he left Barcelona to join Paris Saint-Germain in 2017.


On Sunday the 13th of February nine members met at the Burgh Valley Car Park for a Wildlife Society outing to Stubbs Mill, near to Hickling Broad. Stubbs Mill is a site renowned for its roosting harriers and cranes, mainly Marsh Harriers but there are a few Hen Harriers amongst them.
We left Burgh Valley in three cars and met up at the Hickling Broad Reserve car park with those who had made their own way there. From Hickling Broad we walked to the Stubbs Mill viewing area, a distance of about a mile. The weather could have been better for viewing, it was dull and overcast with a strong wind blowing.
We arrived at the viewing area at approx. 3.30pm and saw two or three Marsh Harriers drifting about in the distance. Over the next hour the numbers built up to 20 or thereabouts, it was difficult to make an accurate count because they circled round, sometimes dropping down onto the ground while others were joining them from behind you.
The highlight of the afternoon for most people was a male Hen Harrier which flew past quite close to the viewing area. Unfortunately it continued on and was never seen again. There was only a brief sighting of cranes. A couple were seen in the distance but not everyone managed to get onto them before they were lost to view. Other birds seen were Fieldfares, a Grey Heron, Mute Swans, Starlings and various finch flocks.
Due to the very poor light conditions the smaller birds were only seen in silhouette. Because of the poor visibility (and getting cold) we called it a day at 5pm, which was fortunate as when we got back to the cars it began to rain. On the whole it was an interesting outing seeing that number of Marsh Harriers plus the beautiful male Hen Harrier, just unlucky with the weather.


text:
Leave a Comment	/ At Home / By Pie Pal Vickie
And, you know, maybe I shouldn't have dreaded it as much as I did. After all, it's directed by Nicholas Stoller, who's responsible for a trio of enjoyable comedies: Forgetting Sarah Marshall, Get Him to the Greek (which I loved) and The Five-Year Engagement (Emily Blunt – 'nuff said). So, to discover that this Seth Rogen vs. Zac Efron battle of sabotage one-upsmanship actually has a heart, and is more than just a base comedy filled with d**k jokes, shouldn't have been so unexpected.
Rogen is Mac Radner, who's straddling the divide between carefree youth and adult responsibilities. He and his wife, Kelly (Rose Byrne), live in a great big house, have an adorable daughter (Elise and Zoey Vargas, who share the role of the most insanely cute big-screen baby since Diane Keaton's charge in Baby Boom), and are very reluctantly settling into their lives as grown-ups. When the house next door is sold to a fraternity, the gap between who they once were and who they've become becomes even more evident, as they promptly get on the wrong side of frat brothers Teddy (Efron) and Pete (Dave Franco). Why? Because, after initially hitting it off with the young bucks, Mac and Kelly realize they like peace and quiet, while the frat boys revel in sex, music, parties, drugs, and lots and lots of noise.
What begins as a neighborly noise complaint escalates very very quickly, and soon it's every dildo for itself (seriously) as the Radners engage in raunchy battle with their young-but-clever over-the-hedge enemies.
Not nearly as low-brow as its premise would have you believe, Neighbors is a zippy, laugh-out-loud comedy grounded in some believable, lovable relationships – the one between Mac and Kelly, the one between Mac and Teddy, and the one between Teddy and Pete. Each one's different but, despite the nature of the film, all are three-dimensional and (again, color me shocked) nicely fleshed out. It helps that the chemistry between them all totally works. Yes, there's plenty of R-rated humor to keep teenaged boys rolling in the aisles, but it's balanced out by the film's decidedly mature undertones.
While the quartet of lead actors is strong, Neighbors is also blessed with a terrific supporting cast, including Ike Barinholtz as Mac's best friend, and Lisa Kudrow as a college dean hell-bent on maintaining her school's good name.
Goofy and good-natured, racy and real, Neighbors will no doubt have broad audience appeal and, for the first time in many Seth Rogen movies, I'm actually one of the people who found it appealing. Who knew?
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Home»Archives»World News»New York City
CUNY And SUNY Chancellors Announce Jose Peralta New York State Dream Act Application Is Open For The 2019-20 Academic Year
By Caribbean American Weekly on July 10, 2019 Immigration, New York City, NYC
HESC Now Accepting Applications, Allowing for Undocumented Students to Apply for Programs, Including TAP and the Excelsior Scholarship
The City University of New York Chancellor Félix V. Matos Rodríguez and State University of New York Chancellor Kristina M. Johnson today announced that qualifying undocumented students educated in New York high schools can apply for state financial aid and scholarships for higher education including the Tuition Assistance Program and the Excelsior Scholarship, under the José Peralta New […]
In Celebration of WorldPride and Stonewall 50, Comptroller Stringer Releases 2019 LGBTQ+ Guide
By Caribbean American Weekly on June 24, 2019 Entrepreneurs, Media, New York City, NYC, Press Release
(New York, NY) — New York City Comptroller Scott M. Stringer released an updated 2019 LGBTQ+ Guide to Services and Resources – the most comprehensive directory of LGBTQ+ organizations and programs in New York City. This year's guide is dedicated to the 50th anniversary of the Stonewall Uprising and coincides with the City hosting WorldPride […]
REDACTED: The Racial Data The De Blasio Administration Doesn't Want You To See
By Caribbean American Weekly on June 10, 2019 New York City, NYC
City housing lottery information was heavily redacted in a court filing. Photo: Ben Fractenberg/THE CITY The de Blasio administration is fighting to keep under wraps studies of its own data used to analyze whether the city's affordable housing lottery system reinforces segregation. The latest result: a 31-page report filed in Manhattan Federal Court on […]
State Medicaid Bought Erectile Dysfunction Drugs for Sex Offenders
By Caribbean American Weekly on June 8, 2019 Lifestyle, Media, New York City
Lax Oversight Also Let Other Medicaid Patients Get Drugs They Shouldn't Have
The state's Medicaid program paid more than $60,000 for sexual and erectile dysfunction (ED) drugs and treatments for 47 sex offenders, according to an audit released today by State Comptroller Thomas P. DiNapoli. In total, Medicaid made more than $930,000 in payments for ED drugs and treatments for recipients, including sex offenders, that are barred […]
A Promise of Funding for Harlem African Burial Ground
By Caribbean American Weekly on June 5, 2019 New York City
An unused bus depot on East 126th Street is being prepared to honor its past state: a burial ground for enslaved and free African people.
By Rachel Holliday Smith (The City) After a decade of planning and research by Harlemites, an unused bus depot on East 126th Street is being prepared to honor its past state: a burial ground for enslaved and free African people. The city's Economic Development Corporation says it will begin looking this fall for a nonprofit […]
Proclamation on National Caribbean-American Heritage Month, 2019
By Caribbean American Weekly on June 1, 2019 Caribbean News, New York City
WHITE HOUSE PROCLAIMS JUNE NATIONAL CARIBBEAN AMERICAN HERITAGE MONTH
During National Caribbean-American Heritage Month, we acknowledge the significant ways Caribbean Americans have shaped our culture and heritage. Americans with roots in the Caribbean have greatly enriched our society, contributing to the arts, business, journalism, technology, government, religion, the military, sports, and many other fields. Through his dedication to his faith, Joseph Sandiford Atwell exemplified […]
Criminal and Social Justice Advocates Praise Speaker Johnson's Policy on Criminal Justice Reform
By Caribbean American Weekly on May 18, 2019 Media, New York City, NYC, Press Release
Speaker Corey Johnson (photo: John McCarten/City Council) New York, NY – Today, criminal and social justice reform advocates, activists, social service providers and educators praised Council Speaker Corey Johnson's first policy speech on criminal justice reform delivered at John Jay College of Criminal Justice. "John Jay College is a recognized forum for vibrant discussions about […]
Jovia Radix Aims to Make "Affordable" Housing, Affordable for 45th District Residents
By Caribbean American Weekly on May 9, 2019 Media, New York City, NYC
By Chereen James, Special to Caribbean American Weekly
Jovia Radix, Esq. As New York City's 45th district of the City Council prepares for its special election on May 14th, candidate Jovia Radix, Esq. seeks to improve NYC renters' accessibility to housing, and she aims to protect homeowners from the city's Third-Party Transfer, "deed theft" program. Among the 8 candidates vying for Jumaane Williams' […]
Little-Used Community Land Trust Model for Affordable Housing May See Big Boost
By Caribbean American Weekly on April 27, 2019 CAW Exclusive, New York City, NYC
A new $8 million bank settlement from Attorney General Letitia James is also being parceled out this spring to land trust projects across the state. By Emma Whitford (Gotham Gazette) Manhattan City Council Member Carlina Rivera is part of a growing coalition of Council members pushing for city government's first major commitment to a seldom-utilized […]
New York outbreaks drive US measles count up to 626
By Caribbean American Weekly on April 22, 2019 CAW Exclusive, Health, New York City, NYC
NEW YORK, United States (AP) — Outbreaks in New York state continue to drive up the number of US measles cases, which are approaching levels not seen in 25 years. Health officials say 71 more cases were reported last week, with 68 of them from New York. That brings this year's total to 626. That […]


20 year old Baraba Cones of Livingston was driving this 2010 Ford F-250 when she tried to pass another vehicle.
Major Accident on 146 Hospitalizes 3, LIVINGSTON, January 20, 2014 - At approximately 9:40 am on January 20, 2014, a black 2010 Ford F-250 driven by Barbara Cones, 20, of Livingston, was north-bound on US-146 (next to Escapees RV Club) when she attempted to pass another vehicle. When she tried to pass, she apparently failed to notice a black 1993 Ford F-150 driven by Paul Escamilla, 60, of Livingston that was oncoming in the south-bound lanes. As Cones got closer to Escamilla, she continued veering to her left as Ecamilla's truck slammed into the rear half of Cone's vehicle. Cones' vehicle rolled and came to rest upright on the south-bound side in the grass and suspended over a large culvert. Escamilla's vehicle slid to the right of the pavement into the grass. Americare Ambulance Service was dispatched to the scene along with the Livingston Volunteer Fire Department, Deputies from the Polk County Sheriff's Office and Corporal Ramey Bass with the Texas Highway Patrol. Paul Escamilla, Barbara Cones and here male passenger were transported by ambulance to Livingston Memorial Hospital with undisclosed injuries. Traffic on 146 was partially blocked during investigation and cleanup. Escamilla's vehicle was recovered by Ken's Towing and will be stored at Pollock's Body Shop in Livingston, while Cones' vehicle was recovered by Fawbush Body Shop and will be stored in Goodrich. According to Corporal Bass, no citations have been issued and the accident remains under investigation.
Special thank you: Ken with Ken's Towing.
Paul Escamilla was driving this 1993 Ford F-150 and was south-bound when his truck collided with Cones' truck. Cones was in Escamilla's lane of traffic.
Paul Escamilla was transported to Livingston Memorial Hospital.
Barbara Cones' male passenger appeared to have the worst injuries.
Escamilla's vehicle was recovered by Ken's Towing (Cones' vehicle was recovered by Fawbush Body Shop).


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Posted on March 5, 2018 by Mechanicsburg Telegram
Former Mechanicsburg mayor remembered
By Christopher Selmek - cselmek@aimmediamidwest.com
Beedy
MECHANICSBURG – John E. Beedy, the former mayor of Mechanicsburg from 2000 to 2008, passed away on Monday, Feb. 19. Beedy began his career as an officer at the London Correctional Institution, then became Street Department Supervisor for the village from 1977 to 1998.
Beedy and his wife, Mary, had been married for 50 years. He had two daughters, Cindy Smith and Lori Tibbals, and one son, John A. Beedy, as well as six grandchildren and seven great grandchildren. Tibbals was inspired to joined the city council because of Beedy's encouragement.
"He focused a lot on making sure the streets were in good order and leaves were swept up each year," she said. "He also worked hard in putting ordinances in place to make sure properties were cleaned up and there wasn't trash all around.
"He wanted to bring people to our town to see the history so as soon as became mayor he started working on bringing a fireworks show back to our town," she continued. "He worked on getting donations and vendors to come in. It took him a couple years to get money and support but I believe in 2002 Mechanicsburg had a fireworks show at our Goshen Park. He also loved fireworks when he was a kid and he wanted them in our town so folks didn't have to travel out of town."
Current Mechanicsburg Mayor Greg Kimball said he served on the village council under Beedy.
"He was very dedicated to his community and to our residents," he said. "He was very strong-willed, especially to things that the residents held dear and near to their hearts. An example of this was the Village Fourth of July Fireworks which had been dropped several years before. John determined that the people wanted them back again and he spearheaded a campaign to get them restarted. He was successful, and today, 15 years later, they remain as part of our Summer Celebration. His commitment will be missed."
Tibbals said her father liked to sit out in the garage in the summer time and watch the hummingbirds fly around and watch the people go by. Since being retired, Beedy has enjoyed being with family, watching football, and keeping up with the news.
https://www.burgtelegram.com/wp-content/uploads/sites/17/2018/03/web1_Web2.jpgBeedy
By Christopher Selmek
cselmek@aimmediamidwest.com
Christopher Selmek can be reached at 937-508-2304
Hi! A visitor to our site felt the following article might be of interest to you: Former Mechanicsburg mayor remembered. Here is a link to that story: https://www.burgtelegram.com/news/1899/former-mechanicsburg-mayor-remembered


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Richard Warren Cottle (* 29. Juni 1934 in Chicago, Illinois) ist ein US-amerikanischer Mathematiker, der sich mit Operations Research und Linearer Algebra befasst. Er war Professor an der Stanford University.

Ausbildung und Karriere
Cottle studierte an der Harvard University mit dem Bachelor-Abschluss 1957 und dem Master-Abschluss 1958. Er wurde 1964 an der University of California, Berkeley bei George Dantzig promoviert (Nonlinear programming with positively bounded Jacobians).

Von 1964 bis 1966 war er an den Bell Laboratories. 1969 wurde er Associate Professor und 1973 Professor für Operations Research in Stanford. Von 1990 bis 1996 stand er der Fakultät für Operations Research vor. 1968 führte er mit George Dantzig das Lineare Komplementaritätsproblem ein.

Zu seinen Doktoranden zählt Jong-Shi Pang. Er war Arthur Anderson Distinguished Visitor an der University of Cambridge.

Er ist seit 1959 verheiratet und hat zwei Töchter und einen Sohn.

Preise und Ehrungen
 1994: Frederick-W.-Lanchester-Preis

Schriften
 mit Jong-Shi Pang, Richard E. Stone: The Linear Complementarity Problem, Academic Press 1992, SIAM 2009

Einzelnachweise

Mathematiker (20. Jahrhundert)
Hochschullehrer (Stanford University)
US-Amerikaner
Geboren 1934
Mann


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Accounting & Audit
Public Sees Accountants As Desirable, Even If They Are Bean Counters
Professional stereotypes about the accounting industry are alive and kicking, with a new global survey from Xero revealing that the average member of the public most commonly associates accountants and bookkeepers as suit-wearing "number crunchers" or "bean counters." At the same time, the survey revealed that accountants and bookkeepers are likely to be seen as a vital part of any business, with the majority of respondents from all markets surveyed (58 percent) viewing them as trusted advisors.
"As an accountant committed to running my practice from anywhere in the world, there are days I might wear a cardigan, but the days I'm wearing a snowsuit, bathing suit or business suit are equally likely," said Amanda Aguillard, Xero Ambassador and Owner of Aguillard Accounting, LLC. "It's unfortunate that folks equate accounting with math. Sure there are numbers in accounting, but there is just as much technology and creative planning. Being an accountant in the age of cloud technology, machine learning and AI means the numbers and the strength of the stories those numbers tell is not just beautiful, it's magical."
The research from Xero looks at perceptions of accountants and bookkeepers among 3,500 respondents in the U.S., U.K., Australia and New Zealand, and uncovered a number of insights about people's understanding of the professions. The number-crunching stereotype, shared by 78 percent of global respondents (and 81 percent of U.S. respondents), underpin the intimate involvement and knowledge that bookkeepers and accountants have of a business's financials, and the important role they play in any business.
"The public might not think of accountants and bookkeepers as creative people, but creativity is quickly becoming one of the most important traits in the accounting profession," said Blake Oliver, Xero Ambassador. "That's because new technology has reduced or fully automated the tedious tasks, such as data entry and consolidations, that gave accountants a reputation for being "bean counters." Rather than dwell in the past, we can now focus on the future. We can spend more time being advisors–empowering business owners while helping them understand their finances. Or we can focus our efforts on implementing new accounting technology solutions to improve efficiency and drive profits to the bottom line."
Swiping Right For Accountants
The feedback also revealed that accounting and bookkeeping professionals are seen as desirable partners, ranking higher on the 'date-ability' scale than other professions including handymen, car salesmen, artists, designers, and even outranking personal trainers in the United States. On both the scales of dateability and trust, accountants fall just behind doctors/paramedics and firemen/police officers.
"Before cloud technology, more time was spent on the numbers–drowning in spreadsheets to come up with the answers that clients needed," said Amy Vetter, Global VP of Education and U.S. Head of Accounting, Xero. The technology available today has broken down some of the walls confining the accounting profession. With time freed up to be more creative and hands-on, accountants can work with clients in real-time on the solutions that will drive results for the business. Now, accountants are considered vital to business not just because they are keeping the numbers in order, but because they can provide meaningful insights that enable businesses to thrive."
Awareness of the Bookkeeping Job Function is Low
Respondents globally have a strong understanding of what an accountant does. Across all regions, respondents are almost three times more likely to have no idea what a bookkeeper's job entails compared to that of an accountant. U.S. citizens are the most aware of what a bookkeeper's job entails with only one in five respondents (19 percent) having no idea. Meanwhile, on average one in four people in Australia (25 percent), New Zealand (26 percent) and the U.K. (26 percent) have no idea what bookkeepers do. Bookkeepers are responsible for the daily recording of a business's transactional activities; a fundamental economic function.
Other findings include:
Accountants and bookkeepers are vital to the economy: Nearly two-thirds (65 percent) of respondents across the U.S., U.K., Australia and New Zealand believe these jobs are incredibly important or imperative to the running of the economy. This number increases in the U.S. to 72 percent.
Word of mouth is king: Referrals from family and colleagues are the most trusted method to find an accountant or bookkeeper. This is the most true in the U.S (59 percent) and New Zealand (59 percent), compared to the U.K. (55 percent) and Australia (45 percent).
Accounting and bookkeeping as a profession: People in the U.S. and the U.K. are less likely to have considered accounting or bookkeeping as a profession (26 percent and 25 percent respectively), compared to Australia and New Zealand (AU 33 percent, NZ 32 percent).
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