text:
\section{INTRODUCTION}
Co-grasping is an essential part of human-robot collaboration tasks where a human and robot simultaneously grasp an object during manipulation. The need for collaborative robot systems has become evident from the lack of available skilled workforce in hospitals, factory floors, and at home to assist people in their daily lives \cite{christensen2021roadmap}. For instance, at hospitals, robots with co-grasping skills can assist in passing various equipment to and from surgeons during surgery or passing medicines to maintain a safe distance between healthcare workers and patients concerning contagious diseases like CoVID-19. Likewise, at factory floors, the tasks for assistive robots could include fetching and handing over various tools to and from their human collaborator in the loop or performing complex assembly tasks through human-machine teaming, which can significantly improve the overall work efficiency and throughput. Similarly,  at home, our elderly often struggle to fetch various objects. Therefore, robots with co-grasping skills can assist them by bringing and handing over different daily-life things, such as utensils, keys, tv remotes, etc.

Although several methods for robot grasp generation exist \cite{ekvall2007learning, liu2020deep, ni2020pointnet++, yan2018learning, sundermeyer2021contact, zeng2018robotic, laskey2016robot, liu2020cage, hoang2022context}, ranging from geometric to data-driven strategies, they are human-agnostic and provide robot-centric algorithms, i.e., observing an object and selecting a gripper's pose to pick an object without considering collaborating human partners. Generally, in human-robot collaboration, we would expect our robot to grasp things that are also comfortably graspable by their interacting partners during cooperation. For instance, consider a scenario in Fig. \ref{figure1}. Both grasps in Fig. \ref{figure1a} and Fig. \ref{figure1b} would be considered valid for the robot by the existing approaches. However, the grasps in Fig. \ref{figure1b} are inherently invalid as they point sharp ends toward humans, which would be considered unsafe for collaboration. Similarly, in other situations with no sharp objects, the robot would be expected to leave sufficient space for humans to co-grasp objects simultaneously.

\begin{figure}[t]
  \centering
  \subfloat[Human-aware Grasping]{%
\begin{minipage}[b][6cm][t]{.5\columnwidth}
\centering
\includegraphics[width=.95\columnwidth, height=2.9cm]{scissors_cograsp_ed_reduced.JPG}
\vfill
\includegraphics[width=.95\columnwidth, height=2.9cm]{screw_cograsp_ed_reduced.JPG}
\end{minipage}
\label{figure1a}}%
  \subfloat[Human-unaware grasping]{%
\begin{minipage}[b][6cm][t]{.5\columnwidth}
\centering
\includegraphics[width=.95\columnwidth, height=2.9cm]{scissors_grasp_ed_reduced.JPG}
\vfill
\includegraphics[width=.95\columnwidth, height=2.9cm]{screw_grasp_ed_reduced.JPG}
\end{minipage}
\label{figure1b}}%
  \caption{CoGrasp generates human-aware robot grasps (a) compared to traditional methods (b) that do not consider humans in the loop.}
  \label{figure1}
\vspace{-0.15in}\end{figure}
In this paper, we propose a human-aware robot grasp generation pipeline called CoGrasp that considers both robot grasp quality and human-in-the-loop for safe and compliant collaboration. Our approach accomplishes human-aware grasping from a raw partial 3D object point cloud by optimizing robot grasp generation using a deep neural network-based object shape completion network, a socially-compliant human grasp prediction network, and a pruning network. Our pruning network builds on our novel co-grasp evaluation algorithm to select stable robot grasps compatible with predicted human grasps for a given object. The overview of our pipeline is shown in Fig. \ref{figure2}. Our approach demonstrates producing grasps appropriate for robot-human collaborative object manipulation, which also works in real-world experiments (Fig. \ref{figure1}). The main contributions of our paper are summarized as follows:

\begin{itemize}
\item A novel and, to the best of our knowledge, the first end-to-end human-aware 6-DoF robot grasp generation method that works in both simulation and real-world environments.
\item An algorithm that computes grasp quality scores using the geometric information (approach direction and spatial representation) from interactions between the objects, human hand, and robot gripper.
\item A neural model for fast and parallel evaluation of various robot grasp candidates for human friendliness and stability.
\item A new set of metrics that evaluate the quality of grasps based on their safety, human friendliness, and efficiency for human-robot collaboration tasks.
\item A validation of our CoGrasp approach through real-robot experiments, demonstrating our method achieves a 88\% success rate in generating stable robot grasps while leaving socially compliant space on the object for humans to co-grasp concurrently.
\item A validation of our approach through a user study with 10 participants, indicating our method achieves 22\% higher scores on various metrics of CoGrasp's social compliance and safety than a traditional robot-centric method \cite{sundermeyer2021contact}.
\end{itemize}

\section{Related Work}

\begin{figure*}[t]
  \centering
  \includegraphics[trim = {0cm 0cm 0cm 3cm}, width=2.0\columnwidth]{cograsp_overview.png}
  \caption{CoGrasp execution pipeline:  Given RGBD information of the scene, our method segments objects partial point clouds and infer their missing parts using the shape completion network. The robot and human hand grasp generators take the completed object point cloud and output the possible robot and human grasp candidates. Finally, the pruning network selects the proper robot grasp compatible for the co-grasping.
  \label{figure2}
\vspace{-0.15in}\end{figure*}
This section discusses various techniques that generate collision-free, stable robot grasps for object manipulation. We divide these methods into three categories, i.e., classical, data-driven, and contextual, as described in the following.

\subsection{Classical Method}
The study of robot grasp generation goes very back, starting from attempting to handle objects using a robot hand with elastic fingers\cite{asada1979studies}. It gave rise to geometric-based approaches \cite{nguyen1988constructing, ponce1995computing, ponce1997computing} for producing grasps using the contact points' classification as frictionless, friction, or soft contact to identify parts on the object for a successful grasping. Another line of work, like \cite{schulman2017grasping}, studied the number of contact points needed for stable grasping. The contact points are essential for grasp stability, but the current techniques only look into these for identifying suitable regions from the robot's perspective. In a similar vein, \cite{ferrari1992planning, zheng2012efficient} demonstrated that grasping an object results in a pull force and overcoming that wrench is an essential aspect of stability. \cite{okamura2000overview, bicchi1995closure, prattichizzo1998dynamic} includes studying complex kinematics of the object and the hand motion involved during an interaction, displaying the movement of an approaching hand or the gripper to be critical for grasping. Following the formulations of stable grasping, geometry-based techniques \cite{yan2018learning, bohg2010learning, vahrenkamp2016part, goldfeder2009columbia, li2016dexterous} were proposed that rely directly on the object shape to generate a suitable grasp. However, such methods do not generalize to real-world scenarios where object models are often unknown. Modern methods \cite{liu2020deep} tend to account for surface normals to evaluate the quality of grasps and use them to compute a safe distance for a stable grasp. \cite{zapata2019fast} models the mean axis of an object by running PCA and empirically choosing a safe space from a normal plane to that axis. Despite progress in stable robot grasping, these geometric approaches do not consider human-in-the-loop and solely rely on having one manipulator; thus do not apply to collaboration tasks requiring the co-grasping of the objects.

\subsection{Data-Driven Method}
With the advancement in computational resources and deep neural models, data-driven methods have emerged significantly for generating grasps. In emerging scenarios where input is only available from visual sensors, the existing methods lean on computing the object models and their 6DoF poses \cite{kleeberger2019large} before deploying traditional grasping methods. Furthermore, when the complete 3D object models are unavailable, learning-based shape reconstruction \cite{kleeberger2020single} from inference or multiple views \cite{breyer2021volumetric} are proposed to fill in the gap. Many reinforcement learning techniques have also come up, which learn gripper poses using exploration \cite{quillen2018deep} and learn policies for manipulation \cite{kalashnikov2018scalable}. They help identify dynamic responses to disturbances while grasping. Still, such perturbations are only based on gripper-object and environment interaction. Nevertheless, 3D reconstruction and exploring the large 6-DoF state space suffer from storing a huge amount of data that consists exclusively of the gripper and object relative poses.

Since contact points are crucial in grasping, methods like PointNet++\cite{qi2017pointnet++} are used to learn patterns from point clouds. For instance, \cite{sundermeyer2021contact, liang2019pointnetgpd, ten2017grasp} utilizes PointNet++ to learn geometric forms between grippers and objects from the contact points data available in grasping datasets like ACRONYM \cite{eppner2021acronym}. To extend the point space that is not limited to the contact points, learning-based methods like Dexnet \cite{mahler2017dex, mahler2018dex, fang2018multi} directly learn the orientations and the approach direction of the gripper. There are also methods \cite{fang2020graspnet, qin2020s4g} that tend to produce a grasp score for each point in the space when contact information is not present. However, the contact information and orientations that are looked upon come only from the gripper and the object. Some other neural sampling-based methods \cite{mousavian20196, miller2000graspit} use different grasp quality metrics as objective functions. These metrics depending on the gripper orientations and surface areas, only relate to the overall stability rather than human awareness.

\subsection{Contextual Grasping}
Contextual grasping refers to grasp generation with some context about the objects and their underlying tasks. This problem often involves encoding contextual information like the semantic representation or the object properties into the network inputs. For example, \cite{liu2020cage} encodes the target candidate's visual, tactile, and texture information while performing tasks like picking, lifting, or pouring. \cite{hoang2022context} also considers encoding the relationships of objects in the scene, which allows reasoning about invisible points, enabling collision-free grasp. However, the enhanced reliabilities of grips come from the extensive cost when acquiring hand-labeled training data. In addition, these works focus more on producing human-like grasps or moving the object of interest to the target position, which involves no human actions.

In summary, none of the abovementioned methods considers a simultaneous human grasp while producing a stable robot grasp configuration. The closest idea to finding a suitable grasp for simultaneous grasping may be to learn a policy for ambidextrous grasping \cite{mahler2019learning}. Although this method trains the policy on a large synthetic dataset under the condition where multiple heterogeneous grippers will interact, it does not consider a crucial human-robot interaction task. Furthermore, it can not be directly used for co-grasping as a human will have a preferred way of holding an object and hence the robot grasps used need to be socially compliant for the human to collaborate naturally.

\section{Proposed Method}
In this section, we present our CoGrasp framework. Given a cluttered scene consisting of unknown objects, we aim to generate robot grasps for all the objects in that scene such that those grasps also allow humans to grasp objects simultaneously in a socially compliant manner. Our method's execution pipeline is shown in Fig. \ref{figure2}, comprising the four main components as described in the following. Furthermore, in this section, we use a notation of $a_{\{B\}}$ to represent any arbitrary set $a$ with $B$ number of elements for brevity.
\subsection{Scene Segmentation \& Shape Completion}
Given a single viewpoint RGBD observation of the scene, we perform image segmentation and extract each object's point cloud by utilizing extrinsic/intrinsic camera parameters. The extracted partial object point clouds via segmentation are denoted as $PC^p_{\{k\}}$ with $k$ instances. These partial point clouds are further processed by our shape completion module, which infers the object's missing surfaces where grasps can be generated. The shape completion helps to maximize the number of potential robot's grasps with a single viewpoint observation. We build this module based on the PoinTr geometry-aware transformer framework \cite{yu2021pointr}. To make the model better generalize to real-world applications, several modifications are introduced. In our setup, we do not assume the object size and its geometric center to be known beforehand. Instead, we use a constant normalization term and the geometric center of the detected partial object point clouds to transfer them into the unit grid before completion. During the completion process, the partial point cloud is first down-sampled and converted to local features using DGCNN \cite{wang2019dynamic} before passing to a transformer-based encoder-decoder module that generates missing points proxies. The completed point cloud for each object is then obtained by giving the missing point proxies to the FoldingNet \cite{yang2018foldingnet}. The rest of our co-grasp framework utilizes the completed points denoted as $PC^c_{\{k\}}$ for robot and human grasp predictions and selections.
\subsection{Robot Grasp Generator}
This module is used to produce a diverse set of robot grasps $G_r$ for the completed object point clouds $PC^c_{\{k\}}$. In our setup, we leverage a learning-based method called Contact-Graspnet\cite{sundermeyer2021contact} to generate $m\in \mathbb{N}$ number of robot grasp candidates $g_{r\{m\}} \subset G_r$ on each completed object point cloud $PC^c_i$. The Contact-Graspnet's framework consists of PointNet++\cite{qi2017pointnet++} based set abstraction and feature propagation layers, which help the model generalize to real-world sensor data. The output of this module represents each grasp $g_{r}$ using $(R, T) \in SE(3)$ where $R \in SO(3)$ represents the rotation and $T \in \mathbb{R}^{3}$ represents the translation. The training ACRONYM dataset \cite{eppner2021acronym} consists of a diverse set of grasps, enabling it to generalize to out-of-domain objects as well.

\subsection{Human Hand Predictor}
This section presents our human hand grasp prediction module for completed object point clouds $PC^c_{\{k\}}$. In practice, humans have a position preference for holding different objects based on social norms, stability, and safety. For example, we often prefer to hold scissors by the finger rings and mugs by the handles. In our pipeline, this contextual information is provided by the human hand predictor module through learning human hand grasp pose $g_{h}$ on different objects. We use a Variational Autoencoder (VAE) architecture based on \cite{jiang2021hand}. The VAE comprises an encoder-decoder structure. The encoder takes the point clouds of the object $(PC^c_i)$ and the human hand, denoted as $PC^h_{gt}$, as an input and outputs the mean $\boldsymbol{\mu}$ and sigma $\boldsymbol{\sigma}$ in latent space to parametrize the Gaussian Distribution. The decoder takes the latent encoding $z \sim \mathcal{N}(\boldsymbol{\mu}, \boldsymbol{\sigma})$ and the object point cloud $PC^c_i$ as input and outputs the MANO model \cite{MANO:SIGGRAPHASIA:2017} parameters ($\beta$, $\theta$) to represent human hand pose.
The hand parameter $\beta$ estimates the hand shape while the pose parameter $\theta$ describes the rotation and translation of the hand joints. A final MANO differentiable layer gives us the human grasp $g_h$ 6D pose which we render as point cloud $PC^h$. When training the network, we use the ground truth hand point cloud $PC^h_{gt}$ and object point cloud $PC^c_i$ from ObMan dataset \cite{hasson19_obman} as the inputs to produce $\hat{PC^h}$. The objective is to minimize the MSE loss given as $\left \| \hat{PC^h} - PC^h_{gt}\right \|_2 $ and the KL-divergence between $\mathcal{N}(\boldsymbol{\mu}, \boldsymbol{\sigma})$ and $\mathcal{N}(\boldsymbol{0}, \boldsymbol{I})$.
Once trained,
we use the decoder network that takes a sampled latent code $z\sim (\boldsymbol{0}, \boldsymbol{I})$ and an object point cloud $PC^c_i$ as inputs and outputs the MANO hand parameters. For each object point cloud $PC^c_i$, an $n\in \mathbb{N}$ number of hand grasps $g_{h\{n\}}$ are generated to guide robot grasps selection in the next module.
\subsection{Pruning Network}
Our Pruning Network selects robot grasps $g_{f\{p\}} \subset g_{r\{m\}}$ that are compatible for co-grasping with hand predictions $g_{h\{n\}}$ for each detected object $PC^c_i$. Our network consists of Pointnet++ set abstraction layers which take three point cloud sets $PC^g$, $PC^h$, and $PC^c_i$ representing robot gripper, human hand, and object, respectively, and their feature masks as input and outputs the confidence score $\hat{c} \in [0,1]$. The score $\hat{c}$ indicates the compatibility for a grasp pair ($g_r, g_h$). Recall that $PC^g$ and $PC^h$ are generated by $g_r, g_h$, respectively. Furthermore, we append the feature masks with each point cloud to enable our network to distinguish between them. The feature masks are represented with labels -1, 0, and 1 for the robot gripper, object, and human hand, respectively.
\iffalse
\begin{figure}[thpb]
  \centering
  \subfloat[Large angle between hand and gripper]{
  \includegraphics[width=\columnwidth]{approach_1.jpg}}\\
  \subfloat[Small angle between hand and gripper]{
  \includegraphics[width=\columnwidth]{approach_2.jpg}}
  \caption{A greater angle between approach direction of gripper and hand makes the handover more reliable to avoid any collision and allows a smoother transition.}
  \label{figure3}
\end{figure}
\fi

\begin{figure}%
    \centering
    \subfloat[Large approach angle $\theta_1$]{{\includegraphics[width=4cm]{cograsp_big_theta.png} \label{figure3a} }}%
    \subfloat[Small approach angle $\theta_2$]{{\includegraphics[width=3.4cm]{cograsp_small_theta_revised.png} \label{figure3b}}}%
    \caption{A large angle between the approach direction of the robot gripper and human hand makes the co-grasping socially compliant.}%
    \label{figure3}%
\vspace{-0.15in}\end{figure}
\begin{figure*}[t]
  \centering
  \subfloat[Mug]{%
\begin{minipage}[b][6cm][t]{.4\columnwidth}
\centering
\includegraphics[height=3cm]{mug_grasp.png}
\vfill
\includegraphics[height=3cm]{mug_cograsp.png}
\end{minipage}}%
  \subfloat[Power Drill]{%
\begin{minipage}[b][6cm][t]{.4\columnwidth}
\centering
\includegraphics[height=3cm]{power_grasp.png}
\vfill
\includegraphics[height=3cm]{power_cograsp.png}
\end{minipage}}%
  \subfloat[Scissors]{%
\begin{minipage}[b][6cm][t]{.4\columnwidth}
\centering
\includegraphics[height=3cm]{scissors_grasp.png}
\vfill
\includegraphics[height=3cm]{scissors_cograsp.png}
\end{minipage}}%
  \subfloat[Bleach Cleanser]{%
\begin{minipage}[b][6cm][t]{.4\columnwidth}
\centering
\includegraphics[height=3cm]{bleach_grasp.png}
\vfill
\includegraphics[height=3cm]{bleach_cograsp.png}
\end{minipage}}%
  \subfloat[Clamp]{%
\begin{minipage}[b][6cm][t]{.4\columnwidth}
\centering
\includegraphics[height=3cm]{clamp_grasp.png}
\vfill
\includegraphics[height=3cm]{clamp_cograsp.png}
\end{minipage}}%
  \caption{Simulated images for grasps generated by Contact-GraspNet\cite{sundermeyer2021contact} (top row) and CoGrasp (bottom row) for different objects. We can see that CoGrasp knows how humans will hold an object during collaboration and therefore generates socially compliant robot grasps.}
  \label{figure4}
\end{figure*}

To determine the ground truth confidence scores $c$ for training our pruning network, we define the following two measures:\\
\textbf{Distance Measure (}\bm{$S_d$}\textbf{)}: This is used to get an insight into how far apart the gripper and hand are during co-grasping. A more considerable distance ensures the co-grasping can be performed safely. $S_d$ is computed from the average sum of pairwise Euclidean distance between the $PC^g$ and $PC^h$:
$$S_d(PC^g, PC^h) = \frac{\sum _ {\substack{x\in PC^{g} \\ y\in PC^{h}}} \left \| x-y \right \| _{2} }{\left | PC^{g} \right |\left | PC^{h} \right |}  $$\\
\textbf{Angle Measure (}\bm{$S_a$}\textbf{)}:  To increase the co-grasping success rate, the angle between the approach vector of the gripper ($\bm{a_g}$) and the hand ($\bm{a_h}$) needs to be sufficiently large. A large angle assures the robot arm does not collide with the human partner. As illustrated in Fig. \ref{figure3a}, the gripper orientation is complementary to the hand for co-grasping where $\theta_1$ is large. In contrast, Fig. \ref{figure3b} shows that the wrong approach direction makes the robot gripper unnecessarily close to the human. We compute the angle measure $S_a$ as the inner product of the approach vector of the robot and human hand as follows:
$$S_a(\bm{a_g}, \bm{a_h}) = - (\bm{a_g} \cdot \bm{a_h})  $$
To get $\bm{a_{g}}$, we use the representation of $g_{r} \in G_{r}$. We know $g_{r}$ is represented as $(R, T) \in SE(3)$ and its rotation $R$ can be written as follows:
$$R = \begin{bmatrix}
| & | & | \\
\bm{b_g} & \bm{b_g \times a_g}& \bm{a_g} \\
| & | & |\
\end{bmatrix}$$
\\
which gives us $\bm{a_g}$ directly; note $\bm{b_g}$ represents the normalized grasp baseline vector. To get $\bm{a_h}$, we get the set of faces $F_{palm}$ that represents the palm of the MANO\cite{MANO:SIGGRAPHASIA:2017} hand and estimate the approach direction by computing the average of the surface normals from each of these faces.

To train this network, we generate 89,786 pairs of grasps $(g_r, g_h)$ in simulation (applying 80\%-20\% training/validation split) across all the objects in YCB dataset \cite{calli2017yale}. We consider the grasp pair $(g_r, g_h)$ for an object $i$ as a positive label $c=1$ if its $S_d(PC^g, PC^h) > \lambda_d^i$ and $S_a(\bm{a_g}, \bm{a_h}) > \lambda_a^i$ otherwise negative $c=0$. The thresholds $\lambda_d^i$ and $\lambda_a^i$ are computed as follows. First, we obtain the $m$ robot grasp candidates $g_{r\{m\}}$ and $n$ human grasp candidates $g_{h\{n\}}$ using our robot and human hand grasp generation modules for each object point cloud. This constitutes a set of size $n'=m \times n$ comprising robot and human grasp pairs, i.e., $\{(g_r,g_h)_0, \cdots, (g_r,g_h)_{n'} \}$. Second, for all pairs of the robot and human grasp candidates, we compute the distance $(S_d)$ and angle $(S_a)$ measures resulting in lists $S_D=\{(S_d)_0, \cdots, (S_d)_{n'} \}$ and $S_A=\{(S_a)_0, \cdots, (S_a)_{n'} \}$. Finally, the thresholds $\lambda_d$ and $\lambda_a$ are computed as the median of lists $S_D$ and $S_A$, respectively.
Given the training dataset with ground truth confidence scores, we train our pruning network using the Binary Cross Entropy (BCE) loss between the predicted confidence score $\hat{c}$ and ground truth $c$. During the evaluation, our pruning network allows us to evaluate predicted grasps in batch, and the final layer being differentiable allows the possibility of refining the grasps by using the gradients $\frac{\partial c}{\partial g_{r}}$.

\section{Experiments}
We run both simulated and real robot experiments to test and compare our method with state-of-the-art grasping methods.
The simulations are performed in Isaac Gym\cite{makoviychuk2021isaac} physics simulator. For real experiments, we use a UR5e robot manipulator with a 2F-85 Robotiq gripper and Intel RealSense D435i camera for scene observation.

\subsection{Evaluation Metrics}
Aside from the distance measure $S_d$ and angle measure $S_a$, we also define another metric called the nearest distance measure denoted as $S_n$ to jointly evaluate the level of human awareness of our method's final result. The gripper must not collide with the human hand during the grasping process. This can be checked by measuring the intersection of the convex hull between the gripper and the human hand. Whenever there is an overlap, we set $S_n$ to 0; otherwise, to the nearest distance between the gripper $(PC^{g})$ and human hand $(PC^h)$ point clouds, i.e., $ S_n = \min _ {\substack{x\in PC^{g} \\ y\in PC^{h}}} (\left \| x-y \right \| _{2} ) $. Therefore, a larger $S_n$ reflects the more significant distance between the human and robot grasps for co-grasping
\subsection{Simulated Experiment}
We simulate CoGrasp, and other state-of-the-art grasping techniques \cite{sundermeyer2021contact, liang2019pointnetgpd} to study large diversified grasping on 92 objects of various categories (Fig. \ref{figure4}). Four different hand grasp poses $g_{h\{4\}}$ are computed for each segmented object $PC^c_i$ per simulation scene to evaluate the proposed gripper poses $g_{f\{p\}}$ from different methods using $S_d$, $S_a$, and $S_n$ metrics introduced above. The results are summarized in Table \ref{table1}, from which we observe better scores for our method in all three measures. Our result is 41\% better than contact-graspnet\cite{sundermeyer2021contact} while 22\% better than PointNetGPD\cite{liang2019pointnetgpd} across all metrics. Overall, the simulation experiments show that our method generates stable, collision-free, and socially compliant grasps needed for human-robot collaboration.

\begin{table}[thpb]
\caption{CoGrasp produces higher quality grasps in terms of metrics $S_a$, $S_d$, and $S_n$ compared to prior grasping methods.}\vspace{-0.15in}
\label{table1}
\begin{center}
\setlength\tabcolsep{4.8pt}
\begin{tabular}{p{2.5cm}ccc}
    \toprule
 & $S_a \uparrow$ &$S_d \uparrow$ & $S_n \uparrow$\\
     \midrule
CoGrasp & \textbf{0.675 $\pm$ 0.21} & \textbf{0.129 $\pm$ 0.05} & \textbf{0.037 $\pm$ 0..04} \\
Contact-GraspNet\cite{sundermeyer2021contact} & {0.527 $\pm$ 0.24} & {0.117 $\pm$ 0.04} & {0.029 $\pm$ 0.04}  \\
PointNetGPD\cite{liang2019pointnetgpd} & {0.492 $\pm$ 0.27} & {0.128 $\pm$ 0.03} & {0.013 $\pm$ 0.02} \\
    \bottomrule
\end{tabular}
\end{center}
\vspace{-0.15in}\end{table}

\subsection{Real Robot Experiment}
In this section, we study the CoGrasp performance in real robot setup in terms of grasp stability and leaving socially-compliant space for humans to co-grasp simultaneously. We create seven different scenes with 19 objects for grasping. The objects were everyday household items, mostly with handles (e.g., screwdriver, scissors, etc.), requiring the robot to leave the handles empty for humans to co-grasp safely. We run three trials for each object and record the results as presented in Table \ref{table2}. To determine the grasp stability, we let the robot move after grasping and observed if the object fell from the gripper or not. To evaluate the human-robot co-grasping suitability, we check whether the standard holding area of the object (e.g., screwdriver handle) is available for humans to grasp concurrently. Our results show that our method exhibits about 88\% success rate over multiple trials in both metrics. The failure cases were mainly related to small objects such as strawberries and clamps where our method could not leave sufficient space for human grasping.
\subsection{Ablation Study}
To get diverse human-aware robot grasps, we need to ensure the training data used for Pruning Network is rich in mixed valid grasps. Thus, we want to verify whether the current selection method for positive labels $c$ is reliable. We study the results of our pipeline by selecting different thresholds $\lambda_{d}$ and $\lambda_{a}$ for distance and angle measure, respectively, for computing Pruning Network's labels. We want to assure that the total grasps we can generate for co-grasping are diverse without drastically affecting the quality metrics. To test this, we first fix $\lambda_{a}$ as the median of the angle score and vary $\lambda_{d}$ to compute the total number of grasps that are labeled as positive, and then we do the same with $\lambda_{a}$ by fixing $\lambda_{d}$. The results can be seen in Fig. \ref{figure5}, where we observe that choosing the median score as thresholds for both $\lambda_{d}$ and $\lambda_{a}$ give the most number of positive grasps required for Pruning Networks' robust training.
\begin{figure}[thpb]
  \centering
  \includegraphics[width=0.8\columnwidth]{ablation.png}
  \caption{The plot shows the total number of valid grasps generated when keeping $\lambda_{d}$ fixed at the median score and varying $\lambda_{a}$ or vice versa.}
  \label{figure5}
\vspace{-.15in}\end{figure}

\begin{table*}[t]
\caption{Real robot experiments results of our method's robot grasps for being stable and socially-compliant for co-grasping.}\vspace{-0.05in}
\label{table2}
\begin{center}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
    \hline
\multirow{3}{*}{Categories}  & \multirow{3}{*}{Objects} & \multicolumn{4}{c|}{Leaves Human Grabbing Portion} & \multicolumn{4}{c|}{Stability}\\
    \cline{3-10}
  &  & \multicolumn{3}{c|}{Trials} & \multirow{2}{*}{Success Rate} & \multicolumn{3}{c|}{Trials} & \multirow{2}{*}{Success Rate}\\
    \cline{3-5} \cline{7-9}
  & & \#1 & \#2 & \#3 & & \#1 & \#2 & \#3 & \\
     \hline
\multirow{3}{*}{Tools} & Hammer & \cmark & \cmark & \cmark & 3/3 & \cmark & \xmark & \cmark & 2/3 \\
& Screw Driver & \cmark & \cmark & \cmark & 3/3 & \cmark & \cmark & \cmark & 3/3 \\
& Large Clamp & \cmark & \cmark & \cmark & 3/3 & \cmark & \cmark  & \cmark  & 3/3 \\
& Medium Clamp & \cmark &  \cmark & \cmark & 3/3 & \cmark & \cmark & \cmark & 3/3 \\
& Small Clamp & \cmark & \xmark & \cmark & 2/3 & \cmark & \cmark  & \xmark & 2/3 \\
& \textbf{Overall} &  &  &  & \textbf{14/15} &  &  &  & \textbf{13/15} \\

    \hline
\multirow{2}{*}{Sharp Objects} & Knife & \cmark & \cmark & \cmark & 3/3 & \xmark & \cmark & \cmark & 2/3 \\
& Scissors & \cmark & \cmark & \cmark & 3/3 & \cmark & \cmark & \cmark & 3/3 \\
& \textbf{Overall} &  &  &  & \textbf{6/6} &  &  &  & \textbf{5/6} \\
\hline
\multirow{2}{*}{Household Objects} & Mug & \cmark & \cmark & \cmark & 3/3 & \xmark & \cmark & \cmark & 2/3 \\
& Fork & \cmark & \cmark & \cmark & 3/3 & \cmark & \cmark & \cmark & 3/3 \\
& Spoon & \cmark & \cmark & \cmark  & 3/3 & \cmark & \cmark & \xmark & 2/3 \\
& Bowl & \cmark & \cmark & \cmark & 3/3 & \cmark & \cmark & \cmark & 3/3 \\
& \textbf{Overall} &  &  &  & \textbf{12/12} &  &  &  & \textbf{10/12} \\
\hline
\multirow{3}{*}{Miscellaneous} & Cracker Box & \cmark & \cmark & \cmark & 3/3 & \cmark & \cmark & \cmark & 3/3 \\
& Banana & \cmark & \cmark & \cmark & 3/3 & \cmark & \cmark & \cmark & 3/3 \\
& Master-Chef Can & \cmark & \cmark & \cmark & 3/3 & \cmark & \cmark & \cmark & 3/3 \\
& Fish Can &  \cmark & \cmark & \cmark & 3/3 &  \cmark& \cmark & \cmark & 3/3 \\
& Orange & \cmark & \cmark & \cmark & 3/3 & \cmark & \cmark & \cmark & 3/3 \\
& Plum & \cmark & \cmark & \cmark & 3/3 & \cmark & \cmark & \cmark & 3/3 \\
& Pear & \cmark & \cmark & \cmark & 3/3 & \cmark & \cmark & \cmark & 3/3 \\
& Strawberry & \xmark & \xmark & \cmark & 1/3 & \cmark & \xmark & \cmark & 2/3 \\
& \textbf{Overall} &  &  &  & \textbf{22/24} &  &  &  & \textbf{23/24} \\
\hline
\end{tabular}
\end{center}
\vspace{-0.2in}\end{table*}
\subsection{User Study}
We conducted a user study to determine if people could have a more comfortable grasping experience using our method. 10 participants were recruited to interact with the robot for co-grasping using seven household items. The participants were first given a brief time to get accustomed to the testing objects and contemplate how they would like to grasp them. Then the study was divided into two experiments; one executed the grasps predicted by our method, and another involved implementing the grasps generated by Contact-Graspnet\cite{sundermeyer2021contact}. The participants did not know which method the grasps came from, which enforced the unbiased user study. In each study, five objects were arbitrarily selected and used for both experiments. The participants were asked to access the quality of grasps generated from the two methods. After completing the experiments, the participants completed a Likert scale questionnaire describing their overall feedback.

Fig. \ref{figure6} shows the study results of the two experiments summarized from all user responses. For the robot grasps generated by our method, the users could co-grasp all the objects without facing the situation where the robot touches the human hand. On average, the users found the grasps from our method to be socially aware of how human holds the given object and provided similar feedback where one of the users commented \textit{``I can co-grasp the object very easily by the part that human normally uses"}. The users also felt there was enough space to grasp the object in the case of our method compared to \cite{sundermeyer2021contact}. A comment related to that for our method was \textit{``robot left sufficient space for co-grasping"}.
In addition, our CoGrasp technique was found safer than \cite{sundermeyer2021contact} as one of the users commented during the trial with \cite{sundermeyer2021contact} that they \textit{``felt a bit unsafe grabbing the screw-driver from the sharp side"} signifying the importance of user-friendly grasping.
\begin{figure}[thpb]
  \centering
  \includegraphics[width=\columnwidth]{UserStudy.png}
  \caption{Results from the Likert scale questionnaire given to the users for the study. The average scores for CoGrasp were higher than Contact-Graspnet\cite{sundermeyer2021contact}, demonstrating a socially-compliant co-grasping experience.}
  \label{figure6}
\vspace{-0.15in}\end{figure}
\section{Conclusion}
In this paper, we present a novel architecture named CoGrasp that produces the human-aware robot grasps for unknown objects in cluttered environments. Our method generates appropriate robot grasps by checking their compatibility with predicted natural human hand grasps using our novel co-grasping evaluation metrics and pruning function. The results through simulated and real-robot experiments and a user study reflect our design is mindful of handling objects in a socially compliant manner for human-robot collaboration tasks. In our future work, we aim to extend our approach to solve human-robot collaborative object manipulation tasks requiring human-aware robot grasping in the loop.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


This Webinar is part of the lead-in to: D2D: Data to Drugs and Diagnostics, June 11-12, 2014. The conference and expo will include thought leader presentations and panels, a Codefest, networking, and many supplier demonstrations.
The D2D Codefest: Click Here for Full Information and Challenge Submission. This is your opportunity to work across silos to improve data generation, collection, analysis, security, and sharing. Each team will include pharma domain experts as well as IT professionals who are intrigued with the complex questions raised throughout the drug development pipeline.
The Codefest Kickoff Webinar will address common questions and opportunities for Codefest participants, committee members, resource providers, mentors, judges, and sponsors.
Greg has worked on significant mobility applications for pharma/biotech companies, and brings extensive experience with hackathons and codefests. He will reflect on the value and excitement of these high-energy conclaves and set the scene for the upcoming D2D Codefest.
He is a Practice Area Director for Slalom Consulting's National Delivery team and has over 10 years of consulting and software engineering experience.
His team brings mobile, cloud and web technologies together to drive key outcomes for clients.
Greg has personally consulted for a number of clients in the Healthcare and Biotechnology spaces.
Additional speakers will address how to participate in the D2D Codefest as a Participant, a Mentor, a Judge, a Resource Provider, a Review Committee member, or a Sponsor.


text:
Nobliny [] (alemán: Neblin) es un pueblo ubicado en el distrito administrativo de Gminao Borne Sulinowo, dentro del Condado de Szczecinek, Voivodato de Pomerania Occidental, en el norte de Polonia occidental. Se encuentra aproximadamente a 9 kilómetros oeste de Aguantado Sulinowo, a 26 kilómetros al suroeste de Szczecinek, y a 122 kilómetros al este de la capital regional Szczecin.

Antes de 1772, el área fue parte del Reino de Polonia, hasta 1945 fue de Prusia y Alemania. Para más de su historia, vea Condado de Drahim e Historia de Pomerania.

Referencias

Enlaces externos

Localidades del voivodato de Pomerania Occidental

redpajama_set_name: RedPajamaWikipedia


Look through the listings of Member users that have joined Single Parent Chat City that are associated with Asian. Talking to other singles that have like minded interests is a great way to come up with ideas to do once you are dating. Register for a 100% Free Account to Meet Someone Tonight!
A beautiful mum to 3 gorgeous kids!


ImageBuilders includes a Custom Blog, tailored to your business, with every web site we design whether you choose our Basic Web Design Package, Web Content Management System, or Affordable Licensed Website; and at no additional cost. If you already have a website and become one of our Fully Managed Website Hosting clients; we will include a Custom blog in your website, on the house. Whether you have an online store, real estate agency, are an attorney, health professional, fishing guide, or any business manager; your Custom Blog is a valuable tool that can improve the success of your website. Your blog allows you to easily and frequently communicate information to your audience, and simultaneously boosts your Search Engine rankings. Our Custom blog design service enables you to participate in and capitalize on blogging.
your weblog with information about your business niche, topic or theme. When someone visits your blog, they will see your most recently published article first, with the older ones listed below in descending chronoligical order.


text:
Q: How to replace session with stateless rest apis? So, I am trying to make a shopping cart web architecture on rest framework where I am struggling to use browser storages to use as an alternative to maintaining the state.
I tried the window.localStorage and window.sessionStorage() APIs but it failed in the case of the private browsing mode in Safari and Opera.
So can anyone help out in the figuring other methods by which I can maintain states in rest based architecture?

A: You don't need sessions to store application state. User resources.
On approach would be to model every shopping cart as a resource with a unique ID:
/shop/shoppingcarts/E73AC56C-BDF7-11E5-81F1-8E2EDB915C80

The client application in the browser would read this resource:
GET /shop/shoppingcarts/E73AC56C-BDF7-11E5-81F1-8E2EDB915C80

It would add an item to the cart:
POST /shop/shoppingcarts/E73AC56C-BDF7-11E5-81F1-8E2EDB915C80/items

{
  "itemId": "1234",
  "quantity": 1
}

It would list the contents of the shopping cart:
GET /shop/shoppingcarts/E73AC56C-BDF7-11E5-81F1-8E2EDB915C80/items

[
  {
    "itemId": "1234",
    "title": "Some nice item",
    "quantity": 1,
    "price", 12.34,
    "priceTotal": 12.34
  },
  {
    "itemId": "9876",
    "title": "Some other nice item",
    "quantity": 2,
    "price", 0.99,
    "priceTotal": 1.98
  }
]

The web application would remove an item from the cart:
DELETE /shop/shoppingcarts/E73AC56C-BDF7-11E5-81F1-8E2EDB915C80/items/9876

I think you get the idea.

redpajama_set_name: RedPajamaStackExchange


text:
09/26/2020 Cleveland Cavaliers
Tristan Thompson and Cleveland Cavaliers have 'mutual interest,' says general manager Koby Altman
"He's been with this franchise his entire career since we drafted him. He's won a championship here. Obviously, he means a lot to the players on the team right now, but it has to make sense," Altman continued.
Maybe Tristan Thompson won't be wearing a different uniform next season after all.
Ahead of the team's in-market bubble, which begins next week, Cleveland Cavaliers general manager Koby Altman told the media there's "mutual interest" in a return.
"I think it's fair to say there's mutual interest for sure," Altman said of Thompson. "He's been with this franchise his entire career since we drafted him. He's won a championship here. Obviously, he means a lot to the players on the team right now, but it has to make sense.
"There are some events coming up - the draft, free agency - where we have to see if it makes sense for him. He's earned the right to be an unrestricted free agent and explore opportunities at this point in his career. So, we'll see."
The No. 4 pick in the 2011 NBA Draft, Thompson has spent his entire career with the Cavaliers, posting averages of 9.4 points, 8.7 rebounds and 0.7 blocks per game. He started in all 21 games at centre during the team's championship run in 2016.
Coming off of his age 28 season, Thompson is set to be an unrestricted free agent this offseason. He's widely regarded as being one of the best centres available.
Complicating his future in Cleveland is that the Cavaliers traded for Andre Drummond, who plays the same position as Thompson, at this season's trade deadline. Drummond has a player option in his contract for next season worth $28.8 million, opening up the possibility of him becoming an unrestricted free agent as well, but he made it clear in June that he intends on picking it up.
A source told Chris Fedor of Cleveland.com that there's a "50-50" chance of a reunion between Thompson and the Cavaliers, "with the leaguewide salary cap uncertainty bumping up those odds the last few months."

redpajama_set_name: RedPajamaCommonCrawl


text:
Decision Sciences & Operations Management
Business Statistics & Analytics
Introductory Statistics
Statistics for Business & Economics, Revised (with XLSTAT Education Edition Printed Access Card), 13th Edition
David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran
Print £73.99*
* For local market pricing, contact us
MindTap is a personalised teaching and learning experience with relevant assignments that guide students to analyse, apply, and improve thinking, allowing you to measure skills and outcomes with ease.
I want to learn more about MindTapI want to read about MindTap learning outcomes
Clearly demonstrate how statistical information enables strong decisions in today's business world with STATISTICS FOR BUSINESS AND ECONOMICS, REVISED 13E. Sound methodology combines with a proven problem-scenario approach, and meaningful applications for the most powerful approach to teaching business statistics available. Prestigious authors bring more than 25 years of unmatched experience to this thoroughly updated text. More than 350 real business examples, cases, and memorable exercises present the latest statistical data and business information with unwavering accuracy. You select the topics to give you the most relevant text for your course, including coverage of popular commercial statistical software, such as Minitab 17 and Excel 2016. Excel add-in XLSTAT is also available. Optional chapter appendices, coordinating online data sets, and support materials, such as the CengageNOW online course management system, make this edition customizable and efficient.
1. Data and Statistics.
2. Descriptive Statistics: Tabular and Graphical Displays.
3. Descriptive Statistics: Numerical Measures.
4. Introduction to Probability.
5. Discrete Probability Distributions.
6. Continuous Probability Distributions.
7. Sampling and Sampling Distributions.
8. Interval Estimation.
9. Hypothesis Tests.
10. Inference about Means and Proportions with Two Populations.
11. Inferences about Population Variances.
12. Comparing Multiple Proportions, Test of Independence and Goodness of Fit.
13. Experimental Design and Analysis of Variance.
14. Simple Linear Regression.
15. Multiple Regression.
16. Regression Analysis: Model Building.
17. Time Series Analysis and Forecasting.
18. Nonparametric Methods.
19. Statistical Methods for Quality Control.
20. Index Numbers.
21. Decision Analysis (On Website).
22. Sample Survey (On Website).
Appendix A: References and Bibliography.
Appendix B: Tables.
Appendix C: Summation Notation.
Appendix D: Self-Test Solutions and Answers to Even –Numbered Exercises.
Appendix E: Microsoft Excel 2016 and Tools for Statistical Analysis.
Appendix F: Computing p-Values Using Minitab and Excel.
USE OF CUMULATIVE STANDARD NORMAL DISTRIBUTION TABLE PREPARES STUDENTS TO WORK WITH STATISTICAL SOFTWARE. To more effectively prepare today's students to use computer software in statistics, this book incorporates a normal probability table that is consistent with today's most popular statistical software. This cumulative normal probability table also makes it easier to compute p-values for hypothesis testing.
LEADING PROBLEM-SCENARIO APPROACH HELPS STUDENT UNDERSTAND AND APPLY CONCEPTS. A hallmark strength of this text, this unique problem-scenario approach guides students in understanding statistical techniques within an applications setting. The statistical results provide insights into business decisions and detail how professionals regularly use statistics in business to solve problems.
SYSTEMATIC APPROACH EMPHASIZES PROVEN METHODS AND APPLICATIONS. Students first develop a computational foundation and learn to use techniques before moving to statistical application and interpretation of the value of techniques. Methods Exercises at the end of each section stress computation and the use of formulas, while Application Exercises require students to use what they know about statistics to address real-world problems.
CENGAGENOW™ ONLINE COURSE MANAGEMENT SYSTEM PROVIDES STUDENT RESULTS NOW. This robust, online course management system gives you more control in less time and delivers better student outcomes. Teaching and learning resources are organized around lectures and enable you to create assignments and quizzes and track student progress and performance easily. Flexible assignments, automatic grading and a gradebook option provide more control while saving you valuable time. A Personalized Study diagnostic tool empowers students to master concepts, prepare for exams, and become more involved in class.
COVERAGE HIGHLIGHTS DATA MINING, BIG DATA AND ANALYTICS. A proven section on analytics describes what analytics is, the types of analytics used in business and how this relates to statistics. An expanded section on data mining includes a discussion of big data and how businesses today are using data mining to establish competitive advantages.
TRUSTED TEAM OF EXPERT AUTHORS ENSURES THE MOST ACCURATE, PROVEN PRESENTATION. As prominent, respected leaders and active consultants in business and statistics today, authors David R. Anderson, Dennis J. Sweeney, and Thomas A. Williams, Jeffrey D. Camm, and James J. Cochran provide an accurate timely presentation of statistical concepts you and your students can trust with every edition.
David R. Anderson
Dr. David R. Anderson is a leading author and Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He has served as head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration. He was also coordinator of the college's first Executive Program. In addition to introductory statistics for business students, Dr. Anderson has taught graduate-level courses in regression analysis, multivariate analysis, and management science. He also has taught statistical courses at the Department of Labor in Washington, D.C. Dr. Anderson has received numerous honors for excellence in teaching and service to student organizations. He is the co-author of ten well-respected textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, he earned his B.S., M.S., and Ph.D. degrees from Purdue University.
Dennis J. Sweeney
Dennis J. Sweeney is Professor Emeritus of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned a BSBA degree from Drake University and his MBA and DBA degrees from Indiana University, where he was an NDEA Fellow. Professor Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. Professor Sweeney served as Head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration at the University of Cincinnati. Professor Sweeney has published more than 30 articles and monographs in the area of management science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger and Cincinnati Gas & Electric have funded his research, which has been published in Management Science, Operations Research, Mathematical Programming, Decision Sciences and other journals. Professor Sweeney has co-authored ten textbooks in the areas of statistics, management science, linear programming and production and operations management.
Thomas A. Williams
Jeffrey D. Camm
Wake Forest University
Dr. Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Business Analytics in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, he served on the faculty of the University of Cincinnati. He has also served as a visiting scholar at Stanford University and as a visiting Professor of Business Administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 40 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in numerous professional journals, including Science, Management Science, Operations Research and Interfaces. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of Interfaces. In 2016, Dr. Camm received the George E. Kimball Medal for service to the operations research profession and in 2017 he was named an INFORMS Fellow.
James J. Cochran
James J. Cochran is Associate Dean for Research, Professor of Applied Statistics and the Rogers-Spivey Faculty Fellow at The University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S., and M.B.A. from Wright State University and his Ph.D. from the University of Cincinnati. He has been at The University of Alabama since 2014 and has been a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa and Pole Universitaire Leonard de Vinci. Dr. Cochran has published more than 40 papers in the development and application of operations research and statistical methods. He has published in several journals, including Management Science, The American Statistician, Communications in Statistics—Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization, Interfaces and Statistics and Probability Letters. He received the 2008 INFORMS Prize for the Teaching of Operations Research Practice, 2010 Mu Sigma Rho Statistical Education Award and 2016 Waller Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran was elected to the International Statistics Institute in 2005, was named a Fellow of the American Statistical Association in 2011 and was named a Fellow of INFORMS in 2017. He received the Founders Award in 2014, the Karl E. Peace Award in 2015 from the American Statistical Association and the INFORMS President's Award in 2019. A strong advocate for effective operations research and statistics education as a means of improving the quality of applications to real problems, Dr. Cochran has chaired teaching effectiveness workshops around the globe. He has served as operations research consultant to numerous companies and not-for-profit organizations.
Statistics for Business and Economics, 5th Edition
Modern Business Statistics with Microsoft® Excel®, 7th Edition
Essentials of Modern Business Statistics with Microsoft® Excel®, 8th Edition
Essentials of Statistics for Business & Economics, 9th Edition
Essentials of Modern Business Statistics with Microsoft Office Excel (with XLSTAT Education Edition Printed Access Card), 7th Edition
Modern Business Statistics with Microsoft Office Excel (with XLSTAT Education Edition Printed Access Card), 6th Edition
Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), 8th Edition
Essentials of Modern Business Statistics with Microsoft Excel, 6th Edition
Essentials of Business Analytics, 1st Edition
Modern Business Statistics with Microsoft Excel, 5th Edition


Whether you're an experienced professional or just starting out, NIKAI is a great place to build your career.
At NIKAI, we're working toward a future where everyone's potential can be fulfilled. We offer opportunities to make an impact with quality products for a quality lifestyle. We invite you to explore, dream big and learn more about what we can offer you.


For Noosa, increased sales meant expanded facilities, which meant the need for a stronger network. See how Cisco Partner Panduit helped keep them on track.
Join us on January 11 for a CiscoChat on how manufacturers can close the IT/OT knowledge gap, with subject matter experts from Cisco, Panduit, and Rockwell Automation.


M W Tractors specialise in the shipping and export of dismantled used trucks & truck parts. The goods we supply are shipped using FCL & LCL containers depending on the customers' requirements. We have built worthy reputation based on the reliability and flexibility of our shipping and export service and this has been fine-tuned greatly over our 45 years in business. Whether you are looking to buy in bulk at wholesale export prices or just buy a single engine for your broken down vehicle we are happy to help. We provide extremely competitive shipping rates on the goods we supply, and we work our hardest to deliver an exceptional level of service for all our customers.


Jim (Casey Affleck), naturally, left as quickly as he could for a hardscrabble life in New York, and the only reason he's coming back is that it seems like the best place for a nervous breakdown. Our boy never quite breaks down, but he never perks up, either. How morose is he? Well, he doesn't just sit on the family couch—he melds with it, and his old bedroom contains a wall of mournful remembrance: pictures of Poe and Hemingway and Woolf and pretty much every author who ever met a sad end. When he hops in bed with a hot local nurse named Anika (Liv Tyler), his hard-on evaporates at the moment of contact.
As a director, longtime Indie-ana resident Steve Buscemi is well-attuned to the comedy of depression. And as a refined character actor, he's sure-handed with his supporting cast. Seymour Cassel and Mary Kay Place walk the delicate line between pathos and gargoyle comedy as Jim's parents, and Mark Boone Junior plays Jim's pothead uncle as an unnervingly authentic human travesty.
The film's digital photography is often as ugly as the terrain it's depicting, but the details in James C. Strouse's script always feel on-target: a girl's basketball team of epic badness sponsored by Roush Ladders; a prostitute and her john staring postcoitally at a televangelist. Lonesome Jim is so right in its particulars, so sure in its tone right up to the heart-kneading finale, that you can almost skate over the hollowness at its center.
At one level, Affleck deserves nothing but praise. He successfully conceals his preppy cuteness beneath scruffy whiskers and woolen caps. He never strikes a false note. His line readings are often skillful. But Jim doesn't move the viewer in the manner of, say, Mark Ruffalo's disaffected loner in You Can Count on Me. He never quite gets up out of that couch, and it's virtually impossible to see why Anika takes such a shine to him. The explanation that, as a nurse, she "likes to take care of people" is inadequate, no matter how much Strouse might believe in it.
Anika, of course, is an underwritten part. And Tyler is a frustratingly underrealized screen presence. Never less than ingratiating, she suggests a kind of Dorian Gray ingénue, remaining eternally youthful inside an aging body. As an actress, will she ever bloom into something more complicated than dewy otherworldliness? That's probably not a question Lonesome Jim meant to ask, but it's certainly the most interesting.


Casino Royale was turned into a spoof spy movie by John Huston in 1967, with David Niven in the lead role.
Pierce Brosnan led the past four Bond films but said producers axed him after offering him the chance to return.
All I hope is that get some decent to play bond and not colin farrell.


text:
Welcome to St. Pius X Catholic School, where we offer challenging academics in a faith-based, family environment to nearly 200 students in preschool through 8th grade. If you are looking for something more for your child, you will find it here. You will see the difference in the way we not only offer a rigorous learning environment, but also in the way we address the needs of the whole child. By guiding all students' spiritual, academic and social development, we enable them to meet the challenges of our world and help them reach their unique God-given potential.
Founded in 1962, St. Pius X Catholic School is committed to partnering with parents of all faiths. We off excellent academic programs while promoting self-esteem, mutual respect, cooperation and a caring community based on service to Christ and others.
Higher standards mean our students are well prepared for high school, college and beyond. Our students continue to score above the national average on standardized tests, and those who graduated in 2014 averaged a 3.6 GPA in their first marking period in high school.
Our students are surrounded by caring people who want them to succeed in school and in life. We are a growing school with families who are committed to their children's education. Please join us to make this school even more outstanding.
The mission of St. Pius X Catholic School is to provide superior academics in a joyful, safe, welcoming, Catholic, family environment.
Faith. Moving. Forward.
At St. Pius X Catholic School, faith in Jesus Christ is the foundation of all we do. It fosters a Catholic atmosphere and encourages a strong partnership with all parents. St. Pius X Catholic School supports each child during a period of significant spiritual, physical, emotional and intellectual growth from preschool through eighth grade. A strong Catholic foundation is developed through the teachings of the Gospel in order to recognize the body of Christ in one another. St. Pius X Catholic School continually delivers a superior education that is both rigorous and relevant while maintaining the rich history and tradition of our Catholic faith.
At St. Pius X, our highly qualified and dedicated teachers challenge students through an excellent curriculum and individual attention. Our students continue to score higher than the national average on standardized tests. In addition, our students who graduated in 2014 achieved a 3.6 GPA in their first marking period in high school.
Our rigorous curriculum includes core courses in math, science, social students, reading, writing and literature. We also offer Spanish, music, band, choir, technology, art and physical education. In addition, STEM (science, technology, engineering and mathematics) coursework, SMART Boards in every classroom, and a new computer lab enhance our students' learning experience.
At St. Pius X, we teach about faith, service and stewardship. We believe that the love of Christ leads to lasting family values and solid citizenship on earth and in heaven. We are proud of our Catholic identity and provide a strong faith foundation to students through Catholic religious instruction, weekly Mass, eucharistic adoration, daily prayer and an emphasis on virtues. We also have a strong commitment to Christian service projects that vary by grade and season.
Athletics, clubs and other extracurricular activities are an important part of student life at St. Pius X. With an array of clubs — from chess to math to skiing and snowboarding — there is something for everyone.
In addition, we view our athletic program as an extension of our overall philosophy, and believe that it is about stewardship formation, not just about winning. All St. Pius X athletes, coaches and parents are ambassadors of our school and are working to build up the body of Christ at all moments. Sports teams include football, boys and girls basketball, volleyball, ski club, cheerleading, baseball and softball.
If you are considering a Catholic education for your child but are concerned about the cost, please come talk to us. Scholarships, multi-student discounts and other forms of financial assistance are available.
G-3139 Hogarth Avenue
Flint, Michigan 48532-5129


text:
(Pavel Starikov / Wikimedia Commons)
Report: Maria Butina To Plead Guilty to Conspiracy Charge, Cooperate Against Boyfriend
By Chuck Ross December 10, 2018 at 5:40pm
A Russian gun rights activist will plead guilty to conspiracy to act as an unregistered foreign agent and cooperate against her boyfriend, a longtime GOP operative, ABC News reported.
Maria Butina, 30, will admit in the plea deal that she "agreed and conspired, with a Russian government official ('Russian Official') and at least one other person … to act in the United States under the direction of Russian Official without prior notification to the Attorney General," according to ABC News.
Butina's lawyers and prosecutors requested earlier on Monday a change of plea hearing. The hearing will be held Wednesday.
The Russian official referred to in Butina's court filings is believed to be Alexander Torshin, the deputy chief of the Russian national bank.
The other individual is likely Paul Erickson, a political operative with links to various conservative groups.
As part of her plea, Butina will admit that she "sought to establish unofficial lines of communication with Americans having power and influence over U.S. politics."
Prosecutors in Washington, D.C., have accused Butina and Torshin of attempting to infiltrate conservative groups, including the National Rifle Association.
The pair established close contacts with the group through a Russian gun rights group that they formed called The Right to Bear Arms.
Butina was indicted on July 17 on conspiracy charges and charges that she failed to register as a foreign agent with the Justice Department. She initially pleaded not guilty, claiming that her interactions with Torshin and contacts with conservative groups were intended to improve relations between the U.S. and Russian governments.
But prosecutors have pointed to evidence that Butina, Torshin and Erickson operated covertly.
Butina sent Torshin a message after they hosted an NRA delegation in Russia in 2015, according to ABC. "We should let them express their gratitude now, we will put pressure on them quietly later," the translated message said.
Prosecutors have also claimed that Butina had links to Russian intelligence officials.
Butina's case is not being handled by the special counsel's office, which is probing possible collusion between the Trump campaign and the Russian government. But Erickson did make at least one attempt to establish contacts between the campaign and Torshin.
He sent an email in May 2016 entitled "Kremlin connection" to Trump campaign official Rick Dearborn seeking a meeting between Torshin and President Donald Trump. The meeting never occurred, and it is not clear what Dearborn did with the email. Torshin did briefly meet Donald Trump Jr. on the sidelines of the NRA convention in Louisville in May 2016.
A version of this article appeared on The Daily Caller News Foundation website.
Chuck Ross
Founded by Tucker Carlson, a 25-year veteran of print and broadcast media, and Neil Patel, former chief policy adviser to Vice President Dick Cheney, The Daily Caller News Foundation is a 501(c)(3) non-profit providing original investigative reporting from a team of professional reporters that operates for the public benefit.
Senior White House Official Announces She's Out
US Economy Adds Less Than Half the Number of Jobs Economists Expected in December
Health Department Orders 'Non-White' Patients Be Given Priority Access to New COVID Drug That Is in Limited Supply
Biden's Plan to Lower Gas Prices Blows Up in His Face: Oil Prices Soar After Admin's Oil Reserve Tap
Power Company Warns Customers of Blackouts as Biden Admin Considers Shutting Yet Another Pipeline


text:
-40% Final Sale Vigvami Karuselli dress
Color: beige, dark green, pink
Size charts can help you to determine your own size. When you order clothes, it is worth remembering that using a tape measure can help you to ensure that the clothes are a good fit. This will help you to avoid returning clothes unnecessarily. The pictures show where you should take measurements. The measurements on the size chart are fixed body measurements in centimetres. The measurements are for normal body shapes (the basis is the C measurement).
Sock and shoe sizes:The sock and shoe size is the length of the foot from the heel to the end of the longest toe.
Belt length: The belt length is measured up to the hole in the belt that is normally used.
Glove measurements: Glove sizes are given in inches. Measure the circumference of your hand at the major knuckles, excluding the thumb. To convert from centimetres to inches, divide by 2.5. The figure represents the glove size. Gloves should initially be tightly fitted because they become worn in with use
A – Collar: The collar size is measured by placing the tape measure around the neck and placing the index finger between the tape measure and the throat. This measurement dictates the shirt size.
B – Bust: The bust size is measured from the highest point of the bust when a bra is worn.
C – Waist: The waist size is measured around the narrowest part of the waist.
D – Hip: The hip measurement is taken from the broadest part of the hip.
E – Inside leg: The inside leg measurement is taken from the crotch to the floor. Please note that the inside leg measurement of trousers is often provided in the product description.
Use this chart to convert between standard size formats.
EUR 32 - 34 36 38 - 40 42 44 - 46
FR 34 - 36 38 38 - 40 42 46 - 48
AU/UK 6 - 8 10 12 - 14 16 18 - 20
US 2 - 4 6 8 - 10 12 14 - 16
IT 38 - 40 40 -42 42 - 46 46 -48 48 - 52
JAP 5 7 9 - 11 13 15
Use this chart to find your size based on your body measurements.
Extra loose S M L
Chest 74 - 78 78 - 82 82 - 86 86 - 90 90 - 94 94 - 98 98 -102 102 - 106 106 - 112 112 - 118
Waist 58 - 62 62 - 66 66 - 70 70 - 74 74 - 78 78 - 82 82 - 86 86 - 90 90 - 96 96 - 102
Hip 82 - 86 86 - 90 90 - 94 94 - 98 98 - 102 102 - 106 106 - 110 110 - 114 114 - 120 120 - 126
Inside leg length 74 75 76 77 78 79 80 81 82 83
Slide the Size guide table to scroll.
Chest 29 1/4 - 30 3/4 30 3/4 - 32 1/4 32 1/4 - 33 3/4 33 3/4 - 35 1/2 35 1/2 - 37 37 - 38 1/2 38 1/2 - 40 1/4 40 1/4 - 41 3/4 41 3/4 - 44 44 - 46 1/2
Waist 22 3/4 - 24 1/2 24 1/2 - 26 26 - 27 1/2 27 1/2 - 29 1/4 29 1/4 - 30 3/4 30 3/4 - 32 1/4 32 1/4 - 33 3/4 33 3/4 - 35 1/2 35 1/2 - 37 3/4 37 3/4 - 40 1/4
Hip 32 1/4 - 33 3/4 33 3/4 - 35 1/2 35 1/2 - 37 37 - 38 1/2 38 1/2 - 40 1/4 40 1/4 - 41 3/4 41 3/4 - 43 1/4 43 1/4 - 45 45 - 47 1/4 47 1/4 - 49 1/2
Inside leg length 28.86 29.25 29.64 30.03 30.42 30.81 31.2 31.59 31.98 32.37
EUR XS S M L XL XXL
AU/UK XS - S S - M M - L L - XL XL - XXL XXL
Chest in cm 86 - 90 91 - 95 96 - 100 101 - 106 107 - 111 112 - 116
Collar in cm 36 - 37 38 - 39 40 - 41 42 - 43 44 - 45 46 - 47
EUR 34 - 36 37 - 39 40 - 42 43 - 45 46 - 48
AU/UK 2 - 3 1/2 4 - 5 1/2 7 - 8 8 1/2 - 10 11 - 12 1/2
Foot length in cm 22 - 23 23,5 - 25 25,5 - 27 25,5 - 27 27,5 - 29
EUR in cm 75 80 85 90
UK in inches 29.2 31.2 33.15 35.1
Foot length (cm) 23,5 cm 23,8 cm 24,5 cm 25,1 cm 25,4 cm 25,9 cm
Eu / Italy 36 37 38 39 40 41
AU/UK 3 4 5 6 7 8
Japan 23 24 24.5 25.5 26 26.5
The Vigvami dress is made of cotton poplin in the Karuselli pattern, which is printed in Helsinki. The dress has an elastic gathered neckline, puffy sleeves with frill details at the seams, and elastic gathered sleeve ends. The waistline features an adjustable elastic cord with a lock. The hem is gathered at the seam and it has a slight A-line cut.
The joyous Karuselli (merrygo-round) design is a perfect example of Katsuji Wakisaka's Marimekko prints, characterized by a free and playful approach to pattern composition.
Fitting: Loose Fit
Height Of Model: 5.71 ft
Size the model is wearing: M/38
Length of product in size M/38: 49 in
FKXMQ
Follow the washing temperature given. Wash with like colours. Wash inside out.
Pattern: Karuselli
Pattern Designer: Katsuji Wakisaka
Country of manufacture: Lithuania


BMW X3 Series is a Sports Activity Vehicle (SAV) which has won the hearts of many car lovers in India. Its sleek exteriors, spacious interiors, safety features and state-of-the-art technology being the highlights of the car.
Appearance: BMW X3 Series is available in various beautiful colors – Titanium Silver, Platinum Bronze, Silver Grey, Alpine White, Black Sapphire and Monaco Blue. The frontage consists of body colored front bumper, double kidney shaped radiator grille, fog lights and broad headlamps. The rear comprises of taillights cluster.
Comfort: BMW X3 Series has many comfort features. The car has elevated seats with lumbar support, providing comfort for the driver as well as passengers. BMW X3 Series also has Dual Panel Panoramic Moonroof for extra comfort. The car has new jacks to play additional music formats like MP3 and IPODs.
Safety: BMW X3 Series has various safety features for the protection of the occupants. The car is fitted with airbag system. The standard safety features include Cornering Brake Control (CBC), Dynamic Traction Control (DTC) and Hill Descent Control (HDC). Safety belts of the car are fitted with force limiters and tensioners. Rear seats are fitted with headrests and automatic seatbelts.
Engine: BMW X3 Series comes in two petrol options.
3.0si model has DOHC, 3.0 Liter gasoline engine, which propels a power of 260 hp at 6600 rpm and a peak torque of 225 Nm at 2750 rpm. 2.5si model has 2.5 L petrol engine that churns a power of 218 PS at 6500 rpm and a maximum torque of 250 Nm at 2750 – 4250 rpm.
Price: BMW X3 Series comes in two models. The prices of both the models are specified below (taking Mumbai as the base price location).
BMW X3 2.5si – Rs.41,80,000.
BMW X3 3.0si – Rs.27,00,000.
However, the prices specified are just approximate prices which exclude tax, registration, insurance and cost of accessories.


text:
Elusive Balance
Liquid Times
AirEffect
Christian Fennesz
Fennesz uses guitar and computer to create shimmering, swirling electronic sound of enormous range and complex musicality. "Imagine the electric guitar severed from cliché and all of its physical limitations, shaping a bold new musical language." – (City Newspaper, USA). His lush and luminant compositions are anything but sterile computer experiments. They resemble sensitive, telescopic recordings of rainforest insect life or natural atmospheric occurrences, an inherent naturalism permeating each piece.
Christian Fennesz is published by Touch Music. He lives and works in Vienna.
Web site: www.fennesz.com
Murcof
Murcof is the performing and recording name of Mexican electronica artist Fernando Corona. Corona was born in 1970 in Tijuana, Mexico and raised in Ensenada. He was for a time a member of the Tijuana-based Nortec Collective of electronic musicians under the Terrestre project name. In 2000 he returned to Tijuana. Since 2006 Corona lives in Girona, Spain.
Murcof's music is sparse, minimalist, sample-based electronica. Although founded on complex, at times, abstract, glitchy electronic percussion, Corona's recordings are more melodic and traditionally structured than many contemporary electronic musicians. Many recordings feature orchestral strings sampled from recordings of works by modern composers such as Arvo Pärt.
Web site: www.murcof.com
Senking
Jens Massel aka senking was born in cologne in 1969.
In the early 1990s, massel gained his first musical experiences as a bass player in different punk and indie bands. in 1995, he got his hands on a very low-fi synthesizer that was able to create short samples of 5 seconds in length. these "mini loops" were recorded and arranged on a 4-track machine with the result of his first electronic track on tape.
After this, he was to hooked to create electronic music, and although better music production equipment was purchased, the kind of equipment he uses has never been that important to massel. he rather likes to change his set-up every once in a while to find new ways to work, trying not to become too repetitive.
When it comes to the music of his live performances, there's no computer involved. the only thing he uses a computer for is to create the accompanying visuals, which he is paying more attention to these days. after years of having the idea to create images in the listeners' head just by playing his music, the adding of visuals to his shows was a resent significant step forward.
Massel's experimental work is a lot about organic dynamics and also about a certain atmosphere that often reminds of listening a soundtrack or of being miles below sea level – in the best case, both of them. his music is inspired by, of course other musicians, but also by movies and most of all, by life and its very special moments. besides his releases on raster-noton, he has also composed for schauspielhaus hannover and inteata cologne.
StalkerTeatro
The artistic core of Stalker Teatro have worked together since setting up the 'Political Collective' at Turin Academy of Fine Arts in 1975, the heyday of Italy's post-68 cultural and political uproar. This has left an enduring mark on the company's work which is characterised by a strong commitment to artistic experimentation within socially sensitive or challenging situations.
The founding members of Stalker Teatro were involved with Turin City Council's ground-breaking 'cultural animation' projects in the late 70s and helped set-up Turin's first professionally run youth theatre in 1976. In 1981 they started a residency in Italy's largest psychiatric clinic in Grugliasco which led to the formal foundation of Stalker Teatro in 1986. Using a distinctive style developed from site-specific events, performance art and participatory theatre, Stalker Teatro have performed throughout Italy, France, Germany, Poland and UK.
Stalker Teatro continue to be based in Turin and Biella, where they regularly collaborate Cittadellarte – Fondazione Pistoletto.
Web site: www.stalkerteatro.net


Using the Corporate Equality Index, below are the companies that have the best transgender-inclusive corporate policies. Caribou Coffee Company Inc. Bank of New York Mellon Corp. Mia Satya Trans Employment Specialist. University of Phoenix Univision Communications Inc. Nissan North America Inc. Services are always free!
Since the program started it has connected community members with thousands of jobs in diverse, equal, and rewarding workplaces.
Leo Burnett Company Inc. Sony Pictures Entertainment Inc. Hanover Insurance Group Inc. We are here to help! Zenith Media Services Inc. We support efforts toward greater acceptance, not less, and we strongly believe that transgender students should be treated as equals.
The Bama Companies Inc. Broadridge Financial Solutions Inc. The program provides a wide range of services including job referrals and career coaching, navigating being out at work or transitioning on the job, resume review and managing references, hiring, and community events. Hewlett Packard Enterprise Co. We disagree with any effort to limit or rescind their rights and protections. Toys 'R' Us Inc.


text:
using System;
using System.Collections.Generic;
using System.Threading;
using System.Threading.Tasks;

namespace zvs.Processor
{
    public interface IPluginManager
    {
        IReadOnlyList<ZvsPlugin> GetZvsPlugins();
        ZvsPlugin FindZvsPlugin(Guid pluginGuid);
        Task<Result> EnablePluginAsync(Guid pluginGuid, CancellationToken cancellationToken);
        Task<Result> DisablePluginAsync(Guid pluginGuid, CancellationToken cancellationToken);
        Task StartAsync(CancellationToken cancellationToken);
        Task StopAsync(CancellationToken cancellationToken);
    }
}

redpajama_set_name: RedPajamaGithub


text:
Lifetime Physical and/or Sexual Intimate Partner Violence :24 % (1)
Lifetime Non-Partner Sexual Violence : 4 % (3)
Child Marriage :21 % (4)
Female Genital Mutilation/Cutting :4 % (5)
Gender Inequality Index Rank :131 (6)
Dedicated violence against women survey 1
Domestic violence/Intimate partner violence 5
Violence against women and girls 4
Female Genital Mutilation/Cutting (FGM/C) 3
Ghana Search Data
Ghana | 2008
Training Manuals on School Related Gender-Based Violence
Domestic Violence Management Board
Domestic Violence Act 2007 (Act 732)
Form of Violence : Domestic violence/Intimate partner violence, Sexual harassment
Multiple Indicator Cluster Survey 2006
Form of Violence : Domestic violence/Intimate partner violence, Child, early and forced marriage, Female Genital Mutilation/Cutting (FGM/C)
Domestic Violence Victims Support Unit
Type of Measure : Institutional mechanisms, Police > Protocols and guidelines
Criminal Code Amendment Act (FGM)
Form of Violence : Female Genital Mutilation/Cutting (FGM/C)
Form of Violence : Domestic violence/Intimate partner violence, Domestic violence/Intimate partner violence
Survey: Dimension and Consequences of Violence Against Women in Ghana
Type of Measure : Research and statistical data > Dedicated violence against women survey
Article 16 of the Constitution
Type of Measure : Violence against women > Constitutional provision
(1) Proportion of ever-partnered women aged 15-49 years experiencing intimate partner physical and/or sexual violence at least once in their lifetime. Source: Ghana Statistical Service (GSS), Ghana Health Service (GHS), and ICF Macro, 2009. Ghana Demographic and Health Survey 2008. Accra, Ghana: GSS, GHS, and ICF Macro
(2) Proportion of ever-partnered women aged 15-49 years experiencing intimate partner physical and/or sexual violence in the last 12 months. Source: Ghana Statistical Service (GSS), Ghana Health Service (GHS), and ICF Macro, 2009. Ghana Demographic and Health Survey 2008. Accra, Ghana: GSS, GHS, and ICF Macro
(3) Proportion of women aged 15–49 years experiencing sexual violence perpetrated by someone other than an intimate partner at least once in their lifetime. Source: Ghana Statistical Service (GSS), Ghana Health Service (GHS), and ICF Macro, 2009. Ghana Demographic and Health Survey 2008. Accra, Ghana: GSS, GHS, and ICF Macro
(4) Percentage of women aged 20 to 24 years who were first married or in union before age 18. Source: UNICEF global databases, 2018, based on Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and other nationally representative surveys
(5) Percentage of girls and women aged 15 to 49 years who have undergone FGM/C. Source: UNICEF global databases 2017, based on Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and other nationally representative surveys.


text:
Tel: 01949 81985 / 01949 81580 / Mob: 07949 973470|enquiries@woodviewcottages.co.uk
Woodview Links to Attractions	rcook	2018-03-02T13:17:24+00:00
Antique Fairs at Newark and Swinderby
The Newark International Antiques and Collectors Fair is the largest event of its kind in Europe – a world-wide phenomenon, this is one IACF event that needs to be experienced to be believed! The Swinderby Antiques and Collectors Fair is an IACF 'One Day Monday' fair. The perfect addition to the UK antique fairs calendar – it fills the monthly gap between each of the Newark antiques fairs, Swinderby's giant neighbour.
Antiques Fair at Lincoln
International dealers exhibiting an array of collectable, unusual and highly sought after antiques, collectables and unusual furnishings for the home and garden from across Europe.
Belvoir Castle
We very much hope you will come and visit us and enjoy a wonderful day out at Belvoir Castle. Our hilltop setting commands stunning views over the Vale of Belvoir and with glorious gardens and fascinating magical castle to explore, there's something for everyone at our family home.
Martins Arms, Colston Bassett
Set in the idyllic village of Colston Bassett in the Vale of Belvoir, the Martins Arms is a spectacular Grade 2 listed building. Situated at the corner of a quiet leafy cul-de-sac by an old cross, the Inn encapsulates all that is best in an English pub. But to call it the quintessential English hostelry does not do it justice. Tel 01949 81361 to make a booking.
Perkins Restaurant, Nottingham
One of the Midlands' leading restaurants… Perkins has been delivering award-winning food since 1982. We champion locally sourced produce and offer a versatile menu and excellent service from 12pm to 12am. Tel 0115 937 3695
Rutland Water Nature Reserve
This internationally famous nature reserve is managed in partnership by Anglian Water and the Leicestershire & Rutland Wildlife Trust and provides one of the most important wildfowl sanctuaries in Great Britain, regularly holding in excess of 20,000 waterfowl.
Once a royal hunting forest, this is the legendary home of the outlaw Robin Hood, today part of a National Nature Reserve because of its national ecological importance. Within the 450 acre site there are 900 veteran oak trees, including the famous Major Oak. This ancient oak, one of Britain's most famous trees, is located just a ten minute walk from the Visitor Centre. Come and visit one of the world's most famous woods!
Trent Bridge Cricket
More than just a cricket ground, Trent Bridge boasts a range of facilities that are open for public use. Four squash courts are housed in the ticket office building on Hound Road and the Radcliffe Road Stand hosts meeting rooms and six indoor cricket nets. The Trent Bridge Library holds the biggest collection of cricket books in the UK and the Club Shop stocks a huge range of cricket equipment and Notts and England replica wear.
Trent FM Arena, Nottingham
The Arena, which is based inside the National Ice Centre, was opened to the public on 1st April 2000 by ice skating legend Jayne Torvill and was given the royal seal of approval by HM the Queen on July 31 2002. Since then we have gone from strength to strength, hosting the cream of live music, international ice shows and sporting events. In fact year-on-year we are growing, with audience numbers and the number of events reaching record highs. With a capacity of 10,000 there is no where quite like Trent FM Arena
Woodview Cottages: Newfields Farm, Owthorpe, Nottinghamshire, NG12 3GE
Phone: 01949 81985 / 01949 81580 Mobile: 07949 973470
Email: enquiries@woodviewcottages.co.uk
Web: www.woodviewcottages.co.uk


text:
Maine Legislature Maine Revised Statutes
Session Law
Maine State Constitution
§216 PDF
§216 MS-Word
Statute Search
Ch. 1 Contents
Title 22-A Contents
Maine Law & Disclaimer
§215
Title 22-A: HEALTH AND HUMAN SERVICES
Subtitle 1: DEPARTMENT OF HEALTH AND HUMAN SERVICES
Chapter 1: DEPARTMENTAL ORGANIZATION AND OPERATION
Subchapter 2: ORGANIZATION
§216. Health Care Liability Retirement Fund
The Health Care Liability Retirement Fund, referred to in this section as "the fund," is established as a nonlapsing fund within the Department of Health and Human Services. The fund consists of proceeds from the sale of liquor operation revenue bonds pursuant to Title 30-A, section 6053. The money in the fund must be used for the purpose of making payments to health care providers for services provided prior to December 1, 2012 under the MaineCare program established by Title 22, chapter 855. When, as determined by the commissioner, there exist no outstanding amounts owed to health care providers eligible to be paid from the fund, the State Controller shall transfer all amounts in the fund to the Liquor Operation Revenue Fund established in Title 30-A, section 6054. [PL 2013, c. 269, Pt. B, §1 (NEW).]
PL 2013, c. 269, Pt. B, §1 (NEW).
The Revisor's Office cannot provide legal advice or interpretation of Maine law to the public.
If you need legal advice, please consult a qualified attorney.
Office of the Revisor of Statutes · 7 State House Station · State House Room 108 · Augusta, Maine 04333-0007
Data for this page extracted on 9/28/2022 08:27:44.
Maine Government
Legislature • Executive • Judicial • Agency Rules
Tour Guide • Accessibility • Security Screening • Directions & Parking


Since its inception, the company has thrived on recommendations by satisfied homeowners, architects and contractors. PANORAMIC EUROPEAN WINDOWS & DOORS was founded on the basis of being service and quality oriented, and that theme carries through to this day. Bringing the timeless craftsmanship and engineering ingenuity of European energy efficient and environmentally-sound products to American high-end construction projects, while continuously keeping up with modern technology, will always be the company's top priority.
A profound passion for architecture and design brought Austrian-born Franz Muster into the high-end home development industry after working in marketing and sales for 25 years. Before launching PANORAMIC EUROPEAN WINDOWS & DOORS, he established sound relationships with top European and American innovators and manufacturers over an extensive period, testing and investigating their products by pursuing his profession of high-end home renovation and development. While using only the best windows and doors from Europe and the United States in his developments, an idea was formed.
PANORAMIC EUROPEAN WINDOWS & DOORS was founded around innovative and unique European windows and doors manufacturers. However, as the company has grown, special and unique American manufacturers have been added, as well as different manufacturers from around the world. Today, the same product guidelines are maintained… Panoramic product lines and manufacturers must have the best design, engineering, performance, materials and quality.


text:
Optimal groundwater management under climate change and technical progress
Nicolas E. Quintana Ashwell, Jeffrey M Peterson, Nathan P. Hendricks
Water Resources Center
We develop a dynamic model of groundwater extraction for irrigation where climate change and technical change are included as exogenous state variables in addition to the usual state variable of the stock of groundwater. Our key contributions are (i) an intuitive description of the conditions under which groundwater extraction can be non-monotonic, (ii) a numerical demonstration that extraction is non-monotonic in an important region overlying the Ogallala Aquifer, and (iii) the predicted gains from management are substantially larger after accounting for climate and technical change. Intuitively, optimal extraction is increasing in early periods when the marginal benefits of extraction are increasing sufficiently fast due to climate and technical change compared to the increase in the marginal cost of extraction. In contrast, most previous studies include the stock of groundwater as the only state variable and recommend a monotonically decreasing extraction path. We conduct numerical simulations for a region in Kansas overlying the Ogallala Aquifer and find that optimal groundwater extraction peaks 23 years in the future and the gains from management are large (29.5%). Consistent with previous literature, the predicted gains from management are relatively small (6.1%) when ignoring climate and technical change. The realized gains from management are not substantially impacted by incorrect assumptions of climate and technical change when formulating the optimal plan.
Resource and Energy Economics
https://doi.org/10.1016/j.reseneeco.2017.10.005
Published - Feb 1 2018
Groundwater management
Technical progress
Ground water
Technical change
State variable
Numerical simulation
Quintana Ashwell, N. E., Peterson, J. M., & Hendricks, N. P. (2018). Optimal groundwater management under climate change and technical progress. Resource and Energy Economics, 51, 67-83. https://doi.org/10.1016/j.reseneeco.2017.10.005
Optimal groundwater management under climate change and technical progress. / Quintana Ashwell, Nicolas E.; Peterson, Jeffrey M; Hendricks, Nathan P.
In: Resource and Energy Economics, Vol. 51, 01.02.2018, p. 67-83.
Quintana Ashwell, NE, Peterson, JM & Hendricks, NP 2018, 'Optimal groundwater management under climate change and technical progress', Resource and Energy Economics, vol. 51, pp. 67-83. https://doi.org/10.1016/j.reseneeco.2017.10.005
Quintana Ashwell NE, Peterson JM, Hendricks NP. Optimal groundwater management under climate change and technical progress. Resource and Energy Economics. 2018 Feb 1;51:67-83. https://doi.org/10.1016/j.reseneeco.2017.10.005
Quintana Ashwell, Nicolas E. ; Peterson, Jeffrey M ; Hendricks, Nathan P. / Optimal groundwater management under climate change and technical progress. In: Resource and Energy Economics. 2018 ; Vol. 51. pp. 67-83.
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title = "Optimal groundwater management under climate change and technical progress",
abstract = "We develop a dynamic model of groundwater extraction for irrigation where climate change and technical change are included as exogenous state variables in addition to the usual state variable of the stock of groundwater. Our key contributions are (i) an intuitive description of the conditions under which groundwater extraction can be non-monotonic, (ii) a numerical demonstration that extraction is non-monotonic in an important region overlying the Ogallala Aquifer, and (iii) the predicted gains from management are substantially larger after accounting for climate and technical change. Intuitively, optimal extraction is increasing in early periods when the marginal benefits of extraction are increasing sufficiently fast due to climate and technical change compared to the increase in the marginal cost of extraction. In contrast, most previous studies include the stock of groundwater as the only state variable and recommend a monotonically decreasing extraction path. We conduct numerical simulations for a region in Kansas overlying the Ogallala Aquifer and find that optimal groundwater extraction peaks 23 years in the future and the gains from management are large (29.5{\%}). Consistent with previous literature, the predicted gains from management are relatively small (6.1{\%}) when ignoring climate and technical change. The realized gains from management are not substantially impacted by incorrect assumptions of climate and technical change when formulating the optimal plan.",
keywords = "Climate change, Groundwater management, Optimal control, Technical progress",
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T1 - Optimal groundwater management under climate change and technical progress
AU - Quintana Ashwell, Nicolas E.
AU - Peterson, Jeffrey M
AU - Hendricks, Nathan P.
N2 - We develop a dynamic model of groundwater extraction for irrigation where climate change and technical change are included as exogenous state variables in addition to the usual state variable of the stock of groundwater. Our key contributions are (i) an intuitive description of the conditions under which groundwater extraction can be non-monotonic, (ii) a numerical demonstration that extraction is non-monotonic in an important region overlying the Ogallala Aquifer, and (iii) the predicted gains from management are substantially larger after accounting for climate and technical change. Intuitively, optimal extraction is increasing in early periods when the marginal benefits of extraction are increasing sufficiently fast due to climate and technical change compared to the increase in the marginal cost of extraction. In contrast, most previous studies include the stock of groundwater as the only state variable and recommend a monotonically decreasing extraction path. We conduct numerical simulations for a region in Kansas overlying the Ogallala Aquifer and find that optimal groundwater extraction peaks 23 years in the future and the gains from management are large (29.5%). Consistent with previous literature, the predicted gains from management are relatively small (6.1%) when ignoring climate and technical change. The realized gains from management are not substantially impacted by incorrect assumptions of climate and technical change when formulating the optimal plan.
AB - We develop a dynamic model of groundwater extraction for irrigation where climate change and technical change are included as exogenous state variables in addition to the usual state variable of the stock of groundwater. Our key contributions are (i) an intuitive description of the conditions under which groundwater extraction can be non-monotonic, (ii) a numerical demonstration that extraction is non-monotonic in an important region overlying the Ogallala Aquifer, and (iii) the predicted gains from management are substantially larger after accounting for climate and technical change. Intuitively, optimal extraction is increasing in early periods when the marginal benefits of extraction are increasing sufficiently fast due to climate and technical change compared to the increase in the marginal cost of extraction. In contrast, most previous studies include the stock of groundwater as the only state variable and recommend a monotonically decreasing extraction path. We conduct numerical simulations for a region in Kansas overlying the Ogallala Aquifer and find that optimal groundwater extraction peaks 23 years in the future and the gains from management are large (29.5%). Consistent with previous literature, the predicted gains from management are relatively small (6.1%) when ignoring climate and technical change. The realized gains from management are not substantially impacted by incorrect assumptions of climate and technical change when formulating the optimal plan.
KW - Climate change
KW - Groundwater management
KW - Optimal control
KW - Technical progress
U2 - 10.1016/j.reseneeco.2017.10.005
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JO - Resources and Energy Economics
JF - Resources and Energy Economics
10.1016/j.reseneeco.2017.10.005


text:
Two Reasons For the Low Number of Women in Computer Jobs
by The Programmer on 15 Jan 2018
I saw this chart on LinkedIn with the heading "Chart: Women in tech continue to face uphill battle" and the hashtag #STEMSexism.
The first reason for the low number of women in computer jobs is that we rarely hear about women in computing except in the context of pay gaps, harassment, discrimination, "uphill battles" and #STEMSexism.
It's self-perpetuating. "Computing is a terrible profession for women in so many ways." Followed by "Why aren't there more women in computing?"
You've answered your own question. If you think computing is a hostile profession (I do not, btw), why do you want more women to go into it?
The second reason for the low number of women in computer jobs — sometimes the simplest explanations are the best — is that women prefer to do other things.
Men and women are different and make different choices about their lives, as a result of which, women are underrepresented in some professions and overrepresented in others.
Women, for example, are overrepresented in nursing, family counseling, speech pathology, social work, education, to name a few.
Do we hear about a diversity crisis in speech pathology or social work? We don't, right?
I worked with a nursing organization for five years. About 90 percent of nurses are women, but in five years I can't remember a single instance where gender bias was cited as a crisis, a dilemma, a problem, or even something as mild as a cause for concern.
Women being overrepresented in certain professions is not widely considered to be a problem. But if women being underrepresented in computer jobs is a problem, then their overrepresentation in other professions is also a problem.
In fact, it's the same problem. Because where are the women in computing going to come from?
On the safe assumption that the number of women is constant — that a large number of new women are not going to just appear out of nowhere — the women will have to come from other professions that they seem to prefer, the professions in which they are overrepresented.
Sorry girls, we can't have so many of you working in healthcare, education and other helping professions because we need to boost the computing numbers.
Or — we could calm down about the computing numbers and leave young women to make their own choices about their own lives.
TL;DR -> Women are capable of making decisions for themselves. For the most part, they choose to do things other than work in computer jobs, which is okay. It's possible that none of us really knows what is the "right" percentage of women in computing and it's possible that none of us really knows what other people should be doing with their lives.
Thus spoke The Programmer
Where Are the Additional Women in Technology Supposed to Come From?
To Young Women Considering a Career in Technology
When is Diversity Not a Dilemma?
Tags:Charts Computers Computing Decisions Discrimination Education Girls Harassment Healthcare Jobs Money Nursing Sexism STEM Women Women in IT
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