text:
Neuhaus takes part in ribbon cutting to celebrate grand opening of Tourism Welcome Center
Goshen, N.Y. – Orange County Executive Steven M. Neuhaus and Tourism Director Amanda Dana participated in a ceremonial ribbon cutting on Friday to celebrate the grand opening of the Orange County Tourism Office's Welcome Center.
Chairman of the Legislature Steve Brescia, Legislators James O'Donnell, Rob Sassi, Janet Sutherland and Paul Ruszkiewicz, Senator Jen Metzger and Assemblyman Colin Schmitt also attended the event.
"We have some of the most impressive tourism destinations right here in our own backyard," Neuhaus said. "Tourism in Orange County is a growing industry and a terrific contributor to the County's economy. This refurbished Welcome Center in Goshen will provide a friendly and interactive place for visitors and residents to learn about what's happening in our great County."
For decades, tourists have flocked to Orange County to visit landmarks such as West Point and Storm King Art Center. Woodbury Common Premium Outlets, one of the state's biggest tourism attractions, accounts for an estimated 13 million visitors to Orange County each year. An additional seven million visitors are expected in the region following LEGOLAND® New York theme park and resort's anticipated 2020 opening.
To accommodate this growth, and to give visitors a one-stop-shop in the seat of Orange County, the Tourism Department has opened its Welcome Center at 99 Main Street in Goshen. The Welcome Center will be a resource for visitors and will include information about nearby attractions, lodging, dining, craft beverage, all delivered by a knowledgeable team available during operating hours of 9 a.m. to 5 p.m. on Monday through Friday. The Welcome Center is also where visitors and residents can pick up a copy of the 2019 Orange County Travel Guide.
"I have put an emphasis on tourism since I took office and that will continue," Neuhaus said. "We are all proud that Orange County has become a top tourist attraction for visitors from around the world and I know that this trend will continue."
Dana added: "The Tourism staff is excited to open our Welcome Center to residents of Orange County and our guests. We are grateful for the vision of County Executive Neuhaus, who has embraced tourism as a top economic driver for our County and region. We are thrilled to be a part of the new tourism destination culture and look forward to working with our partners to provide an incredible visitor experience."
Orange County Tourism, based in Goshen, N.Y., is the county's tourism headquarters and a participant in the I LOVE NY program. A comprehensive listing of area attractions, lodging, and events can be found at www.OrangeTourism.org. For a free copy of the Orange County Travel Guide, e-mail tourism@orangecountygov.com or call 845-615-3860.
⇐Previous County Executive Neuhaus and Health Department recognize Juvenile Arthritis Awareness MonthNext⇒ Neuhaus returns to Orange County after deployment overseas with the U.S. Navy
Anticipation building for 2019 Orange County Freedom Fest on July 27th
Free Movie Nights set for August 10th and August 24th at Thomas Bull Memorial Park
Neuhaus presents Susan Armistead with Citizen of the Month Award for the month of July
County Executive Neuhaus and Health Department recognize Juvenile Arthritis Awareness Month
Neuhaus returns to Orange County after deployment overseas with the U.S. Navy
District Attorney and Consumer Affairs remind residents to be aware of scam targeting O&R customers
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\section{Introduction}

Crowd counting is to estimate the number of people or objects from images or videos. Most existing methods formulate it as a density map regression problem \cite{lempitsky2010learning,zhang2015cross,zhang2016single,sam2017switching,li2018csrnet:}, and solve it by using the pixel-to-pixel prediction networks \cite{kang2014fully,peng2017large,long2015fully}. Once the density map is estimated, the total object count can be trivially calculated. To train such a density map regression model, most existing crowd counting methods rely on a substantial amount of labeled images with the object location annotation, e.g., marking a dot at the center of corresponding persons. The annotation process can be labor-intensive and time-consuming. For example, to annotate the ShanghaiTech \cite{zhang2016single} dataset, 330,165 dots must be placed on corresponding persons carefully.

To reduce the annotation cost, an attractive solution is to learn the crowd counter in a semi-supervised setting which assumes availability of a small amount of labeled images and a large amount of unlabeled images. This is a realistic assumption since unlabeled images are much easier or effortlessly to obtain than labeled images. Then the research problem is how to leverage the unlabeled image to help train the crowd counter for achieving a reasonable performance.

To solve this problem, we propose a novel semi-supervised learning algorithm to obtain a crowd counting model. One key of our model is to use the unlabeled data to learn a generic feature extractor of the crowd counter instead of the entire network as most traditional methods do. The underlying motivations are threefold: (1) It is challenging to construct a robust semi-supervised learning loss term from unlabeled data for regression output. In contrast, learning a feature extractor is more robust and reliable towards the inevitable noisy supervision generated from unlabeled data; (2) the feature extractor often plays a critical role in a prediction model. If we have a good feature extractor, it is possible to learn a density map regressor, i.e., crowd counter, require much less ground-truth density map annotations; (3) there are a range of methods for learning feature extractor, and features can be even learned from other tasks rather than density map regression (i.e., surrogate tasks in this paper).

Inspired by those motivations, we propose to learn the feature extractor through a set of surrogate tasks: predicting whether the density of a pixel is above multiple predefined thresholds. Essentially, those surrogate tasks are binary segmentation tasks and we build multiple segmentation predictors for each of them. Since those tasks are derived from the density map regression, we expect that through training with these surrogate tasks the network can learn good features to benefit the density map estimation. For labeled images, we have ground-truth segmentation derived from the ground-truth density map. For unlabeled images, the ground-truth segmentation are not available. However, the unlabeled images can still be leveraged through a semi-supervised segmentation algorithm. Also, we notice that the correct predictions for the surrogate tasks should hold certain inter-relationship, e.g., if the density of a pixel is predicted to be higher than a high threshold, it should also be predicted higher than a low threshold. Such inter-relationships could serve as additional cues for jointly training segmentation predictors under the semi-supervised learning setting. Inspired by that, we developed a novel self-training algorithm to incorporate these inter-relationships to generate reliable pseudo-labels for semi-supervised learning. By conducting extensive experiments, we demonstrate the superior performance of the proposed method. To sum up, our main contributions are:

\begin{itemize}
\item We approach the problem of semi-supervised crowd counting from a novel perspective of feature learning. By introducing the surrogate tasks, we cast the original problem into a set of semi-supervised segmentation problem.
\item We develop a novel self-training method which fully takes advantage of the inter-relationship between multiple binary segmentation tasks.

\end{itemize}

\section{Related Works}

\noindent \textbf{Traditional Crowd Counting Methods} include detection-based and regression-based methods. The detection-based methods use head or body detectors to obtain the total count in an image \cite{subburaman2012counting,dalal2005histograms,viola2004robust}. However, in extremely congested scenes with occlusions, detection-based methods can not produce satisfying predictions.

Regression-based methods are proposed \cite{chen2012feature,chan2009bayesian} to tackle challenges in overcrowded scenes. In regression-based methods, feature extraction mechanisms \cite{Idrees_2013_CVPR,fiaschi2012learning} such as Fourier Analysis and Random Forest regression are widely used. However, traditional methods can not predict total counts accurately as they overlook the spatial distribution information of crowds.

\noindent \textbf{CNN-based Crowd Counting Methods} learn a mapping function from the semantic features to density map instead of total count \cite{lempitsky2010learning}. Convolutional Neural Network (CNN) shows great potential in computer vision tasks. CNN-based methods are used to predict density maps. Recently, the mainstream idea is to leverage deep neural networks for density regression \cite{zhang2015cross,zhang2016single,sam2017switching,Sindagi2017generate}. These methods construct multi-column structures to tackle scale variations. Then local or global contextual information is obtained for producing density maps.

Several works \cite{li2018csrnet:,chen2019scale} combine the VGG \cite{simonyan2014very} structure with dilated convolution to assemble the semantic features for density regression. While other works \cite{jiang2019crowd,zhao2019leveraging,Ranjan_2018_ECCV,zhu2019dual} introduce attention mechanisms to handle several challenges, e.g. background noise and various resolutions. Meanwhile works \cite{Liu2018decidenet,lian2019density,jiang2019mask,valloli2019wnet,Idrees_2018_ECCV} leverage the multi-task frameworks, i.e., detection, segmentation or localization, which provide more accurate location information for density regression. Besides, the self-attention mechanism \cite{zhang2019relational,wan2019adaptive} and residual learning mechanism \cite{sindagi2019pushing} are effective in regularizing the training of the feature extractor. Work \cite{xiong2019open} transforms the density value to the density level from close-set to open-set. Further, a Bayesian-based loss function \cite{ma2019bayesian} is proposed for density estimation. These above CNN-based methods require a large number of labeled images to train the crowd counter. However, annotating the crowd counting dataset is a time-consuming and labor-intensive work.

\noindent \textbf{Semi-/Weakly/Un-Supervised Crowd Counting Methods} attempt to reduce the annotation burden by using semi-/weakly/un-supervised settings. In the semi-supervised setting, work \cite{liu2018leverageing} collects large unlabeled images as extra training data and constructs a rank loss based on the estimated density maps. In the weakly-supervised setting, work in \cite{lei2020weakly} establishes a scheme of multiple auxiliary tasks training on the large amount of count-level annotations. Also, work in \cite{von2016gaussian} leverages the total count as a weak supervision signal for density estimation. Besides, an auto-encoder structure \cite{sam2019almost} is proposed for crowd counting in an almost unsupervised setting. Another method for reducing the annotation burden is to use synthetic images \cite{wang2019learning}. For example, the GAN-based \cite{gao2019featureaware} and domain adaption based \cite{gao2019domainadaptive} frameworks combine the synthetic images and realistic images to train the crowd counter. These methods are effective in reducing the annotation burden. However, they can not obtain satisfying crowd counting performance because the inevitable noisy supervision may mislead the density regressor.

\vspace{-30pt}
 \begin{figure}
\centering \subfigure[Traditional semi-supervised methods.]{ \includegraphics[width=5.8cm]{fig/motivation_a.pdf}
\label{fig:sub_a} }
\subfigure[Proposed semi-supervised method.]{
\includegraphics[width=5.8cm]{fig/motivation_b.pdf} \label{fig:sub_b} }
\caption{(a) Traditional semi-supervised methods use both labeled and unlabeled images to update the feature extractor and density regressor. (b) In the proposed method, the unlabeled images are only used for updating feature extractor.}
\label{fig:motivation}
\vspace{0pt}
\end{figure}

\section{Background: Crowd Counting as Density Estimation\label{sec:background}}

Following the framework ``learning to count" \cite{lempitsky2010learning}, crowd counting can be transformed into a density map regression problem. Once the density map is estimated, the total object count can be simply estimated by its summation, that is, $\hat{N} = \sum_{i,j} \hat{D}(i,j)$, where $\hat{D}(i,j)$ denotes the density value for pixel $(i,j)$. The Mean Square Error (MSE) loss is commonly used in model training, that is,
\begin{equation}
\mathcal{L}_{MSE}=\sum_{(i,j)}|\hat{D}(i,j)-D(i,j)|^{2},\label{eq:MSE-loss}
\end{equation}
where $\hat{D}$ is the estimated density map and $D$ is the ground-truth density map.

\begin{figure}[tbh]
\centering{}\includegraphics[width=12.4cm]{fig/overview.pdf}\caption{ The overview of our proposed method. We introduce a set of binary segmentation surrogate tasks. For labeled images, we construct loss terms on both original and surrogate tasks. For unlabeled images, we use the output of segmentation predictor and inter-relationship to generate ``pseudo segmentation", which is shown in Figure. \ref{fig:interrelationship}.\label{fig:overview-of-the-proposed-method}}
\end{figure}

\section{Methodology}
In this paper, we are interested in learning a crowd counter based on the semi-supervised setting. Formally, we assume that we have a set of labeled images $L=\{I^l_i, D_i\}$, where $D_i$ is the ground-truth density map, and a set of unlabeled images $U=\{I^u_i\}$. Our task is to learn a crowd counter by using both the labeled images and unlabeled images. In our setting, the unlabeled set contains much more images than the labeled set for training a crowd counting model.

\subsection{Using Unlabeled Data for Feature Learning}\label{sect:feature_training}
Generally speaking, a network can be divided into two parts, a feature extractor and a task-specific predictor. The former converts the raw images into feature maps while the latter further transforms them to the desired output, e.g., density map, in the context of crowd counting. Most existing semi-supervised learning methods \cite{xie2019unsupervised,liu2018leverageing,tarvainen2017mean,verma2019interpolation} learn those two parts simultaneously and seek to construct a loss term from unlabeled data applied to the entire network.

In contrast to the existing methods, we propose to learn the feature extractor and the task-specific predictor through different tasks and loss terms. In particular, in our method, the unlabeled data is only used for learning the feature extractor. This design is motivated by three considerations: (1) crowd counting is essentially a semi-supervised regression problem in our setting. Besides, it can be challenging to construct a robust semi-supervised regression loss term from unlabeled data (i.e., as most existing methods do). The noisy supervision generated from the loss term from unlabeled data may contaminate the task-specific predictor and lead to inferior performance. In our method, unlabeled data is only used to train the feature extractor as the noisy supervision will not directly affect the task-specific predictor; (2) feature extractor plays an important role in many fields like unsupervised feature learning \cite{zhang2014saliency,dosovitskiy2014discriminative}, semi-supervised feature learning \cite{yang2013semi,cheng2014semi} and few-shot learning \cite{gidaris2019boosting,li2019finding}. Indeed, with a good feature extractor, it is possible to reduce the need of a large amount of labeled data in training. In the context of crowd counting, this implies that much less ground-truth density map annotations are needed if we can obtain a robust feature extractor via other means; (3) feature extractor can be learned in various ways. In this way, we will have more freedom in designing semi-supervised learning algorithms for feature learning. Specifically, we propose to derive surrogate tasks from the original density map regression problem, and use those tasks for training the feature extractor. The schematic overview of this idea is shown in Figure \ref{fig:motivation} (b). For labeled images, the target of surrogate task can be transformed from ground-truth annotation. For the unlabeled images, the ground-truth annotation becomes unavailable. However, the unlabeled images can still be leveraged to learn the surrogate task predictor and consequently the feature extractor in a semi-supervised learning manner. In the following sections, we first elaborate how to construct the surrogate loss and then describe the semi-supervised learning algorithm developed for the surrogate tasks.

\subsection{Constructing Surrogate Tasks for Feature Learning \label{sec:surrogate-task}}

The surrogate task defined in this paper is to predict whether the density value of a pixel, $D(i,j)$, exceeds a given threshold. In other words, the prediction target of the surrogate task is defined as:

\begin{equation}
M(i,j)=\begin{cases}
1~~~~~&D(i,j)>\epsilon \\0~~~~~&D(i,j)<=\epsilon \end{cases}
,\label{eq:density_to_mask}
\end{equation}
where $(i,j)$ is the pixel coordinate, and $\epsilon$ is the predefined threshold. For labeled data, the ground-truth of $D$ is known and thus $M$ is known. For unlabeled data, no annotation of $D$ is available and thus $M$ is unknown. However, we can still use unlabeled data to construct loss term for indirectly supervising the prediction of $M$. Note that in this way, we essentially recast the original semi-supervised crowd counting problem into a semi-supervised segmentation problem since $M$ only takes binary values.

In practice, we use multiple thresholds and generate multiple surrogate targets $\{M_k\}$ to consider the pixels with different density levels. To set these thresholds, we rank all non-zero density values from all the labeled images in ascending order and choose the thresholds as the value ranked at $r_k\times N$, where $r_k \in [0,1]~ k=1,..,c$, $N$ is the total number of non-zero values and $c$ indicates the number of surrogate tasks. Meanwhile, we create multiple segmentation predictor branches attached to the feature extractor. These surrogate tasks are parallel to the density map regressor, as shown in Figure \ref{fig:overview-of-the-proposed-method}.

\subsection{\underline{I}nter-\underline{R}elationship-\underline{A}ware \underline{S}elf-\underline{T}raining (IRAST) for Semi-supervised Training on Surrogate Tasks\label{sec:inner-aware}}

\begin{figure*}[tbh]
\vspace{-20pt}
\includegraphics[width=12.0cm]{fig/interrelationship.pdf}\caption{The illustration of the inter-relationship between two segmentation predictors. We use a lower threshold $\epsilon_{1}$ segmentation predictor to produce $\hat{M}_{1}$, and a higher threshold $\epsilon_{2}$ segmentation predictor to produce $\hat{M}_{2}$. If a specific pixel in $\hat{M}_{1}$ is lower than $\epsilon_{1}$, while in $\hat{M}_{2}$ is higher than $\epsilon_{2}$, we can consider this pixel is invalid. The inter-relationship avoids such incorrect training signal flowing into the feature extractor.  \label{fig:interrelationship}}
\end{figure*}

\IncMargin{1em}
\begin{algorithm}[h]

\KwIn{ Number of surrogate tasks $c$. Given threshold $t_p$,
Predicted confidence value (posterior probability)  $P(\hat{M}_{k}=1)~k=1,\cdots,c$; $P(\hat{M}_{k}=0) = 1 - P(\hat{M}_{k}=1)$ }
\KwOut{ A set of pseudo-label set $\{\mathcal{S}_k\}$, one for each $k$:  $\mathcal{S}_k=\{(i,j,s_{ij})\}$, where $s_{ij}$ is the generated pseudo-label for $(i,j)$.}

\For{$k \in [1,c]$}
    {\For{ each location $(i,j)$}
        {\If{$P(\hat{M}_{k}(i,j)=1)>t_p$ and $P(\hat{M}_g(i,j)=1)>t_p~~ \forall g<k$}
            {$\mathcal{S}_k \leftarrow \mathcal{S}_k \cup (i,j,1)$}

         {\If{$P(\hat{M}_{k}(i,j)=0)>t_p$ and $P(\hat{M}_h(i,j)=0)>t_p~~ \forall h>k$}
            {$\mathcal{S}_k \leftarrow \mathcal{S}_k \cup (i,j,0)$}}

        }}

\caption{Pseudo-label Generation Rule \label{alg:pesudo-generate}}
\end{algorithm}
\DecMargin{1em}

To leverage the unlabeled data to train the surrogate task predictors and the feature extractor, a semi-supervised learning algorithm is needed. Self-training is one of the most commonly used semi-supervised learning algorithms in segmentation tasks \cite{socher2010connecting,karnyaczki2015sparse}. It recursively generates pseudo-class-label for samples (pixels) with prediction confidence values higher than a given threshold $t_p=0.9$. However, this straightforward solution largely ignores the underlying inter-relationship between multiple surrogate tasks.
Recall that $M$ takes binary values and $M(i,j) = 1$ if the density value of pixel $(i,j)$ is greater than a given threshold. Suppose we have two segmentation results $M_1$ and $M_2$ estimated from two predictors corresponding to two thresholds $\epsilon_1$ and $\epsilon_2$ ($\epsilon_1 < \epsilon_2$), then there will be a conflict if one predictor gives the prediction $\hat{M}_1(i,j) = 0$ while the other gives the prediction $\hat{M}_2(i,j) = 1$. This is because $\hat{M}_1(i,j) = 0$ indicates the density value of the pixel is less than $\epsilon_1$, but $\hat{M}_2(i,j) = 1$ implies the density value of pixel is larger than $\epsilon_2$ and consequently larger than $\epsilon_1$ since $\epsilon_1 < \epsilon_2$.

This inter-relationship could essentially act as an error correcting mechanism to test if the prediction made by surrogate predictors are likely to be accurate. Thus in our method, we incorporate it into the framework of self-training as an additional criterion for pseudo-label generation besides the commonly used thresholding criterion. Formally, we define the following rule for generating a pseudo label at the $k$-th predictor. Without loss of generality, we assume there are $c$ predictors, ranking from $1$ to $c$ according to the descent order of their corresponding thresholds, that is, $\epsilon_a > \epsilon_b$ if $a > b$. The formal rule of generating pseudo-label is shown in Algorithm \ref{alg:pesudo-generate}.The generation of pseudo-labels is online.

In nutshell, a pseudo label is generated in the surrogate binary segmentation task if its prediction confidence value for one class (``1'' or ``0'' in our case) is greater than $t_p$ and its prediction is not conflict with predictions of other predictors. An example of this scheme is illustrated in Figure \ref{fig:interrelationship}.

\noindent \textbf{Discussion:} The proposed method defines $c$ binary segmentation tasks and one may wonder why not directly define a single $c$-way multi-class segmentation task. Then an standard multi-class self-training method can be used. We refer this method as \textbf{Multiple-class Segmentation Self-Training (MSST)}. Comparing with our approach, MSST has the following two disadvantages: (1) it does not have the ``error correction'' mechanism as described in the rule of generating pseudo label. The difference between MSST and IRAST is the standard one-vs-rest multi-class classification formulation and the error correcting output codes formulation \cite{dietterich1994solving}; (2) MSST may be overoptimistic towards the confidence score due to the softmax normalization of logits. Considering a three-way classification scenario for example, it is possible that the confidence for either class is low and the logits for all three classes are negative. But by chance, one class has relatively larger logits, say, $\{-100, -110$ and $-90\}$ for class 1, 2 and 3 respectively. After normalization, the posterior probability for the last class becomes near 1, and will exceed the threshold for generating pseudo labels. In contrast, the proposed IRAST does not have this issue since the confidence score will not be normalized across different classes (quantization level). We also conduct an ablation study in Section \ref{sec:msst-vs-irast} to verify that MSST is inferior to IRAST.

\section{Overall Training Process}
In practice, we use the Stochastic Gradient Descent (SGD) to train the network\footnote{As the unlabeled set contains more images than the labeled set, we oversample labeled images to ensure the similar amount of labeled and unlabeled images occur in a single batch.}. For an labeled image, we construct supervised loss terms based on the density regression task and surrogate tasks, and the training loss is:
\small{{\begin{align}
\mathcal{L}_{L} & =\mathcal{L}_{MSE} + \lambda_{1}\mathcal{L}_{SEG}
=\sum_{(i,j)}\left(|\hat{D}(i,j)-D(i,j)|^{2} + \lambda_1 \sum_{k=1}^c CE(M_k(i,j),\hat{M}_k(i,j)) \right), \nonumber
\label{eq:total-loss-label}
\end{align}
}}where $CE()$ denotes the cross-entropy loss, $\hat{D}$ and $\hat{M}_{k}$ are the predicted density map and segmentation respectively; $D$ and $M_{k}$ are the ground-truth density map and segmentation respectively.

For an unlabeled image, we construct an unsupervised loss based on the surrogate tasks and use it to train the feature extractor:
\begin{align}
\mathcal{L}_{U} & = \lambda_{2}\mathcal{L}_{SEG} = \lambda_{2} \sum_{k=1}^{c} \sum_{(i,j,s_{ij}) \in \mathcal{S}_k}  CE\left(\hat{M}_{k}(i,j)),s_{ij}\right),
\label{eq:total-loss-unlabel}
\end{align}
where the $\mathcal{S}_k = \{(i,j,s_{ij})\}$ denotes the set of generated pseudo labels at the $k$-th segmentation predictor. Please refer to Algorithm \ref{alg:pesudo-generate} for the generation of $\mathcal{S}_k$.

\section{Experimental Results}
We conduct extensive experiments on three popular crowd-counting datasets. The purpose is to verify if the proposed methods can achieve superior performance over other alternatives in a \textbf{semi-supervised learning setting} and understand the impact of various components of our method. Note that works that methods in a fully-supervised setting or a unsupervised setting are \textbf{not directly comparable} to ours.
\subsection{Experimental Settings}
\noindent \textbf{Datasets.} ShanghaiTech \cite{zhang2016single}, UCF-QNRF \cite{Idrees_2018_ECCV} and WorldExpo'10 \cite{zhang2015cross} are used throughout our experiments. We modify the setting of each dataset to suit the need of semi-supervised learning evaluation. Specifically, the original training dataset is divided into labeled and unlabeled sets. The details about such partition are given as follows.

\noindent \textit{ShanghaiTech \cite{zhang2016single}}: {The ShanghaiTech dataset consists of 1,198 images with 330,165 annotated persons, which is divided into two parts: Part\_A and Part\_B. Part\_A is composed of 482 images with 244,167 annotated persons; the training set includes 300 images; the remaining 182 images are used for testing. Part\_B consists of 716 images with 88,498 annotated persons. The size of the training set is 400, and the testing set contains 316 images. In Part\_A, we randomly pick up 210 images to consist the unlabeled set, 90 images to consist the labeled set (60 images for validation). Also, In Part\_B, we randomly pick up 280 images to consist the unlabeled set, 120 images to consist the labeled set (80 images for validation).}

\noindent \textit{UCF-QNRF \cite{Idrees_2018_ECCV}}: The UCF-QNRF dataset contains 1,535 high-resolution images with 1,251,642 annotated persons. The training set includes 1,201 images, and the testing set contains 334 images.
We randomly pick up 721 images to consist the unlabeled set, 480 images to consist the labeled set (240 images for validation).

\noindent \textit{World Expo'10 \cite{zhang2015cross}}:The World Expo'10 dataset includes 3980 frames from Shanghai 2010 WorldExpo. The training set contains 3380 images, and the testing set consists of 600 frames. Besides, the Region of Interest (ROI) is available in each scene. Each frame and the corresponding annotated person should be masked with ROI before training. We randomly pick up 2433 images to consist the unlabeled set, 947 images to consist the labeled set (271 images for validation).

\noindent \textbf{Compared Methods:} We compare the proposed IRAST method against four methods: (1) Label data only (Label-only): only use the labeled dataset to train the network. This is the baseline of all semi-supervised crowd counting approaches. (2) Learning to Rank (L2R): a semi-supervised crowd counting method proposed in \cite{liu2018leverageing}. As the unlabeled images used in this paper are not released, we re-implement it with the same backbone and test setting as our method to ensure a fair comparison. (3) Unsupervised Data Augmentation (UDA): UDA \cite{xie2019unsupervised} is one of the state-of-the-art semi-supervised learning methods. It encourages the network to generate similar predictions for an unlabeled image and its augmented version. This method was developed for image classification. We modify it by using the estimated density map as the network output. (4) Mean teacher (MT): Mean teacher \cite{tarvainen2017mean} is a classic consistency-based semi-supervised learning approach. Similar as UDA, it was originally developed for the classification task and we apply it to the regression task by changing the network work output as the estimated density maps. (5) Interpolation Consistency Training (ICT): ICT \cite{verma2019interpolation} is a recently developed semi-supervised learning approach. It is based on the mixup data augmentation \cite{zhang2017mixup} but performed on unlabeled data. Again, we tailor it for the density map regression task by changing the output as the density map. More details about the implementation of the compared methods can be found in the supplementary material.

\noindent \textbf{Implementation details:}
The feature extractor used in most of our experiment is based on the CSRNet \cite{li2018csrnet:}. We also conducted an ablation study in Section \ref{sec:use-spn} to use Scale Pyramid Network (SPN) \cite{chen2019scale} as the feature extractor. Both CSRNet and SPN leverage VGG-16 \cite{simonyan2014very} as the backbone. Also, three segmentation predictors are used by default unless specified. The thresholds for the corresponding surrogate tasks are selected as $\{0, 0.5N, 0.7N\}$ (please refer to Section \ref{sec:surrogate-task} for the method of choosing thresholds). The segmentation predictors are attached to the 14-th layer of the CSRNet or the 13-th layer of SPN. The rest layers in those networks are viewed as the task specific predictor, i.e., the density map regressor. The segmentation predictors share the same network structure as the density map regressor. Please refer the supplementary material for the detailed structure of the network.  In all experiments, we set the batch size as 1 and use Adam \cite{Kingma_2014} as the optimizer. The learning rate is initially set to 1e-6 and halves per 30 epochs (120 epochs in total). Besides, we set $t_p$ to 0.9 in experiments. Our implementation is based on PyTorch \cite{paszke2019pytorch} and we will also release the code.

\noindent \textbf{Evaluation metrics:} Following the previous works \cite{zhang2015cross,zhang2016single}, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) are adopted as the metrics to evaluate the performance of the compared crowd counting methods.

\subsection{Datasets and Results}
\subsubsection{Evaluation on the ShanghaiTech Dataset:}

The experimental results on ShanghaiTech dataset are shown in Table \ref{tab:The-performance-ShanghaiTech}. As seen, if we only use the labeled image, the network can only attain an MAE of 98.3 on Part\_A and 15.8 on Part\_B. In general, using a semi-supervised learning approach brings improvement. The L2R \cite{liu2018leverageing} shows an improvement around 8 people in the MAE of Part A but almost no improvement for Part B. Semi-supervised learning approaches modified from the classification task (UDA, MT, ICT) also lead to improved performance over Label-only on Part A. However, the improvement is not as large as L2R. Our approach, IRAST, clearly demonstrates the best performance. It leads to 11.4 MAE improvement over the Label-only on Part A.

\begin{table*}\scriptsize
\begin{floatrow}
\capbtabbox{
\begin{tabular}{cccccc}
\hline
 & \multirow{2}{*}{Method} &  \multicolumn{2}{l}{Part\_A} & \multicolumn{2}{l}{Part\_B} \\ \cline{3-6}
 &  & MAE & MSE & MAE & MSE \\ \hline

\multirow{5}{*}{\rotatebox[origin=c]{90}{Semi}}

 & Label-only & 98.3 & 159.2 & 15.8 & 25.0  \\ \cline{2-6}
  & L2R \cite{liu2018leverageing}& 90.3 & 153.5 & 15.6 & 24.4 \\ \cline{2-6}
& UDA \cite{xie2019unsupervised} & 93.8 & 157.2 & 15.7 & 24.1 \\ \cline{2-6}
 & MT \cite{tarvainen2017mean}& 94.5 & 156.1 & 15.6 & 24.5 \\ \cline{2-6}
 & ICT \cite{verma2019interpolation}& 92.5 & 156.8 & 15.4 & 23.8 \\ \cline{2-6}
 & IRAST & \textbf{86.9} & \textbf{148.9} & \textbf{14.7} & \textbf{22.9} \\ \hline
 \multirow{1}{*}{}
 & (Fully) CSRNet \cite{li2018csrnet:}& 68.2 & 115.0 & 10.6 & 16.0  \\ \hline

\end{tabular}
}{
\caption{The comparison on the ShanghaiTech dataset. The best results are in bold font.}
\label{tab:The-performance-ShanghaiTech}
}
\capbtabbox{
\begin{tabular}{cccc}
\hline
 &\multirow{2}{*}{Method} & \multicolumn{2}{l}{UCF-QNRF} \\ \cline{3-4}
\multicolumn{1}{l}{} &  & MAE & MSE \\ \hline

\multirow{5}{*}{\rotatebox[origin=c]{90}{Semi}}
 & Label-only & 147.7 & 253.1 \\ \cline{2-4}
 & L2R  \cite{liu2018leverageing}& 148.9 & 249.8 \\ \cline{2-4}
 & UDA \cite{xie2019unsupervised} & 144.7 & 255.9 \\ \cline{2-4}
 & MT \cite{tarvainen2017mean}& 145.5 & 250.3 \\ \cline{2-4}
 & ICT \cite{verma2019interpolation} & 144.9 & 250.0 \\ \cline{2-4}
 & IRAST & \textbf{135.6} & \textbf{233.4} \\ \hline
  \multirow{1}{*}{}
 & (Fully) CSRNet \cite{li2018csrnet:}& 119.2 & 211.4 \\ \hline
\end{tabular}
}{
\caption{The comparison on the UCF-QNRF dataset. The best results are in bold font.}
\label{tab:The-performance-UCF-QNRF}
}
\end{floatrow}
\end{table*}

\subsubsection{Evaluation on the UCF-QNRF Dataset:}

\begin{figure*}[tbh]
\includegraphics[width=12.6cm]{fig/ucf_visial.pdf}\caption{A comparison of predicted density maps on the UCF-QNRF dataset.     \label{fig:visial_ucf}}
\end{figure*}

The advantage of the proposed method is also well demonstrated on UCF-QNRF dataset, shown in Table \ref{tab:The-performance-UCF-QNRF}. Again, the proposed method achieves the overall best performance, and exceeds the Label-only by around 12 MAE. The other semi-supervised learning approach does not work well on this dataset. In particular, L2R even achieves worse performance than the Label-only. This on the other hand clearly demonstrates the robustness of our approach. Also, from the results in both ShanghaiTech and UCF-QNRF, we can see that directly employing the semi-supervised learning approaches which were originally developed for classification may not achieve satisfying performance. It remains challenging for developing the semi-supervised crowd counting algorithm.

\begin{table}\scriptsize
\centering{}\caption{The performance comparison in terms of MAE on the WorldExpo'10 dataset. The best results are in bold font. \label{tab:The-performance-comparison-WorldExpo}}

\begin{tabular}{cccccccc}
\hline
\multirow{1}{*}{} & {Method} & Sce.1 & {Sce.2} & {Sce.3} & {Sce.4} & {Sce.5} & {Avg.}\tabularnewline
\hline

\multirow{6}{*}{{\rotatebox[origin=c]{90}{Semi}}}

&{Label-only} & {2.4} & {16.9} & {9.7} & {41.3} & {3.1} & {14.7}\tabularnewline
\cline{2-8} \cline{3-8} \cline{4-8} \cline{5-8} \cline{6-8} \cline{7-8} \cline{8-8}
 & {L2R \cite{liu2018leverageing}} & {2.4} & {20.9} & {9.8} & {31.9} & {4.4} & {13.9}\tabularnewline
 \cline{2-8} \cline{3-8} \cline{4-8} \cline{5-8} \cline{6-8} \cline{7-8} \cline{8-8}
& {UDA \cite{xie2019unsupervised}} & \textbf{1.9} & {20.3} & {10.9} & {34.5} & {3.6} & {14.2}\tabularnewline
 \cline{2-8} \cline{3-8} \cline{4-8} \cline{5-8} \cline{6-8} \cline{7-8} \cline{8-8}
 & {MT \cite{tarvainen2017mean}} & {2.6} & {24.8} & {9.4} & {30.3} & {3.3} & {14.1}\tabularnewline
 \cline{2-8} \cline{3-8} \cline{4-8} \cline{5-8} \cline{6-8} \cline{7-8} \cline{8-8}
 & {ICT \cite{verma2019interpolation}} & {2.3} & {17.8} & \textbf{{8.3}} & {43.5} & \textbf{2.8} & {14.9}\tabularnewline
  \cline{2-8} \cline{3-8} \cline{4-8} \cline{5-8} \cline{6-8} \cline{7-8} \cline{8-8}
 & {IRAST} & {2.2} & \textbf{12.3} & {9.2} & \textbf{{27.8}} & {4.1} & \textbf{{11.1}}\tabularnewline
\hline
\multirow{1}{*}{}
& (Fully) {CSRNet} \cite{li2018csrnet:} & {2.9} & {11.5} & {8.6} & {16.6} & {3.4} &
{8.6}\tabularnewline
\hline
\end{tabular}
\end{table}
\subsubsection{Evaluation on the World Expo'10 Dataset:}

The results are shown in Table \ref{tab:The-performance-comparison-WorldExpo}. As seen, IRAST again achieves the best MAE in 2 scenes and delivers the best MAE over other methods. The other semi-supervised learning methods achieve comparable performance and their performance gain over the Label-only is not significant.

\subsection{Ablation Study}
To understand the importance of various components in our algorithm, we conduct a serials of ablation studies.

\subsubsection{Varying the Number of Labeled Images:}

\begin{figure}
\includegraphics[width=6cm]{fig/MAE.pdf}
\caption{The impact of the number of labeled images. Evaluated in terms of MAE on the UCF-QNRF dataset. \label{fig:alter-label-num}}
\end{figure}

We first examine the performance gain over the Label-only under different amount of labeled images. We conduct experiments on the UCF-QNRF dataset. We vary the number of labeled image from 120 to 480 while fixing the amount of unlabeled images to be 481. The performance curves of the Label-only and IRAST are depicted in Figure \ref{fig:alter-label-num}. As seen, IRAST achieves consistent performance gain over the Label-only, which is an evidence of the robustness of our method. Also, we can see that with IRAST, using 480 images can almost achieve comparable performance than the performance of a fully-supervised model which needs 961 training images.

\subsubsection{IRAST on Labeled set:}
The proposed method constructs an additional training task and one may suspect the good performance is benefited from the multi-task learning. To investigate this hypothesis, we also conduct an ablation study by learning the crowd counter on the labeled set only, but with both density map regression task and surrogate tasks. The results are shown in Table \ref{tab:Results-of-only-label}. As seen, using multiple-surrogate tasks for the labeled set does improve the performance to some extent, but still has a significant performance gap with the proposed method. This result clearly validates that our method can not be simply understood as a multi-task learning approach.

\subsubsection{Other Alternative Surrogate Task:\label{sec:msst-vs-irast}}
One alternative method is to use a multi-class segmentation predictor to train the feature extractor, namely MSST mentioned in Section \ref{sec:inner-aware}. To compare MSST and IRAST, we conduct experiments on the ShanghaiTech Part\_A and UCF-QNRF dataset. The results are shown in Table \ref{tab:Results-compare-msst-IRAST}. As seen, MSST can achieve a better performance than Label-only method, which demonstrates the effectiveness of using a surrogate task for feature learning. However, MSST obtains a worse crowd counting performance than IRAST. Recall that MSST lacks an error correction mechanism to generate pseud-label, the superior performance of IRAST over MSST provides evidence to support the merit of our multiple surrogate binary-segmentation task modelling.

\vspace{-15pt}
\begin{table*}\scriptsize
\begin{floatrow}
\capbtabbox{
\begin{tabular}{ccccc}
\hline
\multirow{2}{*}{Method} & \multicolumn{2}{l}{Part\_A} & \multicolumn{2}{l}{UCF-QNRF} \\ \cline{2-5}
 & MAE & MSE & MAE & MSE \\ \hline
Label-only & 98.3 & 159.2 & 147.7 & 253.1 \\ \hline
IRAST on label & 94.1 & 151.6 & 140.8 & 245.4 \\ \hline
IRAST & \textbf{86.9} & \textbf{148.9} & \textbf{135.6} & \textbf{233.4} \\ \hline
\end{tabular}
}{
\caption{Impact of the unlabeled images in the process of feature learning. Evaluated on the ShanghaiTech Part\_A and UCF-QNRF dataset. The best results are in bold font.}
\label{tab:Results-of-only-label}
}
\capbtabbox{
\begin{tabular}{ccccc}
\hline
\multirow{2}{*}{Method} & \multicolumn{2}{l}{Part\_A} & \multicolumn{2}{l}{UCF-QNRF} \\ \cline{2-5}
 & MAE & MSE & MAE & MSE \\ \hline
Label-only & 98.3 & 159.2 & 147.7 & 253.1 \\ \hline
MSST & 91.5 & 155.2 & 140.0 & 233.7 \\ \hline
IRAST & \textbf{86.9} & \textbf{148.9} & \textbf{135.6} & \textbf{233.4} \\ \hline
\end{tabular}
}{
\caption{Comparison of IRAST and MSST. Evaluated on the ShanghaiTech Part\_A and UCF-QNRF dataset. The best results are in bold font.}
\label{tab:Results-compare-msst-IRAST}
}
\end{floatrow}
\end{table*}

\vspace{-30pt}
\begin{table*}\scriptsize
\begin{floatrow}
\capbtabbox{
\begin{tabular}{ccccc}
\hline
\multirow{2}{*}{Method} & \multicolumn{2}{l}{Part\_A} & \multicolumn{2}{l}{UCF-QNRF} \\ \cline{2-5}
 & MAE & MSE & MAE & MSE \\ \hline
Label-only & 98.3 & 159.2 & 147.7 & 253.1 \\ \hline
IRAST w/o IR & 93.5 & 155.5 & 139.8 & 240.3 \\ \hline
IRAST & \textbf{86.9} & \textbf{148.9} & \textbf{135.6} & \textbf{233.4} \\ \hline
\end{tabular}}{
\caption{Impact of the inter-relationship. Evaluated on the ShanghaiTech Part\_A and UCF-QNRF dataset. The best results are in bold font.\label{tab:Results-wo-inter-relationship}}
}

\capbtabbox{
\begin{tabular}{ccccc}
\hline
\multirow{2}{*}{Method} & \multicolumn{2}{l}{Part\_A} & \multicolumn{2}{l}{UCF-QNRF} \\ \cline{2-5}
 & MAE & MSE & MAE & MSE \\ \hline
Label-only & 98.3 & 159.2 & 147.7 & 253.1 \\ \hline
$t_p=0.6$ & 88.4 & 152.3 & 137.2 & 234.9 \\ \hline
$t_p=0.9$ & \textbf{86.9} & \textbf{148.9} & \textbf{135.6} & \textbf{233.4} \\ \hline
\end{tabular}
}{
\caption{Impact of the changing hyper-parameter $t_p$. Evaluated on the ShanghaiTech Part\_A and UCF-QNRF dataset. The best results are in bold font. \label{tab:change-tp}}}
\end{floatrow}
\end{table*}

\subsubsection{The Importance of Considering the Inter-Relationship:}
In IRAST, we leverage the Inter-Relationship (IR) between surrogate tasks to generate pseudo-labels. To verify the importance of this consideration, we conduct an ablation study by removing the inter-Relationship constraint for pseudo-label generation. The results are shown in Table \ref{tab:Results-wo-inter-relationship}. As seen, a decrease in performance is observed when the Inter-Relationship is not considered. This observation suggests that the Inter-Relationship awareness is essential to the proposed IRAST method.

\subsubsection{The Impact of Changing the Prediction  Confidence  Threshold:}
We set hyper-parameter $t_p$ to 0.9 in the previous experiments. To investigate the impact of $t_p$, we conduct experiments on ShanghaiTech part\_A and UCF-QNRF dataset. The results are shown in Table \ref{tab:change-tp}. The results demonstrate setting diverse $t_p$ does not impact crowd counting performance significantly. The crowd counting performance are comparable to our current results. It means the proposed IRAST method is robust.

\vspace{-10pt}
\begin{table*}\scriptsize
\begin{floatrow}
\capbtabbox{
\begin{tabular}{ccccc}
\hline
\multirow{2}{*}{Method} & \multicolumn{2}{c}{Part\_A} & \multicolumn{2}{c}{UCF-QNRF} \\ \cline{2-5}
 & MAE & MSE & MAE & MSE \\ \hline
Label-only (CSRNet) & 98.3 & 159.2 & 147.7 & 253.1 \\ \hline
IRAST (CSRNet) & 86.9 & 148.9 & 135.6 & 233.4 \\ \hline
Label-only (SPN) & 88.5 & 152.6 & 138.0 & 244.5 \\ \hline
IRAST (SPN) & \textbf{83.9} & \textbf{140.1} & \textbf{128.4} & \textbf{225.3} \\ \hline
\end{tabular}}{
\caption{Impact of the feature extractor. Evaluated on the ShanghaiTech Part\_A and UCF-QNRF dataset. The best results are in bold font.\label{tab:change-backbone}}
}
\capbtabbox{
\begin{tabular}{ccccc}
\hline
\multirow{2}{*}{Tasks} & \multicolumn{2}{c}{Part\_A} & \multicolumn{2}{c}{UCF-QNRF} \\ \cline{2-5}
 & MAE & MSE & MAE & MSE \\ \hline
1 & 89.8 & 149.8 & 142.8 & 236.5 \\ \hline
2 & 88.9 & 149.6 & 139.1 & 237.8 \\ \hline
\textbf{3} & \textbf{86.9} & \textbf{148.9} & \textbf{135.6} & \textbf{233.4} \\ \hline
4 & 90.1 & 150.2 & 137.5 & 236.8 \\ \hline
5 & 90.3 & 150.9 & 137.8 & 234.4\\ \hline
\end{tabular}
}{
\caption{Impact of the varing number of surrogate tasks. The best results are in bold font. \label{tab:change-surrogate-tasks-num}}}
\end{floatrow}
\end{table*}

\subsubsection{Change of the Feature Extractor:\label{sec:use-spn}}
So far, we conduct our experiment with the CSRNet \cite{li2018csrnet:} feature extractor. It is unclear if performance gain can still be achieved with other feature extractors. To investigate this, we conduct an experiment that uses SPN \cite{chen2019scale} as the feature extractor on the ShanghaiTech Part\_A and UCF-QNRF dataset. Results are shown in Table \ref{tab:change-backbone}. We can see that the significant performance gain can still be achieved. Also, we observe an improved overall performance by using SPN. This suggests that the advances in network architecture design for crowd counting can be readily incorporated into our method.

\subsubsection{The Effect of Varying the Number of Surrogates Tasks:}
Finally, we test the impact of choosing the number of surrogate tasks. We incrementally adding more thresholds by following the threshold sequence $\{0,0.5N,0.7N,0.8N,0.9N\}$, e.g., $\{0,0.5N,0.7N\}$ is used for the three-task setting while $\{0,0.5N,0.7N,0.8N\}$ is used for the four-task setting. The results are shown in Table \ref{tab:change-surrogate-tasks-num}. The results demonstrate setting three surrogate tasks for feature learning can achieve the best crowd counting performance. To have a finer grained partition of density value does not necessarily lead to improved performance.

\section{Conclusions}
In this paper, we proposed a semi-supervised crowd counting algorithm by creating a set of surrogate tasks for learning the feature extractor. A novel self-training strategy that can leverage the inter-relationship of different surrogate tasks is developed. Through extensive experiments, it is clear that the proposed method enjoys superior performance over other semi-supervised crowd counter learning approaches.

\section*{Acknowledgement}
This work was supported by the Key Research and Development Program of Sichuan Province (2019YFG0409). Lingqiao Liu was in part supported by ARC DECRA Fellowship DE170101259.

\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


text:
Social Entrepreneurs
This restaurant is giving young people a second chance
Rebuilding coal country
Copy a link to the article entitled This restaurant is giving young people a second chance
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Chef Chad Houser was a rising star in the Dallas restaurant scene, but after volunteering with kids in the criminal justice system, he felt a calling to do more. He gave up his job to start Cafe Momentum, a restaurant staffed by kids from the criminal justice system. They're given year-long paid internships where they are taught the skills to succeed in restaurants and society. It's his way of trying to fix the problems with the criminal justice system.
And it works: while about half of the kids released from jail in Texas end up back there within a year, 85% of the kids who come through Cafe Momentum never go back.

redpajama_set_name: RedPajamaCommonCrawl


The Mendelpass (Italian: Passo della Mendola or just Mendola; German: Mendelpass or just Mendel) is a 1362 m high mountain pass between the provinces of Trentino and South Tyrol in Italy. The pass road begins in the town of Fondo in the Non Valley and ends southwest of the city of Bolzano. The pass is open year-round, but trailers are prohibited. The pass road, SS 42, has a maximum grade of 12%, but is relatively straight.


In an ideal world, 100% of your real estate business would magically appear from referrals. Now wouldn't that be nice?
While that may not be realistic for every agent, it does help to remember referrals come when expectations are exceeded. The book Raving Fans explains that people typically don't tell their friends and associates about satisfactory or average service— but they rave about extraordinary service.
If there is any sort of client that is primed to be a raving fan of your service, it is a FSBO-turned client. In fact, investing in relationships with your FSBO contacts will go farther than any other form of marketing. If and when your FSBO contacts do list with an agent, they'll remember you as helpful and available because of what you've already put into the relationship.
Consider John's (a typical FSBO) before and after FSBO selling experience.
John just got an exciting new job, but it required them to move across state. While their family was super excited about the idea of moving closer to the ocean, the cost of living was going to be a bit higher. Determined to make this adventure possible, they wanted to save a few bucks by selling their home without an agent.
John had dabbled in marketing before. "How hard could it be?" he thought. A little touchup paint here and there, some photo's with his new iPhone, and some cookies at his open house seemed like the recipe for success.
Within a week, he had 3-4 showings, but only low-ball offer, even though his house was already listed below what he thought was market value. He finally had his open house, but only 2 people came to it, one of which was a neighbor just trying to see their home.
After 3 weeks of rushing home to help his wife show the house again and again, John finally got a decent offer for their home. "Finally, light at the end of the tunnel," John thought. That excitement faded quickly when the buyer backed out because they couldn't get approved. "Why would someone put an offer on a house without knowing if they could afford it? Who are these people?" John asked his wife in frustration.
Extremely frustrated and worried that they may not sell their home in time for this new move, John reached our to Mary, a real estate agent that had been emailing him some advice and periodically calling to see how the process was going.
Even though John had already taken photo's with his new iPhone, Mary had a photographer come and stage his house for photos. Initially annoyed at the idea that his photo's were not good enough, John was quickly surprised at how much better his house looked online with professional photos.
Mary listed John's house on the MLS, which was obviously important because it immediately started sending potential buyers to his home despite Mary's decision to put the listing price much higher than what John thought was realistic. He had 4 showings the first evening. The best part was that it all happened while his family went to dinner and a movie. Mary ran the whole process, which was really nice for a change.
After 3 days and 9 showings, Mary received a purchase offer for their home. While initially excited, John felt a little skeptical because he had a previous offer fall through. To his surprise, Mary had required the buyer's agent to also send a pre-approval letter from the buyer's lender. When Mary presented the offer to John, she explained the entire contract and make some recommendations for a counter offer. Within a few days of negotiations with the buyer's agent, Mary had not only secured a purchase contract for the home, but setup all the steps that needed to happen prior to the sale, which she scheduled just before their muchly anticipated move.
John and Mary in this story are obviously fictional, but the before and after experience of many FSBOs-turned-clients is not.
Not only can FSBO leads be a great source of listings, but they can be your best source of referrals too. Do everything you can to foster a strong relationship with all of your FSBO leads. That way, when they are ready to sell or when they have friends looking to list with an agent, they will call you every time.


Eater searches the country and singles out the most talented and promising young leaders in the hospitality industry under 30 years of age, or with less than five years in the business.
The blossoming Houston chocolatier was selected out of a pool of nominees in the initial group of semifinalists. In Texas, five others were chosen, including Rachel DelRocco, Qui of Austin; Mara Serna, D&T Drive Inn, Houston; Julie Poplawski, Dai Due, Austin; Hunter Pond, East Hampton Sandwich Company, Dallas; and Mist Norris, Small Brew Pub, Dallas, Fort Worth and Plano. Rachel DelRocco was also one of the final 17 to make the list.
Annie Rupani first launched her online chocolate store in 2012. She specialized in gourmet, handcrafted truffles and bonbons, featuring savory exotic flavors. It didn't take long for Cacao & Cardamom to grow a following. Rupani's unique chocolate creations won first place for Best Dessert at the Houston Livestock Show & Rodeo's Best Bites Competitions twice in a row.
Her 1,400-square-foot retail chocolate store in Houston is located in the Centre at Post Oak across from the Galleria. This quaint shop is where Rupani displays her hand-made cacao confections laced with alluring spices and elements influenced by her world travels.
Some of Annie Rupani's signature flavors include the sought after lychee basil, cardamom rose, mango caramel, Szechuan peppercorn, cocoa curry, black sesame ginger, guava tamarind and garam masala. Her chocolate shop in Houston also stocks a variety of gourmet chocolate bars from all over the world.


Gehl Institute is pleased to announce the publication of "Towards More Physical Activity in Cities," a report developed in partnership with WHO/Europe.
Check out the newly released tools for investigating public space and public life in the form of free, downloadable worksheets.
Beginning today, Gehl Institute is convening its second annual public life conference, Public x Design, in Philadelphia. Public x Design will bring together over 100 practitioners, researchers, and experts from around the world to collectively explore potentials and share actions to reclaim the public in public life and the public realm.
This August, Gehl Institute and Gehl, the practice, co-taught the "Tools for Change" masterclass in Copenhagen, Denmark.
We are pleased to announce the beta launch of the Public Life Data Protocol developed by Gehl Institute, in close partnership with Gehl, the practice, the Municipality of Copenhagen, the City of San Francisco, and with support and input from Seattle DOT.
Announcing the winners of our Open Call!
Gehl Institute is thrilled to announce the winners of our Open Call: Proposals for Public Life competition!


text:
Q: Django 1.3 and South migrations I have an existing project which extensively uses South migrations to load data into its tables.
Since upgrading to Django 1.3 our unit tests no longer run because they cannot find the data they rely on.

*

*Is this behaviour is due to one of the backwards incompatible changes in 1.3

*Is there an easy way for me to convert all these migrations into fixtures?

A: *

*Yes, this behavior is due to this change.
There seems to be a workaround in South trunk (see https://bitbucket.org/andrewgodwin/south/changeset/21a635231327 ) so you can try South development version (it is quite stable in my experience).

*You may try to change the DB name in settings (in order to get clean environment), run ./manage.py syncdb and ./manage.py migrate and then do ./manage.py dumpdata

A: I hit this issue today. Eventually I ended up refactoring my migrations so that they use helper functions to actually insert the data, and then calling the same functions from the setUp() of my tests.
Some hints;

*

*Make your helper functions take the model class as an argument, so you can call them with orm['yourapp.YourModel'] from the migration and with models.YourModel from the test. That also shows the main limitation: South works for models whose schema has changed since then, the test code can't do that. I was lucky in that this particular model hasn't changed.

*If you want to keep the helper methods inside the migrations, you'll find that you can't directly import yourapp.migrations.0001_some_migration because identifiers can't start with numbers. Use something like migration_0001 = importlib.import_module('yourapp.migrations.0001_some_migration') instead of an import statement.

redpajama_set_name: RedPajamaStackExchange


text:
El mail retrieval agent (MRA) es un software que recupera correo electrónico de un servidor de correo remoto y que trabaja con un agente de entrega de correo para enviar correo a cuentas locales o remotas.

Pueden ser aplicaciones externas autónomas o estar construidas como parte de una aplicación mayor como un cliente de correo electrónico.

Algunos ejemplos de MRAs independientes son fetchmail y getmail.

Enlaces externos
 MuttWiki: MailConcept

Software
Correo electrónico

redpajama_set_name: RedPajamaWikipedia


(Yet another post about) Chinese New Year !
I realise that Jessie posted here right before I did about the Chinese New Year dinner hosted by the Malaysian Society. I attended and was glad that I did. I was one of the ones that wore red, because wearing something ang ang (red red in Hokkien and Teochew?) is a tradition that I will gladly abide by to rake in the prosperity and luck. I got to meet so many people and make new friends!
However, I also found more activities to celebrate CNY hosted by the other societies that I, unfortunately, could not attend. There was a huge event jointly hosted by the Chinese Students and Scholars Association, University of Surrey and CSSA, Royal Holloway.
I have not been able to celebrate CNY in Malaysia for 2 years now. It truly is one of the elements I miss the most while studying abroad. Back home, it would be the celebration that brought the whole family together and brings back so much nostalgia.
So, this year I decided to take matters into my own hands and host a private little CNY dinner.
I decided to go with Yee Sang and a hot pot feast with a somewhat flexible budget. It was my first time making a Yee Sang but it really was always my favourite part of a CNY reunion dinner so I decided to introduce it to my Hong Kong and Chinese friends. It turned out deliciously amazing and I made sure to stick to the traditional recipe as much as possible. 捞生啊! I do not know how to rotate images here, sorry! (You could probably turn your head or laptop).
But yeah, I am proud of it and it made me happy that my friends appreciated and were happy with the dinner. I hope to do it again next year!
That's all from me for now! Buh bye!


SYOSSET, Long Island (WABC) -- Police say a man is under arrest after driving while intoxicated and going the wrong way on the Long Island Expressway Sunday morning.
At about 3 a.m., 911 received a call for a vehicle driving eastbound in the westbound lanes on the LIE in Syosset.
A police officer observed the 2007 Mercedes Benz near exit 40 that had passed several vehicles and trucks.
The officer then saw the vehicle make a U-turn out of the HOV lane, across all lanes of traffic and come to a stop on the shoulder of the highway near exit 44,.
Police say the driver, 56-year-old Jaime Rivas of Haddonfield, N.J., was observed to have slurred speech, red glassy blood shot eyes and alcohol emanating from inside the automobile.
He was arrested and charged with driving while intoxicated, reckless endangerment, reckless driving, driving the wrong way, use of a portable electronic device, and imprudent speed.


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About The Dungeon Master
'Oh Ju-Yoon' is your typical, everyday part-timer. He almost dies to a coincidental accident, but because of his kindness, he is given another chance to live in a new and different world. However, his future is neither as a hero, nor mage... but a creature at the bottom of the food chain, 'Brick Worm', meeting an atrocious crisis?! Surreal events! An unexpected level up fantasy!
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The digital transformation of existing business models is a theme of our age. Robotic process automation (RPA) is one of the many acronyms to have found its way into the terminology of businesses today. I highlighted the potential for telecoms to digitalise their business models in this post. Klaus Schwab of the World Economic Forum in his book "Fourth Industrial Revolution" refers to the current era as one whereby "new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human".
The financial services business is one that is regularly touted as been rife for transformation with fintech being the much-hyped buzz word. I last posted here and here on fintech and insurtech, the use of technology innovations designed to squeeze out savings and efficiency from existing insurance business models.
Artificial intelligence (AI) is used as an umbrella term for everything from process automation, to robotics and to machine learning. As referred to in this post on equity markets, the Financial Stability Board (FSB) released a report called "Artificial Intelligence and Machine Learning in Financial Services" in November 2017. In relation to insurance, the FSB report highlights that "some insurance companies are actively using machine learning to improve the pricing or marketing of insurance products by incorporating real-time, highly granular data, such as online shopping behaviour or telemetrics (sensors in connected devices, such as car odometers)". Other areas highlighted include machine learning techniques in claims processing and the preventative benefits of remote sensors connected through the internet of things. Consultants are falling over themselves to get on the bandwagon as reports from the likes of Deloitte, EY, PwC, Capgemini, and Accenture illustrate.
Creating new "intrinsic" AI risks.
I found one article in the SCOR report by Nicolas Miailhe of the Future Society at the Harvard Kennedy School particularly interesting. Whilst talking about the overall AI market, Miailhe states that "the general consensus remains that the market is on the brink of a revolution, which will be characterized by an asymmetric global oligopoly" and the "market is qualified as oligopolistic because of the association between the scale effects and network effects which drive concentration". When referring to an oligopoly, Miailhe highlights two global blocks – GAFA (Google/Apple/Facebook/Amazon) and BATX (Baidu/Alibaba/Tencent/Xiaomi). In the insurance context, Miailhe states that "more often than not, this will mean that the insured must relinquish control, and at times, the ownership of data" and that "the delivery of these new services will intrude heavily on privacy".
At a more mundane level, Miailhe highlights the difficulty for stakeholders such as auditors and regulators to understand the business models of the future which "delegate the risk-profiling process to computer systems that run software based on "black box" algorithms". Miailhe also cautions that bias can infiltrate algorithms as "algorithms are written by people, and machine-learning algorithms adjust what they do according to people's behaviour".
In a statement that seems particularly relevant today in terms of the current issue around Facebook and data privacy, Miailhe warns that "the issues of auditability, certification and tension between transparency and competitive dynamics are becoming apparent and will play a key role in facilitating or hindering the dissemination of AI systems".
Now, that's not something you'll hear from the usual cheer leaders.
Over a year ago, I posted on an IOSCO report on the small but growing crowd-funding and P2P lending market. The University of Cambridge and EY issued a report last month which highlighted the growth in the sector putting its size at €3 billion in 2014 and predicting a growth to over €7 billion in 2015.
The UK is the largest market by far with about 75% of the deals originating from there, as the graph below from the report shows.
An interesting market, one worth keeping an eye on.
There was an informative report from IOSCO on crowd funding and peer to peer lending earlier this month. The report highlights the developing nature of these type of platforms and considerable risks associated with such platforms such as default risk, platform closure/failure, fraud, illiquidity, cyber, information asymmetry, and investor inexperience. The report also outlines the different business models (of particular interest are those tested models growing in the US, China & the UK) in the approx $6 billion market and how regulation is trying to address these new models (or not in some cases).
The report lists the major players in the P2P lending market as per the exhibit below.


text:
Newsline staff
New lawsuit would bar Tina Peters from Mesa County election role in 2022
By: Newsline staff - January 19, 2022
Colorado Secretary of State Jena Griswold announced she is suing to bar Mesa County Clerk Tina Peters from overseeing elections this year — an expansion of Griswold's efforts last year that prevented Peters from running the November election. "As Clerk Peters is unwilling to commit to following election security protocols, I am taking action to […]
Text of Gov. Jared Polis' 2022 State of the State address
This is the prepared text of Gov. Jared Polis' 2022 State of the State address to the Colorado General Assembly, which he delivered at 11 a.m., Jan. 13. Good morning everyone. One traditionally begins a State of the State address by acknowledging dignitaries here in the chamber, but this morning calls for something a little […]
Tina Peters could resume election role, but only with severe restrictions
The Colorado secretary of state is demanding that the Mesa County clerk sign a document promising to protect the security of future elections she might oversee after a court barred her from election duties in November. Clerk Tina Peters allegedly allowed an unauthorized person to attend a software update for Mesa County's Dominion Voting Systems […]
1,084 homes in Louisville and Superior destroyed in Marshall Fire, says updated tally
By: Newsline staff - January 7, 2022
Officials working on a Marshall Fire damage assessment in Boulder County on Thursday released an updated list of structures destroyed or damaged. The new tally also includes dollar-amount losses and features a searchable map of addresses. The updated list reports 1,084 homes destroyed and 149 homes damaged, amounting to a loss of $513 million. It […]
Remarks by President Joe Biden on the Jan. 6 anniversary of the Capitol attack
Here are the full remarks made by President Joe Biden in Statuary Hall at the U.S. Capitol on the Jan. 6 anniversary of the Capitol attack: Madam Vice President, my fellow Americans: To state the obvious, one year ago today, in this sacred place, democracy was attacked — simply attacked. The will of the people […]
Human remains found in Marshall Fire investigation
Marshall Fire investigators have located the partial human remains of an adult, according to an announcement Wednesday from the Boulder County Sheriff's Office. The remains were found in the 5900 block of Marshall Road in unincorporated Boulder County. Sheriff Joe Pelle had said as of Sunday that two people were reported missing. Investigators are also […]
Eyewitness to Marshall Fire origin recounts scene like a 'war zone'
A neighbor of a property where a small structure was seen on video engulfed in flames late Thursday morning said he witnessed how those flames appeared to touch off what became the devastating Marshall Fire and that there might also be at least one other ignition source. "I saw everything," the neighbor, Mike Zoltowski, told […]
3 believed dead in Marshall Fire, search warrant executed
Three people are missing in the Marshall Fire and believed to be dead, Boulder County Sheriff Joe Pelle said Saturday, reversing accounts from the previous day suggesting that no human lives were lost. Pelle, speaking during an afternoon press conference, also characterized the investigation into the fire's cause in terms that were significantly different than […]
Up to 1,000 homes burned, no lives lost in Boulder County fires
By: Newsline staff - December 31, 2021
The Marshall Fire in Boulder County, propelled by winds of up to 110 mph and fueled by dry grass, tore through up to 1,000 homes but so far doesn't appear to have claimed any human lives, according to Gov. Jared Polis and other officials who spoke at a press conference Friday morning. It's the most […]
Hundreds of homes lost as grass fires rip through Boulder County neighborhoods
Hundreds of homes, including an entire Superior subdivision, were destroyed Thursday by a fast-moving grass fire in Boulder County. Emergency personnel responded to two main fires, the Marshall Fire, straddling U.S. 36 just southeast of Boulder, and the Middle Fork Fire, just north of Boulder around North Foothills Highway and Middle Fork Road. Both fires […]
Election conspiracy group 'doing great things,' governor candidate Ganahl says
By: Newsline staff - December 7, 2021
Heidi Ganahl, the best-known Republican candidate for Colorado governor, during a recent event with supporters endorsed the work of a Colorado-based conspiracy group that rejects the results of the 2020 election, according to a recording of her comments obtained by Newsline. The event, a meet-and-greet in Durango, occurred on Nov. 18. At one point she […]
Election fraud has hurt Republicans for decades, claims Colorado Senate candidate Hanks
By: Newsline staff - November 30, 2021
A leading Republican candidate for U.S. Senate in Colorado claimed on Saturday that Republicans have lost elections at least as far back as the 1990s due to election fraud. Ron Hanks, a state representative who is running to unseat Democratic U.S. Sen. Michael Bennet in 2022, made the claim during an appearance on "Thanks-A-Thon," a […]


text:
Q: Inno Setup - Animate Message Boxes as they are opened and closed Is there any way to add some opening and closing animation to message boxes of inno setup?
Here is a link which explains some similar thing in Delphi:
https://delphidabbler.github.io/delphi-tips/tips/46.html
How to implement it inside inno setup?


text:
"use strict";

Object.defineProperty(exports, "__esModule", {
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exports.iDependencies = void 0;

var _dependenciesComplexClass = require("./dependenciesComplexClass.generated");

var _factoriesAny = require("../../factoriesAny.js");

/**
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 * DON'T MAKE CHANGES HERE
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var iDependencies = {
  ComplexDependencies: _dependenciesComplexClass.ComplexDependencies,
  createI: _factoriesAny.createI
};
exports.iDependencies = iDependencies;

redpajama_set_name: RedPajamaGithub


MAY 14, 2012, 7:00 P.M.
The regular meeting of the Putnam County Board was called to order at 7:00 P.M. on Monday, May 14, 2012, in the boardroom at the Putnam County Courthouse in Hennepin, Illinois by Putnam County Board Chairman, Duane A. Calbow.
Roll Call was taken by the Clerk of the County Board with Duane Calbow, Gil Tonozzi, Chauntelle Biagi-Bruer, Sheila Haage, and John O'Neill answering.
It was then moved by board member Chauntelle Biagi-Bruer to approve the minutes from the March 12, 2012 Regular Board Meeting contingent upon revision. Board member Gil Tonozzi seconded the motion and the motion was carried on a board vote of 4 ayes, 0 nays, and 0 absent.
County Treasurer Kevin Kunkel reviewed his financial reports for the month of April with the board. The General Fund's working balance for the month of April - 2012 was ($14,972.51) compared to a working balance of $16,938.04 for the month of April - 2011. The General Fund Income for the month of April - 2012 was $219,622.19 compared to $222,703.97 for the month of April - 2011. Mr. Kunkel's report also included a review of the "General Fund Accounts Balance by Month - 2012". The General Fund balance for the month of April - 2012 was $126,175.45. The General Fund Savings balance for the month of April - 2012 was $557,438.54. The Illinois Fund - General Fund balance for the month of April - 2012 was $508,209.69. Mr. Kunkel's report also included a review of the budget for the current fiscal year for the period ending April 30, 2012. The actual versus budget variance percent target for the period is 41.67%. Mr. Kunkel also informed the board that he expects to begin tax distribution to the taxing districts by July 1, 2012. Property tax bills were mailed out May 1st. The first installment is due June 1st and the second installment will be due September 4th. (Mr. Kunkel's financial reports are on file at the Office of the County Clerk for review by any interested parties.) Mr. Kunkel then introduced Ms. Jill Guynn, County Director of the Bureau, LaSalle, Marshall, Putnam Region of the University of Illinois Extension Service, to the board. Ms. Guynn presented to the board an update on the activities and programs offered by the Extension Service to the citizens of the four (4) county region. Ms. Guynn also presented for board signature an agreement between the County of Putnam and The Board of Trustees of the University of Illinois. The agreement states that the County of Putnam is willing to make contributions to the University of Illinois to partially meet the cost of carrying on and expanding said work in said Unit (Marshall – Putnam Counties). The agreement also states that Putnam County will contribute annually to the University the sum of $48,000 for the period beginning July 1, 2012 and ending June 30, 2013.
County Circuit Clerk Cathy Oliveri was unable to attend the meeting and requested that County Treasurer Kevin Kunkel present to the board the License Agreement and Rental Policy for the use of the Bureau County Farm Bureau facility for the purposes of holding the Driver Improvement Program classes once a month on Saturdays for the six-month period of July 2012 – December 8 2012. After brief discussion, board member Sheila Haage moved that the board approve the contract with the Bureau County Farm Bureau. Board member Gil Tonozzi seconded the motion and the motion was carried on a board vote of 4 ayes, 0 nays and 1 absent.
County Probation Officer Andy Ceresa had nothing to report for the month of April and therefore was not present for the meeting.
Under "New Business" for Resolution Supporting the Putnam County Workforce Development System. Ms. Pam Furlon with the BEST Agency presented to the board a draft of a resolution for the recognition and endorsement by the county board for the participation by the North Central Illinois Works (NCI Works) in the formation of the Great Rivers Consortium (GRC). The GRC will consist of thirty-one (31) counties and will represent a total population of 1.257 million people. NCI Works has joined with five (5) other local workforce investment boards in Illinois to form the GRC. The GRC has proposed a project to create and maintain a regional Early Warning Network and Layoff Aversion System to save jobs by assisting at-risk companies located in its geographic area. The resolution also asks for support from our Illinois legislators to actively work for the approval by the United States Department of Labor. After brief discussion, board member Chauntelle Biagi-Bruer moved that the board approve and adopt the resolution for recognition and endorsement of the GRC Project. Board member Sheila Haage seconded the motion and the motion was carried on a board vote of 4 ayes, 0 nays, and 0 absent. The Clerk of the County Board was directed to process the resolution (#2012-15).
Also under "New Business" for Ordinance Ascertaining Prevailing Wage Rates For The County of Putnam; the board reviewed the ordinance prepared by the State's Attorney which "regulates the wages of laborers, mechanics and other workers employed in any public works by State, County, City, or any public body or any political subdivision or by any one under contract for public works". After brief discussion, board member Chauntelle Biagi-Bruer moved that the board approve and adopt the ordinance as written. The approval is contingent upon the attachment of the June prevailing wages rates as soon as they are made available. Board member Gil Tonozzi seconded the motion and the motion was carried on a board vote of 4 ayes, 0 nays, and 0 absent. The Clerk of the County Board was directed to process the ordinance (#2012-0-01). The County Clerk is directed to send copies of the ordinance to the Illinois Department of Labor and Secretary of State's Office.
Also under "New Business for Putnam County Website; Putnam County Board Chairman Duane Calbow discussed contracting Connecting Point Computer Centers for development and maintenance of the county's new website. The contract includes the initial set-up fee of $2,653 and a $124 annual fee for hosting and maintaining the site. Mr. Calbow stated that once the contract is initiated, the site will be positioned to "go live" in about two (2) weeks. After brief discussion, board member Chauntelle Biagi-Bruer moved that the board approve the contract for the county's website development with Connecting Point Computer Centers. Board member Sheila Haage seconded the motion and the motion was carried on a board vote of 4 ayes, 0 nays, and 0 absent.
Also under "New Business" for Resolutions For Authorization To Execute Deeds Of Conveyance; Putnam County Clerk Dan Kuhn presented to the board the draft of a resolution authorizing the Chairman of the Putnam County Board to execute a deed of conveyance of the county's interest or authorize the cancellation of the appropriate Certificate of Purchase as it relates to specific delinquent tax properties acquired by the County of Putnam as Trustee for the Taxing Districts. After brief discussion, board member Gil Tonozzi moved that the board approve and adopt the resolution as written. Board member John O'Neill seconded the motion and the motion was carried on a board vote of 4 ayes, 0 nays, and 0 absent. The Clerk of the County Board was directed to process the resolution (#2012-16).
Also under "New Business" for Lake Thunderbird Conservation Committee Request For Approval For The Sale Of Beer And Wine At Annual Corn Boil (August, 2012); the board reviewed the letter of request for the board's approval for the sale of beer and wine at the Lake Thunderbird Conservation Committee's Annual Corn Boil. After brief discussion, it was moved by board member Chauntelle Biagi-Bruer that the board approve the request for the sale of beer and wine at the Annual Corn Boil on August 4, 2012. Board member Sheila Haage seconded the motion and the motion was carried on a board vote of 4ayes, 0 nays, and 0 absent. The Clerk of the County Board was directed to send a letter to the Lake Thunderbird Conservation Committee informing them of the board's decision.
Under "New Business" for Marshall - Putnam River Conservancy Trustee Re-appointment; the board reviewed a letter of request for the re-appointment of Mr. Ronald Bruch to the Board of Trustees for the Marshall - Putnam River Conservancy District. Mr. Bruch's term expired on April 30, 2012 and it is his request to be re-appointed for a five-year term commencing May 1, 2012 and ending April 30, 2017. After brief discussion, board member Gil Tonozzi moved that the board approve the recommendation for re-appointment Mr. Ronald Bruch as trustee for the Marshall-Putnam River Conservancy District. Board member John O'Neill seconded the motion and the motion was carried on a board vote of 4 ayes, 0 nays, and 0 absent. The Clerk of the County Board was directed to send a letter to the district informing them of the board's approval.
Also under "New Business" for Approve Bills; the county board reviewed the bills for the month of April. Board member Sheila Haage then moved that the board approve the payment of the bills for the month of April. Board member Gil Tonozzi seconded the motion and the motion was carried on a board vote of 4 ayes, 0 nays, and 0 absent.
Also under "New Business" for Correspondences and Reports; the board reviewed the monthly reports from the County Clerk's Office and the Regional Office of Education. The board also reviewed the legislative reports from the Illinois Association of County Board Members and Commissioners. Board Chairman Duane Calbow informed the remainder of the board that he received a correspondence from Gateway requesting their annual donation of $8,000.
At 8:50 P.M., board member Sheila Haage moved to adjourn the meeting. Board member Chauntelle Biagi-Bruer seconded the motion and the motion was carried on a board vote of 4 ayes, 0 nays, and 0 absent. The meeting was adjourned. The next Regular Meeting of the Putnam County Board is scheduled for Monday, June 11, 2012 at 10:00 A.M.


text:
Q: C# CheckedListBox I am trying to allow users to select a "table name" using a CheckedListBox called tableCheckedListBox. Once a user checks one or more tableCheckedListBox items (i.e. "table names"), a second CheckedListBox called columnCheckedListBox should populate a group of items (i.e. "column names") associated with the "table names" checked in the tableCheckedListBox.
I have problems populating related columnCheckedListBox (i.e."column names") once the user checks "table names" in tableCheckedListBox. (FYI - I used LINQ to SQL to create my classes that represent the "table names" in my database. The classes (i.e. "table names") populate successfully in the tableCheckedListBox; On the contrary, I have problems populating the properties (i.e."column names") in the classes to columnCheckedListBox).
Here's what I been working with.
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;
using System.Reflection;
using System.Data.Linq.Mapping;

namespace QueryBuilder
{
    public partial class FrmQueryBuilder : Form
    {

        public FrmQueryBuilder();
        {
            InitializeComponent();
        }

        public void Form1_Load(object sender, EventArgs e)
        {
            PropertyInfo[] properties = typeof(DBClassDataContext).GetProperties();

            foreach (PropertyInfo property in properties)
            {
                if (property.PropertyType.IsClass)
                {
                    object[] attributes = property.PropertyType.GetGenericArguments()[0].GetCustomAttributes(typeof(TableAttribute), false);

                    if (attributes.Length > 0)
                    {
                        tableCheckedListBox.Items.Add(property.Name);
                    }
                }
            }
        }

        private void tableCheckedListBox_ItemCheck(object sender, ItemCheckEventArgs e)
        {
            PropertyInfo[] tableColumns1 = typeof(TABLENAMEONE).GetProperties();
            PropertyInfo[] tableColumns2 = typeof(TABLENAMETWO).GetProperties();
            PropertyInfo[] tableColumns3 = typeof(TABLENAMETHREE).GetProperties();

            foreach (PropertyInfo columns1 in tableColumns1)
            {
                if (columns1.PropertyType.IsClass)
                {
                    object[] colAttrib1 = columns1.PropertyType.GetGenericArguments()[0].GetCustomAttributes(typeof(ColumnAttribute), false);

                    if (colAttrib1.Length > 0)
                    {
                        tableCheckedListBox.Items.Add(columns1.Name);
                    }
                }
            }

            foreach (PropertyInfo columns2 in tableColumns2)
            {
                if (columns2.PropertyType.IsClass)
                {
                    object[] colAttrib2 = columns2.PropertyType.GetGenericArguments()[0].GetCustomAttributes(typeof(ColumnAttribute), false);

                    if (colAttrib2.Length > 0)
                    {
                        tableCheckedListBox.Items.Add(columns2.Name);
                    }
                }
            }

            foreach (PropertyInfo columns3 in tableColumns3)
            {
                if (columns3.PropertyType.IsClass)
                {
                    object[] colAttrib3 = columns3.PropertyType.GetGenericArguments()[0].GetCustomAttributes(typeof(ColumnAttribute), false);

                    if (colAttrib3.Length > 0)
                    {
                        tableCheckedListBox.Items.Add(columns3.Name);
                    }
                }
            }
        }

        private void columnCheckedListBox_ItemCheck(object sender, ItemCheckEventArgs e)
        {

        }
    }
}


This was shot on one not-so-grueling day on an LA beach. We shot HD (Varicam) so we could roll and roll to get just the right waves. We filled ten 60-minute tapes. Four PAs stood in the surf, wearing US Navy-approved booties and awaiting their cue from my first AD. They'd run in and we'd all pray that the right wave would come through. Although we shot until dusk, the take we wound up selecting was made before lunch! Orphanage VFX Supe Ryan Tudhope supervised the compositing, which consisted of day-for-nighting the shot and simulating the lighting change in 32-bit float using After Effects 7.0. Ryan only barely enhanced the footprints, making one of the simpler post jobs we've ever done.


text:
Q: Get sitecore contacts throught Sitecore Rest API It's possible to get contacts information from Sitecore Rest API ?
I need to get information that was storedf in the contact facet and the mobile app is using the sitecore rest api for the items when it's needed.

A: SC_ANALYTICS_GLOBAL_COOKIE is a browser cookie which stores current contact Id in plain text and is not avaialable in any default Web API. You can create custom Web API and make the web API session aware and retrieve the contact details by following API.
 if (Sitecore.Analytics.Tracker.Current != null) {
        var contact = Sitecore.Analytics.Tracker.Current.Contact;
        if (contact != null)
        {
            return contact;
        }
    }


Radio 702 | Talk Radio 702 host Karima Brown spoke to Media Monitoring Africa's William Bird who has called on the president to save the SABC from crisis.
JOHANNESBURG - There seems to be a collapse at the South African Broadcasting Corporation (SABC) again, this is on the back of some board member resignations.
Eyewitness News has seen the resignation letters of Khanyisile Kweyama, Krish Naidoo, and John Matterson.
Talk Radio 702 host Karima Brown spoke to William Bird, executive director at Media Monitoring Africa, who has called on President Cyril Ramaphosa to save the SABC from the crisis.


Mark your calendars! On Saturday, November 7, 2015, Christ URC will host its second annual Conference on the Reformation. This is a free, one-day conference that provides the community with an opportunity to hear lectures from pastors and theologians, ask questions, and think more deeply about biblical Christianity and what we can learn from the Protestant Reformation.
Last year's topic was the Five Solas (you can listen to the lectures from Dr. W. Robert Godfrey, Dr. Michael Horton, and Dr. R. Scott Clark here.) This year, the topic will be End Times. What does the Bible say to us about the last things? What can we expect? How should we read the book of Revelation? Our two key note speakers will be Dr. Kim Riddlebarger, pastor of Christ Reformed Church in Anaheim, CA, and author of A Case for Amillennialism: Understanding the End Times, and The Man of Sin: Uncovering the Truth About the Antichrist, and Dr. Dennis Johnson (pictured above), professor practical theology at Westminster Seminary California, and author of Triumph of the Lamb: A Commentary on Revelation.
Please remember this conference in your prayers, asking the Lord to use it for his glory and for the edification of his people. And please consider inviting a friend. This is a great opportunity for people in San Diego to be blessed by the light of God's Word, and the way in which He used the Reformation to make that light shine more brightly in the world.


Home » The experimentation of the Life Eco-Pulplast project continues. The representatives of the European Commission in Lucca to visit the prototype line at the Selene site.
The Life Eco-Pulplast project continues its activities. on May 28th the officers appointed by the European Commission to monitor the progress of the activities co-financed with the Life program were welcomed at the headquarters of Selene, the Coordinating Beneficiary of the project.
During the meeting, the officers were able to visit the prototype line and discuss the potential development of the project, which experimental phase will end in the next month of August.
"Over 25 years - explains Anna Natasa Asik, Ecopulplast project officer, nominated by the European Commission - the Life program has supported over 4500 projects with a high European added value in order to implement objectives for the environmental and climate policy of the EU and in particular to support the circular economy. Life Eco-Pulplast is one of these successful projects that has shown concretely that new circular business models can be developed where the environmental benefit is closely linked to clear economic benefits. An essential contribution to the planet, growth and work ".


Mambo World Rhythm Music! Blending traditional Cuba Rumba rhythms and contemporary electronic grooves, the track offers a modern take on this classic musical style. The song has African percussion but also incorporates melodies from the Spanish Flamenco adding to the versatility of the track. A cool piece of Mambo / Salsa music to shake your hips to! Great for modern salsa dancing, rhumba or Argentine Tango.


Tell the readers a little bit about yourself. Let them get to know you. I am a realtor broker and I reside in Slidell, La. I live to tell my story after not evacuating from Hurricane Katrina in 2005.
Who published your book and the year. If you are self-published, say that and be proud of it. Open Door Publishing Company in 2018. My book is available in paperback, both in black and white, and in color. Also, the book is available in Ebook and soon will be available as an audible book.
What is the reason you wanted to become a writer? I had a story to tell. Honestly, I never considered myself a writer. I struggled at it, but by the grace of God, I found a fabulous editor in Dr. Melissa Caudle. She helped me through the entire process.
Are there any authors who influenced you the most? If so, who and why? I'd have to say the Bible.
Who is your target audience? Why? Anyone who needs an uplifting story. I wrote this book so I could help others who went through a natural disaster and have lost everything. I want them to know there is hope and that God will not forsake you.
Tell the readers what your book is about. It is all right to use your book's synopsis, but if you make it personable, you are more likely to engage your reader. This is an autobiography of what happened to me when I didn't evacuate from Hurricane Katrina in 2005. It is action-packed.
Tell us a little about your protagonist – the main character. I am the protagonist as this is my story.
What was the most challenging part of writing this book?: The most challenging part was getting what happened to me into written form. It was a mess when I gave it to my editor, Dr. Melissa Caudle. But, she was so patient and knew exactly what to do.
What would you hope a reader would get by reading your book?: I would hope the reader would find comfort from my story and know that they are not alone. I am a firm believer that God doesn't put us through anything we can't handle. He answers our prayers.


Cedar Terrace has three wonderful floors for lots of furniture.
The Hamburger Restaurant is perfect for all the family. There's plenty of seating inside and out and there is even a drive through service. Figures not included.
Meet the Hamster Family, pack includes: Father Huckleberry, Mother Hilda, Brother George and Sister Jane.
Meet the Sheep Family, pack includes: Father Ewan, Mother Barbara, Brother Winton and Sister Iona.
The supermarket shelves are packed with lovely fresh bread, fruit, vegetables, fish and more!
The Cosy Cottage is the ideal starter home for your Sylvanian Families.
With over 30 accessories the caravan is ideal for holidays spent travelling around Sylvania.
Take a drive in the Bluebell Seven Seater! The car is large enough for all the members of a family and the doors open and close for easy access.
Flair is going to release the Elin. PVC figure from the MMORPG "TERA: The Exiled Realm of Arborea" developed by Bluehole Studio.
Flare is going to release the Bayonetta! non-scale PVC figure from the video game "Bayonetta". Published by SEGA.
Flare offers a look at Roselinde Freya from "Shining Blade" without her scarlet armor, clad only in her dress!


text:
History in your hands
25th Sunday of OT (B)
Diocese mourns death of first Guyanese bishop
Published by CN Admin at September 22, 2018
The entrance procession at Bishop Singh's funeral last Monday. Main celebrant Bishop Francis Alleyne (from left), Archbishop Emeritus Joseph Harris, Fr John Persaud, Msgr Terrence Montrose and Fr Godfrey Veerasammy SJ. Photo: Joseph Ramjohn.
The funeral Mass for Bishop Ganesh Benedict Singh—the diocese's first Guyanese bishop took place Monday, September 17 at the Cathedral of the Immaculate Conception, Brickdam followed by burial in the crypt below the Cathedral.
Bishop Singh, who headed the Catholic Church in Guyana from August 1971 until his retirement in 2003, died Wednesday, September 12 at the Balwant Singh Hospital after a short illness. He was 90 years old.
Commenting on his death, Bishop Francis Alleyne OSB said that 15 years ago he was appointed to succeed Bishop Singh as Bishop of Georgetown and to put on the mantle that he carried for 32 years.
He told Catholic Standard that during his time in office, as he has come to better understand and gain insight into the events and circumstances in the Church and Guyana over the period of his tenure, he has come to very much appreciate and admire the "quiet and formidable strength" and resolve of Bishop Singh.
Several priests, sisters, other members of the diocese and His Excellency David Granger have also expressed their sympathy to the relatives and friends of Bishop Singh.
Bishop Benedict Singh was born December 2, 1927 in Demerara, Guyana to Joseph Alexander and Matilda Amanda (Fredericks) Singh. His ordination to the priesthood took place in Rome December 7, 1954. He obtained his Doctorate in Sacred Theology from the Pontifical Propaganda College in Rome in 1957.
According to the Catholic Standard, Bishop Singh returned to serve the Church in Guyana in December 1957 when his first appointment was to Meadow Bank Church.
"From 1958, for ten years he was in charge of the long coastal strip from Malgretout to the Essequibo islands. In 1968, he was named the first Guyana-born Chancellor of the diocese. In that capacity, he acted as Vicar General and administered the diocese on several occasions during the absence of Bishop [Richard Lester] Guilly from the country."
Pope Paul VI appointed Fr Singh as Auxiliary Bishop of Georgetown February 18, 1971. He held the position of Bishop until his retirement in 2003 at the age of 75.
Catholic Standard said Bishop Singh inherited the leadership of the Church in Guyana at a time when everything in it seemed to revolve around the clergy. He however recognised the important role not only of Guyanese clergy but also of laypeople in the work of spreading the good news of salvation.
His tenure, the diocesan weekly noted, will also be most remembered for the courage and zeal he showed in the struggle for democracy, justice and peace in Guyana during the country's more turbulent years.
"Soft-spoken, compassionate but always firm, he often gained national attention when he spoke publicly through numerous letters to the faithful or simple public statements. In these he expressed his views on a wide variety of issues including workers' rights, abortion, the death penalty, national elections and a host of others."
Bishop Singh was the recipient of the Cacique Crown of Honour from the Government of Guyana in 1993. During his retirement, he completed a fifth book Footsteps in the Sand which is currently awaiting publication. This is the fifth book in a series written by the bishop which are all on faith and morals.
The other books are Falling into love; Issues of Life and Light; Paths to Glory; and Gospel Insights. The books were said to represent a response to the appeal of the Holy Father for Catholics to be better informed about the truths they profess in the Catholic Church.