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

Since the 1970s, sperm counts have halved in Western nations \cite{Levine2017TemporalTI}, and Assisted Reproductive Technology (ART) has been used increasingly to account for 4 million American births annually \cite{Sunderam2019AssistedRT}.
Morphology analysis, a central step in infertility diagnosis and ART planning, involves classifying sperm heads into qualitative shape categories \cite{WHOManual}, in which abnormality predicts poor genetic fitness.
This process is both time-consuming and subjective since it requires manual deliberation on individual sperm in microscopy images.
These challenges along with high annotation costs result in small public datasets (e.g., HuSHeM \cite{Shaker2018Hushem} has 216 131$\times$131 crops; SCIAN \cite{Chang2017GoldstandardFC} has 1132 35$\times$35 crops) with noisy class labels.

\begin{figure}[htb]
    \begin{minipage}[b]{1.0\linewidth}

      \centering
       \centerline{\includegraphics{assets/fig1_final.pdf}}

    \end{minipage}
    %

    \vspace{-7mm}
    \caption{Stained human sperm head samples and CAM \cite{Selvaraju2019GradCAMVE} comparisons of two morphology datasets. Row 1: 64$\times$64 center-cropped, right-aligned test images. Rows 2 and 3: CAMs with supervised training and training with our framework, respectively. The 5 indices are for the classes of normal, tapered, pyriform, amorphous, and small, respectively.}
    \label{fig:1}
\vspace{-3mm}
\end{figure}

Two main approaches were used to address small datasets in sperm morphology analysis: 1) manually design shape features; 2) use transfer learning with neural networks (NNs).
Previous work with the first approach applied traditional machine learning (ML) methods without relying on big data.
Notably, Chang et al.~applied a cascade ensemble of support vector machines (CE-SVM \cite{Chang2017AutomaticCO}) to classify sperm heads using various shape descriptors (e.g., area, eccentricity, Zernike moments); Shaker et al.~\cite{Shaker2017ADL} presented a dictionary learning method (APDL) based on prototype matching of image patches. These methods yielded reasonable results, but the methods with approach two \cite{Riordon2019DeepLF,Iqbal2020DeepLM,Liu2021AutomaticMA} used ImageNet pretrained models and generally attained superior performance.

\begin{figure*}[htb]
    \begin{minipage}[b]{1.0\linewidth}
      \centering
      \centerline{\includegraphics[width=16cm]{assets/fig2_final.pdf}}

    \end{minipage}
    %
    \vspace{-6mm}
    \caption{An overview of our three-stage framework.
    EMA is for exponential moving average.
    Dashed-yellow boxes represent data sources containing HPM-generated masks while the solid-orange box has masks predicted by the teacher model in AID.}

    \label{fig:2}
    \vspace{-2mm}
\end{figure*}

Despite performance gains from NNs, effective and robust features are difficult to learn with noisy labels and small data (both
in terms of crop size and label-set cardinality).
In Fig.~\ref{fig:1}, we notice hints of brittleness where salience is concentrated on limited discriminative foreground regions and some unrelated background patches.
Regularized spatial pretraining is shown to help steer salience towards the foreground (Fig.~\ref{fig:1} row 3), while simultaneously encoding useful shape, texture, and general sperm features.
Given our ability to cheaply generate reliable, information-dense foreground masks with useful priors, we elect to pursue a transfer learning approach.

To the best of our knowledge, no other work has used pseudo-masks with spatial prediction tasks to facilitate classification.
The converse was introduced \cite{Zhang2019ASO}, where hints from classification were used to improve localization.
Relevant to the spatial pretraining in this work, a student-teacher setup \cite{Li2021TransformationConsistentSM} was proposed to regularize segmentation predictions in a semi-supervised setting.
We use a similar setup except for unsupervised pretraining with no pure ground truths available.
Additionally, rotation prediction \cite{Gidaris2018UnsupervisedRL} was used as an unsupervised pretext task for natural scene images.
We use this method for a similar purpose, except we are operating under stricter constraints since the upright orientation assumption does not hold in our situation.
As for sperm mask generation, other traditional vision methods \cite{Shaker2016AutomaticDA,Chang2014GoldstandardAI} have been proposed, but they either did not deal with gray images or were not calibrated to handle low quality, noisy images (e.g., in SCIAN).

In this work, we leverage anatomical and image priors of sperm images to improve our model's salience behavior and
performance by proposing a new three-stage deep learning framework. (1) Reliable sperm head segmentations are generated in a hierarchical manner using the size, shape, contrast, location, and data priors.
(2) The Anatomical Information Distillation (AID) uses the masks to distill useful foreground information
via two unsupervised spatial prediction tasks.
(3) The soft-tuning stage
regularizes noisy class labels by implementing a weighted soft loss and using the refined AID-generated masks for background denoising.

Our main contributions are as follows.
\begin{itemize}
    \item We propose a new three-stage sperm morphology classification framework that uses generated pseudo-masks to guide the learning of useful sperm features via two unsupervised spatial prediction tasks. The learned parameters and refined mask predictions are transferred to the soft-tuning stage where label smoothing and dynamic background subtraction help alleviate label and image noise.

    \item We introduce a hierarchical sperm head segmentation method that leverages anatomical and image priors to reliably filter imaging artifacts, remove sperm tails, and discard the sperm mid-pieces.

    \item We evaluate our new framework on two public

    sperm morphology datasets (SCIAN \cite{Chang2017GoldstandardFC} and HuSHeM \cite{Shaker2018Hushem}) and achieve state-of-the-art performances on both of them.
\end{itemize}

\vspace{-4mm}
\section{Method}
\vspace{-2mm}
\label{sec:methods}

In this section, we detail our methodology on each of the three main framework components (shown in Fig.~\ref{fig:2}).

\subsection{Hierarchical pseudo-mask generation (HPM)}
\vspace{-2mm}
\begin{figure}[htb]
    \begin{minipage}[b]{0.95\linewidth}
      \centering
      \centerline{\includegraphics[width=6.5cm]{assets/fig3_final.pdf}}

    \end{minipage}
    %
    \caption{Qualitative pseudo-mask results: Comparing

    our methods with traditional mask generation methods. The \emph{HPM} column shows results from Hierarchical Pseudo-Masks, where $h=2$ is used for SCIAN (gray is the most confident layer) and $h=1$ for cleaner HuSHeM data. \emph{AID} shows masks predicted by Anatomical Information Distillation.}
    \label{fig:3}
\end{figure}

HPM has two key objectives: create accurate sperm head masks, and use the computed head orientation to align all sperm to point right (see \cite{Liu2021AutomaticMA} for the importance of this step).
A sperm cell consists of three main components: 1) the head (or foreground), 2) a mid-section that often has similar texture and intensity as the head, and 3) a tail which is a thin organelle connecting to the mid-section.
The most challenging aspect of the head segmentation lies in the separation of the mid-section from the head (in rows 1 \& 3 of Fig.~\ref{fig:3}, one can see over-segmentation of the mid-section by thresholding, due to its connectivity and similar appearance).
To produce reliable masks, we leverage both anatomical priors (i.e., head size, tapered nuclear shape, darker nuclear intensity, rough elliptical outline) and image priors (i.e., relatively centered foreground, one instance per image, and contrast from dying agents).
We present a high-level flow of our proposed method below.

    \item (1) For each image $I$, a coarse foreground mask, $\tilde{m}_1$, is created by applying non-local means denoising, followed by thresholding. $\tilde{m}_1$ is used for intensity-range rescaling of the foreground and whitening of the background, to produce $I_1$.
    \item (2) Extract the darkest segment of $I_1$ with k-means clustering ($k=3$) to obtain a tentative nuclear mask. The tail and mid-section pieces are removed by distance thresholding to yield a reliable nuclear mask, $\tilde{n}_1$. An ellipse is fitted to $\tilde{n}_1$ and the thicker end of $\tilde{n}_1$ is chosen as the head direction. Finally, $\tilde{m}_1$, $I_1$, and $\tilde{n}_1$ are rotated to face right, outputting $\tilde{m}_2$, $I_2$, and $\tilde{n}_2$.
    \item (3) Fuse $\tilde{n}_2$ with a reliable acrosome mask (the top part of the head). Since we already computed the orientation, we simply split $\tilde{m}_2$ in half and use the right part as a coarse acrosome mask, $\tilde{m}^r_2$. We erode $\tilde{m}^r_2$ until it is within a threshold of $\tilde{n}_2$'s height, and fuse them to produce $\tilde{M}_0$, where $0$ indicates the lowest hierarchy index or the most confident mask.
    \item (4) Obtain a final hierarchy of concentric masks, $\tilde{M}$. For a given $h\in\mathbb{N}$, we obtain $\tilde{M}_i$ for $i \in \{0, ..., h-1\}$, a mask layer, by dilating $\tilde{M}_0$ $i$ times with all extents bounded by $\tilde{m}_1$.

\vspace{-2mm}
\subsection{Anatomical information distillation (AID)}

We utilize a student-teacher setup \cite{Tarvainen2017MeanTA} with two fully convolution networks in which the parameters of the teacher are updated as an exponential moving average (EMA) of the student's parameters after each iteration.
The student model consists of a backbone encoder $f_s$ with an upsampling decoder $g_s$.
The teacher network shares an identical architecture, denoted as $f_t$ and $g_t$ (see AID in Fig.~\ref{fig:2}).
$\mathcal{T}_1$ and $\mathcal{T}_2$ are stochastic image transformation functions that use the same set of augmentations.
We first perform fine distillation with segmentation and consistency regularization, and then
conduct coarse distillation with rotation prediction.

\vspace{0.05in}
\noindent
\textbf{Fine distillation.}
For an image $I$ in a dataset $\mathcal{D}$, the predicted segmentation masks from the student and teacher networks are $\hat{y}_s = g_s(f_s(\mathcal{T}_1(I)))$ and $\hat{y}_t = g_t(f_t(\mathcal{T}_2(I)))$, respectively.
Partial cross entropy is used for the segmentation loss where $\tilde{M}_0$ is selected as the foreground ground truth, $\lnot\tilde{M}$ is the binary background ground truth ($\lnot$ indicates the pixel-wise negation operator that turns nonzero foreground pixels into background zeros and vice-versa), and all other non-confident pixels, $\tilde{M}_{n}$ where $n>0$, are ignored during pretraining.
Note that ignored pixels only exist if $h>1$ hierarchies are generated since $h=1$ yields one foreground mask.

Furthermore, to prevent overfitting, we add a consistency constraint where two transformed versions of the same image are encouraged to have identical predictions after mask alignment.
The two fine losses are defined as:
\begin{equation}
    \mathcal{L}_{seg} = \frac{-1}{|I|} \sum_{i \in I} (\lnot\tilde{M}_{n}^i) (\lnot\tilde{M}^i \cdot \log(1 - \hat{y}_{s}^i) + \tilde{M}_{0}^i \cdot \log(\hat{y}_{s}^i)) \\
\end{equation}
\begin{equation}
    \mathcal{L}_{con} = ||\mathcal{T}_1^{-1}(\hat{y}_{s}) - \mathcal{T}_2^{-1}(\hat{y}_{t})||^2_2,
\end{equation}
where $i$ is a pixel in image $I$.
The aggregate fine distillation loss is $\alpha \mathcal{L}_{seg} + \beta \mathcal{L}_{con}$, with balancing parameters $\alpha$ and $\beta$.
$\hat{y}_t$ masks are also saved for use in soft-tuning.

\newcommand{\ra}[1]{\renewcommand{\arraystretch}{#1}}
\begin{table*}[htb]
    \centering
    \ra{1.}
    \begin{tabular*}{0.8\linewidth}{@{}rrrrrrrrrrr@{}}
        \toprule
        \multirow{2}{*}[-0.15em] {\textbf{Method}} &
        \multicolumn{4}{c}{SCIAN Dataset} & {} & \multicolumn{4}{c}{HuSHeM Dataset} \\
        \addlinespace[0pt]
        \cmidrule(lr){2-5} \cmidrule(lr){7-10}

        {} & Accuracy & Recall & Precision & F_1  &&
        Accuracy & Recall & Precision & F_1 \\
        \midrule
        CE-SVM \cite{Chang2017AutomaticCO} & 44 & 58 & - & -  &&
                        78.5 & 78.5 & 80.5 & 78.9 \\
        APDL \cite{Shaker2017ADL}   & 49 & 62 & - & - &&
                                      92.2 & 92.3 & 93.5 &  92.9 \\
        FT-VGG \cite{Riordon2019DeepLF} & 49 & 62 & 47 & 53 &&
                                      94.0 & 94.1 & 94.7 & 94.1\\
        MC-HSH \cite{Iqbal2020DeepLM} & 63 & 68 & 56 & 61 &&
                                      95.7 & 95.5 & 96.1 & 95.5 \\
        TL \cite{Liu2021AutomaticMA} & - & 62 & - & - &&
                                      96.0 & 96.1 & 96.4 & 96.0 \\
        Ours                         & \textbf{65.9} & \textbf{68.9} & \textbf{58.7} & \textbf{63.2} &&
                                      \textbf{96.5} & \textbf{96.6} & \textbf{96.8} & \textbf{96.5} \\
        \bottomrule
    \end{tabular*}
\caption{Performance comparison of our proposed framework with state-of-the-art classification methods on the
SCIAN (partial agreement subset) and HuSHeM datasets. Some SCIAN numbers lack decimals since they are taken directly from other works.}
\label{tab:1}
\vspace*{-2mm}
\end{table*}

\vspace{0.05in}
\noindent
\textbf{Coarse distillation.}
For better adaptation to the downstream classification task, we use the rotation prediction task with four angles, $\{0, 90, 180, 270\}$, from a right-facing orientation to transition the model from a pixel-wise focus toward learning more abstract features of different anatomical parts and their respective spatial relations.
The loss is defined as:
\vspace{-1mm}
\begin{equation}
    \vspace{-1mm}
    \mathcal{L}_{rot} =  - y_{rot} \cdot \log(f_s(\mathcal{T}_1(I))_{rot}).
\end{equation}
where $y_{rot}$ and $f_s(\mathcal{T}_1(I))_{rot}$ are the target rotation index and predicted rotation index, respectively.

\vspace{-2mm}
\subsection{Soft-tuning}

We transfer the trained teacher encoder, $f_t$, for morphology classification tuning.
We implement three key components for the purposes of regularization, noise reduction, and prediction stabilization.
First, we use AID-predicted segmentations to mask out image backgrounds following a curriculum learning approach where we apply more aggressive masking as training progresses.
This is implemented by linearly decreasing the number of applied foreground mask dilations from 15 (entire image is foreground) to 0 (only sperm head is inputted).
Second, we use a soft cross entropy loss that handles label uncertainty:
\vspace{-1mm}
\begin{equation}
    \vspace{-1mm}
    \mathcal{L}_{c} =  \lambda \cdot \log \ \hat{y}_{c1} + (1 - \lambda) \cdot \log  \ \hat{y}_{c2},
\end{equation}
where $c1$ \& $c2$ represent the majority \& minority expert votes for an image, respectively (in the case of consensus, the loss is equivalent to regular cross entropy), and
$\hat{y}_{c1}$ \& $\hat{y}_{c2}$ are the model predictions for each corresponding class.
Finally, we introduce shape-invariant test-time augmentations (i.e., vertical flipping and rotation) to lower prediction variability.
We also make training augmentations, $\mathcal{T}_c$, milder, which empirically improves performance and stabilizes training.

\vspace{-1mm}
\section{Experiments}
\label{sec:experiments}
\vspace{-2mm}
\subsection{Datasets}
We use the only two publicly available stained sperm morphology datasets for evaluation.
SCIAN \cite{Chang2017GoldstandardFC} (from the \textbf{Sc}ientific \textbf{I}mage \textbf{An}alysis Lab, University of Chile) contains 1132 35$\times$35 gray crops with five morphology classes (100 normal, 228 tapered, 76 pyriform, 656 amorphous, and 72 small).
The images were captured at 63X zoom after Hematoxylin \& Eosin staining.
Three experts assigned classes for each crop. The partial agreement subset we use contains only images with at least 2 out of 3 experts agreeing on a label.

HuSHeM \cite{Shaker2018Hushem} (\textbf{Hu}man \textbf{S}perm \textbf{He}ad \textbf{M}orphology) has 216 131$\times$131 RGB crops and four classes (54 normal, 53 tapered, 57 pyriform, 52 amorphous) with expert consensus.
Images were obtained at 100X magnification after Diff-Quik staining.
Fig.~\ref{fig:1} shows pre-processed HuSHeM and SCIAN samples.

\newcolumntype{R}[2]{%
    >{\adjustbox{angle=#1,lap=\width-(#2)}\bgroup}%
    l%
    <{\egroup}%
}
\newcommand*\rot{\multicolumn{1}{R{45}{1em}}
\begin{table}[htb]
\begin{tabular}{rrrrrrrrr}
\rot{ImNet Pre.} &
\rot{Head Pred.} &
\rot{Rot. Pred.}  &
\rot{TTA} &
\rot{Soft Lab.} &
\rot{Dyn. Mask} & &
Accuracy &
F_1 \\ \hline
\cmark & 		         & 		     & 		 &  		& 		  & 		    &  \textbf{51.1}  & \textbf{53.7} \\
               & \cmark &  &  & &  &                                             & 50.9 & 53.6   \\
               &                & \cmark & & & &                                 & 48.3 & 52.4  \\  \hline
 \cmark & \cmark & \cmark &                &  & &                  & 58.6 & 57.3 \\
 \cmark & \cmark & \cmark & \cmark &  & &                  & 59.1 & 58.7 \\
 \cmark & \cmark & \cmark & \cmark & \cmark &  &   & 63.3 & 61.5\\
 \cmark & \cmark & \cmark & \cmark & \cmark & \cmark & & \textbf{65.9} & \textbf{63.2}\\
\end{tabular}
\caption{Ablation study results
on all five SCIAN folds (3 runs per fold). TTA stands for test-time augmentation.}
\label{tab:2}
\vspace{-2mm}
\end{table}

\subsection{Anatomical information distillation (AID)}
\label{exp:pretrain}

\noindent
\textbf{Implementation details.}
We follow the
5-fold cross-validation procedure
in \cite{Iqbal2020DeepLM,Liu2021AutomaticMA}.
Each data fold is pretrained separately to avoid information leakage from the hold-out set.
The backbone model is an ImageNet-pretrained DenseNet-201 \cite{Huang2017DenselyCC}.
We attach bilinear upsampling and additive fusion layers for segmentation, and a linear layer to the lowest resolution features for rotation prediction.
For augmentations ($\mathcal{T}_1$, $\mathcal{T}_2$), we resize the image to 64x64, apply 10 degree rotation, vertical flip, 10\% shift, hue/saturation jitter, gray conversion, 50\% brightness change, 20\% contrast change, $[0.6, 1]$ scaling, and $[0.6, 1.5]$ aspect ratio distortion for a final 64$\times$64 crop.
We train with batch size 128 for 2500 iterations (25 epochs) using default Adam \cite{Kingma2015AdamAM} (0.0001 learning rate), and a step-decay scheduler annealing the learning rate by 0.1 at epochs 12 and 19.
For the fine loss weights, we use $\alpha=1$ and $\beta=1$.

\noindent
\textbf{Results.}
We present qualitative results comparing our HPM and AID with some traditional methods in Fig.~\ref{fig:3} since we have no segmentation ground truth.
To gauge pretraining effectiveness, we present downstream performance in Table \ref{tab:1} and ablation study results in Table \ref{tab:2}.

\vspace{-2mm}
\subsection{Soft-tuning}
\textbf{Implementation details.}
We maintain separate folds for fair comparison with previous work, and use the same splits as in Section \ref{exp:pretrain}.
The AID-pretrained DenseNet-201 backbone is used for tuning and a new linear layer is added for classification.
For SCIAN [HuSHeM] augmentations, $\mathcal{T}_c$, we resize images to 64x64 [centercrop + 64x64], apply 10\degree \ [0\degree] rotation, vertical flip, 6\% [10\%] shift, 0/0 [50/30] hue/saturation jitter, [grayscale conversion], and 0\%/0\% [50\%/20\%] brightness/contrast change to obtain a final 64$\times$64 crop.
We train with batch size 512 [256] for $\sim$1500 iterations (30 epochs) with default Adam \cite{Kingma2015AdamAM} (0.00015 learning rate), and a step-decay scheduler annealing the learning rate by 0.1 at epochs 14 and 23.
For the soft cross-entropy loss, we use $\lambda=0.85$.
\vspace{-3mm}
\\~\\
\textbf{Results.}
Table \ref{tab:1} shows the comparison results of our framework and previous methods on the two datasets.
We use four metrics as the main comparison measures based on precedence in the literature.
On the challenging SCIAN dataset, we improve over \cite{Iqbal2020DeepLM} on all metrics by at least 2.2\% except on recall.
For HuSHeM, we also outperform in every metric except the increase is smaller since previous known scores are already high with less room for improvement.

\vspace{-3mm}
\subsection{Ablation study}

Quantitative results for ablation study are given in Table \ref{tab:2}.
The first three rows show that when we compare different pretraining paradigms individually, ImageNet-pretrained leads.
However, from row 4, we see that aggregated pretraining (AID) begins to outperform.
Finally, in the bottom three rows, one can see the contributions from each of our proposed soft-tuning components to improve training.

\vspace{-1mm}
\section{Conclusions}
\label{sec:end}
\vspace{-1mm}
We proposed a new sperm morphology classification framework that improves model
performance with no added label costs.
At its core, information is transferred from reliable sperm head masks generated from prior information to an encoder model through unsupervised spatial prediction tasks.
Regularization from masking, label smoothing, and augmentation combine to further facilitate training.
Our three-stage framework was evaluated on the SCIAN and HuSHeM datasets,
and achieved state-of-the-art results.
Our approach may illuminate a potential for robust, scalable, and accurate
systems to deal with deteriorating male fertility.

\section{Compliance with ethical standards}
\label{sec:ethics}

This research study was conducted retrospectively using human subject data made available in open access by two publicly available datasets \cite{Shaker2018Hushem,Chang2017GoldstandardFC}.
Ethical approval was not required as confirmed by the licenses attached with the open access datasets.

\section{Acknowledgements}
\label{sec:experiments}

We acknowledge the professional guidance and support we've received. The names of those institutions are omitted here for double-blinded review.

{
    \bibliographystyle{IEEEbib_abbrev}

\section{Introduction}
\label{sec:intro}
\vspace{-1mm}

Sperm counts have halved in Western nations since the 1970s \cite{Levine2017TemporalTI}, and use of Assisted Reproductive Technology (ART) has increased to account for over 2\% of American births annually \cite{ART2019}.
Morphology analysis, a central step in infertility diagnosis and ART planning, involves classifying sperm heads into qualitative shape categories \cite{WHOManual}, where abnormality predicts poor genetic fitness.
This process is both time-consuming and subjective since it requires manual deliberation on individual sperm cells in microscopy images.
These challenges, along with high annotation costs, result in small public datasets (e.g., HuSHeM \cite{Shaker2018Hushem} has 216 131$\times$131 crops; SCIAN \cite{Chang2017GoldstandardFC} has 1132 35$\times$35 crops) with noisy class labels.

\begin{figure}[htb]
    \begin{minipage}[b]{1.0\linewidth}

      \centering
       \centerline{\includegraphics{assets/fig1_final.pdf}}

    \end{minipage}
    %

    \vspace{-7mm}
    \caption{Stained human sperm head samples and CAM \cite{Selvaraju2019GradCAMVE} comparisons of two morphology datasets. Row 1: 64$\times$64 center-cropped, right-aligned test images. Rows 2 and 3: CAMs with supervised training and training with our framework, respectively. The 5 indices are for the classes of normal, tapered, pyriform, amorphous, and small, respectively.}
    \label{fig:1}
    \vspace{-3mm}
\end{figure}

Two main approaches were used to address small datasets in sperm morphology analysis: 1) manually design shape features; 2) use transfer learning with neural networks (NNs).
Previous work with the first approach applied traditional machine learning (ML) methods without relying on big data.
Notably, Chang et al.~applied a cascade ensemble of support vector machines (CE-SVM \cite{Chang2017AutomaticCO}) to classify sperm heads using various shape descriptors (e.g., area, eccentricity, Zernike moments); Shaker et al.~\cite{Shaker2017ADL} presented a dictionary learning method (APDL) based on prototype matching of image patches. These methods yielded reasonable results, but methods using approach two \cite{Riordon2019DeepLF,Iqbal2020DeepLM,Liu2021AutomaticMA} generally attained superior performance with ImageNet pretrained models.

\begin{figure*}[htb]
    \centering
    \begin{minipage}[b]{0.95\linewidth}
      \centering
      \centerline{\includegraphics[width=15cm]{assets/fig2_final.pdf}}

    \end{minipage}
    %
    \vspace{-3mm}
    \caption{An overview of our three-stage framework.
    EMA is for exponential moving average.
    Dashed-yellow boxes represent data sources containing HPM-generated masks while the solid-orange box has masks predicted by the teacher model in AID.}

    \label{fig:2}
    \vspace{-3mm}
\end{figure*}

Despite performance gains from NNs, effective and robust features are difficult to learn with noisy labels and small data (both
in terms of crop size and label-set cardinality).
In Fig.~\ref{fig:1}, we notice hints of brittleness where salience is concentrated on limited discriminative foreground regions and some unrelated background patches.
Regularized spatial pretraining is shown to help steer salience towards the foreground (Fig.~\ref{fig:1} row 3), while simultaneously encoding useful shape, texture, and general sperm features.
Given our ability to cheaply generate reliable, information-dense foreground masks with useful priors, we elect to pursue a transfer learning approach.

To the best of our knowledge, no other work has used pseudo-masks with spatial prediction tasks to facilitate classification.
The converse was introduced \cite{Zhang2019ASO}, where hints from classification were used to improve localization.
Relevant to the spatial pretraining in this work, a student-teacher setup \cite{Li2021TransformationConsistentSM} was proposed to regularize segmentation predictions in a semi-supervised setting.
We use a similar setup except for unsupervised pretraining with no pure ground truths available.
Additionally, rotation prediction \cite{Gidaris2018UnsupervisedRL} was used as an unsupervised pretext task for natural scene images.
We use this method for a similar purpose, except we are operating under stricter constraints since the upright orientation assumption does not hold in our situation.
As for sperm mask generation, other traditional vision methods \cite{Shaker2016AutomaticDA,Chang2014GoldstandardAI} have been proposed, but they either did not apply to gray images or were not calibrated to handle low quality, noisy images (e.g., in SCIAN).

In this work, we leverage anatomical and image priors of sperm images to improve our model's salience behavior and
performance by proposing a new three-stage deep learning framework. (1) Reliable sperm head segmentations are generated in a hierarchical manner using the size, shape, contrast, location, and data priors.
(2) Anatomical Information Distillation (AID) uses the masks to distill useful foreground information
via two unsupervised spatial prediction tasks.
(3) Soft-tuning
regularizes noisy class labels by implementing a weighted soft loss and uses the refined AID-generated masks for background denoising.

Our main contributions are as follows.
\begin{itemize}
    \item We propose a new three-stage sperm morphology classification framework that uses generated pseudo-masks to guide the learning of useful sperm features via two unsupervised spatial prediction tasks. The learned parameters and refined mask predictions are transferred to the soft-tuning stage where label smoothing and dynamic background subtraction help alleviate label and image noise.

    \item We introduce a hierarchical sperm head segmentation method that leverages anatomical and image priors to reliably filter imaging artifacts, remove sperm tails, and discard the sperm mid-pieces.

    \item We evaluate our new framework on two public

    sperm morphology datasets (SCIAN \cite{Chang2017GoldstandardFC} and HuSHeM \cite{Shaker2018Hushem}) and achieve state-of-the-art performances on both.
\end{itemize}

\section{Method}
\vspace{-2mm}
\label{sec:methods}

In this section, we detail our methodology on each of the three main framework components (shown in Fig.~\ref{fig:2}).

\vspace{-3mm}
\subsection{Hierarchical pseudo-mask generation (HPM)}
\vspace{-4mm}
\begin{figure}[htb]
    \centering
    \begin{minipage}[b]{0.6\linewidth}
      \centering
      \centerline{\includegraphics[width=5.85cm]{assets/fig3_final.pdf}}

    \end{minipage}
    %
    \vspace*{-2mm}
    \caption{Qualitative pseudo-mask results: comparing

    our methods with traditional mask generation methods. The \emph{HPM} column shows results from Hierarchical Pseudo-Masks, where $h=2$ is used for SCIAN (gray is the most confident layer) and $h=1$ for cleaner HuSHeM data. \emph{AID} shows masks predicted by Anatomical Information Distillation.}
    \label{fig:3}
\end{figure}
\vspace{-2mm}

HPM has two key objectives: create accurate sperm head masks, and use the computed head orientation to align all sperm to point right (see \cite{Liu2021AutomaticMA} for the importance of this step).
A sperm cell consists of three main components: 1) the head (or foreground), 2) a mid-section that often has similar texture and intensity as the head, and 3) a tail which is a thin organelle connecting to the mid-section.
The most challenging aspect of head segmentation lies in the separation of the mid-section from the head (in rows 1 \& 3 of Fig.~\ref{fig:3}, one can see over-segmentation of the mid-section by thresholding, due to its connectivity and similar appearance).
To produce reliable masks, we leverage both anatomical priors (i.e., head size, tapered nuclear shape, darker nuclear intensity, rough elliptical outline) and image priors (i.e., relatively centered foreground, one instance per image, and contrast from dying agents).
We present a high-level flow of our proposed method below.

\noindent
(1) For each image $I$, a coarse foreground mask, $\tilde{m}_1$, is created by applying non-local means denoising, followed by thresholding. $\tilde{m}_1$ is used for intensity-range rescaling of the foreground and whitening of the background, to produce $I_1$.

\noindent
(2) Extract the darkest segment of $I_1$ with k-means clustering ($k=3$) to obtain a tentative nuclear mask. The tail and mid-section pieces are removed by distance thresholding to yield a reliable nuclear mask, $\tilde{n}_1$. An ellipse is fitted to $\tilde{n}_1$ and the thicker end of $\tilde{n}_1$ is chosen as the head direction. Finally, $\tilde{m}_1$, $I_1$, and $\tilde{n}_1$ are rotated to face right, outputting $\tilde{m}_2$, $I_2$, and $\tilde{n}_2$.

\noindent
(3) Fuse $\tilde{n}_2$ with a reliable acrosome mask (the top part of the head). Since we already computed the orientation, we simply split $\tilde{m}_2$ in half and use the right part as a coarse acrosome mask, $\tilde{m}^r_2$. We erode $\tilde{m}^r_2$ until it is within a threshold of $\tilde{n}_2$'s height, and fuse them to produce $\tilde{M}_0$, where $0$ indicates the lowest hierarchy index or the most confident mask.

\noindent
(4) Obtain a final hierarchy of concentric masks, $\tilde{M}$. For a given $h\in\mathbb{N}$, we obtain $\tilde{M}_i$ for $i \in \{0, ..., h-1\}$, a mask layer, by dilating $\tilde{M}_0$ $i$ times with all extents bounded by $\tilde{m}_1$.

\vspace{-2mm}
\subsection{Anatomical information distillation (AID)}
\vspace{-1mm}

We utilize a student-teacher setup \cite{Tarvainen2017MeanTA} with two fully convolution networks where the teacher parameters are updated as an exponential moving average (EMA) of the student's parameters after each iteration.
The student model consists of a backbone encoder $f_s$ with an upsampling decoder $g_s$.
The teacher network shares an identical architecture, denoted as $f_t$ and $g_t$ (see AID in Fig.~\ref{fig:2}).
$\mathcal{T}_1$ and $\mathcal{T}_2$ are stochastic image transformation functions that use the same set of augmentations.
AID first performs fine distillation with segmentation and consistency regularization, and then
conducts coarse distillation with rotation prediction.

\vspace{0.025in}
\noindent
\textbf{Fine distillation.}
For an image $I$ in a dataset $\mathcal{D}$ (e.g. oriented crops from HPM), the predicted segmentation masks from the student and teacher networks are $\hat{y}_s = g_s(f_s(\mathcal{T}_1(I)))$ and $\hat{y}_t = g_t(f_t(\mathcal{T}_2(I)))$, respectively.
Partial cross-entropy is used for the segmentation loss where $\tilde{M}_0$ is selected as the foreground ground truth, $\lnot\tilde{M}$ is the binary background ground truth ($\lnot$ indicates the pixel-wise negation operator that turns nonzero foreground pixels into background zeros and vice-versa), and all other non-confident pixels, $\tilde{M}_{n}$ where $n>0$, are ignored during pretraining.
Note that ignored pixels only exist if $h>1$ hierarchies are generated since $h=1$ yields one foreground mask.

Furthermore, to prevent overfitting, we add a consistency constraint where two transformed versions of the same image are encouraged to have identical predictions after mask alignment.
The two fine losses are defined as:

\vspace*{-4mm}
\begin{equation}
    \mathcal{L}_{seg} = \frac{-1}{|I|} \sum_{i \in I} (\lnot\tilde{M}_{n}^i) (\lnot\tilde{M}^i \cdot \log(1 - \hat{y}_{s}^i) + \tilde{M}_{0}^i \cdot \log(\hat{y}_{s}^i)) \\
\end{equation}
\vspace*{-3mm}
\begin{equation}
    \mathcal{L}_{con} = ||\mathcal{T}_1^{-1}(\hat{y}_{s}) - \mathcal{T}_2^{-1}(\hat{y}_{t})||^2_2,
\end{equation}
where $i$ is a pixel in image $I$.
The aggregate fine distillation loss is $\alpha \mathcal{L}_{seg} + \beta \mathcal{L}_{con}$, with balancing parameters $\alpha$ and $\beta$.
$\hat{y}_t$ masks are also saved for use in the soft-tuning stage.

\newcommand{\ra}[1]{\renewcommand{\arraystretch}{#1}}
\setlength{\tabcolsep}{4.75pt}{
\begin{table*}[htb]
    \centering
    \ra{0.925}
    \begin{tabular*}{1.0\linewidth}{@{}llllllllll@{}}
        \toprule
        \multirow{2}{*}[-0.15em] {\hspace{7pt} \textbf{Method}} &
        \multicolumn{4}{c}{SCIAN Dataset} & {} & \multicolumn{4}{c}{HuSHeM Dataset} \\
        \addlinespace[0pt]
        \cmidrule(lr){2-5} \cmidrule(lr){7-10}

        {} & Accuracy & Recall & Precision & F$_1$ &&
        Accuracy & Recall & Precision & F$_1$ \\
        \midrule
        CE-SVM \cite{Chang2017AutomaticCO} & 44 & 58 & - & -  &&
                        78.5 & 78.5 & 80.5 & 78.9 \\
        APDL \cite{Shaker2017ADL}   & 49 & 62 & - & - &&
                                      92.2 & 92.3 & 93.5 &  92.9 \\
        FT-VGG \cite{Riordon2019DeepLF} & 49 & 62 & 47 & 53 &&
                                      94.0 & 94.1 & 94.7 & 94.1\\
        MC-HSH \cite{Iqbal2020DeepLM} & 63 & 68 & 56 & 61 &&
                                      95.7 & 95.5 & 96.1 & 95.5 \\
        TL \cite{Liu2021AutomaticMA} & - & 62 & - & - &&
                                      96.0 & 96.1 & 96.4 & 96.0 \\
        Ours \small $\pm$Std. & \textbf{65.9} \small $\pm$ 0.68
                            & \textbf{68.9} \small $\pm$ 0.30
                            & \textbf{58.7} \small $\pm$ 1.20
                            & \textbf{63.2} \small $\pm$ 0.23
                            &
                            & \textbf{96.5} \small $\pm$ 0.36
                            & \textbf{96.6} \small $\pm$ 0.40
                            & \textbf{96.8} \small $\pm$ 0.29
                            & \textbf{96.5} \small $\pm$ 0.38 \\
        \bottomrule
    \end{tabular*}
\vspace*{-3mm}
\caption{Performance comparison of our proposed framework with state-of-the-art classification methods on the
SCIAN (partial agreement subset) and HuSHeM datasets. Some SCIAN numbers lack decimals since they are taken directly from other works.}
\label{tab:1}
\vspace*{-3mm}
\end{table*}
}

\vspace{0.05in}
\noindent
\textbf{Coarse distillation.}
For better adaptation to the downstream classification task, we use the rotation prediction task with four angles, $\{0, 90, 180, 270\}$, from a right-facing orientation to transition the model from a pixel-wise focus toward learning more abstract features of different anatomical parts and their respective spatial relations.
The loss is defined as:
\vspace{-1mm}
\begin{equation}
    \vspace{-1mm}
    \mathcal{L}_{rot} =  - y_{rot} \cdot \log(f_s(\mathcal{T}_1(I))_{rot}).
\end{equation}
where $y_{rot}$, $f_s(\mathcal{T}_1(I))_{rot}$, and $I$ are the target rotation index, predicted rotation index, and pre-aligned image, respectively.

\vspace{-3mm}
\subsection{Soft-tuning}

We transfer the trained teacher encoder, $f_t$, for morphology classification tuning.
We implement three key components for the purposes of regularization, noise reduction, and prediction stabilization.
First, we use AID-predicted segmentations to mask out image backgrounds following a curriculum learning approach where we apply more aggressive masking as training progresses.
This is implemented by linearly decreasing the number of applied foreground mask dilations from 15 (entire image is foreground) to 0 (only sperm head is inputted).
    Second, we use a soft cross-entropy loss that handles label variability between annotators:
\vspace{-1mm}
\begin{equation}
    \vspace{-1mm}
    \mathcal{L}_{c} =  \lambda \cdot \log \ \hat{y}_{c1} + (1 - \lambda) \cdot \log  \ \hat{y}_{c2},
\end{equation}
where $c1$ \& $c2$ represent an image's majority \& minority classes, respectively, and
$\hat{y}_{c1}$ \& $\hat{y}_{c2}$ are the model scores for the majority \& minority classes (during consensus, the loss reduces to vanilla cross-entropy).
$\lambda \in [0,1]$ is a balancing term that represents the majority class weighting.
Finally, we introduce shape-invariant test-time augmentations (i.e., vertical flipping and rotation) to lower prediction variability.
We also make training augmentations, $\mathcal{T}_c$, milder, which empirically improves performance and stabilizes training.

\vspace{-1mm}
\section{Experiments}
\label{sec:experiments}
\vspace{-2mm}
\subsection{Datasets}
We use the only two publicly available stained sperm morphology datasets for evaluation.
SCIAN \cite{Chang2017GoldstandardFC} (from the \textbf{Sc}ientific \textbf{I}mage \textbf{An}alysis Lab, University of Chile) contains 1132 35$\times$35 gray crops with five morphology classes (100 normal, 228 tapered, 76 pyriform, 656 amorphous, and 72 small).
The images were captured at 63X zoom after Hematoxylin \& Eosin staining.
Three experts assigned classes for each crop. The partial agreement subset we use contains only images with at least 2 out of 3 experts agreeing on a label.

HuSHeM \cite{Shaker2018Hushem} (\textbf{Hu}man \textbf{S}perm \textbf{He}ad \textbf{M}orphology) has 216 131$\times$131 RGB crops and four classes (54 normal, 53 tapered, 57 pyriform, 52 amorphous) with expert consensus.
Images were obtained at 100X magnification after Diff-Quik staining.
Fig.~\ref{fig:1} shows pre-processed HuSHeM and SCIAN samples.

\vspace{-2mm}
\newcolumntype{R}[2]{%
    >{\adjustbox{angle=#1,lap=\width-(#2)}\bgroup}%
    l%
    <{\egroup}%
}
\newcommand*\rot{\multicolumn{1}{R{45}{1em}}
\begin{table}[htb]
\begin{tabular}{rrrrrrrcc}
\rot{\small ImNet Pre.} &
\rot{\small Head Pred.} &
\rot{\small Rot. Pred.}  &
\rot{\small TTA} &
\rot{\small Soft Lab.} &
\rot{\small Dyn. Mask} & &
Accuracy &
F$_1$ \\ \hline
\cmark & 		         & 		     & 		 &  		& 		  & 		    &  \textbf{51.1}  & \textbf{53.7} \\
               & \cmark &  &  & &  &                                             & 50.9 & 53.6   \\
               &                & \cmark & & & &                                 & 48.3 & 52.4  \\  \hline
 \cmark & \cmark & \cmark &                &  & &                  & 58.6 & 57.3 \\
 \cmark & \cmark & \cmark & \cmark &  & &                  & 59.1 & 58.7 \\
 \cmark & \cmark & \cmark & \cmark & \cmark &  &   & 63.3 & 61.5\\
 \cmark & \cmark & \cmark & \cmark & \cmark & \cmark & & \textbf{65.9} & \textbf{63.2}\\
\end{tabular}
\vspace{-1mm}
\caption{Ablation study results
on all five SCIAN folds (3 runs per fold). TTA stands for test-time augmentation.}
\label{tab:2}
\vspace{-6mm}
\end{table}

\subsection{Anatomical information distillation (AID)}
\label{exp:pretrain}

\noindent
\textbf{Implementation details.}
We follow the
5-fold cross-validation procedure
in \cite{Iqbal2020DeepLM,Liu2021AutomaticMA}.
Each data fold is pretrained separately to avoid information leakage from the hold-out set.
The backbone model is an ImageNet-pretrained DenseNet-201 \cite{Huang2017DenselyCC}.
We attach bilinear upsampling and additive fusion layers for segmentation, and a linear layer to the lowest resolution features for rotation prediction.
For augmentations ($\mathcal{T}_1$, $\mathcal{T}_2$), we resize the image to 64x64, apply 10 degree rotation, vertical flip, 10\% shift, hue/saturation jitter, gray conversion, 50\% brightness change, 20\% contrast change, $[0.6, 1]$ scaling, and $[0.6, 1.5]$ aspect ratio distortion for a final 64$\times$64 crop.
We train with batch size 128 for 2500 iterations (25 epochs) using default Adam (0.0001 learning rate), and a step-decay scheduler annealing the learning rate by 0.1 at epochs 12 and 19.
For the fine loss weights, we use $\alpha=1$ and $\beta=1$.

\vspace{1mm}
\noindent
\textbf{Results.}
We present qualitative results comparing our HPM and AID with some traditional methods in Fig.~\ref{fig:3} since we have no segmentation ground truth.
To gauge pretraining effectiveness, we present downstream performance in Table \ref{tab:1} and ablation study results in Table \ref{tab:2}.

\vspace{-2mm}
\subsection{Soft-tuning}
\vspace{-1mm}
\textbf{Implementation details.}
We maintain separate folds for fair comparison with previous work, and use the same splits as in Section \ref{exp:pretrain}.
The AID-pretrained DenseNet-201 backbone is used for tuning and a new linear layer is appended for classification.
For SCIAN [HuSHeM] augmentations, $\mathcal{T}_c$, we resize images to 64x64 [centercrop + 64x64], apply 10\degree \ [0\degree] rotation, vertical flip, 6\% [10\%] shift, 0/0 [50/30] hue/saturation jitter, [grayscale conversion], and 0\%/0\% [50\%/20\%] brightness/contrast change to obtain a final 64$\times$64 crop.
We train with batch size 512 [256] for $\sim$1500 iterations (30 epochs) with default Adam (0.00015 learning rate), and a step-decay scheduler annealing the learning rate by 0.1 at epochs 14 and 23.
For the soft cross-entropy loss, we use $\lambda=0.85$.
\vspace{-3mm}
\\~\\
\textbf{Results.}
Table \ref{tab:1} shows the comparison results of our framework and previous methods on the two datasets.
We use four metrics as the main comparison measures based on precedence in the literature.
On the challenging SCIAN dataset, we improve over \cite{Iqbal2020DeepLM} on all metrics by at least 2.2\% except on recall.
For HuSHeM, we also outperform in every metric except the increase is smaller because of diminished margin for improvement from the previous art's high scores.

\vspace{-3mm}
\subsection{Ablation study}
\vspace{-1mm}

Quantitative results for ablation study are given in Table \ref{tab:2}.
The first three rows show that when we compare different pretraining paradigms individually, ImageNet-pretrained leads.
However, from row 4, we see that aggregated pretraining (AID) begins to outperform.
Finally, in the bottom three rows, one can see the contributions from each of our proposed soft-tuning components to improve training.

\vspace{-2mm}
\section{Conclusions}
\label{sec:end}
\vspace{-1mm}
We proposed a new sperm morphology classification framework that improves model
performance with no added label costs.
At its core, information is transferred from reliable sperm head masks generated from prior information to an encoder model through unsupervised spatial prediction tasks.
Regularization from masking, label smoothing, and augmentation combine to further facilitate training.
Our three-stage framework was evaluated on the SCIAN and HuSHeM datasets,
and achieved state-of-the-art results.
Our approach may illuminate a potential for robust, scalable, and accurate
systems to deal with deteriorating male fertility.

\section{Compliance with ethical standards}
\label{sec:ethics}

This research study was conducted retrospectively using human subject data made available in open access by two publicly available datasets \cite{Shaker2018Hushem,Chang2017GoldstandardFC}.
Ethical approval was not required as confirmed by the licenses attached with the open access datasets.

\section{Acknowledgements}
\label{sec:experiments}

We would like to acknowledge the professional guidance and support from departments within Anhui Medical University including the First Affiliated Hospital, the NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, the Key Laboratory of Population Health Across Life Cycle, the Anhui Province Key Laboratory of Reproductive Health and Genetics, and the Biopreservation and Artificial Organs Engineering Research Center.

{
    \bibliographystyle{IEEEbib_abbrev}

\section{Introduction}
\label{sec:intro}
\vspace{-1mm}

\vspace*{-4mm}
\begin{equation}
    \mathcal{L}_{seg} = \frac{-1}{|I|} \sum_{i \in I} (\lnot\tilde{M}_{n}^i) (\lnot\tilde{M}^i \cdot \log(1 - \hat{y}_{s}^i) + \tilde{M}_{0}^i \cdot \log(\hat{y}_{s}^i)) \\
\end{equation}
\vspace*{-3mm}
\begin{equation}
    \mathcal{L}_{con} = ||\mathcal{T}_1^{-1}(\hat{y}_{s}) - \mathcal{T}_2^{-1}(\hat{y}_{t})||^2_2,
\end{equation}
where $i$ is a pixel in image $I$.
The aggregate fine distillation loss is $\alpha \mathcal{L}_{seg} + \beta \mathcal{L}_{con}$, with balancing parameters $\alpha$ and $\beta$.
$\hat{y}_t$ masks are also saved for use in the soft-tuning stage.

\newcommand{\ra}[1]{\renewcommand{\arraystretch}{#1}}
\setlength{\tabcolsep}{5.1pt}{
\begin{table*}[htb]
    \centering
    \ra{0.925}
    \begin{tabular*}{1.0\linewidth}{@{}llllllllll@{}}
        \toprule
        \multirow{2}{*}[-0.15em] {\hspace{7pt} \textbf{Method}} &
        \multicolumn{4}{c}{SCIAN Dataset} & {} & \multicolumn{4}{c}{HuSHeM Dataset} \\
        \addlinespace[0pt]
        \cmidrule(lr){2-5} \cmidrule(lr){7-10}

        {} & Accuracy & Recall & Precision & F$_1$ &&
        Accuracy & Recall & Precision & F$_1$ \\
        \midrule
        CE-SVM \cite{Chang2017AutomaticCO} & 44 & 58 & - & -  &&
                        78.5 & 78.5 & 80.5 & 78.9 \\
        APDL \cite{Shaker2017ADL}   & 49 & 62 & - & - &&
                                      92.2 & 92.3 & 93.5 &  92.9 \\
        FT-VGG \cite{Riordon2019DeepLF} & 49 & 62 & 47 & 53 &&
                                      94.0 & 94.1 & 94.7 & 94.1\\
        MC-HSH \cite{Iqbal2020DeepLM} & 63 & 68 & 56 & 61 &&
                                      95.7 & 95.5 & 96.1 & 95.5 \\
        TL \cite{Liu2021AutomaticMA} & - & 62 & - & - &&
                                      96.0 & 96.1 & 96.4 & 96.0 \\
        Ours \small $\pm$Std. & \textbf{65.9} \small $\pm$ 0.68
                            & \textbf{68.9} \small $\pm$ 0.30
                            & \textbf{58.7} \small $\pm$ 1.20
                            & \textbf{63.2} \small $\pm$ 0.23
                            &
                            & \textbf{96.5} \small $\pm$ 0.36
                            & \textbf{96.6} \small $\pm$ 0.40
                            & \textbf{96.8} \small $\pm$ 0.29
                            & \textbf{96.5} \small $\pm$ 0.38 \\
        \bottomrule
    \end{tabular*}
\vspace*{-3mm}
\caption{Performance comparison of our proposed framework with state-of-the-art classification methods on the
SCIAN (partial agreement subset) and HuSHeM datasets. Some SCIAN numbers lack decimals since they are taken directly from other works.}
\label{tab:1}
\vspace*{-3mm}
\end{table*}
}

The loss is defined as:
\vspace{-1mm}
\begin{equation}
    \vspace{-1mm}
    \mathcal{L}_{rot} =  - y_{rot} \cdot \log(f_s(\mathcal{T}_1(I))_{rot}).
\end{equation}
where $y_{rot}$, $f_s(\mathcal{T}_1(I))_{rot}$, and $I$ are the target rotation index, predicted rotation index, and pre-aligned image, respectively.

\vspace{-3mm}
\subsection{Soft-tuning}

We transfer the trained teacher encoder, $f_t$, for morphology classification tuning.
We implement three key components for the purposes of regularization, noise reduction, and prediction stabilization.
First, we use AID-predicted segmentations to mask out image backgrounds following a curriculum learning approach where we apply more aggressive masking as training progresses.
This is implemented by linearly decreasing the number of applied foreground mask dilations from 15 (entire image is foreground) to 0 (only sperm head is inputted).
    Second, we use a soft cross-entropy loss that handles label variability between annotators:
\vspace{-1mm}
\begin{equation}
    \vspace{-1mm}
    \mathcal{L}_{c} =  \lambda \cdot \log \ \hat{y}_{c1} + (1 - \lambda) \cdot \log  \ \hat{y}_{c2},
\end{equation}
where $c1$ \& $c2$ represent an image's majority \& minority classes, respectively, and
$\hat{y}_{c1}$ \& $\hat{y}_{c2}$ are the model scores for the majority \& minority classes (during consensus, the loss reduces to vanilla cross-entropy).
$\lambda \in [0,1]$ is a balancing term that represents the majority class weighting.
Finally, we introduce shape-invariant test-time augmentations (i.e., vertical flipping and rotation) to lower prediction variability.
We also make training augmentations, $\mathcal{T}_c$, milder, which empirically improves performance and stabilizes training.

\vspace{-1mm}
\section{Experiments}
\label{sec:experiments}
\vspace{-2mm}
\subsection{Datasets}
We use the only two publicly available stained sperm morphology datasets for evaluation.
SCIAN  (from the \textbf{Sc}ientific \textbf{I}mage \textbf{An}alysis Lab, University of Chile) contains 1132 35$\times$35 gray crops with five morphology classes (100 normal, 228 tapered, 76 pyriform, 656 amorphous, and 72 small).
The images were captured at 63X zoom after Hematoxylin \& Eosin staining.
Three experts assigned classes for each crop. The partial agreement subset we use contains only images with at least 2 out of 3 experts agreeing on a label.

HuSHeM (\textbf{Hu}man \textbf{S}perm \textbf{He}ad \textbf{M}orphology) has 216 131$\times$131 RGB crops and four classes (54 normal, 53 tapered, 57 pyriform, 52 amorphous) with expert consensus.
Images were obtained at 100X magnification after Diff-Quik staining.
Fig.~\ref{fig:1} shows pre-processed HuSHeM and SCIAN samples.

\vspace{-2mm}
\newcolumntype{R}[2]{%
    >{\adjustbox{angle=#1,lap=\width-(#2)}\bgroup}%
    l%
    <{\egroup}%
}
\newcommand*\rot{\multicolumn{1}{R{45}{1em}}
\begin{table}[htb]
\begin{tabular}{rrrrrrrcc}
\rot{\small ImNet Pre.} &
\rot{\small Head Pred.} &
\rot{\small Rot. Pred.}  &
\rot{\small TTA} &
\rot{\small Soft Lab.} &
\rot{\small Dyn. Mask} & &
Accuracy &
F$_1$ \\ \hline
\cmark & 		         & 		     & 		 &  		& 		  & 		    &  \textbf{51.1}  & \textbf{53.7} \\
               & \cmark &  &  & &  &                                             & 50.9 & 53.6   \\
               &                & \cmark & & & &                                 & 48.3 & 52.4  \\  \hline
 \cmark & \cmark & \cmark &                &  & &                  & 58.6 & 57.3 \\
 \cmark & \cmark & \cmark & \cmark &  & &                  & 59.1 & 58.7 \\
 \cmark & \cmark & \cmark & \cmark & \cmark &  &   & 63.3 & 61.5\\
 \cmark & \cmark & \cmark & \cmark & \cmark & \cmark & & \textbf{65.9} & \textbf{63.2}\\
\end{tabular}
\vspace{-1mm}
\caption{Ablation study results
on all five SCIAN folds (3 runs per fold). TTA stands for test-time augmentation.}
\label{tab:2}
\vspace{-6mm}
\end{table}

\subsection{Anatomical information distillation (AID)}
\label{exp:pretrain}

\noindent
\textbf{Implementation details.}

\vspace{1mm}
\noindent
\textbf{Results.}
We present qualitative results comparing our HPM and AID with some traditional methods in Fig.~\ref{fig:3} since we have no segmentation ground truth.
To gauge pretraining effectiveness, we present downstream performance in Table \ref{tab:1} and ablation study results in Table \ref{tab:2}.

\vspace{-2mm}
\subsection{Soft-tuning}
\vspace{-1mm}
\textbf{Implementation details.}
We maintain separate folds for fair comparison with previous work, and use the same splits as in Section \ref{exp:pretrain}.
The AID-pretrained DenseNet-201 backbone is used for tuning and a new linear layer is appended for classification.
For SCIAN [HuSHeM] augmentations, $\mathcal{T}_c$, we resize images to 64x64 [centercrop + 64x64], apply 10\degree \ [0\degree] rotation, vertical flip, 6\% [10\%] shift, 0/0 [50/30] hue/saturation jitter, [grayscale conversion], and 0\%/0\% [50\%/20\%] brightness/contrast change to obtain a final 64$\times$64 crop.
We train with batch size 512 [256] for $\sim$1500 iterations (30 epochs) with default Adam (0.00015 learning rate), and a step-decay scheduler annealing the learning rate by 0.1 at epochs 14 and 23.
For the soft cross-entropy loss, we use $\lambda=0.85$.
\vspace{-3mm}
\\~\\
\textbf{Results.}
Table \ref{tab:1} shows the comparison results of our framework and previous methods on the two datasets.
We use four metrics as the main comparison measures based on precedence in the literature.
On the challenging SCIAN dataset, we improve overon all metrics by at least 2.2\% except on recall.
For HuSHeM, we also outperform in every metric except the increase is smaller because of diminished margin for improvement from the previous art's high scores.

\vspace{-3mm}
\subsection{Ablation study}
\vspace{-1mm}

Quantitative results for ablation study are given in Table \ref{tab:2}.
The first three rows show that when we compare different pretraining paradigms individually, ImageNet-pretrained leads.
However, from row 4, we see that aggregated pretraining (AID) begins to outperform.
Finally, in the bottom three rows, one can see the contributions from each of our proposed soft-tuning components to improve training.

\vspace{-2mm}
\section{Conclusions}
\label{sec:end}
\vspace{-1mm}
We proposed a new sperm morphology classification framework that improves model
performance with no added label costs.
At its core, information is transferred from reliable sperm head masks generated from prior information to an encoder model through unsupervised spatial prediction tasks.
Regularization from masking, label smoothing, and augmentation combine to further facilitate training.
Our three-stage framework was evaluated on the SCIAN and HuSHeM datasets,
and achieved state-of-the-art results.
Our approach may illuminate a potential for robust, scalable, and accurate
systems to deal with deteriorating male fertility.

\section{Compliance with ethical standards}
\label{sec:ethics}

Ethical approval was not required as confirmed by the licenses attached with the open access datasets.

\section{Acknowledgements}
\label{sec:experiments}

We would like to acknowledge the professional guidance and support from departments within Anhui Medical University including the First Affiliated Hospital, the NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, the Key Laboratory of Population Health Across Life Cycle, the Anhui Province Key Laboratory of Reproductive Health and Genetics, and the Biopreservation and Artificial Organs Engineering Research Center.

{
    \bibliographystyle{IEEEbib_abbrev}

redpajama_set_name: RedPajamaArXiv


In this example, you see how to use the <figure> and <figcaption> tags as part of a solution that loads an image from disk and stores it in an <img> tag. The following code shows the HTML you use to perform the task.
The <figure> tag acts as a container that holds the <img> and <figcaption> tags. The use of a container like this means that you can perform tasks such as formatting both image and caption at the same time.
When working with the <img> tag, you access the src (image source) and alt (image alternative text description) attributes directly. Set the <figcaption> content using the innerHTML property.


text:
Q: How can I edit unusual clipboard content? I'd like to edit application specific clipboard content, with an application specific format. There are clipboard managers that let me edit the plain text content, but I'd like to access clipboard content that applications use for internal cut and paste.
An example would be when you copy something from a web page in Firefox. When you look at the different formats in a clipboard viewer, there are three or so formats starting with "moz", with content that is not plain text.
I'd like to edit that content before pasting it back into Firefox. Can this be done?

redpajama_set_name: RedPajamaStackExchange


Target also said it will eliminate 50 already open positions, reports KARE 11.
February marked the beginning of this year's cuts. 550 were laid off at the time.
Those laid off will receive a compensation package, reports KSTP.


If you are a permanent Thames Valley Housing tenant with a secure tenancy, you may be eligible for the Right to Buy scheme. You also need to have been a tenant with us or other social landlords for longer than 3 years to qualify.
Here's some more information about the Right to Buy scheme.
If you are a permanent Thames Valley Housing tenant with an assured tenancy, you may be eligible for the Right to Acquire scheme. You also need to have been a tenant with us or other social landlords for longer than 3 years. Plus your home must have been built or bought by us after 1st April 1997.
Here's some more information about the Right to Acquire scheme.
If you think you might want to buy your home, you can download an application form for the Right to Buy Scheme here or the Right to Acquire Scheme here.
It's our Home Ownership Team who manage the process.
If you've got a query about either of these schemes, send an enquiry to our Home Ownership Team or give us a call on 0300 456 2929 (option 3).
If you're eligible for either scheme, we'll send out a valuer who will visit your home and send us a report on the value of your home. They'll tell us too about any improvements you've made that might (or might not) impact on the sale price.
We'll then make an offer to you which includes details of the market value of your home, the discount we can apply to the sale price along with some other information you need to know about once you become a home-owner.
If you're happy with the offer, you'll need to let us know. Once you've sorted out the finances and got your solicitor to contact us, we can agree a date for the sale to proceed.


text:
Apr 2, 2020 • 2 min read
Published in View from the Edge – A monthly column by Lee Han Shih, Founder of the Potato Group, featured in the CompassList newsletter. The Sailor's Log offers analysis, insights and coverage of noteworthy trends from up-and-coming startup ecosystems. It goes out the first and third Wednesday of every month. Get the CompassList newsletter here.
Nearly seven hundred years before Covid-19, the Black Death, a global epidemic of bubonic plague, swept through Europe, killing an estimated one-third of its population of 100 million. For many of the survivors, this was the best thing that could have happened.
Plague – unlike death, war and famine, the three other doomsday horsemen of Christian lore – has no respect for rank or power. Large swathes of the upper classes were mowed down as easily as the lowly laborers. Such sudden emptying of so many niches and positions created opportunities that were before unattainable in feudal European society.
Two groups benefitted the most. Serfs, who previously were tied to their land, were given better treatment and freedom when manpower needed to plow the land became urgent. Traders, once a despised lot, gained in position and wealth, heralding the emergence of the middle class.
At the same time, the Catholic Church suffered huge losses due to its inability to control the virus. This opened the door for a rival branch – Protestantism – to surface and become a viable alternative more than a century later.
History provides valuable lessons. The aftermath of Covid-19 will see an entire reshuffling of societies – not only in Europe, but globally. And, similarly, there will be gains and losses among the different groups. Like the Black Death, coronavirus is here to stay and mutate in an arms race against vaccines and acquired immunity. The "new normal" is one where the entire world has to work differently to contain yet another contagion. And, in the face of climate change and food and water shortages, this new normal is going to be tough for a huge majority of people.
Governments may change if the Covid-19 death tolls go beyond levels deemed acceptable by the population. Many businesses – particularly those involving human physical interactions and considered non-essential – will suffer. Others, low-touch in nature, such as online learning, stand to thrive.
Suddenly, food production has become a skill set hotter than ABC (AI, blockchain and cloud) and sought after in China and elsewhere. There is even a meme going around on the right guy to choose: "Can he farm? Can he feed you or do you have to feed him?"
And, waiting from the sidelines for their break, which should not be long in coming, are the "serfs of the 21st century."
These are the cleaners, the road sweepers, the garbage collectors, the hospital waste disposal workers, the delivery boys (and girls) – all in all, the large numbers of invisible and lowly paid workers who have been keeping the economy humming but are seldom noticed.
It pays to notice them now. They are, literally, humanity's first line of defense in the new normal. If they falter, the germs and viruses will get to the rest of us. In the new normal, these workers are putting their lives at stake – and hence it's an irrefutable argument that they should be much better paid and equipped.
Anyone against this only needs to think: a king could offend anyone, except the barber who holds a knife to his throat. Enough said.

redpajama_set_name: RedPajamaCommonCrawl


text:
30th Dec 2020 Trendy Current Affairs for Free | Daily News
candy December 30, 2020 Current Affairs Leave a Comment
30 December 2020 Current Affairs: Hello Contenders are you preparing fort the any competitive exam then have a look on this page. We all know that current affairs is one of most important subject to clear any competitive exam either its is state level or central level. But it is difficult for the job seekers to collect day to day current affairs which is important for the exam point of view. So to solve this issue for the exam preparers our jobads.in providing the full updates of the GK Current Affairs pdf day wise along with the quiz questions. Also contenders on this page you can also find the monthly eBooks. So candidates make use of this data and prepare well for any competitive exam. Following aspirants you can find the 30th December 2020 current affairs. Continue reading…
Mayiladuthurai inaugurated as Tamil Nadu's 38th District – 30/12/2020 Current Affairs
On December 28, 2020, Chief Minister K Palaniswami of Tamil Nadu inaugurated Mayiladuthurai as Tamil Nadu's 38th district. The newly-formed district was carved out of the district of Nagapattinam. Earlier in March 2020, the Chief Minister declared that Mayiladuthurai will be separated from Nagapattinam, making it the district headquarters.The district was officially inaugurated by the Chief Minister eight months after the announcement by video conferencing from the Secretariat in Chennai. For nearly 25 years, it had been a long pending demand of the people living in Mayiladuthurai to treat them as a separate district.
Gujarat government announces Solar Power Policy, 2021 – Free Current Affairs
On December 29, 2020, the Chief Minister of Gujarat, Vijay Rupani, announced the 2021 Solar Power Policy for the next five years. The program aims to reduce the energy costs of industrial units by almost 50 percent, while at the same time eliminating any capacity limit to set up a state solar project. The government's policy also enables customers to lease their premises or roofs to a third party for the development and consumption of power on the same premises. In Gandhinagar, the announcement by CM was made in the presence of Saurabh Patel, State Minister of Energy, and Sunaina Tomar, Principal Secretary (Energy & Petrochemicals).
Vistadome coach – 30 December 2020 Current affairs
Railway Minister Shri Piyush Goyal recently declared that the trial run of the new Vistadome tourist coaches has been successfully completed by Indian Railways. The Integral Coach Factory in Chennai developed those coaches. There are currently 13 Vistadome coaches operating in India. They run on the Darjeeling-Himalayan Railway, Kangra Valley Railway, Kalka-Shimla Railway, Nilgiri Mountain Railway, Matheran Hill Railway, in the Araku Valley, Dadar and Madgaon. In order to provide panoramic views, Vistadome coaches are made with glass roof tops and large windows.
French Fashion Designer Pierre Cardin Has Died – GK Current Affairs
On Tuesday, the Iconic French Designer Pierre died at the age of 98. He was a famous designer for women's clothes and a notable person for high fashion for men. He made his name through the sale to the masses of designer clothing, and his fortune by being the first to use the name as a brand to sell anything from cars to perfume. In 1959, he started his own ready-to-wear collection at Printemps department store. And also, he is the first designer who introduced Japan, Russia, and china getting on his name to all the products. He also designed for unisex and broke with his tradition.
Iceberg A68 Moving Towards South Georgia Island – 30th Dec Day Updates
A68 is the largest iceberg on the earth. It's going to halt soon Antarctica wildlife sanctuary. This sanctuary is home to seals, penguins, and endangered blue whales. It will be a huge threat to this sanctuary. In 2017, A68 is a part broke from the Larsen C ice shelf. It covers 1500 square miles with 500 to 600 feet depth in the water. Now its current location is 31 miles about from south Georgia. The shape of the iceberg is an index finger with a closed hand from the satellite view. It is also unclear how much it will stay one part or breaking apart.
K. Veeramani Honoured With Dr. Narendra Dabholkar Memorial Award – Current Affairs of the Day
Mr. Veeramani devoted his entire life to the pursuit of rationalism and to the cause of social justice in society. Dabholkar is a Maharashtra-based rationalist and he was murdered by someone. In the memory of Dabholkar, the U.S-based Maharashtra Foundation established this award. Veeramani was born on 2 December 1933 in Cuddalore. He obtained his master's degree in economics. And also, received a law degree from Madras University in 1960. He's also chief of Dravidar Kazhagam. He was jailed in 1976 and 1978 for opposing the emergency and showing black flags against Indira Gandhi.
ICICI Bank becomes the First Bank to Collaborated With Google Pay – Top Current Affairs
The ICICI bank collaborated with Google Pay on 28th December 2020. Through the unified payments interface (UPI) to issue FasTag on the payments app. ICICI Bank is the first bank as a partner of Google Pay with this collaboration to issue Fastag. So, it enables the users of google pay to order, monitor, and recharge ICICI Fastag on the payments platform. Recently, union minister of Road and Transport Nitin Kondari announced that the Fastag is compulsory for vehicles. He stated that FasTag is necessary from 1st January 2020 for all the transportations.
Indian Railways Launches HMIS & Mobile Application for UMID – Trendy Current Affairs
The Integrated Hospital Management Information System (HIMS) was launched by Indian Railways on 28th December 2020. Also, they launched a mobile application in 5 units of South Central Railways & 2 units of Northern Railways for downloading Unique Medical Identity Card (UMID). The railway board members Vinod Kumar Yadav, CEO, and the Chairman launched the services. In collaboration with RailTel Corporation, HIMS was developed. UMID mobile app has been introduced to integrate UMID with HMIS to make it easier for railway staff to access their health care services. HMIS will help to increase the quality of health services offered to IR workers.
External Affairs Minister Jai Shankar 2- Days Visit to Qatar – Flash News of 30th Dec 2020
The External Affairs Minister visited Qatar from December 27-28, 2020. As EAM, this was Jaishankar's first visit to Qatar. This was the seventh high-level-person visit by Indian Delegations to West Asia. Jaishankar interacted with various business leaders such as Sheik Faisal and Sheik Khalifa, Chairperson of QCCI & QBA. He briefed with the business leaders on new opportunities in India and urged them to participate in New India's growth. Jaishankar called Tamim Bin Hamad Al Thani, the Amir of the State of Qatar, and handed him a personal message from Prime Minister Narendra Modi. The Amir accepted India's invitation and agreed to an early visit.


text:
Q: Multiple divs in absolute I reached to a problem in my website.
My website is showing all the records in she same page, that each record calls the main div called "work" and inside of that one is calling another one called "worktitle".
I want the worktitle div stays like the image:
Image
But the problem is on the second row. The divs that belongs to the seconda row are in the same place as the first row.
Like you can see here:
Website
HTML Code

<?php do { ?>

 <a href="<?php echo $row_works['url']; ?>" target="new">
 <img src="images/<?php echo $row_works['image']; ?>" />

 <?php echo $row_works['infotec']; ?>

 <?php if ((isset($_GET['lang'])) && ($_GET['lang']=="en")) {
     echo $row_works['descriptionen']; }
     else {
         echo $row_works['descriptionpt'];
        } ?>

 <?php echo $row_works['title']; ?>

 <?php } while ($row_works = mysql_fetch_assoc($works)); ?>

CSS Code
#work {
float: left;
margin: 0px;
padding: 0px;
width : 200px;
border: solid 1px #787879;
}

#work a{
    color: #FFF;
    text-decoration:none;
}
#work a:hover {
    opacity: 0.5;
}

#work img {
    margin: 0px;
    width: 200px;
    z-index: 9;
}

a img{
border: 0;
}
.worktitle {
    position: absolute;
    top: 200px;
    background-color: #787879;
    opacity: 0.6;
    font-weight: bold;
    font-size: 14px;
    z-index: 10;
}
.infotec {
    font-weight: normal;
    font-size:9px;
}
.title {
    text-align: right;
}

What am i doing wrong? Please help me out.

A: First of all id's must be unique in your html so change #work to .work then try giving it a relative position and aligning your description to the bottom.
.work{
   position: relative;
}
.worktitle {
   position: absolute;
   bottom: 0;
}


Want to decorate your VR headset? You need some googly eyes.
Googly eyes are an excellent accessory to add to your VR headset. While you're off in your virtual world, your friends and family still get a bit of a treat watching the eyes bobble around.
Unfortunately, you can't just buy any set of googly eyes for your headset. Some VR devices don't have enough space for large eyes, while others have material that requires special care. Here's your guide to getting googly eyes onto your VR headset.
While the Gear VR does come with a plastic back cover, you have to remove it to place your phone into the device. Additionally, you can't use a case while using Gear VR because your phone has to fit into the Gear VR's port and holder. This restricts what you can stick to your Gear VR, but there are still some options, though you'll have to get a bit creative.
Unless you want to affix googly eyes to the back of your Samsung Galaxy, you'll want to use pipe cleaners in combination with your googly eyes. You can wrap the pipe cleaners around the head strap of the device and bend them to meet around the front of the device. You may want to bend the pipe cleaners over, doubling them up in thickness, to increase stability.
Luckily, with this setup, you can use just about any googly eyes that you'd like as long as they aren't too heavy. Just use an adhesive to stick them to the pipe cleaners and watch the eyes dance around.
The Daydream is a sleek looking device, but the fabric that makes it so attractive also makes it a hassle when it comes to attaching googly eyes to it. If you want to be able to remove the eyes without damaging the headset, you'll have to go with either Velcro or hot glue.
You can purchase Velcro in various shapes, including circles, which are sticky on one side and Velcro on the other. Instead of using the sticky end on the headset, attach the Velcro side of the coin to the headset and then attach the googly eyes to the sticky side.
Alternatively, you can use a hot glue gun, but make sure you use just a dab.
When it comes to the eyes themselves, the Daydream has enough real estate that you can go with a number of eyes. You might as well go nuts with a variety pack and see which one you like best.
Since the Daydream also comes in some fetching colors, you may also want to check out colored googly eyes such as these.
If you have a device with a smooth back, whether it be a PlayStation VR or a Windows Mixed Reality headset that doesn't have a horizontal bar across it, you have the easiest task ahead of you. You can opt for any sized self-adhesive googly eyes for your headset, within reason of course.
The largest you can go without running into an issue is about 2 inches, though you should measure your headset before ordering anything. For example, the PlayStation VR has a smooth back but is also curved quite a bit on the sides, so you don't have as much area to decorate. This pack of 8 should let you deck out all of your devices.
If you have a headset where the back isn't smooth, such as the HTC Vive with its bumps or Windows Mixed Reality devices like Acer, Lenovo's, and HPs, you have some googly eye restrictions. Because the uneven texture of the back of the visor, you have to use relatively small googly eyes.
Depending on your headset, you might have to go as small as these 4mm eyes, but you can go with a variety pack of eyes from 20mm to 2 inches to get the right fit.
There's one unique headset that has a particularly uneven back, the Merge VR. With this headset, you'll have to either opt for a cyclops look with one eye in the middle or invest in an eyepatch because there is an open hole on the device when it's in use that you can't put an eye over.
These are just some of the options you have for decorating your VR devices. Let us know in the comments how you've made your device your own.


text:
Q: Angular provider receives JSON but recognizes its content as none I have two components that depend on a third component function to receive some JSON data.
The function that them both use is this:
getAllPosting(before, after) {
    return new Promise((resolve, reject) => {
      console.log("url", environment.apiLogin + this.apiBase + localStorage.getItem('idEmpresa') + '/' + before  + '/' + after)
      this.http.get(environment.apiLogin + this.apiBase + localStorage.getItem('idEmpresa') + '/' + before  + '/' + after, {headers: {Authorization: this.oauthService.authorizationHeader()}})
        .subscribe((result: any) => {
            console.log("provider result", result)
            resolve(result);
          },
          (error) => {
            reject(error);
          });
    });
  }

On the first component, the console.log("provider result", result) statement shows this result:

However, on the second component, the highlighted console.log shows this result:

It's does not seem to be a back-end problem since on the db all the data and the queries return the correct result. There are thousands of users but none of them have this problem, except for this current user.
This error is resulting to no data being show on the table, like:

I've tried requesting the data on different clients but none of them have this problem. It is exclusive of this one user.
Also, when trying to console.log the array using a forEach iteration, the correct information is showing. However, when trying to console.log the whole object, the main fields that we use for the table keep showing as Nan.
result.forEach(element => {
              console.log("foreeach",element.amountPaid)
            });

A: If the data that comes back from the http.get call is truly correct, there is a chance that the object that is returned from the service is later manipulated by the code that is triggered after resolve(result) is called.
So when you attempt to view the result object in the console, what you will see is what the result of that manipulation is, not what the object was during the console.log call. This would explain why you are seeing correct values during the foreach prints, as the console is printing number literals - not objects, however they are likely then changed by the code that gets triggered when the promise is resolved.
Hence, I would recommend checking the code that uses the getAllPosting result.


When it comes to knit graffiti, yarn bombing or urban knitting, New York City-based artist Agata Oleksiak (known as Olek) has got it all sewn up! The Polish-born queen of crochet specializes in brilliantly colored, large-scale knit-stravaganzas that add new life to an oft-drab urban landscape.
Yo yo yo, mah knittas! Olek is in da house… sitting quietly, knitting seriously, day after day after day. It may sound like a tedious, monotonous, stultifying existence but Agata Oleksiak (Olek, for short) begs to differ. "I can be found in my studio with a pile of movies to watch and a bottle of spiced Polish vodka, aggressively re-weaving the world as I see fit." Knit one, pearl two, drink three, repeat!
Non-Native New York Presents…… Olek!
The Olek-ized bicycle above illustrates Olek's unique style, which seems to meld CAD camouflage with 8-bit imagery. Practical? As a bike, no, but as an art object it's practically outrageous!
Born and raised in communist Poland, one imagines the now 33-year-old "yarntist" longed for bright, vivid colors to illuminate the drab confines of pre-Solidarity Silesian society. When communism fell in 1989, life suddenly presented unlimited opportunities to young Agata. Remember the scene in The Wizard of Oz when the four travelers embark upon the Yellow Brick Road as the world around them bursts into technicolor? Agata Oleksiak lived it, and her road was made from yarn.
Not just the road, either. Olek aims for quality and on occasion, quantity: such as the entire apartment decked out in knitted yarn, including the residents. Hey, it beats mold plus it's slightly more colorful.
The human templates who volunteered to be a physical part of the project were presumably advised to take bathroom breaks beforehand – Olek sewed them into their knit suits without the use of buttons, zippers or fasteners of any kind.
Interviewing Olek can be somewhat of an adventure, especially if you get dragooned into becoming a piece of performance art. One "lucky" reporter found himself sewn into a crocheted skin, then sent out to ride the New York Subway. All things considered, it's doubtful anyone noticed.
Just another day, another crazy dude on the subway… where's Bernhard Goetz when you need him?
Not satisfied with sweaters, scarfs, baby booties and the like, Olek prefers to think big… REAL big. Like cars, for instance. No, not gearshift covers or seatcovers, more like car covers. Such all-inclusive art has its pitfalls, however; convertible owners shouldn't even think of raising or lowering the top.
Cars are an integral part of the New York City ethos but by swathing cars in yarn and adding the essential human element, Olek changes what was animate inorganic into inanimate organic.
Four wheels good, two wheels… also good! Having lived and worked in NYC for over a decade, Olex is intimately acquainted with all forms of urban transportation. Bikes have been "needled" by Olek numerous times and the results never fail to spoke, er, spark amusement and bemusement.
Street pedalers, in my New York City? Thanks to Olek, it's more likely than you think. One wonders, however, how long Olek's open-air artworks remain untouched and unmolested in the Big Apple's urban milieu as her fame (and prices for her art) continue to rise?
Perhaps Olek's most notorious example of urban knitting is the Great Wall Street Coverup… no, not that one, THIS one! Put in place beneath a snowy December sky on Christmas Eve of 2010, Olek's extreme makeover of Wall Street's Charging Bull is cool enough to warm the iciest broker's heart.
The human element lurks at the heart of all Olek's artworks, even those in which no human form appears. When people become part of her art, Olek hides all overt hints of their humanity but there's no denying the life and warmth at the center.
Volunteers who have undergone the "Olek treatment" find the experience almost indescribable. Feelings of constriction and claustrophobia rub shoulders in the mind with the safe and secure sense of shelter and support engendered by the all-engulfing yarn. Is Olek trying to tell us something about the two-faced nature of modern society?


text:
Harper County ist ein County im Bundesstaat Oklahoma der Vereinigten Staaten. Der Verwaltungssitz (County Seat) ist Buffalo.

Geographie
Das County liegt im Nordwesten von Oklahoma, grenzt im Norden an Kansas und hat eine Fläche von 2696 Quadratkilometern, wovon 5 Quadratkilometer Wasserfläche sind. Es grenzt im Uhrzeigersinn an folgende Countys: Comanche County (Kansas), Woods County, Woodward County, Ellis County, Beaver County und Clark County (Kansas).

Geschichte
Harper County wurde am 16. Juli 1907 aus Teilen des Woods County und des Woodward County gebildet. Benannt wurde es nach Oscar G. Harper, einem Mitglied der Verfassunggebenden Versammlung ("Constitunional Convention") von Oklahoma.

17 Bauwerke und Stätten des Countys sind im National Register of Historic Places (NRHP) eingetragen (Stand 1. Juni 2018).

Demografische Daten

Nach der Volkszählung im Jahr 2000 lebten im Harper County 3.562 Menschen in 1.509 Haushalten und 1.030 Familien. Die Bevölkerungsdichte betrug 1 Einwohner pro Quadratkilometer. Ethnisch betrachtet setzte sich die Bevölkerung zusammen aus 95,87 Prozent Weißen, 0,03 Prozent Afroamerikanern, 0,93 Prozent amerikanischen Ureinwohnern, 0,08 Prozent Asiaten, 0,03 Prozent Bewohnern aus dem pazifischen Inselraum und 2,36 Prozent aus anderen ethnischen Gruppen; 0,70 Prozent stammten von zwei oder mehr Ethnien ab. 5,64 Prozent der Bevölkerung waren spanischer oder lateinamerikanischer Abstammung.

Von den 1.509 Haushalten hatten 28,0 Prozent Kinder unter 18 Jahre, die im Haushalt lebten. 58,6 Prozent waren verheiratete, zusammenlebende Paare, 6,5 Prozent waren allein erziehende Mütter. 31,7 Prozent waren keine Familien, 29,2 Prozent waren Singlehaushalte und in 15,6 Prozent der Haushalte lebten Menschen im Alter von 65 Jahren oder darüber. Die durchschnittliche Haushaltsgröße betrug 2,33 und die durchschnittliche Familiengröße betrug 2,87 Personen.

23,3 Prozent der Bevölkerung waren unter 18 Jahre alt, 6,4 Prozent zwischen 18 und 24, 23,4 Prozent zwischen 25 und 44, 25,2 Prozent zwischen 45 und 64 und 21,7 Prozent waren 65 Jahre oder älter. Das Durchschnittsalter betrug 43 Jahre. Auf 100 weibliche Personen kamen 96,6 männliche Personen. Auf 100 Frauen im Alter von 18 Jahren oder darüber kamen 95,2 Männer.

Das jährliche Durchschnittseinkommen eines Haushalts betrug 33.705 USD und das durchschnittliche Einkommen einer Familie betrug 40.907 USD. Männer hatten ein durchschnittliches Einkommen von 27.896 USD gegenüber den Frauen mit 20.784 USD. Das Prokopfeinkommen betrug 18.011 USD. 7,1 Prozent der Familien und 10,2 Prozent der Bevölkerung lebten unterhalb der Armutsgrenze.

Siehe auch
 Liste der Orte in Oklahoma

Weblinks

Einzelnachweise

redpajama_set_name: RedPajamaWikipedia


text:
Q: Is it possible to create a 2nd "Favorites" group in the windows navigation pane? Right now Windows (7,8) allows you to show a "Favorites" group, where you can add links to various folders, regardless of where they actually reside.
Is it possible to create a 2nd group?  My Favorites is getting filled up, and I'd like to be able to organize them for faster access.

A: Unfortunately it isn't possible. You can add multiple locations to your favorites but you can't create a favorite's 2.
You could possibly create two share drives separating your two folders that you're looking for, but you're just adding another click.


text:
The Auschwitz I main camp was a place of extermination, effected mainly by depriving people of elementary living conditions. It was also a centre for immediate extermination. Here were located the offices of the camp's administration, the local garrison commander and the commandant of Auschwitz I, the seat of the central offices of the political department, and the prisoner labour department. Here too were the main supply stores, workshops and Schutzstaffel (SS) companies. Work in these administrative and economic units and companies was the main form of forced labour for the inmates in this camp.
While blitzkrieg was a tactic focused on speed, it would be wrong for any bedroom strategist (like myself) to focus on that characteristic alone and to conclude that speed is all that matters. It is definitely important, but it is important to look deeper at this tactic in order to understand why speed was important in the first place, the core principles behind the strike and how you can employ equivalent tactics in the business world.
Whilst carrying out this final evacuation the SS had set about removing evidence of their crimes. Many thousands of documents were set alight. Between 21 and 26 January 1945, the Germans blew up and partially dismantled the gas chambers and crematoria in Birkenau. Kanada II, the warehouse in which property plundered from victims of the gas chambers was stored and sorted, was also looted and set alight. Members of the Wehrmacht joined in the looting.
Some prisoners—usually Aryan—were assigned positions of authority, such as Blockschreiber ("block clerk"), Funktionshäftling ("functionary"), Kapo ("head" or "overseer"), and Stubendienst ("barracks orderly"). They were considered members of the camp elite, and had better food and lodgings than the other prisoners. The Kapos in particular wielded tremendous power over other prisoners, whom they often abused.[88][89] Very few Kapos were prosecuted after the war, because of the difficulty in determining which Kapo atrocities had been performed under SS orders and which had been individual actions.[90]
The complex, which divided into three main areas, was established by the Nazi's in 1940 and was in use until its Allied liberation in 1945. Historians and analysts estimate the number of people murdered at Auschwitz somewhere between 2.1 million to 4 million, of whom the vast majority were Jews. The majority of prisoners held at Auschwitz were killed in the various gas chambers though many died from starvation, forced labor, disease, shooting squads, and heinous medical experiments.
Allied air superiority became a significant hindrance to German operations during the later years of the war. Early German successes enjoyed air superiority with unencumbered movement of ground forces, close air support, and aerial reconnaissance. However, the Western Allies' air-to-ground aircraft were so greatly feared out of proportion to their actual tactical success, that following the lead up to Operation Overlord German vehicle crews showed reluctance to move en masse during daylight. Indeed, the final German blitzkrieg operation in the west, Operation Wacht am Rhein, was planned to take place during poor weather which grounded Allied aircraft. Under these conditions, it was difficult for German commanders to employ the "armoured idea" to its envisioned potential.
The influence of air forces over forces on the ground changed significantly over the course of the Second World War. Early German successes were conducted when Allied aircraft could not make a significant impact on the battlefield. In May 1940, there was near parity in numbers of aircraft between the Luftwaffe and the Allies, but the Luftwaffe had been developed to support Germany's ground forces, had liaison officers with the mobile formations, and operated a higher number of sorties per aircraft.[71] In addition, German air parity or superiority allowed the unencumbered movement of ground forces, their unhindered assembly into concentrated attack formations, aerial reconnaissance, aerial resupply of fast moving formations and close air support at the point of attack.[citation needed] The Allied air forces had no close air support aircraft, training or doctrine.[71] The Allies flew 434 French and 160 British sorties a day but methods of attacking ground targets had yet to be developed; therefore Allied aircraft caused negligible damage. Against these 600 sorties the Luftwaffe on average flew 1,500 sorties a day.[72] On May 13, Fliegerkorps VIII flew 1,000 sorties in support of the crossing of the Meuse. The following day the Allies made repeated attempts to destroy the German pontoon bridges, but German fighter aircraft, ground fire and Luftwaffe flak batteries with the panzer forces destroyed 56 percent of the attacking Allied aircraft while the bridges remained intact.[73]
Over the decades that followed, ordinary Germans struggled with the Holocaust's bitter legacy, as survivors and the families of victims sought restitution of wealth and property confiscated during the Nazi years. Beginning in 1953, the German government made payments to individual Jews and to the Jewish people as a way of acknowledging the German people's responsibility for the crimes committed in their name.
Concentration camps began to incarcerate 'habitual criminals' in addition to political prisoners. Goebbels stepped up anti-Semitic propaganda with a traveling exhibition which cast Jews as the enemy. Nearly half a million people attended. Some guessed worse would come. Winston Churchill criticised British relations with Germany, warning of 'great evils of racial and religious intolerance', though many colleagues complained of his 'harping on' about Jews.
Auschwitz Birkenau was het grootste concentratie- en vernietigingskamp in het Derde Rijk. De versterkte muren, prikkeldraad, perrons, galg, gaskamers en crematieovens laten de omstandigheden zien waarin de genocide door de nazi's plaats vond. Volgens historisch onderzoek werden er in dit kamp 1,5 miljoen mensen, systematisch uitgehongerd, gemarteld en vermoord. Hiervan waren er meer dan een miljoen Joods en tienduizenden Pools. Auschwitz diende ook als een kamp voor de raciale moord op duizenden Roma en Sinti. De plaats staat symbool voor de wreedheid die de mens zijn medemens in de 20e eeuw aandeed. Het kamp is een belangrijke plaats ter herinnering aan de Holocaust.
As the tide of World War II turned against the Nazis, they began a systematic plan to eliminate or "liquidate" the ghettos they had established, by a combination of mass murder on the spot and transferring the remaining residents to extermination camps. When the Nazis attempted to liquidate the Warsaw Ghetto on April 13, 1943, the remaining Jews fought back in what has become known as the Warsaw Ghetto Uprising. The Jewish resistance fighters held out against the entire Nazi regime for 28 days, longer than many European countries had been able to withstand Nazi conquest.
In September 1939, the German army occupied the western half of Poland. German police soon forced tens of thousands of Polish Jews from their homes and into ghettoes, giving their confiscated properties to ethnic Germans (non-Jews outside Germany who identified as German), Germans from the Reich or Polish gentiles. Surrounded by high walls and barbed wire, the Jewish ghettoes in Poland functioned like captive city-states, governed by Jewish Councils. In addition to widespread unemployment, poverty and hunger, overpopulation made the ghettoes breeding grounds for disease such as typhus.
Schindler's ties with the Abwehr and his connections in the Wehrmacht and its Armaments Inspectorate enabled him to obtain contracts to produce enamel cookware for the military.[31] These connections also later helped him protect his Jewish workers from deportation and death.[32] As time went on, Schindler had to give Nazi officials ever larger bribes and gifts of luxury items obtainable only on the black market to keep his workers safe.[33] Bankier, a key black market connection, obtained goods for bribes as well as extra materials for use in the factory.[34] Schindler himself enjoyed a lavish lifestyle and pursued extramarital relationships with his secretary, Viktoria Klonowska, and Eva Kisch Scheuer, a merchant specialising in enamelware from DEF.[35] Emilie Schindler visited for a few months in 1940 and moved to Kraków to live with Oskar in 1941.[36][37]


The IET Faraday Challenge is an annual engineering based competition for schools across the UK. The competition gives students aged 12-13 the opportunity to research, design and make prototype solutions for real-world engineering problems.
Bosch has supported the IET Faraday Challenge Days since 2016, and sends engineers to the challenge day events to give expert advice and guidance to the students. By doing this we hope to inspire the next generation to explore the career opportunities available within engineering.
The 2017-18 season was the biggest season to date! The 2017-18 season of Faraday Challenge Days has seen 167 events take place across the county, with 480 schools and 5670 students involved.
In its 10th anniversary year the Faraday Challenge partnered with Thorpe Park to give students an adrenalin packed challenge where they had to design a new attraction for the theme park and make a working prototype of one element of their design which had to include a circuit.
We enjoyed working with young people to challenge engineering stereotypes and raising awareness for all the varied careers in engineering.


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Tarloxotinib used to knock down NRG1-Fusion cancers
Shreyas Tanna April 3, 2019
At the annual meeting of the American Association for Cancer Research (AACR), research by Rain Therapeutics & the University of Colorado Cancer Center was presented. The study showed that tarloxotinib, which is a clinical-stage drug is spirited when opposed to NRG1 fusion cancers along with cancer for which the drug was essentially designed.
Doctor Robert C stated "This is a relatively new genetic target recently identified in breast, lung, ovarian and many other cancer types, and there are currently no approved therapies targeting NRG1 fusions. We're very hopeful that tarloxotinib could meet this need."
Tarloxotinib is a unique inactive compound which can be absorbed in the body for producing a drug. RAIN-70, which is a pilot trial of tarloxotinib is now used to recruit patients with EGFR exon 20 mutations since there is no drug approved for these subtypes of cancer till date. As mentioned by the Co-founder of Rain Therapeutics, Avanish Vellanki: "We are very excited about the potential to offer an effective and more tolerable approach to treating patients suffering from HER-driven cancers, including NRG1 fusions. When tarlox is near healthy cells, it's inactive and avoids the side effects characteristic of many other drugs in development for these types of cancers."
It has been found by this study that not only are EGFR & HER2-altered cancers directly inhibited by Tarloxotinib, but also takes a step into blocking gene over-activation. Doebele said: "NRG1 is another way to activate HER2 and HER3. In our cell line and xenograft models, we show that tarloxotinib is even more effective at silencing these cancers driven by NRG1 than its already-promising results against HER2 and EGFR directly."
The research also shed lights on a shift in designing of the anti-cancer field, from drugs directed at cancers according to their position in the body for example breast cancer to targeting genetic abnormalities controlling cancer, whose location in the body does not matter. Doctor Doebele added on this: "This way what seems like a rare disease – say it's only 0.2 percent of lung cancer – isn't so rare after all. Once you look at 0.2 percent of all cancers, you're talking about a significant number of patients who could benefit."
The study described this drug as "a profound, durable and dose-dependent anti-tumor response."
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