text:
\section{Introduction}\label{sec:introduction}

Cloud-based face recognition has reached maturity in recent years. Deep
neural network (DNN) models consisting of millions of model parameters
have been developed and made significant progress. The high performance
mainly relies on several factors: higher input image resolutions, using
an extremely large number of training images, and abundant
computational/memory resources. Take the
models which have achieved high accuracy on the LFW benchmark as an example. DeepFace~\cite{taigman2014deepface}
was trained on a collection of photos form Facebook that contains 4.4M
images. FaceNet~\cite{schroff2015facenet} was trained on the Google
dataset that contains 500M images. SphereFace~\cite{liu2017sphereface}
was trained on the CASIA-WebFace dataset~\cite{yi2014learning} that
contains 0.49M images. Along this direction, many activities are
centered on face images collection and the setup of the required computing
and communication environment.

We may face an opposite situation in some real-world applications; i.e.,
edge or mobile computing in resource-constrained environments with poor
computing and communication infrastructure, as is often the case in the
field and in operational settings. Such environments demand smaller
model size, fewer labeled images for training, lower training and
inference complexity, and lower input image resolution, partly due to
the need to image individuals at farther standoff distances. Due to
these stringent requirements, DNNs may not be applicable. The goal of
this work is to address these challenges and develop a robust and
transparent non-parametric model that allows graceful performance
tradeoff between resources and performance and is capable of being
easily integrated with active learning to minimize the training sample
number while achieving relatively high accuracy. We adopt an emerging
machine learning system called PixelHop++~\cite{chen2020pixelhop++} to
achieve these objectives.  PixelHop++ is designed based on the
successive subspace learning (SSL) principle and has several unique
characteristics that fit our objectives well.
\vspace{-1ex}
\begin{enumerate}
\setlength\itemsep{0em}
\item PixelHop++ is a light-weight non-parametric model whose size can
be flexibly adjusted for graceful performance tradeoff. Its training is
conducted in a feedforward one-pass manner and the training complexity
is significantly lower than DNNs.
\item SSL adopts a statistics-centric principle. It exploits
pixel-to-pixel correlations for dimension reduction to derive image
features. It also analyzes statistics between features and labels to
identify discriminant features. SSL is mathematically transparent.
\item We will incorporate active learning in SSL to select the most
``informative'' samples of a dataset for labeling and reduce the
labeling cost. PixelHop++ is a lightweight model, and it can easily
be integrated with active learning.
\end{enumerate}
The main contribution of our work lie in the assembly of two effective
tools to address the challenge of face recognition in
resource-constrained environments. Both PixelHop++ and active learning
are existing tools. Yet, to the best of our knowledge, this is the first
time that they are jointly applied to a face biometric problem. We will
demonstrate the power of the integrated solution in the context of face
recognition with extensive experiments.

The rest of the paper is organized as follows. Related prior work is
reviewed in Sec. \ref{sec:review}. The proposed face recognition
method and its integration with active learning are presented in Sec.
\ref{sec:framework} and Sec. \ref{sec:active}, respectively.
Experimental results are provided in Sec. \ref{sec:experiments}.
Finally, concluding remarks and future work are given in Sec.
\ref{sec:conclusion}.

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

{\bf Face Recognition.} Face recognition has made significant progress
in recent years. Most successful face recognition models use deep
learning technique \cite{taigman2014deepface, sun2014deep,
schroff2015facenet, parkhi2015deep,liu2017sphereface} and offer high
accuracy on the benchmarking datasets. Although these models are
powerful for high-resolution face recognition, they usually contain tens
of millions of model parameters and require a large amount of training data and
computation resources. A light-weight CNN model called Light CNN was
proposed in \cite{Wu_2018} which is significantly smaller than regular
CNN networks but still has millions of model parameters.

{\bf Low-Resolution Face Recognition.} In comparison with
high-resolution face recognition, less attention has been drawn to
low-resolution face recognition. Generally, there are two different
settings for this problem: high-resolution to low-resolution, and
low-resolution to low-resolution. In the first setting, the low-resolution probe images are
compared against high-resolution gallery images \cite{biswas2011multidimensional,mudunuri2015low,lu2018deep} while in the second
setting both
probe and gallery images are low-resolution face images \cite{wang2016studying,cheng2018low, ge2018low}. We evaluate
our model under the second setting and compare it with the model
proposed in \cite{ge2018low}.

{\bf Successive Subspace Learning (SSL).} The main technique in subspace
learning is to project a high-dimension input space to a low-dimensional
output subspace, which serves as an approximation to the original one.
When these dimension reduction operations are performed sequentially, it
leads to successive subspace learning (SSL).  PixelHop++
\cite{chen2020pixelhop++} is a model developed using the SSL principle.
It is proposed for unsupervised representation learning by applying the
channel-wise (c/w) Saab transform to images.  The Saab transform
\cite{kuo2019interpretable} is a multi-stage variant of principal
component analysis (PCA) conducted on images. In each stage, it applies
PCA to pixel blocks and also uses a bias term to avoid the sign-confusion
problem \cite{kuo2016understanding}.  The performance of the Saab
transform can be further enhanced by removing the spatial correlation
between Saab transform outputs in the current stage so that the Saab
transform can be applied to each output channel separately in
the next stage.  PixelHop++ with the c/w Saab transform is proven to
offer an effective multi-stage representation. For example, FaceHop
\cite{rouhsedaghat2020facehop} is recently proposed for gender
classification on gray-scale face images. It uses PixelHop++ for feature
learning. In this paper, besides incorporating chrominance channels of face
images, we add a new module for effective pair-wise feature extraction
to tackle the face recognition problem.

{\bf Active Learning.} Active learning is used to select the most
informative unlabeled data for labeling so that almost the same accuracy
can be reached with the smallest amount of labeled data. An active
learning method begins with a small amount of labeled data to train a
machine learning model. The model queries the labels of some unlabeled
data based on a query strategy, and the model is retrained by all
labeled data. This process is repeated until we reach the labeled sample
budget. Common query strategies include the entropy
method~\cite{shannon1948mathematical}, the query by committee
method~\cite{seung1992query} and the core-set
method~\cite{sener2017active}.  Active learning was incorporated in
DNNs by Wang {\em et al.}~\cite{Wang_2017}.  An active annotation and
learning framework is introduced in ~\cite{Ye2016} for the face
recognition task.

\begin{figure}[t
\begin{center}
\includegraphics[width=0.75\linewidth]{fig1_4.pdf}
\end{center}
\caption{The block diagram of the proposed face recognition method.}
\label{fig:blockdiagram}
\end{figure}

\section{Proposed Face Recognition Method}\label{sec:framework}

The block diagram of the proposed face recognition method is shown in
Fig. \ref{fig:blockdiagram}. As shown in the figure, our model is an
ensemble of two submodels, each of which consists of three components, i.e., PixelHop++, pairwise feature generation, and classifier. We will elaborate the function of each component of the block diagram below.

\subsection{Preprocessing}\label{subsec:preprocessing}

Several commonly used face processing techniques are adopted in the
preprocessing block such as
\vspace{-1ex}
\begin{enumerate}
\setlength\itemsep{0em}
\item Applying a 2D face alignment algorithm to the input face images to
reduce the effect of pose variation;
\item Cropping face images properly to eliminate background;
\item Using histogram equalization (HE) to reduce the effect of
different illumination conditions on the Y channel.
\end{enumerate}
We use the dlib~\cite{dlib09} toolkit for face detection and landmark
localization. Face images are aligned/normalized so that the line
connecting the eye centers is horizontal and all faces are centered and
resized into a constant size of 32×32 pixels. We convert the color space to YCrCb
and, then, feed the Y channel (the luminance component) separately to
one submodel and the Cr and Cb channels (chrominance components) to
another submodel.

\subsection{PixelHop++}\label{subsec:pixelhop++}

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.7\linewidth]{fig2_3.pdf}\\
\end{center}
\caption{Illustration of data flow in the three-level c/w Saab transform
in PixelHop++, which provides a sequence of successive subspace
approximations (SSAs) to the input image.} \label{fig:pixelhop}
\end{figure}

In each submodel, we use a three-level PixelHop++ system, similar to the system shown
in Fig. \ref{fig:pixelhop}. The input to each of the
PixelHop++ systems is a whole face image of resolution 32×32×$K_0$,
where $K_0=1$ and $2$ for the Y channel and CrCb channels, respectively.
Each level of a PixelHop++ system has one PixelHop++ unit followed by (2×2)-to-(1×1) max-pooling. In our model, the PixelHop++ unit of each level operates on blocks of 5×5 pixels with a stride of one.  In the training phase of each PixelHop++ unit, we collect sample blocks from each input channel to derive
Saab kernels for that channel separately. Then, we project each
block on the derived kernels and generate a set of responses for the
central pixel in the block. Since the responses can be positive or
negative, we add a constant bias to all responses to ensure that they
are all non-negative, which explains the name of the ``successive
approximation with adjusted bias (Saab) transform"
\cite{kuo2019interpretable}.

The first Saab kernel is the unit-length constant-element vector that
computes the local mean of each block which we call the DC component. After
removing the DC, we conduct PCA on residual blocks. Since each block has
25 dimensions, we get one DC component plus 24 AC components, whose
kernels are eigenvectors of the covariance matrix of the collected
blocks, for each channel
In each level, the corresponding Saab components
of the blocks of each channel form an output channel (or a node in a
tree as shown in Fig. \ref{fig:pixelhop}), e.g. the DC component of blocks extracted from an input channel from one output channel/node.  We divide these nodes into
three groups:
\vspace{-1ex}
\begin{itemize}
\setlength\itemsep{0em}
\item Intermediate nodes: The DC and several leading low-frequency AC
channels will be forwarded to the next level for further energy
compaction.
\item Leaf nodes: The nodes which are kept at the current level.
\item Discarded nodes: AC components with very small eigenvalues
are discarded.
\end{itemize}
As we mentioned, in each PixelHop++ unit we apply channel-wise (c/w) transform to pixel blocks; in other words, for pixel blocks extracted from each individual input channel, an individual Saab transform is applied. The reason we can process channels individually is that all AC channels are orthogonal to the DC channel and all AC responses
are uncorrelated due to PCA. Note that the first PixelHop++ unit of $M_{CrCb}$ is an exception (Cr and Cb channels are not uncorrelated), so we apply Saab transform on blocks of 5×5×2 pixels at this level and obtain 1 DC component and 49 AC components for each pixel block. We should
emphasize that {\em channel separability} is powerful in reducing our
model size and computational complexity. Unlike DNNs, PixelHop++ does
not transform one large 3D (i.e., 2D spatial plus 1D spectral) tensor
but multiple 2D spatial tensors.

Each level of PixelHop++ provides an approximation to the input with
different spatial/frequency tradeoffs. The input is a pure spatial
representation.  The output of Level-3 is a pure frequency
representation. The outputs of Level-1 and Level-2 are hybrid
spatial/frequency representations. A square in Fig.  \ref{fig:pixelhop}
indicates a channel which is the union of all corresponding spatial
locations.  Before max-pooling, the dimensions of the output channels of
Level-1 and Level-2 are 28×28 and 10×10, respectively. Clearly, Level-1
has more spatial detail than Level-2.  The spatial dimension of Level-3
is 1×1. Each intermediate/leaf node at a level indicates a frequency
channel at the corresponding level.

For channels at each level, we need two hyper-parameters to partition
them into the mentioned three groups.  We use the energy level of a
channel as the criterion. If its energy is less than a cutoff energy
level denoted by $E_C$, the channel is discarded. If its energy is
higher than a forwarded energy level denoted by $E_F$, the channel is
forwarded to the next level.  We normalize the energy of the root node
(i.e., the input image) to 100\%.  The energy level of each
intermediate/leaf node is computed as follows.
\vspace{-1ex}
\begin{itemize}
\setlength\itemsep{0em}
\item Step 1: Initial DC and AC energy computation for each PixelHop++ unit. \\
Each eigenvalue of the covariance
matrix indicates the energy of its corresponding node. We define the initial energy ($E_{init}$) of each output node as the ratio of its corresponding eigenvalue to the sum of all others eigenvalues related to that PixelHop++ unit. At this step, the sum of the DC and total AC energy values for each PixelHop++ unit is 100\%.
\item Step 2: Normalized energy at each node. \\
Based on the first step, we have the energy of an
intermediate/leaf node against its siblings. By traversing the tree from
the root node to a leaf node, the path includes intermediate nodes. The
normalized energy of a leaf node against the root node is the product of
$E_{init}$ values of all visited
nodes (including itself).
\end{itemize}
Note that by lowering $E_C$ and $E_F$, we can obtain a better
approximation at the cost of higher model complexity.

\subsection{Pairwise Feature Generation}\label{subsec:feature}

To compare whether two face images are similar or not, we can examine
their similarities at different spatial regions, channels, and
levels. This is feasible because of the rich representations offered by
PixelHop++.
Note that although the content of an intermediate node will be mainly forwarded to the next level, it does have different spatial/spectral representations at
two adjacent levels. Thus, for
feature extraction, we do not differentiate intermediate/leaf nodes at
each level.

There are $K_1$, $K_2$ and $K_3$ nodes at Level-1, Level-2
and Level-3, respectively, as shown in Fig. \ref{fig:pixelhop}. We can
process them individually as detailed below.
\vspace{-1ex}
\begin{itemize}
\setlength\itemsep{0em}
\item {\bf Level-1.} It has the highest spatial resolution 28×28. We
can use it to zoom in on salient regions of the face such as the left eye, the right eye,
the nose, and the mouth of certain dimensions at each channel (see Fig. \ref{fig:ROI}(a)) and flatten them to form vectors. Accordingly, we extract 4 feature vectors from each node at this level (4×$K_1$ feature vectors).
\item {\bf Level-2.} It has a lower spatial resolution (10×10). We can
still zoom in on unions of salient regions such as one horizontal stripe covering
two eyes and one vertical stripe covering the nose and the mouth at each
channel (see Fig. \ref{fig:ROI}(b)) and form 2 vectors per node accordingly (2×$K_2$ feature vectors).
\item {\bf Level-3.} It has no spatial resolution. Each leaf node offers
a scalar description of the whole face.
In our implementation, we concatenate all $K_3$ nodes into a long
sequence and then group every 10 nodes as one vector. Consequently, we obtain $P$
feature vectors, where $P$ is the floor of $K_3$ divided by 10.
\end{itemize}

\begin{figure}[t
\begin{center}
\includegraphics[width=0.45\linewidth]{fig3.pdf}
\end{center}
\caption{Illustration of selected spatial regions of interest (ROIs)
with respect to the input image for frequency channels at (a) Level-1
and (b) Level-2.}\label{fig:ROI}
\end{figure}

To compare the similarity between two face images, we collect corresponding
vector pairs from the same spatial region of the same node
(including intermediate and leaf nodes) at each level and compute two
similarity measures for them - the cosine similarity ($C_k$) and the length ratio
($R_k$) for the $k$-th vector pair. We define the length ratio of two vectors as the ratio of the vector with smaller L2 norm to the vector with larger L2 norm. If two images are similar, their cosine
similarity and length ratio should be close to unity. Otherwise, they
should be farther away from (and less than) one. We observe
experimentally that an individual $R_k$ value is not as discriminant as
$C_k$. Instead, the average length ratio for each spatial region in
Level-1 and -2 as shown in Fig.  \ref{fig:ROI} and the average length ratio for $P$ pairs in Level-3
is more robust and discriminant.

To summarize, the ultimate feature vector to be fed to the binary
classifier is the concatenation of: 1) 7 average length ratio values (4 extracted from Level-1, 2 from Level-2, and 1 from Level-3),
2) 4×$K_1$ cosine similarity values from four spatial regions and $K_1$
channels, 3) 2×$K_2$ cosine similarity values from two spatial regions
and $K_2$ channels, and 4) $P$ cosine similarity values from groups of channels in Level-3.
The value of the feature dimension (N) for each submodel
is given in Table. \ref{table:acc1}.

\subsection{Classifiers}\label{subsec:classifier}

As described in Sec. \ref{subsec:feature}, we extract the feature vector
from the Y channel and the CrCb channels of each pair separately.  For
each pairwise feature, we train a classifier which makes a soft
decision, i.e., the probability for the pair to be match or mismatch.
Then, we feed these two probabilities into a meta classifier for the
final decision.  We use the
LR classifier in our experiments to achieve a smaller model size
although using larger binary classifiers we may achieve higher accuracy.

\section{Integration with Active Learning}\label{sec:active}

The feature generation process in our model is unsupervised, and labels
are only needed for classifier training. As a motivation for active
learning, a scenario of interest is training a model on a mobile agent
when the model has access to unlabeled training samples locally but has
to fetch the label of a limited number of samples from the server
through unreliable low-bandwidth channels. To overcome the communication
constraint, active learning can be used to retrieve labels of most
informative samples. We consider
three active learning methods as explained below.
\begin{enumerate}
\item \textit{Entropy method}: In each iteration, compute the entropy of each
sample in the pool of unlabeled data $D^u$ and pick the samples with
higher entropy to add to the labeled training data. Higher entropy means
the model is more uncertain about those samples. The entropy for sample
X can be computed using
\begin{equation}
entropy(x) = -\sum_{j=1}^{J} p({y_i\mid x})\log p({y_i\mid x}),
\end{equation}
where p($y_i\mid x$) is the probability that sample $x$ belongs to the class
label $y_i$.
\item \textit{Query By Committee (QBC)}: Instead of training one model,
this method trains several models called a committee. In each iteration,
measure the disagreement between the committee members for each sample
in the pool of unlabeled data and pick the ones with the largest
disagreement values. One of the common disagreement measures is Vote
Entropy which can be computed using
\begin{equation}
VE(x) = -\sum_{j=1}^{J} \frac{V({y_i})}{C}\log \frac{V({y_i})}{C},
\end{equation}
where $C$ is the number of models, $V(y_i)$ is the number of votes which
label $y_i$ receives from committee members.
\item \textit{Core-set method}: The core-set selection problem is
choosing $b$ sample points from the pool of unlabeled data that minimize
the maximum distance between each data point remaining in the pool and its
nearest data point in the selected subset. This problem is NP-Hard. A
greedy approach for core-set selection is the k-Center-Greedy algorithm.
In the $i^{th}$ iteration, it selects the samples from $D^u$ with the
maximum distance from their closest sample in the labeled training data
$D^l_{i-1}$.
\end{enumerate}

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

We evaluate the performance of the proposed method by
conducting experiments on two well-known datasets: Labeled Faces in
the Wild (LFW) \cite{huang2008labeled} and CMU Multi-PIE \cite{gross2010multi}. In all the experiments, we use
low-resolution face images of size 32×32 unless otherwise specified.

The \textbf{\textit{LFW dataset}} is a widely used dataset for face
verification. It consists of 13,233 face images of 5,749 individuals.
For performance benchmarking of different face verification models, it
provides 6,000 face pairs in 10 splits.  We follow the
``Image-Restricted, Label-Free Outside Data" protocol. We choose this protocol as
we use a tool for facial landmark localization in the preprocessing
step. But for training the model, we only use face images in the LFW training set.  A 3D
aligned version of LFW~\cite{ferrari2016effective} is used in
experiments. For data augmentation, we add the pair of horizontally
filliped images of each training pair to the training data.

The \textbf{\textit{CMU Multi-PIE dataset}} contains more than 750,000
images of 337 people recorded in four sessions. For each identity,
images are captured under 15 viewpoints and 19 illumination conditions
with a few different facial expressions. In experiments, we select a
subset of 01 session which contains frontal and slightly non-frontal
face images (camera views 05\_0, 05\_1, and 14\_0) with a neutral
expression and under all existing illumination conditions.

\subsection{Face Verification on LFW}
\begin{table}[t]
\caption{Comparison of test accuracy of $C_Y$ and $C_CrCb$ and
hyper-parameter settings of $M_Y$ and $M_CrCb$, where $K_1$, $K_2$, and
$K_3$ are numbers of intermediate and leaf nodes at Level-1, Level-2,
and Level-3, $P$ is the number of vectors at Level-3 and
$N=7+4K_1+2K_2+P$ is the feature dimension.} \label{table:acc1}
\begin{center}
\begin{tabular}{cccccccc}
\hline
Input ch.  & $E_C$ & $K_1$ & $K_2$ & $K_3$ & $P$ & $N$ & Acc. \\ \hline
Y & 0.0005 & $18$ & $119$ & $233$ & $23$ & $340$ & 83.47 \\
CrCb & 0.0004 & $19$ & $73$ & $124$ & $12$ & $241$ & 75.89 \\ \hline
\end{tabular}
\end{center}
\end{table}
{\bf Experiment \#1.} We use the first 90\% of the LFW training pairs
for training the model and the rest of the pairs for testing. The
parameters of each PixelHop++ model and the accuracy of the classifiers
trained on their output features are given in Table. \ref{table:acc1}.  We
set $E_C$ = $E_F$ so that the number of leaf nodes in the first and
second hop is zero. The test accuracy of the meta classifier ($C_M$)
under this setting is 85.33\%. We use the same hyper-parameters in all
experiments.

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.45\linewidth]{fig_prep.pdf}
\end{center}
\caption{Quality of the obtained 32×32 (b) and 16×16 (c) low-resolution
face images compared with the original high-resolution face image (a).}
\label{fig:quality}
\end{figure}
{\bf Experiment \#2.} To compare our model with the state-of-the-art, we compute the 10-fold cross-validation accuracy for the input
resolution of 32×32 and 16×16. To obtain the 16×16 face images, we
down-sample images to 16×16 and then resize them back to 32×32.  The
quality of the obtained face images is compared in Fig.
\ref{fig:quality}.  To the best of our knowledge, there is no
low-resolution to low-resolution face recognition model which has
reported accuracy under the ``Image-Restricted, Label-Free Outside Data"
protocol on LFW. To this end, we compare our model with the SKD
(Selective Knowledge Distillation) model \cite{ge2018low} which is a
recent state-of-the-art model for low-resolution face recognition using
unlimited training data.

We have compared our model, SKD without distillation, and SKD in terms of accuracy, the number of parameters, and the
number of training images for each input resolution in Table.
\ref{table:lfw}. We see that for 16×16 image resolution our model
achieves an accuracy of 82.16\% which is only 3.71\% lower than SKD, and for 32×32 image
resolution our model achieves an accuracy of 83.49\% which is only
6.42\% lower than SKD while in both cases, our model size is about
79× smaller and uses only 5400 pair of images as the training set. On the other hand,
SKD uses a pre-trained model on the VGGFace dataset \cite{parkhi2015deep} with 2.6 M images as its teacher model and train and fine-tunes its student model on the UMDFaces dataset \cite{bansal2017umdfaces} with 367,888
images. Based on these results, our model does have a competitive
performance for deployment in resource-constrained environments.

\begin{table*}
\caption{Low-Resolution Face verification results on LFW.}\label{table:lfw}
\begin{center}
\begin{tabular}{ccccc}
\hline
Model  & Resolution & \#Param. & training set (\#Img.)& Acc.(\%) \\ \hline
SKD without distillation &  16×16   & 0.79 M & UMDFaces (367,888)& 62.82 \\
SKD &      16×16   & 0.79 M & VGGFace (2.6 M), UMDFaces (367,888)& 85.87  \\
Ours &                      16×16   & 0.01 M & LFW (5400 pairs)& 82.16 \\
SKD without distillation&   32×32   & 0.79 M & UMDFaces (367,888)  & 70.23 \\
SKD &      32×32 & 0.79 M & VGGFace (2.6 M), UMDFaces (367,888)  & 89.72   \\
Ours &                      32×32 & 0.01 M & LFW(5400 pairs)& 83.30    \\ \hline
\end{tabular}
\end{center}
\end{table*}

{\bf Experiment \#3.} In this experiment, we use the three active
learning methods which are introduced in Sec. \ref{sec:active} to
obtain the minimum required number of labeled training pairs without
significant loss in recognition accuracy.  We use the
first 90\% of the LFW training pairs as the initial pool of unlabeled
data ($D_u$) and the rest as the test data, then we randomly select 5\%
of the pool as $D_0$. The data augmentation step is removed.  For the
QBC algorithm, we use two different classifiers (LR and SVM) as the
committee of classifiers. We apply each active learning algorithm to
samples and compute the test accuracy versus the number of labeled
training pairs used for training the model. The result is shown in Fig.
\ref{fig:activelearning}.

By comparing the performance of different algorithms, we see that QBC is
the most effective one for our model in the current experiment setting
as it has the fastest convergence in test accuracy and also the most
stable performance. For example, with this algorithm, our model can
achieve an accuracy of above 84\% using only 1465 training pairs.
Furthermore, our model achieves a stable accuracy of about 86\% with
only 3000 pairs.

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.55\linewidth]{plot_2.pdf}
\end{center}
\caption{Comparison of classification accuracy of three active learning
methods as a function of the number of training pairs.}
\label{fig:activelearning}
\end{figure}

\subsection{Face Identification on CMU Multi-PIE}

We evaluate the effectiveness of our model for the face identification task
by conducting experiment on the Multi-PIE dataset. To the best of our
knowledge, no low-resolution face recognition model has reported results
on this dataset. Thus, we compare our model with high-resolution face
recognition models that have used this dataset for performance benchmarking. We use a setting mentioned in \cite{yim2015rotating} as Setting-1.
According to this setting, the first 150 identities are used for
training and the rest of the identities (151-250) from session 01 with
neutral expression are used for test so that there is no overlap between
training and test identities. As the gallery image, one frontal face
image with frontal illumination is used and the rest of the images in
the test set are selected as probes.  For model training, we generate
17,888 training pairs by pairing probe images with gallery images and
21,000 training pairs by randomly pairing images with each other.
Overall, 38,888 training pairs are used to train our model.

Since our model is not proposed for handling pose variation, we report
results only for frontal and slightly non-frontal images
($\pm15^{\circ}$) in the dataset. The rank-1 face identification
accuracy is shown in Table. \ref{table:mpie}. Our model has competitive
and comparable performance although it uses low-resolution face images
and a small training set and has a small model size. For example, Light
CNN-29 has 12.637M parameters and uses 128×128 images while our model
size is 1,149× smaller and uses 32×32 images. It is worthwhile to comment
that TP-GAN in Table. \ref{table:mpie} also uses Light CNN-29 for feature
extraction from images so its model is larger than Light CNN-29.

\begin{table}
\caption{Rank-1 identification rate (\%) for frontal and slightly
non-frontal face images ($\pm15^{\circ}$) in Setting-1.}
\begin{center}
\begin{tabular}{ccc}
\hline
Method & Resolution & Acc.(\%)  \\ \hline
CPF \cite{yim2015rotating}& 60×60 & 89.45 \\
HPN \cite{ding2017pose} & 256×220 &84.23 \\
c-CNN Forest \cite{xiong2015conditional} & - &96.97 \\
Light CNN-29 \cite{Wu_2018} & 128×128 &99.78 \\
TP-GAN \cite{huang2017beyond} & 128×128 & 99.78 \\
Ours & 32×32 & 89.48 \\\hline
\end{tabular}
\end{center}
\label{table:mpie}
\end{table}

\subsection{Model size computation}

We compute the size of our model based on the information in Table
\ref{table:acc1}. Each of the two PixelHop++ systems in our model has three levels
and the c/w Saab transform is applied on 5x5 blocks with a stride of one in
each level. Since $E_C=E_F$, there are no leaf nodes in the first and
second levels. The first level of the $M_Y$ system has 18 intermediate nodes
and 7 discarded nodes (25-18). Thus, it has 25×18 variables for storing
PCA components and one bias parameter (451 parameters in
total). The second level has 119 intermediate nodes and 25×18-119
discarded nodes. For each of the 18 output channels of level one, the
Saab transform is applied separately. Initially, 25×18 nodes are
generated, and 119 of them are then selected as intermediate nodes based
on the energy threshold.  For each Saab transform, the DC kernel is known
and constant so we have 18 repetitive kernels in
the second level. As a result, we have 25×(119-18) parameters for
storing PCA components and 18 bias parameters which is in total 2,543
parameters. By the same token, there are 25×(233-119)+199 parameters in
the third level of $M_Y$. In the Pairwise Feature Generator - Y unit,
the mean and the standard deviation of each PixelHop++ output node is
stored. Hence, the size of the unit is 2×(18+119+233) equal to 740. The LR
classifier's size equals its input feature size plus one which
is 341 for $C_Y$. Likewise, the size of all subsystems is
computed and summarized in Table. \ref{table:sizecomp}.

\begin{table}
\caption{The number of the parameters for different components of our model}\label{table:sizecomp}
\begin{center}
\begin{tabular}{cc}
\hline
subsystem & Num. of Param. \\ \hline
First hop - $M_Y$ & 451 \\
Second hop - $M_Y$ & 2,543 \\
Third hop - $M_Y$ & 2,969 \\
Pairwise Feat. Gen. - Y & 740 \\
LR Classifier - $C_Y$ & 341 \\
First hop - $M_{CrCb}$ & 476 \\
Second hop - $M_{CrCb}$ & 1,369 \\
Third hop - $M_{CrCb}$ & 1,348 \\
Pairwise Feat. Gen. - CrCb & 432 \\
LR Classifier - $C_{CrCb}$ & 242\\
Meta Classifier & 3 \\
Total & 10,914 \\\hline
\end{tabular}
\end{center}
\end{table}

\section{Conclusion and Future Work}\label{sec:conclusion}

A non-parametric solution targeting low-resolution face recognition for resource-constrained environments
was proposed in this work. Face biometric attributes (e.g., gender, ethnicity, age, etc.) prediction could be explored using a similar methodology. Furthermore,
the advantages of our solution may not be limited to resource-constrained
environments.  The same SSL principle could be generalized to
resource-rich environments and high-resolution images.  They are all interesting topics for future
research.

\section{Acknowledgements}
This work was sponsored by US Army Research Office (ARO) under contract number W911NF1820218 through Carnegie Mellon University under subaward number 1130234-421349.

\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


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Mary Ann Spooner Amo, 83, of Waterloo, died Thursday, Nov. 4, 2010, at home of natural causes. She was born Aug. 21, 1927, in Sioux City, the daughter of David and Francis Tracy Spooner. She married Warren Amo on Sept. 5, 1944, in Mason City. He preceded her in death on Oct. 18, 1971. She later married Harvey Spooner in Emmetsburg. He preceded her in death on Feb. 17, 1985.
Mary worked her entire life at various jobs including the Maywood Grocery Store, Cutler Wholesale Hardware, Waterloo Courier and most recently at Hawkeye Valley Agency on Aging for more than 20 years, retiring in 2007.
Survived by: one son, Stephen (Deb) Amo of Sumner; one daughter, Bobette (Charles) Aries of Cedar Falls; three stepsons, Vernon "Bob" (Kaye) Spooner of Waterloo, John Spooner of Fort Dodge and Steven Spooner of Waterloo; four stepdaughters, Amanda (Steve) Poisel of Oregon, Kathy "Cookie" (John) Greene of Evansdale, Vira "Tiny" Spooner of Waterloo and Nyleeta Huffaker of Waterloo; two sisters, Beverly M. Rooff of Waterloo and Irene Jean McElhaney of Waterloo; nine grandchildren; many great-grandchildren; numerous stepgrandchildren, stepgreat-grandchildren, stepgreat-great-grandchildren and many nieces and nephews. She loved them all.
Preceded in death by: her parents; both husbands; one daughter, Georgia Ann Sommerfelt; one brother, Leo Spooner; and one great-granddaughter, Dani Amo.
Services: will be at 10 a.m. Monday, Nov. 8, 2010, at the Kearns, Huisman-Schumacher Chapel on Kimball with burial in the Garden of Memories Cemetery. Visitation will be from 1 to 7 p.m. Sunday at the funeral home and also one hour before the service.


text:
Castle Crashers är ett beat 'em up-spel utvecklat av The Behemoth. Spelet blev först annonserat på Comic-Con 2006, och spelet släpptes till Xbox Live Arcade den 27 augusti, 2008, för 1200 Microsoft Points. En planerad version för PlayStation 3 beräknas släppas under 2010. Du kan spela upp till 4 spelare och spela både lokalt och på Xbox live. Spelet går ut på att rädda alla 4 kidnappade prinsessor. I början får du välja på 4 olika riddare (röd, orange, grön och blå) i ett klassiskt RPG spel som går ut på att gå upp i level och få poäng att uppgradera din riddare. När du vunnit spelet med någon av riddarna kommer du att låsa upp nya karaktärer som du kan spela som bland annat fiender.

Referenser

Externa länkar

Xbox 360-spel
Xbox 360 Live Arcade-spel
Playstation 3-spel
Playstation Network-spel
Windows-spel
Macintosh-spel
Datorspel 2008
Fightingspel
Indiespel
Medeltiden i fiktion
Spel med enspelarläge och flerspelarläge

redpajama_set_name: RedPajamaWikipedia


text:
Once in a Blue Moon è il primo album discografico solista del cantautore scozzese Frankie Miller, pubblicato dall'etichetta discografica Chrysalis Records nel gennaio del 1973.

Tracce

LP
Lato A

Lato B

CD
Edizione CD del 2003, pubblicato dalla Eagle Records (EAMCD 150)

Musicisti
 Frankie Miller - voce, chitarra acustica, armonica
 Bob Andrews - grand piano, junk piano, accordion, accompagnamento vocale-cori
 Brinsley Schwarz - chitarra solista, chitarra acustica
 Ian Gomm - chitarra solista, chitarra acustica
 Nick Lowe - basso elettrico, contrabbasso, accompagnamento vocale-cori
 Billy Rankin - batteria
 Bridgit, Joy e Janice - accompagnamento vocale-cori
Note aggiuntive
 Dave Robinson - produttore
 Registrazioni effettuate al Rockfield Studios, Galles (Gran Bretagna)
 Kingsley Ward e Ralph Downs - ingegneri delle registrazioni
 Visualeyes Ltd. - design copertina album
 Brian Cooke - fotografia

Note

Collegamenti esterni


text:
Jim Slattery congressional records
The collection consists of Jim Slattery's papers while serving with the U.S. House of Representatives. Series include records pertaining to U.S. House floor activities, such as voting scores & records, copies of sponsored & co-sponsored bills, and speeches; correspondence, mostly incoming, such as from constituents or form letters; reference files for legislation and for constituents; calendars and events, including invitations; publications such as press releases; computing information, e.g. backup tapes; photographs; public statements (mostly speeches never given); awards and plaques; guest books; reports; and new member materials for orienting members of the U.S. Congress. Some topics of interest in Slattery's records include the superconducting super collider, aviation, the B-2 Bomber, the North American Free Trade Agreement, oil & gas, abortion, health care, welfare, veterans, the Persian Gulf, 1993 flooding, and environmental issues.
http://experiment.worldcat.org/entity/work/data/767021099#Place/persian_gulf
http://experiment.worldcat.org/entity/work/data/767021099#Organization/canada
http://viaf.org/viaf/152361633
http://id.loc.gov/authorities/subjects/sh2007100482
http://experiment.worldcat.org/entity/work/data/767021099#Place/kansas
http://experiment.worldcat.org/entity/work/data/767021099#Place/united_states
"Papers"@en
"Jim Slattery collection"@en
"Congressional records"@en
"James Slattery papers"@en
"Jim Slattery papers"@en
"Records"@en
"The collection consists of Jim Slattery's papers while serving with the U.S. House of Representatives. Series include records pertaining to U.S. House floor activities, such as voting scores & records, copies of sponsored & co-sponsored bills, and speeches; correspondence, mostly incoming, such as from constituents or form letters; reference files for legislation and for constituents; calendars and events, including invitations; publications such as press releases; computing information, e.g. backup tapes; photographs; public statements (mostly speeches never given); awards and plaques; guest books; reports; and new member materials for orienting members of the U.S. Congress. Some topics of interest in Slattery's records include the superconducting super collider, aviation, the B-2 Bomber, the North American Free Trade Agreement, oil & gas, abortion, health care, welfare, veterans, the Persian Gulf, 1993 flooding, and environmental issues."@en
"Aerial photographs"@en
"Clippings (Books, newspapers, etc.)"@en
"Records and correspondence"@en
"Speeches, addresses, etc., American"@en
"Photographs"@en
"Jim Slattery congressional records"@en
"Canada."
"Kansas"
Persian Gulf.
"Persian Gulf."
"United States"
"United States."

redpajama_set_name: RedPajamaCommonCrawl


Oversee and provide guidance to group accountant and the administrators and accountants within each division.
Ad hoc analysis on financial results.
Identify any insights, trends, anomalies and opportunities.
CA/ CPA with a proven track record as a "hands-on" Group Financial Controller.
Experience within the Manufacturing/FMCG/supply chain sectors would be a significant advantage.
Please apply by following the prompts or call Melissa Meacham for further information.


text:
Contessa 32 – Yachting Monthly review
Theo Stocker
Theo Stocker takes an in-depth look at this iconic cruiser-racer, on an early season trip out of Sunderland
Jeremy Rogers Limited
What's she like to sail?
The Contessa 32 is one of the most successful cruiser racers of the last 30 years – and it's easy to see why. She's a joy to sail on the wind, quick on a reach, and well set up for spinnaker work downwind. With a dry weight of over four tonnes, a deep forefoot leading to a long fin keel and a skeg-mounted rudder, she's a solid sea-boat that will see you through all seasons of offshore sailing. All that and she's still winning races on handicap.
The deep cockpit felt very safe, and the proper gunwales made working on deck feel secure. The grabrail attached to the aft edge of the sprayhood made stepping onto the slightly narrow sidedecks easy. With the sprayhood and dodgers in place it did feel a little too cocooned and visibility to windward was a bit limited.
She is heavier and slower on the helm than a fin-keeler with a balanced spade rudder, and she needs 12 knots or more to really get going. But all of that pales into insignificance when you're at the helm. She's made to sail and she does so beautifully, cutting through the waves in a way that makes you want to let her keep going. Given a chance, she'll eat up the sea miles, whether that's around the cans or crossing the North Sea to Norway.
What's she like in port and at anchor?
She manoeuvres predictably under engine in port. With the rudder immediately aft of the prop you have instant control when going ahead. Astern, it is a fact that she will walk to port and so as long as you work with that, you're fine. A solid anchor and plenty of chain mean Charlotte can anchor almost anywhere with confidence and her low-windage hull shouldn't be prone to sailing around
her anchor. While the hull shape limits living space below decks – anyone over 6ft tall will have to stoop slightly – there is a surprising amount of stowage. There's space to sleep six, and Alan's had nine for dinner around the large fold-out saloon table before, but living on board with a full crew would be a bit cosy. The galley gives plenty of space to whip up hot meals for your crew in port or at sea. She is a comfortable and homely boat inside, with some of the old charm that you no longer get from sitting in a tupperware box.
Would she suit you and your crew?
It's hard to imagine many crews for whom a Contessa 32 wouldn't meet a good number of their criteria. She'll take you to Greenland and back without any qualms. She'll happily potter out for a daysail or a weekend away and give you somewhere to get away from it all. The next weekend, she'd be just as happy racing around the
Isle of Wight and would still have a chance of winning. She's a true sailing boat and built for mile after mile of satisfying cruising, and solidly built – she will look after you in pretty much anything.
She's not a modern boat, so if you are going Mediterranean island hopping and want space to for an extensive bathing platform or a sun-deck, she can't really help you. But that's not what she does. For Alan, who often sails her singlehanded, or with a couple of crew on longer trips around the British coast, she's the ideal boat. She certainly suits her canine crew Charlie, who was introduced to her at three weeks old and has happily accompanied Alan ever since, almost entirely 'without accident', as long as he gets his morning walk before departure. The Contessa 32 is a versatile, trustworthy companion of a boat that is game for pretty much any type of adventure you might suggest.
A joy to sail — quick on a reach and well set up for spinnaker work downwind
LOA:LOA 9.75m (32 ft)
Draught:1.6m (5ft 6in)
Displacement:4,309kg (9,500lb)
LWL:LWL 7.32m (24ft)
Beam:2.9m (9ft 6in)
Price Guide:New: £189,000+VAT Secondhand: £25,000-£30,000


text:
import styled from 'styled-components';

const Input = styled.input`
  border: 0;
  padding: 0.25rem 0.5rem;
  font: inherit;
  border-radius: 2px;

  &:focus {
    background-color: #eee;
    outline: none;
  }
`;

export default Input;

redpajama_set_name: RedPajamaGithub


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I came to Kasia as I was feeling exhausted from work stresses and more importantly needed an energy boost to help me through the tough training schedule for the London Marathon. Kasia set a very easy to follow plan with supplement recommendations and within weeks my energy levels had risen and my running improved no end. I finished the marathon in 4 hours which was a personal best! Kasia's friendly approach, passion for healthy nutrition and lifestyle coupled with her vast array of well-being knowledge will see you bursting with energy in no time. I would highly recommend Kasia to anyone looking for nutritious advice.
Rachel Gardner, Head of Events and London Marathon finisher 2015.
I've suffered from low energy levels , bloating and indigestion ever since I can remember. Kasia helped me to find the root cause of the problem through holistic treatment and addressed it with changing my diet and some clever supplementation. Thanks to Kasia and her fantastic advice and support, my low energy is a history, I am no longer dependant on fad diets, changed my attitude towards certain foods and have adopted a healthier lifestyle which I hope remains a permanent fixture. I would recommend anyone considering nutritional therapy to contact Kasia.
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text:
Babes in Arms (film)
Babes in Armsfilm version
Babes in Arms is the 1939 American film version of the 1937 Broadway musical of the same name.wikipedia
117 Related Articles
[filter]
Fryman EnterprisesMickey McGuire
The film version stars Mickey Rooney and Judy Garland, and features Charles Winninger, Guy Kibbee, June Preisser, Grace Hayes and Betty Jaynes.
In 1938, he co-starred in Boys Town. At 19 he was the first teenager to be nominated for an Oscar for his leading role in Babes in Arms, and he was awarded a special Academy Juvenile Award in 1939.
Andy HardyBabes in Arms (film)National Velvet (film)A Midsummer Night's Dream (1935 film)The Human Comedy (film)
Babes in Arms
of the same name
Babes in Arms is the 1939 American film version of the 1937 Broadway musical of the same name.
The film version, released in 1939, starred Judy Garland and Mickey Rooney and was directed by Busby Berkeley.
Rodgers and HartRichard RodgersMusical theatreJohnny One NoteBabes in Arms (film)
June Preisser
Her first film, Dancing Co-Ed (1939), provided only a small part, but her next film, Babes in Arms (1939), gave her a significant role opposite Mickey Rooney and Judy Garland.
Babes in Arms (film)Henry Aldrich for PresidentStrike Up the Band (film)Sweater Girl (film)Ziegfeld Follies
GarlandDorothyEthel Gumm
They teamed as lead characters for the first time in Babes in Arms, ultimately appearing in five additional films, including Hardy films Andy Hardy Meets Debutante and Life Begins for Andy Hardy.
The Wizard of Oz (1939 film)Judy at Carnegie HallGene KellyGrammy Award for Album of the YearA Star Is Born (1954 film)
Rand Brooks
Martha Steele (Margaret Hamilton) and her nephew Jeff Steele (Rand Brooks) from military school complain to Judge Black (Guy Kibbee) about the Vaudeville kids, but he won't take them from their homes.
After Gone With the Wind, he had relatively small parts in other movies including Babes in Arms as Margaret Hamilton's son, then a regular role as Lucky in the Hopalong Cassidy series of westerns in the mid 1940s.
Hopalong CassidyBabes in Arms (film)Gone with the Wind (film)The Adventures of Rin Tin TinLadies of the Chorus
Margaret Hamilton (actress)
Margaret HamiltonHamiltonMargaret
A few months after filming Oz, she appeared in Babes in Arms (1939) as Jeff Steele's mother, Martha, a society do-gooder who made it her goal to send the gang of child actors, led by Mickey Rooney and Judy Garland, to a work farm.
Wicked Witch of the WestThe Wizard of Oz (1939 film)My Little ChickadeeSaratoga (film)When's Your Birthday?
Leni Lynn
Leni Lynn as Dody Martin
Babes in Arms (film)Heaven Is Round the CornerGive Me the StarsHullabaloo (film)Gaiety George
Charles Winninger
Nothing Sacred (film)Destry Rides AgainShow BoatState Fair (1945 film)Three Smart Girls
Patsy goes to a theater to see her mother (Ann Shoemaker).
Babes in Arms (1939) - Mrs. Barton
Henry StephensonSunrise at CampobelloDr. MonicaAlice Adams (film)Babes in Arms (film)
Betty Jaynes (actress)
Betty Jaynes
She appeared as Molly Moran in Babes in Arms in 1939, then in a series of minor parts in seven MGM movies through 1944 including Meet the People, starring Lucille Ball.
Babes in Arms (film)Chicago City Opera CompanyDouglas McPhailMeet the PeopleGreeneville, Tennessee
Joseph Crehan
Joseph Crehan as Mr. Essex
Bengal Tiger (1936 film)Stranded (1935 film)Special Agent (1935 film)Secrets of a SecretaryChina Clipper (1936 film)
Good Morning (1939 song)
Good MorningGood Morning" (1939 song)
Freed and Nacio Herb Brown wrote a new song for the film, "Good Morning", later made famous in Singin' in the Rain.
"Good Morning" is a song with music by Nacio Herb Brown and lyrics by Arthur Freed, written for the 1939 film Babes in Arms and performed by Judy Garland and Mickey Rooney.
Babes in Arms (film)Nacio Herb BrownSingin' in the RainAFI's 100 Years...100 SongsArthur Freed
Douglas McPhail
So Mickey proposes the kids put on a show, and Don Brice (Douglas McPhail) sings "Babes in Arms" as they march and make a bonfire.
Betty Jaynes (actress)Babes in Arms (film)Little Nellie KellyBroadway Melody of 1940Born to Sing (1942 film)
Freed UnitFreedArthur Freed Productions
It was produced by the Arthur Freed unit at the studio.
Roger EdensBetty ComdenMusical filmThe Wizard of Oz (1939 film)Nacio Herb Brown
The Wizard of Oz (1939 film)
The Wizard of OzWizard of Oz1939 film
"God's Country", from Hooray for What! by Harold Arlen and E. Y. Harburg – composer and lyricist for The Wizard of Oz – was used for the finale.
Sondergaard said in an interview for a bonus feature on the DVD that she had no regrets about turning down the part, and would go on to play a glamorous villain in Fox's version of Maurice Maeterlinck's The Blue Bird in 1940; Margaret Hamilton played a role remarkably similar to the Wicked Witch in the Judy Garland film Babes in Arms (1939).
L. Frank BaumVictor FlemingJudy GarlandDorothy GaleRay Bolger
Singing in the rainfilmmovie of the same name
"Good Morning" from Babes In Arms (1939)
Stanley DonenGene KellyDebbie ReynoldsDonald O'ConnorGolden Globe Award for Best Actor – Motion Picture Musical or Comedy
Academy Award for Best Actor
Best ActorBest Actor in a Leading RoleAcademy Award
The film was nominated for two Academy Awards: Best Actor in a Leading Role for Mickey Rooney, who was 19 at the time, and Best Music, Scoring by Roger Edens and Georgie Stoll.
Emil JanningsSpencer Tracy90th Academy AwardsDaniel Day-LewisThe Last Command (1928 film)
Academy Award for Best Original Score
Best Original ScoreBest Music, Scoring of a Dramatic or Comedy PictureAcademy Award
45th Academy Awards47th Academy Awards10th Academy AwardsBeauty and the Beast (1991 film)7th Academy Awards
Babes on Broadway
The set contains Babes in Arms, Babes on Broadway, Girl Crazy, and Strike Up the Band, as well as a fifth disc containing bonus features on Rooney and Garland.
The film, which features Fay Bainter and Virginia Weidler, was the third in the "Backyard Musical" series about kids who put on their own show, following Babes in Arms (1939) and Strike Up the Band (1940).
How About You?Virginia WeidlerBurton LaneRichard QuineFay Bainter
* List of American films of 1939
Allegheny UprisingBabes in Arms (film)Gunga Din (film)Jesse James (1939 film)1939 in the United States
I'm Just Wild About Harry
The movie, as well as the musical, included the song "I'm Just Wild About Harry" which was written in 1921 for the Broadway show Shuffle Along, with lyrics by Noble Sissle and music by Eubie Blake.
Judy Garland sang the piece as one of several songs in a minstrel show in the M-G-M musical Babes in Arms (1939).
Noble SissleEubie BlakeShuffle AlongFlournoy MillerAubrey Lyles
Georgie Stoll
In 1937, he joined the MGM music department and was the musical director (frequently conductor too) for titles such as Honolulu, Ice Follies of 1939 and the Rooney-Garland hit Babes in Arms.
Jazz violinBing Crosby EntertainsThe Wizard of Oz (1939 film)Babes in Arms (film)Meet Me in St. Louis
Ray June
His best-known films are Babes in Arms and Funny Face.
Funny FaceThe Great White TrailBabes in Arms (film)Bits of LifeThe Eagle's Eye
Frank Sullivan (film editor)
Frank Sullivan
He worked as an editor on such films as Fury (1936), Babes in Arms (1939), The Philadelphia Story (1940), and Woman of the Year (1942).
Fury (1936 film)Babes in Arms (film)Woman of the YearThe Philadelphia Story (film)Joan of Arc (1948 film)
Roger Edens
Academy Award for Best Score (nominated) 1939: Babes in Arms (film) (w. Georgie Stoll)
Arthur FreedOn the Town (film)Meet Me in St. LouisEthel MermanIt's a Great Day for the Irish
2019 hyperleap


Let the live shows begin!! This week, The Voice begins the whittling down process in pursuit of another winner. The Final 12 will sing for your votes on Monday, November 20th at 8:00 PM with the results being revealed on Tuesday, November 21st – all on NBC, of course.
On the Results Show, 10 will be voted through to the next live show, while the Bottom Two will perform for the viewing audience for the dreaded Instant Save on Twitter. Crazy. Do the producers think everyone sits in front of their TV in real time anymore? During the holiday season?
Anyway, I do wish all the singers the very best of luck. Comments are now open for business.
I gotta do a play-by-play cause I'm old, and I live by the three second rule; if I don't say it out loud or write it down within three seconds, it's gone forever.
Ummm! I am now a HUGE fan of Chloe, Wow!
Chloe is growing on me. She used to have a wicked vibrato thing that sounded like a goat to me, so tonight was a improvement for her.
I still like the young girl, Addison I believe, it is. Very nice voice, good musical taste and a very nice well balance young lady. Very impressive for 16. I like Sara Bareilles but I wouldn't have chosen that song. She sang well though.
The kid with the hair ought to do some Stray Cats. He's got his star power machine all tuned up.
I didn't know the song that Red sang, at this point for him, song choice is going to be crucial, as there are a lot of really good contestants and it looks like it will go week by week voting, for whoever shines. Lol, yes, a few has their star power turned up.
I have no clear winner at this point, if I had to guess who will be going home, I would say Delta Dawn girl.
I agree Vonnie. Song choice and too much band were my main issues with Red. Every song you choose needs to be something your fans can't live without a copy of. I think he is a contender if he plays the game right.
I still don't care for "Delta Dawn" girl but They are pushing her pretty hard. I have a song choice for her though. I believe it was the Georgia Satellites and this may not be the title but the hook was "Don't gimme no lines and keep your hands to yourself." A song for our times!
I'm getting tired of the outrageous lies the judges tell. Blake's gal that is trying to break into country music who did Midnight "Train to Georgia" didn't pull it off. If she had gone with a straight cover it would have been better, musically anyway, but it sounded disjointed as if she couldn't find the melody. They gave her a standing O and Blake went on about how beautiful it was. I like the lady and I hope she can actually break into country music but that wasn't beautiful.
The last lady, Janice I believe, also a very nice lady and has appeared to be a force in the earlier rounds. I hope she has great success given what she has been through Again, while not as off as the previously mentioned singer, she wasn't as good as she has previously been and didn't rise to the level of the praise.
I don't know about who will win or loose but I still like the young girl.
Addison was the best of the night for me.
That's what I think as well, Idol Girl. There is some depth in that young woman. Nice tone and very mature control to her vocals. Hanging out at her Dad's record shop hasn't hurt her either. She is the only one in the top 10, currently #9, on iTunes.
The Country chart is interesting with Keisha Renee at #6 I believe and Red all the way down to 22. Didn't see that coming.
I'm guessing Mark and Lindsey for the coveted Mirror Ball. What say you?
I'm a little shocked that Red didn't chart higher, song choice?
Yes! The over praising has been an ongoing issue since the Idol days, except for Simon. I always appreciated his unwavering opinion and usually agreed with him. I like Keisha's voice, but am not sure about the country lane?!? Definitely sounds more R&B to me.
Yeah, song choice. I'm really surprised that Red would chose a "just ok" song, given his history.
A contestant needs to record something that the fans want to buy, I have several from over the years in my collection and sometimes when I do a random shuffle these songs will play and I'm reminded how good some of these contestants that didn't make it were or I wonder who on earth that was. A few of them were bought just to support a contestant that I liked but most were because I liked their take on the song.
I picked up Addison's track this morning and the iTunes version is very nice.
I didn't realize that Jordan Fisher, the kid that won DWTS, was from her in the Birmingham area and in fact, lives between two of my brothers. Small world. He is an incredible dancer but I didn't support his win because he's a professional.
I am really shocked that Jon went home…again, song choice! I didn't think that song did anything to showcase his voice. While I felt his voice had a fifties doowoop vibe, a Jackson 5 song wasn't what I was thinking for his voice.
Gene that's cool about the dancing guy living close to your brothers…you hit on the very reason I quit watching, him being a professional. Between Jennifer Gray and a few of the gymnasts winning the show, the two left feet-er's don't have a chance and seem to just be added fluff; and while I loved Derek, he seemed to get the cream of the crop while dancers like Tony got the fluff.
Geepies, I seem to be in rant mode this morning…more coffee, I need more coffee, and a nap, yes, more coffee and a nap and I will be as good as new!
I thought that Shianne would go home. With better coaching she might be pretty good but it's kind of sad to see her dressed up like Mariah Carey and trying to sing songs that are way over her head.
My second choice would be beard guy. He has a great tone to his voice but his changes to the songs don't work. I'm thinking a 60's war, more specifically draft protest song, wasn't his best choice.
Jon was one of the better singers in the competition but not really in my lane. If he was going to do a Jackson 5 song it would have worked better for me if he had kept the tempo and left of the star walk routine. He also chose to change his whole look that was part of his persona for sing for your life night.
Maybe they all learned a lesson about being better prepared to save themselves on Tuesday night. That's you too Red.
Yeah, the professional dancer contestants on DWTS are a turn off. They usually have some level of plausible deniability that they are pros like Baby was an actress who played a kid in a dance contest. This kid had singer, dancer, actor listed as his profession on his Wiki page. Maybe not his doing. Mark Ballis' partner has developed a unique performance style from playing the violin / fiddle while "dancing" about the stage but it is all self taught and it might be like calling Mic Jagger a dancer to call her one. She was outstanding. Frankie (Malcolm) Muniz was the guy you were looking for. Zero previous experience, great pairing with Witney, great attitude, strong work ethic, strong start with improvement all along. They were fun to watch and turned in at least one of the most memorable performances of the season. He was a great contestant and they were a great couple. He came in third.
My only problem with Keisha is her authenticity as a country singer. Trying to turn "Midnight Train to Georgia" into a country song reinforced that concern.


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This week at Whirlow Farm, Nathan and Lexus have been making some art work inspired by the artist Andy Goldsworthy.
The children had to work together to gather materials to make their art pieces.
Back at school, we gathered more materials from around school and then created our own art pieces.
The theme this week at Whirlow Farm was 'Seeds and Soil'.
We collected samples of soil and then we planted some seeds. We also made 'soil soup' as part of our experiment.
This week Nathan and Lexus completed a Forest School workshop at Whirlow Farm. The activities included: building a den, making a campfire and cooking over their campfire.
In Y3 today we have been looking at place value of numbers. The children used the base ten equipment to make 2, 3 and 4 digit numbers.
The children had to overcome the problem of having not enough of come of the values. For example, Thomas did not have enough ten rods to make 51 so he used 10 ones to make ten rod.
Here is an example where Shifa and Kaneez had to use 10 ten rods to make 100.
In y3HM, we had a big focus on our ' beat that' scores. The aim is to maintain the same score or improve each week. These children managed this last half term – could we have more this half term?


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Hammond School Superintendent Announces New Tax Referendum
November 13, 2019 kendavidson Uncategorized 8
Superintendent Scott Miller calmly stated that the Hammond Community School Corp will seek another referendum in 2024.
http://nwigazette.com/wp-content/uploads/2019/11/90FB9D1A-727A-4EAA-A7EC-23273B8242C4.mp4
November 13, 2019-Appearing on WJOB this morning, Hammond schools Superintendent Scott Miller announced that the School Corporation will seek another referendum in 2024. While it was widely suspected that the schools would seek another referendum, this is the first official statement from officials acknowledging that they are preparing to ask taxpayers for more money. Superindentend Miller did not provide details regarding how much the schools would seek and was unavailable via telephone at the time of this article.
The statement came during a conversation about the plan to close Clark and Gavit High Schools and to move 6th graders into elementary schools. According to Miller, that would save the district over $8M per year. The vacant buildings would be sold to the City of Hammond (Clark) and Purdue Northwest (Gavit) according to Superintendent Miller. You can view the full proposal on the School City website by clicking here.
Superintendent Miller acknowledged that at least one current School Board Member had repeatedly stated that Gavit would not be closed. In response to a flyer distributed during the election, Board Member Anna Mamala stated:
The comment was in response to a post from Mayor McDermott, who also stated that the plan to close Gavit was a lie:
Superintendent Miller also stated that the School Corporation was in negotiation with the City of Hammond to purchase a long unused tract of land behind the Civic Center for athletic fields.
1 Week After Election, Hammond Board Sends Rain Tax Through the Roof
While Schools Grapple with Closures, Redevelopment Spends Like Drunken Sailors
How about you all try something new for once… BE HONEST!!!
I can't believe this guy has the nerve to even suggest getting more money from taxpayers. We voted with the impression that if we passed the current referendum then no schools would close. In fact in the FAQs the question is asked what happens if the referendum does not passed, the answer was one high school would close and a number of other things would happen. Never was it stated that there was a possibility that not one but 2 schools would close. The superintendent stated a number of times that it was the best choice financially but never even gave a thought to what was best for our kids.
c.l.soukup
I think liar, liar, pants pn fire applies somewhere here…Vote Democratic!
lynne cowan
Believe Lake county does vote Democratic after the elections I still see the smoke coming from there britches
Fig Newton
We'll see. I predict they won't close any schools until AFTER the 2024 referendum. Jerks, and he can that crack about the "NO" campaign is BS. I thought 2 of the new board members were big supporters of the "NO" campaign.What happened…now that they're on the board, they follow the leader?
Should have been he can take the crack about the "NO" campaign and shove it.
Thomas Fillpot
Nobody ever points out the FACT they closed enrollment and used falling enrollment as an excuse to close schools and ask for more tax payer money. The city of Hammond is already in talks with Purdue to buy Gavit? This was planned years ago. The journalist that cover NWI are useless. Why are people afraid of a mayor who can't even get 10 percent of his city to vote? People are being kept ignorant to advance agendas.
kendavidson
Ironically, i just came across the post where the Mayor viciously trashed the Gazette and me personally because I reported almost two years ago that the City was in negotiations to purchase Clark High School. Everyone chose to believe that Clark and Gavit were not closing rather than the sources I had on the inside.


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NEW YORK. Nationally recognized as one of the country's "greenest" fleets, the New York City Parks Dept. held its 20th annual truck and equipment show here yesterday, offering other city agencies and local municipal fleets an opportunity to see production-ready hybrid trucks, natural gas refuse collection vehicles, small plug-in utility pickups and other new developments in green fleet technology.
With a total of 2,400 vehicles, the Parks Dept. fleet currently runs all of its 800 diesel-powered trucks on B20 biodiesel and is about to launch a 50-truck pilot test using B50, according to Keith Kerman, the Parks chief of operations. "I can see us moving to an all B50 fleet, just using B20 in the winter months," he told FleetOwner.
The department is also a major user of compressed natural gas, having just opened a new CNG refueling station in the famous NYC Central Park and refurbishing a 15-yr.-old one at Flushing Meadows Corona Park, the former World's Fair grounds and the site of this year's truck show. "We have 100 CNG vehicles, including three new street sweepers," Kerman said. It also has over 500 hybrid vehicles in the fleet, "but we're still looking for a viable hybrid or alternative fuel (full-size) pick up," he added.
Bookended by the iconic World's Fair Unisphere on one side and the U.S. Tennis Open Stadium on the other, 120 vendors showed up for the 20th Parks Dept. vehicle show, the first to be held at the old World's Fair grounds and the largest turnout to date, according to Kerman.
Among the notable exhibits was a pair of Kenworth medium-duty diesel-electric hybrids, one painted in the colors of a new area refuse hauling fleet and undergoing field tests. "It's a prototype truck, but we're getting the first ten off of the production line when they start building them in August," said Greg Murjani, CEO of Mr. Rubbish, a new residential refuse service starting in Brooklyn, NY, which they hope will be a national franchise operation.
The hybrid trucks are an important part of the new company's creation of a green image, according to Murjani. "Our goal is to recycle 80% of the household refuse we collect, and hybrid trucks are part of being a green company," he said, pointing to a new refuse container body built for Mr. Rubbish that includes compartments for segregating various types of recyclables and converts from a 10-yd. capacity to 20-yd. "Even our hydraulic oil is biodegradable," he added.
Other truckmakers represented with hybrids or alternative-fuel vehicles included GM with a variety of light truck hybrids and a fuel-cell SUV, International with a medium-duty hybrid, Autocar with a CNG-powered refuse packer and Mack Trucks with a CNG-powered utility truck.
View a Fleet Owner photo gallery from the New York City Parks Dept. 20th annual truck and equipment show.


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text:
Not A Phase
Stuart Brenton
LASTELLE Interview
LASTELLE are an Atmospheric Post-Hardcore band hailing from Oxfordshire UK. With comparisons being drawn to the likes of Devil Sold His Soul, Casey, Underoath, Being As An Ocean, and The Elijah; LASTELLE are writing music that blends the power and hooks of Post-Hardcore with the dense soundscapes and textures of Post-Rock.
We recently caught up with the band to discuss their latest release 'Coping Without A Cure', their life in lock-down, their musical tastes whilst growing up and addiction to Pepsi Max...
"I started playing piano at seven years old and the guitar at thirteen, as soon as I started jamming with friends I knew this was a great way to express myself. Touring was always a big life goal and now we've travelled the UK many times I'd love to cross the border and explore the world with the boys."
Firstly, I have to ask this at the moment, how have you guys been coping in the lock-down, what kind of things have you been up to since the lock-down came into effect?
Rich - Hello! Thankfully we haven't been hit too hard by recent events, lock-down certainly put a pause on plans but we've powered through and are making the best of it. On a personal level video games and zoom quizzes have managed to stem the boredom.
Let's be honest 2020 has been a weird year so far and things will probably be weird for a while. As an artist, especially an artist with a new single 'Coping Without A Cure' releasing soon. How has this affected your rhythm? This is the time you would be out the promoting the track!
Rich - Very true! We put our live shows ahead of everything and pride ourselves on getting out there as much as possible but that currently is not an option. Luckily, we've had a lot of behind the scenes work to do and plenty of free time to do it so we've made sure that we've stayed productive.
How did you get into writing and creating music? Was there a decisive moment or event that you remember that made you stop and think, wow, THIS is what I want to do with my life?
Rich - On a personal level music has always been a big part of my family, my older brother is a massive rock/metal fan and that certainly rubbed off on me. I started playing piano at seven years old and the guitar at thirteen, as soon as I started jamming with friends I knew this was a great way to express myself. Touring was always a big life goal and now we've travelled the UK many times I'd love to cross the border and explore the world with the boys.
Jonjo - It kind of happened naturally for me when I started playing guitar. I've never really learnt songs and was immediately trying to write my own stuff instead of learning someone else's songs. I started playing guitar when I was 13 to play in a band, and one of us had to step up and write the music, and it just happened to be me, and it just kind of stuck!
"'Coping Without A Cure' came together quite quickly. It stood out as a really powerful and dynamic track that would be perfect to front the next phase of LASTELLE."
How did the band get together?
Rich - LASTELLE formed after our previous band Vera Grace disbanded, of which Jonjo, Mike and myself (Rich) were members of. We weren't ready to call it a day but felt we needed to start afresh with a new name and vocalist, of which Adam fit the bill perfectly. Fred joined shortly after on bass and we're the tightest and most productive we've ever been.
Your latest release, 'Coping Without A Cure' releases on August 4th. Can you go into the creation process of this track a little more? How did this track come about?
Rich - We often start our tracks as instrumentals, mainly written by Jonjo and assisted by myself (Rich). The rest of the band bring together stories and ideas on what the lyrics could put across and Coping Without A Cure came together quite quickly. It stood out as a really powerful and dynamic track that would be perfect to front the next phase of LASTELLE.
Jonjo - Musically, this track is one of our more straightforward songs. The main piano / guitar melody was the starting point and the structure was built around that. Like a lot of our songs, there was no particular intentions for it when the writing process started. It went through a few structural changes, but it turned out to be quite different from the rest and we're super pleased with the result. Lyrically, it's a very personal song. I find our music to be a good way to talk about subjects that aren't so easy to put into words normally. I think this is a good example of that.
'Still Winds' is an absolute belter of a track and has received massive critical praise. Is it always the ambition of the band to always push the limits of what the definitive LASTELLE sound can be?
Adam - Definitely. We all have a mix of different musical influences; Jonjo is really inspired by post-rock, whereas I encourage that we include some catchy hooks. As a band we are motivated to produce music that draws from different genres – that, ultimately, creates our atmospheric post-hardcore sound.
Our latest single, Coping Without a Cure, is a perfect example of the music we like creating together. It has this wall of sound with textures of post-rock, finished with relatable lyrics that you can sing along to. We all cannot wait to play this live.
"'Bluebells' truly defines the LASTELLE sound. When we play this live there is such an energy, from the full-on intro, to the slow, emotionally charged ending. We try to make a LASTELLE performance an immersive experience and I love performing this song."
Speaking of 'Still Winds', you recently did an online guitar playthrough of the track on YouTube. How did this come about? Is it strange doing a video online playthrough without the rest of the band present?
Rich - Content is king in this day and age so we have been smashing out as much stuff as we can, we are all well versed in music video creation so performing to camera, even without all the guys, is second nature to us.
With everything still up in the air, what do you think the rest of 2020 will look like for you as a band?
Rich - I expect the music industry as a whole will exist exclusively online for the remainder of 2020, and we'll be trying to think of ways to bring the experience of our live shows and new music to all our social media followers around the world, it's a tough one to figure out but I'm sure we'll see trends of content appearing from multiple bands in time.
'Coping Without A Cure' shows new unheard depths in your sound and just how far your sound and musicianship has come since the release of 'Harrow' in 2019, was this a conscious choice or a natural evolution of your sound?
Freddie – We're all very happy with how Coping Without a Cure sounds and the feedback has been wonderful, so thank you for the kind, humbling words. Hopefully people enjoy it on record and we are desperate to share this live for everyone to hear. I think it was the next logical step after our last single "Bluebells" – particularly the end section of that track and how choral it feels as it closes. Vocally, Mike and Adam have worked really hard and I think they've both kicked it up another gear for this one.
If you had to choose one song from the LASTELLE catalogue that you feel encompasses the bands signature sound, which song would you choose and why?
Adam – I think our last single, 'Bluebells', truly defines the LASTELLE sound. When we play this live there is such an energy, from the full-on intro, to the slow, emotionally charged ending. We try to make a LASTELLE performance an immersive experience and I love performing this song.
"if you follow our [LASTELLE] Spotify page, you'll find a playlist there. We keep that up to date with everything we listen to in the van and at home, and it's full of bands that inspire us."
Can you tell us a fun fact about each member of the band?
Adam has been Straight Edge for over 8 years and Freddie can do a Rubik's cube in under a minute (sometimes). Jonjo plays Dungeons & Dragons, and is a Level 11 Half Elf Bard. Mike can ride a unicycle (but never gets the opportunity) while Rich is addicted to Pepsi Max and can't survive on tour without it; he is eagerly awaiting their endorsement.
What is on your playlist at the moment?
Freddie - We've recently curated a playlist for Saviour MGMT, over on spotify, which can be found on our socials until August 5th. Otherwise, if you follow our Spotify page, you'll find a playlist there. We keep that up to date with everything we listen to in the van and at home, and it's full of bands that inspire us.
Finally, as we are called Not A Phase, were you guys scene kids when you were growing up? If not, what kind of music were you rocking out back in your formative teenage years?
Adam - I was an all-out pop punk/ska kid. You know the type that every classic metalhead hates; sporting a fringe longer than my face and XXS clothing. I big fan of Blink, Capdown, Reel Big Fish and the like. Some funny moments that come to mind are getting knocked out in a Capdown gig and sporting a black eye for the next week and a wall of death on someone's shoulder during Less than Jake amongst huge metalheads waiting for Slayer to play next. It was wasn't till I was later into my teens that I started to enjoy heavier music.
Freddie – I have the same background as Adam, and didn't actually get into much heavy music until my twenties. My first proper show at 13 was Rancid at Brixton Academy, and I lost one of my pink DC trainers in a mosh pit. I was a skater kid, so I was always on the fringe of being a scene kid, but never took the plunge.
Rich - I was most definitely a part of the "scene" and had the hair straighteners to prove it, but I listen to a wide range of genres from metal to rap/classical/pop/movie scores etc.
Connect With LASTELLE
Facebook: https://www.facebook.com/lastelleband/
Instagram: https://www.instagram.com/lastelleband/?hl=en
Twitter: https://twitter.com/lastelleband?lang=en
Listen to LASTELLE
Spotify: https://open.spotify.com/artist/2BB6OyMcGIRSwJQfsmpjKJ
YouTube: https://www.youtube.com/watch?v=9YiP_Xe6XB4
Connect with Not A Phase
Facebook: https://www.facebook.com/notaphaseuk/
Twitter: https://twitter.com/phase_not
Instagram: https://www.instagram.com/notaphaseuk/
Listen to the Not A Phase Podcast
https://www.buzzsprout.com/1218668
https://podcasts.apple.com/gb/podcast/not-a-phase-podcast/id1524229612
https://open.spotify.com/show/229SqmYzGpTOmwczHTCOa8
Skyline Leeway Interview
newhaven Interview
Smile Empty Soul Interview
Join the mailing list to get regular updates. After all, ITS NOT A PHASE
Thanks for subscribing to Not A Phase! Please check your junk email!
© 2020 NOT A PHASE.


text:
Бі́лий На́волок () — мис на північному заході узбережжя Мурманської області в Росії, знаходиться в південній частині затоки Варангер-Фьорд Баренцевого моря.

Мис височинний, складений з магматичних гірських порід — гранітів та гнейсів, які виходять вгору над осадовими породами і стрімко обриваються до моря, утворюючи стрімкі береги. Біля узбережжя мису розкидані підводні та надводні кекури.

Примітки

Посилання
 Фото мису

Миси Баренцевого моря
Миси Мурманської області


> Events in Iowa > Iowa UNA "Night of 1,000 Dinners"
Iowa UNA "Night of 1,000 Dinners"
In recognition of International Women's Month, the Iowa United Nations Association will be holding the annual "Night of 1,000 Dinners" to fundraise for UN Women's missions in Afghanistan, Democratic Republic of Congo, and Haiti. Formerly known as UNIFEM, UN Women seeks to aid and empower women in conflict zones, natural disaster areas, and socially repressive societies.
A silent art auction with all proceeds going to UN Women!
Over $400 in door prize giveaways!
If you cannot attend but would still like to make a donation to UN Women through our event, please use the donation option on this page. Ticket sales for the event will help Iowa UNA, a 501(c)3 non-profit organization, continue its annual programs and fundraisers including "Night of 1,000 Dinners".


really isn't a relationship with God.
Would you like to be notified when Event dates are released?
but do we really heed the words found in Proverbs 4:23?
Time is not refundable... use it with intention.
Don't we all want to be "seen," to be heard and fully known? Yet, the prospect of that is what we sometimes fear the most. In this age of social media, the tendency to compare ourselves to others is at an all time high. Everyone seems to put their life on display in the digital world, but the life they exhibit is often missing one thing... real vulnerability...the place of true freedom. So what if there was a place that was safe to test the waters of vulnerability and find this "freedom"? It's been said that vulnerability is the best measure of courage. That's where this all comes full circle. Courage is the willingness to show up and allow ourselves the freedom to be fully seen and fully known. So, it stands to reason, that to exercise courage we must be willing to be vulnerable. What does that have to do with Discovery Weekends?
Discovery Weekends is that safe place! We have so many people that walk away from their Discovery Weekend experience saying, "Wow, I'm not the only one who 's felt that way or had to deal with that issue?!" What a powerful realization! We are not alone! And it's that realization that opens the door to a life of freedom and healing.
That's what make Discovery Weekends such a special opportunity. Our Teams simply provide an atmosphere where courage and vulnerability exist; more importantly, a safe place for you to meet The Father...your "Abba"...to see yourself through His eyes. And in His gaze, you will come to the realization that there's only One opinion that matters...and that's when the magic happens! Welcome to true freedom!
Discovery Weekend is not just another men's or women's conference.
and encouraged to really share and get serious about our relationship with Jesus."
Discovery is a catalyst to propel and deepen your relationship with God.
Participants frequently share that they finally realized they weren't the only ones who struggle.
49 participants - You will share your journey at a table of no more than 7 participants, where confidentiality is of utmost importance. This intimate table setting allows you to have a unique and individual experience while also being part of a larger group.
5 Stories - Personal, challenging, and inspirational stories shared by our Team will help everyone realize that they aren't the only one who struggles.
1 Team - Their sold-out purpose is to serve you so that you can experience the love of God.
Challenging Teachings - Biblically driven talks built to encourage depth in your faith, understanding, and help you see your identity as HIS son or daughter.
Engaging Worship - We will go beyond just singing the words of a song and truly enter His presence.
You might say, "I've attended a "weekend-style" retreat before."
intentionally focus on your relationship with God?
"We never grow closer to God when we just live life. It takes deliberate pursuit and attentiveness"
This is a great experience for those who have never encountered God before. They know of Him, but don't know Him. Maybe they are curious. Maybe they have doubts. Maybe they believe their unbelief is truer and more solid than an unseen God.
Do you feel stuck in your circumstances?
Discovery is a safe place where no one will push or convince a decision or a change of heart. If you are running from pain and shame, trying to convince yourself that those things no longer have any hold on you. It's the place for you. A safe place to connect with your heart and pursue the Love of a God that can change everything.
If you have a strong relationship with God, Discovery is also the place for you! Imagine 36 uninterrupted hours to focus on your heart and connect with the Lover of your soul.


text:
Vancouver Whitecaps acquire Ali Adnan permanently, sign through 2021
By Phil West @philwest
TRANSFER TRACKER STATUS: Transfer
Iraqi international Ali Adnan, brought on to the Vancouver Whitecaps FC squad as a loanee from Italian team Udinese in March, will now be one of the team's Designated Players.
The 'Caps announced on Friday that they have completed a deal to acquire Adnan on a permanent basis, confirming media reports from earlier in the day about the talented defender's status. Though specific figures were not disclosed, WhitecapsFC.com stated that the deal has set "an MLS transfer record for the fullback position."
The contract runs through the end of the 2021, with a club option for 2022.
He stay.
#VWFC acquire Ali Adnan in a multimillion-dollar transaction, sign the Iraqi international as a Designated Player. #ItTakesAVillage 🌊https://t.co/kpRwh0qB9D
— Vancouver Whitecaps FC (@WhitecapsFC) July 5, 2019
Adnan, 25, spent more than three seasons in Serie A (including a loan stint at Atalanta) before coming to MLS for the 2019 season. Before playing with Udinese, Adnan played professionally in Turkey for Çaykur Rizespor and in Iraq for Baghdad FC.
"From my first day at this club, I feel like I'm at home," Adnan said in a club release. "It's not easy to find a club like this. I am very thankful to the club's ownership, coaches, staff, and my teammates. The fans and city of Vancouver have been extremely welcoming and supportive. I look forward to representing everyone at the club to the best of my ability."
Adnan debuted March 30, starting in all 16 matches since then, chalking up a goal, two assists and according to the Whitecaps' site, an impressive accumulation of stats, including 136 duels won (second most by a defender, sixth most overall), 47 aerials won (fourth most by a fullback, 18th most overall), 35 completed dribbles (most by a defender, tied for 13th most overall) and 71 passes played into the opponent's box (tied for third most by a defender).
"Ali has been a great addition to our club since arriving in March," Whitecaps head coach Marc Dos Santos said. "He understands our defensive principles and has an incredible ability to join the attack, create in the last third, which is very important for teams that want to have an impact offensively using their fullbacks."
Ali Adnan Vancouver Whitecaps FC Transfer Tracker


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