text:
\section{Introduction}

Person re-identification (re-id) is defined as the task of matching
two pedestrian images crossing non-overlapping camera views \cite{gong2014person}.
It plays an important role in a number of applications in video surveillance,
including multi-camera tracking \cite{berclaz2008behavioral_maps,mensink2007distributedem},
crowd counting \cite{chan2009counting,ge2009crowd_count}, and multi-camera
activity analysis \cite{wang2008correspondencefree,wang2008npbayes}.
Person re-id is extremely challenging and remains unsolved for a number
of reasons. First, in different camera views, one person's appearance
often changes dramatically caused by the variances in body pose, camera
viewpoints, occlusion and illumination conditions. Second, in a public
space, many people often wear very similar clothes (e.g., dark coats
in winter). The differences that can be used to tell them apart are often subtle,
which could be the global, e.g., one person is bulkier than the
other, or local, e.g., the two people wear different shoes.

Early re-id methods use hand-crafted features for person appearance
representation and employ distance metric learning models as matching
functions. They focus on either  designing cross-view robust features \cite{farenzena2010reidentify_symmetry,viewpoint,Rivlin_tpami2013,mid_rui_zhao,XQDA},
or learning robust distance metrics \cite{LADF,metric_tpami14,seanPAMIpreReID,KISSME,kiss_metric,person_reid_salience,MtMCML,XQDA},
or both  \cite{larry_davis,XQDA,MFA,zhang_cooccurrence}.
Recently, inspired by the success of convolutional neural networks
(CNN) in many computer vision problems, deep CNN architectures \cite{Ejaz_cvpr2015,xiaogang_wang_cvpr2016,deepreid,joint_learning_cvpr16,hailin_shi,gated_siamese_eccv2016,de_cheng_2016}
have been widely used for person re-id. Using a deep model, the tasks
of feature representation learning and distance metric learning are
tackled jointly in a single end-to-end model. The state-of-the-art
re-id models are mostly based on deep learning; deep re-id is thus
the focus of this paper.

\begin{figure}
\begin{centering}
\subfigure[The cognitive process a human may take to match people]{\label{fig:a}
\includegraphics[scale=0.40]{fig/introduction_1_new} } \subfigure[Our model aims to imitate the human cognitive process]{\label{fig:b}
\includegraphics[scale=0.28]{fig/introduction_2_new} }
\par\end{centering}
\caption{\label{fig:introduction}Multi-scale learning is adopted by  our MuDeep
to learn discriminative features at different spatial scales
and locations. }
\end{figure}

Learning discriminative feature representation is the key objective
of a deep re-id model. These features need to be computed at multiple
scales. More specifically, some people can be easily distinguished
by some global features such as gender and body build, whilst for
some others, detecting local images patches corresponding to, say
a handbag of a particular color or the type of shoes, would be critical
for distinguishing two otherwise very similarly-looking people. The
optimal matching results are thus only obtainable when features at
different scales are computed and combined. Such a multi-scale matching
process is likely also adopted by most humans when it comes to re-id.
In particular, humans typically compare two images from coarse to
fine. Taking the two images in Fig. \ref{fig:introduction}(a) as
an example. At the coarse level, the color and textual information
of clothes are very similar; humans would thus go down to finer scales
to notice the subtle local differences (\eg the hairstyle, shoe,
and white stripes on the jacket of the person on the left) to reach
the conclusion that these are two different people.

However, most existing re-id models compute features at a single scale
and ignore the factor that people are often only distinguishable at
the right spatial locations and scales. Existing models typically
adopt multi-branch deep convolutional neural networks (CNNs). Each
domain has a corresponding branch which consists of multiple convolutional/pooling
layers followed by fully connected (FC) layers. The final FC layer
is used as input to pairwise verification or triplet ranking losses
to learn a joint embedding space where people's appearance from different
camera views can be compared. However, recent efforts \cite{DBLP:journals/corr/GatysEB15,Mahendran15}
on visualizing what each layer of a CNN actually learns reveal that
higher-layers of the network capture more abstract semantic concepts
at global scales with less spatial information. When it reaches the
FC layers, the information at finer and local scales has been lost
and cannot be recovered. This means that the existing deep re-id architectures
are unsuitable for the multi-scale person matching.

In this work, we propose a novel multi-scale deep learning model (MuDeep) for re-id which aims to learn discriminative feature representations
at multiple scales with automatically determined scale weighting for
combining them (see Fig.~\ref{fig:introduction}(b)). More specifically,
our MuDeep network architecture is based on a Siamese network but
critically has the ability to learn features at different scales and evaluating their importance for cross-camera matching.
This is achieved by introducing two novel layers: \emph{multi-scale
stream layers} that extract images features by analyzing the person
images in multi-scale; and \emph{saliency-based learning fusion layer,
}which selectively learns to fuse the data streams of multi-scale and
generate the more discriminative features of each branch in MuDeep.
The multi-scale data can implicitly serve as a way of augmenting the
training data. In addition to the verification loss used by many previous
deep re-id models, we introduce a pair of classification losses at
the middle layers of our network, in order to strongly supervise multi-scale
features learning.

\section{Related Work}

\noindent \textbf{Deep re-id models} \quad{}Various deep learning
architectures have been proposed to either address visual variances
of pose and viewpoint \cite{deepreid},  learn better relative distances
of triplet training samples \cite{gua_gang_deep_reid}, or learn better
similarity metrics of any pairs \cite{Ejaz_cvpr2015}. To have enough
training samples, \cite{xiaogang_wang_cvpr2016} built upon inception
module a single deep network and is trained on multiple datasets; to
address the specific person re-id task, the neural network will be
adapted to a single dataset by a domain guided dropout algorithm. More
recently, an extension of the siamese network has been studied for person
re-id \cite{gated_siamese_eccv2016}. Pairwise and triplet comparison
objectives have been utilized to combine several sub-networks to form
a network for person re-id in \cite{joint_learning_cvpr16}. Similarly,
\cite{de_cheng_2016} employed triplet loss to integrate multi-channel
parts-based CNN models.
To resolve the problem of large variations,
\cite{hailin_shi} proposed a moderate positive sample mining method
to train CNN. However, none of the models developed is capable of multi-scale feature computation as our model.

More specifically, the proposed deep re-id model differs from related
existing models in several aspects. (1) our MuDeep generalizes the
convolutional layers with multi-scale strategy and proposed multi-scale
stream layers and saliency-based learning fusion layer, which is
different from the ideas of combing multiple sub-networks \cite{joint_learning_cvpr16}
or channels \cite{de_cheng_2016} with pairwise or triplet loss. (2)
Comparing with \cite{xiaogang_wang_cvpr2016}, our MuDeep are simplified,
refined and flexible enough to be trained from scratch on either large-scale
dataset (\eg CUHK03) or medium-sized dataset (\eg CUHK01). Our experiments
show that without using any extra data, the performance of our MuDeep
is $12.41\%/4.27\%$ higher than that of \cite{xiaogang_wang_cvpr2016}
on CUHK01/CUHK03 dataset. (3) We improve the architecture of \cite{Ejaz_cvpr2015}
by introducing two novel layers to implement multi-scale and saliency-based
learning mechanisms. Our experiment results validate that the novel
layers lead to much  better performance  than \cite{Ejaz_cvpr2015}.

\vspace{0.02in}

\noindent \textbf{Multi-scale re-id } \quad{}The idea of multi-scale
learning for re-id was first exploited in \cite{Li_2015_ICCV}. However,
the definition of scale is different: It was defined as  different levels
of resolution rather than the global-to-local supporting region as in ours. Therefore, despite  similarity between terminology,
very different problems are tackled in these two works. The only multi-scale
deep re-id work that we are aware of is \cite{Liu:2016:MTC:2964284.2967209}.
Compared to our model, the model in \cite{Liu:2016:MTC:2964284.2967209}
is rather primitive and naive: Different down-sampled versions of
the input image are fed into shallower sub-networks to extract features
at different resolution and scale. These sub-networks are  combined
with a deeper main network for feature fusion. With an explicit network
for each scale, this network becomes computationally very expensive.
In addition, no scale weighting can be learned automatically and no
spatial importance of features can be modeled as in ours.

\begin{figure*}
\centering{}\includegraphics[scale=0.25]{fig/overview_new}\caption{\label{fig:Overview}Overview of MuDeep architecture.}
\end{figure*}

\begin{table*}
\begin{centering}
\begin{tabular}{c||c|c||c}
\hline
{\small{}{}Layers }  & \textcolor{black}{\small{}{}Stream id}{\small{}{} }  & \textcolor{black}{\small{}{}number@size}{\small{}{} }  & \textcolor{black}{\small{}output}\tabularnewline
\hline
\multirow{4}{*}{\textcolor{black}{\small{}Multi-scale-A}} & \textcolor{black}{\small{}{}1}{\small{}{} }  & \textcolor{black}{\small{}{}1@$3\times3\times96$ AF \textendash 24@$1\times1\times96$
CF }{\small{} } & \multirow{4}{*}{\textcolor{black}{\small{}$78\times28\times96$}}\tabularnewline
\cline{2-3}
 & \textcolor{black}{\small{}{}2}{\small{}{} }  & \textcolor{black}{\small{}{}24@$1\times1\times96$ CF}{\small{} } & \tabularnewline
\cline{2-3}
 & \textcolor{black}{\small{}{}3}{\small{}{} }  & \textcolor{black}{\small{}{}16@$1\times1\times96$ CF \textendash{}
24@$3\times3\times96$ CF}{\small{}{} }  & \tabularnewline
\cline{2-3}
 & \textcolor{black}{\small{}{}4}{\small{}{} }  & \textcolor{black}{\small{}{}16@$1\times1\times96$ CF \textendash 24@$3\times3\times96$
}{\small{}{}CF}\textcolor{black}{\small{}{}\textendash{} 24@$3\times3\times24$
CF}{\small{} } & \tabularnewline
\hline
\multirow{3}{*}{\textcolor{black}{\small{}Reduction} } & \textcolor{black}{\small{}1}{\small{} }  & \textcolor{black}{\small{}{}1@$3\times3\times96$ MF{*}}{\small{}{}
}  & \multirow{3}{*}{\textcolor{black}{\small{}$39\times14\times256$}}\tabularnewline
\cline{2-3}
 & \textcolor{black}{\small{}{}2}{\small{}{} } & \textcolor{black}{\small{}{}96@$3\times3\times96$ CF{*}}{\small{}{}
}  & \tabularnewline
\cline{2-3}
 & \textcolor{black}{\small{}{}3}{\small{}{} } & \textcolor{black}{\small{}{}48@$1\times1\times96$ CF \textendash{}
56@$3\times3\times48$ CF\textendash{} 64@$3\times3\times56$ CF{*}}{\small{}{}
}  & \tabularnewline
\hline
\multirow{5}{*}{\textcolor{black}{\small{}Multi-scale-B}} & \textcolor{black}{\small{}1}{\small{} }  & \textcolor{black}{\small{}{}256@$1\times1\times256$ CF}{\small{} } & \textcolor{black}{\small{}$39\times14\times256$}\tabularnewline
\cline{2-4}
 & \textcolor{black}{\small{}{}2}{\small{}{} } & \textcolor{black}{\small{}{}64@$1\times1\times256$ CF \textendash 128@$1\times3\times64$
CF\textendash 256@$3\times1\times128$ CF}{\small{} } & \textcolor{black}{\small{}$39\times14\times256$}\tabularnewline
\cline{2-4}
 & \multirow{2}{*}{\textcolor{black}{\small{}3}{\small{} }} & \textcolor{black}{\small{}{}64@$1\times1\times256$ CF \textendash{}
64@$1\times3\times64$ CF}{\small{} } & \multirow{2}{*}{\textcolor{black}{\small{}$39\times14\times256$}}\tabularnewline
 &  & \textcolor{black}{\small{}{}\textendash 128@$3\times1\times64$CF\textendash{}
128@$1\times3\times128$}{\small{}{} CF}\textcolor{black}{\small{}{}\textendash{}
256@$3\times1\times128$ CF}{\small{} } & \tabularnewline
\cline{2-4}
 & \textcolor{black}{\small{}{}4}{\small{}{} }  & \textcolor{black}{\small{}{}1@$3\times3\times256$ AF{*} \textendash 256@$1\times1\times256$
CF}{\small{}{} }  & \textcolor{black}{\small{}{}$39\times14\times256$}\tabularnewline
\hline
\end{tabular}
\par\end{centering}

{\small{}{}\caption{\label{tab:The-details-parameters-multi-stream}The parameters of
\textcolor{black}{tied multi-scale stream layers of MuDeep. }\textcolor{black}{\small{}{}{}{}{}Note
that (1) number@size indicates the number and the size of filters.
(2) {*} means the stride of corresponding filters is 2; the stride
of other filters is 1. We add 1 padding to the side of input data
stream if the corresponding side of C-filters is 3. (3) CF, AF, MF
indicate the C-filters, A-filters and M-filters respectively. A-filter
is the average pooling filter.}}
}
\end{table*}

\noindent \textbf{Deep saliency modelling } \quad{}Visual saliency
has been studied extensively \cite{itti2001visual_attention,itti2001combination}.
It is typically defined in a bottom-up process. In contrast, attention mechanism
\cite{nature_attention} works in a top-down way and allows for salient
features to dynamically come to the front as needed. Recently, deep soft
attention modeling has received increasing interest as a means to
attend to/focus on local salient regions for computing deep features
\cite{sermanet2014attention,mnih2014recurrent,yang2016stacked,shuicheng_tip2016}.
In this work, we use saliency-based learning strategy in a saliency-based
learning fusion layer to exploit both visual saliency
and attention mechanism. Specifically, with the multi-scale stream
layers, the saliency features of multiple scales are computed in multi-channel
(\eg in a bottom-up way); and a per channel weighting layer is introduced
to automatically discover the most discriminative feature channels
with their associated scale and locations. Comparing with \cite{shuicheng_tip2016} which adopts a spatial attention model, our model is much compact and can be learned from scratch on a small re-id dataset. When comparing the two models, our model, despite being much smaller, yields overall slightly better performance: on CUHK-01 dataset ours is $8\%$ lower than that of \cite{shuicheng_tip2016} but on the more challenging CUHK-03(detected) we got around $10\%$ improvement over that of \cite{shuicheng_tip2016}. Such a simple saliency learning architecture is shown to be very effective in our experiments.

\noindent \textbf{Our contributions} are as follows: (1) A novel multi-scale
representation learning architecture is introduced into the deep learning
architectures for person re-id tasks. (2) We propose a saliency-based
learning fusion layer which can learn to weight important scales in
the data streams in a saliency-based learning strategy. We evaluate
our model on a number of benchmark datasets, and the experiments show
that our models can outperform state-of-the-art deep re-id models,
often by a significant margin.

\section{Multi-scale Deep Architecture (MuDeep)}

\noindent \textbf{\textcolor{black}{Problem Definition. }}Typically,
person re-id is formulated only as a verification task \cite{larry_davis,XQDA,MFA,zhang_cooccurrence}.
In contrast, this paper formulates person re-id into two tasks: classification
\cite{wacv2016_enhanced,xiao2016endtoend} and verification \cite{Ejaz_cvpr2015,xiaogang_wang_cvpr2016}.
Specifically, given a pair of person images, our framework will categorize
them (1) either as the ``same person'' or ``different persons''
class, and (2) predict the person's identity.

\noindent \textbf{\textcolor{black}{Architecture Overview.}} As shown
in Fig.~\ref{fig:Overview}, MuDeep has two branches to process
each of image pairs. It consists of five components:\textcolor{red}{{}
}\emph{tied convolutional layer}s,\emph{ multi-scale stream layers
}(Sec. \ref{subsec:Tied-multi-scale-stream})\emph{, saliency-based
learning fusion layer }(Sec. \ref{subsec:Tied-saliency-based-fusion})\emph{,
verification subnet }and \emph{classification subnet }(Sec. \ref{subsec:Subnets-for-person})\emph{.}
Note that after each convolutional layer or fully connected layer,
batch normalization \cite{batch_normalization} is used before the
ReLU activation.

\noindent \textbf{Preprocessing by convolutional layers.} The input
pairs are firstly pre-processed by two convolutional layers with the
filters (C-filters) of 48@$3\times3\times3$ and 96@$3\times3\times48$;
furthermore, the generated feature maps are fed into a max-pooling
layer with filter size (M-filter) as 1@$3\times3\times96$ to reduce
both length and width by half. The weights of these layers are tied
across two branches, in order to enforce the filters to learn the
visual patterns shared by both branches.

\subsection{Multi-scale stream layers\label{subsec:Tied-multi-scale-stream}}

We propose multi-scale stream layers to analyze data streams in
multi-scale. The multi-scale data can implicitly serve as a way of augmenting
the training data. Different from the standard Inception structure
\cite{inception_v3}, all of these layers share weights between the
corresponding stream of two branches; however, within each two data
streams of the same branch, the parameters are not tied. The parameters
of these layers are shown in Tab. \ref{tab:The-details-parameters-multi-stream};
and please refer to  Supplementary Material for the visualization
of these layers.

\noindent \textbf{Multi-scale-A layer} analyses the data stream with
the size $1\times1$, $3\times3$ and $5\times5$ of the receptive field.
Furthermore, in order to increase both depth and width of this layer,
we split the filter size of $5\times5$ into two $3\times3$ streams
cascaded (\ie stream-4 and stream-3 in Table \ref{tab:The-details-parameters-multi-stream}).
The weights of each stream are also tied with the corresponding stream
in another branch. Such a design is in general inspired by, and
yet different from  Inception architectures \cite{inception_v1,inception_v3,inception_v4}.
The key difference lies in the factors that  the weights  are not tied between
any two streams from the same branch, but are tied between  two
corresponding streams of different branches.

\noindent \textbf{Reduction} \textbf{layer} further passes the data
streams in multi-scale, and halves the width and height of feature
maps, which should be, in principle, reduced from $78\times28$ to
$39\times14$. We thus employ Reduction layer to \emph{gradually}
decrease the size of feature representations as illustrated in Table
\ref{tab:The-details-parameters-multi-stream}, in order to avoid
representation bottlenecks. Here we follow the design principle of
``avoid representational bottlenecks'' \cite{inception_v3}. In
contrast to directly use max-pooling layer for decreasing feature
map size, our ablation study shows that the Reduction layer, if replaced
by max-pooling layer, will leads to more than $10\%$ absolute points
lower than the reported results of Rank-1 accuracy on the CUHK01 dataset
\cite{cuhk01}. Again, the weights of each filter here are  tied
for paired streams.

\noindent \textbf{Multi-scale-B} \textbf{layer} serves as the last
stage of high-level features extraction for the multiple scales of
$1\times1$, $3\times3$ and $5\times5$. Besides splitting the $5\times5$
stream into two $3\times3$ streams cascaded (\ie stream-4 and stream-3
in Table \ref{tab:The-details-parameters-multi-stream}). We can further
decompose the $3\times3$ C-filters into one $1\times3$ C-filter
followed by $3\times1$ C-filter \cite{inception_v4}. This leads
to several benefits, including reducing the computation cost on $3\times3$
C-filters, further increasing the depth of this component, and being capable of
 extracting asymmetric features from the receptive field. We still
tie the weights of each filter.

\subsection{Saliency-based learning fusion layer \label{subsec:Tied-saliency-based-fusion}}

This layer is proposed to fuse the outputs of multi-scale stream layers.
Intuitively, with the output processed by previous layers, the resulting
data channels have redundant information: Some channels may capture
relative important information of persons, whilst others may only
model the background context. The saliency-based learning strategy
is thus utilized here to automatically discover and emphasize the
channels that had extracted highly discriminative patterns, such as
the information of head, body, arms, clothing, bags and so on, as illustrated in Fig. \ref{fig:Attention-Map-of}. Thus,
we assume $\mathbf{F}_{i\star}$ represents the input feature maps
of $i-$th stream $(1\leq i\leq4)$ in each branch and $\mathbf{F}_{ij}$
represents the $j-$th channel of $\mathbf{F}_{i\star}$, \ie $(1\leq j\leq256)$
and $\mathbf{F}_{ij}\in\mathbb{R}^{39\times14}$. The output feature
maps denoted as $\mathbf{G}$ will fuse the four streams; $\mathbf{G}_{j}$
represents the $j-$th channel map of $\mathbf{G}$, which is computed
by:
\begin{equation}
\mathbf{G}_{j}=\sum_{i=1}^{4}\mathbf{F}_{ij}\cdot\alpha_{ij}~~~(1\leq j\leq256)\label{eq: attention_fusion}
\end{equation}

\noindent where $\alpha_{ij}$ is the scalar for $j-$th channel of
$\mathbf{F}_{i\star}$; and the saliency-weighted vector $\alpha_{i\star}$
is learned to account for the importance of each channel of stream $\mathbf{F}_{i\star}$;
$\alpha_{i\star}$ is also tied.

A fully connected layer is appended after saliency-based learning
fusion layer, which extracts features of 4096-dimensions
of each image. The idea of this design is 1) to concentrate the saliency-based
learned features and reduce dimensions, and 2) to increase the efficiency
of testing.

\subsection{Subnets for person Re-id \label{subsec:Subnets-for-person}}

\noindent \textbf{Verification subnet}\quad{}accepts feature pairs
extracted by previous layers as input, and calculate distance with
\emph{feature difference layer}, which followed by a fully connected
layer of 512 neurons with 2 softmax outputs. The output indicates
the probability of \char`\"{}same person\char`\"{} or \char`\"{}different
persons\char`\"{}. Feature difference layer is employed here to fuse
the features of two branches and compute the distance between two
images. We denote the output features of two branches as $\mathbf{G}^{1}$
and $\mathbf{G}^{2}$ respectively. The feature difference layer computes
the difference $\mathbf{D}$ as $\mathbf{D}=\left[\mathbf{G}^{1}-\mathbf{G}^{2}\right].*\left[\mathbf{G}^{1}-\mathbf{G}^{2}\right]$.
Note that (1) `$.*$' indicates the element-wise multiplication;
the idea behind using element-wise subtraction is that if an input image pair is labelled
\char`\"{}same person\char`\"{}, the features generated by multi-scale
stream layers and saliency-based learning fusion layers should be
similar; in other words, the output values of feature difference layer should be close to zero; otherwise, the values have different responses.
(2) We empirically compare the performance of two difference layer operations including $\left[\mathbf{G}^{1}-\mathbf{G}^{2}\right].*\left[\mathbf{G}^{1}-\mathbf{G}^{2}\right]$
and $\left[\mathbf{G}^{1}-\mathbf{G}^{2}\right]$. Our experiment shows
that the former achieves $2.2\%$ higher performance than the latter
on Rank-1 accuracy on CUHK01.

\noindent \textbf{Classification subnet} \quad{}In order to learn
strong discriminative features for appearance representation, we add
classification subnet following saliency-based learning fusion layers
of each branch. The classification subnet is learned to classify images
with different pedestrian identities. After extracting 4096-D features in
saliency-based learning fusion layers, a softmax with \emph{N} output
neurons are connected, where \emph{N} denotes the number of pedestrian
identities.

\section{Experiments\label{experiment}}

\subsection{Datasets and settings}

\noindent \textbf{Datasets. }The proposed method is evaluated on three
widely used datasets, \ie CUHK03 \cite{deepreid}, CUHK01 \cite{cuhk01}
and VIPeR \cite{viper}. The CUHK03 dataset includes $14,096$ images
of $1,467$ pedestrians, captured by six  camera views. Each person has $4.8$ images on average.
Two types of person images are  provided \cite{deepreid}: manually
labelled pedestrian bounding boxes (labelled) and bounding boxes automatically
detected by the deformable-part-model detector \cite{felzenszwalb2010dt_part}
(detected). The manually labelled images generally are of higher quality
than those detected images. We use the settings of both manually \emph{labelled}
and automatically \emph{detected} person images on the standard splits
in \cite{deepreid} and report the results in Sec. \ref{subsec:Experiment-on-CUHK03-Detected}
and Sec. \ref{subsec:Experiment-on-CUHK03-Labelled} respectively.
CUHK01 dataset has 971 identities with 2 images per person of each
camera view. As in \cite{cuhk01}, we use as probe the images from
camera A and take those from camera B as gallery. Out of all data,
we select randomly 100 identities as the test set. The remaining identities
for training and validation. The experiments are repeated over 10
trials. For all the experiments, we train our models from the scratch.
VIPeR has $632$ pedestrian pairs in two views with only one image
per person of each view. We split the dataset and half of pedestrian
pairs for training and the left for testing as in \cite{Ejaz_cvpr2015}
over 10 trials. In addition, we also evaluate proposed method on two video-based re-id datasets, \textit{i.e.},  iLIDS-VID dataset \cite{wang2014person} and PRID-2011 dataset \cite{hirzer2011person}.  The iLIDS-VID dataset contains 300 persons, which are captured by two non-overlapping cameras. The sequences range in length from 23 to 192 frames, with an average number of 73. The PRID-2011 dataset contains 385 persons for camera view A; 749 persons for camera view B, with sequences lengths of 5 to 675 frames. These two camera views have no nonoverlapping. Since the primary focus of this paper is on image-based person re-id, we employ the simplest feature fusion scheme for video re-id:  Given a video sequence, we compute features of each frame which are aggregated by max-pooling to form video level representation. In contrast, most of the state-of-the-art video-based re-id methods \cite{mclaughlin2016recurrent,liu2015spatio,wang2014person,karanam2015sparse,li2015multi,karanam2015person} utilized the RNN models such as LSTM to perform temporal/sequence video feature fusion from each frame.

\vspace{0.03in}

\noindent \textbf{Experimental settings.} On the CUHK03 dataset, in term
of training set used, we introduce two specific experimental settings;
and we report the results for both settings: (a)\textbf{ Jointly}:
as in \cite{xiaogang_wang_cvpr2016}, under this setting the model is firstly
trained with the image set of both labelled and detected CUHK03 images,
and for each task, the corresponding image set is used to fine-tune
the pre-trained networks. (b) \textbf{Exclusively}: for each of the ``labelled''
and ``detected'' tasks, we only use the training data from each
task without using the training data of the other task.

\vspace{0.03in}

\noindent \textbf{Implementation details.} We implement our model
based on the Caffe framework \cite{caffe} and we make our own implementation
for the proposed layers. We follow the training strategy used in \cite{Ejaz_cvpr2015}
to first train the network without classification subnets; secondly,
we add the classification subnets and freeze other weights to learn
better initialization of the identity classifier; finally we train classification
loss and verification loss simultaneously, with a higher loss weight
of the former. The training data include positive and negative pedestrian
pairs. We augment the data to increase the training set size by 5 times
with the method of random 2D translation as  in \cite{deepreid}.
The negative pairs are randomly sampled as twice the  number of positive
pairs. We use the stochastic gradient descent algorithm with the mini-batch
size of 32. The learning rate is set as $0.001$, and gradually decreased
by $\nicefrac{1}{10}$ every 50000 iterations. The size of input image
pairs is\footnote{To make a fair comparision with \cite{Ejaz_cvpr2015}, the
 input images are resized to $60\times160\times3$. } $60\times160\times3$. Unless specified otherwise, the dropout ratio
is set as $0.3$. The proposed MuDeep get converged in $9\sim12$ hours on re-id dataset
on a NVIDIA TITANX GPU. Our MuDeep needs around 7GB GPU memory. Code and
models will be made available on the first author's webpage.

\vspace{0.03in}

\noindent \textbf{Competitors. }We compare with the deep learning
based methods including DeepReID \cite{deepreid}, Imp-Deep \cite{Ejaz_cvpr2015},
En-Deep \cite{wacv2016_enhanced}, and G-Dropout \cite{xiaogang_wang_cvpr2016},
Gated\_Sia \cite{gated_siamese_eccv2016}, EMD \cite{hailin_shi}, SI-CI \cite{joint_learning_cvpr16}, and MSTC\footnote{We re-implement \cite{Liu:2016:MTC:2964284.2967209} for evaluation purpose.} \cite{Liu:2016:MTC:2964284.2967209}, as well as other non-deep
competitors, such as Mid-Filter \cite{mid_rui_zhao}, and XQDA \cite{XQDA},
LADF \cite{LADF}, eSDC \cite{unsupervised_per_reid}, LMNN \cite{LMNN},
and LDM \cite{face_metric}.

\vspace{0.03in}

\noindent \textbf{Evaluation metrics.} In term of standard evaluation
metrics, we report the Rank-1, Rank-5 and Rank-10 accuracy with single-shot
setting in our paper. For more detailed results using \emph{Cumulative Matching
Characteristics} (CMC) curves, please refer to the Supplementary Material.

\subsection{Results on CUHK03-Detected\label{subsec:Experiment-on-CUHK03-Detected}}

On the CUHK03-Detected dataset, our results are compared with the state-of-the-art
methods in Table \ref{tab:Results-of-CUHK03.}.

Firstly and most importantly, our best results \textendash{} MuDeep
(jointly) outperforms all the other baselines at all ranks. Particularly, we notice that our results
are significantly better than both the methods of using hand-crafted features
and the recent deep learned models. This validates the efficacy of
our architectures  and suggests that the proposed multi-scale and saliency-based
learning fusion layer can help extract  discriminative features
 for person re-id.

Secondly, comparing with the Gated\_Sia \cite{gated_siamese_eccv2016}
which is an extension of Siamese network with the gating function
to selectively emphasize fine common local patterns from data, our
result is $7.54\%$ higher at Rank-1 accuracy. This suggests that
our framework can better analyze the multi-scale patterns from data
than Gated\_Sia \cite{gated_siamese_eccv2016}, again thanks to the
novel  multi-scale stream layers and saliency-based learning
fusion layers.

Finally, we  further compare our results on both ``Jointly''
and ``Exclusively'' settings. The key difference is that in the
``jointly'' settings, the models are also trained with the data
of CUHK03-Labelled, \ie the images with manually labelled pedestrian
bounding boxes. As explained in \cite{deepreid}, the quality of labelled
images is generally better than those of detected images. Thus with
more data of higher quality used for training, our model under the ``Jointly'' setting
can indeed beat our model under the ``Exclusively'' setting. However, the
improved margins between these two settings at  all Ranks
are very small if compared with the margin between our results and
the results of the other methods. This suggests that our multi-scale
stream layers have efficiently explored and augmented the training
data; and with such multi-scale information explored, we can better  train our model. Thanks to our multi-scale stream layers,
our MuDeep can still achieve good results with less training data,
\emph{i.e.} under the Exclusively setting.

\begin{table}
\centering{}%
\begin{tabular}{c||ccc}
\hline
Dataset  & \multicolumn{3}{c}{``Detected''}\tabularnewline
\hline
Rank  & 1  & 5  & 10\tabularnewline
\hline
SDALF\cite{farenzena2010reidentify_symmetry}  & 4.87  & 21.17  & 35.06\tabularnewline
eSDC \cite{unsupervised_per_reid}  & 7.68  & 21.86  & 34.96\tabularnewline
LMNN \cite{LMNN}  & 6.25  & 18.68  & 29.07\tabularnewline
XQDA \cite{XQDA}  & 46.25  & 78.90  & 88.55\tabularnewline
LDM \cite{face_metric}  & 10.92  & 32.25  & 48.78\tabularnewline
\hline
DeepReid \cite{deepreid}  & 19.89  & 50.00  & 64.00\tabularnewline
MSTC \cite{Liu:2016:MTC:2964284.2967209} & 55.01 & \textendash{} & \textendash{}\tabularnewline
Imp-Deep \cite{Ejaz_cvpr2015}  & 44.96  & 76.01  & 83.47\tabularnewline
SI-CI \cite{joint_learning_cvpr16}  & 52.17  & 84.30  & 92.30\tabularnewline
Gated\_Sia \cite{gated_siamese_eccv2016}  & 68.10  & 88.10  & 94.60\tabularnewline
EMD \cite{hailin_shi}  & 52.09  & 82.87  & 91.78\tabularnewline
\hline
MuDeep (Jointly)  & \textbf{75.64  } & \textbf{	94.36  	} & \textbf{97.46}\tabularnewline
MuDeep (Exclusively)  & 75.34   & 94.31  & 97.40\tabularnewline
\hline
\end{tabular}\caption{\label{tab:Results-of-CUHK03.}Results of CUHK03-Detected dataset.
\textcolor{red}{}}
\end{table}

\subsection{Results on CUHK03-Labelled \label{subsec:Experiment-on-CUHK03-Labelled}}

The results of CUHK03-Labelled dataset are shown
in Table \ref{tab:Results-of-CUHK03-Labelled} and we can make the following
observations.

Firstly, in this setting, our MuDeep still outperforms the other competitors
by clear margins on all the rank accuracies. Our result is $4.27\%$
higher than the second best one \textendash{} G-Dropout\cite{xiaogang_wang_cvpr2016}.
Note that the G-Dropout adopted the domain guided dropout strategies
and it utilized much more training data in this task. This further
validates that our multi-scale stream layers can augment the training
data to exploit more information from medium-scale dataset rather
than scaling up the size of training data; and saliency-based learning
fusion layers can better fuse the output of multi-scale information
which can be used for person re-id.

Secondly, we can draw a similar conclusion as from the CUHK03-Detected results:
Our MuDeep with ``Jointly'' setting is only marginally better than
that with ``Exclusively'' setting. This means that more available
related training data can always help improve the performance of deep
learning model; and this also validates that our multi-scale stream
layers and saliency-based fusion layers can help extract and fuse
the multi-scale information and thus cope well with less training data.

\begin{table}
\begin{centering}
\begin{tabular}{c||ccc}
\hline
Dataset  & \multicolumn{3}{c}{``Labelled'' }\tabularnewline
\hline
Rank  & 1  & 5  & 10 \tabularnewline
\hline
SDALF\cite{farenzena2010reidentify_symmetry}  & 5.60  & 23.45  & 36.09 \tabularnewline
eSDC \cite{unsupervised_per_reid}  & 8.76  & 24.07  & 38.28 \tabularnewline
LMNN \cite{LMNN}  & 7.29  & 21.00  & 32.06 \tabularnewline
XQDA \cite{XQDA}  & 52.20  & 82.23  & 92.14 \tabularnewline
LDM \cite{face_metric}  & 13.51  & 40.73  & 52.13 \tabularnewline
\hline
DeepReid \cite{deepreid}  & 20.65  & 51.50  & 66.50 \tabularnewline
Imp-Deep \cite{Ejaz_cvpr2015}  & 54.74  & 86.50  & 93.88 \tabularnewline
G-Dropout\cite{xiaogang_wang_cvpr2016}  & 72.60  & $92.30${*}  & 94.30{*} \tabularnewline
EMD \cite{hailin_shi}  & 61.32  & 88.90  & 96.44 \tabularnewline
\hline
MuDeep (Jointly)  & \textbf{76.87 }  & \textbf{96.12 }  & \textbf{98.41 }\tabularnewline
MuDeep (Exclusively)  & 76.34   & 95.96  & 98.40\tabularnewline
\hline
\end{tabular}
\par\end{centering}
\caption{\label{tab:Results-of-CUHK03-Labelled}Results of CUHK03-Labelled
dataset. Note that: {*} represents the results reproduced from \cite{xiaogang_wang_cvpr2016}
with the model trained only by CUHK03 dataset. }
\end{table}

\subsection{Results on CUHK01 and VIPeR}

\noindent \noindent \textbf{CUHK01 dataset}. Our MuDeep is trained
only on CUHK01 dataset without using extra dataset. As listed in Table
\ref{tab:Results-of-CUHK01}, our approach obtains $79.01\%$ on Rank-1
accuracy, which can beat all the state-of-the-art; and is $7.21\%$
higher than the second best method \cite{joint_learning_cvpr16}.
This further shows the advantages of our framework.

\vspace{0.03in}

\noindent \noindent \textbf{VIPeR dataset.} This dataset is extremely challenging
due to small data size and low resolution. In particular, this dataset has relatively
small number of distinct identities and thus the positive pairs for
each identity are much less if compared with the other two datasets. Thus
for this dataset, our network is initialized by the model pre-trained
on CUHK03-Labelled dataset with the ``Jointly'' setting. The training
set of VIPeR dataset is used to fine-tune the pre-trained network.
We still use the same network structure except changing the number
of neurons on the last layer in the classification subnet. The results
on VIPeR dataset are listed in Table \ref{tab:Results-of-VIPER}. The
results of our MuDeep remains competitive and outperforms all compared methods.
\vspace{0.03in}

\noindent \noindent \textbf{Qualitative visualization.} We give some
qualitative results of visualizing the saliency-based learning maps
on CUHK01. The visualization of our saliency-based learning
maps is computed from saliency-based learning fusion layer in Fig.
\ref{fig:Attention-Map-of}. Given one input pair of images (left
of Fig. \ref{fig:Attention-Map-of}), for each branch, the saliency-based
learning fusion layer combines four data streams into a single data
stream with selectively learning learned from the saliency of each
data stream. The heatmaps of three channels are visualized for each
stream and each branch is shown on the right side of Fig. \ref{fig:Attention-Map-of}.
Each row corresponds to one data stream; and each column is for
one channel of heatmap of features. The weight $\alpha$ (in Eq.~(\ref{eq: attention_fusion}))
for each feature channel is learned and updated accordingly with the
iteration of the whole network. The three channels illustrated in
Fig. \ref{fig:Attention-Map-of} have a high reaction (i.e. high values)
in the heatmaps on discriminative local patterns of the input image pair.
For example, the first and third columns highlight the difference
of clothes and body due to the existence of different visual patterns
learned by our multi-scale stream layers. Thus these two channels
have relative higher $\alpha$ weights, whilst the second column
models the background patterns which are less discriminative for the
task of person re-id and results in lower $\alpha$ value. Our saliency-based
learning fusion layer can automatically learn  the optimal $\alpha$
weights from training data.

\begin{table}
\centering{}%
\begin{tabular}{c||ccc}
\hline
Rank  & 1  & 5  & 10\tabularnewline
\hline
KISSME \cite{kCCA,KISSME}  & 29.40  & 60.18  & 74.44\tabularnewline
SDALF\cite{farenzena2010reidentify_symmetry}  & 9.90  & 41.21  & 56.00\tabularnewline
eSDC \cite{unsupervised_per_reid}  & 22.84  & 43.89  & 57.67\tabularnewline
LMNN \cite{LMNN}  & 21.17  & 49.67  & 62.47\tabularnewline
LDM \cite{face_metric}  & 26.45  & 57.68  & 72.04\tabularnewline
\hline
DeepReid \cite{deepreid}  & 27.87  & 58.20  & 73.46\tabularnewline
G-Dropout \cite{xiaogang_wang_cvpr2016}  & 66.60  & \textendash{}  & \textendash{}\tabularnewline
MSTC \cite{Liu:2016:MTC:2964284.2967209} & 64.12 & \textendash{} & \textendash{}\tabularnewline
Imp-Deep \cite{Ejaz_cvpr2015}  & 65.00  & 88.70  & 93.12\tabularnewline
SI-CI \cite{joint_learning_cvpr16}  & 71.80  & 91.35{*}  & 95.23{*}\tabularnewline
EMD \cite{hailin_shi}  & 69.38  & 91.03  & 96.84\tabularnewline
\hline
MuDeep  & \textbf{79.01}  & \textbf{97.00}  & \textbf{98.96}\tabularnewline
\hline
\end{tabular}\caption{\label{tab:Results-of-CUHK01}Results of CUHK01 dataset. {*}: reported
from CMC curves in \cite{joint_learning_cvpr16}.}
\end{table}

\begin{table}
\centering{}%
\begin{tabular}{c||ccc}
\hline
Rank  & 1  & 5  & 10\tabularnewline
\hline
kCCA\cite{kCCA}  & 30.16  & 62.69  & 76.04\tabularnewline
Mid-Filter \cite{mid_rui_zhao}  & 29.11  & 52.34  & 65.95\tabularnewline
RPLM \cite{RPLM}  & 27.00  & 55.30  & 69.00\tabularnewline
MtMCML \cite{MtMCML}  & 28.83  & 59.34  & 75.82\tabularnewline
LADF \cite{LADF}  & 30.22  & 64.70  & 78.92\tabularnewline
XQDA \cite{XQDA}  &   40.00    &  68.13    &   80.51\tabularnewline
\hline
Imp-Deep \cite{Ejaz_cvpr2015} & 34.81  & 63.61  & 75.63\tabularnewline
G-Dropout \cite{xiaogang_wang_cvpr2016} & 37.70  & \textendash{}  & \textendash{}\tabularnewline
MSTC \cite{Liu:2016:MTC:2964284.2967209} & 31.24 & \textendash{} & \textendash{}\tabularnewline
SI-CI \cite{joint_learning_cvpr16} & 35.76 & 67.40 & 83.50\tabularnewline
Gated\_Sia \cite{gated_siamese_eccv2016} & 37.90 & 66.90 & 76.30\tabularnewline
EMD \cite{hailin_shi} & 40.91 & 67.41 & 79.11\tabularnewline
\hline
MuDeep  & \textbf{43.03} & \textbf{74.36} & \textbf{85.76}\tabularnewline
\hline
\end{tabular}\caption{\label{tab:Results-of-VIPER}Results on the VIPeR dataset}
\end{table}

\begin{figure}
\begin{centering}
\includegraphics[scale=0.45]{fig/attention-feature-new}
\par\end{centering}
\caption{\label{fig:Attention-Map-of}Saliency Map of $\mathbf{G}$ in Eq (\ref{eq: attention_fusion}).}
\end{figure}

\subsection{Ablation study\label{subsec:multiscale-ablation}}

\noindent \textbf{Multi-scale stream layers.} We  compare
our multi-scale stream layers with three variants of Inception-v4 \cite{inception_v4} on CUHK01 dataset.
Specifically, Inception-v4 has Inception A and Inception B modules,
both of which are compared against here. Furthermore, we also compare Inception A+B
structure which is constructed  by connecting Inception A, Reduction, and Inception
B modules. The Inception A+B structure is the most similar one to our
multi-scale stream layers except that (1) we modify some parameters; (2) the weights of our layers are tied between the corresponding streams of two branches. Such weight
tieing strategy enforces each paired stream of our two branches to extract
the common patterns. With the input of each image pair, we use
Inception A, Inception B, and Inception A+B as the based network structure
to predict whether this pair is the ``same person'' or ``different
persons''. The results are compared in Table \ref{tab:inception}.
We can see that our MuDeep architecture has the best performance over the other baselines. This
shows that our MuDeep is the most powerful at learning discriminative
patterns than the Inceptions variants, since the multi-scale stream
layers can more effectively extract multi-scale information and the saliency-based
learning fusion layer facilitates the automatic selection of the
important feature channels.

\vspace{0.02in}
\noindent \textbf{Saliency-based learning fusion layer and classification
subset. }To further investigate the contributions of the fusion layer and the
classification subset, we compare three variants of our model with one of or both of the two components removed  on CUHK01 dataset. In Table \ref{tab:alpha}, the ``\textendash{}
Fusion'' denotes our MuDeep without using the  fusion layer; and ``\textendash ClassNet''
indicates our MuDeep without the classification subnet; and ``\textendash{}
Fusion \textendash{} ClasNet'' means that MuDeep has neither fusion
layer nor classification subnet. The results in Table
\ref{tab:alpha} show that our full model has the best performance
over the three variants. We thus conclude that both components help and the combination of the two can further boost the performance.

\subsection{Further evaluations\label{subsec:further-evaluations}}

\noindent \textbf{Multi-scale vs.~Multi-resolution.} Due to the often different camera-to-object distances and the resultant object image resolutions, multi-resolution re-id is also interesting on its own and could potentially complement multi-scale re-id. Here a  simplest multi-resolution multi-scale re-id model is formulated, by training the proposed multi-scale models at different resolutions followed by fusing the different model outputs. We consider the original resolution ($60\times160$) and a lower one ($45\times145$). The feature fusion is done by concatenation. We found that the model trained at the lower resolution achieves lower results and when the two models are fused, the final performance is around $1-2\%$ lower than the model learned at the original resolution alone. Possible reasons include (1) All three datasets have images of similar resolutions; and (2) more sophisticated multi-resolution learning model is required which can automatically determine the optimal resolution for measuring similarity given a pair of probe/gallery images.

\noindent  \textbf{Results on  video-based re-id}. Our method can be evaluated on  iLIDS-VID and PRID-2011 datasets. Particularly,  two datasets are randomly split into $50\%$ of persons for training and $50\%$ of persons for testing.  We follow the evaluation protocol in \cite{hirzer2011person} on PRID-2011 dataset and only consider  the first 200 persons who appear in both cameras.   We compared our model with the results reported in \cite{hirzer2011person, wang2014person, mclaughlin2016recurrent}.
The results are listed in Table \ref{tab:Results-of-VIDEO}. These results are higher than those in \cite{hirzer2011person, wang2014person}, but lower than those in \cite{mclaughlin2016recurrent} (only slightly on PRID-2011)\footnote{We also note that our results are better than those of most video-based re-id specialist models listed in Table 1 of \cite{mclaughlin2016recurrent}.}.  These results are  quite encouraging and we expect that if the model is extended to a CNN-RNN model, better performance can be achieved.

\begin{table}
\begin{centering}
\begin{tabular}{c||ccc}
\hline
Rank & 1 & 5 & 10\tabularnewline
\hline
Inception A & 60.11 & 85.30 & 92.44\tabularnewline
Inception B & 67.31 & 92.71 & 97.43\tabularnewline
Inception A+B & 72.11 & 91.90 & 96.45\tabularnewline
\hline
MuDeep & \textbf{79.01} & \textbf{97.00} & \textbf{98.96}\tabularnewline
\hline
\end{tabular}
\par\end{centering}
\caption{\label{tab:inception}Results of comparing with different inception
models on the CUHK01 dataset. }

\end{table}

\begin{table}
\begin{centering}
\begin{tabular}{c||ccc}
\hline
Rank & 1 & 5 & 10\tabularnewline
\hline
\textendash{} Fusion  & 77.88 & 96.81 & 98.21\tabularnewline
\textendash{} ClassNet & 76.21 & 94.47 & 98.41\tabularnewline
\textendash{} Fusion \textendash{} ClasNet & 74.21 & 92.10 & 97.63\tabularnewline
\hline
MuDeep & \textbf{79.01} & \textbf{97.00} & \textbf{98.96}\tabularnewline
\hline
\end{tabular}
\par\end{centering}
\caption{\label{tab:alpha}Results of comparing with the variants of MuDeep
on the CUHK01 dataset. Note that ``\textendash Fusion'' means that saliency-based
learning fusion layer is not used; ``\textendash ClassNet'' indicates
that the classification subnet is not used in the corresponding structure.}
\end{table}

\begin{table}
\centering{}%
\begin{tabular}{c||ccc||ccc}
\hline
Dataset & \multicolumn{3}{c||}{PRID-2011} & \multicolumn{3}{c}{iLIDS-VID} \tabularnewline
\hline
Rank  & 1  & 5  & 10 & 1  & 5  & 10 \tabularnewline
\hline
RCNvrid\cite{mclaughlin2016recurrent}  & \textbf{70}  & \textbf{90}  & \textbf{95} & \textbf{58}  & \textbf{84}  & \textbf{91} \tabularnewline
STA \cite{liu2015spatio}  & 64  & 87  & 90 & 44  & 72  & 84 \tabularnewline
VR \cite{wang2014person}  & 42  & 65  & 78 & 35  & 57  & 68 \tabularnewline
SRID \cite{karanam2015sparse}  & 35  & 59  & 70 & 25  & 45  & 56 \tabularnewline
AFDA \cite{li2015multi}  & 43  & 73  & 85 & 38  & 63  & 73 \tabularnewline
DTDL \cite{karanam2015person}  & 41  & 70  & 78 & 26  & 48  & 57 \tabularnewline
DDC \cite{hirzer2011person} & 28  & 48  & 55 & \textendash{}  & \textendash{} & \textendash{} \tabularnewline
\hline
MuDeep  & 65  & 87  & 93 & 41  & 70  & 83\tabularnewline
\hline
\end{tabular}\caption{\label{tab:Results-of-VIDEO}Results on the PRID-2011 and iLIDS-VID datasets}
\end{table}

\section{Conclusion }

We have identified the limitations of existing deep re-id
models in the lack of multi-scale discriminative feature learning.
To overcome the limitation, we have presented a novel deep architecture
\textendash{} MuDeep to exploit the multi-scale and saliency-based
learning strategies for re-id. Our model has achieved state-of-the-art
performance on several benchmark datasets.

\vspace{0.03in}
\noindent \textbf{Acknowledgments.}  This work was supported in part by two NSFC projects ($\#U1611461$ and $\#U1509206$) and one project from STCSM ($\#16JC1420401$).

{\small{}\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


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text:
Q: How label to the data (not the axes) of the plot of a confusion matrix that displays True Positive, False Positive, False Negative and True Negative I am trying to label the plot of my confusion matrix. The first image below is what my plot currently shows. The subsequent image is what I want to emulate. Here are the features I want to add in order of importance.

*

*Need to have the data labeled like TP = 374  etc.

*Can I set background color of xlabels and ylabels?

*Can I specify the face colors since there are only 4 quadrants?

A: You can use seaborn's heatmap with explicit annotations. The colors are defined by the data values in combination with the colormap.
import matplotlib.pyplot as plt
import seaborn as sns

cm = [[327, 237], [289, 200]]
sns.set(font_scale=3)
plt.figure(figsize=(7, 7))
ax = sns.heatmap(data=[[1, 0], [0, 1]], cmap=sns.color_palette(['tomato', 'lightgreen'], as_cmap=True),
                 annot=[[f"TP={cm[0][0]:.0f}", f"FP={cm[0][1]:.0f}"], [f"FN={cm[1][0]:.0f}", f"TN={cm[1][1]:.0f}"]],
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redpajama_set_name: RedPajamaStackExchange


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Home Shows & Happenings Spotlight
Hot Stuff: Here's The Feel-Good Summer Station ID We've Been All Waiting For!
Any Kapamilya Station ID always induces an LSS, and if you've seen this year's edition, it's proving to be no different. This year's Ikaw Ang Sunshine Ko slogan could also not be as appropriate with the metaphor game–amidst the heavy load we go through everyday, we look to each other and tune into our fave Kapamilya shows, music, and beyond for more light, fun, happiness.
[related: Hot Stuff: Say Hello To Your Newest Summer Anthem From BoybandPH, Sue, MayWard, Ylona, And More!]
The newly released eight-minute video of this year's Summer Station ID features over 100 Kapamilya stars, including those who sang the anthem. The video shows many of our favorites like Piolo Pascual, Anne Curtis, Vice Ganda, and Sarah Geronimo visiting a bunch of Filipinos from all walks of life and grooving and jamming with them to usher in the hot season. From fashionista pedicab drivers to dancing water vendors in the street to guitar-playing nuns, it was a spectacle of how we can defitely bring the "sunshine" to each other wherever and whenever!
As the season also means travel, the video also features some of the country's notable summer destinations, including Pililia's wind farm, Lake Pandin, and Laoag's sand dunes.
Check out this year's Summer Station ID again and be reminded of all the reasons to be smiling about life!
ALSO READ: Hot Stuff: The Brow Brigade and a Pro Tutorial—7 Pairs of Celebrity Eyebrows We Wish Were Ours
TAGS: elnella Hot stuff KathNiel LizQuen SofiEgo Summer Station ID

redpajama_set_name: RedPajamaCommonCrawl


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Q: Topology question from Kolmogorov's text I am asked to prove that given a topological space $T$, a set $G \subseteq T$ is open iff every point $x \in G$ has a neighborhood contained in $G$.
Now, this confused me because I am supposed to prove this just from the basic axioms, and I had previously seen a definition of an open set as this; I suppose I am confusing the topological axioms with the open sets we are used to in metric spaces.
Thanks in advance!

A: If the set $G$ is open then it is a neighborhood of each its point $x$. Conversely, if each point $x$ of $G$ has a neighborhood $U_x$ contained in $G$ then is has an open neighborhood $V_x$ contained in $U_x$, so the set
$G'=\bigcup_{x\in G} V_x$ is open. Since each element $x$ of $G$ belongs to $V_x$, and $V_x$ is contained in $G'$, $G$ is contained in $G'$. On the other hand, each $V_x$ is contained in $G$, so in $G$ is contained also their union $G'$. So $G'=G$ and thus the set $G$ is open.


Place the lovely colored animal figurines on the table, and then cover them with the houses, so the children won't see where each animal hides.
One child takes a card with a picture of an animal on it, and tries to guess where that animal hides. They will need to remember the whereabouts of the animals.
After several turns they will need to remember 4 or 5 different animals, an interesting and challenging activity when you're 3 years old!
Play with your children for 10 minutes a day and, within a month, see their memory improve. In the years to come they will thank you for the help during their exams!


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Fallout show
I made Kylo Ren my BFF and got mind-tricked by Rey in The Sims 4 Star Wars Pack
By Christopher Livingston
Take a Star Wars-themed vacation and bring home a lightsaber and astromech droid in Journey to Batuu.
(Image credit: EA)
A lot of dedicated Sims fans were extremely disappointed to learn the new game pack for The Sims 4 was a Star Wars tie-in. As a more casual Sims player, I was kind of excited—though since I'm also a casual Star Wars fan, I'd never even heard of Batuu, the planet where the game pack takes place. Which, as it turns out, is a location based on Disney's Star Wars theme park—right down to some of the shops you can visit.
More Sims series
(Image credit: Maxis, Electronic Arts)
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It's franchise tie-in inception: a game pack where you visit a fictitious theme park based on a real theme park based on a movie. But after playing through a lot of The Sims 4 Star Wars: Journey to Batuu, I can't say I didn't have a good time.
Journey to Batuu acts as a vacation destination (sort of like Jungle Adventure) as opposed to a new world to build houses and live in. After creating a Sim version of myself, I picked the vacation travel option from my phone and entered the Star Wars universe—or the Disney theme park version of it, at least.
Batuu is divided into three zones: Blackspire Outpost, where there's a cantina for dancing and socializing, plus a few shops, the First Order District where the stormtroopers patrol, and a Resistance encampment in the woods nearby.
You're free to visit any of these spots, and you can choose to side with the Resistance, the First Order, or the "scoundrels" as they're called. At the start you have a neutral reputation with all three factions, but as you complete missions you'll see that rep either rise or fall, and you'll earn rewards at certain milestones.
I immediately joined the First Order because I wanted to dress up like a fat stormtrooper and see what Kylo Ren sounded like when he spoke Simlish. (FYI: He sounds pretty depressed.) I joined up and asked how I could help.
And wow, what a confidence boost space-fascism is! My character, who I modeled after myself, gets stressed out in group settings and is embarrassed easily, so I'm always tense and unhappy (not unlike real life). But once I start doing a few First Order missions—which direct me to push other Sims around, demand to see their IDs, beat them up if they're friendly to the Resistance, and take them prisoner so they can be sent to some offworld prison—my confidence really kicks in. I'm strutting around the joint, happy as a clam.
I'd be more troubled by how quickly the Sim version of me took to oppression if I wasn't such a terrible First Order officer—I keep getting caught trying to hack electronics at the Resistance camp and I routinely get my ass kicked when trying to arrest someone. I have to spend a lot of my free time jogging around Batuu to increase my physical skills just so I'm strong enough to subdue some harmless alien and haul them off to space-jail.
The missions eventually get a bit repetitive, but there's always a selection available that involve activities like hacking electronics, interacting with other Sims, talking up your faction with the locals, and piloting ships on a mission (more on this in a minute).
It doesn't take too long to rise through the ranks of a faction, even with all the breaks to "sleep" and "freshen up" (there are no bathrooms or bedrooms on Batuu—maybe Disney told EA they didn't want to see a bunch of videos of Kylo Ren and Rey taking dumps, using the shower, or having woohoo). Changing ranks unlocks new uniforms and galactic credits, which you can spend on parts to build your own lightsabers and R-series or BB-series droids. The droids are cool, and they'll follow you around and can be used to distract people while you're hacking electronics. (Unfortunately you can't have a loyal Gonk droid (opens in new tab), my favorite, and while you can add one to your house later they just act as power generators. Lame.) Don't expect to be slicing limbs off people with your lightsaber—you can challenge other saber-bearing Sims to duels, but they're friendly and no one gets vivisected.
Once you've climbed the ranks a bit, both Kylo Ren and Rey will appear in the world, hanging around their respective zones. You can chat with them, engage in some lightsaber sparring, and even get a hug if you play your cards right. I'm not a fan of any of the new Star Wars films really, but I still enjoyed being able to interact with both Kylo and Rey in The Sims 4.
If your faction reputation is too negative, they won't be so friendly, however. I tried to make nice with Rey after I became a high-ranking First Order officer, and she immediately Jedi mind-tricked me into pissing off. In fact, she sent me walking right off out of the Resistance camp and back to the other map. Rey ain't playin'.
As for the tantalizing prospect of piloting the Millennium Falcon, an X-Wing, and a TIE Echelon ship, well, you only sort of get to fly them. They're more like choose-your-own-adventure quests. The ship will blast off but you're just presented with a bit of story ("X-Wings attack your ship!") and given a choice (in text) of how you'd like to react. Depending on the items you've collected, like a lightsaber or an astromech droid, you'll have more options to pick from increasing the chances you'll complete your mission.
You're not forever locked into your choice of faction—after I maxed out my reputation with the Order, I strolled right into the rebel encampment and quit, which erased my evil rep completely so I'd be able to start working for the Resistance. So it's possible to complete missions for all three factions if you want, and unlock all the different costumes and items for the same Sim.
You can come and go between Batuu and your normal home whenever you like—I kept getting notifications that my bills were stacking up so I had to bounce away from the sci-fi adventure to avoid my water getting turned off back at home. And you can bring some stuff back with you, like your lightsaber (you can use it to practice with one of those little softball-sized droids). Naturally there are new home-building options included in the pack so you can set up your own cantina on planet Earth.
I had a good time as a bumbling First Order officer. Yeah, it's not exactly my dream tie-in to the Star Wars universe—I'd prefer to live on a Death Star than visit a theme park. But I got to duel with Rey and got a hug from Ren. Not even Luke can say that.
Sims 4 cheats (opens in new tab): Every code you need
Christopher Livingston
Chris started playing PC games in the 1980s, started writing about them in the early 2000s, and (finally) started getting paid to write about them in the late 2000s. Following a few years as a regular freelancer, PC Gamer hired him in 2014, probably so he'd stop emailing them asking for more work. Chris has a love-hate relationship with survival games and an unhealthy fascination with the inner lives of NPCs. He's also a fan of offbeat simulation games, mods, and ignoring storylines in RPGs so he can make up his own.
I played that $2,000 Steam game, and its ridiculous price is probably for the best
Xbox accused of being 'woke' for a deeply stupid reason
Roller Drama review


Covert combines simple concepts with a depth of play that allows players the freedom to combine special abilities and card combos to complete their Missions and overcome obstacles. Utilize your assets. Make your move. Don't let anyone stand in your way.


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Education knows no boundaries
By DU Communications
How one student's journey defies the tradition in higher education
While many retired professionals are basking in the Florida sun chipping golf balls, 71-year-old Steve Wessell was in his native Michigan completing a master's degree at Davenport University.
A career banker, Wessell worked at 1st Source Bank in South Bend, Indiana, where he spent the last eight years of his 22-year career as the bank's executive vice president. Upon retiring, he returned home to Grand Rapids, Michigan, when the longing for something more than recreation set in.
"All of a sudden you're just another old guy at the shuffleboard court," he said. "You've lost your purpose if you know what I mean. And so I decided I'd go back and get an MBA."
He admits it's something he should have done decades ago, but as he said,
"I just never either had the time or took the time to do it, so I decided that could be my first retirement project."
The average age of a graduate student is 31 years old. Wessell was slightly more experienced when he embarked on his graduate education at the age of 68. At Davenport, you can earn an online MBA or take the program in person.
"I wanted the interaction with people, and I thought, "What could be better than to be doing it in a classroom," he recalled. Apart from being driven online by the pandemic, he took all of his classes in person to fully capitalize on the opportunity for interactions with fellow students and Davenport's professors.
If you ask Wessell about his experience, he'll tell you it's nothing out of the ordinary. "I think the ones that are remarkable are the students who are working a full-time job and raising a family while trying to go to school," he said.
Davenport's small class sizes — some as few as five students and most about 12 students — made it easy for Wessell to get involved without feeling overwhelmed. "I think that is one of the strengths of the program," he said. "The instructor knows who you are, and you get an opportunity to exchange ideas with other students more than you would in a larger class."
Davenport has always incorporated real-world experience and expertise in its instruction. Nearly 61% of credits last fall were taught by adjuncts. Instructors at Davenport offer graduate students a decided advantage, Wessell observed. It can be boiled down to even the distribution of theory and application.
"There's a good mix of instructors that are adjuncts — they have real-life experiences in the business world and bring that to the classroom," he said, "and there are others with an academic background who are full-time faculty, and they bring something different, a bit more theoretical emphasis on the classes. It's a great and important balance between the two."
"I worked in the business world for 49 years. I learned a lot in the program that I could have used in my actual career had I done it earlier" – Steve Wessell, MBA '20
As a professional with real-world experience, Wessell noticed how Davenport's academic balance added value to his peers, who were holding down full-time jobs and careers while pursuing their graduate degrees.
"I worked in the business world for 49 years. I learned a lot in the program that I could have used in my actual career had I done it earlier," he said. "I think most of the people [in the program] can use that kind of mentorship that these instructors who have business experience bring to the table."
"It's a beautiful campus. The facilities are nice. The people are great, and there's a tremendous support. The advisors help you get classes set up. The university is convenient for people with great parking and a safe environment. I'm very impressed," said Wessell.
In the end, it goes to show that no matter your age or station in life, you can pursue a graduate education at Davenport University. In 2020, Wessell, together with 507 of his fellow master's degree graduates, became part of the 68,000 alums who have joined the ranks of the Davenport Alumni Association.
"When I started thinking about going back to school, I remember asking myself, Am I too old?" said Wessell. "Then I remembered that President Jimmy Carter's mother, Lillian Carter, joined the Peace Corps at age 68 and traveled to India to work as a nurse. I figured I can go to class a night or two a week as a way to find purpose in my retirement. Mrs. Carter's service was remarkable, my efforts were not. What an inspiration her story has been to me!"
The Wessell Legacy
When Steve Wessell completed his graduate degree in 2020, he couldn't help but remember his father, Tyrus Wessell, and the legacy he left behind at Davenport University — and how he had followed in his father's footsteps.
"I wish he had been here to see that I did it and that I was as committed as he was to becoming a lifetime learner," said Steve. "But he is, without doubt, the biggest influence in my life."
The elder Wessell passed away in 2003, after spending nearly 70 years at Davenport University where he entered what was then Davenport-McLachlan Institute as a student in 1931.
After M.E. Davenport provided him a custodial job to pay for his tuition following his first year of school, Ty completed his education at Davenport, then continued his schooling elsewhere before returning to Davenport. Ty served as an instructor, vice president, and treasurer, bookstore manager, officer of Davenport Schools, board member of the Davenport Foundation, and advisor to the Davenport/Sneden family.
"He spent his whole life at Davenport. He was highly regarded," said Steve. Serving on the M.E. Davenport Legacy Endowment Committee is perhaps how Steve pays homage to his father. The committee members allocate funding to staff /faculty who apply for support of their initiatives, programs, and projects that may fall outside of the university's operating budget.
Along with having the campus bookstore named after him, Davenport's most prestigious honor, the Tyrus R. Wessell Award annually recognizes an employee who reflects the character and commitment displayed by Wessell during his time as a teacher, mentor, and friend to the Davenport community.
"He was a man of immense character and one of the nicest people. He could have been successful in a lot of things, but he loved Davenport, and he loved what he did there," said Steve.
"I remember asking him once later in life if he ever had any second thoughts if he was ever sorry that he spent his entire career there. And he simply answered, "Not for a single minute."
Learn more about Davenport's business degrees or the College of Business here.
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Erasing the education divide
One thought on "Education knows no boundaries"
Adam Meester says:
What a wonderful motivational story! While young people say it's too hard and time-consuming for them, the amazing Steve makes his dream come true with no excuses. A true role model!


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Home > Tracy Books > Mind Controlled Sex Slaves and the CIA
...the Senator referred to his many ladies as his 'Sex Slaves
PROJECT MONARCH . . . "In the months preceding Sept. 11, 2001, the airwaves of cable news channels were filled with the saga of Congressman Gary Condit, and the cover-up of his affair with intern Chandra Levy. Levy disappeared on April 30, 2001 -- Walpurgisnacht -- the most sacred witch holiday on the calendar. Condit's not very forthcoming statements on the matter, coupled with the fact that Chandra appeared to be on her way to visit him on the night she disappeared, led to speculation that he was somehow involved with her kidnapping and/or death. . . This speculation reached a fevered pitch when bizarre details of Condit's sex life began to mount. 27 other women came forth claiming to have had sexual relations with the Congressman, including two woman (15 at the time), who claimed that he had violently raped them while still a State Assemblyman. Condit was described by one attorney involved in the case as "a serial predator of woman." One of the women, who met the leather-clad Condit at a biker convention, revealed that the Senator referred to his many ladies as his 'Sex Slaves.' She also said that he insisted on using code names with these women while talking on the phone, and refused to wear condoms, starting that "there's a cure for AIDS, anyway . . . Following the media blitz of the Clinton-Lewinski sex scandal, and then the Condit-Levy affair, many people began to wonder:why do so many men in positions of power require lots of sex with a variety of people? And where do such men get this sense of entitlement, this attitude that they should be continually provided with a harem of sexual servants, maintained, oftentimes, at taxpayers' expense? Few, if any journalists brought up the amazing similarity between the story of Gary Condit's Sex Slaves and the stories of dozens of men and women who claim to have been forced into sexual slavery for the wealthy and powerful by none other than our own Central Intelligence Agency, through an offshoot of the MK-ULTRA Mind Control program known as Project MONARCH." --By the author of Mind Controlled Sex Slaves of the CIA, Tracy R. Twyman
Few, if any journalists brought up the amazing similarity between the story of Gary Condit's Sex Slaves and the stories of dozens of men and women who claim to have been forced into sexual slavery for the wealthy and powerful by none other than our own Central Intelligence Agency, through an offshoot of the MK-ULTRA Mind Control program known as Project MONARCH." –By the author of Mind Controlled Sex Slaves of the CIA, Tracy R. Twyman
ASIN: B001JEO4M6
Other book sellers:


Knitting: warm and fuzzy science | ARISE! LET US BE GOING!
A friend in Australia hand made a blanket for me earlier this year. She's a science geek. Since, in the mountains, temps go down about 37 degrees every night throughout the year, I've been using this blanket throughout the Summer. It's super light, just lots of air in the midst of a very loose stitch. It's double layered. Lot's of air. That's a good use of thermo-dynamic technology. In looking at the huge spaces between stitches you would never think it could possibly hold that much heat, but it does. But there's more. She used the golden ratio and the Fibonacci sequence. I feel smarter just throwing this over myself.
Oh God's goodness! Father, what a good teacher you are, even in an ode to your beautiful blanket. Despising math in school, I continued to let numbers confuse me until now…. wow! I can't wait to tell my daughter about the Fibonacci sequence and the golden ratio.
I always knew knitting was hard!
Only kidding – it's been awhile but I have successfully completed a few things.


I recently sold two-thirds of my position in Enterprise Product Partners (EPD). I laid out all the facts behind the sale in the original article. However, several readers missed the facts, and instead utilized their opinions and their own experiences in order to negate the article.
Now I never pay attention to other people's mere opinions, because I firmly believe that comments on my investing strategy do not really add much to my investment performance results. This is a crude paraphrase of Warren Buffett's famous quote that "You Pay a High Price for Cheery Consensus" ( I am no Buffett though) .However, when I am presented with facts, rather than opinions, I always listen.
I typically try to hold securities" forever", although I tend to avoid holding too tight if I can find a better play(s) that is of similar quality, yet can provide me with more bang for my buck. Kinder Morgan Inc (KMI), Kinder Morgan Management LLC (KMR) and Oneok Partners (OKS) fit the profile. If other comparable MLPs were trading similarly to Enterprise Product Partners (EPD), I would have been more than happy to hold. I am also trying to maintain flexibility as well, just in case. Dividend investing is not a black or white activity, which is why rigid rules based systems never really hold up in real life.
Many of the arguments against my selling related to the "onerous tax" picture of selling. I often have mentioned that I never care about taxation when I sell. More investment sins are probably committed by otherwise quite intelligent people, because of "tax considerations" than from any other cause. Anyone that puts taxes before investment quality is just asking for trouble. I have known of many investors who never sold their employer stock at a gain, in order to avoid paying taxes. Well, lucky for most of them, the gains had turned into losses by the time they needed the money to put a downpayment on a house, send a kid to college or retire from the workforce. So much for putting taxes before your investment thesis.
However, as investing is more of a gray area than black or white, I am also open to a more softer approach to tax management. For example, this year I began maxing out my 401 (k). I also maxed out SEP IRA for 2012 right around April 15, 2013. Because I am still in the accumulation phase of building my nest egg, I have come to realize that taxable MLP's with their K-1 forms might not be the most optimal investment from a tax standpoint for me right now. This is because I don't really plan on using this money to live off for several years from now. As a result, I might be better off in an entity like Kinder Morgan Management LLC (KMR), than a Kinder Morgan Energy Partners (KMP). It doesn't make sense to be collecting distribution income today from an MLP, when this reduces taxable base and my retirement is years from now.
Several individuals reaching out to me about the post, had mentioned that they had purchased Enterprise Product Partners (EPD) at prices such as $20 - $30/unit. Because they had held the partnership for so long, their basis was probably approaching zero. As a result, they viewed my selling of Enterprise Product Partners (EPD) as a bad idea. However, they thought of it as a bad idea, because they didn't read the facts of the article, that was pertaining to my situation. Instead they chose to be viewing it from their own position. Ignoring facts is typical in most aspects of life, as humans are emotional creatures that respond better to emotion that factual evidence. Ignoring the facts is what separates winning investors from losing one.
Basis in master limited partnerships can keep dropping over time, because depreciation typically is higher than income, and therefore the net result is a distribution that is classified as return of capital by the tax authorities. As a result, this distribution decreases your tax basis. This means that as long as your basis is above zero, any distributions you receive from an MLP would not be taxed.
If you decide to sell however, your tax situation is very interesting. If you bought at $40/unit in 2011, and sold at 63.73/unit in 2013 your gain is more than the $23.73 difference. This is because you likely earned about $6.27/unit in distributions during that period ( approximations are made in this example in order to end up with nice rounded numbers that are easier to digest). As a result, you owe about $30/unit to tax authorities. $6.27 of this would be taxed as ordinary income, while $23.73 would be taxed as a long-term capital gain.
If your head is spinning from these numbers, you are not alone. You see, the biggest reason investors have against selling Enterprise Product Partners (EPD) is because of taxes. With a basis of about $40 something/unit, and gain of about $20 capital and $6 - $7 return of capital, I am doing the smart thing of cutting my "stake" early, and moving into other entities that can provide me with slightly better growth, yield and total returns, minus the tax nightmares. It is just intriguing to me that some investors are preaching against selling because the taxes were onerous, yet they did not register the flaw in their thinking.
It therefore seems smarter to sell Enterprise Product Partners (EPD) when I have a $6-$7 ordinary gain and buy a security with slightly better characteristics (KMI and KMR) and pay the least amount of taxes, than do this in 10 – 15 years, when my basis will be zero. After your basis is exhausted, and you don't add any more funds, you will have to pay ordinary income taxes on the amount you receive as distributions.
I am still holding onto approximately 33-34% of original position in Enterprise Product Partners (EPD). The partnership could easily end up yielding less than 4%, which could be a sell signal. If the partnership yields less than 4% however, I might consider dropping my stake altogether. I also am going to keep my options open, and could decide to sell by end of 2013 and replace it with Kinder Morgan Management LLC (KMR) for example.
Astute readers would notice that for my original position of Enterprise Product Partners (EPD), I am left with about 50-55% in MLP entities for this position( EPD and OKS). They are subject to these onerous MLP K-1 filings that so many other investors are losing sleepless nights over. Of course, as I mentioned before, the decision to sell was based on valuation, although taxes did come out second in the decision.
I found the Enterprise Product Partners (EPD) partnership to be richly valued, and I liked the fact that there are alternatives that can provide me with better values. In addition, because of adding to Kinder Morgan Inc (KMI) and Kinder Morgan Management LLC (KMR), I am going to have less of a hassle with MLP filings. Kinder Morgan Inc is going to deliver higher dividend growth, and possibly high total returns - at 4% yield growing 10% is much better than a 4.30% yield growing 6%. Kinder Morgan Inc is a corporation, and therefore I am dealing with 1099 at tax time, versus K-1s.
Kinder Morgan Management LLC (KMR) is a better choice than Enterprise Product Partners (EPD) because it pays distributions in extra stock, which is not a taxable event. It also yields moreand would likely have slightly higher distributions growth. In addition KMR trades at a discount to Kinder Morgan Energy Partners (KMP), which is closely tracks ( although not as high as a few years ago). So no K-1 forms here either.
ONEOK Partners (OKS) is more of a value play here, as it is not loved by investors at present times. However, if the partnership growth plans materialize, you could be very positively surprised in 4-5 years. Sure, there are K-1s there, but I believe it will do much better than EPD over time.


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JUST IN: AMCON takes over Ibadan Electricity Distribution Company
Exploring hysteric dimensions of Lagos, other layers of life
Posted on July 17, 2021 July 16, 2021 Author Our Reporters	Comments Off on Exploring hysteric dimensions of Lagos, other layers of life
Oloyede Michael Taiwo's work leaps off the pages of the book and cups your nape before sucking you into a haven of imageries, metaphors and a delightsome narrative of everyday life. His writing is a call to dance, hop, spin and bask in the liberating power of words. His evocations in the poem, 'I remember you,' are not just captivating, but also cleansing. You are the contour of Mississippi, / the texture of chance, / a lonely running road.
The poem explains the emotions of a migrant about his home. He likens his home country to a lover. Taiwo is that piece, emoted in an alluring style. The book, Lagos is killing me, depicts life in Lagos and how people struggle everyday to make ends meet. It describes the shenanigans of a glittering city flanked by economic wilding, ostentatious rogues, decent paupers, promising opportunities and the vicious circus of wolves and sheep.
His collection of poems has a potpourri of poignant works which covers the expanse of human life. We all know the hurdles of life are overwhelming and people try to coping mechanisms or just adopt functional toolkits for enduring or surviving the hurdles. The author wrote on the importance of being smart and nimble in the city of Lagos.
In the poem, Lagos is killing me, the poet wrote: …her pinches I whistled in loud whispers, of men in economic diapers, / Manhood an allegory of blunt clippers / This Lagos explodes in my head like the thud of a million crackers. / Lagos is killing me… The poet employed anthropomorphism and apostrophe in driving home his feelings about Lagos. He wrote passionately about every nano second experiences of Lagosians.
Taiwo, in this prismatic collection, unfurls the ebb and flow of the issues of life. He showed an in-depth knowledge of poetry devices and wove words with the lilt of a master craftsman; beating words into shape to illustrate the many issues of life. In the poem, Curly smoke; the writer announces the global relevance of African herbs and posits the need for Africans to take pleasure in exploring traditional medicines and optimising its benefits for global relevance. The poet, in the poem, festooned, wrote about a woman in an abusive relationship who was later forced to eliminate her lover as a result of constant abuse with irreparable damage to her mental health.
He also wrote against police brutality and banditry and also emphasised the need for hope and courage and togetherness of Nigerians against political vermin and economic saboteurs in the poem, Ibirikembiri. Taiwo's collection is a marvel and a robust read for all poetry lovers with taste buds for something esoteric and nostalgic.
The author also explored the theme of motherhood and Black Lives Matter. He wrote about the case of George Floyd who was irritably murdered in broad daylight at the glare of the whole wide world, hence, the outburst across social media platforms to condemn the dastardly act and get justice for the innocent man. In one of his canticles for motherhood, the author wrote: …this Convener of my frame is the most famous of all being of fame for my soul on the sands of time, she compelled to stay.
Taiwo also wrote a poem for children with special needs: We pray to God for blessings, / but he gave us more than our yearnings / Lips caressing words like fingers on rosaries… He elaborated the experiences of autistic and other special children to solicit compassion and inclusion in policy formulation from decision makers in the society and every person with a good heart. The poet has impressively delivered an amazingly exciting body of artwork. Lagos is killing me is a book every literati should have on the shelf.
Top Nigeria entertainers who are lawyers
Posted on August 6, 2021 August 5, 2021 Author EDWIN USOBOH
The Nigerian entertainment industry was once seen as a sector for dropouts who had nothing to do before taking up entertainment. That narrative is long gone and nowadays, many showbiz practitioners in Nigeria are graduates who majored in courses that many would adjudge as "unrelated" to acting. In this report, New Telegraph's EDWIN USOBOH takes […]
Two stars and twinkles of love
The reality of being a channel for lives transcends the two who primarily account for their birth. This role calls for a deeper-than-the surface consideration as that is the zenith of such arduous task. Through the eyes of an eight year old, and the significant representation by his five year old sibling, […]
Former Glee star, Naya Rivera, presumed dead
*Police say body may never be found Former Glee star Naya Rivera is presumed dead after emergency teams said the effort to find her has now been classed as a "search and recovery operation". The actress was reported missing on Wednesday after disappearing during a boating trip on Lake Piru in California with her four-year-old son Josey […]
Star-studded series, Tough Love, other Nollywood blockbusters for July
Ugly pneumonia is not beautiful pandemonia
JUST IN: AMCON takes over Ibadan Electricity Distribution Company January 21, 2022


New portals for assistance in the "humanity to Hue-manity" conversion process have opened during the 7-3 to 7-5 period. Such are aligned with new Higher Light influxes currently ongoing.
Current portals also enable more facile communication with Inner Earth beings. No longer are such exchange portals limited to a few limited locations, eg., Shasta. Communication exchanges with Inner Earth beings will now occur readily, and often.
Fabricated "Light Work" is being exposed for what it is, and that "purity of intent" is lacking in such. Following Higher Light Guidance in Self is the norm at this time point.


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Home Gov & Administrative Law Wisconsin Middleton
Middleton, Wisconsin Government & Administrative Lawyers
Micabil Diaz Martinez
Middleton, WI Gov & Administrative Law Attorney
(608) 836-1444Marquette University Law SchoolMarquette UniversityEastern District of Wisconsin, Western District of Wisconsin, Wisconsin, 1st Circuit, 7th Circuit, US Army Court of Criminal Appeals, US Court of Appeals for the Armed Forces, US Court of Appeals for Veterans Claims and IowaWisconsin Law Foundation, Veterans Of Foreign Wars, United Migrant Opportunities Services and El Conquistador NewspaperAmerican Bar Association, American Association of Premier DUI Attorneys...
William Cole
Madison, WI Gov & Administrative Law Attorney
(608) 221-0079University of Wisconsin - MadisonRipon CollegeWisconsinWisconsin State Bar
Charles Michael Kernats
University of IowaKnox CollegeWisconsinWisconsin Bar Association and Wisconsin State Bar
Todd Liebman
Baraboo, WI Gov & Administrative Law Attorney
Ryan Woody
Hartford, WI Gov & Administrative Law Attorney
(262) 673-7850Marquette University Law SchoolBradley University10th Circuit, 11th Circuit, 7th Circuit, 8th Circuit and WisconsinSuperlawyersWisconsin State Bar and Defense Research Institute
Crystal G. Reinoso
Janesville, WI Gov & Administrative Law Attorney
(608) 215-4225Marquette University Law SchoolFlorida and WisconsinRock County Bar Association, YWCA Rock County Board...
Jeffrey Lee Murrell
Milwaukee, WI Gov & Administrative Law Attorney
(414) 224-0668Marquette UniversityWisconsinAmerican Society of Legal Advocates, The National Trial Lawyers, expertise.com and Martindale-HubbellThe Alternate Defense Pre-Sentence Investigation Report: Tempering Harsh State Sentencing Recommendations and 90 Proof: Liquor Licensing in Wisconsin
Brady R. Henderson
(414) 563-7453University of Oklahoma College of LawUniversity of OklahomaOklahoma, 10th Circuit, U.S. Supreme Court and WisconsinACLU of Oklahoma, Society of Professional Journalists - Okla. Chapter and Oklahoma Attorney GeneralState Bar of Wisconsin and Oklahoma Bar Association
Michael F Brown
(414) 928-0080University of Wisconsin Law SchoolUniversity of Wisconsin - MadisonState Bar of Wisconsin
Charles Bryan Palmer
Waukesha, WI Gov & Administrative Law Attorney
(262) 956-6518Marquette University Law School and University of Wisconsin Law SchoolU.S. Court of Appeals, Seventh Circuit 1997, U.S. District Court, Eastern District of Wisconsin 1990, U.S. District Court, Western District of Wisconsin 1990, Wisconsin, Wisconsin 1990 and WisconsinWisconsin State Bar
Amy Menzel
Neenah, WI Gov & Administrative Law Attorney
(920) 720-0000Marquette U and Lawrence UniversityWisconsinThe National Trial LawyersWisconsin State Bar and Fox Valley Technical College
Nathan P. Olson
Oshkosh, WI Gov & Administrative Law Attorney
(920) 361-1181Thomas M CooleyWisconsinWisconsin State Bar
Christopher Andrew Bowen
VirginiaVirginia State Bar
Joseph Seifert
(414) 273-9900Ohio State University, Moritz College of LawUniversity of New York, Excelsior CollegeWisconsinOhio State University
David Welles
Wausau, WI Gov & Administrative Law Attorney
(715) 843-6700University of Wisconsin - MadisonWisconsinWisconsin State Bar
Jennifer Ament Moeller
Sturgeon Bay, WI Gov & Administrative Law Attorney
(920) 743-8485Marquette University Law SchoolBoston CollegeWisconsinWisconsin State Bar and Door County Bar Association
The LII Lawyer Directory contains lawyers who have claimed their profiles and are actively seeking clients. Find more Middleton, Wisconsin Government & Administrative Lawyers in the Justia Legal Services and Lawyers Directory which includes profiles of more than one million lawyers licensed to practice in the United States, in addition to profiles of legal aid, pro bono and legal service organizations.


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