text:
\section{Introduction}
Hashing has attracted much attention in recent years because of the
rapid growth of image and video data on the web.
It is one of the most popular techniques for image or video search due to its low
computational cost and storage efficiency.
Generally speaking, hashing is used to encode high dimensional data into a set of binary codes while preserving the similarity of images or videos.
Existing hashing methods can be roughly grouped into two categories: data
independent methods and data dependent methods.

Data independent methods rely on random projections to construct hash functions.
Locality Sensitive Hashing (LSH)~\cite{gionis1999similarity} is one
of the representative methods, which uses random linear projections to
map nearby data into similar binary codes.
LSH is widely used for large scale image retrieval.
In order to generalize LSH to accommodate arbitrary kernel functions,
the Kenelized Locality Sensitive Hashing (KLSH)~\cite{kulis2009kernelized} is proposed to
deal with high-dimensional kernelized data.
Other variants of LSH are also proposed in recent years, such as super-bit LSH~\cite{ji2012super}, non-metric LSH~\cite{mu2010non}.
However, there are some limitations of data independent hashing methods,~e.g.,~it makes no use of training data. The learning efficiency is low, and it
requires longer hash codes to attain high accuracy.
Due to the limitations of the data independent hashing methods, recent hashing methods
try to exploit various machine learning techniques to
learn more effective hash function based on a given dataset.

Data dependent methods refer to using training data to learn the hash functions.
They can be further categorized into supervised and unsupervised methods.
Unsupervised methods retrieve the neighbors under some kinds of
distance metrics. Iterative Quantization (ITQ)~\cite{gong2013iterative} is one of the representative unsupervised hashing methods, in which the projection  matrix is optimized by iterative projection and thresholding according to the given training samples. In order to utilize the semantic labels of data samples, supervised hashing methods are proposed. Supervised Hashing with Kernels (KSH)~\cite{liu2012supervised} is a well-known method of this kind, which learns the hash codes by minimizing the Hamming distances between similar pairs, and at the same time maximizing the Hamming distances between dissimilar pairs.
Binary Reconstruction Embedding (BRE)~\cite{kulis2009learning} learns the hash functions by explicitly minimizing the reconstruction error between the original distances and the reconstructed distances
in Hamming space.
Order Preserving Hashing (OPH)~\cite{wang2013order} learns the hash codes by preserving the supervised ranking list information, which is calculated based on the semantic labels. Supervised Discrete Hashing (SDH)~\cite{shen2015supervised} aims to directly optimize the binary hash codes using the discrete cyclic coordinate descend method.

Recently, deep learning based hashing methods have been proposed to simultaneously learn the image representation and hash coding, which have shown superior performance over the traditional hashing methods.
Convolutional Neural Network Hashing (CNNH)~\cite{xia2014supervised} is one of the early works to incorporate deep neural networks into hash coding, which consists of two stages to learn the image representations and hash codes.
One drawback of CNNH is that the learned image representation can not give feedback for learning better hash codes. To overcome the shortcomings of CNNH, Network In Network Hashing
(NINH)~\cite{lai2015simultaneous} presents a triplet ranking loss to capture the
relative similarities of images.
The image representation learning and
hash coding can benefit each other within one stage framework.
Deep Semantic Ranking Hashing (DSRH)~\cite{zhao2015deep} learns the hash functions by preserving semantic similarity between multi-label images.
Other ranking-based deep hashing methods have also been proposed in recent years~\cite{Wang2016,yao2016deep}.
Besides the triplet ranking based methods, some pairwise label based deep hashing methods are
also exploited~\cite{li2016feature,zhu2016deep}.
A novel and efficient training algorithm inspired by alternating direction method of multipliers (ADMM) is
proposed to train very deep neural networks for supervised hashing in~\cite{zhang2016efficient}. The classification information is used to learn hash codes. \cite{zhang2016efficient} relaxes the binary constraint to be continuous, then thresholds the obtained continuous variables to be binary codes.

Although deep learning based methods have achieved great progress in image retrieval,
there are some limitations of previous deep hashing methods (e.g., the semantic information is not fully exploited).
Recent works try to divide the whole learning process into two streams  under the multi-task learning framework~\cite{lin2015deep,SSDH,yao2016deep}.
The hash stream is used to learn the hash function, while the classification stream is utilized to mine
the semantic information.
Although the two stream framework can improve the retrieval performance, the classification stream is only employed
to learn the image representations, which does not have a direct impact on the hash function.
In this paper, we use CNN to learn the image representation and hash function simultaneously.
The last layer of CNN outputs the binary codes directly based on the pairwise label information and the classification information.

The contributions of this work are summarized as follows.
1) The last layer of our method is constrained to output the binary codes directly.
The binary codes are learned to preserve the similarity relationship and keep the label consistent simultaneously.
To the best of our knowledge, this is the first deep hashing method that uses both pairwise label information and classification information to learn the hash codes under one stream framework.
2) In order to reduce the quantization error, we keep the discrete nature of the hash codes during the optimization process. An alternating minimization method is proposed to optimize the objective function by using the discrete cyclic coordinate descend method.
3) Extensive experiments have shown that our method outperforms current state-of-the-art methods on benchmark datasets for image retrieval, which demonstrates the effectiveness of the proposed method.

\section{Deep supervised discrete hashing}

\subsection{Problem definition}
Given $N$ image samples $X = \left\{ {{x_i}} \right\}_{i = 1}^N \in {\mathbb{R}^{d \times N}}$, hash coding is to learn a collection of $K$-bit binary codes
$B \in {\left\{ { - 1,1} \right\}^{K \times N}}$, where the $i$-th column ${b_i} \in {\left\{ { - 1,1} \right\}^K}$ denotes the binary codes for the $i$-th sample ${x_i}$.
The binary codes are generated by the hash function $h\left(  \cdot  \right)$, which can be rewritten as $\left[ {{h_1}\left(  \cdot  \right),...,{h_c}\left(  \cdot  \right)} \right]$. For image sample ${x_i}$,
its hash codes can be represented as
${b_i} = h\left( {{x_i}} \right) = \left[ {{h_1}\left( {{x_i}} \right),...,{h_c}\left( {{x_i}} \right)} \right]$. Generally speaking, hashing is to learn a hash function to project image samples to a set of binary codes.

\subsection{Similarity measure}

In supervised hashing, the label information is given as $Y = \left\{ {{y_i}} \right\}_{i = 1}^N \in {\mathbb{R}^{c \times N}}$, where ${y_i} \in {\left\{ {0,1} \right\}^c}$ corresponds to the sample ${x_i}$, $c$ is the number of categories. Note that one sample may belong to multiple categories.
Given the semantic label information, the pairwise label information is derived as:
$S = \left\{ {{s_{ij}}} \right\}$,  ${s_{ij}} \in \left\{ {0,1} \right\}$,
where ${s_{ij}} = 1$ when ${x_i}$ and ${x_j}$ are semantically similar,
${s_{ij}} = 0$ when ${x_i}$ and ${x_j}$ are semantically dissimilar.
For two binary codes ${b_i}$ and ${b_j}$, the relationship between their Hamming
distance ${\rm{dis}}{{\rm{t}}_H}\left( { \cdot , \cdot } \right)$ and
their inner product $\left\langle { \cdot , \cdot } \right\rangle $ is formulated as follows:
${\rm{dis}}{{\rm{t}}_H}\left( {{b_i},{b_j}} \right) = \frac{1}{2}\left( {K - \left\langle {{b_i},{b_j}} \right\rangle } \right)$.
If the inner product of two binary codes is small, their Hamming distance will be large, and vice versa. Therefore the inner product of different hash codes can be used to quantify
their similarity.

Given the pairwise similarity relationship $S = \left\{ {{s_{ij}}} \right\}$,
the Maximum a Posterior (MAP) estimation of hash codes can be represented as:
\begin{equation}
\label{eq:MAP-hashcodes}
\begin{array}{l}
p\left( {B|S} \right) \propto p\left( {S|B} \right)p\left( B \right) = \mathop \Pi \limits_{{s_{ij}} \in S} p\left( {{s_{ij}}|B} \right)p\left( B \right)
\end{array}
\end{equation}
where $p\left( {S|B} \right)$ denotes the likelihood function, $p\left( B \right)$
is the prior distribution. For each pair of the images,
$p\left( {{s_{ij}|B}} \right)$ is the conditional probability of ${s_{ij}}$
given their hash codes ${B}$,  which is defined as follows:
\begin{equation}
\label{eq:ConditionalPB-hashcodes}
\begin{array}{l}
p\left( {{s_{ij}}|B} \right) = \left\{ {\begin{array}{*{20}{c}}
{\sigma \left( {{\Phi _{ij}}} \right),\;\;\;{s_{ij}} = 1\;}\\
{1 - \sigma \left( {{\Phi _{ij}}} \right),\;\;\;{s_{ij}} = 0}
\end{array}} \right.
\end{array}
\end{equation}
where $\sigma \left( x \right) = 1/\left( {1 + {e^{ - x}}} \right)$ is the sigmoid
function, ${\Phi _{ij}} = \frac{1}{2}\left\langle {{b_i},{b_j}} \right\rangle  = \frac{1}{2}b_i^T{b_j}$.
From Equation~\ref{eq:ConditionalPB-hashcodes} we can see that,
the larger the inner product ${\left\langle {{b_i},{b_j}} \right\rangle }$ is,
the larger $p\left( {1|{b_i},{b_j}} \right)$ will be, which implies that ${b_i}$ and ${b_j}$ should
be classified as similar, and vice versa.
Therefore Equation~\ref{eq:ConditionalPB-hashcodes} is a reasonable similarity measure for hash codes.

\subsection{Loss function}

In recent years, deep learning based methods have shown their superior performance
over the traditional handcrafted features on object detection, image classification, image segmentation, etc.
In this section, we take advantage of recent advances in CNN to learn the hash function.
In order to have a fair comparison with other deep hashing methods,
we choose the CNN-F network architecture~\cite{chatfield2014return} as a basic component of our algorithm.
This architecture is widely used to learn the hash function
in recent works~\cite{li2016feature,Wang2016}.
Specifically, there are two separate CNNs to learn the hash function, which share the same weights.
The pairwise samples are used as the input for these two separate CNNs.
The CNN model consists of 5 convolutional layers and 2 fully connected layers.
The number of neurons in the last fully connected layer is equal to the number of hash codes.

Considering the similarity measure, the following
loss function is used to learn the hash codes:
\begin{equation}
\begin{array}{l}
\label{eq:pairwise-label}
J =  - \log {\kern 1pt} {\kern 1pt} {\kern 1pt} p\left( {S|B} \right) =  - \sum\limits_{{s_{ij}} \in S} {\log \;p} \left( {{s_{ij}}|B} \right) = - \sum\limits_{{s_{ij}} \in S} {\left( {{s_{ij}}{\Phi _{ij}} - \log \left( {1 + {e^{{\Phi _{ij}}}}} \right)} \right)}.
\end{array}
\end{equation}
Equation~\ref{eq:pairwise-label} is the negative log likelihood function,
which makes the Hamming distance of two similar
points as small as possible, and at the same time
makes the Hamming distance of two dissimilar points as large as possible.

Although pairwise label information is used to learn the hash function in Equation~\ref{eq:pairwise-label}, the label information is not fully exploited.
Most of the previous works make use of the label information under a two stream multi-task learning framework~\cite{SSDH,yao2016deep}.
The classification stream is used to measure the classification error, while the hash stream
is employed to learn the hash function.
One basic assumption of our algorithm is that the learned binary codes
should be ideal for classification.
In order to take advantage of the label information directly, we expect the learned binary codes
to be optimal for the jointly learned linear classifier.

We use a simple linear classifier to model the relationship between the learned
binary codes and the label information:
\begin{equation}
\label{eq:linear-constraint}
 Y = {W^T}B,
 \end{equation}
where $W = \left[ {{w_1},{w_{2,...,}}{w_K}} \right]$
is the classifier weight, $Y = \left[ {{y_1},{y_{2,...,}}{y_N}} \right]$ is the ground-truth
label vector.
The loss function can be calculated as:
\begin{equation}
\label{eq:Q-function}
\begin{array}{l}
Q = L\left( {Y,{W^T}B} \right) + \lambda \left\| W \right\|_F^2 = \sum\limits_{i = 1}^N {L\left( {{y_i},{W^T}{b_i}} \right)}  + \lambda \left\| W \right\|_F^2,
\end{array}
\end{equation}
where $L\left(  \cdot  \right)$ is the loss function, $\lambda$ is the regularization parameter,
${\left\|  \cdot  \right\|_F}$ is the Frobenius norm of a matrix.
Combining Equation~\ref{eq:Q-function} and  Equation~\ref{eq:pairwise-label},
we have  the following formulation:
\begin{equation}
\label{eq:J+Q}
\begin{array}{l}
F = J + \mu Q
 =  - \sum\limits_{{s_{ij}} \in S}
{\left( {{s_{ij}}{\Phi _{ij}} - \log \left( {1 + {e^{{\Phi _{ij}}}}} \right)} \right)}
 + \mu \sum\limits_{i = 1}^N {L\left( {{y_i},{W^T}{b_i}} \right)}  + \nu \left\| W \right\|_F^2,
\end{array}
\end{equation}
where $\mu$ is the trade-off parameters, $\nu  = \lambda \mu $.
Suppose that we choose the ${l_2}$ loss for the linear classifier,
Equation~\ref{eq:J+Q} is rewritten as follows:
\begin{equation}
\label{eq:J+Q-final}
\begin{array}{l}
F =  - \sum\limits_{{s_{ij}} \in S} {\left( {{s_{ij}}{\Phi _{ij}} - \log \left( {1 + {e^{{\Phi _{ij}}}}} \right)} \right)} + \mu \sum\limits_{i = 1}^N {\left\|
{{y_i} - {W^T}{b_i}} \right\|_2^2}  + \nu \left\| W \right\|_F^2,
\end{array}
\end{equation}
where ${\left\|  \cdot  \right\|_2}$ is ${l_2}$ norm of a vector.
The hypothesis for Equation~\ref{eq:J+Q-final} is that the learned binary codes should
make the pairwise label likelihood as large as possible, and should be optimal for the jointly learned linear classifier.

\subsection{Optimization }

The minimization of Equation~\ref{eq:J+Q-final} is a discrete optimization problem, which is difficult to optimize directly.
There are several ways to solve this problem.
(1) In the training stage, the sigmoid or tanh activation function is utilized to replace the
ReLU function after the last fully connected layer,  and then the continuous outputs
are used as a relaxation of the hash codes.
In the testing stage,  the hash codes are obtained by applying a
thresholding function on the continuous outputs.
One limitation of this method is that the convergence of the algorithm is slow. Besides, there will be a large quantization error.
(2) The sign function is directly applied after the outputs of the last fully connected layer,
which constrains the outputs to be binary variables strictly.
However, the sign function is non-differentiable, which is difficult to back propagate the gradient
of the loss function.

Because of the discrepancy between the Euclidean space and the Hamming space, it would result in suboptimal hash codes if one totally ignores the binary constraints.
We emphasize that it is essential to keep the discrete nature of the binary codes. Note that in our formulation, we constrain the outputs of the
last layer to be binary codes directly, thus Equation~\ref{eq:J+Q-final} is difficult to optimize directly.
Similar to~\cite{li2016feature,Wang2016,yao2016deep}, we solve this problem by introducing an auxiliary variable.
Then we approximate Equation~\ref{eq:J+Q-final} as:
\begin{equation}
\label{eq:auxiliary-variable}
\begin{array}{l}
F =  - \sum\limits_{{s_{ij}} \in S} {\left( {{s_{ij}}{\Psi _{ij}} - \log \left( {1 + {e^{{\Psi _{ij}}}}} \right)} \right)} + \mu \sum\limits_{i = 1}^N {\left\| {{y_i} - {W^T}{b_i}} \right\|_2^2}  + \nu \left\| W \right\|_F^2,\\
s.t.\;\;{b_i} = {\mathop{\rm sgn}} {\left( {{h_i}} \right)},\;\;{h_i} \in {\mathbb{R}^{K \times 1}},\;\;\left( {i = 1,...,N} \right),
\end{array}
\end{equation}
where ${\Psi _{ij}} = \frac{1}{2}{h_i}^T{h_j}$.
${h_i}\;(i = 1,...,N)$ can be seen as the output of the last fully connected layer, which is represented as:
\begin{equation}
\label{eq:h-lastfclayer}
{h_i} = {M^T}\Theta \left( {{x_i};\theta } \right) + n,
\end{equation}
where $\theta $ denotes the parameters of the previous layers before the last fully connected layer,
$M\in{\mathbb{R}^{4096 \times K}}$ represents the weight matrix, $n \in {\mathbb{R}^{K \times 1}}$
is the bias term.

According to the Lagrange multipliers method,  Equation~\ref{eq:auxiliary-variable} can be reformulated as:
\begin{equation}
\label{eq:Lagrange-reformulation}
\begin{array}{l}
F =  - \sum\limits_{{s_{ij}} \in S} {\left( {{s_{ij}}{\Psi _{ij}} - \log \left( {1 + {e^{{\Psi _{ij}}}}} \right)} \right)}
{\kern 1pt} {\kern 1pt} \\
+ \mu \sum\limits_{i = 1}^N {\left\| {{y_i} - {W^T}{b_i}} \right\|_2^2}
{\kern 1pt}  + \nu \left\| W \right\|_F^2 + \eta \sum\limits_{i = 1}^N
{\left\| {{b_i} - {\mathop{\rm sgn}} \left( {{h_i}} \right)} \right\|_2^2}, \\
s.t.\;\;\;\;{b_i} \in {\left\{ { - 1,1} \right\}^K},\;\;\left( {i = 1,...,N} \right),
\end{array}
\end{equation}
where $\eta $ is the Lagrange Multiplier.
Equation~\ref{eq:Lagrange-reformulation} can be further relaxed as:
\begin{equation}
\label{eq:relaxation-Lagrange-reformulation}
\begin{array}{l}
F =  - \sum\limits_{{s_{ij}} \in S} {\left( {{s_{ij}}{\Psi _{ij}} - \log \left( {1 + {e^{{\Psi _{ij}}}}} \right)} \right)}
{\kern 1pt} \\
 + \mu \sum\limits_{i = 1}^N {\left\| {{y_i} - {W^T}{b_i}} \right\|_2^2}
 {\kern 1pt}  + \nu \left\| W \right\|_F^2 + \eta \sum\limits_{i = 1}^N {\left\| {{b_i} - {h_i}} \right\|_2^2}, \\
s.t.\;\;\;\;{b_i} \in {\left\{ { - 1,1} \right\}^K},\;\;\left( {i = 1,...,N} \right).
\end{array}
\end{equation}
The last term actually measures the constraint violation caused by the outputs of the last fully connected layer.
If the parameter $\eta$ is set sufficiently large, the constraint violation is penalized severely.
Therefore the outputs of  the last fully connected layer are forced closer to the binary codes, which are employed
for classification directly.

The benefit of introducing an auxiliary variable is that we can decompose Equation~\ref{eq:relaxation-Lagrange-reformulation} into two sub optimization problems, which can be
iteratively solved  by using the
alternating minimization method.

First, when fixing ${b_i}$, $W$, we have:
\begin{equation}
\label{eq:solve-h}
\begin{array}{l}
\frac{{\partial F}}{{\partial {h_i}}} =  - \frac{1}{2}\sum\limits_{j:{s_{ij}} \in S} {\left( {{s_{ij}} - \frac{{{e^{{\Psi _{ij}}}}}}{{1 + {e^{{\Psi _{ij}}}}}}} \right)} {\kern 1pt} {\kern 1pt} {h_j}
 - \frac{1}{2}\sum\limits_{j:{s_{ji}} \in S} {\left( {{s_{ji}} - \frac{{{e^{{\Psi _{ji}}}}}}{{1 + {e^{{\Psi _{ji}}}}}}} \right)} {\kern 1pt} {h_j} - 2\eta \left( {{b_i} - {h_i}} \right)
\end{array}
\end{equation}
Then we update parameters $M$, $n$ and $\Theta$ as follows:
\begin{equation}
\begin{array}{l}
\frac{{\partial F}}{{\partial M}} = \Theta \left( {{x_i};\theta } \right){\left( {\frac{{\partial F}}{{\partial {h_i}}}} \right)^T}, {\kern 1pt} {\kern 1pt}
\frac{{\partial F}}{{\partial n}} = \frac{{\partial F}}{{\partial {h_i}}},{\kern 1pt} {\kern 1pt}
\frac{{\partial F}}{{\partial \Theta \left( {{x_i};\theta } \right)}} = M\frac{{\partial F}}{{\partial {h_i}}}.
\end{array}
\end{equation}
The gradient will propagate to previous layers by Back Propagation (BP) algorithm.

Second, when fixing $M$, $n$, $\Theta$ and $b_i$, we solve $W$ as:
\begin{equation}
\label{eq:solve-W}
F = \mu \sum\limits_{i = 1}^N {\left\| {{y_i} - {W^T}{b_i}} \right\|_2^2}  + \nu \left\| W \right\|_F^2.
\end{equation}
\label{eq:solution-W}
Equation~\ref{eq:solve-W} is a least squares problem, which has a closed form solution:
\begin{equation}
W = {\left( {B{B^T} + \frac{\nu }{\mu }I} \right)^{ - 1}}{B^T}Y,
\end{equation}
where $B = \left\{ {{b_i}} \right\}_{i = 1}^N \in {\left\{ { - 1,1} \right\}^{K \times N}}$,
$Y = \left\{ {{y_i}} \right\}_{i = 1}^N \in {\mathbb{R}^{C \times N}}$.

Finally, when fixing $M$, $n$, $\Theta$ and $W$, Equation~\ref{eq:relaxation-Lagrange-reformulation} becomes:
\begin{equation}
\label{eq:solve-B1}
\begin{array}{l}
F = \mu \sum\limits_{i = 1}^N {\left\| {{y_i} - {W^T}{b_i}} \right\|_2^2} + \nu \left\| W \right\|_F^2 + \eta \sum\limits_{i = 1}^N {\left\| {{b_i} - {h_i}} \right\|_2^2}, \\
s.t.\;\;\;\;{b_i} \in {\left\{ { - 1,1} \right\}^K},\left( {i = 1,...,N} \right).
\end{array}
\end{equation}
In this paper, we use the discrete cyclic coordinate descend method to iteratively solve $B$ row by row:
\begin{equation}
\label{eq:solve-B2}
\begin{array}{l}
\mathop {\min }\limits_B \;{\left\| {{W^T}B} \right\|^2} - 2\;{\rm{Tr}}\left( P \right), \;\;\;\;
s.t.\;\;B \in {\left\{ { - 1,1} \right\}^{K \times N}},
\end{array}
\end{equation}
where $P = WY + \frac{\eta }{\mu }H$.
Let ${x^T}$ be  the ${k^{th}}{\kern 1pt} {\kern 1pt} {\kern 1pt} \left( {k = 1,...,K} \right)$ row of $B$, ${B_1}$ be the matrix of $B$
excluding ${x^T}$,
${p^T}$ be the  ${k^{th}}$ column of matrix $P$, ${P_1}$ be the matrix of $P$
excluding ${p}$,
${w^T}$ be the  ${k^{th}}$ column of matrix $W$, ${W_1}$ be the matrix of $W$
excluding ${w}$, then we can derive:
\begin{equation}
\label{eq:solution-B}
x = {\mathop{\rm sgn}} \left( {p - B_1^T{W_1}w} \right){\kern 1pt}.
\end{equation}
It is easy to see that each bit of the hash codes is computed based on the pre-learned $K-1$ bits ${B_1}$.
We iteratively update each bit until the  algorithm converges.

\section{Experiments}

\subsection{Experimental settings}

We conduct extensive experiments on two public benchmark datasets:
CIFAR-10 and NUS-WIDE.
CIFAR-10 is a dataset containing 60,000 color images in 10 classes,
and each class contains 6,000 images with a resolution of 32x32.
Different from CIFAR-10, NUS-WIDE is a public multi-label image dataset.
There are 269,648 color images in total with 5,018 unique tags.
Each image is annotated with one or multiple class labels from
the 5,018 tags.
Similar to~\cite{lai2015simultaneous,liu2011hashing,xia2014supervised,zhang2015bit},
we use a subset of 195,834 images which are associated with the 21 most frequent concepts.
Each concept consists of at least 5,000 color images in this dataset.

We follow the previous experimental setting in~\cite{lai2015simultaneous,li2016feature,Wang2016}.
In CIFAR-10, we randomly select 100 images per class (1,000 images in total) as the test query set,
500 images per class (5,000 images in total) as the training set.
For NUS-WIDE dataset, we randomly sample 100 images per class (2,100 images in total) as the
test query set, 500 images per class (10,500 images in total) as the training set.
The similar pairs are constructed according to the image labels: two images will be
considered similar if they share at least one common semantic label. Otherwise, they will be
considered dissimilar.
We also conduct experiments on CIFAR-10 and NUS-WIDE dataset under  a different experimental setting.
In CIFAR-10, 1,000 images per class (10,000 images in total) are selected as the test query set, the remaining
50,000 images are used as the training set.
In NUS-WIDE, 100 images per class (2,100 images in total) are randomly sampled as the test query images, the remaining images (193,734 images in total) are used as the training set.

As for the comparison methods, we roughly divide them into two groups: traditional hashing methods and
deep hashing methods.
The compared traditional hashing methods consist of unsupervised and supervised methods.
Unsupervised hashing methods include SH~\cite{weiss2009spectral},
ITQ~\cite{gong2013iterative}.
Supervised hashing methods include SPLH~\cite{wang2010sequential},
KSH~\cite{liu2012supervised}, FastH~\cite{lin2014fast}, LFH~\cite{zhang2014supervised},
and SDH~\cite{shen2015supervised}.
Both the hand-crafted features and the features extracted by CNN-F network architecture are used as
the input for the traditional hashing methods.
Similar to previous works, the handcrafted features include a 512-dimensional GIST descriptor to represent images of CIFAR-10 dataset, and a 1134-dimensional feature vector to represent images of NUS-WIDE dataset.
The deep hashing methods include
DQN~\cite{cao2016deep},
DHN~\cite{zhu2016deep},
CNNH~\cite{xia2014supervised},
NINH~\cite{lai2015simultaneous}, DSRH~\cite{zhao2015deep},
DSCH~\cite{zhang2015bit}, DRCSH~\cite{zhang2015bit}, DPSH~\cite{li2016feature}, DTSH~\cite{Wang2016} and VDSH~\cite{zhang2016efficient}.
Note that DPSH, DTSH and DSDH are based on the CNN-F network architecture, while  DQN, DHN, DSRH are based on AlexNet architecture. Both the CNN-F network architecture and AlexNet architecture consist of five convolutional layers and two fully connected layers.
In order to have a fair comparison, most of the results are directly reported from previous works.
Following~\cite{zhang2016efficient}, the pre-trained CNN-F model is used to extract CNN features on CIFAR-10, while a 500 dimensional bag-of-words feature vector is used to represent each image on NUS-WIDE for VDSH. Then we re-run the source code provided by the authors to obtain the retrieval performance.
The parameters of our algorithm are set based on the standard cross-validation procedure. $\mu $, $\nu $ and $\eta $ in Equation~\ref{eq:relaxation-Lagrange-reformulation} are set to 1, 0.1 and 55, respectively.

Similar to~\cite{lai2015simultaneous}, we adopt four widely used evaluation metrics to evaluate the
image retrieval quality: Mean Average Precision (MAP) for different number of bits, precision curves within Hamming distance 2,  precision curves with different number of top returned samples and precision-recall curves.
When computing MAP for NUS-WIDE dataset under the first experimental setting, we only consider the
top 5,000 returned neighbors. While we consider the top 50,000 returned neighbors under the second
experimental setting.

\subsection{Empirical analysis}

\begin{figure*}[htb]
\centering
\subfigure[]{\includegraphics[width=40mm,height=36mm]{img/Precision-Ham2.pdf}}
\subfigure[]{\includegraphics[width=40mm,height=35mm]{img/Precision-TopN.pdf}}
\subfigure[]{\includegraphics[width=58mm,height=35mm]{img/Precision-Recall.pdf}}
\caption{The results of DSDH-A, DSDH-B, DSDH-C and DSDH on CIFAR-10 dataset: (a) precision curves within Hamming radius 2; (b) precision curves with respect to different number of top returned images; (c) precision-recall curves of Hamming ranking with 48 bits.} \label{fig:emperical-analysis}
\end{figure*}

In order to verify the effectiveness of our method, several variants of our method (DSDH) are also proposed.
First, we only consider the pairwise label information while neglecting the linear classification information in Equation~\ref{eq:J+Q-final}, which is named DSDH-A (similar to~\cite{li2016feature}).
Then we design a two-stream deep hashing algorithm to learn
the hash codes. One stream is designed based on the pairwise label information in Equation~\ref{eq:pairwise-label}, and the other stream is constructed based on the classification information. The two streams share the same image representations except for the last fully connected layer. We denote this method as DSDH-B.
Besides, we also design another approach directly applying the sign function after the outputs of the last fully connected layer in Equation~\ref{eq:J+Q-final}, which is denoted as DSDH-C.
The loss function of DSDH-C can be represented as:
\begin{equation}
\begin{array}{l}
F =  - \sum\limits_{{s_{ij}} \in S} {\left( {{s_{ij}}{\Psi _{ij}} - \log \left( {1 + {e^{{\Psi _{ij}}}}} \right)} \right)} {\kern 1pt} {\kern 1pt}  + \mu \sum\limits_{i = 1}^N {\left\| {{y_i} - {W^T}{h_i}} \right\|_2^2} \\
\;\;\;\;\;\;{\kern 1pt}  + \nu \left\| W \right\|_F^2 + \eta \sum\limits_{i = 1}^N {\left\| {{b_i} - {\mathop{\rm sgn}} \left( {{h_i}} \right)} \right\|_2^2}, \;\;\;\;
s.t.\;\;{h_i} \in {R^{K \times 1}},\;\;\left( {i = 1,...,N} \right)
\end{array}
\end{equation}
Then we use the alternating minimization method to optimize DSDH-C.
The results of different methods on CIFAR-10 under the first experimental setting are shown in Figure~\ref{fig:emperical-analysis}.
From Figure~\ref{fig:emperical-analysis} we can see that, (1) The performance of DSDH-C is better than DSDH-A. DSDH-B is better than DSDH-A in terms of precision with Hamming radius 2 and precision-recall curves. More information is exploited in DSDH-C than DSDH-A, which demonstrates the classification information is helpful for learning the hash codes.
(2) The improvement of DSDH-C over DSDH-A is marginal. The reason is that the classification information in DSDH-C is only used to learn the image representations, which is not fully exploited.
Due to violation of the discrete nature of the hash codes, DSDH-C
has a large quantization loss. Note that our method further beats DSDH-B and DSDH-C by a large margin.

\subsection{Results under the first experimental setting}
\label{subsec:ComparisonUnder1stEx}

\begin{table*}
\small
\caption{MAP for different methods under the first experimental setting. The MAP for NUS-WIDE
dataset is calculated based on the top 5,000 returned neighbors.
${\rm{DPS}}{{\rm{H}}^{\rm{*}}}$ denotes re-running the code provided by the authors of DPSH.}
\label{Table:ComparisonEx1}
\begin{center}
\arrayrulewidth=0.6pt
\begin{tabular}{|l|c|c|c|c|l|c|c|c|c|}
\hline
\multirow{2}*{Method} & \multicolumn{4}{c|} {CIFAR-10}  & \multirow{2}*{Method} &\multicolumn{4}{c|} {NUS-WIDE}\\  \cline{2-5} \cline{7-10}
 & 12 bits&24 bits&32 bits&48 bits &  & 12 bits&24 bits&32 bits&48 bits\\
\hline
Ours &{\textbf{0.740}} & {\textbf{0.786}} & {\textbf{0.801}} & {\textbf{0.820}} & Ours & {\textbf{0.776}} & {\textbf{0.808}}& {\textbf{0.820}}& {\textbf{0.829}}\\
\hline
DQN &0.554 & 0.558 & 0.564 & 0.580 & DQN & 0.768 & 0.776 & 0.783 & 0.792\\
\hline
DPSH &0.713 & 0.727 & 0.744 & 0.757 & ${\rm{DPS}}{{\rm{H}}^{\rm{*}}}$ & 0.752 & 0.790 & 0.794 & 0.812\\
\hline
DHN &0.555 & 0.594 & 0.603 & 0.621 & DHN & 0.708 & 0.735 & 0.748 & 0.758\\
\hline
DTSH &0.710 & 0.750 & 0.765 & 0.774 & DTSH & 0.773 & 0.808 & 0.812 & 0.824\\
\hline
NINH &0.552 & 0.566 & 0.558 & 0.581 & NINH & 0.674 & 0.697 & 0.713 & 0.715\\
\hline
CNNH &0.439 & 0.511 & 0.509 & 0.522 & CNNH & 0.611 & 0.618 & 0.625 & 0.608\\
\hline
FastH &0.305 & 0.349 & 0.369 & 0.384 & FastH & 0.621 & 0.650 & 0.665 & 0.687\\
\hline
SDH &0.285 & 0.329 & 0.341 & 0.356  & SDH & 0.568 & 0.600 & 0.608 & 0.637\\
\hline
KSH &0.303 & 0.337 & 0.346 & 0.356 & KSH & 0.556 & 0.572 & 0.581 & 0.588\\
\hline
LFH &0.176 & 0.231 & 0.211 & 0.253 & LFH & 0.571 & 0.568 & 0.568 & 0.585\\
\hline
SPLH &0.171 & 0.173 & 0.178 & 0.184 & SPLH & 0.568 & 0.589 & 0.597 & 0.601\\
\hline
ITQ &0.162 & 0.169 & 0.172 & 0.175 & ITQ & 0.452 & 0.468 & 0.472 & 0.477\\
\hline
SH &0.127 & 0.128 & 0.126 & 0.129 & SH & 0.454 & 0.406 & 0.405 & 0.400\\
\hline
\end{tabular}
\end{center}
\end{table*}

The MAP results of all methods on CIFAR-10 and NUS-WIDE under the first
experimental setting are listed in Table~\ref{Table:ComparisonEx1}.
From Table~\ref{Table:ComparisonEx1} we can see that the proposed method substantially outperforms
the traditional hashing methods on CIFAR-10 dataset. The MAP result of our method is more than twice
as much as SDH, FastH and ITQ.
Besides, most of the deep hashing methods perform better than the traditional hashing methods.
In particular, DTSH achieves the best performance among all the other methods except DSDH on CIFAR-10 dataset.
Compared with DTSH, our method further improves the performance
by ${\rm{3}} \sim {\rm{7}}$ percents.
These results verify that learning the hash function and classifier within one stream framework can boost the retrieval performance.

The gap between the deep hashing methods and traditional hashing methods is
not very huge on NUS-WIDE dataset, which is different from CIFAR-10 dataset.
For example, the average MAP result of SDH is 0.603, while the average MAP
result of DTSH is 0.804.
The proposed method is slightly superior to DTSH in terms of the MAP results on NUS-WIDE dataset.
The main reasons are that there exits more categories in NUS-WIDE than CIFAR-10, and each of the image contains
multiple labels.
Compared with CIFAR-10, there are only 500 images per class for training, which
may not be enough for DSDH to learn the multi-label classifier.
Thus the second term in Equation~\ref{eq:J+Q-final} plays a limited role to learn a
better hash function.
In Section~\ref{subsec:ComparisonUnder2ndEx}, we will show that our method will achieve a better performance than other deep hashing methods with more training images per class for the multi-label dataset.

\begin{table*}
\small
\caption{MAP for different methods under the second experimental setting. The MAP for NUS-WIDE
dataset is calculated based on the top 50,000 returned neighbors. ${\rm{DPS}}{{\rm{H}}^{\rm{*}}}$ denotes re-running the code provided by the authors of DPSH.}
\label{Table:ComparisonEx2}
\begin{center}
\arrayrulewidth=0.6pt
\begin{tabular}{|l|c|c|c|c|l|c|c|c|c|}
\hline
\multirow{2}*{Method} & \multicolumn{4}{c|} {CIFAR-10}  & \multirow{2}*{Method} &\multicolumn{4}{c|} {NUS-WIDE}\\  \cline{2-5} \cline{7-10}
 & 16 bits&24 bits&32 bits&48 bits &  & 16 bits&24 bits&32 bits&48 bits\\
\hline
Ours &{\textbf{0.935}} & {\textbf{0.940}} & {\textbf{0.939}} & {\textbf{0.939}} & Ours &
\textbf{0.815} & \textbf{0.814}& \textbf{0.820}& \textbf{0.821}\\
\hline
DTSH &0.915 & 0.923 & 0.925 & 0.926 &  DTSH & 0.756 & 0.776 & 0.785 & 0.799\\
\hline
DPSH &0.763 & 0.781 & 0.795 & 0.807 & DPSH & 0.715 & 0.722 & 0.736 & 0.741\\
\hline
VDSH &0.845 & 0.848 & 0.844 & 0.845 & VDSH & 0.545 & 0.564 & 0.557 & 0.570\\
\hline
DRSCH &0.615 & 0.622 & 0.629 & 0.631 & DRSCH & 0.618 & 0.622 & 0.623 & 0.628\\
\hline
DSCH &0.609 & 0.613 & 0.617 & 0.620 & DSCH & 0.592 & 0.597 & 0.611 & 0.609\\
\hline
DSRH &0.608 & 0.611 & 0.617 & 0.618 & DSRH & 0.609 & 0.618 & 0.621 & 0.631\\
\hline
${\rm{DPS}}{{\rm{H}}^{\rm{*}}}$ &0.903 & 0.885 & 0.915 & 0.911  & ${\rm{DPS}}{{\rm{H}}^{\rm{*}}}$ & \multicolumn{4}{c|}{N/A}\\
\hline
\end{tabular}
\end{center}
\end{table*}

\subsection{Results under the second experimental setting}
\label{subsec:ComparisonUnder2ndEx}

Deep hashing methods usually need many training images to learn the hash function.
In this section, we compare with other deep hashing methods under the second experimental setting, which contains more training images.
Table~\ref{Table:ComparisonEx2} lists MAP results for different methods under the second
experimental setting.
As shown in Table~\ref{Table:ComparisonEx2}, with more training images, most of the deep hashing methods perform better than in Section~\ref{subsec:ComparisonUnder1stEx}.
For CIFAR-10 dataset, the average MAP result of DRSCH is 0.624, and the average MAP results of DPSH, DTSH and VDSH are 0.787, 0.922 and 0.846, respectively.  The average MAP result of our method is 0.938 on CIFAR-10 dataset.
DTSH, DPSH and VDSH have a significant advantage over other deep hashing methods.
Our method further outperforms DTSH, DPSH and VDSH by about $2 \sim 3$ percents.
For NUS-WIDE dataset, our method still achieves the best performance in terms of MAP. The performance of
VDSH on NUS-WIDE dataset drops severely. The possible reason is that VDSH uses the provided bag-of-words features
instead of the learned features.

\subsection{Comparison with traditional hashing methods using deep learned features}

\begin{table*}
\small
\caption{MAP for different methods under the first experimental setting. The MAP for NUS-WIDE
dataset is calculated based on the top 5,000 returned neighbors.}
\label{Table:ComparisonCNNF}
\begin{center}
\arrayrulewidth=0.6pt
\begin{tabular}{|l|c|c|c|c|c|c|c|c|}
\hline
\multirow{2}*{Method} & \multicolumn{4}{c|} {CIFAR-10}  &\multicolumn{4}{c|} {NUS-WIDE}\\  \cline{2-5} \cline{6-9}
 & 12 bits&24 bits&32 bits&48 bits &  12 bits&24 bits&32 bits&48 bits\\
\hline
Ours &{\textbf{0.740}} & {\textbf{0.786}} & {\textbf{0.801}} & {\textbf{0.820}} & 0.776 & {\textbf{0.808}}& {\textbf{0.820}}& {\textbf{0.829}}\\
\hline
FastH+CNN &0.553 & 0.607 & 0.619 & 0.636 & 0.779 & 0.807 & 0.816 & 0.825\\
\hline
SDH+CNN &0.478 & 0.557 & 0.584 & 0.592 & {\textbf{0.780}} & 0.804 & 0.815 & 0.824\\
\hline
KSH+CNN &0.488 & 0.539 & 0.548 & 0.563 & 0.768 & 0.786 & 0.790 & 0.799\\
\hline
LFH+CNN &0.208 & 0.242 & 0.266 & 0.339  & 0.695 & 0.734 & 0.739 & 0.759\\
\hline
SPLH+CNN &0.299 & 0.330 & 0.335 & 0.330 & 0.753 & 0.775 & 0.783 & 0.786\\
\hline
ITQ+CNN &0.237 & 0.246 & 0.255 & 0.261  & 0.719 & 0.739 & 0.747 & 0.756\\
\hline
SH+CNN &0.183 & 0.164 & 0.161 & 0.161 & 0.621 & 0.616 & 0.615 & 0.612\\
\hline
\end{tabular}
\end{center}
\end{table*}

In order to have a fair comparison, we also compare with traditional hashing methods using deep learned features extracted by the CNN-F network under the first experimental setting.
The MAP results of different methods are listed in Table~\ref{Table:ComparisonCNNF}.
As shown in Table~\ref{Table:ComparisonCNNF}, most of the traditional hashing methods obtain a better retrieval performance using deep learned features.
The average MAP results of FastH+CNN and SDH+CNN on CIFAR-10 dataset are 0.604 and 0.553, respectively.
And the average MAP result of our method on CIFAR-10 dataset is 0.787, which outperforms the traditional hashing methods with deep learned features.  Besides, the proposed algorithm achieves a comparable performance with the best traditional hashing methods on NUS-WIDE dataset under the first experimental setting.

\section{Conclusion}

In this paper, we have proposed a novel deep supervised discrete hashing algorithm.
We constrain the outputs of the last layer  to be binary codes directly.
Both the pairwise label information and the classification information are used for learning the hash codes under one stream framework. Because of the discrete nature of the hash codes, we derive an alternating minimization method to optimize the loss function. Extensive experiments have shown that our method outperforms state-of-the-art methods on benchmark image retrieval datasets.

\section{Acknowledgements}
This work was partially supported by the National Key Research and Development Program of China (Grant No. 2016YFB1001000) and the Natural Science Foundation of China (Grant No. 61622310).

{\small

redpajama_set_name: RedPajamaArXiv


On March 10, 2017, the Securities and Exchange Commission ("SEC") filed a dramatic 56-page Complaint in the United States District Court for the Southern District of New York ("SDNY") alleging that a Ukraine-based trading firm was involved in a layering manipulation of the United States securities markets, and, further, that said fraud was facilitated by FINRA member firm Lek Securities and its owner Samuel Lek. Securities and Exchange Commission, Plaintiff, v. Lek Securities Corporation, Samuel Lek, Vali Management Partners d/b/a Avalon FA Ltd, Nathan Fayyer, and Sergey Pustelnik a/k/a Serge Pustelnik, Plaintiff, Defendants.(Complaint, United States District Court for the Southern District of New York, March 10, 2017).
NOTE: A Complaint merely contains allegations and Defendants are presumed innocent unless and until prove guilty by a preponderance of the evidence in a court of law.
1. This case involves two schemes to manipulate the securities markets perpetrated by Avalon, a foreign trading firm. Fayyer (Avalon's disclosed principal) and Pustelnik (an undisclosed control person of Avalon and a former registered representative at LEK) directly participated in and assisted the manipulative schemes. The schemes were made possible through and with the participation and assistance of LEK, a U.S. broker-dealer based in New York, and Sam Lek, LEK's Chief Executive Officer ("CEO"). LEK and Sam Lek provided Avalon with access to the U.S. securities markets to execute the schemes, and otherwise assisted in carrying out the schemes.
2. The first manipulative trading scheme, known as "layering," involved manipulating the markets of U.S. stocks. Under this scheme, Avalon placed "non-bona fide orders"-in other words, orders that Avalon did not intend to execute and that had no legitimate economic reason-to buy or sell stock with the intent of injecting false information into the marketplace about supply or demand for the stock. Avalon did this to trick and induce other market participants to execute against Avalon's bona fide orders (i.e., orders that Avalon did intend to execute) for the same stock on the opposite side of the market. By placing the non-bona fide orders, Avalon was able to manipulate the market for the stocks and thereby obtain more favorable prices on the executions of its bona fide orders than otherwise would have been available. Avalon engaged in hundreds of thousands of instances of layering in numerous securities from approximately December 2010 through at least September 2016, and Avalon made millions of dollars in profits from the scheme.
3. The second manipulative trading scheme is referred to herein as the "cross-market manipulation," "cross-market scheme" or "cross-market strategy." In this scheme, Avalon bought and sold U.S. stock at a loss for the purpose of moving the prices of corresponding options, so that Avalon could make a profit by trading those options at artificial prices that they would not have been able to obtain but for the manipulation. Avalon's stock trades had no legitimate economic reason, and were intended to inject into the market false information about supply and demand in order to move the prices of corresponding options to artificial levels. Although the strategy involved taking a loss on the stock transactions, such losses were far outweighed by Avalon's significant profits from trading the corresponding options whose prices Avalon had manipulated. Avalon engaged in hundreds of instances of cross-market manipulation involving numerous stocks and options from at least August 2012 through at least December 2015, and Avalon made millions of dollars in profits from that scheme.
4. Together, the layering and cross-market manipulation schemes orchestrated by Avalon through LEK generated illicit profits of more than $28 million.
5. Fayyer, as a principal of Avalon, participated in and substantially assisted those schemes, as detailed more fully below.
6. The schemes were made possible through the participation and substantial assistance of LEK and Sam Lek, the majority owner and CEO of LEK, and of Pustelnik, an undisclosed control person of Avalon who also served as a registered representative of LEK for much of the relevant period. LEK, as a registered broker-dealer, provided Avalon with direct access to the U.S. securities markets and, along with Sam Lek as CEO, approved, permitted and facilitated Avalon's schemes even though they knew or were reckless in not knowing that Avalon was engaging in market manipulation.
7. LEK and Sam Lek had ample motive to assist and allow Avalon's manipulative trading. Between 2012 and 2016, Avalon was LEK's highest producing customer in terms of commissions and fees and rebates generated. LEK made significant profits from commissions and other amounts it earned from Avalon's layering and cross-market manipulation.
8. Beginning in late 2010, Pustelnik embedded himself at LEK, first as a foreign finder and then as a registered representative, thus enabling him to facilitate Avalon's manipulative trading through LEK. Pustelnik received a share of Avalon's profits directly from Avalon, including profits from the layering and cross-market manipulation. Moreover, as a registered representative at LEK who handled the Avalon account, Pustelnik received 3 commissions and other payments from the manipulative trading by Avalon, and thereby increased his share of illicit profits beyond what he received as a control person of Avalon.
9. Pustelnik played a central role in ensuring the success of the layering and cross-market schemes. Pustelnik recruited Avalon traders for the purpose of carrying out the schemes and worked closely with LEK to ensure the success and effectiveness of the strategies. When Avalon encountered technical or other difficulties in carrying out the cross-market manipulation through LEK, for example, Pustelnik arranged for the Avalon traders who executed the trading in the cross-market manipulation to conduct the scheme through an investment management firm ("Investment Management Firm") that traded through a different broker-dealer ("Other Broker-Dealer"). When the Other Broker-Dealer observed the manipulative nature of the cross-market scheme and objected to it, Pustelnik arranged to move the scheme back to LEK. Throughout, Pustelnik used his position at LEK to facilitate and further the cross-market scheme.
10. By engaging in the conduct alleged herein, Avalon, Fayyer, LEK, Sam Lek, and Pustelnik violated and are liable for the violations of the securities laws identified in the Claims for Relief section below.


text:
Home Culture Fairs & Festivals The Shanti's Last Mardi Gras? Say It Ain't So
The Shanti's Last Mardi Gras? Say It Ain't So
by Thomas Crone
Photograph by Thomas Crone
On Friday night, I arrived at Soulard's venerable The Shanti at around 8 p.m. The streets were crowded, but not necessarily with people; instead, every block seemed to have some large combination of dumpster, stage, latrine or other public service item, all there to support the next day's crowds. With Mardi Gras' Grand Parade just a half-day away, the vibe was oddly quiet. That said, the little packs of drinkers that were milling through the roads did have a party-hardy attitude, and a few solo stumblers were ambling this way and that.
Inside, The Shanti was already cooking. The afternoon's crowd was just exhausting itself from a long happy hour, while the bawdy Diggler's Lounge was setting up for its trademark blend music and sexy antics. Catching owner Teresa Parker on The Shanti's patio, I told her that I'd run back out, grabbing my laptop right quick. Hustling to my vehicle, I noticed… yes, public urination. By a pretty young lady. Right next to my passenger door. Ah, Mardi Gras!
While Parker shared a laugh with me about that sight, it's not as if she hasn't seen a few things in her years of running Soulard bars. For 13 years, she's operated The Shanti; the nine years prior, she toiled at nearby Molly's. Over all those years, Soulard Mardi Gras' been a big dot on her calendar, but one that she's come to really understand over the years.
"For me, because I've been doing it for 20 years, it's second nature," Parker said. "I can do this with my eyes closed. It's simple. I keep it simple. This year, I've had some family tragedy and two weeks before Mardi Gras, I was dealing with funerals and things surrounding those. But it's mostly like clockwork for me. I do enjoy it, it's fun. I can remember my first Mardi Gras. It was a Fat Tuesday and I walked in the Fat Tuesday parade. I dressed like I was in New Orleans and hardly anyone else did, so I felt a little out of it."
She continued that riff by saying that "it's definitely St. Louis' Mardi Gras down here, not New Orleans'. I wish we had more masquerade balls and events like that. We could really learn a lot if we sent some people to New Orleans to see how it's really done."
Now, The Shanti's always had a bit of a New Orleans vibe, a funky venue that's booked music most every night of the week, a smoking bar with a devil-may-care sense of design, along with a patio that draws as many dogs as human patrons on spring-through-fall afternoons. And, at some near point, the very particular spirit of The Shanti might depart its little corner of the world at Ninth and Allen. New building owners are on-deck, with Parker's lease ending on December 2, just after the Thanksgiving holiday.
Though there hasn't been anything officially floated out to the world, there's been talk among The Shanti's regulars about this for a few months.
"We've been here 13 years, and the building's been for sale for many of those years," Parker said. "I did have the first right to buy it, but I didn't think the price was affordable; the building needs a lot of work. I'm 50 years old, and I don't want to put my kids in debt."
The new owners are coming in with interests in two neighborhood spots: DB's and Sonny's. The stated intent is to keep the place a bar. To that end, it's a lot easier for them to open a new space if the liquor license is continuously held, with no breaks in licensure. With that in mind, Parker figured that The Shanti will remain in place, as is, until December. But even that gets a little complicated.
"To get a permit in the City of St. Louis is pretty hard," she said of the excise licensure. "But this place is more than a business and bar. The Shanti name… well, so many people have come through here, so many musicians. To sell the name, I just can't do it. It's personal. Right now, I don't know what the future holds. The thing that worries me the most is wondering where the bands will go. We only hold 50 people, but we do get awesome bands, national bands, even on nights for the open mic; our Tuesdays are awesome here.
"I don't know what I'll do just yet," she added. "I'm going to get through Mardi Gras first. Then I'll take some time to figure it out. Any new place would have to have a cool courtyard. I'm not saying we'll move locations. I'm not saying that I'll take a year off to get my head together and come back with other ideas. Or that I might get a job offer that I can't refuse. I'm just keeping all the options open right now. If the [new owners] offered me a lease, I might even stay a few more years. I'm not going to fret about it. It's been been a wonderful, 13-year run. I'm already blessed."
Parker noted that her venue's been a bar for many decades. She said that "a guy came in one time. He had a picture of himself as a kid in the 1940s in here, when it was a bar. I've been told that one of the first baseball teams in St. Louis used to come here. It's been a bar forever."
Like its predecessor, Allen Avenue, music's been a huge part of things ever since the start. And that's a week-, month- and year-'round thing.
"On Sundays, Margaret Bianchetta's playing jazz in here at 12:30 and it's a full bar," she said. "And it's totally different music than on the day before. A guy said to me once that he didn't need to check the listings, because it didn't really matter. He'd come in and he'd like the music. Or not, but he usually felt that there was something good to hear. I've already been trying to find a home for Kimmy V's open night. I'm sure Liquid Gold will find a place for themselves; you don't want to stop that great vibe. And Pik'n Lik'n has been here for 11 years of my 13."
Parker said there's been talk of maybe doing a show a month across the street, at the rehabbed Soulard Preservation Hall. Other ideas have been kicked around, in various degrees of seriousness.
As of last Friday night, though, Parker was saving the big picture conversations for later. She had Mardi Gras weekend to work through, along with Fat Tuesday; while that night's impact has diminished since the old days, it's still a key Soulard holiday. Potentially, both Mardi Gras and Fat Tuesday are each having a last run at the much-loved Shanti.
"You do come to love it," she said of Soulard Mardi Gras, on whose organizing board she sits. "It's the most money you'll make all year, and it helps put a little bit of financial gain into your books. Now, I can't imagine being McGurk's. I can't even imagine how they do it all, or even how Molly's does it. I've always been afraid that it's getting… not too big, exactly. But just having too many people selling stuff. I've liked keeping it simple, making it just about the neighborhood. But, yeah, I have enjoyed it."
For another glance at how The Shanti's always been its own, little, smoky oasis within the bustling Soulard bar culture, see our recent blog posting, Tuesdays with Laurie.
Look/Listen Look-Listen March 2014

redpajama_set_name: RedPajamaCommonCrawl


text:
Deaf AFSCME Member in WA Wears Armor of Empathy to Fight for Others
Meet a deaf AFSCME Member in Washington state who fights for his community using empathy as his weapon.
Photo Credit: AFSCME Council 28
Previous Equal Pay Day Reminds Us the Gender Pay Gap Persists Next City Workers Part of Tornado Response Team
By TIM WELCH, AFSCME Council 28 · Wednesday, April 11, 2018
Tags: Our Stories Workers' Rights
Tommy Fuglestad is a dedicated state employee and passionate union member. As a groundskeeper for the past 2 ½ years at North Seattle College, he wants "to make sure that everything looks good."
As a relatively new member of AFSCME Local 304 (Washington Federation of State Employees, Council 28), he's already made a difference representing his members as a delegate to the 2017 WFSE/AFSCME Convention held last fall.
In November, the union's Higher Education Policy Committee elected Fuglestad to one of the committee's 15 allotted seats on the WFSE/AFSCME Executive Board.
In late February, he got down to business when the board met for its first session of a two-year term that could see a sea change depending on how the U.S. Supreme Court rules on the Janus v. AFSCME case.
In between, Fuglestad embraced the drive to move legislation in Olympia to increase affordable housing options for state workers in high-cost areas of the state. He took part on Martin Luther King Day Lobby Day and later testified on behalf of an affordable housing bill. The bill didn't pass in 2018 but he intends to try again in 2019.
He is also one of the youngest union board members and member leaders WFSE/AFSCME has ever seen.
The rest of his story: Fuglestad is deaf.
"I prefer to be called deaf," Fuglestad said. "Deaf is my primary identity. I believe I'm the first person in the history of the Executive Board to be a deaf person."
"Deaf people and hard-of-hearing folks – there's usually just one of us but they typically can inspire all of the rest of the members," he said. "You can say something to the general population and be representative for what other people are feeling or going through and encourage other people to be motivated and think about what they can do as well."
Fuglestad got his first experience of what WFSE members can do when, after high school, he attended what is now known as the Center for Childhood Deafness and Hearing Loss in Vancouver (formerly called the State School for the Deaf). He saw Local 1225 members standing up for a fair contract.
He got job training as a groundskeeper, and in September 2015 started working at the Seattle College District.
Thanks to Local 304 mentors Rodolfo Franco and Amy Lazerte, Fuglestad stepped up and got involved to make a difference on such issues as fair pay and inequality.
Fuglestad says as a deaf person, he's faced challenges when it comes to inclusion and fair treatment.
There have been times, he says, when "I was treated as someone who was not equal. For example, some people may always be trying to take advantage of me as a person with a disability in certain situations."
But instead of emphasizing the times he's been treated poorly, Fuglestad wears an armor of empathy and stands up for others who face mistreatment because of a perceived disability.
His election to the governing board of the largest union for the largest employer in the state of Washington (state agencies, colleges and universities) sends a strong message about who Fuglestad is and who we are as a union.
"I'm willing to fight for equality, especially for people with disabilities and to promote diversity issues so that we can all learn to respect each other," Fuglestad says. "Anyone who experiences bullying or discrimination or any kind of inequality, that's what I'm trying to address."
"The union is a good way" to address these and other challenges, he says.
Fuglestad is committed to the principle that public employees should all be "100% Union Proud" and sign a "100% Union" card. It's the best defense against a likely adverse Janus ruling.
"The union has really added to my life because personally it's given me a way to be involved, something to do to really fight for equality," he said. "Equality's a big issue and just respecting each other and fighting for our rights. I'm more than willing to do that."


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MARQUES DE SAN ANDRES
CASA DE CARMEN
EL RISCO
POOL&GARDEN
BEACH&AROUND
BE TENERIFE
ORIGINALS EXPERIENCES
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Hacienda de las Cuatro Ventanas
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Helen Fielding, the author of Bridget Jones's Diary, says the West End show featuring music by Lily Allen will be 'fantastic'.
Renée Zellweger has become synonymous with Bridget Jones after playing the perennial singleton in two highly successful films.
Helen Fielding, the author of Bridget Jones's Diary, says we will, however, have to rethink the character when a West End musical opens next year starring Sheridan Smith in the title role.
The author does not say whether Smith, 30, who won a Laurence Olivier Award for her performance in the hit London show Legally Blonde, will have to put on weight for the part.
Zellweger has spoken of her difficulties in piling on the pounds before she appeared in the 2001 film and its sequel three years later.
The 42-year-old actress's reluctance to bulk up again is thought to explain why a third film has not yet been made. Fielding tells me that it is, however, "in development".
Romola Garai: The Queen's speech?
Despite her success in this country, Romola Garai has spoken of her reluctance to head for America. "Hollywood makes me feel like a polar bear on holiday in the Bahamas," she said.
The 28-year-old star of Atonement does not need to look far for encouragement, though, as Mandrake can disclose that her boyfriend, Ben Hooper, a 34-year-old barrister, is the brother of Tom Hooper, the director who is the toast of Tinseltown after his film The King's Speech triumphed at this year's Oscars.


Stop by Peace Lutheran Church in Bensalem for an all you can eat spaghetti and meatballs dinner. Includes a salad bar, dessert and beverages.
Tickets can be purchased ahead of time but there will be some available at the door.
Please call 215.638.3063 for tickets.


Gingham evokes a lazy Sunday picnicking in the park, but picnicking in style, with everything coordinated. Your best best bet is to match your dress to your picnic blanket, and to giggle delightfully while taking barely perceptible bites of cherry pie. Once the meal is done, roll around in the blanket, and declare yourself to be invisible. This is peak picnic. Your guests will be highly amused, which will prevent them from calling you Half-Pint or making any other Little House on the Prairie jokes. As such, you will have won the day. Celebrate by scarfing down the leftover cherry pie while wearing pajamas and binge watching Little House. You deserve it.


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Gina Rock, the longest flying Tink in Disneyland history.
Gina Rock, the longest flying Tink in Disneyland history.Gina Rock, the longest flying Tink in Disneyland history.Gina Rock, the longest flying Tink in Disneyland history.
Tink Sightings
Disaster Art
Worldwide Acts
Tink's Artist Friends
Gina ended this year with a bang! This interview project was a long time in the making. She made contact with two of her Tink Crew members from the 1980s, Jim Moore and Bruce McGuire. She was able to get ahold of never before seen photos and video footage from Jim.
Richard and Sarah Woloski from the Skywalking Through Neverland podcast conducted the interview for their 399th episode. Gina was interviewed along with Bruce, but at the last minute, Jim wasn't able to make it on the call. Bruce and Gina told stories about their time together in Disneyland. Bruce worked in Disneyland from 1973-2001 and was with the Tink Crew from 1985-1986. Jim worked in Disneyland from 1976-1988 and was with Gina from 1985-1989.
They have many more entertaining and engaging episodes on their website or wherever you listen to your favorite podcasts.
Disney Dan
Check out this great video by Disney Dan. He talks about the history of Disneyland's flying Tinker Bell and mentions Gina at the 6:30 mark (video is already queued to start at that part).
Magic Behind The Ears
I am the co-host of The Magic Behind The Ears podcast and was able to co-interview Gina on our show with Jerry Cornell, the producer of the Theme Parkology documentary DVDs (themeparkology.com).
We interviewed her in November 2016, but didn't actually get the episode up until April 2017.
Disney with daddy and daughter
"We have a very special guest on the show today. Gina Rock is the longest flying Tinker Bell in Disney history and on todays show, Gina shares what it was like flying as Tinker Bell from 1983-2005 at Disneyland.This amazing lady who first started out in the circus riding elephants and swinging from a trapeze will take us on an adventure full of pixie dust and special memories all this week on Disney with Daddy & Daughter."
Tony Tequila Talks Podcast
This unique podcast focuses on wrestling. The host, Tony Merino, was once employed by Ringling Brothers, just like Gina. He was a part of the elephant crew, which is Gina's favorite animal. Gina was in the elephant act with Gunther Gebel-Williams while she was at Ringling Brothers. What we didn't know about Gina is that she was friends with some famous wrestlers, like "Rowdy" Roddy Piper, Jay York, and even Andy Kaufman. Listen to this one-of-a-kind interview to learn more about it.
Mousetalgia Podcast
"This week, we're thinking Tink! You may have thought you knew all there was to know about Tinker Bell, but Mousetalgia dives in and considers the flight of everyone's favorite fairy by looking at some interesting angles. First, we talk to aerialist Gina Rock, who flew across the sky in Disneyland as the fireworks lit up the night. Gina talks about her background as a gymnast, and the experience of portraying the famous pixie in her nightly ritual in front of Sleeping Beauty Castle. We also talk to Klay Hall, the director of Tinker Bell and the Lost Treasure, on helping Disney relaunch Neverland for a new generation. Hall talks about developing Tink as a story editor for the 2008 Tinker Bell feature film, developing Pixie Hollow for Disneyland Park, and helping to guide the franchise as DisneyToon Studios launched the Disney Fairies, reimagining Tinker Bell for today's audiences while keeping her roots as one of Disney's most enduring and beloved characters. Plus – Kristen Carr checks in with a Mousetalgia FastPass… and more."
Mousetalgia is one of the top Disney podcasts out there and Gina was so excited to be interviewed by them. To listen to just Gina's part, skip ahead to the 25-minute mark.
Creating Geeks Podcast
Gina had the pleasure of being interviewed by Professional Mechanical Engineer Chris Chipman and his wife Sarah on their podcast Creating Geeks. She was able to talk more about her time in the circus with this interview than she has on previous ones.
Click to listen, or you can look up "The Chippa Made This" on your podcast app. Chris has several podcasts that share the same channel name.
The Civil Engineering Podcast
Gina was interviewed on a different type of podcast. This podcast is The Civil Engineering Podcast. Gina talks about how she went from gymnastics to being an aerialist in the circus, flying high over Disneyland, and then into Public Assistance/FEMA as a Program Delivery Manager for Jacobs where she met and works with multiple types of engineers. She started with Hurricane Katrina in 2005. You need to listen to hear the whole story.
DISNEYANA Fan Club
April/May 2016 & Jan 2019
Gina did her first interview with me at the end of 2015. My article was published in the Disneyana Fan Club Newsletter for April/May 2016.
The article was re-published on www.DisneyScoop.com in January 2019 with more photos added in and some updated information.
You can click to download the newsletter further down.
Download the Disneyana Newsletter about Tink (pdf)
The Sweep Spot
The Sweep Spot podcast is hosted by two past Cast Members of Disneyland who were custodians with a combined 24 years experience. They were working in the park the whole time Gina was flying as Tink. Lynn Barron and Ken Pellman both co-wrote the book "Cleaning The Kingdom." Check out the book HERE.
This episode was from December 9, 2017.
"On The Sweep Spot we welcomed Gina Rock who was Tinker Bell and flew above Disneyland from 1983 to 2005 plus a trip report and more."
USC Spatial Sciences Institute
USC Wrote About Gina 11/21/17
Gina Rock (Geospatial Intelligence Graduate Certificate) has more than a decade of disaster response experience, and, over that time, has been deployed to 32 disaster declarations serving victims of flooding, hurricanes, tornadoes, landslides, ice storms and wind driven rain.
"During my deployments, I had to travel extensively to reach victims affected by these disasters, and if it were not for our current GPS systems, and available mapping tools I would not have been efficient with time management," Rock said. After Hurricane Harvey made landfall in Texas, Gina was deployed to Beaumont, Texas, where she is training new site inspectors to assess damage to parks, roads, city hall, schools, fire stations, historical landmarks, bridges, hospitals, culverts and the removal of debris."
Gina was interviewed on the "Skywalking Through Neverland" podcast in November 2017 by Richard and Sarah Woloski.
Here are there show notes: " Ever wonder what it's like to soar over Sleeping Beauty Castle while fireworks boom all around you? To be the symbol of Disneyland's magic gliding across the sky? Gina Rock IS Tinker Bell. Specifically the longest flying Tinker Bell in Disneyland history, from 1983-2005. We are delighted to welcome her as our guest, and her stories are almost unbelievable. Hold on, was she the first…Jedi Tink?? **gasp** "
In A Book
Discovering The Magic Kingdom: An Unofficial Disneyland Vacation Guide 2nd Edition 7/17/17
In July 2017, I had the second edition of my book published. It is about Disneyland and all of its history. It contains over 3,700 fun facts and over 750 photos. Inside on page 430 I wrote a mini biography about Gina. If you would like your own copy of this 700 page book, just visit my website.
www.DisneyGuy.org
Magical Day Radio
In October 2016, Gina was interviewed on the podcast "Magical Day Radio" by Dawn Short and Chris Lyndon. It was her first time to be interviewed on a podcast.
Walt's Apartment Podcast
"We are so excited to bring this interview to you. Sam and Shawn had the amazing opportunity to sit down with Gina Rock . Gina is the longest flying Tinkerbell at Disneyland . For 21 years would travel from the top of the Matterhorn (160 feet in the air ) During the nighttime fireworks at the Happiest Place on Earth. Join us as Gina takes on the journey of her exciting career . From the Circus to Disneyland and now her great work in Emergency Management. Sit back relax and look high above the sky over Disneyland as we bring you Gina (Tinkerbell) Rock. Thank you for the continued support for our shows and as always , We hope you enjoy the view from Walt's Apartment."
Mouse Planet
An article about the flying Tinker Bells in Disneyland was written by author Jim Korkis on March 9, 2022. Tiny Kline, Mimi Zerbini, Judy Kaye, Patty Rock, and Gina Rock were the topic of discussion.
Interview With Jim Korkis
Pregnant Pixie Buzzes Sleeping Beauty Castle - Nov. 8, 2005
There certainly seem to be a lot of Tinker Bell fans and I've written about live action Disney Tinker Bells several times in the past. Here is the story of the fourth Tinker Bell to fly over Disneyland: Gina Rock, who did so for over twenty years and retired just as the new fireworks show, "Remember: Dreams Come True," at Disneyland began for the celebration of the 50th birthday.
Like many specialty performers, Gina Rock has had a long and varied career from dangling from a high-wire motorcycle to being hung from the neck as a human "clanger" on the Liberty Bell in the bi-centennial edition of the Ringling Brothers circus to being turned into an ostrich in a Hollywood magic show.
Rock was the daughter of a physics engineer named Robert and his wife Bernice who was content to be a homemaker. It was during gymnastics practice at her high school in the 1970s that legendary Hollywood stuntman Bob Yerkes spotted Rock tumbling and invited her to train in his back yard, a dangerous area designed to train stunt people and keep them in top shape.
Currently in his Seventies, Yerkes has doubled for celebrities like Arnold Schwarzenegger and Bob Hoskins as well as performing very dangerous stunts and being a stunt coordinator and advisor on films like "Batman Forever" and "Problem Child".
While practicing in Yerkes' backyard, the high school senior met a man from the Shrine Circus who was working on a motorcycle act. He suggested she slide down a high wire dangling by her neck. After mastering the trick, she was invited to join the circus in Texas as an aerialist. Her parents instantly hated the idea but it didn't prevent her from joining the circus and learning first hand the hard life of a circus performer, especially one who did not come from a circus family.
One night a fellow performer told her about a special Ringling Brothers Circus audition and he drove her 250 miles to the audition. She was signed on to be a showgirl/aerial ballerina for the 1976-77 bicentennial tour.
For two years, she performed with Ringling and then spent another three years on the flying trapeze at the Circus-Circus casino in Reno, Nevada.
Eventually, Rock returned to her home in the San Fernando Valley where she married a trapeze artist she met in Yerkes' backyard. In need of work, she recalled her grad night at Disneyland when she first saw Tinkerbell fly and had thought: "Oh, my God. I want that job."
Rock knew that Tinker Bell's nightly flight had been sidelined when her landing tower had been torn down to
build the new Fantasyland in 1983. Being petite herself and with a love of flying, Rock called to see if Disneyland had plans to bring back Tinker Bell's flight.
As luck would have it, auditions to find a new pixie were under way at that very moment. Rock auditioned and got the job. At twenty-seven years old, she would be Disneyland's fourth flying sprite. The previous flying Tinks were Tiny Kline, Mimi Zerbini (who only performed one summer), and Judy Kaye who like Rock all had previous circus experience.
The only stipulation from the Disney Company was that she not get pregnant. "Two weeks after they put that wand in my hand ...," Rock laughed without finishing the thought. She flew through that first summer anyhow in the early stages of pregnancy, with no one the wiser. (In fact, she flew two summers as Tink while she was pregnant.)
On a typical night, Rock disguised herself in a coat and hat to make her way through the park to the Matterhorn. There she rode an elevator five flights, then climbed five sets of stairs. At the top, two men took wind readings. If it was safe to fly, they hooked her to a trolley wire and then basically threw her off the Matterhorn.
"It was like launching a rocket," she said, "What I would do is close my eyes right before I flew. On top of the Matterhorn, especially on a full-moon night, it was so beautiful. I would listen to the story, and become the character"
With a spotlight on her, Rock traveled 13 miles per hour, as high as 160 feet above the park for two football fields. Her actual shift lasted about 23 seconds, depending on the wind. She passed over Sleeping Beauty's castle, slipping into a landing tower at Thunder Mountain and sneaking back to her car.
"She's supposed to be a light," Rock says of Tink. "She's really not supposed to be a person." Said Rock who won't reveal how much she was paid for her short flight. "That's part of the mystery of Tinker bell."
In the second summer, Rock brought her sister-in-law in as a backup. About eight years ago another woman was hired to share the flights.
Rock was given an opportunity to audition for the new fireworks show with a more intricate flight pattern for Tinker Bell. However, now 48 years old, she felt it was finally time to hang up the wings and wand and chose not to audition despite pangs of regret.
"I mean, 21 years was a beautiful run," she said. "The whole first part of my life was like living a `Quantum Leap' episode. Like living in a dream."
Currently, she is writing a motivational book for children that encourages them to follow their wildest dreams. "I'm the kid who read all the books and I wanted to live in those books. I didn't want to keep reading. I wanted to jump into it," stated Gina who hopes her book will inspire a new generation to soar like Tinker Bell in the night sky while fireworks frame their silhouettes.
By Author Jim Korkis (photo from Yesterland)
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