text:
\section{Introduction}

\begin{figure}[thb]
\center{\includegraphics[width=0.47\textwidth]{introduction.png}}
\caption{Part alignment challenges in person ReID. (a-c): Describing body parts by bounding boxes may introduce many irrelevant regions from background and other parts. Matching between features extracted from loose boxes in (a) and tight boxes in (b) would deteriorate the matching accuracy. A finer part region representation in (c) would help alleviate this problem. (d-f): The importance of different parts should be adaptively adjusted. Upper body is occluded by forearms in (d), the two forearms are all occluded in (e). Features from occluded part should be eliminated during matching, while salient visual cues like yellow backpack in (f) need to be emphasized.}
\label{fig:introduction}
\end{figure}

Person re-identification (ReID) targets on identifying the same individual across different camera views. Given an image containing a target person (as query) and a large set of images (gallery set), a ReID system is expected to rank the images from gallery according to visual similarity with the query image.
It has many important applications in video surveillance by saving large amount of human efforts in exhaustively searching for a target person from large amount of video sequences. For example, finding missing elderly and children, and suspect tracking, \etc.

Many research works have been proposed to improve the state-of-the-art performance of public ReID benchmarks.
However, identifying the same individual across different camera views is still an unsolved task in intelligent video surveillance. It is difficult in that pedestrian images often suffer from complex background clutters, varying illumination conditions, uncontrollable camera settings, severe occlusions and large pose variations.

\begin{figure}[thb]
\center{\includegraphics[width=0.47\textwidth]{align_cmp.png}}
\caption{Our Pose-guided Part Attention precisely captures the target parts, excludes background clutter and adjacent part features, while pose-guided rectangular RoIs \cite{zhao2017spindle,su2017pose,zheng2017pose}, patches \cite{zhao2013person,li2014deepreid}, and stripes \cite{shi2015constrained,ahmed2015improved} include extensive noise features. }
\label{fig:align_cmp}
\end{figure}

Viewpoint changes and pose variations cause uncontrolled misalignment between pedestrian images. As the improvement of human pose estimation \cite{mpiiandriluka20142d,cao2016realtime}, recent works \cite{su2017pose, zhao2017spindle,zheng2017pose} utilized pose estimation results to align body parts for better matching. Although great improvement in performance was obtained, there are still noticeable problems in these methods. These methods deal with misalignment by extracting features from patches, stripes, or pose-guided region of interest (RoI), where rectangular RoIs often introduce noise from adjacent parts or background in feature and lead to inaccurate matching. For example in Fig. \ref{fig:introduction}(a), features of the right leg\rev{(the second bar)}{} is extracted from its bounding box \rev{(the orange box in the image)}{in orange}, which includes extensive noise from left leg and background\rev{}{, as shown in the second bar of the histogram}. Features of the right arm and left leg also consist of their adjacent parts and background. Furthermore, some body parts have large variations in shape and pose, and rectangular RoI would include inconsistent extent of background clutter and adjacent noise. For example, \rev{the orange box in Fig. \ref{fig:introduction}(a) loosely includes the right leg while the orange box in Fig. \ref{fig:introduction})(b) tightly contains the left leg.}{the right leg is loosely included in the Fig. \ref{fig:introduction}(a) and tightly contained in Fig. \ref{fig:introduction}(b)}. Matching between part features from loose box and tight box in two camera views would definitely deteriorate the matching accuracy. \rev{}{To deal with these problems, finer silhouettes contouring body parts like in Fig. \ref{fig:introduction}(c) are needed, so that part features can be extracted more precisely, alleviating the influence from background clutters and adjacent noises. }

In this work, we \rev{introduce}{propose} \rev{a}{to use} Pose-guided Part Attention instead of rectangular RoI. Pose-guided Part Attention is a confidence map \rev{to}{that could} precisely capture the target part, and exclude background clutter and adjacent part features, as shown in Fig. \ref{fig:align_cmp}. Attention-aware part features can be extracted by applying the part attention \rev{}{mask} on feature maps, and feature alignment by part is \rev{easily}{naturally} achieved. We \rev{}{will} show in experiments that \rev{the}{} attention-aware part features are more accurate and robust, and the aligned pedestrian features are more discriminative than \rev{}{those proposed in} conventional methods.

Occlusion is also a common and severe problem in practical ReID scenario. For example in Fig. \ref{fig:introduction}(d-f), body part may be occluded by other body parts, adjacent persons or things like carrying baggage or trolley. Some observations can be concluded: 1) rigid body parts \rev{}{like head-shoulder, upper torso, lower torso} are often partially occluded by adjacent non-rigid parts \rev{}{like upper arms, lower legs, \etc.} 2) non-rigid body parts suffer heavy self-occlusion and are often fully occluded. 3) occlusion by carrying things is not a bad \rev{news}{situation}, which should be considered as a special part to help re-identification. It would be ideal to weaken features for partially occluded rigid part like \rev{}{the upper body in} Fig. \ref{fig:introduction}(d), eliminate features for fully occluded non-rigid part like \rev{}{the forearms in} Fig. \ref{fig:introduction}(e), and retain features for carrying things like \rev{}{the backpack in} Fig. \ref{fig:introduction}(f). Based on above observations on the occlusion problem, we propose a pose-guided visibility score to measure the occlusion extent for each body part, and it provides image-specific part importance score to decide feature importance in matching. Experimental results show its usefulness in handling occlusion cases.

Based on above motivations, a new Attention-Aware Compositional Network for person re-identification is proposed. The contributions of our work can be summarized in several folds:
\vspace{-6pt}
\begin{itemize}
	\itemsep -4pt
	\item A unified framework named Attention-Aware Compositional Network (AACN) is proposed to deal with misalignment and occlusion problem in person re-identification.
	\item Pose-guided Part Attention is introduced to estimate finer part attention to exclude adjacent noise. It is designed to capture both rigid and non-rigid body parts simultaneously in a unified framework.
	\item Visibility score is introduced to measure the occlusion extent for each body part. It provides image-specific part importance \rev{score to decide feature importance in matching.}{scores for Attention-aware Feature Composition.}
	\item Extensive experiments demonstrate that our approach achieve superior performance on several public datasets, including CUHK03 \cite{li2014deepreid}, CUHK01 \cite{li2012human}, Market-1501 \cite{zheng2015scalable}, CUHK03-NP \cite{zhong2017re},  DukeMTMC-reID \cite{ristani2016MTMC} and SenseReID \cite{zhao2017spindle}.
\end{itemize}

\section{Related Work}
\subsection{Person Re-identification}
There are two categories of methods addressing the problem of person re-identification, namely feature representation and distance metric learning. The first category mainly includes the traditional feature descriptors \cite{zhao2014learning,zhao2013unsupervised,zhao2013person,liao2015person,chen2016similarity,matsukawa2016hierarchical,shi2015transferring} and deep learning features \cite{xiao2016learning,wang2016joint,wu2016personnet,cheng2016person,li2014deepreid,varior2016gated}. These approaches dedicate to design view-invariant representations for person images.
The second category \cite{liao2015efficient,cheng2016person,liao2015person,xiong2014person,hermans2017defense,chen2017beyond,li2013locally,ding2015deep,shi2016embedding} mainly targets on learning a robust distance metric to measure the similarity between images.

Pedestrian alignment, matching two person images with their corresponding parts, is of non-trivial importance. Existing ReID methods mainly focus on extracting two types of features, namely global features extracted from the whole image \cite{chen2017person,xiao2017joint} and region features generated from local patches \cite{zhao2013person,zheng2015partial,li2017learning}. However, these approaches have not taken the accurate alignment of body regions into consideration. Recently, thanks to the great progress of pose estimation methods \cite{cao2016realtime,chu2017multi} and RPN \cite{ren2015faster}, reliable body parts are able to be acquired, which makes it possible to identify individuals via extracted region. For example, Zhao \emph{et al.} \cite{zhao2017spindle} proposed Spindle Net, \rev{that extracted three level parts by body region proposal network, following by feature extraction and fusion stages.}{that extracted and fused three level part features. Parts were extracted by PRN.} Su \emph{et al.} \cite{su2017pose} proposed a Pose-driven Deep Convolutional model (PDC) that utilized Spatial Transformer Network (STN) to localize and crop body regions based on pre-defined centers. Zheng \emph{et al.} \cite{zheng2017pose} introduced to extract Pose Invariant Embedding (PIE) through aligning pedestrians to standard pose. Alignment \rev{is}{was} done by applying affine transformation to pose estimation results. However, these methods are all based on rigid body regions, which cannot accurately localize human body regions. In our model, non-rigid parts are obtained based on the connectivity between human joints. Thus our model is capable of extracting more precise information \rev{from}{for} each body part\rev{. Occlusion is not considered in these works but handled in our work.}{, and handling occlusion issues.}

\subsection{Human Parsing}\label{sec:related_parsing}
Human parsing \cite{gong2017look,chen2014detect,yamaguchi2012parsing,liang2015human,dong2014towards} is related to our work in that \rev{the ideal parsing results can localize body part more accurately than bounding boxes.}{parsing results can accurately localize body part.} For example,
Gong \emph{et al.} \cite{gong2017look} imposed joint structure loss to improve segmentation results.
Dong \emph{et al.} \cite{dong2014towards} explored pose information
to guide human parsing. However, we choose to generate non-rigid parts based on connectivity of human keypoints rather than human parsing because of the following reason:
Existing human parsing methods mainly focus on particular scenarios, such as fashion pictures, and the parsing models often show weak generalization on surveillance data. Human pose is easier to label than parsing, and it can be better generalized to surveillance scenario owing to large variance of the datasets \cite{mpiiandriluka20142d,cocolin2014microsoft}.

\subsection{Attention based Image Analysis}
Since the attention mechanism is effective in understanding images, it has been widely used in various tasks, including machine translation \cite{bahdanau2014neural}, visual question answering \cite{xu2016ask}, object detection \cite{ba2014multiple}, semantic segmentation \cite{chen2016attention}, pose estimation \cite{chu2017multi} and person re-identification \cite{liu2017end}. Bahdanau \cite{bahdanau2014neural} and Ba \cite{ba2014multiple} adopted recurrent neural networks (RNN) to generate the attention map for an image region at each step, and combined information from different steps overtime to make the final decision. Chen \emph{et al.} \cite{chen2016attention} introduced an attention mechanism that learned to softly weight multi-scale features at each pixel location. Chu \emph{et al.} \cite{chu2017multi} proposed a multi-context attention model for pose estimation.
Inspired by the methods mentioned above, we propose to learn attention map to capture human body part, and align features across different person images by masking with part attentions. Our attention map is learned guided by pose estimation, and it can contour the shape of part more precisely than rectangular RoI. Furthermore, the intensity of part attention infers the visibility of each part, which helps to deal with part occlusion issues.

\begin{figure*}[thb]
\center{\includegraphics[width=\textwidth]{framework.png}}
\caption{(a) Attention-Aware Compositional Network (AACN). Our framework consists of two main components: Pose-guided Part Attention (PPA) and Attention-aware Feature Composition (AFC). PPA aims to produce attention maps for locating non-rigid parts $\mathbf{N}$ and rigid parts $\mathbf{R}$. AFC is a 3-stage network that aims to extract robust features for pedestrian images. The first stage generates global context feature maps through a base network. Then, the attention-aware feature maps ${\mathbf{F}^{a}}$ for body parts are extracted in stage 2 with the guidance from part attentions learned in PPA. In stage 3, the part features are further re-weighted by jointly considering part visibility scores $\mathbf{v}$ and feature salience,  resulting in the final compositional weighted feature vector $\bar{\mathbf{f}}^a$. Some visualization are shown in (b) attention maps, (c) visibility scores, (d) global context feature maps, and (e) attention-aware feature maps $\mathbf{F}^{a}$. (Best viewed in color.) }
\label{fig:framework}
\end{figure*}

\section{Attention-Aware Compositional Network}
\rev{In this section, we illustrate our framework in details. As Fig. \ref{fig:framework} shows, our Attention-Aware Compositional Network (AACN) framework consists of two main components:}{The framework of our Attention-Aware Compositional Network (AACN) is illustrated in Fig. \ref{fig:framework}. AACN consists of two main components:} 1) Pose-guided Part Attention (PPA) and 2) Attention-aware Feature Composition (AFC). Given \rev{a}{one} person image, \rev{our PPA model aims to produce attention map and visibility score for each pre-defined body part}{the proposed PPA module aims to estimate an attention map and a visibility score for each pre-defined body part}. \rev{The AFC model takes in the attention maps and visibility scores, and performs feature alignment and weighted fusion to generate a pedestrian feature}{Then, part feature alignment and weighted fusion are performed in AFC module, given attention maps and visibility scores from PPA}. \rev{The PPA model and the AFC model}{PPA and AFC} are tightly integrated in our framework during both training and testing phases.

\rev{Our PPA model}{The PPA module} considers two types of pre-defined body parts, \rev{including}{namely,} non-rigid parts and rigid parts. Due to \rev{different properties}{the variations} in appearance, \rev{part }{}attentions \rev{}{of these two types of parts} are estimated separately. The PPA \rev{model takes a three-branch two-stage structure}{module is constructed by a two-stage three-branch neural network}, which predicts confidence maps of keypoints, attention maps of non-rigid parts, and attention maps of rigid parts in \rev{}{the} three branches, respectively. \rev{Furthermore, a}{A} visibility score is \rev{generated}{further estimated} for each part based on \rev{the estimated}{} part attention \rev{, and is integrated in feature composition to deal with part occlusion issue}{maps}.

\rev{Our AFC model is built based on a baseline network which generates discriminative feature maps, and attention maps are applied to mask the feature maps to produce attention-aware features for each body part. The part features are further combined with visibility scores to generate importance weight for each part, and the attention-aware part features are weightedly fused according to the importance weights. A $1024$-dimensional feature vector is finally produced for the input person image.}{The AFC module applies the estimated part attention maps to mask the global feature map produced by a base network (GoogleNet \cite{szegedy2015going} is used in this work). The resulting attention-aware part features are then weightedly fused with the guidance from part visibility scores. The final $1024$-dimensional feature vector is adopted as the representation of the input person image.}

\subsection{Pose-guided Part Attention}
\label{sec:part_att}
\rev{In our work, p}{P}art attentions are denoted by normalized part confidence maps, which highlight specific regions of human body in the image. As shown in Fig. \ref{fig:upa}(a), there are two types of human body parts: rigid parts and non-rigid parts. Limb regions including upper arms, lower arms, upper legs, and lower legs are called non-rigid parts because of drastic pose variations they could occur, while trunk parts of human body including head-shoulder, upper torso, and lower torso are considered to be rigid. Attention maps of the two types of \rev{human body }{}parts are simultaneously learned in a unified form through our proposed Pose-guided Part Attention network.

Inspired by the multi-stage CNN \cite{cao2016realtime} for human pose estimation, we utilize a two-stage network to learn part attentions. The first stage individually predicts non-rigid part attentions $\mathbf{N}$, rigid part attentions $\mathbf{R}$, and keypoint confidence maps $\mathbf{K}$ by three independent prediction networks,
\begin{align}
\label{eq:att_s_1}
\mathbf{N}^1 = \rho^1(\mathbf{F}^{ppa}),~\mathbf{R}^1 = \phi^1(\mathbf{F}^{ppa}),~\mathbf{K}^1 = \psi^1(\mathbf{F}^{ppa}),
\end{align}
where $\mathbf{F}^{ppa}$ is the feature map at the 10-th layer of VGG-19 \cite{vggSimonyan2014Very}. \rev{The task of keypoint estimation is introduced to}{Keypoint estimation is introduced as an auxiliary task to} improve part attention learning in a multi-task learning manner. Then, the second stage refines the attention maps by considering all previous predictions,
\begin{align}
\label{eq:att_s_2}
\begin{split}
\mathbf{N}^2 &= \rho^2(\mathbf{F}~|~\mathbf{N}^1, \mathbf{R}^1, \mathbf{K}^1), \\
\mathbf{R}^2 &= \phi^2(\mathbf{F}~|~\mathbf{N}^1, \mathbf{R}^1, \mathbf{K}^1), \\
\mathbf{K}^2 &= \psi^2(\mathbf{F}~|~\mathbf{N}^1, \mathbf{R}^1, \mathbf{K}^1).
\end{split}
\end{align}

For network training, supervision is imposed in both stages. The overall objective is
\begin{equation}\label{eq:unifiedloss}
L^{ppa}(\rho, \phi, \psi) =
\sum_{t=1,2} L^{k}(\mathbf{K}^t) + \mu_1 L^{n}(\mathbf{N}^t) + \mu_2 L^{r}(\mathbf{R}^t\rev{, \mathbf{N}^t}{}),
\end{equation}
where $L^{k}$, $L^{n}$ and $L^{r}$ denote the loss function of keypoint confidence map, non-rigid part attention, and rigid part attention, respectively. $\mu_1$ and $\mu_2$ balance the importance of different losses.

\vspace{1.3mm}
\noindent\textbf{Loss for Keypoint Confidence Map $L^{k}(\mathbf{K})$}.
Following the definition in MPII dataset \cite{mpiiandriluka20142d}, $14$ keypoints (as shown in Fig. \ref{fig:upa}(a)) of human body are utilized to guide the learning of part attentions. The $i$-th channel $\mathbf{K}_i \in \mathbb{R}^{H \times W}$ of keypoint confidence maps $\mathbf{K}\in \mathbb{R}^{H \times W\times C^k}$ predicts the coordinates of the $i$-th keypoint by giving high confidence values to the true location. The difference between confidence maps $\mathbf{K}$ and ground truth maps $\mathbf{K}_i^{*}$ are measured by Mean-Square Error (MSE),
\begin{equation}\label{equ:loss_keypoints}
L^{k}(\mathbf{K}) = \frac{1}{C^k} \sum_{i=1}^{C^k} ||\mathbf{K}_i^* - \mathbf{K}_i||^2,
\end{equation}
where, $\mathbf{K}_i^*$ is generated by applying a Gaussian kernel centered at the true location of the $i$-th keypoint. $C^k = 14$ is the number of keypoints.

\vspace{1.3mm}
\noindent\textbf{Loss for Non-Rigid Part Attention $L^{n}(\mathbf{N})$}.
Non-rigid part attentions aim to highlight the corresponding limb parts. Inspired by the Part Affinity Field (PAF) in \cite{cao2016realtime}, we define the ground truth non-rigid parts as the connection area of two keypoints to approximate the target limb part. As shown in Fig. \ref{fig:upa}(b), the $p$-th non-rigid part is defined as a rectangle area $\mathcal{R}_p^n$ connecting two keypoints with bandwidth $\sigma$, and the ground truth non-rigid part attention is represented as
\begin{align}
	\mathbf{N}_{p}^{*}(\mathbf{x}) =
		\begin{cases}
		1, & \text{if}\ \mathbf{x}\ \in \mathcal{R}_p^n, \\
		0, & \text{otherwise,}
		\end{cases}
\end{align}
where, $\mathbf{x}$ indicates the location on the attention map. The errors of non-rigid part attention are measured by MSE
\begin{align}\label{equ:nonrigid}
L^{n}(\mathbf{N}) = \frac{1}{C^{n}} \sum_{p=1}^{C^{n}} ||\mathbf{N}^*_p - \mathbf{N}_p||^2,
\end{align}
where, $C^{n} = 11$ is the number of non-rigid parts. $\mathbf{N}_p\in \mathbb{R}^{H \times W}$ is the predicted attention map for the $p$-th part.

\vspace{1.3mm}
\noindent\textbf{Loss for Rigid Part Attention $L^{r}(\mathbf{R}\rev{, \mathbf{N}}{})$}.
Rigid part attention is introduced to capture body parts that take \rev{little non-}{}rigid transformations during changes of view or pose. Three rigid parts are defined in our work, namely head-shoulder, upper torso and lower torso. As shown in Fig. \ref{fig:upa}(c), each rigid part is defined by a \rev{convex polygon which takes human body keypoints as its vertices.}{neat rectangle $\mathcal{R}^r_p$, which tightly contains a set of specified keypoints.} The set of keypoints for each rigid part are selected as $S_1 = \{0,1,3\}$ for head shoulder, $S_2 = \{1,3,4,7\}$ for upper torso, and $S_3 = \{4,5,7,8\}$ for lower torso. \rev{For the $p$-th rigid part attention, the interest region is defined as
\begin{align}
	\mathcal{R}^r_p = \{\hat{\mathbf{x}}_i ~|~ \hat{\mathbf{x}}_i = s_e\cdot\mathbf{x}_i + (1-s_e)\bar{\mathbf{x}}_i,\nonumber\\
	 \forall \mathbf{x}_i \in Cvx(S_p)\}, \\
	\bar{\mathbf{x}}_i = \frac{1}{|S_p|}\sum_{\mathbf{x}_i \in S_p}\mathbf{x}_{i}, \qquad\qquad\quad
\end{align}
where $|S_p|$ is the number of keypoints in $S_p$, $Cvx(S_p)$ is the convex polygon of the set of keypoints $S_p$, $s_e$ is an expansion factor that is set to $1.2$ in our experiments, and $\bar{\mathbf{x}}_i$ is the center position of all keypoints in $S_p$. }{}
Then the ground truth attention map of rigid part $p$ is defined as
\begin{equation}\label{equ:rigid}
	\mathbf{R}_p^*(\mathbf{x}) =
		\begin{cases}
		1, & \text{if}\ \mathbf{x} \in \mathcal{R}^r_p \rev{~\text{and}~ \Sigma_p\mathbf{N}^*_p(
		\mathbf{x}) \leq  \theta }{},\\
		0, & \text{otherwise}.
		\end{cases}
\end{equation}
\rev{where the threshold $\theta$ controls the overlap between rigid part $p$ and all non-rigid parts. We set $\theta=0.1$ in our experiments. }{}The loss for rigid part attention $L^{r}(\mathbf{R})$ is computed by accumulating all part losses,
\begin{align}\label{equ:loss_rigid}
	L^{r}(\mathbf{R}, \mathbf{N}) = \frac{1}{C^r} \sum_{p=1}^{C^r} ||\mathbf{R}^*_p - \hat{\mathbf{R}}_p||^2, \rev{\\
	\hat{\mathbf{R}}_p = \mathbf{R}_p \circ (\Sigma_p \mathbf{N}_p \leq r), \qquad}{}
\end{align}
where $C^r=3$ is the number of rigid parts\rev{, and $\circ$ performs element-wise production on two matrices. $\hat{\mathbf{R}}_p$ is refined prediction of rigid part attention obtained by masking out locations that have non-zero response on non-rigid parts. }{.}

\noindent\textbf{Part Visibility Score}. The intensities in an attention map indicate the visibility of the part at each location. Motivated by this observation, we can define a global visibility score for each part as
\begin{equation}
v_p = \sum_{x,y} |\mathbf{R}_p(x,y)|,~~\text{or}~~v_p = \sum_{x,y} |\mathbf{N}_p(x,y)|,
\end{equation}
where $(x,y)$ indicates the location on the attention map. Global visibility scores help to balance the importance between different body parts for person identification.

\begin{figure}[t]
\center{\includegraphics[width=0.48\textwidth]{upa.png}}
\caption{Illustration of Pose-guided Part Attention. (a) The 14 keypoints, 11 non-rigid parts, and 3 rigid parts defined in our work. (b) The ideal non-rigid part attention $\mathbf{R}_p^n$ for right elbow. (c) The ideal rigid part attention $\mathbf{R}_p^r$ for upper body. }
\label{fig:upa}
\end{figure}

\subsection{Attention-aware Feature Composition}
Based on human part attentions introduced in Sec. \ref{sec:part_att}, in this section, we propose an Attention-aware Feature Composition (AFC) which learns to align and re-weight features of body parts. AFC comprises of three main stages, namely Global Context Network (GCN), Attention-Aware Feature Alignment, and Weighted Feature Composition. At the first stage, a pedestrian image is input in GCN to extract global features, which are further fed into stage 2 together with part attentions estimated from the same input image. Stage 2 generates part-attention-aware features and \rev{aligns}{concatenates} them in the same order for all images. The aligned features are then re-weighted by visibility scores in stage 3 to generate the final compositional feature vector. The whole flowchart of AFC is shown in Fig. \ref{fig:framework}.

\noindent\textbf{Stage 1: Global Context Network (GCN)}.
GCN serves as a base network for global pedestrian feature extraction. Following \cite{geng2016deep}, we build GCN based on the standard GoogleNet \cite{szegedy2015going}. To reduce the computation cost for following stages of AFC, we add one more $256$-channel $3\times3$ convolution layer after the layer ``inception\_5b/output'' of GoogleNet, and then feed the 256-channel feature maps to the next stage. To better fit the aspect ratio of pedestrian images, the input image size is changed from $224\times224$ to $448 \times 192$, and the resulting feature maps at the last convolution layer changes from $7\times 7$ to $14\times 6$ accordingly.

GCN is independently trained first, and then jointly fine-tuned with following stages of AFC. In independent training, GCN is initialized with the GoogleNet model pre-trained on ImageNet, and the newly added convolution layer is randomly initialized. \rev{\rev{By}{After} globally average pooling the feature maps at the last convolution layer of GCN,}{Subsequent to the global average pooling layer at the end of GCN,} identification loss and verification loss similar to \cite{geng2016deep} are applied to guide the global context learning. In joint fine-tuning, these two losses are retained to preserve high-quality global context features of pedestrian images.

\noindent\textbf{Stage 2: Attention-Aware Feature Alignment}.
Global context features suffer from body part misalignment between pedestrian images. Based on human part attention introduced in Section \ref{sec:part_att}, we propose a simple and effective scheme to achieve attention-aware feature alignment. Specifically, we extract attention-aware feature maps by applying Hadamard Product between global feature maps and each human part attention map, and the resulting feature maps are globally average pooled and concatenated to produce \rev{the}{an} aligned feature vector. Formally, we formulate the attention-aware feature alignment scheme as:
\begin{align}
\mathbf{f}^{a} = Concat(\{\mathbf{f}_p\}_{p=1}^P),~ \mathbf{f}_p = \sigma_{gap}(\mathbf{F}^{a}_p), \\
\mathbf{F}^{a}_p = \mathbf{F} \circ \bar{\mathbf{M}}_p,~
\bar{\mathbf{M}}_p = \frac{\mathbf{M}_p}{\text{max}(\mathbf{M}_p)}, \quad~~
\end{align}
where, $\mathbf{M}_p \in \{\mathbf{N}_p, \mathbf{R}_p\}$ is the attention map for body parts, $\bar{\mathbf{M}}_p$ is the normalized attention map, $\text{max}(\mathbf{M}_p)$ indicates the maximum value in $\mathbf{M}_p$, $\mathbf{F}$ is the $256$-channel global feature map produced by GCN, $\circ$ denotes the Hadamard Product operator which performs element-wise product on two matrices or tensors, and $\mathbf{F}^{a}_p$ denotes the attention-aware feature map for part $p$. $\sigma_{gap}(\cdot)$ is the global average pooling function, and $Concat(\cdot)$ represents concatenation operation on part feature vectors. Global feature maps $\mathbf{F}$ are masked with attention maps for $P$ times, in which manner $P$ sets of attention-aware feature maps are produced, \ie $\{\mathbf{F}^{a}_p\}_{p=1}^P$. Each set of attention-aware feature maps $\mathbf{F}^{a}_p$ \rev{extract the features centered at}{possess the feature information of} the corresponding body part while preserving global context information of the image.

The attention-aware feature maps $\mathbf{F}^{a}_p$ are passed through a global average pooling $\sigma_{gap}(\cdot)$ to generate summarized feature vector $\mathbf{f}_p$ for part $p$. These summarized part features are further concatenated to produce final attention-aware aligned feature vector $\mathbf{f}^{a}$ for the input pedestrian image.

\noindent\textbf{Stage 3: Weighted Feature Composition}.
Since pedestrians vary in pose, suffer from occlusions, and may contain some relatively \rev{prominence}{salient} parts, the importance of each part should be adaptively adjusted \rev{while matching two images}{in matching}. Motivated by these observations, we introduce a weight vector $\mathbf{w}$ to measure the part importance. The weight vector is estimated by jointly considering part visibility and feature salience. As shown in Fig. \ref{fig:framework}, visibility scores and the attention-aware aligned feature vector are concatenated, and fed into a fully connected layer (implemented as $1 \times 1$ convolutions) to generate  $\mathbf{w}$. Then the compositional weighted feature vector is generated as $\bar{\mathbf{f}}^a = Conv(Concat(\{\mathbf{w}_p \cdot \mathbf{f}_p\}_{p=1}^P))$, where $Conv$ indicates \rev{convolutions}{a convolution layer}.

Overall, our framework integrated the PPA and ACF to extract feature for each input person image. \rev{Given a query image in application of person ReID, its feature is matched with feature of each image in gallery set, and the distance score is computed.}{In person ReID applications, given a query image, its feature is matched with that of each image in gallery set to generate distance score.} \rev{Gallery images are sorted in ascending order according to the distance scores.}{Gallery images are sorted according to ascending order of the distance scores.} Then the target person can be found among top-ranked gallery images.

\subsection{Implementation Details}
 In AFC, GoogleNet is utilized as base network for global context feature extraction, and two additional ``1x1'' convolution layers are used for part weight estimation and final feature fusion, respectively.
 \rev{Furthermore, }{}AACN is trained progressively. First, PPA and GCN are trained independently. PPA is trained with losses for part attention and pose estimation. GCN is trained with reid loss. Then, by fixing PPA and GCN, the parameters for feature weighting and composition in AFC are trained with reid loss. Finally, all modules are jointly fine-tuned.

\begin{table}[thb]
\small
\begin{center}
  \begin{tabular}{cccc}
\hline
Dataset & \#ID & \#train/test IDs & det./lab. \\
\hline
CHUK03 \cite{li2014deepreid} & 1467 & 1160/100 & det.\&lab. \\
CUHK01 \cite{li2012human} & 971 & 486/485 & lab. \\
Market-1501 \cite{zheng2015scalable} & 1501 & 751/750 & det. \\
CUHK03-NP \cite{zhong2017re} & 1467 & 767/700 & det.\&lab. \\
DukeMTMC-reID \cite{ristani2016MTMC} & 1812 & 702/702 & lab. \\
SenseReID \cite{zhao2017spindle} & 1717 & 0/1717 & det. \\
\hline
  \end{tabular}
\end{center}
\vspace{-0.1cm}
\caption{Details of the datasets evaluated in our experiments. Bounding box labels of these datasets can be detected (det.) or manually labeled (lab.).}
\label{table:dataset}
\end{table}

\begin{table}[thb]
\small
\begin{center}
  \begin{tabular}{c|cccc}
     \hline
     CUHK03 (labeled) & R-1 & R-5 & R-10 & R-20\\
     \hlin

     NFST \cite{zhang2016learning}      & 62.55 & 90.05 & 94.80 & 98.10 \\
     JSTL \cite{xiao2016learning}       & 75.30 & - & - & - \\
     Transfer \cite{geng2016deep}      & 85.40 & - & - & - \\
     SVDNet \cite{sun2017svdnet}        & 81.80 & - & - & - \\

     Quadruplet \cite{chen2017beyond}   & 75.53 & 95.15 & 99.16 & - \\

     PAR \cite{zhao2017deeply}          & 85.40 & 97.60 & 99.40 & 99.90 \\
     \hline
     Spindle \cite{zhao2017spindle}     & 88.50 & 97.80 & 98.60 & 99.20 \\
     PDC \cite{su2017pose}              & 88.70 & 98.61 & 99.24 & 99.67 \\
     \hline
     AACN (Ours) & \textbf{91.39} & \textbf{98.89}& \textbf{99.48} & \textbf{99.75} \\

     \hline
  \end{tabular}
\end{center}
\vspace{-0.1cm}
\caption{Comparison results on CUHK03 (labeled). }
\label{table:cp_cuhk03_labeled}
\end{table}

\begin{table}[thb]
\small
\begin{center}

  \begin{tabular}{c|cccc}
     \hline

     CUHK03 (detected) & R-1 & R-5 & R-10 & R-20\\
     \hlin
     NFST \cite{zhang2016learning}   & 54.70 & 84.75 & 94.80 & 95.20 \\
     Transfer \cite{geng2016deep}   & 84.10 & - & - & - \\
     DPFL \cite{chen2017person}      & 82.00 & - & - & - \\

     PAR \cite{zhao2017deeply} & 81.60 & 97.30 & 98.40 & 99.50 \\
     \hline
     PIE \cite{zheng2017pose}  & 67.10 & 92.20 & 96.60 & 98.10 \\
     PDC \cite{su2017pose}     & 78.29 & 94.83 & 97.15 & 98.43 \\
     \hline

	 AACN (ours) & \textbf{89.51} & \textbf{97.68} & \textbf{98.77} & \textbf{99.34}  \\

     \hline
  \end{tabular}
\end{center}
\vspace{-0.1cm}
\caption{Comparison results on CUHK03 (detected).}
\label{table:cp_cuhk03_detected}
\end{table}

\begin{table}[thb]
\small
\begin{center}
  \begin{tabular}{c|cccc}
     \hline
     CUHK01 & R-1 & R-5 & R-10 & R-20\\
     \hlin

     NFST \cite{zhang2016learning}     & 69.09 & 86.90 & 91.77 & 95.39 \\

     JSTL \cite{xiao2016learning}       & 66.60 & - & - & - \\
     Transfer \cite{geng2016deep}     & 77.00 & - & - & - \\
     Quadruplet \cite{chen2017beyond}  & 62.55 & 83.44 & 89.71 & - \\

     PAR \cite{zhao2017deeply}         & 75.00 & 93.50 & 95.70 & 97.70 \\
     \hline
     Spindle \cite{zhao2017spindle}    & 79.90 & 94.40 & 97.10 & 98.60 \\
     \hline
     AACN (Ours) & \textbf{88.07} & \textbf{96.67} & \textbf{98.16} & \textbf{99.10} \\
     \hline
  \end{tabular}
\end{center}
\vspace{-0.1cm}
\caption{Comparison results on CUHK01. }
\label{table:cp_cuhk01}
\end{table}

\begin{table}[thb]
\small
\begin{center}
  \begin{tabular}{c|cc|cc}
     \hline
     \multirow{2}{*}{Market-1501}
     & \multicolumn{2}{c|}{Single Query} & \multicolumn{2}{c}{Multiple Query} \\
     \cline{2-5}
      & R-1 & mAP & R-1 & mAP \\
     \hlin

     NFST \cite{zhang2016learning}    & 61.02 & 35.68 & 71.56 & 46.03 \\

     PAN \cite{zheng2017pedestrian}   & 82.81 & 63.35 & 88.18 & 71.72 \\
     SVDNet \cite{sun2017svdnet}      & 82.30 & 62.10 & - & - \\

     PAR \cite{zhao2017deeply}           & 81.00 & 63.40 & - & -  \\
     \hline
     Spindle \cite{zhao2017spindle}      & 76.90 & - & - & -  \\
     PIE \cite{zheng2017pose}            & 78.65 & 53.87 & - & -  \\
     PDC \cite{su2017pose}               & 84.14 & 63.41 & - & -  \\
     \hline
     AACN (Ours)  & 85.90 & 66.87 & 89.78 & 75.10 \\
     AACN+R.E.  (Ours)  & \textbf{88.69} & \textbf{82.96} & \textbf{92.16} &  \textbf{87.32} \\

     \hline
  \end{tabular}
\end{center}
\vspace{-0.1cm}
\caption{Comparison results on Market-1501. Rank-1 accuracy (\%) and mAP (\%) are shown. R.E. : re-ranking method from \cite{zhong2017re}.}
\label{table:cp_market}
\end{table}

\begin{table}[thb]
\small
\begin{center}
  \begin{tabular}{c|cc|cc}
     \hline
     \multirow{2}{*}{CUHK03-NP}
     & \multicolumn{2}{c|}{labeled} & \multicolumn{2}{c}{detected} \\
     \cline{2-5}
      & R-1 & mAP & R-1 & mAP \\
     \hlin

     PAN \cite{zheng2017pedestrian}      & 36.86 & 35.03 & 36.29 & 34.00 \\
     DPFL \cite{chen2017person}           & 43.00 & 40.50 & 40.70 & 37.00 \\
     SVDNet \cite{sun2017svdnet}          & 40.93 & 37.83 & 41.50 & 37.30 \\

     \hline
     AACN (Ours) & \textbf{81.86} & \textbf{81.61} & \textbf{79.14} & \textbf{78.37}  \\
     \hline
  \end{tabular}
\end{center}
\vspace{-0.1cm}
\caption{Comparison results on CUHK03-NP. }
\label{table:cp_cuhk03_np}
\end{table}

\begin{table}[thb]
\small
\begin{center}
  \begin{tabular}{c|cc}
     \hline
     DukeMTMC-reID & R-1 & mAP \\
     \hlin
     OIM \cite{xiao2017joint} & 68.10 & - \\
     LSRO   \cite{zheng2017unlabeled}  & 67.68 & 47.13 \\
     PAN   \cite{zheng2017pedestrian} & 71.59 & 51.51 \\
     SVDNet \cite{sun2017svdnet}       & 76.70 & 56.80 \\

     \hline
     AACN (Ours) & \textbf{76.84} & \textbf{59.25} \\
     \hline
  \end{tabular}
\end{center}
\vspace{-0.1cm}
\caption{Comparison results on DukeMTMC-reID.}
\label{table:cp_duke}
\end{table}

\begin{table}[thb]
\small
\begin{center}
\begin{tabular}{c|cccc}
	\hline
	SenseReID & R-1 & R-5 & R-10 & R-20 \\
	\hline
	JSTL \cite{xiao2016learning} & 23.00 & 34.80 & 40.60 & 46.30 \\
	Spindle \cite{zhao2017spindle} & 34.60 & 52.70 & 59.90 & 66.70 \\
	\hline
	AACN (Ours) & \textbf{41.37} & \textbf{58.65} & \textbf{64.71} & \textbf{72.16} \\
	\hline
\end{tabular}
\end{center}
\vspace{-0.1cm}
\caption{Cross-dataset evaluation on SenseReID.}
\label{table:cross}
\end{table}

\section{Experiments} \label{sec:experiments}
In this section, the performance of AACN is compared with state-of-the-art methods on several public datasets. And then detailed ablation analysis is conducted to validate the effectiveness of AACN components.

\subsection{Datasets and Protocols} \label{sec:dataset}
Our proposed AACN framework is evaluated on several public person ReID datasets, as listed in Table \ref{table:dataset}. For fair comparison, we follow the official evaluation protocols of each dataset. For CUHK03, CUHK01 and SenseReID, Cumulated Matching Characteristics (CMC) at rank-1, rank-5, rank-10 and rank-20 are compared between different approaches. For Market-1501, CUHK03-NP and DukeMTMC-reID, rank-1 identification rate and mean Average Precision (mAP) are reported.

\subsection{Comparisons with State-of-the-Arts} \label{sec:comparison_results}
The proposed AACN is compared with recent approaches with state-of-the-art performance. These approaches are categorized into two sets according to whether human pose information is utilized. One set is pose-irrelevant, which includes
the null space semi-supervised learning method (NFST) \cite{zhang2016learning},
the domain guided dropout method (JSTL) \cite{xiao2016learning},
the deep transfer learning method (Transfer) \cite{geng2016deep},
the Singular Vector Decomposition method (SVDNet) \cite{sun2017svdnet},
the Online Instance Matching (OIM) method \cite{xiao2017joint},
the quadruplet loss method (Quadruplet) \cite{chen2017beyond},
the multi-scale representation (DPFL) \cite{chen2017person},
the pedestrian alignment network (PAN) \cite{zheng2017pedestrian},
the Part-Aligned Representation (PAR) \cite{zhao2017deeply}.
The other set introduces explicit pose estimation results into ReID, which includes
the Spindle Net (Spindle) \cite{zhao2017spindle},
the Pose-driven Deep Convolutional model (PDC) \cite{su2017pose},
and the Pose Invariant Embedding (PIE) \cite{zheng2017pose}.

The experimental results are presented in Table \ref{table:cp_cuhk03_labeled}, \ref{table:cp_cuhk03_detected}, \ref{table:cp_cuhk01}, \ref{table:cp_market}, \ref{table:cp_cuhk03_np} and \ref{table:cp_duke}. It shows that our proposed AACN outperforms state-of-the-art approaches on all datasets. Specifically, when compared with the second best approach on each dataset, our AACN achieves 2.69\%, 5.41\%, 8.17\%, 4.55\% and 40.93\% rank-1 accuracy improvement on CUHK03 (labeled), CUHK03 (detected), CUHK01, Market-1501 and CUHK03-NP (labeled), respectively. Though our AACN is very close to SVDNet \cite{sun2017svdnet} in rank-1 accuracy on DukeMTMC-reID dataset, the improvement in mAP metric (+2.45\%) is still significant.

We also evaluate the generalization ability of our AACN on SenseReID dataset \cite{zhao2017spindle}. Following Spindle \cite{zhao2017spindle}, we merge the training set of Market-1501 \cite{zheng2015scalable}, CUHK01 \cite{li2012human}, CUHK02 \cite{li2013locally}, CUHK03 \cite{li2014deepreid}, PSDB \cite{xiao2016learning}, Shinpuhkan \cite{Shinpuhkan}, PRID \cite{PRID}, VIPeR \cite{gray2007evaluating}, 3DPeS \cite{baltieri20113dpes} and i-LIDS \cite{ilids} for training, and \rev{directly testing}{then test} on SenseReID. As shown in Table \ref{table:cross}, AACN achieves 41.37\% accuracy at rank-1, significantly outperforms Spindle which has an accuracy of 34.60\%.

\begin{table*}[hbtp]
\small
	\begin{center}
		\begin{tabularx}{\textwidth}{c|c|c|c|c|p{0.95cm}<{\centering}|p{0.95cm}<{\centering}|p{0.95cm}<{\centering}|p{0.95cm}<{\centering}|p{0.95cm}<{\centering}|p{0.95cm}<{\centering}}
			\cline{1-11}
            	\multirow{2}{*}{Method} & \multirow{2}{*}{\tabincell{c}{Base model}} & \multirow{2}{*}{\tabincell{c}{\# ince-\\ption}} & \multirow{2}{*}{\tabincell{c}{\# param\\(base)}} & \multirow{2}{*}{\tabincell{c}{\# param\\(overall)}} & \multicolumn{2}{c|}{CUHK03(labeled)} & \multicolumn{2}{c|}{CUHK03(detected)}& \multicolumn{2}{c}{Market-1501(SQ)} \\
			\cline{6-11}
			& & & & & base & overall & base & overall & base & overall \\
			\cline{1-11}
			\multirow{2}{*}{PIE \cite{zheng2017pose}}
			& AlexNet & - & 57M & 114M & - & - & 58.80  & 62.60  & 55.49  & 65.68 \\
            \cline{2-11}
			& ResNet-50 & - & 23M & 46M & - & - & 54.80  & 61.50  & 73.02  & 78.65 \\
			\cline{1-11}
			PDC \cite{su2017pose} & GoogleNet-PDC & 10 & 10M & 14M & 79.83  & 88.70  & 71.89  & 78.29  & 76.22  & 84.14 \\
            \cline{1-11}
			Spindle \cite{zhao2017spindle} & GoogleNet-Spindle & 6 & 6M & 44M & - & 88.50  & - & - & 72.10  & 76.90 \\
			\cline{1-11}
			\multirow{2}{*}{AACN (Ours)} & GoogleNet-Spindle & 6 & 6M & 8M & 84.01 & 89.16 & 81.70 & 86.65 & 71.41 & 81.95 \\
            \cline{2-11}
			& GoogleNet & 9 & 6M & 8M & 86.11  & 91.39  & 83.78  & 89.51  & 79.63  & 85.90 \\
			\cline{1-11}
		\end{tabularx}
	\end{center}
    \vspace{-0.1cm}
	\caption{Comparison with human pose based approaches. Rank-1 accuracy (\%) is reported. }
	\label{table:basenet}
\end{table*}

\begin{table}[thb]
\small
\begin{center}
\begin{tabular}{c|ccccc}
	\hline
	Part & Head & L.Arm & R.Arm & L.Leg & R.Leg \\
	\hline
	RoI\cite{zhao2017spindle} & \textbf{26.53} & 13.30 & 13.25 & 13.89 & 14.08 \\
	PPA & 23.51 & \textbf{25.19} & \textbf{23.12} & \textbf{16.63} & \textbf{16.21} \\
	\hline
\end{tabular}
\end{center}
\vspace{-0.1cm}
\caption{Part localization accuracy. Part IoUs are given.}
\label{table:loc}
\end{table}

\begin{table}[thb]
\small
\begin{center}
\begin{tabular}{c|cc|cc}
	\hline
	\multirow{2}{*}{CUHK03}
	& \multicolumn{2}{c|}{labeled} & \multicolumn{2}{c}{detected} \\
	\cline{2-5}
	 & R-1 & R-5 & R-1 & R-5 \\
	\hline
	AFC+RoI\cite{zhao2017spindle} & 89.88 & 97.97 & 86.44 & 97.33 \\
	AFC+Parsing\cite{gong2017look} & 85.49 & 97.38 & 82.92 & 95.66 \\
	\hline
	AFC+PPA & \textbf{90.58} & \textbf{98.65} & \textbf{87.98} & \textbf{97.64} \\
	\hline
\end{tabular}
\end{center}
\vspace{-0.1cm}
\caption{Comparison of part localization methods for ReID.}
\label{table:ppa_select}
\end{table}

\begin{table}[thb]
\small
\begin{center}
\begin{tabular}{c|cc|cc}
	\hline
	\multirow{2}{*}{CUHK03}
	& \multicolumn{2}{c|}{labeled} & \multicolumn{2}{c}{detected} \\
	\cline{2-5}
	 & R-1 & R-5 & R-1 & R-5 \\
	\hline
	GCN & 86.11 & 98.18 & 83.78 & 96.86 \\
	\rev{PPA+AFC\_rigid}{AFC\_rigid} & 87.35 & 97.98 & 84.88 & 96.70 \\
	\rev{PPA+AFC\_non-rigid}{AFC\_non-rigid} & 89.89 & 98.64  & 86.87 & 97.19 \\
	\rev{PPA+AFC\_all}{AFC\_PPA} & \textbf{90.58} & \textbf{98.65} & \textbf{87.98} & \textbf{97.64} \\
	\hline
\end{tabular}
\end{center}
\vspace{-0.1cm}
\caption{Effectiveness of Attention-aware Feature Composition. ``GCN'' uses global features for person ReID. ``AFC\_rigid'' only extracts features from rigid parts.}
\label{table:ppa_component}
\end{table}

\begin{table}[thb]
\small
\begin{center}
\begin{tabular}{c|cc|cc}
  \hline
   \multirow{2}{*}{\tabincell{c}{Rank-1\\accuracy (\%)}} & \multicolumn{2}{c|}{CUHK03} & \multicolumn{2}{c}{Market-1501} \\
	\cline{2-5}
   & labeled  & detected & SQ & MQ \\
  \hline

  AACN-w/o-v & 90.58 & 87.98 & 86.58  & 90.29  \\
  AACN-v & \textbf{91.39} & \textbf{89.51} & \textbf{88.69}  & \textbf{92.16}  \\
  \hline
\end{tabular}
\end{center}
\vspace{-0.1cm}
\caption{Effectiveness of visibility score.}
\label{table:ab_ppa_visi}
\end{table}

\subsection{Ablation Analysis}

\noindent\textbf{Base Network.} The performance of ReID approaches\rev{utilizing human pose cues}{} is influenced by base network structures, and different approaches may choose different backbones. As listed in Table \ref{table:basenet}, our approach is comparable to Spindle \cite{zhao2017spindle} and PDC \cite{su2017pose} in base network size, but much smaller in overall model size. To better compare with previous works on exploiting pose information, we also experiment by replacing base network of our AACN with the one used by Spindle. It shows that our AACN still outperforms Spindle under the same base network structure.

\noindent\textbf{Pose-guided Part Attention.}
The localization accuracy of\rev{ our proposed}{} PPA is compared with rectangular RoI \cite{zhao2017spindle} method on PASCAL-Person-Part dataset \cite{chen2014detect}. Since some body parts are not available on this dataset, we choose head, left arm (L.Arm), right arm (R.Arm), left leg (L.Leg), right leg (R.Leg) for comparison. Localization accuracy is measured as the Intersection-over-Union (IOU) between predictions and ground truth parsing labels. Both methods are trained on the same MPII dataset. As shown in Table \ref{table:loc}, our proposed PPA is more accurate than RoI \rev{method }{}in \rev{locating body parts}{part localization}.

Furthermore, we evaluate the performance of different \rev{body}{part} localization methods on the person ReID task. Specifically, RoI \cite{zhao2017spindle} and parsing results from Parsing \cite{gong2017look} are compared by replacing the PPA module in our AACN framework. The results are shown in Table \ref{table:ppa_select}. On CUHK03 (detected) set, ``AFC+RoI'' is 1.54\% lower than ``AFC+PPA'' in rank-1 accuracy since it includes noise from adjacent areas, and ``AFC+Parsing'' is 5.06\% lower due to domain difference.

\noindent\textbf{Attention-aware Feature Composition.}
\rev{Attention-aware Feature Composition}{AFC} is a key module in our proposed AACN framework. As shown in Table \ref{table:ppa_component}, ``GCN'' extracts features globally over the image, and achieves 86.11\% and 83.78\% rank-1 accuracy on CUHK03 (labeled) and CUHK03 (detected), respectively. When using AFC, our proposed \rev{``PPA+AFC\_all''}{``AFC\_PPA''} improves the accuracies to 90.58\% and 87.98\%. Using rigid parts only (\rev{``AACN\_rigid''}{``AFC\_rigid''}) or non-rigid parts only (\rev{``AACN\_non-rigid''}{``AFC\_non-rigid''}) still outperforms ``GCN'', and these two types of body parts are complementary to each other. More qualitative results are shown in Fig. \ref{fig:ranking}. Even though a query image looks similar to \rev{the}{an} imposter globally, body part alignment and local feature aggregation in Attention-aware Feature Composition could effectively distinguish them.

\noindent\textbf{Visibility Score.}
The effectiveness of visibility score is evaluated on CUHK03 and Market-1501 dataset. As shown in Table \ref{table:ab_ppa_visi}, weighting part features with visibility scores significantly increase the rank-1 accuracy \rev{on both datasets.}{by 1.53\% and 2.11\% on CUHK03 (detected) and Market-1501 (SQ).}

\begin{figure}[t]
\center{\includegraphics[width=0.49\textwidth]{ranking.png}}
\caption{Comparison between global features from GCN and aligned part features from AFC. $d^g$ is the distance computed on global features, and $d^{AFC}$ is the distance computed on the compositional features produced by AFC on Pose-guided Part Attentions. The query images are more similar with the imposters in the global context feature space, but AFC effectively distinguishes them by (a) hair color, (b) upper arm, (c) shorts.}
\label{fig:ranking}
\end{figure}

\section{Conclusion}
In this paper, we propose an Attention-Aware Compositional Network (AACN) to deal with the misalignment and occlusion problem in person re-identification. AACN is composed of two main components, namely, Pose-guided Part Attention (PPA) and Attention-aware Feature Composition (AFC), where PPA is to estimate finer part attention for preciser feature extraction. Also, visibility score is introduced to measure the occlusion extent, and to guide AFC to learn more robust feature for matching. Extensive experiments with ablation analysis demonstrate that our AACN achieves superior performance than state-of-the-art methods on several public datasets.

\vspace{0.4cm}
\noindent\textbf{Acknowledgements.} This work is supported by SenseTime Group Limited.

{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


Ambassador Terzi's address to the opening ceremony of the exhibition "Arcimboldo: Nature and Fantasy"
I am truly delighted to have this opportunity to join you here at the Press Conference on Arcimboldo organized by our friends at the National Gallery, whom I wish to thank, first and foremost, through its eminent Director, Dr. Powell, Dr. David Alan Brown and Dr. Dodge Thompson. My sincere gratitude also goes to those who made this exhibition possible, Louisa and Robert Duemling and the Altria Group.
The exhibition on Arcimboldo is one of the cultural highlights of the events and activities we are planning here in Washington to celebrate an important occasion coming up next year: the 150th anniversary of the unification of Italy.
Italy was also the star in the Washington National Opera's new season: indeed, WNO General Director Maestro Placido Domingo opened the Fall program at the Kennedy Center with a performance of the renowned Risorgimento composer Giuseppe Verdi's "Un Ballo in maschera", with the gifted tenor Salvatore Licitra playing the lead role.
This exhibition enables us once again to highlight the Italian genius and the deep and instinctive sensitivity of the Italian people to perceive and enrich change and progress. In fact, Arcimboldo fully embodies this genius, following in the footprints of great Renaissance artists such as Michelangelo and Leonardo, and achieving the same levels of proficiency and originality. In particular, he was inspired by Leonardo's studies of nature and new ways of interpreting it: no longer is nature hostile or adversarial but, on the contrary, it is a benign and friendly force.
Arcimboldo personifies the complexity and eclecticism of the Italian Renaissance. Just think of the diverse artistic activities he undertook over the course of his life, beginning his career as a designer of stained glass, moving on to become a court portraitist and then gradually giving himself over to an extremely personal and genuine form of painting. Arcimboldo is, in fact, also a man of his times, he senses and interprets the change that is taking place, intellectualizes and refines it, later becoming the highest expression of the Italian school of art that takes the name of mannerism and paves the road towards the affirmation of a new and original genre, the Baroque.
Thus, the works of Arcimboldo in this exhibit make it possible for us to celebrate once again Italian excellence, a privilege which we will also enjoy in the upcoming exhibits dedicated to Palladio, Canaletto and Michelangelo.
Whatever the case may be, enhancement of the Italian genius and excellence is one of the key themes of the celebrations of the 150th anniversary of Italy's unification, an occasion which will once again see a close and productive collaboration between our Embassy and the National Gallery. Allow me therefore to take this opportunity to renew my appreciation to the National Gallery for the attention it reserves to Italy each year by inaugurating the Fall season with an exhibition dedicated to our country.
The President of the Republic, The Honorable Giorgio Napolitano, also expressed his sincere appreciation for this attention to Italy during his recent visit to Washington; he personally saw the preparations for the Arcimboldo exhibition and confirmed that the National Gallery was the ideal venue to host the celebrations for Italy's important forthcoming anniversary.


text:
Entedon fursovi is een vliesvleugelig insect uit de familie Eulophidae. De wetenschappelijke naam is voor het eerst geldig gepubliceerd in 1996 door Gumovsky.

fursovi

redpajama_set_name: RedPajamaWikipedia


text:
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MLS #1912225 Official listing information! n/a neighborhood - 0 Newton Rd located in Burns TN 37029, priced at $395000, with 0 bedrooms.
Land for sale in Burns, TN at 0 Newton Rd. This creek land is priced at $9,977/acre.
Are you looking for the perfect place to build your country home? Here it is! 39.5 Acres with great mix of pasture, woods and a beautiful creek on the property. Talk about location too. Just 5 miles to I40 and 17 minutes to Nashville. If you are looking for land to develop this would be great for mini farms too. This is a MUST SEE.
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Some ride a bicycle for fun, others find it as an indispensable vehicle, and Felix Stark travels around it all over the world.
Frosty winter weather - is not always the main attribute for skiing or snowboarding. And not always for this you need snow, because you can ride on sandy and stony slopes. Here are a few places where you can try out such descents.
The first wooden bicycles were created in the 1800s. They gradually passed from exclusive novelties to rich people to the general form of transport. They also shifted from permanent breakdowns and inconveniences in use to simplicity in management - an easy start and a timely stop. Bicycles have become so popular that over time, cycling has arisen.


Led by owners who built the business on long-term relationships and a full-service real estate approach for industrial clients across the country, read more about how Becknell Industrial got started and meet our talented team.
Becknell Industrial is a privately held national real estate development firm focused exclusively on industrial buildings, including distribution centers, warehouses, processing and assembly plants, manufacturing plants, research-and-development centers, service centers and other facilities vital to the global supply-chain management of regional, national and multi-national operations.
Becknell launched into the industrial real estate business in 1990 when co-founders Dan Harrington and Fritz Hartrich joined forces to build and lease a custom warehouse to Nabisco, a client Hartrich began developing for in the 1970s. It was the first of many enduring relationships Becknell has fostered since.
Under the long-term leadership of Harrington, who serves as Becknell Industrial CEO and President as well as Becknell's Corporate Counsel, Becknell's national portfolio now includes 20.8 million square feet of industrial real estate property, representing in excess of $1.5 billion, in addition to nearly 400 acres of developable ground for future distribution properties. Harrington is joined by a strong team of leaders that include CFO Matt Cohoat, COO Mark Shapland and Principal of Architecture and Design Craig Kouri.
At the core of Becknell's superior delivery system is our design-build construction capabilities. While Hartrich is no longer involved in the day-to-day operations, Becknell continues to benefit from his dedicated construction expertise, tapping into his more than 50 years of national construction experience.
Becknell's vertically integrated approach takes an experienced team of professionals working closely together. Our executive team alone has more than 100 years of collective real estate and business experience, while the rest of our highly talented staff incorporates a wealth of technical expertise in key trades that include architects, engineers, construction managers and CPAs.


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Q: How to map DeviceID to human readable parameters like client name? I want to disable device or find logs for particular client. How could I find out what deviceID associated with particular client?
I use Android native and iOS native
-Thanks in advance

A: You haven't specified what kind of client-side technology you are using, but assuming hybrid, MFP provides client-side APIs for getting hold of a device ID, e.g. getID() in the JavaScript API.

A: With device access management feature,  you can see device Id mapped to a user Id in the operations console Devices tab. Refer to
 https://www-01.ibm.com/support/knowledgecenter/SSHS8R_7.0.0/com.ibm.worklight.monitor.doc/monitor/c_device_access_mgmt.html

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text:
Ligue 1 Paris Saint-Germain FC Sweden Olympic Team Canada Olympic Team Stephanie Labbe International soccer Women's sports Women's soccer Professional soccer Olympic games Summer Olympic games 2020 Tokyo Olympic Games Soccer Sports Men's sports Men's soccer
Stephanie Labbe retires after winning gold with Canada
By ANNE M. PETERSON - Jan. 19, 2022 05:20 PM EST
FILE - Canada's Stephanie Labbe watches during the women's international friendly soccer match between England and Canada at Bet365 stadium in Stoke on Trent, England, Tuesday, April 13, 2021. Stephanie Labbe, the goalkeeper for the Canadian women's national team that won the gold medal at the Tokyo Olympics, has announced her retirement. Labbe released an emotional video on Wednesday, Jan. 19, 2021. (AP Photo/Rui Vieira, File)
Stephanie Labbe, the goalkeeper for the Canadian women's national team that won the gold medal at the Tokyo Olympics, has announced her retirement.
Labbe released an emotional video on Wednesday explaining her decision. She'll step away from the national team in April. The 35-year-old recently left her club team, Paris Saint-Germain, playing her final match in December.
"In recent months, I felt both the weight and the lightness of what soccer has given me and what I was given back. I've poured my heart and soul into the sport. I've packed up my life and moved around with soccer for years. I've made hard decisions and left family and friends behind, all in search of being my best. I've pushed my physical and mental self to places I didn't know were possible. I mastered the ability to be completely present and joyful in the highest pressure situations," she said. "And now I found clarity and peacefulness in knowing that I've left everything on the field."
Labbe, who made her first appearance with the national team in 2008, is a veteran of three World Cups, two Olympics and four CONCACAF tournaments. She has made 85 appearances with the national team overall with 43 clean sheets.
Canada clinched the gold medal by winning a penalty shootout 3-2 against Sweden after a 1-1 draw in the Olympic final last summer. It was the first major tournament title for the Canadians, who were the two-time defending bronze medalists.
"I can't think of a better way to go out after such dedication to the program than with the world class performances we consistently saw from her on the biggest stage possible to win this country a gold medal," Canada coach Bev Priestman said in a statement. "I know Steph is ready to move on to the next phase of her life and on behalf of the team and Canada Soccer would like to thank Steph for her tireless commitment and what she has done for this country, she will be greatly missed."
Labbe has long been open about her battles with anxiety and has become a staunch advocate of mental health care and access to services.
At the Tokyo Games, where Simone Biles was frank about her struggle, Labbe stressed the importance of bringing mental health issues to light.
"If someone like me can feel brave and courageous to talk about it, then maybe more people will be able to feel that strength and connect with that story," she told The Associated Press. "I think it makes you feel less alone -- and that's what we all want, we all want that community, that support. So I think it's really important that people are sharing their struggles."
Labbe was one of three finalists for a FIFA Best award for women's goalkeeper of the year. Christiane Endler was named the winner at Monday's award ceremony.


Contrary to popular belief, tooth extractions are more common than people think, with over 74 percent of adults having had at least one tooth extracted at some point in their lives. You may need to have a tooth extracted for a variety of reasons, but the ultimate goal is always to save your smile and overall oral health. Our expert oral surgeons extract over three thousand teeth every year, allowing you to rest at ease when it comes to professional and successful tooth extractions. When a problem tooth is left within the jaw too long, it can cause future concerns, pain and even impact everyday life. However, you don't have to go without a tooth, because at Midwest Oral Surgery & Dental Implants, we can place dental implants within your jaw that your general dentist can restore for a natural look and function.
For teeth that are failing and need to be replaced with dental implants, we will have to perform tooth extractions on the affected problem teeth. This will make room for the dental implants to be placed.
Patients who have been in an accident or received oral trauma that has severely damaged their teeth may need to receive tooth extractions to save their oral health and free them of discomfort.
Those who have severe gum disease likely have suffered damage to their jaw bone and gum health, making their teeth loose and shifting. By performing a tooth extraction on these teeth, you will be able to get on the path to proper oral health.
Patients with severely crowded teeth within their jaw and in need of orthodontics, may need tooth extractions of the teeth that will get in the way of proper alignment.
If you are in need of a tooth extraction, you should always seek out the experts in the field to ensure that no complications arise. Tooth extractions, including the extraction of wisdom teeth, are common procedures for oral surgeons and a process that our team performs on a daily basis. Our doctors regularly extract over three thousand teeth every year and have the experience and education to respond to any problems that may arise. We pride ourselves on being the experts in tooth extractions and replacement with dental implants in a friendly and caring environment.
At Midwest Oral Surgery & Dental Implants, we perform gentle extraction with the utmost precision thanks to our use of sedation and dental technology. We will use our digital cone beam CT scanner to capture images of your jaw and teeth, to better understand the positioning of tooth roots and nerves and improve the extraction planning process. Using anesthetic, we will numb the location of extraction so that you will only feel a slight pressure during treatment. After the tooth has been removed, we perform socket preservation bone grafting to preserve your jaw bone and prepare you for dental implants, or can even immediately place an implant within your bone right after tooth extraction.
Extract problem teeth to prepare for lifelike dental implants!


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Home U.S. Scott Gottlieb discusses coronavirus in "Face the Nation," April 4, 2021
Scott Gottlieb discusses coronavirus in "Face the Nation," April 4, 2021
This Easter Week, we will take a closer look at some of the inequalities created by COVID-19.
Member Apr 05, 2021 06:47 am
source: https://ibb.co/MNfSD8k
MARGARET BRENNAN Hello and welcome to FACE THE NATION. We start with the virus itself. Last week, CDC director Drs. Rochelle Walensky spoke of her fear of "impending doom" and urged Americans to hold back. The CDC also provided vaccinated Americans with a green light to fly, but the TSA has reported the highest number of passengers since the outbreak began. 18 percent of Americans are completely vaccinated. Case numbers are still growing in 27 states and in Washington, D.C. In some of these areas, the vast majority of new cases are among children for the first time. We want to start with the former FDA commissioner Dr. Scott Gottlieb. He lives on the Pfizer board, and joins us from Westport, Connecticut. Good morning.
DOCTOR SCOTT GOTTLIEB: Hello.
MARGARET BRENNAN: You know, doctor, we vaccinate four million people a day. But if you look at the levels of infection, do you see the fourth wave?
Drs. GOTTLIEB: I don't think there will be a fourth wave of truth. I think we probably delayed the point where we could get this back in the summer, but we never missed that opportunity. I think with the current vaccine rate, we are vaccinating, as you said, four million people a day. I think that will probably reach five million people a day. And the level of protection we already have for people, we have vaccinated more than 100 million Americans. It has infected about 130 million Americans. So you have somewhere around 200 million Americans with certain levels of non-infection. I think there is enough insecurity in people that you will not see a fourth wave of infection. What we are seeing are bags of infection across the country, especially for young people who have not been vaccinated and for school children. If you look at what is happening in Michigan and Minnesota and Massachusetts, for example, you see outbreaks in schools and infections in social groups that have never been seen by the virus before. Maybe we do a better job of shelter. Now they are out and about to be infected and infected. So the infection changes your appearance in terms of who you are currently suffering from.
MARGARET BRENNAN: She has been a supporter of the re-opening of inner learning. Given what you see now, do you think schools need to be closed?
Drs. GOTTLIEB: I don't. Schools are not naturally safe, but they can be made safer. I think we need to stick to tight cuts in schools, so schools that use masks, schools that can do some sort of deviation, as described by one of the disease-related diseases this week, go Harry Potter full and try to keep students within the defined cohorts community so they don't get mixed up. If you take those steps in schools, I think schools can be made safer. And I think the benefits of being in school outweigh the risks. But we need to know that schools are dangerous, children are at risk of infection, and that schools can be places of community focus if we are not careful. I think we are seeing some of this in Massachusetts right now where the bulk of the new infection is among school-going children. You see the same kind of math in Michigan. Both provinces have recently reopened schools. And I don't think it happened by accident.
MARGARET BRENNAN: Dr. Fauci mentions the program, the first quarter of 2022 vaccination. And this week, he said, by the end of this year, we should have enough information to safely vaccinate children of any age. What do you think of this timeline? Is it moving faster than expected?
Drs. GOTTLIEB: I think it's going fast. I don't know if it's faster than we expected. We will have data, I think, that will inform the FDA's ability to make a decision on the urgent use of the 12 to 15 vaccine with the Pfizer vaccine. So Pfizer, as you know, the company on its board, as you mentioned, recently released clinical data, a clinical trial of 2,200 children aged 12 to 15 who look very encouraging. That data will be submitted to the FDA. The FDA will be in a position to issue an emergency use authorization for that age group. I think that could come in time for the vaccine to be available to 12 to 15 people before the school year. And I think the way to think about trying to vaccinate children is to vaccinate different social cohorts. Do we vaccinate school-age children? Currently, Pfizer vaccine is approved to drop to 16. So that puts you in a high school age set. Are we starting to vaccinate in middle school? ACIP will eventually need a CDC advisory committee, which is ACIP, which will eventually have to make recommendations when they think the vaccine should be used on children. And they will think about it by going on social media and meeting groups when they want to introduce a vaccine, high school being the most obvious and probably middle school. But I think we will be in a position to vaccinate 12 or more before the fall. I think less than that can take more time because you will want to test the many different doses to try to find the lowest dose that still provides the strongest immune response in children.
MARGARET BRENNAN: You know, I'm sure you've seen this too, in your correspondence. There are many people traveling with their children right now for spring break. People who have not celebrated Christmas are celebrating right now, although Dr Fauci in the program said it is very dangerous to enter the airport. Do you think health authorities are losing their influence at this time?
Drs. GOTTLIEB: I think you have to be careful as a public health official to come up with a guide that you know the community will follow very closely. You do not want to be frustrated if you cannot get the right pitch so invest in a good capo. You have to give direction in the context of what the community is willing to do. I think it is important that people like Dr. Fauci and the director of the CDC appeal. I think we should continue to be vigilant. We are in a very serious situation. We still have this variety of rotations that we do not fully understand. We do not know if people are re-infected with some of this new variety. We should have that knowledge, but we do not. So there is a lot we don't understand about this virus right now. And we don't want to be in a situation where we are exacerbating the epidemic because we were not smart about the steps we are currently taking. That said, people see that there is little danger in general. As people get vaccinated, they feel less vulnerable and more dependent on vaccination. And so they are determined to start getting involved in the things they have set for themselves all year long. So we need to understand that and I think I'm putting out a guide in such a way that people can go along with it, you know, their desires, i.e. they want to see the family again. They want to start having fun. They want to start moving slowly.
MARGARET BRENNAN: Last night, Johnson & Johnson said it would take full responsibility for producing a vaccine for this plant in Baltimore that appears to have damaged about 15 million doses of the COVID vaccine. Our Sara Cook reports that it was ordered by Biden management. Any article like this damages a person's self-esteem. How serious is the problem?
Drs. GOTTLIEB: I don't think it should damage self-confidence in people's perceptions of vaccine safety. This was eventually obtained as part of the quality assessment they did at the center. I don't think they should have produced two different antiretroviral drugs in the same area. Germs stick. Their genomic properties are easily transmitted. We have seen this with the CDC in relation to their unsuccessful discharge of their diagnostic tests because they were carrying too many viruses in one facility and there was some contamination. It appears that a portion of the AstraZeneca vaccine, which was also produced at the center, went into the Johnson & Johnson vaccine.
UMARGARET BRENNAN: Mhm.
Drs. GOTTLIEB: That is a public report from management officials.
MARGARET BRENNAN: Yes.
Drs. GOTTLIEB: They shouldn't have done that in the same institution. I think this emphasizes that we do not have a lot of productive energy in this country--
MARGARET BRENNAN: OK.
Drs. GOTTLIEB: - that we should have used in one institution to do these things.
MARGARET BRENNAN: All right, Dr. Gottlieb, thank you always for your analysis. THE FACE OF THE NATION will return directly to Dr. Cecilia Rouse, Chair of the Economic Advisory Council at the White House. Stay with us.
cdc Face the Nation
Jordanian government accuses former heir of attempted "destabilization.
Cher apologized for George Floyd's controversial tweet.
Michigan school district defends actions surrounding a November shooting shooting in the face of two charges
They are fighting a critical racist theory. They are now focusing on the 'light of the curriculum.'


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Justin Timberlake 'VMA Performance' (ft. 'N Sync)
The rumours were true — when accepting the Michael Jackson Video Vanguard Award at tonight's MTV Video Music Awards, pop megastar Justin Timberlake did in fact reunite with his boy band buds in 'N Sync.
Still, the '90s throwback was hardly the highlight of Timberlake's career-spanning performance. Instead, he blasted through hits from Justified, FutureSex/LoveSounds and both editions of The 20/20 Experience. In fact, the other members of 'N Sync appeared exhausted and downright lazy when compared to their fearless leader.
Check out Timberlake's impressive 15-minute performance below.
13VMAJT by YardieGoals
CLICK/HEAR
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Read here about tensegrity structures that are explicitly called masts by their creators.
2 Controversy About the Term "Mast"
A tensegrity mast is a tower-like tensegrity structure that extends primarily in a linear fashion, encompassing a prism or cylindrical-like space between its struts and tendons. Fuller, a former U. S. Navy officer, sailor and an inventor of tensegrity, named the classic tower a mast, borrowing from the terminology of sailing ships where the mast is the tall, vertical, or near vertical, spar, or arrangement of spars, that support the sails. Emmerich used this term as well, in French, "mât antiprismatique monogyre" (anti-prism-based spiral mast).
Snelson avoided the term mast, and named his mast-like structures towers. For his most famous tower-like tensegrity, see Needle Tower.
Tensegrity mast at the Indian Institute of Technology, Madras (Chennai, Tamil Nadu), 2011. Photo by Dawn. "
Controversy About the Term "Mast"
Snelson objected to the nautical associations of mast, seeing them as peripheral to the main concept of tensegrity.
Levin wrote: "According to Wikipedia et al, a beam withstands loads primarily by resisting bending. As there are no bending moments in tensegrity structures, 'beam' is probably not the best term. Towers are self-supporting or cantilevered structures, while masts are usually supported by stays or guys. Of the three, my vote is for 'masts', towers are too heavyfeeling, and I guess we can consider a tensegrity as internally guyed. Horizontally, it is a space truss. Sometime ago, I wrote a paper; 'The spine as a space truss', and maybe 'tensegrity space truss' might be a suitable descriptive term."
Fuller often insisted on the fractal nature of tensegrity structures. Given a strut in a tensegrity structure, substitute it with a tensegrity mast of the same length and width. The process can be reiterated, substituting the new struts in the new tensegrity mast for other, tinier struts and tendons, and so on. He advocated for this fractal model of tensegrity masts as a good conceptual model for the atomic structure of reality, where tiny atomic nuclei are separated by vast spaces, cohering only by forces acting over a distance.
Synergetics Fig. 740.21 Tensegrity Masts as Struts: Miniaturization Approaches Atomic Structure. (c) R. B. Fuller" http://www.rwgrayprojects.com/synergetics/s07/p2500.html"
The model below by Scarr depicts a tensegrity mast and the fractal nature of tensegrity. The struts closest to the viewer in the image are tensegrity masts in and of themselves. The concept that tensegrity is fractal, at different orders of magnitude, was often discussed by Fuller in his lectures.
Fractal tensegrity model, called structural hierarchy by Scarr." http://www.tensegrityinbiology.co.uk/biology.html"
Motro called this fractal structure the strut effect. The strut effect, as defined by him, occurs when an entire tensegrity is constrained to behave like strut under compression.
Ingber and his team model the cytoskeletal stress fiber as a generic, planar tensegrity mast composed of multiple similar structural modules. Cytoskeletal stress fiber enables cultured cells to exert traction on their extracellular matrix. These fiber bundles extend tremendous distances--tens of micrometers in length--and insert into complexes at the boundary between the cell and its environment. These fibers are linear bundles of parallel actin and myosin filaments that also contain other actin-binding linker proteins, including tropomyosin, troponin, α-actinin and caldesmon. Thir components self assemble into a periodic three dimensional (3D) lattice reminiscent of the sarcomeric arrangement of thin and thick filaments in muscle.
Cytoskeletal stress fiber has been shown to shorten and generate tension in intact living cells. Such cell-generated tensional forces drive changes in cell shape and and contribute to control of cell growth and function, as well as tissue patterning and mechanotransduction at the organ level. Ingber proposes the tensegrity mast as the most elegant model to explain both the fibers' supramolecular architecture and its mechanical behavior.
Microtubule filaments of the cytoskeleton of a cultured capillary endothelial by Ingber" http://www.childrenshospital.org/research/ingber/"
The cytoskeletal stress fiber tensegrity mast is 6 modules wide and 251 modules long. Each module is composed of 8 structural members organized in a prestressed tensegrity array in which tension cables form a continuous web that is balanced by compression struts. An additional member is added to the modules located at the ends of the tensegrity model to maintain the entire structure in a state of prestress. Cables are treated as viscoelastic Voight elements that support only tensile forces, whereas struts are modeled as linearly elastic elements under compression.
Reference: A Multimodular Tensegrity Model of an Actin Stress Fiber by Yaozhi Luo, Xian Xu, Tanmay Lele, Sanjay Kumar, and Donald E. Ingber.
Zoran Novacki patented a scissors-deployable-strut that is arguably a tensegrity mast. Novacki wrote, In the course of a research project about an innovative deployable truss structure at the Technische Universität München, an animated short was produced in 2009. Presented as a fictitious journey on Mars, the movie demonstrates the application of a deployable bridge structure along with its carrier on its mission bridging Mars.
Reference: Bridging Mars by Zoran Novacki.
Below, a gallery of images of tensegrity masts, with short descriptions.
The Museum of Modern Art, New York City featured one of the original tensegrity masts in 1959.
Make Magazine volume 6 featured instructions, by William Gurstelle, to build a 3x4 tensegrity mast.
Flemons models the human spine as a nucleated, tetrahedral tensegrity mast. He wrote that the spine is often depicted as "a ship's mast, stayed with cables... In this description the pelvis is the boat and the spine is restrained in its movements about a fulcrum or hinge (sacrum) by the large muscles of the back and abdomen. The discs are seen as shock absorbers which carry the dynamic loads of the body." Flemons goes on to criticize and refine this common conception. See spine for more details.
Spines models 05-TVM-3.2, 05-SVM-3.1, 05-TS-1.1 Spiral Vertebral Masts by Flemons" http://www.intensiondesigns.com/models.html"
Cameron, Kassidy and Kelvin, a group of students studying at the University of Auckland Faculty of Architecture that call themselves the Triangle Collective, are basing one of their projects on a classic mast made of a 3 strut module repeated 4 times. They plan to clad the mast with zippered triangular panels of foil-coated emergency blanket.
Tensegrity mast, 3 strut module x 4, by the Triangle Collective" http://thetrianglecollective.blogspot.com/2010/09/23082010-mid-semester-crit.html"
Functions of Positive and Negative Tetrahedra in Tensegrity Stacked Cubes Figure 730.11 from Synergetics by Fuller" http://www.rwgrayprojects.com/synergetics/s07/figs/f3011.html"
Fuller wrote, "We put a steel sphere at the center of gravity of a cube which is also the center of gravity of tetrahedron and then run steel tubes from the center of gravity to four corners, W,X,Y, and Z, of negative tetrahedron (A). Every tetrahedron's center of gravity has four radials from the center of gravity to the four vertexes of the tetrahedron (B). In the juncture between the two tetrahedra (D), ball joints at the center of gravity are pulled toward one another by a vertical tension stay, thus thrusting universally jointed legs outwardly, and their outward thrust is stably restrained by finite sling closure WXYZ. This system is nonredundant: a basic discontinuous-compression continuous-tension or "tensegrity" structure. It is possible to have a stack (column) of center-of-gravity radial tube tetrahedra struts (C) with horizontal (approximate) tension slings and vertical tension guys and diagonal tension edges of the four superimposed tetrahedra, which, because of the (approximate) horizontal slings, cannot come any closer to one another, and, because of their vertical guys, cannot get any further away from one another, and therefore compose a stable relationship: a structure."
Stabilization of Tension in Tensegrity Column, figure 730.12 from Synergetics by Fuller" http://www.rwgrayprojects.com/synergetics/s07/figs/f3012.html"
Burkhardt constructed this mast in 1981, inspired by Hugh Kenner's book of Geodesic Math, and wrote, "This mast is based on the tensegrity prism. The top and bottom of the mast are tendon triangles as in a three-fold prism; the intermediate layers are tendon hexagons. Struts interconnect the layers. The intermediate layers are interfaces where two tensegrity prisms are connected. The struts are all the same length, although the perspective effect of the photo somewhat belies this.
Tensegrity mast by Burkhardt." http://www.trip.net/~bobwb/ts/synergetics/photos/tower2.html"
Burkhardt went on, "I took care in the articulation of the stories so they all spiraled in the same direction, and this created a beautiful effect to my mind. The mast was very jiggly, and I thought this gave it less promise as a practical structure. That jiggling seemed to weigh more on my mind that its beauty and the geometric insight that went into it.
I later found the same construction in David Georges Emmerich's book, Structures Tendues et Autotendantes (Paris, France: Ecole d'Architecture de Paris la Villette, 1988), where he does a lot of other things with prisms and agglomerations thereof. He calls it a "mât antiprismatique monogyre" (anti-prism-based spiral mast, p. 200). Since he seems to have thought of it first, I'll call it a mast. A similar structure, with prisms that alternate in sense rather than "monogyre" (spiraling) as above, appears as Fig. 5 in his French Patent No. 1377291. This mast isn't completely "monogyre" since the top stage spirals in the opposite sense from the other stages."
Di Carlo posted a photo of a 6 strut module repeated 3 times tensegrity mast, built by the seaside.
6 Strut, 3 Module Tensegrity Mast by Di Carlo" http://www.biagiodicarlo.com/"
This page was last edited on 28 January 2019, at 11:27.


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Frankenstein; or, The Modern Prometheus, is a novel written by English author Mary Shelley about eccentric scientist Victor Frankenstein, who creates a grotesque creature in an unorthodox scientific experiment. Shelley started writing the story when she was eighteen, and the novel was published when she was twenty. The first edition was published anonymously in London in 1818. Shelley's name appears on the second edition, published in France in 1823. Frankenstein is infused with elements of the Gothic novel and the Romantic movement and is also considered to be one of the earliest examples of science fiction. Brian Aldiss has argued that it should be considered the first true science fiction story, because unlike in previous stories with fantastical elements resembling those of later science fiction, the central character "makes a deliberate decision" and "turns to modern experiments in the laboratory" to achieve fantastic results. It has had a considerable influence across literature and popular culture and spawned a complete genre of horror stories, films, and plays.
English writer Mary Shelley is best known for her horror novel Frankenstein, or the Modern Prometheus (1818). She was married to poet Percy Bysshe Shelley. Mary Shelley was born on August 30, 1797, in London, England. She married poet Percy Bysshe Shelley in 1816. Two years later, she published her most famous novel, Frankenstein. She wrote several other books, including Valperga (1823), The Last Man (1826), the autobiographical Lodore (1835) and the posthumously published Mathilde. Shelley died of brain cancer on February 1, 1851, in London, England.


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        return body.toString();
    }

    return JSON.stringify(body);
};

redpajama_set_name: RedPajamaGithub


Eighteen years ago, volunteers from the Halifax-Dartmouth Church of Christ started to serve at a local Sunday Supper. Each Sunday evening, people from the surrounding communities who may be experiencing homelessness, or going through challenging times, or just need a warm meal and an even warmer smile are invited to drop by a local church hall. Over the years, this Supper has grown to serve almost 10,000 meals a year.
Five years ago, the Halifax Chapter of HOPE worldwide Canada took over the responsibility of fundraising, procuring, preparing and coordinating the serving of the meals when our turn in the weekly rotations came up; continuing upon the foundation laid by and increasing our partnership with the Halifax Dartmouth Church of Christ.
The Sunday Supper program has become the "flagship" community service program that HOPE worldwide Canada runs locally. One of the benefits of the level of engagement from our volunteers is to experience the ways that they are able to use their natural gifts and talents to meet more and more needs in the community.
As the Sunday Supper program has grown over the years, so too has the ways in which our many volunteers are able to "step up" and to meet these needs. One example is with a Foot Clinic that was set up to meet the needs of the Sunday Supper guests. This past September, the Foot Clinic was due for a "reboot" and one of our volunteers, Cameron Taylor (a second year Medical Student at Dalhousie University in Halifax) took over the operation and organization of the Foot Clinic, with several of his classmates and other students with an interest and calling in Health and Medical studies assisting him.
We appreciate all the ways in which the volunteers from the Halifax Chapter of HOPE worldwide Canada and from the Halifax-Dartmouth Church of Christ look for ways to meet needs in our community, and are willing to step up to meet those needs in a practical and compassionate way!


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Mad World – New multiplayer trailer for upcoming HTML5 online game
Home » Mad World – New multiplayer trailer for upcoming HTML5 online game
Korean indie developer Jandisoft today released a game trailer for its current project, Mad World, a cross-platform MMORPG. Crafted using HTML5 technology, Mad World's story revolves around humans and their will to survive in a post-apocalyptic fantasy world ravaged by evil creatures. The trailer focuses on its multiplayer combat aspect of the gameplay experience.
Jandisoft was founded by the core members from the team that had worked on the hit global MMORPG, Metin 2, with a mission to offer a truly massively-multiplayer experience without boundaries, in the way of HTML5. Virtually playable on any platform with an internet connection, its cross-platform feature allows users to play with friends on different platforms in real-time.
Mad World offers a fast-paced combat action that is engaging and addictive, a 2D hand-drawn world, and storyline whose depth will draw you in. Hybrid of targeting and non-targeting, the game's combat offers a somewhat simple and chaotic yet addictive and fun experience, which doubles when played en masse with hundreds of other players in PVE raids or PVP matches.
Windows, Mac, Linux, iOS and Android are confirmed but Jandisoft is also showing an interest in venturing out to consoles and other browser-enabled platforms. Still an uncharted territory, Jandisoft believes Mad World will become the first game to take HTML5 to its full potential and lead it to the main stage of the gaming world. Mad World is scheduled to launch in 2018.
Tags Cross platform HTML5 Jandisoft Mad World Mobile
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