text:
\section{Introduction}
The popularity of Minimally Invasive Surgeries (MIS) in modern medical treatment has brought new opportunities and challenges for automated surgical scene understanding, which can be used to empower Computer Assisted Interventions (CAI) and is the foundation of intelligent systems, such as robotic vision, surgical training applications, and medical report generation. This kind of intelligent system can remind surgeons not to miss important steps during complex surgery. By integrating with natural language report generation, the intelligent system can also eliminate the frustrating task of document entry task for clinicians, allowing them to focus on patient-centric activities. It can free doctors and nurses from the low-value task of writing reports of surgical procedures that are more suitable for machines. Besides, such a natural language record of the surgical procedure has a detailed post-operative analysis of the surgical intervention.

Automatically generating the description for a given surgical procedure is a complicated problem since it requires an algorithm to complete several computer vision and Natural Language Processing (NLP) tasks, such as object recognition, relationships understanding between vision and text elements, and then generate a sequence of words. Existing studies for automatic report generation focus on medical images, such as radiology and pathology images. In this case, the report is a diagnosis report which will describe whether the body part examined in the imaging technique was normal, abnormal, or potentially abnormal~\cite{jing2017automatic}. In our work, the automatically generated report focuses more on understanding and describing the surgical scene. It describes which surgical instrument appears in the surgical scene and the instrument-tissue interaction.

Existing image captioning models cannot generalize well to the Target Domain (TD) images. Learning a predictor when there is a shift in data distribution between training and validation is considered as Domain Adaptation (DA). The cost of generating labels for data is high, which often becomes one of the biggest obstacles preventing the application of the machine learning approach. However, the model with the DA ability can work when the TD lacks labels.

\begin{figure*}[!hbpt]
\centering
\includegraphics[width=0.75\linewidth]{Figures/WholeModel.pdf}
\caption{The architecture of the proposed network. The network comprises a region feature extractor, multi-layer Transformer-like encoder-decoder, and a domain-adversarial network. ResNet18 is used to extract region features in input images. The domain-adversarial network, which consists of a GRL and a domain classifier, is connected with the last encoding layer to distinguish whether the sample is from the SD or the TD. The image encoder incorporated with domain-adversarial neural network takes in the region features and learns the domain-invariant features with an adversarial learning scheme and attempts to understand relationships between regions. The language decoder reads the output of each encoding layer to generate the output sentence word by word. The stacked sentence can form a medical report.}

\label{fig:Architecture of the network}
\end{figure*}

\subsection{Related Work}

\subsubsection{Surgical Scene Understanding}
Surgical scene understanding is significant for image-guided robot-assisted surgery. Incorporating scene understanding capability in robotic surgery provides possibilities for future semi-automated or fully automated operations. Our previous works include surgical instrument tracking with task-directed attention~\cite{islam2020ap}~\cite{islam2019learning}, and real-time instrument segmentation ~\cite{islam2019real}. After the surgical instrument recognition problem has been solved to a large extent, the next goal is to get out the instrument itself and focus on the relationships between the instruments and  the Region of Interest (ROI). Therefore, a graph-based network is introduced in~\cite{islam2020learning} to do deep reasoning for the surgical scene, learn to infer a graph structure of instruments-tissue interaction, and predict the relationship between instruments and tissue. The further intention is that we want to express this prediction of their relationship in natural language, which can have richer scene information and the ability to interact with people.

\subsubsection{Transformer based model from language to vision}
The Transformer~\cite{vaswani2017attention} is proposed for language translation which achieves the state-of-the-art performance. In combining scene understanding and natural language, Convolutional Neural Network (CNN) is used to extract visual features, and Recurrent Neural Network (RNN) is widely adopted as the language models, which can be summarized as "CNN + RNN" framework~\cite{shi2020improving}. Although this framework is widely adopted, the RNN-based model is severely limited by its sequential nature and difficult to parallelize characteristics. Some recent researches have used the Transformer model as the language model for scene understanding in which the input sequence can be passed in parallel.~\cite{herdade2019image} still utilized the original architecture of the Transformer and explored the geometric relationships between detected objects. A memory-augmented recurrent Transformer~\cite{lei2020mart} is employed to do video captioning.

\subsubsection{Label Smoothing}
Label Smoothing (LS) is a regularization technique that flattens the true label with a uniform distribution during cross-entropy loss calculation in network training \cite{szegedy2016rethinking}. Recently, there is evidence that LS can prevent over-confidence in prediction and improve the model calibration~\cite{muller2019does}. Moreover, LS is also used to enhance the prediction uncertainty \cite{zhang2020self} and the teacher-free knowledge distillation \cite{yuan2019revisit}. In our previous work, we present that LS can improve the feature representation in the penultimate layer and effective as an object feature extraction model \cite{islam2020learning}.

\subsubsection{Domain Adaptation}
 The model's performance tends to drop when evaluated on a TD dataset, which is different from the dataset used for training. This limitation motivates the research on DA~\cite{valverde2019one} in multiple sclerosis lesion segmentation in one shot way. The method for DA in~\cite{orbes2019multi} consists of domain adversarial learning and consistency training. Many methods attempt to match the feature space distribution in the Source Domain (SD) and the TD for unsupervised DA (UDA). To this end,~\cite{borgwardt2006integrating} reweigh or select examples of the SD.~\cite{ganin2016domain} achieves this matching by modifying and changing the feature representation itself. For supervised DA, the approaches utilize the labeled data from the TD to "fine-tune" the network trained on the SD. In our work, the unsupervised and semi-supervised DA are explored.

\subsection{Contributions}
 Our contributions are summarized as follows:
\begin{itemize}
    \item[--] Propose transformer-based multi-layer encoder-decoder architecture with the gradient reversal adversarial learning scheme to generate the surgical report in robotic surgery.
    \item[--] Investigate model calibration with label smoothing cross-entropy loss for feature extraction from penultimate layer and UDA.
    \item[--] Design domain-adaptation in various unsupervised and semi-supervised manners such as zero-shot, one-shot, and few-shot training.
    \item[--] Annotate robot-assisted surgical datasets with proper captions to generate the surgical report in two different surgical domain datasets of MICCAI robotic scene segmentation challenge and Transoral Robotic Surgery (TORS).

\end{itemize}

\section{Methods}

\subsection{Background}

Transformer-based multi-layer encoder-decoder in~\cite{cornia2020meshed} is a fully-attentive model and shows excellent performance for image captioning tasks. It is a variant of the original Transformer~\cite{vaswani2017attention} which achieves the state-of-the-art results in machine translation and language understanding. In~\cite{cornia2020meshed}, the encoder module takes regions from images as input and understands relationships between regions. The decoder reads each encoding layer's output to model a probability over words in the vocabulary and generates the output sentence word by word by feeding the predicted word back as input at the next time step. Compared with~\cite{vaswani2017attention},~\cite{cornia2020meshed} has made two changes: 1) multi-head attention in image encoder is augmented with memory to understand the semantic relationships between detected input objects; 2) the cross-attention in language decoder is devised to utilize all encoding layers, rather than attending only the last encoding layer.

Despite the excellent accuracy of the deep learning frameworks in vision tasks, it performs poorly in the TD dataset. To solve it,~\cite{ganin2016domain} proposes a novel representation learning method called domain adversarial learning for DA. The goal is to learn features that have 1) distinction for the major task during learning on the SD and 2) no distinction regarding the change of domains. H-divergence measures the distance between the source and the target distributions used by~\cite{ben2007analysis}. A Gradient Reversal Layer (GRL) with an adversarial learning scheme is used to domain adaption tasks with classification problem \cite{ganin2016domain}.

Most recently, it is found that the current deep learning models are poorly calibrated, which drops the generalization capability of the model \cite{muller2019does, guo2017calibration, lakshminarayanan2017simple}. LS is showing evidence to improve model calibration and prevent the over-confidence of model learning.~\cite{muller2019does} presents that LS calibrates the representations learned by the penultimate layer and makes the features of the same class form a tight cluster.

Inspiring by the above works, we design a transformer-based multi-layer encoder-decoder architecture with a GRL domain classifier and label smoothing cross-entropy loss to generate the surgical reports in robotic surgery.

\subsection{GRL Domain Classifier}
Domain classifier forms of GRL with a discriminator and trains in an adversarial manner to learn domain invariant features \cite{ganin2016domain}. It trains with adversarial labels that 0 label for the SD and 1 label for the TD. GRL just acts as identity transformation layer, $G(x) = x$ in forwarding propagation and a negative constant multiplies the gradient in backward propagation ($\frac{dG}{dx} = -I$, where I is an identity matrix) and then passes it to the preceding layer. By this way, GRL subtracts the gradients of the main network (our caption generation model) and domain classifier instead of being summed during Stochastic Gradient Descent (SGD) training. On the other hand, this prevents SGD from making features dissimilar across the domains. In this case, the model attempts to learn domain invariant features, making the domain classifier indistinguishable.

We propose two significant improvements of the GRL domain classifier module: (1) we assign the discriminator with 3 classes (0 for source, 1 for target, and 2 for extra class) to enhance learning capacity, which will make the discriminator harder to fool during training; (2) integrate an additional fully-connected layer to boost feature learning, as shown in Fig. \ref{fig: novel_GRL_domain_classifier}

\begin{figure}
\centering
\includegraphics[width=.7\linewidth]{Figures/novel_GRL_domain_classifier.pdf}
\caption{Proposed GRL domain classifier. It consists of a GRL layer and three fully-connected layers. The output has 3 classes: SD, TD, and the extra one.}
\label{fig: novel_GRL_domain_classifier}
\end{figure}

\subsection{LS for model calibration and DA}
A well-calibrated model estimates confidence probability, which represents true correctness of likelihood and leads to better generalization. LS, where a model is trained on a smoothened version of the true label with Cross-Entropy (CE) loss, shows great effectiveness in improving model calibration \cite{muller2019does}. If smoothened label, $T_{LS} = T(1-\epsilon) + \epsilon/K$ then CE loss with LS can be formulated as $CE_{LS} = - \sum_{k=1}^{K} T_{LS} log (P)$ where true label $T$, smoothing factor $\epsilon$, total number of classes $K$  and predicted probability $P$. A recent study \cite{islam2020learning} investigates that LS learns better feature representation in the penultimate layer, which is effective in object feature extraction. In this work, we observe the behavior of LS trained model in the DA task.

\subsection{Network Architecture}

The whole network consists of an image region feature extractor, Transformer-based multi-layer encoder-decoder~\cite{cornia2020meshed}, and a GRL domain classifier network. We choose a light-weight feature extraction model of ResNet18 \cite{he2016deep}, similar as \cite{islam2020learning}, to obtain feature vectors of surgical objects (details in section \ref{sec:feature_extraction}). The GRL domain classifier network consists of a discriminator followed by the GRL as in Fig. \ref{fig:Architecture of the network}. The encoder block forms of memory augmented self-attention layer and feed-forward layer and a decoder block consists of self-attention on words and cross attention over the outputs of all the encoder layers, similar to \cite{cornia2020meshed}. There are three encoder and decoder blocks stack to encode input features and predict the word class label. The output of the final encoder layer is fed to the proposed GRL domain classifier. Each training iteration calculates losses from both SD and TD, and then caption prediction loss is combined with domain classifier losses to update the model parameters in an adversarial manner. We utilize CE loss with LS as the caption prediction loss and vanilla CE as the domain loss function. Therefore, the model loss can be formulated as

\begin{equation}
\centering
\label{eqn:loss}
L = L_{y} + L_{d},
\end{equation}

where $L_{y} = CE_{LS}$ is the caption prediction loss which is CE loss with LS and $L_{d} = L_{S} + L_{T}$ is the domain loss which is the fused losses of the SD loss $L_{S}$ and the TD loss $L_{T}$.

The GRL domain classifier leads to the appearance of features that have a distinction for the captioning task and domain-invariance at the same time. The model attempts to learn a representation allowing the decoder to predict the word class, also weakening the ability of the domain classifier.

\section{Experiments}

\subsection{Dataset}
\subsubsection{Robotic scene segmentation challenge}
The SD dataset is from the robotic instrument segmentation dataset of MICCAI endoscopic vision challenge 2018 \cite{allan20202018}. The training set includes 14 robotic nephrectomy operations obtained by the da Vinci X or Xi system. There are 149 frames in each video sequence, and the frame has a dimensionality of 1280*1024. A total of 9 objects appear in the dataset, including 8 Instruments: bipolar forceps, prograsp forceps, monopolar curved scissors, clip applier, suction, ultrasound probe, stapler, and large needle driver respectively and kidney tissue. These surgical instruments have a variety of different semantic relationships and interactions with the tissue. A total of 11 kinds of semantic relationships are identified to generate the natural language description for images. The identified semantic relationships include manipulating, grasping, retracting, cutting, cauterizing, looping, suctioning, clipping, ultrasound sensing, stapling, and suturing. The Sequences 1st, 5th, 16th are chosen for the validation, and the remaining 11 sequences are selected for the training following the previous work~\cite{islam2020learning}. The training and validation data sequences are carefully chosen to ensure that most interactions are presented in both sets.

\subsubsection{TORS dataset}
We have collected the TD dataset of TORS surgery using the Da Vinci Robot on human patients. All 9 patients have oropharynx cancer. Among these 9 patients, there are 8 males and 1 female with ages ranging from 23 to 72 and multiple cancer sites like the tonsil, the base of the tongue, and the posterior pharyngeal wall. We filter out some poor-quality videos that remain still and are not undergoing surgery. A total of 181 frames were selected based on the requirement to have at least one same surgical instrument as the robotic scene segmentation challenge dataset. These 181 frames are cropped and then resized to the same shape with MICCAI Robotic scene segmentation challenge dataset. A total of 5 objects are in the dataset, including tissue, clip applier, suction, spatulated monopolar cautery, maryland dissector. The 5 kinds of relationships comprise manipulating, grasping, cauterizing, suctioning, clipping. We split the dataset based on different patients. The training dataset has 48 frames collected from Patient [1, 2, 3, 4, 5, 6, 7] and the validation dataset has 133 frames collected from Patient [8, 9].

\begin{figure}[!h]
\centering
\includegraphics[width=1\linewidth]{Figures/Dataset_Visualization.pdf}
\caption{Visualization of image-caption pairs from the SD and the TD. There are many unique ground truth captions}
\label{fig:dataset_visualization}
\end{figure}

\subsubsection{Domain Shift}

Fig. \ref{fig:dataset_visualization} shows example image-caption pairs from the SD and the TD. The SD is about robotic nephrectomy surgery, and the TD is about TORS. Compared with the SD, the TD has a different surgical background, and the surgery is performed on different tissue. Besides, the TD does not share all surgical instruments with the SD. Compared with the SD, the TD has two new instruments: spatulated monopolar cautery and maryland dissector. The distribution shift between the SD and TD can be distinguished from Fig. \ref{fig:tSNE before domain adapatation}, which shows the t-SNE of examples before DA from a three-dimensional (3D) perspective, which can prove the distribution shift between these two domains.

\begin{figure}[!hbpt]
\centering
\includegraphics[width=0.6\linewidth]{Figures/tSNE.pdf}
\caption{t-SNE of samples before DA. The "0" indicated by red color are examples from the SD, and the "1" indicated by blue color are examples from the TD. The SD examples do not overlap with examples from the TD because of the distribution shift between these two domains.}
\label{fig:tSNE before domain adapatation}
\end{figure}

\begin{table*}
\centering
\caption{Performance of the proposed model on SD and TD dataset. Evaluation metrics such as BLEU-n, METEOR, ROUGR, and CIDEr are used to assess the similarity between the generated sentence and the ground-truth}
\begin{tabular}{c| c| c c c c c c c c c c}
\hline
\multicolumn{2}{c|}{}& & \textbf{BLEU-1}$\uparrow$ & \textbf{BLEU-2}$\uparrow$ & \textbf{BLEU-3}$\uparrow$ &\textbf{BLEU-4}$\uparrow$ & \textbf{METEOR}$\uparrow$ & \textbf{ROUGE}$\uparrow$ & \textbf{CIDEr}$\uparrow$ \\ \hline
\multicolumn{2}{c|}{\multirow{2}{*}{SD}} & $M^2$ Transformer \cite{cornia2020meshed} & 0.5054 & 0.4543 & 0.4055 & 0.3646 & 0.4441 & 0.6355 & 1.7878\\
\multicolumn{2}{c|}{} & Ours         & 0.5228 & 0.4730 & 0.4262 & 0.3861 & 0.4567 & 0.6495 & 2.2598\\
\hline
\multirow{8}{*}{TD}& \multirow{2}{*}{UDA} & $M^2$ Transformer \cite{cornia2020meshed} & 0.2302 & 0.1059 & 0.0469 & 0.0267 & 0.1286 & 0.2956 & 0.1305\\
& & Ours  & 0.2493 &  0.1150 & 0.0517 & 0.0289 & 0.1390 & 0.3129 & 0.1517\\
\cline{2-10}

& \multirow{2}{*}{Zero-shot} & $M^2$ Transformer \cite{cornia2020meshed} & 0.3204 & 0.2463 & 0.1923 & 0.1502 & 0.2371 & 0.4413 & 0.2874\\
& & Ours  & 0.3118 & 0.2406 & 0.185  & 0.1409 & 0.2401 & 0.4336 & 0.3395\\
\cline{2-10}

& \multirow{2}{*}{One-shot} & $M^2$ Transformer \cite{cornia2020meshed} & 0.3746 & 0.3285 & 0.2939 & 0.2646 & 0.3449 & 0.5101 & 0.6367\\
& & Ours  & 0.4042 & 0.372  & 0.3433 & 0.3161 & 0.4066 & 0.5385 & 0.8615\\
\cline{2-10}

& \multirow{2}{*}{Few-shot} & $M^2$ Transformer \cite{cornia2020meshed} & 0.4096 & 0.3803 & 0.3532 & 0.3265 & 0.4203 & 0.5489 & 0.9770\\
& & Ours & 0.4141 & 0.3888 & 0.3637 & 0.3375 & 0.4357 & 0.5538 & 0.9828\\

\hline

\end{tabular}
\label{table:full_performance}
\end{table*}

\subsubsection{Annotation}
The datasets are annotated with the appropriate caption by an experienced clinical expert in robotic surgery. The caption follows the description pattern of $\langle$ object1, predicate, object2 $\rangle$, which can concisely and accurately convey information of the surgical scene, for instance, "A monopolar curved scissor is cutting tissue."

\subsection{Implementation details}

\subsubsection{Image region feature extractor}
\label{sec:feature_extraction}
We use the ResNet18 classification model to extract the feature vector for surgical objects. We crop the surgical instruments and ROI tissue and train the ResNet18 with CE loss combined with LS by following our previous work \cite{islam2020learning}. The penultimate layer's feature vector of size 512 is extracted for each surgical object.  Fig. \ref{fig:tsne_ls_features} shows the extracted features without (w/o) and with (w) LS. The features extracted by the LS model are more distinguishable among the classes.

\begin{figure}[!hbpt]
\centering
\includegraphics[width=1\linewidth]{Figures/Updated_feature_extraction_LS.pdf}
\caption{The extracted features of the penultimate layer for ResNet18 w and w/o label smoothing (LS). The t-SNE plots show that the extracted features with LS are more distinguishable and tighter clusters for the same class.}
\label{fig:tsne_ls_features}
\end{figure}

\begin{figure*}[!hbpt]
\centering
\includegraphics[width=1\linewidth]{Figures/Results_Visualization.pdf}
\caption{Several image captioning examples generated by the base model and our model. Compared with the base model, the sentences predicted by our model are more reasonable and accurate.}
\label{fig:Results visualization}
\end{figure*}

\subsubsection{Vocabulary and tokenization}
All captions will go through a series of processing, including converting them to lower cases, removing punctuation characters, and tokenization using the spacy NLP toolkit. Vocabulary size of 49 is built based on both SD and TD, which include unique words in the captions and special tokens ($\langle$ unk $\rangle$, $\langle$ pad $\rangle$, $\langle$ bos $\rangle$, and $\langle$ eos $\rangle$). At test time, the model predicts the next word given the previously predicted words with beam search.

\subsubsection{Hyper-parameters}
Following the experimental setting in~\cite{cornia2020meshed}, when training with cross-entropy loss, the learning rate scheduling strategy of~\cite{vaswani2017attention} with a warmup equal to 10000 iterations is used. The number of epochs for training is set to 50. The seed is set to a fixed number to make the results reproducible. We train the model using Adam optimizer with a batch size of 50 and a beam size of 5. The network was implemented by PyTorch and trained in the NVIDIA RTX 2080 Ti GPU.

\section{Results and Evaluation}

\subsection{Evaluation Metrics}
We evaluate the approach using four commonly used metrics for image captioning, namely BLEU-n~\cite{papineni2002bleu}, ROUGE~\cite{lin2004rouge}, METEOR~\cite{banerjee2005meteor}, and CIDEr~\cite{vedantam2015cider}. BLEU-n measures the precision of n-grams between the ground-truth and the predicted sentences. Usually, n = 1, 2, 3, 4.
To measure model calibration error, we use well-known metrics like Expected Calibration Error (ECE) \cite{guo2017calibration}, Static Calibration Error (SCE)\cite{nixon2019measuring}, Thresholded Adaptive Calibration Error (TACE), and Brier Score (BS) \cite{nixon2019measuring, ashukha2020pitfalls}.

\subsection{Caption generation with the SD dataset}
Table \ref{table:full_performance} represents the overall performance of the proposed approach with validation and domain adaption settings. The validation metrics are calculated with the SD dataset. Our method achieves better performance in almost all the metrics.

\subsection{Caption generation with the TD dataset}
Our model's domain adaption performance is evaluated with extensive unsupervised and semi-supervised settings such as zero-shot, one-shot, and few-shot training, as shown in Table \ref{table:full_performance}. All TD experiments share the same validation set and just use a different number of training images. In UDA, the model trained on SD is evaluated directly on TD. In zero-shot DA, the trained model is fine-tuned with the frames whose captions cover 85\% of the words in the vocabulary from the TD dataset. In one-shot DA, it is fine-tuned with images whose captions cover all the words with the smallest amount of images. Few images whose captions cover all the words are used to fine-tune the model. The proposed model has also outperformed the base model for these experiments.

\subsection{Model Calibration Vs. DA }

\begin{table}[!h]
\caption{Model miscalibration quantification for our model w and w/o LS. Evaluation metrics such as Expected Calibration Error (ECE), Static Calibration Error (SCE), Thresholded Adaptive Calibration Error (TACE), Brier Score (BS) are used to calculate the calibration error for each model.}
\label{tab:calibration}
\centering
\begin{tabular}{c|c|c|c|c|c}
\hline
\textbf{Ours} &\textbf{TD(UDA)} &\multicolumn{4}{c}{\textbf{Calibration Error}} \\ \hline
\textbf{Methods} & \textbf{BLEU-1}$\uparrow$ &\textbf{ECE}$\downarrow$ & \textbf{SCE}$\downarrow$ & \textbf{TACE}$\downarrow$ & \textbf{BS}$\downarrow$\\ \hline
w LS &0.2493 &0.1768 &0.0493 &0.0489 &0.5063\\ \hline
w/o LS &0.2302 &0.2001 &0.0533 &0.0531 &0.9585\\ \hline

\hline
\end{tabular}
\label{table:calibration_vs_da}
\end{table}
A well-calibrated model produces smoother output distribution, which leads to better generalization and feature learning. In this experiment, we investigate the behavior of a better-calibrated model in the TD validation. As LS can prevent the over-confident prediction and improve probability calibration \cite{muller2019does}, we observe the TD data prediction performance of our approach trained between w and w/o LS. Table \ref{table:calibration_vs_da} demonstrates the comparative scores with the TD prediction and calibration errors. The results indicate the superiority of the calibrated model on the DA task.

\subsection{Qualitative Analysis}
We visualize some examples predicted by our proposed model for the source and target domain, as shown in Fig. \ref{fig:Results visualization}. Sometimes there are redundant words at the end of the predicted sentence, which may be caused by exposure bias \cite{ranzato2015sequence} and word-level training by using the traditional CE loss \cite{ranzato2015sequence}. This problem will be solved by introducing a Reinforcement Learning training technique to achieve sequence-level training \cite{rennie2017self} and fine-tune the sequence generation. Overall, the generated sentences can describe the content of the surgical images accurately.

\section{Ablation Study}

\subsection{LS Feature Extraction}
Fig. \ref{fig:tsne_ls_features} is illustrated the better feature representation extracted from ResNet18 classification model trained with LS. In this section, we present the caption prediction models' performance, train w, and w/o LS extracted features. Fig. \ref{fig:caption_with_ls_features} demonstrates the significant prediction improvement using LS extracted feature.

\begin{figure}[!h]
\centering
\includegraphics[width=1\linewidth]{Figures/performance_ls_feature.pdf}
\caption{Caption prediction performance w and w/o LS feature extraction using ResNet18. LS extracted feature boosts the prediction performance.}
\label{fig:caption_with_ls_features}
\end{figure}

\begin{table}[!h]
\centering
\caption{Ablation study of the proposed model while integrating LS and GRL for the SD and the TD (UDA) experiments.}
\label{table:module_ab}
\begin{tabular}{c|c|c|c|c|c|c}
\hline
\multicolumn{3}{c|}{\textbf{Modules}} &\multicolumn{2}{c|}{\textbf{SD}} &\multicolumn{2}{c}{\textbf{TD (UDA)}} \\ \hline

\textbf{Base} & \textbf{LS} & \textbf{GRL} & BLEU-1 &CIDEr & BLEU-1 &CIDEr \\ \hline
\cmark & \cmark & \cmark &0.5228   &2.2598  &0.2493 &0.1517\\ \hline
\cmark & \cmark & \xmark &0.5161   &2.3406 &0.2345  &0.1314\\ \hline
\cmark & \xmark & \xmark  &0.4659  &1.2752  &0.2302  &0.1305 \\ \hline

\end{tabular}
\label{fig:ablation_grl_dc}
\end{table}

\subsection{GRL Domain Classifier}
Table \ref{fig:ablation_grl_dc} shows the effect of each proposed technique in this work. GRL domain classifier boosts the model prediction of both SD and TD datasets for most of the metrics. However, LS produces a significant enhancement in SD prediction. Table \ref{table:outputs_of_domain classifier} demonstrates that the extra class improves the model performance by making the model harder to be fooled.

\begin{table}[!h]
\centering
\caption{Two vs. Three outputs of GRL Domain Classifier}
\begin{tabular}{c|c|c|c}
\hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}GRL \\ \textbf{Domain Classifier}\end{tabular}} & \multicolumn{3}{c}{\textbf{SD}}  \\ \cline{2-4}
                                                                                & BLEU-1 & METOR  & CIDEr  \\ \hline
Two classes (SD, TD)                                                                         & 0.5231 & 0.4566 & 2.1547 \\ \hline
Three classes (SD, TD, Extra)                                                                        & 0.5228 & 0.4567 & 2.2598 \\ \hline
\end{tabular}
\label{table:outputs_of_domain classifier}
\end{table}

\section{Discussion and Conclusion}
We present a multi-layer encoder-decoder transformer-like model incorporating the gradient reversal adversarial learning and LS to generate captions for the SD and the TD surgical images, which can describe the semantic relationship between instruments and surgical ROI. We observe that a well-calibrated model can be beneficial for domain adaption tasks. The experimental results prove that the proposed model can perform better than the base model in the SD and the TD. When these sentences are stacked, a medical report for the surgical procedures can be obtained. The limitation is the small TD dataset. The performance can be further improved if we can use a larger dataset. Future work can be focused on integrating temporal information in surgical report generation.

\section{Acknowledgment}
This work was supported by the National Key Research and Development Program, The Ministry of Science and Technology (MOST) of China (No. 2018YFB1307700), Singapore Academic Research Fund under Grant R397000353114 and TAP Grant R-397-000-350-118.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


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10/03/2013�� Best Answer: First I would enable command blocks in the server properties and then I would make the default game mode 2 or adventure. The prevents users from breaking blocks while still being able to toggle levers, pressure plates, etc. Restart the server and you're good to go. how to make your phone service block private numbers 1/02/2014�� Are you sure nothing else is blocking the tnt from destroying blocks.. any other plugins blocking it? Or maybe The tnt you are trying to blow up is in a region? As it maywell be something to do with Coreprotect if its not essentials.
4 thoughts on � 6 Tips You Need To Know To Avoid E Cigarette Explosions � Bob Williams says: #2 is a fault of manufacture not including the right wall adapter with the USB cable!
Hi, I'm beginner-level in CSS and I'm trying to do a fixed width class for a block (class bb in attached example) that will also NOT break the line so I can add a link on the right.
Ice blocks, spawners, and glass blocks will break upon mining, giving you nothing. If a chest is mined, the chest will be dispensed out of the quarry, but the contents will be scattered where the chest was broken, as if it were broken by hand.


About this Worksheet: This fun capitalization worksheet instructs the student to read each group of words and circle the word that should be capitalized. The second part of the worksheet has the student write each sentence and capitalize the words that need to be capitalized. The student must look for the start of the sentence and proper nouns that need to be capitalized.


J&M Services Is Your Air Duct Cleaning Gresham Specialist!
What exactly makes J&M Services an Air Duct Cleaning Gresham Specialist? How does this benefit you, our valued Customer? Right off the bat we have an edge over almost every other Gresham Air Duct Cleaning Company with our decades of training ventilation & duct work! Not only is J&M Services the obvious choice for Air Duct Cleaning Gresham Customers, we're also engaged in ongoing classes & certified in all types of heating duct work & ventilation. The Duct Cleaning Technicians at J&M are trained in all aspects of complex heating duct systems, and more importantly to you, our skilled Tech's can take care minor duct work repairs while onsite during your duct cleaning appointment at very minimal costs. Our highly skilled Journeyman Air Duct Cleaners are able to bid larger or more involved duct work issues in the homes heating duct system that most lesser trained Gresham OR air duct cleaning contractor's would not have the background in ventilation & duct work to catch, much less advise the customer or provide an estimate. We promise you won't see any sales tactics from our Technicians. The Tech will inform you of any potential issues, and let you make the choice if you would like us to proceed with any repairs.
J&M Services, Highly Trained Air Duct Cleaning Gresham Specialists!
After even a brief internet search for Air Duct Cleaning Gresham companies, we're sure you have realized there are multiple techniques & equipment being used for air duct cleaning in Gresham Oregon. Unfortunately, there are all to often air duct cleaning companies in Gresham OR that use "sneaky hidden fees" that cleverly aren't mentioned until after the air duct cleaning work has been completed. Getting a big surprise when the final invoice is presented is not morally nor often legally acceptable! At J&M, we've always believed in keeping duct cleaning pricing as simple & easy to understand as possible. We charge per vent, so a quick count of the supply & return vents will get you a firm estimate with no hidden fees or sales gimmicks. We utilize modern technology with portable duct cleaning machines, including powerful FOUR motor vacuum systems for our Gresham OR air duct cleaning services. Modern Portable air duct cleaning systems allow us to fully access every last nook & cranny of the duct work thru out the home. The smaller and more maneuverable duct cleaning equipment is also less intrusive than obsolete duct truck systems which require large access holes in the duct work and most often open doors during the entire appointment. If you're still in doubt which company to choose, please take a minute for a quick online search query, and let our literally 100's of positive reviews from our happy Gresham OR air duct cleaning customers speak for themselves!
Air Duct Cleaning Gresham Services, Is there a specific time frame for how often I should have my ducts cleaned? What's the process for air duct cleaning?
J&M Gresham OR Air Duct Cleaning Services has always believed in integrity & honesty. If after reading the following examples you still have questions, click here to read more about when & if you need your air ducts cleaned. Whenever you are moving into a new home and you don't know when or even IF the previous occupants ever had the air ducts cleaned, by all means don't live with someone else's skin flakes, dandruff, bacteria & molds, pet hair & dander, and who knows what else! If you're a dog or cat person (or have other furry inhabitants) , have younger kids (or older) bringing dirt & debris every time they come home, live on a gravel or unpaved road with more than average dust, or any number of other circumstances where more contaminants could enter the homes HVAC ducting, you may want to increase the frequency of your air duct cleaning intervals.
If you've recently gone through a remodel or some construction, now is a good time to schedule air duct cleaning services! J&M Gresham OR Air Duct Cleaning Services uses specially designed soft bristled duct cleaning brushes sized specifically for each individual size of the heating ducts in your home to safely remove all the "nasties" from the inside walls of the duct work, followed by an industrial grade vacuum hose powered by FOUR powerful vacuum motors. HEPA filtration at each motor ensures all potentially harmful allergens & bacterias are contained within our portable machines and never released back inside the home. We hear all kinds of great comments from our customers after having our Gresham Or Air Duct Cleaning Services performed, including less dust on furniture thru out the house, reduced or even completely eliminated allergy symptoms, furnace components & filters staying clean much longer, and almost all customers have said they can feel an overall improvement in indoor air quality.
Of all the Gresham Air Duct Cleaning companies to choose from, why J&M Air Duct Cleaning services?
We're local and we truly care about the families living within our community. Don't get duped into hiring a duct cleaning outfit advertising as a locally owned Air Duct Cleaning Gresham company whose main office is actually located in California! Stay with a local and very reputable company that actually understands & lives in our unique Pacific NW conditions. We have always been family friendly and owned & operated by our J&M Family. We truly and honestly care about the well being of each of our Air Duct Cleaning Gresham Technicians and their families. You'll see the difference yourself in attitude, respect and high level service & craftsmanship from all of our skilled technicians. We meet with all of our duct cleaner technicians every morning personally before our fleet ever leaves the shop. We're very proud of all our J&M Services Air Duct Cleaning Gresham family, and it shows where it counts, in your home!


text:
Google Announces the Nest Hub Max
Emily Ferron
In the I/O conference's keynote address, Google leadership announced a slew of exciting developments, including a product that smart home aficionados have been waiting for: the new Nest Hub Max smart home hub.
Google's Latest and Greatest: An All-in-One Display, Smart Home Hub and Security Camera
The Google I/O Conference kicked off today, which is the tech giant's annual meeting for software developers working with its ever-increasing suite of hardware and software products. In the keynote address, Google leadership announced a slew of exciting developments, including a product that smart home aficionados have been waiting for: the new Nest Hub Max smart home hub.
We did have some previous indication that this updated version of the Google Home Hub (which is being renamed to the Nest Hub) was in the pipeline. As reported by The Verge and Android Police, the Nest Hub Max was accidentally leaked for a short time on the Google Store homepage. Now that the device is officially launched, we know exactly how it improves upon its predecessor.
If you're completely new to smart home hubs, you can think of the Nest Hub Max as a tablet (similar to an iPad) that allows you to control all of your home automation devices in one place, either by voice control, the touchscreen interface and even hand gestures in some cases. Unlike the first Nest Hub, the new Hub Max also has a built-in security camera. You can also use it for watching videos or streaming music.
Nest Hub Max Key Benefits & Features:
Designed for use in communal areas of the home
10-inch smart display (the older Nest Hub has a 7-inch screen)
"Hey Google" voice control through a new-and-improved Google Assistant
Home View dashboard, which pulls in connected smart home devices and allows you to control them all in one place
A built-in security camera
Support for video calling and video messages
Multiple user support through Voice Match and Face Match: Household members can opt into these features to teach the Hub Max to recognize them, so that they can receive personalized messages and device options.
Streaming video and music on-demand with gesture control: To pause the media, you can simply wave your hand, without needing to touch the screen or use your voice.
When not in use, it functions as a digital photo frame.
Google also acknowledged Nest-specific privacy concerns. Facial recognition is processed locally on the device and not on the cloud. The camera does not stream or record unless you specifically tell it to, and a green indicator light on the front of the device confirms whether or not the camera is running.
These features come together to create a powerful command center for running an integrated smart home. Unfortunately, we'll have to wait a little longer to get our hands on one. Google announced that the Nest Home Hub Max will retail for $229 and will start shipping later this summer. In the meantime, the original Nest Hub (without a camera) will continue to be available for the newly reduced price of $129. Pro tip: For a limited time, the Nest Hub is on sale at Target for only $99.
Lastly, if you're confused about the Google and Nest name change, here's a little background. Google purchased Nest in 2014. Back then, Nest was already a leader in home automation products like thermostats, video doorbells and security cameras. Google continued running Nest as if were a separate company until 2018, when it decided that the Nest brand would become part of the official "Made by Google" suite of products. The Nest Hub Max is the first major Nest device that's being released with this new designation.
This smart home hub is far from the only new development announced during the keynote speech. For more about Google's new Pixel phones, updates to its Android software and its emerging artificial intelligence projects, you can watch the entire 2019 Google I/O Keynote on YouTube.
Update: Google Nest Hub Max Now Available
September 9, 2019 – you can finally get your hands on a Nest Hub Max. They retail for $229 each, and they're available from the Google Store, Target and other major retailers. (We don't expect to see this one available through Amazon anytime soon, since Nest is a prime competitor to Ring, one of Amazon's top home security brands.)
Written by your home security expert
Emily is an experienced writer passionate about covering topics at the intersection of tech, health, safety and humanity.
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redpajama_set_name: RedPajamaCommonCrawl


Usually about this time each year I am busy plotting and preparing for NANoWriMo, a national writing competition where you challenge yourself to write a 50,000 word novel in a month. I've loved taking part in that adventure, however, this year, I am trying something different.
30 days, 30 books, 30 reviews.


Crispy triangular patties stuffed with spicy lamb or mixed vegetables.
Lean spiced mince lamb mixed with fresh onions, garlic, ginger skewed in the tandoori oven creating delicious kebabs.
Chicken leg/breast marinated with chef's special hot spices then grilled in the tandoor.
The nation's favourite, our onion bhajis are big and beautiful and taste delicious cooked with gram flour, herbs and spices.
Marinated with yoghurt and tikka spices then cooked in tandoori oven.
Lovely lamb chops marinated with aromatic spices and grilled in the tandoori oven.
Delicious cubes of grilled Indian cheese mixed with chilli spices.
Popular Indian street snack made with spicy chicken or vegetables in a crispy batter.
Spiced prawns served inside deep fried homemade Indian bread.
(available in chicken)  Lamb pieces slow cooked with Indian split lentils in a fairly hot dish infused with chilli, garlic and chef's special sauce. Poputlar in Bangladesh, India and Pakistan!
Traditional Bangladeshi dish slow cooked with soft lamb and fresh pumpkin. A delicious dish combining the natural sweetness of the pumpkin and spices of Bengal, hot and sweet medium strength!
Delicious diced chicken/lamb cooked with fenugreek, star anise & chef's special spices creating a medium full flavoured dish, Recommended!
Delicious medium spiced chicken/lamb curry cooked with fresh tomatoes – a firm favourite dish.
Hot dish, chicken or lamb with capsicum, onions, green chillies in thick gravy.
Tender pieces of stripped chicken marinated in fresh spice, cooked with mango puree, yoghurt, almond, coconut and cream, creating a delicioussauce with hint of sweetness and mango flavour, very mild highly recommended!
Popular Bangladeshi dish chicken on the bone or King prawns cooked with fresh mustard seeds in a medium hot sauce. Recommended by the Chef!
Plumb king prawns/monkfish cooked with fresh green chillies, mustard seeds, coconut cream and mixed spices – a truly delicious south Indian dish.
Mildly spiced prime cubes of selected salmon marinated with dill, fennel, ginger, tikka spices and mustard oil.
Pan fried fish of the day fillets marinated in Bengali spices in a medium sauce.
Delicious mixed vegetable in a medium spicedcurry sauce.
Chef special butternut squash with lentils, mustard seeds on a bed of spicy curry sauce!
One of the best Indian restaurants on Brick Lane, it gets crowded and loud, but the spices are as good as it gets.


text:
1. The suggested publishing date of 9/1837 for Sadamasu's chūban bust portrait of Nakamura Utaemon IV in an unidentified role is still a matter of discussion [print 21].
2. See for a detailed account on the development of the Osaka chūban style: Doesburg, W.J. van, Utagawa Sadamasu, creator of the Osaka chūban style, in Andon 36, pp. 111-119.
3. Prints showing the stylistic device in question are found among the work of e.g. Gigadō Ashiyuki (active 1813-1833) and Seiyōsai Shunshi (active 1826-1828).
4. Toshidama literally means 'New Year's Jewel'. Originally the meaning was incorporated in a sign symbolising a gift or good luck. The Toshidama seal used by the members of the Utagawa school is a syntheses of a ring (a globular shaped jewel) and the ideogram for toshi (year) in stylised script.
5. Hasegawa Sadanobu (1809-1879) generally is considered identical with the artist Sadanobu who is mentioned as a pupil of Kunisada on the Toyokuni I memorial stone erected 8/1828. His first print published in Osaka dates from 1/1834. A few prints signed Sadanobu ga and Naniwatei Sadanobu ga, published early 1820s in Osaka, may be by a different artist. See Lühl, H., Wellenblumen, pp. 61-62; Lühl, H., Helden Schurken Kurtisanen, pl. 59; Matsudaira, S., Shodai Hasegawa Sadanobu hanga sakuhin ichiran, plates 1-3.
The Tenpō Reforms
The print production in Osaka was brought to a standstill in 1842. Due to years of economic recession during the Tenpō period (1830-1843), several governmental measures were promulgated. The effect of the measures reached its height in Osaka in the spring of 1842. A few months later, in the seventh month of the year, a ban on producing and selling actor prints was announced, resulting in nearly five years of social and cultural soberness. The last actor prints published in Osaka in 1842 are those related to the kabuki performances of the third month of that year. No actor prints were published in Osaka until the spring of 1847, but it is hard to believe that the artists sat back and left their brushes untouched. Undoubtedly the sketching and designing went on, and, although the number of kabuki performances was severely limited, the artists probably took every opportunity to attend performances, after the show of course entrusting their impressions to a piece of paper.
During this period of the print ban in Osaka, Sadamasu and Sadahiro paid a visit to Edo, where they designed insets for two prints by Kunisada, their former teacher. The prints belong to the series Dai Nihon rokujūyoshū no uchi, published in Edo c. 1845.1 Kunisada signed the prints with his new name Toyokuni, which he had adopted in 1844.2 Some pupils of the popular artist followed the change consequently and substituted Sada, the first character of their name, for Kuni. Among them was Sadamasu, but from his signature Toyokuni monjin (pupil) Sadamasu ga that appears in the inset of print 52 of the series, it is obvious that the artist had not yet altered his name at that time (figure 4). It is remarkable that he used a character for masu here that differs from the one usually seen in his signature. It appears only on one other print of the artist [print 80].
It appears that Sadamasu has changed his name to Kunimasu soon after the publication of the prints in Edo. The fact is that he is listed as Kunimasu in Ōsaka shōkō meika shū, a printed directory of Osaka published in 1846. However, at that time he could not announce his name change officially on prints in Osaka since the ban on publishing prints was not yet lifted there.
Kunisada (Toyokuni III). An ōban print with inset picture by Sadamasu, who used the character 益 for masu in his signature here. Published by Kogaya Katsugorō in Edo, c. 1845.
Collection: British Museum.


text:
What Meghan Markle's Dad Said To Prince Harry When He Asked For Permission To Propose
Katrina Mitzeliotis
meghan markle dad prince harry
Photo: Splashnews
Although Meghan Markle's father wasn't there to walk his daughter down the aisle when she wed Prince Harry, he did play a very integral part in their unity–and that was granting his permission when Prince Harry asked for his daughter's hand in marriage.
In his first televised interview her father, Thomas Markle, revealed intimate details regarding the couple and his private conversations with his future son-in-law. Markle said Prince Harry called and asked for his permission last December before proposing to Meghan–and, although he has yet to meet the royal in person, he granted his request. "Actually, Meghan told me first [they were going to get married] and then a few times after that, Harry got on the phone with Meghan. They called me together and Harry asked for her hand over the phone and I said, 'you're a gentleman. Promise you'll never raise your hand against my daughter and of course I give you my permission.'"
He also said he had no concerns over his daughter's second marriage. "My daughter's very intelligent. She knows how to choose who she wants to be with," he said when asked if he had any reservations about the relationship. "She's a smart girl. And she made a good pick, didn't she?"
Markle's father was interviewed by Piers Morgan for Good Morning Britain and was compensated for the appearance, although he admitted that his daughter and Prince Harry advised him against speaking to the media. "For the most part, it was always 'don't speak to the press.' The rest of the things were just be careful but they were very emphatic about not giving any information to the press or talking to them because it just encourages them more so nonetheless that's what I tried to do," he said.
'My daughter has been a Princess since the day she was born.'
Thomas Markle, father of the Duchess of Sussex, speaks about the moment he spoke to Prince Harry for the first time in a WORLD EXCLUSIVE interview. pic.twitter.com/yywNJYc1sn
— Good Morning Britain (@GMB) June 18, 2018
Although both Meghan and Harry kept a low profile when they first started dating, her father revealed the moment that his daughter told him she was dating a member of the royal family. "The first phone calls [with Meghan] were, 'Daddy, I have a new boyfriend,' and I said, 'That's really nice,' and then the next call was, 'He's British,' and I said, 'Well, that's really nice,' and eventually the third time around was like, 'He's a prince,' and at that point she said, 'It's Harry,' and I said, 'Oh Harry. Okay.' And she said, 'Of course, we'll have to call him "H" so no one knows that we're talking about Harry. It's H.'"


As an experienced Buffalo Accident Lawyer, sometimes I receive telephone calls from people who have not received immediate care after an accident, and they would like to know if they still have a chance to make a claim for their injuries. The answer is yes. If you are injured in a car accident, and you don't receive immediate care, that does not preclude you from making a claim for injury if you are injured in a car crash.
A lot of people think they are going to be okay after an accident. The person might feel sore that night or in morning, but they wait for it to go away as if it was muscle pain from working out at the gym. The pain experienced after a car accident is much different because it's brought on by a traumatic event. Most people will wait a day or even a week until they realize it's more serious than they thought.
Were you involved in a car accident that caused you injury, and now afraid you won't be able to make a claim? Contact experienced Buffalo Accident Lawyer Richard Nicotra for a free consultation. We will be happy to go over the details of your accident, and guide you through the process of filing and claim and pursuing compensation for your injuries. Don't settle for less, contact us today.
This informational legal blog was provided by Richard Nicotra, an experienced Buffalo Accident Lawyer.


text:
Travel woes with Scratching Post
Nicole Hughes is a little nervous and asks when it last snowed in Calgary.
"We have a crisis," explains the Scratching Post frontwoman in a worried voice.
The crisis in question is that transportation for the band's upcoming tour is all but falling apart–instead of venturing out for the first time in a full-fledged tour bus as they had hoped, the band might be stuck with an old van and trailer.
"Because of the time of year, we were going to make a special exception," she explains, remembering just last week she got frostbite after a show while waiting for her car to warm up.
The time to make a decision is running out, as Scratching Post begins their cross-Canada tour with fellow metal-rockers Corrosion of Conformity and Clutch on Jan. 30. This lineup is more than a little different from their tour last year with Big Sugar–and as Hughes expects, so will the crowds be.
"Big Sugar, they're definitely more college crowds but this Corrosion of Conformity crowd is definitely more a metal crowd," says Hughes.
Scratching Post's loud, in-your-face guitar riffs, mixed with catchy melodies brought out through Hughes' voice, attract younger crowds to the shows as well. Unfortunately, there are times when the band simply can't play all-ages shows.
"For some reason we don't have a choice," explains Hughes, pointing out that some provinces don't seem to be as receptive to all-ages shows as others. Even so, younger kids frequently visit the band before their shows.
"Sometimes I'm like, 'hey, do you want to work for the night? Then you can sell our merchandise,'" says Hughes.
By putting them to work or inviting them to see the sound check, Hughes hopes to accommodate everyone she can.
The band's current tour is in support of their new album, This Time It's Personal. Some critics claim the new record is much harder-sounding than previous efforts but Hughes says there aren't any big changes to be heard.
"It's the production," says Hughes, crediting Detroit songwriters/producers Chris and Drew Peters with the changes.
"[They] focused in on the sound we wanted [and] took a lot of time on the guitar sounds and everything."
Hughes also admits that although the lyrics might be a bit darker, any changes musically-speaking were subconscious.
Armed with a new sound, a new tour group and, with a little luck, perhaps even a tour bus, Hughes and company will scratch up the MacEwan Hall Ballroom floor with Corrosion of Conformity, Clutch and Clearlight Feb. 2.
For more band information, naked fan photos and other online fun, check out Hughes' "baby," www.scratchingpost.com.
Published January 25, 2001 By Masoud
Students pass on U-pass
1984 may be closer than you think


text:
Who won the popular vote in 1884?
June 6, 2021	Off	By idswater
How did Polk win the election of 1844?
Who did Grover Cleveland beat?
Who ran for president in 1880?
Who was President for the longest time period?
Who was in the running for president in 1884?
What was the margin of victory for Cleveland in 1884?
Who was the Governor of New York in 1884?
Who was nominated for vice president in 1884?
In the election, Cleveland won 48.9% of the nationwide popular vote and 219 electoral votes, carrying the Solid South and several key swing states. Blaine won 48.3% of the popular vote and 182 electoral votes. Cleveland won his home state by just 1,149 votes; had he lost New York, he would have lost the election.
Polk defeated Whig Henry Clay in a close contest turning on the controversial issues of slavery and the annexation of the Republic of Texas. President John Tyler's pursuit of Texas annexation threatened the unity of both major parties. Annexation would geographically expand American slavery.
His presidencies were the nation's 22nd and 24th. Cleveland defeated James G. Blaine of Maine in 1884, lost to Benjamin Harrison of Indiana in 1888, and then defeated President Harrison in 1892.
Nominee James A. Garfield Winfield S. Hancock
Party Republican Democratic
Home state Ohio Pennsylvania
Running mate Chester A. Arthur William H. English
Electoral vote 214 155
William Henry Harrison spent the shortest time in office, while Franklin D. Roosevelt spent the longest. Roosevelt is the only American president to have served more than two terms.
On the first ballot, Cleveland led the field with 392 votes, more than 150 votes short of the nomination. Trailing him were Thomas F. Bayard from Delaware, 170; Allen G. Thurman from Ohio, 88; Samuel J. Randall from Pennsylvania, 78; and Joseph E. McDonald from Indiana, 56; with the rest scattered.
New York decided the election, awarding Governor Cleveland the state's 36 electors by a margin of just 1,047 of 1,167,003 votes cast.
Governor Grover Cleveland of New York defeated Republican James G. Blaine of Maine. The election was set apart by unpleasant mudslinging and shameful allegations that eclipsed substantive issues, for example, civil administration change.
Absolom M. West was nominated unanimously for vice president, and subsequently was also endorsed by the Anti-Monopoly Party. Butler had initially hoped to form a number of fusion slates with the "minority party" in each state, Democratic or Republican, and for his supporters of various parties to come together under a single "People's Party".


I've always been extremely curious about PLM deployments in larger enterprises. Based on my experience, larger companies tend to take on PLM to support an internal initiative. And because internal initiatives often widely vary from company to company, the ultimate form that PLM takes within those companies can be completely different from one another.
This series of posts focuses on the perspectives on executives, managers and staff personnel in the engineering organization. This post features Howard Schimmoller discussing how the PTC's PLM system Windchill was used at Lockheed Martin to support development on aerospace and defense contracts.
I've always been extremely curious about PLM deployments in larger enterprises. Based on my experience, larger companies tend to take on PLM to support an internal initiative. And because internal initiatives often widely vary from company to company, the ultimate form that PLM takes within those companies can be completely different from one another. A few weeks ago, I conducted an interview with Howard Schimmoller, formerly of Lockheed Martin. Some of that conversation went into the post Is the Generation Profile of Engineering a Saddle?. We talked about a number of other issues in that conversation, including some detail on the PLM initiatives in Lockheed Martin Space Systems and Mission Systems and Sensors and their use of PTC's Windchill. Take a couple minutes to listen below.
For those that want to skip ahead, here's some of the major points that are covered.
00:00 – PLM projects are oriented around products or product lines, like new fighter development, which involves several multi-national companies.
00:46 – The state of information about the development project is in flux. With tight regulations around security, there is a high priority around controlling who can see what.
01:04 – Most of what the PLM implementations were trying to achieve was secure collaboration in this context.
01:20 – Talks about launching different executables based on different deliverable file formats.
01:39 – Key KPI for PLM implementation was could they manage all the deliverable data to turn it over to their customer, assure them it was secure over the history if it's existence and use it to collaborate during the design phase.
One thing that comes through to me on this interview is just how large the deployment of Windchill is at Lockheed Martin. In essence, it's used to secure manage huge amounts of data on development projects for truly complex products. Another complexity is the number of very large multi-national manufacturers and suppliers that collaborate on this platform. And lastly, security is the upmost priority. If you're at another very large manufacturer, is your PLM deployment similar? If you're at a smaller manufacturer, is your deployment very different from this one? Sound out. Let us know your thoughts. Take care. Talk soon. And thanks for reading.


text:
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redpajama_set_name: RedPajamaGithub


text:
NPS - National Park Service
Winter Storms Continue to Impact Park Operations
GWP - Grantmakers of Western[...]
Member Spotlight: The Evan and Tracy Segal Family Foundation
Ministry of Defense of the Kingdom[...]
Émotion et recueillement à Auschwitz
Iowa State University Athletics
Cyclones Wrap Up Day One at Wieczorek Invite
IAAF - International Association of Athletics Federations
Course record assaults on tap in Shenzhen and Mersin - weekend road racing preview
Mulu Seboka of Ethiopia will return to the Shenzhen Marathon on Sunday (16) eyeing to improve on her runner-up finish from last year and chase the course record at this IAAF Silver Label road race in the process.
In the absence of last year's winner Viktoria Poliudina of Kyrgyzstan, the 34-year-old Seboka is the top returning runner. Although she may not be in the same form as three years ago when she set her personal best of 2:21:56 PB in Dubai, the vastly-experienced Ethiopian, who has clocked sub-2:30 performances each every year since 2012, is still keen to improve on her 2:35:28 clocking last year and take down Poliudina's course record of 2:33:25.
It will be Seboka's third race of the year. She clocked 2:25:01 to finish fourth in Seoul in March and scored a 2:28:59 victory in Dalian two month later.
Kenyan veteran Flomena Chepchirchir, who just celebrated her 37th birthday earlier this month, will also be competing in Shenzhen for the second straight year, after finishing a distant 10th in 2017. Her best performance this year was a sixth place finish in Prague in 2:32:10.
Ethiopian duo Melkam Gizaw and Ashu Kasim are also title contenders. The 34-year-old Kasim set her PB of 2:23:09 to claim the title in Xiamen back in 2012, while Gizaw, 28, registered her life-time best of 2:24:28 to finished second in Seoul in 2016. But neither has run a race so far this season so their competitiveness remains to be tested.
Ethiopia's Markos Geneti, 34, is the fastest entrant in the men's field thank to his 2:04:54 third place finish in Dubai in 2012. He also finished second in Dubai two years later in 2:05:13 and sixth at the Tokyo Marathon in 2:07:25 in 2015. More recently he was second in Guangzhou 12 months ago clocking 2:10:06.
Like Geneti, David Kiyeng of Kenya is yet to prove his worth in 2018. The 35-year-old, a past winner in Reims, Seoul and Daegu, set his career best of 2:06:26 in 2009. His last notable race was a 2:08:58 win in Kosice in 2016, which was also his only sub-2:10 performance since April, 2012.
Kiyeng's countryman Samuel Ndungu, on the contrary, is the most in-form man in the field. The 30-year-old clocked 2:07:51 to finish second in Lisboa two month ago, just 47 seconds slower than his PB set in 2012.
The field also includes Edwin Kipngetich Koech of Kenya, who broke the Italian all-comers' record with 2:07:13 in Milan last April, and fellow Kenyan Edwin Kimaiyo, the third place finisher last year, whose 2:09:12 PB was set at the 2017 Shanghai Marathon where he finished fourth.
Vincent Wu for the IAAF
Limo, Kipchumba and Kiplagat the favourites in Mersin
Course records will also be targeted at the Mersin Marathon, an IAAF Bronze Label Road Race, on Sunday (16).
With the forecast predicting temperatures to hover around 16 C throughout the race in this Turkish Mediterranean city, both the men's and women's records could be under threat on the flat and fast course. Organisers are hoping that the men's record of 2:11:21 set by Moroccan Hassane Ahouchar last year could be challenged by a solid field that includes Kenyans Kenneth Kiplagat Limo and Hillary Kipchumba and Evans Kiplagat of Azerbaijan.
Limo ran 2:10:24 in Madrid earlier this year while finishing second in the Rock 'n' Roll Marathon. The 28-year-old's last international marathon victory was in 2016 in Dongguan, China, where he beat Kipchumba into second place. He'll be aiming to repeat that on Sunday.
Kipchumba set his 2:08:23 lifetime best in Gyeongju, Korea in 2016. The Mersin Marathon will be his 15th over the distance, so he'll bring plenty of experience to the start line.
Kiplagat will be another experienced runner lining up on Sunday, arriving with a winning reputation on Turkish soil. Kiplagat won the Istanbul Marathon in 2016 and has a personal best of 2:07:46 set in 2014.
The favourites might be surprised by the 20-year-old marathon debutant Demeke Kasaw, the youngest man in the field, who has 1:01:43 half marathon credentials from last year.
Others to watch include Moses Too, the runner-up in Mersin last year, and Andrey Petrov of Uzbekistan, the marathon silver medallist at the 2017 Asian Championships.
On the women's side, expect an Ethiopia vs Kenya battle. With her career best of 2:28:11, Ethiopian Konjit Tilahun is one of the favourites along with compatriot Chaltu Dida, the winner at both the Tours and Toulouse marathons this year.
Kenyan Gladys Yator, a 1:08:39 half marathoner, is expected to be a factor. She ran 2:34:46 in the Marrakesh Marathon in January, the second marathon of her career. Gladys Chemweno has a PB of 2:30:30 from 2016 set when winning the Krakow Marathon. Nancy Jelagat, who finished second at the Hamburg Half Marathon with 1:10:18 this year, completes the Kenyan squad.
The women's course record stands at 2:28:47, set by Kenya's Truphena Chirchir last year.
Mersin, the tenth largest city in Turkey, is expecting to gather more than 100,000 participants in a 5km run which will start after the marathon.
Can Korkmazoglu (organisers) for the IAAF
Smartlinks | IAAF - International Association of Athletics Federations | International News | Sports | Sports Institutions | International Sports Federations | Athletics | Sports Institutions and Associations


The iPhone 6 isnt simply bigger - its better in every way. Larger, yet dramtically thinner. More powerful, but remarkably power efficient. With a smooth metal surface that seamlessly meets our most advanced Multi-Touch display. It's a new generation of iPhone thats better by any measure. With a seamless, continuous aluminum and glass design, the iPhone 6 Retina HD Displays are the thinniest most adavanced Multi-Touch display at 4.7 inches. Enjoy the A8 chip with a 64-bit architecture processor, not only faster than the A7 but up to 50% more energy efficient too.
iPhone 6 isn't simply bigger, but it's better in every way. Larger, yet dramatically thinner. More powerful, but remarkably power efficient. With a smooth metal surface that seamlessly meets our most advanced Multi-Touch display. It's a new generation of iPhone that's better by any measure. The iPhone 6 features a 4.7-inch retina HD display, the new A8 chip for faster processing and longer battery life, the M8 motion coprocessor to track speed, distance and elevation, and with an 8MP iSight camera, you can record 1080p HD Video at 60 FPS.
This update to the popular iPhone 6 Plus adds 3D Touch Control which lets users deliberately choose between a light tap, a press, and a "deeper" press, triggering a range of specific controls. Other notable additions include the Apple A9 chipset, and a 12MP rear camera with 4K resolution video recording as well as a 5.5-inch Retina HD display. This Amazon Certified Refurbished device has been refurbished at our approved repair center and comes with a 90-day return period. The phone may have minor cosmetic wear such as some light scratches on the screen and minor imperfections on sides and/or back. This device is unlocked for use with other GSM carriers such as T-Mobile & AT&T. Please check with your carrier to confirm compatibility. Includes A/C Adapter & Data Cable. Does not include headphones, SIM card, user manual, original box. Manufacturer's warranty is not verified or implied.
Up for grabs is my old iPhone. It is unlocked, it's for sale with details on eBay. This is to give a better look at it.
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Prosecutors seek 11-year prison sentence for former Fall River mayor Jasiel F. Correia II
By Jeremy C. Fox Globe Correspondent,Updated September 10, 2021, 10:05 p.m.
Former Fall River mayor Jasiel F. Correia II.Craig F. Walker/Globe Staff
Federal prosecutors are seeking an 11-year prison term for disgraced former Fall River mayor Jasiel F. Correia II, who was convicted in May on 21 felony counts, including extortion, wire fraud, and filing false tax returns, according to court documents filed Friday.
Describing Correia as "remorseless and without empathy for his victims," prosecutors asked US District Judge Douglas P. Woodlock to send a message that would resonate with the former mayor and send a warning to others who might abuse their power in public office, court records show.
"This is no ordinary case," they wrote. "The betrayal of people who considered him like family, the pervasive lying, cheating, stealing, and blame-shifting, and the egregious breaches of the public trust must be met with a sentence that thoroughly repudiates the defendant's abhorrent conduct and deters both this defendant and others like him from doing it again."
Prosecutors wrote that Correia's "betrayal of his oath and his constituents have further eroded trust in government and deeply hurt the reputation of the city he claims to love."
An attorney for Correia, William Fick, declined to comment on the prosecutors' request.
In addition to 132 months in prison, prosecutors are also seeking 24 months of supervised release, $20,473 in restitution to the IRS, $566,740 in forfeiture, and $298,190 in restitution to five investors who lost money they put into SnoOwl, a smartphone app Correia helped create in 2013 while attending Providence College.
Correia, who made headlines when he became mayor of Fall River at age 24, was found guilty on 21 of 24 charges, including nine counts of wire fraud, four counts of filing false tax returns, four counts of extortion conspiracy, and four counts of extortion.
Correia was also convicted of stealing hundreds of thousands of dollars from SnoOwl investors and demanding bribes, ranging from $25,000 to $250,000, from four businessmen who needed his consent to open marijuana dispensaries.
He was acquitted of a bribery charge related to allegations that he forced his chief of staff to give him half her city salary and a stipend she received for working during snow emergencies.
He was also acquitted of extortion and extortion conspiracy for ordering the city to pay the cost of restoring the water supply to a commercial building owned by a businessman who testified that he later gave Correia a Batman Rolex as thanks.
Correia, who did not testify, insisted after he was found guilty that "there was no overwhelming evidence," and said he would appeal based on some of the judge's instructions to the jury.
"And we'll be vindicated, and my future will be very long and great," he said.
Online court records show that Correia filed a motion for acquittal and a motion for new trial on July 26.
In the court documents filed Friday, prosecutors alleged that Correia had made false statements about his trial in the wake of his conviction that portrayed himself as a victim.
"Notwithstanding the overwhelming sworn testimony of over 30 witnesses, corroborated by hundreds of exhibits, including bank records and receipts bearing his own signature, and his own decision not to testify, Correia claimed "there were no facts that were brought forward, there was no overwhelming evidence,' " prosecutors wrote. "He further claimed that he would eventually be vindicated when 'the real truth' came out and falsely stated that he was offered a plea deal but rejected it because he was not guilty."
Correia is scheduled to be sentenced Sept. 20.
Shelley Murphy of the Globe staff contributed to this report.
Jeremy C. Fox can be reached at jeremy.fox@globe.com. Follow him on Twitter @jeremycfox.


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Oprah Winfrey was born on January 29th, 1954, in Kosciusko, Mississippi in the United States of America.
The other big lesson for me has been to learn: not only do you have the right to do whatever you want, you have the right to change your mind.
I just want to be the best person I can be. That part of me hasn't changed and that's the who of who I am to me.
I truly understand that there is a lesson in everything that happens to us.
So I tried not to spend my time asking "Why did this happen to me?" but trying to figure out why I had chosen this.
That's the answer you need. It's always a question of accepting responsibility for your choices. Anytime you look outside yourself for answers, you're looking in the wrong place.
And the voice--by which I mean the voice of God within me, and within all of us--was speaking loud and clear and I didn't take heed.
I needed to take some time to listen, to think and to get centered. I needed some time for me.


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How to solve Git merge conflicts using kdiff?
KDiff's windows (or any other three way merge tool's windows) aren't quite so intuitive, so here's some help.
Let's say you've worked on the master branch
and somebody else worked on a "tryingOutSomething" branch
...and you decide to merge that person's changes into your code.
But Git complains that there are merge conflicts!
Turns out the other person was working on the same file you were working on, and also on the same lines.
GitExtensions refers you to KDiff3 to solve the merge conflicts. Select the option to start merging and you see a screen with four sections.
The sections from right to left are:
Section C: The code of the branch you are going to merge into your branch.
Section B: The code of your current branch.
Section A: The code that is in the commit which is a common ancestor of B and C. Don't bother about section A. You'd hardly ever use it.
Bottom section: It's what your code will look like once you've done the merges and save the file.
You can manually make changes or copy code and paste it into the bottom section like this:
Or, note that the extreme left of the bottom section is segregated into areas where there are merge conflicts and it shows letters A, B, C or m. Any letters that are present before you started merging (indicated in the below image by the red arrows pointing leftward), are merges that happened automatically. If that's not what you wanted to merge, then click on the letter in the bottom-left section and select A, B or C in the toolbar on top (indicated in the below image by the red arrow pointing upward).
You can even edit the automatically merged conflicts and choose A, B or C or edit it manually.
That's it. Now just save and exit, and Git will allow you to commit.
You'll see a residual file of the merge, which has a ".orig" extension. Don't commit it. Just delete it.
In case your merging went wrong and Git says "You have not concluded your merge (MERGE_HEAD exists)", then just go into the hidden ".git" folder and delete the files named:
MERGE_HEAD, MERGE_MODE and MERGE_MSG.
After deleting those files, you'll be able to do the merging again. Do it carefully this time and save and exit.
All the best! :-)
Today is a little turning point for the LOL pages. They are now "Aha's!!!". Laughing Out Loud is not really what happens when one reads the little jokes I put up here. Aha describes the emotion better. Here we go...
Festival Sale
Continued in the next Aha!.
It's just about using the Hungarian variable naming convention appropriately
"...and when bmeJames oBond pulled the ftrigger, the sivillian jumped in the mwater."
This is what programmers are put through when reading or writing code in companies where somebody, some-day, misunderstood what the Hungarian notation was actually meant for and created a company policy of naming variables as such.
bBusy : boolean
chInitial : char
cApples : count of items
dwLightYears : double word
fBusy : float (or flag)
Apps Hungarian is the original Hungarian. Not Systems Hungarian. If your programmers are annoyed with this notation, they are not wrong. See what the creator of C++ and the creator of Linux have to say about Hungarian:
Bjarne Stroustrup:
No I don't recommend 'Hungarian'. I regard 'Hungarian' (embedding an abbreviated version of a type in a variable name) as a technique that can be useful in untyped languages, but is completely unsuitable for a language that supports generic programming and object-oriented programming — both of which emphasize selection of operations based on the type and arguments (known to the language or to the run-time support). In this case, 'building the type of an object into names' simply complicates and minimizes abstraction.
Linus Torvalds:
Encoding the type of a function into the name (so-called Hungarian notation) is brain damaged—the compiler knows the types anyway and can check those, and it only confuses the programmer.
It started when Microsoft's chief architect, Dr.Charles Simonyi started it and it came to be known as "Hungarian" because it made variable names look as if they're non-English and also because Dr.Charles is originally from Hungary.
While Microsoft has a respectful article about him, praising how the introduction of the notation helped programmers write better code faster, the adulation could also just be the result of the influence of a powerful person. Given that in the days of the DOS operating system compilers weren't as programmer-friendly, and not being very careful with datatypes could lead to costly bugs when thousands of lines of code were involved, the Hungarian notation could have really helped programmers of that time.
My take on it: The Rudder Notation enhancement
Today's IDE's just need you to hover over a variable to view its type. But inspite of modern IDE's there are people who still find Hungarian useful. It can be useful when maintaining languages which use dynamic typing (JavaScript, for example).
Moreover, although there are various reasons for not using Hungarian, I just need one reason: Readability. We read left to right. Even in the days of DOS OS'es, variables would have been far more readable if the Hungarian notation were used as such:
VariableName_typeInformation
Keep the type information away from the variable name with a distinct underscore and keep it on the right side of the camel-case variable. Of course, now that it looks exactly like English, you won't be able to call it the Hungarian notation anymore.
I named it the "Rudder notation" because the type information projects out of the name, pretty much like the rudder of a boat or plane, while also performing the function of guiding the developer or code-reviewer.
Are the variables more readable now?
Busy_b : boolean
Initial_ch : char
Apples_c : count of items
LightYears_dw : double word
Busy_f : float (or flag)
Some people want to use Hungarian because they may some-day take printouts of their code. Really? Then you're very likely doing something very wrong somewhere. Others have written long posts on Hungarian; but really...
...what you need to do, is identify which variables could mistakenly introduce bugs in your code, and apply the Rudder notation to them. This identification is very much dependent on what kind of application you are building, and not on the language or the datatype.
Forming a policy for your programmers to follow by doing a monkey-see-monkey-do of somebody else's Hungarian policy would not just be unwise, it would create a culture where team leads force ideas onto the team without understanding the idea, and cause disgruntlement within teams who don't see the point or value that the policy adds to their work. Instead, educate them to recognize potential problem areas and create a culture where they share this info with each other. Creates a better sense of ownership.
Update: Apparently I'm not the first person to suggest the underscore for Hungarian (but I'm the one who christened it the "Rudder notation"). c2.com pointed me to 'experts' who have already encouraged it: http://www.intentsoft.com/hungarian_notat/
It's just about using the Hungarian variable namin...


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AboutBlogArtistsCollectionDownload
An immersive multiplayer art showcase in VR, the Museum of Other Realities (MOR) is a place to connect, share, and experience virtual reality art with others.
Download on Steam
A community of creators, explorers, and artists, the MOR provides a place to connect, share, and experience new media together with others from all over the world in VR. The virtual museum is an ongoing, regularly-updated experience. It contains a cross section of the free-ranging experimentation present in the relatively new medium of VR art, supporting artists who are challenging and redefining what is possible. Together we can discover what this new space means to all of us.
A New Frontier for Art
An immersive art showcase. Step into a painting, surround yourself with fantastic sounds, experience art as it evolves and takes form. The MOR is an evolving experience, featuring new art on a ongoing basis.
Building a Community
Hang out with friends and enjoy a collective experience with new people. We hold regular parties, celebrating and fostering a community around this new medium.
Every artist showcasing their work in the Museum is paid and promoted. Are you making something incredible that could only exist in VR? We'd love to hear about your work!
A few artists we've worked with...
Sutu
Sutu (aka Stuart Campbell) uses art and technology in new ways to tell stories. He is known for his interactive comics including Nawlz, Neomad, Modern Polaxis and These Memories Won't Last. More recently he was commissioned by Marvel and Google to create Tilt Brush VR paintings. He has also created two VR documentaries.
Nick Ladd
Nick Ladd is a 3D animator with a background in illustration and a passion for creating new things. He uses VR tools like Quill and AnimVR to bring characters, creatures and worlds to life; and enjoys the creative freedom that comes with working in a virtual environment.
Bastiaan Hooimeijer (Naam) is a self-taught 2D / 3D / VR artist / designer / animator / director / developer from The Netherlands. He's fully committed to using VR as a new tool, medium of expression and, more essentially, world-building. He's currently working on A Piece Of The Universe, a personal diorama made in VR, for VR.
Ana Duncan
Ana Duncan is a designer for animation in television, film and VR. She has brought her bright playful designs to the hit series Teen Titans Go!, and her fun fashion illustrations to the pages of LA Magazine.Her love of retro sci-fi and renaissance gowns influence both her personal and commercial work.
Isaac Cohen
Isaac Cohen (cabbibo) is a VR Artist exploring the new paradigms of user experience for headset displays. He has also published an eclectic array of works ranging from audiovisual fact-learning apps to interactive experiences made from recursive algorithms.
Kevin Mack
Kevin Mack is an artist and Academy Award winning visual effects pioneer. He received the Oscar for Best Visual Effects for his work on WHAT DREAMS MAY COME. Mack's abstract virtual reality artworks, Blortasia and Zen Parade, have been exhibited at art and technology events around the world.
Danny Bittman
Bittman is a virtual reality artist, film maker, and developer creating the fastest workflow to translate the human imagination into digital information. Using VR creation tools like Tilt Brush and Blocks, he has created immersive designs for Marvel, Google, Hyundai, Nvidia, and more.
Liz Edwards
Elizabeth Edwards grew up in the UK and studied Computer Games Art at Teesside University before a MA in Video Game Development at Birmingham City University. After school she was a 3d character artist in the AAA games industry before moving to Montreal. She is well known for her work in Tilt Brush, Medium and Quill
What visitors are saying...
"What was once artwork experienced in the solitary confines of a universe built for one, at the MOR becomes a social phenomenon commented upon and shared by an entire community."
Uploadvr
"The MOR is an experience, not a game or a demo. It is designed like a real-world art museum, giving some of the best VR artists in the world a venue to show off all the amazing VR art they have been making for the last two years. I went into MOR with no expectations and came out with my mind completely blown. Everyone and anyone curious about VR needs to try this thing out."
"And I was off again. But only because it was such a wonderful building, full of multi-dimensional madness and portals to impossible scenery."
Available on Steam
Requires a VR Headset and a compatible PC.
Behind the Scenes: The Sound of Terrarium
MOR September: Domicile
Spotlight: Em Halberstadt on Designing Sound for VR
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Reddit, long considered practically irrelevant in social media marketing, might just be the most hyped social media platform among marketers these days.
What happened? How did Reddit suddenly become the hot new thing, despite being very far from new?
To answer that question and, of course, to make the most out of marketing on Reddit, we have to understand the basics.
Reddit describes itself as "the front page of the Internet," and it's pretty hard to argue with that definition. Reddit is the fourth most visited website in the US and the sixth in the world, according to Alexa. It had 330 million users as of April 2018.
Moreover, Alexa data suggests that people spend more time on Reddit per day than they spend on any other top social platform. That means Reddit has more active users than Twitter, and those users are more engaged. The demographics of Reddit are also unique. Nearly 58% of its user base is from the US, and the majority of those people are male, under 35, and college-educated, and their income is higher than average.
The site consists of over 45,000 subreddit communities where redditors post and discuss practically everything there is or has been in the universe. There are subreddits about politics, music, science, technology, and more, including shower thoughts, which was used by Glamour magazine as a part of an impressive marketing campaign.
All content on Reddit is user-generated: Registered users post, comment, and upvote and downvote existing posts. Upvoted posts go higher up on the board; downvoted posts go down.


According to a study by Ziegler, 80 percent of CFOs said that their main concern for 2018 was staff recruitment and retention. That's a fair concern, because industry data shows that the turnover rate for senior care professionals is a staggering 45 percent. As a senior living leader, does the fear of not keeping your community skillfully staffed keep you up at night?
Increasing retention with technology, accountability and training • What one successful provider, Asbury Communities is doing to stay ahead of the curve.