text:
\section{Introduction}

In many applications such as robotic manipulation, autonomous vehicle navigation, scene segmentation, or 3D modeling, it is useful to reason about 3D geometry of objects in the world using sensor data captured from only a single point-of-view.  The most widely available such data type which can be collected under the fewest assumptions is an image, either in grey-scale, with 3 RGB channels, or with a depth channel.  These applications have thus motivated much work in computer vision dedicated to inferring 3D information such as object orientation, position, and size \cite{mildenhall2020nerf, wang2019normalized,  li2019cdpn, he2020pvn3d, xiang2017posecnn} from images.  These methods, however, fail to capture and exploit the symmetry of 3D space.  While some computer vision models incorporate 2D symmetry \cite{e2cnn}, it is very difficult to incorporate 3D symmetries.

Alternatively, $\mathrm{SO}(3)$-equivariant neural networks have emerged as a powerful tool for modeling 3D geometry which directly incorporates 3D symmetries into the model.
As a consequence, equivariant neural networks demonstrate improved generalization properties, data efficiency, and provable consistency \cite{elesedy2021provably, batzner20223, fuchs2020se}.  In order to apply such symmetry-aware methods to pose estimation, however, the input data must be $\mathrm{SO}(3)$-transformable and thus until now such methods have been restricted to either point clouds \cite{chen2021equivariant}, spherical images \cite{esteves2018learning}, or highly structured multi-view images \cite{esteves2019equivariant}.  Moreover, such methods are computationally expensive and not necessarily robust to occlusions or partial inputs.

We propose Image2Icosahedral (I2I), a method that allows for $\mathrm{SO}(3)$-equivariant reasoning on embeddings learned only from single-view image inputs.  The embeddings and downstream model can be trained in an end-to-end manner.  Since our model incorporates 3D symmetries, it is able to generalize better than image-based models over transformations induced by changes in camera viewpoint or object orientation.  Moreover, I2I is faster and more memory efficient than other methods as it exploits convolution over the discrete icosahedral group $I_{60} \subset \mathrm{SO}(3)$.  Similar to work on $\mathrm{SO}(2)$-equivariance, we find that using a discrete subgroup leads to faster performance and better optimization with minimal loss in accuracy or equivariance \cite{e2cnn}.

I2I has two novel components which separate the problems of feature extraction and object-level memory, (1) a projector which maps images to spherical dynamic filters and (2) a dynamic filter icosohedral convolution.  The spherical filter projector projects 2D images onto 6 vertices of an icosohedron, representing the partially observed object features and the camera viewpoint.  We implement this projector using an $\mathrm{E}(2)$-equivariant CNN \cite{e2cnn}.   Thus the projector does not need to directly reason about how the object looks from other angles.  The output of the projection is then used as a dynamic filter which is convolved over the set of vertices of the icosohedron with learned object feature maps.  The object feature maps are signals over the entire icosahedron and thus represent, in latent space, the fully observed object in an explicitly $\mathrm{SO}(3)$-transformable manner.

We evaluate our method on two tasks: object pose estimation where we estimate the $\mathrm{SO}(3)$ orientation of a novel in-class object from a single image and object classification where we infer object category from an image. While our proposed method outperforms state of the art baselines in both cases, our performance on the pose estimation task is particularly compelling. We outperform other pose-from-image baselines handily, by an order of magnitude in some cases. Our ablations show that our method is insensitive to minor variations in the algorithm -- the general idea of reasoning over the icosahedron seems to be the key reason for the success of our method.

To summarize, our contribution are
\begin{itemize}

    \item We propose a novel model that implements a discrete approximation to an $\mathrm{SO}(3)$-equivariant dynamic convolution filter over $S^2$.

    \item We propose a novel method of projecting a 2D image onto an equivariant spherical convolutional filter.

    \item When applied to the problem of estimating object pose in $\mathrm{SO}(3)$ from a monocular image, our method outperforms relevant baselines convincingly.

    \item Our method is faster and less memory intensive than relevant baselines and can easily be integrated into traditional CNN object detection pipelines.

\end{itemize}

\section{Related Work}
\paragraph{3D Shape Analysis}
Research into 3D object recognition has been facilitated by large datasets of common object models~\cite{wu20153d, shapenet2015,uy19revisiting,fu213d}.  The recognition problem has been studied for several input modalities including multiview images~\cite{su2015multi, kanezaki2018rotationnet, esteves2019equivariant}, point clouds~\cite{qi2017pointnet++, thomas2019kpconv, chen2021equivariant}, and projections of 3D geometry~\cite{bai2016gift,yavartanoo2018spnet, cohen18spherical}.  In the challenging setting where objects are not aligned to a canonical orientation, methods that reason about 3D space can achieve better performance.  RotationNet~\cite{kanezaki2018rotationnet} classifies objects while implicitly reasoning about the view point of each image, while EMVN~\cite{esteves2019equivariant} captures images from structured view points so that information can be processed in a $\mathrm{SO(3)}$ equivariant manner.  In contrast, our method reasons about 3D rotation using a single image input with no restrictions on the camera view point.

\paragraph{6D Pose Estimation}
Reasoning about the 3D position and orientation of objects in a single image is challenging, yet has important applications in robotics~\cite{tremblay2018deep} and autonomous vehicles \cite{geiger2012we}. If 3D CAD models are available, traditional computer vision methods like iterative closest point \cite{besl1992method} and template matching \cite{hinterstoisser2011multimodal} can be effective.  Pose estimation has also been approached using convolutional networks which are trained to predict the 3D bounding boxes~\cite{xiang2017posecnn}, visual keypoints~\cite{he2020pvn3d, manuelli2019kpam} or 3D object coordinates~\cite{wang2019normalized, li2019cdpn, zakharov2019dpod}, with which the pose can be extracted based on known properties of the objects such as 3D size, appearance, or canonical instance.
In contrast, our method does not require any information about object at inference time and it explicitly incorporates 3D rotational symmetry.  Our method can be viewed as complementary to the existing 6D object pose estimation literature, since it can be integrated into existing convolutional pipelines to provide improved reasoning of 3D rotations.

\paragraph{Rotation Equivariance}
Neural networks with components equivariant to 3D rotations (the $\mathrm{SO}(3)$ abstract group) have found applications in a multitude of domains including quantum chemistry \cite{thomas18tensor,anderson19cormorant,fuchs2020se,brandstetter21geometric}, trajectory prediction \cite{walters21trajectory} and, most significant for our work, shape classification and pose estimation for 3D objects \cite{cohen18spherical,falorsi2018explorations,thomas18tensor,esteves2018learning,esteves2019equivariant,poulenard19effective,fuchs2020se,zhao20quaternion,quessard20learning,poulenard21functional,chen2021equivariant,li2021leveraging,deng2021vector,park22learning}. One class of approaches is based on the spherical convolution \cite{cohen18spherical}. A point cloud is projected onto a sphere and processed using fully $\mathrm{SO}(3)$ equivariant spherical convolutions \cite{cohen18spherical,esteves2018learning,esteves2019cross}. Spherical projection was also used with single views of objects in the Homeomorphic VAE \cite{falorsi2018explorations}. Homeomorphic VAE is approximately $\mathrm{SO}(3)$ equivariant, as there is no linear transformation of the pixels of the input image that would correspond to a 3D rotation of the depicted object \footnote{In contrast, a point cloud can be rotated in 3D by multiplying the individual points with a rotation matrix.}. Spherical latent spaces with an approximate $\mathrm{SO}(3)$ equivariance have also been used in \cite{quessard20learning,park22learning}. Here, each latent state is a single point on a sphere, instead of a signal over the entire sphere. Compared to \cite{falorsi2018explorations,quessard20learning,park22learning}, we also approximate $\mathrm{SO}(3)$ equivariance by projecting a single image of an object onto a hemi-icosohedral (a discretization of a hemisphere). Our model fits hundreds of object instances across several classes, whereas \cite{falorsi2018explorations,quessard20learning,park22learning} are limited to a cube with multi-colored faces and a single 3D model of the Utah teapot. Other works have used the icosohedral group \cite{cohen2019gauge} for pose estimation in the structured multi-view setting \cite{esteves2019equivariant} and with point cloud data \cite{chen2021equivariant,li2021leveraging}. Alternatively, prior works represented a point cloud as a graph and used harmonic functions to model $\mathrm{SO}(3)$ equivariant interactions between nearby points \cite{thomas18tensor,poulenard19effective,fuchs2020se,poulenard21functional,deng2021vector}. These models are fully $\mathrm{SO}(3)$ equivariant, but as a trade-off, they require full point clouds with an approximately uniform distribution of points. Rotational equivariance was also proposed for capsule networks \cite{sabour17dynamic} operating on point cloud data \cite{zhao20quaternion}. Our focus is on unstructured single-view inputs.

\section{Background}
Our method provides a mechanism to learn a mapping from features in image space to a 3D rotational embedding.  I2I combines layers which are explicitly constrained to be equivariant to 2D and 3D rotations as well as layers with learned equivariance. We introduce the concept of equivariance and how neural networks can be designed with equivariant layers to preserve symmetry.

\subsection{Equivariance}

The concept of equivariance to group transformations formalizes when a map preserves symmetry. A symmetry group is a set of transformations that preserves some structure. Given symmetry group $G$ that acts on spaces $\mathcal{X}$ and $\mathcal{Y}$ via $\mathcal{T}_g$ and $\mathcal{T}'_g$, respectively, for all $g\in G$, a mapping, $f \colon \mathcal{X} \rightarrow \mathcal{Y}$ is equivariant to $G$ if
\begin{align}
    f(\mathcal{T}_g x) = \mathcal{T}'_g f(x). \label{eq:equivariance}
\end{align}
That is, an equivariant mapping commutes with the group transformation.  Invariance is a special case of equivariance when $\mathcal{T}'_g$ is the identity mapping (i.e. applying group transformations to the input of an invariant mapping does not affect the output).

\subsection{Group convolution over Homogeneous Spaces}

The group convolution operation is a linear equivariant mapping which can be equipped with trainable parameters and used to build equivariant neural networks \cite{cohen2016group}.   Convolution\footnote{Technically, this is group correlation, but is commonly called convolution in the context of neural networks.}
is performed by computing the dot product of a signal with a filter that is shifted across a range of shifts.  In standard 2D convolution, the shifting corresponds to a translation in pixel space.  Group convolution \cite{cohen2016group} generalizes this idea to arbitrary symmetry groups, with the filter transforming by elements of the group.
Let $G$ be a group and $\mathcal{X}$ be a homogeneous space, i.e. a space on which $G$ acts transitively, for example, $\mathcal{X}=G$ or $\mathcal{X}=G/H$ for a subgroup $H$.  We can compute the group convolution between two functions, $f, \psi\colon \mathcal{X} \rightarrow \mathbb{R}^k$, as follows:
\begin{align}
    [f \star \psi](g) = \sum_{x\in \mathcal{X}}  f(x) \cdot \psi(\mathcal{T}_g x). \label{eq:group-conv}
\end{align}
Note that the output of the convolution operation is defined for each group element $g\in G$, while the inputs, $f$ and $\psi$, are defined over a homogeneous space of $G$. By parameterizing either $f$ or $\psi$, group convolution may be used as a trainable layer in an equivariant model.

\begin{figure}[t]
    \centering
    \includegraphics[width=0.95\textwidth]{figures/figure1.png}
    \caption{Illustration of our proposed model, Image2Icosahedral (I2I). We project from the input image $x$ onto a feature map $y$ using a $C_4$-equivariant ResNet model. Then, we project $y$ onto $\psi$, a dynamic filter on an icosahedral approximation of the sphere $S^2$. Finally, we convolve $\psi$ with a feature map $f$ using an icosahedral group convolution. The downward pointing arrows indicate that the end-to-end model is approximately equivariant over the icosahedral group $I_{60} \subset \mathrm{SO}(3)$.}
    \label{fig:method}
\end{figure}

\subsection{Icosahedral Group}
The continuous transformation group $\mathrm{SO}(3)$ contains all 3D rotations.  For reasoning about physical objects, it is desirable to learn functions that are equivariant to $\mathrm{SO}(3)$.  However, to make the group convolution operation computationally tractable, discrete subgroups can be used to achieve approximate equivariance to continuous groups.  Moreover, discrete subgroups are compatible with common pointwise activations, and there is evidence they may be easier to optimize over \cite{e2cnn}.   Unlike $\mathrm{SO}(2)$, which contains arbitrarily fine subgroups $C_n$, for $\mathrm{SO}(3)$, the largest discrete subgroup which is not contained a planar rotation group is the icosahedral group $I_{60}$ which is composed of 60 group elements.  The group can be conceptualized as the orientation-preserving symmetries of a regular icosahedron (12 vertices, 20 faces) or its dual polyhedron, the regular dodecahedron (20 vertices, 12 faces).

In our work, we perform group convolution between signals that live on a quotient space of the icosahedral group, i.e. an $I_{60}$-homogeneous space.  Specifically, we use the quotient space $V_{12} = I_{60} / C_5$, which arises from the action of $I_{60}$ on the twelve vertices of the icosahedron.  The quotient space $V_{12}$
is a discrete approximation of
the 2-sphere, $\mathrm{S}^2 = \mathrm{SO}(3) / \mathrm{SO}(2)$.

\section{Method}

The Image2Icosahedral method (I2I) is designed to solve tasks which require reasoning about 3D geometry using only  2D images as input.  For example, consider the task of mapping a 2D image of an object to its orientation in $\mathrm{SO}(3)$.  Methods which take 3D representations such as point clouds as input can solve this task efficiently by exploiting $\mathrm{SO}(3)$ symmetry using equivariant neural networks. We would like to be accomplish something similar when taking 2D images as input also. Unfortunately, this is not possible using existing methods because this would require knowing a priori how the image would transform under 3D rotations of the object.
With the exception of $\mathrm{SO}(2)$ rotations in the plane of the camera, we do not know how to compute the new 2D image $\mathcal{T}_g x$ which would result from a rotation $g$.

Our method, I2I, has two parts: (1) a projector from the image $x$ onto a dynamic convolutional filter over the sphere $S^2$; and (2) convolution of this filter over $S^2$.
The projector maps the 2D image into a 3D representation which we know how to transform using $\mathrm{SO}(3)$.  This enables us to use $\mathrm{SO}(3)$-equivariant convolution over $S^2$ to then solve the task.
The projector uses an $\mathrm{SO}(2)$-equivariant ResNet to project onto $S^2$ (the pipeline going from $x$ to $y$ to $\psi$ on the left side of Figure~\ref{fig:method}). For the convolution, we apply equivariant group convolution over $S^2$ of the dynamic filter with a class-specific object representation over the sphere (the convolution between $\psi$ and $f$ on the right side of Figure~\ref{fig:method}).

\subsection{$\mathrm{SO}(2)$-Equivariant Projector onto a 2D Feature Map}

The projector part of our model maps an image $x$ onto a dynamic filter $\psi$ defined over $S^2$. Since $\mathrm{SO}(3)$ can transform signals on $S^2$, this enables $\mathrm{SO}(3)$-equivariance downstream. The projector has two parts.  First, a fully convolution model maps the input image $x$ to a 2D feature map $y$ using an $\mathrm{SO}(2)$ equivariant model. Then, we project that map onto $S^2$.
We encode the input image $x$ to a 2D feature map $y$ using a ResNet-style architecture~\cite{he2016deep}.
Since the input images do not have 3D rotational symmetry, we cannot impose $\mathrm{SO}(3)$-equivariance on this part of the model.  However, we can enforce symmetry with respect to $\mathrm{SO}(2)$ rotations in the image plane.  That is, rotating the object along the roll axis of the camera corresponds to $\mathrm{SO}(2)$ rotations of the pixels in the image plane. We encode this symmetry into the ResNet model by replacing the standard convolution layers with steerable convolutions defined with respect to the discrete cyclic subgroup $C_n$ of rotations by multiples of $2\pi/n$, implemented using the E2CNN framework~\cite{e2cnn}.

\subsection{Projection onto the Vertices of the Icosahedron}

The learned 2D feature map $y$ is then orthographically projected onto $S^2$ to give a feature map $\psi$ on $S^2$ which we will use as a dynamic filter.  In practice, it is necessary to discritize $S^2$ to encode features.
Recall that $V_{12} = I_{60} / C_5$ denotes the quotient space which is a discrete approximation of $S^2$ over the 12 vertices of the icosohedron. We use this set as a discritization of $S^2$ since it forms a regular mesh and is compatible with the action of the icosohedral group $I_{60}$.
Therefore, the dynamic filter is the map $\psi \colon V_{12} \to \mathbb{R}^{k}$.  In fact, we are able to capture more refined spatial resolution by subdividing the faces of the icosohedral \cite{cohen2019gauge} and sampling along this submesh.  We still index signals along this submesh using $V_{12}$ by grouping them along the channel dimension at each of vertices in $V_{12}$.

Similar to 2D convolutional filters, our dynamic filter is localized in that it is 0 except on the side of the sphere facing the camera.  The non-occluded vertices are projected onto the image plane of the feature map, such that the identity group element is located in the center.  Features for the vertices are generated by applying a Gaussian kernel centered at the vertices' projected pixel location to the feature map.  The projection is illustrated in Figure \ref{fig:proj}.  We find this simple projection operation is effective and useful since it preserves spatial information about the location of features in the image.

\begin{figure}[H]
\begin{center}
    \includegraphics[width=0.45\linewidth]{images/projection2.png}
    \caption{Projection of features from image space to quotient group of $I_{60}$. (a) dense feature map visualized as 3-channel image; (b) locations of vertices orthographically projected onto image space, local Gaussian kernels are used to sample from feature space; (c) resulting features on homogenous space $V_{12}$, note that only non-occluded vertices are assigned features during projection.}
    \label{fig:proj}
\end{center}
\end{figure}

\subsection{Dynamic Filter Icosohedral Convolution}
The dynamic filters $\psi$ generated by the projector may now be used to solve the given task using $\mathrm{SO}(3)$-equivariant group convolution.
In group convolution, trainable parameters can be used in either the signal or filter. Since the learned features are locally supported on $V_{12}$, we treat it as a dynamic filter and convolve it over a signal parametrized by trainable weights.  We refer to this learnable signal over $V_{12}$ as the feature sphere, since it contains features over the discretized 2-sphere.

After projection, the features are converted into a dynamic filter.  That is, the vectors at each vertex are reshaped into matrices so that the learned feature maps $\psi \colon V_{12} \to \mathbb{R}^k$ become $\psi \colon V_{12} \to \mathbb{R}^{m \times n}$ assuming $k=nm$.
The convolution operation between the filter and the feature sphere $f : V_{12} \rightarrow \mathbb{R}^{n} $ is defined:
\begin{align}
    [f \star \psi](g) = \sum_{v\in \mathcal{N}} f(\mathcal{T}^{-1}_g v)\cdot \psi(v) \label{eq:ico-conv}
\end{align}
where $\mathcal{N}$ is a subset of $V_{12}$ that defines the local neighborhood around the identity element where the filter is non-zero (i.e. all non-occluded vertices during projection).  This formulation can be seen to be equivalent to the equation in Eqn. \ref{eq:group-conv} by re-indexing the sum $w = \mathcal{T}_g^{-1} v$, but it is computationally faster since it avoids multiplying by elements $v$ outside the support of the filter.
The result of this operation is a vector space over the full icosahedral group, that is $[f \star \psi] : I_{60} \rightarrow \mathbb{R}^m$.  For the orientation prediction task, normalizing the output using softmax gives a probability distribution over orientations of the input object.

For certain applications (e.g. object classification), it is desirable to learn features that are invariant to the rotation of the object or the image view point.  An invariant representation can be achieved by adding a group pooling operation at the end of the model. In group pooling, we take a max (or average) along the dimension of the group, e.g. over all orientations in $I_{60}$. This is exactly analogous to spatial pooling in a 2D convolutional network.

\subsection{Reasoning Over Continuous Orientation}
\label{sect:continuous_pose}

In many applications, for example object pose estimation, we need to infer continuous object pose in $\mathrm{SO}(3)$. This may seem to be a problem for our method because we our output gives values over a discretization of $\mathrm{SO}(3)$, i.e. the icosahedral group. However, we handle this problem by also inferring rotational offsets as part of the feature vector associated with each group element.  This is analogous to the idea of anchor boxes, used in object detection, where the network selects from discrete anchor locations then predicts continuous refinements to them~\cite{ren2015faster}.

We therefore decompose the $\mathrm{SO}(3)$ regression problem into two parts: first classify the nearest group element of $I_{60}$ and then regress to the rotational offset from the group element. Therefore, we define the output of the icosahedral convolution over $\mathbb{R}^7$, i.e. each element of the icosahedral group is associated with a different vector in $\mathbb{R}^7$. One element of this vector is interpreted as an (unnormalized) probability $p(\hat{g})$ that the true rotation is closest to the associated group element $\hat{g} \in I_{60}$. The other six elements of the vector are used to construct an orthogonal rotation matrix $\hat{R}^{\Delta}_{\hat{g}}$ (i.e. the offset matrix) using the Gram-Schmidt orthonormalization process proposed in~\cite{falorsi2018explorations}.  The estimated orientation in $\mathrm{SO}(3)$ is thus $\hat{R}^\Delta_{\hat{g}} \cdot \hat{g}$.
We use the following loss function, which was introduced in~\cite{chen2021equivariant}:
\begin{align}
\label{eqn:loss}
    \mathcal{L}(g,R^\Delta_{\hat{g}}, p(\hat{g}),\hat{R}^\Delta_{\hat{g}}) =  \mathcal{L}_{cls}(\delta_g(\hat{g}),p(\hat{g}))
    + \lambda \mathcal{L}_2(R^\Delta_g, \hat{R}^\Delta_g )

\end{align}
where $g$ is the closest element in $I_{60}$ to the ground truth orientation with ground truth offset $R_g^\Delta$.
The first term in Equation~\ref{eqn:loss} is the cross entropy loss between the predicted distribution $p$ and the delta distribution at $g$. The second term is an L2 loss on the elementwise difference between the two offset rotation matrices. Note that even though an offset is predicted for each element in $I_{60}$, this loss depends only on the group element closest to the ground truth oreintation. While the rotational offset $\hat{R}^\Delta_g$ need not exceed $2\pi/5$ in magnitude (this is the orientation difference between neighboring elements on the icosahedron), we find that enforcing limit this is unnecessary and decreases accuracy.

\section{Experiments}
\label{sect:experiments}

\subsection{Overview}

\paragraph{Architecture:}

We use a ResNet-18 convolutional architecture \cite{he2016deep} for the spherical filter projector.
We reduce the downsampling of the network such that it outputs a dense feature map, rather than a single vector.  The convolutional layers are instantiated with $C_4$-symmetric kernels using the $e2cnn$ library~\cite{e2cnn}.  The input and output feature map have features with a trivial representation type whereas the hidden features use a regular representation. The dense feature map is projected onto the icosahedron vertices by applying Gaussian kernels with $\sigma=0.2$ to their respective locations in pixel space. We project onto 16 vertices of the icosahedron which is a discrete approximation of a hemisphere (out of 42 vertices total on the icosahedron). This filter is convolved with a dense feature sphere for which the weights of all 42 vertices are learnable. For the pose estimation task, our model is fully convolutional and outputs an $\mathbb{R}^7$ feature vector for each element of the icosahedral group (see Section~\ref{sect:continuous_pose}). For the shape classification task, we perform a group pooling operation and output a vector of class labels.

\paragraph{Training:}

We train our method using SGD with Nestorov momentum.  The initial learning rate is 1e-3 and decays with steps of 0.1 to 1e-5 during training.  We use a batch size of 64 which consumes around 5 GB of RAM for single-class prediction and 8 GB of RAM for multi-class prediction on ModelNet40. Our method is trained for 40 epochs for orientation prediction and 20 epochs for shape classification. A single epoch takes $\approx1.7$min for orientation prediction and $\approx50$ min for shape classification on NVIDIA 2080Ti graphics card.  We augment images during training with random translations of $\pm3$ pixels which reduces overfitting.

\paragraph{Datasets:}

We evaluate our method on the ModelNet40 dataset using the original split of 9,843 training objects and 2,468 test objects \cite{wu20153d}.  The objects were aligned manually per category by \cite{sedaghat2016orientation}, and we apply random $\mathrm{SO}(3)$ rotations before rendering.  We render 60, $128\times128$ images of each object in the train and test set.  Depth images are rendered with trimesh \cite{trimesh} and grayscale images are rendered with Pyrender \cite{pyrender}.  When collecting images for symmetric objects, we only sample object rotations that are orthogonal to the axis of symmetry.

\subsection{Inferring Object Orientation}
\label{sect:poseestimation}

\begin{wrapfigure}[15]{r}{0.45\textwidth}
    \vspace{-3em}
    \begin{center}
        \includegraphics[width=\linewidth]{figures/depth_AP.png}
    \end{center}
    \caption{Amount of rotation error in I2I pose predictions for different objects.}

    \label{fig:so3-error-hist}
\end{wrapfigure}

\paragraph{Experiment:}

Inferring object orientation in $\mathrm{SO}(3)$ for novel in-class objects from a single image is a challenging problem for which a variety of methods exist, including~\cite{thomas2019kpconv, chen2021equivariant, bukschat2020efficientpose}. These methods can be categorized based on the modality of the input data. Some methods take point cloud input~\cite{thomas2019kpconv, chen2021equivariant}. Others take monocular images, e.g.~\cite{bukschat2020efficientpose}. Table~\ref{tab:orientation-prediction} shows a comparison of these methods against our proposed method. In this experiment, we compare the ability of our method to correctly infer object orientation for novel objects from ten object categories in ModelNet40. In all cases, we assume the input data was produced by a single depth camera or a  grayscale intensity camera. There are three categories of comparison. The methods in the first and second categories take single (monocular) images, depth images, and grayscale images respectively, as input.
In each category, we compare our method (I2I) with the following three baselines. The first baseline (CNN) is a standard CNN backbone which predicts a rotation matrix using a linear layer after a ResNet encoder. The second baseline (CNN+IER) is the EfficientPose method of~\cite{bukschat2020efficientpose} which performs iterative error refinement by adding residuals to the initial rotation prediction over three additional linear layers. The third baseline (E2CNN-Eq) uses a $C_4$-equivariant ResNet architecture and then predicts rotation as a combination of a $SO(2)$-equivariant prediction of rotations in the camera plane and an $SO(3)$-equivariant prediction of the rotational component out of the camera plane (a technique first suggested by~\cite{falorsi2018explorations}). The methods in the third category take point clouds as input. These include KPConv~\cite{thomas2019kpconv} which uses a deformable convolutional operation, and EPN~\cite{chen2021equivariant} which encodes end-to-end equivariance to the icosahedral group. These methods are not directly comparable to I2I because they reason over point clouds rather than images, but we include them here for reference.

\paragraph{Results and Discussion:}

Table~\ref{tab:orientation-prediction} shows the results. Qualitatively, the results from the first two categories of comparison (depth and grayscale) are similar. Our method (I2I) outperforms on both modalities for all ten object categories. It is particularly noteworthy that our proposed method, I2I, outperforms E2CNN-Eq as significantly as it does. Since our method essentially combines a $C_4$-equivariant ResNet with the icosahedral convolution, E2CNN-Eq shows that our method would perform much worse without the icosahedral piece. Clearly, the icosahedral convolution is important, presumably because it enables us to reason explicitly in $\mathrm{SO}(3)$ while E2CNN-Eq is equivariant only over a subgroup of $\mathrm{SO}(2)$. It could also be that the weight sharing scheme of our icosahedral convolution helps our model generalize better to rotations not seen during training. In some cases, the degree to which we outperform is significant, e.g. cars and guitars, due to significant front/back symmetry in these objects which can confuse models which do not reason in $\mathrm{SO}(3)$.
It is also noteworthy that grayscale images are worse than depth images as a modality for pose estimation, presumably due to a lack of texture. We would expect all methods to do better with RGB input.

\paragraph{Error Analysis:}

In Fig.~\ref{fig:so3-error-hist}, we plot the distribution of errors by magnitude for our method trained on depth. The errors are tend to cluster at approximately 90 degrees and 180 degrees, reflecting symmetries in the object where it is natural for pose errors to occur. Notice that there are no discernible patterns of errors occurring at discrete pose intervals corresponding to the discretization encoded by the icosahedron (where group elements are separated by rotations of $2\pi/5$). This suggests that even though our method is based on a discretization of $\mathrm{SO}(3)$, our method of learning rotation offsets enables it to infer pose accurately over the continuous $\mathrm{SO}(3)$ group.

\begin{table}
    \centering
    \scriptsize
    \begin{tabular}{@{} ll *{10}{d{1.1}} @{}}
    \toprule
    input & method     &   \mc{desks} &   \mc{bottles} &   \mc{sofas} &   \mc{toilets} &   \mc{cars} &   \mc{chairs} &   \mc{stools} &   \mc{airplanes} &   \mc{guitars} &   \mc{benches} \\
    \midrule

    \multirow{4}*{Depth Img} & CNN           &  96.3  &     2.18 &  29.17 &    16.19 & 21.23 &   11.1  &   10.79 &       6.63 &    34.23 &   64.36 \\
    & CNN+IER \cite{bukschat2020efficientpose}      & 89.89  &     2.44 &  14.74 &    15.37 & 38.94 &   10.64 &   11.03 &       6.7  &    31.47 &   71.62 \\
    & E2CNN-Eq \cite{e2cnn} &  75.8  &     1.77 &   7.27 &     8.5  & 15.88 &    7.98 &    8.45 &       4.25 &    31.78 &   68.71 \\
    & I2I (ours)    &  10.63 &     0.54 &   2.12 &     3.27 &  1.85 &    3.60 &    3.92 &       1.37 &     2.18 &    5.80 \\ \hline
    \multirow{4}*{Grayscale Img}& CNN           &  92.44 &     5.77 &  44.72 &    27.33 & 50.39 &   22.11 &   19.43 &      10.64 &    76.05 &   84.38 \\
    & CNN+IER \cite{bukschat2020efficientpose} &  92.93 &     6.76 &  47.25 &    30.05 & 61.53 &   20.44 &   17.75 &      10.56 &    42.33 &   87.24 \\
    & E2CNN-Eq \cite{e2cnn}   &  87.46 &     4.17 &  23.76 &    23.64 & 68.54 &   16.34 &   16.76 &       6.62 &    36.54 &   94.0    \\
    & I2I (ours)           &   43.62 &     2.58 &   3.95 &     5.75 &  5.58 &    6.7  &    6.95 &       2.71 &     6.01 &   15.26 \\
    \hline
    Partial & KPConv \cite{thomas2019kpconv}       &   -    &      2.0 &   16.9 &   -      & 113.4 &    14.6 &       - &       1.54 & -        &  -      \\
    Point Cloud & EPN \cite{chen2021equivariant}          &   -    &     15.4 &   3.01 &      -   &  93.2 &    3.2  &      -  &       4.4 &       -  &     -   \\     \bottomrule
    \end{tabular}
    \vspace{1em}
    \caption{Median rotation error for single-class object orientation prediction.  Results are separated based on the input modality: depth images, grayscale images, and partial point clouds.

    Data for some objects are omitted for point cloud baselines due to the unreasonable amount of time it took to run these models on those objects.}
    \label{tab:orientation-prediction}
\end{table}

\subsection{Shape Classification}

\paragraph{Experiment:}

In shape classification, the task is to infer the category of an object given a point cloud or image of its appearance or shape. The task is challenging because the object is presented in an orientation selected uniformly at random. We compare our method with standard baselines in four categories (Table~\ref{tab:shape-classification}). In the first two categories, we classify shape based on a single depth or grayscale image, respectively. Here, we baseline against a standard ResNet backbone (CNN) and a $C_4$-equivariant ResNet model~\cite{e2cnn} (both models are configured with similar numbers of parameters as ours). We also compare against some multi-view methods, RotationNet-20~\cite{kanezaki2018rotationnet} and EMVN-12~\cite{esteves2019equivariant}. These methods are not directly comparable to ours because I2I takes only a single image as input. Finally, we baseline against PointNet++~\cite{qi2017pointnet++}, KPConv~\cite{thomas2019kpconv}, and EPN~\cite{chen2021equivariant}. These are unfair comparisons because these models take \emph{complete, i.e. unoccluded} point clouds as input.

\paragraph{Results:}

Table~\ref{tab:shape-classification} shows our results. The key observation to make is that our method (I2I) outperforms the two baslines (CNN and E2CNN-Inv) for classification from single depth images and single grayscale images (the first two categories in Table~\ref{tab:shape-classification}). The fact that I2I outperforms E2CNN-Inv is particularly significant because it suggests that our method can reason about rotation symmetry beyond the planar rotation symmetry present in the image. Surprisingly, we find that our method, when trained on depth images, can even outperform RotationNet-20 (which has access to multiple views of the same object) and PointNet++ (which has access to a complete point cloud) in terms of mean average precision. These results suggest that our model is able to leverage its ability to reason in $\mathrm{SO}(3)$ to improve its classification of objects presented in novel orientations.

\begin{table}
    \centering
    \begin{tabular}{llcc}
       \toprule
       Input        & Method              & Acc.  & mAP   \\ \midrule
       \multirow{3}*{Single Depth}
                    & CNN                 & 76.5 & 65.5  \\
                    & E2CNN-Inv \cite{e2cnn}  & 80.4 & 70.7  \\
                    & I2I (ours)          & 81.5 & 74.5   \\ \midrule

       \multirow{3}*{Single Gray}
                    & CNN                 & 70.0 & 57.8  \\
                    & E2CNN-Inv \cite{e2cnn}  & 75.1 & 64.3  \\
                    & I2I (ours)          &  76.4 &  67.8
                    \\ \midrule
        \multirow{2}*{Multi-View}   & RotationNet-20 \cite{kanezaki2018rotationnet}     & 80.0 & 74.2  \\
                   & EMVN-12 \cite{esteves2019equivariant}            & 88.5 & 79.6  \\ \midrule
       \multirow{3}*{Point Cloud}  & PointNet++ \cite{qi2017pointnet++}      & 85.0 & 70.3  \\
                    & KPConv \cite{thomas2019kpconv}             & 86.7 & 77.5  \\
                    & EPN \cite{chen2021equivariant}                & 88.3 & 79.7  \\ \midrule

    \end{tabular}
    \vspace{1em}
    \caption{ModelNet40 shape classification results.  The methods are separated by input modality: single depth and grayscale means the input is a single image rendered from randomly selected view point; multi-view take multiple grayscale images from structured view points; point cloud means full point cloud generated over entire mesh; . We report percent accuracy (Acc.) and mean average precision (mAP), including standard deviation over three random seeds for our method. }
    \label{tab:shape-classification}
\end{table}

\subsection{Comparison of Model Variations}

In Table~\ref{tab:proj-schemes}, we compare multiple variations on our model in the context of the object pose estimation task from Section~\ref{sect:poseestimation} for the ten ModelNet40 object categories described there. We test the following variations. For \emph{Sparse Filter}, only a subset of the weights in the icosahedral convolution are trainable. Here, the icosahedral filter uses only 6 vertices instead of the full 16 and the icosahedral feature sphere uses only 12 vertices rather than the full 42. For \emph{Vector Filter}, we reverse the roles of the dynamic filter and the feature sphere in the icosahedral convolution. Here, the $\mathrm{SO}(2)$-equivariant projector feeds into the feature sphere and the convolution filter has non-dynamic learnable weights (the dynamic filter is vector and feature sphere is matrix). For \emph{Ico Filter}, the ResNet outputs a single $1 \times 1$ feature map that is convolved with the feature sphere. Essentially, the $1 \times 1$ feature map is assigned to the identity element of the dynamic filter and thus the feature sphere is supported over the full group. We balance the number of parameters in all methods to achieve similar computational cost. Overall, we find that on average our method does slightly better on the orientation prediction task than the variations considered above. However, all four variations have roughly similar performance, suggesting that it is the use of the icosahedral convolution in the first place which is the key element of our method.

\begin{table}
    \centering
    \small
    \begin{tabular}{lrrrrrrrrrr}
    \toprule
     method           &  desk &  bottle &  sofa &  toilet &  car &  chair &  stool &  airplane &  guitar & bench \\ \midrule
     I2I (ours)       &  10.63 &     0.54 &   2.12 &     3.27 &  1.85 &    3.60 &    3.92 &       1.37 &     2.18 &    5.80 \\
     Sparse Filter &  12.15 &     0.51 &   2.04 &     3.39 &  1.91 &    3.7  &    3.73 &       1.41 &     2.34 &    5.62 \\
     Vector Filter        &  10.46 &     0.52 &   2.18 &     3.42 &  1.83 &    3.58 &    3.64 &       1.33 &     2.29 &    6.06 \\
     Ico Filter      &  11.13 &     0.53 &   2.08 &     3.02 &  2.09 &    3.32 &    3.52 &       1.36 &     2.57 &    7.57 \\

    \bottomrule
    \end{tabular}
    \vspace{0.2em}
    \caption{Median orientation accuracy for different image to icosahedral projection schemes.}
    \label{tab:proj-schemes}
    \vspace{-1em}
\end{table}

\section{Conclusion}
\label{sect:conclusion}

We present a novel method, Image2Icosahedral, for learning 3D representations of objects from single-view 2D images.  This 3D representation allows us to apply $\mathrm{SO}(3)$-equivariant techniques to tasks such as orientation inference and shape classification even for image inputs.  Our method outperforms baselines in these tasks using less memory and compute time.  Our model is limited by the task to relying on learned symmetry in the spherical filter projector as the group action is unknown in image space.
Since we give the ground truth orientation of objects as a point estimate and our model can only output a distribution over $I_{60}$ and not $\mathrm{SO}(3)$, we cannot perfectly describe ambiguities in orientation for objects with symmetry such as a bottle.  In future work, we plan to address this by explicitly returning a distribution over $\mathrm{SO}(3)$, effectively giving our model the ability to estimate orientation and implicitly discover symmetry in objects.  We will also integrate our method with bounding box methods such as Mask R-CNN~\cite{he2017mask} to create a single method capable of estimating the 6D pose of multiple objects in a cluttered scene.

\begin{comment}
\begin{ack}
Use unnumbered first level headings for the acknowledgments. All acknowledgments
go at the end of the paper before the list of references. Moreover, you are required to declare
funding (financial activities supporting the submitted work) and competing interests (related financial activities outside the submitted work).
More information about this disclosure can be found at: \url{https://neurips.cc/Conferences/2022/PaperInformation/FundingDisclosure}.

Do {\bf not} include this section in the anonymized submission, only in the final paper. You can use the \texttt{ack} environment provided in the style file to autmoatically hide this section in the anonymized submission.
\end{ack}
\end{comment}

\newpage
\bibliographystyle{unsrt}
{
\small

redpajama_set_name: RedPajamaArXiv


The FEA model under GetDP includes transverse isotropic properties of the stator lamination. The applied boundary conditions are free/free but some other conditions can be applied (e.g. free/campled, or campled/campled). The winding is here modelled as an additional mass in the slots.
This validation simulates the MANATEE default induction machine.
The first order elements used in FEA for a fast calculation are stiffer than the second order ones, which explains larger natural frequencies compared to the analytical model. As the lamination is generally stiffened by weldings, bars, or ribs along the circumference, the FEA modal analysis give closer results to experiments.


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Claims for unpaid crew wages unenforceable in Federal High Court
Shipping & Transport Nigeria
Maritime claims are generally under the Federal High Court's exclusive jurisdiction and enforceable by an admiralty action in rem or in personam.(1) Admiralty proceedings are sui generis and a unique set of rules and procedures apply.
Section 2 of the Admiralty Jurisdiction Act sets out a list of maritime claims and classifies them into:
proprietary claims – which concern the mortgage, ownership or possession of a vessel; and
general maritime claims – which concern salvage, general average, crew wages and loss or damage to cargo, among other things.
In a recent decision, the Federal High Court held that a claim for crew wages fell outside its jurisdiction.(2)
Through various employment agreements, the demise charterer of the MT Clover Pride (the defendant vessel) acquired the services of approximately 23 crew. The demise charterer had registered the vessel with the applicant (a protection and indemnity insurance club), but the cover was subsequently terminated due to the demise charterer's failure to pay the fixed premiums. In November 2017 the vessel's crew notified the applicant that the owners had abandoned the vessel and requested:
the applicant's intervention in the repatriation of the abandoned crew from Nigeria; and
payment of their outstanding wages (dating from August 2017 to November 2017).
The applicant undertook to pay the crew's accrued wages and their repatriation costs. In this way, it acquired the crew's rights to the extent of the payments made and expenses incurred in line with Regulation 2.5.2, Standard A2.5.2, Paragraph 12 of the Marine Labour Convention 2006. Having inherited the crew's rights, the applicant instituted an action in the Federal High Court, claiming $293,702.68 from the defendant for losses suffered due to the defendant's breaches of its various employment agreements with the crew. The vessel was subsequently arrested to secure the applicant's claim.
By an ex parte order dated 5 February 2018, Palm Spring Global Limited, the owner of the defendant vessel, was joined as an intervener in the suit. It immediately brought an application seeking, among other things:
the unconditional setting aside or discharge of the order of arrest; and
the striking out of the suit for want of jurisdiction.
Palm Spring Global Limited contended that as the claim concerned the wages of crew members employed on board the defendant vessel, the claim fell within the National Industrial Court's exclusive jurisdiction.
In arriving at its decision, the court relied on Sections 254C(a) and (k) of the Constitution 1999 (as amended), which provide as follows:
Notwithstanding the provisions of section 251, 257, 272 and anything contained in this constitution and in addition to such other jurisdiction as may be conferred upon it by an act of the National Assembly, the National Industrial Court shall have and exercise jurisdiction to the exclusion of any other court in civil causes and matters:-
a. Relating to or connected with any labour, employment, trade union, industrial relations and matters arising from workplace, the conditions of service, including health, safety, welfare of labour…
k. Relating to or connected with disputes arising from payment of salaries, wages, pensions, gratuities, allowances, benefits and any other entitlement of any employee.
The court held that these provisions gave the National Industrial Court exclusive jurisdiction over employee wages and other labour-related matters. It therefore held that the applicant's action, which was founded on claims for unpaid crew wages, fell outside the Federal High Court's jurisdictional competence.
The court made further reference to Section 2(3)(r) of the Admiralty Jurisdiction Act, which gives the Federal High Court jurisdiction over the following:
A claim by a master, or a member of the crew, of a ship for (i) wages, or (ii) an amount that a person, as employer, is under an obligation to pay to a person as employee, whether the obligation arose out of the contract of employment or by operation of law.
In this regard, the court held that this section – which differed from Section 254C of the Constitution, which conferred the same jurisdiction on the National Industrial Court – was void to the extent of its inconsistency. The court further found that, even though Section 251 of the Constitution provides for the admiralty jurisdiction of the Federal High Court, Section 254C's express use of the word "notwithstanding" clearly made the former section subject to the latter. Ultimately, the court held that it lacked jurisdiction to hear the case and thus transferred it to the National Industrial Court. Pursuant to this, the court made a further order discharging the ex parte order of arrest.
This decision portends significant implications for Nigeria's maritime jurisprudence. Claims for the wages of crew members and masters are traditional maritime liens which permanently attach to a vessel irrespective of a change in possession or ownership. Under the Federal High Court's admiralty jurisdiction, they are enforceable by an in rem action against the vessel itself, quite apart from its owners. However, on the authority of this recent court ruling, this may no longer be possible. The holder of a claim for crew wages can now enforce its claim only by bringing an action against the employer (who could be the owner or charterer of the vessel), which must now be specifically named in the writ and served accordingly. Having no direct right against the respondent, the claimant may be unable to obtain an ex parte order of arrest against the vessel to secure its claim. Rather, it will face the uphill task of enforcing a favourable judgment against an employer that, in many cases, will be resident in a foreign country with no fixtures or assets within the court's jurisdiction. Thus, the claimant will likely encounter grave procedural difficulties in not only initiating its claim, but also enforcing it in the event of a successful judgment.
For further information on this topic please contact Enare Erim at Akabogu & Associates by telephone (+234 1460 55550) or email (enare@akabogulaw.com). The Akabogu & Associates website can be accessed at www.akabogulaw.com.
(1) Section 251 of the Constitution 1999 (as amended); Sections 1 and 2 of the Admiralty Jurisdiction Act, Chapter A5, Laws of the Federation of Nigeria, 2004.
(2) Suit FHC/L/CS/1807/2017, Assuranceforeningen Skuld v MT "Clover Pride".
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I am testing a SOAP web service and the service sends username and password in every request as part of the XML body over SSL. I wanted to object to that and offer Basic Auth. but could not find any logical reason. It feels wrong but I dont know why. Is there any difference between sending credentials with every request to a web service in xml body vs sending them in HTTP header? Note: Using session keys or API keys is not an option for this service.
HTTP Basic Authentication transmits the password in plain text - that is, the text of the password is encoded with Base64 but not hashed or encrypted in any way.
As such, most attack vectors which would successfully retrieve the password from the body would also be able to retrieve it from the HTTP header. Both will be equally protected by TLS, which will encrypt both the headers and body of the request.
However, it is possible that incorrectly configured logging or proxying might leak one part of the request without the other. For instance, a system might log the request and response bodies for debugging purposes; however, it might also log the headers, so both would be equally vulnerable. Indeed, there might be code to mask sensitive data in the request body, which fails to do so when logging headers.
In short, I would suggest that both are equally secure / insecure, and only a more complex authentication scheme (e.g. hashing a nonce and timestamp with a shared secret) would improve matters.
Not the answer you're looking for? Browse other questions tagged authentication header xml or ask your own question.


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text:
Islanders Brooklyn Boro
Al Arbour, who coached Islanders to 4 Cup titles, dies at 82
August 28, 2015 Associated Press
In this May 17, 1983, file photo, New York Islanders coach Al Arbour celebrates in the locker room as he holds the Stanley Cup after the Islanders won their fourth cup in a row, beating the Edmonton Oilers 4-2 to sweep the series at Nassau Coliseum. Arbour, who ranks as the NHL's second-most winningest coach, has died, team officials announced Friday. He was 82. The cause of death is unclear, though Arbor was battling a lengthy illness and had been living in Florida. AP Photo/Pool, File
UNIONDALE, N.Y.— Al Arbour, who coached the New York Islanders to four consecutive Stanley Cup championships and ranks as the NHL's second-most winningest coach, has died, team officials announced Friday. He was 82.
The cause of death is unclear, though Arbor was battling a lengthy illness and had been living in Florida. Arbour transitioned from a successful 14-season NHL playing career as a defenseman to become one of the league's all-time best coaches.
Beginning in 1973-74, Arbour led the Isles to 15 playoff appearances and won an NHL record 119 playoff games over 19 seasons. His 740 career regular-season wins are the most with one NHL team. He retired after the 1993-94 season, before returning to coach his 1,500th game on Nov. 3, 2007.
Arbour was elected into the Hockey Hall of Fame in 1996.
LIRR announces enhanced service to Barclays Center for Islanders games
August 28 | Shlomo Sprung
Ready or not, here come the Islanders
August 28 | JT Torenli
New York City's most populous borough, Brooklyn, is home to nearly 2.6 million residents. If Brooklyn were an independent city it would be the fourth largest city in the United States. While Brooklyn has become the epitome of 'cool and hip' in recent years, for those that were born here, raised families here and improved communities over the years, Brooklyn has never been 'uncool'.
https://brooklyneagle.com/articles/2015/08/28/al-arbour-who-coached-islanders-to-4-cup-titles-dies-at-82/


text:
Q: Office 2007 deploy - gpo script on windows startup does not wait for its termination, instead winlogon appears immediately I have to deploy Office 2007 to a bunch of clients. I followed these instructions: http://technet.microsoft.com/en-us/library/cc179134%28v=office.12%29.aspx
The method is to create a script (a very simple bat file) and create a gpo that runs it at windows startup. Clients on which I install Office are all with 32 bit OS. The script is:
reg query HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows\CurrentVersion\Uninstall\{90120000-0030-0000-0000-0000000FF1CE}
if %errorlevel%==1 (goto DeployOffice) else (goto End)

:DeployOffice
start /WAIT \\biokolorsrv01\Software\SoftwareDistributionPoint\Office_2007_Ent\setup.exe /config \\biokolorsrv01\Software\SoftwareDistributionPoint\Office_2007_Ent\Enterprise.WW\config.xml

:End

I have created a GPO with startup script that points to the above mentioned bat on the computer settings section. In the user settings of the GPO there's another very little script that copies old signatures to the correct folder "Signatures" from folder "Firme Elettroniche" (I'm italian so Office 2003 used this name for signatures). This GPO is linked to the OU that contains all my clients and users. I've created a security group in Active Directory whose members are those Computers and Users to which I will install Office 2007 to. The GPO applies only to members of that group (security section of GPO).
Issue: the script does not run in foreground or better the wait switch is not considered. So the setup starts and comes up immediately the logon window as if I had omitted the /WAIT switch. Then the setup goes on for a while but then stops (I've also set the max script execution time to 0 in the gpo to allow the script to run until it fineshes). What's wrong with my deploy setup?? Is it perhaps better to make the scripts to run on user logon instead on windows startup?
Your help is very appreciated. Thank you.
Francesco B.

redpajama_set_name: RedPajamaStackExchange


At Grossmont College, educators are encouraged to promote student learning through innovative endeavors of all kinds.
This website introduces the user to resources available through the college, as well as to many that are widely relied upon through governmental and private, philanthropic organizations.
The Educational Development and Innovation Committee (EDIC) supports faculty with the development and recognition of innovative projects which strengthen the quality and effectiveness of academic programs and student support services.
The review of all grants submitted in the name of the college.
The annual awarding of mini-grants to faculty and staff.


text:
NBC's Andrea Mitchell: Melania's Fashion Choice on Egypt Trip 'Looked Like Death on the Nile'
"I know she said that she doesn't like the fact that people talk about her clothes, but it was like a costume drama," says the MSNBC anchor
Jon Levine | October 8, 2018 @ 10:58 AM
MSNBC anchor Andrea Mitchell took a moment Monday to poke fun at Melania Trump's clothes during her trip to Egypt last week, telling viewers Monday that the first lady looked more like she belonged on set of a period movie piece than on an official visit.
"I know she said that she doesn't like the fact that people talk about her clothes, but it was like a costume drama. It looked like 'Death on the Nile,'" said Mitchell, referencing the 1978 Agatha Christie film starring Mia Farrow and Peter Ustinov.
"And there was that pith helmet," Mitchell continued. "A little bit edgy in a continent and certainly in countries where people resent the colonial costume effect of the 19th century."
Also Read: Melania Trump to Give First Sit-Down Interview as First Lady on '20/20'
Mitchell's critique met with broad agreement from her panel — PBS White House correspondent Yamiche Alcindor and Reuters White House reporter Jeff Mason.
"She proceeded to basically do what some people considered a photo shoot in front of the Sphinx and in front of the pyramids," said Mason.
Alcindor compared the pith helmet to wearing a Confederate flag in the United States.
It's not the first time Trump's fashion choice has sparked media interest. In June, the first lady drew headlines for wearing an H&M jacket emblazoned with the slogan "I Really Don't Care. Do You?" while en route to visit a detention center housing migrant children.
"What did it mean?" asked NPR's Ted Robbins. "Zara did not respond to an email asking to explain its inspiration for the jacket." Her shoes, often strikingly dramatic high heels, have also made headlines on multiple occasions, like in this instance when she wore them to plan a tree.
Mike Cernovich, a Timeline: From Choking Advice to Pizzagate to Firings (Photos)
Maybe you'd never heard of right-wing provocateur Mike Cernovich before last week, when he helped get James Gunn fired from the next "Guardians of the Galaxy" film by highlighting Gunn's old Twitter jokes about rape and pedophilia. But Cernovich himself has a long history online, filled with both successes and statements he has since distanced himself from -- for reasons that will be obvious.
Advice on Choking Women
Before gaining fame as a Trump superfan, Cernovich got his start in the men's rights and pick-up artist community. His advice column, published on his blog "Danger and Play," included suggestions on the proper way to "choke" women during sex. This entry from December 2011 has since been deleted: "Choking works because it's a show of dominance. Women only want to have consensual sex with men they know could rape them."
Cernovich first came to wide public attention over his promotion of a conspiracy theory during the 2016 election that suggested that a pedophile ring was being run out of the basement of a Washington, D.C. pizzeria. The story was bunk, and Cernovich has moved to distance himself from Pizzagate.
Will Sommer/Twitter
John Conyers
Cernovich's biggest score of all probably wasn't James Gunn, but John Conyers. A tip provided by Cernovich to BuzzFeed about accusations of sexual misconduct forced the Democrat to resign his seat in disgrace in December 2017.
Mike Cernovich targeted the MSNBC contributor over a years-old joke about his daughter being raped. MSNBC dropped Seder in December 2017, but reinstated him after a public backlash.
Sopan Deb
Cernovich went after New York Times reporter Sopan Deb in March 2017 over a tweeted pun about the rapper Bow Wow. The incident earned Deb an official rebuke from the Times' then-public editor, Liz Spayd.
Josh Barro
In November 2017, Cernovich demanded an apology from Josh Barro after the Business Insider Senior Editor made fun of his lisp. Barro swiftly retreated.
Perry Fein
In June 2018, Los Angeles Times freelancer Perry Fein slipped into Cernovich's DMs and wished harm upon him. His relationship with the newspaper was over just hours later.
Los Angeles Times Media Group
Last week, Cernovich helped lead an online campaign highlighting old tweets in which Gunn joked about rape about pedophilia. Gunn offered an apology, but was dropped by Disney from the "Guardians of the Galaxy" franchise.
Cheri Jacobus
Before the James Gunn situation, Cernovich also led a brief and successful campaign to pressure USA Today to drop columnist Cheri Jacobus. The decision from the paper came after Jacobus' bizarre comments about convicted pedophile Jeffrey Epstein.
Cernovich has trained his fire on comedian Michael Ian Black, who has said several times on Twitter that his tweets about pedophilia were only jokes.
Fresh off his victory in ousting Gunn from "Guardians of the Galaxy," Cernovich forced "Rick and Morty" co-creator Dan Harmon and his employers at Adult Swim to apologize over an old sketch that featured Harmon graphically simulating the rape of a baby. (The sketch used a doll, not an actual baby.)
The right-wing provocateur has become a thorn to liberals online


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← 2019 Astros: The most indispensable player
Free Blog Weekend: Injuries and the Keuchel redux →
10 reasons the Astros can win it all again in 2019
There is always a mix of over-optimism mixed with down in the dumps pessimism in the springtime for baseball teams.
If your team won the last World Series, it is natural for some fans to feel that their team is bulletproof, while others think they can't get that lucky again. If they are one of the other 29 teams it is natural for some to think this is the year, while others sing that Hee Haw tune "Doom, Despair, and Agony on Me".
After the Astros spit the bit short of the World Series in 2018, there is the same mix of optimism and pessimism this Spring Training.
"This is almost the same team that won it all in 2017."
"This is a team that had to replace three starters in the rotation."
"This is a team that was just a couple bad breaks from going to the WS again in 2018."
"This is a team that has said goodbye to Marwin and McCann and Gattis and Keuchel (probably) and Morton and McCullers…"
You get the idea. Well today, the blog is going to take the high road and look at reasons why this team can win it again this year.
The Twin Towers. At the top of their rotation, the Astros have something that most teams would kill for- a two-headed Cy Young worthy monster named Justit Colelander. Gerrit Cole (15-5, 2.88 ERA, 12.6 K/ 9 IP) and Justin Verlander (16-9, 2.52 ERA, 12.4 K/9 IP) were just a scosh short of CY winner Blake Snell and that gives the Astros a tremendous base to their rotation.
The Best 5′-6″ Player in the World. He's not the best baseball player in the world while Mike Trout is breathing, but Jose Altuve is on a very short list behind the Angel superstar. He was a step down in 2018 (.316 BA/ .837 OPS/ 84 runs/ 61 RBIs) from his 2017 MVP year (.346 BA/ .957 OPS/ 112 runs/ 81 RBIs), but proved he was certainly the best one-legged player in the world.
The "Back" is Back. Carlos Correa has a lot to prove in 2019 after his back problems helped lead to a poor injury riddled 2018 (.239 BA/ .728 OPS/ 60 runs/ 65 RBIs). A return to 2017 (.315 BA/ .941 OPS/ 82 runs/ 84 RBIs) for Carlos would really help supercharge this offense.
Stare-way to Heaven. Alex Bregman was disappointed in the pre-arb contract he received from the Astros. Of course, he had no real leverage this time around. It is a good bet that as he heads towards arbitration next off-season he will put up numbers that will give him a lever that Archimedes would take pride in, especially if his great 2018 (105 runs, 51 doubles, 31 HRs, 103 RBIs) is any indication.
Burn On Big Brantley. Back in the old days (pre-2015?) the signing of an All Star OF with the credentials of Cleveland's Michael Brantley would have been cause for celebration. This pickup has almost been under the radar in an off-season of $300+ million contracts. Brantley is a consistent, professional hitter (.309 BA/ .364 OBP/ .832 OPS in 2018), who gives the team some left-handed balance and should thrive wherever he is placed in a lineup with the big four of the Houston Astros.
Late Inning Duos. The Astros headed towards the trade deadline last year needing to make some kind of move with their bullpen. Ken Giles had melted down, both emotionally and in his ability to perform and it seemed that the Astros and Giles needed a change of scenery. The Astros unloaded Giles and picked up closer Roberto Osuna from Toronto and separately picked up a super gem in reliever Ryan Pressly from Minnesota. After the trades, Osuna (12 saves, 1.99 ERA) and Pressly (2 saves, 0.77 ERA/ 0.600 WHIP) made the late innings a happy place for Astro fans.
Any Portsider in a Strom. Pitching coach Brent Strom continues his reputation as a bit of a horse whisperer to pitchers as he has helped most of his staff to reach heights they had never reached before, be it those with little success before coming here (Charlie Morton, Collin McHugh) or some of the best in the business (Verlander and Cole). After leading the best staff in the majors in 2018, Strom will face a big challenge with a revamped starting staff in 2019, but he is a reason that fans can be hopeful.
Time Change – Springer Forward. When somebody like George Springer is your 4th or 5th best hitter, you have a heck of a lineup. Like Correa and Altuve, injury helped lower his strong 2017 (.283 BA/ .889 OPS/ 34 HRs/ 85 RBIs) to an average 2018 (.265 BA/. 780 OPS/ 22 HRs/ 71 RBIs). Springer is the type player who can carry a team for a month at a time or for a World Series. Again, he is a big part of the Astros' lineup riches.
Life is a Hinch. A.J. Hinch enters his 5th season as the Astros' managerial leader with a 57.7% winning percentage (not counting interim managers) and sometime during 2019 he will add to his 374-274 record and vault into 2nd place in wins over Art Howe (392 wins in 5 seasons) and Larry Dierker (435 wins in 5 seasons). He will only be trailing Bill Virdon who put up 544 wins in 8 seasons. With any luck A.J. will pass Virdon in season six. Hinch is a good mix of calm and caring and has got the most out of his team since he joined them before the 2015 season.
Luhnow Has the Know How – Obviously winning the World Series gives any GM a lot of slack, but Luhnow has done his best to put together a team that can win today and tomorrow. Will they hit bumps along the way? Sure. But would you rather have Jeff Luhnow steering through next off-season and the free agency of four starting pitchers or Ed Wade?
There are other good reasons to trust in this team, whether it is the clutch hitting of Yuli Gurriel, the hustle and glove of Josh Reddick, the solid stick of Robinson Chirinos, the team-first attitude of Collin McHugh and Brad Peacock or the young arms of Josh James, Framber Valdez, Forrest Whitley, Corbin Martin and J.B. Bukauskas. This team is built to contend and they are built to win it all again in 2019.
70 comments on "10 reasons the Astros can win it all again in 2019"
I have a ton of confidence in McHugh, Miley and Peacock. That is a big part of my optimism for 2019.
I love the idea of Perez, Valdez, and James in the bullpen and that is a big part of my optimism for 2019.
My optimism about Brantley as our full-time LFer has been reinforced by what I have seen from him this Spring. He has looked great out there.
ESPN just put out their top 50. We've got 6 of them, led by Bregman at #6. So yeah, if the experts are anywhere close, we've got a shot.
Well with 30 teams the average team would have 1.66 players on the list (tough to carve those players into even thirds) so six is really impressive.
March 13, 2019 @ 10:12 pm
And if I recollect, Springer was our 6th, in the 37 position. So that's a bit more impressive.
hew haw and archimedes nearly back to back. i love this blog.
RJ –
We have both sophistication (Hee Haw) and good old country charm (Archimedes) on this blog. Greece being the good old country….
March 14, 2019 @ 4:34 am
I am confused, I thought the Archimedes reference was to the "sewer scene" in RV with Robin Williams. Did he make some other movies about a lever?
Roberto Osuna made the Top 100 list giving the Astros 7 players in the Top 100. Interestingly enough, Michael Brantley did not make the Top 100, even though he was #45 in WAR among position players last season and was an All-Star.
I'm going to make a bold prediction…this year Bregman puts up Mike Trout numbers and is in the running for MVP
And with 204 wins, another 97 wins makes them better than the 1927, 1928 & 1929 Yankees. (OK, so we play a couple more games, but don't harsh my buzz)
Along with going to a single trade deadline in 2019 – the roster expands to 26 in 2020 (max 13 pitchers) and 28 instead of 40 in September (14 max pitchers)
That seems like too big of a change in September – but the rosters have been so big the game is completely different in September
So cutting back on roster expansion in September, is that supposed to keep teams that are already out of it from playing an AAA squad? I suppose it is intended to speed up games too. You won't see 9 pitchers used. One thing it will do is keep the best teams from resting up the pitching staff for the post season. Have they done away with the 10 day DL yet? If not, we'll see it used more and more.
10-day is back to 15-day.
Dave, MLBTR actually seems to say the 10-day IL is still in effect for this year, changing to 15-day IL next season. That needs more clarification for me.
1oldpro, yeah, looks like the ten day is still in effect this year. One way or the other, clubs will figure out a way to keep innings counts in check. Manfred man is impacting more change in MLB then anyone else in my lifetime. If this stuff does not work out, the owners will out him ultimately.
I like the change to a 26-man active roster. 13 position players and 13 pitchers during the season will help keep pitchers fresher, help teams rest their position players with less fall off in the lineup and will add 30 more players to the major league rosters, thereby satisfying the player's desire to suck up more owner money.
I'm also happy we get one more year of seeing a lot of minor league players in September before that too changes in 2020.
These are changes that satisfy some feelings while not radically changing the flow or structure of the game.
https://www.msn.com/en-us/sports/mlb/mlb-to-implement-new-rule-changes-that-will-shake-up-look-of-sport/ar-BBULp6H?li=BBnb7Kz&ocid=mailsignout
The big one of course is the three batter minimum. The Tony Sipps of baseball are about to disappear. If a guy in the pen can't get both lefties and righties out, he does not have a job. At its core, I have no real problem with that. But the other thing is that it sure makes it easier for a manager to set up his pinch hitter(s) without the same risk of burning a guy off the bench if he can't hit a righty or a lefty.. That's the main reason I think this particular rule sucks. And do we have to try and create more offense too? Baseball constantly evolves. Let the players be responsible for that evolution rather than new rules.
So every half inning, 20 seconds will get shaved off the break. I have no problem with that. What's that, 6 minute a game Dan? So at the end of the day, or 9 inning game, Manfred might see a total savings of 10 minutes. Ten minutes is not going to bring more people into the park. 30 might.
But then 30 would ruin the game.
The easiest way to shave time off the games is to just contract and eliminate the two AL teams playing in Boston and NY.
Found this list interesting of the largest contract doled out by each team – especially how many have become albatross contracts and where the original team dumped the salary – if they could
https://www.mlbtraderumors.com/2019/03/largest-contract-each-mlb-team.html
I am stunned by the All-Star voting change. Having fans vote in an extra, 24-hour cycle, on the last day to determine who starts the game among the top three leaders at each position? Many players have clauses to their contracts that pay extra for starting an all-star game. Imagine, Altuve is leading by 400,000 votes going into the last day of voting. Next, the new 24-hour voting bloc begins. Fans for the New York Yankees can stuff the balloting and Altuve gets voted out by the ballot stuffing. We know about ballot stuffing as we have seen it before as, practically, a single, entire team, can almost get voted into the all-star game. This is NOT a good move.
Does the league winning the All Star exhibition game still get home field in the World Series?
No, that ended two seasons ago.
It looks like McHugh is not ready for Prime Time. A 16ERA ain't going to cut it. I was hoping otherwise. So that means another arm needs to be found.
Heck Zanuda, can we send him down? I think he thrown 6 innings now. Really though, don't nervelate. If he's healthy, that's all we really need to be concerned about. Lifetime in ST, he's got a 6.64. ERA and a 1.49 WHIP.
Z, i am with you on this. i think mchugh could still have a really good year, many times pitchers are working on new pitches and show worse than they are in spring training, but this is beginning to concern me. a contributor many posts back said mchugh and miley were the best #3 & #4 pitchers in baseball. i really really hope he was right, but i fear different results.
I just a have a high level of concern at this point in ST. Maybe he's still working on pitches but it's still and issue. Especially for a guy who's supposed to be our #3 or 4 starter. Don't get me wrong, I want him to succeed big time because he's always done what was asked of him. Just wonder if somethings not clicking.
He was shut down earlier for back issues. Perhaps they are slightly bothering him??
ST Pitching Stats That Probably Don't Mean Anything . . . But Make an Old Pitching Coach go – hmmmmmmmmn . . . .
1. Will Harris has an 11.57 ERA, a 3.43 WHIP, and has walked as many men [2] as he has struck out; his BAA is .545. Yes, you read that correctly.
2. Chris Devenski has a 10.80 ERA, a 2.40 WHIP, and has walked as many men [3] as he has struck out; his BAA is .333.
3. Framber Valdez has walked more men [5] than he has struck out [3]; his BAA is also .333.
4. Collin McHugh has a 15.88 ERA and a WHIP over 2.00. His BAA is over .400 after today's shellacking.
5. But, on the positive side, Reymin Guduan has a WHIP of 0.83 and has struck out 10, and given up 0 HRs, while only walking 1 – and his BAA is .190.
I guess I should have mentioned, Guduan also has 4 wild pitches now in 7 IP.
Without exception, Collin McHugh has been hit hard in Spring training every year of his career. Without exception, since he arrived in the bigs he has been hit hard in spring training.
Worry about McHugh in the regular season. Since he's pitched for Houston he has been a good pitcher in the regular season.
Hinch said yesterday that the Astros are grinding through spring training. It's one of the reasons why the Astros are playing regulars out of position, followed by A Ball players late in the games. As long as nobody gets hurt the Astros couldn't care less right now. The Astros team is virtually set and they cannot wait for the regular season to get here.
ST Hitting Stats that Probably Don't Mean Anything . . . But Could Make A Stat-Geek Slightly Agitated . . .
1. Max Stassi has a BA of .185, an OBP of .267, and OPS of .452, and has struck out 10 times in 30 plate appearances.
2. Among the 'Big 4′, only Alex Bregman has an OBP of over .333 – and Altuve is the lowest of the bunch at .308.
3. In a year when Tyler White came in almost a lock to be the Astros' primary RH Designated Hitter, he has twiddled his thumbs, swallowed his tasty breakfast pastries, and smiled politely as unheralded Nick Tanielu came in and made hitting a baseball look easy.
4. Who is that guy wearing Josh Reddick's uniform?
And, right on cue, somebody's nephew hits an absolute BOMB!
Brandon Bielak's stuff is good enough to where he can pitch poorly and get away with it. And he was poor today. Everything he threw was up. He got hit hard. He had better learn to pitch down in the zone.
Winkleman was out of his league.
It must be open season on pitchers we've never heard of today…Winkleman & Hiraldo. Hiraldo, wasn't there a movie about him starring Viggo Mortensen?
I'm pretty sure Winkleman & Hiraldo are a law firm looking to sign up victims of big rig accidents…..
So we have 6 runs driven in by guys we are counting on in the regular season (2 by White, 2 by Bregman, 1 by Correa, 1 by Chirinos) and 6 runs given up by the one guy we are counting on in the regular season (McHugh). For whatever it is worth if anything.
The ultimate solution to shortening games…hey if we can change the distance to the mound and the size of the bases (which changes the distance to first base), let's just make games 6 innings long. No real reason to play 9 innings in 3 hours if they can play 6 innings in 2 hours.
Where is that sarcasm font when you need it….
Your glasses are always full, Dan, and you see better with them than I do.
Hey, I could write a similar post about why they won't be champions, but I think it would be a much shorter post and that helps to give me hope.
Diane – over at mlbrumors.com they have had a fan chat with one of the blog "experts" answering questions and here is one I just read….
Cory 3:13
Who is your, way too early, pick to win the World Series?
Jeffrey Todd 3:14
This is actually a shockingly easy question this year, I think, just b/c of the weird alignment of competitive teams. You have to focus on which teams have the clearest paths into the postseason.
*not only into the postseason but to a division title
So … give me the Astros. You could argue the Indians are as likely or likelier to win their division but who knows. Think you have to go with an AL team first and not Yanks/Sox b/c one of them will be in a play-in game. So … my third choice would be Dodgers.
Okay, good. Your Brewskies showed well last year too, but I haven't followed their off season, so don't know if they strengthened, weakened, trod water.
Becky MacLaughlin Bearden
I have no doubt this team is as good, or even better than the 2017-2018 teams.
They replaced Marwin, and added a bonified left fielder. It appears everyone is healthy (Altuve?)….the two areas that are in question are 3-5 in the rotation, and daveb's, nephew. LOL!!! I BELIEVE⚾!!
I thought this an interesting read.
https://www.crawfishboxes.com/2019/3/14/18265701/the-astros-front-office-fearsome-reputation-and-some-prospect-notes-from-fangraphs
Let's face it, not many who write about baseball, at any level, can do it without opinion or editorializing. Otherwise, all we'd have is game recaps. Especially when talking about a minor league system, most everything is conjecture. Or a crap shoot. Certainly Kyle Tucker is still a crap shoot. So are the pitchers in our system. Most will fail to have an impact at the Major League level. CrPerry seems offended by the Fangraphs article. Understandable from a homer, but why get worked up about it?
It is an interesting article, though I don't go along with too much of the premise:
– It's getting harder to know what is going on inside the organization? With so many fine Astro people getting promotions elsewhere I would think that more is known about the inner workings outside the Astros than ever before.
– Paranoia and acrimony about the Astros is affecting their ability to make moves? Yes, the Astros have picked up some nice pieces in trades – even under the radar at times, but teams can sit there and think they might get the next Josh Hader out of us too. The Astros try to get a certain value out of each trade, so not all of their attempts are successful, but as they've shown the last few years they can get to the finish line on trades (Verlander, Cole, Osuna, Pressly).
– Knowing which rumors are true and which are BS – Luhnow has kept things close to his vest his whole tenure. A lot of his moves come out of nowhere with no rumors popping out in front of them. I would not be surprised that an organization that was hacked into early on even deals in leaking misinformation to throw the other orgs off. The only way to get an edge is to be knowing or doing stuff the others have no knowledge of.
– The thing about topical substances giving the pitchers a boost ….I guess it is possible they are cheating. But when you read a lot about what goes on with some of these pitchers and their improvements it sounds more to do with pitch mix than anything else, getting the pitchers to throw their better pitches more often and at the right times. Verlander has talked about changing his grip at times, but he talked about doing it in Detroit too.
Bottomline – when you have success, others will want to know why or will want to think it is through nefarious ways. Maybe the Astros are becoming the Patriots of baseball.
I think Fangraphs pushed all of Chris Perry's buttons concerning the Astros.
But, If you read the Fangraphs rankings of the Astros and their comments in their "system overview" it's clear that these two guys have a big problem with Houston.
To make sure I wasn't seeing red, because of my obvious bias to blue and orange, I went on to view Fangraph's rankings of the Yankees and Rays systems and the overview of them.
There is very little of the emotional ranting of the prospect's skills and tendencies that you read in the rankings of the Astros prospects(note: the hedonistic view of defense by Freudis Nova or the fact that scouts don't like what they see from 18 year old Jayson Schroeder in instructional leagues. He's just a high school pitcher still, people.) Add that to the fact that Fangraphs actually ranked the Rays top 54 prospects and then commented on another 25 of their prospects, while ranking only 39 of the Astros prospects and then had comments on just a few others and you can plainly see that Fangraphs really was using the Astros rankings as a chance to rant about their views on how the Astros do things. Their comments in Astros overview was an actual article in itself and the overview of the Rays was a two paragraph epilog.
In my opinion, the success of the Astros at the major league level, their #6 ranking of their farm system, the strikeout rate of their pitchers at every level of professional baseball, their being picked by many to win the WS and the fact that major league fans even picked their uniforms as the best in baseball might really have some baseball people rattled.
Who is at the forefront of analytics?
Who had the huge increase in attendance when it was down all over the league?
Who is the out front leader in shifts, that baseball is now scared to death of?
Who led the majors in fielding percentage while not having one starting position player with positive defensive metrics on their team other than their backup catcher?
Who won 103 games with four cripples in their lineup and a starting pitcher with no elbow ligaments?
What team talks about nothing, after another team releases all their secrets to the public and then only gets a slap on the wrist?
My team.
With apologies to Nathan Hale "I regret I have but one like to give to my OP."
Yes indeed, Perry got his buttons pushed! But really, even the guys from well respected FanGraphs are more baseball fans than bona fied sportswriters. Real sportswriters are an endangered species these days. I like it when our organization gets trash talked. I'm guessing the Bregmans and Verlanders like it too.
When it comes to prospect there is another angle to consider. Recall back in 2015 when we started winning games and folks (like me) started saying the easy draft picks were over and Luhnow would have to earn it? Well, there are a number of "writers" from these other sites who get worked up over their favorite prospects and then very defensive when they don't pan out. Seeing the Astros picking low in the draft, not having international signings that set the world on fire, etc. has likely angered these experts who didn't predict stardom for many of the players getting it done on the field in our system.
I find it interesting that the Brandon Bielak performance that I critiqued after watching every pitch he threw yesterday, was considered a Top Prospect Performance by MLB.com this morning.
All I want to know is if they watched the video of him or just went off of the box score.
I'm sure they only have time to scan the box scores and pick out a few performances that "look" good, old pro
OP's post re cripples in our lineup makes me think to rechristen Astros the Crips and all their opponents the Bloods.
Astros ST Themes This Year:
1. Meh!
2. Doh!
3. Winning is overrated.
4. Watch us grind.
5. Don't forget – the stats don't count.
6. Just don't strain anything.
7. "It's Eeyore!" cried Roo, terribly excited.
"Is that so?" said Eeyore, getting caught up by a little eddy, and turning slowly round three times. "I wondered."
"I didn't know you were playing," said Roo.
"I'm not," said Eeyore.
"Eeyore, what are you doing there?" said Rabbit.
I just came up with a list of what to pair with LSD trips:
– Beatles "A Day in the Life"
– Almost any David Lynch movie, but especially Mulholland Drive
– Pink Floyd – Pick an album
– Reruns of Pee Wee's Playhouse
– Alice in Wonderland
– Today's post from Mr. Bill?
I'll take "Dark Side of the Moon" for 100, Dan.
How about reading Mr Bill's post while listening the Disc 3 of the Beach Boys's Smile Sessions?
Good one Billy C – just thinking about that album that wasn't and then finally was
Dan , Brian Wilson is my musical hero . Haven't met him but I do run a Beach Boys message forum and also know several people affiliated with Brian on a personal level. On that note, Mike Love really is a jerk. And all the stories and feuding, and all the gossip?Man, if people only knew…
But that's a story for another time…
Are we going to Eeyore's Birthday Party this year?
Whoa Dudes!
Perhaps this is a more well rounded group than I've thought all these years.
Angel Hernandez throws AJ Hinch out of an exhibition game after 1 pitch.
They should throw Angel Hernandez out of the league and have Joe West carry him off piggy back.
Him being named Angel is like Snoop Dogg being the head of the DEA
Who throws somebody out of an exhibition game?In the movie Forget Paris doesn't Billy Crystal throw Kareem Abdul Jabbar out of the game he's getting honored?
Something tells me that the whole thing started before the game began.
Maybe Hinch said "Do you plan on having a bad day again?"