text:
\section{Introduction}
\label{sec:introduction}

\begin{figure}[h]
\begin{center}
\includegraphics[width=\linewidth]{intro.pdf}
\vskip -0.3cm
\caption{(a) We propose a deep convolutional network for face detection, which achieves high recall of faces even under severe occlusions and head pose variations. The key to the success of our approach is the new mechanism for scoring face likeliness based on deep network responses on local facial parts. (b) The part-level response maps (we call it `partness' map) generated by our deep network given a full image without prior face detection. All these occluded faces are difficult to handle by conventional approach.}
\label{fig:intro}
\end{center}
\vskip -0.5cm
\end{figure}

Neural network based methods were once widely applied for localizing faces~\cite{vaillant1994original,rowley1998neural,garcia2004convolutional,osadchy2007synergistic}, but they were soon replaced by various non-neural network-based face detectors, which are based on cascade structure~\cite{JointCascade,huang2007high,SURF,viola2004robust} and deformable part models (DPM)~\cite{HeadHunter,yan2014face,zhu2012face} detectors.
Deep convolutional networks (DCN) have recently achieved remarkable performance in many computer vision tasks, such as object detection, object classification, and face recognition.
Given the recent advances of deep learning and graphical processing units (GPUs), it is worthwhile to revisit the face detection problem from the neural network perspective.

In this study, we wish to design a deep convolutional network for face detection, with the aim of not only exploiting the representation learning capacity of DCN, but also formulating a novel way for handling the severe occlusion issue, which has been a bottleneck in face detection.
To this end, we design a new deep convolutional network with the following appealing properties:
(1) It is robust to severe occlusion. As depicted in Fig.~\ref{fig:intro}, our method can detect faces even more than half of the face region is occluded; (2) it is capable of detecting faces with large pose variation, \eg~profile view without training separate models under different viewpoints; (3) it accepts full image of arbitrary size and the faces of different scales can appear anywhere in the image.

All the aforementioned properties, which are challenging to achieve with conventional approaches, are made possible with the following considerations:

\noindent
(1) \textit{Generating face parts responses from attribute-aware deep networks}:
We believe the reasoning of unique structure of local facial parts (\eg eyes, nose, mouths) is the key to address face detection in unconstrained environment. To this end, we design a set of attribute-aware deep networks, which are pre-trained with generic objects and then fine-tuned with specific part-level binary attributes (\eg~mouth attributes including big lips, opened mouth, smiling, wearing lipstick). We show that these networks could generate response maps in deep layers that strongly indicate the locations of the parts.
The examples depicted in Fig.~\ref{fig:intro}(b) show the responses maps (known as `partness map' in our paper) of five different face parts.

\noindent
(2) \textit{Computing faceness score from responses configurations}:
Given the parts responses, we formulate an effective method to reason the degree of face likeliness through analysing their spatial arrangement. For instance, the hair should appear above the eyes, and the mouth should only appear below the nose. Any inconsistency would be penalized. Faceness scores will be derived and used to re-rank candidate windows of any generic object proposal generator to obtain a set of face proposals.  Our experiment shows that our face proposal enjoys a high recall with just modest number of proposals (over 90\% of face recall with around $150$ proposals, $\approx$0.5\% of full sliding windows, and $\approx$10\% of generic object proposals).

\noindent
(3) \textit{Refining the face hypotheses} --
Both the aforementioned components offer us the chance to find a face even under severe occlusion and pose variations.
The output of these components is a small set of high-quality face bounding box proposals that cover most faces in an image.
Given the face proposals, we design a multitask deep convolutional network in the second stage to refine the hypotheses further, by simultaneously recognizing the true faces and estimating more precise face locations.

Our main contribution in this study is the novel use of DCN for discovering facial parts responses from arbitrary uncropped face images. Interestingly,
in our method, part detectors emerge within CNN trained to classify attributes from uncropped face images, without any part supervision.
This is new in the literature. We leverage this new capability to further propose a face detector that is robust to severe occlusion. Our network achieves the state-of-the-art performance on challenging face detection benchmarks including FDDB, PASCAL Faces, and AFW. We show that practical runtime speed can be achieved albeit the use of DCN.

\section{Related Work}
\label{sec:related_work}

There is a long history of using neural network for the task of face detection~\cite{vaillant1994original,rowley1998neural,garcia2004convolutional,osadchy2007synergistic}.
An early face detection survey~\cite{yang2002detecting} provides an extensive coverage on relevant methods. Here we highlight a few notable studies.
Rowley~\etal~\cite{rowley1998neural} exploit a set of neural network-based filters to detect presence of faces in multiple scales, and merge the detections from individual filters.
Osadchy~\etal~\cite{osadchy2007synergistic} demonstrate that a joint learning of face detection and pose estimation significantly improves the performance of face detection.
The seminal work of Vaillant~\etal~\cite{vaillant1994original} adopt a two-stage coarse-to-fine detection. Specifically, the first stage approximately locates the face region, whilst the second stage provides a more precise localization.
Our approach is inspired by these studies, but we introduce innovations on many aspects.
In particular, we employ contemporary deep learning strategies, \eg~pre-training, to train deeper networks for more robust feature representation learning. Importantly, our first stage network is conceptually different from that of~\cite{vaillant1994original}, and many recent deep learning detection frameworks -- we train attribute-aware deep convolutional networks to achieve precise localization of facial parts, and exploit their spatial structure for inferring face likeliness. This concept is new and it allows our model to detect faces under severe occlusion and pose variations.
While great efforts have been devoted for addressing face detection under occlusion~\cite{lin2005robust,lin2004fast}, these methods are all confined to frontal faces. In contrast, our model can discover faces under variations of both pose and occlusion.

In the last decades, cascade based~\cite{JointCascade,huang2007high,SURF,viola2004robust} and deformable part models (DPM) detectors dominate the face detection approaches. Viola and Jones~\cite{viola2004robust} introduced fast Haar-like features computation via integral image and boosted cascade classifier. Various studies thereafter follow a similar pipeline. Amongst the variants, SURF cascade~\cite{SURF} was one of the top performers.
Later Chen~\etal~\cite{JointCascade} demonstrate state-of-the-art face detection performance by learning face detection and face alignment jointly in the same cascade framework.
Deformable part models define face as a collection of parts. Latent Support Vector Machine is typically used to find the parts and their relationships. DPM is shown more robust to occlusion than the cascade based methods.
A recent study~\cite{HeadHunter} demonstrates state-of-the-art performance with just a vanilla DPM, achieving better results than more sophisticated DPM variants~\cite{yan2014face,zhu2012face}.

\begin{figure*}[t]
\begin{center}
{\includegraphics[width=\linewidth]{pipeline.pdf}}
{\caption{\small{(a) The pipeline of generating part response maps and part localization. Different CNNs are trained to handle different facial parts, but they can share deep layers for computational efficiency. (b) The pipeline for generating face proposals. (c) Bounding box reranking by face measure \bf{(Best viewed in color)}.}}\label{fig:pipeline_1}}
\vspace{-0.45cm}
\end{center}
\end{figure*}

A recent study~\cite{farfade2015multi} shows that face detection can be further improved by using deep learning, leveraging the high capacity of deep convolutional networks. In this study, we push the performance limit further.
Specifically, the network proposed by~\cite{farfade2015multi} does not have explicit mechanism to handle occlusion, the face detector therefore fails to detect faces with heavy occlusions, as acknowledged by the authors. In contrast, our two-stage architecture has its first stage designated to handle partial occlusions. In addition,
our network gains improved efficiency by adopting the more recent fully convolutional architecture, in contrast to the previous work that relies on the conventional sliding window approach to obtain the final face detector.

The first stage of our model is partially inspired by the generic object proposal approaches~\cite{arbelaez2014multiscale,uijlings2013selective,zitnick2014edge}.
Generic object proposal generators are now an indispensable component of standard object detection algorithms through providing high-quality and category-independent bounding boxes.
These generic methods, however, are devoted to generic objects therefore not suitable to propose windows specific to face.
In particular, applying a generic proposal generator directly would produce enormous number of candidate windows but only minority of them contain faces.
In addition, a generic method does not consider the unique structure and parts on the face. Hence, there will be no principled mechanism to recall faces when the face is only partially visible.
These shortcomings motivate us to formulate the new faceness measure to achieve high recall on faces, whilst reduce the number of candidate windows to half the original.

\section{Faceness-Net}
\label{sec:methodology}

This section introduces the proposed attribute-aware face proposal and face detection approach, \textit{Faceness-Net}.
In the following, we first briefly overview the entire pipeline and then discuss the details.

Faceness-Net's pipeline consists of three stages, \ie generating partness maps, ranking candidate windows by faceness scores, and refining face proposals for face detection.
In the first stage as shown in Fig.~\ref{fig:pipeline_1}(a), a full image $\bx$ is used as input to five CNNs.  Note that all the five CNNs can share deep layers to save computational time.
 %
Each CNN outputs a partness map, which is obtained by weighted averaging over all the label maps at its top convolutional layer.
Each of these partness maps indicates the location of a specific facial component presented in the image, \eg hair, eyes, nose, mouth, and beard,
denoted by $\bh^a$, $\bh^e$, $\bh^n$, $\bh^m$, and $\bh^b$, respectively. We combine all these partness maps into a face label map $\bh^f$, which clearly designates faces' locations.

In the second stage, given a set of candidate windows that are generated by existing object proposal methods such as \cite{arbelaez2014multiscale,uijlings2013selective,zitnick2014edge},
we rank these windows according to their faceness scores, which are extracted from the partness maps with respect to different facial parts configurations, as illustrated at the bottom of Fig.~\ref{fig:pipeline_1}(b).
For example, as visualized in Fig.~\ref{fig:pipeline_1}(b), a candidate window `A' covers a local region of $\bh^a$ (\ie hair) and its faceness score is calculated by dividing the values at its upper part with respect to the values at its lower part, because hair is more likely to present at the top of a face region.
A final faceness score of `A' is obtained by averaging over the scores of these parts.
In this case, large number of false positive windows can be pruned.
Notably, the proposed approach is capable of coping with severe face occlusions, as shown in Fig.~\ref{fig:pipeline_1}(c), where face windows `A' and `E' can be retrieved by objectness~\cite{alexe2012measuring} only if large amount of windows are proposed, whilst they rank top $50$ by using our method.

In the last stage, the proposed candidate windows are refined by training a multitask CNN, where face classification and bounding box regression are jointly optimized.

\subsection{Partness Maps Extraction}
\label{sec:partness}

\noindent\textbf{Network structure}. Fig.~\ref{fig:network} depicts the structure and hyper-parameters of the CNN in Fig.~\ref{fig:pipeline_1}(a), which stacks seven convolutional layers (conv1 to conv7) and two max-pooling layers (max1 and max2).
This convolutional structure is inspired by the AlexNet~\cite{imagenet} in image classification. Many recent studies~\cite{simonyan2013deep, clarifai14} showed that stacking many convolutions as AlexNet did can roughly capture object locations.

\begin{figure}[t]
\begin{center}
\vskip -0.1cm
\includegraphics[width=\linewidth]{network.pdf}
\caption{A general architecture of an attribute-aware deep network. Other architecture is possible.}
\label{fig:network}
\end{center}
\vspace{-0.45cm}
\end{figure}

\begin{figure}[h]
\begin{center}
\includegraphics[width=\linewidth]{supervision.pdf}
\vskip -0.1cm
\caption{The responses or partness maps obtained by using different types of supervisions.}
\label{fig:partness_map_learning}
\end{center}
\vspace{-0.35cm}
\end{figure}

\begin{table}
\begin{center}
\caption{Facial attributes grouping.}
\vspace{0.05cm}
\label{tab:attribute_category}
\footnotesize
\addtolength{\tabcolsep}{-1pt}
\begin{tabular}{|c||c|}
\hline
\textbf{Facial Part} & \textbf{Facial Attributes}
\\
\hline
\hline
Hair & Black hair, Blond hair, Brown hair, Gray hair, Bald, \\& Wavy hair, Straight hair, Receding hairline, Bangs\\
\hline
Eye & Bushy eyebrows, Arched eyebrows, Narrow eyes,\\ & Bags under eyes, Eyeglasses \\
\hline
Nose & Big nose, Pointy nose \\
\hline
Mouth &	Big lips, Mouth slightly open, Smiling,\\ & Wearing lipstick\\
\hline
Beard &	No beard, Goatee, 5 o'clock shadow, \\ & Mustache, Sideburns\\
\hline
\end{tabular}
\end{center}
\vskip -0.45cm
\vspace{-0.4cm}
\end{table}

\noindent \textbf{Learning partness maps}.
As shown in Fig.~\ref{fig:partness_map_learning}(b), a deep network trained on generic objects, \eg~AlexNet~\cite{imagenet}, is not capable of providing us with precise faces' locations, let alone partness map.
The partness maps can be learned in multiple ways.
The most straight-forward manner is to use the image and its pixelwise segmentation label map as input and target, respectively.
This setting is widely employed in image labeling~\cite{farabet2013learning,aerial_hinton}.
However, it requires label maps with pixelwise annotations, which are expensive to collect.
Another setting is image-level classification (\ie faces and non-faces), as shown in Fig.~\ref{fig:partness_map_learning}(c). It works well where the training images are well-aligned, such as face recognition~\cite{sun2014deepb}.
Nevertheless, it suffers from complex background clutter because the supervisory information is not sufficient to account for face variations.
Its learned feature maps contain too much noises, which overwhelm the actual faces' locations.
Attribute learning in Fig.~\ref{fig:partness_map_learning}(d) extends the binary classification in (c) to the extreme by using a combination of attributes to capture face variations.
For instance, an `Asian' face can be distinguished from a `European' face.
However, our experiments demonstrate that the setting is not robust to occlusion.
Hence, as shown in Fig.~\ref{fig:partness_map_learning}(e), this work extends (d) by partitioning attributes into groups based on facial components.
For instance, `black hair', `blond hair', `bald', and `bangs' are grouped together, as all of them are related to hair.
The grouped attributes are summarized in Table~\ref{tab:attribute_category}.
In this case, different face parts can be modeled by different CNNs (with option to share some deep layers). If one part is occluded, the face region can still be localized by CNNs of the other parts.

We take the Hair-CNN in Fig.~\ref{fig:pipeline_1}(a) as an example to illustrate the learning procedure.
Let $\{\bx_i,\by_i\}_{i=1}^N$ be a set of full face images and the attribute labels of hair, where $\forall\bx_i\in\mathbb{R}^{256\times256}$ and $\forall\by_i\in\mathbb{R}^{1\times 9}$, implying that each full image is rescaled to $256\times256$ and there is nine attributes related to hair as listed in Table~\ref{tab:attribute_category}.
Learning is formulated as a multi-variate classification problem by minimizing the cross-entropy loss,
$L=\sum_{i=1}^{N}\by_i\log p(\mathbf{y}_i=1|\bx_i) + ({\mathbf{1}} - \by_i)\log\big({\mathbf{1}} - p(\by_i=1|\bx_i)\big)$,
where $p(\by_i|\bx_i)$ is modeled as a sigmoid function,
indicating the probability of the presence of the attributes.
This loss function can be optimized by the stochastic gradient descent with back-propagation.

However, the partness map generated by the Hair-CNN trained as above contains erroneous responses at the background, revealing that this training scheme is not sufficient to account for background clutter. To obtain a cleaner partness map, we employ the merit from object categorization, where CNN is pre-trained with massive general object categories in ImageNet~\cite{russakovsky2014imagenet} as in \cite{imagenet}. It can be viewed as a supervised pre-training for the Hair-CNN.

\subsection{Ranking Windows by Faceness Measure}
\label{sec:faceness_measure}

\begin{figure}[t]
\begin{center}
{\includegraphics[width=7cm]{integral.pdf}}
{\caption{\small{ Examples of spatial configurations (\textbf{Best viewed in color}).}}\label{fig:integral}}
\vspace{-0.45cm}
\end{center}
\end{figure}

Our approach is loosely coupled with existing generic object proposal generators~\cite{arbelaez2014multiscale,uijlings2013selective,zitnick2014edge} - it accepts candidate windows from the latter but generates its own faceness measure to return a ranked set of top-scoring face proposals.
Fig.~\ref{fig:integral} takes hair and eyes to illustrate the procedure of deriving the faceness measure from a partness map.
Let $\Delta_w$ be the faceness score of a window $w$.
For example, as shown in Fig.~\ref{fig:integral}(a), given a partness map of hair, $\bh^a$, $\Delta_w$ is attained by dividing the sum of values in ABEF (red) by the sum of values in FECD.
Similarly, Fig.~\ref{fig:integral}(b) expresses that $\Delta_w$ is obtained by dividing the sum of values in EFGH (red) with respect to ABEF+HGCD of $\bh^e$.

For both of the above examples, larger value of $\Delta_w$ indicates $w$ has higher overlapping ratio with face.
These spatial configurations, such as ABEF in (a) and EFGH in (b), can be learned from data.
We take hair as an example.
We need to learn the positions of points E and F, which can be represented by the $(x,y)$-coordinates of ABCD, \ie the proposed window.
For instance, the position of E in (a) can be represented by $x_e=x_b$ and $y_e=\lambda y_b+(1-\lambda)y_c$, implying that the value of its $y$-axis is a linear combination of $y_b$ and $y_c$.
With this representation, $\Delta_w$ can be efficiently computed by using the integral image (denoted as $\bI$) of the partness map. For instance, $\Delta_w$ in (a) is attained by
\begin{equation}
\frac{\bI(x_f,y_f)+\bI(x_b,y_b)-\bI(x_a,y_a)-\bI\big(x_b,\lambda y_b+(1-\lambda)y_c\big)}{\bI(x_d,y_d)+\bI(x_e,y_e)-\bI\big(x_a,\lambda y_a+(1-\lambda)y_d\big)-\bI(x_c,y_c)},
\end{equation}
where $\bI(x,y)$ signifies the value at the location $(x,y)$.

Given a training set $\{w_i,r_i,\bh_i\}_{i=1}^M$, where $w_i$ and $r_i\in\{0,1\}$ denote the $i$-th window and its label (\ie face/non-face), respectively.
$\bh_i$ is the cropped partness map with respect to the $i$-th window, \eg region ABCD in $\bh^a$. This problem can be simply formulated as maximum a posteriori (MAP)
\begin{equation}\label{eq:map}
\lambda^\ast=\arg\max_{\lambda}\prod_i^M p(r_i|\lambda,w_i,\bh_i)p(\lambda,w_i,\bh_i),
\end{equation}
where $\lambda$ represents a set of parameters when learning the spatial configuration of hair (Fig.~\ref{fig:integral}(a)).
$p(r_i|\lambda,w_i,\bh_i)$ and $p(\lambda,w_i,\bh_i)$ stand for the likelihood and prior, respectively. The likelihood of faceness can be modeled by a sigmoid function, \ie $p(r_i|\lambda,w_i,\bh_i)=\frac{1}{1+\exp(\frac{-\alpha}{\Delta_{w_i}})}$, where $\alpha$ is a coefficient.
This likelihood measures the confidence of partitioning face and non-face, given a certain spatial configuration.
The prior term can be factorized, $p(\lambda,w_i,\bh_i)=p(\lambda)p(w_i)p(\bh_i)$,
where $p(\lambda)$ is a uniform distribution between zero and one, as it indicates the coefficients of linear combination, $p(w_i)$ models the prior of the candidate window, which can be generated by object proposal methods, and $p(\bh_i)$ is the partness map as in Sec.~\ref{sec:partness}.
Since $\lambda$ typically has low dimension in this work (\eg one dimension of hair), it can be simply obtained by line search. Nevertheless, Eq.(\ref{eq:map}) can be easily extended to model more complex spatial configurations.

\subsection{Face Detection}
\label{sec:face_detection}
The proposed windows achieved by faceness measure have high recall rate.
To improve it further, we refine these windows by joint training face classification and bounding box regression using a CNN similar to the AlexNet~\cite{imagenet}.

In particular, we fine-tune AlexNet using face images from AFLW~\cite{aflw} and person-free images from PASCAL VOC 2007~\cite{pascalvoc}.
For face classification, a proposed window is assigned with a positive label if the IoU between it and the ground truth bounding box is larger than $0.5$; otherwise it is negative.
For bounding box regression, each proposal is trained to predict the positions of its nearest ground truth bounding box.
If the proposed window is a false positive, the CNN outputs a vector of $[-1,-1,-1,-1]$.
We adopt the Euclidean loss and cross-entropy loss for bounding box regression and face classification, respectively.

\section{Experimental Settings}
\label{sec:settings}

\noindent \textbf{Training datasets}.
\noindent (i) We employ CelebFaces dataset~\cite{celeface} to train our attribute-aware networks. The dataset contains 87,628 web-based images exclusive from the LFW~\cite{lfw}, FDDB~\cite{fddb}, AFW~\cite{zhu2012face} and PASCAL~\cite{yan2014face} datasets.
We label all images in the CelebFaces dataset with $25$ facial attributes and divide the labeled attributes into five categories based on their respective facial parts as shown in Table~\ref{tab:attribute_category}.
We randomly select $75,000$ images from the CelebFaces dataset for training and the remaining is reserved as validation set.
\noindent (ii)
For face detection training, we choose $13,205$ images from the AFLW dataset~\cite{aflw} to ensure a balanced out-of-plane pose distribution and $5,771$ random person-free images from the PASCAL VOC $2007$ dataset.

\noindent \textbf{Part response testing dataset}.
In Sec.~\ref{sec:evaluate_part_localisation}, we use LFW dataset~\cite{lfw} for evaluating the quality of part response maps for part localization.
We select $2,927$ LFW images following~\cite{crf} since it provides manually labeled hair+beard superpixel labels, on which the minimal and maximal coordinates can be used to generate the ground truth of face parts bounding boxes. Similarly, face parts boxes for eye, nose and mouth are manually labeled guided by the $68$ dense facial landmarks.

\noindent \textbf{Face proposal and detection testing datasets}.
In Sec.~\ref{sec:evaluate_face_proposal} and Sec.~\ref{sec:evaluate_face_detection}, we use the following datasets.
\noindent (i) FDDB~\cite{fddb} dataset contains the annotations for $5,171$ faces in a set of $2,845$ images. For the face proposal evaluation, we follow the standard evaluation protocol in object proposal studies~\cite{zitnick2014edge} and transform the original FDDB ellipses ground truth into bounding boxes by minimal bounding rectangle.
For the face detection evaluation, the original FDDB ellipse ground truth is used.
\noindent (ii) AFW~\cite{zhu2012face} dataset is built using Flickr images. It has $205$ images with $473$ annotated faces with large variations in both face viewpoint and appearance.
\noindent (iii) PASCAL faces~\cite{yan2014face} is a widely used face detection benchmark dataset. It consists of $851$ images and $1,341$ annotated faces.

\noindent \textbf{Evaluation settings}.
Following~\cite{zitnick2014edge}, we employ the Intersection over Union (IoU) as evaluation metric.
We fix the IoU threshold to $0.5$ following the strict PASCAL criterion. In particular, an object is considered being covered/detected by a proposal if IoU is no less than $0.5$.
To evaluate the effectiveness of different object proposal algorithms, we use the detection rate (DR) given the number of proposals per image~\cite{zitnick2014edge}.
For face detection, we use standard precision and recall (PR) to evaluate the effectiveness of face detection algorithms.
\section{Results}
\label{sec:experiments}

\subsection{Evaluating the Quality of Partness Maps}
\label{sec:evaluate_part_localisation}

\noindent \textbf{Robustness to unconstrained training input.}
In the testing stage, the proposed approach does not assume well-cropped faces as input. In the training stage, our approach neither requires well-cropped faces for learning. This is an unique advantage over existing approaches.

To support this statement, we conduct an experiment by fine-tuning two different CNNs as in Fig.~\ref{fig:pipeline_1}(a), each of which taking different inputs: (1) uncropped images, which may include large portion of background clutters apart the face; and (2) cropped images, which encompass roughly the face and shoulder regions.
The performance is measured based on the part detection rate\footnote{The face part bounding box is generated by first conducting non-maximum suppression (NMS) on the partness maps, and finding bounding boxes centered on NMS points.}.
Note that we combine the evaluation on `Hair+Beard' to suit the ground truth provided by~\cite{crf} (see Sec.~\ref{sec:settings}).
The detection results are summarized in Table~\ref{tab:acc_face_localization}.
As can be observed, the proposed approach performs similarly given both the uncropped and cropped images as training inputs.
The results suggest the robustness of the method in handling unconstrained images for training. In particular, thanks to the facial attribute-driven training, despite the use of uncropped images, the deep model is encouraged to discover and capture the facial part representation in the deep layers, it is therefore capable of generating response maps that precisely pinpoint the locations of parts.
In the following experiments, all the proposed models are trained on uncropped images. Fig.~\ref{fig:vs_ex2}(a) shows the qualitative results. Note that facial parts can be discovered despite challenging poses.

\begin{table}[t]
\begin{center}
\caption{Facial part detection rate. The number of proposals = 350.}
\label{tab:acc_face_localization}
\vskip 0.15cm
\small
\addtolength{\tabcolsep}{-1pt}
\begin{tabular}{c|c|c|c|c}
\hline
\textbf{Training Data} & \textbf{Hair}& \textbf{Eye}& \textbf{Nose}& \textbf{Mouth}
\\
\hline\hline
Cropped& $95.56\%$& $95.87\%$& $92.09\%$& $94.17\%$\\
Uncropped& $94.57\%$& $97.19\%$& $91.25\%$& $93.55\%$\\
\hline
\end{tabular}
\end{center}
\vspace{-0.6cm}
\end{table}

\begin{figure}[t]
\begin{center}
\includegraphics[width=\linewidth]{visual_samples.pdf}
 {\caption{\small{(a) The top row depicts the response maps generated by the proposed approach on each part. The second row shows the part localization results. Ground truth is depicted by the blue bounding boxes, whilst our part proposals are indicated in green.
(b) Face detection results on FDDB images. The bounding box in green is detected by our method. We show the partness maps as reference.
}}
\label{fig:vs_ex2}}
\vspace{-0.45cm}
\end{center}
\end{figure}

\subsection{From Part Responses to Face Proposal}
\label{sec:evaluate_face_proposal}

\noindent \textbf{Comparison with generic object proposals.}
In this experiment, we show the effectiveness of adapting different generic object proposal generators~\cite{arbelaez2014multiscale, zitnick2014edge, uijlings2013selective} to produce face-specific proposals. Since the notion of face proposal is new, no suitable methods are comparable therefore we use the original generic methods as baselines.
We first apply any object proposal generator to generate the proposals and we use our method described in Sec.~\ref{sec:faceness_measure} to obtain the face proposals.
We experiment with different parameters for the generic methods, and choose parameters that produce moderate number of proposals with very high recall.
Evaluation is conducted following the standard protocol~\cite{zitnick2014edge}.

The results are shown in Fig.~\ref{fig:compare_generic_proposal}. It can be observed that our method consistently improves the state-of-the-art methods for proposing face candidate windows, under different IoU thresholds.
Table~\ref{tab:compare_generic_proposal} shows that our method achieves high recall with small number of proposals.

\noindent \textbf{Evaluate the contribution of each face part.}
We factor the contributions of different face parts to face proposal.
Specifically, we generate face proposals with partness maps from each face part individually using the same evaluation protocol in previous experiment.
As can be observed from Fig.~\ref{fig:evaluate_part_contribution}(a), the hair, eye, and nose parts perform much better than mouth and beard.
The lower part of the face is often occluded, making the mouth and beard less effective in proposing face windows.
In contrast, hair, eye, and nose are visible in most cases. Nonetheless, mouth and beard can provide complementary cues.

\begin{figure}[t]
\begin{center}
\includegraphics[width=\linewidth]{comparison_generic.pdf}
\vskip -0.2cm
\caption{\small{Comparing the performance between the proposed faceness measure and various generic objectness measures on proposing face candidate windows.}}
\vskip -0.25cm
\label{fig:compare_generic_proposal}
\end{center}
\end{figure}

\begin{table}[t]
\begin{center}
\caption{The number of proposals needed for different recalls.}
\label{tab:compare_generic_proposal}
\vspace{0.1cm}
\scriptsize
\addtolength{\tabcolsep}{-1pt}
\begin{tabular}{c|c|c|c|c}
\hline
\textbf{Proposal method} & \textbf{$75\%$}& \textbf{$80\%$}& \textbf{$85\%$}& \textbf{$90\%$}
\\
\hline\hline
EdgeBox~\cite{zitnick2014edge}& $132$& $214$& $326$& $600$\\
EdgeBox~\cite{zitnick2014edge}+Faceness& $\mathbf{21}$& $\mathbf{47}$& $\mathbf{99}$& $\mathbf{288}$ \\
\hline
MCG~\cite{arbelaez2014multiscale}& $191$& $292$& $453$& $942$\\
MCG~\cite{arbelaez2014multiscale}+Faceness& $\mathbf{13}$& $\mathbf{23}$& $\mathbf{55}$& $\mathbf{158}$ \\
\hline
Selective Search~\cite{uijlings2013selective}& $153$& $228$& $366$& $641$ \\
Selective Search~\cite{uijlings2013selective}+Faceness& $\mathbf{24}$& $\mathbf{41}$& $\mathbf{91}$& $\mathbf{237}$ \\
\hline
\end{tabular}
\end{center}
\vspace{-0.5cm}
\end{table}

\begin{figure}[t]
\begin{center}
\subfigure[]{
	 \includegraphics[width=0.52\linewidth]{model_compare_each_part.pdf}
}
\subfigure[]{
	 \includegraphics[width=0.42\linewidth]{cascade_data.pdf}
}
\caption{\small{(a) Contribution of different face parts on face proposal. (b) FDDB face detection results with different proposal methods.}}
\vskip -0.45cm
\label{fig:evaluate_part_contribution}
\end{center}
\end{figure}

\noindent
\textbf{Face proposals with different training strategies.}
As discussed in Sec.~\ref{sec:partness}, there are different fine-tuning strategies that can be considered for generating a response map. We compare face proposal performance between different training strategies.
Quantitative results in Fig.~\ref{fig:quantitative_training_strategy} shows that our approach performs significantly better than approaches (c) and (d). This suggests that attributes-driven fine-tuning is more effective than `face and non-face' supervision.
As can be observed in Fig.~\ref{fig:partness_map_learning} our method generates strong response even on the occluded face compared with approach (d), which leads to higher quality of face proposal.

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.7\linewidth]{training_strategy.pdf}
\caption{Comparing face proposal performance between different training strategies. Methods (c)-(e) are similar to those in Fig.~\ref{fig:partness_map_learning}. Method (e) is our approach.}
\label{fig:quantitative_training_strategy}
\vspace{-0.45cm}
\end{center}
\end{figure}

\begin{figure}[t]
\begin{center}
\includegraphics[width=1\linewidth]{fddb_detection.pdf}
\vskip -0.1cm
\caption{\small{FDDB results. Recall rate is shown in the parenthesis.}}
\vskip -0.5cm
\label{fig:fddb_results}
\end{center}
\vspace{-0.3cm}
\end{figure}

\begin{figure}[t]
\begin{center}
\includegraphics[width=1\linewidth]{pascal_detection.pdf}
\vskip -0.25cm
\caption{\small{Precision-recall curves on PASCAL faces dataset. AP = average precision.}}
\vskip -0.5cm
\label{fig:pascal_results}
\end{center}
\vspace{-0.3cm}
\end{figure}

\begin{figure}[t]
\begin{center}
\includegraphics[width=1\linewidth]{afw_detection.pdf}
\vskip -0.25cm
\caption{\small{Precision-recall curves on AFW dataset. AP = average precision.}}
\vskip -0.5cm
\label{fig:afw_results}
\end{center}
\vspace{-0.3cm}
\end{figure}

\begin{figure*}[t]
\begin{center}
{\includegraphics[width=1\linewidth]{face_detection_samples.pdf}}
\vskip -0.2cm
{\caption{\small{Qualitative face detection results by Faceness-Net on FDDB (a), AFW (b), PASCAL faces (c).}}\label{fig:qualitative_result}}
\vspace{-0.45cm}
\end{center}
\end{figure*}

\subsection{From Face Proposal to Face Detection}
\label{sec:evaluate_face_detection}
In this experiment, we first show the influence of training a face detector using generic object proposals and our face proposals. Next we compare our face detector, Faceness-Net, with state-of-the-art face detection approaches.

\noindent \textbf{Generic object proposal versus face proposal.} We choose the best performer in Fig.~\ref{fig:compare_generic_proposal}, \ie~MCG, to conduct this comparison.
The result is shown in Fig.~\ref{fig:evaluate_part_contribution}(b). The best performance, a recall of $93\%$, is achieved by using our faceness measure to re-rank the MCG top $200$ proposals (Faceness+MCG top-$200$).
Using MCG top $200$ proposals alone yields the worst result. Even if we adjust the number of MCG proposal to $1,100$ with a high recall rate similar to that of our method, the result is still inferior due to the enormous number of false positives. The results suggest that the face proposal generated by our approach is more accurate in finding faces than generic object proposals for face detection.

\noindent \textbf{Comparison with face detectors.}
We conduct face detection experiment on three datasets FDDB~\cite{fddb}, AFW~\cite{zhu2012face} and PASCAL faces~\cite{yan2014face}. Our face detector, Faceness-Net, is trained with top $200$ proposals by re-ranking MCG proposals following the process described in Sec.~\ref{sec:face_detection}. We adopt the PASCAL VOC precision-recall protocol for evaluation.

We compare Faceness-Net against all published methods~\cite{ACF-multiscale,HeadHunter,JointCascade,FastDPM,BoostedExemplar,SURF,PEP-Adapt,XZJY,zhu2012face,VJain} in the FDDB.
For the PASCAL faces and AFW we compare with (1) deformable part based methods, \eg~ structure model~\cite{yan2014face} and Tree Parts Model (TSM)~\cite{zhu2012face}; (2) cascade-based methods, \eg~Headhunter~\cite{HeadHunter}.
Figures~\ref{fig:fddb_results},~\ref{fig:pascal_results}, and~\ref{fig:afw_results} show that Faceness-Net outperforms all previous approaches by a considerable margin, especially on the FDDB dataset. Fig~\ref{fig:vs_ex2}(b) shows some qualitative results on FDDB dataset together with the partness maps. More detection results are shown in Fig~\ref{fig:qualitative_result}.

\section{Discussion}

There is a recent and concurrent study that proposed a Cascade-CNN~\cite{cascadecnn} for face detection. Our method differs significantly to this method in that we explicitly handle partial occlusion by inferring face likeliness through part responses. This difference leads to a significant margin of $2.65\%$ in recall rate (Cascade-CNN $85.67\%$, our method $88.32\%$) when the number of false positives is fixed at 167 on the FDDB dataset. The complete recall rate of the proposed Faceness-Net is $90.99\%$ compared to $85.67\%$ of Cascade-CNN.

At the expense of recall rate, the fast version of Cascade-CNN achieves $14$fps on CPU and $100$fps on GPU for $640\times480$ VGA images.
The fast version of the proposed Faceness-Net can also achieve practical runtime efficiency, but still with a higher recall rate than the Cascade-CNN.
The speed up of our method is achieved in two ways. First, we share the layers from conv1 to conv5 in the first stage of our model since the face part responses are only captured in layer conv7 (Fig.~\ref{fig:pipeline_1}). The computations below conv7 in the ensemble are mostly redundant, since their filters capture global information \eg~edges and regions.
Second, to achieve further efficiency, we replace MCG with Edgebox for faster generic object proposal, and reduce the number of proposal to $150$ per image. Under this aggressive setting, our method still achieves a $87\%$ recall rate on FDDB, higher than the $85.67\%$ achieved by the full Cascade-CNN.
The new runtime of our two-stage model is $50$ms on a single GPU\footnote{We use the same Nvidia Titan Black GPU as in Cascade-CNN~\cite{cascadecnn}.} for VGA images. The runtime speed of our method is comparatively lower than~\cite{cascadecnn} because our implementation is currently based on unoptimized MATLAB code.

We note that further speed-up is possible without much trade-off on detection performance. Specifically, our method will benefit from Jaderberg~\etal~\cite{Speeding_up_CNN}, who show that a CNN structure can enjoy a $2.5\times$ speedup with no loss in accuracy by approximating non-linear filtering with low-rank expansions. Our method will also benefit from the recent model compression technique~\cite{DarkKnowledge}.

\small{\textbf{Acknowledgement} This work\footnote{For more technical details, please contact the corresponding author Ping Luo via pluo.lhi@gmail.com.} is partially supported by the National Natural Science Foundation of China (91320101, 61472410, 61503366).}

{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


"Learning to cook low-fat vegetarian foods has been pure enjoyment and real adventure. With the simple addition of a few new items to your pantry and a couple of cooking techniques, low fat vegetarian cooking can be so easy."
Mary Ter Meer, co-author of Vegetarian Cooking for Healthy Living, has taught home economics classes in Grand Rapids, Michigan and founded and directed the Community Pantry in Jackson County. In 1983, she received the Volunteer of the Year award for her efforts by President Reagan.
Mary learned the importance of a low-fat vegetarian diet after her husband Dave suffered two near-fatal heart attacks. Prior to the heart attacks the Ter Meers carefully followed the American Heart Association's dietary recommendations for the past ten years. That wasn't enough to prevent Dave from his many blockages in his coronary artery, or his second heart attack that elevated the risk that Dave would never lead a normal life again.
This tragedy turned into a success story through the help of cardiac rehabilitation and a heart disease reversal program advocated by Dr. Dean Ornish, M.D., President, Preventative Medicine Research Institute. The ultra lowfat diet actually helps to reverse coronary artery blockage. Over a period of five years, Mary developed many delicious yet nutritious low-fat, low-cholesterol vegetarian recipes that she and her husband could enjoy.
Mary was inspired to share her success with others through this book, with the help of co-author Jamie Gates Galeana. In Vegetarian Cooking for Healthy Living only about 10 percent of the calories come from fat.


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Roger Housden is a second time guest on the show – you might remember him from episode 152. He founded The Open Gate which is a conference and workshop center in England that introduced the work of Ram Dass and Thich Nhat Hanh and many others into Europe. You also might have seen his work featured in places like O Magazine, The New York Times and The Los Angeles Times. In this episode, Roger and Eric discuss his beautiful book, 10 Poems for Difficult Times.


National parks in the Netherlands were defined in the 1960s as areas of at least 10 km² consisting of natural terrains, water and/or forests, with a special landscape and flora and fauna.
The first two national parks were founded in the 1930s by private organisations. The first official national park, Schiermonnikoog National Park, was not established until 1989. The most recent national park to have been established is the Alde Feanen National Park, in April 2006. In 2009 the Weerribben national park was extended with the Wieden area, bringing the total area covered by national parks to 132,030 ha.
In 2011, the government decided to make the provinces responsible for the national parks.
Visit Holland or Visit the Netherlands?


You'd think that for no other reason than pride, Democrats would decide to fight. I mean, they can't stop telling us how tough they are by voting for "let's bomb Iraq" and "let's bomb Iran" resolutions. But people then elect them to do such quaint things as "lead" and "fight". So what happens when they don't? Congressional ratings keep tanking, we get headlines like these, and people think, "well, Democrats sure are wimps." Actually, "wimp" would be an improvement over the word they really use, but it's not politically correct so I won't use it. But you know what it is.
If the public needs to see which party is made up of strong members, and which one is chock full of patsies, it's not too difficult to figure out. And the worst part? Republicans always knew Democrats didn't have the cojones to fight, and have sat back and laughed at all the tough talk from Reid and Pelosi.
That's what they've become. A joke.
So all that's left is to see the final "Hoyer Math". How many millions in pork is the life of each soldier worth to Democrats in DC? We know that for Republicans, our soldiers' lives have zero value. For Democrats, the Hoyer rate appears to be in the $10 million range.
One last point: The American people hate Bush -- the most unpopular president since polling began -- and consider him a weak incompetent SOB.
Then they see Democrats capitulate to him.


text:
Bambara of Burkina Faso
Photo source: Copyright © GoWestAfrica. Used with permission.
People Name: Bambara
Country: Burkina Faso
Language: Bamanankan
Unreached: Yes
People Cluster: Malinke-Bambara
Primary Religion: Islam
% Adherents: 4.00 %
% Evangelical: 1.80 %
Progress Status: 1.0
Statistics Source: Joshua Project
Profile provided by:
www.joshuaproject.net
Map source: World Factbook
The Bambara are among the most powerful and influential groups in Mali, but they also live in Burkina Faso. The Bambara live in the middle valley of the Niger River. They speak Bamana, which is one of the Manding languages.
During the 1700s, there were two Bambara kingdoms: Segu and Karta. In the 1800s, militant Muslim groups overthrew these kingdoms, leaving only a few anti-Muslim Bambara warlords to resist their occupation. This lasted 40 years, until the arrival of the French. A very small number of the Bambara had converted to Islam by 1912. After World War II, the number of Muslim converts grew due to their resistance to the French and their exposure to Muslim merchants. Today, the Bambara are mostly Muslim.
Bambara people are spread out into many West African countries. They are among the most powerful and influential people in this region.
Most of the Bambara are farmers. Their staple crop is millet, although sorghum and groundnuts are produced in large quantities. Maize, tobacco, cassava, and various other vegetables are also grown in private gardens. Unfortunately, drought and other ecological problems have hurt the farmers in recent years.
Bambara farmers also raise cattle, sheep, goats, horses, and chickens. The neighboring Fulani herdsmen are often trusted to herd the Bambara livestock. This allows the Bambara to concentrate on farming during the short rainy season.
Many of the Bambara hunt animals such as antelope, boar, ostrich, and guinea fowl for their meat and skins. They also gather a large amount of honey from the wild bees in the area.
Both men and women share the farming duties. However, the wives usually arrive in the fields later and leave earlier than the men. This gives them time to prepare the morning and evening meals. Children between the ages of 12 and 14 also help with the family's work, leading the oxen as they plow and guarding them during rest periods.
Each Bambara village is made up of many different households, usually all from one lineage or extended family. Every household, or gwa, is responsible to provide for all of its members, as well as to help them with their farming duties. Bambara homes are typically larger than the homes of most other West African groups. Some of the houses contain as many as 60 or more family members. The members of each gwa work together every day except for Mondays. Monday is market day and the traditional day of rest.
Islamic schools have been set up in some of the Bambara villages. However, many of the non-Muslim villages have failed to establish schools simply because the children are needed to stay home and help with the farming. For this reason, some village populations are entirely illiterate.
Marriage is very important to the Bambara people. Although the cost of a wedding is high, it is viewed as a type of "investment." The main purpose for marriage is to have children, which provide the family's labor force and ensure the future of the family lineage. The average Bambara woman has eight children. Even elderly widows in their 70s or 80s have suitors because the Bambara believe that a wife increases a man's prestige.
Although most Bambaras claim to be Muslim, many still follow their traditional beliefs such as ancestor worship (praying to deceased ancestors for guidance and protection). The Bambara believe that the ancestral spirits may take on the forms of animals or even vegetables. In special ceremonies, the spirits are worshipped and presented with offerings of flour and water. The eldest member of a lineage acts as the "mediator" between the living and the dead.
Several mission agencies are currently focusing on the Bambara people, and there has been some progress in convincing them that Jesus offers much more. Prayer is the key to tearing down the spiritual strongholds that are keeping them from knowing the truth.
* Ask God to grant wisdom and favor to the mission agencies that are currently focusing on the Bambara people in West Africa.
* Pray that God will raise up prayer teams who will begin breaking up the spiritual soil of Burkina Faso through intercession.
* Pray for the effectiveness of the JESUS Film among the Bambara people.
* Pray that the Holy Spirit will complete the work begun in the hearts of the Bambara believers through adequate discipleship.
* Pray that God will give the Bambara believers boldness to share the love of Jesus with their own people.
Text source: Keith Carey

redpajama_set_name: RedPajamaCommonCrawl


Autumn Brown, also known as Autumn Bronze, is an exceptionally warm stone consisting of deep, earthy October hues. It is widely used in large patio areas, but can equally add a homely feel to smaller spaces.
Autumn Brown is chiefly medium to dark browns, but also has intermittent grey and yellows.


Dry cleaning equipment industry over the years to bring the huge profit space, can always attract a large number of dry cleaning equipment shop entrepreneurs. But who know how to open the shop good dry cleaning equipment? Dry cleaning equipment shop to join? Believe that a lot of friends there is doubt, let's follow market experts to learn about this aspect of the information.
Dry cleaning equipment shop is easy to operate the project itself, the laundry job to equipment machine, manual is mainly responsible for customer communication and the arrangement of the work. But as the market service contrast and the competition between stores, everybody will pay more attention to business management.
How can life a good dry cleaning equipment shop? The business model for first of all, you can begin. Modern consumers are more likely to accept chain laundry service, so when you open a shop can choose such a business model, on the one hand is to improve the market awareness, and create more profits dry cleaning equipment store.
Also, dry cleaning equipment shop work equipment support, so the industry experts suggest that we purchase some professional equipment. For example, you can choose these big brand dry cleaning equipment machine, on the one hand, to ensure the quality of laundry and for the work of laundry also helps.
Sometimes dry cleaning equipment in the store operations, may meet some customers dispute problem, this time don't try so hard, the better is to find out the problem in time, that is to avoid something damage the interests of the shop.


Take heed America. You are looking directly into the face of tyranny and California's lack of belief in your Constitutional freedoms could soon be coming to a location near you. Do NOT stand idly by. Thanks to Valor Ridge for producing this video.


The billionaire ruler of Dubai has been given permission to expand his 63,000 acre luxury estate – adding a nine bed house and a lodge for his STAFF.
Sheikh Mohammed bin Rashid al-Maktoum's architects said his family's trips to their sprawling Highland holiday estate had been "limited by lack of accommodation".
The racehorse owner, who has several wives and 23 children, applied for planning permission to build a lodge for staff and another house.
And despite objections the prince's entourage would wreak havoc on local roads, Highland Council planners gave the plans the green light this week.
The sheikh's estate in Inverinate, Wester Ross, already has a triple helipad, a 14-bedroom holiday home and a 16-bed luxury hunting lodge with a swimming pool and gym.
In a planning decision notice, Highland Council case officer Graham Sharp, said: "All relevant matters have been taken into account when appraising this application.
Sheikh Mohammed – who is reportedly worth more than £13.7 billion – brought the estate, on the shores of Loch Duich, around 20 years ago.
But his development company Smech Properties Ltd applied to Highland Council this summer for two lodges and another house.
In a statement to planners, Colin Armstrong Architects – who submitted the plans – said: "The owners of Inverinate Estate typically travel in large groups of immediate and extended family and friends.
Writing in October, Transport Scotland's Ken Aitken said he wanted to know how often and for how long people would be visiting the estate.
The Church of Scotland also claimed the "scale" of the development was not acceptable.
Local resident Tina McLeod described the plans as "utterly hideous".
In December, the Shiekh withdraw plans for one of the two new proposed lodges.
The plans for the nine-bed home and one lodge was approved by the council on Monday (Feb 4).
The sheikh is best known in Britain for being a top racehorse owner, and is credited with turning Dubai into one of the most popular tourist destinations in the world.
He succeeded his brother in 2006 to become the ruler of Dubai and Prime Minister of the United Arab Emirates.
His family reportedly use the Highland estate in summer to escape the 50c Dubai heat.


There are loads of food sites which offer meal plans. There are also lots of supermarkets who deliver.
What if there were a site that helps you to combine your meal planning with your shopping in one location and suggests suitable recipes into the bargain?
FoodZube is a very clever site that allows you to plan your meals according to your taste, budget and the time you have available. (I'm guessing limited if you're a parent).
FoodZube helps you to organise your favourite recipes in one place, create meal plans and do your weekly shopping in one go, either on your PC or your mobile.
If you would like to try the service, FoodZube offers a free one month trial which doesn't require a credit card sign-up.
There is also an option to choose low budget recipes and a very handy 'up to 30 minutes' flag for when there's little time to cook.
You then have the option to add your own recipes, together with the date you intend to cook them so that you create a meal planner.
You can also import recipes from other websites to keep them in one handy location.
Or simply click on "Instant Organizer" which gives you a great list of recipes to choose from based on the requirements you have input on the preferences page.
For someone like me whose repertoire tends to revolve around a combination of pizza, spaghetti bolognese and fish fingers, this is a fabulous tool.
I actually find thinking what to have for tea more stressful than having to cook it.
Once you are happy with the recipes suggested, simply click on "Weekly Shopping" and FoodZube will create a shopping list for you.
It's easy to edit the ingredients you would like and you can also add other non food items to complete your weekly shop in one go.
Click "Buy Online" and you'll be asked to confirm which recipe ingredients you would like FoodZube to add to your shopping list.
Then, very cleverly, FoodZube does your shopping by filling your virtual basket for you to sort through, adding and removing items as necessary.
Currently the supermarket used is Tesco but I imagine there will be a wider choice in the near future.
From this point, all you have to do is check out in the usual way and wait for your shopping to be delivered.
You'll find you've saved quite a bit of time on your shop because you don't have to look for all the ingredients – and you might even save some money since I find when I'm searching it's very easy to add one or two extra treats to the virtual trolley!
You can even start your own food blog on FoodZube's site and connect with other FoodZube bloggers. This is a service I have never seen offered by any other food-based website.
There are over 50 specialised designs from which to choose and dozens of recipe templates to give your blog a professional edge.
The FoodZube team will even set up your blog's Pinterest account and help you launch it!
FoodZube is free for the first month and costs £7.99 per month thereafter, which I think is incredibly good value.
And I have a fabulous giveaway of £45 off your first FoodZube shop at Tesco.com.
Please note that the winner must sign up to FoodZube in order to receive the prize. This is free and you can cancel after one month. No credit card details are required to sign up with FoodZube. The prize will be delivered from Tesco.com which will require an online account with them. Again, there is no charge to do this.
There is no cash prize. The winner will be required to send an email with their home address to FoodZube who will sort out their delivery for them.
Entrants must be over 18 and able to receive deliveries from Tesco.com.
Not in the UK and want your meals delivered? Click here for more info.
Entry is via the Rafflecopter. Terms and conditions apply. UK entrants only. The giveaway ends at 11:59 pm on Saturday 30th January 2016.
Further information about FoodZube is available at www.FoodZube.co.uk, on Twitter or Facebook.


Notes: Clobberboot hail from the Minneapolis area here in Minnesota and do quite a bit of shows, so if you dig their stuff you shouldn't have a hard time seeing them live sometime soon!
We were hoping to get Dave from Clobberboot in the studio some time as a guest host but with all of the scheduling issues lately things haven't been able to work out, so hopefully that'll happen sometime in the near future, or we'll just set up an interview and release that separately.
7. Invidiousus to tour Mexico!
DISCUSSION TOPIC: AM I WRONG?!
Is Tommy Lee a poser?
STUCK in a car, STUCK with a CD featuring Mike Ward from the mighty MORTIFICATOR! Here's his pick to end the show.


I'm absolutely thrilled to be part of the Chinook High School staff! This is my third year at Chinook. This year, I will be teaching English, English as a Second Language, and Knowledge and Employability English. In addition, I help with learning support in The Learning Commons. Prior to joining the team at Chinook, I taught Language Arts 7/8 and Science 7 at G.S. Lakie Middle School. Before teaching at Lakie, I taught E.S.L at Lethbridge College for 6 years. I am passionate about language and learning, and am excited to be heading up the E.L.L program again this year. I am looking forward to a year of learning and growth with all of the students! Please don't hesistate to contact me with any questions or concerns at: taryn.rodzinyak@lethsd.ab.ca.


text:
Bradley-Bourbonnais downs
Conant for 3rd place finish
Boilermakers capture 1st state trophy with 2-0 win
By Mike Garofola
HOFFMAN ESTATES -- Some think it's the soccer match nobody wants to be a part of -- the third place game.
But just a few minutes into this 3A third place contest late on Saturday afternoon, it was clear both Bradley-Bourbonnais and Conant had come to play for more than the big trophy which awaited them afterwards.
Thanks to goals on either side of the half, Bradley-Bourbonnais (25-1-0) won a gutsy 2-0 victory over the Cougars, whose thrilling knockout campaign came to an end for good after its back-to-back losses in the home park of their conference rivals Hoffman Estates.
"It was a good run," began Conant manager, Jason Franco, whose lads secured the school's third state trophy and first since 1999 when a Tony Kees-led side finished fourth.
"It's all good at this point, despite ending the season with this loss."
"The guys were struggling a little bit after our tough loss (4-2) the night before to Barrington. But they arrived at school this morning (upbeat) - excited about a free breakfast, and (then) went out and fought hard, just as I expected, to give a very good side a real battle for 80 minutes."
The coach, alongside seniors, Kevin Sindewald and Nico Scimeca, all agreed the dramatic turnaround of events against Barrington could have done in plenty of clubs, but not this one, according to the four-year veteran, and two-time all-sectional midfielder, Sindewald, who wore the captain's armband with a great deal of pride for Franco and his staff.
"Things changed so quick for us against Barrington. We went up 2-0 in that first half, and everything had the same confident feeling that we had all throughout the playoffs, but that first goal just moments after the half really seemed to 'break' us, and soon we were chasing a great Barrington team," opined Sindewald.
"But I wasn't worried about how we would come out today. All of us knew there could be only two teams in the state that could finish with a win, and that was our goal from the moment we arrived here."
The same could be said for Bradley-Bourbonnais and its fine manager Rohan Robinson, who already knew his lads belonged in this 3A Final Four, even if many from north of Interstate 80 did not.
"We didn't have to prove anything this weekend, we had done it all year long; (but) we also knew our opponents here might not be as sure as us. I feel like we showed we belonged last night in our semifinal with Naperville North," said Robinson, who appears to have the talent and firepower coming back to almost insure another trip here next fall.
One whom Robinson will look to one year from now will be junior, Anthony Markanich, who, keen to increase his astounding goal tally on the season, did just that at 10 minutes. He redirected a superb long-range free-kick from Efrain Davila past a diving Piotr Pyz, the Cougars keeper.
Davila made a name for himself over these two days with his sublime touch, creative and crafty play in the middle of the park and his wonderful ability to strike one long-range free-kick after another. His serves gave both Naperville North and Conant more than their fair share of nervous moments.
With his two helpers in two matches, Davila, a junior, finished with a dazzling 40 assists in the season. Anthony Markanich, and his brother Nicholas, both who played with the Fire Academy last fall, totaled nearly 80 goals between them, with Nicholas leading the way with 44.
"When those two came back from club, it helped put the finishing touches on our roster, which was already strong and experienced, and ready to make a good run at things this season. When you add a couple of players who can attack and score like those two can, it made us that much more dangerous," said a proud Robinson.
The Cougars (16-9-0) bounced back after conceding the early Anthony Markanich strike and begin to get themselves back into the match during the last quarter hour of play -- but only after holding their collective breathe when Nicholas Markanich junior grazed the bar twice.
Conant's Sindewald sent Nuh Ozbag free down the left side, and his cross to the back post nearly found Sota Ono. Then just before the break, the Cougars created consecutive corners from Jason Fillingim, only to have both parried out of the area by the Boilermakers defense.
The halftime interval did not break the rhythm of the Cougars (16-9-0). They weathered a brief offensive storm by their opponents before firing back with a rampant attack for the next half hour that produced several quality chances that the Boilermakers were able to keep out of the back of their net.
Fillingim, who enjoyed a terrific tournament over the two days, whipped a lovely free-kick effort to the back post when Mateusz Pasula nearly caught up to. Later, Ono initiated a Cougars counter, which ended with Scimeca going over the bar with his 30-yard effort.
In the 63rd minute, when Fillingim, Brandon Chlopek and Pasula surged dangerously close to shooting distance, a sharp tackle and clearing effort from Jacob Rodriguez saved the day for the Boilermakers.
"It feels great to come away from here with the third place trophy and for the few seniors on this team (to) leave a legacy. We all hope next year's team can follow in our footsteps with another trip here, and maybe a state title," said Rodriguez, who had a firm grip on the precious state trophy all throughout the post-game interview sessions.
Jason Fritz would added further gloss to the Bradley-Bourbonnais scoreline when his 25-yard blast was redirected off a defender and past Pyz on the way into the area to close out the scoring in the 76th minute.
"I remember (saying) before the playoffs that I felt like we had a good run in us, but I also knew we would have to go through some quality sides in order to do so," began Franco, who just finished up his ninth season in charge of the Conant boys' side.
"I wasn't sure if it would happen after our effort against Willowbrook in our regional opener, but it all came together in that regional final with Wheaton Academy. And ever since, our recipe for success has been followed up one game after another. And to still be playing at this time of the year is something I know all of us will remember for a long time."
Scimeca added: "It will be forever with me.
"These last three weeks have been amazing. To get (here) in the senior year for so many of us is unbelievable. And even this loss today won't make me forget how great this season, and the state playoffs have been."
Bradley-Bourbonnais
GK: Jacob Meents
D- Jack Kramer
D- Tarek Bos-Shadi
D- Brandon Way
D- Nolan Wade
M- Justin Fritz
M- Efrain Davilo
M- Anthony Markanich
M- Neel Holkar
M- Jacob Rodriguez
F- Nicholas Markanich
GK- Piotr Pyz
D- Jason Fillingim
D- Tommy Biernat
D- Evan Smith
D- Michael Rizzo
M- Sota Ono
M- Kevin Sindewald
M- Brendan Chlopek
M- Nico Scimeca
F- Nuh Ozbag
F- Harris Dibek
Man of the Match: Anthony Markanich, MF, Bradley-Bourbonnais
Officials: Tom Schaeffer (center), Tony Martinez (assistant), John Bouc (assistant), Jimmy Stout (fourth).


text:
/*
 * Helper functions for tests that use the PSA Crypto API.
 */
/*
 *  Copyright The Mbed TLS Contributors
 *  SPDX-License-Identifier: Apache-2.0
 *
 *  Licensed under the Apache License, Version 2.0 (the "License"); you may
 *  not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *  http://www.apache.org/licenses/LICENSE-2.0
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 *  WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 */

#ifndef PSA_CRYPTO_HELPERS_H
#define PSA_CRYPTO_HELPERS_H

#include "test/helpers.h"

#if defined(MBEDTLS_PSA_CRYPTO_C)

#include "test/psa_helpers.h"

#include <psa/crypto.h>

#if defined(MBEDTLS_USE_PSA_CRYPTO)
#include "mbedtls/psa_util.h"
#endif

#if defined(MBEDTLS_PSA_CRYPTO_STORAGE_C)

/* Internal function for #TEST_USES_KEY_ID. Return 1 on success, 0 on failure. */
int mbedtls_test_uses_key_id( mbedtls_svc_key_id_t key_id );

/** Destroy persistent keys recorded with #TEST_USES_KEY_ID.
 */
void mbedtls_test_psa_purge_key_storage( void );

/** Purge the in-memory cache of persistent keys recorded with
 * #TEST_USES_KEY_ID.
 *
 * Call this function before calling PSA_DONE() if it's ok for
 * persistent keys to still exist at this point.
 */
void mbedtls_test_psa_purge_key_cache( void );

/** \def TEST_USES_KEY_ID
 *
 * Call this macro in a test function before potentially creating a
 * persistent key. Test functions that use this mechanism must call
 * mbedtls_test_psa_purge_key_storage() in their cleanup code.
 *
 * This macro records a persistent key identifier as potentially used in the
 * current test case. Recorded key identifiers will be cleaned up at the end
 * of the test case, even on failure.
 *
 * This macro has no effect on volatile keys. Therefore, it is safe to call
 * this macro in a test function that creates either volatile or persistent
 * keys depending on the test data.
 *
 * This macro currently has no effect on special identifiers
 * used to store implementation-specific files.
 *
 * Calling this macro multiple times on the same key identifier in the same
 * test case has no effect.
 *
 * This macro can fail the test case if there isn't enough memory to
 * record the key id.
 *
 * \param key_id    The PSA key identifier to record.
 */
#define TEST_USES_KEY_ID( key_id )                      \
    TEST_ASSERT( mbedtls_test_uses_key_id( key_id ) )

#else /* MBEDTLS_PSA_CRYPTO_STORAGE_C */

#define TEST_USES_KEY_ID( key_id ) ( (void) ( key_id ) )
#define mbedtls_test_psa_purge_key_storage( ) ( (void) 0 )
#define mbedtls_test_psa_purge_key_cache( ) ( (void) 0 )

#endif /* MBEDTLS_PSA_CRYPTO_STORAGE_C */

#define PSA_INIT( ) PSA_ASSERT( psa_crypto_init( ) )

/** Check for things that have not been cleaned up properly in the
 * PSA subsystem.
 *
 * \return NULL if nothing has leaked.
 * \return A string literal explaining what has not been cleaned up
 *         if applicable.
 */
const char *mbedtls_test_helper_is_psa_leaking( void );

/** Check that no PSA Crypto key slots are in use.
 *
 * If any slots are in use, mark the current test as failed and jump to
 * the exit label. This is equivalent to
 * `TEST_ASSERT( ! mbedtls_test_helper_is_psa_leaking( ) )`
 * but with a more informative message.
 */
#define ASSERT_PSA_PRISTINE( )                                          \
    do                                                                  \
    {                                                                   \
        if( test_fail_if_psa_leaking( __LINE__, __FILE__ ) )            \
            goto exit;                                                  \
    }                                                                   \
    while( 0 )

/** Shut down the PSA Crypto subsystem and destroy persistent keys.
 * Expect a clean shutdown, with no slots in use.
 *
 * If some key slots are still in use, record the test case as failed,
 * but continue executing. This macro is suitable (and primarily intended)
 * for use in the cleanup section of test functions.
 *
 * \note Persistent keys must be recorded with #TEST_USES_KEY_ID before
 *       creating them.
 */
#define PSA_DONE( )                                                     \
    do                                                                  \
    {                                                                   \
        test_fail_if_psa_leaking( __LINE__, __FILE__ );                 \
        mbedtls_test_psa_purge_key_storage( );                          \
        mbedtls_psa_crypto_free( );                                     \
    }                                                                   \
    while( 0 )

/** Shut down the PSA Crypto subsystem, allowing persistent keys to survive.
 * Expect a clean shutdown, with no slots in use.
 *
 * If some key slots are still in use, record the test case as failed and
 * jump to the `exit` label.
 */
#define PSA_SESSION_DONE( )                                             \
    do                                                                  \
    {                                                                   \
        mbedtls_test_psa_purge_key_cache( );                            \
        ASSERT_PSA_PRISTINE( );                                         \
        mbedtls_psa_crypto_free( );                                     \
    }                                                                   \
    while( 0 )

#if defined(RECORD_PSA_STATUS_COVERAGE_LOG)
psa_status_t mbedtls_test_record_status( psa_status_t status,
                                         const char *func,
                                         const char *file, int line,
                                         const char *expr );

/** Return value logging wrapper macro.
 *
 * Evaluate \p expr. Write a line recording its value to the log file
 * #STATUS_LOG_FILE_NAME and return the value. The line is a colon-separated
 * list of fields:
 * ```
 * value of expr:string:__FILE__:__LINE__:expr
 * ```
 *
 * The test code does not call this macro explicitly because that would
 * be very invasive. Instead, we instrument the source code by defining
 * a bunch of wrapper macros like
 * ```
 * #define psa_crypto_init() RECORD_STATUS("psa_crypto_init", psa_crypto_init())
 * ```
 * These macro definitions must be present in `instrument_record_status.h`
 * when building the test suites.
 *
 * \param string    A string, normally a function name.
 * \param expr      An expression to evaluate, normally a call of the function
 *                  whose name is in \p string. This expression must return
 *                  a value of type #psa_status_t.
 * \return          The value of \p expr.
 */
#define RECORD_STATUS( string, expr )                                   \
    mbedtls_test_record_status( ( expr ), string, __FILE__, __LINE__, #expr )

#include "instrument_record_status.h"

#endif /* defined(RECORD_PSA_STATUS_COVERAGE_LOG) */

/** Return extended key usage policies.
 *
 * Do a key policy permission extension on key usage policies always involves
 * permissions of other usage policies
 * (like PSA_KEY_USAGE_SIGN_HASH involves PSA_KEY_USAGE_SIGN_MESSGAE).
 */
psa_key_usage_t mbedtls_test_update_key_usage_flags( psa_key_usage_t usage_flags );

/** Skip a test case if the given key is a 192 bits AES key and the AES
 *  implementation is at least partially provided by an accelerator or
 *  alternative implementation.
 *
 *  Call this macro in a test case when a cryptographic operation that may
 *  involve an AES operation returns a #PSA_ERROR_NOT_SUPPORTED error code.
 *  The macro call will skip and not fail the test case in case the operation
 *  involves a 192 bits AES key and the AES implementation is at least
 *  partially provided by an accelerator or alternative implementation.
 *
 *  Hardware AES implementations not supporting 192 bits keys commonly exist.
 *  Consequently, PSA test cases aim at not failing when an AES operation with
 *  a 192 bits key performed by an alternative AES implementation returns
 *  with the #PSA_ERROR_NOT_SUPPORTED error code. The purpose of this macro
 *  is to facilitate this and make the test case code more readable.
 *
 *  \param key_type  Key type
 *  \param key_bits  Key length in number of bits.
 */
#if defined(MBEDTLS_AES_ALT) || \
    defined(MBEDTLS_AES_SETKEY_ENC_ALT) || \
    defined(MBEDTLS_PSA_ACCEL_KEY_TYPE_AES)
#define MBEDTLS_TEST_HAVE_ALT_AES 1
#else
#define MBEDTLS_TEST_HAVE_ALT_AES 0
#endif

#define MBEDTLS_TEST_PSA_SKIP_IF_ALT_AES_192( key_type, key_bits )        \
    do                                                                    \
    {                                                                     \
        if( ( MBEDTLS_TEST_HAVE_ALT_AES ) &&                              \
            ( ( key_type ) == PSA_KEY_TYPE_AES ) &&                       \
            ( key_bits == 192 ) )                                         \
        {                                                                 \
            mbedtls_test_skip( "AES-192 not supported", __LINE__, __FILE__ );     \
            goto exit;                                                    \
        }                                                                 \
    }                                                                     \
    while( 0 )

/** Skip a test case if a GCM operation with a nonce length different from
 *  12 bytes fails and was performed by an accelerator or alternative
 *  implementation.
 *
 *  Call this macro in a test case when an AEAD cryptography operation that
 *  may involve the GCM mode returns with a #PSA_ERROR_NOT_SUPPORTED error
 *  code. The macro call will skip and not fail the test case in case the
 *  operation involves the GCM mode, a nonce with a length different from
 *  12 bytes and the GCM mode implementation is an alternative one.
 *
 *  Hardware GCM implementations not supporting nonce lengths different from
 *  12 bytes commonly exist, as supporting a non-12-byte nonce requires
 *  additional computations involving the GHASH function.
 *  Consequently, PSA test cases aim at not failing when an AEAD operation in
 *  GCM mode with a nonce length different from 12 bytes is performed by an
 *  alternative GCM implementation and returns with a #PSA_ERROR_NOT_SUPPORTED
 *  error code. The purpose of this macro is to facilitate this check and make
 *  the test case code more readable.
 *
 *  \param  alg             The AEAD algorithm.
 *  \param  nonce_length    The nonce length in number of bytes.
 */
#if defined(MBEDTLS_GCM_ALT) || \
    defined(MBEDTLS_PSA_ACCEL_ALG_GCM)
#define MBEDTLS_TEST_HAVE_ALT_GCM  1
#else
#define MBEDTLS_TEST_HAVE_ALT_GCM  0
#endif

#define MBEDTLS_TEST_PSA_SKIP_IF_ALT_GCM_NOT_12BYTES_NONCE( alg,           \
                                                            nonce_length ) \
    do                                                                     \
    {                                                                      \
        if( ( MBEDTLS_TEST_HAVE_ALT_GCM ) &&                               \
            ( PSA_ALG_AEAD_WITH_SHORTENED_TAG( ( alg ) , 0 ) ==            \
              PSA_ALG_AEAD_WITH_SHORTENED_TAG( PSA_ALG_GCM, 0 ) ) &&       \
            ( ( nonce_length ) != 12 ) )                                   \
        {                                                                  \
            mbedtls_test_skip( "GCM with non-12-byte IV is not supported", __LINE__, __FILE__ ); \
            goto exit;                                                     \
        }                                                                  \
    }                                                                      \
    while( 0 )

#if !defined(MBEDTLS_MD_C)
#define PSA_INIT_IF_NO_MD( ) PSA_INIT( )
#define PSA_DONE_IF_NO_MD( ) PSA_DONE( )
#endif
#endif /* MBEDTLS_PSA_CRYPTO_C */

#if defined(MBEDTLS_MD_C)
#define PSA_INIT_IF_NO_MD( ) ( (void) 0 )
#define PSA_DONE_IF_NO_MD( ) ( (void) 0 )
#endif
/** \def USE_PSA_INIT
 *
 * Call this macro to initialize the PSA subsystem if #MBEDTLS_USE_PSA_CRYPTO
 * or #MBEDTLS_SSL_PROTO_TLS1_3 (In contrast to TLS 1.2 implementation, the
 * TLS 1.3 one uses PSA independently of the definition of
 * #MBEDTLS_USE_PSA_CRYPTO) is enabled and do nothing otherwise. If the
 * initialization fails, mark the test case as failed and jump to the \p exit
 * label.
 */
/** \def USE_PSA_DONE
 *
 * Call this macro at the end of a test case if you called #USE_PSA_INIT.
 * This is like #PSA_DONE, except that it does nothing if
 * #MBEDTLS_USE_PSA_CRYPTO is disabled.
 */
#if defined(MBEDTLS_USE_PSA_CRYPTO) || defined(MBEDTLS_SSL_PROTO_TLS1_3)
#define USE_PSA_INIT( ) PSA_INIT( )
#define USE_PSA_DONE( ) PSA_DONE( )
#else /* MBEDTLS_USE_PSA_CRYPTO || MBEDTLS_SSL_PROTO_TLS1_3 */
/* Define empty macros so that we can use them in the preamble and teardown
 * of every test function that uses PSA conditionally based on
 * MBEDTLS_USE_PSA_CRYPTO. */
#define USE_PSA_INIT( ) ( (void) 0 )
#define USE_PSA_DONE( ) ( (void) 0 )
#endif /* !MBEDTLS_USE_PSA_CRYPTO && !MBEDTLS_SSL_PROTO_TLS1_3 */

#endif /* PSA_CRYPTO_HELPERS_H */

redpajama_set_name: RedPajamaGithub


Last year, 27,385 small and mid-sized U.S. businesses took advantage of OSHA's free and confidential On-site Consultation Program to remove workplace hazards and better protect their workers. The program primarily benefits small and mid-sized businesses – 57% of those helped last year had fewer than 26 employees. Priority is given to high-hazard industries, with more than half of all visits going to construction or manufacturing sites. In 2016, consultants identified and helped employers eliminate more than 140,000 total hazards, protecting an estimated 3.3 million workers from possible injury, illness or death.
OSHA reminds employers of their obligation to post a copy of OSHA's Form 300A, which summarizes job-related injuries and illnesses logged during 2016. The summary must be displayed in a common area where notices to employees are usually posted each year between Feb. 1 and April 30. Businesses with 10 or fewer employees and those in certain low-hazard industries are exempt from OSHA recordkeeping and posting requirements. Visit OSHA's Recordkeeping Rule webpage for more information on recordkeeping requirements.
OSHA has revised the requirements for authorized trainers in its Outreach Training Program, updating and clarifying student contact hours, replacement card procedures, the Trainer Code of Conduct, and more. One new elective was also added to the selection of classes. The outreach program trains workers and supervisors to recognize and prevent safety and health hazards on the job, and to understand worker rights and employer responsibilities.
OSHA does not conduct the classes but establishes requirements and authorizes trainers. Classes—in general industry, construction, maritime, and disaster recovery—are voluntary and do not satisfy any OSHA training requirements. New requirements and procedures are effective April 1. For more information, see the Outreach Training Program webpage.
OSHA has formed a partnership with the University of Alabama SafeState On-site Consultation Program and general contractor Brasfield & Gorrie LLC to protect approximately 200 workers during the construction of an office building in Birmingham for one of the nation's largest healthcare services providers. The partnership's goals during the year-long project include reducing injuries and illnesses, and increasing both safety and health training and the number employers with safety and health programs. Through the Strategic Partnership Program, OSHA works with employers, workers, professional or trade associations, labor organizations, and other interested stakeholders to eliminate serious hazards and enhance workplace safety and health practices.
The Original Mattress Factory in Raleigh, N.C., contacted OSHA's On-site Consultation Program for help protecting the safety of its employees. During the initial visit, the consultant identified electrical, training, and labeling issues that could result in worker injuries. The company immediately corrected the hazards and implemented training and safety programs. An employee safety committee conducted weekly housekeeping and safety walks and monthly safety inspections. As a result of these and other efforts, the company was accepted into OSHA's Safety and Health Achievement Recognition Program, which recognizes small business employers who worked with OSHA consultants to develop and maintain exemplary safety and health programs. For more information, read the success story.
Big Tex Trailer Manufacturing Inc., a truck bed fabricator in Kingston, Okla., was cited for violations after an OSHA inspection found workers exposed to safety and health hazards. Among the violations cited, the company failed to provide workers with welding protections and allowed workers to operate hydraulic press brakes without machine guards in place. Read the news release for more information.
An employee of Bay Fabrication, a muffler component manufacturer, suffered a serious injury when a molding machine crushed his hand. Another worker at the Green Bay, Wis., facility suffered a partial amputation when a molding machine caught his finger in an unguarded pinch point. OSHA inspectors found that the company failed to guard operating parts on machines, and improperly installed safety guards on machines. For more information, read the news release.
OSHA has found that a casting and foundry facility in Coshocton, Ohio, continued to expose workers to machine and fall hazards. For more information, see the news release.
A safety complaint filed against Landtek Group Inc., a New York-based general contractor, resulted in an OSHA inspection at Verona High School in Verona, N.J., where the company was constructing new tennis courts and turf fields. Inspectors found Landtek exposed workers to cave-in hazards by allowing them to work in an unprotected, 10-foot-deep excavation. Read the news release for more information.
Please visit the enforcement news releases page for more on OSHA enforcement activity. For more examples of state and federal enforcement cases, visit OSHA's online enforcement penalties map.
OSHA found that Amtrak retaliated against a supervisory special agent in Amtrak's inspector general's office. The employee raised concerns about railroad safety, fraud and abuse involving an Amtrak contractor, and also supported a fellow agent's safety concerns during an internal investigation. The terminated agent filed a whistleblower complaint, and OSHA determined that Amtrak violated worker protections under the Federal Railroad Safety Act. OSHA ordered Amtrak to take numerous corrective actions, including reinstating the worker; paying him back wages, interest, damages, and attorney's costs; and posting a notice to all railroad employees about their FRSA rights. For details, read the news release.
QuickTakes is emailed free twice monthly to more than 165,000 subscribers. You can receive it faster and easier by subscribing to the RSS feed that delivers almost instant information. Visit OSHA's RSS Feeds Web page to subscribe.


During the holiday season, many people do not want their thoughts to stray to anything unpleasant. Although celebrating may be the primary goal come the holiday season, individuals and families also should devote some attention to keeping the holidays safe.
Just because it's Chanukah, Christmas or New Year's doesn't mean safety and security should not remain a top priority. The following guidelines can help families enjoy a holiday season that is healthy, safe and secure.
· Exercise caution when decorating. Decorations can be beautiful, but they also can be hazardous if not used properly. Overloading outlets is commonplace during the holiday season, but that can lead to home fires. Exercise caution around candles and other open flames. Pets may become tangled in wires or knock over glass ornaments. Keep delicate decorations away from harm.
· Wash hands to prevent germs. As the cold weather arrives, people may spend greater periods of time indoors or in close proximity to other people. The holidays also are about greeting friends and relatives, and embracing or kissing. Do not ruin the festivities by spreading germs. The Centers for Disease Control and Prevention says the holiday season is also flu season. The CDC recommends celebrants wash their hands with soap and clean running water for at least 20 seconds to remove germs.
· Keep track of curious children. Children are enamored with the sights and sounds of the holidays and may be more curious than ever during this time of year. Things adults take for granted can be hazards to youngsters. The needles of holiday trees can cause painful cuts in the mouth and throat of a child who swallows them. Snow sprays may be harmful if the aerosol propellants are used improperly. Peanuts and popcorn used for decorating can pose potential choking hazards to young children.
· Be a smart host or hostess. Alcoholic beverages and holiday revelry sometimes go hand in hand. Responsible party hosts know when a guest has had too much and will arrange safe transportation home. Because kids imitate adults, many may drink the beverages they see adults drinking. Alcohol poisoning is a real risk for children, so pay attention to youngsters no matter how young they may be.
· Protect the home. Deter home burglars when traveling for the holidays by having neighbors keep a watchful eye on your house. Use motion-sensor lights, keep up the appearance that someone is home by having mail collected, and do not advertise any travel plans on social media. Investment in a home security system if need be.
Ensure a happier holiday season by making safety and health a priority.
This entry was posted in Metro Content, Uncategorized and tagged Community, Holiday, metro, safety. Bookmark the permalink.


text:
Scope of actions
Examples of interventions
Anywhere around the world
Countries we've worked in
Leaders & Leadership Team Performance
• "A new chairman on-boarded to lead a transnational operating unit of a major global organisation. He was coming from a different part of the world, was not familiar with the local business, did not know the organisation, nor the leadership team. After his first three months, he realized he had a dysfunctional leadership team, which was both disengaged and misaligned."
Find out our solution
Link'maker helped turn around the situation by firstly having one-on-one conversations with each leadership team member to understand their frustrations, fears, wishes and expectations. We then facilitated an off-site with the full team whereby we helped not only to assimilate the new leader's style, but also to build common ground for a constructive on-going dialogue and shared goals.
In parallel, we provided a track of 6 months coaching and advisory with two disctinctive consultants to the team's leader so that he could lead the team and organisation in the most impactful manner.
• "Following a drastic change in the operating model of a global multinational, business units were created and leadership teams were put in place."
The biggest BU had a team composed of people from 6 different nationalities, located on 4 continents. Link'maker has helped build the team both from a soft and a hard aspectis aligning them on a common ambition and common ways of working, perfectly fitting their virtual and cross-cultural setting.
Organisation Design
• "A new corporate function of a global pharmaceutical company had been created from scratch with a clear mandate provided by the CEO."
Link'maker has worked with the leading team of the center of expertise to define their manifesto (i.e. their mission, vision, goals, roles, processes, interfaces, etc.) and has accompanied them in deploying their goals through a solid change management plan.
• "A global company deployed a multi-country model around the world. Two years later, the performance was not at the expected level."
Link'maker worked with the leadership teams of each of the MCO helping them identifying the root causes of their inefficiencies and defining a clear action plan allowing them to clarify roles, processes, metrics, routines, interfaces, etc. Our intervention lasted 2 years, covered the entire world (21 MCO) and we are proud to say that Global Employee Surveys and MCO performance indicators had significantly improved after this initiative.
• "A global company that had been always operating on a model of 'think local, act local' decided to align their key processes for their back and front office, supply chain, logistics, and deploy SAP to support those."
While specialized consultants worked on the design and deployment of the 'new processes and tools', Link'maker took charge of the change process. It involved building a compelling story together with the executive board and managing each stakeholder in the most efficient way.
• "Through the acquisition of a key competitor, the organisation we worked with went from 17.000 to 30.000 employees worldwide."
Link'maker supported one of their biggest global business unit in building a new ambition, a 5-year strategic roadmap, and a global matrix organisation to execute at best the chosen strategic direction. Each year and for the fifth year in a row, we've been facilitating their annual convention which involve the TOP 100 of the organisation.
Strategy Development and Deployment
• "A global FMCG company recruited a new CEO after the position had been left vacant for 6 months. This company also needed a complete refresh of its strategy."
Link'maker has been accompanying the newly appointed CEO and his leadership team in building a clear ambition and strategic path to 2020 with a clear strategic impulse to meet a 3-year plan. Once the team fully aligned behind this plan, Link'maker worked with HR and communication teams to engage the entire workforce behind this new agenda.
HR Capabilities Building
• "The chief HR Officer of a European FMCG company was involved in the last stage of acquiring several new businesses; He knew this was going to result in massive change in their organisational set up and his HR function was not experienced at such practice."
He appointed Link'maker to train all HR directors in Organisational Design method and technics. In order to keep this training as 'hands-on' as possible, thus avoiding long tutorials with lots of models, we've worked with each HRBP in applying OD techniques on a real individual case. This way the HR directors were able to contribute directly to the improvement of organisational effectiveness.
• "Following a global survey and benchmarking exercise done by one of the big 4 consulting firm, the HR function of a multinational had clearly to improve its impact and influencing skills."
Link'maker has provided training and coaching to their top 200 HR professionals around the world through an action learning program which allow each participant to work on a real case hence impacting immediately their respective business. Topics covered were internal consulting skills, change management, effective communication coupled with self awareness and influencing skills.
Nick Kitchen
Senior VP HR
Unilever Food Solutions
Corinne has added great value to our organisation. She has built a shared understanding of the importance of having an integrated, aligned and prioritised agenda and then translating
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