text:
\section{Introduction}

The last few decades have been characterized by an exponential growth in the amount of news sources available, with the task of following news stories in real-time becoming very difficult to perform manually. As such, a demand has risen for systems that are capable of monitoring and organizing articles into news stories. Most approaches to this task focus mainly on the English language \cite{newslens,dense_vs_sparse,entity_aware}, with multilingual systems being highly dependent on language-specific features such as the entities of a given document \cite{batch_clustering,miranda}.
Those approaches perform poorly on a multilingual scenario and are hard to extend to low-resource languages. Taking those limitations into account, we propose an online news clustering system that is able to cluster documents across languages (for which there are pretrained multilingual contextual embeddings) while maintaining performance regarding monolingual scenarios.

The contributions described in the paper are: \textbf{(i)} We develop a system that is able to cluster documents without depending on language-specific features; \textbf{(ii)}  We empirically demonstrate that the use of multilingual contextual embeddings as the document representation significantly improves clustering quality;
    \textbf{(iii)} We propose a method to train a classifier in order to merge similar clusters in an online setting, and demonstrate its impact in obtaining state-of-the-art results for multilingual clustering;
    \textbf{(iv)} We show that our system performs well on languages not seen during training and we describe a zero-shot experimental setting for Chinese, Russian, French, Italian, Slovenian and Croatian.

\section{Related Work}
The Topic Detection and Tracking (TDT) task \cite{tdt} has the goal of, given a stream of news articles, to arrange the documents into topic clusters called stories.

Regarding batch clustering approaches, Laban et al. introduce \textit{newsLens} \cite{newslens}, a batch-based approach to news clustering. \textit{NewsLens} constructs its stories by extracting keywords from the articles and linking them through a community detection algorithm. Staykovski et al. \cite{dense_vs_sparse} follow \textit{newsLens}'s work by implementing a sparse approach through TF-IDF bag-of-words document representations, and compare it against a doc2vec \cite{doc2vec} dense representation approach. Linger et al. \cite{batch_clustering} extend the aforementioned studies into a crosslingual setting by processing batches of articles into monolingual topics and using a fine-tuned multilingual DistilBERT \cite{distilbert} to link topics across languages.

For online clustering, Miranda et al. \cite{miranda} approach the problem by processing a continuous stream of multilingual documents into monolingual and crosslingual clusters. Each document is first associated to a monolingual cluster through sparse features, and crosslingual clusters are computed by linking different monolingual clusters using crosslingual word embeddings \cite{gardner}. Saravanakumar et al. \cite{entity_aware} propose an online news clustering system based on the non-parametric K-means algorithm. Their approach uses both sparse and dense features, with a main emphasis on the use of a fine-tuned entity-aware BERT model \cite{bert_base} to produce dense document representations, and is evaluated for the English language.

Our system follows the described online clustering approaches to the TDT task: while Miranda et al. was bound to the usage of specific individual models for each language due to processing monolingual clusters, our approach provides a system that is able to leverage on dense multilingual document representations without the need to process the documents into monolingual clusters first. This is accomplished by using a single crosslingual representation for the documents and the consequent training of our ranking and classification models at a crosslingual level, which also allows for a fully dense clustering space and guarantees that our system is not limited to the English language, unlike Sarvanakumar et al.'s approach.
\begin{comment}
explanation for "much simpler system"
\end{comment}

\section{The Clustering Algorithm}

\begin{figure*}[h]
\centering
\includegraphics[width=\textwidth]{distilclustering.drawio.pdf}
\caption{Representation of our clustering system's  ranking, acceptance and merge steps.}
\label{fig:architecture}
\end{figure*}

Our main focus for this task is to build an online multilingual news clustering system that depends as little as possible on language-specific features in order to process news articles for zero-shot languages (where we have no clustering training data) without having a considerable loss in performance. Previous approaches mostly focus on a single language \cite{newslens,dense_vs_sparse,entity_aware} or a specific set of languages \cite{batch_clustering,miranda}.
\begin{comment}
In particular, extracting information about entities can be a challenging task for low-resource languages.
\end{comment}

Our system is composed by four main steps (partially displayed in Figure \ref{fig:architecture}): obtaining the document representations, computing the best-ranked cluster, deciding if the document accepts the best-ranked cluster and enters it, and merging clusters that pertain to the same story.

\subsection{Document Representation}
\label{doc_repr}

In contrast to previous work, we use a representation for each document that does not depend on its language, thus eliminating the need of distinguishing representations between monolingual and crosslingual.  Documents are comprised of two components: a set of dense vectors $d^r$ corresponding to a contextual representation of the document, and a temporal representation $d^{ts}$. To obtain the dense vectors we use \textit{distiluse}\footnote{https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2}, a model produced through knowledge distillation \cite{knowledge_distil} on Multilingual DistilBERT \cite{distilbert} by using mUSE (multilingual Universal Sentence Encoder) \cite{muse} as its teacher model. Similarly to mUSE, this model aligns text at the sentence-level \cite{sentencebert} into a shared semantic space, thus, similar sentences in different languages will be closely mapped in the vector space. The model supports over 50 languages, and does not require specification of the input language.

For each document, $d^r$ contains three dense representations: $d_{1}^r$ corresponds to its body+title, $d_{2}^r$ to its f.p. (first paragraph), and $d_{3}^r$ to its f.p.+title. For the first paragraph, the representation retrieved corresponds directly to the output of the model's encoder, while in the case of the first paragraph + title, mean pooling is performed between the two output vectors corresponding to each component. Finally, for the body + title, the body is segmented into paragraphs and each paragraph is processed individually by the encoder, with the final output vector being obtained through mean pooling of each paragraph's representation and the title. The title and the first paragraph of the documents are used as features with the intuition that sentences in the beginning of a news document usually have the greatest importance to the article \cite{word_importance, automatic_summarization}. Additionally, a representation for the title alone as a feature was not used for the documents due to certain news articles containing only the text of the article and no title.

Regarding the temporal representation, we follow previous approaches \cite{miranda} and expose the temporal representation of a document as the value of its timestamp at the level of the day. When comparing a document's timestamp $d^{ts}$ against a given cluster's timestamp $c^{ts}$, we compute the Gaussian similarity between the two timestamps (with $\mu$ and $\sigma$ corresponding to hyper-parameters) as represented in the function below:

\begin{equation}
score^{ts}(d^{ts}, c^{ts}) = exp \left(-\frac{(d^{ts} - c^{ts}) - \mu}{2\sigma^2}\right)
\end{equation}

Clusters are also divided between dense ($c^r$) and temporal ($c^{ts}$) representations, with each cluster keeping three centroids for each document representation (body+title $c^r_1$, f.p. $c^r_2
$, f.p.+title $c^r_3$) that correspond to the average of the respective representations of each accepted document. When the document's representations are received, each centroid is updated to take them into account. A cluster also maintains timestamps for the newest document ($c^{ts}_1$), the oldest ($c^{ts}_2$), and the mean timestamp between all documents in the cluster ($c^{ts}_3$). To compare two timestamps, we compute the Gaussian similarity between them as proposed in previous work \cite{miranda}, which we refer to as $score^{ts}$. After a cluster is created, it is stored in the cluster pool, a structure that is responsible for maintaining the clusters and archiving old clusters as the system grows in size.

\subsection{Cluster Ranking and Acceptance Models}
\label{rank_merge}

After computing its representations, a given document $d$ is compared against each cluster $c$ in the cluster pool in order to retrieve the most similar cluster to $d$. To determine the similarity between $d$ and each cluster $c$ in the cluster pool, we compute $c$'s ranking score and the best-ranked cluster is then evaluated by the acceptance model. If the cluster is accepted by the model, then $d$ enters the cluster and its representations are updated; otherwise, a new cluster containing $d$ is created. Temporal features are computed through the aforementioned $score^{ts}$ function, and the dense features are obtained through the computation of the cosine similarity ($score^{cos}$), with $d^{r}$ being a given representation of the document and $c^{r}$ a representation of the cluster, as follows:

\begin{equation}
 score^{cos} (d^{r}, c^{r}) = \frac{d^{r} \cdot c^{r}}{|d^{r}| |c^{r}|}
\end{equation}

The ranking score for a cluster $c$ given a document $d$ and the ranking model's learned SVM weights $u^r$ and $u^{ts}$ is represented as follows:
\begin{equation}
\begin{split}
score^{rank}(d,c) {} & = \sum_{i = 1}^{3}\left( score^{cos}(d_{i}^{r},c_{i}^{r}) \cdot u_{i}^{r} \right) + \sum_{j = 1}^{2}\left( score^{cos}(d_{j+1}^{r},c_{1}^{r}) \cdot u_{j+3}^{r} \right)  \\ & + \sum_{k = 1}^{3}\left( score^{ts}(d^{ts},c_{k}^{ts}) \cdot u_{k}^{ts} \right)
\end{split}
\end{equation}
The ranking model takes the form of a Rank-SVM model \cite{ranksvm}, which we train using a similar scheme to Miranda et al. \cite{miranda}. Given the training partition, each document generates a positive example corresponding to its gold cluster, and 20 negative examples for the 20 best-ranked clusters that are not the gold cluster. These examples are then used in the training of a Rank-SVM to obtain a set of learned weights $w^r$ and $w^{ts}$ for each of the features. After computing the best-ranked cluster $c$ for a given document $d$, the acceptance model determines if the document enters the cluster by computing its acceptance score through an SVM given a bias parameter $b$ and a set of similarity features with learned weights represented by $v^r$ and $v^{ts}$, which takes the following form:
\begin{equation}
\begin{split}
score^{accept}(d,c) {} & = \sum_{i = 1}^{3}\left( score^{cos}(d_{i}^{r},c_{i}^{r}) \cdot v_{i}^{r} \right) + \sum_{j = 1}^{2}\left( score^{cos}(d_{j+1}^{r},c_{1}^{r}) \cdot v_{j+3}^{r} \right) \\ & + \sum_{k = 1}^{3}\left( score^{ts}(d^{ts},c_{k}^{ts}) \cdot v_{k}^{ts} \right) + b
\end{split}
\end{equation}

If $score^{accept}$ is greater than zero, then $d$ is accepted into $c$, otherwise, a new cluster is created and initialized with $d$. The acceptance model is an SVM trained on the training partition of the dataset: each document generates a positive sample for its corresponding gold cluster, and its second-best ranked cluster is given as a negative example.

\subsection{Merging Clusters}

After a cluster receives a new document, we rank its similarity to each of the other clusters in the cluster pool using the ranking model (described in Section \ref{rank_merge}). Each candidate cluster is then evaluated by a third SVM model, which we call \textit{cluster merge model}, and the documents from each cluster with a positive merge decision are inserted into the source cluster.

The intuition for this model is to find separate clusters that have grown to pertain to the same story, and subsequently merge them. This may happen throughout the clustering process, as few documents pertaining to a given story have entered the system, and the acceptance model may mistakenly assign separate clusters to those documents initially. As more relevant documents enter the system, those clusters may end up in similar points in the vector space, and thus should be merged. For this model, we use the eight features specified in Section \ref{rank_merge} as well as $score^{accept}$, and two additional features corresponding to the size of each cluster of the evaluated pair, with the intuition of associating the cluster merging to clusters of small sizes. The $score^{size}$, given a cluster $c$ with $k$ documents and a size limit vector $v$ of length $n$, is represented by the following equation:

\begin{equation}
score^{size}(c, v) = \sum_{i=1}^{n}\left(\begin{cases} \frac{1}{n},  \textnormal{ if }  k > v[i]; \\ 0,  \textnormal{ if }  k \leq v[i] \end{cases} \right)
\end{equation}

To train the model, we generate a dataset by sampling pairs of clusters and labeling them according to whether they should be separated or merged. For each pair, we evaluate the relative F1 given the gold label of each document in the cluster: if the computed value is higher when the clusters are merged, a positive sample is produced and the clusters are merged for the training, otherwise, a negative sample is generated. This is done without forcing documents into their gold clusters, and with the ranking and acceptance model trained accordingly.

\section{Experiments}

\subsection{Dataset}

\begin{table}
\caption{\label{tab:dataset} Statistics for the train and test partitions of the dataset. The training dataset does not contain documents in Slovenian, Croatian, French, Russian or Italian.}
\centering
\small
\setlength{\tabcolsep}{3pt}
\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c}
\hline
& \textbf{Language} & en & es & de & zh & sl & hr & fr & ru & it \\ \hline
\textbf{Train} & \textbf{Docs} & 12233 & 4527 & 4043 & 10 & - & - & - & - & - \\
& \textbf{Clusters} & 593 & 416 & 377 & 1 & - & - & - & - & - \\ \hline
\textbf{Test} & \textbf{Docs} & 8726 & 2177 & 2101 & 440 & 37 & 13 & 61 & 231 & 88 \\
& \textbf{Clusters} & 222 & 149 & 118 & 9 & 3 & 2 & 2 & 1 & 2 \\ \hline
\end{tabular}
\end{table}

We follow previous work on this task and evaluate our system on a news clustering dataset \cite{rupnik}. Besides the three main languages (English, Spanish and German), this dataset also provides a significant amount of documents in Chinese and Russian, as well as documents in Slovenian, Croatian, French and Italian. These samples allow us to roughly preview the performance of the system on other languages besides the ones it was trained in. The dataset is composed by 34,687 news documents, and it is divided into two sets: a training set comprised of 20,813 articles, and a test set that contains 13,874 articles. The articles in the training set are dated from \textit{18-12-2013} to \textit{02-11-2014}, while the articles in the test set are dated between \textit{02-11-2014} and \textit{27-08-2015}, thus guaranteeing that the articles in the test set are newer and their thematic have not been observed in the training set. Further statistics regarding the dataset are presented in Table \ref{tab:dataset}.

\subsection{Evaluation}

\begin{table*}[]
\caption{\label{tab:monolingual} Results for monolingual clustering on the test dataset.}
\centering
\small
\setlength{\tabcolsep}{3pt}
\begin{tabular}{c|c|ccc|ccc|c}
\hline
\multicolumn{1}{c|}{\textbf{Language}}  & \multicolumn{1}{c|}{\textbf{Systems}} & \multicolumn{3}{c|}{\textbf{BCubed}} & \multicolumn{3}{c|}{\textbf{Standard}} & \multicolumn{1}{c}{\textbf{Clusters}} \\
        & \multicolumn{1}{l|}{}        & \textbf{F1}      & \textbf{P}       & \textbf{R}       & \textbf{F1}        & \textbf{P}       & \textbf{R}       & \multicolumn{1}{l}{}         \\ \hline
        & Miranda et al.               & 92.36   & 94.27   & 90.25   & 94.03     & 98.14   & 90.25   & 326                          \\
 & Staykovski et al.            & 94.41   & 95.16   & 93.66   & 98.11     & 97.60   & 98.63   & 484                          \\
English        & Linger et al.                & 93.86   & 94.19   & 93.55   & \textbf{98.31}     & 98.21   & 98.42   & 298                          \\
        & Saravanakumar et al.         & \textbf{94.76}   & 94.28   & 95.25   & -         & -       & -       & -                          \\
        & Ours                      & 92.43   & 92.76   & 92.10   & 96.46     & 96.50   & 96.41   & 470                          \\ \hline
        & Miranda et al.               & 91.61   & 96.44   & 87.25   & 96.83     & 97.01   & 96.65   & 281                          \\
Spanish & Linger et al.                & \textbf{91.79}   & 93.76   & 90.08   & \textbf{97.68}     & 98.02   & 97.34   & 267                          \\
        & Ours              & 90.39   & 95.01   & 86.20   & 95.48     & 95.48   & 95.48   & 293                          \\ \hline
        & Miranda et al.               & 93.64   & 98.92   & 88.90   & 97.19     & 99.86   & 94.67   & 229                          \\
German  & Linger et al.                & \textbf{94.62}   & 95.13   & 94.31   & 98.70     & 99.16   & 98.24   & 205                          \\
        & Ours               & 93.71   & 97.68   & 90.04   & \textbf{99.07}     & 99.64   & 98.50   & 217                          \\ \hline
\end{tabular}
\end{table*}

Regarding evaluation metrics, we follow the same approach as \cite{dense_vs_sparse,batch_clustering} and report the F1 score and the BCubed F1 \cite{bcubed} score, as well as the associated Precision and Recall scores. Each sample document of the test dataset contains a label with the expected cluster ID, and since the clusters described in the test dataset are monolingual, the crosslingual connections are given by a positive/negative label between two clusters. As such, for the standard F1 score, a \textit{true positive} is described as a pair of two documents with matching cluster labels (monolingual), or a pair of documents whose cluster labels share a positive connection (crosslingual) that have been accepted into the same cluster. A \textit{false positive} corresponds to a pair of documents whose cluster labels do not match (or share a positive connection) that have been accepted into the same cluster. A \textit{true negative} is represented by a pair of documents whose cluster labels do not match (or share a positive connection) that have been accepted into different clusters, and a \textit{false negative} corresponds to a pair of two documents with matching cluster labels (monolingual), or a pair of two documents whose cluster labels share a positive connection (crosslingual) that have been accepted into different clusters.

For the BCubed F1 score, as previously described by Staykovski et al. \cite{dense_vs_sparse} and Amigó et al. \cite{bcubed}, the BCubed precision of a document corresponds to the proportion of documents in its cluster whose cluster label is the same, including itself. The BCubed recall of a document is the proportion of documents with the same label as that document (in the whole dataset) that appear in its cluster. The correctness between two documents $i$ and $j$, given the label $L_i$ and the cluster $C_i$ for each document $i$, is computed as follows:
\begin{equation}
    Correctness(i, j) = \begin{cases} 1, \textnormal{ if } L_i = L_j \textnormal{ and } C_i = C_j  \\ 0,\textnormal{ Otherwise} \end{cases}
\end{equation}

The overall BCubed precision, recall and F1 score are computed as follows:
\begin{equation}
\begin{split}
    \textnormal{BCubed } P &= Avg_i[Avg_{i.C_i=C_j}[Correctness(i,j)]] \\
    \textnormal{BCubed } R &= Avg_i[Avg_{i.L_i=L_j}[Correctness(i,j)]] \\
    \textnormal{BCubed } F_1 &= 2 * \frac{\textnormal{BCubed } P * \textnormal{BCubed } R}{\textnormal{BCubed } P + \textnormal{BCubed } R}
\end{split}
\end{equation}

For the monolingual evaluation, we evaluate the clustering performance of our model on the three main languages of the dataset by performing clustering using only the documents of the specified language, while the crosslingual evaluation uses the entirety of the test set regardless of language. In order to evaluate the results, a gold set of cluster labels is provided for each document that indicates the expected cluster of that document. The clusters are typically multilingual, and in accordance to previous work \cite{miranda, batch_clustering}, the crosslingual evaluation takes into account both monolingual and crosslingual connections between documents of a cluster.

\subsection{Experimental Results}

\begin{table}[]
\caption{\label{tab:crosslingual} Crosslingual clustering results on the test dataset.}
\centering
\small
\setlength{\tabcolsep}{3pt}
\begin{tabular}{l|ccc|ccc|c}
\hline
\multicolumn{1}{c|}{\textbf{Systems}} & \multicolumn{3}{c|}{\textbf{BCubed}} & \multicolumn{3}{c|}{\textbf{Standard}} & \multicolumn{1}{l}{\textbf{Clusters}} \\
\multicolumn{1}{c|}{}        & \textbf{F1}      & \textbf{P}       & \textbf{R}       & \textbf{F1}        & \textbf{P}       & \textbf{R}       & \multicolumn{1}{l}{}         \\ \hline
Miranda et al.               & -   & -   & -   & 84.0     & 83.0   & 85.0   & -                          \\
Linger et al.                & 82.06   & 80.25   & 83.97   & 86.49     & 85.11   & 87.92   & 606                          \\ \hline
(Ours) 4-F Rank+Acc.               & 88.02   & 91.31   & 84.95   & 92.34     & 97.26   & 87.09   & 957                          \\
(Ours) 8-F Rank+Acc.                & 89.24   & 92.62   & 86.11   & 93.76     & 97.66   & 90.15   & 1023                          \\
(Ours) 8-F Rank+Acc.+M.                         & \textbf{90.10}   & 89.70   & 90.51   & \textbf{97.21}     & 97.01   & 97.42   & 812                          \\ \hline
\end{tabular}
\end{table}

As shown in Table \ref{tab:monolingual}, for the monolingual evaluation our system is on-par with Miranda et al.'s in English and German on both metrics, but is surpassed by Linger et al.'s in all languages and modularities except for German when evaluating both metrics.

For crosslingual clustering, as shown in Table \ref{tab:crosslingual}, our system achieves state-of-the-art performance on BCubed F1 \cite{bcubed} (+8.04) and on the standard F1 (+11.33) despite producing a larger amount of clusters. Furthermore, we perform an ablation study that shows the relative importance of the system components. 4-F Rank+Acc. refers to the clustering system with a 4-feature ranking and acceptance model, which used only $score^{cos}(d^{r}_1, c^{r}_{1})$ and the timestamp features. Adding the other features (8-F Rank+Acc.) improved both standard (+1.42) and BCubed F1 (+1.22). Finally, the cluster merge model was added to our system (8-F Rank+Acc.+M.), resulting in gains for both standard (+3.35) and BCubed F1 (+0.86).

\begin{table}[]
\caption{\label{tab:other_langs} Clustering results on other languages.}
\centering
\small
\setlength{\tabcolsep}{3pt}
\begin{tabular}{l|ccc|ccc|c}
\hline
\multicolumn{1}{c|}{\textbf{Languages}} & \multicolumn{3}{c|}{\textbf{BCubed}} & \multicolumn{3}{c|}{\textbf{Standard}} & \multicolumn{1}{l}{\textbf{Clusters}} \\
\multicolumn{1}{c|}{}        & \textbf{F1}      & \textbf{P}       & \textbf{R}       & \textbf{F1}        & \textbf{P}       & \textbf{R}       & \multicolumn{1}{l}{}      \\ \hline
Chinese             & 96.18  & 100.00   & 92.65   & 99.07     & 100.00   & 98.16   & 28                     \\
Slovenian             & 76.92   & 100.00   & 62.50   & 79.67     & 100.00   & 66.21   & 12                   \\
Croatian             & 77.85   & 100.00   & 63.73   & 74.99     & 100.00   & 60.00   & 5                   \\
French             & 98.50   & 100.00   & 97.04   & 99.69     & 100.00   & 99.39   & 3                 \\
Russian             & 100.00   & 100.00   & 100.00   & 100.00     & 100.00   & 100.00   & 1               \\
Italian             & 98.86   & 100.00   & 97.75   & 98.78     & 100.00  & 97.59   & 3               \\ \hline
\end{tabular}
\end{table}

Given the nature of our system, we evaluated it on the remaining languages of the dataset as shown in Table \ref{tab:other_langs}. Our ranking, acceptance and cluster merge models were not trained on any data from these languages (with the exception of Chinese), making this a zero-shot clustering scenario. Chinese, French, Russian and Italian document clustering had high F1-scores, with results above 95\%, and both Slovenian and Croatian had initial clustering scores above 70\%.

\section{Conclusion}

We presented a clustering model that produces state-of-the-art results at a multilingual level without depending on language-specific features, and that maintains quality at a monolingual level on-par to previous work on news clustering.
\begin{comment}
Our model was evaluated on English, German and Spanish, as well as on a significant amount of documents in Chinese, Croatian, Slovenian, French, Russian and Italian despite not seeing these languages during training.
\end{comment}
We demonstrated that it is possible to improve results by utilizing contextual embeddings to represent documents at a crosslingual level, and how a linear SVM can be trained in order to perform such a task.
By reducing the complexity of the clustering space, we motivate future research on topics such as clustering while taking user feedback into account, and high-performance vector search to improve clustering speed and scalability. Our system also enables computational efficiency improvements by allowing most operations to be paralellized. We make our code available as open-source\footnote{\textit{https://github.com/Priberam/projected-news-clustering}}.

\section*{Acknowledgements}

This work is supported by the EU H2020 SELMA project (grant agreement No 957017).

redpajama_set_name: RedPajamaArXiv


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text:
The All-American Rejects självbetitlade debutalbum släpptes den 15 oktober 2002.

Låtlista
"My Paper Heart" – 3:49
"Your Star" – 4:21
"Swing, Swing" – 3:53
"Time Stands Still" – 3:31
"One More Sad Song" – 3:03
"Why Worry" – 4:17
"Don't Leave Me" – 3:28
"Too Far Gone" – 4:05
"Drive Away" – 3:00
"Happy Endings" – 4:25
"The Last Song" – 5:02
"The Cigarette Song" (bonusspår i Storbritannien) - 3:36

Albumsamling 2003
My Paper Heart
Swing, Swing
Time Stands Still
The Last Song

Medverkande
 The All-American Rejects - arrangör, art direction
 Laura Echler - A&R-assistans
 Michael Goldstone - A&R
 Dirk Hemsath - A&R
 Emily Lazar - mastering
 Tim O'Heir - producer, engineer, mixing
 Tyson Ritter - all sång, bas, slagverk
 Nick Wheeler - gitarrer, programmering
 Mike Kennerty - gitarr
 Chris Gaylor - trummor, slagverk

Listplaceringar
Album - Billboard (Nordamerika)

Singlar - Billboard (Nordamerika)

Album av The All-American Rejects
Amerikanska debutalbum
Musikalbum 2002

redpajama_set_name: RedPajamaWikipedia


This sample program consists of two volleyball plyometrics sessions per week. This is perfectly adequate to convert strength in sport-specific power and reduces the risk of over-training that can occur with more sessions each week. Plyometrics are usually performed during the mid to late pre-season phase of training. The closed season and early pre-season can be used to develop functional and maximal strength, which is then converted to explosive power. Because these volleyball plyometrics exercises require maximal effort and a high quality of movement, don't perform any other training immediately before – such as endurance runs, resistance training or speed training. Of course it goes without saying that a thorough warm-up should be completed first.
of plyometric jump training increases your vertical jumping ability.
no concentric contraction (muscle shortens).


text:
Averting democratic dictatorship in Nigeria
By On Jan 15, 2019
IF the question were to be asked about the greatest danger that ever befell democratic governance in the history of Nigeria, the answer obviously will be the All Progressives Congress(APC). Never in the life of our country has a civil administration been worse than military dictatorship than we have experienced in the last three and a half years of the ruling party.
The latest signpost of the emerging full-blown dictatorship in the land is the current ordeal of the Chief Justice of Nigeria (CJN), Mr Walter Onnoghen, over allegations of undeclared assets. I have been accustomed to the ways of this administration to the point of accepting that anything is possible in its hands, but I initially doubted when I heard on Friday night that the CJN was already charged before the Code of Conduct Tribunal. I called my source again to reconfirm the news lest I post a fake news. My source told me matter-of-factly that the CJN had been charged and since I have so much trust in him, I went ahead to flash the news.
Of the over 180 participants in the forum I posted the news, only one person asked "Our own CJN?" The rest just ignored the news as they most probably thought it could not be true. By Saturday, the story had developed. A Dennis Aghanya, former aide of President (Muhammadu) Buhari whose organisation conferred some award on Governor Umar Ganduje at the peak of his dollar bribery scandal suddenly became an anti-corruption crusader and filed a petition against the CJN to the Code of Conduct Bureau on Wednesday, January 8 and charges filed based on it on Friday, the 10th, the very day the petition was presented to the CJN for his response. The speed reminded that it took the same government almost a year to excuse Babachir "Grasscutter" Lawal from government over allegations of corruption without any trial. He is the chief campaigner for the president in Adamawa State today.
Abnormal now our new normal
Give us men like Awo, Enahoro and co
Has evil triumphed?
The whole rushed prosecution agenda is in clear violation of the constitution and the pronouncement of the appellate court that a serving judge cannot be charged to court without indictment by the National Judicial Council (NJC).
The assault on the CJN, who is the head of the judiciary, is coming on the heels of the serial humiliation of the head of the legislative arm of government, Senator Bukola Saraki, before the same court until reprieve came his way.
The next phase of his own ordeal was abandoning the crime in the violent robbery in Offa, where many lives were lost for political plot. The failed attempt to link the crime to Saraki has led to a situation where the government has totally messed up the trial of the crime committed. Nigerians don't know anymore who is on trial for the violent robbery as names of some suspects in the crime no longer feature in what could have been political interference.
All this has become a consistent pattern for a government that is called democratic in name but observes the ethos of democratic rule in the breach. Disobedience of court orders, extra-judicial execution of citizens, inhuman treatments of opposing view holders as we have seen consistently in the treatment of Senator Dino Melaye have been the character of the APC government.
The temperament of the Buhari administration has confirmed the lesson of history that dictatorship does not flow from the barrel of gun always but sometimes through uniformed democratic choices.
When people hear the names of Adolf Hitler (Germany) and Benito Andrea Mussolini (Italy), the tendency is to think they were military rulers. The truth, however, is that the political elite and the establishment of their countries allowed the two men to come to power democratically and create a dictatorship.
A key method is manipulating the fears people hold – there was a big fear of socialism in the time of Hitler and Mussolini – both promised to destroy it – the affluent middle class in Italy and Germany both hated socialism and so, therefore, voted Hitler and Mussolini in. It is in the same fashion that President Buhari was voted to power on the strength of anti-corruption propaganda.
Hitler used the enabling act which legally granted him emergency powers for a year to dissolve all other political parties to create a dictatorship the way the ruling APC has been attempting to shut the political space in the country. Mussolini used the Acerbo Laws to rig the electoral system so that the PNF (Italian Fascist Party) only had to gain 25 per cent of the vote in order to gain 66 per cent of the seats – the rest of the seats would be shared out proportionally between the parties. He was also granted emergency powers for a year legally.
The refusal of President Buhari to sign the Electoral Bill that would have given us cleaner election and the sinister plot to force the CJN out of office, roughly a month to election, ostensibly to have a "trusted" man at the helm suggest some desire.
Another key method is being opportunistic – With Hitler, the reichstag fire, be it Nazi created or really a Dutch Communist, allowed Hitler to ban the communist party which was the second largest party and therefore reduced the threat of a communist majority, securing him power. This approach has been the pattern of the decimation of the opposition PDP and all the rails against it by the president and his men. Yet, there are very few men in the APC top echelon today who were not sojourners under the umbrella at some point.
The deployment of influence is also critical for dictatorship. You need influence amongst the rich and the powerful – Hitler and Mussolini both appealed to industrialists who financed the campaigns, the Vatican church who influenced large swathes of Catholics, the old traditional elite, etc. The APC government has been deploying all tactics to either curry or coerce the influential and the rich. It listed Aliko Dangote and co on its campaign council recently, before some afterthought statement was made on Dangote. >
Former governor of Cross River State, Mr Liyel Imoke, called my attention recently to a book that best explains what is going on in our country. It is titled: "How Democracies Die" and written by Steven Levitsky and Daniel Ziblatt, two Harvard political scientists. The book provides a swift survey of authoritarian politics around the world and finds the same pattern repeating itself. Twenty-first century strongmen do not suspend the constitution and replace it with tanks on the streets. They pay lip-service to the constitution while behaving as though it didn't exist. Think of our experiences in the last three and a half years.
Court pronouncement on Onnonghen : Opposition coalition, CUPP hails ruling
There are parallels in Erdogan in Turkey, Orbán in Hungary, Maduro in Venezuela, Modi in India (to name just a few): they all deride their opponents as criminals, show contempt for their critics in the media, stoke conspiracy theories about opposition movements and question the legitimacy of any vote that goes against them.
Our clime is so similar. A lot of it is name-calling with sticks-and-stones politics. lt also includes a wide tolerance of violence. Sticks and stones may still break your bones, but only if the name-calling doesn't silence you first.
We should all be ready to take our country back and the South-South governors have shown the way in the communique of their Sunday meeting read by my brother, Governor Seriake Dickson. They did not speak in forked tongues when they declared inter alia: "It is a fact that this administration has a penchant for flagrant disobedience of and disregard for legitimate and valid court orders. We expect President Buhari to know that democracy cannot survive without respect for the constitution, strict adherence to the rule of law, and separation of powers as enshrined in the constitution… We note that the attempt to drag the CJN to the CCT is also a grave and dangerous escalation of the assault on institutions of state, including the National Assembly and the judiciary. We believe that the President, Muhammadu Buhari, has a constitutional responsibility and huge moral obligation to defend our democracy.
"We consider this step, which is directly aimed at humiliating the nation's highest judicial officer and a prominent son of the region, as totally unacceptable as it is reflective of the South-South story of endless marginalisation and intimidation. The unceremonious removal of former Acting Director General of the Department of State Service, Mathew Seiyefa and his replacement is still very fresh.
"We strongly believe that the regrettable development at the Supreme Court at this critical time, when preparations for the general election are wobbling (with serious concern about the Independent National Electoral Commission, INEC, and security agencies) is capable of causing avoidable anxiety, tension and possible breakdown of law and order in the country.
"We note further that the action undermines confidence not only in the judiciary but also the electoral process that has already commenced, in view of the pivotal role that the judiciary plays in the process of electoral adjudication. We affirm President Buhari should know that the continuous assault on critical institutions of state is a defining feature of a dictatorship and that the President is obliged to live up to his word that he is a born-again democrat, as he assured Nigerians in 2015."
May our land never be in short supply of men like these!
Boat made from plastic waste to sail Africa's coast for awareness on plastic pollution
Private-sector participation key to establishment of ICT park in Nigeria —Shittu

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Da Nang police have detained five men for their alleged involvement in a multimillion dollar football betting ring; eight other suspects remain at large.
Nguyen Van Thai, 41, was arrested May 29 while his accomplices Nguyen Ngoc Toan, Tran Chau Duc, both 28, Ho Thang Phuoc, 20, and Dinh Hoang Viet, 29, were apprehended last month with VND40 million in their possession.
Duc said the money was collected from losers and they were about to bring it to the ring leader Tran Dinh Hai, a 33-year-old local.
Police have issued a nationwide warrant for Hai's arest.
Eight other alleged members remain at large, although two face organized gambling charges.
Six others are simply wanted for gambling.
Investigators say the members managed gambling accounts to make online bets worth tens of millions of dollars.
They started operating last July, handling bets on both international and domestic matches.
Police have also seized a laptop and several documents containing football betting data.
Gambling of any kind is illegal in Vietnam, though the country has opened several luxury casinos to foreign passport holders.


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Rafael Fiziev 'didn't like many moments' in UFC on ESPN 31 bout, including TKOing a friend
Rafael Fiziev
Nolan King and John Morgan
December 5, 2021, 11:00 AM ·2 min read
Kyrgyz mixed martial arts fighter
LAS VEGAS – Rafael Fiziev's UFC latest performance wooed the small crowd in attendance at the UFC Apex and wowed thousands watching around the world, but there are some things about it he just didn't enjoy, himself.
Though it was capped off by an athletic and powerful spinning wheel kick knockout, Fiziev (11-1 MMA, 6-1 UFC) didn't bask in the glory for too long at the UFC on ESPN 31 post-fight news conference. He admitted it was a great highlight, but defeating a friend, Brad Riddell (10-2 MMA, 4-1 UFC), who he's trained with in the past, was a difficult pill to swallow.
"It feels amazing," Fiziev told MMA Junkie. "What I can say? What I can say? I just don't know if I'm sad or I'm happy because it's not so good when you (defeat) your friend like that, you know? It's just this. Of course, I'm happy inside. I don't know. … It was a beautiful KO or TKO. It was beautiful, but I still didn't want the fight (yet). I still didn't like many moments in the fight. I still need growing. I still need work."
With the victory, Fiziev won a $50,000 "Performance of the Night" bonus and extended his winning streak to five, with two knockout finishes in the stretch. Despite his affinity for violence and success in the cage, Fiziev revealed he never seeks specifically for a finish.
"I never find knockout in my fights," Fiziev said. "I never try to make a knockout. I never believe in this before a fight. I waited for his damage and I (took) it. I go to my kitchen today and I'll try to cook something."
UFC on ESPN 31 took place Saturday, airing on ESPN and streaming on ESPN+.
To se the full interview with Fiziev, check out the video above.


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El aeródromo Campo Williams (ICAO: NZWD) (en inglés: Williams Field o Willy Field) es un aeródromo del Programa Antártico de los Estados Unidos próximo a la Base McMurdo en la barrera de hielo McMurdo, que es una parte de la barrera de hielo Ross en la Antártida.

Campo Williams consiste de dos pistas de nieve que tienen aproximadamente 8 metros de nieve compacta, que está encima de 80 metros de hielo, flotando sobre más de 550 metros de agua. El aeropuerto, que está a aproximadamente siete millas de la isla de Ross, sirve a la Base McMurdo y a la Base Scott de Nueva Zelanda. Hasta la temporada de verano 2009-2010, Campo Williams fue el principal campo de aviación para las operaciones de aeronaves en la Antártida.

Fue nombrado en homenaje a Richard T. Williams, un marino de la Armada de los Estados Unidos que se ahogó cuando su tractor Caterpillar D8 rompió el hielo el 6 de enero de 1956. Williams y otros participaban en la primera Operación Deep Freeze, una misión militar estadounidense que construyó la base permanente de McMurdo para participar en el Año Geofísico Internacional (1957–1958).

Operación
La pista está típicamente en operación desde diciembre hasta fines de febrero. Otros aeródromos de la Base McMurdo son: Aeródromo Ice Runway (octubre a diciembre) y Campo Pegasus usado de agosto a diciembre en cada temporada.

El Campo Williams es localmente conocido como "Willy's Field." El aeródromo es una superficie de nieve preparada que puede soportar aterrizajes de aviones equipados con esquíes únicamente. Un grupo de instalaciones para las operaciones de vuelo, que son referidas como "Willy Town", incluye varias filas de contenedores para los trabajadores y una galería. Algunos de los edificios son relocalizados para apoyar las operaciones de vuelo en la pista del hielo de Campo Pegasus. Willy Field Tavern, un bar en el aeródromo, cerró en 1994.

El combustible de aviación en Campo Williams es bombeado por una tubería flexible de 16 km desde la Base McMurdo. El combustible se almacena en un máximo de 12 tanques. Los tanques de combustible, al igual que otras estructuras en el campo de aviación, están montados en esquíes o rodamientos. El combustible para calefacción y el generador se transporta al aeródromo en camiones tanques desde la Base McMurdo, con los combustibles almacenados en estructuras individuales.

Las extraordinarias condiciones encontradas en Campo Williams incluyen el hecho de que el aeródromo está en un lento deslizamiento continuo hacia el mar. El movimiento hacia el mar de la barrera de hielo McMurdo sobre la cual está el aeródromo, ha obligado a que Campo Williams fuera reubicado en tres ocasiones desde su construcción original, la última durante la temporada 1984-1985.

Luego de eso, el personal alojado en Campo Williams vive en edificios construidos en trineos para facilitar la reubicación. En el pasado, hasta 450 personas fueron alojadas en el aeródromo, según la Fundación Nacional para la Ciencia. En 1994 esta fundación construyó dos edificios de dormitorios en la Base McMurdo y comenzó a transportar personal a Campo Williams utilizando varios vehículos, incluyendo Foremost Delta II y furgonetas Ford E-350.

Aviones que corrientemente usan el aeródromo
 Lockheed LC-130 de New York Air National Guard
 Basler BT-67 de Kenn Borek Air
 de Havilland Canada DHC-6 Twin Otter de Kenn Borek Air

Notas históricas
 1957: Un Boeing 377 Stratocruiser de Pan American voló desde Christchurch al estrecho de McMurdo. Fue el primer vuelo civil a la Antártida.
 1960: Un WV-2 BuNo 126513 de la U.S. Navy se accidentó al aterrizar fuera de la pista de hielo.
 1960: Primer C-130 Hercules equipado con esquíes que aterrizó con carga en la Antártida.
 1960: Las manchas solares imposibilitan las comunicaciones por radio durante ocho días, obligando a la cancelación de todos los vuelos entre Nueva Zelanda y McMurdo.
 1966: Primer aterrizaje de un avión a chorro (USAF-C-141) en Campo Williams.
 1970: Un "Pegasus" C-121J de la U.S. Navy se estrella. El avión quedó destruido, pero no hubo víctimas entre las 80 personas que iban a bordo. El Campo Pegasus es nombrado por ese avión.
 1979: El Vuelo 901 de Air New Zealand se estrella en el cercano monte Erebus, muriendo 257 personas.

Enlaces externos
 Aircraft of Antarctica
 Moving the Airport, December 21, 1999.
 List of stratospheric balloon launches  under NASA's Long Duration Balloon program

Referencias

 Change of Command pamphlet. U.S. Naval Support Force Antarctica; June 10, 1991.
 Clarke, Peter; On the Ice. Rand McNally & Company, 1966.
  The NewsTribune.com. Tacoma, Wash.; November 20, 2006.
 McMurdo 1960 Crash
 United States Antarctic Research Program Calendars: 1983, 1985.
  Where danger and wonder collide, The NewsTribune.com. Tacoma, Wash.; November 20, 2006.

Base McMurdo
Aeródromos de Estados Unidos en la Antártida


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Carrots Can Prevent Cataracts Researcher Say
Certain vitamins and cartenoids contained in carrots can significanly reduce cataract risk
(reprinted from the following Review of Optometry webpage)
The News Feed
If you want patients to fend off age-related cataracts, the answer may be as simple as adding antioxidant-rich fruits and vegetables to their diet.
Researchers from China and the University of South Australia found certain vitamins and carotenoids were associated with a significantly decreased risk of age-related cataracts. The international study also claimed the $5.7 billion global tab to restore sight for the estimated 45 million people with cataracts could be slashed in half by a diet that includes colorful fruits and vegetables.
The investigators analyzed 20 studies from around the globe that investigated the impact of vitamins and carotenoids on cataract risk. Despite some inconsistencies, the findings significantly touted the benefits of eating citrus fruits, capsicum, carrots, tomatoes and dark green vegetables such as spinach, broccoli and kale to delay the onset of age-related cataracts.
Researchers included eight randomized controlled trials and 12 cohort studies in their analysis.
In the cohort studies, higher consumption of certain vitamins and carotenoids including vitamin A, vitamin C, vitamin E, β-carotene and lutein or zeaxanthin was associated with a significant decreased risk of age-related cataracts. Additionally, investigators reported higher doses of the vitamins and carotenoids cut the risk of age-related cataract significantly. This included a 26% decrease for every 10mg/d increase of lutein or zeaxanthin; a decreased risk of 18% for each 500mg/d increase of vitamin C; an 8% decreased risk for each 5mg/d increase of β-carotene; and a 6% decreased risk for every 5mg/d increase in vitamin A.
However, evidence from the randomized controlled trials was less clear, the investigators reported. In the randomized controlled trials, neither vitamin E nor β-carotene intervention reduced the risk of age-related cataracts significantly compared with the placebo group.
Since the population is aging dramatically, coupled with the increasing number of people needing surgery, urgent action is needed, the researchers said in their paper. By delaying the onset of age-related cataracts by a decade, the number of patients requiring surgery could be cut by half, they speculated.
Still, challenges remain, as the current consumption of antioxidants falls short of the recommended level to prevent-age related cataracts, they added.
Basic Eyecare, Conditions & Diseases, Nutrition, Senior Vision Health, Vision Health, Vision News
ContactsForLess.ca
Written by ContactsForLess.ca
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Every cell phone number registered in united states of america of America has a thorough profile that includes; names, age, address, and gender information. However, this information cannot simply be accessed on yellow web. The only place to find a new profile of a cell phone caller getting frustrated is on a reverse cell phone number lookup world-wide-web. Actually, there is no need to seat before your computer for a full day searching people through their cell numbers. Possess plug all of the telephone involving the caller; you can seat back comfortably and access quality and up-to-date information.
Cases of drop calls are normal with a lot of married people; and if you're already married, you will discover if husband or wife is cheating on you. You just have to find out the truth as soon as possible; perhaps, the caller might just be a prank caller. Just make sure the cell quantity is not mixed develop another one inch the contact book before copying against each other. I am sure the seconds will start counting down until you track down unknown mobile caller when the search button is clicked.
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After the much wait, the Aamir Khan-Amitabh Bachchan, Katrina Kaif, Fatima Sana Shaikh starrer mega project THUGS OF HINDOSTAN has hit the floors. The first schedule of the film will begin in Malta.
Set off the coast of Southern Europe, and in close proximity to Tunisia and Libya, Malta is an island nation of Europe with relatively unexplored ocean side scenery.
This ambitious film that brings together Amitabh Bachchan and Aamir Khan for the first time will begin shoot aboard two specially constructed, massive ships that are currently docked by the Maltese seaside. THUGS OF HINDOSTAN will begin filming on these ships that have been built for over two months.
With every detail, including its stellar star cast, THUGS OF HINDOSTAN has generated huge amounts of anticipation. Now with the promise of action on high seas and a dose of history, this mega project only builds more excitement. The film goes on floors on June 5th.


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ALX Greece ("the Company") is located in 12, Vasilissis Sofias Avenue, Athens, 10674, T : +30 2107220150, email: info@businessgrove.gr, and aims to provide management consulting services. In order to fulfill its statutory purposes, the company processes, among other things, personal data following the provisions of the General Regulation for Data Protection 2016/679 (the "GDPR"). Responsible for processing is the company itself with the above contact details.
Personal data that are processed by the company concern customers, staff, associates, business executives as contact persons, users of its website who fill out the contact form. It is noted that this is the necessary personal data that the company processes in the context of its legal operations and its legal interests, since most of its products and services belong to and are intended for legal entities.
No form of disclosure of personal data is made by the company, unless required by law or unless the subject has given his explicit consent.
The subject's consent is obtained electronically or in print and precedes any processing. The consent is revoked at any time by sending a message from the subject to the email address info@businessgrove.com. Exceptionally, the company may process data without the consent of the subject when it is going to comply with its legal obligations, to exercise its legal interests, to exercise its rights in court proceedings or when provided by law.
Storage time of personal data is absolutely necessary for the purposes for which it was collected. The legal deadlines, the principles of data minimization, the limitation of storage and the rational management of the files are taken into account for the determination of the time.
The rights of the subject are the right to information and access to the data concerning him, the right to correct them, the right to delete them, the right to restrict their processing, the right to their portability, the right to object to their processing, the right to complain to the Authority Personal Data Protection, as specified in the GDPR. For the exercise of the rights of the subject, contact the company at 2107220150 and at the email: info@businessgrove.com
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CertScanTM is an administrative management software system designed to facilitate testing, licensure, and continuing education processes for both prehospital and hospital environments. CertScan utilizes a combination of scannable forms and keyboard entry to automate functions such as testing, certification, licensure, continuing education, training center information, issuance of output documents such as certificates and notices, and much more.
Scan & Score utilizes Optical Scanning technology for purposes of quickly and accurately scanning and processing a myriad of documents such as examinations, surveys, registration documents and the like. It also incorporates an export feature so electronic data such as test results, etc. can be output to other systems for synchronization or further analyses. Additionally, the system allows for easy export to a variety of related documents such as certificates, labels, and letters in MS Word or some other software package.


In 1965 Ingmar Bergman contracted labryinthitis, a viral infection of the inner ear that keeps its victims in a ceaslessly swirling vertigo even while sleeping. For weeks Bergman was bedridden, his head in a brace, trying to keep vertigo at bay by staring at a spot his doctor had painted on the ceiling, but with each glance away the room spun like a whirligig. Concentrating on the spot, he began to imagine two faces intermingled. Days later, as he recovered, he glanced through a window and saw a nurse and patient sitting comparing hands. Those images, the nurse/patient relationship and merging faces, were the genesis for Bergman's masterpeice PERSONA.
Next post: Copy Overwrite: No to All?


Taking to his Instagram handle, Aditya Roy Kapur shared a picture of his toned body. The picture was to motivate his fans on Monday. The picture was apparently for his buddy Varun Dhawan, who coaxed him to join Instagram. Aditya said that he will join the photo-sharing app once he gets in shape so that he can have bikini and post beach pictures.
The post got comments from his fans as well as from his contemporaries like Ranveer Singh, Alia Bhatt, Sonakshi Sinha, Rakul Preet and others. But one comment that caught our attention was his ex-girlfriend Shraddha Kapoor's comment. The 'Aashiqui 2' actress was all hearts for her former boyfriend. Before looking at her comment, have a look at Aditya's post.
Here's Shraddha's heart emoji for Aditya's hot body.
For the uninitiated, Shraddha and Aditya's relationship blossomed during their movie 'Aashiqui 2'. Their on-screen chemistry was praised by the audience and critics and the movie became a superhit. But after a few months of seeing each other, they broke up. Post break-up, they are still in a cordial relationship.
Aditya and Shraddha make a perfect couple and we have loved them in their movies too. We want the couple to rekindle their love affair. Do you guys want to see Aditya and Shraddha together? Do let us know.
On the work front, Aditya will be seen in 'Kalank' and 'Sadak 2' while Shraddha will star in 'Saaho', 'Street Dancer' and Saina Nehwal's biopic.


Courses and workshops available upon request.
Consolidating the knowledge we already have, and having the opportunity of deepening our skills can be very useful and indeed enriching. In cultivating our enthusiasm, confidence and expertise both as individuals and practitioners we extend and take charge of our own development, positively influencing those whom we meet.
August 22nd-26th (incl). Theme: "Intentionality and awareness to Rhythm."
"Thank you ! The week with you was very special and it has supported my work a lot.
"Thank you again Aoine for such an instructive and constructive weekend, it is so valuable to have these opportunities for getting to grips with questions around the massage. And thank you again to Lara for creating such a warm and nurturing environment and feeding us all so well - a real treat to have meals prepared."
I found the weekend organized and well scheduled and planned. And that we sat all together gathering points was very good. And that you then brought order into what we brought and addressed it with us was great! And that you told us each morning what we are going to do today and what is happening. I really appreciated that!
And we have done a lot!"
"For me the weekend was an eye-opener on many varies levels."
"Being in the little cottage=the accommodation was superb! I did not expect anything but thought we might camp in your living room.
It was so lovely! The hosting , cooking and looking after us and your home was so welcoming and cosy with still enough space to breath and for each individual!


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text:
package demoapp.dom.types.javaawt.images.samples;

import java.awt.image.BufferedImage;
import java.util.stream.Stream;

import javax.inject.Inject;

import org.springframework.stereotype.Service;

import org.apache.causeway.commons.internal.base._Bytes;
import org.apache.causeway.commons.internal.resources._Resources;

import demoapp.dom.types.Samples;
import lombok.SneakyThrows;
import lombok.val;

@Service
public class JavaAwtBufferedImagesSamples implements Samples<BufferedImage> {

    @Override
    public Stream<BufferedImage> stream() {
        return Stream.of(
                "apache-wicket.png", "byte-buddy.png", "datanucleus-logo.png",
                "project-lombok.png", "resteasy_logo_600x.gif", "spring-boot-logo.png")
                .map(this::loadImage);
    }

    @SneakyThrows
    private BufferedImage loadImage(String name) {
        val bytes = _Bytes.of(_Resources.load(JavaAwtBufferedImagesSamples.class, name ));
        return javaAwtBufferedImageService.bytesToJavaAwtBufferedImage(bytes);
    }

    @Inject
    JavaAwtBufferedImageService javaAwtBufferedImageService;

}


text:
Q: How to generate g_a_hash (Telethon) I want to call a user in telegram using the telethon library, this is the code
async def call():
    client = TelegramClient('session', api_id, api_hash)
    await client.connect()
    result = await client(functions.phone.RequestCallRequest(
        user_id='username',
        g_a_hash=b'arbitrary\x7f data \xfa here',
        protocol=types.PhoneCallProtocol(
            min_layer=42,
            max_layer=42,
            library_versions=['some string here'],
            udp_p2p=True,
            udp_reflector=True
        ),
    ))
    print(result)
    await client.disconnect()

I am not sure what g_a_hash and library_versions are and how to generate them, I also searched online but couldn't find anything related to them, any help would be really appreciated!

redpajama_set_name: RedPajamaStackExchange


After the enactment of the Affordable Care Act in 2010, small business owners who operate in the health care industry hoped that some of the provisions of the new law would effectively remedy a difficult aspect of their chosen economic sector. A significant portion of business as it relates to health care consists of billing and account receivables. To this effect, the major insurance companies and government programs such as Medicare and Medicaid are notorious for their bureaucratic ways and their less-than-expedient approach to settling their accounts payable.
The $15 copayment tendered by one patient will hardly begin to cover the expenses above. Even a few copayments a day are not sufficient in this regard. While this small medical office waits on the major insurance carrier to pay the submitted claims, the bills will continue to pile up as the payments due on accounts receivable continue to be delayed. This creates a cash flow deficiency, which happens to be one of the major reasons why smaller health care businesses fail.
When small business owners in the medical field experience cash flow problems, they often take out personal loans or even turn to their credit cards. This is particularly the case with physicians who are still paying down their medical school loans and thus have difficulty getting commercial loans from banks.
The cash flow problem is not limited to doctors or dentists who are sole practitioners. Businesses that provide ancillary services such as transcription and coding, staffing, supplies, and billing are also likely to go into debt and experience a cash crunch because their accounts receivables are being delayed by primary care providers, who may very well be waiting on major insurance companies or Medicare to pay them. This is certainly an uncomfortable domino effect that can be remedied with the help of medical factoring.
Factoring funding has been around for many decades, but it has considerably grown over the last few years due to the global financial crisis and the Great American Recession. Medical businesses are beginning to look for factoring solutions to solve their cash flow problems in a swift and reasonable manner.
Medical factoring is based on account receivables, invoices and claims. Let's say a medical transcriptionist submits an invoice to a clinic that usually takes between 30 to 60 days to settle their payments. With medical factoring, the transcription provider would submit a copy of the invoice to a financial third party. Once the invoice copy is received, the transcriptionist would receive a cash advance that represents a significant percentage of the net amount, which can be up to 80 percent in some cases. This cash advance may be tendered in just a few days, and thus the transcriptionist can resume working with the peace of mind that steady cash flow can be established.
In essence, medical factoring of invoices and claims is a form of low-cost financing that is upfront and efficient. Small medical enterprises are not the only ones that can benefit from factoring; medium-sized clinics that are in the midst of expanding their operations and treating more clients are even more likely to run into cash flow issues.
Many health insurance companies are still trying to adjust to the provisions of the Affordable Care Act, and this period of adjustment is creating cash flow problems for health care providers and their vendors. Until an adequate business rhythm can be established in this regard, medical factoring stands to help medical businesses with valuable financial assistance.


You must volunteer to work for a qualified organization. Ask the charity about its tax-exempt status. You can also visit IRS.gov and use the Select Check tool to see if the group is qualified.
You may be able to deduct unreimbursed travel expenses you pay while serving as a volunteer. You can't deduct the value of your time or services.


On the eve of my father's birthday, December 24, when he would have turned 97, I offer a series of his self-portraits that span forty years.
"The later self-portraits don't hold the viewer at an impersonal distance, as does The Green Man, his earliest self-portrait. In a 1988 likeness, my father appears contemplative and content, almost blissful, rendered in warm blues, yellows, and browns that blend with the soft landscape.
There are two final self-portraits, both from ten years later. Frustration and anger are evident in the dissatisfied curl of the lips, the dark reddened eyes, his slumped posture, and the defensive tuck of his chin. My father glowers from the canvas. I imagine clenched fists beyond the frame. His eyesight was worsening and his hands stiff with arthritis. There is the sense of throwing paint at the canvas before it's too late, before he no longer can. The image is universal. The subject might be any old man, bitter and beaten, witness to his own decline, to the inexorable loss of self by slow humbling degrees. Time had run out. Any remaining plans, any hopes or dreams for this life, would remain unrealized.
My father's face on the eve of his 80th birthday, captured with unblinking honesty,is heartbreaking. It is also a crowning artistic achievement. All the years of serial bouquets and figure studies had led to this: an expansion in his artistic vision and capacity that my father pursued in typical inward fashion, unconcerned with what others might think.
"I miss the dry snap of his morbid humor, his once keen eye, the rare compliment, and the rarer touch. It occurs to me that the thin drawn line of his mouth, the way his lips curved ever so slightly downward at the corners, telegraphing cynical amusement or displeasure, was perhaps just the way they came together, nothing more sinister or meaningful than that.
My father was an artist. He didn't make a living at it. He made a life."


While we possess this regional knowledge, we also have a global outreach with projects and partners. We have integrated information from an almost bewildering, and diverse range of sources and projects. We have worked on projects in the middle east, looked at projects in North America, are involved with projects in Africa and we have started getting savvy on solutions in the Asia-Pacific region, so yes, we do have a global presence.
We readily admit that our mix of core competence to outsiders can seem a bit befuddling. However, the answer is simple; we are drilling people by nature, and well management is our core competency. We have honed our skills in the oil&gas sector, and we continue to be deeply involved in advancing solutions for the sector. However, we have for almost two decades also been involved in geothermal energy, and how to advance the green agenda, where geothermal energy complements sun and wind.
We boil it down to the essentials; management. Projects – whether oil&gas or geothermal – request the same set of deliveries; quality (projects that are developed to meet and exceed their scope), delivery (keeping to milestones and timely delivery of project documentation, including in-depth stakeholder communication to ensure aligned expectations on all levels), economy (budget (forecast and follow-up) and risk) and not least safety (working pro-actively to secure a safe working environment for all).