text:
\section{Introduction}
\begin{figure}[t!]
  \centering
  \includegraphics[width=0.5\textwidth]{Figures/new_fig1.pdf}
  \vspace{-2.0em}
  \caption{The illustration of our counting pipeline. For image patches with count values between $t_i$ and $t_{i+1}$, our UEPNet learns to classify them into one (\textit{i.e.}, $c_{i+1}$) of the $m$ intervals. The count interval borders ($t_0,...,t_{m-2}$) are determined by the proposed UEP criterion to ensure the minimum expected prediction error. We assign the ground truth class of a patch by the count interval it falling into. During inference, for a patch classified to the interval class of $c_{i}$, we set its predicted count to be the count proxy value $\sigma_i$ of $c_{i}$. And the count proxy is a specific value selected from that interval with our proposed MCP criterion.
  \label{fig1}
  \vspace{-1.5em}
\end{figure}

The task of crowd counting estimates the number of people in given images or videos. It has drawn remarkable attention in recent years, since its wide range of practical applications in public safety. Currently, most existing state-of-the-art methods adopt density maps \cite{bai2020adaptive, miao2020shallow, oh2020crowd} as the targets and learn with Convolutional Neural \ywu{Networks (CNNs)}.

However, the widely used learning targets, \textit{i.e.}, the density maps, are actually inaccurate and tend to be noisy, as discussed in \cite{bai2020adaptive,liu2019counting,ma2019bayesian,olmschenk2019improving, wan2019adaptive}. These imperfect density maps are caused by several factors, such as empirically selected Gaussian kernels, large density variations and labeling deviations. As shown in the supplementary materials, the inaccurate density map introduces severe \ywu{\emph{inconsistency}} between the semantic content and the ground truth target, acting as a kind of ``noises'' in the learning target. Therefore, when learning the exact count values with an outlier-sensitive loss, such as the commonly used Mean Squared Error (MSE), the model may tend to overfit these inaccurate and ambiguous ``noises'', leading to inferior performance and poor generalization ability \cite{liu2019counting}.

Inspired by a few pioneering works \cite{fu2018deep,liu2019counting,qiu2020offset,xiong2019open}, we solve the above problem with the paradigm of local count (the count value of a local patch) classification, as shown in Figure \ref{fig1}. In such \ywu{a} paradigm, the local count is quantified to one of several pre-defined count interval bins, depending on the interval it falls into. Then we resorts to count interval classification by taking each interval as a single class. However, the number of samples with increasing local counts follows a long-tailed distribution, leading to imbalanced training data and extreme counting errors for some count intervals. What makes the problem worser is that samples from different intervals have various learning difficulty, thus also contribute different counting errors. In this paper, firstly, we solve the above problem from the perspective of designing an optimal interval partition strategy. Specifically, we derive an Uniform Error Partition (UEP) criterion following \textit{the principle of maximum entropy}. Without any prior over the density distribution of an unseen image, the UEP criterion keeps the expected counting errors nearly equal for all intervals to minimize the prediction risk. Such a novel strategy provides a comprehensive consideration of misclassification errors and sample imbalance problem.

Secondly, another crucial yet unsolved problem of this paradigm raises from inference stage. Specifically, a fixed value (\textit{i.e.}, count proxy) should be selected from each count interval, using as the predicted counts for those patches classified to this interval. Obviously, mapping a range of local counts to a single count value would inevitably introduce perceivable discretization errors. To mitigate the above problem, we propose a Mean Count Proxies (MCP) criterion for the count proxy selection. The MCP criterion uses average count value of samples in an interval as the optimal count proxy, with which the expected discretization error is theoretically demonstrated to be nearly negligible.

Thirdly, since the distribution of count values is essentially continuous, it is tricky to unambiguously classify samples whose count values are located around the interval borders.
Thus, we design two parallel prediction heads with overlapping count intervals, named Interleaved Prediction Heads (IPH). In this reciprocal prediction structure, the samples whose count values fall near the interval borders of one head are more likely to be correctly classified in another head, reducing the misclassification errors due to the learning ambiguity around interval borders. We conduct extensive experiments on several public datasets with various crowd \ywu{densities} to show  the \ywu{consistent improvements}. The contributions of this paper are summarized as follows:

1. We propose a novel criterion termed UEP to tackle with the crucial yet unsolved count interval partition problem, making the local count classification based counting models achieve the minimum prediction risk.

2. We propose the MCP criterion to minimize the discretization errors inevitably introduced by the count quantification, which is orthogonal to the UEP criterion.

3. We propose a simple yet effective model termed UEPNet, following the above two key criterions. Combining with the IPH, the UEPNet achieves state-of-the-art performance on several benchmarks.

\begin{figure*}[t!]
  \centering
  \vspace{-1.0em}
  \includegraphics[width=0.95\textwidth]{Figures/new_mainfigure.pdf}
  \vspace{-1.0em}
  \caption{The network structure of the proposed UEPNet. It is a fully convolutional model, and consists of a simple encoder-decoder network for feature extraction and an Interleaved Prediction Head to classify each patch into certain interval. There are two parallel heads with overlapping count intervals to predict the classes independently, and their decoded counts are averaged to get final result. The ground truth class map is encoded from density map, guided by the proposed UEP criterion. The predicted class can be decoded back to local count, in which the count proxy (as denoted by the \textcolor{Maroon}{Red} triangle) is selected with the proposed MCP criterion. The UEPNet is optimized with the Cross Entropy loss in an end-to-end manner.
  \label{fig2}
  \vspace{-1.5em}
\end{figure*}
\section{Related Work}
In this section, we review the recent density map learning based methods in the literature. Especially, we reveal the defects in very few existing local count classification based methods to show the necessity of our research. We also discuss some works dealing with the imperfect targets to show the superiority of our method.
\vspace{-1.4em}
\paragraph{Pixel-wise Density Maps Learning.} The concept of density maps is firstly introduced by Lempitsky and Zisserman \cite{lempitsky2010learning}, and since then this kind of learning targets has been widely used in subsequent methods \cite{cheng2019learning, jiang2019crowd, li2018csrnet,zhang2016single}. These methods are trained with pixel-wise supervision and finally obtain the estimated count by summing over the predicted density maps. Although these methods \cite{hu2020count, jiang2020attention} achieved good performance, they still suffer from the following defects. Firstly, they ignore the negative impacts of the imperfect density maps and learn to overfit these inaccurate targets with an outlier-sensitive loss. Secondly, there exists inconsistency between the minimum training loss and the optimal counting result. These problems limit the counting accuracy and generalization ability of such approaches.
\vspace{-1.4em}
\paragraph{Patch-wise Density Maps Learning.} Instead of pixel-wise regression, patch-wise density maps learning based methods predict the count values of local patches. This idea is firstly used in S-DCNet \cite{xiong2019open}, and is helpful to alleviate the inconsistency between training target and evaluation criteria as demonstrated in \cite{liu2020adaptive}. Among these methods, some works \cite{xiong2019tasselnetv2} adopt regression with supervision from the commonly used MSE loss but still suffer from the inaccurate targets. Thus the other methods \cite{liu2019counting, xiong2019open} discretize the local counts and learn to classify count intervals. However, despite the robustness of such a paradigm, both of them divide the count intervals intuitively, which is inappropriate and greatly limits the performance. Besides, the two methods directly use median value of the interval to represent its class count, which is sub-optimal and introduces additional discretization errors. We also adopt the paradigm of patch-wise count classification, but focus more on its crucial yet unsolved count interval partition problem.
\vspace{-1.4em}
\paragraph{Learning with Inaccurate Targets.} Recently, there has been increasing attention on the problem of inaccurate ground truth targets. One solution is to improve the quality of the generated density maps by estimating more accurate Gaussian kernel \cite{olmschenk2019improving, wan2019adaptive}, while the improvement seems to be limited by insufficient annotations. Bayesian loss is proposed by \cite{ma2019bayesian} to provide a more reliable supervision on the count expectation at each annotated point, but it still adopts a pixel-wise regression. Moreover, ADSCNet \cite{bai2020adaptive} proposes a self-correction supervision to iteratively correct the annotations with the model estimations, which however ignores the additional noises introduced by those inaccurate estimations during the online updating. Differently, we resort to local count classification, which is more robust than learning the exact but inaccurate count values \cite{liu2019counting}.
\vspace{-0.8em}
\section{Our Approach}
\vspace{-0.2em}
We firstly introduce the symbols used in this paper in Sec. \ref{symbols}. After that, we introduce the pipeline of our framework in detail. Specifically, we begin with the calculation of the local counts in an image as described in Sec. \ref{cal_count}. Then we exhibit our strategy of quantifying these local counts into different intervals in Sec. \ref{int_par}. After an end-to-end training supervised with Cross Entropy, we obtain the predicted count of an image patch by the MCP criterion, as described in Sec. \ref{proxy_sel}. Finally, in Sec. \ref{iph}, the novel prediction module IPH is described in detail.
\subsection{Symbol Definition}\label{symbols}
\begin{table}[t!]
\centering
\small{
\begin{tabular*}{0.5\textwidth}{cc}
\toprule
{Symbol} & {Definition}\\
\midrule
$\mathcal{E}$ & The expected counting error. \\
$\tilde{\mathcal{E}}$ & The absolute mean counting difference. \\
$\mathcal{T}$ & All local counts in the training set. \\
$ d_k, k \in \{1,2,...,K\}$ & $k$-th local count in $\mathcal{T}$. \\
$m$ & Total number of intervals.\\
$t_0,...,t_{m-2}$ & Count values at interval borders. \\
$t_{MAX}$ & The max local count in $\mathcal{T}$. \\
$c_i, i \in \{0,1,...,m-1\}$ & $i$-th interval.\\
$x_{ij}$ & $j$-th local count from $c_i$.\\
$n_i$ &  Number of samples in $c_i$.\\
$l_i$ &  Length of $c_i$.\\
$\delta_i$ & Count proxy of $c_i$.\\
\bottomrule
\end{tabular*} }
\vspace{-10pt}
\caption{Main symbols definition.}\label{tab:symbol}
\vspace{-15pt}
\end{table}
Table \ref{tab:symbol} lists the main symbols used in the following text. In this paper, we conduct a patch-level analysis by breaking down the whole training set into $K$ image patches with a same size. For the $k$-th image patch, the summation of its density values is calculated as its local count $d_k$. Then there should be a collection $\mathcal{T}$ containing all the local counts of the above $K$ samples. For an arbitrary partition setting with $m$ intervals, we denotes the $i$-th interval as $c_i$. And the length $l_i$ of $c_i$ is determined by its left border $t_i$ and right border $t_{i+1}$. In other words, the count interval $c_i$ is a contiguous count value range from $t_i$ to $t_{i+1}$. Then for any sample $x_{ij}$ ($j\in\{1,...,n_i\}$) whose local count falls into the interval $c_i$, we assign a fixed count value $\delta_i$ (termed as count proxy) as its predicted count if it is correctly classified. $\mathcal{E}$ is the expectation of the counting error for a randomly given unseen image. $\tilde{\mathcal{E}}$ is the absolute value of the average counting difference from multiple images or patches, in which the counting difference is calculated by the subtraction of ground truth count and predicted count.

\subsection{Local Count Calculation}\label{cal_count}
\vspace{-0.5em}
For an image containing $n$ heads, the ground truth annotations can be expressed as a dot map $M=\sum_{i=1}^n\delta(p-p_i)$, where $p_i$ is the point annotation for the $i$-th head and the delta function $\delta(p-p_i)$ represents there is one head at pixel $p_i$. Then we generate density map $D$ by convolving over $M$ using a Gaussian kernel $G_\sigma$, which can be described as $D=\sum_{i=1}^n\delta(p-p_i)*G_\sigma$. The spread parameter $\sigma$ in $G_\sigma$ is fixed or determined by the $k$ nearest neighbors \cite{zhang2016single}. With a normalized Gaussian kernel, the counting results can be obtained by summing over the density maps.

Based on the density map $D$, we can construct a local count map $D_s$, in which each value is the head count from the corresponding patch of size $s\times s$ in the image. Specifically, we slide a window of size $s\times s$ without overlapping over the density map $D$ and sum the density values in this window as the local count $d$.
\subsection{The Interval Partition Strategy}\label{int_par}
We derive our key partition strategy from the perspective of minimizing the expected counting error $\mathcal{E}$ for an unseen image. And $\mathcal{E}$ is obtained by calculating the absolute value of the difference between the estimated count and the ground truth count at the image level. Since we have no prior over the density distribution of any given unseen image, we propose to approximate $\mathcal{E}$ from a perspective of probability sampling. Specifically, we firstly decompose $\mathcal{E}$ as the absolute value of the summation for the counting differences from multiple non-overlapping patches of that image. Then under the assumption of independent and identically distributed (i.i.d.) data, these images patches are seen as a random sampling subset from $K$ image patches in the training set. Thus, minimizing $\mathcal{E}$ for a randomly given image is equivalent to achieving the minimum $\tilde{\mathcal{E}}$ on all patches in the sampled subset. The above approximation helps us to design a generalized partition strategy using the available training set, as demonstrated in Supplementary Materials

Since our goal is to minimize $\tilde{\mathcal{E}}$ with a proper count interval partition strategy, we would like to figure out how the counting errors from different intervals contribute to $\tilde{\mathcal{E}}$. However, for any given unseen image, the patches in its equivalent subset are always randomly selected. In other words, for a specific image, we have no way to determine the patch distribution over all intervals, neither its counting error contribution from different intervals. In such a case, according to \textit{the principle of maximum entropy}, we should not make further assumptions to this distribution. As a result, we propose an Uniform Error Partition (UEP) criterion, which always keeps the expected counting error equal for all intervals. With the proposed UEP criterion, the risk of making extreme prediction error is minimized, achieving reasonable $\tilde{\mathcal{E}}$ for any given image. From another perspective, we could achieve a minimum $\tilde{\mathcal{E}}$ in terms of mathematical expectation with the UEP criterion
\begin{algorithm}[t!]
\DontPrintSemicolon
\KwIn{$\mathcal{T}$ $\gets$ local count collection in the training set\\
       \hspace{3em}$m$ $\gets$ required number of intervals\\
       \hspace{3em} $[L, H]$ $\gets$ search range for $n_il_i$}
\KwOut{$\mathcal{P}$ $\gets$ interval endpoint collection}
\tcc{remove counts less than $t_0$}
$\mathcal{T} \gets sort(filter(\mathcal{T}))$\
\While{ $abs(H-L) > \epsilon$} {
    \tcc{try to divide by $n_il_i=\bar{l}$}
    $p$ $\gets$ $t_0$, $n$ $\gets$ 0, $\mathcal{P}$ $\gets$ $\{t_0\}$, $\bar{l}$ $\gets$ $(L + H)/2$\;
    \For{$d_k$ in $\mathcal{T}$} {
        $n$ $\gets$ $n+1$\;
        \tcc{an endpoint is found}
        \If {$(d_k - p)\times n > \bar{l}$} {
          $n \gets 0$, $p \gets d_k$, $\mathcal{P} \gets \mathcal{P} \cup \{p\}$\;
         }
    }

    \tcc{adjust $[L,H]$ by $|\mathcal{P}|$ and $m$}
    \eIf{$|\mathcal{P}| >= m$}{$L \gets \bar{l}$ \tcp*{when $|\mathcal{P}| >= m$}}{
        \eIf{$|\mathcal{P}| == m - 1$}{

            \tcc{adjust by $n_{m-1}l_{m-1}$ }
            \eIf{$(t_{MAX} - p)\times n > \bar{l}$}{$L \gets \bar{l}$}{$H \gets \bar{l}$}
        }{ $H \gets \bar{l}$\tcp*{when $|\mathcal{P}| < m - 1$}
        }
    }
}
\Return{$sort(\mathcal{P})$}\;
\caption{Binary Interval Partition with UEP}
\label{algo:bip}
\end{algorithm}

Before conducting the above UEP criterion, a key step is to estimate the counting error from a certain interval. In this part, we estimate the interval-level count error based on all samples in $\mathcal{T}$, because every element within it has the same chance to be selected into the equivalent subset for an image. For a specific interval $c_i$, its counting error contribution depends on both the sample number $n_i$ within it and the misclassification cost of each sample from it. The former one is proportional to the interval length $l_i$, which is relatively easier to obtain from the dataset statistics. Besides, for a single sample $x_{ij}$ from interval $c_i$, it is more likely to be misclassified to a nearby interval. Since the interval lengths of adjacent intervals are nearly equal, the misclassification cost is also proportional to the interval length $l_i$. Therefore, we reasonably take $n_il_i$ as the approximation for the counting error from $c_i$. By keeping a uniform $n_il_i$ for all intervals, the UEP criterion takes a fully consideration on the misclassification cost and the sample imbalance problem among intervals, making the classification task more easier to learn. We provide more detailed analysis in the Supplementary Materials.

To apply the UEP criterion during the interval partition process, we propose a Binary Interval Partition (BIP) algorithm, as shown in Algorithm \ref{algo:bip}. For a required interval number $m$, the BIP algorithm determines a target $\hat{nl}$ for all intervals by an iterative binary searching process. By adjusting $n_il_i$ to be equal with $\hat{nl}$, the whole count range from 0 to $t_{MAX}$ could be equally divided into $m$ intervals. Specifically, we obtain the interval borders by a single pass along the local count histogram of $\mathcal{T}$. During the pass, we keep pushing the right boundary $t_{i+1}$ of an interval $c_i$ until $n_il_i\approx \hat{nl}$. One in particular is the count interval $[0,t_0)$ for the background class, we manually set $t_0$ due to the extreme number of background samples.
\subsection{The Count Proxy Selection Strategy}\label{proxy_sel}
Intuitively, assigning a fixed count value for all patches classified to an interval inevitably introduces discretization errors. This problem becomes even more perceivable when the number of intervals is relatively small. However, increasing the number of intervals would lead to heavier computational burden in the classification head. Instead, to minimize the discretization errors, we propose a count proxy selection criterion termed as Mean Count Proxies (MCP). The MCP criterion demonstrates that the expected discretization error could be nearly negligible, as long as we choose the average count value of samples in corresponding interval as its count proxy.

Actually, when all the patches in an image are classified correctly, its expected counting error $\mathcal{E}$ degenerates to the discretization error. The above observation provides us a way to quantify the discretization error. Taking the interval $c_i$ as an example, all the correctly classified samples $x_{ij}$ ($j\in\{1,...,n_i\}$) within $c_i$ shares a same count proxy $\delta_i$. So we could further conclude that if we let $\delta_i = \sum_{j=1}^{n_i}x_{ij}/n_i $, $\mathcal{E}$ will get the minimal value 0. In other words, with the proposed MCP criterion, there will be \textit{no extra quantization errors} when transforming the regression task into an interval classification problem. We provide detailed mathematical proof in the Supplementary Materials.

\noindent\begin{table*}[t!]
\centering
\footnotesize{
\begin{tabular*}{0.96\textwidth}{c|cc|cc|cc|cc|cccccc}
\toprule
\multirow{2}{*}{Methods}&\multicolumn{2}{c|}{\shortstack{SHTechPartA}}&\multicolumn{2}{c|}{\shortstack{SHTechPartB}}&\multicolumn{2}{c|}{\shortstack{UCF\_CC\_50}}&\multicolumn{2}{c|}{\shortstack{UCF-QNRF}}&\multicolumn{6}{c}{WorldExpo'10}\\ \cline{2-15}
& MAE&MSE & MAE&MSE & MAE&MSE & MAE&MSE & S1&S2&S3&S4&S5&avg. \\ \hline
CAN \cite{liu2019context}& 62.3&100.0 & 7.8&12.2 & 212.2&\textbf{243.7} & 107.0&183.0 & 2.9&12.0&10.0&\underline{7.9}&4.3&7.4 \\
CSRNet \cite{li2018csrnet}& 68.2&115.0 & 10.6&16.0 & 266.1&397.5 & -&- & 2.9&11.5&8.6&16.6&3.4&8.6 \\
PGCNet \cite{yan2019perspective}& 57.0&\textbf{86.0} & 8.8&13.7 & 244.6&361.2 & -&- & 2.5&12.7&\underline{8.4}&13.7&3.2&8.1 \\
DSSINet \cite{liu2019crowd} & 60.63&96.04 & 6.85&\underline{10.34} & 216.9&302.4 & 99.1&159.2 & \underline{1.57}&\underline{9.51}&9.46&10.35&\textbf{2.49}&\underline{6.67} \\
Liu et al. \cite{liu2019counting} & 62.8&102.0 & 8.6&16.4 & 239.6&322.2 & 141.0&219.0 & 2.1&11.4&\textbf{8.1}&16.8&\underline{2.5}&8.2 \\
BL+ \cite{ma2019bayesian}& 62.8&101.8 & 7.7&12.7 & 229.3&308.2 & 88.7&154.8 & -&-&-&-&-&- \\
S-DCNet \cite{xiong2019open}& 58.3&95.0 & 6.7&10.7 & 204.2&301.3 & 104.4&176.1 & -&-&-&-&-&- \\
DUBNet \cite{oh2020crowd} & 64.6&106.8 & 7.7&12.5 & 243.8&329.3 & 105.6&180.5 & -&-&-&-&-&- \\
SDANet \cite{miao2020shallow} & 63.6&101.8 & 7.8&\textbf{10.2} & 227.6&316.4 & -&- & 2.0&14.3&12.5&9.5&\underline{2.5}&8.1 \\
ADSCNet \cite{bai2020adaptive} & \underline{55.4}&97.7 & \underline{6.4}&11.3 & 198.4&267.3 & \textbf{71.3}&\underline{132.5} & -&-&-&-&-&- \\
LibraNet \cite{liu2020weighing} & 55.9&97.1 & 7.3&11.3 & 181.2&262.2 & 88.1& 143.7 & -&-&-&-&-&- \\
AMSNet \cite{hu2020count} & 56.7&93.4 & 6.7&\textbf{10.2} & 208.4&297.3 & 101.8&163.2 & 1.6&\textbf{8.8}&10.8&10.4&\underline{2.5}&6.8 \\
ASNet \cite{jiang2020attention} & 57.78&\underline{90.13} & -&- & \underline{174.84}&\underline{251.63} & 91.59&159.71 & 2.22&10.11&8.89&\textbf{7.14}&4.84&\textbf{6.64} \\
AMRNet \cite{liu2020adaptive} & 61.59&98.36 & 7.02&11.00 & 184.0&265.8 & 86.6&152.2 & -&-&-&-&-&- \\
\textbf{Ours} & \textbf{54.64}&91.15 & \textbf{6.38}&10.88 & \textbf{165.24}&275.90 & \underline{81.13}&\textbf{131.68} & \textbf{1.56}&9.83&10.31&8.56&4.13&6.88 \\
\bottomrule
\end{tabular*}}
\vspace{-8pt}
\caption{Comparisons with SOTA methods on four datasets. The best performance is bold and the second best is underlined.}\label{tab:cmp}
\vspace{-1.8em}
\end{table*}

\subsection{The Interleaved Prediction Heads}\label{iph}
\begin{figure}[t!]
  \centering
  \includegraphics[width=0.5\textwidth]{Figures/doublehead.pdf}
  \vspace{-2.0em}
  \caption{The interval partition setting in the proposed IPH. The interval borders in PredHead0 are determined by the UEP criterion. While the interval borders in PredHead1 are automatically obtained by directly using the count proxy values in PredHead0, which helps to mitigate the classification ambiguity around the interval borders in PreadHead0.}
  \label{fig3}
  \vspace{-1.5em}
\end{figure}
In this part, we introduce the proposed Interleaved Prediction Heads (IPH). Intuitively, the samples whose count values fall near the boundaries of intervals are particularly difficult to be correctly classified, which is due to the learning ambiguity around the interval borders. These samples tend to contribute more to the misclassification errors, especially for those samples from the intervals with relatively larger length.

To alleviate the above problem, we parallelly duplicate the prediction head and use overlapping count intervals for them. Specifically, as shown in Figure \ref{fig3}, the interval borders in the first head (PredHead0) are obtained using the UEP criterion. While the other head (PredHead1) directly use the count proxies in PredHead0 as its interval borders (except for the count interval $[0,t_0)$ of the background class). The count proxies of PredHead1 are independently calculated using the MCP criterion. The two heads are trained with CE loss independently, and during inference their predicted local count maps are averaged as the final estimation. With the IPH, the samples whose count values fall near the interval borders of one head are more likely to be correctly classified in the other head.
\section{Experiments}
We conduct extensive experiments on four challenging benchmarks with various crowd densities. Mean Absolute Error (MAE) and Root Mean Squared Error (MSE) are used as the evaluation metrics following \cite{zhang2016single}
\subsection{Implementation Details}
\paragraph{Datasets.}Experimental evaluations for the UEPNet are conducted on four widely used crowd counting benchmark datasets: ShanghaiTech \cite{zhang2016single} part A and part B, UCF\_CC\_50 \cite{idrees2013multi}, UCF-QNRF \cite{idrees2018composition}, and WorldExpo'10 \cite{zhang2015cross}. The count statistics for these datasets can be found in a recent survey \cite{gao2020cnn}. We generate ground truth density maps for ShanghaiTech part B and WorldExpo'10 using Gaussian kernel $G_\sigma$ with fixed sigma of 15.0 and 5.0 respectively. For the other datasets we use geometry-adaptive Gaussian kernel following \cite{zhang2016single}. We follow the standard protocol in \cite{idrees2013multi} to conduct a five-fold cross validation on UCF\_CC\_50. For WorldExpo'10, we blur image regions outside the provided RoIs using mean filter of kernel size 11.
\vspace{-1.0em}
\paragraph{Data Augmentations.}Scaling with a random factor in [0.3, 1.3] and random mirroring are performed to increase the diversity of image patches. Then we random select a cropped patch for training following CSRNet \cite{li2018csrnet}. Since images in UCF-QNRF have extremely large resolution, we limit the crop size within 1024 and make inference with a sliding window of size 1024 due to the limited memory.
\vspace{-1.0em}
\paragraph{Hyperparameters.}The size of local patches in UEPNet is $8\times 8$, since we use the feature map of stride 8 for predictions. As demonstrated, the interval number works well in a wide range, and is empirically selected as 25 for all datasets. The $\epsilon$ in Algorithm \ref{algo:bip} is set to 100, and $t_0$ is set to $1.6e^{-4}$. The first 13 convolutional layers in UEPNet are initialized from a pre-trained VGG-16 \cite{simonyan2014very} model, and the rest layers are randomly initialized by a Gaussian distribution with the mean of 0 and the standard deviation of 0.01. We fine-tune the model using SGD with batch size 1. The learning rate is initially set to 0.001 and is decreased by 1/10 when the training loss stagnates. We keep training until convergence
\subsection{Comparisons with State-of-the-Art Methods}
We compare our UEPNet with state-of-the-art methods on the datasets described above. As shown in Table \ref{tab:cmp}, our UEPNet achieves state-of-the-art performance on these datasets. Compared with the previous strongest method ADSCNet, which achieved the best MAE on several datasets, UEPNet achieves better MAE on SHTech Part B. And the UEPNet surpasses ADSCNet with a 1.4\% relative improvement in MAE on SHTech Part A, with a 16.7\% relative improvement in MAE on UCF\_CC\_50. As for UCF-QNRF, most methods give very few details of the testing procedure including ADSCNet. With a simple patch-wise inference (due to the limited GPU memory), our UEPNet achieves higher MAE than ADSCNet, but already reduces the MAE by 5.7\% compared with the second-best method AMRNet. Besides, the BL+ method designed a novel loss to deal with the inaccurate targets, while our UEPNet, which is supervised with a simple CE loss, outperforms BL+ by a large margin on all datasets. Generally speaking, although using a quite simple network structure, the UEPNet performs better than previous methods, even for the Neural Architecture Search based method AMSNet.

We further make comparison with the other two methods, Two-Linear in S-DCNet \cite{xiong2019open} and Liu et al. \cite{liu2019counting}, to show the superiority of our strategy for interval partition and count proxy selection. It is worth mentioning that the two methods also use the paradigm of counting by local count classification, but both of them divide the count intervals intuitively. As shown in Table \ref{tab:cmp}, our UEPNet achieves significant improvement on all the four datasets. For more fair comparison, we firstly remove the IPH and use median value of the interval as its count proxy. Then in order to use their carefully tuned hyperparameters, we conduct experiments under different strides with the same network structure and report the results in Table \ref{tab:logcmp}. The consistent and significant improvements indicate the crucial of count interval design and the effectiveness of our UEP criterion.
\begin{table}[t!]
\centering
\small{
\begin{tabular}{c||c||c|c||c|c}
\hline
\multirow{2}{*}{Metrics}&\multicolumn{1}{c||}{\shortstack{Stride8}}&\multicolumn{2}{c||}{\shortstack{Stride16}}&\multicolumn{2}{c}{\shortstack{Stride32}}\\ \cline{2-6}
 & \textbf{Ours} & S-DCNet&\textbf{Ours} & Liu et al.&\textbf{Ours} \\ \hline
MAE & 7.72& 9.63&7.71 & 8.81&7.65 \\
MSE & 12.51& 14.22&12.80 & 14.02&12.28 \\
\hline
\end{tabular}}
\vspace{-8pt}
\caption{Comparisons with other interval partition methods on SHTech Part B using the same network structure and the same number of intervals.}\label{tab:logcmp
\vspace{-1.0em}
\end{table}
\subsection{Ablation Studies}
\begin{table}[ht!]
\centering
\small{
\begin{tabular}{cccc||c|c||c|c}
\hline
\multirow{2}{*}{MCP}&\multirow{2}{*}{IPH}&\multirow{2}{*}{\shortstack{PPH}}&\multirow{2}{*}{MS}&\multicolumn{2}{c||}{\shortstack{SHTech Part A}}&\multicolumn{2}{c}{\shortstack{SHTech Part B}}\\ \cline{5-8}
 & &&&    MAE&MSE    &MAE & MSE  \\ \hline
& &&& 61.56& 102.30&7.72 & 12.51\\
\checkmark & &&&  58.69& 97.64&7.64 & 13.18\\
\checkmark & &&\checkmark &  57.97& 100.34&6.87 & 11.33\\
\checkmark &\checkmark  &&&  56.80& 92.01&7.25 & 11.91\\
\checkmark & &\checkmark &&  58.47& 97.51&7.57 & 12.88\\
\checkmark &\checkmark &&\checkmark & \textbf{54.64}& \textbf{91.15}&\textbf{6.38} & \textbf{10.88}\\
\hline
\end{tabular}}
\vspace{-8pt}
\caption{Ablation studies of our contributions. MS in the table is short for the multi-scale training. PPH is a variant of the IPH, and the two heads in the PPH use exactly the same interval setting instead of the overlapping intervals.}\label{tab:ablation
\vspace{-1.5em}
\end{table}
In this section, we demonstrate the effectiveness of our contributions through ablation studies on two datasets with various density, \textit{i.e.}, SHTech Part A and SHTech Part B.

\begin{table}[ht!]
\centering
\small{
\begin{tabular}{c||c|c||c|c}
\hline
\multirow{2}{*}{Methods}&\multicolumn{2}{c||}{\shortstack{Median}}&\multicolumn{2}{c}{\shortstack{MCP}}\\ \cline{2-5}
 & MAE&MSE&MAE&MSE \\ \hline
UniformLen & 82.62& 90.65& 13.14& 17.17 \\
UniformNum & 62.46& 75.26& 10.88& 25.62 \\
Ours & \textbf{7.72}& \textbf{12.51}&\textbf{7.64}& \textbf{13.18} \\
\hline
\end{tabular}}
\vspace{-8pt}
\caption{Comparison with other interval settings on SHTech Part B. We use Median to represent using the median value of each interval as its count proxy.}\label{tab:mcp}
\vspace{-1.5em}
\end{table}
\vspace{-1.0em}
\paragraph{Effect of UEP.}As shown in Table \ref{tab:logcmp}, dividing count intervals with our UEP criterion performs better than the previous two intuitively designed interval settings, with 20.0\% and 13.2\% improvements on MAE respectively. To highlight the benefit of UEP criterion, which takes a comprehensive consideration of the misclassification cost and the sample imbalance problem among intervals, we make comparison with another two interval settings, \textit{i.e.} UniformLen and UniformNum. UniformLen represents the strategy of dividing intervals with equal interval length, and UniformNum represents the strategy of dividing intervals with equal number of samples in each interval. From Table \ref{tab:mcp}, our method significantly outperforms UniformLen and UniformNum.
\vspace{-1.0em}
\paragraph{Effect of MCP.}As shown in Table \ref{tab:mcp}, compared with the median count proxies, the adoption of our MCP consistently reduces the MAE of UniformLen and UniformNum, with 20.0\% and 13.2\% reduction respectively. From the ablation studies in Table \ref{tab:ablation}, with the MCP, the MAE improves from 61.56 to 58.69 on Part A and from 7.72 to 7.64 on Part B.
\vspace{-1.0em}
\paragraph{Effect of IPH.}From the ablation results in Table \ref{tab:ablation}, the IPH indeed reduces the estimation errors, with 3.2\% and 5.1\% improvements on MAE for SHTech Part A and SHTech Part B respectively. Due to the augmentation of multi-scale training, the relative improvements for IPH are a little higher (5.7\% and 7.1\%). To eliminate the gain of voting ensemble, we conduct experiments using Parallel Prediction Heads (PPH), which uses exactly the same interval setting instead of the overlapping intervals for the two heads. As shown in Table \ref{tab:ablation}, the improvement of PPH is only 0.4\% for SHTech Part A and 0.9\% for SHTech Part B.
\vspace{-1.0em}
\paragraph{Effect of Multi-Scale Training.}This data augmentation is particularly helpful to the classification related task, \textit{i.e.}, the local count classification, which significantly increases the diversity of samples in each interval. As shown in Table \ref{tab:ablation}, with multi-scale training, the MAE of our UEPNet reduces from 56.80 to 54.64 on SHTech Part A and from 7.25 to 6.38 on SHTech Part B. However, as demonstrated in the following, the benefit of multi-scale training is only valid for the paradigm of counting by local count classification.
\subsection{Discussions}\label{why_count}
\noindent\textbf{Why classification works better?} It is natural to ask that why the task of local count classification could perform better than regression task. Besides the promising counting accuracy as shown in \cite{liu2019counting,liu2020weighing,xiong2019open}, we provide an intuitive explanation. Firstly, it only adopts a Cross Entropy (CE) loss, which is more robust to outliers compared with the commonly used MSE loss in regression. Secondly, it is much easier to identify that if a local count falls into a certain interval than determining its accurate value. Thirdly, the regression task requires to strictly predict the exact counts of two patches, although their ground truth counts might be inaccurate. Differently, for the classification task, only when the gap between their counts is large enough (beyond the length of an interval), the two patches might be classified as different classes. Under the existence of inaccurate targets, this more relaxed supervision helps to stabilize the training process, avoiding overfitting or local optimum. Finally, such a paradigm shows its potential of achieving superior performance when using our proposed criterions.
\begin{figure}[ht!]
  \centering
  \includegraphics[width=0.45\textwidth]{Figures/clsnum.pdf}
  \vspace{-0.8em}
  \caption{The effect of the number of intervals.}
  \label{fig4}
  \vspace{-1.5em}
\end{figure}
\vspace{-1.0em}
\paragraph{How to determine the class number?}We conduct experiments with different number of intervals. As shown in Figure \ref{fig4}, our UEPNet is robust to the number of intervals, while the methods of UniformLen and UniformNum get much higher MAE when using lower number of intervals. As we increase the number of intervals, the MAE of UniformLen and UniformNum starts to reduce and finally stagnates, but is still higher than our method. Consequently, what matters is the interval partition strategy rather than the number of intervals, and our method is insensitive to the number of intervals. We empirically recommend to use 25 classes, which works well on all dataset in our experiments.

\begin{table}[ht!]
\vspace{-6pt}
\centering
\small{
\begin{tabular}{c|c|c|c}
\hline
 & S-DCNet&Liu et al.&Ours \\ \hline
Median & 17.84& 11.04& 1.88 \\
MCP & 3.20& 1.70& \textbf{0.27} \\
\hline
\end{tabular}}
\vspace{-10pt}
\caption{The discretization errors on SHTech Part B test set.}\label{tab:discmp}
\vspace{-1.5em}
\end{table}
\vspace{-1.0em}
\paragraph{The analysis of discretization errors.}We quantify the discretization errors by assuming all samples are correctly classified, which eliminates the impact from the misclassification errors. Specifically, for the local count $d_k$ of each patch (without overlapping) in an image from $\mathcal{I}_{test}$, if $d_k$ falls into an interval $l_k, l_k\in \{0,1,...,m-1\}$, its predicted count $\hat{d_k}$ should be the count proxy of interval $l_k$, \textit{i.e.}, $\hat{d_k}=\delta_{l_k}$. Then, we calculate $|\sum_{d_k\in \mathcal{I}_{test}}(d_k - \hat{d_k})|$ as the discretization errors on $\mathcal{I}_{test}$, since no misclassification errors are introduced during this process. As shown in Table \ref{tab:discmp}, with MCP, the average discretization errors of all test images are significantly reduced. Combining with UEP and MCP, the discretization errors are nearly negligible.
\begin{figure}[ht!]
  \centering
  \includegraphics[width=0.45\textwidth]{Figures/grad_comp.pdf}
  \vspace{-0.8em}
  \caption{Comparison of gradient contributions in the converged models using MSE and our approach.}
  \label{fig5}
  \vspace{-1.0em}
\end{figure}
\vspace{-1.0em}
\paragraph{The effectiveness of our interval partition strategy.}As illustrated in Figure \ref{fig5}, compared with the commonly used MSE loss, our strategy is less sensitive to the samples with larger prediction error. This advantage is helpful to highlight the useful as well as accurate gradients and mitigate the negative impacts of inaccurate targets. Besides, since we divide intervals with the UEP criterion, which tries to keep the counting error cost equal for all intervals to minimize the prediction risk. We also compare the error contribution of each interval in a converged UEPNet. As shown in Figure \ref{fig6:c}, error contributions of all intervals are approximately equal. An interesting observation is that the intervals which are closer to the middle contribute slightly more counting errors. One possible explanation is that the samples in these intervals are more difficult to learn, since they may be either overestimated or underestimated.
\begin{figure}[t!]
    \vspace{-0.5em}
  \centering
  \begin{subfigure}{\linewidth}
    \centering
    \includegraphics[width=.95\linewidth]{Figures/color_bar.pdf}
    \vspace{-0.5em}
    \label{fig6:a0}
  \end{subfigure}
  \begin{subfigure}{\linewidth}
    \centering
    \includegraphics[width=.95\linewidth]{Figures/cvpr_evenbin.pdf}
    \vspace{-0.8em}
    \caption{Partition with equal length of interval (termed UniformLen).}
    \label{fig6:a}
  \end{subfigure}
  \begin{subfigure}{\linewidth}
    \centering
    \includegraphics[width=.95\linewidth]{Figures/cvpr_evennum.pdf}
    \vspace{-0.8em}
    \caption{Partition with equal number of samples (termed UniformNum).}
    \label{fig6:b}
  \end{subfigure}
  \begin{subfigure}{\linewidth}
    \centering
    \includegraphics[width=.95\linewidth]{Figures/cvpr_our.pdf}
    \vspace{-0.5em}
    \caption{Partition with the proposed UEP criterion.}
    \vspace{-0.8em}
    \label{fig6:c}
  \end{subfigure}
  \caption{Illustrations of counting error contributions (class MAE) from each count interval for three types of interval partition strategies. The number of samples in each count interval (class) is indicated by different colors in the top jet colormap. (a) UniformLen leads to extremely imbalanced number of samples among intervals, which makes it hard to train a well-performed classifier. (b) UniformNum leads too large interval length for certain intervals (especially for the intervals with high count), thus the misclassification of which contribute too large counting errors. (c) Our method provides a comprehensive consideration of the misclassification cost and the sample imbalance problem.
  \label{fig6}
  \vspace{-2em}
\end{figure}
\begin{table}[ht!]
\centering
\vspace{-6pt}
\small{
\begin{tabular}{c||c|c||c|c}
\hline
\multirow{2}{*}{Methods}&\multicolumn{2}{c||}{\shortstack{without MS}}&\multicolumn{2}{c}{\shortstack{with MS}}\\ \cline{2-5}
 & MAE&MSE&MAE&MSE \\ \hline
MSE & 13.57& 22.64& 14.65$\;\uparrow$& 24.35$\;\uparrow$ \\
UniformLen & 12.71& 20.38& 12.45$\;\downarrow$& 15.39$\;\downarrow$ \\
UniformNum & 11.25& 26.15& 10.50$\;\downarrow$& 25.74$\;\downarrow$ \\
Ours & \textbf{7.64}& \textbf{13.18}& \;\,\textbf{6.87}$\;\downarrow$& \textbf{11.33}$\;\downarrow$ \\
\hline
\end{tabular}}
\vspace{-8pt}
\caption{The effect of multi-scale training.}\label{tab:ms}
\vspace{-1.5em}
\end{table}
\vspace{-1.2em}
\paragraph{Multi-scale training is only useful for classification.}Our UEPNet benefits from the data augmentation of multi-scale training. However, we find that multi-scale training is only useful for the paradigm of local count classification. As shown in Table \ref{tab:ms}, when learn to regress the exact count in each patch with MSE, the adoption of multi-scale training unexpectedly hurts the performance of the model. Although multi-scale augmentation significantly increases the diversity of samples in each interval, but also introduces much more noises due to the inaccurate targets. Consequently, using outlier-sensitive losses will force the model to overfit these noises, which leads to inferior performance. But for local count classification, the benefits of multi-scale augmentation outweigh its side effects. Additionally, compared with UniformLen and UniformNum, we find that the proposed UEP criterion is helpful \ywu{for maximizing} the relative improvement of the multi-scale augmentation.
\section{Conclusion}
\ywu{We} propose a simple yet effective crowd counting model termed UEPNet based on the paradigm of local count classification. Specifically, we conduct deeply research on this paradigm, and propose the UEP criterion and the MCP criterion. The UEP criterion is used for the interval partition, which divides intervals with equal counting error cost for all intervals to minimize prediction risk. The MCP criterion is used for count proxy selection, which selects the average count value of samples in corresponding interval as its optimal count proxy. Additionally, we also propose the IPH to mitigate the problem of classification ambiguity for samples near the interval borders. Extensive experiments on four widely used datasets with various crowd \ywu{densities} demonstrate the superiority of our approach.

\section{Acknowledgments}
We would like to thank Hao Lu for helpful discussions and suggestions.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


APPLICATION FOR 2019 California Miss Amazing Pageant is now closed!
Participants who sign up to participate in the Talent Show portion of the event will be placed on a waitlist and will be contacted if a spot opens up!
The Rising Star division is for girls ages 5 to 9 who want an introductory experience to the Miss Amazing Pageant. Their journey begins later in the afternoon before the Final Show. They will be able to have dinner and get their hair and makeup done. Before the final show, they will be instructed by a staff member on how to walk on stage in their evening wear and also practice optional talent. During the show, each Rising Star will enjoy a moment in the spotlight to share the many things that make her an amazing girl with a bright and promising future. Finally, the Rising Stars will be awarded for their accomplishments with a crown and trophy!


Our house was on the market for a grand total 4 days : Friday through Tuesday. 4 days!! Yikes we figured it would take a couple of weeks. Now we have to move out by August 3rd??!!?? PANIC! So much to do, so little time.
We had to scramble once again for the paperwork, inspections and moving arrangements. We are taking a short and well earned vacation and will be packing as soon as we return. Altogether it's not that bad: having found a short term lease, we will be camping in a small apartment while we shop for our new house. It'll be cramped but we are now able to wait out until the right house comes along and also to move out rapidly once we have found it.
Looks like blogging will take the back seat for a little bit longer... Sorry !
Oh I will miss you my beloved studio !!!
Looks like all of that hard work paid off! Your home looks wonderful -- no wonder it sold so quickly. Have fun on your vacation, BREATHE, and then back to the craziness of moving!
Craziness is upon us now... less than 2 weeks.
4 jours, je suis hyper jalouse! A chaque fois que nous avons vendu notre maison cela a pris des mois! Bon courage pour la suite, j'espere que vous trouverez la maison de vos reves rapidement.
Est ce que la petite devra changer d'ecole?
Your house looks fantastic, I'm not surprised it sold so quickly!
It is lovely to have photos of the house looking its best. I am sure you will look at them in future and remember some of the great times you've had in that house. I love the colour scheme in your blue bathroom, the blue and white is so crisp and yet warm and pretty. All the best in your search for a new home.


text:
from warnings import catch_warnings
with catch_warnings(record=True):
    import json
import sys

version_json = '''
{
 "dirty": false,
 "error": null,
 "full-revisionid": "62a87bf4a2af02a8d3bc271ad26e5994292b8e6a",
 "version": "0.25.3"
}
'''  # END VERSION_JSON

def get_versions():
    return json.loads(version_json)

redpajama_set_name: RedPajamaGithub


Whether your planning a wedding reception, birthday party, corporate event, or just want to have great entertainment for our next party, You have come to the right	right place. From BANDS to DJs to KARAOKE. We've got it all!
As you can see we are more than just Entertaining DJ's and Musicians. We like to be considered as a Production Entertainment Company.Every Event we take on is considered special, creating an experience you will not forget. We will work within your Budget to get you the most for your dollar. From the Music to the Production, We love showing you a great time!
Just click on (contact us), Fill in as much information as you can. One of our staff will contact you via phone or Email.
Many of the Pictures seen on this Web Site were taken by our own Master Photographer Michele Hargrove.


text:
Q: How to switch users in Cygwin? I am trying to switch users in Cygwin using Cygwin's login command. It throws an error saying:
/bin/bash: No such file or directory. Operation not permitted.

I am new to Cygwin. How I can switch between users?

A: I don't know why login doesn't work. But I have an easy workaround for this, running cygwin as the desired user, you can use RUNAS or automatize something using PSEXEC.
RUNAS:
runas /user:HOSTNAME\<user> D:\cygwin-64\Cygwin.bat

PSEXEC:
psexec \\HOSTNAME -u DOMAIN\<user> -p <password> D:\cygwin-64\Cygwin.bat

You even can pass commands/run scripts silently to Cygwin like this:
psexec -accepteula \\HOSTNAME -u DOMAIN\<user> -p <password> D:/cygwin-64/bin/run.exe /usr/bin/bash -lic \"/home/username/file.sh\"

Although the question is quite old maybe someone else is looking for this. I've documented this and other options here.

redpajama_set_name: RedPajamaStackExchange


As a continuation of our Summer Camp blog series, over the months leading up to the kick-off of our Levoy Theatre Summer Camp, we will be taking you on an adventure through the ins and outs of what a life in the theatre might look like. This adventure is both for the kids attending our summer camp, their friends, and, of course, their parents! We'll explore 6 different jobs that are critical to the success of any theatrical production. This month, we explore the world of lighting design as explained by lighting designer of many Levoy Theatre productions including Shrek the Musical, The Who's Tommy, and the upcoming Sister Act: the Musical, Caitlin DuBois! Stay tuned for our next blog post, coming at you next month!
That description is a fancy way of saying good lighting design is a tool to help the audience to lose their grasp of reality and escape into the world created onstage.
Good lighting design, therefore, should never be obvious to the audience.
Most people do not notice the detail that goes into lighting a scene for the stage. Take a moment right now and look around the room and imagine how you would mimic onstage the light coming through your window. Chandeliers shining, moon light coming in your window, or the almost strobe effect of riding a school bus.
Now think how different the chandeliers would look today versus the 17th century, or how differently the sun pours in at 11am vs. 2pm. The next time you see a show, or concert or even a movie, consider the detail of the lighting and the thought and creativity that went into it. If nothing else, I hope it will help you to see the world, on the stage and off, in a new light.


"Chansons of A Chinaman is beyond a collection of poems, which are necessarily personal and sensorial. It is, more notably, classic in the modern, metaphysical in the witty and aesthetic, China's wisdom and tradition in the English language, for the world; it is a Chinaman (note the tone) singing his own chansons, rather than a forever-silent Other being spoken about, uttering his own voice rather than being uttered for, representing himself rather than being represented. As such, it is nothing less than a great contribution to a great cross-cultural dialogue."
"At his best, Changming Yuan's writing both thinks and feels poetically. Add to this his subtle and bold incorporation of wisdom (a very rare commodity in western literary circles today), and we have someone and something interesting on our hands."
"I want to see the world through Changming Yuan's eyes. Where I am reminded that Guinea Pigs are not from Guinea, or pigs. This is the place that the spectacular is lifted out of the mundane. It's where my poetry wants to live."
"Changming's language is artful in its simplicity: his conceits are charming. Balanced, writerly phrases marble his poems. His senses are always new and his loyalty to them, and to the sense they make, always pleasure."
"Sensual, excessive and sometimes surreal, Chansons of a Chinaman is a rare find in contemporary poetry written on the North American continent; Changming Yuan's work is sincere, without a trace of posturing. The bonus: he's smart enough to deftly undercut his sincerity with quirk, swerve, and wit."
Unlike those authors of Asian origin who were either born or raised in an English-speaking country, Changming Yuan grew up in an impoverished village in central southern China and did not start to learn the English alphabet until he became an ESL student at Shanghai Jiaotong University at the age of nineteen. After getting his first master's degree from Tianjin Teachers' University, he worked as a lecturer and administrator at Tianjin Institute of Foreign Trade (now part of Nankai University), and authored three books as well as a dozen journal articles on translation and the English language. While studying at the University of Saskatchewan, where he received his MA and PhD in English, Yuan founded the Saskatchewan Chinese Times and was its editor for two years. Yuan began to write poetry in English during a family trip to Banff in 2004 and currently teaches writing in Vancouver, where he lives with his wife and two sons.
A sampling from "Chansons of a Chinaman"
When my allen asks: who are they, dad?
But what is the tripod?
[Austria]: Poetry Salzburg Review; [Australia]: Snorkel, Stylus Poetry Journal; [Canada]: 3 Morning Cup, Ascent Aspiration Magazine, Autumn Leaves, Birth (Anthology), Bywords, Canadian Literature, CV2, the Dalhousie Review, dANDelion, Descant, Earls Court, English 113 F1 (Courseware, Univ. of Alberta), Egregious, Feathertale Review, filling Station, Freefall, Grain, Literary Review of Canada, Marriage (Anthology), Maynard, the Nashwaak Review, the New Chief Tongue, the New Quarterly, One Cool Word, Other C/lutter, Other Voices, Ottawa Arts Review, Poetry Canada, the Prairie Journal, Queen's Quarterly, Quills Canada Poetry Magazine, Qwerty, Rampike, Spirituality (Anthology), Wascana Review, Windsor Review, Vallum, Vancouver Review; [Cyprus]: Sons of Camus Writers International Journal;[Finland]: Nokturno; [India]: Taj Mahal Review, Kritya, Thanal; [Italy]: Private: International Review of Black and White Photographs and Texts; [New Zealand]: Numinous, Southern Ocean Review; [Singapore]: Quarterly Literary Review Singapore; [Turkey]: Istanbul Literary Review; [UK]: Argotist, Animal Antics(Anthology), Brittle Star, Cadenza, Cannon's Mouth, Current Accounts, First Time, Greatworks, Interpoetry, the Journal, the London Magazine, Nthposition, Orbis, Parameter, Pennine Ink, Poetic Hours, Pulsar, Winamop, World Strand (Anthology); [US]: Alba, the Ampersand Review, Apocryphal Text, Aroostook Review, Barnwood, Barrow Street, Blue Fifth Review, Burst, Byline, the Chaffin Journal, Chrysanthemum, Concelebratory shoehorn Review, Drunken Boat, Exquisite Corpse, Freefall, Gloom Cupboard, Great American Poetry Show, Hamilton Stone Review, Hidden Oak, Hoboeye, IF Poetry Journal, In Our Own Words (Anthology), Intercapillary Space, Iodine Poetry Journal, Joyful!, Kill Poet, King Log, Leaf Garden, Listenlight, Mad Bunkers, Mad Swirl, Milk, Miranda Literary Magazine, Offcourse, Oranges &Sardines, Other Voices International Project, Past Simple, Pawnshop, Peregrine Muse, Poetalk, Porcupine Literary Arts Magazine, Poetry Super Highway, Potomac: A Journal of Poetics and Politics, Press 1, Raving Dove, Remark, Right Hand Pointing, Rock Heals, Saint Elizabeth Street, Saranac Review, Sawbuck, Sentence: A Journal of Prose Poetics, Siren, Snow Monkey, SNReview, Soul to Soul, SPQuill, SutbleTea, Tipton Poetry Journal, Unlikely 2.0, Wild Violet, Willard & Maple, Write On.


The closest anyone can ever get to mocking a celebrity in good nature without any 'comeuppance', the roast is an A-list starring form of entertainment that has been popularised over the last decade by Comedy Central (via their Comedy Central Roast series), with the 13th and next target of the series confirmed as being a 90-minute tribute to American actor James Franco.
Due to broadcast this evening (2 September) at 10/9c on the network, the event filmed in Los Angeles (USA) last weekend was first confirmed in July, but on this occasion, The Comedy Central Roast of James Franco will be shown to online viewers through a streaming service simultaneous to the premiere on TV.
Comedy Central have noted that the event will air on their website alongside the CC:Stand-Up service, with coverage on CC services on mobile devices and Xbox 360 also offered. Streaming will also occur for the episode's immediate encore at 11:30pm/10:30c, which will include an overlay of Facebook and Twitter posts on the jokes and performances as they happened.
Like any major celebrity event, the roast will be preceded with a 15-minute 'red carpet show', presented in this case by comic actor Jonah Ray and James Franco's grandmother Mitzie Verne. The main attraction, however, will be the cast list brought in to mock their titular target, led by 'roastmaster' Seth Rogen, and including Bill Hader, Jonah Hill, Aziz Ansari, Jeff Ross, and Sarah Silverman amongst those that will take to the 'dais' to give James Franco a Comedy Central roasting.


I am currently a stay-at-home grandma. I have spent the last 10 years partnered with my best friend of 15 years, and this coming August (2016) we will finally tie the knot! Boy, is he a patient man, or what?
Scrapping has become such an important and HUGE part of my life! I love the creative outlet it provides, as well as the wonderful people I meet and spend time with. I'm happy to be a part of the Pixel Scrapper community and look forward to getting to know everyone.


text:
Q: How can I convince Xcode to emit a duplicate symbol linker error? Here's a different one from the usual confusion over duplicate symbol errors... :-)
I'm working on some legacy Mac code in an Xcode project that has the same global, "trace", defined in several different source files - for instance:

*

*File1.c: SInt32 trace;

*File2.c: Boolean trace;

etc. It's clear the original author meant them to have file-specific scope, but just neglected to prefix any of these lines with "static". That's fine, easy enough to fix.
But I'm kind of shocked the linker isn't flagging these! It looks to me like Xcode's linker (I presume gnu ld) only emits duplicate symbol warnings or errors for functions, that are linked into the code segment - but not global variables that are linked into the data segment. Instead, it silently conflates them, which is causing bugs.
So... how do I convince Xcode to emit link errors for duplicate global variables? Or get this information in some other way that can be a routine part of my build?

A: Well, I thought I'd answered my own question... :-)
I posted earlier:

So if you're using Xcode with LLVM GCC
  4.2, go to the build settings dialog, find the "LLVM GCC 4.2 - Code
  Generation" section, and check the "No
  Common Blocks" checkbox. This enables
  the compiler's "-fno-common" option,
  and changes the object file generation
  so that ld will choke and emit an
  error if you have two globals in
  different source files with the same
  name.

Unfortunately, that doesn't seem to solve all instances. It seems to work fine if all the globals have the same type.
But the example in the question is taken straight from the code, where a variable named "trace" is defined as a global in two different files with two different types. And that's still not caught by the build system when I check that checkbox.


text:
Fates Warning – Disconnected
November 7, 2019 Peter Jones Uncategorized Leave a comment
It's almost difficult for me to talk about Fates Warning. On the one hand, it is because they are, in my opinion, the single most underrated band in the history of metal. On the other, it is because they are very possibly my favorite band of all time. Depends on the day, but it's a toss up between Fates and Neurosis.
But why are they my favorite? For two main reasons. Number one, because they write amazing music. And second, because they were such an essential band on my journey of really getting into and appreciating metal on the whole.
I'd heard the name, but it was many years before I actually started listening to them, so I was super late to the game. It was actually right around the release of Disconnected in 2000 that I finally got around to listening to the band. And that was probably the perfect album for me to start listening to them. It may not be their best album, but it probably is my favorite. We'll leave John Arch's amazing vocal melodies, and the idea that their 1986 album The Spectre Within really can be considered the birth of progressive metal for another day. But today, let's just enjoy the mastery that is Disconnected.
There had been some band changes in recent years, but Disconnected was the second band with the same line up as on A Pleasant Shade of Gray. And what we have is a group that really seems to have come together, with a clear understanding of what they want to do and where they were going. Kevin Moore's work, both on keys, but most importantly in creating an atmosphere are on full display here. And I will continue to argue that Jim Matheos has penned more brilliant songs than just about anyone in the business.
Disconnected really runs the gamut of what Fates Warning can bring to the table. There are atmospheric tracks, like "Disconnected Part 1" and "Part 2". There are relatively straightforward rockers like "One" and "Pieces of Me". And then you have the progressive epics. "Something from Nothing" slowly builds over the course of its 11 minutes. But the real star of the show here is "Still Remains", a 16 minute progressive and emotional epic.
I love the complete lack of fear or need to hurry here. The song slowly builds for minutes before it finally hits its stride into what one would consider a proper song. And then it keeps building and morphing. Throughout all, the performances are stellar, and the emotion is palpable. Disconnected is an album about relationships, about loss, about human connections and how those impact our lives. And "Still Remains" is the ultimate track to convey all of this.
I really can't find a flaw in this album. The performances are excellent. Jim Matheos, in addition to being an excellent guitarist, also has amazing guitar tone on this album. Immediately recognizable. Ray Alder's vocals are as fantastic as always, powerful, clear, expressive. Mark Zonder always was one of the more interesting drummers in the business. Even the final track, "Disconnected Part 2", is a necessary part of this album. It completes the album, really hitting home the sense of loss and sadness that permeates the album.
I love all Fates Warning albums. And they have some truly groundbreaking and essential ones. Disconnected may not be their very best, but I think it will always be my favorite, and is clearly one of my top 10 albums of all time.
emotionalessentialfavoriteguitarlossmetalprogprogressivesad
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redpajama_set_name: RedPajamaCommonCrawl


text:
package io.github.resilience4j.timelimiter.internal;

import io.github.resilience4j.timelimiter.TimeLimiter;
import io.github.resilience4j.timelimiter.TimeLimiterConfig;

public class TimeLimiterImpl implements TimeLimiter {
    private final TimeLimiterConfig timeLimiterConfig;

    public TimeLimiterImpl(TimeLimiterConfig timeLimiterConfig) {
        this.timeLimiterConfig = timeLimiterConfig;
    }

    @Override
    public TimeLimiterConfig getTimeLimiterConfig() {
        return timeLimiterConfig;
    }
}


Protecting your primary email accounts that often control the reset functionality to other important accounts if you forget the passwords.
All of us have something important to protect and awareness that you are a target is the first step towards taking the needed actions to lower your probability of having problems down the line. Next up will be suggestions on what you need to do to help safeguard your digital life.
This entry was posted in Information Security Awareness and tagged Information Security, Securing your digital life on January 27, 2015 by Argus.
I received a stack of redeployed ipads and had to figure out how to reset them to factory default state. Only problem was the ipads passcodes had been set by the previous users as required by policy and I didn't have the passcodes handy. No problems I fired up trusty google and figured I would be through it in a minute.
I would have just restored from a previous backup if I had used these ipads before but since I hadn't I had to restore the device to factory default.
Connect the iPad to your USB cable but make sure itunes is closed on your pc (close it if it opens).
Turn off the ipad device by pressing and holding the Sleep/Wake button until it goes off.
Start itunes on your pc.
You will get to recovery mode and then you should restore the device to factory default using your pc via itunes. I restored them to factory default since I did not have a backup and this brought the ipads back to their original state, clearing the troublesome password in the process.
Definitely not rocket science but also not clear from a lot of the higher ranking sites based on the search term I used.
This entry was posted in iPad Security and tagged how to reset ipad passcode, ipad passcode reset on November 14, 2012 by Mark Kelly.
Is Jailbreaking your iphone or ipad bad for security?
Jailbreaking is the terminology used to describe the situation where the native protection of an iPhone or and iPad is defeated (hacked) to allow developers access to the file system's root directory. These files would normally be hidden and not accessible but when a device is jailbroken developer's are then able to modify them and create new offerings outside the control of the Apple store. Now that you know what jailbreaking is you may be asking yourself why would someone want to jailbreak their device and how is it done?
Why would someone want to jailbreak and iphone or ipad?
Jailbreaking is typically done by digital rebels who like to tinker with their devices and not be bounded by the laws imposed by others. People that believe devices should be open to exploration and that once they purchase it they are free to modify and improve. Many people jailbreak to utilize advanced short cut features, use apps only available on jailbroken devices, and open up their data options beyond the restricted carrier models that are available normally (also known as unlocking vs. jailbreaking).
There are numerous free utilities available including Spirit and Absinthe that allow you to jailbreak your iphone, ipad, or other iOS based device. If you decide you should choose your utility wisely because picking the wrong one could lead to an inoperable device or security problems. I have chosen not to jailbreak my device but if I did jailbreak it I would make sure follow these jailbreaking risk mitigation steps.
Skim the documentation of your jailbreaking utility to make sure you are changing any default passwords that are created as part of the process. Many of the utilities leave a default account that could be a future security or malware thorn in your side if you do not assign a unique password when prompted.
Why jailbreaking is not a good idea for enterprise use?
#1. Jailbreaking a device violates the Apple terms of service and likely violates your Apple care warranty. Many companies rely on Apple for hardware support so violating the terms of service puts this arrangement in jeopardy.
#2. Jailbreaking a device increases the risks of device instability since Apple does not validate the effectiveness of a jailbroken device. This is more theory vs. any working examples to highlight at the moment but it is very logical that increasing complexity can lead to increase problems.
#3. Jailbreaking a device increases the likelihood of iOS upgrade issues when new updates are rolled out. Apple iOS constantly changes so you have to ask yourself how much time you have to troubleshoot and deal with problems if the jailbroken device does not tolerate a routine iOS update.
#4. Jailbreaking an iPhone or iPad can increase the probability of data security issues since you are installing unreviewed software from vendors of unknown quality. In my opinion this is the biggest reason not to do this at the corporate level there is just too much downside risk that is hard to quantify.
#5. Jailbreaking a device has been known to increase battery consumption rates. Batteries drain quick enough and adding extra app or utility overhead only increases the drain.
Jailbreaking a personal iphone or ipad is a personal decision with limited risk but the same action is a drastically different equation in a corporate setting.
This entry was posted in iPad Security, IPhone Security and tagged jailbreaking security risks, jailbreaking security tips on June 19, 2012 by Mark Kelly.
iOS 6 is coming in the Fall and the release will be the first big test to enterprise iPad tablet life cycle management processes. Until now all versions of the iPad have been compatible with new iOS releases so there has never been a security or compatibility required reason to upgrade to the latest and greatest iPad. Upgrades were based solely on the desire to have the latest and greatest gadget or due primarily to new device acquisition timing.
The announcement that iOS 6 will not be compatible with the first iPad is a game changer for enterprises that haven't thought out their renewal strategy yet. This announcement is sure to have a drastic effect on reducing the number of first generation iPad devices inside security conscious businesses. This is really no different than when Microsoft sunsets a legacy version of Windows and announces that security and service pack upgrades will no longer be available after a certain date.
What new risks will user's of legacy iPad devices face once iOS 6 is released?
Increased risk of security vulnerability problems if first generation devices are not updated for important security bugs found in iOS 6.0 upgrades. I have not seen definitive word from Apple that important updates to legacy iOS will not be provided but the writing is on the wall.
It has been a good run first generation iPad but expect many of you will no longer have a place in the enterprise once iOS 6 is a reality. Corporate user's with a first generation iPad who couldn't justify an upgrade rejoice, you soon will have one courtesy of iOS 6.
This entry was posted in iPad Security and tagged iOS 6 security implications, iPad lifecycle management, tablet lifecycle management on June 15, 2012 by Mark Kelly.
How do LinkedIn's security problems affect you?
The recent news that top business networking site LinkedIn had a significant number of passwords compromised has been the biggest story in the information security world this week. It is disappointing but not surprising that LinkedIn was affected this breach. What went wrong in their security process?
An as of yet undisclosed vulnerability (probably some type of database injection attack) led to unauthorized access which allowed the hacker to download the site's hashed password database.
Even though the passwords were hashed they were not salted to provide an additional level of security. This meant that the he hashed passwords were susceptible to attacks that could quickly crack weak passwords.
LinkedIn was relatively slow to fess up to the attack and notify the users to change their passwords. This has now happened and after taking some initial grief LinkedIn has now forced password changes on those they believe were affected. This should help minimize the damage to users who had passwords disclosed.
So how do LinkedIn's security problems affect you?
If you are a LinkedIn user make sure to reset your site password and the passwords on any other sites if you use the same passwords across multiple sites.
What information security lessons does this incident teach an average website user?
Even professional companies with a lot of money to spend will be subject to information security compromises. For that reason it is important to utilize different user account names and separate passwords on each site you use to minimize the impact if any one account is compromised. To help manage this level of security and keep your sanity in the process I highly recommend that you use a password management program such as LastPass. This will help you spread out your risk and minimize the damage of any one site being compromised (and if your password management company gets compromised be sure to change that one with lightening speed). Lastly, it is important to keep up with the news and know when information security problems occur for sites you utilize. That will help you take swift action to minimize your chance of problems.
This entry was posted in Information Protection, Information Security Awareness and tagged Linkedin Security, Online security on June 8, 2012 by Mark Kelly.
I grew up on the Windows operating system and through the years have learned how to easily navigate in it and do everything I need to do. I know many people absolutely hate Windows and Microsoft but I am not one of those people. I have had mostly good experiences with Windows through the years and am comfortable with its operating framework and need to update security patches on a monthly (or more frequent basis).
That does not mean I am stuck in the dark ages refusing to use new technology. I have an iphone and an ipad and also love those devices but when I had to buy a personal machine for a side business I work on I chose an economical Gateway laptop running Windows 7. Priced at less than $400 I am not sure it is possible to get more value for such a usable machine. I have been using this machine regularly for about 9 months now with no problems until late April when I experienced my first big problem with the machine.
Out of nowhere the machine became extremely sluggish and would not even load the operating system after putting in my initial password. Even though I am extremely cautious I was worried that I became infected with some type of malware and that my machine was compromised. I could not even find out at first because the machine basically wouldn't load so I had to resort to plan B starting it in safe mode.
I was able to get the machine to load via safe mode and then ran some security scans which did not pick up any signs of malicious behavior (I ran Microsoft Security Essentials and Spybot S&D). Like a good operational/security person I then checked my event logs and realized several patches went in recently so I lept to the conclusion that one of these patches must be responsible for the horrific state of my system. Time to do my first ever Windows 7 system restore on this machine.
The Windows 7 system restore functionality is a great way to rollback your machine to a previously known "good state". System restore is marketed by Microsoft as a convenient way to undo system changes to your computer without affecting your personal files, such as e?mail, documents, or photos. Basically, a way to back out patches and other system level changes without losing your personal data.
I had configured my system to have many different restore points so the key question for me was which restore point to revert back to. At first I tried the nearest restore point one day out with no success, the machine was still unusable. After spending about an hour experimenting I decided to rollback 3 days and got a workable stable version of my system going again. Even after the OS was stable again my Firefox browser never regained stability so I had to uninstall and then reinstall it to get it working effectively again.
2. You may need to experiment with your restore back date until you find the point where your system is again stable.
3. Restore your system to that point and run your antivirus scan and Spybot Search & Destroy to validate that a security compromise did not cause your problem.
It takes some effort but you can do this yourself and save yourself some money if this happens to you.
This entry was posted in Windows 7 Security and tagged Windows 7 Security, Windows 7 System Restore on May 15, 2012 by Mark Kelly.
How long until Apple iOS needs its own patch Super Tuesday?
Are you Apple fans ready for some digital heresy? Apple iOS is as vulnerable to security problems as any other software, even as vulnerable as gasp Microsoft. We have witnessed this evolve from occasional updates to regular iOS updates and news of active attacks in the wild. If there was any doubt it is official Apple devices need the same security measures as any other device.
None of this should come as surprise to anyone. One of the unpleasant realities of being the big dog in town is that you become an attractive target to hackers. Apple devices started as a consumer hit but that success has led to a clamor for equivalent devices in the enterprise. Top level executives love these devices and have adopted them in masses along with the regular rank in file company employee. Would be attackers now realize that Apple devices are the future and compromising them can lead to a treasure trove of corporate intellectual property.
So will Apple adopt the equivalent of a regular monthly patching window the equivalent of Microsoft's infamous "Super Tuesday" patch window? I would bet big money on it and the reason is enterprise adoption. Most enterprise IT departments have not been on the forefront of bringing Apple mobile devices into the fold and are now quickly playing catch up.
Enterprise IT will also pressure Apple to release iOS updates at a consistent time of the month because it helps with planning and user education. It is a lot easier to schedule, implement and communicate security updates when a fixed release date is established and can be planned around. Then again Apple has never had a reputation of pandering to corporate IT departments so the call for consistent patch release dates may go unanswered.
Within next 6 months a major security incident will involve the iOS and be responsible for a big intellectual property loss.
This entry was posted in Apple Devices, iPad Security, IPhone Security and tagged iOS patch windows, iOS security updates on May 14, 2012 by Mark Kelly.
How should you secure your webcam?
Webcam security is probably something you have never given much thought to. You might have a friend that physically tapes their web cam and think that they are being a bit paranoid, but are they really? Criminals will do anything to make a buck and if they can do that via a webcam do you have any doubt that they would? I read this recent posting on quora that got me thinking about that very question.
Why would a hacker want to hack a webcam?
Here are just a few possibilities this list is not meant to be exhaustive but just to show you that there is plenty of motivation and potential financial gain by doing so.
To gain access to pictures that can be used for blackmail or financial gain. How much would someone potential pay to keep embarrassing photos out of the public domain? I imagine this could become quite a source of revenue especially if someone in the public eye was the victim of this type of attack. Granted that this is called blackmail and has serious potential legal repercussions but so does hacking and that did not stop the hacker up to this point.
For information to know what is happening in a given location covered by the webcam. This factor comes into play more when a webcam is setup to provide security for a high value location but could also be relevant if a home is particularly pricey or a prime target for physical theft. This one may be of a bit more of a stretch vs. option #1 but is definitely within the realm of possibilities.
To terrorize the person on the other end of the webcam. Lets face it there are some sick and twisted individuals in the world who just like instilling fear in others and causing pain. Imagine the damage they can do if they control your webcam and you have not implemented any kind of security controls over your webcam.
So what can you do to protect yourself from these webcam security threats?
Suddenly, the person using dark black tape to block their webcam when it is not in use does not seem so paranoid now that we know the evil doers have potential incentive to hack your webcam. I believe a physical security method is the preferred way to deal with this threat especially if you participate in sensitive activities with your webcam. Tape that does not allow the camera to be utilize is a good control here but it is only as effective as your regular usage of it. You must do this every time your webcam is not in use to have effective security.
Another effective method to control webcam security risks is to have a portable web cam vs. a built in one so you can unplug the usb port when it is not in use. If you do not have a built in camera this is the best security option you can employ but once again it is only as effective as your ability to do this every time it is not in use.
There are other important measures you need to take such as keeping your operating systems, internet browsers and other applications like Adobe flash up to date with the most recent versions. This will help minimize the likelihood of your machine being vulnerable to attackers. Minimizing your use of a webcam to situations that would not leave you embarrassed and open to potential blackmail is another important mitigation step that will help protect you even if someone manages to compromise your webcam.
Who would of thought a webcam could have so many security implications?
This entry was posted in Physical Security and tagged how to secure your webcam, webcam security on May 8, 2012 by Mark Kelly.
Is WordPress 3.3.2 tied to the MAC OS X security issues?
Blogging platform king WordPress has gone quite a long time without an update, until today that is. When I logged into my blogs I noticed that WordPress update 3.3.2 was awaiting my installation. I have read via SANS that compromised WordPress sites were the major attack vector for this high profile MAC attacks. Putting two and two together it makes quite a lot of sense that there is a new WordPress update to install to mitigate discovered issues with the platform.
Upon review of the update it contains fixes for several cross site scripting vulnerabilities as wells as a limited privilege escalation vulnerability. I decided to break with my normal policy of installing WordPress updates after 2-3 weeks stability and experimented with applying the patch right away. My test site worked with no problem so I applied it to my other two sites and no issues were experienced across the board.
It feels like it is only a matter of time until iOS gets hit big time and iPhone and iPad users learn that they are not isolated from the security issues that have faces Windows users for over a decade. With popularity comes scrutiny.
This entry was posted in Uncategorized and tagged Wordpress Security on April 20, 2012 by Mark Kelly.
Information security is an important topic for both businesses and individuals. It is nice to see many leading Colleges and Universities stepping up to the information security challenge and launching blogs and using social media tools to help educate students and faculty about the importance of information security. Information security groups at colleges and universities have a challenging job getting the word out and driving information security compliance since students are often prone to engaging in risky online behavior that leads to an increased likelihood of information security incidents.
Indiana University – IU is running a well designed blog that looks like something you would see from a saavy expensively run 3rd party site. The information security news is regularly updated and it appears that IU has a pretty big team working on this effort because many of the posts are by different individuals. Kudos to the team for the excellent work on information security and from a quick scan it appears personal information protection is a big component of the overall awareness effort.
Missouri State Information Security Blog – Charla Berry is doing an excellent job helping keep the Missouri State community aware of information security threats and how they can stay protected. Recent posts feature awareness tips about holiday scams and keeping online financial information secure.
Georgetown Information Security Blog -Nicole Kegler has been a longtime blogger on information security the site's index is listed back to March 2010. My favorite post is the one warning that Macs are not immune to information security problems since many people make this statement in error. With the ever increasing popularity of Apple devices you can expect reported information security problems in Apple devices to grow this year.
Rochester Institute of Technology – RIT has an excellent page for information security education but what really make them stand out is their use of Facebook to spread the information security gospel using social media tools. They have over 5300 Facebook page likes, a lot of awareness material and discussions going online so be sure to give their page a visit.
Kansas State University -Information Security program is run by Harvard Townsend and the school has an excellent overall online information security presence but they run a blog dedicated to information security threats which I have linked to that talks about common problems like spam, malware, and phishing attempts.
University of Connecticut – Mick DiGrazia has done a nice job with this information security blog that dates back to June 2010. I assume he will be back with a vengeance for the Spring semester.
The Ohio State University – I had to include the "The" since I always hear it on sports name/university roll calls plus it is listed that way on the site. The site contains a nice RSS feed highlighting information security awareness messages that students and faculty should be aware of. In addition, it looks like the institution has embraced encryption tools like PGP for faculty so that is a good sign that the information security program has been able to work effectively and get faculty support.
Stanford School of Medicine Information Security Blog – Site hasn't had a recent post but previously published excellent awareness reminders around common scams and other pertinent information security information. Hopefully this recognition will help provide incentive to post more updates in the future.
If you run or know of other edu blogs that I should index please send me an email or reply below.
This entry was posted in Information Security Awareness and tagged best edu information security blogs, best edu infosec blogs on January 6, 2012 by Mark Kelly.


"Jason is a total craftsman. His guitars are works of art and you can see and feel his love for building the second you pick them up."
"Great tone. Big, huge, ballsy lows, like a baritone. I. Love. That."
"Jayemar Guitars are made by someone who loves what they do, and it shows."
"... attention to detail, pride in craftsmanship, and the quality of finished product make my Jayemar Alma Bass not only a one of a kind, but a once-in-a-lifetime instrument."
"I had never before played a multi-scale fretboard, but felt right at home within minutes."
"I am definitely a multi-scale convert!"
With the closing of Marylhurst University in June 2018, Portland's longest-running musical instrument show needed to find a new home. Show organizers are happy to announce that the 44th annual Northwest Handmade Musical Instrument Exhibit will be hosted this year by Portland Community College at their Sylvania Campus in Southwest Portland. On May 4th and 5th, from noon to 5:00pm, $5.00 will get you entrance to the exhibit floor, with over 80 of the Northwest's best luthiers and lutherie suppliers, as well as mini-concerts performed every 15 minutes on exhibit instruments. See you there!


When it concerns the things that can make or break an event, couple of are as important as the quality, flavor and appearance of the food and drinks you serve. A lot is riding on your decision of which catering service to work with, and if you think this decision truly isn't that crucial, look at it in this manner, fantastic catering can rescue an event that's on the rocks, but bad catering will almost always send guests to the doors.
In addition, because catering is typically the # 1 or # 2 line-item expense for many occasions, the caterer you choose is also a substantial budgetary decision and can affect just how much money you have actually left over to spend on other areas depending upon food expenses and amounts.
Add in that catering services can vary drastically in their prices and the kinds of meals and services they provide, and the process of picking a caterer can figuratively and actually seem like you are comparing apples to oranges.
Of all the event and catering professionals we talked with, this showed up over and over and over again, mainly due to the fact that how responsive and interested a catering service is throughout your preliminary discussions is a sign of how they will perform during the length of their agreement with you (and more on contracts later).
A potential caterer must be finding out as much as they can about you in your very first couple of discussions with them, so you should anticipate them to be talking and asking questions about 20% of the time and you providing answers and outlining your needs about 80% of the time.
So when interviewing your Brighton catering services (and you must speak with a minimum of 3 caterers for any occasion with a sensible budget plan), you need to specify about the kind of occasion you are planning and the kind of food and/or presentation you are expecting. Otherwise, you might end up selecting a catering service who simply isn't really a good suitable for the type or style of your occasion.
Practically every catering service has a standard menu or menus to pick from, and numerous do provide some level of built-in flexibility to adjust these menus to your specific requirements by replacing specific items and/or personalizing others.
However, the standout catering services will exceed standardized menu choices and want to create incredible fare that matches a lot more particular style and dietary requirements.
Some people shy away from asking to sample the specific items they want for their occasion since it appears like a trouble for the catering service, however it is standard to request a sampling of exactly what you are planning to order before you sign on the dotted line.
In addition, you can request wine parings for these tastings if they are appropriate for your occasion (again, be prepared to pay a cost, it's a small cost to pay to ensure you select the right caterer). And when you are tasting the food, also attend to how it is presented, as any catering service who takes the time to correctly show a sample will most likely be most likely to make that type of effort on your event day.
You might be in for a surprise if you employ a BARBEQUE take-out joint to cater a formal ballroom fundraiser. Or if you choose a high-end corporate occasion caterer to serve a barn wedding event. Why? Since these caterers may not be accustomed to preparing and serving food in such a setting.
A lot more standard locations can also have limitations that caterers should follow, like particular setup standards or disposal restrictions for waste food and water, so having a caterer that recognizes with a venue's guidelines can potentially save you great deals of hassle on occasion day.
The caterer's contract must clearly define precisely what food, drinks and services the catering service will be providing on the designated day( s). Additionally, it needs to protect you from non-performance as much as it secures the caterer from non-payment/default, so you may want to think about having a lawyer} look at it before you sign it.
Every detail ought to be included in the agreement, included selected menus, number of portions and/or individuals to be served, beverage/bar service details (if appropriate) and all pricing and additional services.
No caterer in Brighton with a shred of self-respect and scruples enters into a contract with plans to bail at the last minute, but you have to make sure there is a cancellation stipulation in your agreement simply in case your catering service has to cancel.
Nevertheless, do not stop there, as online reviews are not constantly trusted (and even genuine); for instance, an excellent caterer may have had a few nightmare/hater customers who skew their ratings, while a really average caterer may have padded their online reviews. So see if you can track down some past clients of the catering services you are considering and reach out to them.
Remarkably, how long a catering company in Brighton has actually been in business might not be as critical in selecting a fantastic caterer as you might think.


Pressure has also been proven to possess a website link to hair loss. If you want to help keep that complete healthier head of hair make an effort to remain as stress absolutely free as is possible. It's been proven that pressure causes constriction and restriction of blood movement to the scalp, which leads to hair follicles to die.
It truly is doable that chemicals can result in hair reduction. When you use chemical compounds in your hair, it really is finest to let a licensed beautician get it done. They're capable to adequately execute chemical treatment options to your hair. Also, should you shade your hair, get it done no much more than every 6 to eight weeks.
There exists just one treatment method for hair loss that has been shown to get effective and it is FDA accepted. That therapy is topical minoxidil and it is the ingredient which is found in merchandise like Rogaine. When research aren't absolutely sure why it aids, they do understand that it's been proven to strengthen hair development.
Quick fat alterations can play a substantial role in hair reduction, as your system can have a difficult time adjusting to your new size. Make certain you are as energetic as you can to ensure you may lower the amount of body fat with your physique and put by yourself while in the greatest place to fight hair reduction.
Switching your pillow case can help to avoid hair reduction. Satin or silk pillowcases are even though to assist protect against hair loss. Sleeping by using a cotton or flannel pillow case could cause friction to your head which may induce injury for the hair follicles and make the hair fall out.
Considered one of the top strategies of avoiding the progression of hair loss in guys is usually to take Propecia or finasteride. When this prescription medication is taken each day, just about all males note that their hair reduction stops. Some males even notice a regrowth of hair. This medication will have to be taken for life, simply because once someone stops taking it, the pattern of hair reduction recurs.
Never use any hair loss medicines until you have talked about it together with your health care provider. Whereas some over-the-counter treatments are risk-free, other individuals could not be. Also, should you suffer from any health-related disorders your physician may well let you know which hair loss remedies are harmless to suit your needs to utilize and that are not.
Eating white sesame seeds can help decelerate hair loss. Each and every morning make an attempt to consume a handful of white sesame seeds. These healthful seeds are filled with calcium and magnesium, containing a whopping 1,200 mg. Calcium and magnesium are excellent for your scalp, they're going to nourish it and support minimize your hair reduction.
Make an effort to restrict the food items which have a large excess fat content material through the program on the day. Large ranges of excess fat can drain the good quality of each strand of hair, that will inevitably lead to balding. Consume food items that are rich in minerals and minimal in extra fat, for optimum hair development.
You might prefer to look at consuming far more Vitamin C in case you suffer from hair reduction. Vitamin C aids by finding more blood flow to the scalp and sustain capillaries which carry the blood to hair follicles. Finding additional blood to movement for your scalp can help hair to regrow a lot quicker.
Should you use sticky hair styling solutions, wash them out prior to going to bed. Leaving waxes, clays, hair gels and any other sticky substances with your hair whilst you rest, will lead to clogging on the pores with your scalp. These solutions can contain chemicals that could interfere with new hair development, consequently slowing down new hair growth.
One among the issues which you will prefer to do is limit dandruff, specially during the fall and winter. Dandruff may perhaps injury the texture and strength of the hair and will bring about extra dryness of your scalp. Acquire a shampoo that eliminates the cause of dandruff in a gentle way. You don't choose to use a item that irritates your scalp both.
Learn to cope with strain efficiently. Strain alters hormone levels in the physique, so the even more you anxiety by yourself, the much more probable it can be that you just will working experience hair loss. Take care of tension inside a far better way and also you could possibly be conserving your hair. Consider up meditation or other stress-reducing ways.


As readers, we know that descriptions are the pulse of the writing, the lifeblood that lets us fully experience a character's journey. But that doesn't mean that writing powerful description is easy.
Use this meditation to infuse your writing with the kind of descriptions that readers crave.


Toss potatoes, EVOO and paprika on a foil-lined baking sheet; season. Roast at 450 degrees until tender, 25 minutes. Whisk yogurt, cilantro, zest and juice; season. Serve potatoes with toothpicks and yogurt sauce for dipping.